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WO2024259003A1 - Systèmes et procédés d'évaluation du microbiome et traitements associés - Google Patents

Systèmes et procédés d'évaluation du microbiome et traitements associés Download PDF

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WO2024259003A1
WO2024259003A1 PCT/US2024/033663 US2024033663W WO2024259003A1 WO 2024259003 A1 WO2024259003 A1 WO 2024259003A1 US 2024033663 W US2024033663 W US 2024033663W WO 2024259003 A1 WO2024259003 A1 WO 2024259003A1
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genera
stool
microbial
probiotic
oral
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Xin Zhou
Michael P. Snyder
Xin Chen
Mark M. Davis
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Leland Stanford Junior University
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Leland Stanford Junior University
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P1/00—Drugs for disorders of the alimentary tract or the digestive system
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K35/00—Medicinal preparations containing materials or reaction products thereof with undetermined constitution
    • A61K35/66—Microorganisms or materials therefrom
    • A61K35/74—Bacteria
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K35/00—Medicinal preparations containing materials or reaction products thereof with undetermined constitution
    • A61K35/66—Microorganisms or materials therefrom
    • A61K35/74—Bacteria
    • A61K35/741—Probiotics
    • A61K35/742—Spore-forming bacteria, e.g. Bacillus coagulans, Bacillus subtilis, clostridium or Lactobacillus sporogenes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P19/00—Drugs for skeletal disorders
    • A61P19/02—Drugs for skeletal disorders for joint disorders, e.g. arthritis, arthrosis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P37/00—Drugs for immunological or allergic disorders
    • A61P37/02—Immunomodulators
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    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
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    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
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    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
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    • G01N33/6863—Cytokines, i.e. immune system proteins modifying a biological response such as cell growth proliferation or differentiation, e.g. TNF, CNF, GM-CSF, lymphotoxin, MIF or their receptors
    • G01N33/6869—Interleukin
    • G—PHYSICS
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    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
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    • G01N33/92—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving lipids, e.g. cholesterol, lipoproteins, or their receptors
    • G—PHYSICS
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    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/475—Assays involving growth factors
    • G01N2333/495—Transforming growth factor [TGF]
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    • G01N2333/52—Assays involving cytokines
    • G01N2333/521—Chemokines
    • G01N2333/523—Beta-chemokines, e.g. RANTES, I-309/TCA-3, MIP-1alpha, MIP-1beta/ACT-2/LD78/SCIF, MCP-1/MCAF, MCP-2, MCP-3, LDCF-1or LDCF-2
    • G—PHYSICS
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    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
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    • G01N2333/54—Interleukins [IL]
    • G01N2333/5412—IL-6
    • G—PHYSICS
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    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
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    • G01N2333/5428—IL-10
    • G—PHYSICS
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    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
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    • G—PHYSICS
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    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
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    • G01N2333/54—Interleukins [IL]
    • G01N2333/545—IL-1
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
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    • G01N2333/555—Interferons [IFN]
    • G01N2333/56—IFN-alpha
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    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/52—Assays involving cytokines
    • G01N2333/555—Interferons [IFN]
    • G01N2333/57—IFN-gamma

Definitions

  • the disclosure is generally directed towards systems and methods for assessment of microbiome, and more specifically directed towards systems and methods for analyzing the effect of microbial genera on host health and treatments thereof.
  • Human microbiomes are composed of remarkably dynamic microbial communities that live in and on various body sites, including the gut, skin, nasal cavity, and oral cavity. At each site, the microbial and host cell interactions exhibit territoryspecific complexity.
  • the molecular foundations of microbial ecology and their interactions with the host are being elucidated with new technology-enabled multi-omics profiling, shedding light on their role in both normal physiological processes. For instance, increasing evidence has shown that the microbiome can play an important role in regulating the host’s immune system, while the immune system maintains the key feature of host-microbes symbiosis.
  • This cross-talk between the microbiome and the immune system can provide an understanding of various biological phenomena such as aging and the pathogenesis of diseases such as inflammatory bowel disease (IBD), cardiovascular disease, and type 2 diabetes mellitus (T2DM). Studying the mechanisms underpinning such interactions has the potential to provide novel insights into the development of microbiome-targeted therapeutic interventions.
  • IBD inflammatory bowel disease
  • T2DM type 2 diabetes mellitus
  • the techniques described herein relate to a method for administering microbial genera to an individual, including: measuring levels of a set of one or more gene products in a biological sample of the individual; determining that an amount of one gene product of the set of gene products is above a threshold or is below a threshold; and when the amount of the one gene product is above a threshold, administering to the individual one or more microbial genera negatively correlated with the one gene product, or when the amount of the one gene product is below a threshold, administering to the individual one or more microbial genera positively correlated with the one gene product.
  • the techniques described herein relate to a method further including: measuring a microbial genera composition of a microbiome; wherein the composition of microbial genera is utilized to assist in selecting the one or more microbial genera to be administered.
  • the techniques described herein relate to a method, wherein the one gene product and the one or more correlated microbial genera to be administered is listed within Table 1.
  • the techniques described herein relate to a method, wherein the one gene product is: BDNF, EGF, Eotaxin, INF-a, INF-y, IL-1 [3, IL-6, IL-10, IL-12, IL- 17, IL-22, IL-23, MCP-1 , RANTES, TGF-p, or TNF-a.
  • the techniques described herein relate to a method, wherein the one gene product is Eotaxin, wherein Eotaxin is greater than threshold, wherein the is administered one or more of the following microbial genera: Desulfovibrio or Escherichia_Shigella.
  • the techniques described herein relate to a method, wherein the one gene product is INF-a, wherein INF-a is greater than threshold, wherein the is administered one or more of the following microbial genera: Blautia or Dial ister.
  • the techniques described herein relate to a method, wherein the one gene product is INF- y, wherein INF- y is greater than threshold, wherein the is administered one or more of the following microbial genera: Blautia, Dialister, or Fusicatenibacter.
  • the techniques described herein relate to a method, wherein the one gene product is IL-1 [3, wherein IL-1 p is greater than threshold, wherein the is administered one or more of the following microbial genera: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Faecalibacterium, Lachnospira, Prevotella, Roseburia, Slackia, Subdoligranulum, or Sutterella.
  • the techniques described herein relate to a method, wherein the one gene product is IL-6, wherein IL-6 is greater than threshold, wherein the is administered one or more of the following microbial genera: Collinsella, Dialister, or Fusicatenibacter.
  • the techniques described herein relate to a method, wherein the one gene product is IL-12, wherein IL-12 is greater than threshold, wherein the is administered one or more of the following microbial genera: Butyricimonas, Collinsella, or Fusicatenibacter.
  • the techniques described herein relate to a method, wherein the one gene product is IL-17, wherein IL-17 is greater than threshold, wherein the is administered one or more of the following microbial genera: Dialister, Subdoligranulum, or Senegalimassilia.
  • the techniques described herein relate to a method, wherein the one gene product is IL-23, wherein IL-23 is greater than threshold, wherein the is administered one or more of the following microbial genera: Butyricimonas, Dialister, Holdemanella, or Senegalimassilia.
  • the techniques described herein relate to a method, wherein the one gene product is MCP-1 , wherein MCP-1 is greater than threshold, wherein the is administered one or more of the following microbial genera: Blautia, Desulfovibrio, Dialister, or Slackia.
  • the techniques described herein relate to a method, wherein the one gene product is RANTES, wherein RANTES is greater than threshold, wherein the is administered one or more of the following microbial genera: Butyricimonas, Collinsella, Holdemanella, or Lawson ibacter.
  • the techniques described herein relate to a method, wherein the one gene product is TNF-a, wherein TNF-a is greater than threshold, wherein the is administered one or more of the following microbial genera: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, or Frisingicoccus.
  • the techniques described herein relate to a method, wherein the one gene product is TNF-a, wherein TNF-a is greater than threshold, wherein the is administered one or more of the following microbial genera: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, or Frisingicoccus.
  • the techniques described herein relate to a method, wherein the one gene product is BDNF, wherein BDNF is less than threshold, wherein the is administered one or more of the following microbial genera: Barnesiella, Eggerthella, Lachnospira or Parabacteroides.
  • the techniques described herein relate to a method, wherein the one gene product is EGF, wherein EGF is less than threshold, wherein the is administered one or more of the following microbial genera: Anaerostipes, Barnesiella, Eggerthella, Intestinibacter, Neglecta, Parabacteroides, or Romboutsia.
  • the techniques described herein relate to a method, wherein the one gene product is IL-10, wherein IL-10 is less than threshold, wherein the is administered one or more of the following microbial genera: Hungatella or Monoglobus.
  • the techniques described herein relate to a method, wherein the one gene product is IL-17, wherein IL-17 is less than threshold, wherein the is administered one or more of the following microbial genera: Adlercreutzia, Barnesiella, Butyricicoccus, Cloacibacillus, Dysosmobacter, or Faecalicatena.
  • the techniques described herein relate to a method, wherein the one gene product is IL-22, wherein IL-22 is less than threshold, wherein the is administered one or more of the following microbial genera: Anaerotignum, Butyrivibrio, Cloacibacillus, Dysosmobacter, Frisingicoccus, Gordonibacter, Negativibacillus, Phocea, Pseudoflavonifractor, Raoultibacter, or Turicibacter.
  • the techniques described herein relate to a method, wherein the one gene product is TGF-[3, wherein TGF-p is less than threshold, wherein the is administered one or more of the following microbial genera: Acutalibacter, Akkermansia, Clostridium_sensu_stricto, Clostridium_XVIII, Flavonifractor, Holdemania, or Hungatella.
  • the techniques described herein relate to a method, wherein the one or more microbial genera are to be orally administered.
  • the techniques described herein relate to a method, wherein the one or more genera is provided in a probiotic food, a probiotic beverage, a liquid solution composition, a gel composition, an oil composition, an emulsion composition, a capsule, an enteric-coated capsule, a dragee, a gavage, a lyophilized powder, a freeze- dried powder, or a combination thereof.
  • the techniques described herein relate to a method, wherein the one or more microbial genera are to be rectally administered.
  • the techniques described herein relate to a method, wherein the one or more genera is provided in a probiotic liquid, a probiotic gel, a probiotic suppository, a probiotic fecal transplant, a probiotic enema, a probiotic catheter, a lyophilized powder, a freeze-dried powder, or a combination thereof.
  • the techniques described herein relate to a method for administering microbial genera to an individual, including: measuring levels of a set of one or more analytes in a biological sample of the individual; determining that the measurement of one analyte is not within a healthy range; and administering to the individual one or more microbial genera to individual for the purpose of altering the level of the one analyte into the healthy range, wherein the one or more microbial genera is correlated with the analyte.
  • the techniques described herein relate to a method further including: measuring a microbial genera composition of a microbiome; wherein the composition of microbial genera is utilized to assist in selecting the one or more microbial genera to be administered.
  • the techniques described herein relate to a method, wherein the one analyte and the one or more correlated microbial genera to be administered is listed within Table 2.
  • the techniques described herein relate to a method, wherein the one analyte is selected from: LDL cholesterol, non-HDL cholesterol, or HDL/LDL cholesterol ratio, wherein the one or more microbial genera to be administered includes Haemophilus.
  • the techniques described herein relate to a method, wherein the Haemophilus is to be topically administered.
  • the techniques described herein relate to a method, wherein the Haemophilus is provided in a probiotic suppository, a probiotic oil, a probiotic emulsion, a probiotic ointment, a probiotic lotion, a probiotic powder, a probiotic cream, a lyophilized powder, a freeze-dried powder, or a combination thereof.
  • the techniques described herein relate to a method, wherein the one analyte is A1 C, wherein the one or more microbial genera to be administered includes Akkermansia.
  • the techniques described herein relate to a method, wherein the one or more microbial genera are to be orally administered.
  • the techniques described herein relate to a method, wherein the one or more genera is provided in a probiotic food, a probiotic beverage, a liquid solution composition, a gel composition, an oil composition, an emulsion composition, a capsule, an enteric-coated capsule, a dragee, a gavage, a lyophilized powder, a freeze- dried powder, or a combination thereof.
  • the techniques described herein relate to a method, wherein the one or more microbial genera are to be rectally administered.
  • the techniques described herein relate to a method, wherein the one or more genera is provided in a probiotic liquid, a probiotic gel, a probiotic suppository, a probiotic fecal transplant, a probiotic enema, a probiotic catheter, a lyophilized powder, a freeze-dried powder, or a combination thereof.
  • the techniques described herein relate to a method for treating an individual for a medical condition by administering microbial genera, including: administering to the individual one or more microbial genera to individual, wherein the one or more microbial genera is correlated with a gene product associated with the condition.
  • the techniques described herein relate to a method, wherein the medical condition is psoriasis and the one or more microbial genera is: Finegoldia, Brevibacterium, Halomonas, Methylobacterium, Moraxella, Paracoccus, Dolosigranulum, Neisseria, Methylorubrum, Enhydrobacter, Peptoniphilus, or Roseomonas.
  • the techniques described herein relate to a method, wherein the one or more microbial genera are to be topically administered.
  • the techniques described herein relate to a method, wherein the one or more microbial genera are provided in a probiotic suppository, a probiotic oil, a probiotic emulsion, a probiotic ointment, a probiotic lotion, a probiotic powder, a probiotic cream, a lyophilized powder, a freeze-dried powder, or a combination thereof.
  • the techniques described herein relate to a method, wherein the medical condition is inflammatory bowel disease and the one or more microbial genera is: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Fusicatenibacter, Dialister, Subdoligranulum, Senegalimassilia, or Holdemanella.
  • the techniques described herein relate to a method, wherein the medical condition is rheumatoid arthritis and the one or more microbial genera is: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Subdoligranulum, or Senegalimassilia.
  • the techniques described herein relate to a method, wherein the medical condition is systemic lupus erythematosus and the one or more microbial genera is: Blautia, Dialister, Fusicatenibacter, Collinsella, Subdoligranulum, or Senegalimassilia.
