Host genetic variation in mucosal immunity pathways influences the upper airway microbiome

Abstract

BackgroundThe degree to which host genetic variation can modulate microbial communities in humans remains an open question. Here, we performed a genetic mapping study of the microbiome in two accessible upper airway sites, the nasopharynx and the nasal vestibule, during two seasons in 144 adult members of a founder population of European decent.ResultsWe estimated the relative abundances (RAs) of genus level bacteria from 16S rRNA gene sequences and examined associations with 148,653 genetic variants (linkage disequilibrium [LD] r 2 < 0.5) selected from among all common variants discovered in genome sequences in this population. We identified 37 microbiome quantitative trait loci (mbQTLs) that showed evidence of association with the RAs of 22 genera (q < 0.05) and were enriched for genes in mucosal immunity pathways. The most significant association was between the RA of Dermacoccus (phylum Actinobacteria) and a variant 8 kb upstream of TINCR (rs117042385; p = 1.61 × 10−8; q = 0.002), a long non-coding RNA that binds to peptidoglycan recognition protein 3 (PGLYRP3) mRNA, a gene encoding a known antimicrobial protein. A second association was between a missense variant in PGLYRP4 (rs3006458) and the RA of an unclassified genus of family Micrococcaceae (phylum Actinobacteria) (p = 5.10 × 10−7; q = 0.032).ConclusionsOur findings provide evidence of host genetic influences on upper airway microbial composition in humans and implicate mucosal immunity genes in this relationship.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-016-0227-5) contains supplementary material, which is available to authorized users.

The Relationship Between the Human Genome and Microbiome Comes into View

“…Alpha-diversity and microbial or functional pathway abundances can simply be treated as individual quantitative traits where standard GWAS methods can be applied to each trait (7). The microbiome GWAS to date have used standard additive genetic modeling approaches (4, 10, 18, 30, 68), rank based correlations (5), or combination models, where common taxa are modeled as quantitative traits and rare taxa are modeled as binary traits (65). …”

“…Genes associated with the microbiome of nasal, oral, and skin body sites drove most of the enrichment in these pathways. In a study of the nasal microbiota in a Hutterite population, Igartua et al used the Ingenuity Pathway Analysis Knowledge Base to identify protein-protein interaction (PPI) networks from genes near nasal microbiome associated loci (30). Both of the significant PPI networks identified contain highly connected proteins (hubs in the PPI network) that play important roles in modulating mucosal immunity (including IgA, IgG, IL12/IL12RA, TCR and STAT5A/B).…”

“…Goodrich et al also found an association with a variant in this gene and the abundance of unclassified Clostridiaceae (Heritability: 0.32) (18). The nasal microbiome GWAS performed by Igartua et al identified a significant association between the abundance of Dermacoccus in the nasal vestibule and another variant in this gene (30). SLIT3 is a secreted protein expressed in several tissues including skin, stomach, small intestine, and colon (11).…”

Integrating tumor genomics into studies of the microbiome in colorectal cancer

“…21,22 More recent studies have used a genome-wide approach to calculate the heritability of microbial taxa and identify variants and genes in the human genome that correlate with variation in the microbiome across body sites. 17,18,[23][24][25][26] In addition, several studies have used similar analysis techniques to assess host genetic effects on the microbiome in the context of various human diseases and conditions. [26][27][28][29] Although results across cohorts have not had a high degree of overlap, several loci have been found to be associated across multiple studies, 19,30,31 indicating that in some cases, host genes can affect microbiome composition across different contexts.…”

To progress understanding of disease triggers and modifiers in sarcoidosis, stratification is the key

“…The lung microbiome largely overlaps with the oral microbiome, and is the result of microbial immigration, elimination and relative reproduction rates [10,11]. Microbiome communities are complex and dynamic and vary throughout life under the influence of the season, geography, local environment, body site and host characteristics like genotype, ethnicity, age, sex, body mass index, diet, drugs and health status [9][10][11][12]. This wide variety of influences makes it difficult to develop studies that account for all these factors.…”