Biology subjects

Langille, M. G. I.

Publications and source records attributed to Langille, M. G. I..

8 recordsLinked to original sources

bioRxiv2025

Effect of bedrest on the human gut and oral microbiome: implications for frailty

The physiological effects of spaceflight resemble those of ageing and prolonged inactivity, and ground-based microgravity analogs have emerged as promising models for studying frailty. The human microbiome is increasingly recognised for its role in age-associated decline, although precise mechanisms remain unclear. Here, we evaluate the gut and oral microbiomes of twenty-two participants, aged 55-65, who were enrolled in a head-down tilt bedrest (HDBR) study, the first Canadian HDBR study conducted in an older cohort. Participants were randomly assigned to an inactivity or multi-modality exercise intervention group for fourteen days of HDBR, followed by seven days of rehabilitation and additional follow-up appointments. Gut (n=343) and oral (n=344) taxonomic profiles were generated using V4/V5 16S rRNA gene sequencing from fecal and salivary samples collected throughout the study. Gut functional profiles were generated using metagenomic (n=86) and metabolomic (n=83) data. Frailty was measured using a 36-item frailty index. Inactivity-associated changes to the gut microbiome during HDBR included decreasing -diversity, decreasing Akkermansia and Lactobacillus, and increasing Bacteroides. Exercise-associated changes included increasing gut Roseburia. Interestingly, oral microbiome {beta}-diversity was more strongly associated with frailty than the gut. We conclude that inactivity-associated changes to the human microbiome could contribute to the early stages of frailty development, and that exercise can serve as an effective countermeasure against these effects. These results may inform strategies to preserve the health of both older adults facing prolonged periods of inactivity, as well as astronauts during longer space exploration missions. IMPORTANCEHealthy ageing carries significant demographic, economic, and public health implications globally, and both physical activity and human-associated microorganisms play important roles in its trajectory. Using microgravity simulation in older adults as a human frailty model, we show that short periods of inactivity could have significant effects on these microbial communities, particularly in the gut, which could further contribute to multi-system physiological decline and frailty pathogenesis. Participants who completed a daily, multi-modality exercise intervention did not exhibit these changes, which may help protect both older adults facing periods of inactivity and astronauts in the advent of longer space exploration missions. Interestingly, our results suggested that oral bacteria may be a more sensitive indicator of individual frailty levels than gut bacteria. Future research should ensure that functional aspects of human-associated microbial communities are thoroughly investigated, as these may offer deeper insight into their contributions to age-related decline.

microbiology↗

bioRxiv2025

JarrVis: Visualising Taxa-function relationships from meta-omic data

High-throughput sequencing has necessitated and facilitated the development of various computational tools for dissecting the taxonomic structure and molecular function of microbial communities residing in diverse ecological niches. A wide variety of tools and protocols have been developed to process amplicon sequencing as well as meta-omic (metagenomic, metatranscriptomic and genomic) data to generate relative abundances of taxonomic groups or functional categories on a per-sample basis. A key output from many of these protocols is a stratified relative abundance table that specifies the taxonomic contribution to each function. However, there are a very few tools which can effectively visualize the different taxa-function relationships from these stratified outputs. Here we introduce an R Shiny application called JarrVis (Just Another stRatified Rpkm VISualizer) which can visualize the taxa-function relationships resulting from different types of microbiome data. JarrVis can visualize relationships between samples (which can be combined based on metadata categories), the taxa that are detected in these samples (at any given taxonomic level) and the functions encoded by these taxa in an interactive interface. We utilized JarrVis to visualize and examine taxa-function relationships in 1) a 16S amplicon time-series spanning 4 years with samples collected weekly, 2) functions associated with microbial cobalamin biosynthesis and uptake in metagenome-assembled genomes from marine metagenomes and 3) a set of metatrascriptomes from gut samples of patients with Crohns Disease, Ulcerative Colitis and non-IBD controls. Our analysis was able recapitulate already published taxa-function relationships as well as discover novel insights from these publicly available datasets. JarrVis and related scripts and data are available at https://github.com/dhwanidesai/JarrVis.

bioinformatics↗

bioRxiv2022

Investigating the oral microbiome in retrospective and prospective cases of prostate, colon, and breast cancer.

