Biology subjects

Read, M. N.

Publications and source records attributed to Read, M. N..

4 recordsLinked to original sources

bioRxiv2024

Genome scale metabolic modelling of human gut microbes to inform rational community design

The human gut microbiome impacts host health through metabolite production, notably short-chain fatty acids (SCFAs) derived from digestion-resistant carbohydrates (DRCs). While DRC supplementation offers a means to modulate the microbiome therapeutically, its effectiveness is often limited by the microbial communitys complexity and individual variability in microbiome functionality. We utilized genome-scale metabolic models (GEMs) from the AGORA collection to provide a system-level overview of the metabolic capabilities of human gut microbes in terms of carbohydrate trophic networks and propose improved therapeutic interventions, based on microbial community design. Our study inferred the capability of AGORA strains to consume carbohydrates of varying structural complexities--including DRCs--and to produce metabolites amenable to cross-feeding, such as SCFAs. The resulting functional database indicated that DRC-degrading abilities are rare among gut microbes, suggesting that the presence or absence of specific taxa can determine the success of DRC-based interventions. Additionally, we found that metabolite production profiles exceed family-level variation, highlighting the limitations in predicting intervention outcomes based on gut microbial composition assessed at higher taxonomic levels. In response to these findings, we integrate reverse ecology principles, network analysis and GEM community modelling to guide the design of minimal yet resilient microbial communities to better guarantee intervention response (purpose-based communities). As a proof of principle, we predicted a purpose-based community designed to enhance butyrate production when used in conjunction with DRC supplementation, that displays resilience under nutritional stress, such as amino acid restriction. We further seeded the identified purpose-based community into modelled human microbiomes previously demonstrated to accurately predict SCFA production profiles. The analysis confirmed that such intervention significantly promotes butyrate production across samples, with those that presented a comparatively lower butyrate production pre-intervention displaying the largest increase in butyrate production after seeding. Our work highlights the potential of combining GEMs with community design to infer effective microbiome interventions, ultimately leading to improved health outcomes.

ecology↗

bioRxiv2021

TrackSOM: mapping immune response dynamics through sequential clustering of time- and disease-course single-cell cytometry data

Mapping the dynamics of immune cell populations over time or disease-course is key to understanding immunopathogenesis and devising putative interventions. We present TrackSOM, an algorithm which delineates cellular populations and tracks their development over a time- or disease-course of cytometry datasets. We demonstrate TrackSOM-enabled elucidation of the immune response to West Nile Virus infection in mice, uncovering heterogeneous sub-populations of immune cells and relating their functional evolution to disease severity. TrackSOM is easy to use, encompasses few parameters, is quick to execute, and enables an integrative and dynamic overview of the immune system kinetics that underlie disease progression and/or resolution.

bioinformatics↗

bioRxiv2020

Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre

As the size and complexity of high-dimensional cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end-to-end integration and analysis of high-dimensional cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre-processing, batch alignment, data integration, clustering, dimensionality reduction, visualization and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large high-dimensional datasets, generated by flow cytometry, mass cytometry (CyTOF), or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single-cell RNA sequencing (scRNAseq) or high-dimensional imaging technologies, such as Imaging Mass Cytometry (IMC). The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre).

immunology↗

bioRxiv2018

Nuclear filamentous actin functions in the replication stress response

\"Replication stress\" describes phenomena that alter DNA replication rates 1-3. Multiple architectural challenges within the confined nuclear volume must be navigated during replication to prevent or repair replication stress. Cellular mechanisms potentiating changes in nuclear architecture that facilitate DNA replication remain unclear. Here we show that the ATR, IPMK and mTOR kinases regulate actin polymerisation in human cells to alter nuclear architecture and promote replication fork repair. We demonstrate that replication stress activates mTOR, in an ATR and IPMK-dependent manner, to induce polymerisation of nuclear filamentous actin (F-actin). mTOR and ATR then counteract replication stress-induced nuclear envelope deformation and increase nuclear volume through their regulation of actin dynamics. Additionally, we reveal that FANCD2 labelled replication forks colocalise with actin filaments in late S-phase. mTOR and ATR then regulate the mobility, speed and directionality of stalled replication foci within the three-dimensional nuclear architecture. Importantly, we find nuclear F-actin also acts as a substrate for the directed migration of stalled replication foci to the nuclear periphery. Suppressing mTOR and ATR-dependent actin forces prevents replication fork restart and promotes chromosome segregation errors in primary and cancer cell lines. Together, these data reveal that ATR and mTOR regulate actin dynamics in the replication stress response to alter nuclear architecture and maintain genome stability.

cell biology↗