Host genetic control of gut microbiome composition.
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ABSTRACT:
SUBMITTER: Bubier JA
PROVIDER: S-EPMC8295090 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In the past decade, studies on the mammalian gut microbiome have revealed that different animal species have distinct gut microbial compositions. The functional ramifications of this variation in microbial composition remain unclear: do these taxonomic differences indicate microbial adaptations to host-specific functionality, or are these diverse microbial communities essentially functionally redundant, as has been indicated by previous metagenomics studies? Here, we examine the metabolic content of mammalian gut microbiomes as a direct window into ecosystem function, using an untargeted metabolomics platform to analyze 101 fecal samples from a range of 25 exotic mammalian species in collaboration with a zoological center. We find that mammalian metabolomes are chemically diverse and strongly linked to microbiome composition, and that metabolome composition is further correlated to the phylogeny of the mammalian host. Specific metabolites enriched in different animal species included modified and degraded host and dietary compounds such as bile acids and triterpenoids, as well as fermentation products such as lactate and short-chain fatty acids. Our results suggest that differences in microbial taxonomic composition are indeed translated to host-specific metabolism, indicating that taxonomically distant microbiomes are more functionally diverse than redundant.
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Project description:BackgroundGenetic predisposition increases risk for asthma, and distinct nasal microbial compositions are associated with asthma. Host genetics might shape nasal microbiome composition.ObjectiveWe examined associations between host genetics and nasal microbiome composition.MethodsNasal samples were collected from 584 participants from the Mount Sinai Health System, New York. Seventy-seven follow-up samples were collected from a subset of 40 participants. 16S rRNA sequencing and RNA sequencing were performed on nasal samples. Beta diversity was calculated, variant calling on RNA sequencing data was performed, and genetic relatedness between individuals was determined. Using linear regression models, we tested for associations between genetic relatedness and nasal microbiome composition.ResultsThe median age of the cohort was 14.6 (interquartile range 11.2-19.5) years, with participants representing diverse ancestries and 52.7% of the cohort being female. For participants who provided follow-up samples, the median time between samples was 5.1 (interquartile range 1.4-7.2) months. Nasal microbiome composition similarity as reflected by beta diversity was significantly higher within subjects over time versus between subjects (coefficient = 0.091, P = 2.84-7). There was no significant association between genetic relatedness and beta diversity (coefficient = -0.05, P = .29). Additional analyses exploring the relationship between beta diversity and genetic variance yielded similar results.ConclusionHost genetics has little influence on nasal microbiome composition.
| S-EPMC9643606 | biostudies-literature
Project description:The ecological and genetic changes that underlie the evolution of host-microbe interactions remain elusive, primarily due to challenges in disentangling the variables that alter microbiome composition. To understand the impact of host habitat, host genetics, and evolutionary history on microbial community structure, we examined gut microbiomes of river- and three cave-adapted morphotypes of the Mexican tetra, Astyanax mexicanus, in their natural environments and under controlled laboratory conditions. Field-collected samples were dominated by very few taxa and showed considerable interindividual variation. We found that lab-reared fish exhibited increased microbiome richness and distinct composition compared to their wild counterparts, underscoring the significant influence of habitat. Most notably, however, we found that morphotypes reared on the same diet throughout life developed distinct microbiomes suggesting that genetic loci resulting from cavefish evolution shape microbiome composition. We observed stable differences in Fusobacteriota abundance between morphotypes and demonstrated that this could be used as a trait for quantitative trait loci mapping to uncover the genetic basis of microbial community structure.
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Project description:The gut microbiome contributes to the variation of blood lipid levels, and secondary bile acids are associated with the effect of statins. Yet, our knowledge of how statins, one of our most common drug groups, affect the human microbiome is scarce. We aimed to characterize the effect of rosuvastatin on gut microbiome composition and inferred genetic content in stool samples from a randomized controlled trial (n = 66). No taxa were significantly altered by rosuvastatin during the study. However, rosuvastatin-treated participants showed a reduction in the collective genetic potential to transport and metabolize precursors of the pro-atherogenic metabolite trimethylamine-N-oxide (TMAO, p < 0.01), and an increase of related metabolites betaine and γ-butyrobetaine in plasma (p < 0.01). Exploratory analyses in the rosuvastatin group showed that participants with the least favorable treatment response (defined as < median change in high-density/low-density lipoprotein (HDL/LDL) ratio) showed a marked increase in TMAO-levels compared to those with a more favorable response (p < 0.05). Our data suggest that while rosuvastatin has a limited effect on gut microbiome composition, it could exert broader collective effects on the microbiome relevant to their function, providing a rationale for further studies of the influence of statins on the gut microbiome.
