Emily Van Syoc

Fungi in the gut microbiome, collectively known as the mycobiome, are a prevalent yet neglected component of the human holobiont. A major question in the study of gut microbial communities is whether fungi exhibit eco-evolutionary patterns that are consistent with partner fidelity and long-term associations. We compared gut fungal profiles across natural populations of humans and nonhuman primates and identified significant degrees of primate-mycobiome phylosymbiosis as well as human-enriched fungal taxa. Notably, subsets of fungi are cophylogenetic and exhibit cospeciation patterns in hominids. These findings cautiously originate a new view on the eco-evolutionary potential that can shape the composition of human and primate gut mycobiomes.

Human genetic determinants of the gut mycobiome remain uninvestigated despite decades of research highlighting tripartite relationships between gut bacteria, genetic background, and disease. Here, we present the first genome-wide association study on the number and types of human genetic loci influencing gut fungi relative abundance. We detect 148 fungi-associated variants (FAVs) across 7 chromosomes that statistically associate with 9 fungal taxa. Of these FAVs, several occur in the protein-coding genes PTPRC, ANAPC10, NAV2, and CDH13. Additional FAVs link to tissue-specific gene expression as fungi-associated expression quantitative trait loci. Notably, the relative abundance of gut yeast Kazachstania associates with genetic variation in CDH13 encoding T-cadherin, a protein linked to cardiovascular disease. Kazachstania forms a causal relationship with cardiovascular disease risk in a mendelian two-sample randomization analysis. These findings establish previously unrecognized connections between human genetics, gut fungi, and chronic disease, broadening the paradigm of human-microbe interactions in the gut to the mycobiome.

Metagenomics has revolutionized our understanding of microbial communities, offering unprecedented insights into their genetic and functional diversity across Earth’s diverse ecosystems. Beyond their roles as environmental constituents, microbiomes act as symbionts, profoundly influencing the health and function of their host organisms. Given the inherent complexity of these communities and the diverse environments where they reside, the components of a metagenomics study must be carefully tailored to yield accurate results that are representative of the populations of interest. This Primer examines the methodological advancements and current practices that have shaped the field, from initial stages of sample collection and DNA extraction to the advanced bioinformatics tools employed for data analysis, with a particular focus on the profound impact of next-generation sequencing on the scale and accuracy of metagenomics studies. We critically assess the challenges and limitations inherent in metagenomics experimentation, available technologies and computational analysis methods. Beyond technical methodologies, we explore the application of metagenomics across various domains, including human health, agriculture and environmental monitoring. Looking ahead, we advocate for the development of more robust computational frameworks and enhanced interdisciplinary collaborations. This Primer serves as a comprehensive guide for advancing the precision and applicability of metagenomic studies, positioning them to address the complexities of microbial ecology and their broader implications for human health and environmental sustainability.

Infant consumption of human milk (HM) is associated with a reduced risk of overweight and obesity, but the reasons for this relationship are not completely understood. There is emerging evidence that micro RNAs (miRNAs) regulate infant development and metabolism, but the associations between HM miRNAs and infant growth remain poorly understood. We examined the relationship between HM miRNA consumption and infant obesity in 163 mother–infant dyads to determine (1) if miRNA profiles differentiate infants with obesity, and (2) if individual miRNAs accurately predicted infant obesity status at one year of age. Infant obesity was categorized as weight-for-length (WFL) Z scores or conditional weight gain (CWG) in the 95th percentile. HM miRNA profile was associated with infant age (r² = 6.4%, p = 0.001), but not maternal obesity status (r² = 1.5%, p = 0.87) or infant weight status (WFL Z-score) at birth (r² = 0.6%, p = 0.4), 1 month (r² = 0.5%, p = 0.6), or 4 months (r² = 0.8%, p = 0.2). Nine HM miRNAs were associated with either 12-month CWG or 12-month WFL Z scores. Among these 9 miRNAs, miR-224-5p remained significant in a logistic regression model that accounted for additional demographic factors (estimate = −27.57, p = 0.004). These findings suggest involvement of HM miRNAs and particularly miR-224-5p in infant growth, warranting further investigation. To our knowledge, this is the largest study of HM miRNAs and early-life obesity and contributes to the understanding of the relationship between HM miRNAs and infant growth.