The human microbiome has been proposed as a useful biomarker for several different human diseases including various cancers. To answer this question, we examined salivary samples from two Canadian population cohorts, the Atlantic Partnership for Tomorrows Health project (PATH) and Albertas Tomorrow Project (ATP). Sample selection was then divided into both a retrospective and prospective case control design examining individuals with prostate, breast, or colon cancer. In total 89 retrospective and 260 prospective cancer cases were matched to non-cancer controls and saliva samples were sequenced using 16S rRNA gene sequencing to compare bacterial diversity, and taxonomic composition. We found no significant differences in alpha or beta diversity across any of the three cancer types and two study designs. Although retrospective colon cancer samples did show evidence on visual clustering in weighted beta diversity metrics. Differential abundance analysis of individual taxon showed several taxa that were associated with previous cancer diagnosis in all three groupings within the retrospective study design. However, only one genus (Ruminococcaceae UCG-014) in breast cancer and one ASV (Fusobacterium periodonticum) in colon cancer was identified by more than one differential abundance (DA) tool. In prospective cases of disease three ASVs were associated with colon cancer, one ASV with breast cancer, and one ASV with prostate cancer. None overlapped between the two different study cohorts. Attempting to identify microbial signals using Random Forest classification showed relatively low levels of signal in both prospective and retrospective cases of breast and prostate cancer (AUC range: 0.394-0.665). Contrastingly, colon cancer did show signal in our retrospective analysis (AUC: 0.745) and in one of two prospective cohorts (AUC: 0.717). Overall, our results indicate that it is unlikely that reliable oral microbial biomarkers of disease exist in the context of both breast and prostate cancer. However, they do suggest that further research into the relationship between the oral microbiome and colon cancer could be fruitful. Particularly in the context of early disease progression and risk of cancer development.

microbiology↗

bioRxiv2022

From defaults to databases: parameter and database choice dramatically impact the performance of metagenomic taxonomic classification tools

In metagenomic analyses of microbiomes, one of the first steps is usually the taxonomic classification of reads by comparison to a database of previously taxonomically classified genomes. While different studies comparing metagenomic taxonomic classification methods have determined that different tools are "best", there are two tools that have been used the most to-date: Kraken (k-mer based classification against a user-constructed database) and MetaPhlAn (classification by alignment to clade-specific marker genes), the latest versions of which are Kraken2 and MetaPhlAn 3, respectively. We found large discrepancies in both the proportion of reads that were classified as well as the number of species that were identified when we used both Kraken2 and MetaPhlAn 3 to classify reads within metagenomes from human-associated or environmental datasets. We then investigated which of these tools would give classifications closest to the real composition of metagenomic samples using a range of simulated and mock samples and examined the combined impact of tool-parameter-database choice on the taxonomic classifications given. This revealed that there may not be a one-size-fits-all "best" choice. While Kraken2 can achieve better overall performance, with higher precision, recall and F1 scores, as well as alpha- and beta-diversity measures closer to the known composition than MetaPhlAn 3, the computational resources required for this may be prohibitive for many researchers, and the default database and parameters should not be used. We therefore conclude that the best tool-parameter-database choice for a particular application depends on the scientific question of interest, which performance metric is most important for this question and the limit of available computational resources.

bioinformatics↗

bioRxiv2022

Integrating phylogenetic and functional data in microbiome studies

Microbiome functional data are frequently analyzed to identify associations between microbial gene families and sample groups of interest. This is most often performed with approaches focused on the metagenome-wide relative abundance of microbial functions. Although such approaches can provide valuable insights, it is challenging to distinguish between different possible explanations for variation in community-wide functional profiles by considering functions alone. To help address this problem, we have developed a novel, phylogeny-aware framework to expand taxonomic balance tree-based approaches to identify enriched functions more robustly. The key focus of our approach, termed POMS, is on identifying functions that are consistently enriched in sample groups across independent taxonomic lineages. Based on simulated data we demonstrate that POMS can more accurately identify gene families that confer a selective advantage compared with commonly used differential abundance approaches. We also show that POMS can identify enriched functions in real-world metagenomics datasets that are potential targets of strong selection on multiple members of the microbiome. While this framework may not be able to identify all potential functional enrichments, the enrichments it does identify are more interpretable and conservative compared with those identified by existing differential abundance approaches. More generally, POMS is a novel approach for exploring microbiome functional data, which could be used to complement standard analyses. POMS is freely available as an R package at: https://github.com/gavinmdouglas/POMS.