  • the techniques described herein relate to a method, wherein the medical condition is hypertension and the one or more microbial genera is: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Subdoligranulum, or Senegalimassilia.
  • the techniques described herein relate to a method, wherein the medical condition is atherosclerosis and the one or more microbial genera is: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Hungatella, or Monoglobus.
  • the techniques described herein relate to a method, wherein the medical condition is depression or anxiety and the one or more microbial genera is: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Hungatella, or Monoglobus.
  • the techniques described herein relate to a method, wherein the medical condition is autism and the one or more microbial genera is: Desulfovibrio, Escherichia_Shigella, Blautia, Dialister, Slackia, Butyricimonas, Collinsella, Holdemanella, Lawsonibacter, Fusicatenibacter, Acutalibacter, Akkermansia, Clostridium_sensu_stricto, Clostridium_XVIII, Flavonifractor, Holdemania, or Hungatella.
  • the techniques described herein relate to a method, wherein the medical condition is schizophrenia and the one or more microbial genera is: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Hungatella, or Monoglobus.
  • the techniques described herein relate to a method, wherein the medical condition is metabolic disease and the one or more microbial genera is: Barnesiella, Frisingicoccus, Butyrivibrio, Adlercreutzia, Butyricicoccus, Cloacibacillus, Dysosmobacter, Faecalicatena, Anaerotignum, Cloacibacillus, Dysosmobacter, Gordonibacter, Negativibacillus, Phocea, Pseudoflavonifractor, Raoultibacter, or Turicibacter.
  • the medical condition is metabolic disease and the one or more microbial genera is: Barnesiella, Frisingicoccus, Butyrivibrio, Adlercreutzia, Butyricicoccus, Cloacibacillus, Dysosmobacter, Faecalicatena, Anaerotignum, Cloacibacillus, Dysosmobacter, Gordonibacter, Negativibacillus, Phocea, Pseud
  • the techniques described herein relate to a method, wherein the one or more microbial genera is: Barnesiella, Frisingicoccus, or Butyrivibrio.
  • the techniques described herein relate to a method, wherein the medical condition is type 2 diabetes or obesity and the one or more microbial genera is: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Hungatella, Monoglobus, Faecalibacterium, Lachnospira, Prevotella, Roseburia, Slackia, Subdoligranulum, or Sutterella.
  • the medical condition is type 2 diabetes or obesity and the one or more microbial genera is: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Hungatella, Monoglobus, Faecalibacterium, Lachnospira, Prevotella, Roseburia, Slackia, Subdoligranulum, or Sutterella.
  • the techniques described herein relate to a method, wherein the medical condition is leaky gut syndrome and the one or more microbial genera is: Blautia, Dialister, Fusicatenibacter, Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Faecalibacterium, Lachnospira, Prevotella, Roseburia, Slackia, Subdoligranulum, Sutterella, Hungatella Monoglobus, Acutalibacter, Akkermansia, Clostridium_sensu_stricto, Clostridium_XVIII, Flavonifractor, Holdemania, Anaerostipes, Barnesiella, Eggerthella, Intestinibacter, Neglecta, Parabacteroides, or Romboutsia.
  • the techniques described herein relate to a method, wherein the one or more microbial genera are to be orally administered.
  • the techniques described herein relate to a method, wherein the one or more genera is provided in a probiotic food, a probiotic beverage, a liquid solution composition, a gel composition, an oil composition, an emulsion composition, a capsule, an enteric-coated capsule, a dragee, a gavage, a lyophilized powder, a freeze- dried powder, or a combination thereof.
  • the techniques described herein relate to a method, wherein the one or more microbial genera are to be rectally administered.
  • the techniques described herein relate to a method, wherein the one or more genera is provided in a probiotic liquid, a probiotic gel, a probiotic suppository, a probiotic fecal transplant, a probiotic enema, a probiotic catheter, a lyophilized powder, a freeze-dried powder, or a combination thereof.
  • the techniques described herein relate to a method of determining microbial genera host immune response, including: providing immune responsive organoids in culture; adding a microbial genus culture supernatant to the immune responsive organoids in culture; and measuring one or more gene products to determine organoid response to the microbial genus culture supernatant.
  • the techniques described herein relate to a method for administering a probiotic treatment, including: providing a culture of an immune responsive organoids of an individual; contacting the culture of immune responsive organoids with a culture product of a microbial genus or a combination of microbial genera; determining that the microbial genus or the combination of microbial genera yield a desired response by the immune responsive organoids; and based on the response by the immune responsive organoids, determining a treatment regimen for the individual that includes administration of the microbial genus or the combination of microbial genera.
  • Figure 1 provides a flow diagram of an example of a method to assess microbiome-host interactions to infer treatments.
  • Figure 2 provides a flow diagram of an example of a co-culture organoid assay to assess microbiome-host interactions.
  • Figure 3 provides an example of a method to culture microbial isolates from a microbiome, which can be utilized in co-culture organoid assays.
  • Figures 4A to 4E provide results of an assessment of the immune response of an organoid culture in response to six different microbial samples.
  • Figure 5 provides principal component analysis of the results of the immune response of an organoid culture in response to six different microbial samples, inclusive of the results provided in Figs. 4A to 4E.
  • Figures 6A to 6F provide schematics and data charts depicting study of longitudinal profiles of the microbiome at four body sites.
  • Figure 6A provides a schematic of the study design.
  • Figure 6B provides data showing overlap of sample numbers among different omics types.
  • Figure 60 provides data showing Proportion of stress, insulin resistant and healthy samples.
  • Figure 6D provides LIMAP of microbiome samples by body site.
  • Figure 6E provides data showing density distribution of microbiome richness and evenness.
  • Figure 6F provides data showing Rank prevalence curve of microbiome genera with the 100 highest longitudinal prevalence at each body site.
  • Figures 7A to 7M data charts showing ecological dynamics of microbiome from the four body sites.
  • Figure 7A provides Relative Abundance of Representative Genera Displayed on UMAP.
  • Figure 7B provides Principal Coordinate Analysis Distribution of Samples Differing in Insulin Status.
  • Figure 7C provides Intraclass Correlation of Microbiome at Each Taxonomy Level.
  • Figure 7D provides Microbiome Variance Explained by Individuality, Season, and Residuals.
  • Figure 7E provides Variance in Microbiome Explained by Diet.
  • Figure 7F provides Seasonal effect of Microbiome.
  • Figure 7J provides Relationship Between the Number of Core Microbiome Genera, Steady-State Plasma Glucose, and Body Mass Index.
  • Figure 7K provides Number of Core Microbiome Genera in Insulin Sensitive and Insulin Resistant Individuals.
  • Figure 7L provides Rank Prevalence Curve of the Microbiome at Each Body Site.
  • Figure 7M provides Effect Size of Taxa Differing in Relative Abundance Between Insulin Sensitive and Insulin Resistant Individuals. Significance is indicated as * for p- value ⁇ 0.05, and ** for p-value ⁇ 0.01 . Significance is indicated as * for p-value ⁇ 0.05, and ** for p-value ⁇ 0.01.
  • Figures 8A to 8C provide data charts showing that the individuality of the microbiome differs significantly across genera and body sites.
  • Figure 8A provides Bray Curtis dissimilarity comparisons within individuals, families, and between unrelated participants.
  • Figure 8B provides DMI Scores.
  • Figure 8C provides Average DMI Radar Plot by Body Site and Phylum, with significant Kruskal-Wallis test results for Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, and Other phyla. Significance indicated by asterisks: *p ⁇ 0.05, **p ⁇ 0.01 , ***p ⁇ 0.001.
  • Figures 9A and 9B provide data charts of DMI distribution across sites and health status.
  • Figure 9A provides Histogram Distribution of Degree of Microbial Individuality and Family Score.
  • Figures 10A to 10F provide data charts showing temporal stability of microbiomes associated with individuality and stress events.
  • Figure 10A provides correlations of taxa-recurrence with mean DMI for stool, skin, oral, and nasal samples.
  • Figure 10B provides linear regression data between dissimilarity and collection date interval.
  • Figure 10C provides beta coefficient of individual-based correlation between sample pair's BC distances and the collection date intervals.
  • Figure 10D provides correlations of microbiome abundances within and between body sites.
  • Figure 10E provides DMI differences between correlated and non-correlated genera.
  • Figure 10F provides data showing microbiome shifts during health and stress events over three months. Significance indicated by asterisks: *p ⁇ 0.05, **p ⁇ 0.01 , ***p ⁇ 0.001 .
  • Figures 11A to 11 H provide data charts of longitudinal dynamics and the relations with DMI.
  • Figure 11 A provides Strain Replacement Rate in Insulin Sensitive and Insulin Resistant Individuals.
  • Figure 11 B provides Time-Related Stability Correlation Between Body Sites in Insulin Sensitive Groups.
  • Figure 11 C provides Time-Related Stability Correlation Between Body Sites in Insulin Resistant Groups. The same illustration of Figure S3B for insulin resistant (IR) group.
  • Figure 11 D provides Degree of Microbial Individuality (DMI) Comparison Between Bacteria Genera Correlated and Uncorrelated Between Body Sites.
  • DMI Degree of Microbial Individuality
  • Figure 11 E provides Microbial Evenness Change During Respiratory Viral Infection Among Insulin Sensitive and Insulin Resistant Individuals.
  • Figures 11 F to 11 H provides Microbial Relative Abundance Change During Respiratory Viral Infection. Their trends during infection are visually inspected and grouped into (Fig. 11 F) temporarily increased during infection, (Fig. 11 G) mixed trends of increase and decrease, (Fig. 11 H) temporarily decreased during infection.
  • Figures 12A to 12C provide data charts showing systematic connections between circulating cytokines and microbiomes.
  • Figure 12A provides data showing cytokine-related genera percentages by phylum.
  • Figure 12B provides a density plot of significant cytokine-microbiome correlation coefficients, compared by genera prevalence.
  • Figure 12C provides correlation coefficients by body site and phylum, p-values for positive versus negative associations were annotated in the middle. Significance indicated by asterisks: *p ⁇ 0.05, **p ⁇ 0.01 , ***p ⁇ 0.001.
  • Figures 13A to 13D provide data charts of microbiome-cytokine interactions.
  • Figure 13A provides Relationship Between the Microbiome and Cytokine Based on Their Correlation Coefficient.
  • Figure 13B provides Phyla Composition of Core, Middle, and Opportunistic Genera of Microbiome.
  • Figure 13C provides Correlation between Genera of Stool Proteobacteria and Plasma Cytokines Divided by Prevalence.
  • Figure 13D provides The Correlation between Body Mass Index and Plasma Leptin and Granulocyte- Macrophage Colony-Stimulating Factor.
  • Figures 14A and 14B provide data on microbiome plasma analytics correlation.
  • Figure 14A provides Collinearity of Metabolome, Lipidome, and Proteome.
  • Figure 14B provides Different Interactome of the Stool Microbiome and Internal Plasma Analytics
  • Figures 15A to 15E provide data charts showing interactions between plasma metabolites, lipids, proteomics, and microbiome over time.
  • Figure 15A provides a correlation Network that shows links between microbiome genera relative abundance and plasma analytics, color-coded by type (Microbiomes: Dark yellow, Blue, Dark red, Green;
  • Plasma analytics Dark blue, Orange, Red).
  • Figure 15B provides data showing plasma analytics-microbiome relative abundance correlation summary of Fig. 15A.
  • Figure 15C provides correlations between genera and the metabolite ethyl glucuronide.
  • Figure 15D provides data showing plasma analytics-microbiome relative richness.
  • Figure 15E provides correlations between genera and the metabolite p-Cresol glucuronide.
  • Figure 16 provides data on microbiome-host correlations. Distribution of Correlation Coefficients for Microbiome Interactions Across Four Body Sites.
  • Figures 17A to 17E provide data showing causal inference decodes microbiome-driven phenotypic dynamics mediated by internal molecules and cytokines.
  • Figure 17A provides a data summary of microbiome and phenotype mediation analysis. Comparisons between IS and IR regarding each mediated effect were performed using a Fisher exact t test.
  • Figure 17B provides data showing Akkermansia's Mediation Effect on Blood A1 C Level via Plasma IL-15.
  • Figure 17C provides data showing Akkermansia's Mediation Effect on Blood A1 C Level in Insulin Sensitive Individuals.
  • Figure 17D provides data showing Haemophilus's Mediation Effect on Plasma Triglycerides Level.
  • Figure 17E provides data showing Haemophilus’s Mediation Effect on Plasma Triglycerides Level in Insulin Sensitive Individuals.
  • Table 1 provides data of gene products that were significantly associate with a microbial genus, as determined by their correlation between the product and the microbial genus within a host microbiome.
  • Table 2 provides data of circulatory clinical phenotypes that were significantly associate with a microbial genus, as determined by their correlation between the phenotype measured in blood and the microbial genus within a host microbiome.
  • the phenotype abbreviations are described in Table 3.
  • Table 3 provides circulatory clinical phenotypes assessed and their abbreviations.
  • a microbial genus or a combination of microbial genera that have been found to be beneficial are utilized as supplements and/or medications that can be administered as probiotic supplement or as a treatment, including a prophylactic treatment and/or a prescribed treatment.
  • a supplement and/or treatment is associated with a particular health benefit, such as improvement of a clinical phenotype or a medical condition.
  • a biological sample e.g., blood sample
  • a biological sample derived from a patient can be diagnostically assessed by examining the products of expressed gene products therein to determine a composition of microbial genera within a microbiome, which can be associated with a clinical phenotype or medical condition, and which microbial genera would be beneficial.
  • a microbiome sample derived from a patient can be assessed by examining which microbial genera are present, which can provide a determination of whether the patient could benefit from an administration of beneficial microbial genera.
  • a determination can be made on how to alter a patient’s microbiome to improve the patient’s expression of gene products to improve the individual’s condition.
  • the individual’s microbiome is altered by administering beneficial microbial genera.
