Biology subjects

Maczewski, M.

Publications and source records attributed to Maczewski, M..

4 recordsLinked to original sources

bioRxiv2026

Pulmonary pressure load shapes right ventricular molecular remodelling in dilated cardiomyopathy

Right ventricular (RV) adaptation to pulmonary hypertension determines outcome in dilated cardiomyopathy (DCM), but the molecular mechanisms of the transition to decompensation remain unclear. We analysed RV tissue from explanted hearts of patients with end-stage DCM using single-nucleus RNA sequencing (n=21), mass spectrometry and Olink Reveal proteomics (both n=44), and integrated these molecular profiles with echocardiographic and right-heart catheterisation measures to identify molecular correlates of RV dysfunction. Mean pulmonary arterial pressure was the dominant correlate of RV transcriptional remodelling, particularly in cardiomyocytes, where higher pressure was associated with contractile remodelling, autophagy, vesicle trafficking and glucose metabolism. In contrast, RV decompensation was characterised by immune activation and reduced oxidative phosphorylation exclusively at the proteomic level. Integrative multi-omics factor analysis (MOFA) further identified fibrosis as the dominant molecular program shared across transcriptomic and proteomic layers. Together, these findings indicate molecular adaptation to pressure load and tissue fibrosis during progression towards RV failure.

physiology↗

bioRxiv2026

Dysferlin is a novel regulator of COMP-positive matrifibrocytes in heart failure

Background and AimsCardiac fibrosis is a major contributor to heart failure (HF), yet mechanisms limiting pathological fibroblast activation remain incompletely understood. We identified dysferlin (DYSF), a membrane repair protein, as highly induced in HF fibroblasts and investigated its role in regulating profibrotic responses. MethodsCardiac fibroblasts from patients with end-stage HF and unaffected donor hearts were analysed by liquid chromatography-tandem mass spectrometry and bulk RNA sequencing. Dysferlin expression was validated in independent cohorts. Selected gene/protein expression was validated using single-cell/single-nucleus RNA sequencing and multiplex immunofluorescence of human myocardium from dilated cardiomyopathy (DCM), ischaemic cardiomyopathy (ICM), acute myocardial infarction (AMI), and unaffected hearts. Functional studies were performed in human and mouse cardiac fibroblasts using siRNA-mediated silencing and TGF-{beta} stimulation, and in engineered human 3D cardiac microtissues. Fibrotic remodelling, autophagy, apoptosis, and contractile function were assessed by molecular, histological, biochemical and functional analyses. ResultsDysferlin abundance was markedly increased in HF fibroblasts. Across HF myocardium, DYSF was enriched in activated fibroblasts but largely excluded from COMP-enriched fibrotic regions, consistent with a role in restraining fibroblast state transitions. Although induced by TGF-{beta}, DYSF silencing enhanced extracellular matrix production, increased FOSL2 expression, and promoted differentiation into COMP-positive matrifibrocytes. In engineered human cardiac microtissues, DYSF silencing exacerbated fibrosis, increased apoptosis, and impaired contractility. Mechanistically, dysferlin restrained the TGF-{beta}-FOSL2-autophagy signalling axis, whereas FOSL2 suppressed DYSF expression, defining a reciprocal regulatory circuit. Silencing FOSL2 or MXRA5 increased dysferlin levels, while mRNA-protein discordance implicated S-acylation as a potential regulator of dysferlin protein abundance. ConclusionsDysferlin is a stress-inducible antifibrotic regulator that limits maladaptive fibroblast differentiation and myocardial fibrosis, thereby representing a potential therapeutic target to attenuate adverse cardiac remodelling in HF. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC="FIGDIR/small/745492v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@46ae02org.highwire.dtl.DTLVardef@4a0b99org.highwire.dtl.DTLVardef@13fa574org.highwire.dtl.DTLVardef@18777d4_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

Infarctsize-AI: an efficient infarct size image analysis tool for small rodent myocardial infarction studies

BackgroundMyocardial infarct size (IS) is the gold standard end-point in shorth-term studies on cardioprotection. However, IS quantification in rodent models with standard Evans Blue and 2,3,5-triphenyltetrazolium chloride (TTC) staining is time-consuming and prone to inter-observer variance. Therefore, we aimed to develop an artificial intelligence (AI)-based application to reduce time and inter-observer variability of IS analysis in rodent acute myocardial infarction (MI) models. MethodsWe used TTC/Evans blue-stained heart slice images of independent laboratories from previously published projects. Rat (n = 325 and 248 slices) and mouse (n = 77 slices) datasets were used to train deep learning segmentation models with three different neural network architectures, which were combined into a single AI analysis. AI analysis was compared with manual analysis on rat data from a training laboratory (internal data, n = 496 slices, n = 41 whole-hearts) and data from independent laboratories (external data, n = 60 and 62 slices). Additionally, two independent evaluators performed manual and AI-assisted analysis, consisting of AI-analysis and its manual correction, on internal (n = 36 slices) and external data (n = 37 slices). ResultsLins concordance correlation coefficient (CCC) between IS/AAR values from manual and AI analysis was 0.844 with 95% CI of [0.814; 0.869] for images of internal data heart slices. On external data heart slices, AI accurately annotated slice area and AAR but failed to annotate infarcted area. On internal whole-heart data, CCC between AI and AI-assisted IS/AAR was 0.894 with 95% CI of [0.812; 0.942]. AI-assisted analysis reduced evaluation time on both internal and external datasets and increased region overlap for AAR between the two independent evaluators on dependent data. ConclusionsAI-assisted analysis significantly reduced analysis time and inter-observer variability. For optimal performance, lab-specific AI training is recommended. Infarctsize-AI is available at https://infarctsize.com. Translational perspectiveMyocardial infarct size (IS) is the gold-standard end-point in shorth-term studies to assess potential cardioprotective therapies against acute myocardial infarction (AMI). However, IS quantification in rodent AMI models is time-consuming and prone to inter-observer variance. Therefore, we developed an AI-based software that can reduce analysis time and inter-observer variability and facilitate documentation, which facilitates the clinical translation of potential cardioprotective therapies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=34 SRC="FIGDIR/small/688527v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@187c938org.highwire.dtl.DTLVardef@1ab8bb4org.highwire.dtl.DTLVardef@160dc68org.highwire.dtl.DTLVardef@264617_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Renal cancer cell-derived amphiregulin recruits mesenchymal stromal cells, induces their glycolytic switch, and promotes tumour growth

Mesenchymal stem/stromal cells (MSCs) are multipotent cells that support wound healing. Tumours, often called as wounds that do not heal, recruit MSCs, which in turn support tumour growth. The immunomodulatory MSCs properties are facilitated under hypoxic conditions, while tumours are often hypoxic and immune-suppressed. It is unclear how MSCs wound healing actions are prevented in tumours. Here, we found that renal cancer cells secrete amphiregulin which induces MSCs recruitment and facilitates tumour growth in vivo. In MSCs, AREG triggers HIF1A degradation and transcriptional reprogramming, leading to glycolytic switch, increased migration and attenuation of immunoregulatory profile. In renal cancer cells, AREG stimulates expression of oncogenic proteins, including AKR1C3, leading to increased tumour growth and angiogenesis. To our knowledge, this is the first study showing that glycolytic switch in MSCs is induced by cancer. Our study provides insight into how renal cancer cells attenuate MSCs wound pro-healing properties to facilitate tumour growth.

cancer biology↗