bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.02.17.638761

Proteomic characterisation of the early rheumatoid arthritis-cardiovascular disease multimorbid axis.

Abstract

BackgroundInflammation contributes to the increased risk of cardiovascular disease (CVD) observed in people with rheumatoid arthritis (RA), with increased prevalence observed from time of diagnosis. Subclinical vascular and myocardial abnormalities can be detected with cardiovascular magnetic resonance (CMR) imaging in otherwise low risk individuals. Identifying associated circulating markers could enable diagnostics and implicate biological pathways of RA-CVD. The studys objective was to identify blood-based proteins associated with CMR measures of vascular and myocardial abnormality and associated inflammatory and/or cardio-metabolic pathways in a new-onset RA cohort. MethodsSerum samples (baseline/pre-treatment, N = 75 and year 1, N = 71) from CADERA (Coronary Artery Disease Evaluation in Rheumatoid Arthritis) participants, a subgroup of a randomised controlled trial, who underwent CMR, were used to measure 334 proteins across 4 pre-defined Olink panels (Inflammation, Cardiovascular-II, Cardiovascular-III, Cardiometabolic). Bayesian mixed effects regression analyses, unsupervised hierarchical clustering and protein network analyses were applied. Normalised protein expression from 334 Olink proteins were used as exposures in the regression analyses. CMR measures of vascular stiffness (aortic distensibility and stiffness index) and myocardial tissue characteristics (native T1, myocardial extracellular volume and late gadolinium enhancement) were each used as individual outcomes in regression analyses. Associations were considered significant at 95% threshold for credible intervals. An expanded physical protein-protein interaction (PPI) network created using the significant (seed) proteins was subjected to topological and enrichment analysis to identify enriched biological pathways. Results54/334 proteins were associated significantly with CMR measures at baseline (7 - vascular; 48 - myocardial tissue characteristics; Coagulation factor 11 with both). Two proteins, TNFSF13B (B-cell activating factor) and CRTAC1, were associated with CMR measures at baseline, were sensitive to change over time and co-varied with changes in CMR measure. Topological analysis of expanded PPI network revealed GRB2 as most connected (degree score=64; closeness score=118.83) and four key signalling pathways, including JAK-STAT and EGFR tyrosine kinase inhibitor resistance, emerged as significant. ConclusionsThis first proteomic study of treatment-naive early RA and subclinical cardiovascular pathology identifies proteins that could aid diagnostic test development and implicates signalling pathways in the RA-cardiovascular axis to inform on our understanding of the basis of RA-CVD.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shukla, R., Mohammed, A., Black, N., Erhayiem, B., Fent, G. J., Miller, C. A., Plein, S., Plant, D., Buch, M. H.. 2025-02-23. Proteomic characterisation of the early rheumatoid arthritis-cardiovascular disease multimorbid axis.. https://doi.org/10.1101/2025.02.17.638761

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Limit-pushing overexpression reveals constraints on protein abundance

Proteins are often classified as toxic or non-toxic without measuring the abundance reached, leaving constraints on tolerable protein abundance unresolved. We established a limit-pushing approach in Saccharomyces cerevisiae combining strong inducible expression with gTOW-mediated high-copy selection to counteract copy-number compensation while measuring protein abundance and growth. Nearly all of approximately 80 chromosome I proteins severely inhibited growth or reduced viability at sufficiently high abundance. We established IE50, the expression level associated with a 50% reduction in growth rate, to quantify their widely varying overexpression tolerance. IE50 was positively associated with predicted structural order and cytoplasmic localization propensity and negatively associated with sulphur content. Single-cell imaging linked higher tolerance to proteins remaining cytoplasmic without becoming aggregation-positive and revealed abundance-dependent changes in localization and organelle morphology. At extreme abundance, Fun12, Nup60, and Pex22 generated distinct large-scale intracellular states through specific sequence regions. These findings establish overexpression toxicity as a quantitative property linked to protein characteristics and reveal both constraints on tolerable abundance and sequence-dependent capacities for intracellular organization.

systems biology↗

Accessing Enzyme Kinetic Data and Prediction Methods at Scale

Enzyme kinetic parameters inform metabolic models, yet experimental measurements are sparse. A growing body of work predicts them from protein and substrate features, but software fragmentation hinders adoption, so downstream tools lock into the most accessible method. We present OpenKinetics Predictor (at predictor.openkinetics.org), an open-source platform integrating thirteen methods in isolated environments behind one interface. The platform optionally reports similarity between query proteins and each method's training data to contextualise reliability. A common featurisation-prediction abstraction keeps it extensible, and independent parties, including original authors, contributed many methods. We pair it with a data portal (at data.openkinetics.org) that exposes CatLog, a curated kinetic dataset, with precomputed embeddings, predicted binding sites, and standardised splits. Both offer a web interface and an API, and the GECKO modelling toolbox already calls the predictor API. As a case study, we predict across an E. coli model and find inter-predictor agreement varies with metabolic context and data availability.

systems biology↗

A thermoregulatory design principle for transitions into hypometabolism

Mammals entering torpor or hibernation undergo an abrupt transition from normothermia to hypothermia, yet how thermoregulation enables this switch remains poorly understood. Here, we identify dynamical signatures that precede these transitions and a mathematical principle that can generate them. In fasting-induced torpor in mice, body-temperature fluctuations increased before torpor onset, providing an early-warning signal that tracked proximity to the transition better than temperature decline alone. A heat-balance model showed that reducing how strongly the effective heat-loss coefficient depends on body temperature reorganizes thermoregulatory stability, allowing a low-temperature equilibrium to emerge while the normothermic state remains stable. This organization is consistent with a symmetry-broken pitchfork involving a saddle-node. Similar increases in temperature fluctuations preceded hibernation onset in hamsters. These findings link pre-transition temperature dynamics to changes in the underlying thermoregulatory landscape and provide a framework for detecting and understanding transitions from normothermia to hypothermia.

systems biology↗