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Project description:BackgroundThe composition of bacteria in and on the human body varies widely across human individuals, and has been associated with multiple health conditions. While microbial communities are influenced by environmental factors, some degree of genetic influence of the host on the microbiome is also expected. This study is part of an expanding effort to comprehensively profile the interactions between human genetic variation and the composition of this microbial ecosystem on a genome- and microbiome-wide scale.ResultsHere, we jointly analyze the composition of the human microbiome and host genetic variation. By mining the shotgun metagenomic data from the Human Microbiome Project for host DNA reads, we gathered information on host genetic variation for 93 individuals for whom bacterial abundance data are also available. Using this dataset, we identify significant associations between host genetic variation and microbiome composition in 10 of the 15 body sites tested. These associations are driven by host genetic variation in immunity-related pathways, and are especially enriched in host genes that have been previously associated with microbiome-related complex diseases, such as inflammatory bowel disease and obesity-related disorders. Lastly, we show that host genomic regions associated with the microbiome have high levels of genetic differentiation among human populations, possibly indicating host genomic adaptation to environment-specific microbiomes.ConclusionsOur results highlight the role of host genetic variation in shaping the composition of the human microbiome, and provide a starting point toward understanding the complex interaction between human genetics and the microbiome in the context of human evolution and disease.
| S-EPMC4570153 | biostudies-literature
Project description:To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 × 10-8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 × 10-20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10-10 < P < 5 × 10-8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.
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Project description:Bacterial vaginosis (BV) affects 20% of women worldwide and is associated with adverse reproductive health outcomes and increased risk for HIV. Typically, BV represents a shift in the vaginal microbiome from one that is dominated by Lactobacillus to one that is diverse. Persistent racial differences in BV and diverse vaginal microbiome composition overlap with racial disparities in risks for HIV and sexually transmitted infection, especially among women of African descent. Risk factors for BV and nonoptimal vaginal microbiome include sexual practices, yet racial differences persist when adjusted for behavioral factors, suggesting a host genetic component. Here, we perform a genome-wide association study on vaginal microbiome traits in Kenyan women. Linear regression and logistic regression were performed, adjusting for age and principal components of genetic ancestry, to evaluate the association between Lactobacillus crispatus, Lactobacillus iners, Gardnerella vaginalis, Shannon diversity index, and community state type (CST) with host genetic single nucleotide polymorphisms (SNPs). We identified novel genomic loci associated with the vaginal microbiome traits, though no SNP reached genome-wide significance. During pathway enrichment analysis, Toll-like receptors (TLRs), cytokine production, and other components of innate immune response were associated with L. crispatus, L. iners, and CST. Multiple previously reported genomic loci were replicated, including IL-8 (Shannon, CST), TIRAP (L. iners, Shannon), TLR2 (Shannon, CST), MBL2 (L. iners, G. vaginalis, CST), and MYD88 (L. iners, Shannon). These genetic associations suggest a role for the innate immune system and cell signaling in vaginal microbiome composition and susceptibility to nonoptimal vaginal microbiome.IMPORTANCE Globally, bacterial vaginosis (BV) is a common condition in women. BV is associated with poorer reproductive health outcomes and HIV risk. Typically, BV represents a shift in the vaginal microbiome from one that is dominated by Lactobacillus to one that is diverse. Despite many women having similar exposures, the prevalence of BV and nonoptimal vaginal microbiome is increased for women of African descent, suggesting a possible role for host genetics. We conducted a genome-wide association study of important vaginal microbiome traits in Kenyan women. We identified novel genetic loci and biological pathways related to mucosal immunity, cell signaling, and infection that were associated with vaginal microbiome traits; we replicated previously reported loci associated with mucosal immune response. These results provide insight into potential host genetic influences on vaginal microbiome composition and can guide larger longitudinal studies, with genetic and functional comparison across microbiome sites within individuals and across populations.
