WO2024259003A1

WO2024259003A1 - Systems and methods for assessment of microbiome and treatments thereof

Info

Publication number: WO2024259003A1
Application number: PCT/US2024/033663
Authority: WO
Inventors: Xin Zhou; Michael P. Snyder; Xin Chen; Mark M. Davis
Original assignee: Leland Stanford Junior University
Current assignee: Leland Stanford Junior University
Priority date: 2023-06-12
Filing date: 2024-06-12
Publication date: 2024-12-19
Anticipated expiration: 2025-12-12

Abstract

Systems and methods for assessing microbiome-host interactions are described. Several microbial genera have been found to provide benefit for a variety of medical disorders and phenotypes. Microbial genera can be provided as a composition to provide a particular benefit. Microbial genera can be administered to an individual to alter expression of gene products such as cytokines. Microbial genera can be administered to an individual as a treatment for a medical disorder.

Description

SYSTEMS AND METHODS FOR ASSESSMENT OF MICROBIOME AND TREATMENTS THEREOF

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application Ser. No. 63/507,687, entitled “Systems and Methods for Assessment of Microbiome and Treatments Thereof,” filed June 12, 2023, the disclosure of which is hereby incorporated by reference in its entirety.

FIELD OF THE DISCLOSURE

[0002] 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.

BACKGROUND

[0003] 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. SUMMARY

[0004] In some aspects, 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.

[0005] In some aspects, 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.

[0006] In some aspects, 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.

[0007] In some aspects, 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.

[0008] In some aspects, 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.

[0009] In some aspects, 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.

[0010] In some aspects, 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.

[0011] In some aspects, 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.

[0012] In some aspects, 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.

[0013] In some aspects, 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.

[0014] In some aspects, 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.

[0015] In some aspects, 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.

[0016] In some aspects, 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.

[0017] In some aspects, 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.

[0018] In some aspects, 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.

[0019] In some aspects, 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.

[0020] In some aspects, 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.

[0021] In some aspects, 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.

[0022] In some aspects, 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.

[0023] In some aspects, 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.

[0024] In some aspects, 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. [0025] In some aspects, 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. [0026] In some aspects, the techniques described herein relate to a method, wherein the one or more microbial genera are to be orally administered.

[0027] In some aspects, 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.

[0028] In some aspects, the techniques described herein relate to a method, wherein the one or more microbial genera are to be rectally administered.

[0029] In some aspects, 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.

[0030] In some aspects, 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.

[0031] In some aspects, 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. [0032] In some aspects, 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.

[0033] In some aspects, 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.

[0034] In some aspects, the techniques described herein relate to a method, wherein the Haemophilus is to be topically administered.

[0035] In some aspects, 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.

[0036] In some aspects, 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.

[0037] In some aspects, the techniques described herein relate to a method, wherein the one or more microbial genera are to be orally administered.

[0038] In some aspects, 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.

[0039] In some aspects, the techniques described herein relate to a method, wherein the one or more microbial genera are to be rectally administered.

[0040] In some aspects, 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. [0041] In some aspects, 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.

[0042] In some aspects, 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.

[0043] In some aspects, the techniques described herein relate to a method, wherein the one or more microbial genera are to be topically administered.

[0044] In some aspects, 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.

[0045] In some aspects, 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.

[0046] In some aspects, 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.

[0047] In some aspects, 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.

[0048] In some aspects, 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.

[0049] In some aspects, 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.

[0050] In some aspects, 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.

[0051] In some aspects, 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. [0052] In some aspects, 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.

[0053] In some aspects, 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.

[0054] In some aspects, the techniques described herein relate to a method, wherein the one or more microbial genera is: Barnesiella, Frisingicoccus, or Butyrivibrio.

[0055] In some aspects, 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.

[0056] In some aspects, 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.

[0057] In some aspects, the techniques described herein relate to a method, wherein the one or more microbial genera are to be orally administered.

[0058] In some aspects, 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.

[0059] In some aspects, the techniques described herein relate to a method, wherein the one or more microbial genera are to be rectally administered.

[0060] In some aspects, 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.

[0061] In some aspects, 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.

[0062] In some aspects, 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.

BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The description and claims will be more fully understood with reference to the following figures and data graphs, which are presented as exemplary embodiments of the disclosure and should not be construed as a complete recitation of the scope.

[0064] Figure 1 provides a flow diagram of an example of a method to assess microbiome-host interactions to infer treatments.

[0065] Figure 2 provides a flow diagram of an example of a co-culture organoid assay to assess microbiome-host interactions.

[0066] Figure 3 provides an example of a method to culture microbial isolates from a microbiome, which can be utilized in co-culture organoid assays.

[0067] Figures 4A to 4E provide results of an assessment of the immune response of an organoid culture in response to six different microbial samples.

[0068] 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.

[0069] 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. [0070] 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. Significance for richness (ACE) and evenness (Pielou) is reported for each site: stool (ACE p < 0.0001 , Pielou p = 0.4), skin (ACE p = 0.016, Pielou p = 0.016), oral (ACE p < 0.0001 , Pielou p = 0.017), and nasal (ACE p = 0.0003, Pielou p = 0.39). Figure 7G provides Variance in Microbiome Explained by Exposome. Figure 7H provides Shifts in Diversity and Evenness Between IR and IS Individuals. Figure 7I provides Prevalence by Relative Abundance Plot. 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.

[0071] 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.

[0072] 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. For DMI: Nasal vs. Oral: p=0.29263, Nasal vs. Skin: p=0.38261 , Oral vs. Skin: p=0.00016, Nasal vs. Stool: p=0.000027, Oral vs. Stool: p=0.000002, and Skin vs. Stool: p<0.000001. For FS: Nasal vs. Oral: p=0.076, Nasal vs. Skin: p=0.001 , Oral vs. Skin: p=0.033, Nasal vs. Stool: p<0.001 , Oral vs. Stool: p<0.001 , and Skin vs. Stool: p=0.000011 . Figure 9B provides Comparative Analysis of Microbiome Individuality Across Different Insulin Sensitivity States. The Degree of Personalization, a relative abundance weighted sum up of Degree of Microbial Individuality, calculated for each individual at the four body sites was compared between individuals of different insulin sensitivity states. The p-value from Two-Sided Wilcoxon rank sum tests between insulin sensitive and insulin resistant individuals was shown. IR: insulin resistant; IS: insulin sensitive.

[0073] 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 .

[0074] 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. Significance levels are annotated as follows: * for adjusted p-values < 0.05, ** for adjusted p-values < 0.01 , *** for adjusted p-values < 0.001.**** for adjusted p-values < 0.0001. 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.

[0075] 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.

[0076] 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.

[0077] 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 [0078] 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.

[0079] Figure 16 provides data on microbiome-host correlations. Distribution of Correlation Coefficients for Microbiome Interactions Across Four Body Sites.

[0080] 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.

BRIEF DESCRIPTION OF THE TABLES

[0081] The description and claims will be more fully understood with reference to the following tables, which are presented as exemplary embodiments of the disclosure and should not be construed as a complete recitation of the scope.

[0082] 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.

[0083] 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.

[0084] Table 3 provides circulatory clinical phenotypes assessed and their abbreviations. DETAILED DESCRIPTION

[0085] Turning now to the drawings and data, the various embodiments of the disclosure are related to assessment of microbiome-host interactions and therapeutic applications thereof. In many embodiments, associations between microbial genera of a microbiome and their effect upon host gene expression are exploited to assess and/or to treat individuals, which may be useful for various medical disorders. In several embodiments, 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. In many embodiments, a supplement and/or treatment is associated with a particular health benefit, such as improvement of a clinical phenotype or a medical condition.

[0086] In several embodiments, a biological sample (e.g., blood 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. In many embodiments, 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. Utilizing the gene product data and/or the microbial genera data, 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. In several embodiments, the individual’s microbiome is altered by administering beneficial microbial genera.

[0087] As described in detail herein, it is now known that particular microbial genera can specifically alter a host’s gene expression, especially genes related to immune response and inflammation. Provided in Table 1 is a list of genes whose expression levels are significantly correlated with various microbial genera, establishing a linkage between microbiome composition and the host’s response to that composition. It has been further discovered that administration of microbial genera can induce specific expression of a host’s genes (see Examples and Data herein).

[0088] 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.

[0089] In several embodiments, 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. In many embodiments, 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.

[0090] Several medical disorders are known to develop at least in part by unhealthy expression of immune system modulators, cytokines, chemokines, hormones, growth factors, and other signaling molecules. These medical disorders include (but are not limited to) autoimmune disorders (including autoinflammatory disorders), cardiovascular disorders, mental health disorders, and metabolic disorders. These medical disorders have also been linked to altered microbiomes of the host. Based on the discovery that expression of these genes is significantly correlated and influenced by particular microbial genera, assessments and treatments for these disorders can be prescribed. In several embodiments, a patient can be administered bacterial genera as a probiotic supplement, a prophylaxis, or a treatment for a medical condition, which can be prescribed by a clinical assessment. In some embodiments, a microbial genus or a combination of microbial genera that are beneficial to a healthier condition (e.g., healthy clinical phenotype) can be administered to as a probiotic supplement to an individual, with or without any clinical diagnostic assessment performed. In some embodiments, 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. In some embodiments, 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. In some embodiments, 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.

[0091] To better understand microbial genera effects on a host, in accordance with several embodiments, a co-culture assay comprising an immune responsive organoid and microbial genera can be utilized. In many embodiments, 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. Examples of immune responsive organoids found to work well in this system are tonsil organoids, lymph node organoids, and spleen organoids. In some embodiments, tonsil tissue, lymph node tissue, or spleen tissue is extracted from a human donor and cultured. In some embodiments, an immune responsive organoid is developed via differentiation of pluripotent cells in culture. Immune responsive organoids can be challenged with a bacterial isolate. In some embodiments, the immune response (e.g., cytokine expression, immune cell activation) of an immune responsive organoid culture is assessed. In some embodiments, gene expression products (e.g., RNA, proteins/peptides) of an immune responsive organoid culture are assessed. Assessment of Gene Products to Infer Microbiome Interactions

[0092] Several embodiments of the disclosure are directed to exploiting the correlation between microbial genera, host gene product expression, and medical condition. In several embodiments, 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. In many embodiments, gene product expression levels can be utilized to determine which microbial genera would be beneficial to be administered to the individual. In some embodiments, the composition of a patient’s microbiome is assessed to determine which microbial genera would be beneficial to be administered to the individual. In several embodiments, a patient is administered a beneficial microbial genus or a combination of beneficial microbial genera, which may improve the health of the individual.

Detection of Gene Products Associated with Microbial Genera

[0093] 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. In accordance with numerous embodiments, the method is utilized to determine which microbial genera could be administered to alter host gene expression and thus improve recipient health.

[0094] 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.

[0095] An individual (or patient) can be any individual for assessing the health as related to their microbiome. In some implementations, 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. Examples of 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. In some implementations, 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.

[0096] The biological sample derived from an individual can be any biological sample that would have gene products. Examples of 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). In some implementations, 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. For example, 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) are useful in assessing a number of microbiomes, including microbiomes of the gut, nasal cavity, oral cavity, and skin (see addendum and Table 1 ).

[0097] 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 .

[0098] Method 100 can optionally perform (103) a clinical assay to determine host dysbiosis or assess other clinical phenotypes. As described herein, 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. For assessing dysbiosis, the diversity and/or amount of microbiota within a microbiome sample can be assed. For assessing clinical phenotypes, a biological sample (e.g., blood, plasma, CSF) can be analyzed for circulating analytes. Other clinical assessments can also be performed that provide a phenotype that is correlated with particular microbiota genera.

[0099] In some implementations, 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. For example, it was found that 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.

[0100] 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. [0101] To identify the composition of a microbiome from a microbiome sample, 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).

[0102] 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).

[0103] 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. In other words, an administration of particular microbial genera can be utilized to correct gene product expression that is involved in the pathology of many medical disorders. In some embodiments, it is determined that one or more circulatory gene products are not within a healthy range. In some embodiments, it is determined that one or more circulatory gene products are too high or too low, which can be established based on a threshold.

[0104] In one example, it has been established that insulin sensitive individuals have relatively high levels of IL-17 and IL-22 circulating in plasma whereas type 2 diabetics that are insulin resistant have little to no circulating IL-17 and IL-22. Cytokines IL-17 and IL-22 have also been shown to be protective in leaky gut disorder. As can be inferred from Table 1 , genus Barnesiella within the stool microbiome is highly and positively correlated with circulating IL-17 and genera Frisingicoccus and Butyrivibrio are highly and positively correlated with circulating IL-22. Based on this information, 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).

[0105] And as noted, various clinical phenotypes are significantly correlated with microbiomes of the gut, nasal cavity, oral cavity, and skin. Table 2, for example, provides a list of circulating analytes that are correlated with various microbial genera. And thus clinical phenotypes can be utilized to assist in determining which microbial genera that would provide benefit to the individual.

[0106] In some embodiments, the benefit of certain microbial genera can be further established utilizing patient-derived immune responsive organoids. These organoids can be derived from a patient and utilized to assess a particular patient’s response to administration of particular microbial genera. For example, 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. Based on the results, particular combinations of microbial genera can be identified that best induce IL-17 and IL-22 production specific to that patient in order to determine a patient-specific combination of microbial genera to administer to the patient to treat insulin resistance. An example of a method for assessing immune responsive organoids with microbial genera is discussed further below in the description of Fig. 2.

[0107] 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.

[0108] 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.

[0109] While a specific example of a method for determining beneficial microbial genera for a patient are described above, one of ordinary skill in the art can appreciate that various steps of the process can be performed in different orders and that certain steps may be optional according to some embodiments of the description. As such, it should be clear that the various steps of the method could be used as appropriate to the requirements of specific applications. Furthermore, any of a variety of methods for determining beneficial microbial genera for a patient appropriate to the requirements of a given application can be utilized in accordance with various embodiments of the disclosure. Administration of Beneficial Microbial Genera

[0110] Various embodiments are directed towards a treatment comprising an administration of beneficial microbial genera. In some embodiments, based on the knowledge that the pathology of certain medical conditions is related to an altered expression of gene products, 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.

[0111] In several embodiments, a treatment regimen is administered for a medical condition. In these embodiments, a patient can be diagnosed as having a particular medical condition and based on this diagnosis, the patient can be administered microbial genera.

[0112] An example of a procedure for diagnosis and treatment of a medical condition can be as follows:

(a) diagnose an individual or determine that the individual has been diagnosed with a medical condition

(b) administer to the individual beneficial microbial genera based on the medical condition diagnosis.

[0113] In one example, the medical condition is type 2 diabetes, insulin resistance, leaky gut disease, and/or obesity.

[0114] A number of medical conditions can be treated with administration of microbial genera. Generally, 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.

[0115] Several 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). In some embodiments, 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 ). Accordingly, in some embodiments, an individual having psoriasis is administered one or more of: Finegoldia, Brevibacterium, Halomonas, Methylobacterium, Moraxella, Paracoccus, Dolosigranulum, Neisseria, Methylorubrum, Enhydrobacter, Peptoniphilus, or Roseomonas. And in some embodiments, an individual having psoriasis is administered one or more of: Methylobacterium, Moraxella, or Paracoccus.

[0116] Several 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). In some embodiments, 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. In some embodiments, 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.

[0117] 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 ). Accordingly, in some embodiments, 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.

[0118] 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). In some embodiments, 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 ). Accordingly, in some embodiments, an individual having systemic lupus erythematosus is administered one or more of: Blautia, Dialister, Fusicatenibacter, Collinsella, Subdoligranulum, or Senegalimassilia. And in some embodiments, an individual having systemic lupus erythematosus is administered one or more of: Blautia, Dialister, and Fusicatenibacter.

[0119] 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 ). Accordingly, in some embodiments, 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. [0120] 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). Furthermore, some cytokines have been shown to promote inflammation in atherosclerosis, including TNF-a and IL-6, and some cytokines have been shown to protect from atherosclerosis, including IL-10 (P. Tsioufis, et al., Int J Mol Sci. 2022 Dec 14;23(24): 15937, the disclosure of which is hereby incorporated by reference). In some embodiments, 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 ). Accordingly, in some embodiments, an individual having atherosclerosis is administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Hungatella, or Monoglobus. And in some embodiments, an individual having atherosclerosis is administered one or more of: Collinsella or Dialister.

[0121] 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). In some embodiments, 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 ). Accordingly, in some embodiments, 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.

[0122] 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). In some embodiments, 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 ). Accordingly, in some embodiments, 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. [0123] 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). Furthermore, some cytokines have been shown to circulating at high levels in schizophrenic patients, including TNF-a and IL-6 (B. Dawidowski, et al., J Clin Med. 2021 Aug 27; 10(17):3849, the disclosure of which is hereby incorporated by reference). In some embodiments, 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 ). Accordingly, in some embodiments, 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.

[0124] 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). In some embodiments, the individual is administered beneficial microbial genera that increases IL-17 or IL-22. In some embodiments, 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 ). Other genera were also found to increase IL-22 in the gut, including Anaerotignum, Cloacibacillus, Dysosmobacter, Gordonibacter, Negativibacillus, Phocea, Pseudoflavonifractor, Raoultibacter, and Turicibacter (Table 1 ). In some embodiments, 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.

[0125] Several 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 ). Accordingly, in some embodiments, 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. And in some embodiments, an individual having type 2 diabetes or obesity is administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, or Dialister.

[0126] 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. Meyer, et al., Autoimmun Rev. 2023 Jun;22(6): 103331 ; and X. Tang, et al., Mediators Inflamm. 2016;2016: 1927348; the disclosures of which are hereby incorporated by reference. In some embodiments, an individual having leaky gut syndrome (or any medical disorder commonly associated with 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. Accordingly, in some embodiments, an individual having leaky gut syndrome (or any medical disorder commonly associated with 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. And in some embodiments, an individual having leaky gut syndrome (or any medical disorder commonly associated with leaky gut syndrome) is administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, or Hungatella. [0127] 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. In these embodiments, 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. When expression of gene products is determined not to be within a healthy range (or on the borders of a healthy range), the individual can be administered microbial genera to adjust gene product expression back into a healthy range. As provided in Table 1 , many gene products within the circulatory system are significantly correlated with microbiome composition in the gastrointestinal tract (i.e., as indicated by stool microbiome), nasal cavity, oral cavity, and skin. Further, as described within the Examples and Data below, 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.

[0128] In several embodiments, an individual can be assessed for gene expression products and treated with microbial genera as follows:

(a) collect a biological sample of an individual

(b) determine that the gene product expression level

(c) based on the gene product expression level, administer to the individual one or more microbial genera to adjust gene product expression level.

In many embodiments, the method is performed as part as a screening procedure of the individual. In some embodiments, the individual has not been diagnosed with a particular medical condition prior to the screening assay. In some embodiments, the screening procedure is performed to determine gene product expression associated with a particular medical condition. In some embodiments, 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.

[0129] 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. And in some embodiments, a biological sample (e.g., blood, plasma, CSF) is assessed for one or more of the following gene products: 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-J3, or TNF-a.

[0130] 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 ). [0131] 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. To reduce levels of INF- y, an individual can be administered one or more of: Blautia, Dialister, or Fusicateni bacterio the gastrointestinal tract. To reduce levels of circulatory IL-1 p, 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. To reduce levels of circulatory IL-6, an individual can be administered one or more of: Collinsella, Dialister, or Fusicatenibacter to the gastrointestinal tract. To reduce levels of circulatory IL-12, an individual can be administered one or more of: Butyricimonas, Collinsella, or Fusicatenibacter to the gastrointestinal tract. To reduce levels of circulatory IL-17, an individual can be administered one or more of: Dialister, Subdoligranulum, or Senegalimassilia to the gastrointestinal tract. To reduce levels of circulatory IL-23, an individual can be administered one or more of: Butyricimonas, Dialister, Holdemanella, or Senegalimassilia to the gastrointestinal tract. To reduce levels of circulatory MCP-1 , an individual can be administered one or more of: Blautia, Desulfovibrio, Dialister, or Slackia to the gastrointestinal tract. To reduce levels of circulatory RANTES, an individual can be administered one or more of: Butyricimonas, Collinsella, Holdemanella, or Lawsonibacter to the gastrointestinal tract. To reduce levels of circulatory TNF-a, an individual can be administered one or more of: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, or Frisingicoccus to the gastrointestinal tract.

[0132] To increase levels of circulatory BDNF, an individual can be administered one or more of: Barnesiella, Eggerthella, Lachnospira or Parabacteroides to the gastrointestinal tract. To increase levels of circulatory EGF, an individual can be administered one or more of: Anaerostipes, Barnesiella, Eggerthella, Intestinibacter, Neglecta, Parabacteroides, or Romboutsia to the gastrointestinal tract. To increase levels of circulatory IL-10, an individual can be administered one or more of: Hungatella or Monoglobus to the gastrointestinal tract. To increase levels of circulatory IL-17, an individual can be administered one or more of: Adlercreutzia, Barnesiella, Butyricicoccus, Cloacibacillus, Dysosmobacter, or Faecalicatena to the gastrointestinal tract. To increase levels of circulatory IL-22, 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. To increase levels of circulatory TGF-[3, an individual can be administered one or more of: Acutalibacter, Akkermansia, Clostridium_sensu_stricto, Closthdium_XVIII, Flavonifractor, Holdemania, or Hungatella to the gastrointestinal tract.

[0133] In one example, 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.

[0134] Various embodiments are directed to treatment regimens based on an assessment of clinical phenotype readouts of common blood analytes. In these embodiments, a blood sample is acquired from an individual and examined for levels of blood analytes. 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. When it is determined that a patient does not have a healthy level of one or more analytes, the individual can be administered beneficial microbial genera that can correct the level of the one or more analytes.

[0135] In several embodiments, an individual can be assessed for circulating metabolite levels and treated with microbial genera as follows:

(a) collect a blood or serum sample of an individual

(b) measure the level of one or more analytes in the sample (c) determine that the level of one or more analytes is not within a healthy range (or is near the borderline)

(d) based on the level of one or more analytes, administer to the individual beneficial microbial genera to get the metabolite back into a healthy range.

In many embodiments, the method is performed as part as a screening procedure of the individual. In some embodiments, the individual has not been diagnosed with a particular medical condition prior to the screening assay. In some embodiments, the screening procedure is performed to determine analyte levels associated with a particular condition. In some embodiments, 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.

[0136] Various circulatory analytes that can be assessed are provided in Table 3. Accordingly, in some embodiments, a biological sample (e.g., blood, plasma, CSF) is assessed for one or more analytes listed within Table 3.

[0137] 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).

[0138] In one example, skin microbiomes comprising the microbial genus Haemophilus are shown to be negatively correlated with unhealthy cholesterol (e.g., LDL and non-HDL) (Table 2). Thus, an individual can be topically administered Haemophilus to reduce unhealthy cholesterol levels. When 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.

[0139] In one example, gut microbiomes comprising the microbial genus Akkermansia are shown to be negatively correlated with A1 C (Table 2). Thus, an individual can be orally or rectally administered Akkermansia to reduce A1 C levels. When the circulating analyte levels of an individual is assessed and indicates that the individual has high A1 C, the individual can be administered beneficial microbial genera comprising Akkermansia. [0140] 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.

[0141] 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. In these embodiments, 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. When it is determined that there is a lack of beneficial microbial genera, the individual can be administered beneficial microbial genera to introduce the beneficial microbial genera into the host’s microbiome.

[0142] In several embodiments, an individual can be assessed for microbiome composition and treated with beneficial microbial genera as follows:

(a) collect a microbiome sample of an individual

(b) determine that the composition of the microbiome lacks beneficial genera

(c) based on the composition of the microbiome, administer to the individual beneficial microbial genera to help promote a healthy microbiome composition.

In many embodiments, the method is performed as part as a screening procedure of the patient. In some embodiments, the patient has not been diagnosed with a particular medical condition prior to the screening assay. In some embodiments, the screening procedure is performed to determine microbiome composition associated with a particular medical condition. In some embodiments, 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.

[0143] In one example, 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. When 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. In some embodiments, 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.

