Biology subjects

Watts, K.

Publications and source records attributed to Watts, K..

5 recordsLinked to original sources

bioRxiv2026

Sex-Specific Regulation of Endothelial-to-Mesenchymal Transition (EndMT) During Atherosclerosis

Sex differences in atherosclerotic plaque biology underlie clinically distinct manifestations of acute coronary syndromes, yet the molecular mechanisms driving these differences remain incompletely understood. Endothelial-to-mesenchymal transition (EndMT) is increasingly recognized as a contributor to plaque remodeling, but whether EndMT is regulated in a sex-specific and stage-dependent manner across atherosclerosis has not been systematically examined. Here, we integrated bulk RNA sequencing with single-cell transcriptomic analyses in endothelial cells and human atherosclerotic plaques to define sex-specific EndMT regulation across healthy and advanced-stage disease. Using trajectory analyses in endothelial cells undergoing EndMT, we observed pronounced sex-specific regulation in healthy endothelium, with females exhibiting stronger early EndMT activation, whereas endothelial cells derived from atherosclerotic plaques displayed markedly attenuated sex differences with disease progression. Consistent sex-divergent pseudotime trajectories were observed in human carotid plaque endothelial single-cell RNA-seq data, with females showing greater EndMT activation at earlier stages and males at later stages. Together, these findings support a stage-dependent model of sex-specific EndMT regulation, indicating that the functional consequences of EndMT are highly context dependent and may differ across early and late disease stages. Integration of these datasets prioritized high-confidence sex-specific EndMT regulators, including COL4A1, PECAM1, CD151, JAG1, FN1, NEDD9, PODXL, MAFB, PROCR, and CDH13, providing a mechanistic framework to explain clinically observed sex differences in plaque biology and to guide targeted functional interrogation.

genomics↗

bioRxiv2025

Optimising biodiversity offsetting to account for habitat succession and species colonisation dynamics

Biodiversity offsetting schemes aim to balance habitat loss from development through conservation and restoration, yet their long-term ecological outcomes under repeated impact-offset cycles remain uncertain. Using a conceptual modelling framework based on forest succession, we show that offsetting success hinges on two critical factors: the scale of habitat compensation and the duration of conservation commitments. We show that effective offsetting requires not only restoring areas larger than those impacted, but also sustaining conservation over timeframes that align with the ecological timescales of habitat development. Rapid habitat conversion consistently undermines offset success, particularly since the persistence and recovery of habitat-dependent, colonising species are highly sensitive to how quickly habitats are lost, how long restoration sites are protected, and how much time they need to reach sufficient habitat quality. Moreover, we show that short-term improvements do not necessarily meet long-term biodiversity targets. Given that habitat succession unfolds over decades or more, offset policies must move beyond short-term metrics. We emphasise that offsetting schemes must be scrutinized to ensure sufficient spatial compensation and time for habitat succession. Offsetting schemes should also account for different ecological indicators to avoid the ecological fallacy of short-term restoration success resulting in long-term biodiversity and ecosystem integrity loss.

systems biology↗

bioRxiv2023

Computational Screen for Sex-Specific Drug Effects in a Cardiac Fibroblast Network Model