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Project description:Background - Prepregnancy overweight and obesity promote deleterious health impacts on both mothers during pregnancy and the offspring. Significant changes in the maternal peripheral blood mononuclear cells (PBMCs) gene expression due to obesity are well-known. However, during pregnancy the impact of overweight on immune cell gene expression and its association with maternal and infant outcomes is not well explored. Methods – Blood samples were collected from healthy normal weight (NW, BMI 18.5-24.9) or overweight (OW, BMI 25-29.9) 2nd parity pregnant women at 12, 24 and 36 weeks of pregnancy. PBMCs were isolated from the blood and subjected to mRNA sequencing. Maternal and infant microbiota were analyzed by 16S rRNA gene sequencing. Integrative multi-omics data analysis was performed to evaluate the association of gene expression with maternal diet, gut microbiota, milk composition, and infant gut microbiota. Results - Gene expression analysis revealed that 453 genes were differentially expressed in the OW women compared to NW women at 12 weeks of pregnancy, out of which 354 were upregulated and 99 were downregulated. Several up-regulated genes in the OW group were enriched in inflammatory, chemokine-mediated signaling and regulation of interleukin-8 production-related pathways. At 36 weeks of pregnancy healthy eating index score was positively associated with several genes that include, DTD1, ELOC, GALNT8, ITGA6-AS1, KRT17P2, NPW, POT1-AS1 and RPL26. In addition, at 36 weeks of pregnancy, genes involved in adipocyte functions, such as NG2 and SMTNL1, were negatively correlated to human milk 2’FL and total fucosylated oligosaccharides content collected at 1 month postnatally. Furthermore, infant Akkermansia was positively associated with maternal PBMC anti-inflammatory genes that include CPS1 and RAB7B, at 12 and 36 weeks of pregnancy. Conclusions – These findings suggest that prepregnancy overweight impacts the immune cell gene expression profile, particularly at 12 weeks of pregnancy. Further, deciphering the complex association of PBMC’s gene expression levels with maternal gut microbiome and milk composition and infant gut microbiome may aid in developing strategies to mitigate obesity-mediated effects.
2024-08-27 | GSE273780 | GEO
Project description:Genetics provides a potentially powerful approach to dissect host-gut microbiota interactions. Toward this end, we profiled gut microbiota using 16s rRNA gene sequencing in a panel of 110 diverse inbred strains of mice. This panel has previously been studied for a wide range of metabolic traits and can be used for high-resolution association mapping. Using a SNP-based approach with a linear mixed model, we estimated the heritability of microbiota composition. We conclude that, in a controlled environment, the genetic background accounts for a substantial fraction of abundance of most common microbiota. The mice were previously studied for response to a high-fat, high-sucrose diet, and we hypothesized that the dietary response was determined in part by gut microbiota composition. We tested this using a cross-fostering strategy in which a strain showing a modest response, SWR, was seeded with microbiota from a strain showing a strong response, A×B19. Consistent with a role of microbiota in dietary response, the cross-fostered SWR pups exhibited a significantly increased response in weight gain. To examine specific microbiota contributing to the response, we identified various genera whose abundance correlated with dietary response. Among these, we chose Akkermansia muciniphila, a common anaerobe previously associated with metabolic effects. When administered to strain A×B19 by gavage, the dietary response was significantly blunted for obesity, plasma lipids, and insulin resistance. In an effort to further understand host-microbiota interactions, we mapped loci controlling microbiota composition and prioritized candidate genes. Our publicly available data provide a resource for future studies.
| S-EPMC4579341 | biostudies-literature
Project description:The microbiome is the collection of all microbial genes and can be investigated by sequencing highly variable regions of 16S ribosomal RNA (rRNA) genes. Evidence suggests that environmental factors and host genetics may interact to impact human microbiome composition. Identifying host genetic variants associated with human microbiome composition not only provides clues for characterizing microbiome variation but also helps to elucidate biological mechanisms of genetic associations, prioritize genetic variants, and improve genetic risk prediction. Since a microbiota functions as a community, it is best characterized by β diversity; that is, a pairwise distance matrix. We develop a statistical framework and a computationally efficient software package, microbiomeGWAS, for identifying host genetic variants associated with microbiome β diversity with or without interacting with an environmental factor. We show that the score statistics have positive skewness and kurtosis due to the dependent nature of the pairwise data, which makes p-value approximations based on asymptotic distributions unacceptably liberal. By correcting for skewness and kurtosis, we develop accurate p-value approximations, whose accuracy was verified by extensive simulations. We exemplify our methods by analyzing a set of 147 genotyped subjects with 16S rRNA microbiome profiles from non-malignant lung tissues. Correcting for skewness and kurtosis eliminated the dramatic deviation in the quantile-quantile plots. We provided preliminary evidence that six established lung cancer risk SNPs were collectively associated with microbiome composition for both unweighted (p = 0.0032) and weighted (p = 0.011) UniFrac distance matrices. In summary, our methods will facilitate analyzing large-scale genome-wide association studies of the human microbiome.
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