[0144] 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. In these embodiments, immune responsive tissue (e.g., tonsil tissue) is extracted or otherwise generated from the patient and cultured. 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. When it is determined that 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. [0145] In several embodiments, an individual can be assessed via an immune responsive organoid and treated with microbial genera as follows:

(a) collect and culture or otherwise obtain a culture of immune responsive organoids of an individual

(b) contact the culture of immune responsive organoids with a culture supernatant of a microbial genus or a combination of microbial genera

(c) determine that the microbial genus or the combination of microbial genera yield a desired response by the immune responsive organoids

(d) based on the response by the immune responsive organoids, administer to the individual the microbial genus or the combination of microbial genera. In many embodiments, the method is performed as part as a screening procedure of the individual. In some embodiments, the individual has not been diagnosed with a particular medical condition prior to the screening assay. In some embodiments, the screening procedure is performed to determine microbiome composition associated with a particular medical condition. In some embodiments, 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.

[0146] In one example, 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. 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. In some embodiments, 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.

[0147] Various embodiments are directed to probiotic supplements based on microbial genera that have been determined to be beneficial. In these embodiments, a probiotic supplement can be produced or manufactured for administration to the general public. As discussed in greater detail below, 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. [0148] In one example, 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. Furthermore, gastrointestinal microbiomes comprising the microbial genus Haemophilus have been associated with low levels of cholesterol, especially unhealthy cholesterol (e.g., LDL and non-HDL). Accordingly, 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.

[0149] 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.

[0150] In some embodiments, 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.

[0151] In some embodiments, 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.

[0152] In some embodiments, 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.

[0153] In some embodiments, 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.

[0154] In some embodiments, 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.

[0155] In some embodiments, 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.

[0156] In some embodiments, 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.

[0157] In some embodiments, 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.

[0158] In some embodiments, 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.

[0159] Provided herein are methods of treating various medical disorders in a recipient. In some embodiments, a recipient is administered beneficial microbiota as described herein. In some embodiments, the amount of bacterium for treatment is a therapeutically effective amount of the bacterium. In some embodiments, the bacterium for treatment is lyophilized. In some embodiments, the bacterium for treatment is freeze- dried. In some embodiments, the bacterium for treatment is lyophilized or freeze-dried and subsequently reconstituted.

[0160] In some embodiments, a bacterium is administered in a therapeutically effective amount as part of a course of treatment. As used in this context, 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. In certain embodiments, 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.

[0161] 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.

[0162] 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.

[0163] An "effective amount" is an amount sufficient to effect beneficial or desired results. For example, 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. Moreover, 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.

[0164] 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 adjusted to maintaining a level of the compound within any desired and specified range.

[0165] 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. Examples of bacterial doses of colony forming units (CFU) include from about 1 *10⁵ to about 1 x10¹³, from about 1 x10⁶ to about 1 xi o¹⁰, from about 1 x ⁵ to about 1 x ⁷, from about 1 x ⁶ to about 1 xio⁸, from about 1 X10⁷ to about 1 xi o⁹, from about 1 xi o⁸ to about 1 x10¹⁰, from about 1 xi o⁹ to about 1 xi o¹¹ , from about 1 xi o¹⁰ to about 1 x ¹², and from about 1 xl 0¹¹ to about 1 xio¹³ In various embodiments, the dosage is about 1 xio⁶ CFUs, the dosage is about 1 xi o⁷ CFUs, the dosage is about 1 xl 0⁸ CFUs, the dosage is about 1 xi o⁹ CFUs, the dosage is about 1 xi o¹⁰ CFUs, the dosage is about 1 xio¹¹ CFUs, the dosage is about 1 xio¹² CFUs, or the dosage is about 1 xi o¹³ CFUs. In reference to bacterial CFU dosage, about is ± 50%. [0166] A bacterium can be grown utilizing techniques for cultivation of bacteria, which is appreciated in the art. Generally, a microbiota can be enriched and/or isolated from a microbiome sample. To isolate specific microbial genera, 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. Upon expansion of colonies, the taxonomies of microbial colonies can be identified by any appropriate technique (e.g., sequencing). Alternatively, 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.

[0167] 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. In some embodiments, a composition for storage and/or administration comprises a lyophilized bacterium. In some embodiments, a composition for storage and/or administration comprises a freeze-dried bacterium. In some embodiments, 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. Examples of 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.

Assessing Effect of Microbial Strains on Host Immune Response

[0168] Several embodiments are directed to assessing the effect microbial genera on the immune response of its host. In many embodiments, 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. In some embodiments, a tonsil organoid system is utilized. In some embodiments, a lymph node organoid system is utilized. In some embodiments, 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.

[0169] 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.

[0170] 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. In many embodiments, 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.

[0171] In some embodiments, the immune responsive organoids are derived from lymphoid tissue, including (but not limited to) tonsils, lymph nodes, and spleen. In some embodiments, 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). In some embodiments, 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). In some embodiments, the immune responsive organoids are derived from spleen, which can be collected from a donor as a biopsy. In some embodiments, 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.

[0172] To culture immune responsive organoids, the lymphoid tissue is dissociated, washed, and dispersed into low-attachment tissue-culture wells. In some embodiments, 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. To assist in immune cell culture, 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).

[0173] 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. Any microbiome sample can be utilized. 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.

[0174] 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.

[0175] Returning to Fig. 2, method 200 measures (205) organoid response. After adding microbial genera culture supernatant to the organoid culture, stimulation of the organoid culture is allowed to continue for a period of time. In some embodiments, the stimulation period is between 12 hours and up to 672 hours (4 weeks). In various embodiments, 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. In some embodiments, addition of microbial genera culture supernatant to the organoid culture is repeated over the course of the stimulation period.

[0176] After a period of stimulation, the organoids are assessed for responsiveness. In some embodiments, the supernatant of the organoid culture is utilized for assessment. In some embodiments, the cells of the organoid culture are utilized for assessment. In some embodiments, cell lysates of the organoid culture are utilized for assessment. In some embodiments, nucleic acids of the organoid culture are utilized for assessment. In some embodiments, gene products of the organoid culture are utilized for assessment.

[0177] Any assessment of immune response can be performed. In some embodiments, gene products are assessed to determine which genes were activated. In some embodiments, activation of T-cells is assessed. In some embodiments, humoral response (e.g., antibody production) is assessed. In some embodiments, cytokine and/or chemokine response is assessed.

[0178] While a specific example of a method for assessing microbial genera using an immune response organoid co-culture is described above, one of ordinary skill in the art can appreciate that various steps of the process can be performed in different orders and that certain steps may be optional according to some embodiments of the description. As such, it should be clear that the various steps of the method could be used as appropriate to the requirements of specific applications. Furthermore, any of a variety of methods for microbial genera using an immune response organoid co-culture appropriate to the requirements of a given application can be utilized in accordance with various embodiments of the disclosure.

EXAMPLES AND DATA

[0179] 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. spp', strain 5 is Prevotella.spp', and 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). As can be seen in the results, the various microbial strains each yielded a unique response for each cytokine. These results show that specific strains can induce specific immune responses. Further, the immune response of a microbiome is influenced by the strains within its composition and a change in composition can yield a different result. This indicates that introduction of specific strains can be administered to an individual to promote certain immune responses in order to achieve certain clinical phenotypes or treat medical disorders.

[0180] 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.

STUDY: LONGITUDINAL PROFILING OF THE MICROBIOME AT FOUR BODY SITES REVEALS CORE STABILITY AND INDIVIDUALIZED DYNAMICS DURING HEALTH AND DISEASE

[0181] 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).

[0182] The etiology and pathogenesis of insulin resistance and T2DM have been closely linked to the human microbiome. Patients with impaired insulin and glucose homeostasis exhibit shifted microbiome composition in the gut, skin, and other body sites, reflecting an ecological dysbiosis characterized by altered microbial alpha diversity, decreased compositional stability, and greater inter-individual variability. Compromised mucosal and skin barrier integrity, often associated with insulin resistance, may potentiate microbial translocation, thereby exacerbating systemic inflammation. Although human microbiome studies are often, by necessity, observational, a causal relationship between microbiome dysbiosis and impaired glucose/insulin homeostasis has been demonstrated through human microbiome manipulation.

[0183] While prior studies on the microbiome and glucose homeostasis have been informative, they exhibit certain limitations. Firstly, these studies often lack dense longitudinal sampling, essential for capturing stability features, thus limiting fundamental insights into host-microbe interactions. Secondly, they primarily focus on the microbiome from a single host site, overlooking the importance of simultaneous multi-region sampling for assessing microbiome site-specific dynamics, and their interplay across various host microenvironments. Lastly, most studies do not concurrently measure host clinical and molecular phenotypes, impeding the exploration of the molecular relationships underpinning health and disease-related host-microbiome interactions.

[0184] Collaborative initiatives like the Integrative Human Microbiome Project (iHMP) and Integrative Personal Omics Profiling (iPOP) offer avenues to surmount previous studies' limitations by investigating well-characterized human longitudinal cohorts. In this study, we examined the relationships between multi-site microbiomes and host health in the context of prediabetes, through characterizing the microbiome collected from four body sites in 86 adults for over six years and examining their associations with host omics and clinical characteristics. We describe unique longitudinal trajectories for microbiomes across different body sites, demonstrating their responsiveness to both host-specific and environmental factors. We found that personalized microbiomes exhibit greater stability than communal microbes, highlighting the resilience of the individualized microbiome. Nonetheless, these systems remain vulnerable to disturbances like viral infections, which may precipitate dysbiosis linked to metabolic dysfunctions. Furthermore, our analysis revealed associations between site-specific microbiomes and clinical parameters such as cytokine profiles and insulin sensitivity, highlighting the intertwined nature of immune activity and metabolic well-being. Results

Description of the study design

[0185] We analyzed stool, skin, oral, and nasal microbiome from a human cohort of 86 participants for up to six years (1 ,126.6 ± 455.8 days). (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^A2 (28.31 ± 4.44 kg/m^A2). 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.

[0186] 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. (Fig. 6A). 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). Overall, we analyzed a total of 3,058 visits, 5,432 biological samples (1 ,467 plasma samples, 926 stool samples, 1 ,116 skin samples, 1 ,001 oral samples, and 922 nasal samples) generating a total of 118,124,374 measurements.

Microbial Demographics and Personalization Across Body Sites: Unraveling the Impact of Diet and Environment

[0187] We initially examined the overall demographics of the microbiome at each of the four body sites (Figs. 6D and 7A). Extending previous studies, we observed a separation between body sites, including a clear boundary of the skin and nasal samples, highlighting the pronounced territory specificity of each microbiome.

[0188] 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. The skin and nasal microbiome samples jointly displayed a triangular distribution, primarily driven by three dominant genera: Cutibacterium, Corynebacterium, and Staphylococcus (Figs. 6D and 7 A). The distribution of microbial genera remained consistent with findings from other cohorts, irrespective of participant diversity in insulin sensitivity in our study. (Fig. 7B). Our study, however, extends these observations by longitudinally comparing inter- and intra-individual covariance across all four body sites within the same cohort.

[0189] 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. Notably, despite similar ecological characteristics and dominant bacterial constituents, the nasal microbiome manifested greater personalization than the skin microbiome (Fig. 7D), presumably because nasal microbiome dynamics are more host dependent.

[0190] While environmental factors like season and diet can influence the human microbiome, our results suggest that the impact of seasonality on the gut microbiome is relatively modest. Likely due to its direct external exposure, the skin microbiome exhibited the largest seasonal dynamic. This was followed by the oral microbiome, which may be shaped by seasonal dietary changes, as it explained more dietary variance than other sites (Fig. 7E). We also observed a significant fluctuation of the skin and oral microbiome evenness: these two communities both become more even during summer with an increased number of different microbes (Fig. 7F) possibly triggered by environmental changes such as temperature and humidity. Based on two participants with accessible environmental and chemical exposure data, we found stronger exposome-skin microbiome covariance than in other body sites (Fig. 7G) with noticeable environmental impacts on internal sites like oral and stool microbiomes. By modeling the microbial dynamics across seasons, we found more pronounced alterations in skin and nasal microbiomes than in stool and oral microbiomes. These findings not only extend our knowledge of gut microbiome specificity and individuality to other body sites but also broaden our understanding of environmental influences on microbiomes, with diet shaping oral microbiomes and environmental exposure impacting skin and nasal microbiomes.

Microbiome from Distinct Body Sites are Ecologically Unique and Altered in Insulin Resistance

[0191] Beyond the influence of personal and environmental factors, we observed distinct ecological attributes among the microbiome at the four body sites (Fig. 6E). The stool microbiome exhibited the greatest richness and evenness, underscoring its essential complexity and functional significance. In contrast, the skin microbiome displayed a more skewed population due to its lower evenness compared to the nasal microbiome, despite their similar richness distributions. These ecological features, which often shift with disease progression, aligned with previous findings of IR-related gut dysbiosis, characterized by a significant decrease in stool microbiome's alpha diversity (t = 2.8462, p-value = 0.0067). Furthermore, shifts in the richness-evenness scatter plots for all four body sites (Fig. 7H) suggested systemic dysbiosis in IR individuals, highlighted by significantly higher skin microbiome richness (t = -2.9102, p-value = 0.0057) (Fig. 7H) and evenness (t = -2.4393, p-value = 0.019) (Fig. 7H). These shifts indicate that IR- associated dysbiosis extends beyond the gut microbiome.

[0192] Importantly, extending longitudinal observations from single body sites, we define a "core microbiome" as microbes consistently present over time, representing potentially indispensable genera at each body sites. Interestingly, we found that stool and oral microbiomes maintain more than 25 highly prevalent core genera, whereas nasal and skin microbiomes only had three (Fig. 6F). Importantly, some core genera have low relative abundance, (e.g., Coprococcus Mean Prevalence = 80.75%; Mean Relative Abundance = 0.544%), demonstrating the potential significance of low-abundance strains (Fig. 71). Intriguingly, the richness of core genera in stool and oral microbiome is negatively associated with steady-state plasma glucose (SSPG) (Spearman Rho = -0.52, p-value = 0.00047) and BMI (Spearman Rho = -0.40, p-value = 0.005), respectively (Fig. 7J), indicating that IR and obesity may be associated with a loss of core microbiomes at these sites.

[0193] We further explored the microbiome ecology variations between insulin resistant (IR) and insulin sensitive (IS) individuals. IR subjects had a significantly higher number of skin core genera (t = -2.5856, p-value = 0.014) and a lower number of stool core genera (t = 2.9659, p-value = 0.0051 ) compared to IS individuals (Fig. 7K). Notably, several butyrate-producing bacteria (i.e., Coprococcus, Parasutterella, and Butyricicoccus) were decreased in IR stool core microbiomes, whereas diabetes-related opportunistic skin pathogens (e.g., Finegoldia and Acinetobacter) were enriched in the skin core microbiome of IR individuals. In addition, we found a clear divergence of the rank prevalence curves in stool and skin microbiome (Fig. 7L), demonstrating a global microbial prevalence shift in IR individuals at these two sites.

[0194] Additionally, several taxa differed significantly between IR and IS individuals in relative abundance. The stool microbiome of IR individuals showed an increase of genus Phocaeicola (LEfSe_effect size: 0.03; BH-adjusted p-value = 0.017) and a reduction of the genus Unclassified Ruminococcaceae (LEfSe_effect size: 0.017; BH-adjusted p- value = 0.0039), whereas the skin microbiome exhibited a decrease in the genus Cutibacterium (LEfSe_effect size: 0.069; BH-adjusted p-value = 0.007) and an increase of the genus Peptoniphilus (LEfSe_effect size: 0.0076; BH-adjusted p-value = 0.0022), which has been previously associated with diabetes-related skin dysbiosis and necrotizing infections (Fig. 7M). No such differences were observed in oral and nasal microbiomes. These findings further indicate that skin and stool microbiome stability is altered in IR individuals. Overall, our results reveal that IR participants exhibit a stool microbiome with reduced richness and butyrate-producing bacteria, and a skin microbiome more susceptible to opportunistic pathogens.

Distinct Taxon-Specific Stability and Individuality in Microbiomes Across Body Sites: A Potential Pathway for Personalized Interventions

[0195] We next examined microbiome stability at the genus level across body sites, hypothesizing stability is taxon- and site-specific. We defined a metric "Degree of Microbial Individuality" (DMI) for each genus, which quantifies similarity within an individual relative to the population; a high DMI means the microbes taxa is highly individual-specific. We also calculated a "Family Score" (FS) to evaluate microbial dissimilarity within households. Expanding observations of the stool microbiome, skin, oral and nasal microbiome also demonstrated significantly less intra-individual and within- family variation compared to the variation observed between different individuals (Fig. 8A). Intra-individually, the rate of complete ASV turnover within a genus over time was highest in the nasal microbiome (35.5% of cases), followed by skin (24.4%), stool (24.2%), and oral sites (2.7%). This finding underscores significant intra-individual ecological dynamics at the sub-genus level within microbiomes, despite overall community stability.

[0196] 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.

[0197] Our data revealed substantial DMI variance across body sites, potentially attributable to inherent niche-specific taxonomic complexities. For example, Corynebacterium in nasal and Bacteroides in stool showed the highest DMI, respectively. These results suggest that specific microbial taxa, adapting to their respective niches, may exhibit enhanced individualization. Therefore, the microbial community at each body site is largely shaped by niche-specific interactions. In contrast, environmental genera such as Klebsiella and Haemophilus displayed uniformly low DMI across all examined body sites, indicating that external environmental factors exerts a relatively weaker influence on the individuality of the native host microbiome. Interestingly, after adjusting individuals’ collective DMI by each genera’s relative abundance, we found a notable increase of individuality in the stool microbiomes of IR individuals (Fig. 9B), likely due to the increased Bacteroidetes among IR individuals.

[0198] Overall, 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.

[0199] Microbiome stability is highly personalized. Consequently, we explored the relationship between microbial individuality and stability across body sites. We first examined the genus recolonization rate, measured by ASV consistency when a genus is detectable after being undetected in one or more consecutive samples. The overall recolonization rate (measured by 1 - Pairwise Jaccard Distance) was significantly associated with DMI on all three body sites except for the oral microbiome (Fig. 10A), which intrinsically maintains a high recolonization rate. Such correlations were strongest in the nasal microbiome (Fig. 10A), potentially explaining the high ICC observed above. Surprisingly, no difference in recolonization rate was found between IR and IS individuals. (Fig. 11A). Our results suggest that highly individualized strains are more likely to recolonize, validating a hypothesis raised by fecal transplantation studies and implying its potential efficacy in treating IR-related dysbiosis.

[0200] 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). Additionally, 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).

Intra- and Inter-Individual Correlations of Microbiome Dynamics Across Body Sites: Implications for Microbial Interdependence and Territory Specificity

[0201] We next investigated whether microbiome dynamics were intra-individually coassociated across body sites, both at the community level and for individual taxa. Hierarchical clustering demonstrated a strong link in personal microbiome dynamics between the skin and nasal sites, whereas the dynamics of the stool microbiome was less correlated with other body sites. (Fig. 10C). The inter-site correlation of microbiome stability indicates systemic microbial coordination, potentially regulated by the common mucosal system. Comparison between IS and IR individuals revealed that both groups had a strong skin-nasal link, but a skin-oral correlation was exclusive to IS individuals. (Figs. 11 B and 11 C). This implies that IR status undermines the stability of the skin or oral microbiome, possibly due to the compromised host regulation of microbiome stability or due to impaired mucosal immunity related to IR, a key factor for between-body-sites microbial interactions.

[0202] Since the microbiome often operates as inter-dependent guilds, we questioned which body sites demonstrate the highest microbial inter-dependency. We found that 18.23% of all stool genus pairings (5,671 ) were significantly correlated within individuals, mostly Firmicutes (Fig. 10D). Surprisingly, co-association was highest in the oral microbiome (50.17%), followed by skin (47.51 %) and nasal (38.36%), highlighting strong synergistic interactions among microbiome members at each body site.

[0203] We further investigated the microbial crosstalk between body sites by searching for sample-wise microbial collinearity. Members from the skin and nasal sites were the most correlated (15.57% of all possible pairs) (Fig. 10D). However, remarkable territory specificity was still evident among core microbiomes of each body site. For instance, three predominant core genera - Corynebacterium, Staphylococcus, and Cutibacterium - exhibited no longitudinal correlation between skin and nasal sites. Similarly, oral Prevotella's abundance did not correlate with its abundance in the stool microbiome, consistent with our observation of high FS in oral but not stool Prevotella. Our results suggest translocation cases likely do not involve the niche-specific core taxa. Moreover, genera that are more interdependent exhibit lower DMI compared to less interdependent genera (Figs. 10E and 11 D), indicating that highly individualized microbiomes exhibit greater temporal stability. Expanding on our findings of microbiotypes across body sites (Fig 7A), we observed that dominant taxa driving these biotypes exhibit significant inter-individual correlations. Examples include significant correlations of Cutibacterium levels in one’s skin with their Cutibacterium level in nasal (beta = 0.56, BH-adjusted p-value = 0.0012), their Bacteroides in stool (beta = 0.52, BH- adjusted p-value = 0.0038), and their Leptotrichia in oral microbiomes (beta = 0.43, BH- adjusted p-value = 0.0088). Conversely, individuals with high Unclassified Ruminococcaceae in stool correlates with low Veillonella in oral microbiome (beta = -0.35, BH-adjusted p-value = 0.015). These findings suggest that the establishment of microbiotypes are regulated by personalized factors.

Acute Events Impact Microbiome Dynamics Across Body Sites and are Influenced by Insulin Resistance

[0204] Three strong perturbation events - infection, vaccination, and antibiotic usage - are known to cause stool microbiome disruptions. Within a three-month period, the stool microbiome of IR individuals exhibited greater temporal shifts between healthy visits and perturbations than IS individuals (Fig. 10F). This pattern, however, was not consistently discernible across other body sites, suggesting a site-specific microbiome response to these events. Unlike IS individuals, IR individuals displayed reduced stool microbiome evenness and lacked the perturbation in nasal microbiome during respiratory infection (Fig. 11 E), possibly due to higher IR-associated mucosal inflammation masking or superseding infection-induced local microbiome changes observed in IS individuals. During the course of infection, we identified a transient increase in several genera such as Alistipes in the stool, Peptoniphilus on the skin, Unclassified Prevotellaceae in the oral cavity, and Oribacterium in the nasal cavity. Conversely, we noticed a decrease in the presence of Clostridium cluster IV in the stool, Unclassified Clostridia in the oral cavity, and Unclassified Neisseriales in the nasal cavity (Figs. 11 F-11 H). Although the extent to which transient microbial shifts contribute to IR-related dysbiosis is not clear, the notable increase of core microbiomes in IR skin samples (Fig. 7K) may suggest a correlation between acute perturbations and long-term changes in the core microbiome composition. Overall, our findings indicate that 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.

The Interplay Between Host Immune System and Microbiome Across Body Sites: Insights Into Insulin Resistance and Inflammation

[0205] The dynamics of microbial stability are likely linked to multiple host regulatory mechanisms, particularly the immune system. Using a bayes model, we examined the longitudinal interplay between 62 host circulating cytokines/chemokines, growth factors and microbiome abundance at all four body sites. Based on credible interval, we identified 477 stool, 226 skin, 318 oral, and 221 nasal significant microbiome-cytokine associations that are body-site-specific. The stool and oral microbiome showed a significantly broader microbiome interaction spectrum than skin and nasal microbiome.. Interestingly, the 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), and leptin demonstrated the highest number of interactions with the microbiome. We further identified a subgroup of 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. For example, 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] We next examined whether certain phyla are more frequently interacting with cytokines, potentially influencing their stability and individuality. Members of the Firmicutes phylum (primarily Clostridia) were significantly overrepresented among cytokine-correlated microbes across all body sites (Xstooi² = 19.343, Pstooi = 1.092 w⁵; Xskin² = 10.418, Pskin = 0.0012; Xoral² = 30.935, Poral = 2.668 X 10-⁸; Xnasal²= 31.396, Pnasal = 2.104 x 10’⁸) (Fig. 12A). This interaction may account for Firmicutes’ higher FS and lower DMI in the stool microbiome compared to other phyla. Additionally, an increase of Firmicutes is associated with conditions like obesity-related gut dysbiosis, IBD-related oral dysbiosis, and psoriasis-related skin dysbiosis, which share inflammation as a common factor.

