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WO2024112806A1 - Génération et utilisation de cartes épigénétiques pour la découverte de médicaments - Google Patents

Génération et utilisation de cartes épigénétiques pour la découverte de médicaments Download PDF

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Publication number
WO2024112806A1
WO2024112806A1 PCT/US2023/080772 US2023080772W WO2024112806A1 WO 2024112806 A1 WO2024112806 A1 WO 2024112806A1 US 2023080772 W US2023080772 W US 2023080772W WO 2024112806 A1 WO2024112806 A1 WO 2024112806A1
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Prior art keywords
epigenetic
cell
inhibitor
target
state
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Inventor
Arash Jamshidi
Justin K. VALLEY
Rigel Joseph KISHTON
Samuel Gross
Pedro Juan MENDEZ ROMERO
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Moonwalk Biosciences Inc
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Moonwalk Biosciences Inc
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N5/00—Undifferentiated human, animal or plant cells, e.g. cell lines; Tissues; Cultivation or maintenance thereof; Culture media therefor
    • C12N5/06—Animal cells or tissues; Human cells or tissues
    • C12N5/0602—Vertebrate cells
    • C12N5/0634—Cells from the blood or the immune system
    • C12N5/0636—T lymphocytes
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6881—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/154—Methylation markers

Definitions

  • Described herein is a method of generating a differential cellular state map for epigenetic editing comprising (a) generating a first epigenetic map of a first cellular state; (b) generating a second epigenetic map of a second cellular state; and (c) determining a difference between the first epigenetic map and the second epigenetic map.
  • the first cellular state and the second cellular state are different states of differentiation.
  • the first cellular state and the second cellular state are selected from the group consisting of a na ⁇ ve T-cell state, a central memory T-cell state, an effector T-cell state, and an effector memory T-cell state.
  • the first cellular state or the second cellular state is treated with an epigenetic modulator.
  • the epigenetic modulator is a transcription factor inhibitor.
  • Also described herein is a method of assessing the effect of an epigenetic modulator, comprising (a) defining a target epigenetic state based on a target epigenetic map of a target cellular state wherein the target epigenetic map provides a methylation state of each genomic site of a plurality of target genomic sites in a target cell; (b) contacting a cell in an initial cellular state with an epigenetic modulator, thereby producing a treated cell; (c) generating a treated epigenetic map of the treated cell wherein the epigenetic map provides the methylation state of each genomic site of the plurality of target genomic sites in a treated cell; and (d) comparing the target epigenetic map and the treated epigenetic map to determine the alteration in the methylation state of each genomic site of the plurality of target genomic sites between the target cell and the treated cell.
  • the epigenetic modulator reduces the expression or activity of a transcription factor.
  • the transcription factor comprises at least one footprint enriched in a differentially methylated region (DMR).
  • the transcription factor comprises differential methylations states.
  • the transcription factor comprises at least one footprint enriched in a differentially methylated region (DMR) and further comprises differential methylations states.
  • the transcription factor is selected from a group consisting of AP-1, bHLEH40, RUNX1, FOXN3, ELK1, HIC1, SP1, NF-kB, BATF, JUNE, IRF4, NFAT, STAT5, STAT3, Fra, Fos, ATF, RUNX2, bHLEH41, CLOCK, BMAL, NPASS, Max, ELK, Fli, Eomes, GATA1, Prop1, ZNF189, ROR, ZNF415, RUNT, T-Bet, MADs, HOX, and ZNF317.2a5.
  • the epigenetic modulator is a transcription factor inhibitor.
  • the transcription factor inhibitor comprises an AP-1 inhibitor, a BATF inhibitor, a Fra inhibitor, a FOXN3 inhibitor, a Fos inhibitor, an ATF inhibitor, a RUNX1 inhibitor, a RUNX2 inhibitor, a bHLH inhibitor, a CLOCK inhibitor, an ELK1 inhibitor, a HIC1 inhibitor, an IRF inhibitor, an NFAT inhibitor, an NF-kB inhibitor, a STAT inhibitor, a FLI inhibitor, a BMAL inhibitor, a GATA inhibitor, a ROR inhibitor, a PROP inhibitor, a ZNF-189 inhibitor, a Tbet inhibitor, a Tcf7 inhibitor, an EOMES inhibitor, a RUNT inhibitor, an NPAS inhibitor, a HOX inhibitor, a ZNF317 inhibitor, a MADS inhibitor, a DNA hypomethylation agent, or a Max inhibitor.
  • the epigenetic modulator is a GSK-3 ⁇ inhibitor selected from a group consisting of a small ion cation, TWS-119, CHIR98014, CHIR98023, CHIR99021, SB-216763, SB-415286, AR-A014418, kenpaullone, alsterpaullone, 1-azakenpaullone, Bacpaullone, a pyrazolo [3,4-b] quinoxaline derivative, a 9-oxo-thiazolo [5,4-f] quinazoline-2-carbonitrile derivative, tideglusib, and manzamine A.
  • GSK-3 ⁇ inhibitor selected from a group consisting of a small ion cation, TWS-119, CHIR98014, CHIR98023, CHIR99021, SB-216763, SB-415286, AR-A014418, kenpaullone, alsterpaullone, 1-azakenpaullone, Bacpaullone, a pyrazolo
  • the transcription factor inhibitor is an AP-1 inhibitor selected from the group consisting of SR-11302, T-5224, E3330, 1-Methyl-6-oxo-1,6-dihydropyridine-3-carboxylic acid, Ganoderic acid H, Chloranthalactone B, Torilin, SPC 839, Glucocorticoid receptor modulator 1, Timolumab, Vepalimomab, Vapaliximab, IMM-H007, SSAO/VAP-1 inhibitor 1, DL-Syringaresinol, and (+)- Glaucarubinone.
  • AP-1 inhibitor selected from the group consisting of SR-11302, T-5224, E3330, 1-Methyl-6-oxo-1,6-dihydropyridine-3-carboxylic acid, Ganoderic acid H, Chloranthalactone B, Torilin, SPC 839, Glucocorticoid receptor modulator 1, Timolumab, Vepalimom
  • the transcription factor inhibitor is a RUNX1 inhibitor selected from the group consisting of Ro5-3335, and 2-pyridyl benzimidazole AI-4-57.
  • the transcription factor inhibitor is a mTOR inhibitor selected from the group consisting of sirolimus (rapamycin), everolimus, temsirolimus, TORIN1, TORIN2, ridaforolimus, and KU-0063794.
  • the epigenetic modulator modulates expression of an intracellular target of a transcription factor.
  • a method of modulating the expression of a target gene located in a target cell comprising (a) determining the epigenetic state of a methylation state of each genomic site of a plurality of target genomic sites located in the target cell in an initial cellular state; and (b) contacting the target cell with a first epigenetic modulator to modulate the expression of the target gene, wherein the gene is a transcription factor.
  • the transcription factor comprises at least one footprint enriched in a differentially methylated region (DMR).
  • the transcription factor comprises differential methylations states.
  • the transcription factor comprises at least one footprint enriched in a differentially methylated region (DMR) and further comprises differential methylations states.
  • the transcription factor is selected from a group consisting of AP-1, bHLEH40, RUNX1, FOXN3, ELK1, HIC1, SP1, NF- kB, BATF, JUNE, IRF4, NFAT, STAT5, STAT3, Fra, Fos, ATF, RUNX2, bHLEH41, CLOCK, BMAL, NPASS, Max,ELK, Fli, Eomes, GATA1, Prop1, ZNF189, ROR, ZNF415, RUNT, T- Bet, MADs, HOX, and ZNF317.2a5.
  • the method further comprises generating a first epigenetic map of the target cell wherein the first epigenetic map provides a methylation state of each genomic site of a plurality of target genomic sites in the target cell before contacting with the epigenetic modulator. In some embodiments, the method further comprises generating a second epigenetic map of the cell after contacting with the first epigenetic modulator. In some embodiments, the second epigenetic map provides the methylation state of each genomic site of the plurality of target genomic sites in the target cell after contacting with the epigenetic modulator.
  • the method further comprises comparing the first and the second epigenetic map and determining the difference between the first and the second map to identify if a desired methylation state of each genomic site of the plurality of target genomic sites in the target cell is achieved. In some embodiments, the method further comprises contacting the target cell with a second epigenetic modulator if desired methylation state of each genomic site of the plurality of target genomic sites in the target cell is not achieved. In some embodiments, the method further comprises comparing the first and the second epigenetic map and determining changes in methylation states in each genomic site of a plurality of a non-target genomic sites to identify off-target edits.
  • the method further comprises contacting the target cell with a third epigenetic modulator to reverse the methylation states in each genomic site of the plurality of the non-target genomic sites to restore the methylation state of methylation states in each genomic site of the plurality of the non-target genomic sites to the methylation state prior to contacting the target cell with the first epigenetic modulator.
  • FIG.1 shows a schematic of using epigenetic maps for discovery of new targets for reprogramming cell state.
  • FIG.2 shows a list of footprints of known transcription factors that are turned on during differentiation of na ⁇ ve cells to effector cells.
  • FIG.3 shows comparison of epigenetic maps of RUNX1 gene in na ⁇ ve cells versus effector cells and where the footprint of RUNX1 is enriched in each cell type. Light gray indicates methylated regions, and dark gray indicates unmethylated regions.
  • FIGs.4A-4C ifllustrate transcription factors with distinct epigenetic states (Light gray indicates methylated regions, and dark gray indicates unmethylated regions).
  • FIG.4A shows comparison of epigenetic maps of FOXN3 gene in na ⁇ ve cells versus effector cells.
  • FIG.4B shows comparison of epigenetic maps of ELK1 gene in na ⁇ ve cells versus effector cells.
  • FIG. 4C shows comparison of epigenetic maps of BACH2 gene in na ⁇ ve cells versus effector cells.
  • FIG.5 shows a Venn diagram of transcription factors with footprint enriched in differentially methylated regions, transcription factors that have differential methylation states, and transcription factors that have differential methylation states and are also footprint enriched in differentially methylated regions.
  • FIG.6 shows a ranked order list of transcription factors that are involved during differentiation of central memory cells (CM) to effector cells, and during differentiation of effector memory cells (EM) to effector cells.
  • FIGs.7A-7B illustrate network of transcription factors that are active in all the subsets of CD4 and CD8 cells.
  • FIG.7A shows network of transcription factors that are involved of all the subsets of CD4 cells during differentiation.
  • FIG.7B shows network of transcription factors that are involved during differentiation of all the subsets of CD8 cells.
  • FIGs.8A-8B illustrates how a network of interactions among SMAD4, RUNX1, and SATB1 is created during differentiation of na ⁇ ve cells to effector cells with epigenetic mapping (light gray indicates methylated regions, and dark gray indicates unmethylated regions).
  • FIG. 8A shows identification of SMAD4 footprint enriched in the differentially methylated regions of RUNX1, suggesting that during differentiation SMAD4 turns on first and activates RUNX1.
  • FIG.8B shows identification of RUNX1 footprint enriched in the differentially methylated regions of SATB1 suggesting during differentiation RUNX1 activates SATB1.
  • FIG.9 shows complete interaction matrix of transcription factors and differentially methylated regions (DMR) for TH17 and Treg cells (light gray indicates methylated regions, and dark gray indicates unmethylated regions).
  • Table 16 provides the list of transcription factors and differentially methylated regions (DMR) for TH17 and Treg cells.
  • FIG.10 shows epigenetic maps produced for CD8 differential subsets to show changes in DMRs during differentiation of CD8 from na ⁇ ve cells to central memory (CM) cells (left panel), from na ⁇ ve cells to effector cells (E) (middle panel), and from na ⁇ ve cells to both CM and effector memory cells (EM) (right panel). Light gray indicates methylated regions, and dark gray indicates unmethylated regions.
  • FIGs.11A-11H illustrate differentially methylated regions of genes that are downstream or upstream of the GSK3 beta pathway (light gray indicates methylated regions, and dark gray indicates unmethylated regions).
  • FIG.11A shows a schematic diagram of GSK3 beta pathway.
  • FIG.11B shows differentially methylated regions of GSK3.
  • FIG.11C shows differentially methylated regions of AXIN1.
  • FIG.11D shows differentially methylated regions of AXIN2.
  • FIG.11E shows differentially methylated regions of LEF1, which is downstream of GSK3.
  • FIG.11F shows differentially methylated regions of TCF7, which is downstream of GSK3.
  • FIG.11G shows differentially methylated regions of BCL11B, which is downstream of GSK3.
  • FIG.11H shows differentially methylated regions of TLE, which is downstream of GSK3.
  • FIGs.12A-12C illustrate differentially methylated regions of genes related to AP-1 and RUNX (e.g., NFATC2, RUNX1) in na ⁇ ve cells versus effector cells (light gray indicates methylated regions, and dark gray indicates unmethylated regions).
  • FIG.12A and FIG.12B show differentially methylated regions of NFATC2.
  • FIG.12C shows differentially methylated regions of RUNX1.
  • FIG.13 shows histogram of association scores for Rapamycin-treated cells versus effector, na ⁇ ve or vehicle-treated cells.
  • FIGs.14A-14D shows differentially methylated regions of GSE1, IL2RB, DHX9, and IFNG-AS1 gene in rapamycin-treated cells versus other cell populations (e.g., effector cells, na ⁇ ve cells, vehicle-treated cells) (light gray indicates methylated regions, and dark gray indicates unmethylated regions).
  • FIG.14A shows differentially methylated regions of GSE1 gene in Rapamycin-treated cells versus other cell populations (e.g., effector cells, na ⁇ ve cells, vehicle-treated cells).
  • the Na ⁇ ve/Vehicle cells cluster similarly, while the Effector and Rapamycin-treated cells cluster separately and are distinct.
  • FIG.14B shows differentially methylated regions of IL2RB gene in Rapamycin-treated cells versus other cell populations (e.g., effector cells, na ⁇ ve cells, vehicle-treated cells).
  • the Rapamycin-treated cells appears to have a separate epigenetic state compared to the Na ⁇ ve/Vehicle and Effector.
  • FIG.14C shows differentially methylated regions of DHX9 gene in Rapamycin-treated cells versus other cell populations (e.g., effector cells, na ⁇ ve cells, vehicle- treated cells).
  • the Rapamycin-treated cells clusters primarily with the Effector population, while the Na ⁇ ve and Vehicle population cluster separately.
  • FIG.14D shows differentially methylated regions of IFNG-AS1 gene in Rapamycin-treated cells versus other cell populations (e.g., effector cells, na ⁇ ve cells, vehicle-treated cells).
  • the Rapamycin-treated cells clusters closely with the Na ⁇ ve population but distinct from the Effector and Vehicle population.
  • FIG.15 shows histogram of association scores for TWS119-treated cells versus effector, na ⁇ ve or vehicle-treated cells.
  • FIGs.16A-16D illustrate differentially methylated regions of various genes in TWS119- treated cells versus other cell populations (e.g., effector cells, na ⁇ ve cells, vehicle-treated cells) (light gray indicates methylated regions, and dark gray indicates unmethylated regions).
  • FIG. 16A shows differentially methylated regions of BCL11B in TWS119-treated cells versus other cell populations (e.g., effector cells, na ⁇ ve cells, vehicle-treated cells).
  • FIG.16B shows differentially methylated regions of AKAP13 in TWS119-treated cells versus other cell populations (e.g., effector cells, na ⁇ ve cells, vehicle-treated cells).
  • FIG.16C shows differentially methylated regions of AXIN1 in TWS119-treated cells versus other cell populations (e.g., effector cells, na ⁇ ve cells, vehicle-treated cells).
  • FIG.16D shows differentially methylated regions of SEPTIN9 in TWS119-treated cells versus other cell populations (e.g., effector cells, na ⁇ ve cells, vehicle-treated cells).
  • FIGs.17A-17E illustrate differentially methylated regions of various genes in Th17 versus Treg cells.
  • FIG.17A shows differentially methylated regions of CCND2 in Th17 versus Treg cells.
  • FIG.17B shows differentially methylated regions of CD247 in Th17 versus Treg cells.
  • FIG.17C shows differentially methylated regions of ENOX2 in Th17 versus Treg cells.
  • FIG.17D shows differentially methylated regions of YBEY in Th17 versus Treg cells.
  • FIG. 17E shows differentially methylated regions of ARX in Th17 versus Treg cells.
  • FIG.18 depicts an exemplary device, in accordance with some embodiments.
  • FIG.19 depicts an exemplary system, in accordance with some embodiments.
  • FIG.20 shows comparison of actual and reference null data sets for TCF7. Columns are CpGs in TCF7, rows are individual fragments spanning TCF7. Dark gray indicates methylated CpG status. Light gray indicates unmethylated CpG status.
  • FIG.22 shows a heatmap of TCF7 showing optimal number of clusters based on the Gap Statistic (dark gray indicates methylated CpG status, light gray indicates unmethylated CpG status).
  • Row annotation (gray) are CpG annotations showing various transcripts from the UCSC database (increasing gray bar height corresponds to introns, promoters, and exons, respectively).
  • FIGs.23A-23Z, FIGs.23AA-23HH illustrate heatmaps of various T cell related genes showing optimal number of clusters based on the Gap Statistic (dark gray indicates methylated CpG status, light gray indicates unmethylated CpG status).
  • Row annotation (gray) are CpG annotations showing various transcripts from the UCSC database (increasing gray bar height corresponds to introns, promoters, and exons, respectively).
  • FIG.23A shows a heatmap of CD8A.
  • FIG.23B shows a heatmap of CD4.
  • FIG.23C shows a heatmap of TIGIT.
  • FIG.23D shows a heatmap of LAG3.
  • FIG.23E shows a heatmap of CCR7.
  • FIG. 23F shows a heatmap of SELL.
  • FIG.23G shows a heatmap of TNFRSF9.
  • FIG.23H shows a heatmap of CTLA4.
  • FIG. 23I shows a heatmap of CXCR3.
  • FIG.23J shows a heatmap of SLAMF8.
  • FIG.23K shows a heatmap of CD69.
  • FIG.23L shows a heatmap of FOXP3.
  • FIG.23M shows a heatmap of EOMES.
  • FIG.23N shows a heatmap of TBX21.
  • FIG.23O shows a heatmap of GZMB.
  • FIG. 23P shows a heatmap of CD19.
  • FIG.14Q shows a heatmap of KLF4.
  • FIG.23R shows a heatmap of MYC.
  • FIG.23S shows a heatmap of SOX2.
  • FIG.23T shows a heatmap of IL2.
  • FIG.23U shows a heatmap of IFNG.
  • FIG.23V shows a heatmap of IL2RG.
  • FIG.23W shows a heatmap of MKI67.
  • FIG.23X shows a heatmap of CD101.
  • FIG.23Y shows a heatmap of IL7R.
  • FIG.23Z shows a heatmap of CD30.
  • FIG.23AA shows a heatmap of CD3E.
  • FIG.23BB shows a heatmap of CD27.
  • FIG.23CC shows a heatmap of CD28.
  • FIG.23DD shows a heatmap of IL7R.
  • FIG.23EE shows a heatmap of IL2RB.
  • FIG.23FF shows a heatmap of CXCR1.
  • FIG. 23GG shows a heatmap of CDCR4.
  • FIG.23HH shows a heatmap of BCL6 [0036]
  • FIG.24 shows histogram of the optimal number of clusters based on the Gap Statistic for >14,000 Hg38 genes.
  • FIG.25A-25E shows histogram of the optimal number of clusters per chromosome based on the Gap Statistic for >14,0000 Hg38 genes.
  • FIGs.26A-26Z and FIGs.26AA-26II illustrate heatmaps of various genes located on the X chromosome showing optimal number of clusters based on the Gap Statistic (dark gray indicates methylated CpG status, light gray indicates unmethylated CpG status).
  • Row annotation are CpG annotations showing various transcripts from the UCSC database (increasing gray bar height corresponds to introns, promoters, and exons, respectively).
  • FIG.26A shows a heatmap of EOLA2.
  • FIG.26B shows a heatmap of EMD.
  • FIG.26C shows a heatmap of PGRMC1.
  • FIG.26D shows a heatmap of RPL10.
  • FIG.26E shows a heatmap of EOLA1.
  • FIG. 26F shows a heatmap of HTATSF1.
  • FIG.26G shows a heatmap of NDUFB11.
  • FIG.26H shows a heatmap of CCNQ gene.
  • FIG.26I shows a heatmap of IKBKG.
  • FIG.26J shows a heatmap of SLC25A5.
  • FIG.26K shows a heatmap of TMEM185A.
  • FIG.26L shows a heatmap of ZBTB33.
  • FIG.26M shows a heatmap of AMER1.
  • FIG.26N shows a heatmap of DYNLT3.
  • FIG.26O shows a heatmap of PRPS1.
  • FIG.26P shows a heatmap of ZNF449.
  • FIG.26Q shows a heatmap of BCAP31.
  • FIG.26R shows a heatmap of ZNF711.
  • FIG.26S shows a heatmap of NALF2.
  • FIG.26T shows a heatmap of MORF4L2.
  • FIG.26U shows a heatmap of UBL4A.
  • FIG.26V shows a heatmap of ZNF41.
  • FIG.26W shows a heatmap of ARX.
  • FIG.26X shows a heatmap of FAM199X.
  • FIG.17Y shows a heatmap of RAP2C.
  • FIG.26Z shows a heatmap of F8A2.
  • FIG.26AA shows a heatmap of MCTS1.
  • FIG.26BB shows a heatmap of MED12.
  • FIG. 26CC shows a heatmap of PRDX4.
  • FIG.26DD shows a heatmap of PRPS2.
  • FIG.26EE shows a heatmap of ERCC6L.
  • FIG.26FF shows a heatmap of LONRF3.
  • FIG.26GG shows a heatmap of SOWAHD.
  • FIG.26HH shows a heatmap of SYP.
  • FIG.26II shows a heatmap of TCEAL3.
  • FIG.27 shows a heatmap and plot of calculated information gain for the LAG3 gene (dark gray indicates methylated CpG status, light gray indicates unmethylated CpG status). Higher values of information gain indicate those CpGs are more important in defining the clusters.
  • FIG.28 shows a heatmap and plot of calculated information gain for the MYC gene (dark gray indicates methylated CpG status, light gray indicates unmethylated CpG status).
  • FIGs.29A-29Z, FIGs.29AA-29ZZ, FIGs.29AB-29AZ, FIGs.29BA, 20BC-20BZ illustrate the comparison of epigenetic maps of targets in Th17 cells versus Treg cells (light gray indicates methylated regions, and dark gray indicates unmethylated regions).
  • FIG.29A illustrate the comparison of epigenetic maps of TYMS in Th17 cells versus Treg cells.
  • FIG.29B illustrate the comparison of epigenetic maps of STAT3 in Th17 cells versus Treg cells.
  • FIG.29C illustrate the comparison of epigenetic maps of IL2RA in Th17 cells versus Treg cells.
  • FIG. 29D illustrate the comparison of epigenetic maps of CD40LG in Th17 cells versus Treg cells.
  • FIG.29E illustrate the comparison of epigenetic maps of RORA in Th17 cells versus Treg cells.
  • FIG.29F illustrate the comparison of epigenetic maps of PTGER3 in Th17 cells versus Treg cells.
  • FIG.29G illustrate the comparison of epigenetic maps of TNFSF8 in Th17 cells versus Treg cells.
  • FIG.29H illustrate the comparison of epigenetic maps of CD6 in Th17 cells versus Treg cells.
  • FIG.29I illustrate the comparison of epigenetic maps of ITGB2 in Th17 cells versus Treg cells.
  • FIG.29J illustrate the comparison of epigenetic maps of TNFRSF1B in Th17 cells versus Treg cells.
  • FIG.29K illustrate the comparison of epigenetic maps of INPP5F in Th17 cells versus Treg cells.
  • FIG.29L illustrate the comparison of epigenetic maps of ADAM12 in Th17 cells versus Treg cells.
  • FIG.29M illustrate the comparison of epigenetic maps of ITGB1 in Th17 cells versus Treg cells.
  • FIG.29N illustrate the comparison of epigenetic maps of NET1 in Th17 cells versus Treg cells.
  • FIG.29O illustrate the comparison of epigenetic maps of an unidentified gene in Th17 cells versus Treg cells.
  • FIG.29P illustrate the comparison of epigenetic maps of Th17 cells versus Treg cells.
  • FIG.29Q illustrate the comparison of epigenetic maps of SIK3 in Th17 cells versus Treg cells.
  • FIG.29R illustrate the comparison of epigenetic maps of BCL9L and UPK2 in Th17 cells versus Treg cells.
  • FIG.29S illustrate the comparison of epigenetic maps of KIRREL3 in Th17 cells versus Treg cells.
  • FIG.29T illustrate the comparison of epigenetic maps of SNORD28 and SLC3A2 in Th17 cells versus Treg cells.
  • FIG.29U illustrate the comparison of epigenetic maps of AIP in Th17 cells versus Treg cells.
  • FIG.29V illustrate the comparison of epigenetic maps of MYO7A in Th17 cells versus Treg cells.
  • FIG.29W illustrate the comparison of epigenetic maps of ALDH1L2 and NOPCHAP1 in Th17 cells versus Treg cells.
  • FIG.29X illustrate the comparison of epigenetic maps of LRRC43 in Th17 cells versus Treg cells.
  • FIG.29Y illustrate the comparison of epigenetic maps of TCTN2 in Th17 cells versus Treg cells.
  • FIG.29Z illustrate the comparison of epigenetic maps of an unidentifiable gene in Th17 cells versus Treg cells.
  • FIG.29AA illustrate the comparison of epigenetic maps of LINC02384 in Th17 cells versus Treg cells.
  • FIG.29BB illustrate the comparison of epigenetic maps of LINC00385 and LINC00384 in Th17 cells versus Treg cells.
  • FIG.29CC illustrate the comparison of epigenetic maps of DLEU1 in Th17 cells versus Treg cells.
  • FIG.29DD illustrate the comparison of epigenetic maps of TBC1D4 in Th17 cells versus Treg cells.
  • FIG.29EE illustrate the comparison of epigenetic maps of MBNL2 in Th17 cells versus Treg cells.
  • FIG.29FF illustrate the comparison of epigenetic maps of DOCK9 in Th17 cells versus Treg cells.
  • FIG.29GG illustrate the comparison of epigenetic maps of UBAC2 in Th17 cells versus Treg cells.
  • FIG.29HH illustrate the comparison of epigenetic maps of LOC102724945 in Th17 cells versus Treg cells.
  • FIG.29II illustrate the comparison of epigenetic maps of SGPP1 in Th17 cells versus Treg cells.
  • FIG.29JJ illustrate the comparison of epigenetic maps of SPTB in Th17 cells versus Treg cells.
  • FIG.29KK illustrate the comparison of epigenetic maps of DGLUCY in Th17 cells versus Treg cells.
  • FIG.29LL illustrate the comparison of epigenetic maps of HERC2 in Th17 cells versus Treg cells.
  • FIG.29MM illustrate the comparison of epigenetic maps of RORA in Th17 cells versus Treg cells.
  • FIG.29NN illustrate the comparison of epigenetic maps of RORA in Th17 cells versus Treg cells.
  • FIG.29OO illustrate the comparison of epigenetic maps of ANP32A in Th17 cells versus Treg cells.
  • FIG.29PP illustrate the comparison of epigenetic maps of ATF7IP2 in Th17 cells versus Treg cells.
  • FIG.29QQ illustrate the comparison of epigenetic maps of METTL9 in Th17 cells versus Treg cells.
  • FIG.29RR illustrate the comparison of epigenetic maps of ZNRF1 in Th17 cells versus Treg cells.
  • FIG. 29SS illustrate the comparison of epigenetic maps of CMIP in Th17 cells versus Treg cells.
  • FIG.29TT illustrate the comparison of epigenetic maps of COTL1 in Th17 cells versus Treg cells.
  • FIG.29UU illustrate the comparison of epigenetic maps of USP10 in Th17 cells versus Treg cells.
  • FIG.29VV illustrate the comparison of epigenetic maps of ZCCHC14-DT and ZCCHC14 in Th17 cells versus Treg cells.
  • FIG.29WW illustrate the comparison of epigenetic maps of LOC101927817 and ANKRD11 in Th17 cells versus Treg cells.
  • FIG.29XX illustrate the comparison of epigenetic maps of RAB11FIP4 in Th17 cells versus Treg cells.
  • FIG.29YY illustrate the comparison of epigenetic maps of Th17 cells versus Treg cells.
  • FIG.29ZZ illustrate the comparison of epigenetic maps of an unidentifiable gene in Th17 cells versus Treg cells.
  • FIG.29AB illustrate the comparison of epigenetic maps of LO101059954 in Th17 cells versus Treg cells.
  • FIG.29AC illustrate the comparison of epigenetic maps of LOC105374945 in Th17 cells versus Treg cells.
  • FIG.29AD illustrate the comparison of epigenetic maps of NAGLU in Th17 cells versus Treg cells.
  • FIG.29AE illustrate the comparison of epigenetic maps of MSI2 in Th17 cells versus Treg cells.
  • FIG.29AF illustrate the comparison of epigenetic maps of GGA3 in Th17 cells versus Treg cells.
  • FIG.29AG illustrate the comparison of epigenetic maps of SNORD1B, SNORD1A, CYGB in Th17 cells versus Treg cells.
  • FIG. 29AH illustrate the comparison of epigenetic maps of USP36 in Th17 cells versus Treg cells.
  • FIG.29AI illustrate the comparison of epigenetic maps of TTC39C, TTC39C-AS1 in Th17 cells versus Treg cells.
  • FIG.29AJ illustrate the comparison of epigenetic maps of MBP in Th17 cells versus Treg cells.
  • FIG.29AK illustrate the comparison of epigenetic maps of ELL in Th17 cells versus Treg cells.
  • FIG.29AL illustrate the comparison of epigenetic maps of Th17 cells versus Treg cells.
  • FIG.29AM illustrate the comparison of epigenetic maps of DPY19L3 in Th17 cells versus Treg cells.
  • FIG.29AN illustrate the comparison of epigenetic maps of S1PR1, S1PR1-DT in Th17 cells versus Treg cells.
  • FIG.29AO illustrate the comparison of epigenetic maps of SPEN, FLJ37453 in Th17 cells versus Treg cells.
  • FIG.29AP illustrate the comparison of epigenetic maps of PIGR in Th17 cells versus Treg cells.
  • FIG.29AQ illustrate the comparison of epigenetic maps of HDAC1 in Th17 cells versus Treg cells.
  • FIG.29AR illustrate the comparison of epigenetic maps of Th17 cells versus Treg cells.
  • FIG.29AS illustrate the comparison of epigenetic maps of TGFBR3 in Th17 cells versus Treg cells.
  • FIG.29AT illustrate the comparison of epigenetic maps of NFATC2 in Th17 cells versus Treg cells.
  • FIG. 29AU illustrate the comparison of epigenetic maps of ZNF831 in Th17 cells versus Treg cells.
  • FIG.29AV illustrate the comparison of epigenetic maps of SLC17A9 in Th17 cells versus Treg cells.
  • FIG.29AW illustrate the comparison of epigenetic maps of TRAPPC10 in Th17 cells versus Treg cells.
  • FIG.29AX illustrate the comparison of epigenetic maps of ADARB1 in Th17 cells versus Treg cells.
  • FIG.29AY illustrate the comparison of epigenetic maps of RRM2 in Th17 cells versus Treg cells.
  • FIG.29AZ illustrate the comparison of epigenetic maps of Th17 cells versus Treg cells.
  • FIG.29BA illustrate the comparison of epigenetic maps of UXS1 in Th17 cells versus Treg cells.
  • FIG.29BC illustrate the comparison of epigenetic maps of an unidentifiable gene in Th17 cells versus Treg cells.
  • FIG.29BD illustrate the comparison of epigenetic maps of ZEB2 in Th17 cells versus Treg cells.
  • FIG.29BE illustrate the comparison of epigenetic maps of Th17 cells versus Treg cells.
  • FIG.2BF illustrate the comparison of epigenetic maps of CDCA7 in Th17 cells versus Treg cells.
  • FIG.29BG illustrate the comparison of epigenetic maps of Th17 cells versus Treg cells.
  • FIG.29BH illustrate the comparison of epigenetic maps of CTLA4 in Th17 cells versus Treg cells.
  • FIG.29BI illustrate the comparison of epigenetic maps of an unidentifiable gene in Th17 cells versus Treg cells.
  • FIG.29BJ illustrate the comparison of epigenetic maps of IKZF2 in Th17 cells versus Treg cells.
  • FIG.29BK illustrate the comparison of epigenetic maps of EPHA4 in Th17 cells versus Treg cells.
  • FIG.29BL illustrate the comparison of epigenetic maps of Th17 cells versus Treg cells.
  • FIG.29BM illustrate the comparison of epigenetic maps of LINC01814 in Th17 cells versus Treg cells.
  • FIG.29BN illustrate the comparison of epigenetic maps of IRAK2 in Th17 cells versus Treg cells.
  • FIG.29BO illustrate the comparison of epigenetic maps of TRAT1 in Th17 cells versus Treg cells.
  • FIG.29BP illustrate the comparison of epigenetic maps of MED12L, GPR171 in Th17 cells versus Treg cells.
  • FIG.29BQ illustrate the comparison of epigenetic maps of NBEAL2, NRADDP in Th17 cells versus Treg cells.
  • FIG.29BR illustrate the comparison of epigenetic maps of FOXP1, FOXP1-AS1 in Th17 cells versus Treg cells.
  • FIG.29BS illustrate the comparison of epigenetic maps of FOXP1 in Th17 cells versus Treg cells.
  • FIG.29BT illustrate the comparison of epigenetic maps of TREM154 in Th17 cells versus Treg cells.
  • FIG.29BU illustrate the comparison of epigenetic maps of LINC02481, S100P in Th17 cells versus Treg cells.
  • FIG.29BV illustrate the comparison of epigenetic maps of TCF7 in Th17 cells versus Treg cells.
  • FIG.29BW illustrate the comparison of epigenetic maps of ADAM19 in Th17 cells versus Treg cells.
