Biology subjects

Grewal, J.

Publications and source records attributed to Grewal, J..

6 recordsLinked to original sources

bioRxiv2025

The cellular mammalian clock regulates Staphylococcus aureus invasion in epithelial cells

An endogenous biological clock, the circadian clock, coordinates life with the 24-hour day/night environmental cycle. In mammals, a central pacemaker in the suprachiasmatic nucleus of the hypothalamus coordinates timing between peripheral clocks and with the environment and, for example, modulates immune responses to infections. However, its role in controlling bacterial infections at a cellular level is not understood. Here, we investigate the role of the host cellular clock during infection by a highly drug-resistant human pathogen, Staphylococcus aureus. Our findings revealed that S. aureus invasion into epithelial cells was dependent on the host circadian phase. Interestingly, cells deficient in BMAL1, a transcriptional activator and an essential clock protein, demonstrated increased bacterial uptake compared to parental A549 cells. The BMAL1-knockdown (KD) cells showed significant induction of GP340, a receptor of the S. aureus adhesin, SraP. An S. aureus sraP mutant did not exhibit rhythmic uptake into A549 cells or an increased uptake into BMAL1-KD compared to parental A549 cells. Of note, other bacterial adhesin mutants showed rhythmic and higher uptake in BMAL1-KD cells. Hence, we report that S. aureus epithelial cell invasion is clock-modulated and mediated through the S. aureus SraP-GP340 pathway, suggesting potential for host clock-directed therapy against this pathogen.

microbiology↗

bioRxiv2025

Cationic antimicrobial copolymers reveal immunomodulatory properties in LPS stimulated macrophages in vitro

Antimicrobial polymers, which have emerged as a promising alternative to antibiotics in the fight against antimicrobial resistance, are based on the design of cationic host defence peptides (CHDPs). Being a part of the mammalian innate immune system, CHDPs possess both antimicrobial and immunoregulatory effects to manage bacterial infections. However, the immunomodulatory effects of antimicrobial polymers remain largely unexplored. Within this work, a library of 15 copolymers were synthesised by reversible addition-fragmentation chain transfer (RAFT) polymerisation and their abilities to modulate pro-inflammatory pathways in LPS-activated murine and human macrophages were investigated. We found that two diblock copolymers with cationic units copolymerised with either apolar or hydrophilic comonomers appeared to have anti-inflammatory activity through suppression of the activation of the NF-{kappa}B signalling pathway, scavenging of reactive oxygen species and reduced production of the pro-inflammatory cytokine IL-6. Furthermore, the cationic-apolar copolymer exhibits significant antimicrobial activity against P. aeruginosa. Thus, this promising copolymer holds potential as a dual-action therapeutic, effectively combating bacterial infections while curbing prolonged inflammation and thereby preventing sepsis at the site of infection.

microbiology↗

bioRxiv2023

EGGNet, a generalizable geometric deep learning framework for protein complex pose scoring

Computational prediction of molecule-protein interactions has been key for developing new molecules to interact with a target protein for therapeutics development. Past work includes two independent streams of approaches: (1) predicting protein-protein interactions (PPI) between naturally occurring proteins and (2) predicting the binding affinities between proteins and small molecule ligands (aka drug target interaction, or DTI). Studying the two problems in isolation has limited the ability of these computational models to generalize across the PPI and DTI tasks, both of which ultimately involve non-covalent interactions with a protein target. In this work, we developed an Equivariant Graph of Graphs neural Network (EGGNet), a geometric deep learning framework for molecule-protein binding predictions that can handle three types of molecules for interacting with a target protein: (1) small molecules, (2) synthetic peptides and (3) natural proteins. EGGNet leverages a graph of graphs (GoGs) representation constructed from the molecule structures at atomic-resolution and utilizes a multi-resolution equivariant graph neural network (GNN) to learn from such representations. In addition, EGGNet leverages the underlying biophysics and makes use of both atom- and residue-level interactions, which improve EGGNets ability to rank candidate poses from blind docking. EGGNet achieves competitive performance on both a public proteinsmall molecule binding affinity prediction task (80.2% top-1 success rate on CASF-2016) and an synthetic protein interface prediction task (88.4% AUPR). We envision that the proposed geometric deep learning framework can generalize to many other protein interaction prediction problems, such as binding site prediction and molecular docking, helping accelerate protein engineering and structure-based drug development.

synthetic biology↗

bioRxiv2018

Text-mining clinically relevant cancer biomarkers for curation into the CIViC database

Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing and drug response markers is essential. Several knowledgebases have been created by different groups to collate evidence for these associations. These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. These databases rely on time-consuming manual curation from skilled experts who read and interpret the relevant biomedical literature. To aid in this curation and provide the greatest coverage for these databases, particularly CIViC, we propose the use of text mining approaches to extract these clinically relevant biomarkers from all available published literature. To this end, a group of cancer genomics experts annotated biomarkers and their clinical associations discussed in 800 sentences and achieved good inter-annotator agreement. We then used a supervised learning approach to construct the CIViCmine knowledgebase (http://bionlp.bcgsc.ca/civicmine/) extracting 128,857 relevant sentences from PubMed abstracts and Pubmed Central Open Access full text papers. CIViCmine contains over 90,992 biomarkers associated with 7,866 genes, 402 drugs and 557 cancer types, representing 29,153 abstracts and 40,551 full-text publications. Through integration with CIVIC, we provide a prioritised list of curatable biomarkers as well as a resource that is valuable to other knowledgebases and precision cancer analysts in general.

bioinformatics↗

bioRxiv2018

CancerMine: A literature-mined resource for drivers, oncogenes and tumor suppressors in cancer

Understanding a mutation in cancer requires knowledge of the different roles that genes play in cancer as drivers, oncogenes and tumor suppressors. We present CancerMine, a high-quality text-mined knowledgebase that catalogues over 856 genes as drivers, 2,421 as oncogenes and 2,037 as tumor suppressors in 426 cancer types. We compile 3,485 genes that are not in the IntOGen resource of drivers and complement the Cancer Gene Census with 3,136 new genes identified as oncogenes and tumor suppressors. CancerMine provides a method for gene-centric clustering of cancer types illustrating genetic similarities between cancer types of different organs and was validated against data from the Cancer Genome Atlas (TCGA) project. Finally with 178 novel cancer gene mentions in publications each month, this resource will be updated monthly, pre-empting the need to manually curate the ever-increasing number of novel cancer associated genes. CancerMine is viewable through a web portal (http://bionlp.bcgsc.ca/cancermine/) and available for download (https://github.com/jakelever/cancermine).

bioinformatics↗

bioRxiv2016

Enhancing Knowledge Discovery from Cancer Genomics Data with Galaxy

We present a collection of Galaxy tools representing many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We implemented methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability on cloud-based infrastructure and commodity hardware. Some tools represents extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate several of these to perform some standard data integration and visualization tasks are demonstrated on a cohort of 96 diffuse large B-cell lymphomas, enabling the discovery of multiple candidate lymphoma-related genes that have not been reported previously.

genomics↗