[go: up one dir, main page]

WO2023282900A1 - Méthode et système de diagnostic et de traitement d'une maladie neurodégénérative et des crises d'épilepsie - Google Patents

Méthode et système de diagnostic et de traitement d'une maladie neurodégénérative et des crises d'épilepsie Download PDF

Info

Publication number
WO2023282900A1
WO2023282900A1 PCT/US2021/040878 US2021040878W WO2023282900A1 WO 2023282900 A1 WO2023282900 A1 WO 2023282900A1 US 2021040878 W US2021040878 W US 2021040878W WO 2023282900 A1 WO2023282900 A1 WO 2023282900A1
Authority
WO
WIPO (PCT)
Prior art keywords
rna
disease
subject
blood
patient
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2021/040878
Other languages
English (en)
Inventor
Robert Meller
Simon P. ROGER
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Morehouse School of Medicine Inc
Original Assignee
Morehouse School of Medicine Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Morehouse School of Medicine Inc filed Critical Morehouse School of Medicine Inc
Priority to PCT/US2021/040878 priority Critical patent/WO2023282900A1/fr
Publication of WO2023282900A1 publication Critical patent/WO2023282900A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P25/00—Drugs for disorders of the nervous system
    • A61P25/28—Drugs for disorders of the nervous system for treating neurodegenerative disorders of the central nervous system, e.g. nootropic agents, cognition enhancers, drugs for treating Alzheimer's disease or other forms of dementia
    • 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
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • 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
    • C12Q2600/158—Expression markers
    • 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
    • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA

Definitions

  • the present disclosure relates to the treatment of neurodegenerative disease, in particular pre-clinical Alzheimer’s disease and mild cognitive impairment, as well as treatment and prevention of seizures.
  • AD Alzheimer’s disease
  • MCI mild cognitive impairment
  • An aspect of the application is a method of pre-clinical detection for incipient neurodegenerative disease, comprising the steps of: extracting a whole blood sample from a subject; preparing an RNA library from the whole blood sample; sequencing the RNA library; determining differential expression of a plurality of RNA sequences comprised within the RNA library, wherein the plurality of RNA sequences comprises both protein coding and non-coding RNA (ncRNA); creating a blood RNA transcriptome profile based on the differential expression of the RNA sequences; comparing the blood RNA transcriptome profile to a reference blood RNA transcriptome profile derived from a subject with neurodegenerative disease; detecting incipient neurodegenerative disease based on the correspondence between the blood RNA transcriptome profile and the reference profile derived from a subject with neurodegenerative disease.
  • ncRNA protein coding and non-coding RNA
  • the neurodegenerative disease is Alzheimer's disease. In a particular embodiment, the neurodegenerative disease is pre-clinical AD. In certain embodiments, the method further comprises the step of detecting pro-dromal Alzheimer's disease. In specific embodiments, the RNA library further comprises miRNA and mRNA. In other embodiments, the method further comprises comparing the blood RNA transcriptome profile to a reference blood RNA transcriptome profile from a subject with a dementia selected from one or more of the group consisting of frontal temporal dementia, CADASIL and mild cognitive impairment (MCI).
  • MCI mild cognitive impairment
  • the subject is selected based on one or more characteristics selected from the group consisting of geographical location, race, sex, age, weight, height (BMI), blood pressure, heartrate, body temperature, medications, routine admission blood studies and drug screens.
  • the neurodegenerative disease is one or more selected from the group consisting of Huntington's disease, Parkinson's disease, trinucleotide repeat disorders (DRPLA, SBMA, SCA1, SCA2, SCA3, SCA6, SCA7, SCA17, FRAXA, FXTAS, FRAXE, FRDA, DM1, SCA8, SCA12), amyotrophic lateral sclerosis and Batten disease.
  • Another aspect of the application is a method of enhancing treatment of preclinical Alzheimer's disease, comprising the steps of: extracting a whole blood sample from a subject; preparing an RNA library from the whole blood sample; sequencing the RNA library; determining differential expression of a plurality of RNA sequences comprised within the RNA library, wherein the plurality of RNA sequences comprises non-coding RNA (ncRNA); creating a blood RNA transcriptome profile based on the differential expression of the RNA sequences; comparing the blood RNA transcriptome profile to a reference blood RNA transcriptome profile derived from a subject with preclinical Alzheimer's disease; detecting preclinical Alzheimer's disease based on the correspondence between the blood RNA transcriptome profile and the reference profile derived from a subject with preclinical Alzheimer's disease; treating the subject with a therapy for Alzheimer's disease.
  • ncRNA non-coding RNA
  • the therapy for Alzheimer's disease comprises: administering cholinesterase inhibitors.
  • the cholinesterase inhibitors are selected from the group consisting of one or more of donepezil, rivastigimine and galantamine.
  • the therapy for Alzheimer’s disease include antibody therapies, such as treatment with one or more of aducanumab, bapineuzumab, gantenerumab, crenezumab, BAN2401, GSK 933776, AAB-003, SAR228810, BIID037/BART and solaneuzumab.
  • Another aspect of the application is a method of enhancing treatment of preclinical Parkinson's disease, comprising the steps of: extracting a whole blood sample from a subject; preparing an RNA library from the whole blood sample; sequencing the RNA library; determining differential expression of a plurality of RNA sequences comprised within the RNA library, wherein the plurality of RNA sequences comprises non-coding RNA (ncRNA); creating a blood RNA transcriptome profile based on the differential expression of the RNA sequences; comparing the blood RNA transcriptome profile to a reference blood RNA transcriptome profile derived from a subject with preclinical Parkinson's disease; detecting preclinical Parkinson's disease based on the correspondence between the blood
  • ncRNA non-coding RNA
  • RNA transcriptome profile and the reference profile derived from a subject with preclinical Parkinson's disease treating the subject with a therapy for Parkinson's disease.
  • Another aspect of the application is a method of enhancing treatment of epileptic seizures, comprising the steps of: extracting a whole blood sample from a subject; preparing an RNA library from the whole blood sample; sequencing the RNA library; determining differential expression of a plurality of RNA sequences comprised within the RNA library, wherein the plurality of RNA sequences comprises non-coding RNA (ncRNA); creating a blood RNA transcriptome profile based on the differential expression of the RNA sequences; comparing the blood RNA transcriptome profile to a reference blood RNA transcriptome profile derived from a subject with an epileptic seizure; detecting epileptic seizure based on the correspondence between the blood RNA transcriptome profile and the reference profile derived from a subject with epileptic seizure; treating the subject with a therapy for epileptic seizure.
  • ncRNA non-coding RNA
  • Fig. 1 shows analysis of RNA expression profiles distinguishes AD patients from healthy controls.
  • AD and Controls subject to ROC analysis has an accuracy of 0.83.
  • AUG for AD vs. other dementias was 0.53, and vs.
  • Panel B Hierarchical cluster analysis of differentially expressed genes shows a clear separation of healthy control (blue) and AD patient profiles (yellow).
  • Panel C Principal component analysis (PCA) shows clear separation of variability between samples.
  • Fig. 2 shows analysis of RNA expression profiles distinguishes AD patients from MCI patients.
  • Panel A Using the exon expression values identified above to distinguish AD from healthy controls, the PCA analysis was reperformed with the MCI data as well. Note how the MCI data fall in between the controls and AD patient groups.
  • Panel B A separate analysis of just MCI and AD patient groups was performed. Hierarchical cluster analysis of differentially expressed genes shows a clear separation of MCI and AD patient profiles.
  • Panel C Principal component analysis (PCA) shows clear separation of variability between patient groups.
  • PCA Principal component analysis
  • Fig. 3 shows analysis of RNA expression profiles distinguishes AD patients from patients with other forms of dementia.
  • hierarchical cluster analysis of differentially expressed genes shows a clear separation of AD and other dementia patient profiles.
  • Fig. 4 shows use of post-seizure blood RNA profiles to develop an algorithm to retrospectively diagnose a seizure event.
  • Blood samples from patients undergoing EEG monitoring are analyzed for RNA expression patterns at various times following seizure. These data are modeled to identify RNAs to predict the occurrence of a seizure, retrospectively.
  • Panel A Analysis of RNA expression profiles to African American pan genome, by race or ethnicity (AA- African American, CC Caucasian, HA Hispanic). Unique alignments make up 0.2% of mapped reads.
  • Panel B Comparison of alignment metrics of standard and de-novo transcriptome annotation guide generated using Blood RNA-seq data. More RNAs are called exons with the custom guide, and quantitated.
  • Fig. 5 shows the use of temporal blood RNA profiles to identify the nature of a seizure event. Blood samples from patients undergoing EEG monitoring are analyzed for RNA expression patterns at various times following seizure. These data are modeled to identify RNAs to predict the occurrence of a seizure, retrospectively.
  • tissue refers to an aggregation of cells that are morphologically or functionally related, or cell systems.
  • tissue in vitro cultured cells, as well as tissues, organs, and the like, are encompassed by the term tissue.
  • library refers to a collection of polynucleotides derived from nucleic acid sequences of a particular tissue, in particular RNA or cDNA.
  • the polynucleotides of a library may be, but are not necessarily, cloned into a vector or set in a microarray.
  • nucleic acid polynucleotide
  • oligonucleotide may be used interchangeably herein and refer to a deoxyribonucleotide or ribonucleotide polymer in either single- or double-stranded form.
  • a “subsequence” or “segment” refers to a sequence of nucleotides that comprise a part of a longer sequence of nucleotides.
  • a "gene,” for the purposes of the present disclosure, includes a DNA region encoding a gene product.
