Biology subjects

Armstrong, H.

Publications and source records attributed to Armstrong, H..

3 recordsLinked to original sources

bioRxiv2023

Proportionality-based association metrics in count compositional data

MotivationCompositional data comprise vectors that describe the constituent parts of a whole. Data arising from various -omics platforms such as 16S and RNA-sequencing are compositional in nature. However, correlations between features on raw counts have no meaningful interpretation. Metrics of proportionality were formulated to address this problem. However, there is an inherent bias that arises when calculating these metrics empirically on count-based measures due to variability in read depths. ResultsWe quantify the bias introduced by empirically calculating proportionality-based association metrics in count data. Additionally, we propose a means of estimating these metrics within a logit-normal multinomial model in pursuit of more accurate estimates. The model-based estimates are shown to outperform empirical estimates in simulated data, and are additionally applied to a mouse embryonic stem-cell single-cell sequencing dataset as well as a pediatric-onset multiple sclerosis metagenomic dataset. Availability and ImplementationAn R package is available at https://CRAN.R-project.org/package=countprop. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2023

PoseR - A deep learning toolbox for decoding animal behavior

The actions of animals provide a window into how their minds work. Recent advances in deep learning are providing powerful approaches to recognize patterns of animal movement from video recordings using markerless pose estimation models. There is an increasingly rich field of unsupervised and supervised methods for classifying animal behavior built upon the outputs of pose estimation models. However, these methods often rely on species and task-specific feature engineering of trajectories, kinematics and task programming. Highly generalized solutions that use only pose estimations and the inherent structure of animals and their environment provide an opportunity to develop foundational, contextual and, importantly, standardized animal behavior models for efficient and reproducible behavioral analysis. Here, we present PoseRecognition (PoseR), a behavioral classifier leveraging action recognition models using spatio-temporal graph convolutional networks. We show that it can be used to classify animal behavior quickly and accurately from pose estimations, using zebrafish larvae, Drosophila melanogaster, mice, and rats as model organisms. PoseR can be accessed using a Napari plugin, which facilitates efficient behavioral extraction for bout-like behaviour, annotation, model training and deployment. Our tool simplifies the behavioral analysis workflow by transforming coordinates of animal position and pose into semantic labels with speed and precision. Furthermore, we contribute a novel method for unsupervised clustering of behaviors and provide open-source access to our zebrafish datasets and models. The design of our tool ensures scalability and versatility for use across multiple species and contexts, improving the efficiency of behavioral analysis across fields.

neuroscience↗

bioRxiv2023

Mouse mammary tumor virus is implicated in severity of colitis and dysbiosis in the IL-10-/- mouse model of inflammatory bowel disease

BackgroundFollowing viral infection, genetically manipulated mice lacking immunoregulatory function may develop colitis and dysbiosis in a strain specific fashion that serves as a model for inflammatory bowel disease (IBD). We found that one such model of spontaneous colitis, the interleukin (IL)-10 knockout (IL-I0-/-) model derived from the SvEv mouse, had evidence of increased mouse mammary tumor virus (MMTV) viral RNA expression compared to the SvEv wildtype. MMTV is endemic in several mouse strains as an endogenously encoded betaretrovirus that is passaged as an exogenous agent in breast milk. As MMTV requires a viral superantigen to replicate in the gut associated lymphoid tissue prior to the development of systemic infection, we evaluated whether MMTV may contribute to the development of colitis in the IL-10-/- model. ResultsViral preparations extracted from IL-10-/- weanling stomachs revealed augmented MMTV load compared to the SvEv wildtype. Illumina sequencing of the viral genome revealed that the two largest contigs shared 96.4% - 97.3% identity with the mtv-1 endogenous loci and the MMTV(HeJ) exogenous virus from the C3H mouse. The MMTV sag gene cloned from IL-10-/- spleen encoded the MTV-9 superantigen that preferentially activates T cell receptor V{beta}-12 subsets, which were expanded in the IL-10-/- versus the SvEv colon. Evidence of MMTV cellular immune responses to MMTV Gag peptides was observed in the IL-10-/- splenocytes with amplified interferon-{gamma} production versus the SvEv wildtype. To address the hypothesis that MMTV may contribute to colitis, we used HIV reverse transcriptase inhibitors, tenofovir and emtricitabine, and the HIV protease inhibitor, lopinavir boosted with ritonavir, for 12 weeks treatment versus placebo. The combination anti-retroviral therapy with known activity against MMTV was associated with reduced colonic MMTV RNA and improved histological score in IL10-/- mice, as well as diminished secretion of pro-inflammatory cytokines and modulation of the microbiome associated with colitis. ConclusionsThis study suggests that immunogenetically manipulated mice with deletion of IL-10 may have reduced capacity to contain MMTV infection in a mouse-strain specific manner, and the antiviral inflammatory responses may contribute to the complexity of IBD with the development of colitis and dysbiosis.

microbiology↗