The human gut teems with a diverse ecosystem of microbes, yet non-bacterial portions of that community are overlooked in studies of metabolic diseases firmly linked to gut bacteria. Type 2 diabetes mellitus (T2D) is associated with compositional shifts in the gut bacterial microbiome and the mycobiome, the fungal portion of the microbiome. However, whether T2D and/or metformin treatment underpins fungal community changes is unresolved. To differentiate these effects, we curated a gut mycobiome cohort spanning 1,000 human samples across five countries and validated our findings in a murine experimental model. We use Bayesian multinomial logistic normal models to show that T2D and metformin both associate with shifts in the relative abundance of distinct gut fungi. T2D is associated with shifts in the Saccharomycetes and Sordariomycetes fungal classes, while the genera Fusarium and Tetrapisipora most consistently associate with metformin treatment. We confirmed the impact of metformin on individual gut fungi by administering metformin to healthy mice. Thus, metformin and T2D account for subtle, but significant and distinct variation in the gut mycobiome across human populations. This work highlights for the first time that metformin can confound associations of gut fungi with T2D and warrants the need to consider pharmaceutical interventions in investigations of linkages between metabolic diseases and gut microbial inhabitants. IMPORTANCE: This is the largest to-date multi-country cohort characterizing the human gut mycobiome, and the first to investigate potential perturbations in gut fungi from oral pharmaceutical treatment. We demonstrate the reproducible effects of metformin treatment on the human and murine gut mycobiome and highlight a need to consider metformin as a confounding factor in investigations between type 2 diabetes mellitus and the gut microbial ecosystem.

The human gut teems with a diverse ecosystem of microbes, yet non-bacterial portions of that community are overlooked in studies of metabolic diseases firmly linked to gut bacteria. Type 2 diabetes mellitus (T2D) associates with compositional shifts in the gut bacterial microbiome and fungal mycobiome, but whether T2D and/or pharmaceutical treatments underpin the community change is unresolved. To differentiate these effects, we curated a gut mycobiome cohort to-date spanning 1,000 human samples across 5 countries and a murine experimental model. We use Bayesian multinomial logistic normal models to show that metformin and T2D both associate with shifts in the relative abundance of distinct gut fungi. T2D associates with shifts in the Saccharomycetes and Sordariomycetes fungal classes, while the genera Fusarium and Tetrapisipora most consistently associate with metformin treatment. We confirmed the impact of metformin on individual gut fungi by administering metformin to healthy mice. Thus, metformin and T2D account for subtle, but significant and distinct variation in the gut mycobiome across human populations. This work highlights for the first time that oral pharmaceuticals can confound associations of gut fungi with T2D and warrants the need to consider pharmaceutical interventions in investigations of linkages between metabolic diseases and gut microbial inhabitants.

As the applications of microbiome science in agriculture expand, laboratory methods should be constantly evaluated to ensure optimization and reliability of downstream results. Most animal microbiome research uses fecal samples or rectal swabs for profiling the gut bacterial community; however, in birds, this is difficult given the unique anatomy of the cloaca where the fecal, urinary, and reproductive tracts converge into one orifice. Therefore, avian gut microbiomes are usually sampled from cloacal swabs, creating a need to evaluate sample preparation methods to optimize 16S sequencing. We compared four different DNA extraction methods from two commercially available kits on cloacal swabs from 10 adult commercial laying hens and included mock communities and negative controls, which were then subjected to 16S rRNA amplicon sequencing. Extracted DNA yield and quality, diversity analyses, and contaminants were assessed. Differences in DNA quality and quantity were observed, and all methods needed further purification for optimal sequencing, suggesting contaminants due to cloacal contents, method reagents, and/or environmental factors. However, no differences were observed in alpha or beta diversity between methods. Importantly, multiple bacterial contaminants were detected in each mock community and negative control, indicating the prevalence of laboratory and handling contamination as well as method-specific reagent contamination. We found that although the extraction methods resulted in different extraction quality and yield, overall sequencing results were not affected, and we did not identify any method that would be an inappropriate choice in extracting DNA from cloacal swabs for 16S rRNA sequencing. Overall, our results highlight the need for careful consideration of positive and negative controls in addition to DNA isolation method and lend guidance to future microbiome research in poultry.