  • FIG.29BX illustrate the comparison of epigenetic maps of MIR3142HG, MIR146A in Th17 cells versus Treg cells.
  • FIG.29BY illustrate the comparison of epigenetic maps of SPEF2 in Th17 cells versus Treg cells.
  • FIG.29BZ illustrate the comparison of epigenetic maps of FYB1 in Th17 cells versus Treg cells.
  • FIG.30 shows an epigenetic map depicting the methylation patterns of a region of chromosome 19 for edited cells and control cells (light gray indicates methylated regions, and dark gray indicates unmethylated regions).
  • FIG.31 shows an epigenetic map depicting the methylation patterns of a region of chromosome 12 for edited cells and control cells (light gray indicates methylated regions, and dark gray indicates unmethylated regions).
  • FIG.32A and 32B show exemplary methods for reprogramming a cell.
  • FIG.33 shows exemplary method for selecting a cellular reprogramming protocol.
  • FIG.34 shows exemplary method for selecting a cellular reprogramming protocol incorporating blocking reagents.
  • FIG.35 depicts an example of sorting CD8+ T cells into na ⁇ ve, central memory (CM), effector (Eff), and effector memory (EM) populations.
  • CM central memory
  • Eff effector
  • EM effector memory
  • Exemplary degrees of error are within 20 percent (%), typically, within 10%, and more typically, within 5% of a given value or range of values. Reference to “about” or “approximately” a value or parameter includes (and describes) embodiments directed to that value or parameter per se.
  • “Determining,” “measuring,” “evaluating,” “assessing,” “assaying,” and “analyzing” refer to forms of measurement. include determining whether an element is present or not (for example, detection). can include quantitative, qualitative, or quantitative and qualitative determinations. Assessing can be relative or absolute. “Detecting the presence of” can include determining the amount of something present in addition to determining whether it is present or absent depending on the context.
  • Cancer cells are used interchangeably herein. These terms refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist alone within an animal, or can be a non-tumorigenic cancer cell, such as a leukemia cell. These terms include, for example, a hematologic cancer, a solid tumor, a soft tissue tumor, or a metastatic lesion. “Cancer” includes premalignant, as well as malignant cancers. [0057] “Cell” refers to a biological cell.
  • a cell can be the basic structural, functional and/or biological unit of a living organism.
  • a cell can originate from any organism having one or more cells. Some non-limiting examples include: a prokaryotic cell, eukaryotic cell, a bacterial cell, an archaeal cell, a cell of a single-cell eukaryotic organism, a protozoa cell, a cell from a plant, an algal cell, a fungal cell an animal cell, a cell from an invertebrate animal (e.g.
  • a cell from a vertebrate animal e.g., fish, amphibian, reptile, bird, mammal
  • a cell from a mammal e.g., a pig, a cow, a goat, a sheep, a horse, a rodent, a rat, a mouse, a non-human primate, a human, etc.
  • a cell can be a somatic cell, for example, a skin cell, a nerve cell, a muscle cell, a blood cell, a muscle cell, a liver cell, a skin cell, an immune cell, a pancreatic cell, a nerve cell, a gastric cell, a cardiac cell, a gonad cell, or a fat cell, a bone cell (e.g., osteoblast, osteocyte, osteoclast, osteoprogenitor cell), a brain cell (e.g., neuron, astrocyte, glial cell), an optic cell, an olfactory cell, an auditory cell, or a kidney cell, or a germ cell, e.g., an oocyte, a sperm.
  • a somatic cell for example, a skin cell, a nerve cell, a muscle cell, a blood cell, a muscle cell, a liver cell, a skin cell, an immune cell, a pancreatic cell, a nerve cell, a gastric cell,
  • the cell may be an adult cell, e.g., adult somatic cell, a sperm, an oocyte.
  • the somatic cell is an “adult somatic cell,” by which is meant a cell that is present in or obtained from an organism other than an embryo or a fetus or results from proliferation of such a cell in vitro.
  • the compositions and methods for rejuvenating a somatic cell can be performed both in vivo and in vitro, where in vivo is practiced when a somatic cell is present within a subject, and where in vitro is practiced using an isolated somatic cell maintained in culture.
  • the cell may be a stem cell, e.g., an embryonic stem cell, an adult stem cell, an induce pluripotent stem cell (iPSC). Induced pluripotent stem cells can be derived, for example, from adult somatic cells such as skin or blood cells.
  • the stem cell may be a totipotent stem cell, a pluripotent stem cell, a multipotent stem cell, or an unipotent stem cell.
  • iPSC induce pluripotent stem cell
  • the stem cell may be a totipotent stem cell, a pluripotent stem cell, a multipotent stem cell, or an unipotent stem cell.
  • A “Allogeneic cell” refers to a cell obtained from an individual who is not the intended recipient of the cell as a therapy (the cell is allogeneic to the subject).
  • Allogeneic cells of the disclosure may be selected from immunologically compatible donors with respect to the subject of the methods of the disclosure. Allogeneic cells of the disclosure may be modified to produce “universal” allogeneic cells, suitable for administration to any subject without unintended immunogenicity. Allogeneic cells of the disclosure include, but are not limited to, hematopoietic cells and stem cells, such as hematopoietic stem cells.
  • Allogeneic cells of the disclosure include, but are not limited to, hematopoietic cells and stem cells, such as hematopoietic stem cells.
  • Autologous cell refers to a cell obtained from the same individual to whom it may be administered as a therapy (the cell is autologous to the subject). Autologous cells of the disclosure include, but are not limited to, hematopoietic cells and stem cells, such as hematopoietic stem cells.
  • Cell therapy refers to the delivery of a cell or cells into a recipient for therapeutic purposes. Cells described herein may be used in compositions and methods of cell therapy.
  • Hematopoietic cell may refer to a cell that arises from a hematopoietic stem cell. This includes, but is not limited to, myeloid progenitor cells, lymphoid progenitor cells, megakaryocytes, erythrocytes, mast cells, myeloblasts, basophils, neutrophils, eosinophils, macrophages, thrombocytes, monocytes, natural killer cells, T lymphocytes, B lymphocytes and plasma cells.
  • E “Induced pluripotent stem cell” (iPS or iPSC) refer to a pluripotent stem cell that can be generated directly from a somatic cell. This includes, but is not limited to, specialized cells such as skin or blood cells derived from an adult.
  • F “Mesenchymal cell” refers to a cell that is derived from a mesenchymal tissue. In some cases, cells of the disclosure may be mesenchymal cells.
  • G “Mesenchymal stromal cell” (MSC) may refer to a spindle shaped plastic-adherent cell isolated from bone marrow, adipose, and other tissue sources, with multi potent differentiation capacity in vitro.
  • a mesenchymal stromal cell can differentiate into osteoblasts (bone cells), chondrocytes (cartilage cells), myocytes (muscle cells), and adipocytes (fat cells which give rise to marrow adipose tissue).
  • the term mesenchymal stromal cell is suggested in the scientific literature to replace the term “mesenchymal stem cell.”
  • cells of the disclosure may be mesenchymal stromal cells.
  • Mesenchyme refers to a type of animal tissue included of loose cells embedded in a mesh off proteins and fluid, i.e., the extracellular matrix.
  • Multipotent refer to a cell that can develop into more than one cell type but is more limited than a pluripotent cell. For example, adult stem cells and cord blood stem cells may be considered as multipotent.
  • PSC pluripotent stem cell
  • stem cell refers to a cell that can maintain an undifferentiated state indefinitely and can differentiate into most, if not all cells of the body.
  • stem cell refers to an undifferentiated or partially differentiated cell that can differentiate into various types of cells and proliferate indefinitely to produce more of the same stem cell.
  • T-lymphocyte refers to a hematopoietic cell that normally develops in the thymus.
  • T-lymphocytes or T-cells include, but are not limited to, natural killer T cells, regulatory T cells, helper T cells, cytotoxic T cells, memory T cells, gamma delta T cells, and mucosal invariant T cells.
  • M Transfect,” “transform” and “transduce” refer to a process by which exogenous nucleic acid is transferred or introduced into a cell or a host cell.
  • a “transfected” or “transformed” or “transduced” cell is one which has been transfected, transformed, or transduced with exogenous nucleic acid or progeny of the cell.
  • “Complementary” and “complementarity” refer to the association of double-stranded nucleic acids by base pairing through specific hydrogen bonds.
  • the base paring may be standard Watson-Crick base pairing (e.g., 5'-A G T C-3' pairs with the complementary sequence 3'-T C A G-5') or other non-traditional type. Complementarity is typically measured with respect to a duplex region and thus, excludes overhangs, for example.
  • Complementarity between two strands of the duplex region may be partial and expressed as a percentage (e.g., 80%), if only some (e.g., 80%) of the bases are complementary.
  • CpG Island refers to a region with a high frequency of CpG sites. The region is at least 200 bp, with a GC percentage greater than 50%, and an observed-to-expected CpG ratio greater than 60%.
  • Diagnose” and “diagnosis” refer to the identification or classification of a molecular or pathological state, disease, or condition (e.g., cancer). For example, “diagnosis” may refer to identification of a particular type of cancer.
  • Diagnosis may also refer to the classification or staging of a particular subtype of cancer, for instance, by histopathological criteria, or by molecular features (e.g., a subtype characterized by expression of one or a combination of biomarkers (e.g., genes or proteins encoded by said genes)).
  • Domain refers to a section or portion of a polypeptide or a nucleic acid sequence encoding the section or the portion of the polypeptide that contributes to a specified function to the polypeptide.
  • a domain may comprise a contiguous region or more than one distinct non- contiguous regions of a polypeptide.
  • Edit and “editing” with reference to a nucleic acid refers to any change in nucleic acid, including insertion, deletion, and correction. “Editing” can also refer to any epigenetic changes or epigenetic editing. In some cases, “epigenetic editing” refers to the selective and reversible modification of DNA (e.g., methylation, demethylation) and histones (methylation, demethylation, acetylation, deacetylation). The changes can be in a genome of a cell. “Insertion,” “deletion,” and “correction” have the following meanings: (A) “Insertion” refers to an addition of one or more nucleotides in a DNA sequence.
  • Insertions can range from small insertions of a few nucleotides to insertions of large segments such as a cDNA or a gene.
  • “Deletion” refers to a loss or removal of one or more nucleotides in a DNA sequence or a loss or removal of the function of a gene.
  • a deletion can include, for example, a loss of a nucleotide, a few nucleotides, an exon, an intron, a gene segment, or the entire sequence of a gene. Deletion of a gene may include any deletion sufficient result in the elimination or reduction of the function or expression of the gene or its gene product.
  • (C) “Correction” refers to a change of one or more nucleotides of a genome in a cell, whether by insertion, deletion, or substitution. [0063] Editing may also result in a gene knock-in, knock-out or knock-down, each defined as follows: (A) “Knock-in” refers to an addition of a DNA sequence, or fragment thereof into a genome. (B) “Knockout” refers to the elimination of a gene or the expression of a gene. (C) “Knock-down” refers to reduction in the expression of a gene or its gene product(s).
  • “Effector,” “epigenetic effector” and “effector polypeptide” refer to a polypeptide engineered to bind a specific target sequence in chromosomal DNA and modify the DNA or protein(s) associated with DNA at or near the target sequence and modify the target sequence.
  • An epigenetic effector may, in some cases, include a nucleic acid binding moiety and one or more effector moieties.
  • “Effector moiety” refers to a domain that can alter the expression of a target gene when localized to an appropriate site in the nucleus of a cell, e.g., in a target nucleotide sequence.
  • “Epigenetic map” refs to epigenetic state or a portion of a genome (e.g., a gene, a promoter, an enhancer, a transcription start element).
  • “Gene” refers to a combination of polynucleotide elements, that when operatively linked in either a native or recombinant manner, provide some product or function. “Gene” is to be interpreted broadly and can encompass mRNA, cDNA, cRNA and genomic DNA forms of a gene. In some uses, “gene” encompasses the transcribed sequences, including 5' and 3' untranslated regions (5'-UTR and 3'-UTR), exons and introns.
  • the transcribed region will contain “open reading frames” that encode polypeptides.
  • a “gene” comprises only the coding sequences (e.g., an “open reading frame” or “coding region”) necessary for encoding a polypeptide.
  • a “gene” may not encode a polypeptide, for example, ribosomal RNA genes (rRNA) and transfer RNA (tRNA) genes.
  • rRNA ribosomal RNA genes
  • tRNA transfer RNA
  • a “gene” may include not only the transcribed sequences, but in addition, also includes non- transcribed regions including upstream and downstream regulatory regions, enhancers, and promoters.
  • RNA refers to any RNA molecule (or a group of RNA molecules collectively) that facilitates binding of a polypeptide, such as a Cas protein, to a specific location of a target nucleic acid.
  • a single guide RNA can comprise a crRNA and tracrRNA that are fused together.
  • a guide RNA can comprise a crRNA segment and/or a tracrRNA segment.
  • Exemplary guide RNAs include, but are not limited to, crRNAs, pre-crRNAs (e.g., DR-spacer-DR), and mature crRNAs (e.g., mature JDR- spacer, mature DR-spacer-mature JDR).
  • crRNAs include, but are not limited to, crRNAs, pre-crRNAs (e.g., DR-spacer-DR), and mature crRNAs (e.g., mature JDR- spacer, mature DR-spacer-mature JDR).
  • Guide RNA also encompasses an RNA molecule or suitable group of molecular segments that binds a Cas protein other than Cas9 (e.g., Cpfl protein) and that possesses a guide sequence within the single or segmented strand of RNA comprising the functions of a guide RNA which include Cas protein binding to form a gRNA:Cas protein complex capable of binding, nicking and/or cleaving a complementary target sequence in a target polynucleotide.
  • “Homolog” refers to a gene or a protein that is related to another gene or protein by a common ancestral DNA sequence and is functionally similar. Homologous proteins may but need not be structurally related or are only partially structurally related.
  • Ortholog refers to a gene or protein that is related to another gene or protein by a speciation event. Orthologous proteins may in some cases be structurally related or only partially structurally related. In some cases, an ortholog may retain the same function as the gene or protein to which they are orthologous.
  • Non-limiting examples of Cas9 orthologs include: Akkermansia muciniphila Cas9 (AmCas9), Bifidobacterium longum Cas9 (BlCas9), Campylobacter jejuni Cas9 (CjCas9), Francisella novicida Cas9 (FnCas9), Geobacillus stearothermophilus Cas9 (GeoCas9), Legionella pneumophila Cas9 (LpCas9), Neisseria lactamica Cas9 (NlCas9), Neisseria meningitidis Cas9 (NmCas9), Oscillospira luneus Cas9 (OlCas9), Staphylococcus aureus Cas9 (SaCas9), Streptococcus pyogenes Cas9 (SpCas9), Streptococcus thermophilus CRISPR1 Cas9 (St
  • Homologs and orthologs may be identified by homology modeling (e.g., see Filipek, S. (2023). Homology modeling: Methods and protocols. Humana Press.).
  • “Individual,” “patient,” and “subject” refer to any single subject, e.g., a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates) for which treatment is desired.
  • the patient is a human.
  • Methods and “methylating” refer to (i) the addition one or more methyl groups to one or more cysteine residues, or (ii) the replacement of one or more unmethylated cysteine residues with one or more methylated cysteine residues, or (iii) the addition of one or more methyl to one or more sites to one or more histones.
  • “Demethylate” and “demethylating” refer to (i) the removal of one or more methyl groups from one or more cysteine residues, or (ii) the replacement of one or more methylated cysteine residues with one or more unmethylated cysteine residues, or (iii) the removal of one or more methyl residues from one or more sites on one or more histones. [0071] “Modifying,” “modification,” “modulate” and “modulating” refer to a change in the structure, expression levels or activity of a gene or polypeptide as detected by standard art known methods such as those described herein.
  • a modification includes a 10% change in expression levels, a 25% change, a 40% change, and a 50% or greater change in expression levels.
  • “Polynucleotide,” “oligonucleotide,” “nucleic acid,” and “nucleic acid sequence” are used interchangeably to refer to a polymeric form of nucleotides, such as deoxyribonucleotides, ribonucleotides, NS analogs thereof. Polynucleotides may be provided in single-, double-, or multi-stranded form in a linear, branched, or circular conformation.
  • a polynucleotide can be exogenous (e.g., a sequence that is not native to the cell, or a chromosomal sequence whose native location in the genome of the cell is in a different chromosomal location) or endogenous (e.g., a chromosomal sequence that is native to the cell) to a cell.
  • a polynucleotide can exist in a cell-free environment.
  • a polynucleotide can be a gene or fragment thereof.
  • a polynucleotide can be DNA.
  • a polynucleotide can be RNA, e.g., an mRNA.
  • a polynucleotide can comprise one or more analogs (e.g., altered backbone, sugar, or nucleobase).
  • modifications include addition (or removal) of acetyl groups, amino groups, carboxyl groups, carboxymethyl groups, hydroxyl groups, methyl groups, phosphoryl groups, and thiol groups, as well as the substitution of the carbon and nitrogen atoms of the bases with other atoms (e.g., 7- deaza purines).
  • Nucleotide analogs also include dideoxy nucleotides, 2'-0-methyl nucleotides, locked nucleic acids (LNA), peptide nucleic acids (PNA), and morpholinos.
  • Profile refers to a set of one or more biological features determined from a sample. Exemplary features that may be included in a profile include, but are not limited to, epigenetic features (e.g., methylation and/or acetylation status of a CpG site or histone), nucleic acid sequence data, expression data, proteomics data, metabolomics data, results from a functional assay, cellular morphological characteristics, etc.
  • epigenetic features e.g., methylation and/or acetylation status of a CpG site or histone
  • nucleic acid sequence data e.g., expression data, proteomics data, metabolomics data, results from a functional assay, cellular morphological characteristics, etc.
  • Cellular profile refers to phenotypic and epigenetic state of a whole cell.
  • Cellular profile also refers to the epigenetic characteristics of a cell’s genome. Non-limiting examples of epigenetic characteristics include DNA methylation, DNA demethylation, histone methylation, histone demethylation, histone acetylation, histone deacetylation and combinations thereof.
  • B “Epigenetic profile” and “epigenome profile” refer to the epigenetic state of a whole genome.
  • Epigenetic profile and “epigenome profile” also refer to epigenetic characteristics of genomic sequences in cells or tissues. Non-limiting examples of epigenetic characteristics include DNA methylation, DNA demethylation, histone methylation, histone demethylation, histone acetylation, histone deacetylation and combinations thereof.
  • C “Personalized differential cellular state profile” refers to the cellular profile of a cell compared to a healthy and/or young cell of similar type.
  • “Reprogram,” “transdifferentiate” and the like refer to a process that alters or reverses the differentiation state of a differentiated cell (e.g., a somatic cell).
  • Reprogramming can encompass complete reversion of the differentiation state of a differentiated cell (e.g., a somatic cell) to a pluripotent state or a multipotent state.
  • Reprogramming can encompass complete or partial reversion of the differentiation state of a differentiated cell (e.g., a somatic cell) to an undifferentiated cell (e.g., an embryonic-like cell).
  • Reprogramming can result in expression of particular genes by the cells, the expression of which further contributes to reprogramming.
  • Programming of a differentiated cell (e.g., a somatic cell) according to the methods of the disclosure can cause a differentiated cell to assume a less differentiated state, or an undifferentiated state (e.g., an undifferentiated cell).
  • sample refers to a composition that is obtained or derived from a subject and/or individual of interest that contains or may contain a cellular and/or other molecular entity that is to be characterized and/or identified, for example, based on physical, biochemical, chemical, and/or physiological characteristics.
  • Samples include, but are not limited to, tissue samples, primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, vitreous fluid, lymph fluid, synovial fluid, follicular fluid, seminal fluid, amniotic fluid, milk, whole blood, plasma, serum, blood-derived cells, urine, cerebro-spinal fluid, saliva, sputum, tears, perspiration, mucus, tumor lysates, and tissue culture medium, tissue extracts such as homogenized tissue, tumor tissue, cellular extracts, and combinations thereof.
  • tissue homology and “sequence identity” refer to sequence similarity between two peptides or between two nucleic acid molecules.
  • Homology can be determined by comparing a position in each sequence which can be aligned for purposes of comparison. When a position in the compared sequence can be occupied by the same base or amino acid, then the molecules can be homologous at that position. A degree of homology between sequences can be a function of the number of matching or homologous positions shared by the sequences.
  • any particular sequence can be at least 50%, 60%, 70%, 80%, 85%, 90%, 92%, 95%, 96%, 97%, 98% or 99% identical to any sequence described he(which can correspond with a particular nucleic acid sequence described herein), such particular polypeptide sequence can be determined conventionally using known computer programs such as the Bestfit program (Wisconsin Sequence Analysis Package, Version 8 for Unix, Genetics Computer Group, University Research Park, 575 Science Drive, Madison, Wis.53711).
  • the parameters can be set such that the percentage of identity can be calculated over the full length of the reference sequence and that gaps in sequence homology of up to 5% of the total reference sequence can be allowed.
  • sequence identity between a reference sequence (query sequence, i.e., a sequence of the disclosure) and a subject sequence also referred to as a global sequence alignment, can be determined using the FASTDB computer program based on the algorithm of Brutlag et al. (Comp. App. Biosci.6:237-245 (1990)).
  • the subject sequence can be shorter than the query sequence due to N- or C-terminal deletions, not because of internal deletions, a manual correction can be made to the results to take into consideration the fact that the FASTDB program does not account for N- and C-terminal truncations of the subject sequence when calculating global percent identity.
  • the percent identity can be corrected by calculating the number of residues of the query sequence that can be lateral to the N- and C-terminal of the subject sequence, which can be not matched/aligned with a corresponding subject residue, as a percent of the total bases of the query sequence.
  • a determination of whether a residue can be matched/aligned can be determined by results of the FASTDB sequence alignment. This percentage can be then subtracted from the percent identity, calculated by the FASTDB program using the specified parameters, to arrive at a final percent identity score. This final percent identity score can be used for the purposes of this embodiment. In some cases, only residues to the N- and C-termini of the subject sequence, which can be not matched/aligned with the query sequence, can be considered for the purposes of manually adjusting the percent identity score. That is, only query residue positions outside the farthest N- and C-terminal residues of the subject sequence can be considered for this manual correction.
  • a 90-residue subject sequence can be aligned with a 100-residue query sequence to determine percent identity.
  • the deletion occurs at the N-terminus of the subject sequence, and therefore, the FASTDB alignment does not show a matching/alignment of the first ten residues at the N-terminus.
  • the ten unpaired residues represent 10% of the sequence (number of residues at the N- and C-termini not matched/total number of residues in the query sequence) so 10% can be subtracted from the percent identity score calculated by the FASTDB program. If the remaining ninety residues were perfectly matched, the final percent identity can be 90%.
  • a 90-residue subject sequence can be compared with a 100-residue query sequence.
  • the deletions can be internal deletions, so there can be no residues at the N- or C-termini of the subject sequence which can be not matched/aligned with the query.
  • the percent identity calculated by FASTDB can be not manually corrected.
  • residue positions outside the N- and C-terminal ends of the subject sequence, as displayed in the FASTDB alignment, which can be not matched/aligned with the query sequence can be manually corrected for.
  • Any suitable mammal can be treated by a method or composition described herein.
  • mammals include humans, non-human primates (e.g., apes, gibbons, chimpanzees, orangutans, monkeys, macaques, and the like), domestic animals (e.g., dogs and cats), farm animals (e.g., horses, cows, goats, sheep, pigs) and experimental animals (e.g., mouse, rat, rabbit, guinea pig).
  • a mammal is a human.
  • a mammal may be any age or at any stage of development (e.g., an adult, teen, child, infant, or a mammal in utero).
  • a mammal may be male or female.
  • a mammal can be a pregnant female.
  • a subject may be a human.
  • a human may be more than about: 1 day to about 10 months old, from about 9 months to about 24 months old, from about 1 year to about 8 years old, from about 5 years to about 25 years old, from about 20 years to about 50 years old, from about 1 year old to about 130 years old or from about 30 years to about 100 years old.
  • Humans can be more than about: 1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, or 120 years of age.
  • Humans can be less than about: 1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120 or 130 years of age.
  • Epigenetic Maps The methods described herein utilize epigenetic maps of cells of different cellular states and cell types to identify unique methylation markers and patterns that may be contributors to a desired cellular state.
  • an epigenetic map may be represented by coordinates compared to a reference genome.
  • an epigenetic map may be represented graphically.
  • An epigenetic map may be physically displayed, e.g., on a computer monitor.
  • the mapping information can be obtained from the sequence reads to the region.
  • sequence read abundance i.e., the number of times a particular sequence or nucleotide is observed in a collection of sequence reads may be calculated.
  • the epigenetic map depicting peak signals of sequence reads can be generated.
  • the resultant epigenetic map can provide an analysis of the chromatin in the region of interest.
  • the sequence reads are analyzed computationally to produce a number of numerical outputs that are mapped to a representation (e.g., a graphical representation) of a region of interest.
  • an epigenetic map may depict one or more of the following: chromatin accessibility along the region; DNA binding protein (e.g., transcription factor) occupancy for a site in the region, and/or chromatin states along the region.
  • An epigenetic map may further represent the global occupancy of a binding site for the DNA binding protein by, e.g., aggregating data for one DNA binding protein over a plurality of sites to which that protein binds.
  • the map can be annotated with sequence information, and information about the sequence (e.g., the positions of introns, exons, transcriptional start sites, promoters, enhancers, etc.) so that the epigenetic information can be viewed in context with the annotation.
  • an epigenetic map represents global changes in the methylation of across the entire genome of an organism, e.g., a human as well as changes in methylation of a plurality of different regions, e.g., coding sequences, intergenic spacers, regulatory regions, e.g., promoters, etc., of the entire genome, a portion of the genome or near or around or within a particular gene or genes.
  • an epigenetic map can represent the methylation level values of all CpG positions within entire genome of an organism, e.g., a human.
  • an epigenetic map can represent the methylation level values of all CpG positions within a plurality of different regions, e.g., coding sequences, intergenic spacers, regulatory regions, e.g., promoters, etc., of the entire genome, a portion of the genome or near or around or within a particular gene or genes.
  • computationally implemented scripts or tools can be used to generate epigenetic/epigenomic maps.
  • Exemplary scripts or tools that can be utilized include make_homer_ucsc_file, which can create a .bedGraph file which allows for genome-wide pileups of fragment counts; and homer_bedgraph_to_bigwig which can convert the bedGraph file to a binary-compressed bigWig file, used by most genome browsers to visualize fragment coverage across the genome.
  • the analysis can include generating a metric associated with particular elements of a gene. For example, such metrics can include accessibility over a promoter of an annotated gene, or over the coding region of an annotated gene.
  • annotation and generation of metric can be used for further downstream analysis, e.g., comparing epigenetic profiles, clustering and/or biological pathway analysis to produce a differential epigenetic map.
  • an epigenetic map may be a differential epigenetic map.
  • a differential epigenetics map provides a representation of epigenetic modifications that have been made to across a plurality of different regions, e.g., coding sequences, intergenic spacers, regulatory regions, e.g., promoters, etc., of the entire genome, a portion of the genome or near or around or within a particular gene or genes compared to a reference.
  • a differential epigenetics map provides a comparative representation of a first epigenetic map taken at a point in time and a second epigenetic map generated at another point of time to determine what changes have taken place in a specific time period.
  • a differential epigenetics map provides a comparative representation of a first epigenetic map taken obtained before epigenetic modifications that have been made to across a plurality of different regions, e.g., coding sequences, intergenic spacers, regulatory regions, e.g., promoters, etc., of the entire genome, a portion of the genome or near or around or within a particular gene or genes and a second epigenetic map obtained after epigenetic modifications that have been made to across a plurality of different regions, e.g., coding sequences, intergenic spacers, regulatory regions, e.g., promoters, etc., of the entire genome, a portion of the genome or near or around or within a particular gene or genes.
  • a differential epigenetics map provides a representation of epigenetic differences between a plurality of different regions, e.g., coding sequences, intergenic spacers, regulatory regions, e.g., promoters, etc., of the entire genome, a portion of the genome or near or around or within a particular gene or genes located within a first cell and a plurality of different regions, e.g., coding sequences, intergenic spacers, regulatory regions, e.g., promoters, etc., of the entire genome, a portion of the genome or near or around or within a particular gene or genes located within a second cell.
  • the first cell and the second cell are of same type.
  • the first cell and the second cell are of different type. In some embodiments, the first cell and the second cell are of same age. In some embodiments, the first cell and the second cell are of different age, e.g., the first cell is an old cell, and the second cell is a young cell of the same type or vice versa. In some embodiments, the first cell and the second cell are in same cellular state. In some embodiments, the first cell and the second cell are in the different cellular state, e.g., the first cell is in a healthy state and the second cell is in a diseased state or vice versa. [0085] In some embodiments, the epigenetic map can provide information regarding active regulatory regions and/or the transcription factors that are bound to the regulatory regions.
  • the methods described herein comprise generating an epigenetic map of a cellular state.
  • the cellular state can be a state of differentiation, a state of rejuvenation, a state of exhaustion, a state of memory, a biological age, a state of health, a state of disease, or a state of dysfunction.
  • a cellular state can comprise a level of stemness, a stem-like characteristic, or a memory characteristic.
  • a cellular state can comprise a level of exhaustion, a level of differentiation, a disease-associated characteristic, a dysfunction- associated characteristic, or an age-associated characteristic.
  • the methods described herein describe generating an epigenetic map of a cellular state of a cell or tissue type.
  • the cell or tissue type can be defined by one or more characteristics, such as phenotypic properties (e.g., cell surface markers) or certain functional characteristics (e.g., ability to release cytokines).
  • the cell type can be classified by its tissue of origin.
  • the methods described herein can comprise mapping a cellular state of a lymphocyte (e.g., a natural killer cell, a T-cell, or a B-cell).
  • the lymphocyte can be a T-cell.
  • the T-cell can be a CD8+ T-cell, a CD4+ T-cell, or a regulatory T-cell.
  • the methods described herein can comprise mapping a T-cell differentiation state.
  • a T-cell differentiation state can be a na ⁇ ve T-cell state, a central memory T-cell state, an effector T-cell state, or an effector memory T-cell state.
  • the methods described herein can comprise mapping a T-cell treated with an agent.
  • the agent can be a small molecule inhibitor or activator.
  • the agent can be a transcription factor inhibitor.
  • Non-limiting example of transcription factor inhibitor include an AP-1 inhibitor, a BATF inhibitor, a Fra inhibitor, a FOXN3 inhibitor, a Fos inhibitor, an ATF inhibitor, a RUNX1 inhibitor, a RUNX2 inhibitor, a bHLH inhibitor (e.g., a bHLH40/41 inhibitor), a CLOCK inhibitor, an ELK1 inhibitor, a HIC1 inhibitor, an IRF inhibitor (e.g., an IRF4 inhibitor), an NFAT inhibitor, an NF-kB inhibitor, a STAT inhibitor (e.g., a STAT5 inhibitor, a STAT3 inhibitor), a FLI inhibitor, a BMAL inhibitor, a GATA inhibitor, a ROR inhibitor, a PROP inhibitor, a ZNF-189 inhibitor, a Tbet inhibitor, aTcf7 inhibitor, an EOMES inhibitor, a RUNT inhibitor, an NPAS inhibitor, a HOX inhibitor, a ZNF317 inhibitor, a MADS inhibitor, or
  • the transcription factor inhibitor can be a RUNX1 inhibitor.
  • a RUNX1 inhibitor include Ro5-3335, 2-pyridyl benzimidazole AI-4-57 or an analog thereof (e.g., AI-10- 104).
  • the transcription factor inhibitor can be a DNA hypomethylation agent (e.g., a DNMT inhibitor).
  • the DNA hypomethylation agent include, but not limited to, decitabine, guadecitabine, Fdcyd, zebularine, CP-4200, RG108, Nanaomycin A, or azacytidine.
  • the transcription factor inhibitor can be a mTOR inhibitor.
  • Non-limiting examples of a mTOR inhibitor is a sirolimus (rapamycin), everolimus, temsirolimus, TORIN1, TORIN2, ridaforolimus, or KU-0063794.
  • the agent can be a GSK-3 ⁇ inhibitor.
  • Non-limiting examples of a GSK-3 ⁇ inhibitor include a small ion cation, e.g., lithium chloride, TWS-119, CHIR98014, CHIR98023, CHIR99021, SB-216763, SB-415286, AR-A014418, kenpaullone, alsterpaullone, 1-azakenpaullone, Bacpaullone, a pyrazolo [3,4-b] quinoxaline derivative, a 9-oxo-thiazolo [5,4- f] quinazoline-2-carbonitrile derivative, tideglusib, or manzamine A.
  • the transcription factor inhibitor can be an AP-1 inhibitor.
  • Non-limiting examples of AP-1 inhibitor includes SR-11302, T-5224, E3330, 1-Methyl-6-oxo-1,6-dihydropyridine-3-carboxylic acid, Ganoderic acid H, Chloranthalactone B, Torilin, SPC 839, Glucocorticoid receptor modulator 1, Timolumab, Vepalimomab, Vapaliximab, IMM-H007, SSAO/VAP-1 inhibitor 1, DL- Syringaresinol, or (+)-Glaucarubinone.
  • the methods described herein describe methods of generating an epigenetic map.
  • generating an epigenetic map can comprise nanopore sequencing.
  • generating an epigenetic map can comprise methylome sequencing.
  • the methylome sequencing can provide information about methylation states of different sites in a gene or multiple genes.
  • the methylome sequencing can be whole methylome sequencing and provide information about methylation states across the whole genome.
  • methylome sequencing can provide information about the methylation state at specific CpG sites or DNA methylations regions that regulate gene expression through transcriptional silencing of the corresponding gene.
  • DNA methylation states can differ in different cell types or tissue types.
  • DNA methylation states can differ based on state of differentiation, a state of rejuvenation, a state of exhaustion, a state of memory, a biological age, a state of health, a state of disease, or a state of dysfunction.
  • generating an epigenetic map can comprise single-cell methylome sequencing of a cell in a cellular state.
  • the methylome sequencing can be whole methylome sequencing.
