Biology subjects

Revote, J.

Publications and source records attributed to Revote, J..

5 recordsLinked to original sources

bioRxiv2026

High-throughput screening in hiPSC-cardiac models reveals cardiomyocyte-specific cell cycle regulatory mechanisms

Introductory ParagraphMultiple regulatory mechanisms govern cardiomyocyte proliferation including epigenetic modifications, metabolism and mechanical load. However, it is unclear whether such mechanisms can be pharmacologically targeted to induce cardiomyocyte proliferation without affecting other cell types. Here, we develop a dual-reporter (TNNT2eGFP; PCNAmScarlet-I) and a high-throughput image-based pipeline in human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes, with counter screening in non-myocytes, to identify compounds that selectively promote cardiomyocyte proliferation without affecting other cell types. We identify the PIM kinase inhibitor GDC-0339 as a cardiomyocyte-selective pro-proliferative compound. GDC-0339 induced proliferation of hiPSC-derived cardiomyocytes without activity in non-myocytes, non-cardiac fibroblasts or epithelial cells. Phosphoproteomic profiling of GDC-0339 in cardiomyocytes and non-cardiac fibroblasts revealed a cardiomyocyte-specific mechanism of action involving sarcomere disassembly via remodelling of the F-actin cytoskeleton and metabolic reprogramming to anaerobic metabolism via Pyruvate Dehydrogenase Kinases (PDKs). Thus, we uncover cardiomyocyte-specific mechanisms governing the cell cycle that are potentially druggable.

cell biology↗

bioRxiv2022

TCR_Explore: a novel webtool for T cell receptor repertoire analysis

T cells expressing either alpha-beta or gamma-delta T cell receptors (TCR) are critical sentinels of the adaptive immune system, with receptor diversity being essential for protective immunity against a broad array of pathogens and agents. Programs available to profile TCR clonotypic signatures can be limiting for users with no coding expertise. Current analytical pipelines can be inefficient due to manual processing steps, open to data transcription errors and have multiple analytical tools with unique inputs that require coding expertise. Here we present a bespoke webtool designed for users irrespective of coding expertise, coined TCR_Explore, incorporating automated quality control steps that generates a single output file for creation of flexible and publication ready figures. TCR_Explore will elevate a users capacity to undertake in-depth TCR repertoire analysis of both new and pre-existing datasets for identification of T cell clonotypes associated with health and disease. The web application is located at https://tcr-explore.erc.monash.edu for users to interactively explore TCR repertoire datasets. Key PointsO_LIBespoke program for non-specialists in computerised methodologies for deep exploration of TCR repertoire analysis C_LIO_LIAutomated QC and analysis pipelines for Sanger based TCR sequencing coupled with immunophenotyping, with the capacity for integration of other sequencing platform outputs C_LIO_LIAutomated summary processes to aid data visualisation and generation of publication-ready graphical displays C_LI