  • Table 2 Provided in Table 2 is a list of circulatory clinical phenotypes that are significantly correlated with various microbial genera, establishing a linkage between a microbiome composition and the clinical phenotype. Assessments of gene product expression and/or clinical data can thus infer microbial genera composition within a gene host. Further, microbial genera can be provided to a patient to induce healthier gene product expression or improvements in clinical phenotypes. In several embodiments, these linkages between the microbial genera and the host response are exploited to provide assessments of and/or treatments to a host.
  • a biological sample of a patient is assessed to determine the expression levels of various gene products, including immune system modulators, cytokines, chemokines, hormones, growth factors, and other signaling molecules.
  • the gene products that are assessed have known association with one or more microbial genera that can be found within an individual’s microbiome and thus based on the gene product expression, the composition of a host’s microbiome can be predicted.
  • a microbial genus or a combination of microbial genera that are beneficial to a healthier condition can be administered to as a probiotic supplement to an individual, with or without any clinical diagnostic assessment performed.
  • a microbial genus or a combination of microbial genera that are beneficial to medical condition can be administered as a treatment to a patient diagnosed with a medical condition.
  • an individual’s biological sample is assessed for gene products as a diagnostic to determine which microbial genus or combinations of microbial genera would be beneficial; in some embodiments, based on the patient’s gene product expression in the biological sample, the patient is administered the microbial genus or combinations of microbial genera that can alter gene expression.
  • an individual’s microbiome sample is assessed to determine microbiome composition as a diagnostic to determine whether the patient would benefit from increasing beneficial microbial genera within the patient’s microbiome; in some embodiments, the patient is administered a beneficial microbial genus or a beneficial combination of microbial genera to reestablish a healthy balance of microbiome populations.
  • a co-culture assay comprising an immune responsive organoid and microbial genera can be utilized.
  • bacterial isolates are generated from a microbiome sample and cultured; a bacterial isolate can be applied to the immune responsive organoid to assess the immune response the isolate stimulates.
  • immune responsive organoids found to work well in this system are tonsil organoids, lymph node organoids, and spleen organoids.
  • tonsil tissue, lymph node tissue, or spleen tissue is extracted from a human donor and cultured.
  • an immune responsive organoid is developed via differentiation of pluripotent cells in culture.
  • Immune responsive organoids can be challenged with a bacterial isolate.
  • the immune response e.g., cytokine expression, immune cell activation
  • gene expression products e.g., RNA, proteins/peptides
  • an individual can be assessed to determine expression levels of gene products to infer presence and levels of particular microbial genera within the patient’s microbiome.
  • gene product expression levels can be utilized to determine which microbial genera would be beneficial to be administered to the individual.
  • the composition of a patient’s microbiome is assessed to determine which microbial genera would be beneficial to be administered to the individual.
  • a patient is administered a beneficial microbial genus or a combination of beneficial microbial genera, which may improve the health of the individual.
  • Fig. 1 Provided in Fig. 1 is an example of a method to determine microbial genera that would provide benefit to a patient.
  • the method can be utilized in various applications that involve the association of microbial genera, host gene product expression, and phenotype.
  • the method is utilized to determine which microbial genera could be administered to alter host gene expression and thus improve recipient health.
  • Method 100 can begin with measuring (101 ) gene products in a biological sample that is collected from a patient. Assessment of gene products can be useful for various purposes related to host-microbiome interactions and health status, as will be discussed in greater detail below. Generally, gene products can infer a microbiome composition, an imbalance of a particular class of gene products (e.g., cytokines), or an appropriate treatment of beneficial microbial genera.
  • a particular class of gene products e.g., cytokines
  • An individual can be any individual for assessing the health as related to their microbiome.
  • an individual has been diagnosed with a medical condition.
  • Medical conditions that may be of interest include any clinical phenotype, medical disorder, or any other health-related condition that has a relationship with host-microbiome.
  • classes of medical disorders that may be of interest include autoimmune disorders (including autoinflam matory disorders), cardiovascular disorders, mental health disorders, and metabolic disorders. These disorders are also marked with unhealthy expression of immune system modulators, cytokines, chemokines, hormones, growth factors, and other signaling molecules, which may be related to host-microbiome interactions.
  • a patient is considered healthy or is otherwise not diagnosed with a medical disorder.
  • Screening of individuals may be part of routine screening or performed upon a diagnosis that related to a disorder (e.g., having symptoms indicative of a disorder). Accordingly, measuring gene products can be useful in assessing the individual’s health status as it is related to the microbiomes (and composition thereof) of the individual.
  • the biological sample derived from an individual can be any biological sample that would have gene products.
  • biological samples include (but are not limited to) blood, plasma, serum, lymph, cerebral spinal fluid, urine, stool, saliva, spittle, nasal fluid, vaginal fluid, pulmonary fluid, sweat, and swabs of a cavity (e.g., nasal, oral, urethral, vaginal, rectal, or ear canal).
  • the biological sample source is local to the microbiome to be assessed.
  • Various microbiomes that may be assessed include (but are not limited to) microbiomes of the gastrointestinal tract, the oral cavity, the nasal cavity, skin, a wound, lungs, urethral canal, vaginal canal, ocular surface, and ear canal.
  • assessment of the nasal cavity microbiome may be achieved by assessing the local nasal fluids or a nasal swab.
  • Circulatory fluid samples e.g., blood and plasma
  • the gene products within a biological sample can be any analyte that can be influenced by expression of a gene.
  • the gene products can also have an association with microbiome-host interaction in which various genera can influence the expression levels.
  • These gene products include (but are not limited to) RNA, proteinaceous species (e.g., proteins and peptides), and metabolites.
  • Classes of analytes that are useful include (but are not limited to) gene products of: immune system modulators, cytokines, chemokines, hormones, growth factors, and other signaling molecules.
  • Gene products found to associate with presence and microbial genera are provided in Table 1 .
  • Method 100 can optionally perform (103) a clinical assay to determine host dysbiosis or assess other clinical phenotypes.
  • several clinical phenotypes are influenced by various microbiota, such as dysbiosis and circulating analytes (Table 2). These assessments can assist in determining which microbial genera would be beneficial to provide as a supplement to the individual.
  • dysbiosis the diversity and/or amount of microbiota within a microbiome sample can be assed.
  • a biological sample e.g., blood, plasma, CSF
  • Other clinical assessments can also be performed that provide a phenotype that is correlated with particular microbiota genera.
  • the method further determines a composition of a microbiome of the individual.
  • the composition of a microbiome can more help further determine whether a health status is related to a relative ratio of microbial genera, and often referred to an imbalance of microbial genera when influencing undesired health phenotypes.
  • Certain medical conditions and health statuses significantly correlate with microbiome composition. For example, as described herein, type 2 diabetes and insensitivity to insulin significantly correlates with low microbiome diversity and particular microbial genera in the microbiomes of the gut, the oral cavity, the nasal cavity, and skin.
  • butyrate-producing bacteria such as Coprococcus, Parasutterella, and Butyricicoccus were more likely to be in stool microbiome samples of insulin sensitive individuals; whereas diabetes-related opportunistic pathogens such as Finegoldia and Acinetobacterwere more prevalent in skin microbiome samples of insulin resistant individuals.
  • composition of a microbiome can be determined from a microbiome sample.
  • Microbiome samples can be obtained from the microbiome source and excretions or waste of that source (e.g., stool sample to determine gastrointestinal tract microbiome). Accordingly, microbiome samples can be obtained from one the following sources (or an excretion or waste of): the gastrointestinal tract, the oral cavity, the nasal cavity, skin, a wound, lungs, urethral canal, vaginal canal, ocular surface, and ear canal.
  • sources or an excretion or waste of
  • any biochemical methodology for identifying microbial genera can be utilized, which may be combined with computational and/or statistical analysis.
  • Biochemical methodologies include high-throughput sequencing, biomarker analysis, and bacterial genus isolation techniques (see, e.g. , J. Galloway-Pena and B. Hanson, Dig Dis Sci. 2020 Mar;65(3):674- 685, the disclosure of which is hereby incorporated by reference).
  • composition of a microbiome can also be determined from host biomarkers that can act as surrogates of microbiome composition. For example, a set of twelve cytokines was found to be able to predict the composition of the microbiome within a stool sample (see, D. Yang, et al., Sci Rep. 2019 Dec 27;9(1 ):20082, the disclosure of which is incorporated hereby by reference).
  • Method 100 determines (105) microbial genera that would provide benefit to the individual. Based on research described herein, it is now understood that many gene products within the circulatory system are significantly correlated with microbiomes of the gut, nasal cavity, oral cavity, and skin. Table 1 provides a list of circulating gene products that are correlated with various microbial genera. Furthermore, several microbial genera have been shown to specifically induce expression of gene products (see Examples and Data). It has further been shown that imbalances of gene products in circulation (e.g., cytokines) and/or imbalances in microbiome composition is associated with a number of disease states. Accordingly, the gene product expression and/or microbiome composition of an individual can provide insight on whether administration of particular microbial genera can improve health.
  • cytokines cytokines
  • an administration of particular microbial genera can be utilized to correct gene product expression that is involved in the pathology of many medical disorders.
  • a combination of one or more of these microbial genera would provide benefit to individuals with low circulating IL-17 and/or IL-22, individuals lacking these genera in their gut microbiome, insulin resistant individuals, type 2 diabetics, individuals experiencing leaky gut, and/or overweight individuals (especially obese individuals).
  • the benefit of certain microbial genera can be further established utilizing patient-derived immune responsive organoids.
  • organoids can be derived from a patient and utilized to assess a particular patient’s response to administration of particular microbial genera.
  • tonsil organoids can be extracted from an insulin resistant patient with low circulating IL-17 and IL-22.
  • the tonsil organoids can be maintained in culture and particular microbial genera, such as (for example) Barnesiella, Frisingicoccus, and Butyrivibrio, can be added to the culture individually or as some combination thereof to determine their effect on stimulating the expression of IL-17 and IL-22.
  • Method 100 can optionally administer beneficial microbial genera to the patient. With the determination that particular microbial genera can be beneficial to a patient, the patient can be administered the microbial genera. Certain microbial genera within particular microbiomes have been found to significantly correlate with circulatory gene products (Table 1 ), and with circulatory analytes (Table 2). In some embodiments, an individual is administered one or more microbial genera to increase an amount of a circulatory gene product. In some embodiments, an individual is administered one or more microbial genera to decrease an amount of a circulatory gene product. In some embodiments, an individual is administered one or more microbial genera to alter the level of a clinical analyte or otherwise adjust a clinical phenotype. Further description of gene products (and clinical phenotypes) that can be altered by administering a certain microbial genera are discussed below.
  • Administration of the microbial genera can be accorded to the particular microbiomes that provide the benefit. For example, some microbial genera can provide benefit within the gastrointestinal tract while other microbial genera can provide benefit on the skin. Therefore, the administration of microbial genera that provides benefit to the gastrointestinal tract can be orally administered (for example) within food, a beverage, or enteric-coated capsule, or can be rectally administered (for example) within a suppository or fecal transfer. Administration of microbial genera that provides benefit on the skin can be administered topically (for example) via an ointment, a cream, a lotion, or a powder. Further description of modes of administration are discussed below.
  • Various embodiments are directed towards a treatment comprising an administration of beneficial microbial genera.
  • this knowledge is leveraged to develop treatments for a medical disorder that can adjust the expression level of the gene products by administering microbial genera that have are significantly associated with the expression of those gene products.
  • the administered microbial genera can increase or decrease gene product expression, as dependent on the positive or negative association of gene product expression with particular microbial genera and the desired outcome to yield expression levels commiserate with healthy pathologies.
  • a treatment regimen is administered for a medical condition.
  • a patient can be diagnosed as having a particular medical condition and based on this diagnosis, the patient can be administered microbial genera.
  • An example of a procedure for diagnosis and treatment of a medical condition can be as follows:
  • the medical condition is type 2 diabetes, insulin resistance, leaky gut disease, and/or obesity.
  • a number of medical conditions can be treated with administration of microbial genera.
  • any medical condition associated with an interaction between the host and a microbiome can be treated.
  • Classes of medical disorders that can be treated include autoimmune disorders (including autoinflam matory disorders), cardiovascular disorders, mental health disorders, and metabolic disorders.
  • Autoimmune disorders that can be treated with microbial genera include (but are not limited to) psoriasis, ulcerative colitis, Crohn’s disease, inflammatory bowel disease, rheumatoid arthritis, and systemic lupus erythematosus.
  • Cardiovascular disorders that can be treated with microbial genera include (but are not limited to) hypertension, pulmonary artery hypertension, heart failure, atherosclerosis, vascular inflammation, and thrombosis.
  • Mental health disorders that can be treated with microbial genera include (but are not limited to) depression, anxiety, autism, and schizophrenia.
  • Metabolic disorders that can be treated with microbial genera include (but are not limited to) type 2 diabetes, insulin resistance, leaky gut disease, and obesity.
  • cytokines have been shown to promote inflammation in psoriasis, including TNF-a, IL-12, IL-17, IL-22 and IL-23 (I. Sieminska, et al., The Immunology of Psoriasis— Current Concepts in Pathogenesis. Clinic Rev Allerg Immunol (2024), the disclosure of which is hereby incorporated by reference).
  • an individual having psoriasis is administered microbial genera to reduce one or more of: TNF-a, IL-12, IL-17, IL-22 or IL-23.
  • Genera found to reduce TNF-a on the skin include Finegoldia (Table 1 ).
  • Genera found to reduce IL-12 on the skin Brevibacterium, Halomonas, Methylobacterium, Moraxella, and Paracoccus (Table 1 ).
  • Genera found to reduce IL-17 on the skin include Dolosigranulum, Neisseria, and Methylorubrum (Table 1 ).
  • Genera found to reduce IL-22 on the skin include Enhydrobacter, Moraxella, Paracoccus, Peptoniphilus, and Roseomonas (Table 1 ).
  • Genera found to reduce IL-23 on the skin include Methylobacterium and Moraxella (Table 1 ).
