Biology subjects

Reddel, R. R.

Publications and source records attributed to Reddel, R. R..

7 recordsLinked to original sources

bioRxiv2025

Mechanosensitive Stanniocalcin-1 Attenuates Pulmonary Arterial Hypertension by Suppressing Smooth Muscle Cell Proliferation

BackgroundIdiopathic pulmonary arterial hypertension (IPAH) is driven by progressive pulmonary vascular remodeling, particularly pulmonary arterial smooth muscle cell (PASMC) proliferation. Current combination vasodilator therapies have markedly improved outcomes; however, prognosis remains poor in subgroups such as patients with respiratory comorbidities, highlighting the need for novel therapies. Elevation of intravascular hydrostatic pressure is a hallmark of IPAH, yet its direct role in PASMC pathobiology remains largely unexplored. We developed a cyclic hydrostatic pressurization culture system to model hypertensive hemodynamic stress in vitro and identify pressure-responsive mediators. MethodsPASMCs from 4 patients with IPAH were exposed to high hydrostatic pressure (70/40 mmHg, 60 bpm). Transcriptomic profiling identified differentially expressed genes, validated by qPCR. Functional studies included piezo-type mechanosensitive ion channel component 1 (PIEZO1) modulation, recombinant human stanniocalcin-1 (rhSTC1) treatment, bromodeoxyuridine (BrdU) incorporation, and western blotting for cell-cycle regulators. In vivo, chronic hypoxia-induced pulmonary hypertension was assessed in wild-type and Stc1-/- mice by hemodynamic and histological analyses, with or without intratracheal rhSTC1 administration. ResultsRNA sequencing revealed STC1 as a robustly pressure-induced gene in IPAH PASMCs. PIEZO1 activation upregulated STC1, whereas knockdown blunted this response. Elevated STC1 expression was observed in PASMCs of IPAH lung tissues, and rhSTC1 reduced PASMC proliferation and increased p-p53, p21, and p27 expression. In the chronic hypoxia model, Stc1-/- mice exhibited higher right ventricular systolic pressure (RVSP) (43.7 {+/-} 1.3 vs. 30.6 {+/-} 0.9 mmHg) and greater pulmonary arterial medial thickness (39.1 {+/-} 2.5% vs. 26.5 {+/-} 1.3%) than wild-type mice. CD68-positive macrophages were increased in Stc1-/- mice under normoxia and further elevated with hypoxia. In wild-type and Stc1-/- PAH models, intratracheal administration of rhSTC1 markedly reduced medial thickening, CD68-positive macrophage accumulation, and RVSP in both wild-type and Stc1-/- mice. ConclusionsWe demonstrate that elevated hydrostatic pressure drives STC1 expression via PIEZO1, conferring potent anti-remodeling effects in IPAH. STC1 supplementation represents a potential therapeutic strategy that addresses an urgent medical need not fulfilled by conventional therapies. Clinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSO_LIWe established a novel hydrostatic pressurization system to recapitulate idiopathic pulmonary arterial hypertension (IPAH) hemodynamic conditions in vitro. C_LIO_LIStanniocalcin-1 (STC1) is a hydrostatic pressure-responsive gene in pulmonary arterial smooth muscle cells (PASMCs) from IPAH patients, induced via the mechanosensitive receptor piezo type mechanosensitive ion channel component 1 (PIEZO1). C_LIO_LIExogenous STC1 suppresses PASMC proliferation and attenuates pulmonary vascular remodeling in chronic hypoxia-induced PAH models. C_LI What Are the Clinical Implications?O_LISTC1 supplementation represents a potential therapeutic strategy for IPAH, acting through a non-vasodilatory mechanism. C_LIO_LISTC1 supplementation may offer benefit in patients with limited response to current vasodilator therapies or with comorbid respiratory disease. C_LIO_LITargeting mechanotransduction pathways could expand treatment options for pulmonary hypertension. C_LI

molecular biology↗

bioRxiv2025

OnCorr: A pan-cancer mRNA-protein correlation tool for precision oncology

Proteins are ultimately responsible for cellular phenotypes and are targeted by most anticancer drugs. However, beyond immunohistochemistry, proteins are not typically measured in precision oncology, meaning transcriptomics is used as a proxy. To determine how informative mRNA is for guiding personalised treatments, mRNA-protein correlations were analysed in three large pan-cancer datasets and made available in a web portal (https://procan.shinyapps.io/OnCorr/). OnCorr can be integrated into precision medicine programs to augment transcriptomics.

