Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

Xin Zhou¹, Xiaotao Shen², Jethro S Johnson³, Daniel J Spakowicz⁴, Melissa Agnello⁵, Wenyu Zhou², Monica Avina⁶, Alexander Honkala⁷, Faye Chleilat⁶, Shirley Jingyi Chen⁸, Kexin Cha⁸, Shana Leopold⁹, Chenchen Zhu⁶, Lei Chen¹⁰, Lin Lyu¹¹, Daniel Hornburg⁶, Si Wu⁶, Xinyue Zhang⁶, Chao Jiang¹², Liuyiqi Jiang¹³, Lihua Jiang⁶, Ruiqi Jian⁶, Andrew W Brooks⁶, Meng Wang⁶, Kévin Contrepois⁶, Peng Gao⁶, Sophia Miryam Schüssler-Fiorenza Rose⁶, Thi Dong Binh Tran¹⁴, Hoan Nguyen¹⁴, Alessandra Celli⁶, Bo-Young Hong¹⁵, Eddy J Bautista¹⁶, Yair Dorsett¹⁷, Paula B Kavathas¹⁸, Yanjiao Zhou¹⁷, Erica Sodergren¹⁴, George M Weinstock¹⁴, Michael P Snyder¹⁹

Affiliations

¹ Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
² Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA.
³ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Oxford Centre for Microbiome Studies, Kennedy Institute of Rheumatology, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7FY, UK.
⁴ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Division of Medical Oncology, Ohio State University Wexner Medical Center, James Cancer Hospital and Solove Research Institute, Columbus, OH 43210, USA.
⁵ Medical Affairs, uBiome, San Francisco, CA 94103, USA.
⁶ Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁷ Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA.
⁸ Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁹ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA.
¹⁰ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC.
¹¹ Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC.
¹² Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC.
¹³ Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC.
¹⁴ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
¹⁵ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Woody L Hunt School of Dental Medicine, Texas Tech University Health Science Center, El Paso, TX 79905, USA.
¹⁶ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Headquarters-Mosquera, Cundinamarca 250047, Colombia.
¹⁷ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Medicine, University of Connecticut Health Center, Farmington, CT 06032, USA.
¹⁸ Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT 06520, USA.
¹⁹ Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: mpsnyder@stanford.edu.

PMID: 38479397
PMCID: PMC11022754
DOI: 10.1016/j.chom.2024.02.012

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

Xin Zhou et al. Cell Host Microbe. 2024.

. 2024 Apr 10;32(4):506-526.e9.

doi: 10.1016/j.chom.2024.02.012. Epub 2024 Mar 12.

Authors

Affiliations

¹ Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
² Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA.
³ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Oxford Centre for Microbiome Studies, Kennedy Institute of Rheumatology, University of Oxford, Roosevelt Drive, Headington, Oxford OX3 7FY, UK.
⁴ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Division of Medical Oncology, Ohio State University Wexner Medical Center, James Cancer Hospital and Solove Research Institute, Columbus, OH 43210, USA.
⁵ Medical Affairs, uBiome, San Francisco, CA 94103, USA.
⁶ Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁷ Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA.
⁸ Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA.
⁹ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA.
¹⁰ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC.
¹¹ Shanghai Institute of Immunology, Shanghai Jiao Tong University, Shanghai 200240, PRC.
¹² Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC.
¹³ Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, PRC.
¹⁴ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
¹⁵ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Woody L Hunt School of Dental Medicine, Texas Tech University Health Science Center, El Paso, TX 79905, USA.
¹⁶ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Headquarters-Mosquera, Cundinamarca 250047, Colombia.
¹⁷ The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Medicine, University of Connecticut Health Center, Farmington, CT 06032, USA.
¹⁸ Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT 06520, USA.
¹⁹ Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Center for Genomics and Personalized Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford, CA 94305, USA; Stanford Healthcare Innovation Labs, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: mpsnyder@stanford.edu.

PMID: 38479397
PMCID: PMC11022754
DOI: 10.1016/j.chom.2024.02.012

Abstract

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Keywords: insulin resistance; longitudinal profiling; microbiome host interaction; microbiome stability; nasal microbiome; oral microbiome; precision medicine; prediabetes; skin microbiome; stool microbiome.

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Conflict of interest statement

Declaration of interests M.P.S. is a co-founder and the scientific advisory board member of Personalis, Qbio, January, SensOmics, Filtricine, Akna, Protos, Mirvie, NiMo, Onza, Oralome, Marble Therapeutics, and Iollo. He is also on the scientific advisory board of Danaher, Genapsys, and Jupiter. A.H. is a founder and shareholder of Arxeon. Y.Z. and G.M.W. are co-founders of General Biomics.

