DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer.

The field of computational drug repurposing aims to uncover novel therapeutic applications for existing drugs through high-throughput data analysis. However, there is a scarcity of drug repurposing methods leveraging the cellular-level information provided by single-cell RNA sequencing data. To address this need, we propose DrugReSC, an innovative approach to drug repurposing utilizing single-cell RNA sequencing data, intending to target specific cell subpopulations critical to disease pathology. DrugReSC constructs a drug-by-cell matrix representing the transcriptional relationships between individual cells and drugs and utilizes permutation-based methods to assess drug contributions to cellular phenotypic changes. We demonstrate DrugReSC's superior performance compared to existing drug repurposing methods based on bulk or single-cell RNA sequencing data across multiple cancer case studies. In summary, DrugReSC offers a novel perspective on the utilization of single-cell sequencing data in drug repurposing methods, contributing to the advancement of precision medicine for cancer.

Identification of cell subpopulations associated with disease phenotypes from scRNA-seq data using PACSI.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has revolutionized the transcriptomics field by advancing analyses from tissue-level to cell-level resolution. Despite the great advances in the development of computational methods for various steps of scRNA-seq analyses, one major bottleneck of the existing technologies remains in identifying the molecular relationship between disease phenotype and cell subpopulations, where "disease phenotype" refers to the clinical characteristics of each patient sample, and subpopulation refer to groups of single cells, which often do not correspond to clusters identified by standard single-cell clustering analysis. Here, we present PACSI, a method aimed at distinguishing cell subpopulations associated with disease phenotypes at the single-cell level. RESULTS: PACSI takes advantage of the topological properties of biological networks to introduce a proximity-based measure that quantifies the correlation between each cell and the disease phenotype of interest. Applied to simulated data and four case studies, PACSI accurately identified cells associated with disease phenotypes such as diagnosis, prognosis, and response to immunotherapy. In addition, we demonstrated that PACSI can also be applied to spatial transcriptomics data and successfully label spots that are associated with poor survival of breast carcinoma. CONCLUSIONS: PACSI is an efficient method to identify cell subpopulations associated with disease phenotypes. Our research shows that it has a broad range of applications in revealing mechanistic and clinical insights of diseases.

SubtypeDrug: a software package for prioritization of candidate cancer subtype-specific drugs.

SUMMARY: Cancer can be classified into various subtypes by its molecular, histological or clinical characteristics. Discovering cancer-subtype-specific drugs is a crucial step in personalized medicine. SubtypeDrug is a system biology R-based software package that enables the prioritization of subtype-specific drugs based on cancer expression data from samples of many subtypes. This provides a novel approach to identify the subtype-specific drug by considering biological functions regulated by drugs at the subpathway level. The operation modes include extraction of subpathways from biological pathways, identification of dysregulated subpathways induced by each drug, inference of sample-specific subpathway activity profiles, evaluation of drug-disease reverse association at the subpathways level, identification of cancer-subtype-specific drugs through subtype sample set enrichment analysis, and visualization of the results. Its capabilities enable SubtypeDrug to find subtype-specific drugs, which will fill the gaps in the recent tools which only identify the drugs for a particular cancer type. SubtypeDrug may help to facilitate the development of tailored treatment for patients with cancer. AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and available under GPL-2 license from the CRAN website (https://CRAN.R-project.org/package=SubtypeDrug). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide haplotype association study identifies risk genes for non-small cell lung cancer.

Lung cancer is the leading cause of cancer-related death worldwide. Most lung cancer is non-small cell lung cancer (NSCLC), in which malignant cells form in the lung epithelium. Mutations in multiple genes and environmental factors both contribute to NSCLC, and although some NSCLC susceptibility genes have been characterized, the pathogenesis of this disease remains unclear. To identify genes conferring NSCLC risk and determine their associated pathological mechanism, we combined genome-wide haplotype associated analysis with gene prioritization using 224,677 SNPs in 37 NSCLC cell lines and 116 unrelated European individuals. Five candidate genes were identified: ESR1, TGFBR1, INSR, CDH3, and MAP3K5. All of these have previously been implicated in NSCLC, with the exception of CDH3, which can therefore be considered a novel indicator of NSCLC risk. Functional annotation confirmed the relationship between these five genes and NSCLC. Our findings are indicative of the underlying pathological mechanisms of NSCLC and provide information to support future directions in diagnosing and treating NSCLC.