[0207] Interestingly, 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. At all four body sites we found that opportunistic microbes demonstrated a stronger correlation with cytokines than core microbiomes (stool: W = 1 ,715, p-value = 1.181 x10-12, skin: W = 223, p-value = 0.004041 , oral: W = 1 ,055, p-value = 7.877x10-6, nasal: W = 482, p-value = 0.004691 ) (Fig. 12B). 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. Our study revealed that the correlations between cytokines and Proteobacteria abundance are mostly negative, except for Proteobacteria members in the nasal microbiome (Fig. 12C). The negative correlation of Proteobacteria is stronger in stool (W = 25,315, p-value = 0.04439), skin (W = 4,995, p-value = 0.005214), and oral sites (W = 9,443, p-value = 9.649x10-5), but weaker in nasal sites (W = 5,218, p-value = 0.2693). Furthermore, all cytokine correlations (n =10) from opportunistic stool Proteobacteria are negative, while many high prevalence Proteobacteria members exhibit positive correlations with cytokines (Fig. 13C). Our results suggest that inflammation might contribute to the adaptation of more prevalent Proteobacteria. [0208] The host response by cytokines and chemokines may be linked with the observed richness of bacterial genera, in addition to their relative abundance. We therefore examined the correlation between cytokines and the richness of the 20 most diverse genera per body site. The richness of several prevalent stool microbiome genera within the Bacteroidetes phylum, such as Prevotella, Phocaeicola, and Parabacteroides, form a cluster with primarily negative associations, echoing our finding that the relative abundances of Bacteroidetes members are more likely to be negatively correlated with cytokines (Fig. 12C). Also, leptin and GM-CSF, both strongly associated with BMI (Fig. 13D), show the strongest overall correlation with richness. Furthermore, we discovered that seven skin genera positively correlated with a cytokine cluster, indicating that the richness of specific skin genera (e.g., Rothia, Veillonella, Streptococcus) is positively associated with the level of plasma cytokines. Consequently, the observed increase in the skin microbiome richness in IR individuals (Fig. 16A) may suggest a diminished host selection of the skin microbiome during inflammatory periods.

[0209] Overall, our analysis elucidates the interplay between chronic low-grade inflammation and microbial dynamics across various body sites. It connects the immune system with microbiome composition and individualization, and shed lights on how these dynamics are both influenced by and contribute to insulin resistance.

The Microbiome is Highly Connected with Host Molecules: Unraveling the Role in Insulin Resistance and Inflammation

[0210] To comprehensively explore the relationship between the microbiome and internal host molecules and its role in IR, we analyzed the correlations between microbiome genera and plasma proteins, lipids, and metabolites in the host. We first modularized the lipidomics data to address its high collinearity (Fig. 14A), then applied a linear mixed model to residualize all omics data, reducing between-individual variation and emphasizing longitudinal intra-individual relationships.

[0211] Interestingly, the 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. Notably, 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. However, 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.

[0212] Many taxa-molecule interactions were consistent across body sites, validating the robustness of these relationships. The skin microbiome exhibited the broadest connectivity in the microbe-lipidome association, whereas the stool microbiome was mainly connected to the metabolome and proteome, as expected (Fig. 15B). Proteome pathway annotation further supported these connections and their potential functional implications. The skin microbiome-related proteins were enriched for pathways regulating lipid transport and metabolism, whereas the stool, nasal, and oral microbiomes were more strongly connected to the host immune response, including complement activation and humoral immune response. The oral microbiome exhibited significant associations with proteins linked with regulation of proteolysis, hydrolase activity, and enzyme, peptidase, and lipase inhibitor activity. Strikingly, the complexity of the network between the stool microbiome and host molecular relationships was significantly reduced in individuals with IR (Fig. 14B), indicating a loss of balance in these individuals.

[0213] Our study reveals several metabolites, such as alcohol associated metabolite ethyl glucuronide, interact with microbiomes across all four body sites, demonstrating a more global effects of alcohol beyond the gut and oral microbiome. Notably, skin Neisseria and Klebsiella, recognized for their acetaldehyde- and ethanol-producing potential, showed a positive correlation with plasma ethyl glucuronide level (Fig. 15C). Conversely, Faecalimonas, typically an acetate-producing bacteria and sensitive to alcohol, exhibited a significant negative correlation with ethyl glucuronide. Furthermore, Desulfovibrio, a key genus promoting microbiome-related metabolic syndrome, was positively correlated with ethyl glucuronide. These findings suggest that alcohol metabolism is associated with microbial dysbiosis across various body sites.

[0214] We also observed notable interactions between microbiome genera richness, a recognized indicator of host metabolic status, and internal plasma analytics. These interactions were most pronounced in the stool microbiome, likely due to its high richness. (Figs. 15D). Intriguingly, we discovered a positive association between p-Cresol glucuronide, a bacteriostatic metabolite produced exclusively by the anaerobic gut microbiome and linked to insulin resistance, and the richness of Unclassified Ruminococcaceae and Oscillibacterm the stool. Conversely, it was negatively associated with genera typically considered metabolically beneficial, such as Roseburia. (Fig. 15E) These findings suggest that p-Cresol glucuronide may influence the development of insulin resistance-related dysbiosis by altering host tolerance to certain genera belonging to Clostridia.

[0215] In conclusion, our results not only validate several previous findings but also generate many novel host-microbial associations, enhancing our understanding of the complex interplay between the human microbiome, metabolites, and host health.

Elucidating Microbiome-Mediated Effects on Clinical Phenotypes: A Comprehensive Mediation Analysis across Four Body Sites

[0216] We next investigated potential causal linkages between microbes, host molecules and clinical phenotypes using mediation analysis. This method measures the contribution of mediators, such as plasma omics or cytokines/chemokines, to the assumed microbiome-clinical phenotype connection, providing confidence levels by inverting the roles of mediator and dependent variable. Using this assumption, we identified 330 directionally significant mediation effects involving microbial taxa across all sites, with 207 and 164 of these mediation effects detected in IS and IR participants, respectively (Fig. 17A). These outcomes suggest potential microbiome-host causality exist and vary with IR status, though unaccounted confounders remain possible.

[0217] Compared to IS, we observed a significant decrease in cytokine-mediated effects of stool microbiome on hematologic parameters (p-value = 0.002) (Figs. 17B and 17C) and metabolome-mediated effects of stool microbiome on immune phenotypes (p- value = 0.002) in IR individuals. Furthermore, there was an absence of lipidome-mediated associations between skin microbiome and host plasma lipids/cholesterol in IR participants (p-value = 0.031 ), suggesting a dysregulation in specific microbiome- metabolic interactions related to hematologic/immune and lipid/cholesterol homeostasis in IR (Figs. 17D and 17E). In contrast, the oral microbiome mediated a large proportion of immune profiles via the modulation of lipidome (p-value = 0.035) and cytokines (p- value = 0.0001 ) in IR relative to IS participants, primarily through a negative relationship among major oral core microbiomes such as Veillonella. These causal relationships align with previous findings suggesting that diabetes-related oral dysbiosis may arise from a combination of the loss of core commensal oral taxa and an increase in the pathogenicity of resident oral bacteria during impaired glucose metabolism.

Conclusions

[0218] This study presents a systematic analysis of longitudinal multi-site microbiome ecology and host dynamics. With date-matched microbiome and host omics, we not only expand our knowledge of the stability and individuality of microbiome from various body sites, but also provide mechanism-generating hypotheses on host-microbiome interactions in the context of prediabetes. We demonstrate a number of novel observations: 1 ) There is a “core” microbiome that is highly stable over time and an opportunistic microbiome that is highly variable and more potent to the immune system; these are niche-specific. 2) Correlations between microbiomes across body sites and extensive interactions with host factors indicate systemic coordination and interactions across the human body. 3) Highly individualized microbiomes are associated with distinct environmental factors (i.e., season, diet, chemical and biological exposome) in a nichespecific manner. However, these effects do not override the variance contributed by individuals, suggesting that the host is still the largest confounding factor for the variation observed in the microbiome. 4) Individuals with IR have a less stable microbiome with more diverse microbiome members on skin, possibly associated with upper respiratory infection, as well as significantly altered host-microbiome interactions. [0219] Our study revealed that the stool and oral microbiomes exhibit the highest level of individualization, likely due to the unique influence of personalized dietary habits and host-specific factors, such as IR, impacting the digestive system. Meanwhile, the skin and nasal microbiomes are less individualized, possibly owing more to individual environmental exposure. We observed site-specific impact of environmental factors, such as season, on the microbiomes of these four body sites. For instance, a decrease in stool microbiome richness in late summer corresponded with previous findings of worsened insulin sensitivity during this period. Similarly, we noted 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. Meanwhile, changes in humidity from January to April might explain the richness increase in skin and decrease in nasal microbiome. These observations underscore the critical role of the host in modulating the microbiome in response to environmental fluctuations.

[0220] Leveraging dense longitudinal sampling, we were able to quantify the stability and degree of individuality of the microbiomes at different body sites. The strong correlation between microbiome stability and individuality suggests an active role of the host in the establishment of commensal bacterial populations. Notably, this individuality appears to be taxon-specific, pointing to the possibility that the stability of an individual's microbiome may be influenced by the dominant bacterial taxa they carry. For instance, the higher DMI in stool Bacteroidetes links traditional Bacteroidetes/Firmicutes ratio or enterotypes to new insights in the context of metabolic disorders. Furthermore, microbiotypes across different body sites within the same individual correlate. Individuals with high levels of Bacteroides in the stool also have high Cutibacterium on the skin and Prevotella in the oral microbiome. Combined with our findings on the correlation between microbiome individuality and stability, the low-level engagement between the core microbiome and cytokines, and the clear host molecular type-specific patterns in the hostmicrobiome interactome, we propose that the colonization and adaptation of bacteria across multiple body sites is not random but precisely regulated by host factors. [0221] Niche-specific traits often emerge through multi-site comparisons; for instance, we identified unique characteristics of the oral microbiome. The recurrence rate of oral bacteria does not increase with DM I, possibly due to a stable and conserved community structure within the oral microbiome. This, coupled with the oral microbiome's high intrasite interdependence and minimal correlation with other body sites, underscores its specialization. The unique oral environment, shaped by factors such as saliva, teeth, gums, tongue, and distinct nutrient availability, likely contributes to this specialization.

[0222] 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. Notably, 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. Given the established association of IR with low-grade chronic inflammation, the observed changes in diversity among IR individuals might be related to their unique cytokine profiles.

[0223] We also identified a list of interactions between host plasma biomolecules and microbiomes. We also find many correlations across body sites with host factors; for example, the correlated Bacteroides in the stool and Cutibacterium in the skin are both closely related to lipid metabolism. Our multi-omics analyses suggest potential causality of host factors in these relationships, reinforcing the idea of host-driven systematic microbial coordination across body sites as proposed in gut-brain axis and gut-lung axis theories. Since many of the metabolites, lipids and proteins are signaling molecules (e.g., chemokines, hormones, peptides), these molecules may play important roles in organismal communication across the entire host-microbiome ecosystem. Importantly, this interaction presumably occurs individually, and significantly altered at disease stage. [0224] Intriguingly, we found Klebsiella on skin was positively associated with metabolites of alcohol. This indicates that alcohol intake may change the host into a /ebs/e/Za-tolerant environment, resulting in the adaptation and expansion of pathogenic Klebsiella. This observation supports previous findings in alcohol-associated pneumonia, where alcohol consumption and increased susceptibility to Klebsiella in the lungs may be a result of either intestinal Klebsiella-specific T-cell sequestration or alcohol-related impairment of tryptophan catabolite production/processing in the gut microbiome, which restricts pulmonary immune cell trafficking.

[0225] Insulin resistance (IR) appears to disrupt the intricate balance between the host and microbiome, demonstrated by an unstable, dysbiotic microbiome in IR individuals. In our study, we discovered marked differences in the microbiome composition, diversity, and core members of the stool and skin microbiomes in IR individuals compared to their insulin-sensitive counterparts. Notably, 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. Consistently, we detected reduced host-microbe coordination and a missing association between oral and skin microbiome stability. Further, our mediation analysis validates our result about the reduced gut microbiomecytokine interaction in IR individuals, and revealed heightened pro-inflammatory signals linked to the microbiomes of the upper respiratory tract (oral and nasal microbiome) in IR individuals. Our in-depth exploration of acute microbiome changes during infection provides additional substantiation for this theory. We observed a significant increase in IR-enriched skin genera such as Peptoniphilus and Intrasporangium (Fig. 11 D) during the course of a respiratory viral infection, while a decline in specific genera in the stool microbiome known for butyrate production, like Clostridium_\\/, Lawsonibacter, and Intestinimonas. While these findings do not explicitly validate the hypothetic link between respiratory viral infection, chronic metabolic dysregulation, and related complications, they suggest that infection-related microbial shifts may be associated with, or even cause, the dysbiosis and complications of metabolic syndrome.

Methods

Microbiome Sample Collection and Sequencing

[0226] 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.

[0227] After 30 minutes of beads-beating lysis, skin and oral samples were processed using a silica-guanidinium thiocyanate-based nucleic acid isolation protocol on a liquidhandling robot. The 16S rRNA variable region V4 was amplified by 35 cycles of PCR. The DNA from each sample was barcoded and combined to create a sequencing library. The sequencing library was then purified using columns and microfluidic DNA fractionation to reduce unwanted DNA fragments. Bio-Rad MyiQ was used to quantify the DNA concentration of the library using the Kapa iCycler qPCR kit (Bio-Rad Laboratories, Hercules, CA, USA). Sequencing was performed on the Illumina NextSeq 500 Platform (Illumina, San Diego, CA, USA) via 2 * 150 bp paired-end sequencing protocol.

[0228] Raw sequencing data from the stool samples and nasal samples are acquired. Briefly, 16S rRNA gene from V1 -V3 hyper-variable region was barcoded and sequenced on the Illumina MiSeq sequencing platform through a V3 2 x 300 sequencing protocol. The same cutoff used in skin and oral sequencing data was applied to stool and nasal sequencing data in demultiplexing. After demultiplexing, reads with Q-scores less than 35 and ambiguous bases (Ns) are trimmed for additional analysis. Microbiome data processing

[0229] Demultiplexed sequenced samples were saved as FASTQ files using BCL2FASTQ software (Version 2.20, Illumina, CA, USA). Sequences with barcode mismatches, primer mismatches exceeding one, or Q-scores below 30 were excluded. Due to low overlap between forward and reverse reads according to FLASH (Version 1.2.11 ), only forward reads were selected for further processing.

[0230] Microbiome sequencing data from four body sites were combined and processed using the DADA2 R package (version 1.16). Sequences were filtered to remove ambiguous bases (maxN=0) and those with more than two expected errors (maxEE=2). After filtering, inter-sample composition analysis was performed based on the learned error rate. An amplicon sequence variant (ASV) table was constructed, and chimeras were removed using the DADA2 workflow consensus method. Reads passing all filters were aligned against a trained database of target 16S rRNA gene sequences and taxonomic annotations derived from Version 18 of The Ribosomal Database Project (RDP) Taxonomy release (Aug 14, 2020). 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 Local Outlier Factor (LOF) of each point was calculated on a sequencing depth to richness (observed ASV) plot, and samples with a LOF greater than 3 (n=7) were removed due to an abnormal richnesssequencing depth relationship. 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. Batch effects were estimated using PERMANOVA analysis when both batch and subject ID were included, with results showing that the total variance explained by the 23 batches was 2% for stool, 3% for nasal, 0.6% for oral, and 3.6% for skin samples. The batch effects were considered small, and no correction for batch effects was applied in the analysis. Lipidomics analyses

[0231] 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.

[0232] To address the high collinearity of the lipidomic data, a customized clustering method was designed. Specifically, the 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.

Metabolomics Analyses

[0233] 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). 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.

Proteomics Analyses

[0234] Plasma proteins were characterized using a TripleTOF 6600 system (Sciex) via liquid chromatography-mass spectrometry (LC-MS) with SWATH acquisition. In every injection, 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 [0235] 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.

Exposome and Associated Environmental Analyses

[0236] 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. Chemical extraction involved the resuspension of zeolite beads in a sterile Eppendorf LoBind tube filled with 1 mL of Mass Spec grade methanol. Following a 20-minute incubation period at room temperature, the samples were subjected to a 20-minute centrifugation process at 22,000 g, also at room temperature. The samples were then analyzed using a Waters UPLC-coupled Exactive Orbitrap Mass Spectrometer (Thermo, Waltham, MA, USA), yielding a collection of 158 exposome chemicals. Environmental data, on the other hand, were sourced from several origins. Parameters such as temperature, humidity, and sampling flow rate were directly recorded by the MicroPEM. GPS coordinates of the participants provided geographical data. Additional meteorological and demographic information was obtained from public data repositories including the Climate Data Online (CDO), the US Census Bureau, and local weather stations. This culminated in the collection of 10 environmental feature data points.

Dietary Analyses

[0237] A total of 25 food items were included in a questionnaire that was completed voluntarily by participants during their routine visit in the study, using a diet questionnaire. The frequency of consuming each food item was scored in downstream analysis. For breads, biscuits, cakes, pies and pastries, we score the frequency from 0~4, with the associated frequency per day: 0) less than 1 , 1 )1 per day, 2)2-3 per day, 3)4-5 per day, 4)6 or more. For other foods we ask the participants to state the frequency from 6+ times per day to less than once per month. Insulin-Suppression Test

[0238] A subset of eligible consenting participants (N=58) underwent an Insulinsuppression Test (1ST), as a measure of insulin-mediated glucose uptake, to evaluate the insulin sensitivity status. 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. The mean value of the four steady-state plasma glucose (SSPG) and insulin concentrations were subsequently calculated. Participants were then categorized based on their SSPG values: those with SSPG < 150 mg/dl were classified as insulin sensitive (IS) (n=28), while those with SSPG > 150 mg/dl were classified as insulin resistant (IR) (n =30). Participants who were unable to provide measurements due to personal or medical circumstances were allocated to an indeterminate group (Unknown) (n= 28).

Clinical Lab Test

[0239] 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.

UMAP for Microbiome Distribution

[0240] The distribution of the microbiome was visualized using Uniform Manifold Approximation and Projection (UMAP), facilitated by the R package "Seurat (Version 4.0)". Prior to the application of UMAP, the count data were first normalized to relative abundance and scaled to represent one million reads per sample. A total of 1524 variable features were identified and encapsulated within a Seurat object. From this point, a distance matrix was produced through the utilization of the R package "Vegan (Version 2.6-2)", employing Bray Curtis dissimilarity. This distance matrix was subsequently transformed via Principal Coordinate Analysis (PCoA). The first ten dimensions from the PCoA (out of a total of 1094 generated) were used to determine neighboring relationships. The UMAP projection was then calculated, with default settings employed. This projection was made possible by invoking Python's UMAP via reticulate. The final UMAP results were visualized using the first two dimensions.

[0241] Intraclass correlation Coefficient (ICC) 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 ~ 1 + Days + (1\SubjectlD)

Where 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.

[0242] We then used ICC as the proportion of total variation explained by subject structure in the cohort by Vsubjectio/Vtotai, in which V was the variance from the corresponding component extracted by Varcorr and Vtotai was 1 .

Permutation Test on Bray Curtis Distance Comparison

[0243] 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.

[0244] For each microbial genus, a 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.

[0245] 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 .

[0246] Degree of Microbial Individuality (DM I) was measured as the mathematical difference between a given genus regarding their populational median of the interindividual BC distance and the median of the intra-individual BC distance. To assess the robustness and variability of the DMI estimates, we employed a bootstrap resampling technique.

[0247] First, we summarized the between-sample distance of genera with longitudinal prevalence greater than 10% at a given body site. The distance of sample pairs was then allocated into inter-individual or intra-individual groups. For each genus, we resampled the data with replacement and computed the DMI using the following formula:

DMI i = BC inter-individual - BC intra-individual

[0248] This resampling process was repeated multiple times to obtain a distribution of DMI estimates, which enabled us to assess the variability and robustness of our results.

[0249] By employing the bootstrap resampling technique, we aimed to gain insights into the reliability of our DMI calculations and understand how sensitive they were to potential variations in the data. This approach provided a more comprehensive understanding of the degree of microbial individuality across various body sites and taxa. To ensure the accuracy of our results, we considered a DMI to be reliable only if that DMI’s standard deviation (SD) of the bootstrapped distribution is less than 15 times of the mean DMI. This criterion helped to filter out any DMI estimates that might be influenced by high variability or uncertainty in the data. Detail of the bootstrap result was included in our supplementary materials, where we used column “confidence” to distinguish DMI values with high or low reliability. The total number of genera reported per region after bootstrapping included: 105 for stool, 35 for skin, 63 for oral, and 33 for nasal samples. [0250] To quantify the cumulative DMI for each 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.

Family Score

[0251] To assess the impact of a shared living environment on microbiome variability, we introduced a metric called the Family Score (FS). The FS represents the relative influence of a shared environment on the inter-individual dissimilarity of a given genus within cohabitating pairs. We began by excluding genera with a longitudinal prevalence of less than 10%, leaving 141 for stool, 119 for skin, 41 for oral, and 33 for nasal samples. For each remaining genus, we computed the "within-family" inter-individual Bray-Curtis (BC) distance between cohabiting pairs. We also calculated the median inter-individual BC distance (BC inter-individual) and intra-individual BC distance (BC intra-individual). Using these values, we computed the FS for a given genus '/' with the following formula:

Family Score i = (BC inter-individual ~ BC intra-family) / (BC inter-individual ~ BC intra-individual)

[0252] 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. We truncated the FS at 0 and 1 to maintain consistency in the comparison scale. Any FS values greater than or equal to 1 were assigned a value of 1 , and values less than or equal to 0 were assigned a value of 0. Classification of The Microbiome Genera by Their Longitudinal Prevalence

[0253] 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%.

Body Site-Specific Longitudinal Model for Bray-Curtis distance

[0254] To estimate the effect of body site on the change in BC distance over time, we used a linear mixed effects regression with the BC distance as the response variable implemented in the “Ime4” package in R. The BC distance of all pairwise samples was transformed to a more normally distributed dataset. The transformation method used was the log-log transformation, which has been widely used in survival analysis studies. Specifically, the transformation was performed by applying the formula: transformed_dist - Iog10(-log10(1-dist))

[0255] A total of eight different transformations were evaluated (variations of logarithmic, square root, arcsine, and ratio transformations of the original distance) to identify the most suitable transformation for our analysis. To assess the normality of the transformed distances, an Anderson-Darling test was performed, and the best method was selected by Anderson-Darling’s statistics A.

[0256] Fixed effects included an interaction between the body site and time, and with a random intercept for each individual. Time was normalized to the days from the first sample for each individual. The model was optimized using the “nlopwrap” method and nested models were compared by likelihood ratio test using the “Imtest” package in R. Differences in the slopes of the body sites over time were assessed by F-test with Satterthwaites's degrees of freedom. For the comparisons between body sites, the model was constructed as: dist ~ -1 + diffdays*dataset + (1 1 subjectjd)

[0257] For the comparison between IR and IS participants, the model was constructed as: dist ~ -1 + IRIS *diffdays + (1 1 subjectjd) + (1 + IRIS | subjectjd)

Where dist was the pairwise BC distance, diffdays was the date interval between two samples, subjectjd was the subject ID associated with each participant, and IRIS was the insulin sensitivity status of each participant.

