Biology subjects

Sweeney, M. D.

Publications and source records attributed to Sweeney, M. D..

5 recordsLinked to original sources

bioRxiv2026

Molecule-resolved inference reveals the spatial organization of transcriptional programs

Transcriptional programs encode cellular identity and physiological activity, linking gene regulation to cellular phenotypes. Although spatial transcriptomics measures individual RNA molecules across intact tissues, most analyses aggregate them into cells or pixels, obscuring molecular level functional dynamics. We introduce FICTURE2, a statistical model that infers the transcriptional program from which each individual molecule is generated, using gene identity and spatial context alone without segmentation or binning. FICTURE2 learns programs with distinct spatial distributions, ranging from millimeter-scale continuous transition to micron-scale subcellular localization, the coexisting patterns of which then reveal both cellular identity and physiological activity with unified interpretations. FICTURE2 supports both unsupervised and reference-guided inference and is orders of magnitude more efficient than alternative approaches, enabling systematic analysis of large-scale spatial datasets as we demonstrate in data across diverse tissue types profiled by various spatial technologies.

genomics↗

bioRxiv2026

Practical utility of sequence-to-omics models for improving the reproducibility of genetic fine-mapping

Recent advances in deep learning have led to the development of sequence-to-omics (S2O) models that predict molecular phenotypes directly from DNA sequences. Here, we systematically evaluate the utility of these models, e.g., AlphaGenome, Borzoi, Enformer, and Sei, for improving the reproducibility of genetic fine-mapping across expression quantitative trait loci (eQTL) datasets from Genotype-Tissue Expression (GTEx), Trans-Omics Precision Medicine (TOPMed), and Multi-Ancestry Analysis of Gene Expression (MAGE) projects. We show that purely statistical fine-mapping often yields high replication failure rates (RFRs), but integrating S2O model predictions substantially reduces RFRs and enhances the accuracy of prioritizing SNPs replicated in other consortia. We describe a generalized framework for functionally informed fine-mapping that combines traditional posterior inclusion probabilities (PIPs) from statistical fine-mapping methods with scores from S2O models to generate functionally informed PIPs (fiPIPs) that improve reproducibility. Our findings demonstrate that S2O models, particularly newer ones like AlphaGenome and Borzoi, enable robust identification of replicated variants across consortia, highlighting their promise for scalable, functionally aware genetic mapping.

genetics↗

bioRxiv2025

Opposite directions of association of higher physical activity and higher insulin resistance with human skeletal muscle cell type abundance and fiber type-level gene expression

To investigate the interplay between physical activity and cardiometabolic traits in human skeletal muscle, we characterized gene expression and chromatin accessibility across skeletal muscle cell types in 263 Finnish individuals from the FUSION Tissue Biopsy Study. We analyzed skeletal muscle single-nucleus RNA-seq data (168,309 nuclei, 23,849 genes), ATAC-seq data (242,069 nuclei, 927,588 peaks), and bulk RNA-seq data (22,309 genes). Lower insulin resistance (HOMA-IR) and higher total physical activity were both associated with higher proportions of Type 1 nuclei and lower proportions of Type 2x nuclei. We identified cell-type-level and tissue-level gene expression-trait and gene set-trait associations for cardiometabolic and physical activity traits, and a smaller proportion of cell-type-level chromatin accessibility-trait associations. Traits typically associated with better health--lower trait values of cardiometabolic traits (BMI, HOMA-IR, normal glucose tolerance vs. type 2 diabetes, 2-hour plasma glucose) and higher physical activity levels (total and vigorous)--were associated with higher expression of energy metabolism genes and lower expression of signaling pathway genes across muscle fiber types, total pseudobulk, and to some extent in bulk tissue. For HOMA-IR and physical activity, these directions of association remained when adjusting for both traits in the same model, indicating apparently independent associations in the same pathways.

genomics↗

bioRxiv2025

Enhancement of Prednisolone efficacy and safety in Duchenne muscular dystrophy via neutrophil elastase inhibition

Duchenne muscular dystrophy (DMD) is a genetic disorder characterized by muscle wasting, and leading to premature death. The pathogenesis of DMD is complex. It is triggered by loss of the cytoskeletal protein dystrophin, which causes muscle fiber instability and damage, followed by chronic inflammation and fibrotic replacement of damaged muscle tissue. Here we investigated the hypothesis that inhibition of neutrophil elastase, which is increased in dystrophic mice and impairs myogenesis, could provide a therapeutic effect in DMD. While neutrophil elastase inhibition, via the orally bioavailable compound Alvelestat, enhances myogenesis both in vitro and in vivo and reduces muscle damage and fibrosis, it does not produce a significant beneficial effect on the functional outcomes. However, when co- administered with the standard of care Prednisolone, Alvelestat dramatically enhances Prednisolone efficacy in a mouse model of DMD. In key functional tests, the performance of mice co-dosed with Alvelestat and Prednisolone is almost three times better than that of mice treated with Prednisolone alone. Additionally, co-administration of Alvelestat and Prednisolone reduces Prednisolones negative effects on muscle mass and resistance to fatigue. These findings are consistent with the finding that Prednisolone induces neutrophil elastase expression in patients affected by DMD. Our data support a beneficial role for neutrophil elastase inhibition in a mouse model of DMD that is also treated with Prednisolone, suggesting that a combinatorial therapy for DMD including a corticosteroid and a neutrophil elastase inhibitor could be trialed in humans. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/640472v3_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@f20853org.highwire.dtl.DTLVardef@b7fforg.highwire.dtl.DTLVardef@12fe312org.highwire.dtl.DTLVardef@e4cf0c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2023

Cardiomyocyte-restricted expression of IL11 causes cardiac fibrosis, inflammation, and dysfunction.

AbstractBackground: Cardiac fibrosis is a common pathological process in heart disease and represents a therapeutic target. TGF{beta} is the canonical driver of cardiac fibrosis and was recently shown to be dependent on IL11 for its profibrotic effects in fibroblasts. In the opposite direction, recombinant human IL11 has been reported as anti-fibrotic and also anti- inflammatory in the mouse heart. Objectives: In this study, we determined the effects of IL11 expression in cardiomyocytes on cardiac pathobiology and function. Methods: We used the Cre-loxP system to generate a tamoxifen-inducible mouse with cardiomyocyte-restricted murine Il11 expression. Using protein assays, bulk RNA-sequencing, and in vivo imaging we analysed the effects of IL11 on myocardial fibrosis, inflammation and cardiac function and challenge previous reports suggesting cardioprotective potential of IL11. Results: TGF{beta} stimulation of cardiomyocytes caused Il11 upregulation. As compared to wild-type controls, Il11 expressing hearts demonstrated severe cardiac fibrosis and inflammation that was associated with the upregulation of cytokines, chemokines, complement factors and increased inflammatory cells. IL11 expression also activated a programme of endothelial-to- mesenchymal transition and resulted in left ventricular dysfunction. Conclusion: Our data define species matched IL11 as strongly profibrotic and proinflammatory when secreted from cardiomyocytes and further establish IL11 as a disease factor.

molecular biology↗