Figures

**Figure 1.. Longitudinal profiles of the microbiome at four body sites.**
A. Study Design. B. Overlap of sample numbers among different omics types. C. Proportion of stress, insulin resistant and healthy samples. D. UMAP of microbiome samples by body site. E. Density distribution of microbiome richness and evenness. F. Rank prevalence curve of microbiome genera with the 100 highest longitudinal prevalence at each body site.

**Figure 2.. The individuality of the microbiome differs significantly across genera and body sites.**
A. Bray Curtis dissimilarity comparisons within individuals, families, and between unrelated participants. B. DMI Scores (see STAR Methods). C. Average DMI Radar Plot by Body Site and Phylum, with significant Kruskal-Wallis test results for *Actinobacteria*, *Bacteroidetes*, Firmicutes, *Proteobacteria*, and Other phyla. D. DMI Cladogram representing stool, skin, oral, and nasal microbiomes. Significance indicated by asterisks: *p < 0.05, **p < 0.01, ***p < 0.001.

**Figure 3.. Temporal stability of microbiomes associated with individuality and stress events.**
A. Correlations of taxa-recurrence with mean DMI for stool, skin, oral, and nasal samples. B. Linear regression between dissimilarity and collection date interval. C. Beta coefficient of individual-based correlation between sample pair’s BC distances and the collection date intervals. D. Correlations of microbiome abundances within and between body sites. E. DMI differences between correlated and non-correlated genera. F. Microbiome shifts during health and stress events over three months. Significance indicated by asterisks: *p < 0.05, **p < 0.01, ***p < 0.001.

**Figure 4.. Systematic connections between circulating cytokines and microbiomes.**
A. Mixed-effects model outputs for cytokine-microbiome correlations; red lines for positive, blue for negative associations; circle size denotes significant correlation count. B. Cytokine-related genera percentages by phylum. C. Density plot of significant cytokine-microbiome correlation coefficients, compared by genera prevalence. D. Correlation coefficients by body site and phylum. p-values for positive versus negative associations were annotated in the middle. E. Hierarchical clustering of Spearman coefficients between cytokines and diverse genera. Significance indicated by asterisks: *p < 0.05, **p < 0.01, ***p < 0.001.

**Figure 5.. Interactions between plasma metabolites, lipids, proteomics, and microbiome over time.**
A. Correlation Network 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). B. Plasma analytics-microbiome relative abundance correlation summary of Fig. 5A. C. Correlations between genera and the metabolite ethyl glucuronide. D. Plasma analytics-microbiome relative richness summary of Fig. S6A. E. Correlations between genera and the metabolite p-Cresol glucuronide.

**Figure 6.. Causal inference decodes microbiome-driven phenotypic dynamics mediated by internal molecules and cytokines.**
A. Microbiome and phenotype mediation analysis. Comparisons between IS and IR regarding each mediated effect were performed using a Fisher exact t test. B. *Akkermansia*’s Mediation Effect on Blood A1C Level via Plasma IL-15. C. *Akkermansia*’s Mediation Effect on Blood A1C Level in Insulin Sensitive Individuals. D. *Haemophilus*’s Mediation Effect on Plasma Triglycerides Level. E. *Haemophilus’*s Mediation Effect on Plasma Triglycerides Level in Insulin Sensitive Individuals.

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Update of

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease.
Zhou X, Shen X, Johnson JS, Spakowicz DJ, Agnello M, Zhou W, Avina M, Honkala A, Chleilat F, Chen SJ, Cha K, Leopold S, Zhu C, Chen L, Lyu L, Hornburg D, Wu S, Zhang X, Jiang C, Jiang L, Jiang L, Jian R, Brooks AW, Wang M, Contrepois K, Gao P, Schüssler-Fiorenza Rose SM, Binh Tran TD, Nguyen H, Celli A, Hong BY, Bautista EJ, Dorsett Y, Kavathas P, Zhou Y, Sodergren E, Weinstock GM, Snyder MP. Zhou X, et al. bioRxiv [Preprint]. 2024 Feb 1:2024.02.01.577565. doi: 10.1101/2024.02.01.577565. bioRxiv. 2024. Update in: Cell Host Microbe. 2024 Apr 10;32(4):506-526.e9. doi: 10.1016/j.chom.2024.02.012. PMID: 38352363 Free PMC article. Updated. Preprint.

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Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

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Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

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Conflict of interest statement

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