Bayesian Mixed-Effects Model for Microbial Taxa and Cytokine Interactions

[0258] We utilized a Bayesian negative-binomial longitudinal mixed-effects model to analyze the interactions between microbial taxa and cytokines. This model choice addresses several key characteristics of our data: the compositional nature of microbiome data, the presence of zero-inflation and high skewness in cytokine measurements, and the repeated measurements of both cytokine and microbiome data in our study cohort (n=62 with longitudinal, date-matching measurements). The model's capacity to handle the highly skewed cytokine data was a critical factor in its selection, especially given the dramatic range of cytokine surges observed in our dataset, ranging from a 10.8-fold increase for MIP1 B to as much as a 618-fold increase for LEPTIN. Each genus’s reads count was modeled as a sparse-matrix response variable, with a plasma cytokine level MFI quantity and time as fixed effects and a random intercept for each individual, following the formula:

Mj - Xj /3+Zj b_i+s_i

Where 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, and d is a zero-centered error term. [0259] Posterior sampling was performed using four chains, 5,000 iterations per sample, and a 1 ,000-iteration burn-in of a No-U-Turn Sampler implemented in the “brms (Version 2.18.0)” package in R. The iteration plots and posterior predictive distributions were visually inspected for chain convergence. Microbe genera and cytokines above the limit of detection in less than 10% of the samples were excluded from the analysis. A microbe-cytokine association was considered significant if the 95% credible interval on the fit coefficient of the cytokine term did not include zero.

[0260] In our model, the beta represents the effect size. We estimated the variance explained by the model using the “bayes_R2” function from the brms package, and this information is included in the final table. While we provide p-values for each pair, as inferred from a method detailed previously, and the subsequent BH-adjusted p-values, it's important to note that these are supplementary. In our Bayesian analysis, 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

[0261] 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.

[0262] The 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.

[0263] To account for repeated sampling from the same subject, a linear mixed-effects model was utilized, incorporating subject ID as a random effect using the R package "Ime4" (Version 1.1 -30). Spearman correlations were then calculated, and p-values were adjusted using the Benjamini & Hochberg method. For correlation network construction, BH adjusted p-values < 0.2 were included. In the linear mixed model assessing longitudinal effects, the estimated correlation coefficient served as the measure of effect size (Fig. 16A).

[0264] To gage the robustness of the point estimation for the proportion of microbiome genera that are significantly correlated, we employed a bootstrapping approach. Specifically, we determined the percentage of significantly correlated genera by dividing the number of significantly correlated pairs by the total possible pairs. We bootstrapped this procedure 20 times to obtain mean and standard deviation values for the percentage of significantly correlated microbiome genera. These values were then used to compare the scale of interdependency of microbiome across different body sites. To ascertain if one body site exhibits a significantly greater interdependency compared to another, we employed the two-sided Wilcoxon rank-sum test. The significance of these comparisons was determined using p-values adjusted for multiple testing using the BH correction.

[0265] The estimation of inter-individual microbiome-wide correlation was conducted utilizing the SparCC method (R package discordant, Version 1.25.0), a methodology specifically tailored for compositional data. To prepare the microbiome relative abundance data for this analysis, the data were multiplied by 20000 and subsequently rounded, a process designed to convert proportions into data resembling counts. Following this, the SparCC method was employed to compute the correlation matrix. This matrix was then transformed into a long format data frame, with each row representing a pair of genera and their corresponding correlation coefficient. To assess the statistical significance of the observed correlations, a permutation test was implemented. For each pair of genera, the sample labels were randomly permuted, and the permuted SparCC correlation coefficient was calculated. We executed this procedure 10,000 times to produce a null distribution of SparCC correlation coefficients. Following this, we counted the instances where our observed correlation coefficient was encompassed within this null distribution. A finding was deemed "non-significant" if the correlation coefficient from any of the 10,000 permutations was more extreme than the coefficient from our original analysis, and subsequent raw p-value was computed based on the permutation results. Results with the BH adjusted p-value < 0.1 from this permutation was included in our final report.

[0266] The inter-omics correlation network was visualized using the R packages "ggraph" (Version 2.0.5), "igraph" (Version 1.3.2), and "tidygraph" (Version 1.2.1 ) under the "kk" layout.

Pathway Enrichment for Proteins Correlated with the Microbiome

[0267] The enrichment of pathways corresponding to proteins associated with the microbiome from four different body sites was achieved via the R package "clusterProfiler (Version 3.15)". The proteins in question served as input for pathway enrichment, specifically through Gene Ontology (GO) processes, enabling the identification of statistically significant pathways. The determination of these pathways was reliant on Fisher’s exact test.

[0268] GO terms of interest were isolated based on their Benjamini-Hochberg adjusted p-values; terms with p-values below 0.05 were retained for ensuing analyses. For the GO terms that demonstrated significant enrichment, the R package "simplifyEnrichment" was employed. This package facilitated the calculation of similarities between each pair of GO terms. The construction of a network only incorporated edges that exhibited similarities exceeding 0.70.

[0269] In order to discern modules within the correlation network, community analysis was undertaken utilizing the R package "igraph (Version 1.3.4)". To encapsulate each module, only the GO term that presented with the smallest Benjamini-Hochberg adjusted p-value was preserved.

Mediation Analysis

[0270] 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 associations between the microbiome and phenotype (Direct Effect), as well as internal omics data (Indirect Effect), were initially determined as outlined in the 'Correlation network analysis' section. Only significant associations (BH adjusted p-values < 0.2) were considered for subsequent mediation analysis. The linear regression model from R package "mediation" was employed for the mediation analysis. Ultimately, 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.

[0271] To control the false discovery rate (FDR), a reverse mediation analysis was performed by exchanging the mediator with effects (i.e., the microbiome influencing internal omics data via phenotype), and pairs with significant ACME in reverse mediation (p-values < 0.05) were excluded from the final results. Comparisons between body sites and insulin sensitivity statuses were conducted for each dataset using the Fisher exact test. Initially, it was hypothesized that the true meditative effect of each mediated pathway (e.g., genus / ~ cytokine immune ) was considerably large (n= 1 x 10⁸). Subsequently, the presence of a significantly different meditative linkage between the designated comparison pairs was assessed.

Principal Variance Component Analysis (PVCA)

[0272] To assess the variation in microbiome data based on individual and season, the principal variance component analysis (PVCA) was performed via R package "pvca (Version 3.15)”. 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. For microbiome samples in each body site, 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.

Deconvolute the Environmental Effect on the Microbiome

[0273] 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.

[0274] Microbiome samples with matching exposome chemical data within a 3-day period were selected for subsequent analysis (Nchemicai = 8, NEnvironmentai = 32 (Nparticipanti = 13; Npartici_Pant2 = 19)). 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.

[0275] Seasonal Effects on Microbiome'. Z-score normalized microbial data or microbial diversity was systematically analyzed using Generalized Additive Mixed Models (GAMMs). For each genus of interest, the model was formulated as: genera/diversity ~ IRIS + s(Time, bs = "cc") + (1\Subject_ID), knots = Hst(TimeOfYear = c(0, 366)

[0276] In this model, 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). The term (1\Subject_ID) includes a random intercept for each subject, to account for within-subject correlation.

[0277] 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. [0278] 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.

[0279] The Effects of Infection on Microbiome: The analysis of the effects of infection on the microbiome involved genera that were significantly altered during infection periods. The methodology implemented was informed by a previous method for estimating infection processes based on self-reported symptoms.

[0280] 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.

[0281] The categorization of these states was designed as a continuous progression from the pre-healthy to post-healthy state, with the average duration of an infection event being 88 days. 58 infection events were detected among 32 participants (IS: 7 participants, IR:12 participants, Unknown:13 participants) in our study. Each event was assigned a unique identification consisting of the subject ID and the event number. The progression of infection states within an event was tracked in the order of pre-healthy, event early, event late, recovery, and post-healthy.

[0282] In order to assess the effects of these infection states and insulin sensitivity status on microbiome genera, GAMM was constructed: genera ~ IRIS + s(lnfection_status, bs - "cc", k =5) + (1\event)

[0283] In this model, 'genera' represents the z-score normalized microbial relative abundance, IRIS indicates the insulin sensitivity status of each participant, and '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.

[0284] For the evenness changes during infection among insulin resistant (IR) and insulin sensitive (IS) individuals, the model was reformulated as follows: evenness ~ s(lnfection_status, bs = "cc", k =5) + (1\ event)

[0285] In this adjusted model, 'evenness' denotes the outcome variable, specifically the Subject ID based Z-score transformed Pielou's evenness measure of the microbiome sample, indicating the diversity of the microbial community. The remaining components of the model and their interpretations align with the previously described model.

Table 1. Microbial Genera Correlated with Gene Products

Gene ... . . . _ Correlation

_. . Microbial Genus Microbiome

Product Coefficient

VCAM1 Anaerostipes 0.000289651 stool

VCAM1 Ruminococcus 0.000294312 stool

VCAM1 Odoribacter 0.000253657 stool

VCAM1 Dorea 0.000240125 stool

IL22 Frisingicoccus 0.00610077 stool

IL22 Unclassified_Muribaculaceae 0.007980052 stool

IL22 Butyrivibrio 0.009078662 stool

VCAM1 Ruminococcus2 0.000224485 stool

IL17A Barnesiella 0.023720287 stool

BDNF Anaerotignum -0.000134791 stool

BDNF Barnesiella 0.000227538 stool

BDNF Eggerthella 0.00033774 stool

BDNF Lachnospira 0.000135762 stool

BDNF Parabacteroides 0.000120461 stool

BDNF Phocea -0.000214052 stool

BDNF Ruthenibacterium -0.000180588 stool

CD40L Barnesiella 0.001579901 stool

CD40L Cloacibacillus 0.002375157 stool

CD40L Eggerthella 0.000993913 stool

CD40L Faecalicatena 0.0016263 stool

CD40L Holdemania 0.00067425 stool

CD40L Neglecta 0.000900094 stool

CD40L Oxalobacter -0.001681997 stool

CD40L Raoultibacter 0.002963052 stool

CD40L Turicibacter 0.001288915 stool

EGF Anaerostipes 0.000423086 stool

EGF Anaerotignum -0.000497834 stool

EGF Barnesiella 0.001868111 stool

EGF Eggerthella 0.001494512 stool

EGF Intestinibacter 0.001263134 stool

EGF Neglecta 0.000878965 stool

EGF Parabacteroides 0.000457256 stool

EGF Romboutsia 0.001349344 stool

EGF Unclassified_Clostridiales 0.000926419 stool

EGF Unclassified_Erysipelotrichaceae 0.002032961 stool

EGF Unclassified_Firmicutes 0.002128915 stool ENA78 Lacrimispora -0.001144014 stool

EOTAXIN Desulfovibrio -0.010848097 stool

EOTAXIN Escherichia_Shigella -0.011180209 stool

EOTAXIN Turicibacter 0.007628332 stool

EOTAXIN Unclassified_Bacteroidales -0.012845766 stool

FASL Acidaminococcus 0.036081167 stool

FASL Agathobacter -0.008003902 stool

FASL Clostridium_sensu_stricto 0.015969159 stool

FASL Eggerthella 0.015722457 stool

FASL Faecalicatena 0.028892863 stool

FASL Flavonifractor 0.011491978 stool

FASL Holdemanella -0.045713024 stool

FASL Holdemania 0.017860593 stool

FASL Longicatena 0.027755531 stool

FASL Ruminococcus2 0.006084543 stool

FASL Ruthenibacterium 0.011902919 stool

FGFB Butyricimonas -0.010787169 stool

FGFB Clostridium_sensu_stricto 0.013916096 stool

FGFB Flavonifractor 0.008273645 stool

FGFB Holdemania 0.013875107 stool

FGFB Prevotella -0.032855335 stool

FGFB Unclassified_Firmicutes 0.024536687 stool

GCSF Bilophila 0.010484907 stool

GCSF Dialister -0.030073788 stool

GCSF Eggerthella 0.014013278 stool

GCSF Escherichia_Shigella 0.020662677 stool

GCSF Ihubacter 0.007380794 stool

GCSF Ruminococcus2 0.004680606 stool

GCSF Sutterella -0.02891904 stool

GMCSF Agathobacter 0.007003765 stool

GMCSF Anaerotignum 0.006011156 stool

GMCSF Butyrivibrio 0.069231687 stool

GMCSF Desulfovibrio -0.03320304 stool

GMCSF Frisingicoccus 0.025944554 stool

GMCSF Intestinibacter -0.014766296 stool

GMCSF Raoultibacter -0.023021936 stool

GMCSF Terrisporobacter -0.042799336 stool

GMCSF Unclassified_Muribaculaceae 0.087674902 stool

GROA Agathobacter -0.005591251 stool

GROA Clostridium_sensu_stricto 0.008285412 stool GROA Dialister -0.031243623 stool

GROA Eggerthella 0.009090599 stool

GROA Unclassified_Erysipelotrichaceae 0.011331288 stool

GROA Unclassified_Firmicutes 0.01713992 stool

HGF Akkermansia 0.014304115 stool

HGF Parabacteroides 0.004479667 stool

HGF Phascolarctobacterium -0.005971741 stool

HGF Ruminococcus 0.007984103 stool

HGF Slackia -0.034037925 stool

HGF Unclassified_Alphaproteobacteria 0.025250546 stool

HGF Unclassified_Bacteroidales -0.05148197 stool

HGF Unclassified_Eggerthellaceae -0.012234568 stool

ICAM1 Acidaminococcus 0.001479992 stool

ICAM1 Butyricimonas -0.001105617 stool

ICAM1 ClostridiumJV -0.000721899 stool

ICAM1 Lachnotalea -0.001897297 stool

ICAM1 Oscillibacter -0.000379454 stool

ICAM1 Phascolarctobacterium -0.000402251 stool

ICAM1 Phocaeicola -0.000251424 stool

ICAM1 Unclassified_Lachnospiraceae -0.000201871 stool

IFNA Blautia -0.002232792 stool

IFNA Dialister -0.015688163 stool

IFNB Acutalibacter 0.002765402 stool

IFNB Adlercreutzia 0.002297885 stool

IFNB Cloacibacillus 0.004086069 stool

IFNB Faecalicatena 0.005571887 stool

IFNB Holdemania 0.001867272 stool

IFNB Raoultibacter 0.004661352 stool

IFNB Unclassified_Muribaculaceae -0.040033926 stool

IFNG Blautia -0.004210135 stool

IFNG Dialister -0.041802762 stool

IFNG Fusicatenibacter -0.005871796 stool

IFNG Unclassified_Bacteroidales -0.037519118 stool

IFNG Unclassified_Firmicutes 0.025713731 stool

IL10 Collinsella -0.010893793 stool

IL10 Hungatella 0.017801879 stool

IL10 Monoglobus 0.016451797 stool

IL12P40 Akkermansia 0.00497678 stool

IL12P40 Parabacteroides 0.002189194 stool

IL12P40 Raoultibacter 0.007935895 stool IL12P70 Butyricimonas -0.021850854 stool

IL12P70 Collinsella -0.014728946 stool

IL12P70 Fusicatenibacter -0.006471325 stool

IL12P70 Monoglobus 0.023234982 stool

IL13 Faecalimonas 0.035950575 stool

IL13 Oscillibacter -0.005869153 stool

IL13 Unclassified_Muribaculaceae -0.084113381 stool

IL13 Unclassified_Ruminococcaceae -0.005025178 stool

IL15 Butyricimonas -0.017000417 stool

IL15 Clostridium_sensu_stricto 0.00994687 stool

IL15 Collinsella -0.011125976 stool

IL15 Faecalibacterium -0.003933203 stool

IL15 Lachnospira -0.004659097 stool

IL15 Turicibacter 0.013486613 stool

IL17A Dialister -0.038411954 stool

IL17A Subdoligranulum -0.005612308 stool

IL17F Adlercreutzia 0.001200576 stool

IL17F Butyricicoccus 0.000779244 stool

IL17F Cloacibacillus 0.002639199 stool

IL17F Dysosmobacter 0.000850256 stool

IL17F Faecalicatena 0.003677757 stool

IL17F Hungatella 0.003150082 stool

IL17F Senegalimassilia -0.005303245 stool

IL17F Unclassified_Bacteroidales -0.004999527 stool

IL17F Unclassified_Muribaculaceae -0.022204281 stool

IL18 Agathobacter -0.00259136 stool

IL18 Dialister -0.010787712 stool

IL18 Eggerthella 0.004819637 stool

IL18 Holdemanella -0.013650035 stool

IL18 Prevotella -0.011707982 stool

IL18 Ruminococcus2 0.002009433 stool

IL18 Unclassified_Firmicutes 0.008573517 stool

IL18 Unclassified_Muribaculaceae -0.024601534 stool

IL1A Dialister -0.006025285 stool

IL1A Hungatella 0.004128605 stool

IL1A Intestinibacter 0.003735435 stool

IL1A Ruminococcus2 0.001251757 stool

IL1A Unclassified_Erysipelotrichaceae 0.005375817 stool

IL1A Unclassified_Firmicutes 0.00526722 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

IL1 RA Butyricimonas -0.026253837 stool IL1 RA Clostridium_IV -0.050491305 stool IL1 RA Clostridium_XIVa 0.038351727 stool IL1 RA Desulfovibrio -0.070357111 stool IL1 RA Dialister -0.109064526 stool IL1 RA Faecalimonas 0.096776199 stool IL1 RA Lachnospira -0.019977394 stool IL1 RA Monoglobus 0.045541088 stool IL1 RA Subdoligranulum -0.024036921 stool IL1 RA Unclassified_Bacteroidales -0.1330559 stool IL1 RA Unclassified_Erysipelotrichaceae 0.058537554 stool IL1 RA Unclassified_Muribaculaceae -0.18261 1396 stool

IL2 Butyrivibrio 0.034238689 stool IL2 Coprococcus 0.006414868 stool IL2 Holdemanella 0.03885044 stool IL2 Paraprevotella 0.010400522 stool IL2 Pseudoflavonifractor -0.020516513 stool IL2 Roseburia -0.003983732 stool IL2 Unclassified_Bacteroidales -0.04751 1913 stool IL2 Veillonella -0.030407825 stool IL21 Beduinibacterium 0.007984823 stool IL21 Butyricicoccus 0.00329297 stool IL21 Cuneatibacter -0.005288291 stool IL21 Faecalibacillus -0.005162563 stool

IL21 Faecalibacterium -0.002960464 stool IL21 Intestinibacter 0.00900859 stool IL21 Romboutsia 0.006404378 stool IL21 Ruminococcus -0.003852243 stool

IL21 Subdoligranulum -0.004750763 stool

IL21 Turicibacter 0.008994764 stool

IL21 Unclassified_Erysipelotrichaceae 0.013012289 stool

IL22 Anaerotignum 0.000771414 stool

IL22 Cloacibacillus 0.00294123 stool

IL22 Dysosmobacter 0.000764219 stool

IL22 Gordonibacter 0.00192863 stool

IL22 Negativibacillus 0.001708288 stool

IL22 Parabacteroides -0.000478617 stool

IL22 Phocea 0.001334783 stool

IL22 Pseudoflavonifractor 0.002145118 stool

IL22 Raoultibacter 0.002464405 stool

IL22 Turicibacter 0.002225878 stool

IL22 Unclassified_Alphaproteobacteria 0.002452588 stool

IL22 Unclassified_Burkholderiales -0.003991865 stool

IL23 Butyricimonas -0.013497129 stool

IL23 Dialister -0.033766465 stool

IL23 Erysipelatoclostridium 0.028103072 stool

IL23 Flavonifractor 0.007633745 stool

IL23 Holdemanella -0.032277119 stool

IL23 Holdemania 0.01700053 stool

IL23 Longicatena 0.027334691 stool

IL23 Senegalimassilia -0.030973044 stool

IL23 Unclassified_Muribaculaceae -0.054285013 stool

IL27 Butyricimonas -0.013065102 stool

IL27 Collinsella -0.011423826 stool

IL27 Dialister -0.036483623 stool

IL27 Flavonifractor 0.010396319 stool

IL27 Fusicatenibacter -0.006137989 stool

IL27 Holdemanella -0.037308444 stool

IL27 Unclassified_Proteobacteria -0.043066812 stool

IL31 Butyrivibrio -0.246452667 stool

IL31 Clostridium_XIVa 0.03819049 stool

IL31 Eggerthella 0.046603173 stool

IL31 Erysipelatoclostridium 0.065610275 stool

IL31 Faecalicatena 0.069554405 stool

IL31 Fusicatenibacter -0.022859541 stool

IL31 Holdemanella -0.104590275 stool

IL31 Holdemania 0.054223213 stool IL31 Longicatena 0.06886041 stool

IL4 Butyricimonas -0.010692544 stool

IL4 Dialister -0.030502205 stool

IL4 Flavonifractor 0.007980374 stool

IL4 Unclassified_Muribaculaceae -0.06461488 stool

IL5 Butyrivibrio 0.045894662 stool

IL5 Coprobacter -0.011987386 stool

IL5 Howardella -0.039054478 stool

IL5 Turicibacter 0.019756964 stool

IL5 Unclassified_Bacteroidales -0.04143017 stool

IL6 Clostridium_sensu_stricto 0.021221282 stool

IL6 Collinsella -0.019841523 stool

IL6 Dialister -0.054231042 stool

IL6 Eggerthella 0.022271917 stool

IL6 Fusicatenibacter -0.008950338 stool

IL6 Holdemania 0.026083473 stool

IL6 Hungatella 0.035489712 stool

IL6 Monoglobus 0.027373901 stool

IL6 Unclassified_Firmicutes 0.03705534 stool

IL7 Acutalibacter 0.011 197703 stool

IL7 Butyricimonas -0.009176703 stool

IL7 Coprobacter -0.008076931 stool

IL7 Dialister -0.014839058 stool

IL7 Erysipelatoclostridium -0.020961458 stool

IL7 Frisingicoccus -0.015192341 stool

IL7 Pseudoflavonifractor -0.010556299 stool

IL7 Raoultibacter 0.011083634 stool

IL7 Unclassified_Alphaproteobacteria -0.017041608 stool

IL7 Unclassified_Eggerthellaceae 0.007622815 stool

IL8 Dialister -0.02679321 stool

IL8 Faecalicatena 0.012642395 stool

IL8 Holdemania 0.006846535 stool

IL8 Ruminococcus2 0.002864058 stool

IL8 Unclassified_Firmicutes 0.01378914 stool

IL9 Clostridium_sensu_stricto 0.013934699 stool

IL9 Dialister -0.059939758 stool

IL9 Eggerthella 0.01495402 stool

IL9 Faecalicatena 0.029652432 stool

IL9 Flavonifractor 0.009902202 stool

IL9 Holdemania 0.015877777 stool IL9 Hungatella 0.028869072 stool

IL9 Longicatena 0.0248893 stool

IL9 Ruthenibacterium 0.00927959 stool

IL9 Unclassified_Firmicutes 0.040655211 stool

IP10 Dialister -0.008666218 stool

IP10 Mediterraneibacter -0.00184684 stool

LEPTIN Agathobacter 0.000102584 stool

LEPTIN Barnesiella -0.000217943 stool

LEPTIN Catabacter -0.000158317 stool

LEPTIN Desulfovibrio -0.000457887 stool

LEPTIN Faecalicatena -0.000267062 stool

LEPTIN Frisingicoccus 0.000304507 stool

LEPTIN Howardella -0.00035646 stool

LEPTIN Intestinibacter -0.000290552 stool

LEPTIN Odoribacter -0.000105252 stool

LEPTIN Phascolarctobacterium -0.000108387 stool

LEPTIN Sutterella -0.000310315 stool

LEPTIN Terrisporobacter -0.000464882 stool

LEPTIN Unclassified_Muribaculaceae 0.001262933 stool

LIF Anaerotignum -0.006190125 stool

LIF Neglecta 0.008592342 stool

LIF Raoultibacter 0.024629824 stool

LIF Turicibacter 0.013787659 stool

MCP1 Barnesiella 0.001193502 stool

MCP1 Blautia -0.000409801 stool

MCP1 Butyrivibrio 0.004282364 stool

MCP1 Desulfovibrio -0.004630613 stool

MCP1 Dialister -0.003993384 stool

MCP1 Slackia -0.005349208 stool

MCP1 Unclassified_Alphaproteobacteria 0.002163889 stool

MCP3 Agathobacter -0.010622037 stool

MCP3 Butyricimonas -0.021831162 stool

MCP3 Desulfovibrio -0.045141037 stool

MCP3 Faecalibacterium -0.009753873 stool

MCP3 Flavonifractor 0.014013267 stool

MCP3 Fusicatenibacter -0.010116411 stool

MCP3 Lachnospira -0.014441714 stool

MCP3 Oscillibacter -0.009298859 stool

MCP3 Slackia -0.04393316 stool

MCP3 Subdoligranulum -0.015052353 stool MCP3 Unclassified_Bacteroidales -0.064689541 stool