  • an individual having psoriasis is administered one or more of: Finegoldia, Brevibacterium, Halomonas, Methylobacterium, Moraxella, Paracoccus, Dolosigranulum, Neisseria, Methylorubrum, Enhydrobacter, Peptoniphilus, or Roseomonas.
  • an individual having psoriasis is administered one or more of: Methylobacterium, Moraxella, or Paracoccus.
  • cytokines have known roles in inflammatory bowel disease (inclusive of ulcerative colitis and Crohn’s disease); TNF-a, IL-12, IL-17, and IL-23 promote inflammation and IL-22 has shown to improve maintenance of the gut epithelial layer (h. Nakase, et al., Autoimmun Rev. 2022 Mar;21 (3):103017, the disclosure of which is hereby incorporated by reference).
  • an individual having ulcerative colitis or Crohn’s disease is administered microbial genera to reduce one or more of: TNF-a, IL- 12, IL-17, or IL-23.
  • Genera found to reduce TNF-a in the gut include Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, and Frisingicoccus (Table 1 ).
  • Genera found to reduce IL-12 in the gut include Butyricimonas, Collinsella, and Fusicatenibacter (Table 1 ).
  • Genera found to reduce IL-17 in the gut include Dialister, Subdoligranulum, and Senegalimassilia.
  • Genera found to reduce IL-23 in the gut include Butyricimonas, Dialister, Holdemanella, and Senegalimassilia.
  • an individual having ulcerative colitis or Crohn’s disease is administered microbial genera to increase IL-22.
  • Genera found to increase IL-22 in the gut include Frisingicoccus, Butyrivibrio, Anaerotignum, Cloacibacillus, Dysosmobacter, Gordonibacter, Negativibacillus, Phocea, Pseudoflavonifractor, Raoultibacter, and Turicibacter (Table 1 ). Accordingly, in some embodiments, an individual having ulcerative colitis or Crohn’s disease is administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Fusicatenibacter, Dialister, Subdoligranulum, Senegalimassilia, or Holdemanella. And in some embodiments, an individual having ulcerative colitis or Crohn’s disease is administered one or more of: Butyricimonas, Collinsella, Frisingicoccus, Dialister, or Senegalimassilia.
  • Gut microbiota has been shown to influence rheumatoid arthritis (T. Zhao, et al., Front Immunol. 2022 Sep 8; 13: 1007165, the disclosure of which is hereby incorporated by reference). Furthermore, several cytokines have been shown to promote inflammation in rheumatoid arthritis, including TNF-a, IL-6, and IL-17 (N. Kondo, et al., Int J Mol Sci. 2021 Oct 10;22(20): 10922, the disclosure of which is hereby incorporated by reference). In some embodiments, an individual having rheumatoid arthritis is administered microbial genera to reduce one or more of: TNF-a, IL-6, or IL-17.
  • Genera found to reduce TNF-a in the gut include Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, and Frisingicoccus (Table 1 ).
  • Genera found to reduce IL-6 in the gut include Collinsella, Dialister, and Fusicatenibacter (Table 1 ).
  • Genera found to reduce IL-17 in the gut include Dialister, Subdoligranulum, and Senegalimassilia (Table 1 ).
  • an individual having rheumatoid arthritis is administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Subdoligranulum, or Senegalimassilia. And in some embodiments, an individual having rheumatoid arthritis is administered one or more of: Collinsella or Dialister.
  • Gut microbiota has been shown to influence systemic lupus erythematosus (H. Yaigoub, et al., Clin Immunol. 2022 Nov;244:109109, the disclosure of which is hereby incorporated by reference). Furthermore, several cytokines have been shown to promote inflammation in systemic lupus erythematosus, including INF-a, INF-y, IL-6, and IL-17 (K. Ohl and K. Tenbrock J Biomed Biotechnol. 2011 ; 2011 :432595, the disclosure of which is hereby incorporated by reference).
  • an individual having systemic lupus erythematosus is administered microbial genera to reduce one or more of: INF-a, INF-y, IL-6, or IL-17.
  • Genera found to reduce INF-a in the gut include Blautia and Dialister.
  • Genera found to reduce INF-y in the gut include Blautia, Dialister, and Fusicatenibacter (Table 1 ).
  • Genera found to reduce IL-6 in the gut include Collinsella, Dialister, and Fusicatenibacter (Table 1 ).
  • Genera found to reduce IL-17 in the gut include Dialister, Subdoligranulum, and Senegalimassilia (Table 1 ).
  • an individual having systemic lupus erythematosus is administered one or more of: Blautia, Dialister, Fusicatenibacter, Collinsella, Subdoligranulum, or Senegalimassilia.
  • an individual having systemic lupus erythematosus is administered one or more of: Blautia, Dialister, and Fusicatenibacter.
  • Gut microbiota has been shown to influence hypertension (D. Yan, et al., Animal Model Exp Med. 2022 Dec;5(6):513-531 , the disclosure of which is hereby incorporated by reference). Furthermore, several cytokines have been shown to promote inflammation in hypertension, including TNF-a, IL-6, and IL-17 (Z. Zhang, et al., Front Immunol. 2023 Jan 10; 13: 1098725, the disclosure of which is hereby incorporated by reference). In some embodiments, an individual having hypertension is administered microbial genera to reduce one or more of: TNF-a, IL-6, or IL-17.
  • Genera found to reduce TNF-a in the gut include Agathobacter, Butyrici monas, Collinsella, Desulfovibrio, and Frisingicoccus (Table 1 ).
  • Genera found to reduce IL-6 in the gut include Collinsella, Dialister, and Fusicatenibacter (Table 1 ).
  • Genera found to reduce IL-17 in the gut include Dialister, Subdoligranulum, and Senegalimassilia (Table 1 ).
  • an individual having hypertension is administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Subdoligranulum, or Senegalimassilia. And in some embodiments, an individual having hypertension is administered one or more of: Collinsella or Dialister.
  • Gut microbiota has been shown to influence atherosclerosis (A. Al Samarraie, et al., Int J Mol Sci. 2023 Mar 12;24(6):5420, the disclosure of which is hereby incorporated by reference).
  • an individual having atherosclerosis is administered microbial genera to reduce one or more of: TNF-a or IL-6, and/or to increase IL-10.
  • Genera found to reduce TNF-a in the gut include Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, and Frisingicoccus (Table 1 ).
  • Genera found to reduce IL-6 in the gut include Collinsella, Dialister, and Fusicatenibacter (Table 1 ).
  • Genera found to increase IL-10 in the gut include Hungatella and Monoglobus (Table 1 ).
  • an individual having atherosclerosis is administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Hungatella, or Monoglobus.
  • an individual having atherosclerosis is administered one or more of: Collinsella or Dialister.
  • Gut microbiota has been shown to influence depression and anxiety (A. Kumar, et al., Pharmaceuticals (Basel). 2023 Apr 9; 16(4):565, the disclosure of which is hereby incorporated by reference). Furthermore, some cytokines have been shown to circulating at high levels in depressed and anxious patients, including TNF-a and IL-6, and some cytokines have been shown to be neurotrophic, including BDNF (J.C. Felger and F.E. Lotrich, Neuroscience. 2013 Aug 29;246: 199-229; and F. Santoft, et al., Brain Behav Immun Health. 2020 Feb 5;3: 100045; the disclosures of which are hereby incorporated by reference).
  • an individual having depression or anxiety is administered microbial genera to reduce one or more of: TNF-a or IL-6, and/or to increase BDNF.
  • Genera found to reduce TNF-a in the gut include Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, and Frisingicoccus (Table 1 ).
  • Genera found to reduce IL-6 in the gut include Collinsella, Dialister, and Fusicatenibacter (Table 1).
  • Genera found to increase BDNF in the gut include Barnesiella, Eggerthella, Lachnospira and Parabacteroides (Table 1 ).
  • an individual having depression or anxiety is administered one or more of: Agathobacter, Butyrici monas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Hungatella, or Monoglobus. And in some embodiments, an individual having depression or anxiety is administered one or more of: Collinsella or Dialister.
  • Gut microbiota has been shown to influence autism (M.A. Taniya, et al., Front Cell Infect Microbiol. 2022 Jul 22;12:915701 , the disclosure of which is hereby incorporated by reference). Furthermore, some cytokines have been shown to be circulating at increased levels in autistic individuals having more severe symptoms, including Eotaxin, MCP-1 , RANTES and IL-6, and some cytokines have been shown to be circulating at reduced levels in autistic individuals having more severe symptoms, including TGF-[3 (A. Masi, et al., Neurosci Bull. 2017 Apr;33(2): 194-204, the disclosure of which is hereby incorporated by reference).
  • an individual having autism is administered microbial genera to reduce one or more of: Eotaxin, MCP-1 , RANTES or IL-6, and/or to increase TGF-[3.
  • Genera found to reduce Eotaxin in the gut include Desulfovibrio and Escherichia_Shigella (Table 1 ).
  • Genera found to reduce MCP- 1 in the gut include Blautia, Desulfovibrio, Dialister, and Slackia (Table 1 ).
  • Genera found to reduce RANTES in the gut include Butyricimonas, Collinsella, Holdemanella, and Lawsonibacter (Table 1).
  • Genera found to reduce IL-6 in the gut include Collinsella, Dialister, and Fusicatenibacter (Table 1 ).
  • Genera found to increase TGF-p in the gut include Acutalibacter, Akkermansia, Clostridium_sensu_sthcto, Clostridium_XVIII, Flavonifractor, Holdemania, and Hungatella (Table 1 ).
  • an individual having autism is administered one or more of: Desulfovibrio, Escherichia_Shigella, Blautia, Dialister, Slackia, Butyricimonas, Collinsella, Holdemanella, Lawsonibacter, Fusicatenibacter, Acutalibacter, Akkermansia, Clostridium_sensu_stricto, Clostridium_XVIII, Flavonifractor, Holdemania, or Hungatella.
  • Gut microbiota has been shown to influence schizophrenia (K. Tsamakis, et al., Microorganisms. 2022 May 29;10(6):1121 , the disclosure of which is hereby incorporated by reference).
  • an individual having schizophrenia is administered microbial genera to reduce one or more of: TNF-a or IL-6.
  • Genera found to reduce TNF- a in the gut include Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, and Frisingicoccus (Table 1 ).
  • Genera found to reduce IL-6 in the gut include Collinsella, Dialister, and Fusicatenibacter (Table 1 ).
  • an individual having schizophrenia is administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Hungatella, or Monoglobus. And in some embodiments, an individual having schizophrenia is administered one or more of: Collinsella or Dialister.
  • Cytokines IL-17 and IL-22 have shown to be associated with positive outcomes in metabolic disease (type 2 diabetes, insulin resistance and/or obesity).
  • the individual is administered beneficial microbial genera that increases IL-17 or IL-22.
  • the individual is administered one or more of: Barnesiella, Frisingicoccus, and Butyrivibrio. Other genera were also found to increase IL-17 in the gut, including Adlercreutzia, Butyricicoccus, Cloacibacillus, Dysosmobacter, and Faecalicatena (Table 1 ).
  • the individual is administered one or more of: Barnesiella, Frisingicoccus, Butyrivibrio, Adlercreutzia, Butyricicoccus, Cloacibacillus, Dysosmobacter, Faecalicatena, Anaerotignum, Cloacibacillus, Dysosmobacter, Gordonibacter, Negativibacillus, Phocea, Pseudoflavonifractor, Raoultibacter, or Turicibacter.
  • cytokines have been associated with negative outcomes related to obesity and type 2 diabetes including TNF-a, IL-6 and IL-1 [3; and some cytokines have been associated with positive outcomes including IL-10 (N. Esser, et al., Diabetes Res Clin Pract. 2014 Aug; 105(2): 141 -50, the disclosure of which is hereby incorporated by reference).
  • Genera found to reduce TNF-a in the gut include Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, and Frisingicoccus (Table 1 ).
  • Genera found to reduce IL-6 in the gut include Collinsella, Dialister, and Fusicatenibacter (Table 1).
  • Genera found to reduce IL-1 p in the gut include Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Faecalibacterium, Lachnospira, Prevotella, Roseburia, Slackia, Subdoligranulum, and Sutterella (Table 1 ).
  • Genera found to increase IL-10 in the gut include Hungatella and Monoglobus (Table 1 ).
  • an individual having type 2 diabetes or obesity is administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Hungatella, Monoglobus, Faecalibacterium, Lachnospira, Prevotella, Roseburia, Slackia, Subdoligranulum, and Sutterella.
  • an individual having type 2 diabetes or obesity is administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, or Dialister.
  • Leaky gut syndrome is a medical condition in which the epithelium lining of the gastrointestinal track is damaged, resulting in increased permeability of damaging substances to reach into the blood stream. Leaky gut results in greater damage to other organs of the body and is commonly associated with inflammatory disorders, cardiovascular disorders, neurological disorder and metabolic disorders (R.S. Aleman, et al., Molecules. 2023 Jan 7;28(2):619, the disclosure of which is hereby incorporated by reference). Some cytokines have been shown to increase intestinal permeability, including INF-y, TNF-a and IL-1 P; and some cytokines and factors have been shown to improve intestinal barrier function including IL-10, TGF-p, and EGF (F.
  • an individual having leaky gut syndrome is administered microbial genera to reduce one or more of: INF-y, TNF-a or IL-1 p, and or to increase one or more of: IL-10, TGF-p, or EGF.
  • Genera found to reduce INF-y in the gut include Blautia, Dialister, and Fusicatenibacter (Table 1).
  • Genera found to reduce TNF-a in the gut include Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, and Frisingicoccus (Table 1).
  • Genera found to reduce IL-1 p in the gut include Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Faecalibacterium, Lachnospira, Prevotella, Roseburia, Slackia, Subdoligranulum, and Sutterella (Table 1 ).
  • Genera found to increase IL-10 in the gut include Hungatella and Monoglobus (Table 1 ).
  • Genera found to increase TGF-[3 in the gut include Acutalibacter, Akkermansia, Clostridium_sensu_stricto, Clostridium_XVIII, Flavonifractor, Holdemania, and Hungatella (Table 1 ).