  • the region can also include DNA regions that regulate the production of the gene product, whether or not such regulatory sequences are adjacent to coding and/or transcribed sequences.
  • This term in science also encompasses RNAs which are expressed by a cell, but that are not translated into a protein, such as a non-coding RNA, micro RNA, piRNA, etc.
  • a gene can include, without limitation, promoter sequences, terminators, translational regulatory sequences such as ribosome binding sites and internal ribosome entry sites, enhancers, silencers, insulators, boundary elements, replication origins, matrix attachment sites and locus control regions.
  • Gene expression refers to the conversion of the information, contained in a gene, into a gene product.
  • a gene product can be the direct transcriptional product of a gene (e.g, mRNA, tRNA, rRNA, antisense RNA, ribozyme, structural RNA, or a novel RNA whose function is as yet to be determined) or a protein produced by translation of a mRNA.
  • Gene products also include RNAs which are modified, by processes such as capping, polyadenylation, methylation, and editing, and proteins modified by, for example, methylation, acetylation, phosphorylation, ubiquitination, ADP-ribosylation, myristilation, and glycosylation.
  • transcriptome refers to the set of all RNA molecules found in one cell or found in a population of cells. It is herein used to refer to all RNAs unless otherwise stated (e.g, the transcriptome is all RNA species, and their parts such as different isoforms (transcripts) and exons (small parts)).
  • the transcriptome differs from the exome in that the transcriptome consists of only those RNA molecules contained in a specified cell population, and normally concerns the amount or concentration of each RNA molecule in addition to their molecular identities.
  • the term can be applied to the whole set of transcripts in a given organism, or to a particular subset of transcripts found in a specific cell type. In contrast to the genome, the transcriptome can vary with external environmental conditions. Since the transcriptome comprises all RNA transcripts in the cell, the transcriptome reflects the active expression of different genes at any given time (although accounting for mRNA degradation phenomena, such as transcriptional attenuation, as an exception).
  • neurodegenerative disease refers to the progressive loss of structure or function of neurons, including death of neurons.
  • Many neurodegenerative diseases - including amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease, and Huntington's disease occur as a result of neurodegenerative processes. Such diseases are incurable, resulting in progressive degeneration and/or death of neuron cells.
  • many similarities appear that relate these diseases to one another on a sub-cellular level. Discovering these similarities offers hope for therapeutic advances that could ameliorate many diseases simultaneously.
  • There are many parallels between different neurodegenerative disorders including atypical protein assemblies as well as induced cell death. Neurodegeneration can be found in many different levels of neuronal circuitry ranging from molecular to systemic.
  • Alzheimer's disease refers to a chronic neurodegenerative disease characterized by loss of neurons and synapses in the cerebral cortex and certain subcortical regions. This loss results in gross atrophy of the affected regions, including degeneration in the temporal lobe and parietal lobe, and parts of the frontal cortex and cingulate gyrus.
  • Parkinson's disease refers to a long-term degenerative disorder of the central nervous system that mainly affects the motor system.
  • the mechanism is by which the brain cells in Parkinson's are lost is not understood, but may consist of an abnormal accumulation of the protein alpha-synuclein bound to ubiquitin in the damaged cells.
  • the alpha-synuclein-ubiquitin complex cannot be directed to the proteasome. This protein accumulation forms proteinaceous cytoplasmic inclusions called Lewy bodies.
  • ALS Amyotrophic lateral sclerosis
  • motor neuron disease for a group of conditions of which ALS is the most common. ALS is characterized by stiff muscles, muscle twitching, and gradually worsening weakness due to muscles decreasing in size. It may begin with weakness in the arms or legs, or with difficulty speaking or swallowing. About half of people develop at least mild difficulties with thinking and behavior and most people experience pain. Most eventually lose the ability to walk, use their hands, speak, swallow, and breathe.
  • the term “dementia” refers to a broad category of brain diseases that cause a long-term and often gradual decrease in the ability to think and remember that is great enough to affect a person's daily functioning. Other common symptoms include emotional problems, difficulties with language, and a decrease in motivation. A person's consciousness is usually not affected. A dementia diagnosis requires a change from a person's usual mental functioning and a greater decline than one would expect due to aging. The most common type of dementia is Alzheimer's disease, which makes up 50% to 70% of cases. Other common types include vascular dementia (25%), Lewy body dementia (15%), and frontotemporal dementia.
  • dementia Less common causes include normal pressure hydrocephalus, Parkinson's disease dementia, syphilis, and Creutzfeldt-Jakob disease among others. More than one type of dementia may exist in the same person. A small proportion of cases run in families. In the DSM-5, dementia was reclassified as a neurocognitive disorder, with various degrees of severity.
  • MCI mild cognitive impairment
  • Diagnosis of MCI is often difficult, as cognitive testing may be normal. Often, more in-depth neuropsychological testing is necessary to make the diagnosis.
  • the most commonly used criteria are called the Peterson criteria and include: memory or other cognitive (thought-processing) complaint by the person or a person who knows the patient well. The person must have a memory or other cognitive problem as compared to a person of the same age and level of education. The problem must not be severe enough to affect the person's daily function. The person must not have dementia.
  • MCI can present with a variety of symptoms, when memory loss is the predominant symptom it is termed “amnestic MCI” and is frequently seen as a prodromal stage of Alzheimer's disease. Studies suggest that these individuals tend to progress to probable Alzheimer's disease at a rate of approximately 10% to 15% per year.
  • preclinical Alzheimer’s disease refers to is a newly defined stage of the disease reflecting current evidence that changes in the brain may occur years before symptoms affecting memory, thinking or behavior can be detected by affected individuals or their physicians.
  • MCI mild cognitive impairment
  • a biomarker is something that can be measured to accurately and reliably indicate the presence of disease.
  • An example of a biomarker is fasting blood glucose (blood sugar) level, which indicates the presence of diabetes if it is 126 mg/dL or higher.
  • ADNI Alzheimer’s Disease Neuroimaging Initiative
  • AIBL Biomarker & Lifestyle Flagship Study of Ageing
  • amyloid plaque build-up is present in the brains of the healthy individuals being studied. The number is dependent on their age and genetic background, but ranges from approximately 20-40%. Interestingly, the percentage of amyloid-positive normal individuals detected at a given age closely parallels the percentage of individuals diagnosed with Alzheimer’s dementia a decade later.
  • RNA RNA that consists of rRNA ( ⁇ 90% of total RNA for most cells). Although there is less tRNA by mass, their small size results in their molar level being higher than rRNA. Other abundant RNAs, such as mRNA, snRNA, and snoRNAs are present in aggregate at levels that are about 1-2 orders of magnitude lower than rRNA and tRNA. Certain small RNAs, such as miRNA and piRNAs can be present at very high levels; however, this appears to be cell type dependent.
  • ncRNA non-coding RNA
  • the term “ncRNA” refers to an RNA molecule that is not translated into a protein.
  • the number of non-coding RNAs within the human genome is unknown; however, recent transcriptomic and bioinformatics studies suggest that there are thousands of them. Many of the newly identified ncRNAs have not been validated for their function. It is also likely that many ncRNAs are non functional (sometimes referred to as junk RNA), and are the product of spurious transcription.
  • RNAs Abundant and functionally important types include transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs), as well as small RNAs such as microRNAs, siRNAs, piRNAs, snoRNAs, snRNAs, exRNAs, scaRNAs and the long ncRNAs such as Xist and HOTAIR.
  • tRNAs transfer RNAs
  • rRNAs ribosomal RNAs
  • small RNAs such as microRNAs, siRNAs, piRNAs, snoRNAs, snRNAs, exRNAs, scaRNAs and the long ncRNAs such as Xist and HOTAIR.
  • the ncRNA may have some associated activity that may be deleterious. Most often the major concern is whether it will be translated into short random peptides.
  • ncRNAs long non-coding RNAs