Heart disease is the leading cause of death in both men and women. Cardiac fibrosis is the uncontrolled accumulation of extracellular matrix proteins which can exacerbate the progression of heart failure, and there are currently no drugs approved specifically to target matrix accumulation in the heart. Computational signaling network models (SNMs) can be used to facilitate discovery of novel drug targets. However, the vast majority of SNMs are not sex-specific and/or are developed and validated using data skewed towards male in vitro and in vivo samples. Biological sex is an important consideration in cardiovascular health and drug development. In this study, we integrate a previously constructed cardiac fibroblast SNM with estrogen signaling pathways to create sex-specific SNMs. The sex-specific SNMs maintained previously high validation when compared to in vitro experimental studies in the literature. A sex-specific perturbation analysis and drug screen uncovered several potential pathways that warrant further study in the pursuit of sex-specific treatment recommendations for cardiac fibrosis. Author SummaryHeart failure is a leading cause of death for both men and women, but we still do not have adequate therapies to prevent or reverse this disease. One factor that contributes to heart failure is scarring of cardiac tissue, also known as fibrosis. Computer models can help find new heart failure drugs by simulating hundreds of biological reactions that regulate fibrosis at the molecular level. Unfortunately, the differences in male and female patients are not usually considered for these drug discovery simulations, which can result in drugs that work well for some individuals but not for other individuals. In our study, we added sex-specific biological reactions to a computer model in order to identify drugs that could treat fibrosis differently in male and female patients. Our simulations also predicted why premenopausal women may generally develop less fibrosis than men, while post-menopausal women may develop similar levels of fibrosis as men.

systems biology↗

bioRxiv2021

Network Modeling Predicts Personalized Gene Expression and Drug Responses in Valve Myofibroblasts Cultured with Patient Sera

Aortic valve stenosis (AVS) patients experience pathogenic valve leaflet stiffening due to excessive extracellular matrix (ECM) remodeling. Numerous microenvironmental cues influence pathogenic expression of ECM remodeling genes in tissue-resident valvular myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a manually curated myofibroblast signaling network with a data-driven transcription factor network to predict patient-specific myofibroblast gene expression signatures and drug responses. Using transcriptomic data from myofibroblasts cultured with AVS patient sera, we produced a large-scale, logic-gated differential equation model in which 11 biochemical and biomechanical signals are transduced via a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis-related genes. Correlations were found between personalized model-predicted gene expression and AVS patient echocardiography data, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Further, global network perturbation analyses revealed signaling molecules with the most influence over network-wide activity including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor {beta} (TGF{beta}) along with downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Lastly, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in VICs cultured with AVS patient sera and treated with or without bosentan - a clinically approved ET1 receptor inhibitor. In sum, our work advances the ability of computational approaches to provide a mechanistic basis for clinical decisions including patient stratification and personalized drug screening.

systems biology↗

bioRxiv2020

RangeShifter 2.0: An extended and enhanced platform for modelling spatial eco-evolutionary dynamics and species' responses to environmental changes.

O_LIProcess-based models are becoming increasingly used tools for understanding how species are likely to respond to environmental changes and to potential management options. RangeShifter is one such modelling platform, which has been used to address a range of questions including identifying effective reintroduction strategies, understanding patterns of range expansion and assessing population viability of species across complex landscapes. C_LIO_LIHere we introduce a new version, RangeShifter 2.0, which incorporates important new functionality. It is now possible to simulate dynamics over user-specified, temporally changing landscapes. Additionally, the genetic and evolutionary capabilities have been strengthened, notably by introducing an explicit genetic modelling architecture, which allows for simulation of neutral and adaptive genetic processes. Furthermore, emigration, transfer and settlement rules can now all evolve, allowing for sophisticated simulation of the evolution of dispersal. We illustrate the potential application of RangeShifter 2.0s new functionality by two examples. The first illustrates the range expansion of a virtual species across a dynamically changing UK landscape. The second demonstrates how the software can be used to explore the concept of evolving connectivity in response to land-use modification, by examining how movement rules come under selection over landscapes of different structure and composition. C_LIO_LIRangeShifter 2.0 is built using object-oriented C++ providing computationally efficient simulation of complex individual-based, eco-evolutionary models. The code has been redeveloped to enable use across operating systems, including on high performance computing clusters, and the Windows GUI has been enhanced. Furthermore, the recoding of the package has supported the development of a new version running under the R platform, RangeShiftR. C_LIO_LIRangeShifter 2.0 will facilitate the development of in-silico assessments of how species will respond to environmental changes and to potential management options for conserving or controlling them. By making the code available open source, we hope to inspire further collaborations and extensions by the ecological community. C_LI

ecology↗