  • Genera found to increase EGF in the gut include Anaerostipes, Barnesiella, Eggerthella, Intestinibacter, Neglecta, Parabacteroides, and Romboutsia.
  • an individual having leaky gut syndrome is administered one or more of: Blautia, Dialister, Fusicatenibacter, Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Faecalibacterium, Lachnospira, Prevotella, Roseburia, Slackia, Subdoligranulum, Sutterella, Hungatella Monoglobus, Acutalibacter, Akkermansia, Clostridium_sensu_stricto, Clostridium_XVIII, Flavonifractor, Holdemania, Anaerostipes, Barnesiella, Eggerthella, Intestinibacter, Neglecta, Parabacteroides, or Romboutsia.
  • an individual having leaky gut syndrome is administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, or Hungatella.
  • Various embodiments are directed to treatment regimens based on an assessment of expression of gene products of the patient to indicate which gene products are unhealthy in the patient.
  • a biological sample is acquired from an individual and examined for expression of gene products and based on the expression profile, a determination of whether gene product expression is within a healthy range can be made.
  • the individual can be administered microbial genera to adjust gene product expression back into a healthy range.
  • microbial genera can alter gene product expression patterns of immune responsive organoids, suggesting that administration of beneficial microbial genera would alter a host’s gene product expression back into a healthy range.
  • an individual can be assessed for gene expression products and treated with microbial genera as follows:
  • the method is performed as part as a screening procedure of the individual.
  • the individual has not been diagnosed with a particular medical condition prior to the screening assay.
  • the screening procedure is performed to determine gene product expression associated with a particular medical condition.
  • the individual has been diagnosed with (or determined to be at risk for) a particular medical condition and the gene products assessed are related to the medical condition.
  • Various circulatory gene products that can be assessed are provided in Table 1. Numerous circulatory gene products that have been associated with various medical disorders, such as (for example) BDNF, EGF, Eotaxin, INF-a, INF-y, IL-1 [3, IL-6, IL-10, IL-12, IL-17, IL-22, IL-23, MCP-1 , RANTES, TGF- , and TNF-a. Accordingly, in some embodiments, a biological sample (e.g., blood, plasma, CSF) is assessed for one or more gene products listed within Table 1.
  • a biological sample e.g., blood, plasma, CSF
  • a biological sample e.g., blood, plasma, CSF
  • BDNF e.g., blood, plasma, CSF
  • EGF Eotaxin
  • INF-a INF-a
  • INF-y IL-1 [3, IL-6, IL-10, IL-12, IL-17, IL-22, IL-23, MCP-1 , RANTES, TGF-J3, or TNF-a.
  • An individual can be administered one or more microbial genera to increase or to decrease a circulatory gene products, which can be based on assessment of circulatory gene product expression level.
  • a gene product can be lowered by administering one or more microbial genera that are negatively correlated with the gene product (see Table 1).
  • a gene product can be increased by administering one or more microbial genera that are positively correlated with the gene product (see Table 1 ).
  • To reduce levels of circulatory Eotaxin an individual can be administered one or more of: Desulfovibrio or Escherichia_Shigella to the gastrointestinal tract.
  • To reduce levels of circulatory INF-ct an individual can be administered one or more of: Blautia or Dialister to the gastrointestinal tract.
  • an individual can be administered one or more of: Blautia, Dialister, or Fusicateni bacterio the gastrointestinal tract.
  • an individual can be administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Faecalibacterium, Lachnospira, Prevotella, Roseburia, Slackia, Subdoligranulum, or Sutterella to the gastrointestinal tract.
  • an individual can be administered one or more of: Collinsella, Dialister, or Fusicatenibacter to the gastrointestinal tract.
  • an individual can be administered one or more of: Butyricimonas, Collinsella, or Fusicatenibacter to the gastrointestinal tract.
  • an individual can be administered one or more of: Dialister, Subdoligranulum, or Senegalimassilia to the gastrointestinal tract.
  • an individual can be administered one or more of: Butyricimonas, Dialister, Holdemanella, or Senegalimassilia to the gastrointestinal tract.
  • an individual can be administered one or more of: Blautia, Desulfovibrio, Dialister, or Slackia to the gastrointestinal tract.
  • an individual can be administered one or more of: Butyricimonas, Collinsella, Holdemanella, or Lawsonibacter to the gastrointestinal tract.
  • an individual can be administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, or Frisingicoccus to the gastrointestinal tract.
  • an individual can be administered one or more of: Barnesiella, Eggerthella, Lachnospira or Parabacteroides to the gastrointestinal tract.
  • an individual can be administered one or more of: Anaerostipes, Barnesiella, Eggerthella, Intestinibacter, Neglecta, Parabacteroides, or Romboutsia to the gastrointestinal tract.
  • an individual can be administered one or more of: Hungatella or Monoglobus to the gastrointestinal tract.
  • an individual can be administered one or more of: Adlercreutzia, Barnesiella, Butyricicoccus, Cloacibacillus, Dysosmobacter, or Faecalicatena to the gastrointestinal tract.
  • an individual can be administered one or more of: Anaerotignum, Butyrivibho, Cloacibacillus, Dysosmobacter, Frisingicoccus, Gordonibacter, Negativibacillus, Phocea, Pseudoflavonifractor, Raoultibacter, or Turicibacter to the gastrointestinal tract.
  • an individual can be administered one or more of: Acutalibacter, Akkermansia, Clostridium_sensu_stricto, Closthdium_XVIII, Flavonifractor, Holdemania, or Hungatella to the gastrointestinal tract.
  • gene products IL-17 and IL-22 are assessed in an individual. Expression of IL-17 and IL-22 within the circulatory system has been associated with healthy insulin sensitivity and low or absent expression of these products has been associated with insulin resistance and type 2 diabetes. Accordingly, an individual can be orally or rectally administered one or more of: Barnesiella, Frisingicoccus, and Butyrivibho to improve IL-17 or IL-22 levels. When a biological sample of a patient is assessed and indicates that expression of IL-17 and/or IL-22 within the circulatory system is low or absent, the patient is administered beneficial microbial genera comprising one or more of: Barnesiella, Frisingicoccus, or Butyrivibho.
  • Various embodiments are directed to treatment regimens based on an assessment of clinical phenotype readouts of common blood analytes.
  • a blood sample is acquired from an individual and examined for levels of blood analytes.
  • Table 2 Provided in Table 2 is a list of common blood analyte readouts that are significantly associated with microbial genera and the constituents that mediate the phenotypic result.
  • the individual can be administered beneficial microbial genera that can correct the level of the one or more analytes.
  • an individual can be assessed for circulating metabolite levels and treated with microbial genera as follows:
  • the method is performed as part as a screening procedure of the individual.
  • the individual has not been diagnosed with a particular medical condition prior to the screening assay.
  • the screening procedure is performed to determine analyte levels associated with a particular condition.
  • the individual has been diagnosed with (or determined to be at risk for) a particular medical condition and the analytes assessed are related to the medical condition.
  • a biological sample e.g., blood, plasma, CSF
  • a biological sample is assessed for one or more analytes listed within Table 3.
  • An individual can be administered one or more microbial genera to increase or to decrease a circulatory analyte, which can be based on assessment of circulatory analytes.
  • An analyte can be lowered by administering one or more microbial genera that are negatively correlated with the gene product (see Table 2).
  • An analyte can be increased by administering one or more microbial genera that are positively correlated with the gene product (see Table 2).
  • skin microbiomes comprising the microbial genus Haemophilus are shown to be negatively correlated with unhealthy cholesterol (e.g., LDL and non-HDL) (Table 2).
  • unhealthy cholesterol e.g., LDL and non-HDL
  • an individual can be topically administered Haemophilus to reduce unhealthy cholesterol levels.
  • the circulating analyte levels of an individual is assessed and indicates that the individual has high cholesterol, high LDL cholesterol, high non-HDL, and/or high LDL to HDL ratio, the individual can be administered beneficial microbial genera comprising Haemophilus.
  • gut microbiomes comprising the microbial genus Akkermansia are shown to be negatively correlated with A1 C (Table 2).
  • an individual can be orally or rectally administered Akkermansia to reduce A1 C levels.
  • the individual can be administered beneficial microbial genera comprising Akkermansia.
  • Many other microbial genera have been found to promote healthy analyte levels and thus a method can be performed to administer microbial genera to promote healthy analyte levels in accordance with various embodiments.
  • Various embodiments are directed to treatment regimens based on an assessment of microbiome composition of the patient to indicate whether the patient could benefit from administration of microbial genera.
  • a microbiome sample is acquired from an individual and examined for microbiome composition, a determination of whether a healthy amount of beneficial microbial genera can be made.
  • the individual can be administered beneficial microbial genera to introduce the beneficial microbial genera into the host’s microbiome.
  • an individual can be assessed for microbiome composition and treated with beneficial microbial genera as follows:
  • the method is performed as part as a screening procedure of the patient.
  • the patient has not been diagnosed with a particular medical condition prior to the screening assay.
  • the screening procedure is performed to determine microbiome composition associated with a particular medical condition.
  • the patient has been diagnosed with (or determined to be at risk for) a particular medical condition and the microbial genera assessed are related to the medical condition.
  • gastrointestinal microbiomes comprising microbial genera Barnesiella, Frisingicoccus, and Butyrivibrio have been associated with healthy insulin sensitivity and gastrointestinal microbiomes having a low presence or an absence of these microbial genera has been associated with insulin resistance and type 2 diabetes.
  • the gastrointestinal microbiome of a patient is assessed and indicates that microbiome comprises low presence or an absence of one or more of the microbial genera Barnesiella, Frisingicoccus, and Butyrivibrio
  • the patient is administered beneficial microbial genera comprising one or more of: Barnesiella, F singicoccus, and Butyrivibrio.
  • a gastrointestinal microbiome is assessed via a stool sample. Many other microbial genera have been found to help promote a healthy microbiome composition and thus a method can be performed to administer these microbial genera as determined by whether the composition of a microbiome lacks these beneficial genera in accordance with various embodiments.
  • Various embodiments are directed to treatment regimens based on an assessment of an immune responsive organoid derived from an individual to indicate whether the individual would benefit from administration of microbial genera.
  • immune responsive tissue e.g., tonsil tissue
  • the immune responsive is treated with a microbial genus or a combination of microbial genera to determine if the microbial genus or the combination of microbial genera can induce healthy gene product response.
  • the individual can be administered the microbial genus or the combination of microbial genera can induce healthy gene product response.
  • an individual can be assessed via an immune responsive organoid and treated with microbial genera as follows:
  • the method is performed as part as a screening procedure of the individual.
  • the individual has not been diagnosed with a particular medical condition prior to the screening assay.
  • the screening procedure is performed to determine microbiome composition associated with a particular medical condition.
  • the individual has been diagnosed with (or determined to be at risk for) a particular medical condition and the microbial genera assessed are related to the medical condition.
  • gastrointestinal microbiomes comprising microbial genera Barnesiella, Frisingicoccus, and Butyrivibrio have been associated with healthy insulin sensitivity and gastrointestinal microbiomes having a low presence or an absence of these microbial genera has been associated with insulin resistance and type 2 diabetes. Accordingly, a culture of immune responsive organoids of a patient can be contacted with culture products of a microbial genus or a combination of microbial genera comprising one or more of Barnesiella, Frisingicoccus, and Butyrivibrio.
  • the patient derived immune responsive organoid culture When the patient derived immune responsive organoid culture is assessed and indicates that the organoid culture produced a desired response by the microbial genus or the combination of microbial genera comprising one or more of Barnesiella, Frisingicoccus, and Butyrivibrio, the patient is administered the microbial genus or the combination of microbial genera.
  • the immune responsive organoid culture is derived from an individual’s tonsil tissue, lymph node tissue, or spleen tissue. Many other microbial genera have been found to provide a desired response by immune responsive organoids and can be utilized in accordance with various embodiments.
  • a probiotic supplement can be produced or manufactured for administration to the general public.
  • the probiotic supplement can be produced in any means for microbial genera administration.
  • Probiotic supplements can be sold with the intent that individuals of the general public are to self-administer beneficial microbial genera.
  • gastrointestinal microbiomes comprising microbial genera Barnesiella, Frisingicoccus, and Butyrivibrio have been associated with healthy insulin sensitivity and gastrointestinal microbiomes having a low presence or an absence of these microbial genera has been associated with insulin resistance and type 2 diabetes.
  • gastrointestinal microbiomes comprising the microbial genus Haemophilus have been associated with low levels of cholesterol, especially unhealthy cholesterol (e.g., LDL and non-HDL).
  • a probiotic supplement can be produced or manufactured with a microbial genus or a combination of microbial genera comprising one or more of Barnesiella, Frisingicoccus, Butyrivibrio, and Haemophilus.
  • Individuals of the general public can administer the probiotic supplement, which may provide metabolic health benefits.
  • Many other microbial genera have been found to provide health benefits and can be included a probiotic for general public administration in accordance with various embodiments.
  • Microbial genera can be administered by a variety of modes. Generally, the mode of administration relates to a local microbiome to be augmented by the microbial genera.
  • administration of microbial genera to augment a gastrointestinal microbiome can comprise an oral administration or a rectal administration.
  • Oral administration can comprise the administration of one or more of: a probiotic food, a probiotic beverage, a liquid solution composition, a gel composition, an oil composition, an emulsion composition, a capsule, an enteric-coated capsule, a dragee, a gavage, a lyophilized powder, a freeze-dried powder, a combination thereof, or any other means to orally administer microbial genera to the gastrointestinal tract.
  • Rectal administration can comprise the administration of one or more of: a probiotic liquid, a probiotic gel, a probiotic suppository, a probiotic fecal transplant, a probiotic enema, a probiotic catheter, a lyophilized powder, a freeze-dried powder, a combination thereof, or any other means to rectally administer microbial genera to the gastrointestinal tract.
  • administration of microbial genera to augment an oral microbiome can comprise an oral administration to the oral cavity.