  • Short ncRNAs include miRNA, siRNA, short enhancer RNAs (eRNAs), circular RNAs and piRNA.
  • miRNA generally bind to a specific target messenger RNA with a complementary sequence to induce cleavage, or degradation or block translation.
  • Short interfering RNAs function in a similar way as miRNAs to mediate post-transcriptional gene silencing (PTGS) as a result of mRNA degradation.
  • PTGS post-transcriptional gene silencing
  • pi-interacting RNAs Piwi-interacting RNAs (piRNA) are so named due to their interaction with the piwi family of proteins. The primary function of these RNA molecules involves chromatin regulation and suppression of transposon activity in germline and somatic cells.
  • PiRNAs that are antisense to expressed transposons target and cleave the transposon in complexes with PlWI-proteins. This cleavage generates additional piRNAs which target and cleave additional transposons. This cycle continues to produce an abundance of piRNAs and augment transposon silencing.
  • Transcriptomic techniques include DNA microarrays and RNA-Seq. All transcriptomic methods require RNA to first be isolated from the experimental organism before transcripts can be recorded. Although biological systems are incredibly diverse, RNA extraction techniques are broadly similar and involve mechanical disruption of cells or tissues, disruption of RNase with chaotropic salts, disruption of macromolecules and nucleotide complexes, separation of RNA from undesired biomolecules including DNA, and concentration of the RNA via precipitation from solution or elution from a solid matrix. Isolated RNA may additionally be treated with DNase to digest any traces of DNA. Transcription can also be studied at the level of individual cells by single-cell transcriptomic.
  • RNA-Seq refers to RNA-Seq (RNA sequencing), sometimes also referred to as whole transcriptome shotgun sequencing (WTSS).
  • WTSS whole transcriptome shotgun sequencing
  • RNA-Seq uses high-throughput sequencing to illuminate the existence and relative quantities of RNA molecules at a given moment in a biological sample.
  • RNA-Seq is used to study the continuously changing cellular transcriptome.
  • RNA-Seq enables overview in different groups or treatments of alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations/SNPs and changes in gene expression over time, or differences in gene expression.
  • RNA-Seq can also look at different populations of RNA to include the whole RNS transcriptome (such as miRNA or tRNA). RNA-seq can be performed by single cell sequencing and also in situ sequencing of fixed tissue.
  • RNA-Seq works in concert with a range of high-throughput DNA sequencing technologies. However, prior to sequencing of the extracted RNA transcripts, several key processing steps are performed. Methods differ in the use of transcript enrichment, fragmentation, amplification, single or paired-end sequencing, and whether to preserve strand information.
  • Methods differ in the use of transcript enrichment, fragmentation, amplification, single or paired-end sequencing, and whether to preserve strand information.
  • RNA- Seq is not limiting on the invention discussed herein.
  • Tissue specificity Gene expression varies within and between tissues, and RNA-Seq measures this mix of cell types. This may make it difficult to isolate the biological mechanism of interest. Single cell sequencing can be used to study each cell individually, mitigating this issue.
  • Time dependence Gene expression changes over time, and RNA-Seq only takes a snapshot. Time course experiments can be performed to observe changes in the transcriptome.
  • Data generation artifacts also known as technical variance: The reagents (e.g., library preparation kit), personnel involved, and type of sequencer (e.g., Ion Torrent, Oxford Nanopore, Illumina, Pacific Biosciences) can result in technical artifacts that might be mis-interpreted as meaningful results. As with any scientific experiment, it is prudent to conduct RNA-Seq in a well controlled setting. If this is not possible or the study is a meta- analysis, another solution is to detect technical artifacts by inferring latent variables (typically principal component analysis or factor analysis) and subsequently correcting for these variables.
  • latent variables typically principal component analysis or factor analysis
  • RNA-Seq experiment in humans is usually on the order of 1 Gb. This large volume of data can pose storage issues.
  • One solution is compressing the data using multi-purpose computational schemas (e.g., gzip) or genomics- specific schemas. The latter can be based on reference sequences or de novo.
  • Another solution is to perform microarray experiments, which may be sufficient for hypothesis-driven work or replication studies (as opposed to exploratory research).
  • extracts may be typically 40-60% ribosomal RNA; in such cases, rRNA is not removed nor is the extract enriched for mRNA, which increases sample to sample variability; ncRNA is also not enriched and blood extracts are used as is, ncRNA may be high expression in blood.
  • RNA extracts may be typically 98% ribosomal RNA.
  • Enrichment for transcripts can be performed by poly- A affinity methods or by depletion of ribosomal RNA using sequence-specific probes. Degraded RNA may affect downstream results; for example, mRNA enrichment from degraded samples will result in the depletion of 5' mRNA ends and an uneven signal across the length of a transcript Snap-freezing of tissue prior to RNA isolation is typical, and care is taken to reduce exposure to RNase enzymes once isolation is complete.
  • RNA-Seq analysis can be enhanced by enriching RNA classes of interest while depleting known abundant RNAs.
  • the mRNA molecules can be removed by using oligonucleotides probes that bind their poly-A tails.
  • abundant but uninformative ribosomal RNAs rRNAs
  • ribo-depletion can also introduce some bias via non- specific depletion of off-target transcripts, so is not preferred for the methods herein.
  • Gel electrophoresis and extraction can be used to purify small RNAs, such as micro RNAs, by their size.
  • RNA transcripts are frequently fragmented prior to sequencing. Fragmentation may be achieved by chemical hydrolysis, nebulisation, sonication, or reverse transcription with chain-terminating nucleotides. Alternatively, fragmentation and cDNA tagging may be done simultaneously by using transposase enzymes.
  • Fragmentation may be achieved by chemical hydrolysis, nebulisation, sonication, or reverse transcription with chain-terminating nucleotides.
  • fragmentation and cDNA tagging may be done simultaneously by using transposase enzymes.
  • cDNA copies of transcripts may be amplified by PCR to enrich for fragments that contain the expected 5’ and 3’ adapter sequences. Amplification is also used to allow sequencing of very low input amounts of RNA, down to as little as 50 pg in extreme applications.
  • Spike-in controls of known RNAs can be used for quality control assessment to check libraiy preparation and sequencing, in terms of GC-content, fragment length, as well as the bias due to fragment position within a transcript.
  • Unique molecular identifiers UMIs are short random sequences that are used to individually tag sequence fragments during libraiy preparation so that every tagged fragment is unique.
  • UMIs provide an absolute scale for quantification, the opportunity to correct for subsequent amplification bias introduced during library construction, and accurately estimate the initial sample size. UMIs are particularly well-suited to single-cell RNA-Seq transcriptomics, where the amount of input RNA is restricted and extended amplification of the sample is required.
  • transcript molecules can be sequenced in just one direction (single-end) or both directions (paired-end).
  • a single-end sequence is usually quicker to produce, cheaper than paired-end sequencing and sufficient for quantification of gene expression levels.
  • Paired-end sequencing produces more robust alignments/assemblies, which is beneficial for gene annotation and transcript isoform discovery.
  • Strand-specific RNA-Seq methods preserve the strand information of a sequenced transcript Without strand information, reads can be aligned to a gene locus but do not inform in which direction the gene is transcribed.
  • Stranded-RNA-Seq is useful for deciphering transcription for genes that overlap in different directions and to make more robust gene predictions in non-model organisms.
  • the particular strands used in sequencing are not limiting on the invention described herein.
  • RNA-Seq analysis generates a large volume of raw sequence reads which have to be processed to yield useful information.
  • Data analysis usually requires a combination of bioinformatics software tools that vary according to the experimental design and goals. The process can be broken down into four stages: quality control, alignment, quantification, and differential expression.
  • Most popular RNA-Seq programs are run from a command-line interface, either in a Unix environment or within the R/Bioconductor statistical environment.
  • Sequence reads are not perfect, so the accuracy of each base in the sequence needs to be estimated for downstream analyses.