MCP3 Unclassified_Muribaculaceae -0.125439768 stool

MCSF Agathobacter -0.003590619 stool

MCSF Clostridium_sensu_stricto 0.006483421 stool

MCSF Dialister -0.027310146 stool

MCSF Flavonifractor 0.003740888 stool

MCSF Holdemanella -0.022839033 stool

MCSF Holdemania 0.007804654 stool

MCSF Unclassified_Firmicutes 0.013616732 stool

MCSF Unclassified_Muribaculaceae -0.027765095 stool

MIG Cuneatibacter -0.002139919 stool

MIG Dialister -0.010380744 stool

MIG Escherichia_Shigella 0.009869947 stool

MIG Faecalibacterium -0.000970212 stool

MIG Holdemanella -0.009424729 stool

MIG Hungatella 0.009125534 stool

MIP1A Butyricicoccus 0.001949442 stool

MIP1A Cuneatibacter -0.002633422 stool

MIP1A Faecalibacillus -0.003266799 stool

MIP1A Faecalibacterium -0.001633972 stool

MIP1A Flavonifractor 0.002119382 stool

MIP1A Unclassified_Erysipelotrichaceae 0.006708773 stool

MIP1 B Agathobacter -0.001043384 stool

MIP1 B Dialister -0.007532884 stool

MIP1 B Hungatella 0.003600485 stool

MIP1 B Unclassified_Erysipelotrichaceae 0.002894411 stool

MIP1 B Unclassified_Firmicutes 0.003569358 stool

NGF Faecalimonas 0.050207171 stool

NGF Flavonifractor 0.014295134 stool

NGF Hungatella 0.03392372 stool

PAH Colidextribacter 0.000606969 stool

PAH Dialister -0.00094413 stool

PAH Longicatena -0.000493216 stool

PAH Paraprevotella 0.00040653 stool

PAH Prevotella -0.00073446 stool

PAH Roseburia 9.22327E-05 stool

PAH Unclassified_Alphaproteobacteria 0.00045124 stool

PAH Unclassified_Lachnospiraceae 9.33283E-05 stool

PDGFBB Anaeromassilibacillus -0.001835699 stool

PDGFBB Anaerotruncus -0.002773335 stool PDGFBB Beduinibacterium -0.007295546 stool

PDGFBB Butyricimonas -0.00381083 stool

PDGFBB Collinsella -0.002381736 stool

PDGFBB Dialister -0.008340663 stool

PDGFBB Eisenbergiella -0.002032026 stool

PDGFBB Frisingicoccus -0.009340087 stool

PDGFBB Gordonibacter -0.005633074 stool

PDGFBB Oscillibacter -0.001154277 stool

PDGFBB Pseudoflavonifractor -0.003678912 stool

PDGFBB Raoultibacter -0.004965421 stool

PDGFBB Unclassified_Muribaculaceae -0.022520768 stool

RANTES Bacteroides 0.000140813 stool

RANTES Butyricimonas -0.000423609 stool

RANTES Collinsella -0.000568929 stool

RANTES Erysipelatoclostridium 0.00136417 stool

RANTES Holdemanella -0.001284575 stool

RANTES Holdemania 0.000555785 stool

RANTES Lawsonibacter -0.000349005 stool

RANTES Unclassified_Proteobacteria -0.000956901 stool

RESISTIN Anaerotignum -0.000243468 stool

RESISTIN ClostridiumJV -0.000496477 stool

RESISTIN Eggerthella 0.000709653 stool

RESISTIN Flavonifractor -0.000296407 stool

RESISTIN Fournierella -0.00139654 stool

RESISTIN Holdemanella -0.00151956 stool

RESISTIN Parasutterella -0.000426515 stool

RESISTIN Slackia -0.000942794 stool

SCF Agathobacter -0.006881125 stool

SCF Anaerotignum -0.0093102 stool

SCF Barnesiella 0.022484608 stool

SCF Clostridium_sensu_stricto 0.014134588 stool

SCF Fusicatenibacter -0.004950778 stool

SCF Holdemanella -0.023566395 stool

SCF Lachnotalea 0.023699046 stool

SCF Monoglobus 0.022295137 stool

SCF Para bacteroides 0.005498003 stool

SCF Parasutterella -0.012974796 stool

SCF Romboutsia 0.011882799 stool

SCF Unclassified_Firmicutes 0.040747134 stool

SDF1A Butyricicoccus 0.001558823 stool SDF1A Intestinibacter 0.004462932 stool

SDF1A Terrisporobacter 0.005914262 stool

SDF1A Turicibacter 0.004454183 stool

SDF1A Unclassified_Erysipelotrichaceae 0.005934726 stool

TGFA Agathobacter -0.006753947 stool

TGFA Clostridium_XIVa 0.012391071 stool

TGFA Dialister -0.0450502 stool

TGFA Dorea -0.003431423 stool

TGFA Dysosmobacter 0.006047134 stool

TGFA Eggerthella 0.012607949 stool

TGFA Faecalicatena 0.022443056 stool

TGFA Fusicatenibacter -0.003710445 stool

TGFA Holdemania 0.009896751 stool

TGFA Hungatella 0.022073461 stool

TGFA Senegalimassilia -0.075490403 stool

TGFA Unclassified_Bacteroidales -0.042081433 stool

TGFA Unclassified_Muribaculaceae -0.187095623 stool

TGFB Acutalibacter 0.021000784 stool

TGFB Akkermansia 0.024640204 stool

TGFB Clostridium_sensu_stricto 0.021636562 stool

TGFB Clostridium_XVIII 0.019400084 stool

TGFB Flavonifractor 0.009468295 stool

TGFB Holdemania 0.013110007 stool

TGFB Howardella -0.047880193 stool

TGFB Hungatella 0.020967933 stool

TGFB Odoribacter -0.009816867 stool

TGFB Unclassified_Bacteroidales -0.053067158 stool

TGFB Unclassified_Muribaculaceae -0.105134821 stool

TNFA Agathobacter -0.004577381 stool

TNFA Akkermansia 0.008155313 stool

TNFA Barnesiella 0.009203345 stool

TNFA Butyricimonas -0.007475865 stool

TNFA Cloacibacillus 0.016399493 stool

TNFA Clostridium_sensu_stricto 0.008382473 stool

TNFA Collinsella -0.006431388 stool

TNFA Desulfovibrio -0.018853045 stool

TNFA Frisingicoccus -0.011810401 stool

TNFA Romboutsia 0.005618619 stool

TNFA Unclassified_Bacteroidales -0.022473161 stool

TNFB Clostridium_sensu_stricto 0.008322313 stool TNFB Holdemanella -0.03683056 stool TNFB Holdemania 0.007640474 stool TNFB Unclassified_Firmicutes 0.014967865 stool TRAIL Butyricicoccus 0.001997998 stool TRAIL Faecalibacterium -0.000983721 stool TRAIL Flavonifractor 0.002117492 stool TRAIL Intestinibacter 0.004806593 stool TRAIL Ruminococcus2 0.001373216 stool TRAIL Turicibacter 0.004260526 stool TRAIL Unclassified_Erysipelotrichaceae 0.007607594 stool TRAIL Unclassified_Muribaculaceae -0.023857717 stool VCAM1 Agathobacter 0.000273746 stool VCAM1 Alistipes 0.000254529 stool VCAM1 Bacteroides 0.000224947 stool VCAM1 Butyrivibrio -0.000943235 stool VCAM1 Coprococcus 0.000196485 stool VCAM1 Desulfovibrio -0.000753207 stool VCAM1 Lachnospira 0.000268678 stool VCAM1 Oscillibacter 0.000242672 stool VCAM1 Streptococcus -0.000311343 stool VCAM1 Unclassified_Firmicutes -0.000680261 stool VCAM1 Unclassified_Ruminococcaceae 0.000289439 stool

VEGF Dialister -0.003822994 stool VEGF Erysipelatoclostridium 0.004728722 stool VEGF Faecalibacillus -0.001168741 stool VEGF Flavonifractor 0.001022455 stool VEGF Hungatella 0.00292533 stool VEGF Ruminococcus2 0.000779398 stool VEGF Unclassified_Bacteroidales -0.006816505 stool VEGFD Anaerotignum 0.003832907 stool VEGFD Bacteroides 0.002143678 stool VEGFD Barnesiella 0.01437816 stool VEGFD Beduinibacterium 0.008664877 stool VEGFD Eisenbergiella 0.005127058 stool VEGFD Faecalicatena 0.018001987 stool VEGFD Flavonifractor 0.004483783 stool VEGFD Ihubacter 0.006086828 stool VEGFD Negativibacillus 0.006386131 stool VEGFD Neglecta 0.006741434 stool VEGFD Ruthenibacterium 0.005388215 stool VEGFD Unclassified_Clostridiales_lncertae_Sedis_XIII 0.007334668 stool

BDNF Abiotrophia 0.000536338 skin

BDNF Anaerococcus 0.000240646 skin

BDNF Moraxella -0.000931689 skin

BDNF Porphyromonas 0.000530736 skin

BDNF Sphingomonas -0.000427629 skin

BDNF Streptococcus 0.000225882 skin

CD40L Brucella -0.00106773 skin

CD40L Halomonas -0.002694922 skin

CD40L Lactococcus 0.00269535 skin

CD40L Moraxella -0.008926067 skin

EGF Actinomyces 0.002249684 skin

EGF Bradyrhizobium -0.001314709 skin

EGF Fusobacterium 0.003410263 skin

EGF Lactococcus 0.003356359 skin

EGF Leptotrichia 0.002780075 skin

EGF Moraxella -0.007639462 skin

EGF Pelomonas -0.001277298 skin

EGF Porphyromonas 0.002372244 skin

EGF Rothia 0.002299633 skin

EGF Schaalia 0.002182672 skin

EGF Streptococcus 0.001258973 skin

EGF Unclassified_Carnobacteriaceae 0.003541909 skin

EGF Veillonella 0.002263307 skin

ENA78 Dolosigranulum -0.0047828 skin

ENA78 Fusobacterium -0.0021 16835 skin

ENA78 Haemophilus -0.000920996 skin

ENA78 Neisseria -0.001293509 skin

ENA78 Veillonella -0.000913979 skin

EOTAXIN Pseudomonas -0.005396801 skin

EOTAXIN Sphingomonas -0.005638281 skin

FASL Corynebacterium -0.010841753 skin

FASL Lactococcus 0.064922338 skin

FASL Streptococcus 0.016504821 skin

FGFB Lactococcus 0.044853458 skin

FGFB Moraxella -0.093970033 skin

GCSF Lactococcus 0.041899607 skin

GMCSF Aminobacter 0.014207141 skin

GMCSF Anaerococcus 0.017811879 skin

GMCSF Campylobacter 0.024517325 skin GMCSF Cutibacterium -0.00358289 skin

GMCSF Dolosigranulum -0.123327575 skin

GMCSF Neisseria -0.022440691 skin

GMCSF Peptoniphilus 0.023031005 skin

GROA Halomonas -0.040425571 skin

GROA Moraxella -0.062225623 skin

GROA Peptoniphilus -0.015012382 skin

HGF Chryseobacterium -0.036510872 skin

HGF Dolosigranulum -0.044836415 skin

HGF Enhydrobacter 0.029199049 skin

HGF Moraxella -0.038953227 skin

IFNA Corynebacterium -0.007556344 skin

IFNB Unclassified_Lachnospiraceae -0.003840154 skin

IFNG Moraxella -0.06979999 skin

IL10 Brevibacterium -0.049186389 skin

IL10 Campylobacter 0.026478821 skin

IL10 Lactococcus 0.037683932 skin

IL10 Leptotrichia 0.019257116 skin

IL10 Moraxella -0.069576979 skin

IL10 Streptococcus 0.012144904 skin

IL12P40 Porphyromonas 0.006360168 skin

IL12P70 Brevibacterium -0.063391215 skin

IL12P70 Halomonas -0.060542662 skin

IL12P70 Lactococcus 0.054771409 skin

IL12P70 Methylobacterium -0.024517658 skin

IL12P70 Moraxella -0.137579399 skin

IL12P70 Paracoccus -0.067589566 skin

IL12P70 Streptococcus 0.01348681 skin

IL13 Corynebacterium -0.015871068 skin

IL13 Lactococcus 0.057176479 skin

IL15 Aeri bacillus -0.031672897 skin

IL15 Finegoldia -0.027252272 skin

IL15 Halomonas -0.032028406 skin

IL15 Peptoniphilus -0.02609808 skin

IL17A Dolosigranulum -0.046545847 skin

IL17A Neisseria -0.019510939 skin

IL17F Enhydrobacter 0.002458215 skin

IL17F Methylorubrum -0.003797254 skin

IL17F Unclassified_Lachnospiraceae -0.003649868 skin

IL18 Abiotrophia 0.011307895 skin IL18 Halomonas -0.011812797 skin

IL18 Moraxella -0.021004399 skin

IL1A Lactococcus 0.007706777 skin

IL1A Pseudomonas 0.003046704 skin

IL1 B Aeribacillus -0.07534059 skin

IL1 B Brevibacterium -0.142097894 skin

IL1 B Dolosigranulum -0.16688471 skin

IL1 B Finegoldia -0.106428477 skin

IL1 B Halomonas -0.123795018 skin

IL1 B Methylobacterium -0.033700085 skin

IL1 B Moraxella -0.293060203 skin

IL1B Paracoccus -0.128626856 skin

IL1 B Peptoniphilus -0.053372477 skin

IL1 B Roseomonas -0.182387176 skin

IL1 B Stenotrophomonas -0.022909701 skin

IL1 RA Capnocytophaga 0.075797217 skin

IL1 RA Leptotrichia 0.053332377 skin

IL1 RA Methylorubrum -0.092822155 skin

IL1 RA Neisseria 0.06078996 skin

IL1 RA Porphyromonas 0.094852877 skin

IL1 RA Rothia 0.05054727 skin

IL1 RA Schaalia 0.063780455 skin

IL1 RA Streptococcus 0.035656216 skin

IL1 RA Unclassified_Carnobacteriaceae 0.068561682 skin

IL1 RA Veillonella 0.057185686 skin

IL2 Bradyrhizobium 0.008282689 skin

IL2 Cutibacterium -0.003483211 skin

IL2 Lactococcus 0.040668194 skin

IL2 Leptotrichia 0.015461369 skin

IL2 Lysinibacillus 0.010746802 skin

IL2 Pelomonas 0.006176818 skin

IL2 Pre vote Ila 0.014644666 skin

IL2 Pseudomonas 0.008017963 skin

IL2 Rhodococcus 0.01880483 skin

IL2 Schaalia 0.01337165 skin

IL2 Unclassified_Lachnospiraceae 0.015329283 skin

IL21 Cutibacterium -0.002341251 skin

IL21 Delftia 0.005503342 skin

IL21 Dolosigranulum -0.047492974 skin

IL21 Klebsiella 0.016352581 skin IL21 Pseudomonas 0.006226291 skin

IL21 Roseomonas 0.018052352 skin

IL22 Capnocytophaga 0.003625285 skin

IL22 Enhydrobacter -0.004384481 skin

IL22 Gemella 0.003502605 skin

IL22 Moraxella -0.007510545 skin

IL22 Paracoccus -0.006981724 skin

IL22 Peptoniphilus -0.002505765 skin

IL22 Ralstonia 0.001546725 skin

IL22 Roseomonas -0.010177682 skin

IL22 Unclassified_Lachnospiraceae 0.003622051 skin

IL22 U n class ifi ed_St re pto p hyta -0.002661779 skin

IL23 Lactococcus 0.059220651 skin

IL23 Methylobacterium -0.035917612 skin

IL23 Moraxella -0.099789273 skin

IL27 Corynebacterium -0.014913073 skin

IL27 Methylobacterium -0.031641477 skin

IL31 Methylobacterium -0.049203418 skin

IL4 Abiotrophia 0.032970221 skin

IL4 Corynebacterium -0.01076371 skin

IL4 Lactococcus 0.042721409 skin

IL4 Moraxella -0.083367918 skin

IL4 Streptococcus 0.015022031 skin

IL5 Lactococcus 0.039164576 skin

IL5 Methylobacterium -0.01844986 skin

IL5 Moraxella -0.06031857 skin

IL5 Paracoccus -0.042923713 skin

IL5 Rhodococcus 0.023806997 skin

IL6 Halomonas -0.063255172 skin

IL6 Methylobacterium -0.039003661 skin

IL7 Actinomyces -0.014537947 skin

IL8 Abiotrophia 0.01824409 skin

IL8 Halomonas -0.021054629 skin

IL8 Lactococcus 0.023149301 skin

IL9 Halomonas -0.060233905 skin

IL9 Methylobacterium -0.020064695 skin

IP10 Citrobacter -0.005030939 skin

LEPTIN Anaerococcus 0.000153095 skin

LEPTIN Dolosigranulum -0.001040612 skin

LEPTIN Klebsiella -0.000400793 skin LEPTIN Neisseria -0.000308455 skin

LEPTIN Unclassified_Bacteroidales -0.000314769 skin

LIF Aeri bacillus -0.051735808 skin

LIF Granulicatella -0.04863437 skin

LIF Kocuria -0.070152451 skin

LIF Lactococcus 0.023663895 skin

LIF Moraxella -0.104828469 skin

MCP1 Acinetobacter -0.0014207 skin

MCP1 Citrobacter -0.001728757 skin

MCP1 Methylobacterium -0.003183579 skin

MCP1 Pelomonas -0.001141264 skin

MCP1 Sphingomonas -0.002231019 skin

MCP3 Finegoldia -0.043778993 skin

MCP3 Lactococcus 0.078233309 skin

MCP3 Leptotrichia 0.036035256 skin

MCP3 Moraxella -0.17495995 skin

MCSF Halomonas -0.026712434 skin

MCSF Lactococcus 0.02980974 skin

MIG Delftia 0.004391035 skin

MIG Kocuria -0.017850201 skin

MIG Pseudomonas 0.003876705 skin

MIP1A Cutibacterium -0.001121333 skin

MIP1A Delftia 0.00343345 skin

MIP1A Dolosigranulum -0.023522846 skin

MIP1A Klebsiella 0.007911926 skin

MIP1A Pseudomonas 0.002996885 skin

MIP1 B Delftia 0.002253734 skin

MIP1 B Enhydrobacter 0.005202562 skin

MIP1 B Moraxella -0.010333042 skin

MIP1 B Pseudomonas 0.002183103 skin

NGF Corynebacterium -0.019446557 skin

PAH Delftia 0.000239771 skin

PAH Lactobacillus -0.000743643 skin

PAH Paracoccus -0.000640769 skin

PAH Phocaeicola 0.0007489 skin

PDGFBB Acinetobacter -0.003693594 skin

PDGFBB Dolosigranulum -0.013931988 skin

PDGFBB Unclassified_Streptophyta 0.004520648 skin

RANTES Abiotrophia 0.001275105 skin

RANTES Microbacterium 0.000708712 skin RESISTIN Moraxella -0.003455011 skin

RESISTIN Rhodococcus 0.000786873 skin

RESISTIN Unclassified_Bacteroidales 0.00083942 skin

SCF Lactococcus 0.041 166218 skin

SCF Methylobacterium -0.018670405 skin

SCF Moraxella -0.077987501 skin

SCF Streptococcus 0.012234258 skin

SDF1A Delftia 0.003174991 skin

SDF1A Klebsiella 0.008003942 skin

SDF1A Lactococcus 0.008294058 skin

SDF1A Pseudomonas 0.002912504 skin

TGFB Lactococcus 0.048851688 skin

TGFB Moraxella -0.080605673 skin

TGFB Streptococcus 0.014542255 skin

TNFA Finegoldia -0.02062196 skin

TNFA Peptoniphilus -0.01358227 skin

TNFA Rhodococcus 0.016616302 skin

TNFB Halomonas -0.027845642 skin

TNFB Lactococcus 0.022860952 skin

TNFB Moraxella -0.049603537 skin

TNFB Streptococcus 0.007354676 skin

TRAIL Klebsiella 0.008800696 skin

TRAIL Pseudomonas 0.003784475 skin

VEGF Gemella 0.003111662 skin

VEGF Lactococcus 0.006401278 skin

VEGF Streptococcus 0.001944397 skin

VEGFD Moraxella -0.035335148 skin

VCAM1 Haemophilus 0.000407326 oral

VCAM1 Unclassified_Lachnospiraceae 0.000210808 oral

VCAM1 Leptotrichia 0.000357607 oral

VCAM1 Lachnoanaerobaculum 0.000165234 oral

VCAM1 Eubacterium 0.000132735 oral

VCAM1 Unclassified_Bacteroidales 0.000227899 oral

VCAM1 Solobacterium 0.000207138 oral

VCAM1 Porphyromonas 0.000236611 oral

BDNF Abiotrophia 0.000227987 oral

BDNF Alloprevotella -0.000219159 oral

BDNF Corynebacterium 0.000170397 oral

BDNF Megasphaera -9.7278E-05 oral

BDNF Parvimonas -0.000136724 oral BDNF Schaalia 5.0213E-05 oral

BDNF Unclassified_Bacteroidetes -0.00026859 oral

BDNF Unclassified_Firmicutes -0.000306633 oral

BDNF Unclassified_Prevotellaceae -0.000188389 oral

CD40L Actinomyces 0.000381225 oral

CD40L Schaalia 0.000240751 oral

CD40L Unclassified_Firmicutes -0.001456959 oral

CD40L Unclassified_Flavobacteriaceae -0.002872674 oral

CD40L Unclassified_Neisseriaceae -0.001628685 oral

EGF Aminipila 0.001273356 oral

EGF Corynebacterium 0.000789445 oral

EGF Delftia 0.001457457 oral

EGF Eikenella -0.001049189 oral

EGF Filifactor 0.001 173733 oral

EGF Meta mycoplasma 0.001128587 oral

EGF Peptoanaerobacter 0.001389923 oral

EGF Schaalia 0.000385966 oral

EGF Treponema 0.000947267 oral

EGF Unclassified_Bacteroidetes -0.001121719 oral

EGF Unclassified_Peptostreptococcaceae 0.000815529 oral

EGF Veillonella -0.000157882 oral

ENA78 Alloprevotella -0.000709671 oral

ENA78 Lautropia -0.000695151 oral

ENA78 Neisseria -0.000352324 oral

ENA78 Ori bacterium 0.00016911 oral

ENA78 Parvimonas -0.000612741 oral

ENA78 Streptococcus 0.000148155 oral

ENA78 Tannerella -0.000816861 oral

ENA78 Unclassified_Bacteroidales -0.000458744 oral

ENA78 Unclassified_Bacteroidetes -0.001667417 oral

ENA78 Unclassified_Lachnospiraceae 0.000377041 oral

ENA78 Unclassified_Peptostreptococcaceae -0.001313326 oral

EOTAXIN Eikenella -0.008430216 oral

EOTAXIN Kingella -0.005648613 oral

FASL Solobacterium 0.004757717 oral

FGFB Solobacterium 0.002897835 oral

FGFB Unclassified_Clostridia -0.006673433 oral

GCSF Corynebacterium 0.0127401 oral

GCSF Unclassified_Bacteroidales 0.007086948 oral

GMCSF Aminipila -0.024788404 oral GMCSF Gemella 0.005202844 oral

GMCSF Kingella 0.011956984 oral

GMCSF Lautropia -0.00767403 oral

GMCSF Neisseria -0.006901037 oral

GMCSF Peptoanaerobacter -0.033285442 oral

GMCSF Rothia 0.004604207 oral

GMCSF Streptococcus 0.003699778 oral

GMCSF Unclassified_Neisseriaceae -0.023027122 oral

GMCSF Unclassified_Peptostreptococcaceae -0.018954052 oral

GMCSF Veillonella 0.002808365 oral

GROA Actinomyces 0.002612423 oral

GROA Porphyromonas -0.002695966 oral

GROA Solobacterium 0.002180571 oral

GROA Unclassified_Clostridia -0.003796987 oral

GROA Unclassified_Flavobacteriaceae -0.026317675 oral

GROA Unclassified_Selenomonadaceae 0.002455312 oral

HGF Actinomyces 0.004949589 oral

HGF Unclassified_Firmicutes -0.01022495 oral

ICAM1 Peptoanaerobacter 0.000829929 oral

ICAM1 Unclassified_Flavobacteriales 0.000491406 oral

ICAM1 Unclassified_Pasteurellaceae 0.000588531 oral

IFNA Unclassified_Bacteroidales 0.003654078 oral

IFNA Unclassified_Firmicutes -0.007879864 oral

IFNB Actinomyces 0.00108778 oral

IFNB Neisseria -0.00154155 oral

IFNB Schaalia 0.000545933 oral

IL10 Abiotrophia 0.009596782 oral

IL10 Anaerosinus 0.009878953 oral

IL10 Corynebacterium 0.008634493 oral

IL10 Unclassified_Bacteroidales 0.007814707 oral

IL10 Unclassified_Clostridia -0.009214702 oral

IL10 Unclassified_Firmicutes -0.015769493 oral

IL10 Unclassified_Selenomonadaceae 0.005510079 oral

IL12P40 Actinomyces 0.001676426 oral

IL12P40 Delftia 0.004738213 oral

IL12P40 Lancefieldella 0.001241499 oral

IL12P40 Unclassified_Clostridia -0.003978607 oral

IL12P70 Abiotrophia 0.013489312 oral

IL12P70 Unclassified_Bacteroidales 0.010219085 oral

IL12P70 Unclassified_Selenomonadaceae 0.006144919 oral IL13 Unclassified_Bacteroidales 0.007344111 oral

