Biology subjects

Thaxton, C.

Publications and source records attributed to Thaxton, C..

3 recordsLinked to original sources

bioRxiv2025

Matrix metalloproteinase-3 promotes arteriovenous fistula failure by regulating FAK-AKT signaling

Objective- Surgically created upper extremity arteriovenous fistulae (AVF) are the preferred vascular access for patients requiring dialysis. It is estimated, however, that 50% of AVF fail within one year due to aggressive neointimal hyperplasia, which significantly increases morbidity and mortality. Matrix metalloproteinase-3 (MMP-3), also known as stromelysin-1, is a member of the metalloproteinase family that plays a critical role in the pathogenesis of many human disorders by degrading extracellular matrix and regulating molecular signaling pathways. The role of MMP- 3 in AVF neointimal failure has not been explored. Approach and Results- We observed that MMP-3 was induced in a time-dependent fashion by fetal bovine serum (FBS) and the growth factor PDGF-BB in cultured venous SMC. MMP-3 was also highly expressed in the neointimal SMCs of the outflow veins and the juxta-anastomotic area in an AVF mouse model, as well as in human AVF specimens. Knockdown of MMP-3 significantly suppressed venous SMC proliferation, whereas overexpression of MMP-3 facilitated cell growth in vitro. Importantly, deficiency of global and SMC-specific MMP-3 significantly reduced neointimal hyperplasia and improved patency after AVF creation. Mechanistic studies showed that MMP-3-mediated SMC proliferation and AVF neointimal hyperplasia were regulated via the FAK-AKT signaling pathway. Conclusions- These data suggest that MMP-3 is a key mediator of AVF neointimal failure. Targeting local MMP-3 activity may be a novel therapeutic strategy to prevent AVF neointimal failure and improve outcomes in patients requiring hemodialysis.

cell biology↗

bioRxiv2024

Expert Panel Curation of 31 Genes in Relation to Limb Girdle Muscular Dystrophy

IntroductionLimb girdle muscular dystrophies (LGMDs) are a group of genetically heterogeneous autosomal conditions with some degree of phenotypic homogeneity. LGMD is defined as having onset >2 years of age with progressive proximal weakness, elevated serum creatine kinase levels and dystrophic features on muscle biopsy. Advances in massively parallel sequencing have led to a surge in genes linked to LGMD. MethodsThe ClinGen Muscular Dystrophies and Myopathies gene curation expert panel (MDM GCEP, formerly Limb Girdle Muscular Dystrophy GCEP) convened to evaluate the strength of evidence supporting gene-disease relationships (GDR) using the ClinGen gene-disease clinical validity framework to evaluate 31 genes implicated in LGMD. ResultsThe GDR was exclusively LGMD for 17 genes, whereas an additional 14 genes were related to a broader phenotype encompassing congenital weakness. Four genes (CAPN3, COL6A1, COL6A2, COL6A3) were split into two separate disease entities, based on each displaying both dominant and recessive inheritance patterns, resulting in curation of 35 GDRs. Of these, 30 (86%) were classified as Definitive, 4 (11%) as Moderate and 1 (3%) as Limited. Two genes, POMGNT1 and DAG1, though definitively related to myopathy, currently have insufficient evidence to support a relationship specifically with LGMD. ConclusionsThe expert-reviewed assertions on the clinical validity of genes implicated in LGMDs form an invaluable resource for clinicians and molecular geneticists. We encourage the global neuromuscular community to publish case-level data that help clarify disputed or novel LGMD associations.

genetics↗

bioRxiv2020

MACE2K: A Text-Mining Tool to Extract Literature-based Evidence for Variant Interpretation using Machine Learning

Interpretation of a given variants pathogenicity is one of the most profound challenges to realizing the promise of genomic medicine. A large amount of information about associations between variants and diseases used by curators and researchers for interpreting variant pathogenicity is buried in biomedical literature. The development of text-mining tools that can extract relevant information from the literature will speed up and assist the variant interpretation curation process. In this work, we present a text-mining tool, MACE2k that extracts evidence sentences containing associations between variants and diseases from full-length PMC Open Access articles. We use different machine learning models (classical and deep learning) to identify evidence sentences with variant-disease associations. Evaluation shows promising results with the best F1-score of 82.9% and AUC-ROC of 73.9%. Classical ML models had a better recall (96.6% for Random Forest) compared to deep learning models. The deep learning model, Convolutional Neural Network had the best precision (75.6%), which is essential for any curation task.

bioinformatics↗