  • the cell prior to single-cell methylome sequencing, the cell may be sorted based on expression level of one or more cell markers.
  • a T-cell can be sorted based on expression of or absence of cell marker CD62L.
  • a T-cell can be sorted based on expression of or absence of cell marker CD62RO. In some embodiments, a T-cell can be sorted based on expression of or absence of cell marker CD62RO and expression of or absence of cell marker CD62L. For example, a na ⁇ ve T-cell can be sorted based on a CD62L+ / CD45RO- phenotype. A central memory CD8+ T-cells can be sorted based on a CD62L+ / CD45RO+ phenotype. An effector memory CD8+ T-cells can be sorted based on a CD62L- / CD45RO+ phenotype.
  • An effector CD8+ T-cells can be sorted based on a CD62L- / CD45RO- phenotype.
  • generating an epigenetic map can comprise methylome sequencing of genomic DNA extracted from a cell in a cellular state.
  • the present disclosure also provides methods for associating a cell identity marker with a specific cell type.
  • a cell identity marker can be associated with a specific cell type by comparing epigenetic maps of known cell types, which may be generated by any of the methods described elsewhere herein, e.g., using single cell methylome sequencing of a known cell or using methylome sequencing of extracted genomic DNA from a known cell.
  • a cell identity marker can be a methylation pattern that is unique to a specific cell type.
  • Generating a Differential Cellular State Maps [0095] The methods described herein allow for the direct comparison of two cellular state maps.
  • the present disclosure provides a method of generating a differential state map to identify differential methylation regions (DMRs). The method can comprise (a) generating a first epigenetic map of a first cellular state. The method can further comprise (b) generating a second epigenetic map of a second cellular state.
  • the first cellular state and the second cellular state can be different states of differentiation, different states of rejuvenation, or different states of treatment with an agent (e.g., transcription factor inhibitor, Rapamycin, TWS119, decitabine).
  • the first cellular state and the second cellular state can comprise different levels of stemness, different levels of stem-like characteristic, or different levels of memory characteristics.
  • the method can further comprise (c) determining a difference between the first epigenetic map and the second epigenetic map.
  • the first cellular state and the second cellular state can be both lymphocyte states.
  • the first cellular state and the second cellular state can both be tumor infiltrating lymphocyte states.
  • the first cellular state and the second cellular state can both be regulatory T-cell states. In some embodiments, the first cellular state and the second cellular state can both be CD8+ T-cell states. In other embodiments, the first cellular state and the second cellular state can both be CD4+ T-cell states. [0097] In some embodiments, the first cellular state and the second cellular state can be T-cell differentiation states. In some embodiments, the first cellular state or the second cellular state can be a na ⁇ ve T-cell state, a central memory T-cell state, an effector memory T-cell state, or an effector T-cell state. In some embodiments, the first cellular state and second cellular state can be different T-cell differentiation states.
  • the first cellular state can be a na ⁇ ve T-cell state
  • the second cellular state can be a central memory T-cell state.
  • the first cellular state can be a na ⁇ ve T-cell state
  • the second cellular state can be an effector memory T-cell state.
  • the first cellular state can be a na ⁇ ve T-cell state
  • the second cellular state can be an effector T-cell state.
  • the first cellular state can be a central memory T-cell state
  • the second cellular state can be an effector memory T-cell state.
  • the first cellular state can be a central memory T-cell state, and the second cellular state can be an effector T-cell state. In some embodiments, the first cellular state can be an effector memory T-cell state, and the second cellular state can be an effector T-cell state. [0098] In some embodiments, the first cellular state can be an exhausted state and the second cellular state can be a rejuvenated state relative to the first cellular state. In some embodiments, the first cellular state can be a diseased state and the second cellular state can be a healthy state relative to the first cellular state. In some embodiments, the first cellular state and the second cellular state can be of different cell types. In some embodiments, the different cell types can be from different tissues.
  • the first cellular state and second cellular state can be cellular states from a same individual.
  • the first cellular state and second cellular state can be cellular states of two different cells in the individual at a same time point.
  • the first cellular state can be the state of a na ⁇ ve T-cell and the second cellular state can be the state of an effector T-cell, where the na ⁇ ve T-cell and effector T-cell can be both present in the individual at the same time period.
  • the first cellular state and second cellular state can be cellular states of two different cells in the individual at different time points.
  • the first cellular state can be the state of a diseased cell in an individual at a first time point
  • the second cellular state can be the state of a healthy cell in an individual in a second time point, after the individual has undergone a therapeutic treatment.
  • the first cellular state and second cellular state can be cellular states of different individuals.
  • the first cellular state can be mapped for an older individual, and the second cellular state may be mapped for a younger individual.
  • the first cellular state can be mapped for an individual that has a disease
  • the second cellular state can be mapped for a healthy individual.
  • the method can further comprise generating a cellular state map for a third, fourth, fifth, sixth, seventh, eighth, nineth, tenth or more cellular states.
  • the method can comprise direct comparison among at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or more cellular state maps.
  • the cellular state maps in direct comparison can be derived from a same individual.
  • the cellular state maps can be derived from different cells in the individual at a same time point.
  • the cellular state maps in direct comparison can be derived from different individuals.
  • the cellular state maps can be derived from different cells in different individuals at a same time point.
  • the present disclosure provides methods of generating two epigenetic maps.
  • generating the first epigenetic map comprises single-cell methylome sequencing of a first cell in the first cellular state
  • generating the second epigenetic map comprises single-cell methylome sequencing of a second cell in the second cellular state.
  • the method can comprise generating additional epigenetic maps.
  • the method can comprise generating at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10 or more epigenetic maps.
  • the cells may be sorted based on expression level of one or more cell markers (e.g., CD3, PD1, T-bet, TCF1, TIM3, CD38, CD101, CXCR5, CD45RO, CD45RA, CD39, and CD62L).
  • the first cell can be sorted by its CD62L expression and lack of CD45RO expression of as a CD62L+ / CD45RO- subtype
  • the second cell can be sorted by its CD45RO expression and lack of CD62L expression as a CD62L- / CD45RO+ subtype.
  • generating the first epigenetic map can comprise methylome sequencing of genomic DNA extracted from a first cell in the first cellular state
  • generating the second epigenetic map comprises methylome sequencing of genomic DNA extracted from a second cell in the second cellular state.
  • a third, fourth, fifth, sixth, seventh, eighth, nineth, tenth or more epigenetic maps of different cellular states can be generated by methylome sequencing of genomic DNA extracted from cells.
  • generating a differential cellular state map can further comprise identifying a first methylation pattern specific to the first cellular state and/or identifying a second methylation pattern specific to the first cellular state.
  • identifying the first methylation pattern and/or identifying the second methylation pattern occurs after (c) determining the difference between the first epigenetic map and the second epigenetic map.
  • generating a differential cellular state map can further comprise associating a first methylation pattern to the first cellular state and/or associating a second methylation pattern to the second cellular state.
  • a methylation pattern can be identified for a specific cell type following comparison of the first and second epigenetic maps. In some cases, the methylation pattern can be associated with a specific cell type following comparison of the first and second epigenetic maps.
  • the methylation pattern associated with a specific cell type can be used to identify a nucleic acid from that cell type from a plurality of nucleic acids from a mixture of cell types.
  • the methylation pattern can be used as a cell identity marker and can help identify and/or distinguish sequencing reads associated with the specific cell type. This can aid in the generation of an epigenetic map for a specific cell type from sequencing reads of nucleic acids from a mixture of cell types.
  • the methylation pattern can be used to identify differentially methylated regions.
  • the differentially methylated regions can further identify a nucleic acid associated with differentiation from a first cellular state to a second cellular state.
  • the differentially methylated regions can further identify an epigenetic modulator (e.g., transcription factor, small molecule, epigenetic editor) associated with differentiation from a first cellular state to a second cellular state. Selecting an Epigenetic Modulator Based on a Target Epigenetic Map [0106]
  • the present disclosure provides a method of selecting an epigenetic modulator based on a differential cellular state map generated according to any of the embodiments described herein.
  • the method of selecting an epigenetic modulator can be to identify modulator for cellular reprogramming.
  • the epigenetic modulator can be a transcription factor, a small molecule, an epigenetic editor, or combination thereof.
  • the epigenetic modulator can be a transcription factor.
  • the method of selecting a transcription factor can comprise (a) generating a first epigenetic map of a first cellular state; (b) generating a second epigenetic map of a second cellular state; (c) comparing the first cellular state and the second cellular state to identify differentially methylated regions; and (d) analyzing the differentially methylated regions for enrichment of transcription factor motifs, wherein the motifs represent a target transcription factor.
  • the target transcription factor can have its motif (e.g., footprint) enriched in differentially methylated regions (DMRs) between two cellular states (e.g., cellular differentiation states).
  • the target transcription factor can have its motif (e.g., footprint) enriched in DMRs between two cellular states (e.g., na ⁇ ve T cells versus effector T cells).
  • Non-limiting families of transcription factors that have their motif (e.g., footprint) enriched between two cellular states (e.g., na ⁇ ve T cells versus effector T cells) include bZIP family (e.g., AP-1, VATF, Fra, Fos, ATF), bHLH family (e.g., bHLH40/41, CLOCK, BMAL, NPAS, Max), RUNt family (e.g., RUNX1/2), or combinations thereof.
  • transcription factors include AP-1, bHLEH40, RUNX1, FOXN3, ELK1, HIC1, SP1, NF-kB, BATF, JUNE, IRF4, NFAT, STAT5, STAT3, Fra, Fos, ATF, RUNX2, bHLEH41, CLOCK, BMAL, NPASS, Max,ELK, Fli, Eomes, GATA1, Prop1, ZNF189, ROR, ZNF415, RUNT, T-Bet, MADs, HOX, or ZNF317.
  • the target transcription factor can have its motif (e.g., footprint) enriched in differentially methylated regions between Th17 and Treg.
  • Non-limiting families of transcription factors that have their motif (e.g., footprint) enriched between Th17 and Treg include bZIP family (e.g., AP-1, VATF, Fra, Fos, ATF), ETS family (e.g., ELK, Fli) or combinations thereof.
  • the method of selecting a transcription factor can comprise identifying target transcription factor with differential methylation states (e.g., distinct epigenetic states).
  • the method can comprise (a) generating a first epigenetic map of a first cellular state; (b) generating a second epigenetic map of a second cellular state; (c) comparing the first cellular state and the second cellular state to identify a gene with one or more differential methylation states, wherein the gene is the target transcription factor.
  • the target transcription factor can have differential methylation states between two cellular states (e.g., na ⁇ ve T cells versus effector T cells).
  • the target transcription factor with differential methylation states can be involved in cellular differentiation (e.g., transcription factors involved in differentiation of T cells).
  • the target transcription factor can be involved in CD8 cell differentiation (e.g., differentiation of na ⁇ ve T cells to effector T cells, differentiation of central memory T cells to effector T cells, differentiation of effector memory T cells to effector T cells, or any other combinations thereof).
  • CD8 cell differentiation e.g., differentiation of na ⁇ ve T cells to effector T cells, differentiation of central memory T cells to effector T cells, differentiation of effector memory T cells to effector T cells, or any other combinations thereof.
  • target transcription factors involved in CD8 cell differentiation include AP-1, bHLH, IRF, Eomes, Tbet, Tcf7, Runt, Hox, or MADS.
  • the target transcription factor can be involved in the CD4 differentiation (e.g., differentiation of Th0 to Th1, Th2, Th17, Treg or any other combinations thereof).
  • Non-limiting examples of target transcription factors involved in CD4 cell differentiation include AP-1, GATA, STAT, IRF, Prop1, ZNF189, or ROR.
  • the target transcription factor with differential methylation state can comprise of ELK1, BACH2, RUNX1, or FOXN3.
  • the target transcription factor with differential methylation state can be involved in signaling pathways (e.g., GSK3 signaling pathway).
  • the target transcription factor with differential methylation states can be involved downstream of a signaling pathway (e.g., GSK3 signaling pathway).
  • the target transcription factor with differential methylation states can be involved upstream of a signaling pathway (e.g., GSK3 signaling pathway).
  • the target transcription factor with differential methylation states can be involved in GSK3 signaling pathway (e.g., AXIN1, AXIN2, GSK, LEF1, BCL11B, TCF7, TLE). ).
  • the target transcription factor with differential methylation states can be related to AP-1 or RUNX, such as NFATC2 or RUNX1.
  • the target transcription factor can have both differential methylation states and have its motif (e.g., footprint) enriched in differentially methylated regions.
  • the target transcription factor can have a motif (e.g., footprint) that correspond to sequence of a promoter or an enhancer of gene.
  • the promoter or an enhancer can be of a gene involved in cellular reprogramming.
  • the epigenetic modulator can be a small molecule.
  • the method of selecting a target small molecule can comprise (a) identifying a transcription factor according to any embodiments described herein and identifying the target small molecule that act directly on the transcription factor.
  • the small molecule can be a small molecule inhibitor or activator.
  • the small molecule inhibitor or activator can be capable of enhancing methylation at a target nucleic acid.
  • the small molecule inhibitor or activator may be capable of repressing methylation at a target nucleic acid.
  • the small molecule inhibitor or activator may be capable of enhancing methylation at a target histone. In some embodiments, the small molecule inhibitor or activator may be capable of repressing methylation at a target histone. In some embodiments, the small molecule inhibitor or activator may be capable of enhancing addition of an acetyl group at a target histone. In some embodiments, the small molecule inhibitor or activator may be capable of repressing addition of an acetyl group at a target histone.
  • the small molecule inhibitor may be capable of inhibiting a DNA methyltransferase, DNA demethylase, a histone methyltransferase, a histone demethylase, a histone acetyltransferase, or a histone deacetylase.
  • the small molecule activator may be capable of activating a DNA methyltransferase, DNA demethylase, a histone methyltransferase, a histone demethylase, a histone acetyltransferase, or a histone deacetylase.
  • a small molecule inhibitor can be a transcription factor inhibitor.
  • a small molecule inhibitor can be selected from a GSK-3 ⁇ Inhibitor, an AP-1 inhibitor, a BATF inhibitor, a Fra inhibitor, a FOXN3 inhibitor, a Fos inhibitor, an ATF inhibitor, a RUNX1 inhibitor, a RUNX2 inhibitor, a bHLH inhibitor (e.g., a bHLH40/41 inhibitor), a CLOCK inhibitor, an ELK1 inhibitor, a HIC1 inhibitor, an IRF inhibitor (e.g., an IRF4 inhibitor), an NFAT inhibitor, an NF-kB inhibitor, a STAT inhibitor (e.g., a STAT5 inhibitor, a STAT3 inhibitor), a FLI inhibitor, a BMAL inhibitor, a GATA inhibitor, a ROR inhibitor, a PROP inhibitor, a ZNF-189 inhibitor, a Tbet inhibitor, aTcf7 inhibitor, an EOMES inhibitor, a RUNT inhibitor, an NPAS inhibitor, a HOX inhibitor
  • a small molecule inhibitor or activator can be capable of inhibiting a signaling pathway or activating a signaling pathway.
  • the small molecule inhibitor can be capable of deactivating a GSK3 ⁇ signaling pathway.
  • GSK-3 ⁇ inhibitor include a small ion cation, e.g., lithium chloride, TWS-119, CHIR98014, CHIR98023, CHIR99021, SB-216763, SB-415286, AR- A014418, kenpaullone, alsterpaullone, 1-azakenpaullone, spywarepaullone, a pyrazolo [3,4-b] quinoxaline derivative, a 9-oxo-thiazolo [5,4-f] quinazoline-2-carbonitrile derivative, tideglusib, or manzamine A.
  • the small molecule inhibitor can be capable of activating a GSK3 ⁇ signaling pathway. In some embodiments, the small molecule inhibitor or activator can be capable of inhibiting a signaling pathway of activating a signaling pathway involving AP-1 gene. In some embodiments, the small molecule inhibitor can be an AP-1 inhibitor.
  • AP-1 inhibitors include but not limited to SR-11302, T-5224, E3330, 1- Methyl-6-oxo-1,6-dihydropyridine-3-carboxylic acid, Ganoderic acid H, Chloranthalactone B, Torilin, SPC 839, Glucocorticoid receptor modulator 1, Timolumab, Vepalimomab, Vapaliximab, IMM-H007, SSAO/VAP-1 inhibitor 1, DL-Syringaresinol, or (+)-Glaucarubinone.
  • the small molecule inhibitor or activator can be capable of inhibiting a signaling pathway of activating a signaling pathway involving RUNX gene.
  • the small molecule inhibitor can be a RUNX1 inhibitor.
  • RUNX1 inhibitors include but not limited to a Ro5-3335, 2-pyridyl benzimidazole AI-4-57 or an analog thereof (e.g., AI-10-104).
  • the small molecule inhibitor can be a DNA hypomethylation agent, e.g., a DNMT inhibitor.
  • DNA hypomethylation agent include but not limited to a decitabine, guadecitabine, Fdcyd, zebularine, CP-4200, RG108, Nanaomycin A, and azacytidine.
  • the small molecule inhibitor can be a mTOR inhibitor (e.g., sirolimus (rapamycin), everolimus, temsirolimus, TORIN1, TORIN2, ridaforolimus, or KU-0063794).
  • the present disclosure provides a method of selecting an epigenetic editor based on a differential cellular state map generated according to any of the embodiments described herein.
  • the epigenetic editor can be capable of methylating a target nucleic acid.
  • an epigenetic editor can be or comprise a DNA methyltransferase.
  • the epigenetic editor can be capable of demethylating a target nucleic acid.
  • an epigenetic editor may be or comprise a DNA demethylase.
  • the epigenetic editor can be capable of methylating a target histone.
  • an epigenetic editor can be or comprise a histone methyltransferase.
  • the epigenetic editor can be capable of demethylating a target histone.
  • an epigenetic editor can be or comprise a histone demethylase.
  • the epigenetic editor can be capable of adding an acetyl group to a target histone.
  • an epigenetic editor can be or comprise a histone acetyltransferase.
  • the epigenetic editor can be capable of removing an acetyl group to a target histone.
  • an epigenetic editor can be or comprise a histone deacetylase.
  • the epigenetic editor can be capable of binding to a target nucleic acid.
  • the epigenetic editor comprises a nucleic acid binding domain.
  • the nucleic acid binding domain can be a CRISPR/Cas domain, a zinc finger domain, or a TAL domain.
  • the nucleic acid binding domain is fused to an effector moiety (e.g., DNA methyltransferase, DNA demethylase, a histone methyltransferase, a histone demethylase, a histone acetyltransferase, or a histone deacetylase).
  • an effector moiety e.g., DNA methyltransferase, DNA demethylase, a histone methyltransferase, a histone demethylase, a histone acetyltransferase, or a histone deacetylase.
  • the fusion protein of the nucleic acid binding domain and effector moiety is capable of mediating targeted epigenetic modulators, such as targeted methylation or demethylation at a target nucleic acid site.
  • the fusion protein of the nucleic acid binding domain and effector moiety is capable of mediating targeted epigenetic modulators, such as targeted methylation or demethylation at a target nucleic histone site.
  • targeted epigenetic modulators such as targeted methylation or demethylation at a target nucleic histone site.
  • the present disclosure also provides a method of generating a network of epigenetic modulator based on a differential cellular state map generated according to any of the embodiments described herein.
  • the network of epigenetic modulator can be a network of transcription factors involved in cellular reprogramming.
  • the method of generating a network of transcription factors involved in cellular reprogramming comprises (a) generating a first epigenetic map of a first cellular state; (b) generating a second epigenetic map of a second cellular state; (c) comparing the first cellular state and the second cellular state to identify a first differentially methylated region; (d) analyzing the first differentially methylated region for enrichment of a first transcription factor motif, wherein the first motif represents a first target transcription factor; (e) identifying a second differentially methylation region of the first target transcription factor; and (f) analyzing the second differentially methylated region for enrichment of a second transcription factor motif, wherein the second motif represents a second target transcription factor.
  • the method of generating a network of transcription factors can further comprise identifying a third, a fourth, a fifth, a sixth, a seventh, an eighth, a nineth, a tenth or more transcription factor motif (e.g., footprint).
  • the network of epigenetic modulator can include at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or more transcription factors.
  • the network of epigenetic modulator can further include a small molecule.
  • the network of epigenetic modulator can be a network of small molecules.
  • the network of epigenetic modulator can include at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or more small molecules.
  • the method of selecting an epigenetic modulator can comprise defining an initial epigenetic state and a target epigenetic state based on the differential cellular state map generated according to any of the embodiments described herein.
  • the initial epigenetic state can be defined by a methylation pattern or a combination of methylation patterns that are associated with a specific biological age, an exhausted state, a diseased state, or a dysfunctional state.
  • the target epigenetic state can be defined by a methylation pattern or a combination of methylation patterns that are associated with a rejuvenated state, a healthy state, a biological age, or a high functioning state.
  • the method can further comprise contacting a cell in the initial epigenetic state with an epigenetic modulator (e.g., a small molecule inhibitor, a transcription factor, or an epigenetic editor), thereby producing a treated cell.
  • an epigenetic modulator e.g., a small molecule inhibitor, a transcription factor, or an epigenetic editor
  • the method can further comprise generating a third epigenetic map of the treated cell according to any of the methods described elsewhere herein.
  • the method can further comprise selecting the epigenetic modulator based on the third epigenetic map and the differential cellular state map.
  • the third epigenetic map can inform the epigenetic state of the treated cell (e.g., treated epigenetic state), which can be compared with the initial epigenetic state and the target epigenetic state.
  • the selecting can comprise assessing the difference between the treated epigenetic state and the initial epigenetic state, and/or assessing the difference between the treated epigenetic state and the target epigenetic state.
  • the epigenetic modulator can be selected based on how close the treated epigenetic state is to the target epigenetic state. [0117]
  • the present disclosure provides a method of selecting a combination of epigenetic modulators for cellular treatment based on a differential cellular state map generated according to any of the embodiments described herein.
  • the method can comprise defining an initial epigenetic state and a target epigenetic state based on the differential cellular state map generated from a first epigenetic map and a second epigenetic map according to any of the embodiments described herein.
  • the method can further comprise generating a third epigenetic map of a first cell in the initial epigenetic state contacted with a first epigenetic modulator (e.g., a small molecule, a transcription factor, or an epigenetic editor).
  • a first epigenetic modulator e.g., a small molecule, a transcription factor, or an epigenetic editor.
  • the method can further comprise generating a fourth epigenetic map of a second cell in the initial epigenetic state contacted with a second epigenetic modulator (e.g., a small molecule, a transcription factor, or an epigenetic editor).
  • a second epigenetic modulator e.g., a small molecule, a transcription factor, or an epigenetic editor.
  • the third epigenetic map and/or fourth epigenetic map can be generated according to any of the methods described elsewhere herein.
  • the method can further comprise selecting a combination of the first epigenetic modulator and the second epigenetic modulator for cellular treatment based on the third epigenetic map, the fourth epigenetic map, and the differential cellular state map.
  • Comparison of the epigenetic states of the treated cells informed by the third and fourth epigenetic maps with the initial epigenetic state and the target epigenetic state informed by the differential cellular state map can inform that combining the effect of the two epigenetic modulators can bring the epigenetic state closer to the target epigenetic state than either epigenetic modulator can do alone.
  • Such analyses can inform the selection of a combination of epigenetic modulators for treating a cell, for example, treating a cell to be reprogrammed to a different cellular state (e.g., na ⁇ ve T cell to effector T cell).
  • the present disclosure provides a method of assessing the effect of an epigenetic modulator (e.g., transcription factor, small molecule, epigenetic editor).
  • the method comprises (a) defining a target epigenetic state based on a target epigenetic map of a target cellular state, wherein the target epigenetic map provides a methylation state of each genomic site of a plurality of target genomic sites in a target cell; (b) contacting a cell in an initial cellular state with an epigenetic modulator, thereby producing a treated cell; (c) generating a treated epigenetic map of the treated cell, wherein the epigenetic map provides the methylation state of each genomic site of the plurality of target genomic sites in a treated cell; and (d) comparing the target epigenetic map and the treated epigenetic map to determine the alteration in the methylation state of each genomic site of the plurality of target genomic sites between the target cell and the
  • a target epigenetic state can be defined by a methylation pattern or a combination of methylation patterns that are associated with a rejuvenated state, a healthy state, a biological age, or a high functioning state.
  • the epigenetic map of the treated cell can inform the epigenetic state of the treated cell (e.g., treated epigenetic state), which can be compared with the initial epigenetic state and the target epigenetic state.
  • assessing the effect of an epigenetic modulator can comprise identifying the difference between the treated epigenetic state and the initial epigenetic state, and/or identifying the difference between the treated epigenetic state and the target epigenetic state.
  • assessing the effect of an epigenetic modulator can further comprise determining whether the epigenetic modulator changes the epigenetic state of a cell in a direction that is closer or more similar to the target epigenetic state.
  • an epigenetic modulator can be a modulator that is capable of mediating a change in an epigenetic state.
  • an epigenetic modulator is capable of enhancing methylation in a target nucleic acid.
  • epigenetic modulator is capable of repressing methylation in a target nucleic acid.
  • an epigenetic modulator is capable of enhancing methylation in a target histone.
  • an epigenetic modulator is capable of repressing methylation in a target histone.
  • the epigenetic modulator reduces the expression or activity of a transcription factor.
  • the transcription factor comprises at least one footprint enriched in a differentially methylated region (DMR).
  • the transcription factor comprises differential methylation states.
  • the transcription factor comprises at least one footprint enriched in a DMR and further comprises differential methylations states.
  • the transcription factor is selected from AP-1, bHLEH40, RUNX1, FOXN3, ELK1, HIC1, SP1, NF-kB, BATF, JUNE, IRF4, NFAT, STAT5, STAT3, Fra, Fos, ATF, RUNX2, bHLEH41, CLOCK, BMAL, NPASS, Max,ELK, Fli, Eomes, GATA1, Prop1, ZNF189, ROR, ZNF415, RUNT, T-Bet, MADs, HOX, and ZNF317.
  • the epigenetic modulator is an epigenetic editor. In some embodiments, the epigenetic modulator is a small molecule inhibitor or activator.
  • the epigenetic modulator is a transcription factor inhibitor.
  • the transcription factor inhibitor is selected from a an AP-1 inhibitor, a BATF inhibitor, a Fra inhibitor, a FOXN3 inhibitor, a Fos inhibitor, an ATF inhibitor, a RUNX1 inhibitor, a RUNX2 inhibitor, a bHLH inhibitor (e.g., a bHLH40/41 inhibitor), a CLOCK inhibitor, an ELK1 inhibitor, a HIC1 inhibitor, an IRF inhibitor (e.g., an IRF4 inhibitor), an NFAT inhibitor, an NF-kB inhibitor, a STAT inhibitor (e.g., a STAT5 inhibitor, a STAT3 inhibitor), a FLI inhibitor, a BMAL inhibitor, a GATA inhibitor, a ROR inhibitor, a PROP inhibitor, a ZNF-189 inhibitor, a Tbet inhibitor, a Tcf7 inhibitor, an EOMES inhibitor, a RUNT inhibitor, an NPAS inhibitor,
  • the transcription factor inhibitor is a RUNX1 inhibitor.
  • a RUNX1 inhibitor include Ro5-3335, 2-pyridyl benzimidazole AI-4-57 or an analog thereof (e.g., AI-10-104).
  • the transcription factor inhibitor is a DNA hypomethylation agent (e.g., a DNMT inhibitor).
  • the DNA hypomethylation agent include, but not limited to, decitabine, guadecitabine, Fdcyd, zebularine, CP-4200, RG108, Nanaomycin A, or azacytidine.
  • the transcription factor inhibitor is a mTOR inhibitor.
  • Non-limiting examples of a mTOR inhibitor is a sirolimus (rapamycin), everolimus, temsirolimus, TORIN1, TORIN2, ridaforolimus, or KU-0063794.
  • the epigenetic modulator modulates expression of an intracellular target of a transcription factor (e.g., CD151).
  • the epigenetic modulator is a GSK-3 ⁇ inhibitor.
  • Non-limiting examples of a GSK-3 ⁇ inhibitor include a small ion cation, e.g., lithium chloride, TWS-119, CHIR98014, CHIR98023, CHIR99021, SB-216763, SB-415286, AR-A014418, kenpaullone, alsterpaullone, 1-azakenpaullone, Bacpaullone, a pyrazolo [3,4-b] quinoxaline derivative, a 9- oxo-thiazolo [5,4-f] quinazoline-2-carbonitrile derivative, tideglusib, or manzamine A.
  • the transcription factor inhibitor is an AP-1 inhibitor.
  • Non-limiting examples of AP-1 inhibitor includes SR-11302, T-5224, E3330, 1-Methyl-6-oxo-1,6-dihydropyridine-3- carboxylic acid, Ganoderic acid H, Chloranthalactone B, Torilin, SPC 839, Glucocorticoid receptor modulator 1, Timolumab, Vepalimomab, Vapaliximab, IMM-H007, SSAO/VAP-1 inhibitor 1, DL-Syringaresinol, or (+)-Glaucarubinone.
  • contacting a cell in an initial epigenetic state with an epigenetic modulator can result in no change in epigenetic state of the treated cell.
  • contacting a cell in an initial epigenetic state with an epigenetic modulator e.g., transcription factor, small molecule, epigenetic editor
  • contacting the cell with an epigenetic modulator e.g., transcription factor, small molecule, epigenetic editor
  • contacting the cell with an epigenetic modulator can result in a decrease in methylation in a target nucleic acid.
  • contacting the cell with an epigenetic modulator e.g., transcription factor, small molecule, epigenetic editor
  • contacting the cell with an epigenetic modulator e.g., transcription factor, small molecule, epigenetic editor
  • the epigenetic modulator (e.g., transcription factor, small molecule, epigenetic editor) can bind to a target nucleic acid.
  • the epigenetic modulator e.g., transcription factor, small molecule, epigenetic editor
  • the epigenetic modulator can enhance methylation of at least one CpG site of a target nucleic acid.
  • the epigenetic modulator e.g., transcription factor, small molecule, epigenetic editor
  • contacting the cell with an epigenetic modulator (e.g., transcription factor, small molecule, epigenetic editor) can result in activation of a signaling pathway.
  • contacting the cell with an epigenetic modulator can result in inhibition of a signaling pathway.
  • contacting the cell with an epigenetic modulator e.g., transcription factor, small molecule, epigenetic editor
  • the epigenetic modulator e.g., transcription factor, small molecule, epigenetic editor
  • the method for reprogramming comprises (a) determining the epigenetic state of the target cell in an initial cellular state; and contacting the target cell with a first epigenetic modulator to reprogram the target cell.
  • the present disclosure provides a method for modulating an expression of a gene with an epigenetic modulator described herein.
  • the method of modulating the expression of a gene comprises (a) determining the epigenetic state of the target cell in an initial cellular state; and (b) contacting the target cell with a first epigenetic modulator to reprogram the target cell, wherein the gene is a transcription factor.
  • the method can modulate the expression of at least a first gene and a second gene wherein the first gene and the second gene are involved in a genetic pathway.
  • the method can comprise (a) determining the epigenetic state of the target cell in an initial cellular state; (b) generating a first epigenetic map of the target cell wherein the first epigenetic map provides a methylation state of each genomic site of a plurality of target genomic sites located in the first gene in the target cell before contacting with the epigenetic modulator; (c) contacting the target cell with a first epigenetic modulator; (d) generating a second epigenetic map of the target cell wherein the second epigenetic map provides the methylation state of each genomic site of a plurality of target genomic sites located in the first gene and the methylation state of each genomic site of a plurality of target genomic sites located in the second gene in the target cell; and (e) comparing the first and the second epigenetic map and determining the difference between the first and the second map
  • the method disclosed herein can modulate the expression of a gene, wherein a protein encoded by the gene is an intracellular target of a transcription factor.
  • the method can comprise (a) determining the epigenetic state of the target cell in an initial cellular state; (b) generating a first epigenetic map of the target cell wherein the first epigenetic map, provides a methylation state of each genomic site of a plurality of target genomic sites located in the gene in the target cell before contacting with the epigenetic modulator; (c) contacting the target cell with a first epigenetic modulator; (d) generating a second epigenetic map of the cell wherein the second epigenetic map, provides the methylation state of each genomic site of a plurality of target genomic sites located in the gene in the target cell; and (e) comparing the first and the second epigenetic map and determining the difference between the first and the second map to identify if a desired methylation state of each genomic site of the plurality of target
  • the gene can encode CD151.
  • the gene can encode a protein that is involved in a genetic pathway selected from one or more of: PI3K/Akt, NF- ⁇ B, ERK, Wnt / ⁇ -catenin, or GPCR signaling pathway.
  • the gene can encode a protein that interacts with a second protein selected from one or more of MMP9, RAC1, MET, RNF128, USP13, PRDM8, SNCA, PLK4, MAML3, ZDHHC11B, LINC00461, PCDHB17P, SOX30, RGS14, DCDC, or XIAP.
  • the present disclosure provides a method of modulating an expression of a first transcription factor and a second transcription factor with an epigenetic modulator described herein.
  • the method of modulating an expression of a first transcription factor and a second transcription factor comprises (a) determining the epigenetic state of the target cell in an initial cellular state; (b) generating a first epigenetic map of the target cell wherein the first epigenetic map provides a methylation state of each genomic site of a plurality of target genomic sites located in a first gene encoding the first transcription factor in the target cell before contacting with the epigenetic modulator; (c) contacting the target cell with a first epigenetic modulator, (d) generating a second epigenetic map of the target cell wherein the second epigenetic map provides the methylation state of each genomic site of a plurality of target genomic sites located in the first gene encoding the first transcription factor and the methylation state of each genomic site of a plurality of target genomic sites located in
  • the first transcription factor can comprise at least one footprint enriched in a DMR.
  • the second transcription factor can comprise at least one footprint enriched in a DMR.
  • the first transcription factor can comprise differential methylations states.
  • the second transcription factor can comprise differential methylations states.
  • the first transcription factor can comprise at least one footprint enriched in a DMR and further comprises differential methylations states.
