PAI-1 Regulates the Cytoskeleton and Intrinsic Stiffness of Vascular Smooth Muscle Cells.

BACKGROUND: Plasma concentration of PAI-1 (plasminogen activator inhibitor-1) correlates with arterial stiffness. Vascular smooth muscle cells (SMCs) express PAI-1, and the intrinsic stiffness of SMCs is a major determinant of total arterial stiffness. We hypothesized that PAI-1 promotes SMC stiffness by regulating the cytoskeleton and that pharmacological inhibition of PAI-1 decreases SMC and aortic stiffness. METHODS: PAI-039, a specific inhibitor of PAI-1, and small interfering RNA were used to inhibit PAI-1 expression in cultured human SMCs. Effects of PAI-1 inhibition on SMC stiffness, F-actin (filamentous actin) content, and cytoskeleton-modulating enzymes were assessed. WT (wild-type) and PAI-1-deficient murine SMCs were used to determine PAI-039 specificity. RNA sequencing was performed to determine the effects of PAI-039 on SMC gene expression. In vivo effects of PAI-039 were assessed by aortic pulse wave velocity. RESULTS: PAI-039 significantly reduced intrinsic stiffness of human SMCs, which was accompanied by a significant decrease in cytoplasmic F-actin content. PAI-1 gene knockdown also decreased cytoplasmic F-actin. PAI-1 inhibition significantly increased the activity of cofilin, an F-actin depolymerase, in WT murine SMCs, but not in PAI-1-deficient SMCs. RNA-sequencing analysis suggested that PAI-039 upregulates AMPK (AMP-activated protein kinase) signaling in SMCs, which was confirmed by Western blotting. Inhibition of AMPK prevented activation of cofilin by PAI-039. In mice, PAI-039 significantly decreased aortic stiffness and tunica media F-actin content without altering the elastin or collagen content. CONCLUSIONS: PAI-039 decreases intrinsic SMC stiffness and cytoplasmic stress fiber content. These effects are mediated by AMPK-dependent activation of cofilin. PAI-039 also decreases aortic stiffness in vivo. These findings suggest that PAI-1 is an important regulator of the SMC cytoskeleton and that pharmacological inhibition of PAI-1 has the potential to prevent and treat cardiovascular diseases involving arterial stiffening.

Endocrine-disrupting chemicals and hormonal profiles in PCOS women: A comparative study between urban and rural environment.

Polycystic Ovary Syndrome (PCOS), a multifaceted endocrine disorder, affects a significant proportion of women globally, with its etiology rooted in both genetic and environmental factors. This study delves into the environmental aspect, particularly focusing on the role of endocrine-disrupting chemicals (EDCs) in the context of urbanization and industrialization. This research examines the impact of endocrine-disrupting chemicals (EDCs) - Bisphenol A (BPA), Mono-ethyl Hexyl Phthalate (MEHP), and Di-ethyl Hexyl Phthalate (DEHP) - on 40 women with Polycystic Ovary Syndrome (PCOS) across urban and rural Gujarat. Employing High-Performance Liquid Chromatography (HPLC) and chemiluminescence, we analyzed their blood samples for EDCs levels and hormonal parameters. Urban individuals displayed significantly higher BPA and DEHP concentrations, highlighting the environmental exposure differences. Notably, urban exposure to MEHP and DEHP correlated with a marked decrease in estradiol levels, while rural DEHP exposure was associated with an increase in estradiol but a decrease in prolactin and DHEAS levels. These findings illuminate the variable effects of EDC exposure on hormonal profiles in PCOS, influenced by geographical and environmental contexts. The study underscores the critical need for tailored environmental health policies to mitigate the diverse impacts of EDCs, advocating for a nuanced approach to PCOS management that considers environmental exposures. Our insights contribute to the understanding of PCOS's hormonal dynamics, emphasizing the significance of addressing EDC exposure in different settings.

CrossMP: Enabling Cross-Modality Translation between Single-Cell RNA-Seq and Single-Cell ATAC-Seq through Web-Based Portal.

In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend.

IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network.

INTRODUCTION: Immune checkpoint inhibitors (ICIs) are potent and precise therapies for various cancer types, significantly improving survival rates in patients who respond positively to them. However, only a minority of patients benefit from ICI treatments. OBJECTIVES: Identifying ICI responders before treatment could greatly conserve medical resources, minimize potential drug side effects, and expedite the search for alternative therapies. Our goal is to introduce a novel deep-learning method to predict ICI treatment responses in cancer patients. METHODS: The proposed deep-learning framework leverages graph neural network and biological pathway knowledge. We trained and tested our method using ICI-treated patients' data from several clinical trials covering melanoma, gastric cancer, and bladder cancer. RESULTS: Our results demonstrate that this predictive model outperforms current state-of-the-art methods and tumor microenvironment-based predictors. Additionally, the model quantifies the importance of pathways, pathway interactions, and genes in its predictions. A web server for IRnet has been developed and deployed, providing broad accessibility to users at https://irnet.missouri.edu. CONCLUSION: IRnet is a competitive tool for predicting patient responses to immunotherapy, specifically ICIs. Its interpretability also offers valuable insights into the mechanisms underlying ICI treatments.

