Biology subjects

Armour, C. R.

Publications and source records attributed to Armour, C. R..

6 recordsLinked to original sources

bioRxiv2026

A single cell atlas of mouse podocytes upon injury identifies kidney zone-dependent responses.

There are regional differences in the kidney in focal segmental glomerulosclerosis (FSGS) where podocyte injury is more severe in the juxta-medulla (JM) compared to the outer cortex (OC). Single nuclear RNA-sequencing was performed to determine any regional transcriptomic differences. 1055 differentially expressed genes (DEGs) were identified between healthy OC and JM podocytes. Of 53 podocyte canonical genes, only Magi1 and Mapt, and Npnt were higher in healthy OC and JM podocytes respectively. Hallmark pathway analysis showed that in normal mice, healthy JM podocytes are enriched for oxidative phosphorylation, glycolysis and fatty acid metabolism compared to OC podocytes. At day 7 in an experimental FSGS model induced in mice with a cytopathic anti-podocyte antibody, 226 and 225 DEGs were higher in OC and JM podocytes respectively, and 166 overlapped. Five podocyte subclusters were identified, of which the most severe (subcluster 4) was enriched for a senescence phenotype, including the p53 pathway. Inducing FSGS in mice in which p53 was deleted specifically in podocytes had lower glomerular injury compared to diseased wildtype mice. These results are consistent with differences in podocytes in the OC and JM, which might underlie regional differences in FSGS.

bioinformatics↗

bioRxiv2022

Streamlined implementation of a machine learning model to classify screen relevant neoplasia using reference-based OTU clustering

The ability to use 16S rRNA gene sequence data to train machine learning classification models offers the opportunity diagnose patients based on the composition of their microbiome. In some applications the taxonomic resolution that provides the best models may require the use of de novo OTUs whose composition changes when new data are added. We previously developed a new reference-based approach, OptiFit, that fits new sequence data to existing de novo OTUs without changing the composition of the original OTUs. While OptiFit produces OTUs that are as high quality as de novo OTUs, it is unclear whether this method for fitting new sequence data into existing OTUs will impact the performance of classification models relative to models trained and tested only using de novo OTUs. We used OptiFit to cluster sequences into existing OTUs and evaluated model performance in classifying a dataset containing samples from patients with and without colonic screen relevant neoplasia (SRN). We compared the performance of this model to standard methods including de novo and database-reference-based clustering. We found that using OptiFit performed as well or better in classifying SRNs. OptiFit can streamline the process of classifying new samples by avoiding the need to retrain models using reclustered sequences.

microbiology↗

bioRxiv2021

A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters for Microbiome-Based Classification of Colorectal Cancer

Colorectal cancer is a common and deadly disease in the United States accounting for over 50,000 deaths in 2020. This progressive disease is highly preventable with early detection and treatment, but many people do not comply with the recommended screening guidelines. The gut microbiome has emerged as a promising target for non-invasive detection of colorectal cancer. Most microbiome-based classification efforts utilize taxonomic abundance data from operational taxonomic units (OTUs) or amplicon sequence variants (ASVs) with the goal of increasing taxonomic resolution. However, it is unknown which taxonomic resolution is optimal for microbiome-based classification of colorectal cancer. To address this question, we used a reproducible machine learning framework to quantify classification performance of models based on data annotated to phylum, class, order, family, genus, OTU, and ASV levels. We found that model performance increased with increasing taxonomic resolution, up to the family level where performance was equal (p > 0.05) among family (mean AUC: 0.689), genus (mean AUC: 0.690), and OTU (mean AUC: 0.693) levels before decreasing at the ASV level (p < 0.05, mean AUC: 0.676). These results demonstrate a trade-off between taxonomic resolution and prediction performance, where coarse taxonomic resolution (e.g. phylum) is not distinct enough but fine resolution (e.g. ASV) is to individualized to accurately classify samples. Similar to the story of Goldilocks and the three bears, mid-range resolution (i.e. family, genus, and OTU) is just right for optimal prediction of colorectal cancer from microbiome data. ImportanceDespite being highly preventable, colorectal cancer remains a leading cause of cancer related death in the United States. Low-cost, non-invasive detection methods could greatly improve our ability to identify and treat early stages of disease. The microbiome has shown promise as a resource for detection of colorectal cancer. Research on the gut microbiome tends to focus on improving our ability to profile species and strain level taxonomic resolution. However, we found that finer resolution impedes the ability to predict colorectal cancer based on the gut microbiome. These results highlight the need for consideration of the appropriate taxonomic resolution for microbiome analyses and that finer resolution is not always more informative.

