Biology subjects

Wallis, R.

Publications and source records attributed to Wallis, R..

4 recordsLinked to original sources

bioRxiv2023

SenPred: A single-cell RNA sequencing-based machine learning pipeline to classify senescent cells for the detection of an in vivo senescent cell burden

Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required for senescent cell identification. However, emerging scRNA-seq datasets have enabled increased understanding of the heterogeneity of senescence. Here we present SenPred, a machine-learning pipeline which can identify senescence based on single-cell transcriptomics. Using scRNA-seq of both 2D and 3D deeply senescent fibroblasts, the model predicts intra-experimental and inter-experimental fibroblast senescence to a high degree of accuracy (>99% true positives). We position this as a proof-of-concept study, with the goal of building a holistic model to detect multiple senescent subtypes. Importantly, utilising scRNA-seq datasets from deeply senescent fibroblasts grown in 3D refines our ML model leading to improved detection of senescent cells in vivo. This has allowed for detection of an in vivo senescent cell burden, which could have broader implications for the treatment of age-related morbidities.

cell biology↗

bioRxiv2022

Quantitative Systems Pharmacology Modeling Framework of Autophagy in Tuberculosis: Application to Adjunctive Metformin Host-Directed Therapy

BackgroundQuantitative systems pharmacology (QSP) modeling of the host-immune response against Mtb can inform rational design of host-directed therapies (HDTs). We aimed to develop a QSP framework to evaluate the effects of metformin-associated autophagy-induction in combination with antibiotics. MethodsA QSP framework for autophagy was developed by extending a model for host-immune response to include AMPK-mTOR-autophagy signalling. This model was combined with pharmacokinetic-pharmacodynamic models for metformin and antibiotics against Mtb. We compared the model predictions to mice infection experiments, and derived predictions for pathogen and host-associated dynamics in humans treated with metformin in combination with antibiotics. ResultsThe model adequately captured the observed bacterial load dynamics in mice Mtb infection models treated with metformin. Simulations for adjunctive metformin therapy in newly diagnosed patients suggested a limited yet dose-dependent effect of metformin on reducing the intracellular bacterial load and selected pro-inflammatory cytokines. Our predictions suggest that metformin may provide beneficiary effects when overall bacterial load, or extracellular-to-intracellular bacterial ratio is low, either early after infection or late during antibiotic treatment. ConclusionsWe present the first QSP framework for HDTs against Mtb, linking cellular-level autophagy effects to disease progression. This framework may be extended to guide design of HDTs against Mtb.

systems biology↗

bioRxiv2021

Identification of the receptor-binding protein of Clostridium difficile phage CDHS-1 reveals a new class of receptor-binding domains.

As natural bacterial predators, bacteriophages have the potential to be developed to tackle antimicrobial resistance, but our exploitation of them is limited by understanding their vast uncharacterised genetic diversity1,2. Fascinatingly, this genetic diversity reflects many ways that phages can make proteins, performing similar functions that together form the familiar phage particle. Critical to infection are phage receptor-binding proteins (RBPs) that bind bacterial receptors and initiate bacterial entry3. Here we identified and characterised Gp22, a novel RBP for phage CDHS-1 that infects pathogenic C. difficile, but that had no recognisable RBPs. We showed that Gp22 antibodies neutralised CDHS-1 infection and used immunogold-labelling and transmission electron microscopy to identify their location on the capsid. The Gp22 three-dimensional structure was resolved by X-ray crystallography revealing a new RBP class with an N-terminal L-shaped -helical superhelix domain and a C-terminal Mg2+-binding domain. The findings provide novel insights into C. difficile phage biology and phage-host interactions. This will facilitate optimal phage development and future engineering strategies4,5. Furthermore, the AlphaFold2-predicted Gp22 structure, which was strikingly accurate, paves the way for a structurome based transformation and guidance of future phage studies where many proteins lack sequence homology but have recognisable protein structures.

molecular biology↗

bioRxiv2020

Early growth response 2 (EGR2) is a novel regulator of the senescence program

Senescence, a state of stable growth arrest, plays an important role in ageing and age-related diseases in vivo. Although the INK4/ARF locus is known to be essential for senescence programs, the key regulators driving p16 and ARF transcription remain largely underexplored. Using siRNA screening for modulators of the p16/pRB and ARF/p53/p21 pathways in deeply senescent human mammary epithelial cells (DS HMECs) and fibroblasts (DS HMFs), we identified EGR2 as a novel regulator of senescence. EGR2 expression is up-regulated during senescence and its ablation by siRNA in DS HMECs and HMFs transiently reverses the senescent phenotype. We demonstrate that EGR2 activates the ARF and p16 promoters and directly binds to the ARF promoter. Loss of EGR2 downregulates p16 levels and increases the pool of p16- p21- reversed cells in the population. Moreover, EGR2 overexpression is sufficient to induce senescence. Our data suggest that EGR2 is a regulator of the p16/pRB and direct transcriptional activator of the ARF/p53/p21 pathways in senescence and a novel marker of senescence.

cell biology↗