  • Raw data is examined to ensure: quality scores for base calls are high, the GC content matches the expected distribution, short sequence motifs (k-mers) are not over-represented, and the read duplication rate is acceptably low.
  • sequence quality analysis including FastQC and FaQCs. Abnormalities may be removed (trimming) or tagged for special treatment during later processes.
  • transcript sequences are aligned to a reference genome or de novo aligned to one another if no reference is available.
  • the key challenges for alignment software include sufficient speed to permit billions of short sequences to be aligned in a meaningful timeframe, flexibility to recognize and deal with intron splicing of eukaryotic mRNA, and correct assignment of reads that map to multiple locations.
  • Software advances have greatly addressed these issues, and increases in sequencing read length reduce the chance of ambiguous read alignments.
  • One of ordinary skill will understand the choice of high- throughput sequence aligners that are available and may be selected for analyses.
  • Alignment of primary transcript mRNA sequences derived from eukaryotes to a reference genome requires specialized handling of intron sequences, which are absent from mature mRNA.
  • Short read aligners perform an additional round of alignments specifically designed to identify splice junctions, informed by canonical splice site sequences and known intron splice site information. Identification of intron splice junctions prevents reads from being misaligned across splice junctions or erroneously discarded, allowing more reads to be aligned to the reference genome and improving the accuracy of gene expression estimates. Since gene regulation may occur at the mRNA isoform level, splice-aware alignments also permit detection of isoform abundance changes that would otherwise be lost in a bulked analysis.
  • transcriptome sequences There are two general methods of inferring transcriptome sequences.
  • One approach maps sequence reads onto a reference genome, either of the organism itself (whose transcriptome is being studied) or of a closely related species.
  • Microarray data is recorded as high-resolution images, requiring feature detection and spectral analysis.
  • Microarray raw image files are each about 750 MB in size, while the processed intensities are around 60 MB in size.
  • Multiple short probes matching a single transcript can reveal details about the intron- exon structure, requiring statistical models to determine the authenticity of the resulting signal.
  • RNA-Seq studies produce billions of short DNA sequences, which must be aligned to reference genomes composed of millions to billions of base pairs.
  • de novo transcriptome assembly uses software to infer transcripts directly from short sequence reads.
  • De novo assembly of reads within a dataset requires the construction of highly complex sequence graphs.
  • RNA-Seq operations are highly repetitious and benefit from parallelized computation but modem algorithms mean consumer computing hardware is sufficient for simple transcriptomics experiments that do not require de novo assembly of reads.
  • One of ordinary skill will understand that the type of hardware is not limiting on the invention discussed herein.
  • De novo assembly can be used to align reads to one another to construct full- length transcript sequences without use of a reference genome.
  • Challenges particular to de novo assembly include larger computational requirements compared to a reference-based transcriptome, additional validation of gene variants or fragments, and additional annotation of assembled transcripts.
  • the metrics used to describe transcriptome assemblies are known to one of ordinary skill in the art. Annotation-based metrics may be used to assess assembly completeness (e.g. contig reciprocal best hit count). Once assembled de novo, the assembly can be used as a reference for subsequent sequence alignment methods and quantitative gene expression analysis.
  • Quantification of sequence alignments may be performed at the gene, exon, or transcript level.
  • Typical outputs include a table of read counts for each feature supplied to the software; for example, for genes in a general feature format file. Gene and exon read counts may be calculated quite easily using HTSeq, for example.
  • Quantitation at the transcript level is more complicated and requires probabilistic methods to estimate transcript isoform abundance from short read information; for example, using cufflinks software. Reads that align equally well to multiple locations must be identified and either removed, aligned to one of the possible locations, or aligned to the most probable location.
  • Some quantification methods can circumvent the need for an exact alignment of a read to a reference sequence altogether.
  • the kallisto software method combines pseudoalignment and quantification into a single step that runs two orders of magnitude faster than contemporary methods such as those used by tophat/cufflinks software, with less computational burden.
  • a genome-guided approach relies on the same methods used for DNA alignment, with the additional complexity of aligning reads that cover non-continuous portions of the reference genome. These non-continuous reads are the result of sequencing spliced transcripts.
  • alignment algorithms have two steps: 1) align short portions of the read (i.e., seed the genome), and 2) use dynamic programming to find an optimal alignment, sometimes in combination with known annotations.
  • Software tools that use genome-guided alignment include Bowtie, TopHat (which builds on BowTie results to align splice junctions), Subread, STAR, HISAT2, Sailfish, Kallisto, and GMAP.
  • the quality of a genome guided assembly can be measured with both 1) de novo assembly metrics (e.g., N50) and 2) comparisons to known transcript, splice junction, genome, and protein sequences using precision, recall, or their combination (e.g., Fl score).
  • de novo assembly metrics e.g., N50
  • comparisons to known transcript, splice junction, genome, and protein sequences using precision, recall, or their combination e.g., Fl score
  • in silico assessment could be performed using simulated reads.
  • RNA- Seq RNA- Seq with 30 million 100 bp sequences per sample. This example would require approximately 1.8 gigabytes of disk space per sample when stored in a compressed fastq format. Processed count data for each gene would be much smaller, equivalent to processed microarray intensities. Sequence data may be stored in public repositories, such as the Sequence Read Archive (SRA). RNA-Seq datasets can be uploaded via the Gene Expression Omnibus, or similar software platforms.
  • SRA Sequence Read Archive
  • Treatment with cholinesterase inhibitors can begin once preclinical AD has been identified, e.g, donepezil, rivastigimine, galantamine. Treatments may also begin to address behavioral issues such as irritability, anxiety or depression.
  • Antidepressants may include one or more drugs such as citalopram, fluoxetine, paroxetine, sertraline and trazodone.
  • Anxiolytics may include one or more drugs such as lorazepam and oxazepam.
  • Antipsychotics may include one or more drugs such as aripiprazole, clozapine, haloperidol, olanzapine, quetiapine, risperidone and ziprasidone.
  • Other drugs for mood stabilization may include carbamazepine.
  • Treatments for sleep changes may include one or more drugs such as tricyclic antidepressants (e.g nortriptyline, trazodone), benzodiazepines (e.g, lorazepam, oxazepam and temazepam), zolpidem, zaleplon, chloral hydrate, risperidone, onlanzapine, quetiapine, and haloperidol.
  • tricyclic antidepressants e.g nortriptyline, trazodone
  • benzodiazepines e.g, lorazepam, oxazepam and temazepam
  • zolpidem
  • Other therapies may include one or more such as caprylic acid and coconut oil, coenzyme Q10, coral calcium, Ginkgo biloba, huperzine A, omega-4 fatty acids, phosphatidylserine, and tramiprosate.
  • Parkinson’s disease treatments for other very early stages of neurodegenerative diseases, such as Parkinson’s disease, may also be used when those diseases are identified by the RNA transcriptome profiles discussed herein.
  • Treatments for Parkinson’s disease may include one or more drugs such as levodopa, carbidopa, dopamine agonists, catechol O-methyltransferase (COMT) inhibitors, anticholinergics, amantadine and monoamine oxidase type B (MAO-B) inhibitors.
  • drugs such as levodopa, carbidopa, dopamine agonists, catechol O-methyltransferase (COMT) inhibitors, anticholinergics, amantadine and monoamine oxidase type B (MAO-B) inhibitors.
  • the present methods disclosed herein may also be used to distinguish epileptic seizures from other types of seizure, and thus enhance treatment effectiveness. Seizures, or spells, often frequently present as episodic transient loss of consciousness (TLOC). A major clinical challenge is to distinguish between patients who have an epileptic seizure (nearly 40%) from those whose spells are from syncope (25%), psychogenic non-epileptic seizures (PNES), or other non-epileptic spells (10%).