bioinformatics↗

bioRxiv2020

Predicting and validating protein degradation in proteomes using deep learning

Age, disease, and exposure to environmental factors can induce tissue remodelling and alterations in protein structure and abundance. In the case of human skin, ultraviolet radiation (UVR)-induced photo-ageing has a profound effect on dermal extracellular matrix (ECM) proteins. We have previously shown that ECM proteins rich in UV-chromophore amino acids are differentially susceptible to UVR. However, this UVR-mediated mechanism alone does not explain the loss of UV-chromophore-poor assemblies such as collagen. Here, we aim to develop novel bioinformatics tools to predict the relative susceptibility of human skin proteins to not only UVR and photodynamically produced ROS but also to endogenous proteases. We test the validity of these protease cleavage site predictions against experimental datasets (both previously published and our own, derived by exposure of either purified ECM proteins or a complex cell-derived proteome, to matrix metalloproteinase [MMP]-9). Our deep Bidirectional Recurrent Neural Network (BRNN) models for cleavage site prediction in nine MMPs, four cathepsins, elastase-2, and granzyme-B perform better than existing models when validated against both simple and complex protein mixtures. We have combined our new BRNN protease cleavage prediction models with predictions of relative UVR/ROS susceptibility (based on amino acid composition) into the Manchester Proteome Susceptibility Calculator (MPSC) webapp http://www.manchesterproteome.manchester.ac.uk/#/MPSC (or http://130.88.96.141/#/MPSC). Application of the MPSC to the dermal proteome suggests that fibrillar collagens and elastic fibres will be preferentially degraded by proteases alone and by UVR/ROS and protease in combination, respectively. We also identify novel targets of oxidative damage and protease activity including dermatopontin (DPT), fibulins (EFEMP-1,-2, FBLN-1,-2,-5), defensins (DEFB1, DEFA3, DEFA1B, DEFB4B), proteases and protease inhibitors themselves (CTSA, CTSB, CTSZ, CTSD, TIMPs-1,-2,-3, SPINK6, CST6, PI3, SERPINF1, SERPINA-1,-3,-12). The MPSC webapp has the potential to identify novel protein biomarkers of tissue damage and to aid the characterisation of protease degradomics leading to improved identification of novel therapeutic targets.

bioinformatics↗

bioRxiv2020

MonaGO: a novel Gene Ontology enrichment analysis visualisation system

MonaGO is a novel web-based visualisation system that provides an intuitive, interactive and responsive interface for performing gene ontology (GO) enrichment analysis and visualising the results. MonaGO combines dynamic clustering and interactive visualisation as well as customisation options to assist biologists in obtaining meaningful representation of overrepresented GO terms, producing simplified outputs in an unbiased manner. MonaGO supports gene lists as well as GO terms as inputs. Visualisation results can be exported as high-resolution images or restored in new sessions, allowing reproducibility of the analysis. An extensive comparison between MonaGO and 11 state-of-the-art GO enrichment visualisation tools based on 9 features revealed that MonaGO is the only platform that simultaneously allows interactive visualisation within one single output page, directly accessible through a web browser with customisable display options. In summary, MonaGO will facilitate the interpretation of GO analysis and will assist the biologists into the representation of the results.

bioinformatics↗

bioRxiv2019

PRISM: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact

Post-translational modifications (PTMs) play very important roles in various cell signalling pathways and biological process. Due to PTMs extremely important roles, many major PTMs have been thoroughly studied, while the functional and mechanical characterization of major PTMs is well-documented in several databases. However, most currently available databases mainly focus on protein sequences, while the real 3D structures of PTMs have been largely ignored. Therefore, studies of PTMs 3D structural signatures have been severely limited by the deficiency of the data. Here, we develop PRISMOID, a novel publicly available and free 3D structure database for a wide range of PTMs. PRISMOID represents an up-to-date and interactive online knowledge base with specific focus on 3D structural contexts of PTMs sites and mutations that occur on PTMs and in the close proximity of PTM sites with functional impact. The first version of PRISMOID encompasses 17,145 non-redundant modification sites on 3,919 related protein 3D structure entries pertaining to 37 different types of PTMs. Our entry web page is organized in a comprehensive manner, including detailed PTM annotation on the 3D structure and biological information in terms of mutations affecting PTMs, secondary structure features and per-residue solvent accessibility features of PTM sites, domain context, predicted natively disordered regions and sequence alignments. In addition, high-definition JavaScript packages are employed to enhance information visualization in PRISMOID. PRISMOID equips a variety of interactive and customizable search options and data browsing functions; these capabilities allow users to access data via keyword, ID, and advanced options combination search in an efficient and user-friendly way. A download page is also provided to enable users to download the SQL file, computational structural features, and PTM sites data. We anticipate PRISMOID will swiftly become an invaluable online resource, assisting both biologists and bioinformaticians to conduct experiments and develop applications supporting discovery efforts in the sequence-structural-functional relationship of PTMs and providing important insight into mutations and PTM sites interaction mechanisms. The PRISMOID database is freely accessible at http://prismoid.erc.monash.edu/. The database and web interface are implemented in MySQL, JSP, JavaScript, and HTML with all major browsers supported.

bioinformatics↗