Scaffold protein SH3BP2 signalosome is pivotal for immune activation in nephrotic syndrome.

Despite clinical use of immunosuppressive agents, the immunopathogenesis of minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) remains unclear. Src homology 3-binding protein 2 (SH3BP2), a scaffold protein, forms an immune signaling complex (signalosome) with 17 other proteins, including phospholipase Cγ2 (PLCγ2) and Rho-guanine nucleotide exchange factor VAV2 (VAV2). Bioinformatic analysis of human glomerular transcriptome (Nephrotic Syndrome Study Network cohort) revealed upregulated SH3BP2 in MCD and FSGS. The SH3BP2 signalosome score and downstream MyD88, TRIF, and NFATc1 were significantly upregulated in MCD and FSGS. Immune pathway activation scores for Toll-like receptors, cytokine-cytokine receptor, and NOD-like receptors were increased in FSGS. Lower SH3BP2 signalosome score was associated with MCD, higher estimated glomerular filtration rate, and remission. Further work using Sh3bp2KI/KI transgenic mice with a gain-in-function mutation showed ~6-fold and ~25-fold increases in albuminuria at 4 and 12 weeks, respectively. Decreased serum albumin and unchanged serum creatinine were observed at 12 weeks. Sh3bp2KI/KI kidney morphology appeared normal except for increased mesangial cellularity and patchy foot process fusion without electron-dense deposits. SH3BP2 co-immunoprecipitated with PLCγ2 and VAV2 in human podocytes, underscoring the importance of SH3BP2 in immune activation. SH3BP2 and its binding partners may determine the immune activation pathways resulting in podocyte injury leading to loss of the glomerular filtration barrier.

FatPlants: a comprehensive information system for lipid-related genes and metabolic pathways in plants.

FatPlants, an open-access, web-based database, consolidates data, annotations, analysis results, and visualizations of lipid-related genes, proteins, and metabolic pathways in plants. Serving as a minable resource, FatPlants offers a user-friendly interface for facilitating studies into the regulation of plant lipid metabolism and supporting breeding efforts aimed at increasing crop oil content. This web resource, developed using data derived from our own research, curated from public resources, and gleaned from academic literature, comprises information on known fatty-acid-related proteins, genes, and pathways in multiple plants, with an emphasis on Glycine max, Arabidopsis thaliana, and Camelina sativa. Furthermore, the platform includes machine-learning based methods and navigation tools designed to aid in characterizing metabolic pathways and protein interactions. Comprehensive gene and protein information cards, a Basic Local Alignment Search Tool search function, similar structure search capacities from AphaFold, and ChatGPT-based query for protein information are additional features. Database URL: https://www.fatplants.net/.

Natural and artificial selection of multiple alleles revealed through genomic analyses.

Genome-to-phenome research in agriculture aims to improve crops through in silico predictions. Genome-wide association study (GWAS) is potent in identifying genomic loci that underlie important traits. As a statistical method, increasing the sample quantity, data quality, or diversity of the GWAS dataset positively impacts GWAS power. For more precise breeding, concrete candidate genes with exact functional variants must be discovered. Many post-GWAS methods have been developed to narrow down the associated genomic regions and, ideally, to predict candidate genes and causative mutations (CMs). Historical natural selection and breeding-related artificial selection both act to change the frequencies of different alleles of genes that control phenotypes. With higher diversity and more extensive GWAS datasets, there is an increased chance of multiple alleles with independent CMs in a single causal gene. This can be caused by the presence of samples from geographically isolated regions that arose during natural or artificial selection. This simple fact is a complicating factor in GWAS-driven discoveries. Currently, none of the existing association methods address this issue and need to identify multiple alleles and, more specifically, the actual CMs. Therefore, we developed a tool that computes a score for a combination of variant positions in a single candidate gene and, based on the highest score, identifies the best number and combination of CMs. The tool is publicly available as a Python package on GitHub, and we further created a web-based Multiple Alleles discovery (MADis) tool that supports soybean and is hosted in SoyKB (https://soykb.org/SoybeanMADisTool/). We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. Finally, we identified a candidate gene for the pod color L2 locus and predicted the existence of multiple alleles that potentially cause loss of pod pigmentation. In this work, we show how a genomic analysis can be employed to explore the natural and artificial selection of multiple alleles and, thus, improve and accelerate crop breeding in agriculture.