  • PNES psychogenic non-epileptic seizures
  • the diagnosis of epileptic seizures is based on history with the semiology of events and clinical examination. However, extensive testing is often required with long-term clinical and electroencephalogram (EEG) monitoring to capture spells and characterize their electrographic pattern.
  • EEG electroencephalogram
  • RNAs in whole blood samples persist 24h following epileptic seizure termination. This enables discrimination between an epileptic seizure and non-epileptic seizures; this in turn can lead to more effective selection of treatments and therapies for both kinds of seizure.
  • the risk for recurrent seizure is similar between males and females, as is the likelihood of ultimate remission of epilepsy. Although most epilepsy syndromes are equally or more commonly found in males than in females, childhood absence epilepsy and the syndrome of photosensitive epilepsy are more common in females. In addition, some genetic disorders with associated epilepsy (e.g., Rett syndrome and Aicardi syndrome) and eclamptic seizures in pregnancy can only occur in females.
  • the methods disclosed herein can be applied with respect to the sex, whether male or female, of a patient, so as to enhance care with respect to sex (identified gender).
  • Epilepsy can be treated by either medications, implanted devices, diet, surgery or a combination of these therapies. Most people are able to control the seizures caused by their epilepsy with medications called anti-epileptic drugs or AEDs. The type and severity of the seizure will determine what and how much medication is needed.
  • the treatment for epileptic seizures may comprise: administering one or more drugs selected from the group consisting of brivaracetam, ezogabine, pregabalin, cannabidiol oral solution, felbamate, primidone, carbamazepine, fenfluramine, rufinamide, carbamazepine-XR, gabapentin, stiripentol, cenobamate, lacosamide, tiagabine hydrochloride, lamotrigine, clobazam, levetiracetam, topiramate, clonazepam, levetiracetam XR, topiramate XR, diazepam nasal, lorazepam, valproic acid, diazepam rectal, oxcarbazepine, vigabatrin, divalproex sodium-ER, phenobarbital, eslicarbazepine acetate, phenytoin and ethosuximide.
  • drugs
  • CT/ MRI/ PET results CT/ MRI/ PET results
  • biomarker status Ab, Tau and p-Tau
  • RNA-Seq Library Preparation RNA-Seq libraries are prepared as in preliminary studies. Blood (3. 0 mL) is drawn into PAXgene blood collection (Rainen L, et al. Stabilization of mRNA expression in whole blood samples. Clin Chem.
  • RNA quality is verified using a Bioanalyzer (RNA chip, Agilent Bioanalyzer 2100) prior to library preparation. Only samples with an A260/A280 ratio >2. 0 and a 28S/18S RNA ratio >5 are subjected to further analysis. The expected total RNA yield from whole blood is 3-8 ⁇ g/3 mL of whole blood. RNA (2 ⁇ g) is subjected to ribosomal RNA depletion. ERCC spike in libraries will assess library preparation. RNA-Seq Libraries are prepared for the Ion Torrent sequencer using the Ion Total RNA-seq Kitv2. The libraries are verified with a Bioanalyzer chip (DNA Nano Kit, Agilent). Templates are prepared using the Ion One Touch system.
  • Samples are run in batches of four, across two-three chips to give an estimated read depth of 40 million reads/ sample.
  • Samples undergo 200bp sequencing using Ion 540 chips (540). Following sequencing, reactions are analyzed to ensure appropriate base and GC content distribution. The goal is to obtain 20-40M aligned reads/ blood sample in this study, based on depth analysis.
  • RNA-Seq data files (BAM files) are used to generate gene expression values (reads). Exon expression and transcript expression values are also calculated. Once gene read values are determined, they will be transferred to a database for storage. Sequencing data are stored on a local sever prior to upload to NIH. A de-identified database of clinical phenotype data is maintained alongside each transcriptome.
  • Biomarker Assessment Biomarkers are measured at the Emory ADRC Biomarker Core. Qualitative levels (positive or negative) are used, via the predetermined thresholds set by the unit, in particular whether they are above or below the threshold levels.
  • RNAs for modeling will be selected by performing multifactorial analysis of variance, compensating for age and sex. Models with the highest normalized correct rate are further validated using bootstrap and two-level cross validation.
  • RNAs differentially expressed are determined and then those expression values are used to generate models to predict the single and combined results of the biomarker assessments.
  • Validation an additional 30-40 blood samples are analyzed from the ADRC and MSM to determine whether the biomarker approach can identify the biomarker status in an independent validation group. The best performing models from the testing phase (based on accuracy, sensitivity and specificity) are then tested against this mixed sample set. Accuracy is determined by the AUC/ overall accuracy, sensitivity and specificity measures (determined using partek software as previously described (Hardy JJ, et al. Assessing the accuracy of blood RNA profiles to identify patients with post-concussion syndrome: A pilot study in a military patient population. PLoS One. 2017;12(9)).
  • RNA-Seq libraries were assembled, sequenced on an Ion Torrent S5 sequencer, and aligned to the Hgl9 reference genome. Samples had on average 20 million aligned reads, and there were no significant differences in read number between samples, nor mapping (see Fig. 1). Differential expression was determined using Partek on normalized data values, and differentially expressed genes were used for subsequent analysis and modeling.
  • Example 1 Blood RNA profiles distinguish between healthy controls and AD patients
  • PCA hierarchical cluster and principle component analysis
  • Example 2 Blood RNA profiles distinguish between AD and MCI patients [0104] The AD patient selective model was used and MCI data was added to this model for PCA analysis (Fig 2, Panel C). As can be seen there is a distinct grouping of the MCI patients in-between the healthy controls and the AD patients.
  • RNA profiles generated from high-throughput sequencing of collected blood samples have remarkable accuracy for clinical status prediction.
  • This additional reference can be used with the data to enhance detection of novel RNAs in the AD cohort. Reference genomes are based on known and predicted RNA sequences; unpredicted RNAs must be identified via direct sequencing studies and such novel transcripts are highly tissue dependent. Cufflinks was used to create a blood specific annotation guide from the blood samples. This increases the number of detectable and quantifiable RNAs from 196,398 to over 208,000 transcripts for a novel blood transcriptome. These data show the ability to align to the Pan-Genome and generate novel annotation guides.
  • the present disclosure enables a blood test for AD, which identifies patients earlier who are at very high risk of developing AD. These patients can be identified when their symptoms are less severe, so any therapy may be able to at least halt the progression of the disease.
  • the blood test is effective in these patients and can be a routine screening tool for all people aged 40, and then every ten years thereafter to identify if they have the signature indicative of the AD process.
  • Example 5 Characterizing the temporal profile of blood transcriptome response following an EEG confirmed seizure.
  • Blood is obtained from patients undergoing video EEG monitoring in an epilepsy-monitoring unit. Blood is collected at baseline and following the occurrence of an EEG confirmed seizure or psychogenic non-epileptic seizures at 6h, at 24h and at 72h post- event. Blood is subjected to RNA sequencing to identify temporal profiles of RNA expression following the seizure. Gene expression profiles are observed to change following the onset of the seizure with some genes increasing or decreasing transiently, and others changing for the entire duration.
  • Example 6 Statistical and bioinformatics analysis to identify the most accurate set of RNA expression patterns to determine seizure occurrence, temporal profile and persistence of the transcriptome response
  • Temporal profiles of whole blood RNA signatures are different in patients following an epileptic seizure and those with psychogenic non-epileptic seizures (Fig. 5).
  • RNA expression patterns are compared between patients with epileptic seizures and those with psychogenic non-epileptic seizures. Using mathematical models, signatures of RNA are identified that are the most effective classifiers to discriminate between patients with EEG confirmed seizures and non-epileptic seizures. Data are analyzed to determine temporal profiles that predict time of seizure. A resulting diagnostic panel identifies with over 90% accuracy that a seizure event occurred. This data distinguishes epileptic seizure from non-seizure events. Diagnosed epilepsy patients (or patients who suffered a seizure), receive improved medical treatment by reducing diagnosis delay, cost, and unnecessary medication.

