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Shadrina, M.

Publications and source records attributed to Shadrina, M..

2 recordsLinked to original sources

Integrative Omics Identifies Candidate Plasma Biomarkers and Cellular Targets Associated with Thoracic Aortic Aneurysm

ObjectivesTo define the cellular landscape of thoracic aortic aneurysm (TAA) and identify circulating biomarkers associated with disease burden. BackgroundTAA is marked by progressive aortic wall weakening and dilation, predisposing to rupture and dissection. However, its cellular architecture remains incompletely defined, and reliable circulating biomarkers are lacking. MethodsSingle-cell RNA sequencing was performed on 17 aortic tissue samples from 10 patients undergoing TAA repair and integrated with publicly available datasets to characterize disease-associated cell states. In parallel, tomographic imaging and plasma proteomics were used to identify biomarkers associated with aortic diameter. Key findings were further assessed through integration with single-cell data, external validation, and in vitro stimulation of primary human adventitial fibroblasts with fibroblast growth factor 23 (FGF-23). ResultsWe identified 25 cellular subsets, including macrophages, endothelial cells, vascular smooth muscle cells, and fibroblasts, with substantial heterogeneity in cellular composition and transcriptional state. Genome-wide association study candidate genes, including JUN and TPM3, showed cell type-specific upregulation. Plasma proteomics identified multiple biomarkers associated with aortic diameter, of which FGF-23 was independently validated in the UK Biobank as elevated in individuals with TAA. FGFR1, the receptor for FGF-23, was selectively expressed in fibroblasts and subsets of vascular smooth muscle cells, with strongest downstream signaling in fibroblasts. FGF-23 stimulation induced inflammatory and extracellular matrix remodeling programs in primary human adventitial fibroblasts. ConclusionsThese findings define the cellular landscape of TAA and identify the FGF-23-FGFR1 axis as a biomarker-linked pathway that may contribute to aneurysm progression. Condensed AbstractThoracic aortic aneurysm (TAA) is characterized by progressive aortic dilation and risk of rupture, yet its cellular architecture and circulating biomarkers remain incompletely defined. We performed single-cell RNA sequencing on 17 aortic samples from 10 patients and integrated these data with public datasets to define the cellular landscape of TAA. In parallel, imaging and plasma proteomics (n=10) were used to identify biomarkers associated with aortic diameter. We identified 25 cellular subsets, including macrophages, endothelial cells, vascular smooth muscle cells, and fibroblasts, with notable transcriptional heterogeneity and cell type-specific upregulation of GWAS-associated genes (e.g., JUN, TPM3). Plasma proteomics identified fibroblast growth factor 23 (FGF-23) as associated with aortic diameter and elevated in TAA in the UK Biobank. FGFR1, its receptor, was selectively expressed in fibroblasts and VSMCs, and FGF-23 stimulation induced inflammatory and extracellular matrix remodeling programs in fibroblasts. These findings link a circulating biomarker to stromal cell signaling in TAA. HighlightsO_LIIntegrative single-cell RNA sequencing defined a diverse cellular landscape in thoracic aortic aneurysm tissue, identifying 25 distinct cell populations. C_LIO_LICross-dataset harmonization revealed marked differences in cellular composition across studies while supporting shared stromal and immune programs in thoracic aortic aneurysm. C_LIO_LIPlasma proteomics identified FGF-23 as a candidate biomarker associated with aortic diameter and independently linked to thoracic aortic aneurysm presence in the UK Biobank C_LIO_LIFGFR1 was enriched in fibroblasts and subsets of vascular smooth muscle cells, and FGF-23 induced inflammatory and extracellular matrix remodeling programs in human primary adventitial fibroblasts. C_LI

cell biology↗

Automated Identification of Germline de novo Mutations in Family Trios: A Consensus-Based Informatic Approach

Accurate identification of germline de novo variants (DNVs) remains a challenging problem despite rapid advances in sequencing technologies as well as methods for the analysis of the data they generate, with putative solutions often involving ad hoc filters and visual inspection of identified variants. Here, we present a purely informatic method for the identification of DNVs by analyzing short-read genome sequencing data from proband-parent trios. Our method evaluates variant calls generated by three genome sequence analysis pipelines utilizing different algorithms--GATK HaplotypeCaller, DeepTrio and Velsera GRAF--exploring the assumption that a requirement of consensus can serve as an effective filter for high- quality DNVs. We assessed the efficacy of our method by testing DNVs identified using a previously established, highly accurate classification procedure that partially relied on manual inspection and used Sanger sequencing to validate a DNV subset comprising less confident calls. The results show that our method is highly precise and that applying a force-calling procedure to putative variants further removes false-positive calls, increasing precision of the workflow to 99.6%. Our method also identified novel DNVs, 87% of which were validated, indicating it offers a higher recall rate without compromising accuracy. We have implemented this method as an automated bioinformatics workflow suitable for large- scale analyses without need for manual intervention.

bioinformatics↗