cancer biology↗

bioRxiv2024

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning

Multi-omic characterization and integration remains a challenge due to data complexity and sparsity. Addressing this, our study introduces an unsupervised deep learning model, MOVE (Multi-Omic Variational Encoder), specifically designed to integrate and augment the Cancer Dependency Map (DepMap). Harnessing orthogonal multi-omic information, this model successfully generates molecular and phenotypic profiles, resulting in an increase of 32.7% in the number of multi-omic profiles and thereby generating a complete DepMap for 1,523 cancer cell lines. The synthetically enhanced data increases statistical power, uncovering less studied mechanisms associated with drug resistance, and refines the identification of genetic associations and clustering of cancer cell lines. By applying SHAP for model interpretation, MOVE reveals multi-omic features essential for cell clustering and biomarker identification related to drug and gene dependencies. This understanding is crucial for the development of much-needed, effective strategies in prioritizing cancer targets.

systems biology↗

bioRxiv2023

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

cancer biology↗

bioRxiv2022

Transformer-based deep learning integrates multi-omic data with cancer pathways

Multi-omic data analysis incorporating machine learning has the potential to significantly improve cancer diagnosis and prognosis. Traditional machine learning methods are usually limited to omic measurements, omitting existing domain knowledge, such as the biological networks that link molecular entities in various omic data types. Here we develop a Transformer-based explainable deep learning model, DeePathNet, which integrates cancer-specific pathway information into multi-omic data analysis. Using a variety of big datasets, including ProCan-DepMapSanger, CCLE, and TCGA, we demonstrate and validate that DeePathNet outperforms traditional methods for predicting drug response and classifying cancer type and subtype. Combining biomedical knowledge and state-of-the-art deep learning methods, DeePathNet enables biomarker discovery at the pathway level, maximizing the power of data-driven approaches to cancer research. DeePathNet is available on GitHub at https://github.com/CMRI-ProCan/DeePathNet. HighlightsO_LIDeePathNet integrates biological pathways for enhanced cancer analysis. C_LIO_LIDeePathNet utilizes Transformer-based deep learning for superior accuracy. C_LIO_LIDeePathNet outperforms existing models in drug response prediction. C_LIO_LIDeePathNet enables pathway-level biomarker discovery in cancer research. C_LI

bioinformatics↗

bioRxiv2022

Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities

The proteome provides unique insights into biology and disease beyond the genome and transcriptome. Lack of large proteomic datasets has restricted identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant workflow to quantify 8,498 proteins, these data capture evidence of cell type and post-transcriptional modifications. Integrating multi-omics, drug response and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline revealed thousands of protein-specific biomarkers of cancer vulnerabilities. Proteomic data had greater power to predict drug response than the equivalent portion of the transcriptome. Further, random downsampling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger), available at https://cellmodelpassports.sanger.ac.uk, is a comprehensive resource revealing principles of protein regulation with important implications for future clinical studies.

systems biology↗

bioRxiv2020

Alternative Lengthening of Telomeres is not synonymous with mutations in ATRX/DAXX

The PCAWG Consortium has recently released an unprecedented set of tumor whole genome sequence (WGS) data from 2,658 cancer patients across 38 different primary tumor sites1. WGS is able to document the quantity and distribution of telomeric repeats2. In one of the papers analyzing the PCAWG dataset, Sieverling et al.3 confirmed previous data4 indicating that tumors with truncating ATRX or DAXX alterations, referred to as ATRX/DAXXtrunc, have an aberrant telomere variant repeat (TVR) distribution. By regarding these mutations, vs. TERT modifications (TERTmod; i.e. promoter mutations +/- amplifications +/- structural variations), as indicators of Alternative Lengthening of Telomeres (ALT) vs. telomerase, they built a random forest classifier for ALT-probability, and then associated genomic characteristics with the putative Telomere Maintenance Mechanism (TMM)3. However, we show here that equating ATRX/DAXXtrunc and TERTmod with ALT and telomerase, respectively, results in TMM predictions which correlate poorly with TMM assay data. ATRX/DAXXtrunc mutations are heterogeneously distributed in ALT-positive (ALT+) tumors of different types, as are TERTmod in telomerase-positive tumors4. Although these mutations are strongly associated with TMM, most tumors do not harbor them, making them an inadequate basis for building a classifier in a large-scale pan-cancer study4-7. Here, we provide a new analysis of the PCAWG data, based on C-circle assay (CCA)8 that is available for a subset of these tumors4,9,10. We show that the Sieverling et al. score overestimates the proportion of ALT associated with ATRX/DAXXtrunc and misclassifies ALT tumors when these mutations are absent. We also show some TVR correlate with ATRX/DAXXtrunc mutations, regardless of TMM. Finally, we propose a new classifier to identify ALT tumors in the PCAWG cohort.

cancer biology↗