ANTXR1 as a potential sensor of extracellular mechanical cues

Disulfidptosis-related subtype and prognostic signature in prostate cancer

BACKGROUND

Disulfidptosis refers to cell death caused by the accumulation and bonding of disulfide in the cytoskeleton protein of SLC7A11-high level cells under glucose deprivation. However, the role of disulfidptosis-related genes (DRGs) in prostate cancer (PCa) classification and regulation of the tumor microenvironment remains unclear.

METHODS

Firstly, we analyzed the expression and mutation landscape of DRGs in PCa. We observed the expression levels of SLC7A11 in PCa cells through in vitro experiments and assessed the inhibitory effect of the glucose transporter inhibitor BAY-876 on SLC7A11-high cells using CCK-8 assay. Subsequently, we performed unsupervised clustering of the PCa population and analyzed the differentially expressed genes (DEGs) between clusters. Using machine learning techniques to select a minimal gene set and developed disulfidoptosis-related risk signatures for PCa. We analyzed the tumor immune microenvironment and the sensitivity to immunotherapy in different risk groups. Finally, we validated the accuracy of the prognostic signatures genes using single-cell sequencing, qPCR, and western blot.

RESULTS

Although SLC7A11 can increase the migration ability of tumor cells, BAY-876 effectively suppressed the viability of prostate cancer cells, particularly those with high SLC7A11 expression. Based on the DRGs, PCa patients were categorized into two clusters (A and B). The risk label, consisting of a minimal gene set derived from DEGs, included four genes. The expression levels of these genes in PCa were initially validated through in vitro experiments, and the accuracy of the risk label was confirmed in an external dataset. Cluster-B exhibited higher expression levels of DRG, representing lower risk, better prognosis, higher immune cell infiltration, and greater sensitivity to immune checkpoint blockade, whereas Cluster A showed the opposite results. These findings suggest that DRGs may serve as targets for PCa classification and treatment. Additionally, we constructed a nomogram that incorporates DRGs and clinical pathological features, providing clinicians with a quantitative method to assess the prognosis of PCa patients.

CONCLUSION

This study analyzed the potential connection between disulfidptosis and PCa, and established a prognostic model related to disulfidptosis, which holds promise as a valuable tool for the management and treatment of PCa patients.

A simplified and efficient extracellular vesicle-based proteomics strategy for early diagnosis of colorectal cancer

Colorectal cancer (CRC) is a major cause of cancer-related death worldwide and an effective screening strategy for diagnosis of early-stage CRC is highly desired. Although extracellular vesicles (EVs) are expected to become some of the most promising tools for liquid biopsy of early disease diagnosis, the existing EV-based proteomics methods for practical application in clinical samples are limited by technical challenges in high-throughput isolation and detection of EVs. In the current study, we have developed a simplified and efficient EV-based proteomics strategy for early diagnosis of CRC. DSPE-functionalized beads were specifically designed that enabled direct capture of EVs from plasma samples in 10 minutes with good reproducibility and comprehensive proteome coverage. The single-pot, solid-phase-enhanced sample-preparation (SP3) technology was then combined with data-independent acquisition mass spectrometry (DIA-MS) for in-depth analysis and quantification of EV proteomes. From a cohort with 30 individuals including 11 healthy controls, 8 patients with adenomatous polyp and 11 patients with early-stage CRC, our streamlined workflow reproducibly quantified over 800 proteins from their plasma-derived EV samples, from which dysregulated protein signatures for molecular diagnosis of CRC were revealed. We selected a panel of 10 protein markers to train a machine learning (ML) model, which resulted in accurate prediction of polyp and early-stage CRC in an independent and single-blind validation cohort with excellent diagnostic ability of 89.3% accuracy. Our simplified and efficient clinical proteomic strategy will serve as a valuable tool for fast, accurate, and cost-effective diagnosis of CRC that can be easily extended to other disease samples for discovery of unique EV-based biomarkers.