Iridoid glycosides from the fruits of Cornus officinalis.

Four new iridoid glycosides named cornusphenosides A-D (1-4) were isolated from an ethanol extract of the fruits of Cornus officinalis (shan zhu yu). The structures of these compounds were elucidated on the basis of spectroscopic data (UV, IR, HRESIMS, and 1D and 2D NMR) and chemical evidence. The neuroprotective effects of compounds 1-4 were also assessed in vitro.

Establishment and validation of nomogram model for predicting major adverse cardiac events in patients with acute ST-segment elevation myocardial infarction based on glycosylated hemoglobin A1c to apolipoprotein A1 ratio: An observational study.

The objective of the current study is to assess the usefulness of HbA1cAp ratio in predicting in-hospital major adverse cardiac events (MACEs) among acute ST-segment elevation myocardial infarction (STEMI) patients that have undergone percutaneous coronary intervention (PCI). Further, the study aims to construct a ratio nomogram for prediction with this ratio. The training cohort comprised of 511 STEMI patients who underwent emergency PCI at the Huaibei Miners' General Hospital between January 2019 and May 2023. Simultaneously, 384 patients treated with the same strategy in First People's Hospital of Hefei formed the validation cohort during the study period. LASSO regression was used to screen predictors of nonzero coefficients, multivariate logistic regression was used to analyze the independent factors of in-hospital MACE in STEMI patients after PCI, and nomogram models and validation were established. The LASSO regression analysis demonstrated that systolic blood pressure, diastolic blood pressure, D-dimer, urea, and glycosylated hemoglobin A1c (HbA1c)/apolipoprotein A1 (ApoA1) were significant predictors with nonzero coefficients. Multivariate logistic regression analysis was further conducted to identify systolic blood pressure, D-dimer, urea, and HbA1c/ApoA1 as independent factors associated with in-hospital MACE after PCI in STEMI patients. Based on these findings, a nomogram model was developed and validated, with the C-index in the training set at 0.77 (95% CI: 0.723-0.817), and the C-index in the validation set at 0.788 (95% CI: 0.734-0.841), indicating excellent discrimination accuracy. The calibration curves and clinical decision curves also demonstrated the good performance of the nomogram models. In patients with STEMI who underwent PCI, it was noted that a higher HbA1c of the ApoA1 ratio is significantly associated with in-hospital MACE. In addition, a nomogram is constructed having considered the above-mentioned risk factors to provide predictive information on in-hospital MACE occurrence in these patients. In particular, this tool is of great value to the clinical practitioners in determination of patients with a high risk.

Transcriptomic analysis of mRNAs in human whole blood identified age-specific changes in healthy individuals.

Older age is one of the most important shared risk factors for multiple chronic diseases, increasing the medical burden to contemporary societies. Current research focuses on identifying aging biomarkers to predict aging trajectories and developing interventions aimed at preventing and delaying the progression of multimorbidity with aging. Here, a transcriptomic changes analysis of whole blood genes with age was conducted. The age-related whole blood gene-expression profiling datasets were downloaded from the Gene Expression Omnibus (GEO) database. We screened the differentially expressed genes (DEGs) between healthy young and old individuals and performed functional enrichment analysis. Cytoscape with Cytohubba and MCODE was used to perform an interaction network of DEGs and identify hub genes. In addition, ROC curves and correlation analysis were used to evaluate the accuracy of hub genes. In total, we identified 29 DEGs between young and old samples that were enriched mainly in immunoglobulin binding and complex, humoral immune response, and immune response-activating signaling pathways. In combination with the PPI network and topological analysis, 4 hub genes (IGLL5, Jchain, POU2AF1, and Bach2) were identified. Pearson analysis showed that the expression changes of these hub genes were highly correlated with age. Among them, 3 hub genes (IGLL5, POU2AF1, and Bach2) were identified with good accuracy (AUC score > 0.7), indicating that these genes were the best indicators of age. Together, our results provided potential biomarkers IGLL5, POU2AF1, and Bach2 to identify individuals at high early risk of age-related disease to be targeted for early interventions and contribute to understanding the molecular mechanisms in the progression of aging.

