Biology subjects

Wallis, R.

Publications and source records attributed to Wallis, R..

7 recordsLinked to original sources

bioRxiv2026

Characterisation of the conformational changes of GlnH that stimulate PknG activity in Mycobacteria and Corynebacterium glutamicum

GlnH is an amino acid binding protein that senses aspartate to regulate metabolism via the PknG pathway in diverse Actinobacteria. Information about ligand occupancy of periplasmic GlnH is conveyed to PknG via an uncharacterised transmembrane protein GlnX. This pathway is important in the virulence of Mycobacterium tuberculosis, and in regulating valuable industrial fermentations by Corynebacterium glutamicum. GlnH has a "Venus flytrap"-like structure, comprising two lobes that surround the ligand aspartate. However, the conformational changes that allow GlnH to initiate this signalling pathway are unknown. To address this question, we produced GlnH from pathogens M. tuberculosis and Mycobacterium marinum and non-pathogens Mycobacterium smegmatis and C. glutamicum and used X-ray crystallography and cryo-EM to determine their structures. The results show that amino acid specificity is conserved in all homologues. However, GlnH from Mycobacteria was monomeric and bound aspartate with nanomolar affinity, whereas GlnH from C. glutamicum bound aspartate with micromolar affinity and dimerised upon binding. Whilst GlnH of the non-pathogens was stable at neutral pH, GlnH from the pathogens was most stable at acidic pH, reflecting the environment of host phagosomes. Structures were determined for all homologues, but only M. smegmatis GlnH crystallised in both unbound (Apo) and Asp-bound forms. GlnH has an open structure with a cleft between the lobes to permit access to aspartate. The Asp-bound structure is more compact with the lobes locked together, completely enclosing the ligand. AlphaFold was used to design mutations to disrupt the predicted GlnH-GlnX interface, and these variants failed to complement the metabolic defect of glnX knockout in M. smegmatis, supporting the predicted complex and suggesting how the GlnH conformational change is transmitted GlnX to initiate signalling. Author summaryBacteria sense their environment and respond to changes using a variety of sensors and regulators. We investigated a widely conserved sensor that allows Mycobacterium tuberculosis to detect amino acids in order to regulate its metabolism in different environments within the human body. A key requirement of any sensor is the ability to change shape in reponse to its stimulus. We have determined the structures of the sensor in the presence and absence of amino acid to identify the changes in shape and how these could be passed into the bacterial cell to change its behaviour. We used four related organisms to understand how sensing differs between pathogens and non-pathogens.

microbiology↗

bioRxiv2025

SAMP-Score: A morphology-based machine learning classification method for screening pro-senescence compounds in p16 positive cancer cells

BackgroundSenescence identification is rendered challenging due to a lack of universally available biomarkers. This represents a bottleneck in efforts to develop pro-senescence therapeutics - agents designed to induce the arrest of cellular proliferation associated with a senescence response in cancer cells for therapeutic gain. This is particularly true in contexts such as basal-like breast cancer (BLBC), which often express high levels of widely reported senescence hallmarks, which has led to the designation of these subtypes as senescence marker positive (Sen-Mark+). Unfortunately, these are often cancers with the most limited treatment options, where novel pro-senescence compounds would be of potential clinical utility. ResultsTo address these challenges, we have developed SAMP-Score, a machine learning classification tool for identifying senescence induction in Sen-Mark+ cancers. This technique builds upon our previous observation that senescent cells develop distinct senescence-associated morphological profiles (SAMPs), which can be assessed readily in traditionally challenging contexts for senescence identification, including high-throughput screens. ConclusionsThrough application of SAMP-Score, we have identified QM5928, a novel pro-senescence compound, that is able to induce senescence in a variety of Sen-Mark+ cancers and has potential utility as a tool molecule to explore the mechanisms and pathways through which senescence induction occurs in these cells.

cancer biology↗

bioRxiv2023

The complement pattern recognition molecule CL-11 promotes invasion and injury of respiratory epithelial cells by SARS-CoV-2.

Collectin-11 is a soluble C-type lectin produced at epithelial surfaces to initiate pathogen elimination by complement. Given the respiratory epithelium is a source of CL-11 and downstream complement-pathway components, we investigated the potential of CL-11 to impact the pathogenicity of SARS-CoV-2. While the SARS-CoV-2 spike trimer could bind CL-11 and trigger complement activation followed by MAC formation, the virus was resistant to lysis. Surprisingly, virus production by infected respiratory epithelial cells was enhanced by CL-11 opsonisation of virus but this effect was fully inhibited by sugar-blockade of CL-11. Moreover, SARS-CoV-2 spike protein expressed at the bronchial epithelial cell surface was associated with increased CL-11 binding and MAC formation. We propose that SARS-CoV-2 pathogenicity is exacerbated both by resistance to complement and CL-11 driven respiratory cell invasion and injury at the portal of entry. Contrary to expectation, CL-11 blockade could offer a novel approach to limit the pathogenicity of SARS-CoV-2.

immunology↗

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↗