microbiology↗

bioRxiv2021

Developing and deploying an integrated workshop curriculum teaching computational skills for reproducible research

Inspired by well-established material and pedagogy provided by The Carpentries (Wilson 2016), we developed a two-day workshop curriculum that teaches introductory R programming for managing, analyzing, plotting and reporting data using packages from the tidyverse (Wickham et al. 2019), the Unix shell, version control with git, and GitHub. While the official Software Carpentry curriculum is comprehensive, we found that it contains too much content for a two-day workshop. We also felt that the independent nature of the lessons left learners confused about how to integrate the newly acquired programming skills in their own work. Thus, we developed a new curriculum that aims to teach novices how to implement reproducible research principles in their own data analysis. The curriculum integrates live coding lessons with individual-level and group-based practice exercises, and also serves as a succinct resource that learners can reference both during and after the workshop. Moreover, it lowers the entry barrier for new instructors as they do not have to develop their own teaching materials or sift through extensive content. We developed this curriculum during a two-day sprint, successfully used it to host a two-day virtual workshop with almost 40 participants, and updated the material based on instructor and learner feedback. We hope that our new curriculum will prove useful to future instructors interested in teaching workshops with similar learning objectives.

scientific communication and education↗

bioRxiv2020

Interspecies comparative metagenomics reveals correlated gut microbiome functional capacities among vertebrates

While recent research reveals that the gut microbiome drives vertebrate health, little is known about whether the mechanisms these microbes employ to interact with physiology are consistent across host species. To help close this knowledge gap, we compared gut metagenomes across 10 vertebrate species, including biomedical animal models, to define the inter-species variation in the biochemical pathways encoded by gut microbiota. Doing so revealed gut-enriched pathways conserved across vertebrates, as well as pathways that vary concordantly with host evolutionary history. Overall, the functional capacity of the non-human gut microbiome generally reflects that of humans, though a subset of the pathways encoded by human gut microbiota are not well represented in non-human microbiomes. Collectively, these results support the use of animal models to study the mechanisms through which gut microbes impact human health, but suggest that researchers should cautiously consider which model will optimally represent a specific mechanism of interest. SignificanceEfforts to understand how the gut microbiome interacts with human physiology frequently relies on the use of animal models. However, it is generally not understood if the biochemical pathways encoded in gut microbiomes of these different animal models - which define the routes of interaction between gut microbes and their hosts - reflect those found in the human gut. To address this question, we compared gut metagenomes generated 10 different vertebrate lineages. In so doing, our study revealed that non-human gut metagenomes generally encode a set of pathways that are consistent with those found in the human gut. However, some human metagenome pathways are poorly represented in non-human guts, including pathways implicated in disease. Moreover, our analysis identified pathways that appear to be conserved across vertebrates, as well as pathways that are linked to the evolutionary history of their hosts, observations that hold potential to clarify the basis for phylosymbiosis.

microbiology↗

bioRxiv2018

A Metagenomic Meta-Analysis Reveals Functional Signatures of Health and Disease in the Human Gut Microbiome

While recent research indicates that human health depends, in part, upon the symbiotic relationship between gut microbes and their host, the specific interactions between host and microbe that define health are poorly resolved. Metagenomic clinical studies clarify this definition by revealing gut microbial taxa and functions that stratify healthy and diseased individuals. However, the typical single-disease focus of microbiome studies limits insight into which microbiome features robustly associate with health, indicate general deviations from health, or predict specific diseases. Additionally, the focus on taxonomy may limit our understanding of how the microbiome relates to health given observations that different taxonomic members can fulfill similar functional roles. To improve our understanding of the association between the gut microbiome and health, we integrated about 2,000 gut metagenomes obtained from eight clinical studies in a statistical meta-analysis. We identify characteristics of the gut microbiome that associate generally with disease, including functional alpha-diversity, beta-diversity, and beta-dispersion. Moreover, we resolve microbiome modules that stratify diseased individuals from controls in a manner independent of study-specific effects. Many of the differentially abundant functions overlap multiple diseases suggesting a role in host health, while others are specific to a single disease and may associate with disease-specific etiologies. Our results clarify potential microbiome-mediated mechanisms of disease and reveal features of the microbiome that may be useful for the development of microbiome-based diagnostics. Ultimately, our study clarifies the definition of a healthy microbiome and how perturbations to it associate with disease.

bioinformatics↗