Metformin inhibits the pathogenic functions of AChR-specific B and Th17 cells by targeting miR-146a.

Myasthenia gravis (MG) is characterized by fatigable skeletal muscle weakness with a fluctuating and unpredictable disease course and is caused by circulating autoantibodies and pathological T helper cells. Regulation of B-cell function and the T-cell network may be a potential therapeutic strategy for MG. MicroRNAs (miRNAs) have emerged as potential biomarkers in immune disorders due to their critical roles in various immune cells and multiple inflammatory diseases. Aberrant miR-146a signal activation has been reported in autoimmune diseases, but a detailed exploration of the relationship between miR-146a and MG is still necessary. Using an experimental autoimmune myasthenia gravis (EAMG) rat model, we observed that miR-146a was highly expressed in the spleen but expressed at low levels in the thymus and lymph nodes in EAMG rats. Additionally, miR-146a expression in T and B cells was also quite different. EAMG-specific Th17 and Treg cells had lower miR-146a levels, while EAMG-specific B cells had higher miR-146a levels, indicating that targeted intervention against miR-146a might have diametrically opposite effects. Metformin, a drug that was recently demonstrated to alleviate EAMG, may rescue the functions of both Th17 cells and B cells by reversing the expression of miR-146a. We also investigated the downstream target genes of miR-146a in both T and B cells using bioinformatics screening and qPCR. Taken together, our study identifies a complex role of miR-146a in the EAMG rat model, suggesting that more caution should be paid in targeting miR-146a for the treatment of MG.

Inference of Subpathway Activity Profiles Reveals Metabolism Abnormal Subpathway Regions in Glioblastoma Multiforme.

Glioblastoma, also known as glioblastoma multiforme (GBM), is the most malignant form of glioma and represents 81% of malignant brain and central nervous system (CNS) tumors. Like most cancers, GBM causes metabolic recombination to promote cell survival, proliferation, and invasion of cancer cells. In this study, we propose a method for constructing the metabolic subpathway activity score matrix to accurately identify abnormal targets of GBM metabolism. By integrating gene expression data from different sequencing methods, our method identified 25 metabolic subpathways that were significantly abnormal in the GBM patient population, and most of these subpathways have been reported to have an effect on GBM. Through the analysis of 25 GBM-related metabolic subpathways, we found that (S)-2,3-Epoxysqualene, which was at the central region of the sterol biosynthesis subpathway, may have a greater impact on the entire pathway, suggesting a potential high association with GBM. Analysis of CCK8 cell activity indicated that (S)-2,3-Epoxysqualene can indeed inhibit the activity of U87-MG cells. By flow cytometry, we demonstrated that (S)-2,3-Epoxysqualene not only arrested the U87-MG cell cycle in the G0/G1 phase but also induced cell apoptosis. These results confirm the reliability of our proposed metabolic subpathway identification method and suggest that (S)-2,3-Epoxysqualene has potential therapeutic value for GBM. In order to make the method more broadly applicable, we have developed an R system package crmSubpathway to perform disease-related metabolic subpathway identification and it is freely available on the GitHub (https://github.com/hanjunwei-lab/crmSubpathway).

Metformin attenuates autoimmune disease of the neuromotor system in animal models of myasthenia gravis.

Metformin, the most widely used medicine for type 2 diabetes, displays anti-inflammatory functions via activating AMP-activated protein kinase (AMPK). Circulating autoantibodies and disequilibrium of helper T cells and regulatory T cells are pathological hallmarks of myasthenia gravis (MG). Rectify the imbalance of different T cell populations has become an important therapeutic strategy to treat MG. In this study, we assessed the effect of metformin on the development of autoimmunity using an experimental autoimmune myasthenia gravis (EAMG) rat model. We first provided evidence that oral administration of metformin attenuated the onset of EAMG. This effect was accompanied by a substantial decrease of circulating auto-antibody levels with no effect on blood glucose level. While metformin treatment in vitro showed little effect on inducible Treg, metformin strongly inhibited Th17 cell differentiation through the increase of reactive oxygen species and AMPK. Furthermore, an attenuation of antigen-induced IgG2b antibody production by two different doses of metformin was also observed in the AChR-specific recall response. In conclusion, the above results indicate that metformin may have therapeutic value for the clinical treatment of MG.