It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2’s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses general linear models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g. counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2’s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel disease (IBD) across multiple time points and omics profiles.

The thanatomicrobiome (thanatos, Greek for death) is a relatively new term and is the study of the microbes colonizing the internal organs and orifices after death. Recent scientific breakthroughs in an initial study of the thanatomicrobiome have revealed that a majority of the microbes within the human body are the obligate anaerobes, Clostridium spp., in the internal postmortem microbial communities. We hypothesized that time-dependent changes in the thanatomicrobiome within internal organs can estimate the time of death as a human body decays. Here we report a cross-sectional study of the sampling of 27 human corpses from criminal cases with postmortem intervals between 3.5–240 hours. The impetus for examining microbial communities in different internal organs is to address the paucity of empirical data on thanatomicrobiomic succession caused by the limited access to these organs prior to death and a dearth of knowledge regarding the movement of microbes within remains. Our sequencing results of 16S rRNA gene amplicons of 27 postmortem samples from cadavers demonstrated statistically significant time-, organ-, and sex-dependent changes. These results suggest that comprehensive knowledge of the number and abundance of each organ’s signature microorganisms could be useful to forensic microbiologists as a new source of data for estimating postmortem interval.

Human thanatomicrobiome studies have established that an abundant number of putrefactive bacteria within internal organs of decaying bodies are obligate anaerobes, Clostridium spp. These microorganisms have been implicated as etiological agents in potentially life-threatening infections; notwithstanding, the scale and trajectory of these microbes after death have not been elucidated. We performed phylogenetic surveys of thanatomicrobiome signatures of cadavers’ internal organs to compare the microbial diversity between the 16S rRNA gene V4 hypervariable region and V3-4 conjoined regions from livers and spleens of 45 cadavers undergoing forensic microbiological studies. Phylogenetic analyses of 16S rRNA gene sequences revealed that the V4 region had a significantly higher mean Chao1 richness within the total microbiome data. Permutational multivariate analysis of variance statistical tests, based on unweighted UniFrac distances, demonstrated that taxa compositions were significantly different between V4 and V3-4 hypervariable regions (p < 0.001). Of note, we present the first study, using the largest cohort of criminal cases to date, that two hypervariable regions show discriminatory power for human postmortem microbial diversity. In conclusion, here we propose the impact of hypervariable region selection for the 16S rRNA gene in differentiating thanatomicrobiomic profiles to provide empirical data to explain a unique concept, the Postmortem Clostridium Effect.

ObjectivesGut-produced trimethylamine N-oxide (TMAO) is postulated as a possible link between red meat intake and poor cardiometabolic health. We investigated whether gut microbiome could modify associations of dietary precursors with TMAO concentrations and cardiometabolic risk markers among free-living individuals.DesignWe collected up to two pairs of faecal samples (n=925) and two blood samples (n=473), 6 months apart, from 307 healthy men in the Men’s Lifestyle Validation Study. Diet was assessed repeatedly using food-frequency questionnaires and diet records. We profiled faecal metagenome and metatranscriptome using shotgun sequencing and identified microbial taxonomic and functional features.ResultsTMAO concentrations were associated with the overall microbial compositions (permutational analysis of variance (PERMANOVA) test p=0.001). Multivariable taxa-wide association analysis identified 10 bacterial species whose abundance was significantly associated with plasma TMAO concentrations (false discovery rate <0.05). Higher habitual intake of red meat and choline was significantly associated with higher TMAO concentrations among participants who were microbial TMAO-producers (p<0.05), as characterised based on four abundant TMAO-predicting species, but not among other participants (for red meat, P-interaction=0.003; for choline, P-interaction=0.03). Among abundant TMAO-predicting species, Alistipes shahii significantly strengthened the positive association between red meat intake and HbA1c levels (P-interaction=0.01). Secondary analyses revealed that some functional features, including choline trimethylamine-lyase activating enzymes, were associated with TMAO concentrations.ConclusionWe identified microbial taxa that were associated with TMAO concentrations and modified the associations of red meat intake with TMAO concentrations and cardiometabolic risk markers. Our data underscore the interplay between diet and gut microbiome in producing potentially bioactive metabolites that may modulate cardiometabolic health.