IL15 Actinomyces 0.003715882 oral

IL15 Filifactor 0.015642026 oral

IL15 Lancefieldella 0.003435306 oral

IL15 Mogibacterium 0.003335591 oral

IL15 Neisseria -0.005084625 oral

IL15 Solobacterium 0.004015731 oral

IL15 Unclassified_Bacteroidales 0.006365923 oral

IL15 Unclassified_Clostridia -0.009707922 oral

IL15 Unclassified_Firmicutes -0.029124371 oral

IL15 Unclassified_Neisseriaceae -0.021778666 oral

IL17A Aggregatibacter -0.007300695 oral

IL17A Alloprevotella -0.011815708 oral

IL17A Lautropia -0.009106726 oral

IL17A Mogibacterium 0.003349902 oral

IL17A Neisseria -0.00665637 oral

IL17A Ori bacterium 0.002837625 oral

IL17A Parvimonas -0.007091575 oral

IL17A Solobacterium 0.003896905 oral

IL17A Unclassified_Lachnospiraceae 0.005437351 oral

IL17A Unclassified_Neisseriaceae -0.014850467 oral

IL17A Unclassified_Pasteurellaceae -0.016054601 oral

IL17F Actinomyces 0.000573654 oral

IL17F Capnocytophaga -0.000702477 oral

IL17F Fusobacterium -0.000798732 oral

IL17F Lautropia -0.001015996 oral

IL17F Neisseria -0.001162507 oral

IL17F Schaalia 0.000379876 oral

IL17F Unclassified_Weeksellaceae -0.000604571 oral

IL18 Rothia 0.001255488 oral

IL18 Unclassified_Clostridia -0.002751973 oral

IL18 Unclassified_Firmicutes -0.005513137 oral

IL1A Solobacterium 0.000833766 oral

IL1A Unclassified_Clostridia -0.002644823 oral

IL1A Unclassified_Firmicutes -0.003246132 oral

IL1 B Actinomyces 0.007403594 oral

IL1 B Anaerosinus 0.012109018 oral

IL1 B Butyrivibrio 0.009535833 oral

IL1 B Filifactor 0.031306234 oral

IL1 B Lancefieldella 0.005506451 oral IL1 B Mogibacterium 0.005557435 oral

IL1 B Neisseria -0.00818248 oral

IL1 B Peptoanaerobacter -0.076443021 oral

IL1 B Treponema 0.014828413 oral

IL1 B Unclassified_Bacteroidales 0.009720854 oral

IL1 B Unclassified_Clostridia -0.020171161 oral

IL1 B Unclassified_Firmicutes -0.073332553 oral

IL1 B Unclassified_Flavobacteriaceae -0.085685593 oral

IL1 B Unclassified_Leptotrichiaceae 0.015549258 oral

IL1 B Unclassified_Neisseriaceae -0.052892625 oral

IL1 B Unclassified_Selenomonadaceae 0.009114823 oral

IL1 RA Alloprevotella -0.024944078 oral

IL1 RA Alloscardovia 0.038919029 oral

IL1 RA Eubacterium -0.014903577 oral

IL1 RA Tannerella -0.025385628 oral

IL1 RA Unclassified_Bacteroidales 0.0201885 oral

IL1 RA Unclassified_Burkholderiales -0.084595619 oral

IL1 RA Unclassified_Clostridia -0.028806125 oral

IL1 RA Unclassified_Firmicutes -0.036707081 oral

IL1 RA Unclassified_Leptotrichiaceae -0.026226813 oral

IL2 Alloprevotella -0.010289366 oral

IL2 Capnocytophaga 0.005377537 oral

IL2 Catonella 0.003929783 oral

IL2 Granulicatella 0.010655905 oral

IL2 Unclassified_Bacteroidales 0.008088224 oral

IL2 Unclassified_Neisseriaceae -0.019207951 oral

IL21 Actinomyces 0.003075282 oral

IL21 Cardiobacterium -0.01080457 oral

IL21 Schaalia 0.001596649 oral

IL21 Treponema -0.008469958 oral

IL21 Unclassified_Bacteroidetes -0.011103072 oral

IL21 Unclassified_Clostridia -0.007876087 oral

IL21 Unclassified_Firmicutes -0.011828411 oral

IL21 Unclassified_Peptostreptococcaceae -0.013667303 oral

IL21 Unclassified_Weeksellaceae -0.004704863 oral

IL22 Abiotrophia 0.001572578 oral

IL22 Granulicatella 0.001017178 oral

IL22 Treponema 0.001019606 oral

IL22 Unclassified_Clostridia 0.001050197 oral

IL22 Unclassified_Flavobacteriales -0.002117511 oral IL22 Unclassified_Weeksellaceae 0.000658343 oral

IL23 Solobacterium 0.004525337 oral

IL27 Unclassified_Bacteroidales 0.010786449 oral

IL27 Unclassified_Candidatus_Saccharibacteria -0.006981277 oral

IL27 U ncl assified_Clostrid i a -0.008933948 oral

IL31 Schaalia -0.0051 17797 oral

IL4 Abiotrophia 0.01229669 oral

IL4 Corynebacterium 0.008086489 oral

IL4 Solobacterium 0.004094872 oral

IL4 Unclassified_Bacteroidales 0.00715592 oral

IL4 Unclassified_Firmicutes -0.013613186 oral

IL5 Alloprevotella -0.011900289 oral

IL5 Aminipila -0.035540357 oral

IL5 Peptoanaerobacter -0.043721175 oral

IL5 Unclassified_Bacteroidales 0.009902087 oral

IL5 Unclassified_Flavobacteriaceae -0.046735808 oral

IL5 Unclassified_Neisseriaceae -0.024668682 oral

IL5 Unclassified_Peptostreptococcaceae -0.016600838 oral

IL6 Filifactor 0.024266547 oral

IL6 Unclassified_Bacteroidales 0.008494245 oral

IL6 Unclassified_Selenomonadaceae 0.006057722 oral

IL7 Schaalia -0.001367961 oral

IL7 Unclassified_Neisseriaceae -0.008251868 oral

IL8 Solobacterium 0.00173429 oral

IP10 Cardiobacterium 0.001947322 oral

IP10 Unclassified_Bacteroidales 0.00077437 oral

IP10 Unclassified_Flavobacteriaceae 0.002750138 oral

LEPTIN Abiotrophia 0.00012854 oral

LEPTIN Aminipila -0.000610415 oral

LEPTIN Gemella 6.10446E-05 oral

LEPTIN Lautropia -0.000152152 oral

LEPTIN Metamycoplasma -0.000137774 oral

LEPTIN Peptoanaerobacter -0.000466643 oral

LEPTIN Streptococcus 4.50976E-05 oral

LEPTIN Unclassified_Bacteroidetes -0.000180415 oral

LEPTIN Unclassified_Carnobacteriaceae 3.99916E-05 oral

LEPTIN Unclassified_Flavobacteriaceae -0.000403145 oral

LEPTIN Unclassified_Flavobacteriales -0.00022052 oral

LEPTIN Unclassified_Neisseriaceae -0.000231105 oral

LEPTIN Unclassified_Peptostreptococcaceae -0.000233248 oral LIF Actinomyces 0.003464037 oral

LIF Unclassified_Firmicutes -0.060757536 oral

LIF Unclassified_Neisseriaceae -0.022203798 oral

MCP3 Corynebacterium 0.016270483 oral

MCP3 Filifactor 0.043454056 oral

MCP3 Granulicatella 0.015809268 oral

MCP3 Peptoanaerobacter -0.030266065 oral

MCP3 Unclassified_Bacteroidales 0.011974481 oral

MCP3 Unclassified_Clostridia -0.020930292 oral

MCP3 Unclassified_Firmicutes -0.023199236 oral

MCSF Solobacterium 0.002142003 oral

MCSF Unclassified_Clostridia -0.004125218 oral

MIG Unclassified_Peptostreptococcaceae 0.001566813 oral

MIP1A Cardiobacterium -0.005762257 oral

MIP1A Unclassified_Bacteroidetes -0.005192318 oral

MIP1A Unclassified_Clostridia -0.004651399 oral

MIP1A Unclassified_Weeksellaceae -0.001462293 oral

MIP1 B Unclassified_Clostridia -0.001652029 oral

MIP1 B Unclassified_Firmicutes -0.002166973 oral

NGF Corynebacterium 0.016572113 oral

PAH Abiotrophia 0.000225566 oral

PAH Catonella 0.00010295 oral

PAH Corynebacterium 0.000172079 oral

PAH Gemella 8.97915E-05 oral

PAH Kingella -0.000362774 oral

PAH Ori bacterium 5.69406E-05 oral

PAH Prevotella -4.89425E-05 oral

PAH Unclassified_Peptostreptococcaceae 0.000328524 oral

PAH Unclassified_Weeksellaceae 9.75873E-05 oral

PDGFBB Alloprevotella -0.002713123 oral

PDGFBB Eubacterium -0.001048214 oral

PDGFBB Gemella -0.00098586 oral

PDGFBB Haemophilus -0.000726382 oral

PDGFBB Peptoanaerobacter -0.005040786 oral

PDGFBB Peptostreptococcus -0.001569688 oral

PDGFBB Unclassified_Clostridia -0.002574486 oral

PDGFBB Unclassified_Selenomonadaceae 0.001140061 oral

PDGFBB Unclassified_Weeksellaceae -0.001068853 oral

RANTES Aggregatibacter -0.00036338 oral

RANTES Butyrivibrio 0.00034021 oral RANTES Gemella -0.000227705 oral

RANTES Solobacterium 0.000228617 oral

RANTES Unclassified_Carnobacteriaceae -0.000148231 oral

RESISTIN Alloscardovia -0.000610584 oral

RESISTIN Butyrivibrio 0.000347084 oral

RESISTIN Treponema 0.000471021 oral

RESISTIN Unclassified_Bacteroidales 0.000308967 oral

RESISTIN Unclassified_Firmicutes -0.000453316 oral

SCF Corynebacterium 0.009412207 oral

SCF Filifactor 0.015304901 oral

SCF Unclassified_Neisseriaceae -0.017494168 oral

SCF Unclassified_Peptostreptococcaceae 0.012135136 oral

SDF1A Capnocytophaga -0.001445993 oral

SDF1A Solobacterium 0.000956355 oral

SDF1A Unclassified_Bacteroidetes -0.003222062 oral

SDF1A Unclassified_Clostridia -0.00351094 oral

SDF1A Unclassified_Firmicutes -0.002935421 oral

SDF1A Unclassified_Weeksellaceae -0.001115404 oral TGFA Fusobacterium -0.008134474 oral TGFA Neisseria -0.006038188 oral

TGFB Abiotrophia 0.013721658 oral

TGFB Peptoanaerobacter -0.036477679 oral

TGFB Solobacterium 0.005986337 oral

TGFB Unclassified_Firmicutes -0.0281 14714 oral

TGFB Unclassified_Neisseriaceae -0.025885735 oral

TN FA Actinomyces 0.004066688 oral

TN FA Filifactor 0.013195861 oral

TN FA Neisseria -0.006159569 oral

TNFA Slackia -0.009075645 oral

TN FA Unclassified_Bacteroidales 0.003557869 oral

TNFA Unclassified_Clostridia -0.0081526 oral

TNFA Unclassified_Firmicutes -0.014536987 oral

TNFA Unclassified_Neisseriaceae -0.007959898 oral

TNFA Unclassified_Selenomonadaceae 0.003880596 oral

TNFB Solobacterium 0.002479642 oral

TNFB Unclassified_Bacteroidales 0.003825737 oral

TNFB Unclassified_Selenomonadaceae 0.003027929 oral

TRAIL Unclassified_Bacteroidetes -0.003793605 oral

TRAIL Unclassified_Clostridia -0.003181023 oral

TRAIL Unclassified_Firmicutes -0.004572103 oral TRAIL Unclassified_Peptostreptococcaceae -0.005887147 oral TRAIL Unclassified_Weeksellaceae -0.001517907 oral VCAM1 Actinomyces 0.000135823 oral VCAM1 Campylobacter 0.000241768 oral VCAM1 Capnocytophaga 0.000269748 oral VCAM1 Catonella 0.00019482 oral VCAM1 Gemella 0.000212821 oral VCAM1 Lancefieldella 0.000139212 oral VCAM1 Lautropia 0.000194642 oral VCAM1 Mogibacterium 9.98376E-05 oral VCAM1 Ori bacterium 0.000223096 oral VCAM1 Streptococcus 0.000284904 oral VCAM1 Unclassified Carnobacteriaceae 0.00018887 oral VCAM1 Unclassified_Weeksellaceae 0.000187249 oral

VEGF Actinomyces 0.000705117 oral VEGF Parvimonas -0.001037787 oral VEGF Unclassified_Clostridia -0.002261335 oral VEGF Unclassified_Firmicutes -0.002232097 oral VEGFD Aggregatibacter -0.005631654 oral VEGFD Alloprevotella -0.010057538 oral VEGFD Delftia 0.008719631 oral VEGFD Filifactor 0.012164212 oral VEGFD Gemella -0.002665731 oral VEGFD Haemophilus -0.00225082 oral VEGFD Unclassified_Carnobacteriaceae -0.002765905 oral VCAM1 Streptococcus 0.000372235 nasal

IL5 Unclassified_Betaproteobacteria 0.042569492 nasal MIP1A Stenotrophomonas 0.00916581 nasal BDNF Blautia 0.000443376 nasal BDNF Paenibacillus 0.000468971 nasal BDNF Peptoniphilus -0.000176299 nasal BDNF Roseomonas 0.000382978 nasal BDNF Unclassified Candidatus Saccharibacteria 0.000269608 nasal CD40L Micrococcus -0.002063026 nasal EGF Bacillus -0.002923033 nasal EGF Massilia -0.001759001 nasal EGF Pelomonas -0.002694101 nasal EGF Phocaeicola 0.002849445 nasal EGF Porphyromonas -0.001426774 nasal

EGF Roseomonas 0.002576263 nasal EOTAXIN Capnocytophaga -0.007955161 nasal

EOTAXIN Corynebacterium 0.001765317 nasal

EOTAXIN Lautropia -0.009483537 nasal

EOTAXIN Unclassified_Candidatus_Saccharibacteria -0.00620504 nasal

FASL Dolosigranulum -0.039545052 nasal

FGFB Dolosigranulum -0.034689697 nasal

GCSF Dolosigranulum -0.036204977 nasal

GCSF Kingella -0.028502658 nasal

GCSF Roseomonas 0.032091027 nasal

GMCSF Corynebacterium -0.005049111 nasal

GMCSF Dolosigranulum -0.027119031 nasal

GMCSF Massilia 0.015009875 nasal

GMCSF Microbacterium 0.012587308 nasal

GMCSF Moraxella -0.039542931 nasal

GMCSF Staphylococcus 0.006930242 nasal

GMCSF Unclassified_Neisseriales 0.017483648 nasal

GROA Lautropia -0.022507692 nasal

HGF Moraxella 0.034814936 nasal

HGF Roseomonas 0.024888288 nasal

HGF Unclassified_Betaproteobacteria 0.019626633 nasal

ICAM1 Kocuria -0.000952276 nasal

ICAM1 Microbacterium -0.000973196 nasal

ICAM1 Peptoniphilus -0.001002897 nasal

IFNA Dolosigranulum -0.026480576 nasal

IFNA Roseomonas 0.015316732 nasal

IFNB Micrococcus -0.003533718 nasal

IFNB Unclassified_Betaproteobacteria -0.00417 nasal

IFNG Dolosigranulum -0.045816673 nasal

IFNG Moraxella -0.042874252 nasal

IL10 Dolosigranulum -0.03201302 nasal

IL10 Micrococcus -0.017851652 nasal

IL10 Streptococcus 0.009006942 nasal

IL12P70 Micrococcus -0.028640037 nasal

IL12P70 Staphylococcus 0.007753609 nasal

IL13 Dolosigranulum -0.039799387 nasal

IL13 Kingella -0.049642499 nasal

IL13 Staphylococcus 0.004935136 nasal

IL15 Micrococcus -0.021841153 nasal

IL15 Streptococcus 0.007239795 nasal

IL17A Corynebacterium -0.003180329 nasal IL17A Dolosigranulum -0.016580313 nasal

IL17F Capnocytophaga -0.001961637 nasal

IL17F Micrococcus -0.002255052 nasal

IL17F Negativicoccus 0.003467024 nasal

IL18 Dolosigranulum -0.008009746 nasal

IL18 Ko curia -0.006775793 nasal

IL18 Staphylococcus 0.001318094 nasal

IL1A Dolosigranulum -0.008152849 nasal

IL1A Peptoniphilus -0.003622319 nasal

IL1A Stenotrophomonas 0.00779035 nasal

IL1 B Bacillus -0.039285349 nasal

IL1 B Pelomonas -0.090738554 nasal

IL1 B Streptococcus 0.012419908 nasal

IL1 RA Kingella -0.071394616 nasal

IL1 RA Lactobacillus -0.072841629 nasal

IL1 RA Unclassified_Betaproteobacteria 0.074169 nasal

IL2 Brevundimonas 0.011301183 nasal

IL2 Cutibacterium 0.004491992 nasal

IL2 Finegoldia -0.017861032 nasal

IL2 Lachnoanaero baculum -0.032919513 nasal

IL2 Lawsonella -0.022843043 nasal

IL2 Massilia 0.009920733 nasal

IL2 Microbacterium 0.011154585 nasal

IL2 Micrococcus -0.041783419 nasal

IL2 Negativicoccus 0.029060551 nasal

IL2 Unclassified_Betaproteobacteria 0.03597751 nasal

IL2 Unclassified_Neisseriales 0.011199161 nasal

IL21 Capnocytophaga -0.0098861 14 nasal

IL21 Dolosigranulum -0.017016524 nasal

IL21 Neisseria -0.009344016 nasal

IL21 Pelomonas -0.016458409 nasal

IL21 Porphyromonas -0.01010965 nasal

IL21 Stenotrophomonas 0.016230269 nasal

IL22 Actinomyces 0.001551142 nasal

IL22 Dermabacter -0.004477334 nasal

IL22 Dolosigranulum -0.002327801 nasal

IL22 Gemella 0.001507788 nasal

IL22 Phocaeicola -0.003467661 nasal

IL22 Porphyromonas 0.002350317 nasal

IL22 Schaalia 0.002510326 nasal IL22 Unclassified_Clostridiales -0.004708921 nasal