Landscapes

  • Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Organic Chemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Neurosurgery (AREA)
  • General Health & Medical Sciences (AREA)
  • Genetics & Genomics (AREA)
  • Neurology (AREA)
  • Biomedical Technology (AREA)
  • Zoology (AREA)
  • Wood Science & Technology (AREA)
  • Analytical Chemistry (AREA)
  • Psychiatry (AREA)
  • Microbiology (AREA)
  • Biochemistry (AREA)
  • Pathology (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
  • Immunology (AREA)
  • Hospice & Palliative Care (AREA)
  • Physics & Mathematics (AREA)
  • Biotechnology (AREA)
  • General Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Molecular Biology (AREA)
  • Medicinal Chemistry (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Pharmacology & Pharmacy (AREA)
  • Animal Behavior & Ethology (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

L'invention concerne un procédé permettant d'identifier un sujet atteint d'une maladie d'Alzheimer pré-clinique parmi ceux présentant des symptômes similaires, mais d'autres formes de démence telles qu'un trouble cognitif léger. Le profil transcriptomique intégral de l'ARN sanguin d'un sujet suspecté de souffrir d'une maladie d'Alzheimer pré-clinique est obtenu et analysé par rapport à un profil transcriptomique intégral de l'ARN sanguin de référence d'un sujet présentant une autre forme de démence telle que la démence fronto-temporale, la CADASIL ou un trouble cognitif léger. Le profil transcriptomique intégral de l'ARN sanguin comprend la présence et la quantification de l'ARNnc. L'invention concerne également des méthodes visant à améliorer le traitement des crises d'épilepsie.
PCT/US2021/040878 2021-07-08 2021-07-08 Méthode et système de diagnostic et de traitement d'une maladie neurodégénérative et des crises d'épilepsie Ceased WO2023282900A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/US2021/040878 WO2023282900A1 (fr) 2021-07-08 2021-07-08 Méthode et système de diagnostic et de traitement d'une maladie neurodégénérative et des crises d'épilepsie