  • Oral administration to the oral cavity can comprise the administration of one or more of: a probiotic gel, a probiotic suppository, a probiotic oil, a probiotic emulsion, a probiotic sublingual strip, a probiotic mouthwash, a lyophilized powder, a freeze-dried powder, a combination thereof, or any other means to orally administer microbial genera to the oral cavity.
  • administration of microbial genera to augment a nasal microbiome can comprise a nasal administration to the nasal cavity.
  • Nasal administration can comprise the administration of one or more of: a probiotic gel, a probiotic suppository, a probiotic oil, a probiotic emulsion, a probiotic inhaler, a probiotic nasal wash, a lyophilized powder, a freeze-dried powder, a combination thereof, or any other means to administer microbial genera to the nasal cavity.
  • administration of microbial genera to augment a skin microbiome or a wound microbiome can comprise a topical administration to the skin.
  • Topical administration can comprise the administration of one or more of: a probiotic gel, a probiotic suppository, a probiotic oil, a probiotic emulsion, a probiotic ointment, a probiotic lotion, a probiotic powder, a probiotic cream, a lyophilized powder, a freeze- dried powder, a combination thereof, or any other means to topically administer microbial genera to the skin or wound.
  • administration of microbial genera to augment a pulmonary microbiome can comprise a pulmonary administration to the lungs.
  • Pulmonary administration can comprise the administration of one or more of: a probiotic inhaler, a probiotic nebulizer, a lyophilized powder, a freeze-dried powder, a combination thereof, or any other means to administer microbial genera to the lungs.
  • administration of microbial genera to augment a urethral microbiome can comprise a urethral administration to the urethral canal.
  • Urethral administration can comprise the administration of one or more of: a probiotic gel, a probiotic suppository, a probiotic oil, a probiotic emulsion, a probiotic catheter, and any other means to administer microbial genera to the urethral canal.
  • administration of microbial genera to augment a vaginal microbiome can comprise a vagina administration to the vaginal canal.
  • Urethral administration can comprise the administration of one or more of: a probiotic gel, a probiotic suppository, a probiotic oil, a probiotic emulsion, a probiotic cream, a probiotic ointment, a probiotic vaginal wash, a probiotic catheter, a lyophilized powder, a freeze- dried powder, a combination thereof, or any other means to administer microbial genera to the vaginal canal.
  • administration of microbial genera to augment an ocular surface can comprise an ocular administration to the ocular surface.
  • Ocular administration can comprise the administration of one or more of: a probiotic gel, a probiotic cream, a probiotic cream, a probiotic ocular drops, a probiotic ocular wash, a lyophilized powder, a freeze-dried powder, a combination thereof, or any other means to administer microbial genera to the ocular surface.
  • administration of microbial genera to augment an ear canal can comprise an otic administration to the ear canal.
  • Otic administration can comprise the administration of one or more of: a probiotic gel, a probiotic cream, a probiotic cream, a probiotic otic drops, a probiotic otic wash, a lyophilized powder, a freeze-dried powder, a combination thereof, or any other means to administer microbial genera to the ocular surface.
  • a recipient is administered beneficial microbiota as described herein.
  • the amount of bacterium for treatment is a therapeutically effective amount of the bacterium.
  • the bacterium for treatment is lyophilized.
  • the bacterium for treatment is freeze- dried.
  • the bacterium for treatment is lyophilized or freeze-dried and subsequently reconstituted.
  • a bacterium is administered in a therapeutically effective amount as part of a course of treatment.
  • to "treat” means to ameliorate or prevent at least one symptom of the disorder to be treated or to provide a beneficial physiological effect.
  • a therapeutically effective amount can be an amount sufficient to prevent reduce, ameliorate or eliminate the symptoms of diseases or pathological conditions susceptible to such treatment.
  • a therapeutically effective amount is an amount sufficient to (for example) reconstitute a balanced microbiome, correct expression level of a gene product, correct level of an analyte, or improve a medical disorder phenotype.
  • Dosage, toxicity and therapeutic efficacy of a pharmaceutical composition can be determined, e.g., by standard pharmaceutical procedures in cell cultures or experimental animals, e.g., for determining the LDso (the dose lethal to 50% of the population) and the EDso (the dose therapeutically effective in 50% of the population).
  • the dose ratio between toxic and therapeutic effects is the therapeutic index and it can be expressed as the ratio LDso/EDso.
  • Compounds that exhibit high therapeutic indices are preferred. While compounds that exhibit toxic side effects may be used, care should be taken to design a delivery system that targets such compounds to the site of affected tissue in order to minimize potential damage to uninfected cells and, thereby, reduce side effects.
  • Data obtained from cell culture assays or animal studies can be used in formulating a range of dosage for use in humans. If a bacterium comprising a polypeptide is provided systemically, the dosage of effector polypeptides lies preferably within a range of circulating concentrations that include the EDso with little or no toxicity. The dosage may vary within this range depending upon the dosage form employed and the route of administration utilized. A dose may be formulated in animal models to achieve a local environment concentration in a range that includes an ICso. Such information can be used to more accurately determine useful doses in humans. Levels in plasma may be measured, for example, by immunological based assays or liquid chromatography.
  • an "effective amount” is an amount sufficient to effect beneficial or desired results.
  • a therapeutically effective amount is one that achieves the desired therapeutic effect. This amount can be the same or different from a prophylactical ly effective amount, which is an amount necessary to prevent onset of disease or disease symptoms.
  • An effective amount can be administered in one or more administrations, applications or dosages. The skilled artisan will appreciate that certain factors may influence the dosage and timing required to effectively treat a subject, including but not limited to the severity of the disease or disorder, previous treatments, the general health and/or age of the subject, and other diseases present.
  • treatment of a subject with a therapeutically effective amount of a bacterium described herein can include a single treatment or a series of treatments. For example, several divided doses may be administered daily, one dose, or cyclic administration of the compounds to achieve the desired therapeutic result.
  • Frequency of administration for a bacterium, inclusive of the various beneficial microbiota described herein, can be at least once a year, at least once every six months, at least once every five months, at least once every four months, at least once every three months, at least once every two months, at least once a month, at least once every four weeks, at least once every three weeks, at least once every two weeks, at least once a week, at least twice a week, at least three times a week, at least four times a week, at least five times a week, at least six times a week, daily, two times per day, three times per day, four times per day, five times per day, six times per day, eight times per day, nine times per day, ten times per day, eleven times per day, twelve times per day, at least once every 12 hours, at least once every 6 hours, at least once every 2 hours, at least once every hour, at least once every 30 min, at least once every 20 min, or at least once every 10 min.
  • Administration can also be continuous and
  • a bacterium may be administered in an amount effective to yield a desired result, such as correcting gene product expression level within the subject, reduction of inflammation, improvement in glucose sensitivity, improvement of a neurological symptom, reduction of HDL cholesterol levels, etc.
  • bacterial doses of colony forming units include from about 1 *10 5 to about 1 x10 13 , from about 1 x10 6 to about 1 xi o 10 , from about 1 x 5 to about 1 x 7 , from about 1 x 6 to about 1 xio 8 , from about 1 X10 7 to about 1 xi o 9 , from about 1 xi o 8 to about 1 x10 10 , from about 1 xi o 9 to about 1 xi o 11 , from about 1 xi o 10 to about 1 x 12 , and from about 1 xl 0 11 to about 1 xio 13
  • the dosage is about 1 xio 6 CFUs, the dosage is about
  • a bacterium can be grown utilizing techniques for cultivation of bacteria, which is appreciated in the art.
  • a microbiota can be enriched and/or isolated from a microbiome sample.
  • a microbiome can be sorted using a cell sorter or a dilution technique to yield single cells within a field or within a individual wells of plate.
  • the taxonomies of microbial colonies can be identified by any appropriate technique (e.g., sequencing).
  • direct targeting of particular taxa can be achieved using a selective antibody.
  • Various cultures may need optimization of media and/or a co-culture system. For further details and examples of particular methodologies to cultivate microbiome-derived bacteria, see X. Wan, et al., Microorganisms. 2023 Apr 20; 11 (4): 1080, the disclosure of which is hereby incorporated by reference.
  • Microbial cultures can be lyophilized and/or freeze dried, which can yield a shelf-stable powder. Lyophilized and/or freeze-dried bacteria can be administered in that form or reconstituted prior to administration.
  • a composition for storage and/or administration comprises a lyophilized bacterium.
  • a composition for storage and/or administration comprises a freeze-dried bacterium.
  • a composition comprising a lyophilized and/or freeze-dried bacterium can further comprise one or more protectant agents, which can enhance the survivability of the bacterium.
  • protectant agents include (but are not limited to) dimethylsulfoxide (Me2SO), glycerol, blood serum, serum albumin, skimmed milk powder, whey protein, peptone, yeast extract, sucrose, glucose, trehalose, lactose, methanol, polyvinylpyrrolidone (PVP), sorbitol, sodium ascorbate, and malt extract.
  • Me2SO dimethylsulfoxide
  • glycerol glycerol
  • blood serum serum albumin
  • skimmed milk powder whey protein
  • peptone yeast extract
  • sucrose glucose
  • trehalose lactose
  • lactose lactose
  • methanol polyvinylpyrrolidone
  • sorbitol sodium ascorbate, and malt extract.
  • an immune responsive organoid culture system is utilized to asses the effect of microbial genera. Any organoid culture system capable of indicating an immune response can be utilized.
  • a tonsil organoid system is utilized.
  • a lymph node organoid system is utilized.
  • a spleen tissue organoid system is utilized. Immune responsive culture systems and methods are described in U.S. Appl. No. 18/094,851 , the disclosure of which is hereby incorporated by reference.
  • Fig. 2 Provided in Fig. 2 is an example of a method to assess microbial genera effect on gene host expression.
  • the method utilizes an immune responsive organoid culture system that is co-cultured with microbial genera.
  • the co-culture system can assess the effect the microbial genera on the immune system, including T-cell activation, humoral response, and gene product expression.
  • Method 200 can begin by providing (201 ) immune responsive organoids in culture.
  • An immune responsive organoid is an in vitro cluster of immune cells, which can be derived from lymphoid tissue or differentiated from stem cells.
  • the cluster of immune cells comprises one or more of: T-cells, antigen presenting cells, dendritic cells, and B-cells.
  • T-cells can comprise CD8+ T-cells and/or CD4+ T-cells.
  • B- cells can comprise CD38+ B-cells and/or CD27+ B-cells.
  • the immune responsive organoids are derived from lymphoid tissue, including (but not limited to) tonsils, lymph nodes, and spleen.
  • the immune responsive organoids are derived from tonsil tissue, which can be collected from a donor as a biopsy or as whole tonsils (e.g., tonsillectomy).
  • the immune responsive organoids are derived from lymph nodes, which can be collected from a donor as a biopsy or as whole lymph nodes (e.g., lymphadenectomy).
  • the immune responsive organoids are derived from spleen, which can be collected from a donor as a biopsy.
  • the immune responsive organoids are collected from a patient, which may be used for patient-specific response assessment. Tissue and cells can be cryopreserved until ready for culture.
  • the lymphoid tissue is dissociated, washed, and dispersed into low-attachment tissue-culture wells.
  • tissue-culture wells with permeable membranes can be utilized to facilitate the collection and/or exchange of media of the organoid culture.
  • the dissociated tissue can be kept in an appropriate medium, can be allowed to reaggregate, and can be developed into immune responsive organoids.
  • factors for promoting immune cell health and/or maturity can be provided, such as (for example) B-cell activating factor and one or more adjuvants (e.g., aluminum hydroxide).
  • Method 200 adds (203) a microbial culture or a microbial culture supernatant to the organoid culture.
  • Microbial genera isolates (or a population of mixed microbial genera) can be collected and isolated from a microbiome sample.
  • the isolated microbial genera isolates (or a population of mixed microbial genera) can be further cultured.
  • a singular isolated microbial genera isolate, a mixture of genera isolates, or a population of mixed microbial genera can be utilized as a microbial culture for assessment.
  • the organoid culture can be contacted with a live microbial culture, an attenuated or killed microbial culture, or a supernatant of microbial culture to induce a response of the organoids.
  • Microbiome samples can be obtained from the microbiome source and excretions or waste of that source (e.g., stool sample). Culturing of microbial genera isolates (or population of mixed microbial genera) can be extracted and cultured as described herein.
  • Fig. 3 Provided in Fig. 3 is one example of a method yield a microbial genera culture supernatant product.
  • the method can collect microbial genera isolates and then grow in a liquid culture.
  • the culture can be pelted via centrifugation and supernatant is extracted and filtered.
  • the filtered supernatant is collected, which can be added to the organoid culture.
  • method 200 measures (205) organoid response.
  • stimulation of the organoid culture is allowed to continue for a period of time.
  • the stimulation period is between 12 hours and up to 672 hours (4 weeks).
  • the stimulation period is about 12 hours, the stimulation period is about 24 hours, the stimulation period is about 48 hours, the stimulation period is about 72 hours, the stimulation period is about 96 hours, the stimulation period is about 120 hours, the stimulation period is about 144 hours, the stimulation period is about 168 hours, the stimulation period is about 240 hours, the stimulation period is about 336 hours, the stimulation period is about 504 hours, or the stimulation period is about 672 hours.
  • addition of microbial genera culture supernatant to the organoid culture is repeated over the course of the stimulation period.
  • the organoids are assessed for responsiveness.
  • the supernatant of the organoid culture is utilized for assessment.
  • the cells of the organoid culture are utilized for assessment.
  • cell lysates of the organoid culture are utilized for assessment.
  • nucleic acids of the organoid culture are utilized for assessment.
  • gene products of the organoid culture are utilized for assessment.
  • any assessment of immune response can be performed.
  • gene products are assessed to determine which genes were activated.
  • activation of T-cells is assessed.
  • humoral response e.g., antibody production
  • cytokine and/or chemokine response is assessed.