  • the second transcription factor can comprise at least one footprint enriched in a DMR and further can comprise differential methylations states.
  • the first transcription factor can comprise at least one footprint enriched in a DMR and the second transcription factor can comprise at least one footprint enriched in a DMR.
  • the first transcription factor can comprise at least one footprint enriched in a DMR and the second transcription factor can comprise differential methylations states. In some embodiments, the first transcription factor can comprise at least one footprint enriched in a DMR and the second transcription factor can comprise at least one footprint enriched in a DMR and further comprises differential methylations states. In some embodiments, the first transcription factor comprises differential methylations states and the second transcription factor can comprise at least one footprint enriched in a DMR. In some embodiments, the first transcription factor can comprise differential methylations states and the second transcription factor can comprise differential methylations states.
  • the first transcription factor can comprise differential methylations states and the second transcription factor can comprise at least one footprint enriched in a differentially methylated region (DMR) and further can comprise differential methylations states.
  • the first transcription factor can comprise at least one footprint enriched in a differentially methylated region (DMR) and further comprises differential methylations states and the second transcription factor comprises at least one footprint enriched in a differentially methylated region (DMR).
  • the first transcription factor can comprise at least one footprint enriched in a differentially methylated region (DMR) and further can comprise differential methylations states and the second transcription factor comprises differential methylations states.
  • the first transcription factor can comprise at least one footprint enriched in a differentially methylated region (DMR) and further can comprise differential methylations states and the second transcription factor can comprise at least one footprint enriched in a differentially methylated region (DMR) and further can comprise differential methylations states.
  • DMR differentially methylated region
  • DMR differentially methylated region
  • the method of reducing off-target edits in a target cell comprises (a) determining the epigenetic state of the target cell in an initial cellular state; (b) generating a first epigenetic map of the target cell wherein the first epigenetic map, provides a methylation state of each genomic site of a plurality of target genomic sites located in the target cell before contacting with the epigenetic modulator; (c) generating a second epigenetic map of the cell wherein the second epigenetic map, provides the methylation state of each genomic site of a plurality of target genomic sites located in the target cell; and (d) comparing the first and the second epigenetic map and determining the difference between the first and the second map to identify if a desired methylation state of each genomic site of the plurality of target genomic sites in the target cell is achieved.
  • the method further can comprise generating a second epigenetic map of the cell after contacting with the first epigenetic modulator. In some embodiments, the method further can comprise contacting the target cell with a second epigenetic modulator if desired methylation state of each genomic site of the plurality of target genomic sites in the target cell is not achieved. In some embodiments, the method disclosed herein can be used for bi-directional editing. In some embodiments, the method disclosed herein can be used for bi-directional editing to prevent cross talk as disclosed in US 63/487,538, which is incorporated by reference herein in its entirety.
  • the plurality of target genomic site can be selected from one or more of a differentially methylated region, an enhancer, a promoter, a transcription start site (TSS), or a CTCF binding site.
  • the target genomic site can comprise a CTCF binding site.
  • the target genomic site can comprise a differentially methylated region. The method of any preceding embodiments, wherein the differentially methylated region is unmethylated and the epigenetic modulator has methylation activity. In some embodiments, the differentially methylated region can be hypomethylated and the epigenetic modulator can have methylation activity.
  • the differentially methylated region can be hypermethylated and the epigenetic modulator can have demethylation activity. In some embodiments, the differentially methylated region can be unmethylated in the cell and the epigenetic modulator can have methylation activity. [0132] In some embodiments, the first and the second epigenetic modulator can be identical. In some embodiments, the first and the second epigenetic modulator cannot identical. In some embodiments, the method further can comprise comparing the first and the second epigenetic map and determining changes in methylation states in each genomic site of a plurality of a non- target genomic sites to identify off-target edits.
  • the method further can comprise contacting the target cell with a third epigenetic modulator to reverse the methylation states in each genomic site of the plurality of the non-target genomic sites to restore the methylation state of methylation states in each genomic site of the plurality of the non-target genomic sites to the methylation state prior to contacting the target cell with the first epigenetic modulator.
  • the plurality of non-target genomic site can be selected from one or more of a differentially methylated region, an enhancer, a promoter, a transcription start site (TSS), or a CTCF binding site.
  • the first, the second, and the third epigenetic modulator can identical.
  • the first, the second, and the third epigenetic modulator cannot be identical. In some embodiments, the first and the second modulator can be identical, and the third epigenetic modulator cannot be identical. In some embodiments, the first and the third modulator can be identical, and the second epigenetic modulator cannot be identical. In some embodiments, the second and the third modulator can be identical, and the first epigenetic modulator can not be identical. Blocking Modifications at Off-Target Genomic Sites [0133] In another aspect, the present disclosure provides a method of blocking a modification at an off-target genomic site.
  • An off-target genomic site can be a genomic site that is unintentionally targeted or a site where a modification is undesired.
  • an off-target genomic site comprises an epigenetic cellular identity marker.
  • An epigenetic cellular identity marker can be correlated with the identity (i.e., cellular differentiation state) of cell, as described elsewhere herein.
  • loss of the epigenetic cellular identity markers causes the cell to lose its cellular identity.
  • Cell identity can be dictated by the specific set of genes expressed and proteins produced in the cell that are activated by the epigenetic state of the cell to enable its unique function. Altering the epigenetic state of the epigenetic cellular identity markers can cause a loss of cellular state identity.
  • the methods described herein can preserve the epigenetic state of the one or more epigenetic cellular identity markers, e.g., through blocking a modification at an off-target genomic site comprising a cellular identity marker.
  • the cell can be contacted with a blocking reagent that specifically binds to one or more selected epigenetic cellular identity markers.
  • the modification enzymes are sterically prevented from modifying the protected marker.
  • the cellular identity of the cell may be preserved when epigenetic cellular identity markers are protected by the blocking reagent.
  • the blocking reagent can include a nucleic acid binding moiety that specifically binds to an off-target genomic site, e.g., an epigenetic cellular identity marker.
  • the nucleic acid binding moiety may specifically bind based on the nucleic acid sequence at the epigenetic locus (that is, the nucleic acid binding moiety can bind to the locus irrespective of the status of the epigenetic marker).
  • the nucleic acid binding moiety can be a nuclease-deficient targeted nucleic acid binding moiety.
  • the blocking reagent may include a CRISPR-based editing platform, which can include a dead endonuclease domain (e.g., a dead Cas9) domain.
  • the CRISPR-based editing platform of the blocking reagent may further include one or more single guide RNA (sgRNA) molecules that targets one or more epigenetic cellular identity markers, e.g., a blocking guide RNA.
  • sgRNA single guide RNA
  • a blocking guide RNA can comprise a nucleic acid sequence that is complementary to the off-target genomic site identified by any of the methods described herein.
  • the blocking guide RNA is configured to bind to a CRISPR/Cas domain, wherein the CRISPR/Cas domain – blocking guide RNA complex binds to the off-target genomic site.
  • the CRISPR/Cas domain can be catalytically inactive.
  • the nuclease-deficient targeted DNA binding domain comprises a transcription activator-like effector (TALE) nucleic acid binding moiety or a zinc finger nucleic acid binding moiety that specifically bind the off-target genomic site, e.g., an epigenetic cellular identity marker.
  • TALE transcription activator-like effector
  • the CRISPR/Cas domain – blocking guide RNA complex, the TALE nucleic acid binding moiety, or the zinc finger nucleic acid binding moiety prevents a modification, e.g., methylation, demethylation, acetylation, or acetylation, from occurring at the off-target genomic site.
  • the nucleic acid binding moiety used with the blocking reagent is not fused or bound to an epigenetic effector.
  • Reprogramming a Cell provides a method of reprogramming a cell, which may in some cases be a partial reprogramming.
  • a cell can be reprogrammed, e.g., partially reprogrammed, by contacting the cell with a blocking reagent that specifically binds to an off-target genomic site, e.g., a site that comprises an epigenetic cellular identity marker or a site where it is undesired to introduce a modification; and contacting the cell with an epigenetic effector described elsewhere herein that modifies a target site.
  • the blocking reagent inhibits modification of the off-target genomic site.
  • the cell is simultaneously contacted with the blocking reagent and the epigenetic effector such that the blocking reagent inhibits the epigenetic effector from modifying the off-target site.
  • the cell is contacted with the blocking reagent, wherein the blocking reagents binds to an off-target genomic site, and then subsequently contacted with an epigenetic effector, wherein the epigenetic effector introduces a modification at a target site.
  • the method may further include culturing the cell after contacting the cell with the blocking reagent and the epigenetic effector.
  • the blocking reagent can comprise a first CRISPR/Cas domain that is deficient in nuclease activity and a first guide RNA, e.g., a blocking guide RNA, that binds to the off-target genomic site.
  • the first guide RNA can hybridize to a sequence at the off-target genomic site and recruit the first CRISPR/Cas domain to the off-target genomic site, thereby blocking the off-target genomic site from epigenetic editing.
  • the blocking reagent comprises a TALE domain or a zinc finger domain that specifically binds to the off-target genomic site.
  • the epigenetic effector can comprise a second CRISPR/Cas domain and a second guide RNA, a TALE domain, or a zinc finger domain that specifically binds to the target site.
  • the epigenetic effector can comprise an effector moiety as described elsewhere herein that introduces an epigenetic modification to the target site.
  • the blocking reagent comprises a first CRISPR/Cas domain
  • the epigenetic effector comprises a second CRISPR/Cas domain, wherein the first CRISPR/Cas domain and the second CRISPR/Cas domain do not cross-react. Examples of orthogonal CRISPR/Cas domains that do not cross-react are described elsewhere herein.
  • the method may further include identifying and/or selecting the off-target genomic site and/or selecting the target site for guide epigenetic editing using one or more methods described elsewhere herein.
  • Identification and/selection of the off-target genomic site or the target site may be, for example, based a known association between the epigenetic marker and a cellular identity, disease state, and/or biological age.
  • identification and/selection may be based on the epigenetic markers of a desired cellular state profile, an undesired cellular state profile, and/or a differential profile.
  • identification and/selection may be based on a differential detected between two or more epigenetic maps, as described elsewhere herein.
  • the method further includes selecting one or more off-target genomic sites; selecting one or more target sites for guided epigenetic editing, wherein the one or more selected target sites excludes the one or more off-target genomic sites; and contacting the cell with one or more epigenetic effectors targeted to the one or more selected target sites.
  • the method may further include contacting the cell with a blocking reagent that specifically binds to the one or more selected off-target genomic sites, wherein the blocking reagent inhibits modification of the selected one or more off-target genomic sites.
  • the methods described herein allow for parallel, multiple epigenetic editing of targets (e.g., CpG sites and/or histones) and associated modifications (e.g., methylation, demethylation, acetylation and/or deacetylation) for the epigenetic markers, and/or the preservation of multiple epigenetic makers (e.g., cellular identity markers) at off-target genomic sites.
  • targets e.g., CpG sites and/or histones
  • modifications e.g., methylation, demethylation, acetylation and/or deacetylation
  • multiple epigenetic makers e.g., cellular identity markers
  • the desired edits and/or targeted preservations may be selected computationally by leveraging previous experimental data and/or other available datasets (e.g., publicly available data sets).
  • the selection process may be iterative. For example, after initial reprogramming, the modified cells may be profiled to evaluate multi-omic and functional effects of the epigenetic modifications.
  • Methods of reprograming a cell may be performed in vivo (e.g., in a subject), ex vivo (e.g., outside of a subject), or in vitro (e.g., using a cell line).
  • the one or more epigenetic effectors and/or blocking reagents may be administered to an individual.
  • the epigenetic effectors and/or blocking reagents may be administered, for example, using a vector (such as a viral vector), which allows for expression of the epigenetic effectors and/or blocking reagents in the cell, which causes the partial reprogramming.
  • the vector may be targeted to a particular cell type.
  • the method may be performed ex vivo, for example by obtaining a cell (or population of cells) from a subject. In some embodiments, the reprogrammed cell taking from the subject may then be readministered to the subject. [0142] In some embodiments, the method may be used to reprogram an immune cell. In some embodiments, the method may be used to reprogram an immune cell ex vivo. In some embodiments, the method may be used to reprogram an immune cell in immunosenescence. In some embodiments, the method may be used to reprogram an immune cell out of immunosenescence. In some embodiments, the method may be used to reprogram an immune cell for adoptive cell therapy.
  • the reprogrammed cell may be, in some embodiments, administered to a subject, which may be the same subject or a different subject from which the original cell was obtained.
  • the method may be used to reprogram a cell in vivo. Such partial reprogramming may be used to treat, for example, fibrosis in lung, liver, kidney, heart, or neurodegenerative disease, or type 2 diabetes.
  • the method may be used in reprogram a pancreatic beta cell in vivo.
  • FIG.32A shows an exemplary method for reprogramming a cell. Although the figure is shown representing steps in a particular order, the illustrated steps may be performed in any suitable order.
  • one or more epigenetic cellular identity markers are selected.
  • the one or more epigenetic cellular identity markers may be associated (i.e., correlated) with the identity of the cell subject to the partial reprogramming method.
  • one or more target epigenetic markers are selected.
  • the one or more target epigenetic markers are those epigenetic markers intended to be modified, for example an epigenetic marker associated with biological aging or a disease state.
  • a blocking reagent that specifically binds to the one or more selected epigenetic cellular identity markers is contacted with the cell. In some embodiments, the blocking reagent is added to a cellular medium containing the cell.
  • the blocking reagent is expressed in the cell, for example using a heterologous vector controlled by an inducible promoter.
  • exemplary forms of the blocking agent may include mRNA, integrative DNA, non-integrative DNA, and/or proteins.
  • Exemplary methods of introducing the blocking reagent into the cell include (1) passive uptake through the media, (2) transfection, (3) transduction (e.g., using various viruses, lentivirus, AAV, etc.), (4) activation of endogenous genes, and (5) lipid nanoparticles.
  • dCAS9 with guide RNAs may be used for specific cell identity markers may be introduced into the cell through transduction using AAV2.
  • dCAS9 protein and guide RNAs are introduced into the cell directly through electroporation.
  • the cell is contacted with one or more targeted cellular reprogramming factors to modify the target epigenetic markers.
  • the one or more cellular reprogramming factors may be introduced in the same manner or different manner as the blocking agent.
  • the one or more cellular reprogramming factors are added to a cellular medium containing the cell.
  • the one or more cellular reprogramming factors are expressed in the cell, for example using a heterologous vector controlled an inducible promoter.
  • Exemplary methods of introducing the cellular reprogramming factors into the cell include (1) passive uptake through the media, (2) transfection, (3) transduction (e.g., using various viruses, lentivirus, AAV, etc.), (4) activation of endogenous genes, and (5) lipid nanoparticles.
  • FIG.1A shows step 106 occurring prior to step 108, these steps may occur in either order or simultaneously.
  • the cell is cultured in the presence of the blocking reagent and the one or more modification enzymes, which allows the modification enzymes to modify the targeted epigenetic marker while the blocking regent protects the one or more selected epigenetic cellular identity markers.
  • the method may occur in vivo.
  • FIG.32B shows an exemplary method for reprogramming a cell, which includes at least rejuvenating the cell. Although the figure is shown representing steps in a particular order, the illustrated steps may be performed in any suitable order.
  • one or more epigenetic cellular identity markers are selected. The one or more epigenetic cellular identity markers may be associated (i.e., correlated) with the identity of the cell subject to the partial reprogramming method.
  • one or more target epigenetic markers are selected. The one or more target epigenetic markers are those epigenetic markers intended to be modified, for example an epigenetic marker associated with biological aging or a disease state.
  • the cell is at least rejuvenated, for example by contacting the cell with one or more non-targeted cellular reprogramming factors (e.g., one or more transcription factors, such as one or more Yamanaka factors).
  • Contacting the cell with the one or more non-targeted cellular reprogramming factors can include, for example, adding the one or more non-targeted cellular reprogramming factors to the cell medium containing the cell.
  • contacting the cell with the one or more non-targeted cellular reprogramming factors can include expressing the one or more transcription factors in the cell, for example using a heterologous vector controlled an inducible promoter.
  • Exemplary methods of introducing the non-targeted cellular reprogramming factors into the cell include (1) passive uptake through the media, (2) transfection, (3) transduction (e.g., using various viruses, lentivirus, AAV, etc.), (4) activation of endogenous genes, and (5) lipid nanoparticles.
  • a blocking reagent that specifically binds to the one or more selected epigenetic cellular identity markers is contacted with the cell.
  • the blocking reagent is added to a cellular medium containing the cell.
  • the blocking reagent is expressed in the cell, for example using a heterologous vector controlled by an inducible promoter.
  • Exemplary forms of the blocking agent may include mRNA, integrative DNA, non-integrative DNA, and/or proteins.
  • Exemplary methods of introducing the blocking reagent into the cell include (1) passive uptake through the media, (2) transfection, (3) transduction (e.g., using various viruses, lentivirus, AAV, etc.), (4) activation of endogenous genes, and (5) lipid nanoparticles.
  • dCAS9 with guide RNAs may be used for specific cell identity markers may be introduced into the cell through transduction using AAV2.
  • dCAS9 protein and guide RNAs are introduced into the cell directly through electroporation.
  • the cell is contacted with one or more targeted cellular reprogramming factors to modify the target epigenetic markers.
  • the one or more modification enzymes or fragments are added to a cellular medium containing the cell.
  • the one or more modification enzymes or fragments are expressed in the cell, for example using a heterologous vector controlled an inducible promoter.
  • Exemplary methods of introducing the targeted cellular reprogramming factors into the cell include (1) passive uptake through the media, (2) transfection, (3) transduction (e.g., using various viruses, lentivirus, AAV, etc.), (4) activation of endogenous genes, and (5) lipid nanoparticles.
  • FIG.32B shows step 116 occurring prior to step 118, and step 118 occurring prior to step 120, these steps may occur in either order or simultaneously.
  • the cell is cultured in the presence of the blocking reagent and the one or more modification enzymes, which allows the modification enzymes to modify the targeted epigenetic marker while the blocking regent protects the one or more selected epigenetic cellular identity markers.
  • the method may occur in vivo. Selecting a Cellular Reprogramming Protocol [0146]
  • the present disclosure provides a cellular reprogramming protocol selected to reprogram a cell to a target cell.
  • the target cell can have a target epigenetic map, which can include a methylation state of each genomic site of the target cell or an epigenetic status of one or more epigenetic cellular identity markers.
  • the target epigenetic map may also include an epigenetic status of one or more target epigenetic markers.
  • the target epigenetic map need not include the statuses of all epigenetic markers of the target cell; for example, certain epigenetic markers may not significantly alter the cell’s identity or age/disease status.
  • the selected protocol can optimally modify the epigenetic markers of the cell being modified to match the target cell. [0147] To select the cellular reprogramming protocol, a plurality of cell samples can be tested in parallel using different cellular reprogramming protocols. The term “parallel” as used herein does not require the plurality of cell samples to be tested simultaneously.
  • An epigenetic map of the cells in the plurality of cell samples can be obtained during the testing protocol and compared to the test epigenetic map to determine a differential.
  • the cellular reprogramming protocols can be examined longitudinally (i.e., using different protocols) and longitudinally (i.e., at different time points) to select the protocol and timepoint that matches the target epigenetic map.
  • the process can be performed iteratively to further optimize the protocol.
  • a plurality of cell samples may be cultured according to different cellular reprogramming protocols for a period of m days, wherein the epigenetic map that matches the target epigenetic profile is obtained by culturing a cell sample according to protocol x for n days (wherein n is less than m) but may not fully reach the target epigenetic map.
  • protocol x for n days wherein n is less than m
  • a plurality of cell samples may be cultured according to protocol x for n days, followed by different protocols for an additional time, with epigenetic maps obtained at different time points during this additional time and compared to the target epigenetic map to determine a differential and identify the optimal protocol that reprograms a cell to match the selected target epigenetic map.
  • the method can include selecting a target epigenetic map.
  • the target epigenetic map is an epigenetic map of a cell desired to be matched according to the optimized cellular reprogramming protocol.
  • the target epigenetic map may be selected, for example, from a database of epigenetic maps or empirically determined.
  • the epigenetic map of a target cell may be determined (for example, using a methylation sequencing (methyl-seq) method).
  • Exemplary profiling techniques may include, for example, epigenetic profiling, transcriptomic profiling, proteomic profiling, cell imaging, determining a cellular state, a functional assay, multi-omics profiling, metabolic profiling, flow cytometry, whole genome bisulfite sequencing, single-cell sequencing, ATAC sequencing, single-cell ATAC sequencing, a methylation microarray profiling, methylation sequencing, single-cell methylation sequencing, single-cell RNA sequencing, or nucleic acid sequencing.
  • the target cell is profiled using single-cell sequencing, methylation sequencing, or single-cell methylation sequencing.
  • the target cell includes a desired identify characteristic (e.g., a particular type of cell or cellular state) and can include one or more additional desired phenotypes (for example, a desired age or desired disease status associated with an epigenetic map).
  • the target epigenetic map can include one or more cellular identity markers and an associate maker status for each of the one or more cellular identity markers.
  • the target epigenetic map may further include one or more target epigenetic markers and an epigenetic status of the one or more target epigenetic markers.
  • the target epigenetic markers are markers other than the one or more cellular identity markers that are associated with the desired phenotype of the cell.
  • the one or more target epigenetic markers may be associated (i.e., correlated) with a biological age or disease state.
  • the epigenetic map of the plurality of cell samples at the start of the method should be the same or approximately so that changes can be effectively monitored according to the different tested protocols. That is, differences in the epigenetic maps obtained during the reprogramming protocol should be due to the protocol itself rather than differences between the starting epigenetic statuses of the cells.
  • Cells in the plurality of cell samples are cultured under different conditions according to a plurality of different cellular reprogramming protocols.
  • the cellular reprogramming protocol may include contacting the cells with one or more epigenetic modulators (e.g., epigenetic effector) that modify one or more epigenetic markers of the cells and/or a blocking reagent that specifically binds to an off-target genomic site, e.g., a site that comprises an epigenetic cellular identity marker or a site where it is undesired to introduce a modification.
  • epigenetic modulators e.g., epigenetic effector
  • a blocking reagent that specifically binds to an off-target genomic site, e.g., a site that comprises an epigenetic cellular identity marker or a site where it is undesired to introduce a modification.
  • the blocking reagent can inhibit modification of a selected epigenetic marker by the one or more cellular reprogramming factors, for example to preserve an existing epigenetic status of the selected epigenetic marker.
  • the epigenetic modulators e.g., epigenetic effectors
  • blocking reagents may be contacted with the different cell samples at different doses (i.e., the amount of cellular reprogramming factor or blocking reagent) and/or at different dosing schedules.
  • different doses may include a 1x, 1.5x, 2x, 2.5x, 3x, 4x, 4.5x, 5x, etc. difference in dosing of the cellular reprogramming factors and/or blocking reagent for the different cell samples.
  • Different dosing schedules may include differences of when the cells in the different plurality of cell samples are contacted with the cellular reprogramming factors and/or blocking reagents.
  • different dosing schedules can include twice daily dosing, once daily dosing, dosing every two days, dosing every three days, dosing every four days, etc.
  • the dosing schedule may include one or more dosing periods and one or more non-dosing periods.
  • a dosing schedule may include dosing period at a selected dosing frequency (e.g., daily, every other day, etc.) for a first period of time (e.g., one week, two weeks, etc.) followed by a non-dosing period where the cells are not contacted with the cellular reprogramming factors and/or blocking reagents for a second period of time (e.g., one week, two weeks, etc.).
  • the dosing schedule may include a first dosing period at a first selected dosing frequency and a second dosing period at a second selected dosing frequency (e.g., where the first and second selected dosing frequencies are different), which may optionally be interceded by a non-dosing period.
  • cells in the plurality of cell samples may be contacted with different epigenetic modulators (e.g., epigenetic effectors) and/or different blocking reagents.
  • the cell samples may be contacted with these different epigenetic modulators (e.g., epigenetic effectors) and/or different blocking reagents according to different dosing schedules, as discussed above.
  • the blocking reagent is added to a cellular medium containing the cell.
  • the blocking reagent is expressed in the cell, for example using a heterologous vector controlled by an inducible promoter.
  • Exemplary forms of the blocking agent may include mRNA, integrative DNA, non-integrative DNA, and/or proteins.
  • Exemplary methods of introducing the blocking reagent into the cell include (1) passive uptake through the media, (2) transfection, (3) transduction (e.g., using various viruses, lentivirus, AAV, etc.), (4) activation of endogenous genes, and (5) lipid nanoparticles.
  • dCAS9 with guide RNAs may be used for specific cell identity markers may be introduced into the cell through transduction using AAV2.
  • dCAS9 protein and guide RNAs are introduced into the cell directly through electroporation.
  • Epigenetic maps for the cells in the plurality of cell samples i.e., test epigenetic maps
  • the epigenetic maps may be obtained, for example, at predetermined intervals.
  • the epigenetic maps may be obtained daily, every two days, every three days, every four days, every five days, every six days, every seven days, or any other desired interval.
  • the epigenetic map(s) of cells in the plurality of cell samples may be determined (for example, using a methylation sequencing (methyl-seq) method).
  • methyl-seq methylation sequencing
  • By obtaining an epigenetic map for the different cell samples at a plurality of time points it is possible to monitor the progress of the epigenetic modifications and recognize when the status of a particular epigenetic marker has been modified. Thus, for example, it is possible to determine that a particular reprogramming protocol is desirable until a time point wherein an undesired epigenetic change is made.
  • a differential between the target epigenetic maps and test epigenetic maps can be obtained, thereby providing a differential epigenetic map.
  • the differential epigenetic map indicates differences between the target epigenetic map and the test epigenetic map.
  • a cellular reprogramming protocol may be selected based on the differential epigenetic maps for the plurality of cell samples.
  • the selected cellular reprogramming protocol may be the cellular reprogramming protocol that provides the test epigenetic map closest (which may be quantitatively determined) to the target epigenetic map.
  • the differential epigenetic map may be analyzed to determine a number of epigenetic markers in the test epigenetic map that have a status that differs from corresponding epigenetic markers in the target epigenetic profile. Not all epigenetic markers need to be evaluated to determine this number, and the analysis may be limited to preselected epigenetic markers.
  • the differential epigenetic map may be analyzed to determine a number of preselected epigenetic cellular identity markers in the test epigenetic profile that have a status that differs from corresponding epigenetic cellular identity markers in the target epigenetic map.
  • the selected cellular reprogramming map does not result in a loss of a predetermined threshold number the one or more selected cellular identity markers of the target epigenetic map.
  • the selected cellular reprogramming protocol does not result in a loss of a predetermined proportion of the one or more cellular identity markers of the target epigenetic map.
  • the selected cellular reprogramming protocol may provide a test epigenetic profile that has less than 40%, less than 30%, less than 20%, less than 10%, or less than 5% loss of the one or more cellular identity markers of the target epigenetic map.
  • said selected protocol may be used to reprogram a cell. Such reprogramming may be performed, for example, in vitro.
  • An exemplary method for selecting a cellular reprogramming protocol is shown in FIG. 33.
  • a target epigenetic map is selected.
  • the target epigenetic map includes preselected epigenetic markers for a target cell, which may be a real or hypothetical cell.
  • the ideal protocol selected according to the method would result in the epigenetic map a of a treated according to the selected protocol cell to match the target epigenetic map.
  • the target epigenetic map can include one or more prelected epigenetic cellular identity markers.
  • the epigenetic cellular identity markers are correlated with the cellular identity of the target cell.
  • the target epigenetic map may include epigenetic markers in addition to the cellular identity markers, for example epigenetic markers correlated with a biological age or disease state.
  • the target epigenetic map further includes one or more target epigenetic markers, which may be associated with a biological age or disease state.
  • the one or more epigenetic cellular identity markers and/or the one or more target epigenetic markers may include, for example, one or more CpG sites and/or one or more histones.
  • cells in the plurality of cell samples are cultured by contacting the cells with one or more epigenetic modulators (e.g., epigenetic effectors) that modify one or more epigenetic markers of the cells.
  • the cells in the plurality of cell samples may be obtained, for example, from a cell line.
  • Exemplary cells in the plurality of cell samples include fibroblasts, keratinocytes, peripheral mononuclear blood cells, hepatocytes, neural cells, blood cells, immune cells, lung cells, pancreatic cells (e.g., pancreatic beta cells or pancreatic alpha cells), cardiomyocytes, oligodendrocytes, or epithelial cells.
  • Different cell samples are contacted with different doses of the one or more epigenetic modulators (e.g., epigenetic effectors) and/or using different dosing schedules for the one or more cellular reprogramming factors.
  • epigenetic modulators e.g., epigenetic effectors
  • culturing the plurality of cell samples at 331 may include contacting the cells in the plurality of cell samples with one or more epigenetic modulators (e.g., epigenetic effectors) that target the one or more target epigenetic markers.
  • the one or more epigenetic modulators (e.g., epigenetic effectors) that target the one or more target epigenetic markers may be targeted, for example, using a nuclease-deficient targeted DNA binding protein.
  • the one or more epigenetic modulators (e.g., epigenetic effectors) that target the one or more target epigenetic markers are targeted using a CRISPR-based editing platform.
  • the CRISPR- based editing platform of the one or more epigenetic modulators can include one or more single guide RNA (sgRNA) molecules that targets one or more target epigenetic markers.
  • the CRISPR-based editing platform of the one or more epigenetic modulators comprises a dead Cas9 endonuclease.
  • the nuclease-deficient targeted DNA binding protein of the one or more CRISPR-based editing platform comprises a transcription activator-like (TAL) effector DNA- binding domain or a zinc finger DNA binding domain.
  • TAL transcription activator-like
  • culturing the plurality of cell samples at 331 may further include contacting the cells in the plurality of cell samples with a blocking reagent that specifically binds to one or more of the one or more epigenetic cellular identity markers.
  • the plurality of cells need not be dosed with the same amount of blocking reagent or at the same dosing schedule.
  • culturing comprises contacting the cells in the plurality of cell samples with a blocking reagent at different doses of the blocking reagent or using different dosing schedules for the blocking reagent.
  • the method can further include obtaining test epigenetic maps for the cells in the plurality of cell samples at a plurality of time points during the culturing. For example, a subsample from each of the cell samples may be taken from each of the cellular samples and analyzed to determine an epigenetic map.
  • the term “test” epigenetic map is used to distinguish from the “target” epigenetic profile.
  • the test epigenetic maps of from the plurality of cell samples at the different time points are compared to the target epigenetic map at 333, thereby determining a differential epigenetic map for each of the obtained test epigenetic maps.
  • a cellular reprogramming protocol is selected at 334 based on the differential epigenetic maps.
  • the protocol that provides the fewest epigenetic differences between the associated test epigenetic map and the target epigenetic map is selected.
  • the selected cellular reprogramming protocol does not result in a loss of a predetermined threshold number of the one or more cellular identity markers of the target epigenetic map.
  • the selected cellular reprogramming protocol does not result in a loss of a predetermined threshold proportion of the one or more cellular identity markers of the target epigenetic map.
  • the selected cellular reprogramming protocol modifies a predetermined threshold number or proportion of the one or more target epigenetic markers.
  • the method may further include reprogramming a cell (for example a cell obtained from an individual (i.e., not a cell line) is reprogrammed according to the selected cellular reprogramming protocol.
  • a cell for example a cell obtained from an individual (i.e., not a cell line) is reprogrammed according to the selected cellular reprogramming protocol.
  • FIG.34 Another exemplary method for selecting a cellular reprogramming protocol is shown in FIG.34.
  • a target epigenetic map is selected.
  • the target epigenetic map includes preselected epigenetic markers for a target cell, which may be a real or hypothetical cell.
  • the ideal protocol selected according to the method would result in the epigenetic map a of a treated according to the selected protocol cell to match the target epigenetic map.
  • the target epigenetic map can include one or more prelected epigenetic cellular identity markers.
  • the epigenetic cellular identity markers are correlated with the cellular identity of the target cell.
  • the target epigenetic map may include epigenetic markers in addition to the cellular identity markers, for example epigenetic markers correlated with a biological age or disease state.
  • the target epigenetic profile further includes one or more target epigenetic markers, which may be associated with a biological age or disease state.
  • the one or more epigenetic cellular identity markers and/or the one or more target epigenetic markers may include, for example, one or more CpG sites and/or one or more histones.
  • cells in the plurality of cell samples are cultured by contacting the cells with one or more epigenetic modulators (e.g., epigenetic effectors) that modify one or more epigenetic markers of the cells and a blocking reagent that specifically binds to an off-target genomic site, e.g., a site that comprises an epigenetic cellular identity marker or a site where it is undesired to introduce a modification.
  • epigenetic modulators e.g., epigenetic effectors
  • a blocking reagent that specifically binds to an off-target genomic site, e.g., a site that comprises an epigenetic cellular identity marker or a site where it is undesired to introduce a modification.
  • the cells in the plurality of cell samples may be obtained, for example, from a cell line.
  • Exemplary cells in the plurality of cell samples include fibroblasts, keratinocytes, peripheral mononuclear blood cells, hepatocytes, neural cells, blood cells, immune cells, lung cells, pancreatic cells (e.g., pancreatic beta cells or pancreatic alpha cells), cardiomyocytes, oligodendrocytes, or epithelial cells.
  • pancreatic cells e.g., pancreatic beta cells or pancreatic alpha cells
  • cardiomyocytes oligodendrocytes
  • epithelial cells epithelial cells.
  • test epigenetic profile is used to distinguish from the “target” epigenetic map.
  • the test epigenetic maps of from the plurality of cell samples at the different time points are compared to the target epigenetic map at 343, thereby determining a differential epigenetic map for each of the obtained test epigenetic maps.
  • a cellular reprogramming protocol is selected at 344 based on the differential epigenetic maps. For example, in some implementations, the protocol that provides the fewest epigenetic differences between the associated test epigenetic map and the target epigenetic map is selected. In some implementations, the selected cellular reprogramming protocol does not result in a loss of a predetermined threshold number of the one or more cellular identity markers of the target epigenetic map.