bioinformatics↗

bioRxiv2021

Microbiome differential abundance methods produce disturbingly different results across 38 datasets

Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods have been applied for this purpose, which are largely used interchangeably in the literature. Although it has been observed that these tools can produce different results, there have been very few large-scale comparisons to describe the scale and significance of these differences. In addition, it is challenging for microbiome researchers to know which differential abundance tools are appropriate for their study and how these tools compare to one another. Here, we have investigated these questions by analyzing 38 16S rRNA gene datasets with two sample groups for differential abundance testing. We tested for differences in amplicon sequence variants and operational taxonomic units (referred to as ASVs for simplicity) between these groups with 14 commonly used differential abundance tools. Our findings confirmed that these tools identified drastically different numbers and sets of significant ASVs, however, for many tools the number of features identified correlated with aspects of the tested study data, such as sample size, sequencing depth, and effect size of community differences. We also found that the ASVs identified by each method were dependent on whether the abundance tables were prevalence-filtered before testing. ALDEx2 and ANCOM produced the most consistent results across studies and agreed best with the intersect of results from different approaches. In contrast, several methods, such as LEfSe, limma voom, and edgeR, produced inconsistent results and in some cases were unable to control the false discovery rate. In addition to these observations, we were unable to find supporting evidence for a recent recommendation that limma voom, corncob, and DESeq2 are more reliable overall compared with other methods. Although ALDEx2 and ANCOM are two promising conservative methods, we argue that those researchers requiring more sensitive methods should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.

bioinformatics↗

bioRxiv2020

Assessing the variation within the oral microbiome of healthy adults

Over 1000 different species of microbes have been found to live within the human oral cavity where they play important roles in maintaining both oral and systemic health. Several studies have identified the core members of this microbial community, however, the factors that determine oral microbiome composition are not well understood. In this study we exam the salivary oral microbiome of 1049 Atlantic Canadians using 16S rRNA gene sequencing in order to determine which dietary, lifestyle, and anthropometric features play a role in shaping microbial community composition. Features that were identified as being significantly associated with overall composition were then additionally examined for genera and amplicon sequence variants that were associated with these features. Several associations were replicated in an additional secondary validation dataset. Overall, we found that several anthropometric measurements including waist hip ratio, height, and fat free mass, as well as age and sex, were associated with oral microbiome composition in both our exploratory and validation cohorts. We were unable to validate dietary impacts on the oral microbiome but did find evidence to suggest potential contributions from factors such as the number of vegetable and refined grain servings an individual consumes. Interestingly, each one of these factors on their own were associated with only minor shifts in the oral microbiome suggesting that future biomarker identification for several diseases associated with the oral microbiome may be undertaken without the worry of confounding factors obscuring biological signal. ImportanceThe human oral cavity is inhabited by a diverse community of microbes known as the human oral microbiome. These microbes play a role in maintaining both oral and systemic health and as such have been proposed to be useful biomarkers of disease. However, to identify these biomarkers, we first need to determine the composition and variation of the healthy oral microbiome. Within this report we investigate the oral microbiome of 1049 healthy individuals to determine which genera and amplicon sequence variants are commonly found between individual oral microbiomes. We then further investigate how lifestyle, anthropometric, and dietary choices impact overall microbiome composition. Interestingly, the results from this investigation showed that while many features were significantly associated with oral microbiome composition no single biological factor explained a variation larger than 2%. These results indicate that future work on biomarker detection may be encourage by the lack of strong confounding factors.

microbiology↗

bioRxiv2019

PICRUSt2: An improved and extensible approach for metagenome inference

One major limitation of microbial community marker gene sequencing is that it does not provide direct information on the functional composition of sampled communities. Here, we present PICRUSt2 (https://github.com/picrust/picrust2), which expands the capabilities of the original PICRUSt method1 to predict the functional potential of a community based on marker gene sequencing profiles. This updated method and implementation includes several improvements over the previous algorithm: an expanded database of gene families and reference genomes, a new approach now compatible with any OTU-picking or denoising algorithm, and novel phenotype predictions. Upon evaluation, PICRUSt2 was more accurate than PICRUSt1 and other current approaches overall. PICRUSt2 is also now more flexible and allows the addition of custom reference databases. We highlight these improvements and also important caveats regarding the use of predicted metagenomes, which are related to the inherent challenges of analyzing metagenome data in general.

bioinformatics↗