Mechanical strain regulates osteogenesis via Antxr1/LncRNA H19/Wnt/β-catenin axis

Alleviating bone loss is an essential way to prevent osteoporotic fractures. Proper exercise improves bone density without the side effects of long-term medications, but the mechanism is unclear. Our study explored the role of Antxr1/LncRNA H19/Wnt/β-catenin axis in the process of exercise-mediated alleviation of bone loss. Here we discovered that moderate-intensity treadmill exercise alleviates bone loss caused by ovariectomy and ameliorates bone strength accompanied by an increased lncRNA H19 expression. Concomitantly, Antxr1, a mechanosensitive protein was found downregulated by exercise but upregulated by ovariectomy. Interestingly, knockdown expression of Antxr1 increased lncRNA H19 expression and Wnt/β-catenin signaling pathway in bone marrow mesenchymal stem cells, whereas overexpression of Antxr1 decreased lncRNA H19 expression and Wnt/β-catenin signaling pathway. Hence, our study demonstrates the regulation of Antxr1/LncRNA H19/Wnt/β-catenin axis in the process of mechanical strain-induced osteogenic differentiation, which provides further mechanistic insight into the role of mechanical regulation in bone metabolism.

ANTXR1 deficiency promotes fibroblast senescence: implications for GAPO syndrome as a progeroid disorder

ANTXR1 is one of two cell surface receptors mediating the uptake of the anthrax toxin into cells. Despite substantial research on its role in anthrax poisoning and a proposed function as a collagen receptor, ANTXR1's physiological functions remain largely undefined. Pathogenic variants in ANTXR1 lead to the rare GAPO syndrome, named for its four primary features: Growth retardation, Alopecia, Pseudoanodontia, and Optic atrophy. The disease is also associated with a complex range of other phenotypes impacting the cardiovascular, skeletal, pulmonary and nervous systems. Aberrant accumulation of extracellular matrix components and fibrosis are considered to be crucial components in the pathogenesis of GAPO syndrome, contributing to the shortened life expectancy of affected individuals. Nonetheless, the specific mechanisms connecting ANTXR1 deficiency to the clinical manifestations of GAPO syndrome are largely unexplored. In this study, we present evidence that ANTXR1 deficiency initiates a senescent phenotype in human fibroblasts, correlating with defects in nuclear architecture and actin dynamics. We provide novel insights into ANTXR1's physiological functions and propose GAPO syndrome to be reconsidered as a progeroid disorder highlighting an unexpected role for an integrin-like extracellular matrix receptor in human aging.

Prenatal onset GAPO syndrome with a novel ANTXR1 variant in an Indian child: Expansion of the phenotype & literature review

GAPO syndrome is a rare genetic condition caused by bi-allelic variants in ANTXR1 gene & is an abbreviation for its core features - growth retardation, alopecia, pseudo-anodontia & optic atrophy. Certain additional features involving various other systems have been reported over the years & contribute to the expanding spectrum of this evolving phenotype. We report GAPO syndrome in a 3.75 year old Indian female child, who presented with some unique features such as sagittal craniosynostosis with scaphocephaly & bilateral choroid plexus cysts, alongside the core phenotype. We also report a novel frameshift variant in our patient & offer first evidence for the prenatal onset of some features.

Identification of diagnostic biomarkers for osteoarthritis through bioinformatics and machine learning

Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by cartilage degradation, inflammatory arthritis, and joint dysfunction. Currently, there is a lack of effective early diagnostic methods and treatment strategies for OA. Bioinformatics and biomarker research provide new possibilities for early detection and personalized therapy of OA. In this study, we investigated the molecular mechanisms of OA and important signaling pathways involved in disease progression through bioinformatics analysis. Firstly, using the limma package, we analyzed the differentially expressed genes (DEGs) between normal healthy samples and OA cartilage tissue samples. These DEGs were found to be primarily involved in biological processes such as extracellular matrix (ECM) binding, immune receptor activity, and cytokine activity, as well as signaling pathways including cytokine receptors, ECM-receptor interaction, and PI3K-Akt. Gene set enrichment analysis revealed that in the OA group, signaling pathways such as AMPK, B cell receptor, IL-17, and PPAR were downregulated, while calcium signaling pathway, cell adhesion molecules, ECM-receptor interaction, TGF-β signaling pathway, and Wnt signaling pathway were upregulated. Additionally, we constructed a co-expression module network using WGCNA and identified key modules associated with OA, from which we selected 7 most predictive OA characteristic genes. Among them, ANTXR1, KCNS3, SGCD, and LIN7A were correlated with the level of immune cell infiltration. This study elucidates the mechanisms underlying the development of OA and identifies potential diagnostic markers and therapeutic targets, providing important insights for early diagnosis and treatment of OA.