  • the selected cellular reprogramming protocol does not result in a loss of a predetermined threshold proportion of the one or more cellular identity markers of the target epigenetic map. In some implementations, the selected cellular reprogramming protocol modifies a predetermined threshold number or proportion of the one or more target epigenetic markers.
  • the method may further include reprogramming a cell (for example a cell obtained from an individual (i.e., not a cell line) is reprogrammed according to the selected cellular reprogramming protocol.
  • Cellular identity marker As described herein, the present disclosure in part provides a cellular identity marker.
  • the epigenetic cellular identity marker can be correlated with the identity (i.e., cellular differentiation state) of cell. Loss of the epigenetic cellular identity markers may cause the cell to lose its cellular identity. See, for example, Basu et al.,Epigenetic reprogramming of cell identity: lessons from development for regenerative medicine, Clinical Epigenetics, vol.13, no. 144 (2021).
  • the cellular identity of a cell can be the cellular differentiation state, for example, an immune cell (or particular type of immune cell), neural cell, epithelial cell, etc. In some cases, cell identity is dictated by the specific set of genes expressed and proteins produced in the cell that are activated by the epigenetic state of the cell to enable its unique function.
  • the epigenetic cellular identity marker is selected from a database.
  • a database may be generated, for example, by comparing epigenetic profiles of different types of cells. The specific epigenetic sites across the genome of the different types of cells are compared and sites that are highly specific to a given tissue and cell are selected. For example, this could be in the form of a specific set of CpG sites in particular location in the genome that are unmethylated for cardiomyocytes but are methylated in all other tissues.
  • Exemplary cellular identity markers are described in Moss et al., Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease, Nat. Commun., vol.9, no. 5068 (2016); Loyfer et al., A human DNA methylation atlas reveals principles of cell type- specific methylation and identifies thousands of cell type-specific regulatory elements, Biorxiv 2022.01.24.477547 (2022); and Cui et al., A human tissue map of 5-hydroxymethylcytosines exhibits tissue specificity through gene and enhancer modulation, Nat. Commun., vol.11, no. 6161 (2020).
  • the present disclosure provides a blocking reagent.
  • the blocking reagent can be capable of blocking an off-target genomic site from an epigenetic modification.
  • the blocking reagent can include a nucleic acid binding moiety that is capable of specifically binding to an off-target genomic site, e.g., an epigenetic cellular identity marker.
  • the nucleic acid binding moiety may be configured to bind based on the nucleic acid sequence at the epigenetic locus (that is, the nucleic acid binding moiety can bind to the locus irrespective of the status of the epigenetic marker).
  • the nucleic acid binding moiety can be a nuclease-deficient targeted nucleic acid binding moiety.
  • the blocking reagent may include a CRISPR-based editing platform, which can include a dead endonuclease domain (e.g., a dead Cas9) domain.
  • the CRISPR-based editing platform of the blocking reagent may further include one or more single guide RNA (sgRNA) molecules that targets one or more epigenetic cellular identity markers, e.g., a blocking guide RNA.
  • sgRNA single guide RNA
  • a blocking guide RNA can comprise a nucleic acid sequence that is complementary to the off-target genomic site identified by any of the methods described herein.
  • the blocking guide RNA is configured to bind to a CRISPR/Cas domain, wherein the CRISPR/Cas domain – blocking guide RNA complex binds to the off-target genomic site.
  • the present disclosure in part provides an epigenetic modulator.
  • the modulator increases or decreases the expression of a target gene, e.g., a transcription factor.
  • the epigenetic modulator further comprises a Cas9 protein.
  • the epigenetic modulator demethylates the target sequence.
  • the epigenetic modulator activates the target gene.
  • the epigenetic modulator may be or comprises a epigenetic modulator is a transcription factor inhibitor, e.g., a transcription factor inhibitor selected from a an AP-1 inhibitor, a BATF inhibitor, a Fra inhibitor, a FOXN3 inhibitor, a Fos inhibitor, an ATF inhibitor, a RUNX1 inhibitor, a RUNX2 inhibitor, a bHLH inhibitor (e.g., a bHLH40/41 inhibitor), a CLOCK inhibitor, an ELK1 inhibitor, a HIC1 inhibitor, an IRF inhibitor (e.g., an IRF4 inhibitor), an NFAT inhibitor, an NF-kB inhibitor, a STAT inhibitor (e.g., a STAT5 inhibitor, a STAT3 inhibitor), a FLI inhibitor, a BMAL inhibitor, a GATA inhibitor, a ROR inhibitor, a PROP inhibitor, a ZNF-189 inhibitor, a Tbet inhibitor, aTcf7 inhibitor, an EO
  • a transcription factor inhibitor
  • the epigenetic modulator is a GSK-3 ⁇ inhibitor.
  • GSK-3 ⁇ inhibitor include a small ion cation, e.g., lithium chloride, TWS-119, CHIR98014, CHIR98023, CHIR99021, SB-216763, SB-415286, AR-A014418, kenpaullone, alsterpaullone, 1-azakenpaullone, Bacpaullone, a pyrazolo [3,4-b] quinoxaline derivative, a 9- oxo-thiazolo [5,4-f] quinazoline-2-carbonitrile derivative, tideglusib, or manzamine A.
  • Non-limiting examples of a RUNX1 inhibitor include Ro5-3335, 2-pyridyl benzimidazole AI-4-57 or an analog thereof (e.g., AI-10-104).
  • the transcription factor inhibitor is a DNA hypomethylation agent, e.g., a DNMT inhibitor, e.g., decitabine, guadecitabine, Fdcyd, zebularine, CP-4200, RG108, Nanaomycin A, or azacytidine.
  • the nucleic acid is a DNA, e.g., genomic DNA.
  • the nucleic acid is a RNA, e.g., mRNA.
  • the effector moiety is capable of altering methylation profile of a genome of a cell.
  • effector moiety can modify a nucleic acid by increasing or decreasing methylation in a target nucleic acid.
  • the effector moiety modifies the chromatin structure of a cell through histone modifications, e.g., via modulating histone methylation and/or acetylation profile.
  • the epigenetic modulator may be capable of binding to a site in a CpG island of a target nucleic acid and introducing an epigenetic modification at a desired target site. In some embodiments, the epigenetic modulator may be capable of methylating or demethylating at least one CpG site of a target nucleic acid. [0182] In some embodiments, the epigenetic modulator is capable of binding to a transcription regulatory element. In some embodiments, the epigenetic modulator is capable of binding to a transcription regulatory element selected from a promoter, an enhancer, a silencer, an insulator, a locus control region, or a transcription start site operably linked to a gene.
  • the epigenetic modulator is capable of binding to a promoter element. In some embodiments, the epigenetic modulator is capable of binding to a promoter element selected from a TATA box, a CAAT box, a GC box, an INR, a DPE, an MTE, a DCE, or a BRE. In some embodiments, the epigenetic modulator is capable of binding to a TATA box. In some embodiments, the epigenetic modulator is capable of binding to a CAAT box. In some embodiments, the epigenetic modulator is capable of binding to a GC box. In some embodiments, the epigenetic modulator is capable of binding to an INR.
  • the epigenetic modulator is capable of binding to a DPE. In some embodiments, the epigenetic modulator is capable of binding to an MTE. In some embodiments, the epigenetic modulator is capable of binding to a DCE. In some embodiments, the epigenetic modulator is capable of binding to a BRE.
  • the consensus sequences of exemplary promoter elements are provided in Table 1 below.
  • the promoter may be constitutively active. Alternatively, in some embodiments, the promoter may be conditionally active (e.g., where transcription is initiated only under certain physiological conditions).
  • the epigenetic modulator is capable of binding to an enhancer. In some embodiments, the epigenetic modulator is capable of binding to a silencer.
  • the epigenetic modulator is capable of binding to an insulator. In some embodiments, the epigenetic modulator is capable of binding to a locus control region. In some embodiments, the epigenetic modulator is capable of binding to a transcription start site. [0183] Table 1.
  • a nucleic acid binding moiety binds to its target sequence with a KD of less than or equal to 500, 450, 400, 350, 300, 250, 200, 150, 100, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.09, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01, 0.005, 0.003, 0.002, or 0.001M.
  • a nucleic acid binding moiety does not bind, e.g., does not detectably bind to a non-target sequence.
  • the nucleic acid binding moiety comprises a sequence complimentary, e.g., at least 5%, at least 10%, at least 15%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 99%, or 100% complimentary to the target sequence.
  • an epigenetic modulator may comprise a fusion protein comprising a nucleic acid binding domain and an effector domain.
  • the nucleic acid binding domain of an epigenetic modulator may be located at the N-terminus or C-terminus of the effector domain. In some cases, the nucleic acid binding domain is located at the N-terminus of the effector domain.
  • the epigenetic modulator may comprise any combination of arrangements of the nucleic acid binding moiety and the effector moiety described in this disclosure.
  • the epigenetic modulator e.g., an epigenetic modulator described herein may be capable of methylation, demethylation, acetylation, and/or deacetylation.
  • the epigenetic modulator is capable of adding or removing a methyl group in a nucleic acid.
  • the epigenetic modulator is capable of adding or removing a methyl group in a histone.
  • the epigenetic modulator is capable of adding or removing an acetyl group in a histone.
  • the effector moiety comprises M.TaqI, M.EcoDam, M.CcrMI, or CamA.
  • the epigenetic modulator e.g., an epigenetic effector described herein may comprise multiple effector moieties, e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 effector moieties.
  • the 2nd, 3rd, 4th, 5th, 6th, 7th, 8th, 9th, or 10th effector moiety is selected from one or more of DNMT3A1, DNMT3A2, DNMT3B1, DNMT3B2, DNMT3B3, DNMT3B4, DNMT3B5, DNMT3B6, DNMT3L, TRDMT1, MQ1, MET1, DRM2, CMT2, CMT3, TET1, TET2, TET3, SETDB1, SETDB2, EHMT2 (i.e., G9A), EHMT1 (i.e., GLP), SUV39H1, EZH2, EZH1, SUV39H2, SETD8, SUV420H1, SUV420H2, KDM1A (i.e., LSD1), KDM1B (i.e., LSD2), KDM2A, KDM2B, KDM5A, KDM5B, KDM5C, KDM5D, KDM4B
  • KDM1A i
  • the epigenetic modulator e.g., an epigenetic effector described herein may simultaneously demethylate and transcriptionally activate a target site. In some embodiments, the epigenetic modulator, e.g., an epigenetic effector described herein may simultaneously acetylate and transcriptionally repress a target site. In some embodiments, the epigenetic modulator, e.g., an epigenetic effector described herein may simultaneously deacetylate and transcriptionally activate a target site. [0190] In some embodiments, the effector moiety of the epigenetic effector may enhance or repress methylation in a target nucleic acid.
  • the effector moiety may be or may comprise a moiety capable of effecting DNA demethylation.
  • the effector moiety may be or comprise a DNA demethylase.
  • the effector moiety may comprise a member of the TET family.
  • the effector moiety may be selected from TET1, TET2, and TET3, or a functional equivalent thereof.
  • the effector moiety may be or comprise TDG.
  • the effector moiety of the epigenetic effector may increase or decrease methylation or acetylation in a histone. Increasing or decreasing methylation or acetylation in a histone can modify chromatin structure.
  • the effector moiety comprises DOT1L, PRDM9, PRMT1, PRMT2, PRMT3, PRMT4, PRMT5, NSD1, NSD2, NSD3, ROM2, AtHD3A, HDAC11, HDAC8, SIRT3, SIRT6, HST2, a SETDB1 domain, a NuRD domain, or a TET family protein domain.
  • the effector moiety of the epigenetic effector may be or comprise a histone demethylase or a functional equivalent thereof.
  • the effector moiety of the epigenetic effector may be or comprise a transcription factor regulator or DNA-binding domain.
  • the transcription factor regulator or DNA-binding domain may be selected from a KRAB domain, KAP1 domain, MECP2 domain, VP16, P64, p65, FOXA1, FOXA2, FOXO3, FOXO1, TOX, TOX3, TOX4, ID2, ID1, CREM, SCX, TWST1, CREB1, TERF1, ID3, GSX1, ATF1, TWST2, ZMYM3, I2BP1, RHXF1, I2BL, TRI68, HXB13, HEY1, PHC2, FIGLA, SAM11, KMT2B, HEY2, JDP2, ASCL4, HHEX, GSX2, ASCL3, PHC1, OTP, I2BP2, VGLL2, HXA11, PDLI4, ASCL2, CDX4, ZN860, NKX25, ISL1, CDX2, PROP1, H
  • the epigenetic or chromatin modifier may be selected from a TET protein (e.g., TET1), an ERF protein (e.g., ERFl, ERF3), LSD1, PYGO1, KRAB, MeCP2, SIN3A, HDT1, MBD2B, NIPP1, VP64, HP1A, Rb, SUVR4, COBB, NCOR, or HP1A.
  • TET1 TET1
  • ERF protein e.g., ERFl, ERF3
  • an epigenetic modulator comprises a protein having a sequence as recited in Uniprot ref: Q8NFU7 or a protein encoded by a nucleotide sequence as recited in NCBI Accession: Accession: NM_030625.3, GI: 1519311914; or Accession: NM_001406365.1 , GI: 2238345226; or Accession: NM_001406367.1, GI: 2238345083; or Accession: NM_001406368.1, GI: 2238345245; or Accession: NM_001406369.1, GI: 2238345201; or Accession: NM_001406370.1, GI: 2238345031; or Accession:
  • an epigenetic modulator comprises a functional fragment or variant of any thereof, or a polypeptide with a sequence that has at least 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% identity to any of the above-referenced sequences.
  • an epigenetic modulator comprises a protein having a sequence as recited in Uniprot ref: Q9Y6K1 or a protein encoded by a nucleotide sequence as recited in NCBI Accession: NM_001320892.2, GI: 1677500358; or Accession: NM_001320893.1, GI: 1003701584; or Accession: NM_001375819.1, GI: 1034612234; or Accession: NM_022552.5, GI: 1812533218; or Accession: NM_153759.3, GI: 371940994; or Accession: NM_175629.2, GI: 371940990; or Accession: NM_175630.1,GI: 28559070.
  • an epigenetic modulator comprises a protein having a sequence as recited in Uniprot ref: Q9UJW3 or a protein encoded by a nucleotide sequence as recited in NCBI Accession: NM_013369.4, GI: 1676318741; or Accession: NM_175867.3, GI: 1732746326.
  • an epigenetic modulator comprises a functional fragment or variant of any thereof, or a polypeptide with a sequence that has at least 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% identity to any of the above-referenced sequences.
  • the epigenetic modulator can be part of a construct that comprises a Cas9 protein. In some embodiments, the epigenetic modulator methylates the target sequence. In some embodiments, the epigenetic modulator deactivates the target gene. [0206] In some embodiments, an epigenetic modulator comprises a protein having a sequence as recited in Uniprot ref: P21506 or a protein encoded by a nucleotide sequence as recited in NCBI Accession: NM_015394.5, GI: 1519244023.
  • an epigenetic modulator comprises a functional fragment or variant of any thereof, or a polypeptide with a sequence that has at least 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% identity to any of the above- referenced sequences.
  • the epigenetic modulator can be part of a construct that comprises a Cas9 protein.
  • the epigenetic modulator methylates the target sequence.
  • the fusion construct deactivates the target gene.
  • the epigenetic modulator is a transcriptional enhancer. A transcriptional enhancer can increase gene transcription.
  • the transcriptional enhancer is a transcriptional activator, a protein that acts via recruitment of transcription activator proteins, modifier of target gene, such as demethylation, recruitment of DNA modifier, modulator of histones associated with target DNA, recruitment of a histone modifier (e.g., acetylation and/or methylation of histones).
  • the epigenetic modulator comprises multiple transcriptional enhancers, e.g., at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 transcriptional enhancers.
  • proteins (or fragments thereof) that can be used in increase transcription include but are not limited to: transcriptional activators such as VPR (e.g., VPR-p65-Rta), VP16, VP64, VP48, VP160, MyoD1, HSF1,RTA, SET7/9, a p65 subdomain (e.g., from NFkB), and activation domain of EDLL and/or TAL activation domain (e.g., for activity in plants); histone lysine methyltransferases such as SET1A, SET1B, MLL1 to 5, ASH1, SYMD2, NSD1, and the like; histone lysine demethylases such as JHDM2a/b, UTX, JMJD3, and the like; histone acetyltransferases such as GCN5, PCAF, CBP, p300, p300 core, TAF1, TIP60/PLIP, MOZ/MYST3, MORF/MYST4, S
  • the transcriptional enhancer is a VP64. In some embodiments, the transcriptional enhancer is a p300 or a p300 core. In some embodiments, the transcriptional enhancer is p300. In some embodiments, the transcriptional enhancer comprises H3K27ac. In some embodiments, the transcriptional enhancer is a BRD4.
  • the expression level of a target gene that is enhanced via the transcriptional enhancer disclosed herein is at least about 50%, at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 91%, at least about 92%, at least about 93%, at least about 94%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, at least about 99%, at least about 100%, at least about 101%, at least about 102%, at least about 103%, at least about 104%, at least about 105%, at least about 106%, at least about 107%, at least about 108%, at least about 109%, at least about 110%, at least about 115%, at least about 120%, at least about 125%, at least about 130%, at least about 140%, at least about 150%, at least about 160%, at least about 170%, at least about 180%, at least about 190%, at least about 200%, or more
  • the expression level of a target gene that is enhanced via the transcriptional enhancer disclosed herein is at least about 0.1-fold, at least about 0.2-fold, at least about 0.3-fold, at least about 0.4-fold, at least about 0.5-fold, at least about 0.6-fold, at least about 0.7-fold, at least about 0.8-fold, at least about 0.9-fold, at least about 1-fold, at least about 1.1-fold, at least about 1.2-fold, at least about 1.3-fold, at least about 1.4-fold, at least about 1.5-fold, at least about 1.6-fold, at least about 1.7- fold, at least about 1.8-fold, at least about 1.9-fold, at least about 2-fold, at least about 2.5-fold, at least about 3-fold, at least about 3.5-fold, at least about 4-fold, at least about 4.5-fold, at least about 5-fold, at least about 6-fold, at least about 7-fold, at least about 8-fold, at least about 9-fold, at least about 10-fold,
  • the transcriptional enhancer described herein can be part of a construct comprising any one or more of the nucleic acid binding domains described herein.
  • the transcriptional enhancer can be part of a construct comprising any one or more of the nucleic acid binding domains described herein and any one or more of the epigenetic effectors described herein (e.g., Cas9-DNMT3A-VP64).
  • the construct can comprise a nucleic acid binding domain and an epigenetic effector, and further comprise another nucleic acid binding domain and a transcriptional enhancer (e.g., Cas9- DNMT3A+Cas9-VP64).
  • the transcriptional enhancer can be located at the N-terminus or C-terminus of the nucleic acid binding domain. In some embodiments, the transcriptional enhancer can be located at the N-terminus or C-terminus of the epigenetic effector. In some embodiments, the epigenetic effector can be located at the N-terminus or C- terminus of the nucleic acid binding domain. In some embodiments, the transcriptional enhancer can be placed in a spatial orientation which allows it to affect the transcription of the target. In some embodiments, a transcriptional enhancer can be advantageously positioned to affect the transcription of the target, and a nuclease can be advantageously positioned to cleave or partially cleave the target.
  • a transcriptional repressor moiety described herein can be part of a construct comprising any one or more of the nucleic acid binding domains described herein. In some embodiments, the transcriptional repressor moiety can be part of a construct comprising any one or more of the nucleic acid binding domains described herein and any one or more of the epigenetic effectors described herein (e.g., Cas9-DNMT3A-KRAB).
  • the construct can comprise a nucleic acid binding domain and an epigenetic effector, and further comprise another nucleic acid binding domain and a transcriptional repressor moiety (e.g., Cas9- DNMT3A+Cas9-KRAB).
  • a transcriptional repressor moiety e.g., Cas9- DNMT3A+Cas9-KRAB.
  • the transcriptional repressor moiety can be located at the N-terminus or C-terminus of the nucleic acid binding domain.
  • the transcriptional repressor moiety can be located at the N-terminus or C- terminus of the epigenetic effector.
  • the epigenetic effector can be located at the N-terminus or C-terminus of the nucleic acid binding domain.
  • the transcriptional repressor moiety can be placed in a spatial orientation which allows it to affect the transcription of the target.
  • a transcriptional repressor moiety can be advantageously positioned to affect the transcription of the target, and a nuclease can be advantageously positioned to cleave or partially cleave the target. This may include positions other than the N ⁇ /C ⁇ terminus of the nucleic acid binding domain.
  • nucleic acid binding moiety of a nucleic acid binding domain may bind to a target nucleic acid, e.g., a DNA, e.g., a genomic RNA, e.g., a RNA, e.g., an mRNA.
  • the nucleic acid binding moiety may bind one or more genomic sequences in a cell.
  • the nucleic acid binding moiety may be or comprise a CRISPR/Cas domain, a zinc finger domain, a TAL domain, a tetR domain, a meganuclease, or an oligonucleotide.
  • a nucleic acid binding moiety may be or comprises a CRISPR/Cas domain.
  • the CRISPR/Cas domain comprises one or more RNA molecules, which can be a crRNA and/or a tracrRNA and/or optionally, an engineered guide RNA or sgRNA.
  • the CRISPR/Cas domain forms a complex with its partner RNA or RNAs.
  • the CRISPR/Cas domain and RNA complex utilizes RNA-DNA base pairing to determine the binding site to a target nucleic acid, to each genomic site of the plurality of target genomic sites in a target cell.
  • the CRISPR/Cas domain optionally complexed with its partner sgRNA or sgRNAs binds to a CpG site in a target nucleic acid. In some embodiments, the CRISPR/Cas domain optionally complexed with its partner sgRNA or sgRNAs binds to a protospacer adjacent motif (PAM) sequence in the target nucleic acid. In some embodiments, the PAM sequence is located within a CpG Island in a target nucleic acid. [0213] In some embodiments, the CRISPR/Cas domain may comprise a CRISPR/Cas protein.
  • a CRISPR/Cas domain may be derived from a protein involved in a Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) system or have structural and/or functional similarities to a protein involved in the CRISPR system and optionally a guide RNA, e.g., a single guide RNA (sgRNA).
  • CRISPR Clustered Regularly Interspaced Short Palindromic Repeats
  • sgRNA single guide RNA
  • Three classes (I-III) of CRISPR systems have been identified.
  • the class II CRISPR systems use a single Cas endonuclease (rather than multiple Cas proteins).
  • One class II CRISPR system includes a type II Cas endonuclease such as Cas9, a CRISPR RNA (“crRNA”), and a trans-activating crRNA (“tracrRNA”).
  • the crRNA contains a “guide RNA,” typically about 20-nucleotide RNA sequence that corresponds to a target DNA sequence.
  • crRNA also contains a region that binds to the tracrRNA to form a double-stranded structure which is cleaved by RNase III, resulting in a crRNA/tracrRNA hybrid.
  • a crRNA/tracrRNA hybrid then directs Cas9 endonuclease to recognize and cleave a target DNA sequence.
  • Another class II CRISPR system includes the type V endonuclease Cpfl, which is smaller than Cas9; examples include AsCpfl (from Acidaminococcus sp.) and LbCpfl (from Lachnospiraceae sp.).
  • Cpfl -associated CRISPR arrays are processed into mature crRNAs without the requirement of a tracrRNA; in other words, a Cpfl system requires only Cpfl nuclease and a crRNA to cleave a target DNA sequence.
  • the CRISPR/Cas protein may be selected from a type I, type II, type III, type IV, type V Cas protein, and type VI Cas protein.
  • the CRISPR/Cas protein may be selected from Cas1, Cas1B, Cas2, Cas3, Cas4, Cas5, Cas5d, Cas5t, Cas5h, Cas5a, Cas6, Cas7, Cas8, Cas8a, Cas8b, Cas8c, Cas9, Cas10, Cas10d, Cas12a/Cpf1, Cas12b/C2c1, Cas12c/C2c3, Cas12d/CasY, Cas12e/CasX, Cas12g, Cas12h, Cas12i, Csy1 , Csy2, Csy3, Csy4, Cse1, Cse2, Cse3, Cse4, Cse5e, Csc1, Csc2, Csa5, Csn1, Csn2, Csm1, Csm2, Csm3, Csm4, Csm5, Csm6, Cmr1, Cmr3, Cmr4, Cmr5, C
  • the CRISPR/Cas protein may be or comprise a Cas9 ortholog.
  • the Cas9 protein may be selected from SpCas9, SaCas9, ScCas9, StCas9, NmCas9, VRERCas9, VERCas9, xCas9, espCas91.0, espCas1.1, Cas9HF1, hypaCas9, evoCas9, HiFiCas9, and CjCas9.
  • the CRISPR/Cas protein may be or comprise a Cas12 ortholog.
  • the Cas12 protein may be selected from Cpf1, FnCas12a, LbCas12a, AsCas12a, LbCas12a, TsCas12a, SaCas12a, Pb2Cas12a, PgCas12a, MiCas12a, Mb2Cas12a, Mb3Cas12a, Lb4Cas12a, Lb5Cas12a, FbCas12a, CrbCas12a, CpbCas12a, CMaCas12a, BsCas12a, BfCas12a, BoCas12a.
  • the CRISPR/Cas protein may be derived from a bacterium or has one or more components derived from a bacterium, and wherein the one or more components may optionally be derived from different bacteria.
  • the bacteria origin of the CRISPR/Cas protein of each of the epigenetic effectors may be selected from Streptococcus pyogenes, Streptococcus thermophilus, Streptococcus sp., Staphylococcus aureus, Nocardiopsis rougevillei, Streptomyces pristinae spiralis, Streptomyces viridochromo genes, Streptomyces viridochromogenes, Streptosporangium roseum, Streptosporangium roseum, AlicyclobacHlus acidocaldarius, Bacillus pseudomycoides, Bacillus selenitireducens, Bacillus niameyensis, Bacillus okhensis, Capnocytophaga can
  • subtilis subtilis, Corynebacterium glutamicum, Streptomyces sp., Clostridium difficile, Clostridium saccharoperbutylacetonicum N1-4, Acaryochloris marina, Leptotrichia shahii, and Francisella novicida.
  • the CRISPR/Cas protein may be derived from a virus, e.g., a phage virus, e.g., a bacteriophage, e.g., a Biggievirus or has one or more components derived from a virus, e.g., a phage virus, e.g., a bacteriophage, e.g., a Biggievirus and wherein the one or more components may optionally be derived from different virus.
  • the CRISPR/Cas domain comprises a modified form of a wild type Cas protein.
  • the modified form of the wild type Cas protein can comprise one or more amino acid changes (e.g., deletion, insertion, or substitution).
  • the endonuclease domain may comprise one or more amino acid substitutions as compared to a wild type endonuclease domain.
  • the CRISPR/Cas domain comprises an endonuclease domain that has modified or reduced nuclease activity as compared to a wild type protein.
  • the endonuclease domain can have less than 90%, less than 80%, less than 70%, less than 60%, less than 50%, less than 40%, less than 30%, less than 20%, less than 10%, less than 5%, or less than 1% nuclease activity of the wild type Cas protein.
  • the CRISPR/Cas domain comprises a catalytically inactive CRISPR/Cas protein (e.g., dCas9) or a CRISPR/Cas protein with substantially reduced nuclease activity compared to a wild type CRISPR/Cas protein.
  • a catalytically inactive CRISPR/Cas protein e.g., dCas9
  • CRISPR/Cas protein with substantially reduced nuclease activity compared to a wild type CRISPR/Cas protein.
  • Many catalytically inactive CRISPR/Cas proteins are known in the art.
  • a catalytically inactive CRISPR/Cas protein or a CRISPR/Cas protein that has reduced DNA cleavage activity with respect to both strands of a double-stranded target DNA can result from deletion or mutation of all of the nuclease domains of a CRISPR/Cas protein (e.g., both RuvC and HNH nuclease domains in a Cas9 protein; RuvC nuclease domain in a Cpf1 protein).
  • a catalytically inactive S e.g., both RuvC and HNH nuclease domains in a Cas9 protein; RuvC nuclease domain in a Cpf1 protein.
  • pyogenes Cas9 can result from a D10A (aspartate to alanine at position 10) mutation in the RuvC domain and H939A (histidine to alanine at amino acid position 839) or H840A (histidine to alanine at amino acid position 840) in the HNH domain.
  • a catalytically inactive CRISPR/Cas protein e.g., dCas, dCas9 can bind to a target polynucleotide but may not cleave the target polynucleotide.
  • mutations in Cas9 include but are not limited to D10A, D11A, D16A, D17A, H557A, H558A, H588A, N611A, N612A, H589A, H820A, H821A, D839A, H840A, N863A, N864A, D917A, D918A, H969A, H970A, E993A,E994A, N995A, N996A, E1006A, E1007A, D1255A, D1256A, or any combination thereof.
  • a spCas9 mutation include e.g., D10A/H820A, D1OA, D10A/D839A/H840A, and D10A/D839A/H840A/N863A or any combination thereof.
  • the CRISPR/Cas domain comprises a CRISPR/Cas domain that has single strand DNA cleavage activity when contacted with a double stranded DNA sequence.
  • the CRISPR/Cas domain comprises a CRISPR/Cas domain (i.e., a nickase) that can generate a single-strand break but not a double-strand break.
  • a CRISPR/Cas nickase can result from deletion or mutation of one of the nuclease domains in a Cas protein comprising at least two nuclease domains (e.g., Cas9).
  • an S. pyogenes Cas9 nickase can result from a D10A (aspartate to alanine at position 10) mutation in the RuvC domain or a H839A (histidine to alanine at amino acid position 839) or H840A (histidine to alanine at amino acid position 840) mutation in the HNH domain.
  • a Cas protein described herein is a mature Cas protein, e.g., lacking a N terminal methionine.
  • a Cas protein can be a chimeric Cas protein that is fused to other proteins or polypeptides.
  • a Cas protein can be a chimera of various Cas proteins, for example, comprising domains of Cas proteins from different organisms.
  • a Cas9 is a chimeric Cas9, e.g., modified Cas9, e.g., synthetic RNA-guided nucleases (sRGNs), e.g., modified by DNA family shuffling, e.g., sRGN3.1, sRGN3.3.
  • sRGNs synthetic RNA-guided nucleases
  • the DNA family shuffling comprises, fragmentation and reassembly of parental Cas9 genes, e.g., one or more of Cas9s from Staphylococcus hyicus (Shy), Staphylococcus lugdunensis (Slu), Staphylococcus microti (Smi), and Staphylococcus pasteuri (Spa).
  • PAM sequences A target DNA sequence must generally be adjacent to a “protospacer adjacent motif’ (“PAM”) that is specific for a given Cas endonuclease; however, PAM sequences appear throughout a given genome.. In some embodiments, the PAM is required for target binding of the Cas protein.
  • PAM protospacer adjacent motif
  • the specific PAM sequence required for Cas protein recognition may depend on the specific type of the Cas protein.
  • a PAM can be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides in length. In some embodiments, a PAM is between 2-6 nucleotides in length.
  • the PAM can be a 5’ PAM (i.e., located upstream of the 5’ end of the protospacer). In some embodiments, the PAM can be a 3’ PAM (i.e., located downstream of the 5’ end of the protospacer).
  • the Cas protein of a prime editor recognizes a canonical PAM, for example, a SpCas9 recognizes 5’-NGG-3’ PAM.
  • a Cas protein described herein has altered PAM specificity.
  • a Cas protein described herein may have one or mutations in a PAM recognition motif. Examples of specific PAM sequences are provided in Table 2 below. As used in PAM sequences in Table 2 and consensus sequences of exemplary promoter elements in Table 1, “N” refers to any one of nucleotides A, G, C, and T, “R” refers to nucleotide A or G, “Y” refers to nucleotide C or T, “W” refers to nucleotide A or T, “K” refers to nucleotide G or T, “M” refers to nucleotide A or C, “B” refers to nucleotide C or G or T, “D” refers to nucleotide A or G or T, “H” refers to nucleotide A or C or T, and “V” refers to nucleotide A or C or G.
  • a nucleic acid binding moiety may be or comprises a Zn finger domain.
  • Zn finger proteins and methods for design and construction of fusion proteins are known to those of skill in the art.
  • the Zn finger domain may comprise or consist essentially of or consist of 1-10, 1-9, 1-8, 1-7, 1-6, 1-5, 1-4, 1-3, 1-2, 2-10, 2-9, 2-8, 2-7, 2-6, 2-5, 2-4, 2-3, 3- 10, 3-9, 3-8, 3-7, 3-6, 3-5, 3-4, 4-10, 4-9, 4-8, 4-7, 4-6, 4-5, 5-10, 5-9, 5-8, 5-7, 5-6, 6-10, 6-9, 6- 8, 6-7, 7-10, 7-9, 7-8, 8-10, 8-9, or 9-10 zinc fingers.
  • Zn finger proteins and/or multi fingered Zn finger proteins may be linked together, e.g., as a fusion protein, using any suitable linker sequences.
  • the Zn finger domain may include any combination of suitable linkers between the individual Zn finger proteins and/or multi-fingered Zn finger proteins of the Zn finger molecule.
  • the Zn finger domain of an epigenetic modulator may comprise a Zn finger molecule comprising an engineered zinc finger protein that binds (in a sequence- specific manner) to a DNA sequence in a target nucleic acid.
  • Engineering methods include, but are not limited to, rational design and various types of selection.
  • Rational design includes, for example, using databases comprising triplet (or quadruplet) nucleotide sequences and individual Zn finger amino acid sequences, in which each triplet or quadruplet nucleotide sequence is associated with one or more amino acid sequences of zinc fingers which bind the particular triplet or quadruplet sequence.
  • databases comprising triplet (or quadruplet) nucleotide sequences and individual Zn finger amino acid sequences, in which each triplet or quadruplet nucleotide sequence is associated with one or more amino acid sequences of zinc fingers which bind the particular triplet or quadruplet sequence.
  • a Zn finger molecule may comprise a two-handed Zn finger protein.
  • Two handed Zn finger proteins are those proteins in which two clusters of zinc finger proteins are separated by intervening amino acids so that the two Zn finger domains bind to two discontinuous target DNA sequences.
