Identification of molecular mechanisms for achieving HIV-1 control in the absence of antiretroviral therapy

TMEM132C and LIPE protein molecules drive synovial hyperplasia via the PPARγ signaling axis: Mechanistic insights into core pathogenic proteins in rheumatoid arthritis

The pathogenesis of rheumatoid arthritis (RA) involves a variety of cellular and molecular signaling pathways. TMEM132C and LIPE, as potential key protein molecules, may play an important role in synovial hyperplasia of RA. The main objective of this study was to reveal the function of TMEM132C and LIPE in the proliferation of RA synovial cells, and to explore the mechanism of their regulation through the PPARγ signaling axis. In this study, differential expression genes (DEGs) were screened through data acquisition and preprocessing. Then, principal component analysis (PCA) and functional enrichment analysis were used to clarify the importance of PPARγ signal axis in RA. Then, machine learning technology was used to identify key proteins, and SHAP-driven analysis was used to interpret the results of Logistic regression model. The expression and signaling activities of TMEM132C and LIPE were verified by cell culture, transfection, proliferation detection, RNA extraction, real-time quantitative fluorescence PCR (qPCR) and Western blot experiments. A series of significant DEGs were identified, and functional enrichment analysis showed that the PPARγ signal axis plays a key role in RA. TMEM132C and LIPE were identified as key genes mainly enriched in the PPAR signaling pathway, suggesting that they play an important role in the pathogenesis of RA.

Hepatitis B virus pathogenesis relevant immunosignals uncovering amino acids utilization related risk factors guide artificial intelligence-based precision medicine

Background: Although immune microenvironment-related chemokines, extracellular matrix (ECM), and intrahepatic immune cells are reported to be highly involved in hepatitis B virus (HBV)-related diseases, their roles in diagnosis, prognosis, and drug sensitivity evaluation remain unclear. Here, we aimed to study their clinical use to provide a basis for precision medicine in hepatocellular carcinoma (HCC) via the amalgamation of artificial intelligence. Methods: High-throughput liver transcriptomes from Gene Expression Omnibus (GEO), NODE (https://www.bio.sino.org/node), the Cancer Genome Atlas (TCGA), and our in-house hepatocellular carcinoma patients were collected in this study. Core immunosignals that participated in the entire diseases course of hepatitis B were explored using the "Gene set variation analysis" R package. Using ROC curve analysis, the impact of core immunosignals and amino acid utilization related gene on hepatocellular carcinoma patient's clinical outcome were calculated. The utility of core immunosignals as a classifier for hepatocellular carcinoma tumor tissue was evaluated using explainable machine-learning methods. A novel deep residual neural network model based on immunosignals was constructed for the long-term overall survival (LS) analysis. In vivo drug sensitivity was calculated by the "oncoPredict" R package. Results: We identified nine genes comprising chemokines and ECM related to hepatitis B virus-induced inflammation and fibrosis as CLST signals. Moreover, CLST was co-enriched with activated CD4+ T cells bearing harmful factors (aCD4) during all stages of hepatitis B virus pathogenesis, which was also verified by our hepatocellular carcinoma data. Unexpectedly, we found that hepatitis B virus-hepatocellular carcinoma patients in the CLSThighaCD4high subgroup had the shortest overall survival (OS) and were characterized by a risk gene signature associated with amino acids utilization. Importantly, characteristic genes specific to CLST/aCD4 showed promising clinical relevance in identifying patients with early-stage hepatocellular carcinoma via explainable machine learning. In addition, the 5-year long-term overall survival of hepatocellular carcinoma patients can be effectively classified by CLST/aCD4 based GeneSet-ResNet model. Subgroups defined by CLST and aCD4 were significantly involved in the sensitivity of hepatitis B virus-hepatocellular carcinoma patients to chemotherapy treatments. Conclusion: CLST and aCD4 are hepatitis B virus pathogenesis-relevant immunosignals that are highly involved in hepatitis B virus-induced inflammation, fibrosis, and hepatocellular carcinoma. Gene set variation analysis derived immunogenomic signatures enabled efficient diagnostic and prognostic model construction. The clinical application of CLST and aCD4 as indicators would be beneficial for the precision management of hepatocellular carcinoma.