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2021/040878 WO2023282900A1 (fr) 2021-07-08 2021-07-08 Méthode et système de diagnostic et de traitement d'une maladie neurodégénérative et des crises d'épilepsie

Publications (1)

Publication Number Publication Date
WO2023282900A1 true WO2023282900A1 (fr) 2023-01-12

Family

ID=84800923

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2021/040878 Ceased WO2023282900A1 (fr) 2021-07-08 2021-07-08 Méthode et système de diagnostic et de traitement d'une maladie neurodégénérative et des crises d'épilepsie

Country Status (1)

Country Link
WO (1) WO2023282900A1 (fr)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060134663A1 (en) * 2004-11-03 2006-06-22 Paul Harkin Transcriptome microarray technology and methods of using the same
US20130116132A1 (en) * 2011-11-03 2013-05-09 Diagenic Asa Alzheimer's probe kit
US20140271542A1 (en) * 2011-10-05 2014-09-18 The United States Of America As Represented By The Secretary, Department Of Health And Human Service Genetic marker for predicting prognosis in patients infected with hepatitis c virus
US20160022573A1 (en) * 2013-03-13 2016-01-28 Neuroderm, Ltd. Method for treatment of parkinson's disease
US20160030431A1 (en) * 2011-05-09 2016-02-04 Eip Pharma, Llc Compositions and methods for treating alzheimer's disease
US20160289762A1 (en) * 2012-01-27 2016-10-06 The Board Of Trustees Of The Leland Stanford Junior University Methods for profiliing and quantitating cell-free rna
US20190011461A1 (en) * 2016-01-04 2019-01-10 Evogen, Inc. Biomarkers and methods for detection of seizures and epilepsy

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060134663A1 (en) * 2004-11-03 2006-06-22 Paul Harkin Transcriptome microarray technology and methods of using the same
US20160030431A1 (en) * 2011-05-09 2016-02-04 Eip Pharma, Llc Compositions and methods for treating alzheimer's disease
US20140271542A1 (en) * 2011-10-05 2014-09-18 The United States Of America As Represented By The Secretary, Department Of Health And Human Service Genetic marker for predicting prognosis in patients infected with hepatitis c virus
US20130116132A1 (en) * 2011-11-03 2013-05-09 Diagenic Asa Alzheimer's probe kit
US20160289762A1 (en) * 2012-01-27 2016-10-06 The Board Of Trustees Of The Leland Stanford Junior University Methods for profiliing and quantitating cell-free rna
US20160022573A1 (en) * 2013-03-13 2016-01-28 Neuroderm, Ltd. Method for treatment of parkinson's disease
US20190011461A1 (en) * 2016-01-04 2019-01-10 Evogen, Inc. Biomarkers and methods for detection of seizures and epilepsy

Similar Documents

Publication Publication Date Title
Li et al. Epigenome-wide association study of Alzheimer’s disease replicates 22 differentially methylated positions and 30 differentially methylated regions
Li et al. Epigenetic dysregulation of enhancers in neurons is associated with Alzheimer’s disease pathology and cognitive symptoms
US20210074378A1 (en) Methods for Analyzing Genetic Data to Classify Multifactorial Traits Including Complex Medical Disorders
Hossein-Nezhad et al. Transcriptomic profiling of extracellular RNAs present in cerebrospinal fluid identifies differentially expressed transcripts in Parkinson’s disease
Raman et al. Apparent bias toward long gene misregulation in MeCP2 syndromes disappears after controlling for baseline variations
US20230348980A1 (en) Systems and methods of detecting a risk of alzheimer's disease using a circulating-free mrna profiling assay
Grunert et al. Altered microRNA and target gene expression related to Tetralogy of Fallot
WO2020237314A1 (fr) Méthode de traitement ou de prophylaxie
Liu et al. Systematic analysis of RNA regulatory network in rat brain after ischemic stroke
JP2023182591A (ja) バイオマーカー
Liu et al. Elevated hsa_circRNA_101015, hsa_circRNA_101211, and hsa_circRNA_103470 in the human blood: novel biomarkers to early diagnose acute pancreatitis
Crawford et al. Golgi apparatus, endoplasmic reticulum and mitochondrial function implicated in Alzheimer’s disease through polygenic risk and RNA sequencing
Yang et al. Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits
Li et al. TBX6 as a cause of a combined skeletal‐kidney dysplasia syndrome
WO2015069900A1 (fr) Méthode pour profiler et quantifier un arn acellulaire
Huang et al. Identification and experimental validation of marker genes between diabetes and Alzheimer’s disease
Fisher et al. Unique transcriptional signatures correlate with behavioral and psychological symptom domains in Alzheimer’s disease
Arockiaraj et al. Methylation data processing protocol and comparison of blood and cerebral spinal fluid following aneurysmal subarachnoid hemorrhage
Wang et al. Prenatal diagnosis of Walker–Warburg syndrome due to compound mutations in the B3GALNT2 gene
US20230250481A1 (en) Method and system of diagnosing and treating neurodegenerative disease and seizures
Dhawan Extracellular miRNA biomarkers in neurologic disease: is cerebrospinal fluid helpful?
Moon et al. Brain structure and allelic associations in Alzheimer's disease
WO2023282900A1 (fr) Méthode et système de diagnostic et de traitement d'une maladie neurodégénérative et des crises d'épilepsie
WO2022272251A2 (fr) Systèmes et méthodes d'analyse de données génétiques pour l'évaluation de l'activité de régulation génique
Yang et al. MED12 variants associated with X-linked recessive partial epilepsy without intellectual disability

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21949490

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21949490

Country of ref document: EP

Kind code of ref document: A1