  • An example of a two-handed type of zinc finger binding protein is SIP1, where a cluster of four zinc finger proteins is located at the amino terminus of the protein and a cluster of three Zn finger proteins is located at the carboxyl terminus (Remade et al.1999).
  • Each cluster of zinc fingers in these proteins is able to bind to a unique target sequence and the spacing between the two target sequences can comprise many nucleotides.
  • the Zn finger domain comprises a ZIM3, ZNF436, ZNF257, ZNF675, ZNF490, ZNF320, ZNF331, ZNF816, ZNF680, ZNF41, ZNF189, ZNF528, ZNF543, ZNF554, ZNF140, ZNF610, ZNF264, ZNF350, ZNF8, ZNF582, ZNF30, ZNF324, ZNF98, ZNF669, ZNF677, ZNF596, ZNF677, ZNF596, ZNF214, ZNF37A, ZNF34, ZNF250, ZNF547, ZNF273, ZNF354A, ZNF82, ZNF224, ZNF33A, ZNF45, ZNF175, ZNF595, ZNF184, ZNF419, ZNF28-1, ZNF28-2, ZNF18, ZNF213, ZNF394, ZNF1, ZNF14, ZNF416, ZNF557, ZNF566, ZNF729, ZIM2, ZNF254, ZNF1, ZNF14, ZNF41
  • a nucleic acid binding moiety is or comprises a TAL domain.
  • a TAL domain is derived from a TAL effector molecule that specifically binds a DNA sequence.
  • TAL effectors typically comprise a plurality of TAL effector domains or fragments thereof, and optionally one or more additional portions of naturally occurring TAL effectors (e.g., N- and/or C-terminal of the plurality of TAL effector domains). More than 113 TAL effector sequences are known to date.
  • Non-limiting examples of TAL effectors from Xanthomonas include, Hax2, Hax3, Hax4, AvrXa7, AvrXalO and AvrBs3.
  • TAL effectors comprise a central repeat domain of tandemly arranged repeats (the repeat-variable di-residues, RVD domain) that determine the specific binding of TAL effectors. These repeats are typically 33 or 34 amino acids. Different TAL effectors may have a different number of repeats (typically ranging from 1.5 to 33.5 repeats) and a different order of their repeats. The C-terminal repeat is usually shorter in length (e.g., about 20 amino acids) and is generally referred to as a “half-repeat”.
  • Each repeat of the TAL effector generally correlates to one base-pair in the target DNA sequence with different repeat types exhibiting different base- pair specificity. A smaller number of repeats generally results in weaker protein-DNA interactions. A number of 6.5 repeats in a TAL effector has been shown to be sufficient to activate transcription of a reporter gene (Scholze et ah, 2010). [0226] Many variations between repeats occur at amino acid positions 12 and 13, which have been termed "hypervariable" and are responsible for the specificity of the interaction with the target DNA promoter sequence, as shown in Table 3 listing exemplary repeat variable di- residues (RVD) and their corresponding nucleic acid base targets. [0227] Table 3.
  • RVDs and Nucleic Acid Base Specificity include a T flanking the 5' base targeted by the first repeat.
  • the TAL domain described herein may be derived from a TAL effector from any bacterial species (e.g., Xanthomonas species such as the African strain of Xanthomonas oryzae pv. Oryzae (Yu et al.2011), Xanthomonas campestris pv. raphani strain 756C and Xanthomonas oryzae pv. oryzzco /a strain BLS256 (Bogdanove et al.2011).
  • Xanthomonas species such as the African strain of Xanthomonas oryzae pv. Oryzae (Yu et al.2011), Xanthomonas campestris pv. raphani strain 756C and Xanthomonas oryzae pv. oryzzco
  • the TAL domain comprises an RVD domain as well as flanking sequence(s) (sequences on the N-terminal and/or C-terminal side of the RVD domain) also from the naturally occurring TAL effector. It may comprise more or fewer repeats than the RVD of the naturally occurring TAL effector.
  • the TAL domain can be designed to target a given nucleic acid sequence based on Table 3 and other nucleic acid base specificities known in the art.
  • the TAL domain of an epigenetic effector can comprise a number of TAL effector domains (e.g., repeats (monomers or modules)) selected based on the desired binding site to a target nucleic acid.
  • TAL effector domains may be removed or added in order to suit a specific binding target sequence.
  • the TAL domain of an epigenetic effector may comprise between 6.5 and 33.5 TAL effector domains, e.g., repeats.
  • TAL domain of an epigenetic effector may comprise between 8 and 33.5 TAL effector domains, between 10 and 25 TAL effector domains, or between 10 and 14 TAL effector domains.
  • the TAL domain of an epigenetic effector may comprise TAL effector domains that correspond to a perfect match to the DNA target sequence.
  • the TAL domain of an epigenetic effector may comprise a mismatch between a repeat and a target base-pair in the target nucleic acid as along as it allows for the function of the epigenetic effector comprising the TAL effector molecule.
  • the TAL domain of an epigenetic effector comprises no more than 7 mismatches, 6 mismatches, 5 mismatches, 4 mismatches, 3 mismatches, 2 mismatches, or 1 mismatch, and optionally no mismatch, with the target DNA sequence.
  • TAL binding is inversely correlated with the number of mismatches.
  • the binding affinity of the TAL domain to the target nucleic acid is thought to depend on the sum of matching repeat-DNA combinations.
  • TAL effector molecules having 25 TAL effector domains or more may be able to tolerate up to 7 mismatches.
  • the TAL domain of an epigenetic effector may comprise additional sequences derived from a naturally occurring TAL effector.
  • the length of the C-terminal and/or N-terminal sequence(s) included on each side of the TAL effector domain portion of the TAL domain can vary and be selected by one skilled in the art.
  • a number of C-terminal and N-terminal truncation mutants in Hax3 derived TAL-effector based proteins have been characterized (Zhang et al.2011) and key elements have been identified that contribute to optimal binding to the target sequence and activation of transcription. Transcriptional activity was generally found to inversely correlate with the length of N-terminus.
  • an important element for DNA binding residues was identified within the first 68 amino acids of the Hax 3 sequence.
  • a TAL domain in an epigenetic effector comprises 1) one or more TAL effector domains derived from a naturally occurring TAL effector; 2) at least 70, 80, 90, 100, 110, 120, 130, 140, 150, 170, 180, 190, 200, 220, 230, 240, 250, 260, 270, 280 or more amino acids from the naturally occurring TAL effector on the N-terminal side of the TAL effector domains; and/or 3) at least 68, 80, 90, 100, 110, 120, 130, 140, 150, 170, 180, 190, 200, 220, 230, 240, 250, 260 or more amino acids from the naturally occurring TAL effector on the C-terminal side of the TAL effector domains.
  • the epigenetic modulator further comprises a linker, e.g., a linker connecting the domains of the epigenetic modulator.
  • a linker may connect a polypeptide to another polypeptide.
  • a linker may connect a polypeptide to a nucleic acid.
  • a linker may connect a nucleic acid to another nucleic acid.
  • a linker connects the nucleic acid binding domain and the effector domain of an epigenetic modulator.
  • a linker may be a chemical bond.
  • a linker may be a covalent bond.
  • a linker may be a noncovalent bond.
  • a linker may be a peptide linker. In some cases, a peptide linker may be at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more amino acids in length. In some cases, a linker may be a rigid linker. As well known by one of skill in the art, rigid linkers may comprise an alpha helix structure or Pro-rich sequence. Rigid linkers maintain a substantially fixed spatial distance between domains. In other cases, a linker may be a flexible linker. As well known by one of skill in the art, flexible linkers may comprise small amino acids (e.g., Gly, Ser, or Ala). Flexible linkers allow the domains they connect to have flexibility of movement relative to each other.
  • a linker may be a cleavable linker.
  • Cleavable linkers may utilize the reversible nature of a disulfide bond.
  • a cleavable linker comprises a cleavage site motif for a protease.
  • a cleavable linker may be a self-cleaving linker. In vivo cleavage of linkers in compositions described herein may be cleaved in specific conditions. Cleavable linkers are well known by one of skill in the art.
  • an epigenetic modulator described herein does not comprise a linker, e.g., the nucleic acid binding moiety is fused to the effector moiety.
  • an epigenetic modulator described herein may comprise one or more nuclear localization sequence (NLS) (e.g., an SV40 NLS).
  • NLS nuclear localization sequence
  • the one or more NLS facilitates the import of the polypeptide comprising an NLS into the cell nucleus.
  • the epigenetic modulator may comprise 1 NLS.
  • the epigenetic modulator may comprise 2 NLSs.
  • the epigenetic modulator may comprise 3 NLSs.
  • the epigenetic modulator may comprise more than 3, 4, 5, 6, 7, 8, 9, or 10 NLSs.
  • the NLS is located at the N-terminus, C-terminus, or in an internal region of the polypeptide.
  • an NLS is fused to the N-terminus of the nucleic acid binding domain of an epigenetic modulator described herein. In some cases, an NLS is fused to the C- terminus of the nucleic acid binding domain of an epigenetic modulator. In some cases, an NLS is fused to the N-terminus of the effector domain of an epigenetic modulator. In some cases, an NLS is fused to the C-terminus of the effector domain of an epigenetic modulator. In some cases, the nucleic acid binding domain of the epigenetic modulator does not comprise an NLS. In some cases, the effector domain of the epigenetic modulator does not comprise an NLS.
  • an NLS is fused to the N-terminus of a CRISPR/Cas effector protein. In some cases, an NLS is fused to the C-terminus of a CRISPR/Cas effector protein. Examples of NLS are provided in Table 4 below.
  • the disclosure provides in part, polynucleotides encoding all epigenetic modulators, effector domains, nucleic acid binding domains, polypeptides, and functional RNAs disclosed herein. Methods for design and construction of polynucleotides are known to those of skill in the art. Nucleotides are referred to by their commonly accepted single-letter codes. Unless otherwise indicated, nucleotide sequences are written left to right in 5' to 3' orientation. Nucleotides are referred to herein by their commonly known one-letter symbols recommended by the IUPAC- IUB Biochemical Nomenclature Commission.
  • A represents adenine
  • C represents cytosine
  • G represents guanine
  • T represents thymine
  • U represents uracil.
  • Non-limiting examples of polynucleotides include coding or non- coding regions of a gene or gene fragment, loci (locus) defined from linkage analysis, exons, introns, messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA (rRNA), short interfering RNA (siRNA), short-hairpin RNA (shRNA), micro-RNA (miRNA), ribozymes, cDNA, recombinant polynucleotides, branched polynucleotides, plasmids, vectors, isolated DNA of any sequence, isolated RNA of any sequence, cell-free polynucleotides including cell- free DNA (cfDNA) and cell-free RNA (cfRNA), nucleic acid probes, and primers.
  • loci locus
  • locus defined from linkage analysis, exons, introns, messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA (rRNA), short interfer
  • a nucleic acid binding moiety may be or comprise a domain from an obligate mobile element-guided activity (OMEGA) system.
  • OMEGA domain can comprise an RNA-programmable nuclease domain.
  • the OMEGA domain can comprise a distinct transposon-encoded protein domain, for example, an IscB domain, an IsrB domain, an IshB domain, or an TnpB domain.
  • the OMEGA domain can be an ancestor or a variant of an ancestor of a CRISPR nuclease domain, for example, a Cas9 domain or a Cas12 domain.
  • An IscB domain or an TnpB domain can be encoded in a family of IS200/IS605 transposons.
  • the OMEGA domain can comprise a nuclease domain.
  • the OMEGA domain comprises a RuvC domain or an HNH domain.
  • the OMEGA domain comprises a RuvC domain and an HNH domain. In other cases, the OMEGA domain can comprise an HNH domain but no RuvC domain.
  • the OMEGA domain can further comprise a PLMP domain.
  • the OMEGA domain is catalytically active.
  • the OMEGA domain can, for example, comprise nickase activity.
  • the OMEGA domain can be mutated to be deficient in nuclease activity.
  • the OMEGA domain is catalytically inactive.
  • the OMEGA domain can comprise RNA-guided activity. In some cases, an OMEGA domain can comprise an RNA-guided nuclease.
  • An OMEGA domain can be capable of specifically interacting with or binding to a specific noncoding RNA, for example, an ⁇ RNA.
  • the noncoding RNA can be configured to recruit the OMEGA domain to a specific target sequence, for example, by hybridization of a segment of the noncoding RNA to the target sequence.
  • hybridization of the segment of the nonRNA to the target sequence triggers the OMEGA domain to activate its nuclease domain and carry out double-stranded DNA cutting or a single-stranded DNA nick at the target sequence.
  • the noncoding RNA that interacts with the OMEGA domain comprises a CRISPR repeat sequence or a sequence from a CRISPR array.
  • the OMEGA domain is associated with a CRISPR array.
  • the OMEGA domain is capable of associating with a particular target adjacent motif (TAM).
  • TAM target adjacent motif
  • the OMEGA domain may require binding to the TAM in order to activate its RNA-guided activity.
  • an OMEGA domain is a part of an epigenetic effector described elsewhere herein.
  • an OMEGA domain is a part of a blocking reagent described elsewhere herein.
  • An OMEGA domain can be the nucleic acid binding domain of an epigenetic effector.
  • An OMEGA domain can be coupled to an effector moiety described elsewhere herein, for example, as a fusion protein.
  • an OMEGA domain can be the nucleic acid binding domain of a blocking reagent described elsewhere herein.
  • a nucleic acid binding moiety may be or comprise a Fanzor domain.
  • the Fanzor domain can comprise an RNA-programmable nuclease domain.
  • the Fanzor domain is derived from a eukaryotic cell or an engineered variant thereof.
  • the Fanzor domain can be derived from a metazoan, fungus, choanoflagellate, algae, rhodophyta, a unicellular eukaryote, plant, or animal.
  • the Fanzor domain is derived from a virus or an engineered variant thereof.
  • the Fanzor domain can be derived from Phycodnaviridae, Ascoviridae, or Mimiviridae.
  • the Fanzor domain is derived from the Acanthamoeba polyphaga mimivirus, Mercenaria, Dreissena polymorpha, Batillaria attramentaria, Klebsormidium nitens, or Chlamydomonas reinhardtii.
  • the Fanzor domain can comprise a homolog of a TnpB domain.
  • a Fanzor domain can be capable of associating with a eukaryotic transposase.
  • a Fanzor domain is capable of associating with a LINE, CMC, Crypton, Mariner/Tc1, hAT, IS607, EnSpm, Sola, or Helitron transposon.
  • the Fanzor domain can comprise a nuclease domain.
  • the Fanzor domain comprises a RuvC domain.
  • the Fanzor domain can further comprise a WED domain.
  • the Fanzor domain is catalytically active.
  • the Fanzor domain can, for example, comprise nickase activity.
  • the Fanzor domain can be mutated to be deficient in nuclease activity.
  • the Fanzor domain is catalytically inactive.
  • the Fanzor domain can comprise RNA-guided activity.
  • an Fanzor domain can comprise an RNA-guided nuclease.
  • a Fanzor domain can be capable of specifically interacting with or binding to a specific noncoding RNA, for example, an ⁇ RNA.
  • the noncoding RNA can be configured to recruit the Fanzor domain to a specific target sequence, for example, by hybridization of a segment of the noncoding RNA to the target sequence. In some cases, hybridization of the segment of the nonRNA to the target sequence triggers the Fanzor domain to activate its nuclease domain. In some cases, an activated Fanzor domain carries out double-stranded DNA cutting or a single-stranded DNA nick at the target sequence.
  • the Fanzor domain is capable of associating with a particular target adjacent motif (TAM).
  • TAM target adjacent motif
  • the Fanzor domain may require binding to the TAM in order to activate its RNA-guided activity.
  • the Fanzor domain can be smaller in size compared to a CRISPR Cas9 protein or a CRISPR Cas12 protein.
  • a Fanzor domain is a part of an epigenetic effector described elsewhere herein.
  • a Fanzor domain is a part of a blocking reagent described elsewhere herein.
  • a Fanzor domain can be the nucleic acid binding domain of an epigenetic effector.
  • a Fanzor domain can be coupled to an effector moiety described elsewhere herein, for example, as a fusion protein.
  • a Fanzor domain can be the nucleic acid binding domain of a blocking reagent described elsewhere herein.
  • Vectors e.g., a viral vector and/or a non-viral vector.
  • An epigenetic effector or a blocking reagent described herein can be delivered via a vector into a cell via electroporation, chemical transformation, nucleofection, viral transduction, viral transfection, or other similar techniques.
  • the vector is a viral vector. Examples of viral vectors include expression vectors, replication vectors, probe generation vectors, and sequencing vectors.
  • a suitable vector contains an origin of replication functional in at least one organism, a promoter sequence, convenient restriction endonuclease sites, and one or more selectable markers.
  • An expression vector may be used to express natural or synthetic nucleic acids by operably linking a nucleic acid encoding the gene of interest to a promoter.
  • Vectors can be suitable for replication and integration in eukaryotes. Typical cloning vectors contain transcription and translation terminators, initiation sequences, and promoters useful for expression of the desired nucleic acid sequence.
  • Viral vectors including those derived from retroviruses such as lentivirus, are suitable tools to achieve long-term gene transfer since they allow long-term, stable integration of a transgene and its propagation in daughter cells.
  • An expression vector may be provided to a cell in the form of a viral vector.
  • Viral vector technology is well known in the art and described in a variety of virology and molecular biology manuals.
  • Viruses, which are useful as vectors include, but are not limited to, retroviruses, adenoviruses, adeno- associated viruses (AAV), herpes viruses, and lentiviruses.
  • An AAV can be AAV1, AAV2, AAV4, AAV5, AAV6, AAV8, AAV9, AAV 10 or any combination thereof.
  • AAV recombinant AAV
  • rAAV recombinant AAV
  • rAAVs utilizes the cis- acting 145-bp ITRs to flank vector transgene cassettes, providing up to 4.5 kb for packaging of foreign DNA.
  • a vector comprises an expression cassette comprising the nucleic acid encoding a protein or functional RNA.
  • the protein or functional RNA in the expression cassette is operatively linked to a promoter sequence that controls the expression of the protein or functional RNA.
  • the promoter may be an inducible promoter that is capable of turning on expression of a polynucleotide sequence to which it is operatively linked, when such expression is desired. In some embodiments, the inducible promoter is capable of turning off expression when expression is not desired.
  • the vector comprising an expression cassette may contain a selectable marker gene (e.g., antibiotic resistance gene) or a reporter gene (e.g., luciferase, beta- galactosidase, green fluorescent protein gene) to facilitate identification and selection of cells containing the vector.
  • a selectable marker gene e.g., antibiotic resistance gene
  • a reporter gene e.g., luciferase, beta- galactosidase, green fluorescent protein gene
  • the present disclosure provides a composition of a vector or vector set encoding an epigenetic effector, a blocking reagent, a guide RNA, or any polypeptide or nucleic acid described elsewhere herein.
  • provided vectors may be or include DNA, RNA, e.g., mRNA, or any other nucleic acid moiety or entity as described herein, and may be prepared by any technology described herein or otherwise available in the art (e.g., synthesis, cloning, amplification, in vitro or in vivo transcription, etc.).
  • provided nucleic acids that encode an epigenetic effector, a blocking reagent, a guide RNA, or a nucleic acid in a guided epigenetic editing composition described elsewhere herein may be operationally associated with one or more replication, integration, and/or expression signals appropriate and/or sufficient to achieve integration, replication, and/or expression of the provided nucleic acid in a system of interest (e.g., in a particular cell, tissue, organism, etc.).
  • the vector is a non-viral vector, e.g., liposome, exosome, lipid nanoparticle.
  • the vector may be selected from a lipid nanoparticle, a liposome, an exosome, and a micro vesicle.
  • the viral vector may be derived from an adenovirus, a retrovirus, an adeno-associated virus, a vaccinia virus, a lentivirus, a phage virus, a herpes simplex virus, or a polio virus.
  • the lipid nanoparticle may comprise an ionizable lipid.
  • the lipid nanoparticle further comprises one or more of neutral lipids, ionizable amine-containing lipids, biodegradable alkyne lipids, steroids, phospholipids, polyunsaturated lipids, structural lipids (e.g., sterols), PEG, cholesterol, or polymer conjugated lipids.
  • the vector may be provided as a component of a reaction mixture. In some embodiments, the vector may be provided as a component of a composition comprising the vector and a pharmaceutically acceptable carrier. In some embodiments, the vector may be provided as a component of a culture comprising a cell. In some embodiments, the vector may be provided as a component of a production vector.
  • the present disclosure provides methods of reprogramming a cell, e.g., a target cell from one cell type to another.
  • the present disclosure provides methods of reprogramming a target cell from one cellular state, e.g., a first cellular state to another, e.g., a second cellular state.
  • the first cellular state and the second cellular state are of the same cell type.
  • the first cellular state and the second cellular state are of the different cell types.
  • the reprogramming may be from pluripotent stem cell to adult cell.
  • the reprogramming may be from adult cell to pluripotent stem cell.
  • the reprogramming may be from one stem cell type to another stem cell type. In some embodiments, the reprogramming may be from one adult cell type to another adult cell type. In some embodiments, the reprogramming may be from one adult somatic cell type to another adult somatic cell type. In some embodiments, the reprogramming may be from one blood cell type to another blood cell type. In some embodiments, the reprogramming may be from one immune cell type to another immune cell type. In some embodiments, the reprogramming may be from first cellular state, e.g., an exhausted cell state to a second cellular state, e.g., a rejuvenated state relative to the first cellular state.
  • first cellular state e.g., an exhausted cell state to a second cellular state, e.g., a rejuvenated state relative to the first cellular state.
  • the reprogramming may be from first cellular state, e.g., a diseased cellular state to a second cellular state, e.g., a healthy cellular state relative to the first cellular state.
  • the reprogramming may comprise inducing a change in epigenetic state, chromatin structure, transcription, mRNA splicing, post-transcriptional modification, mRNA stability and/or half-life, translation, post-translational modification, protein stability and/or half-life and/or protein activity of the at least one component of the cell.
  • the first cellular state and the second cellular state are T cell states, e.g., regulatory T-cell states, e.g., CD8+ T-cell states, e.g., CD4+ T-cell states.
  • the first cellular state is selected from the group consisting of: a na ⁇ ve T-cell state, a central memory T-cell state, an effector T-cell state, and an effector memory T-cell state.
  • the second cellular state is selected from the group consisting of: a na ⁇ ve T- cell state, a central memory T-cell state, an effector T-cell state, and an effector memory T-cell state.
  • the somatic cell being reprogrammed may be selected from a fibroblast cell, a liver cell e.g., a liver hepatocyte, a cardiac cell, a CNS cell, a PNS cell, a kidney cell, a lung cell, a hematopoietic cell, a pancreatic beta cell, a bone cell, a skeletal muscle cell, a skin cell, an immune cell, e.g., a T cell (e.g., CD4+ T cell, CD8+ T cell), a follicular cell, a vascular cell, a neural cell, an osteoblast cell, an osteoclast cell, and an endothelial cell.
  • a liver cell e.g., a liver hepatocyte, a cardiac cell, a CNS cell, a PNS cell, a kidney cell, a lung cell, a hematopoietic cell, a pancreatic beta cell, a bone cell, a skeletal muscle cell, a skin
  • the somatic cell being produced by the reprogramming may be selected from a fibroblast cell, a liver cell, a cardiac cell, a CNS cell, a PNS cell, a kidney cell, a lung cell, a hematopoietic cell, a pancreatic beta cell, a bone cell, a skeletal muscle cell, a skin cell, an immune cell, a follicular cell, a vascular cell, a neural cell, an osteoblast cell, an osteoclast cell, and an endothelial cell.
  • the cell may be isolated from an in-vivo tissue in a subject.
  • the cell may be isolated from a biopsy.
  • the cell may be isolated from a cancer biopsy.
  • the cell may be isolated from a blood sample. In some embodiments, the cell may be isolated from a subject having a disease. In some embodiments, the cell may be isolated from a healthy subject. [0254] In some embodiments, the contacting step may be accomplished in-vitro. In some embodiments, the contacting step may be accomplished in-vivo. In some embodiments, the contacting step may be accomplished ex-vivo. Software, Systems, and Devices [0255] In some other aspects, provided herein are non-transitory computer-readable storage media.
  • the non-transitory computer-readable storage media comprise one or more programs for execution by one or more processors of a device, the one or more programs including instructions which, when executed by the one or more processors, cause the device to generate a cellular state map (e.g., a cellular state map for a modified cell, a current cellular sate map or a rejuvenated cellular state map), a differential cellular state map, and/or obtain a target list of epigenetic markers and associate modifications, for example based on inputted profiling data.
  • a cellular state map e.g., a cellular state map for a modified cell, a current cellular sate map or a rejuvenated cellular state map
  • a differential cellular state map e.g., a target list of epigenetic markers and associate modifications, for example based on inputted profiling data.
  • FIG.18 illustrates an example of a computing device or system in accordance with one embodiment.
  • Device 1800 can be a host computer connected to a
  • device 1800 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet.
  • the device can include, for example, one or more processor(s) 1810, input devices 1820, output devices 1830, memory or storage devices 1840, communication devices 1860, and a profiling data generation device (e.g., a nucleic acid sequencer) 1870.
  • Software 1850 residing in memory or storage device 1840 may comprise, e.g., an operating system as well as software for executing the methods described herein.
  • Input device 1820 and output device 1830 can generally correspond to those described herein and can either be connectable or integrated with the computer.
  • Input device 1820 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device.
  • Output device 1830 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.
  • Storage 1840 can be any suitable device that provides storage (e.g., an electrical, magnetic or optical memory including a RAM (volatile and non-volatile), cache, hard drive, or removable storage disk).
  • Communication device 1860 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device.
  • Software module 1850 which can be stored as executable instructions in storage 1840 and executed by processor(s) 1810, can include, for example, an operating system and/or the processes that embody the functionality of the methods of the present disclosure.
  • Software module 1850 can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described herein, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a computer-readable storage medium can be any medium, such as storage 1840, that can contain or store processes for use by or in connection with an instruction execution system, apparatus, or device. Examples of computer- readable storage media may include memory units like hard drives, flash drives and distribute modules that operate as a single functional unit. Also, various processes described herein may be embodied as modules configured to operate in accordance with the embodiments and techniques described above.
  • Software module 1850 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a transport medium can be any medium that can communicate, propagate or transport programming for use by or in connection with an instruction execution system, apparatus, or device.
  • the transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.
  • Device 1800 may be connected to a network (e.g., network 1904, as shown in FIG.19 and described below), which can be any suitable type of interconnected communication system.
  • the network can implement any suitable communications protocol and can be secured by any suitable security protocol.
  • the network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T1 or T3 lines, cable networks, DSL, or telephone lines.
  • Device 1800 can be implemented using any operating system, e.g., an operating system suitable for operating on the network.
  • Software module 1850 can be written in any suitable programming language, such as C, C++, Java or Python.
  • application software embodying the functionality of the present disclosure can be deployed in different configurations, such as in a client/server arrangement or through a Web browser as a Web-based application or Web service, for example.
  • the operating system is executed by one or more processors, e.g., processor(s) 1810.
  • Device 1800 can further include, for example, a nucleic acid sequencer 1870, which can be any suitable nucleic acid sequencing instrument.
  • Exemplary sequencers can include, without limitation, Roche/454’s Genome Sequencer (GS) FLX System, Illumina/Solexa’s Genome Analyzer (GA), Illumina’s HiSeq 2500, HiSeq 3000, HiSeq 4000, and NovaSeq 6000 Sequencing Systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, or Pacific Biosciences’ PacBio RS system.
  • FIG.19 illustrates an example of a computing system in accordance with one embodiment.
  • device 1800 e.g., as described above and illustrated in FIG.18
  • device 1906 is a sequencer.
  • Exemplary sequencers can include, without limitation, Roche/454’s Genome Sequencer (GS) FLX System, Illumina/Solexa’s Genome Analyzer (GA), Illumina’s HiSeq 2500, HiSeq 3000, HiSeq 4000 and NovaSeq 6000 Sequencing Systems, Life/APG’s Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator’s G.007 system, Helicos BioSciences’ HeliScope Gene Sequencing system, Pacific Biosciences’ PacBio RS system, MinION, GridION, or PromethION.
  • GS Genome Sequencer
  • GA Genome Analyzer
  • SOLiD Support Oligonucleotide Ligation Detection
  • Polonator Polonator’s G.007 system
  • Helicos BioSciences HeliScope Gene Seque
  • Devices 1800 and 1906 may communicate, e.g., using suitable communication interfaces via network 1904, such as a Local Area Network (LAN), Virtual Private Network (VPN), or the Internet.
  • network 1904 can be, for example, the Internet, an intranet, a virtual private network, a cloud network, a wired network, or a wireless network.
  • Devices 1800 and 1906 may communicate, in part or in whole, via wireless or hardwired communications, such as Ethernet, IEEE 802.11b wireless, or the like. Additionally, devices 1800 and 1906 may communicate, e.g., using suitable communication interfaces, via a second network, such as a mobile/cellular network.
  • Communication between devices 1800 and 1906 may further include or communicate with various servers such as a mail server, mobile server, media server, telephone server, and the like.
  • devices 1800 and 1906 can communicate directly (instead of, or in addition to, communicating via network 1904), e.g., via wireless or hardwired communications, such as Ethernet, IEEE 802.11b wireless, or the like.
  • devices 1800 and 1906 communicate via communications 1908, which can be a direct connection or can occur via a network (e.g., network 1904).
  • One or all of devices 1800 and 1906 generally include logic (e.g., http web server logic) or are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and/or receiving information via network 1904 according to various examples described herein.
  • logic e.g., http web server logic
  • devices 1800 and 1906 generally include logic (e.g., http web server logic) or are programmed to format data, accessed from local or remote databases or other sources of data and content, for providing and/or receiving information via network 1904 according to various examples described herein.
  • EXAMPLES Example 1 Library preparation for long read whole methylome sequencing with average reads of ⁇ 30 Kb in length
  • This example shows a method of preparing a sequencing library for whole methylome sequencing with average N50 reads of ⁇ 30 Kb in length using the Oxford Nanopore Technologies Sequencing platform.
  • DNA shearing, end-repair, and purification [0270]
  • purified genomic DNA was first sheared using a 26-gauge blunt end needle (ThermoFisher UK Ltd HCA-413-030Y GC Syringe Replacement Parts 26g, 51mm) attached onto a 1ml luer-loc syringe.
  • the needle and syringe were used to draw up a sample of cell free DNA (3 ug of DNA in a volume of 50 ⁇ L of 10 mM Tris HCl pH8.0, 0.1 mM EDTA) in a 1.5 ⁇ L LoBind sample tube. Once all the liquid from the bottom of the tube was drawn into the needle, the sample was expelled back into the tube.
  • End repair was performed on the sheared DNA by preparing the following mix in a 0,2 mL thin-walled PCR tube: 47 ⁇ L of the sheared DNA, 1 ⁇ L of DNA Control Sample (optional), 3.5 ⁇ L NEBNext FFPE DNA Repair Buffer, 2 ⁇ L NEBNext FFPE DNA Repair Mix (NEB, M6630), 3.5 ⁇ L Ultra II End-prep Reaction buffer, and 3 ⁇ L Ultra II End-prep Enzyme mix (NEB, E7546) and incubating in a thermocycler with the following thermal program: 1) 20°C for 5 min., 2) 65°C for 5 min.
  • the end-repaired DNA was then purified using AXP beads (included in the Ligation Sequencing Kit V14; Oxford Nanopore catalog #SQK-LSK114).
  • a volume of 60 ⁇ L of resuspended AXP beads were added to the end-prep reaction and mixed by flicking the tube. The mixture was incubated for 5 min. at room temperature. The sample was spun down and pelleted on a magnet for 10 minutes until the supernatant was clear and colorless. The supernatant was removed, and the beads were washed with 200 ⁇ L of freshly prepared 70% ethanol while the tube was kept on the magnet to not disturb the pellet. The 70% ethanol was removed using a pipette and discarded.
  • the beads were washed a second time with 200 ⁇ l of freshly prepared 70% ethanol and following removal of the ethanol, were resuspended in 61 ⁇ L of nuclease-free water and incubated for 2 min. at RT. The tube was placed back in the magnet for 1 min., following which the supernatant was transferred into a clean 1.5 mL low binding tube, and 1 ⁇ L was quantified in Qubit.
  • Adapter ligation and clean up [0274] The following mixture was prepared for adapter ligation, by adding in the following order into a 1.5 mL Eppendorf DNA LoBind tube: 60 ⁇ L of the purified end-repaired DNA, 25 ⁇ L of Ligation Buffer (LNB) from the Ligation Sequencing Kit, 10 ⁇ L of NEBNext Quick T4 DNA Ligase, and 5 ⁇ L of Ligation Adapter (LA). The reaction mixture was incubated for 10 minutes at room temperature. To purify the library, a volume of 40 ⁇ l of AXP beads provided in the ligation kit, were added to the reaction and incubated for 10 minutes at room temperature, mixing the sample gently every 30 seconds.
  • LNB Ligation Buffer
  • LA Ligation Adapter
  • the sample was spun down and pelleted on a magnet. While the tube was kept on the magnet, the supernatant was removed.
  • the beads were washed by resuspending in 250 ⁇ l Long Fragment Buffer (LFB), spun down, and pelleted for at least 5 minutes on a magnetic rack before removing the supernatant.
  • the beads were washed a second time with 250 ⁇ l Long Fragment Buffer (LFB), spun down, and pelleted on the magnet before removing any residual supernatant.
  • the beads were allowed to dry for ⁇ 30 seconds, taken off the magnetic rack, resuspended in 25 ⁇ l Elution Buffer (EB), and incubated for 10 minutes at 37°C.