IL23 Dolosigranulum -0.042782173 nasal

IL23 Peptoniphilus -0.009986695 nasal

IL23 Roseomonas 0.025901987 nasal

IL27 Dolosigranulum -0.047700774 nasal

IL27 Roseomonas 0.031520225 nasal

IL27 Staphylococcus 0.00542153 nasal

IL31 Lachnoanaero baculum 0.044758665 nasal

IL31 Lactococcus -0.065411315 nasal

IL31 Neisseria 0.03126298 nasal

IL31 Peptoniphilus -0.059003042 nasal

IL31 Prevotella 0.027054605 nasal

IL31 Rothia 0.035324275 nasal

IL31 Streptococcus 0.030754901 nasal

IL31 Veillonella 0.036125615 nasal

IL4 Dolosigranulum -0.039844983 nasal

IL5 Brevundimonas 0.015313487 nasal

IL5 Cutibacterium 0.006251825 nasal

IL5 Finegoldia -0.013044924 nasal

IL5 Massilia 0.013385158 nasal

IL5 Microbacterium 0.013221515 nasal

IL5 Micrococcus -0.02061677 nasal

IL5 Negativicoccus 0.026672655 nasal

IL5 Unclassified_Neisseriales 0.01498203 nasal

IL5 Unclassified_Streptophyta 0.00743235 nasal

IL6 Streptococcus 0.017770743 nasal

IL7 Haemophilus 0.011392856 nasal

IL7 Klebsiella -0.010543803 nasal

IL8 Dolosigranulum -0.016404322 nasal

IL8 Ko curia -0.013139869 nasal

IL8 Peptoniphilus -0.004483514 nasal

IL8 Prevotellamassilia 0.011833897 nasal

IL9 Dolosigranulum -0.031977469 nasal

IL9 Kingella -0.03314127 nasal

IL9 Micrococcus -0.029252469 nasal

IP10 Kocuria -0.009846899 nasal

LEPTIN Agathobacter -0.000417577 nasal

LEPTIN Corynebacterium -6.48766E-05 nasal

LEPTIN Finegoldia 0.000149689 nasal

LEPTIN Granulicatella 0.000152987 nasal LEPTIN Leptotrichia 0.000211631 nasal

LEPTIN Moraxella -0.000511293 nasal

LEPTIN Schaalia 0.000218026 nasal

LEPTIN Staphylococcus 7.03337E-05 nasal

LIF Micrococcus -0.027311315 nasal

MCP1 Capnocytophaga -0.002303036 nasal

MCP1 Kingella -0.002596795 nasal

MCP1 Lachnoanaero baculum -0.003162682 nasal

MCP1 Neisseria -0.00147736 nasal

MCP1 Unclassified_Candidatus_Saccharibacteria -0.001798903 nasal

MCP1 Unclassified_Streptophyta 0.000979849 nasal

MCP1 Zea 0.001737002 nasal

MCP3 Peptoniphilus -0.019805892 nasal

MCSF Dolosigranulum -0.014061624 nasal

MCSF Lawsonella 0.004281319 nasal

MCSF Micrococcus -0.014782555 nasal

MCSF Peptoniphilus -0.005196789 nasal

MIG Abiotrophia -0.007670908 nasal

MIG Finegoldia 0.002427913 nasal

MIG Fusobacterium -0.005808301 nasal

MIG Haemophilus -0.005509633 nasal

MIG Kingella -0.010675088 nasal

MIG Leptotrichia -0.004640825 nasal

MIG Stenotrophomonas 0.012265183 nasal

MIG Veillonella -0.003971203 nasal

MIP1A Blautia -0.010842443 nasal

MIP1A Capnocytophaga -0.004952892 nasal

MIP1A Dolosigranulum -0.008524427 nasal

MIP1A Fusobacterium -0.004473259 nasal

MIP1A Klebsiella -0.005610383 nasal

MIP1A Lactobacillus -0.007446599 nasal

MIP1A Leptotrichia -0.004455845 nasal

MIP1A Neisseria -0.004443855 nasal

MIP1A Pelomonas -0.009031677 nasal

MIP1A Porphyromonas -0.00643677 nasal

MIP1A Unclassified_Lachnospiraceae -0.00913657 nasal

MIP1A Unclassified_Streptophyta -0.00309214 nasal

MIP1 B Capnocytophaga -0.002504904 nasal

MIP1 B Haemophilus -0.003147552 nasal

MIP1 B Kingella -0.00428603 nasal MIP1 B Peptoniphilus -0.002790509 nasal

MIP1 B Staphylococcus 0.000845688 nasal

MIP1 B Unclassified_Betaproteobacteria 0.004549639 nasal

NGF Dolosigranulum -0.044298686 nasal

NGF Roseomonas 0.047226242 nasal

PAH Abiotrophia 0.000474854 nasal

PAH Finegoldia -0.000296447 nasal

PAH Negativicoccus -0.000722473 nasal

PDGFBB Acinetobacter -0.006537294 nasal

PDGFBB Peptoniphilus -0.002488672 nasal

PDGFBB Unclassified_Clostridiales -0.005881642 nasal

RANTES Campylobacter -0.000857606 nasal

RANTES Ko curia -0.001237001 nasal

RESISTIN Klebsiella 0.000674271 nasal

RESISTIN Moraxella 0.001374893 nasal

RESISTIN Peptoniphilus -0.000360478 nasal

SCF Unclassified_Neisseriales -0.014454018 nasal

SDF1A Capnocytophaga -0.005567839 nasal

SDF1A Dolosigranulum -0.00920301 nasal

SDF1A Fusobacterium -0.003690515 nasal

SDF1A Kingella -0.007360429 nasal

SDF1A Peptoniphilus -0.003325778 nasal

SDF1A Porphyromonas -0.004734855 nasal

SDF1A Stenotrophomonas 0.009445661 nasal

TGFA Ko curia -0.02710462 nasal

TGFA Lachnoanaero baculum 0.011176634 nasal

TGFA Micrococcus -0.028207012 nasal

TGFA Unclassified_Neisseriales -0.017890993 nasal

TGFB Dolosigranulum -0.034489062 nasal

TGFB Micrococcus -0.033568752 nasal

TGFB Staphylococcus 0.006733814 nasal

TNFA Lautropia -0.013305054 nasal

TNFA Streptococcus 0.00634315 nasal

TNFB Peptoniphilus -0.006123412 nasal

TNFB Streptococcus 0.005378276 nasal

TRAIL Capnocytophaga -0.006029025 nasal

TRAIL Dolosigranulum -0.010414052 nasal

TRAIL Kingella -0.007336625 nasal

TRAIL Lactobacillus -0.007196453 nasal

TRAIL Lautropia -0.0074415 nasal TRAIL Peptoniphilus -0.003582894 nasal

TRAIL Stenotrophomonas 0.009582553 nasal

VCAM1 Moraxella 0.000781511 nasal

VCAM1 Subdoligranulum 0.000956596 nasal

VEGF Dolosigranulum -0.003963652 nasal

VEGF Peptoniphilus -0.002042995 nasal

Table 2. Microbial Genera Correlated with Clinical Analytes

Correlation

Microbial Genus Analytes Microbiome Coefficient

Agathobacter GLOB -0.005356815 Gut

Agathobacter TP -0.005514113 Gut

Agathobacter LYM 0.094716616 Gut

Agathobacter PLT -0.947360127 Gut

Agathobacter CHOLHDL 0.010646257 Gut

Agathobacter OR 0.003290014 Gut

Agathobacter LDLHDL 0.008465834 Gut

Agathobacter RDW 0.023020492 Gut

Agathobacter CHOLHDL 0.011035142 Gut

Agathobacter CR 0.001092536 Gut

Agathobacter EOS 0.0277714 Gut

Agathobacter LDLHDL 0.010609055 Gut

Ruminococcus HGB 0.021276863 Gut

Akkermansia A1C -0.008338842 Gut

Akkermansia BASO -0.002921234 Gut

Akkermansia CO2 0.025766098 Gut

Akkermansia BASO -0.003633019 Gut

Akkermansia BASO -0.002982775 Gut

Akkermansia BUN -0.0541 12981 Gut

Akkermansia BASO -0.00298568 Gut

Akkermansia A1C -0.027373098 Gut

Akkermansia BASO -0.002666482 Gut

Akkermansia A1C -0.01571406 Gut

Akkermansia A1C -0.01853334 Gut

Akkermansia A1C -0.010785664 Gut

Akkermansia BASO -0.00326816 Gut

Akkermansia A1C -0.008463498 Gut

Akkermansia BUN -0.052379875 Gut

Akkermansia EOSAB -0.002055755 Gut

Phascolarctobacterium HDL 0.172236584 Gut

Phascolarctobacterium RDW 0.018277776 Gut

Phascolarctobacterium TP 0.005041736 Gut

Phascolarctobacterium TP 0.008963111 Gut

Blautia CR -0.003055391 Gut

Blautia CR -0.002320667 Gut

Barnesiella EGFR 0.161040459 Gut

Romboutsia ALT -0.436239518 Gut

Romboutsia MCV 0.093117999 Gut Romboutsia ALKP 0.273234223 Gut Akkermansia BUN -0.049426933 Gut Akkermansia EOSAB -0.001510737 Gut Akkermansia EOSAB -0.00254701 Gut Akkermansia BUN -0.046467891 Gut Akkermansia EOSAB -0.001729992 Gut Akkermansia CR 0.001103788 Gut Akkermansia A1C -0.007396889 Gut Sutterella ALKP -0.429706297 Gut Sutterella LDL -0.466368944 Gut Sutterella NHDL -0.479153298 Gut Faecalibacillus HDL 0.281626026 Gut Faecalibacillus ALKP 0.333992903 Gut Faecalibacillus TP 0.008335581 Gut Faecalibacillus HDL 0.31839612 Gut Faecalibacillus HDL 0.206317972 Gut Faecalibacillus CHOLHDL 0.016479411 Gut Faecalibacillus LDLHDL 0.013131203 Gut Faecalibacillus CHOLHDL 0.012839837 Gut Faecalibacillus LDLHDL 0.010923805 Gut Faecalibacillus ALKP 0.514056051 Gut Faecalibacillus TP 0.010130811 Gut Faecalibacillus HDL 0.15355654 Gut Faecalibacillus CHOLHDL 0.010354343 Gut Faecalibacillus LDLHDL 0.008950255 Gut Faecalibacillus CO2 0.028680837 Gut Faecalibacillus CHOLHDL 0.024170583 Gut Faecalibacillus LDLHDL 0.020013546 Gut Faecalibacillus TP 0.006137132 Gut Faecalibacillus ALKP 0.742056695 Gut Faecalibacillus HDL 0.290912852 Gut Faecalibacillus TP 0.014724029 Gut Ruthenibacterium BUN 0.064091712 Gut Romboutsia ALKP 0.25363153 Gut Romboutsia ALT -0.265706744 Gut Romboutsia UALB -0.102961194 Gut Romboutsia EGFR -0.107613237 Gut Romboutsia ALKP 0.420223528 Gut Romboutsia ALKP 0.290241511 Gut Romboutsia ALKP 0.342089774 Gut Intestinimonas TBIL -0.005185854 Gut Ihubacter ALCRU 0.89515941 Gut Akkermansia BASO -0.003237466 Gut

Akkermansia BASO -0.003139056 Gut

Akkermansia BUN -0.121459953 Gut

Akkermansia EOSAB -0.004328684 Gut

Akkermansia BASO -0.005413098 Gut

Akkermansia EOSAB -0.002275303 Gut

Akkermansia BASO -0.00448212 Gut

Akkermansia EOSAB -0.003177104 Gut

Akkermansia BASOAB -0.000136196 Gut

Roseburia PLT 1 .884756707 Gut

Roseburia GLOB 0.005426228 Gut

Fusicatenibacter ALT -0.150881618 Gut

Fusicatenibacter ALKP 0.300319665 Gut

Fusicatenibacter ALKP 0.368838714 Gut

Fusicatenibacter OR -0.002041458 Gut

Fusicatenibacter ALKP 0.217076429 Gut

Fusicatenibacter OR -0.003037505 Gut

Fusicatenibacter ALT -0.213656361 Gut

Fusicatenibacter ALT -0.210714154 Gut

Fusicatenibacter ALKP 0.89440186 Gut

Fusicatenibacter ALKP 0.727390507 Gut

Agathobacter ALT 0.083317973 Gut

Agathobacter OR 0.003342294 Gut Ruminococcus MCH 0.085455661 Gut Ruminococcus MCV 0.285981967 Gut

Unclassified_Lachnospiraceae ALKP 0.253137507 Gut

Unclassified_Lachnospiraceae ALKP 0.737913061 Gut

Phascolarctobacterium GLU -0.336219345 Gut

Phascolarctobacterium IGM 0.737993807 Gut

Phascolarctobacterium MCHC 0.049722994 Gut

Phascolarctobacterium RBC 0.01170495 Gut

Phascolarctobacterium GLU -0.198345589 Gut

Phascolarctobacterium IGM 1.305597012 Gut

Phascolarctobacterium TP 0.006728837 Gut

Oscillibacter MONOAB 0.002699862 Gut

Oscillibacter TGL 0.740307109 Gut

Roseburia CA -0.006976242 Gut

Roseburia PLT 0.596631489 Gut

Roseburia TP -0.007858667 Gut

Roseburia PLT 1 .258949756 Gut

Roseburia NEUTAB -0.021826641 Gut

Roseburia PLT 1.231061422 Gut Roseburia PLT 2.164453976 Gut

Faecalibacillus ALKP 0.295260365 Gut Faecalibacillus MCH 0.037089741 Gut Faecalibacillus LDLHDL 0.030864842 Gut

Odoribacter EGFR 0.134538457 Gut Odoribacter EGFR 0.420012646 Gut

Anaerobutyricum K -0.010093359 Gut Veillonella EGFR -0.097224222 Gut

Butyricimonas IGM 0.926843011 Gut Butyricimonas IGM 1.780763432 Gut Intestinimonas TBIL -0.006805047 Gut Intestinimonas TBIL -0.005513367 Gut Intestinimonas EGFR -0.106150821 Gut Intestinimonas MCH 0.035854551 Gut Staphylococcus RDW 0.008745474 Skin Staphylococcus RDW 0.0127358 Skin Staphylococcus CR -0.001412777 Skin Haemophilus LDL -0.552175761 Skin Haemophilus MCV -0.112268059 Skin Haemophilus MONO 0.037038648 Skin Staphylococcus UALB 0.084740197 Skin Staphylococcus CR -0.001410136 Skin Staphylococcus UALB 0.055123076 Skin Staphylococcus CHOLHDL -0.009663252 Skin Staphylococcus LDLHDL -0.008713585 Skin Staphylococcus UALB 0.072100455 Skin Staphylococcus CA -0.004023835 Skin Staphylococcus CR -0.001798809 Skin Staphylococcus UALB 0.058360612 Skin Staphylococcus CHOLHDL -0.005853482 Skin Staphylococcus LDLHDL -0.005666458 Skin Corynebacterium RDW 0.021492961 Skin Corynebacterium ALKP -0.353104807 Skin Corynebacterium ALKP -0.250379265 Skin Haemophilus CHOL -1.184949819 Skin Haemophilus CHOLHDL -0.044596366 Skin Haemophilus LDL -0.905490147 Skin Haemophilus LDLHDL -0.027678253 Skin Haemophilus MONO 0.040062998 Skin Haemophilus NHDL -1.670570802 Skin Haemophilus CHOLHDL -0.027657663 Skin Haemophilus LDLHDL -0.014087302 Skin Haemophilus MONO 0.048104022 Skin

Haemophilus NHDL -0.750586557 Skin

Haemophilus CHOL -1.515470111 Skin

Haemophilus CHOLHDL -0.01584292 Skin

Haemophilus LDL -1.009545706 Skin

Haemophilus LDLHDL -0.010557627 Skin

Haemophilus TGL -1.71789091 Skin

Haemophilus CHOLHDL -0.044939467 Skin

Haemophilus LDLHDL -0.039053518 Skin

Haemophilus TGL -2.048063105 Skin

Haemophilus CHOLHDL -0.040281071 Skin

Haemophilus HCT -0.054833432 Skin

Haemophilus HGB -0.024044714 Skin

Haemophilus LDL -1.800290082 Skin

Haemophilus LDLHDL -0.035628405 Skin

Haemophilus NHDL -2.068429393 Skin

Haemophilus TGL -1.350505754 Skin

Haemophilus CHOL -1.5163154 Skin

Haemophilus CHOLHDL -0.032933421 Skin

Haemophilus LDL -1.486704109 Skin

Haemophilus LDLHDL -0.028905258 Skin

Haemophilus NHDL -1.704188174 Skin

Haemophilus TGL -1.095257125 Skin

Haemophilus CHOLHDL -0.034238777 Skin

Haemophilus LDL -0.384774797 Skin

Haemophilus LDLHDL -0.02213169 Skin

Haemophilus MONO 0.052060275 Skin

Haemophilus NHDL -0.913585247 Skin

Haemophilus TGL -2.654025634 Skin

Haemophilus CHOL -0.788921315 Skin

Haemophilus CHOLHDL -0.03909901 Skin

Haemophilus LDL -0.605950789 Skin

Haemophilus LDLHDL -0.02433314 Skin

Haemophilus MONO 0.032082071 Skin

Haemophilus NHDL -1.25259791 Skin

Haemophilus LDLHDL -0.023703414 Skin

Haemophilus CHOL -1.400605195 Skin

Haemophilus LDL -1.378535264 Skin

Haemophilus LDLHDL -0.048029106 Skin

Haemophilus NHDL -2.334356144 Skin

Haemophilus ALB -0.004820878 Skin

Haemophilus CHOLHDL -0.009604306 Skin Haemophilus LDL -1.145442623 Skin

Haemophilus NHDL -1.384082925 Skin

Haemophilus TGL -1.197854229 Skin

Haemophilus CHOL -1.229854033 Skin

Haemophilus CHOLHDL -0.016606184 Skin

Haemophilus LDL -0.948182064 Skin

Haemophilus LDLHDL -0.012691788 Skin

Haemophilus MONO 0.059200055 Skin

Haemophilus NHDL -1 .234534299 Skin

Haemophilus TGL -1.431591819 Skin

Haemophilus CHOL -0.832809521 Skin

Haemophilus NHDL -0.538145671 Skin

Haemophilus TGL -0.982939064 Skin

Haemophilus ALB -0.00609757 Skin

Haemophilus CHOL -1.111506293 Skin

Haemophilus CHOLHDL -0.033438598 Skin

Haemophilus LDL -0.86865389 Skin

Haemophilus LDLHDL -0.022509869 Skin

Haemophilus CHOL -0.897733137 Skin

Haemophilus CHOLHDL -0.032224568 Skin

Haemophilus LDL -0.987820008 Skin

Haemophilus LDLHDL -0.026138309 Skin

Haemophilus TGL -1.361095992 Skin

Haemophilus CHOLHDL -0.034019036 Skin

Haemophilus LDL -1.633912342 Skin

Haemophilus LDLHDL -0.029694779 Skin

Haemophilus NHDL -1.903920557 Skin

Haemophilus TGL -1 .356348344 Skin

Haemophilus CHOLHDL -0.042593608 Skin

Haemophilus LDL -1.708706903 Skin

Haemophilus LDLHDL -0.036165692 Skin

Haemophilus NHDL -2.038593875 Skin

Haemophilus TGL -1.66687622 Skin

Haemophilus CHOL -0.993204136 Skin

Haemophilus LDL -0.784151651 Skin

Haemophilus NHDL -0.629352407 Skin

Haemophilus CHOLHDL -0.02542401 Skin

Haemophilus LDL -1 .626746563 Skin

Haemophilus LDLHDL -0.025204375 Skin

Haemophilus MONO 0.022267348 Skin

Haemophilus TGL -0.54389773 Skin Corynebacterium CR -0.001386031 Skin Pelomonas ALKP 0.532263176 Skin Pelomonas ALKP 0.370851978 Skin Pelomonas LYMAB 0.014341238 Skin

Staphylococcus OR -0.002074128 Skin Prevotella CHOLHDL -0.012825189 Skin Prevotella LDLHDL -0.011565831 Skin Prevotella EGFR 0.243740531 Skin Prevotella CHOLHDL -0.01 1196991 Skin Prevotella CHOLHDL -0.021620369 Skin Prevotella LDLHDL -0.017654933 Skin Prevotella CHOLHDL -0.026013204 Skin Prevotella LDLHDL -0.023019414 Skin Prevotella CHOLHDL -0.027236858 Skin Prevotella LDLHDL -0.023524247 Skin

Corynebacterium RDW 0.009475153 Skin Corynebacterium IGM -0.400012563 Skin Unclassified_Carnobacteriaceae TGL -2.481816875 Skin Dermabacter EGFR -0.285470223 Skin

Stenotrophomonas LDL 0.764191091 Skin Stenotrophomonas IGM 0.991001056 Skin Prevotella ALKP -0.15652465 Oral cavity Prevotella HGB 0.029486323 Oral cavity Prevotella ALB 0.00583 Oral cavity Prevotella ALKP -0.418619358 Oral cavity Veillonella ALKP -0.508347633 Oral cavity Schaalia ALB -0.004443067 Oral cavity Schaalia TP -0.010089685 Oral cavity

Unclassified Candidatus Saccharibacteria IGM -0.474202691 Oral cavity

Unclassified Carnobacteriaceae TBIL 0.002328499 Oral cavity

Oribacterium NA. 0.065379937 Oral cavity

Lachnoanaerobaculum CHOLHDL 0.012584583 Oral cavity

Streptococcus HGB 0.015257364 Oral cavity

Alloprevotella EOS 0.016789815 Oral cavity

Unclassified_Candidatus_Saccharibacteria MONOAB 0.001547602 Oral cavity

Peptostreptococcus HCT -0.028997985 Oral cavity

Peptostreptococcus HGB -0.008646219 Oral cavity

Lachnoanaerobaculum CHOLHDL 0.015445205 Oral cavity

Lachnoanaerobaculum LDLHDL 0.01157626 Oral cavity

Actinomyces CHOL -0.293260336 Oral cavity

Actinomyces LDL -0.32525834 Oral cavity

Actinomyces NHDL -0.434097316 Oral cavity Mogibacterium CHOLHDL -0.026897512 Oral cavity Aggregatibacter PLT 0.312489595 Oral cavity Aggregatibacter PLT 0.578788878 Oral cavity Aggregatibacter RDW 0.014594145 Oral cavity Haemophilus HDL -0.190508736 Oral cavity Haemophilus HDL -0.196987866 Oral cavity Haemophilus HDL -0.266371409 Oral cavity Streptococcus ALB -0.011971878 Oral cavity Lancefieldella CHOL -0.667227301 Oral cavity Snodgrassella UALBCR -0.248336142 Oral cavity Streptococcus HCT 0.036273824 Oral cavity Streptococcus HGB 0.013167246 Oral cavity Schaalia TP -0.009800657 Oral cavity

Lancefieldella LDL -0.2981 15165 Oral cavity

Unclassified_Neisseriaceae MONOAB -0.001836822 Oral cavity Unclassified_Neisseriaceae NEUTAB -0.016107387 Oral cavity Lautropia GLU -0.358969323 Nasal cavity Lautropia GLU -0.423151134 Nasal cavity Lautropia GLU -0.279619676 Nasal cavity Lautropia GLU -0.338142129 Nasal cavity Lautropia GLU -0.387273523 Nasal cavity Lautropia GLU -0.292268396 Nasal cavity Phocaeicola HSCRP -0.041976261 Nasal cavity Phocaeicola IGM 1 .623351072 Nasal cavity Phocaeicola MCH 0.072360029 Nasal cavity Phocaeicola HSCRP -0.10974401 Nasal cavity Phocaeicola IGM 1 .624552686 Nasal cavity Phocaeicola MCH 0.052501153 Nasal cavity Phocaeicola UALBCR -0.334712619 Nasal cavity

Blautia A1C -0.012691739 Nasal cavity

Schaalia CHOL 1 .430885564 Nasal cavity Schaalia MCV 0.127753182 Nasal cavity Schaalia GLOB 0.006986732 Nasal cavity Phocaeicola IGM 1 .601471146 Nasal cavity Lawsonella CR -0.001367122 Nasal cavity

Granulicatella BUN 0.217947353 Nasal cavity Granulicatella LDL 0.53512433 Nasal cavity Granulicatella BUN 0.161241793 Nasal cavity Granulicatella LDL 1 .423163611 Nasal cavity Granulicatella BUN 0.162239148 Nasal cavity Granulicatella LDL 1 .738518478 Nasal cavity Phascolarctobacterium CL 0.173394905 Gut Phascolarctobacterium CL 0.10624458 Gut Phascolarctobacterium CL 0.121383049 Gut

Phascolarctobacterium CL 0.065287669 Gut

Phascolarctobacterium CL 0.096273549 Gut

Phascolarctobacterium CL 0.149290433 Gut

Phascolarctobacterium MCHC 0.044032151 Gut

Parasutterella HGB 0.032948873 Gut

Anaerobutyricum K -0.011092835 Gut

Anaerobutyricum BASOAB -0.000462188 Gut

Anaerobutyricum K -0.008745319 Gut

Mediterraneibacter MCV 0.228864394 Gut

Beduinibacterium GLOB -0.006981216 Gut

Ruminococcus LDLHDL -0.021391415 Gut

Parasutterella HGB 0.029976406 Gut

Parasutterella MCV 0.186317022 Gut

Parasutterella LYMAB -0.017958071 Gut

Parasutterella HGB 0.047267557 Gut

Parasutterella LYMAB -0.028772248 Gut

Parasutterella HDL 0.472728498 Gut

Parasutterella MCV 0.390153858 Gut

Parasutterella HGB 0.030585611 Gut

Parasutterella MCV 0.186072442 Gut

Unclassified_Eggerthellaceae NHDL -0.766389165 Gut

Unclassified_Eggerthellaceae LDLHDL -0.033611634 Gut

Delftia LYMAB 0.014867311 Skin

Delftia MCHC 0.072997638 Skin

Delftia MCHC 0.048820318 Skin

Acinetobacter HDL -0.540550622 Skin

Haemophilus EGFR 0.380519833 Skin

Haemophilus MONOAB 0.005465883 Skin

Haemophilus MONOAB 0.0063244 Skin

Unclassified_Candidatus_Saccharibacteria PLT 2.352087922 Skin

Pseudomonas MCV -0.20151087 Skin

Pseudomonas EGFR -1 .076587008 Skin

Pseudomonas EGFR -1.16913125 Skin

Prevotella EOS 0.047309478 Oral cavity

Prevotella EOSAB 0.003562748 Oral cavity

Prevotella NA. 0.06870046 Oral cavity

Prevotella ALKP -0.501598926 Oral cavity

Prevotella NA. 0.10579263 Oral cavity

Prevotella TP -0.006401312 Oral cavity

Prevotella NA. 0.067488443 Oral cavity

Veillonella EOS 0.098533185 Oral cavity Veillonella EOSAB 0.006158765 Oral cavity