  • Figs. 4A to 4E Provided in Figs. 4A to 4E is the results of immune response of six microbial strains utilized in the tonsil co-culture assay.
  • the immune response measured is the induced expression of cytokines: IL-1 A (Fig. 4A); IL-1 RA (Fig. 4B); CCL3 (Fig. 4C); M- CSF (Fig. 4D); and IL-6 (Fig. 4E).
  • the six microbial strains tested are as follows: strain 1 is Clostridia. spp., strain 2 is Coprococcus. spp., strain 3 is E. coli.spp', strain 4 is Bacteriodes.
  • strain 5 is Prevotella.spp'
  • strain 6 is a mix of Roseburia.spp. Used as controls are phosphate buffered saline (PBS), culture media; staphylococcal eneterotoxin B (SEB); toll-like receptor agonist (TLR), and live attenuated influenza vaccine (LIAV).
  • PBS phosphate buffered saline
  • SEB staphylococcal eneterotoxin B
  • TLR toll-like receptor agonist
  • LIAV live attenuated influenza vaccine
  • Fig. 5 Provided in Fig. 5 is principal component analysis of the tonsil co-culture assay results. These results show that the tonsil co-culture system can generate strain specific immune response that is highly repeatable and phylogenetically relevant. The results of the different individuals for each strain all clustered together. Further, Bacteroides and Prevotella are known to be very close phylogenetically, and as the results show, these two genera clustered close to one another.
  • the human microbiome comprises highly dynamic microbial communities inhabiting various body sites, engaging in intricate host-microbial interactions that display territory-specific complexity. Advancements in multi-omics technologies have catalyzed the elucidation of the molecular mechanisms underlying microbial ecology and their interactions with host, unveiling the critical roles of the microbiome in normal physiological processes such as aging as well as diseases including inflammatory bowel disease (IBD), cardiovascular disease, and type 2 diabetes mellitus (T2DM).
  • IBD inflammatory bowel disease
  • T2DM type 2 diabetes mellitus
  • FIG. 6A The cohort comprised 41 males and 45 females, aged between 29 and 75 years old (55 ⁇ 9.8 years old), with BMIs ranging from 19.1 to 40.8 kg/m A 2 (28.31 ⁇ 4.44 kg/m A 2). Sampling occurred quarterly, with an additional 3-7 samples collected within five weeks (12% of the total) during periods of stress, such as respiratory illness, vaccination, or antibiotic use.
  • the 16S ribosomal RNA gene sequencing method employed in this study targeted a variable region to facilitate the detection of amplicon sequence variants (ASVs), enabling the identification and differentiation of most bacterial taxa at the genus and species levels.
  • ASVs amplicon sequence variants
  • a unique feature of this cohort is the multi-omics phenotyping of participants at each timepoint (Fig. 6B).
  • Untargeted proteomics (302 proteins), untargeted metabolomics (724 annotated metabolic features), targeted lipidomics (846 annotated lipids), and 62 targeted cytokine and growth factor measurements were performed, along with 51 clinical markers, including C-reactive protein (CRP), fasting glucose (FG), hemoglobin A1 C (HbA1 C), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) from plasma samples.
  • CRP C-reactive protein
  • FG fasting glucose
  • HbA1 C hemoglobin A1 C
  • LDL low-density lipoprotein
  • HDL high-density lipoprotein
  • Glucose control assessments comprising an annual oral glucose tolerance test for all participants and a gold-standard steady-state plasma glucose (SSPG) measurement for 58 individuals, classified 28 individuals as insulin-sensitive (IS) and 30 as insulin-resistant (IR) (Fig. 6C).
  • SSPG steady-state plasma glucose
  • I insulin-sensitive
  • IR insulin-resistant
  • Micro-biotypes like enterotypes in the stool microbiome, are present in all body sites, with their community structure predominantly influenced by specific taxa.
  • the stool microbiome primarily exhibited a gradient of abundance distributions between Bacteroidetes and Firmicutes, except for a few samples with high Prevotella.
  • the recently identified core genus Phocaeicola had minimal impact on the overall Bacteroidetes/Firmicutes gradient, but samples with high Phocaeicola and Bacteroides were clearly separated.
  • Fig. 7A The oral microbiome was primarily composed of Prevotella, Streptococcus, Veillonella, Haemophilus, Neisseria, and Leptotrichia.
  • Intraclass (intra-individual) correlation coefficient (ICC) analysis confirmed that microbial personalization is more pronounced at the ASV level than at broader taxonomic resolutions (Fig. 7C), highlighting stronger individualization with finer taxonomy resolution.
  • ICC Intraclass correlation coefficient
  • the DMI irrespective of relative abundance, was high in the stool microbiome (Fig. 8B), particularly within the Bacteroidetes phylum (Fig. 8C), possibly due to its pronounced adaptive evolution and substantial colonization resistance. Furthermore, the stool microbiome had the lowest FS, whereas oral and nasal microbiomes shared greater similarity within households (Fig. 9A), likely due to common living environments or direct microbiome exchanges.
  • the DMI and FS metrics for each specific genus offer an overarching perspective on microbial host specificity. Meanwhile, they provide crucial insights into the taxonomic composition of the community and potential influences of environmental factors on the host's microbiome. Additionally, the DMI measurements provide important ecological characteristics about micro-biotypes, including 'enterotype' in stool microbiome or 'cutotypes' in skin microbiome.
  • the longitudinal data also enabled tracking of microbiome stability over time by quantifying the dissimilarity between sample pairs in relation to collection date-intervals, which was reported to be higher in IBD-related gut dysbiosis.
  • Our analysis revealed that the stool microbiome changed more slowly over time, with the nasal site exhibiting the fastest rate of change (p-value ⁇ 0.001 ) (Fig. 10B).
  • IR individuals showed significantly lower stability in stool and skin microbiomes than IS individuals, as evidenced by linear mixed models (Stool p-value'. 1.82 x 10-06, Skin p-value: 2.84 x 10-12), corroborating our findings of greater microbial abundance disparities in these two body sites between IR and IS participants. (Fig. 7M).
  • dysbiosis can manifest differently across body sites, potentially through site-specific mechanisms. For instance, IR-related temporary disruptions in the stool microbiome seem to be characterized by a loss of core microbiome species producing short chain fatty acids. In contrast, in less complex skin and nasal microbiomes, dysbiosis might involve the acquisition of opportunistic pathogenic species such as Peptoniphilus.
  • cytokines associated with epithelial/endothelial growth and vascular inflammation /.e., EGF, VCAM-1 , IL-22
  • IL-1 family members /.e., IL-1 b, IL-1 Ra
  • leptin demonstrated the highest number of interactions with the microbiome.
  • cytokines including IL-1 b, IL-1 Ra, MCP3(CCL-7), and IL-23 as the strongest correlative cytokines with the microbiome via effect size (Fig. 13A).
  • the clear pattern of body-site- specific interactions may contribute to the taxa niche-specificity.
  • Moraxella shows a negative correlation with 23 cytokines on the skin, yet only with three in the nasal cavity. This reduced microbe-immune interaction in the nasal cavity may explain the higher prevalence of Moraxella in nasal. [0206]
  • cytokines appear to play a pivotal role in shaping an individual's core microbiome and in curbing the colonization of non-commensal bacteria, including many from the Proteobacteria phylum.
  • Proteobacteria This correlation is largely driven by Proteobacteria rather than Firmicutes, as Proteobacteria consistently constitutes a larger segment of the opportunistic microbiome compared to the core microbiome (Fig. 13B).
  • Proteobacteria often carry potent lipopolysaccharides (LPS) and instigate the downstream immune cascade.
  • LPS lipopolysaccharides
  • cytokines and chemokines may be linked with the observed richness of bacterial genera, in addition to their relative abundance.
  • leptin and GM-CSF both strongly associated with BMI (Fig. 13D), show the strongest overall correlation with richness.
  • the Microbiome is Highly Connected with Host Molecules: Unraveling the Role in Insulin Resistance and Inflammation
  • microbiome-host molecule interaction network partitions according to internal molecular composition rather than body sites of the microbiome (Fig. 15A), suggesting that certain taxa are primarily influenced by internal molecules interactions over influencing host molecular composition.
  • three enterotypes driving taxa Bacteroides, Prevotella, and Unclassified Ruminococcaceae, exhibit a clear preference for the lipidome, proteome, and metabolome regions, respectively (Fig. 15A).
  • the close association between Prevotella and proteins has been previously documented, as well as the relationship between Bacteroides and lipids.
  • our findings reinforce this understanding to include both additional taxa and multiple body sites, suggesting that these connections are not only site- and taxa-specific but also systemic and robust.
  • the skin and nasal microbiomes are less individualized, possibly owing more to individual environmental exposure.
  • environmental factors such as season
  • a decrease in stool microbiome richness in late summer corresponded with previous findings of worsened insulin sensitivity during this period.
  • a decline in the richness and evenness of the oral microbiome from late summer through winter suggesting a potential influence of environmental factors like the availability of fresh food and changes in sunlight durations.
  • changes in humidity from January to April might explain the richness increase in skin and decrease in nasal microbiome.
  • Microbial individuality and stability are closely related to the host immune system, which is well known to interact with microbes at multiple body sites. This interaction modulates both the colonization of microbes, as well as their functional benefits (e.g., epithelium barrier integrity maintainance).
  • Our Bayesian model reveals that the interactions between the microbiome and cytokines, while present, are subtle. Certain genera exhibit an approximate 1.5-fold change in response to cytokine variations. The interaction between inflammatory cytokines and the microbiome demonstrated that low prevalence genera (7.e. , stool Proteobacteria) are likely reduced during host inflammatory events. We also revealed a systematic relationship between cytokines and the genera complexity of the microbiome at each body site.
  • the diversity within a subset of the skin microbiome positively correlates, while that within the stool microbiome negatively correlates with the same group of cytokines.
  • the observed changes in diversity among IR individuals might be related to their unique cytokine profiles.
  • Insulin resistance appears to disrupt the intricate balance between the host and microbiome, demonstrated by an unstable, dysbiotic microbiome in IR individuals.
  • IR Insulin resistance
  • the systematic shift in microbiome prevalence indicates an entire microbial community's transformation instead of the abnormality of a few isolated members (Fig. 7L). This dysbiosis can potentially alter the complex hostmicrobiome interaction in IR subjects.
  • Stool samples were self-collected by participants and other samples were collected by study coordinators following iPOP study standard operating procedures (SOP), as adapted from HMP_SOP corresponding sections (HMP_MOP_Version12_0_072910). Briefly, retroauricular areas were rubbed with premoistened swabs under pressure for skin sampling, anterior nares for nasal sampling, and rear of the oropharynx for oral sampling. Samples are stored at -80 C immediately after arrival. Stool and nasal samples were further processed and sequenced in-house at the Jackson Laboratory for Genomic Medicine (JAX-GM, Farmington, CT, USA), while oral and skin samples were sent to uBiome (uBiome, San Francisco, CA, USA) for further processing.
  • SOP study standard operating procedures
  • RDP Ribosomal Database Project
  • Relative ASV abundance was determined by dividing the count associated with that taxon by the total number of filtered reads. Samples with depths below 1 ,000 reads were removed due to insufficient sequencing depths following the HMP consortium standard.
  • the average sample sequencing depth after quality control was 23,554 for stool microbiome, 74,515 for skin microbiome, 132,912 for oral microbiome, and 24,899 for nasal microbiome.
  • Lipid extraction and data generation was performed as follows. Briefly, complex lipids were extracted from 40 pL of EDTA-plasma using a mixture of methyl tertiary-butyl ether, methanol, and water, followed by biphasic separation. Lipids were then analyzed using the Lipidyzer platform, which consists of a DMS device (SelexION Technology, Framingham, MA, USA) and a QTRAP 5500 (Sciex). Lipids were quantified using a mixture of 58 labeled internal standards provided with the platform (cat# 5040156, Sciex, Redwood City, CA, USA), and lipid abundances were reported in nmol/g.
  • lipidomics data were divided into six clusters using Fuzzy c-means clustering (R package “Mfuzz” (version 3.15)). For the lipids within each cluster, correlations were computed, and lipids with high correlative relationships (Spearman correlation > 0.8 and BH-adjusted p-values ⁇ 0.05) were grouped into the same module.
  • Community analysis (‘fastgreedy.community’ function from R package “igraph” (v1.3.5)) was employed to detect the modules. For lipids not assigned to any of the modules, their original lipid species annotations were used for downstream analysis.
  • Untargeted metabolic profiling was performed using a broad-spectrum LC-MS platform using a combination of reverse-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) separations and high-resolution MS. Briefly, plasma metabolites were extracted following solvent precipitation using a mixture of ice-cold acetone, acetonitrile, and methanol (1 :1 :1 , v/v).
  • RPLC reverse-phase liquid chromatography
  • HILIC hydrophilic interaction liquid chromatography
  • Hydrophilic metabolites were separated on a ZIC-HILIC (2.1 x 100 mm, 3.5 pm, 200 A; Merck Millipore) while hydrophobic metabolites were separated on a Zorbax SBaq columns (2.1 x 50 mm, 1.7 pm, 100 A; Agilent Technologies). Data was acquired on a Thermo Q Exactive plus mass spectrometer for HILIC and a Thermo Q Exactive mass spectrometer for RPLC. Raw data were processed using Progenesis QI (v2.3, Nonlinear Dynamics, Waters) and metabolites were formally identified by matching fragmentation spectra and retention time to analytical-grade standards or matching experimental MS/MS to fragmentation spectra in publicly available databases. A total of 726 annotated metabolites were retained for downstream analysis.
  • Progenesis QI v2.3, Nonlinear Dynamics, Waters
  • Plasma proteins were characterized using a TripleTOF 6600 system (Sciex) via liquid chromatography-mass spectrometry (LC-MS) with SWATH acquisition.
  • LC-MS liquid chromatography-mass spectrometry
  • 8-pg of tryptic peptides, derived from undepleted plasma were loaded onto a ChromXP C18 column (0.3 x 150 mm, 3 pm, 120 A, Sciex). The separation of peptides was achieved through a 43-minute gradient ranging from 4% to 32% B.