  • the beads were then pelleted on a magnet for 10 minutes until the eluate was clear and colorless before transferring 25 ⁇ l of eluate containing the DNA library into a clean 1.5 ml Eppendorf DNA LoBind tube. Then, 1 ⁇ l of eluted sample was quantified using a Qubit fluorometer, and the library was sequenced in three splits into three libraries of 300 ng (10-20 fmol) in 32 ⁇ l using Elution Buffer (EB). Each of the three aliquots of the library was loaded when 25% of the sequencing pores lost their sequencing capacity, by mixing 300 ng of library in 32 ⁇ l of Elution Buffer (EB). This procedure yields ⁇ 90 Gb, and ⁇ 30X coverage across the genome.
  • EB Elution Buffer
  • Example 2 Single-cell methylome sequencing of CD8+ T cells in different differentiation states
  • This example shows an example of single-cell methylome sequencing of CD8+ T cells in the following differentiation states: na ⁇ ve CD8+ T-cells, central memory CD8+T-cells, effector memory CD8+ T-cells, and effector CD8+ T-cells.
  • Cell Thawing and Incubation [0277] T cells from a donor were thawed and incubated overnight to allow for re-expression of cell surface markers including CD62L in preparation for staining and sorting.
  • Vials of PBMCs from donor TIS006, CEL021, Aliquot CHS-0001504791 were taken from a liquid nitrogen stock and thawed in a 37 °C water bath for 2-3 minutes or until only small chunks of frozen contents can be visualized.
  • PBS Phosphate Buffered Saline
  • the cells were mixed by gentle pipetting and then diluted in pre-warmed PBMC cell thaw medium, such that the final volume of PBMC cell thaw medium to cryopreserved cell stock is at 10:1 (v:v) ratio.
  • Multiple PBMC vials from the same donor can be thawed and pooled by scaling the volume of the PBMC thaw proportionally.
  • the cells were centrifuged at 600 x g for 5 minutes at room temperature.
  • the cells were resuspended in culture media (RMPI 1640 + 10% FBS + 1x Glutamax) at a concentration of 10,000,000 cells/mL.
  • the cells were incubated overnight to allow for re- expression of CD62L and other cell surface markers.
  • CD8+ T cells were isolated from the PBMCs utilizing the StemCell Human CD8+ T cell Isolation kit.
  • Cell Staining In total, about 100 million cells were stained in preparation for sorting. The following antibodies were used for staining: APC anti-human CD45RO and an anti-human CD62L antibody.
  • the T cells were spun at 600 x g for 5 minutes and resuspended in 2 mL of FACS buffer (Mg 2+ /Ca 2+ -free 1x PBS + 2% HI FBS).
  • the T-cells were then sorted into the populations: 1) Na ⁇ ve CD8+ T-cells, 2) central memory CD8+ T-cells, 3) effector memory CD8+ T-cells, and 4) effector CD8+ T-cells, as shown in FIG.35.
  • the cells can be index sorted into an Eppendorf twin-tech, loBind 96-well plate, partitioned into wells of a) single cells, b) pools of 4 cells, and c) pools 10 cells containing 2.5 ⁇ L of lysis buffer (10 mM Tris HCl, pH8.0, 0.67 mg/mL Proteinase K and 9 pg of Unmethylated lambda DNA for single cell methylome sequencing).
  • the sorted CD8+ cells are used to prepare single cell methylome sequencing libraries for sequencing using Illumina platforms. Each library (single-cell or mini-pool of 4 or 10 cells) requires 25-50 million reads.
  • Cell Lysis [0286] First, a volume of 7 ⁇ L of mineral oil are added to the partitioned cells. The cells are incubated at 98°C to lyse the cells and denature the proteins. A volume of 3 ⁇ L of Single-cell Lysis buffer (10 mM Tris HCl, pH8.0, 0.67 mg/mL Proteinase K) is added to each well. The samples are gently vortexed (speed 4-5/10) and centrifuged for 5 min. at 2000 rpm at room temperature.
  • a volume of 4.5 ⁇ L of molecular biology grade water was added for a final volume of 10 ⁇ L.
  • the cells are incubated at 55°C for 10 min. in a thermocycler to digest proteins.
  • Bisulfite conversion [0288] The DNA in the cell lysate is then subjected to bisulfite conversion.
  • the CT conversion reagent is prepared by resuspending 1 CT conversion tube with 790 ⁇ L of M-solubilization buffer and 300 ⁇ L M-dilution buffer.
  • the CT conversion reagent is incubated at 50 C for 5-10 min. and vortexed every 30 seconds until no precipitates are visible.
  • a volume of 160 ⁇ l of M- Reaction Buffer is added and vortexed.
  • a volume of 65 ⁇ L of CT Conversion reagent is then added to each well of cell lysate and incubated in the thermocycler with the following program: 1) 98°C for 8 min., 2) 65°C for 180 min, 3) hold at 4°C.
  • Desulfonation and Purification of Bi-sulfite-Converted DNA the DNA is first bound to MagBinding Beads. A volume of 5 ⁇ l of MagBinding Beads is added to 300 ⁇ l of M-Binding Buffer into 96-Well 1 mL polypropylene plate.
  • the 75 ⁇ L of bi-sulfite-converted DNA sample s transferred to the MagBinding Beads and M-Binding Buffer mixture.
  • the wells are rinsed with 75 ⁇ L of this mixture to collect any remaining sample and combined with the MagBinding Beads and M-Binding Buffer mixture and mixed by vortexing.
  • the mixture is incubated at room temperature for 5 min. to bind the DNA to the MagBinding Beads.
  • the plate is centrifuged for 1 min. at 1500 rpm at RT and then placed on a magnet for 5 min. (or until the solution clears), before the supernatant s removed and discarded.
  • the plate is removed from the magnet, and the beads were washed in 200 ⁇ L of M-wash buffer. The plate is placed on a magnet for 3 min. (or until the solution clears) and the supernatant is removed. [0291] To desulfonate the DNA, the plate is removed from the magnet, and 100 ⁇ L of M- Desulfonation Buffer was added and mixed thoroughly. The plate is then incubated at room temperature for 15 min. The plate is placed on a magnet for 3 min. (or until the solution clears) and the supernatant is removed and discarded. [0292] The DNA-bound beads are then washed twice with M-wash buffer.
  • the plate is centrifuged at 500 x g for 10 s at 15-25°C to collect all the liquid content in the bottom and adding 2.5 ⁇ L of a freshly prepared solution of 1x Blue buffer, 0.4 mM dNTP mix, 4 ⁇ M preamplification oligo, and 10 U/ ⁇ L Klenow exo- was added.
  • a fifth round of first-strand synthesis is performed by mixing by gentle vortex, spinning the plate to collect the liquid at the bottom, and incubating in a thermocycler with the following program: 1) 4°C for 5 min., 2) 4-37°C for 8.25 min, 3) 37°C for 90 min., and 4) hold at 4°C.
  • the samples are treated with Exonuclease I by adding 2 ⁇ L of Exonuclease I and 48 ⁇ L of Molecular BioGrade Water.
  • the samples are incubated in the thermocycler at 37°C for 1 hour with the lid temperature set to 50°C. At this point, the 1 st strand product can be stored at 4°C overnight or at -20°C for at least 1 month.
  • the preamplified samples are then purified by washing the DNA using AMPure XP beads. A volume of 64 ⁇ L (0.8X) of Ampure XP beads are added to each sample, mixed by pipetting up and down and incubated at room temperature for 10 min.
  • the plate is placed on a magnet for 3 minutes or until the solution cleared and the supernatant is removed and discarded.
  • the plate is removed from the magnet and 200 ⁇ L of 80% (vol/vol) ethanol is added for a first wash.
  • the sample is mixed gently by pipetting up and down twice.
  • the plate is returned to the magnet, and, once the beads have pelleted, the supernatant is removed.
  • a second wash with 200 ⁇ L of 80% (vol/vol) ethanol is performed following the same procedure, and the supernatant is removed after pelleting the beads using the magnet.
  • the AMPure XP beads are dried for 5-10 min.
  • Adapter Tagging and Purification of Double-Tagged Products [0301] The resuspended AMPure XP beads in the adapter oligo mix are incubated 10 min. at RT to elute the DNA from the beads. Next, they are heated to 95°C for 45 s in a thermocycler, and immediately cooled on ice using an aluminum rack. The PCR plate is spun down at 500 x g for 10 s at 15–25°C to collect liquid at the bottom.
  • Klenow exo- 50 U/ ⁇ l stock
  • 1 ⁇ L of Klenow exo- 50 U/ ⁇ l stock
  • the plate is vortexed gently and spun down at 500 x g for 10 s at 15– 25°C and incubated in a thermocycler with the following program: 1) 4°C for 5 min., 2) 4-37°C for 8.25 min. (ramp rate of 0.1°C/s), 3) 37°C for 90 min., and 4) hold at 4°C.
  • the double-tagged products are purified.
  • a PEG buffer (18% PEG 8,000, 2.5 M NaCl, 10 mM Tris–HCl (pH 8.0), 1 mM EDTA and 0.05% (vol/vol) Tween 20), is equilibrated at room temperature for 30 min.
  • 50 ⁇ L of elution buffer (EB) and 80 ⁇ L of PEG buffer are added to the adaptor tagging product and AMPure XP beads, mixed by pipetting up and down 10 times, and incubated for 10 min. at room temperature.
  • the mixture is placed on a magnet for 3 min. or until the solution clears, and the supernatant is removed and discarded.
  • the mixture is removed from the magnet and 200 ⁇ L of 80% (vol/vol) ethanol is added.
  • the mixture is mixed gently by pipetting up and down twice and returned to the magnet.
  • the supernatant is removed and the beads AMPure XP are washed again with 200 ⁇ L of 80% (vol/vol) ethanol on the magnet.
  • the supernatant is removed, and the beads were dried for 5-10 min. at room temperature.
  • the AMPure XP beads are resuspended in 38 ⁇ L of a PCR reaction mix (final concentration of 1x KAPA HiFi Fidelity Buffer GXL Buffer, 0.2 mM GXL dNTPs, 0.02 U/ ⁇ uL KAPA HiFi HotStart polymerase).
  • elution buffer elution buffer
  • 80 ⁇ L of PEG buffer elution buffer
  • the mixture is mixed by pipetting up and down 10 times and incubated for 10 min. at room temperature.
  • the libraries are then transferred to a polypropylene, 96-deep well plate.
  • the beads are washed twice with 200 ⁇ L of 80% (vol/vol) ethanol. The ethanol is removed, and the beads are dried on a magnet for 10 min.
  • 17.5 ⁇ L of EB buffer is added and the beads are mixed by gently vortexing.
  • the plate is removed from the magnet, and the mixture is incubated at room temperature for 10 min. [0307]
  • the plate is placed on the magnet for 2 min.
  • each library was then transferred to a new Eppendorf loBind 96-well PCR plate.
  • the size distribution and potential presence of adapter dimers of each library are verified by digital electrophoresis with the Fragment Analyzer system, using the HS NGS Fragment Kit (1-6000bp) Kit.
  • Each library is quantified with qPCR and diluted down to 4 nM. Equal volumes are pooled into a single aliquot, which is then spiked with 15% of Phix Control V3 and sequenced in an Illumina platform using V2 chemistry, 2 x 76 bp, to verify library mapping rates and bisulfite conversion efficiency.
  • Example 3 Generating Epigenetic Maps Based on Unsupervised Clustering of Epigenetic States Using Long Read Sequencing
  • This example shows a method of generating epigenetic maps that depict methylation patterns in DNA from methylation sequence data that can be used in differential analysis to identify favorable epigenetic editing modulators.
  • Unsupervised clustering scheme was developed to identify epigenetic states on a whole genome and gene-level bases, using long read sequencing with methylation calling.
  • Oxford Nanopore Technologies (ONT) was used to generate sequencing reads from CD8+ T-cells, isolated from three normal, healthy donors.
  • Simple Matching which evaluates the number of CpGs that match (e.g., both unmethylated or both methylated) and normalizes to the total number of comparable (i.e., CpGs) in the ROI was used.
  • various fragments were grouped (clustered) to optimize an inter-cluster metric (e.g., minimize inter-cluster average distance) and an intra- cluster metric (e.g., maximize the distance between the two closest residents of two separate clusters).
  • the two most common methods for clustering are hierarchical and k-means clustering. In this approach, hierarchical clustering was performed.
  • the optimal number of clusters was determined.
  • Common methods for determining the appropriate number of clusters include the Elbow Method, Silhouette, and the Gap Statistic.
  • the appropriate number of clusters was determined by computing a figure of merit (FOM) while varying the number of clusters and selecting an optimal cluster number derived from the graph of the FOM vs. clusters (e.g., the elbow, maximum, etc.).
  • FOM figure of merit
  • a version of the Gap Statistic was used.
  • the Gap Statistic provides a method to evaluate the correct number of clusters by comparing the dispersion of inter-cluster distances to that obtained using a reference null distribution in which all samples are equidistant from one another (i.e., there should only be 1 cluster for the null hypothesis).
  • a state (1 or 0) from the distribution of fragments that span that CpG was randomly sampled.
  • the resultant reference null data set eliminated the dependency structure of the actual data by ensuring all features (i.e., CpGs) were independent of one another.
  • actual and reference null data sets for TCF7 was compared.
  • the columns represent CpGs in TCF7, and the rows represent individual fragments spanning TCF7. Dark gray indicates methylated CpG status. Light gray indicates unmethylated CpG status.
  • This process was repeated multiple times (e.g., 50 times) to generate many reference null distributions. For each reference null distribution, a dispersion FOM (log(Wk)) was calculated. This was repeated for varying cluster number (up to a maximum determined by the number of fragments for that gene). The mean of the reference distribution FOM for each cluster number was compared to that obtained from the actual data and the Gap Statistic was calculated.
  • the standard error of the reference null FOM for each cluster number was used.
  • This may require one to link the states defined for one gene to those arising from a different gene. This may be accomplished through the use of fragments that span multiple genes (thereby enabling one to understand inter- genic correlations of epigenetic states). Alternatively, the inter-genic state relationships using other data modalities such as single cell methylation profiling and/or gene expression may be mapped. Ensuring that the resultant clusters represent true epigenetic states will require further optimization.
  • Methods to improve may involve tightening the gap statistic selection criteria (increasing the number of SE(k+1)'s that Gap(k+1) must be from Gap(k)), placing an upper limit on the number of allowed epigenetic states per gene (currently it is capped by the number of available fragments), denoising techniques to account for technical/biological noise, and incorporating various heuristics (e.g. weighting CpGs in promoter regions more heavily than introns in distance calculations, developing heuristics for accommodating known biological phenomenon such as X-inactivation).
  • various heuristics e.g. weighting CpGs in promoter regions more heavily than introns in distance calculations, developing heuristics for accommodating known biological phenomenon such as X-inactivation.
  • Example 4 Assessing the Relative Importance of CpGs to a given Classification
  • Read ONT data from CD8+ T-cells described in Example 3 was used to assess the relative importance of CpGs to a given classification (e.g., cluster, experimental condition), which can aid in differential analysis to identify favorable epigenetic editing target sites.
  • a given classification e.g., cluster, experimental condition
  • the region of interest was selected and subjected to clustering. These clusters then defined the classification.
  • information gain for each CpG in a gene was calculated. Information gain measures the gain in information (reduction in entropy) when partitioning a dataset on a given attribute (e.g., CpG methylation value).
  • Information gain is commonly used in decision tree creation where it is used in a recursive fashion to select the order of attributes to partition on to maximize classification accuracy.
  • a) can be interpreted as the Expected value of the resulting entropy when the dataset is partitioned on attribute, a.
  • the methylation of a CpG how much information is gained regarding the underlying random variable (e.g., epigenetic state) can be calculated.
  • Epigenetic maps for discovery of new targets for reprogramming cell state can enable discovery of targets or modulators that can reprogram cell state. As shown in FIG.1, epigenetic maps can be coupled with machine learning (ML) and/or artificial intelligence (AI) to identify 1) specific epigenetic sites, 2) transcription factors or 3) small molecules that can reprogram cell state.
  • ML machine learning
  • AI artificial intelligence
  • Specific epigenetic sites can be targeted through direct epigenetic editing.
  • methylation state of effector T cells versus na ⁇ ve T cells across the whole genome are compared to identify exact methylation sites that are different between the two cells.
  • This will significantly reduce the potential target space from 28 million CpG sites in the genome to a small fraction (e.g., ⁇ 0.1%), which are organized in a few hundred regions in the genome.
  • Direct epigenetic editing of the target sites e.g., via epigenetic editing screen
  • Transcription factors [0327] Specific transcription factors can bind to differential epigenetic regions.
  • footprint of known transcription factors enriched in those regions can be analyzed to predict which transcription factors are active in defining a particular cell state. For example, methylation state of effector versus na ⁇ ve T cells are compared to identify methylation sites that are different between the two cells. Next, transcription factors are analyzed within these differential methylation sites, reducing the possible 1600 transcription factors in the genome to only the transcription factors where their footprints are enriched within the methylation sites. Next, the network is put together with transcription factors and the genes or genomic locations they interact with through the epigenetic maps. If multiple transcription factor motifs are found in a spatially adjacent region of the genome, the transcription factor complexes that may be involved and interacting for gene regulation can be identified.
  • Example 3 Small molecules [0328] With the identified transcription factors or network, small molecules can also be identified. Small molecules are used to either directly act on the transcription factors or act on the predicted network upstream of transcriptions factors and the genes they impact to effect cellular phenotype and/or function.
  • Example 6 Epigenetic maps for discovery of transcription factors [0329] Population of CD8+ T cells were sorted into na ⁇ ve, central memory (CM), effector memory (EM), and effector subsets as described in Example 2. Next, each subset was subjected to epigenetic sequencing and mapping through unsupervised clustering as described in Example 3. Epigenetic maps of na ⁇ ve cells were compared to epigenetic maps of effector cells to identify differentially methylated regions (DMRs).
  • DMRs differentially methylated regions
  • FIGs.4A-4C shows transcription factors ( FOXN3, ELK1, BACH2, respectively) with distinct epigenetic states, where for some transcription factors, the window (region) was unmethylated in na ⁇ ve cells while in some transcription factors, the window (region) was unmethylated in effector cells (light gray indicates methylated regions, and dark gray indicates unmethylated regions). Furthermore, the transcription factors with footprint enriched in differentially methylated region were compared to transcription factors that have differential methylation states.
  • RUNX1, FOXN3 were found to have both footprints enriched in differentially methylated regions and have differential methylation states, adding additional support that these transcription factors may be causal in reprogramming of cell states, such as from na ⁇ ve cells to effector cells.
  • FIG.6 a ranked order list of transcription factors that are involved during differentiation of central memory cells (CM) to effector cells and effector memory cells (EM) to effector cells were also identified. The footprints of these transcription factors were analyzed within open differential methylation windows derived from epigenetic maps of genes in central memory cells versus effector cells and effector memory cells versus effector cells to identify potential causal transcription factors.
  • Example 7 Network of transcription factor interactions [0331] As described in Example 6, footprints of transcription factors can be identified in the DMRs via Homer analysis, using epigenetic maps of na ⁇ ve cells versus effector cells. Footprints in the DMRs can be used to help build the network of transcription factor interactions.
  • the footprint of SMAD4 was found to be enriched in the DMR regions of RUNX1 (light gray indicates methylated regions, and dark gray indicates unmethylated regions). Furthermore, RUNX1, which itself is a transcription factor, had its footprint enriched in SATB1 (FIG.8B), suggesting during differentiation RUNX1 activates SATB1, and creating a network of interactions among SMAD4, RUNX1, and SATB1.
  • This data showed that by analyzing all DMRs and transcription factors, it may be possible to determine the full interaction network between the DMRs and which of the known 400+ transcription factor footprints are enriched in each DMR.
  • DMR differentially methylated regions
  • Table 16 provides the list of transcription factors and differentially methylated regions (DMR) for TH17 and Treg cells.
  • DMR differentially methylated regions
  • epigenetic maps were produced for CD8 differential subsets to show changes in DMRs during differentiation of CD8 from na ⁇ ve cells to central memory (CM) cells, from na ⁇ ve cells to effector cells (E), and from na ⁇ ve cells to both CM and effector memory cells (EM) (light gray indicates methylated regions, and dark gray indicates unmethylated regions).
  • CM central memory
  • E effector cells
  • EM effector memory cells
  • Example 10 Assessing the impact of various treatments on the methylome of CD8+ T cells [0335] This example shows a methodology to assess the impact of various treatments on the methylome of CD8+ T cells. Methylome sequencing via nanopore sequencing, as described in Example 1 and 2, was performed from sorted na ⁇ ve, central memory (CM), effector memory (EM), and effector CD8+ T cell subsets.
  • CD8+ T cells treated with vehicle, rapamycin, GSK- 3beta inhibitor (TWS119), or hypomethylation agent (decitabine) were also subjected to sequencing.
  • reference DMR from effector and na ⁇ ve cells were created by first merging the sequencing data files (e.g., beta files) from effector and na ⁇ ve cells. DMR files were then generated between the merged effector and na ⁇ ve data files (e.g., beta files) and the DMR files were consolidated for epigenetic map generation.
  • DMR configuration required fisher exact test comprising of 1) p-value ⁇ dmrThreshold (0.01) 2) max base pair (bP) spanned by CpGs ⁇ bpWin (100 bp) (fisherFixed DMR.r) 3) minimum number of CpGs in a region of interest (ROI) ⁇ minROIbpc (3). All DMRs within a 5000 base pair window were combined and then centered in the 5000 base pair window. Table 6 shows the number of CpGs contained in the DMRs between various subsets (e.g., na ⁇ ve, CM, EM, and effector) and treated T-cells (treatment with vehicle, rapamycin, TWS119, decitabine).
  • subsets e.g., na ⁇ ve, CM, EM, and effector
  • Rapamycin-treated cells clustered primarily with the effector population
  • the na ⁇ ve and vehicle population clustered separately in a DMR in the DHX9 gene (FIG.14C)
  • light gray indicates methylated regions
  • dark gray indicates unmethylated regions
  • association scores were calculated and analyzed for TWS119 versus the other labels.
  • the differentially methylated regions in TWS119 and Rapamycin-treated cells were found to be distinct with minimal overlap in specific DMRs. Histogram of all the association scores for TWS119 versus the other labels for all na ⁇ ve-effector DMRs showed that the TWS119 label associated most closely with the vehicle and na ⁇ ve populations (FIG.15). This data indicated that there are little differences between the vehicle and treatment groups, and further, that both groups are more similar to the na ⁇ ve methylome than the effector methylome baseline.
  • Example 11 Epigenetic maps of Th17 versus Treg DMRs [0346] Similar to the methodology described in Example 10, DMR and epigenetic maps were generated for sorted Th17 and Treg cells, and then analyzed to obtain label-cluster association scores.
  • the label-cluster association was filtered to get region of interests (ROIs) with Th17 Purity Score > 0.7 Treg Label Association Scores ⁇ 0.3 to balance the selection of ROIs/DMRs that have high average purity in a cluster (e.g., most of the fragments for a label fall in a single cluster) and low association (e.g., most of the fragments for a label fall in different clusters from one another).
  • ROIs/DMRs region of interests
  • Treg Label Association Scores ⁇ 0.3 to balance the selection of ROIs/DMRs that have high average purity in a cluster (e.g., most of the fragments for a label fall in a single cluster) and low association (e.g., most of the fragments for a label fall in different clusters from one another).
  • Homer transcription factor motif analysis was performed on DMRs that were extracted from the filtered ROIs to look for enriched transcription factor motifs or footprints.
  • FIGs. 17A-17E shows the corresponding DMR (light gray indicates methylated regions, and dark gray indicates unmethylated regions).
  • Th17 and Treg cells were sorted from peripheral blood mononuclear cells (PMBC) of a healthy donor. Similar to the methodology described in Example 11, DMR and epigenetic maps were generated for sorted Th17 and Treg cells.
  • PMBC peripheral blood mononuclear cells
  • the maps were filtered for Label Purity > 0.75 and Association Score ⁇ 0.4. The maps were further filtered to obtain maps located on protein coding genes. Next, the list of resulting genes was filtered for predicted druggable targets that fall within the druggable genome, clinically actionable, or kinase subcategories by using a public database (e.g., The Drug Gene Interaction Database (DGIDB)). Using this methodology, 28,000,000 CpGs were filtered to 15,520 CpGs. Of the 15,520 CpGs, 83 DMRs were identified. Furthermore, 83 DMRs were filtered to identify 28 druggable targets.
  • DGIDB The Drug Gene Interaction Database
  • FIGs.29A-29Z, FIGs.29AA-29ZZ, FIGs.29AB- 29AZ, and FIGs.29BA, 20BC-20BZ shows the comparison of epigenetic maps of targets in Th17 cells versus Treg cells (light gray indicates methylated regions, and dark gray indicates unmethylated regions).
  • FIGs.29A-29J and FIG.29AQ shows the comparison of epigenetic maps of 11 targets (CD40LG, IL2RA, CD6, RORA, STAT3, TYMS, TNFRSF1B, PTGER3, ITGB2, TNFSF8, HDAC1) in Th17 cells versus Treg cells. This data showed that epigenetic maps can be used to enable drug discovery, small molecule targets, and epigenetic editing targets. [0355] Table 14. Druggable targets discovered from Th17 versus Treg cell epigenetic maps
  • This example demonstrates a method of analyzing the effects of a CRISPR epigenetic editing system across the epigenome and the location of the modifications. This method of analysis can be useful to locate unintended modifications at off-target sites and contribute to designing approaches to minimize unintended modifications, such as selectively blocking off- target sites during CRISPR-guided epigenetic editing to block those sites from being modified. Unintended modifications can result from direct off-target editing by the CRISPR-guided epigenetic editing system or from a long-range effect from an epigenetic edit by the CRISPR- guided epigenetic editing system (e.g., by modulating a signaling pathway).
  • FIGs.30 and 31 are example epigenetic maps that were generated that show differentially methylated regions between the control cells and the edited cells in regions of chromosome 19 (FIG. 30) and chromosome 12 (FIG.31) (light gray indicates methylated regions, and dark gray indicates unmethylated regions). Some of these differentially methylated regions may be a result of direct off-target editing by the CRISPR epigenetic editing system.
  • the first base in a chromosome is numbered 0.
  • Column 3. chromEnd - The ending position of the feature in the chromosome or scaffold.
  • Column 4. Name – Defines the name of BED line.
  • Column 7. thickStart The starting position at which the feature is drawn thickly (for example, the start codon in gene displays).
  • thickStart and thickEnd are usually set to the chromStart position.
  • Column 8. thickEnd The ending position at which the feature is drawn thickly (for example the stop codon in gene displays).
  • Column 9. itemRgb - An RGB value of the form R,G,B (e.g., 255,0,0). If the track line itemRgb attribute is set to "On", this RBG value will determine the display color of the data contained in this BED line.
  • blockCount The number of blocks (exons) in the BED line.
  • Column 11. blockSizes A comma-separated list of the block sizes.
  • Analyzing the locations of the off-target modifications can be used to refine editing methods by designing selective blockers that can be incorporated during CRISPR-guided epigenetic editing to block important off-target sites from epigenetic editing.
  • a method of selectively blocking an off-target site while simultaneously editing a target site is using combinations of orthogonal Cas systems (or Cas systems that do not cross-react), wherein one or more orthogonal Cas systems can be used to selectively block one or more off-target sites (using guide RNAs that guide the respective Cas protein(s) to bind to the off-target sites, thereby blocking epigenetic modifications), while another orthogonal Cas system introduces an epigenetic modification to a specific target site.
  • epigenetic mapping was used to identify the location of off-target modifications in chromosome 19 and chromosome 12 resulting from the CRISPR-guided epigenetic editing of CD151.
  • Guide RNAs for an orthogonal Cas system comprising a catalytically inactive orthogonal Cas protein can be designed to selectively block those sites of interest via binding.
  • Such an orthogonal Cas system targeting the off-target sites for binding can be used together with the ExpOFF epigenetic editing system (e.g., epigenetic editing system comprising ZNF10 KRAB, DNMT3A, and DNMT3L domains fused to a catalytically inactive S. pyogenes dCas9) targeting CD151 for methylation to refine epigenetic editing.
  • a method of generating a differential cellular state map for epigenetic editing comprising: (a) generating a first epigenetic map of a first cellular state; (b) generating a second epigenetic map of a second cellular state; and (c) determining a difference between the first epigenetic map and the second epigenetic map.
  • a method of assessing the effect of an epigenetic modulator comprising: (a) defining a target epigenetic state based on a target epigenetic map of a target cellular state wherein the target epigenetic map provides a methylation state of each genomic site of a plurality of target genomic sites in a target cell; (b) contacting a cell in an initial cellular state with an epigenetic modulator, thereby producing a treated cell; (c) generating a treated epigenetic map of the treated cell wherein the epigenetic map provides the methylation state of each genomic site of the plurality of target genomic sites in a treated cell; and (d) comparing the target epigenetic map and the treated epigenetic map to determine the alteration in the methylation state of each genomic site of the plurality of target genomic sites between the target cell and the treated cell.
  • the epigenetic modulator reduces the expression or activity of a transcription factor.
  • the transcription factor comprises at least one footprint enriched in a differentially methylated region (DMR). 5.
  • the transcription factor comprises differential methylations states. 6.
  • the transcription factor comprises at least one footprint enriched in a differentially methylated region (DMR) and further comprises differential methylations states. 7.
  • the transcription factor is selected from AP-1, bHLEH40, RUNX1, FOXN3, ELK1, HIC1, SP1, NF-kB, BATF, JUNE, IRF4, NFAT, STAT5, STAT3, Fra, Fos, ATF, RUNX2, bHLEH41, CLOCK, BMAL, NPASS, Max,ELK, Fli, Eomes, GATA1, Prop1, ZNF189, ROR, ZNF415, RUNT, T-Bet, MADs, HOX, and ZNF317.2a5.
  • the epigenetic modulator is a transcription factor inhibitor.
  • the transcription factor inhibitor is selected from, an AP-1 inhibitor, a BATF inhibitor, a Fra inhibitor, a FOXN3 inhibitor, a Fos inhibitor, an ATF inhibitor, a RUNX1 inhibitor, a RUNX2 inhibitor, a bHLH inhibitor (e.g., a bHLH40/41 inhibitor), a CLOCK inhibitor, an ELK1 inhibitor, a HIC1 inhibitor, an IRF inhibitor (e.g., an IRF4 inhibitor), an NFAT inhibitor, an NF-kB inhibitor, a STAT inhibitor (e.g., a STAT5 inhibitor, a STAT3 inhibitor), a FLI inhibitor, a BMAL inhibitor, a GATA inhibitor, a ROR inhibitor, a PROP inhibitor, a ZNF-189 inhibitor, a Tbet inhibitor, a Tcf7 inhibitor, an EOMES inhibitor, a RUNT inhibitor, an NPAS inhibitor, a HOX inhibitor, a ZNF317 inhibitor, a
  • the epigenetic modulator is a GSK-3 ⁇ inhibitor selected from a small ion cation, e.g., lithium chloride, TWS-119, CHIR98014, CHIR98023, CHIR99021, SB-216763, SB-415286, AR-A014418, kenpaullone, alsterpaullone, 1-azakenpaullone, Bacpaullone, a pyrazolo [3,4-b] quinoxaline derivative, a 9-oxo-thiazolo [5,4-f] quinazoline-2-carbonitrile derivative, tideglusib, and manzamine A. 11.
  • a small ion cation e.g., lithium chloride, TWS-119, CHIR98014, CHIR98023, CHIR99021, SB-216763, SB-415286, AR-A014418, kenpaullone, alsterpaullone, 1-azakenpaullone, Bacpaullone, a pyrazol
  • the transcription factor inhibitor is an AP-1 inhibitor selected from SR-11302, T-5224, E3330, 1-Methyl-6-oxo-1,6-dihydropyridine- 3-carboxylic acid, Ganoderic acid H, Chloranthalactone B, Torilin, SPC 839, Glucocorticoid receptor modulator 1, Timolumab, Vepalimomab, Vapaliximab, IMM- H007, SSAO/VAP-1 inhibitor 1, DL-Syringaresinol, or (+)-Glaucarubinone 12.
  • AP-1 inhibitor selected from SR-11302, T-5224, E3330, 1-Methyl-6-oxo-1,6-dihydropyridine- 3-carboxylic acid, Ganoderic acid H, Chloranthalactone B, Torilin, SPC 839, Glucocorticoid receptor modulator 1, Timolumab, Vepalimomab, Vapaliximab
  • the transcription factor inhibitor is a RUNX1 inhibitor selected from Ro5-3335, 2-pyridyl benzimidazole AI-4-57 or an analog thereof (e.g., AI-10-104).
  • the transcription factor inhibitor is a DNA hypomethylation agent, e.g., a DNMT inhibitor.
  • the DNA hypomethylation agent is selected from decitabine, guadecitabine, Fdcyd, zebularine, CP-4200, RG108, Nanaomycin A, and azacytidine.
  • the epigenetic modulator is a mTOR inhibitor selected from sirolimus (rapamycin), everolimus, temsirolimus, TORIN1, TORIN2, ridaforolimus, and KU-0063794. 16.
  • the epigenetic modulator modulates expression of an intracellular target of a transcription factor, e.g., CD151. 17.
  • a method of for reprogramming a target cell comprising, (a) determining the epigenetic state of the target cell in an initial cellular state; and (b) contacting the target cell with a first epigenetic modulator to reprogram the target cell. 18.
  • a method of modulating the expression of a target gene located in a target cell comprising: (a) determining the epigenetic state of a methylation state of each genomic site of a plurality of target genomic sites located in the target cell in an initial cellular state; and (b) contacting the target cell with a first epigenetic modulator to modulate the expression of the target gene, wherein the gene is a transcription factor. 19.
  • a method of modulating the expression of a target gene comprising (a) determining the epigenetic state of a methylation state of each genomic site of a plurality of target genomic sites located in the target gene in an initial cellular state; and (b) contacting the target gene with a first epigenetic modulator to reprogram the target gene, wherein the gene is a transcription factor.