Veillonella EOS 0.09911502 Oral cavity

Veillonella TGL 1 .760510765 Oral cavity

Veillonella ALB -0.007241136 Oral cavity

Veillonella EOS 0.060848705 Oral cavity

Veillonella EOSAB 0.00500501 Oral cavity

Veillonella EOS 0.076014919 Oral cavity

Veillonella EOSAB 0.003259312 Oral cavity

Veillonella EOS 0.08962076 Oral cavity

Veillonella EOS 0.047704008 Oral cavity

Veillonella EOSAB 0.003296531 Oral cavity

Veillonella NA. 0.063894749 Oral cavity

Veillonella EOS 0.094925097 Oral cavity

Veillonella EOS 0.119795678 Oral cavity

Veillonella EOS 0.062068522 Oral cavity

Veillonella EOSAB 0.004830499 Oral cavity

Veillonella TGL 1 .882033584 Oral cavity

Veillonella EOS 0.083361518 Oral cavity

Veillonella EOSAB 0.006535404 Oral cavity

Veillonella TGL 2.135351141 Oral cavity

Veillonella EOS 0.06541934 Oral cavity

Veillonella EOSAB 0.005553419 Oral cavity

Veillonella EOS 0.095643056 Oral cavity

Veillonella ALB -0.010267975 Oral cavity

Veillonella EOS 0.096020553 Oral cavity

Veillonella EOS 0.094788314 Oral cavity

Veillonella EOSAB 0.006187639 Oral cavity

Veillonella NA. 0.099900377 Oral cavity

Veillonella TGL 2.024843033 Oral cavity

Streptococcus NA. 0.073507131 Oral cavity

Eggerthia UALBCR 0.556805547 Oral cavity

Streptococcus HOT -0.147525531 Oral cavity

Streptococcus HGB -0.053722197 Oral cavity

Streptococcus HOT -0.145650564 Oral cavity

Streptococcus HGB -0.047071329 Oral cavity

Rothia HOT -0.122746945 Oral cavity

Rothia RBC -0.012792313 Oral cavity

Rothia MOV 0.090899403 Oral cavity

Rothia HOT -0.138528011 Oral cavity

Rothia HGB -0.055769132 Oral cavity

Rothia AST 0.230842036 Oral cavity

Rothia HOT -0.127149221 Oral cavity Rothia HGB -0.042617532 Oral cavity Rothia HCT -0.231970924 Oral cavity Rothia HCT -0.200779662 Oral cavity Rothia HGB -0.072142455 Oral cavity Rothia AST 0.221332298 Oral cavity

Tannerella TBIL 0.005593862 Oral cavity Tannerella TGL -1.17478603 Oral cavity Tannerella TBIL 0.004828695 Oral cavity Tannerella TBIL 0.006939616 Oral cavity Parvimonas HSCRP 0.036569603 Oral cavity Parvi monas TBIL 0.009333738 Oral cavity Parvimonas HSCRP 0.043641236 Oral cavity Parvimonas RDW 0.014568879 Oral cavity Parvimonas HSCRP 0.045892021 Oral cavity Parvimonas RDW 0.01998389 Oral cavity Parvimonas HSCRP 0.057464364 Oral cavity Parvimonas HSCRP 0.069440926 Oral cavity Parvimonas HSCRP 0.070898033 Oral cavity Parvimonas TBIL 0.004959719 Oral cavity Parvimonas HSCRP 0.042502018 Oral cavity Parvimonas HSCRP 0.050527355 Oral cavity Parvimonas HSCRP 0.062010967 Oral cavity Parvimonas TBIL 0.005478911 Oral cavity Parvimonas HSCRP 0.071178123 Oral cavity Parvimonas RDW 0.016014332 Oral cavity Parvimonas RDW 0.020865824 Oral cavity

Parvimonas HSCRP 0.057033827 Oral cavity Parvimonas RDW 0.014153768 Oral cavity

Veillonella TGL 1 .881798538 Oral cavity Parvimonas HSCRP 0.051863861 Oral cavity Parvimonas TBIL 0.012949337 Oral cavity Parvimonas HSCRP 0.03706207 Oral cavity Parvimonas TBIL 0.010596901 Oral cavity Leptotrichia NEUTAB 0.033878608 Nasal cavity Leptotrichia WBC 0.045915247 Nasal cavity

Corynebacterium WBC -0.028391453 Nasal cavity Corynebacterium EGFR -0.313840112 Nasal cavity Corynebacterium NEUTAB -0.026996399 Nasal cavity Corynebacterium WBC -0.050732114 Nasal cavity Corynebacterium EGFR -0.281132985 Nasal cavity Corynebacterium NEUTAB -0.053888213 Nasal cavity Corynebacterium NEUTAB -0.037715524 Nasal cavity Corynebacterium WBC -0.062186969 Nasal cavity

Corynebacterium WBC -0.025718864 Nasal cavity

Corynebacterium NEUTAB -0.027386027 Nasal cavity

Corynebacterium EGFR -0.472979258 Nasal cavity

Corynebacterium WBC -0.026126619 Nasal cavity

Corynebacterium WBC -0.055569147 Nasal cavity

Corynebacterium NEUTAB -0.036071286 Nasal cavity

Corynebacterium WBC -0.068875961 Nasal cavity

Corynebacterium NEUTAB -0.059974995 Nasal cavity

Corynebacterium NEUTAB -0.02497876 Nasal cavity

Corynebacterium WBC -0.035517784 Nasal cavity

Corynebacterium NEUTAB -0.022312095 Nasal cavity

Corynebacterium NEUTAB -0.036974896 Nasal cavity

Corynebacterium WBC -0.066767283 Nasal cavity

Corynebacterium EGFR -0.300750257 Nasal cavity

Corynebacterium NEUTAB -0.047263975 Nasal cavity

Corynebacterium EGFR -0.317532569 Nasal cavity

Corynebacterium EGFR -0.323855092 Nasal cavity

Corynebacterium WBC -0.036123049 Nasal cavity

Corynebacterium EGFR -0.366640462 Nasal cavity

Corynebacterium NEUTAB -0.025894117 Nasal cavity

Corynebacterium WBC -0.045364563 Nasal cavity

Corynebacterium WBC -0.037314036 Nasal cavity

Schaalia CHOLHDL 0.049759841 Nasal cavity

Schaalia CHOL 2.57570761 Nasal cavity

Schaalia CHOLHDL 0.041296826 Nasal cavity

Schaalia LDL 1.931521106 Nasal cavity

Schaalia CHOL 1.97963853 Nasal cavity

Schaalia NHDL 1.900519514 Nasal cavity

Schaalia CHOL 2.685406899 Nasal cavity

Schaalia LDL 1.737995581 Nasal cavity

Schaalia NHDL 2.264858863 Nasal cavity

Schaalia CHOL 1 .894260468 Nasal cavity

Schaalia CHOLHDL 0.04324007 Nasal cavity

Schaalia LDL 1 .469065339 Nasal cavity

Schaalia NHDL 2.121599277 Nasal cavity

Agathobacter EOS -0.066357989 Gut

Agathobacter WBC -0.038338712 Gut

Agathobacter LDLHDL 0.042777411 Gut

Ruminococcus HCT 0.093828618 Gut Ruminococcus MCV 0.193004955 Gut Ruminococcus ALB 0.008351637 Gut Ruminococcus MCHC 0.084694643 Gut

Ruminococcus MCV 0.160296844 Gut

Ruminococcus ALT 0.705559653 Gut

Ruminococcus ALT 0.226408437 Gut

Ruminococcus IGM -0.600776622 Gut

Ruminococcus ALB 0.009347043 Gut

Ruminococcus MCV 0.283824914 Gut

Clostridium_IV A1C 0.019008417 Gut

Clostridium_IV HDL 0.362556056 Gut

Clostridium_IV HDL 0.26251623 Gut

Unclassified_Lachnospiraceae MCH 0.112518327 Gut

Unclassified_Lachnospiraceae MCV 0.353413137 Gut

Phascolarctobacterium ALT -0.533525298 Gut

Phascolarctobacterium CR -0.004052644 Gut

Phascolarctobacterium GLOB 0.013654171 Gut

Phascolarctobacterium ALT -0.498806886 Gut

Phascolarctobacterium ALT -0.389225113 Gut

Phascolarctobacterium CA -0.010269818 Gut

Phascolarctobacterium CR -0.002028435 Gut

Oscillibacter MCV 0.287806796 Gut

Romboutsia MCV 0.109922511 Gut

Romboutsia MCH 0.043827829 Gut

Romboutsia MCH 0.051105717 Gut

Romboutsia MCHC 0.061195951 Gut

Romboutsia MCV 0.137396335 Gut

Collinsella HDL -1.119353876 Gut

Collinsella MCV -0.280660786 Gut

Neglecta MONOAB 0.004089111 Gut

Unclassified_Firmicutes NEUTAB -0.028878222 Gut

Unclassified_Firmicutes NEUTAB -0.01891224 Gut

Unclassified_Firmicutes WBC -0.021599353 Gut

Lawsonibacter RDW -0.035634689 Gut

Phocaeicola ALB -0.013056044 Gut

Phocaeicola CL -0.313190325 Gut

Phocaeicola CHOLHDL -0.03326046 Gut

Phocaeicola LDLHDL -0.030638292 Gut

Agathobacter BASOAB 0.000433963 Gut

Agathobacter NEUT -0.25326201 Gut

Agathobacter MONO 0.028944461 Gut

Oscillibacter ALKP -0.555071655 Gut

Oscillibacter LDL -0.8375149 Gut

Oscillibacter LDL -0.895893694 Gut Romboutsia CL 0.30071509 Gut Collinsella HDL -0.453022451 Gut Collinsella LYMAB 0.022386475 Gut Collinsella RDW -0.044607896 Gut Collinsella TBIL 0.004510922 Gut

Dorea MCH -0.255919713 Gut

Victivallis NEUT -0.435511769 Gut Victivallis NEUT -0.354486107 Gut Victivallis RDW -0.066573375 Gut Phocaeicola IGM 1 .005207043 Gut Phocaeicola RDW 0.069599197 Gut Phocaeicola RDW 0.057354406 Gut Roseburia ALB -0.008458927 Gut Roseburia CR -0.00435502 Gut Roseburia ALB -0.006224646 Gut Roseburia TBIL -0.006398253 Gut Roseburia TBIL -0.006450848 Gut Roseburia TBIL -0.006846036 Gut Roseburia CR -0.003294947 Gut Roseburia BASO 0.007308602 Gut Roseburia CR -0.003807951 Gut

Mediterraneibacter AST -0.232971219 Gut Mediterraneibacter CHOLHDL -0.047788597 Gut Mediterraneibacter LDLHDL -0.040362901 Gut Phocaeicola RDW 0.064140948 Gut Phocaeicola ALB -0.017454994 Gut Phocaeicola IGM 1 .352846359 Gut

Alistipes IGM -0.917252788 Gut Agathobacter AG -0.059083894 Gut Agathobacter LDL 1.053129173 Gut Agathobacter LDLHDL 0.052072106 Gut Agathobacter NHDL 1 .395366306 Gut Agathobacter PLT -1.894565612 Gut Agathobacter MCV -0.156724538 Gut Agathobacter BUN -0.108703705 Gut Agathobacter EOS -0.059361291 Gut Agathobacter LDLHDL 0.02150706 Gut Agathobacter BASOAB 0.000318323 Gut Agathobacter EOS -0.1421 10197 Gut Agathobacter LDLHDL 0.018957759 Gut Agathobacter BASOAB 0.000579081 Gut Agathobacter EOS -0.1092004 Gut Agathobacter NHDL 1.157841736 Gut Ruminococcus HDL -0.496617171 Gut Clostridium_IV A1C 0.026213567 Gut Clostridium_IV RDW 0.043925119 Gut Faecalibacterium ALKP -0.601245281 Gut Oscillibacter HDL -0.745604325 Gut Oscillibacter HDL -0.825734418 Gut Oscillibacter MONO 0.068024731 Gut Roseburia BASO 0.005282745 Gut Roseburia CR -0.006667451 Gut Roseburia TBIL -0.009774855 Gut Roseburia CR -0.006545447 Gut Roseburia ALB -0.015166839 Gut Roseburia CR -0.00799078 Gut Roseburia NA. -0.35730465 Gut Roseburia TP -0.020309534 Gut Roseburia CR -0.005324318 Gut Roseburia EOS -0.172943301 Gut Collinsella HGB 0.032659549 Gut Collinsella TBIL 0.006976783 Gut Collinsella TBIL 0.017783203 Gut Collinsella HDL -0.58981017 Gut Collinsella MCHC -0.081585003 Gut Collinsella MCV -0.290771115 Gut Collinsella LYMAB 0.019474294 Gut Collinsella MCV -0.185026952 Gut Collinsella MCHC -0.063974885 Gut Collinsella MCV -0.184602076 Gut Dorea EGFR 0.436877028 Gut Dorea EGFR 0.329919944 Gut

Neglecta WBC 0.043601464 Gut Odoribacter NEUT 0.393006321 Gut Unclassified_Muribaculaceae CHOL 0.92515022 Gut Unclassified_Muribaculaceae LDLHDL 0.023104293 Gut Butyricimonas K -0.018828132 Gut Butyricimonas EGFR 0.286388444 Gut Anaerotruncus RBC -0.021273879 Gut Anaerotruncus HCT -0.200038524 Gut Anaerotruncus RBC -0.021474344 Gut Cutibacterium NEUT 0.161512133 Skin Cutibacterium NEUT 0.250581244 Skin Cutibacterium NEUT 0.274702185 Skin Cutibacterium NEUT 0.238289923 Skin

Staphylococcus RDW 0.015129358 Skin

Streptococcus NEUT 0.3829266 Skin

Escherichia/Shigella NEUTAB -0.052602057 Skin

Unclassified_Bacillaceae_1 MCH -0.131672552 Skin

Unclassified_Bacillaceae_1 MCV -0.454006248 Skin

Cutibacterium IGM -0.968923788 Skin

Cutibacterium RDW 0.014480371 Skin

Cutibacterium IGM -0.521261201 Skin

Cutibacterium IGM -0.479430037 Skin

Staphylococcus UALB 0.106506672 Skin

Staphylococcus UALB 0.128362956 Skin

Staphylococcus UALB 0.059341499 Skin

Haemophilus CHOLHDL -0.036436192 Skin

Haemophilus TGL -2.094857441 Skin

Haemophilus CHOLHDL -0.048402988 Skin

Haemophilus CHOLHDL -0.025452846 Skin

Haemophilus TGL -1.81502573 Skin

Haemophilus TGL -2.194921869 Skin

Gemella MCV 0.35092088 Skin

Gemella MCV 0.552148984 Skin

Gemella RDW 0.106175664 Skin

Gemella RDW 0.055288612 Skin

Cutibacterium IGM -0.532233187 Skin

Staphylococcus MCV 0.070300765 Skin

Staphylococcus EGFR 0.103004848 Skin

Anaerococcus LYM 0.223412568 Skin

Anaerococcus LYM 0.169102942 Skin

Delftia A1C 0.017395891 Skin

Delftia LYM -0.128532879 Skin

Delftia NEUT -0.581369812 Skin

Delftia TBIL -0.005632438 Skin

Delftia NEUT -0.376737636 Skin

Delftia EOS -0.049368948 Skin

Delftia MCV -0.167981044 Skin

Delftia NEUT -0.21 1674029 Skin

Delftia ALKP 0.412429198 Skin

Stenotrophomonas AST -0.518211312 Skin

Stenotrophomonas MONO 0.108079915 Skin

Stenotrophomonas AST -0.373166104 Skin

Stenotrophomonas MONO 0.078142581 Skin

Unclassified_Candidatus_Saccharibacteria EOS 0.024227216 Oral cavity Unclassified_Candidatus_Saccharibacteria IGM 0.440843675 Oral cavity

Unclassified_Candidatus_Saccharibacteria IGM 0.830510535 Oral cavity

Unclassified_Candidatus_Saccharibacteria IGM 0.695987075 Oral cavity

Gemella fpg_mg_ml 0.005658297 Oral cavity

Catonella MCV -0.23912178 Oral cavity

Butyrivibrio ALB 0.006782488 Oral cavity

Butyrivibrio CA -0.007761113 Oral cavity

Unclassified_Prevotellaceae CR -0.001869638 Oral cavity

Unclassified_Neisseriaceae PLT 1.129148922 Oral cavity

Streptococcus NA. 0.360452595 Oral cavity

Streptococcus CA 0.030654801 Oral cavity

Streptococcus CA 0.017257938 Oral cavity

Streptococcus CA 0.029069067 Oral cavity

Streptococcus CA 0.014939285 Oral cavity

Streptococcus CA 0.020409531 Oral cavity

Streptococcus CL 0.219259739 Oral cavity

Streptococcus NA. 0.317809345 Oral cavity

Streptococcus CA 0.021338504 Oral cavity

Streptococcus NA. 0.270463965 Oral cavity

Streptococcus NA. 0.345022098 Oral cavity

Streptococcus NA. 0.230160815 Oral cavity

Unclassified_Candidatus_Saccharibacteria HDL 0.275441944 Oral cavity

Capnocytophaga BUN 0.109489712 Oral cavity

Unclassified_Neisseriaceae NEUTAB -0.033335892 Oral cavity

Unclassified_Neisseriaceae NEUTAB -0.032398653 Oral cavity

Unclassified_Neisseriaceae NEUTAB -0.061861693 Oral cavity

Capnocytophaga BUN 0.209308767 Oral cavity

Capnocytophaga BUN 0.063484954 Oral cavity

Capnocytophaga BUN 0.121137266 Oral cavity

Catonella MCV -0.143843177 Oral cavity

Unclassified_Neisseriaceae NEUTAB -0.025544871 Oral cavity

Unclassified_Flavobacteriales UALBCR 0.313222805 Oral cavity

Lachnospira UALBCR 1 .498977923 Nasal cavity Table 3. Abbreviations of Analytes of Table 2.

Analyte Clinical Circulatory Phenotype

Class

Abbreviation Measurement

A1 C Hemoglobin A1 C Glucose/lnsulin

AG albumin/globulin Kidney/Hepatic

ALB Albumin Blood Test Kidney/Hepatic

ALCRU Aluminum/Creatinine Ratio, Random, Urine Kidney

ALKP Alkaline Phosphatase Hepatic

ALT alanine aminotransferase Hepatic

AST aspartate aminotransferase Hepatic

BASO Basophils Immune

BASOAB Basophils Absolute Number Immune

BUN blood urea nitrogen Kidney

CA calcium blood test Electrolyte

CHOL complete cholesterol test Cholesterol/Lipid High-density lipoprotein (HDL) cholesterol

Cholesterol/Lipid ratio

CL Chloride Blood Test Electrolyte

CO2 Carbon Dioxide (CO2) in Blood Hematologic

CR creatinine Kidney

EGFR estimated Glomerular Filtration Rate Kidney

EOS Eosinophils Immune

EOSAB Eosinophils Absolute Number Immune

GLOB globulin Kidney/Hepatic

GLU Clucose Glucose/lnsulin

HCT hematocrit Hematologic

HDL High-density lipoprotein (HDL) cholesterol Cholesterol/Lipid

HGB Hemoglobin Hematologic

HSCRP High-sensitivity C-reactive protein Immune

IGM Immunoglobulin M Immune

INSF Insulin, Fasting Glucose/lnsulin

K potassium Electrolyte

LDL Low-density lipoprotein (LDL) cholesterol Cholesterol/Lipid

LDLHDL LDL to HDL ratio Cholesterol/Lipid

LYM Lymphocytes Immune

LYMAB Lymphocytes Absolute Number Immune

MCH mean corpuscular hemoglobin Hematologic

MCHC corpuscular hemoglobin concentration Hematologic

MCV Mean Corpuscular Volume Hematologic

MONO Monocytes Immune

MONOAB Monocytes Absolute Number Immune

NA. sodium Electrolyte NEUT Neutrophils Immune NEUTAB Neutrophils Absolute Number Immune NHDL non-high-density lipoprotein cholesterol Cholesterol/Lipid PLT platelet count Hematologic RBC red blood cell count Immune RDW red cell distribution width Hematologic TBIL Total bilirubin Hepatic TGL triglycerides Cholesterol/Lipid TP total protein Kidney/Hepatic UALB Microalbumin test Kidney UALBCR Urine Albumin-Creatinine Ratio Kidney WBC whitebloodcell Immune

Claims

WHAT IS CLAIMED IS:

1 . A method for administering microbial genera to an individual, comprising: 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.

2. The method of claim 1 further comprising: 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.

3. The method of claim 1 or 2, wherein the one gene product and the one or more correlated microbial genera to be administered is listed within Table 1 .

4. The method of claim 3, wherein the one gene product is: BDNF, EGF, Eotaxin, INF-a, INF-y, IL-1 , IL-6, IL-10, IL-12, IL-17, IL-22, IL-23, MCP-1 , RANTES, TGF-0, or TNF-a.

5. The method of claim 4, 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.

6. The method of claim 4, 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 Dialister.

7. The method of claim 4, 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.

8. The method of claim 4, wherein the one gene product is IL-1 [3, wherein IL-1 [3 is greater than threshold, wherein the is administered one or more of the following microbial genera: Agathobacter, Butyrici monas, Collinsella, Desulfovibrio, Faecalibacterium, Lachnospira, Prevotella, Roseburia, Slackia, Subdoligranulum, or Sutterella.

9. The method of claim 4, 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.

10. The method of claim 4, 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.

11. The method of claim 4, 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.

12. The method of claim 4, 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.

13. The method of claim 4, 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.

14. The method of claim 4, 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 Lawsonibacter.

15. The method of claim 4, 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.

16. The method of claim 4, 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.

17. The method of claim 4, 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.

18. The method of claim 4, 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.

19. The method of claim 4, 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.

20. The method of claim 4, 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.

21. The method of claim 4, 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.

22. The method of claim 4, wherein the one gene product is TGF-|3, wherein TGF-fB is less than threshold, wherein the is administered one or more of the following microbial genera: Acutalibacter, Akkermansia, Clostridium_sensu_stricto, Clostridium VI II, Flavonifractor, Holdemania, or Hungatella.

23. The method of any one of claims 1 -22, wherein the one or more microbial genera are to be orally administered.

24. The method of claim 23, 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.

25. The method of any one of claims 1 -22, wherein the one or more microbial genera are to be rectally administered.

26. The method of claim 23, 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.

27. A method for administering microbial genera to an individual, comprising: 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.

28. The method of claim 27 further comprising: 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.

29. The method of claim 27 or 28, wherein the one analyte and the one or more correlated microbial genera to be administered is listed within Table 2.

30. The method of any one of claims 27-29, 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 comprises Haemophilus.

31 . The method of claim 30, wherein the Haemophilus is to be topically administered.

32. The method of claim 31 , 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.

33. The method of any one of claims 27-29, wherein the one analyte is A1 C, wherein the one or more microbial genera to be administered comprises Akkermansia.

34. The method of claim 33, wherein the one or more microbial genera are to be orally administered.

35. The method of claim 34, 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.

36. The method of claim 33, wherein the one or more microbial genera are to be rectally administered.

37. The method of claim 36, 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.

38. A method for treating an individual for a medical condition by administering microbial genera, comprising: 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.

39. The method of claim 38, 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.

40. The method of claim 39, wherein the one or more microbial genera are to be topically administered.

41 . The method of claim 40, 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.

42. The method of claim 38, 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.

43. The method of claim 38, 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.

44. The method of claim 38, wherein the medical condition is systemic lupus erythematosus and the one or more microbial genera is: Blautia, Dialister, Fusicatenibacter, Collinsella, Subdoligranulum, or Senegalimassilia.

45. The method of claim 38, wherein the medical condition is hypertension and the one or more microbial genera is: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Subdoligranulum, or Senegalimassilia.

46. The method of claim 38, wherein the medical condition is atherosclerosis and the one or more microbial genera is: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Hungatella, or Monoglobus.

47. The method of claim 38, 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.

48. The method of claim 38, 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.

49. The method of claim 38, wherein the medical condition is schizophrenia and the one or more microbial genera is: Agathobacter, Butyricimonas, Collinsella, Desulfovibrio, Frisingicoccus, Dialister, Fusicatenibacter, Hungatella, or Monoglobus.

50. The method of claim 38, 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.

51 . The method of claim 50, wherein the one or more microbial genera is: Barnesiella, Frisingicoccus, or Butyrivibrio.

52. The method of claim 38, 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.

53. The method of claim 38, 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.

54. The method of any one of claims 42-53, wherein the one or more microbial genera are to be orally administered.

55. The method of claim 54, 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.

56. The method of any one of claims 42-53, wherein the one or more microbial genera are to be rectally administered.

57. The method of claim 56, 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.

58. A method of determining microbial genera host immune response, comprising: 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.

59. A method for administering a probiotic treatment, comprising: 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 comprises administration of the microbial genus or the combination of microbial genera.