  • High sensitivity MS/MS mode was utilized to construct variable Q1 window SWATH Acquisition methods (100 windows) with Analyst TF Software (v1 .7).
  • Scoring of peak groups was performed with PyProphet (v2.0.1 ) and alignment of peak groups with TRIC, each adhering to stringent confidence thresholds (1 % FDR at peptide level; 10% FDR at protein level). The abundance of proteins was calculated as the cumulative sum of the three most abundant peptides.
  • Luminex Multiplex Assays for Targeted Cytokine, Chemokine, and Growth Factors The evaluation of circulating cytokines, chemokines, and growth factors was undertaken employing established procedures from the Stanford Human Immune Monitoring Center (HIMC). Specifically, EDTA-plasma was scrutinized using a Human 62-plex Luminex multiplex assay, consisting of conjugated antibodies (Affymetrix, Santa Clara, California). The raw data obtained from the assay were normalized against the median fluorescence intensity (MFI) value. Subsequently, variance stabilizing transformation (VST) was applied to the data to eradicate the batch effect. Measurements featuring background noise (CHEX) exceeding five standard deviations from the mean (mean ⁇ 5 x SD) were omitted from the data.
  • MFI median fluorescence intensity
  • the process of data collection for the exposome and associated environmental elements proceeded accordingly.
  • the chemical exposome was sampled using the RTI MicroPEM V3.2 personal exposure monitor (RTI International, Research Triangle Park, NC, USA) for two participants.
  • the MicroP EM an active air sampling apparatus, operates by circulating air at a rate of 0.5 L/min. It was modified to house a customized cartridge containing 200 mg of zeolite adsorbent beads (Sigma 2-0304, Sigma-Aldrich Corp., St. Louis, MO USA) positioned at the airflow's termination to gather both hydrophobic and hydrophilic compounds. Each sampling session spanned approximately five days. Postsession, the cartridge was detached and preserved at -80 °C until subsequent processing.
  • a subset of eligible consenting participants underwent an Insulinsuppression Test (1ST), as a measure of insulin-mediated glucose uptake, to evaluate the insulin sensitivity status.
  • 1ST Insulinsuppression Test
  • participants Following a 12-hour overnight fast, participants were administered an infusion comprising 0.27 ug/m2 min of octreotide, 25m U/m2 min of insulin, and 240 mg/m2 min of glucose over a three-hour period during their visit to Stanford's Clinical and Translational Research Unit (CTRU). Blood samples were procured at ten-minute intervals during the final half-hour of the infusion, resulting in a total of four blood draws. These samples were analyzed to determine plasma glucose and insulin levels.
  • CTRU Clinical and Translational Research Unit
  • Clinical lab tests were performed at the Stanford Clinical Lab.
  • the test includes a metabolic panel, complete blood count panel, glucose, HbA1 C, insulin measurements, hsCRP, IgM, lipid panel, kidney panel, liver panel.
  • Intraclass correlation Coefficient was calculated from Linear Mixed Models, in which we modeled random intercepts but a fixed slope, allowing different personal levels between individuals. We first linearly transformed each analyte (when applicable) and standardized the total variation to 1 before applying ‘Imer’ function from R package “Ime4 (V1.1-30)”, with the formula as:
  • Exp was the linearly transformed and standardized values of each analyte
  • Days was the length of time individuals participated in the study
  • SubjectID was the subject ID associated with each participant.
  • the Bray-Curtis (BC) distance was used to quantify the degree of similarity between two microbiome samples, with the ASV serving as the unit for calculating dissimilarity for the complete microbiome sample or for specific taxa. Similarity metrics were calculated pairwise for both intra-individual and inter-individual comparisons. A permutation test was employed to estimate the null distribution while accounting for the varying sample sizes of each participant. The null hypothesis being tested was that there is no difference between intra-individual and inter-individual distances.
  • test statistic was calculated as the mean difference in BC distances between intra-individual and inter-individual comparisons. To estimate the null distribution, all sample labels were randomly permuted, and the BC distances were computed pairwise. This process was repeated 10,000 times, generating a null distribution of test statistics.
  • P-values were then calculated by determining the proportion of permuted test statistics that were at least as extreme as the observed test statistic. In the case of multiple comparisons, such as for different microbial genera, p-values were adjusted using the BH procedure to control the false discovery rate. Statistical significance was determined using a threshold of BH adjusted p-value ⁇ 0.1 .
  • DM I Degree of Microbial Individuality
  • DMI i BC inter-individual - BC intra-individual
  • the DMI score was multiplied by the average relative abundance of each genus for a given individual. This generated a weighted DMI (abundance_dmi) that represented the product of the DMI score and the genus's relative abundance.
  • the total DMI for each individual was computed by summing these weighted DMI values across all genera. This approach offered a comprehensive measure of the overall DMI per individual, accounting for the contribution of each genus weighted by its relative abundance in the individual's microbiome.
  • the FS represents the relative influence of a shared environment on the inter-individual dissimilarity of a given genus within cohabitating pairs.
  • BC inter-individual inter-individual
  • intra-individual BC distance BC intra-individual
  • This formula normalizes the FS to a scale that allows for comparisons across families and non-families.
  • An FS of 0 indicates that the shared living environment has no impact on inter-individual dissimilarity, while an FS of 1 suggests that living in the same environment causes inter-individual dissimilarity to resemble intra-individual dissimilarity.
  • the microbiome genera were categorized as the core microbiome, opportunistic microbiome, and middle group based on their longitudinal prevalence. Calculation of prevalence was based on the presence or absence of reads from each sample. For each sample, the relative abundance of each genus was first transformed to 1 if it was greater than 0; then, the proportion of 1 for each genus in each participant was determined as the longitudinal prevalence. Then the genera were assigned to a group based on their longitudinal prevalence: core microbiome: longitudinal prevalence > 80%; middle group: 20% ⁇ longitudinal prevalence ⁇ 80%; opportunistic microbiome: longitudinal prevalence ⁇ 20%.
  • Mi is a vector of the genus-level microbe relative abundances for each participant /
  • Xi is the design matrix for the fixed effects
  • Each row of matrix Xi contains the terms (1 ) time (days post-study start), Di, and (2) cytokine measurements, Yi, from 1 to n.
  • Zi is the random effects design vector of 1 ’s denoting a random intercept
  • bi is a scalar for each participant
  • d is a zero-centered error term.
  • the beta represents the effect size.
  • the determination of significance is based on the commonly practiced use of credible intervals derived from Markov Chain Monte Carlo (MCMC) sampling.
  • Correlation network analysis was conducted to construct a network between the microbiome (from stool, skin, oral, and nasal samples) and internal multi-omics data (proteome, metabolome, and lipidome) from plasma. Initially, time points with unmatched collection dates for each pair of microbiome and internal omics data were excluded. Subjects with fewer than five samples for a specific microbiome type were also removed from the corresponding correlation analysis.
  • microbiome data which included relative abundance or observed ASV richness at the genus level, was processed by retaining only genera detected in at least 10% of all samples.
  • Centered log ratio (CLR) normalization was applied to address compositionality in microbiome data using the R package "compositions" (Version 2.0-4). Proteome, metabolome, and lipidome module data were Iog2 transformed.
  • a mediation analysis was conducted to investigate the potential influence of microbiomes from stool, skin, oral, and nasal sources on phenotypes through internal multi-omics data, including proteome, metabolome, lipidome, and cytokine.
  • Phenotype data were obtained via clinical laboratory tests of plasma samples.
  • the linear regression model from R package "mediation" was employed for the mediation analysis.
  • pairs with significant Average Causal Mediation Effects ACME, p-values ⁇ 0.05 were reported, representing the microbiome's impact on phenotype measurements through internal multi-omics.
  • the PVCA is a combination of the principal component analysis and variance components analysis, which were originally employed to assess batch effects in microarray data and widely used for microbiome related variance decompositions.
  • the season was determined by subtracting the date of collection from the first day of the year (from 1 -365 days). Each sample's participant ID and season were then entered into the PVCA as variables. Then, the "ggtern (Version 3.3.5)" R package was used to visualize the data.
  • Exposome and Diet Data Analysis' To investigate the influence of exposome and diet data on the microbiome from different body sites, exposome data (chemical and environmental) were collected and processed as previously described. Diet data were collected and detailed in the methods section above. As an example, the analysis process for exposome chemical data is described below.
  • Microbiome data were normalized using the centered log ratio (CLR, “clr” function from R package “compositions”), and exposome data were Iog2- transformed and auto-scaled.
  • Principal component analysis (PCA) was performed on both microbiome and exposome data. Principal components (PCs) from the microbiome and exposome were further analyzed, with PCs accounting for over 80% of cumulative explained variation being included.
  • a linear regression model was constructed using PCs of microbiome data as the dependent variable (Y) and corresponding exposome PCs as the independent variable (X).
  • the R2 value was extracted to represent the exposome's contribution to microbiome data.
  • the same method was applied to evaluate the dietary effect on the microbiome from four body sites.
  • the response variable 'genera' represents the z-score normalized microbial relative abundance.
  • the fixed effects components include the status of insulin sensitivity (IRIS) and a cyclic cubic spline smoother for the Time variable, encapsulating potential cyclical patterns across the year (from 0 to 366).
  • IRIS insulin sensitivity
  • a cyclic cubic spline smoother for the Time variable encapsulating potential cyclical patterns across the year (from 0 to 366).
  • (1 ⁇ Subject_ID) includes a random intercept for each subject, to account for within-subject correlation.
  • the model was fitted using the Restricted Maximum Likelihood (REML) method for robust estimation of smoothing parameters in a complex and unbalanced design and incorporated the use of 'ImeControl' function from the 'nlme' package in R to handle the optimization process of the mixed-effects models. This was conducted by specifying 'optim' as the optimizer for the model fit.
  • the resulting model provides insight into the temporal dynamics of gene expression and its relationship with insulin sensitivity status (IRIS), considering the random effects associated with each subject. The graphical representation of these models for each genus and p value for smooth terms were saved for further exploration.
  • the infection status was classified into longitudinal categories: pre-healthy (-H) state, event early (EE) state, event late (EL) state, recovery (RE) state, and post-healthy (+H) state.
  • the pre-healthy state comprised the healthy baselines observed within 186 days preceding the onset of the infection event.
  • the EEs state was characterized by visits occurring between day 1 and day 6 of the event.
  • the EL state spanned visits on days 7 to 14 since the onset of the event.
  • the recovery state included visits within the 15-40- day period since the event's inception, and the post-healthy state encompassed visits within the 186 days following the event.
  • 'genera' represents the z-score normalized microbial relative abundance
  • IRIS indicates the insulin sensitivity status of each participant
  • 'lnfection_status' is a smoothing function of the longitudinal infection states with cyclic cubic regression splines.
  • the term 1 ⁇ event)' is a random intercept for each infection event.
  • VCAM1 Anaerostipes 0.000289651 stool
  • VCAM1 Dorea 0.000240125 stool
  • IL1 B Agathobacter -0.009659334 stool IL1 B Barnesiella 0.015296511 stool IL1 B Butyricimonas -0.03082052 stool IL1 B Cloacibacillus 0.030460049 stool IL1 B Clostridium_sensu_stricto 0.015346376 stool IL1 B Collinsella -0.029731761 stool IL1 B Desulfovibrio -0.061177261 stool IL1 B Faecalibacterium -0.006410001 stool IL1 B Lachnospira -0.010507807 stool IL1 B Pre vote Ila -0.036501256 stool IL1 B Roseburia -0.00537989 stool IL1 B Slackia -0.027556117 stool IL1 B Subdoligranulum -0.006387575 stool IL1 B Sutterella -0.039244818 stool
  • PDGFBB Unclassified_Muribaculaceae -0.022520768 stool
  • GMCSF Dolosigranulum -0.123327575 skin
  • PDGFBB Unclassified_Streptophyta 0.004520648 skin

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Abstract

L'invention concerne des systèmes et des procédés d'évaluation d'interactions microbiome-hôte. Il a été démontré que plusieurs genres microbiens sont bénéfiques pour une variété de troubles médicaux et de phénotypes. Des genres microbiens peuvent être utilisés en tant que composition pour offrir un avantage particulier. Des genres microbiens peuvent également être administrés à un individu afin de modifier l'expression de produits géniques tels que des cytokines. Des genres microbiens peuvent en outre être administrés à un individu en tant que traitement d'un trouble médical.
PCT/US2024/033663 2023-06-12 2024-06-12 Systèmes et procédés d'évaluation du microbiome et traitements associés Pending WO2024259003A1 (fr)

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US20220118030A1 (en) * 2019-02-22 2022-04-21 Evelo Biosciences, Inc. Bacterial membrane preparations

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US20220118030A1 (en) * 2019-02-22 2022-04-21 Evelo Biosciences, Inc. Bacterial membrane preparations

Non-Patent Citations (2)

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T. PESSI; Y. SÜTAS; M. HURME; E. ISOLAURI: "Interleukin‐10 generation in atopic children following oral Lactobacillus rhamnosus GG", CLINICAL & EXPERIMENTAL ALLERGY, vol. 30, no. 12, 7 July 2008 (2008-07-07), UK , pages 1804 - 1808, XP071884025, ISSN: 0954-7894, DOI: 10.1046/j.1365-2222.2000.00948.x *
ZHOU ZHOU, YU LINGZI, CAO JIAJIA, YU JIAMING, LIN ZHIBO, HONG YI, JIANG SIBO, CHEN CONG, MI YULING, ZHANG CAIQIAO, LI JIAN: "Lactobacillus salivarius Promotion of Intestinal Stem Cell Activity in Hens Is Associated with Succinate-Induced Mitochondrial Energy Metabolism", MSYSTEMS, vol. 7, no. 6, 20 December 2022 (2022-12-20), pages 1 - 14, XP093255333, ISSN: 2379-5077, DOI: 10.1128/msystems.00903-22 *

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