  • a method of modulating expressing the expression of at least a first gene and a second gene located in a target cell; wherein the first gene and the second gene is involved in a genetic pathway comprising, (a) determining the epigenetic state of the target cell in an initial cellular state; (b) generating a first epigenetic map of the target cell wherein the first epigenetic map provides a methylation state of each genomic site of a plurality of target genomic sites located in the first gene in the target cell before contacting with the epigenetic modulator; (c) contacting the target cell with a first epigenetic modulator, (d) generating a second epigenetic map of the cell wherein the second epigenetic map provides the methylation state of each genomic site of a plurality of target genomic sites located in the first gene and the methylation state of each genomic site of a plurality of target genomic sites located in the second gene in the target cell; and (e) comparing the first and the second epigenetic map and determining the difference between the first and the second
  • a method of determining off-target edits in a target cell comprising determining the epigenetic state of the target cell in an initial cellular state; (a) generating a first epigenetic map of the target cell wherein the first epigenetic map provides a methylation state of each genomic site of a plurality of non-target genomic sites located in the target cell before contacting with an epigenetic modulator; (b) contacting the target cell with the epigenetic modulator (c) generating a second epigenetic map of the target cell wherein the second epigenetic map provides the methylation state of each genomic site of a plurality of non-target genomic sites located in the target cell; and (d) comparing the first and the second epigenetic map and determining the difference between the first and the second map to identify if the methylation state of each genomic site of the plurality of non-target genomic sites in the target cell is changed.
  • a method of reducing off-target edits in a target cell comprising (a) generating a first epigenetic map of the target cell wherein the first epigenetic map provides a methylation state of each genomic site of a plurality of non-target genomic sites located in the target cell before contacting with a first epigenetic modulator; (b) contacting the target cell with the first epigenetic modulator (c) generating a second epigenetic map of the target cell wherein the second epigenetic map provides the methylation state of each genomic site of a plurality of non-target genomic sites located in the target cell; (d) comparing the first and the second epigenetic map and determining the difference between the first and the second map to identify if the methylation state of each genomic site of the plurality of non-target genomic sites in the target cell is changed; and (e) contacting the cell target cell with a second epigenetic modulator.
  • any of embodiments 22-24 further comprising comparing the second and the third epigenetic map and determining the difference between the first and the second map to identify if the methylation state of each genomic site of the plurality of non-target genomic sites in the target cell is changed. 26. The method of any of embodiments 22-24, further comprising comparing the second and the third epigenetic map and determining the difference between the first and the second map to identify if the methylation state of each genomic site of the plurality of non-target genomic sites in the target cell returned to the methylation state of each genomic site of the plurality of non-target genomic sites prior to contacting with the first epigenetic modulator. 27.
  • a method of reducing off-target edits in a target cell comprising (a) generating a first epigenetic map of the target cell wherein the first epigenetic map provides a methylation state of each genomic site of a plurality of non-target genomic sites located in the target cell before contacting with an epigenetic modulator; (b) contacting the cell target cell with a blocker wherein the blocker binds to the each genomic site of the plurality of non-target genomic sites located in the target cell; and (c) contacting the target cell with the epigenetic modulator.
  • a method of determining the efficacy of a blocker to reduce off-target edits in a target cell comprising: (a) generating a first epigenetic map of the target cell wherein the first epigenetic map provides a methylation state of each genomic site of a plurality of non-target genomic sites located in the target cell before contacting with an epigenetic modulator; (b) contacting the cell target cell with a blocker wherein the blocker binds to the each genomic site of the plurality of non-target genomic sites located in the target cell; (c) contacting the target cell with the epigenetic modulator; (d) generating a second epigenetic map of the target cell wherein the second epigenetic map provides the methylation state of each genomic site of a plurality of non-target genomic sites located in the target cell; and (e) comparing the first and the second epigenetic map and determining the difference between the first and the second map to identify if the methylation state of each genomic site of the plurality of non-target genomic sites in
  • the method further comprises generating a first epigenetic map of the target cell wherein the first epigenetic map provides a methylation state of each genomic site of a plurality of target genomic sites in the target cell before contacting with the epigenetic modulator.
  • the method further comprises generating a second epigenetic map of the cell after contacting with the first epigenetic modulator.
  • the second epigenetic map provides the methylation state of each genomic site of the plurality of target genomic sites in the target cell after contacting with the epigenetic modulator. 34.
  • the method of embodiment 33 further comprising comparing the first and the second epigenetic map and determining the difference between the first and the second map to identify if a desired methylation state of each genomic site of the plurality of target genomic sites in the target cell is achieved.
  • 35 The method of any one of the preceding embodiments , further comprising contacting the target cell with a second epigenetic modulator if desired methylation state of each genomic site of the plurality of target genomic sites in the target cell is not achieved.
  • 36 The method of any one of the preceding embodiments, wherein the first and the second epigenetic modulator are identical.
  • 37. The method of any one of the preceding embodiments, wherein the first and the second epigenetic modulator are not identical. 38.
  • any one of the preceding embodiments further comprising comparing the first and the second epigenetic map and determining changes in methylation states in each genomic site of a plurality of a non-target genomic sites to identify off-target edits. 39. The method of any one of the preceding embodiments, further comprising contacting the target cell with a third epigenetic modulator to reverse the methylation states in each genomic site of the plurality of the non-target genomic sites to restore the methylation state of methylation states in each genomic site of the plurality of the non-target genomic sites to the methylation state prior to contacting the target cell with the first epigenetic modulator. 40.
  • a method of modulating an expression of a first transcription factor and a second transcription factor comprising, (a) determining the epigenetic state of the target cell in an initial cellular state; (b) generating a first epigenetic map of the target cell wherein the first epigenetic map, provides a methylation state of each genomic site of a plurality of target genomic sites located in a first gene encoding the first transcription factor in the target cell before contacting with the epigenetic modulator; (c) contacting the target cell with a first epigenetic modulator, (d) generating a second epigenetic map of the cell wherein the second epigenetic map provides the methylation state of each genomic site of a plurality of target genomic sites located in the first gene encoding the first transcription factor and the methylation state of each genomic site of a plurality of target genomic sites located in a second gene encoding the second transcription factor in the target cell; and (e) comparing the first and the second epigenetic map and determining the difference between the first and the second map
  • the method of embodiment 45, wherein the first transcription factor comprises at least one footprint enriched in a DMR. 47.
  • the method of embodiment 45, wherein the first transcription factor comprises differential methylations states. 49.
  • the method of embodiment 45, wherein the second transcription factor comprises differential methylations states.
  • the method of embodiment 45, wherein the first transcription factor comprises factor comprises at least one footprint enriched in a differentially methylated region (DMR) and further comprises differential methylations states.
  • DMR differentially methylated region
  • the second transcription factor comprises factor comprises at least one footprint enriched in a DMR and further comprises differential methylations states. 52.
  • the method of embodiment 45 wherein the first transcription factor comprises at least one footprint enriched in a DMR and the second transcription factor comprises at least one footprint enriched in a DMR. 53. The method of embodiment 45, wherein the first transcription factor comprises at least one footprint enriched in a DMR, and the second transcription factor comprises differential methylations states. 54. The method of embodiment 45, wherein the first transcription factor comprises at least one footprint enriched in a DMR, and the second transcription factor comprises at least one footprint enriched in a DMR and further comprises differential methylations states. 55. The method of embodiment 45, wherein the first transcription factor comprises differential methylations states and the second transcription factor comprises at least one footprint enriched in a DMR. 56.
  • the method of embodiment 45 wherein the first transcription factor comprises differential methylations states and the second transcription factor comprises differential methylations states. 57. The method of embodiment 45, wherein the first transcription factor comprises differential methylations states and the second transcription factor comprises at least one footprint enriched in a DMR and further comprises differential methylations states. 58. The method of embodiment 45, wherein the first transcription factor comprises at least one footprint enriched in a DMR and further comprises differential methylations states and the second transcription factor comprises at least one footprint enriched in a DMR. 59. The method of embodiment 45, wherein the first transcription factor comprises at least one footprint enriched in a DMR and further comprises differential methylations states and the second transcription factor comprises differential methylations states. 60.
  • a method of modulating the expression of a gene, wherein a protein encoded by the gene is an intracellular target of a transcription factor comprising; (a) determining the epigenetic state of the target cell in an initial cellular state; (b) generating a first epigenetic map of the target cell wherein the first epigenetic map, provides a methylation state of each genomic site of a plurality of target genomic sites located in the gene in the target cell before contacting with the epigenetic modulator; (c) contacting the target cell with a first epigenetic modulator, (d) generating a second epigenetic map of the cell wherein the second epigenetic map, provides the methylation state of each genomic site of a plurality of target genomic sites located in the gene in the target cell; and
  • the method of embodiment 61, wherein the gene encodes CD151.
  • the gene encodes a protein that is involved in a genetic pathway selected from one or more of: PI3K/Akt, NF- ⁇ B, ERK, Wnt / ⁇ -catenin, or GPCR signaling pathway.
  • a method of selecting a combination of epigenetic modulators for cellular treatment comprising: (a) defining a target epigenetic state based on a target epigenetic map of a target cellular state; (b) generating a first epigenetic map of a first cell treated with a first epigenetic modulator; (c) generating a second epigenetic map of a second cell treated with a second epigenetic modulator; and (d) selecting a combination of the first epigenetic modulator and the second epigenetic modulator based on the first epigenetic map, the second epigenetic map, and the target epigenetic map.
  • 66 The method of any preceding embodiments, wherein the first cellular state and the second cellular state are different states of differentiation. 67.
  • first cellular state and the second cellular state have different levels of stemness.
  • first cellular state and the second cellular state are different states of health or disease.
  • first cellular state or the second cellular state is treated with an agent.
  • the agent is a transcription factor inhibitor. 71.
  • the transcription factor inhibitor is selected from an AP-1 inhibitor, a BATF inhibitor, a Fra inhibitor, a FOXN3 inhibitor, a Fos inhibitor, an ATF inhibitor, a RUNX1 inhibitor, a RUNX2 inhibitor, a bHLH inhibitor (e.g., a bHLH40/41 inhibitor), a CLOCK inhibitor, an ELK1 inhibitor, a HIC1 inhibitor, an IRF inhibitor (e.g., an IRF4 inhibitor), an NFAT inhibitor, an NF-kB inhibitor, a STAT inhibitor (e.g., a STAT5 inhibitor, a STAT3 inhibitor), a FLI inhibitor, a BMAL inhibitor, a GATA inhibitor, a ROR inhibitor, a PROP inhibitor, a ZNF-189 inhibitor, a Tbet inhibitor, aTcf7 inhibitor, an EOMES inhibitor, a RUNT inhibitor, an NPAS inhibitor, a HOX inhibitor, a ZNF317 inhibitor, a
  • the agent is a GSK-3 ⁇ inhibitor selected from a small ion cation, e.g., lithium chloride, TWS-119, CHIR98014, CHIR98023, CHIR99021, SB-216763, SB-415286, AR-A014418, kenpaullone, alsterpaullone, 1- azakenpaullone, Bacpaullone, a pyrazolo [3,4-b] quinoxaline derivative, a 9-oxo-thiazolo [5,4-f] quinazoline-2-carbonitrile derivative, tideglusib, and manzamine A. 73.
  • a GSK-3 ⁇ inhibitor selected from a small ion cation, e.g., lithium chloride, TWS-119, CHIR98014, CHIR98023, CHIR99021, SB-216763, SB-415286, AR-A014418, kenpaullone, alsterpaullone, 1- azakenpaullone, Bacp
  • the transcription factor inhibitor is an AP-1 inhibitor selected from SR-11302, T-5224, E3330, 1-Methyl-6-oxo-1,6-dihydropyridine- 3-carboxylic acid, Ganoderic acid H, Chloranthalactone B, Torilin, SPC 839, Glucocorticoid receptor modulator 1, Timolumab, Vepalimomab, Vapaliximab, IMM- H007, SSAO/VAP-1 inhibitor 1, DL-Syringaresinol, or (+)-Glaucarubinone 74.
  • AP-1 inhibitor selected from SR-11302, T-5224, E3330, 1-Methyl-6-oxo-1,6-dihydropyridine- 3-carboxylic acid, Ganoderic acid H, Chloranthalactone B, Torilin, SPC 839, Glucocorticoid receptor modulator 1, Timolumab, Vepalimomab, Vapalixim
  • the transcription factor inhibitor is a RUNX1 inhibitor selected from Ro5-3335, 2-pyridyl benzimidazole AI-4-57 or an analog thereof (e.g., AI-10-104).
  • the transcription factor inhibitor is a DNA hypomethylation agent, e.g., a DNMT inhibitor.
  • the DNA hypomethylation agent is selected from decitabine, guadecitabine, Fdcyd, zebularine, CP-4200, RG108, Nanaomycin A, and azacytidine. 77.
  • the transcription factor inhibitor is a mTOR inhibitor selected from sirolimus (rapamycin), everolimus, temsirolimus, TORIN1, TORIN2, ridaforolimus, and KU-0063794.
  • 78. The method of embodiment 69, wherein the agent is rapamycin. 79.
  • the method of embodiment 79, wherein the GSK3 beta inhibitor is TWS119.
  • the method of embodiment 69, wherein the agent is a hypomethylation agent.
  • 82. The method of embodiment 81, wherein the hypomethylation agent is a decitabine.
  • first cellular state and the second cellular state are of the same cell type.
  • first cellular state and the second cellular state are of different cell types.
  • the first cellular state is of a first cell type and the second cellular state is of a second cell type, and further comprising identifying a methylation pattern specific to the first cell type or the second cell type.
  • the first cellular state and the second cellular state are regulatory T-cell states.
  • the first cellular state and the second cellular state are CD8+ T-cell states. 88.
  • first cellular state and the second cellular state are CD4+ T-cell states.
  • first cellular state and the second cellular state are selected from the group consisting of: a na ⁇ ve T-cell state, a central memory T-cell state, an effector T-cell state, and an effector memory T-cell state.
  • the first cellular state is an induced pluripotent stem cell state and the second cellular state is a target differentiated cell state.
  • the first cellular state is a na ⁇ ve T- cell state and the second cellular state is a central memory T-cell state.
  • the first cellular state is a na ⁇ ve T- cell state and the second cellular state is an effector T-cell state.
  • the first cellular state is a na ⁇ ve T- cell state and the second cellular state is an effector memory T-cell state.
  • the first cellular state is an exhausted state and the second cellular state is a rejuvenated state relative to the first cellular state. 95.
  • first cellular state is a diseased state and the second cellular state is a healthy state relative to the first cellular state.
  • the first cellular state and second cellular state are cellular states of a same individual.
  • generating the first epigenetic map comprises single-cell methylome sequencing of a first cell in the first cellular state
  • generating the second epigenetic map comprises single-cell methylome sequencing of a second cell in the second cellular state.
  • generating the first epigenetic map comprises methylome sequencing of genomic DNA extracted from a first cell in the first cellular state and generating the second epigenetic map comprises methylome sequencing of genomic DNA extracted from a second cell in the second cellular state.
  • generating the first epigenetic map comprises single-cell methylome sequencing of a first cell in the first cellular state
  • generating the second epigenetic map comprises methylome sequencing of genomic DNA extracted from a second cell in the second cellular state.
  • the epigenetic modulator comprises a small molecule inhibitor or activator.
  • the small molecule inhibitor or activator inhibits or activates a signaling pathway.
  • the epigenetic modulator comprises a small molecule inhibitor.
  • the epigenetic modulator comprises a transcription factor.
  • the epigenetic modulator comprises a direct epigenetic editor.
  • the epigenetic editor binds to a target nucleic acid.
  • the epigenetic editor comprises a nucleic acid binding domain. 107.
  • a method of selecting a target transcription factor for cellular reprogramming comprising: (a) generating a first epigenetic map of a first cellular state; (b) generating a second epigenetic map of a second cellular state; (c) comparing the first cellular state and the second cellular state to identify differentially methylated regions; and (d) analyzing the differentially methylated regions for enrichment of transcription factor motifs, wherein the motifs represent the target transcription factor.
  • a method of selecting a target transcription factor for cellular reprogramming comprising: (a) generating a first epigenetic map of a first cellular state; (b) generating a second epigenetic map of a second cellular state; and (c) comparing the first cellular state and the second cellular state to identify a gene with one or more differential methylation states, wherein the gene is the target transcription factor.
  • a method of generating a network of transcription factors involved in cellular reprogramming comprising: (a) generating a first epigenetic map of a first cellular state; (b) generating a second epigenetic map of a second cellular state; (c) comparing the first cellular state and the second cellular state to identify a first differentially methylated region; (d) analyzing the first differentially methylated region for enrichment of a first transcription factor motif, wherein the first motif represents a first target transcription factor; (e) identifying a second differentially methylation region of the first target transcription factor; and (f) analyzing the second differentially methylated region for enrichment of a second transcription factor motif, wherein the second motif represents a second target transcription factor. 110.
  • a method of selecting a target small molecule for cellular reprogramming comprising: (a) identifying a transcription factor comprising, (i) generating a first epigenetic map of a first cellular state; (ii) generating a second epigenetic map of a second cellular state; (iii) comparing the first cellular state and the second cellular state to identify differentially methylated regions; and (iv) analyzing the differentially methylated regions for enrichment of transcription factor motifs, wherein the motifs represent the transcription factor; and (b) identifying the target small molecule that act directly on the transcription factor.
  • 111 The method of any embodiments 107-110, wherein the first cellular state and the second cellular state are different states of differentiation. 112.
  • the method of any embodiments 107-110, wherein the first cellular state and the second cellular state are CD8+ T-cell states. 118. The method of any embodiments 107-110, wherein the first cellular state and the second cellular state are CD4+ T-cell states. 119. The method of any embodiments 107-110, wherein the first cellular state and the second cellular state are selected from the group consisting of: a na ⁇ ve T-cell state, a central memory T-cell state, an effector T-cell state, and an effector memory T-cell state. 120. The method of any embodiments 107-110, wherein the first cellular state is an induced pluripotent stem cell state and the second cellular state is a target differentiated cell state.
  • generating the first epigenetic map comprises methylome sequencing of genomic DNA extracted from a first cell in the first cellular state
  • generating the second epigenetic map comprises methylome sequencing of genomic DNA extracted from a second cell in the second cellular state.
  • generating the first epigenetic map comprises single-cell methylome sequencing of a first cell in the first cellular state
  • generating the second epigenetic map comprises methylome sequencing of genomic DNA extracted from a second cell in the second cellular state.
  • the transcription factor is from bHLH gene family. 128.
  • the method of any preceding embodiments, wherein the transcription factor comprises RUNX1, FOXN3, ELK1, or BACH2.
  • the transcription factor regulates a GSK3beta signaling pathway.
  • the transcription factor upregulates the GSK3beta signaling pathway.
  • the transcription factor downregulates the GSK3beta signaling pathway.
  • the transcription factor comprises GSK3, AXIN1, AXIN2, LEF1, TCF7, BCL11B, or TLE.
  • the small molecule regulates a GSK3beta signaling pathway.
  • the small molecule upregulates the GSK3beta signaling pathway. 135.
  • the method of any preceding embodiments, wherein the small molecule downregulates the GSK3beta signaling pathway. 136.
  • the target cell is selected from a bone cell, a blood cell, a muscle cell, a liver cell, a skin cell, an immune cell, a pancreatic cell, a nerve cell, a gastric cell, a cardiac cell, a gonad cell, or a fat cell.
  • the target cell is a liver hepatocyte.
  • the epigenetic modulator comprises: (i) a first effector moiety; and (ii) a nucleic acid binding moiety.
  • the epigenetic modulator of embodiment 139 wherein the first effector moiety is selected from a DNMT1, DNMT3A1, DNMT3A2, DNMT3B1, DNMT3B2, DNMT3B3, DNMT3B4, DNMT3B5, DNMT3B6, DNMT3L, TRDMT1, MQ1,MET1, DRM2, CMT2, CMT3, TET1, TET2, TET3, SETDB1, SETDB2, EHMT2 (i.e., G9A), EHMT1 (i.e., GLP), SUV39H1, EZH2, EZH1, SUV39H2, SETD8, SUV420H1, SUV420H2, KDM1A (i.e., LSD1), KDM1B (i.e., LSD2), KDM2A, KDM2B, KDM5A, KDM5B, KDM5C, KDM5D, KDM4B, NO66, KAT1, KAT2A, KAT3A, KAT3B,
  • the epigenetic modulator of embodiment 142, wherein the DNA methyltransferase may be selected from m6A methyltransferase, an m4C methyltransferase, and an m5C methyltransferase. 144.
  • the epigenetic modulator of embodiment 139, wherein the first effector moiety may be or comprise a transcription repressor moiety.
  • the epigenetic modulator of embodiment 139, wherein the first effector moiety is or may comprise a protein selected from KRAB, MeCP2, HP1, RBBP4, REST, FOG1, SUZ12, or a functional equivalent.
  • the epigenetic modulator of any preceding embodiments further comprising a linker.
  • the linker of any preceding embodiments, wherein the linker is a non-cleavable linker.
  • linker of any preceding embodiments wherein the linker is a peptide linker 153.
  • 154. The epigenetic modulator of any preceding embodiments, wherein the nucleic acid binding moiety is fused to the effector moiety.
  • 155. The epigenetic modulator of any preceding embodiments, wherein the nucleic acid binding moiety is located 5’ of the effector moiety.
  • the nucleic acid binding moiety is located 3’ of the effector moiety.
  • the epigenetic modulator of any preceding embodiments wherein the 3’ end of nucleic acid binding moiety is connected to the 5’ end of the effector moiety. 158. The epigenetic modulator of any preceding embodiments, wherein the 5’ end of nucleic acid binding moiety is connected to the 3’ end of the effector moiety. 159. The epigenetic modulator of any preceding embodiments, wherein the nucleic acid binding moiety comprises a CRISPR/Cas domain, a zinc finger domain, a TAL domain, a tetR domain, a meganuclease, or an oligonucleotide. 160.
  • the epigenetic modulator of any preceding embodiments, wherein the nucleic acid binding moiety comprises a CRISPR/Cas domain. 161.
  • the epigenetic modulator of any of preceding embodiments, wherein the CRISPR/Cas domain may comprise a CRISPR/Cas protein. 162.
  • the epigenetic modulator of any of preceding embodiments, wherein the CRISPR/Cas protein may be selected from a type I, type II, type III, type IV, type V Cas protein, and type VI Cas protein. 163.
  • the epigenetic modulator of any of preceding embodiments, wherein the CRISPR/Cas protein may be catalytically inactive. 164.
  • the CRISPR/Cas protein may be selected from Cas1, Cas1B, Cas2, Cas3, Cas4, Cas5, Cas5d, Cas5t, Cas5h, Cas5a, Cas6, Cas7, Cas8, Cas8a, Cas8b, Cas8c, Cas9, Cas10, Cas10d, Cas12a/Cpf1, Cas12b/C2c1, Cas12c/C2c3, Cas12d/CasY, Cas12e/CasX, Cas12g, Cas12h, Cas12i, Csy1 , Csy2, Csy3, Csy4, Cse1, Cse2, Cse3, Cse4, Cse5e, Csc1, Csc2, Csa5, Csn1, Csn2, Csm1, Csm2, Csm3, Csm4, Csm5, Csm6, Cm
  • the epigenetic modulator of any preceding embodiments, wherein the CRISPR/Cas protein may be or comprise a Cas9 ortholog.
  • the Cas9 ortholog may be selected from SpCas9, SaCas9, ScCas9, StCas9, NmCas9, VRERCas9, VERCas9, xCas9, espCas91.0, espCas1.1, Cas9HF1, hypaCas9, evoCas9, HiFiCas9, and CjCas9. 167.
  • the epigenetic modulator of any preceding embodiments, wherein the CRISPR/Cas protein may be or comprise a Cas12 ortholog. 169.
  • the epigenetic modulator of any preceding embodiments, wherein the Cas12 ortholog may be selected from Cpf1, FnCas12a, LbCas12a, AsCas12a, LbCas12a, TsCas12a, SaCas12a, Pb2Cas12a, PgCas12a, MiCas12a, Mb2Cas12a, Mb3Cas12a, Lb4Cas12a, Lb5Cas12a, FbCas12a, CrbCas12a, CpbCas12a, CMaCas12a, BsCas12a, BfCas12a, BoCas12a, Cas12j, or Cas12c.
  • the epigenetic modulator of any preceding embodiments, wherein the CRISPR/Cas protein may be derived from a bacterium or has one or more components derived from a bacteria, and wherein the one or more components may optionally be derived from different bacteria. 171.
  • the epigenetic modulator of any preceding embodiments, wherein the bacteria origin of the CRISPR/Cas protein of the epigenetic modulator may be selected from Streptococcus pyogenes, Streptococcus thermophilus, Streptococcus sp., Staphylococcus aureus, Nocardiopsis rougevillei, Streptomyces pristinae spiralis, Streptomyces viridochromo genes, Streptomyces viridochromogenes, Streptosporangium roseum, Streptosporangium roseum, AlicyclobacHlus acidocaldarius, Bacillus pseudomycoides, Bacillus selenitireducens, Bacillus niameyensis, Bacillus okhensis, Capnocytophaga canis, Chryseobacterium gallinarum, Coriobacterium_glomerans_PW2, Dechloromonas denitrificans, Enterococcus ceco
  • subtilis subtilis, Corynebacterium glutamicum, Streptomyces sp., Clostridium difficile, Clostridium saccharoperbutylacetonicum N1–4, Acaryochloris marina, Leptotrichia shahii, and Francisella novicida. 172.
  • the epigenetic modulator of any preceding embodiments, wherein the viral origin of the CRISPR/Cas protein of each of the epigenetic effectors may be selected from_bacteriophages.
  • the epigenetic modulator of any preceding embodiments, wherein the nucleic acid binding moiety of the epigenetic modulator may comprise a zinc finger domain. 175.
  • the epigenetic modulator of any preceding embodiments, wherein the zinc finger domain may comprise or consist essentially of or consist of 1-10, 1-9, 1-8, 1-7, 1-6, 1-5, 1-4, 1-3, 1-2, 2-10, 2-9, 2-8, 2-7, 2-6, 2-5, 2-4, 2-3, 3-10, 3-9, 3-8, 3-7, 3-6, 3-5, 3-4, 4- 10, 4-9, 4-8, 4-7, 4-6, 4-5, 5-10, 5-9, 5-8, 5-7, 5-6, 6-10, 6-9, 6-8, 6-7, 7-10, 7-9, 7-8, 8- 10, 8-9, or 9-10 zinc fingers.
  • the epigenetic modulator of any preceding embodiments, wherein the nucleic acid binding moiety of the epigenetic modulator may comprise a TAL domain.
  • the effector moiety may be a durable effector moiety.
  • the epigenetic modulator of any preceding embodiments, wherein the effector moiety may be a transient effector moiety. 180.
  • the epigenetic modulator of any preceding embodiments, wherein the effector moiety may be capable of effecting methylation profile of a DNA or histone. 181.
  • the epigenetic modulator of any preceding embodiments, wherein the effector moiety may be capable of increasing or decreasing a target gene expression.
  • the epigenetic modulator of any preceding embodiments, wherein the effector moiety may be capable of increasing or decreasing a second gene expression that is not the target gene.
  • the target gene and the second gene interact in a genetic pathway.
  • the target gene located upstream of the second gene in the genetic pathway 185.
  • the epigenetic modulator of any preceding embodiments, wherein the second effector moiety is capable of DNA methylation.
  • the epigenetic modulator of embodiment 188, wherein the DNA methyltransferase may be selected from m6A methyltransferase, an m4C methyltransferase, and an m5C methyltransferase. 190.
  • the epigenetic modulator of embodiment 188, wherein the DNA methyltransferase may be selected from DNMT1, DNMT3A1, DNMT3A2, DNMT3B1, DNMT3B2, DNMT3B3, DNMT3B4, DNMT3B5, DNMT3B6, DNMT3L, TRDMT1, MQ1,MET1, DRM2, CMT2, CMT3, or a functional equivalent thereof. 191.
  • the epigenetic modulator of any preceding embodiments, wherein the second effector moiety is capable of DNA demethylation.
  • the epigenetic modulator of any preceding embodiments, wherein the second effector moiety is selected from TET1, TET2, and TET3 or a functional equivalent thereof. 193.
  • the epigenetic modulator of any preceding embodiments wherein the second effector moiety may be or comprise a transcription repressor moiety. 194.
  • the epigenetic modulator of any preceding embodiments, wherein the second effector moiety is or may comprise a protein selected from KRAB, MeCP2, HP1, RBBP4, REST, FOG1, SUZ12, or a functional equivalent.
  • the epigenetic modulator of any preceding embodiments, wherein the first and the second effector moiety are identical.
  • the epigenetic modulator of any preceding embodiments, wherein the first and the second effector moiety are not identical.
  • NLS nuclear localization signal
  • the polynucleotide of any preceding embodiments, wherein the polynucleotide is a RNA, e.g., an mRNA.
  • a guide RNA is complexed with the epigenetic modulator, wherein the guide RNA targets the epigenetic modulator to each genomic site of the plurality of target genomic sites in the target cell.
  • the plurality of target genomic sites is a DNA sequence segment.
  • the plurality of target genomic sites is an RNA sequence segment, e.g., a mRNA sequence segment.
  • the mRNA may be expressed from the genome of an organism.
  • the plurality of target genomic sites of any preceding embodiments, wherein the organism may be a prokaryote, e.g., a bacterium. 212.
  • the plurality of target genomic sites of any preceding embodiments, wherein the organism may be a eukaryote. 213.
  • the plurality of target genomic sites of any preceding embodiments, wherein the eukaryote may be a vertebrate.
  • the plurality of target genomic sites of any preceding embodiments, wherein the vertebrate may be a mammal. 215.
  • the plurality of target genomic sites of any preceding embodiments, wherein the mammal may be a non-human mammal, e.g., a mouse, a primate. 216.
  • the plurality of target genomic sites of any preceding embodiments wherein the mammal may be a human. 217.
  • the plurality of target genomic sites of any preceding embodiments, wherein the target cell is selected from a fibroblast cell, a liver cell, a cardiac cell, a CNS cell, a PNS cell, a kidney cell, a lung cell, a hematopoietic cell, a pancreatic beta cell, a bone cell, a skeletal muscle cell, a skin cell, an immune cell, a follicular cell, a vascular cell, a neural cell, an osteoblast cell, an osteoclast cell, and an endothelial cell.
  • the plurality of target genomic sites of any preceding embodiments, wherein the target cell is a liver hepatocyte. 220.
  • the plurality of target genomic sites of any preceding embodiments, wherein the target cell is an immune cell, e.g., a T cell. 221.
  • the plurality of target genomic sites of any preceding embodiments, wherein the T cell is CD4+ T cell.
  • the plurality of target genomic sites of any preceding embodiments, wherein the T cell is CD8+ T cell. 223.
  • the plurality of target genomic sites of any preceding embodiments, wherein the target gene is a transcription factor. 224.
  • the transcription factor is selected from AP-1, bHLEH40, RUNX1, FOXN3, ELK1, HIC1, SP1, NF-kB, BATF, JUNE, IRF4, NFAT, STAT5, STAT3, Fra, Fos, ATF, RUNX2, bHLEH41, CLOCK, BMAL, NPASS, Max,ELK, Fli, Eomes, GATA1, Prop1, ZNF189, ROR, ZNF415, RUNT, T-Bet, MADs, HOX, and ZNF317.2a5. 225.
  • the plurality of target genomic sites of any preceding embodiments wherein the plurality of target genomic sites may be associated with a germline gene, e.g., a misregulated germline gene, e.g., a misregulated germline gene associated with a developmental defect or disorder. 226.
  • the plurality of target genomic sites of any preceding embodiments, wherein the target cell expresses CD151. 227.
  • the plurality of target genomic sites of any preceding embodiments, wherein the plurality of target genomic sites may be associated with a disease or a disorder. 228.
  • the plurality of target genomic sites of any preceding embodiments wherein the disease is selected from a cancer, an immune or autoimmune disorder, a pulmonary disorder, a neurodegenerative disorder, a cardiovascular disorder, a fibrotic disorder, an eye or skin disorder, an osteoarthritic disorder, a kidney disorder, or a metabolic disorder. 229.
  • the plurality of target genomic sites of any preceding embodiments, wherein the plurality of target genomic sites may be associated with a genetic pathway involved in cellular regeneration.
  • the plurality of target genomic site is selected from one or more of a differentially methylated region, an enhancer, a promoter, a transcription start site (TSS), or a CTCF binding site. 231.
  • the method of embodiment 230, wherein the target genomic site comprises a CTCF binding site. 232.
  • the method of embodiment 230, wherein the target genomic site comprises an enhancer or a promoter.
  • the method of embodiment 230, wherein the target genomic site comprises a differentially methylated region. 234.
  • the method of any preceding embodiments, wherein the differentially methylated region is unmethylated and the epigenetic modulator has methylation activity.
  • 235 The method of any preceding embodiments, wherein the differentially methylated region is hypomethylated and the epigenetic modulator has methylation activity.
  • 236 The method of any preceding embodiments, wherein the differentially methylated region is hypermethylated and the epigenetic modulator has demethylation activity. 237.
  • the differentially methylated region is unmethylated in the cell and the epigenetic modulator has methylation activity.
  • the guide sequences are ribonucleic acid guide sequences. 239. The method of any preceding embodiments, wherein the guide sequence is from about 10 base pairs to about 150 base pairs in length.
  • the plurality of non-target genomic site is selected from one or more of a differentially methylated region, an enhancer, a promoter, a transcription start site (TSS), or a CTCF binding site. 241.
  • a system comprising: (a) a composition comprising an epigenetic modulator; (b) a robotics arranged to deliver the composition to a target cell; and (c) a computer system programmed to: (i) control the robotics; and (ii) select an epigenetic modulator that is capable of modulating a methylation state of each genomic site of a plurality of target genomic sites located in the target cell.

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Abstract

La présente invention concerne des procédés et des compositions permettant de moduler l'expression des gènes et les voies génétiques. Les préparations et les compositions de l'invention peuvent également être modifiées, par exemple, des variants, des fusions ou des combinaisons.
PCT/US2023/080772 2022-11-21 2023-11-21 Génération et utilisation de cartes épigénétiques pour la découverte de médicaments Ceased WO2024112806A1 (fr)

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