Department of Microbial Pathogenesis and Immunology, Texas A&M Health, College of Medicine, Bryan, TX 77807, USA. Electronic address:
Published: March 2024
AI Article Synopsis
Article Abstract
Tight regulation of macrophage immune gene expression is required to fight infection without risking harmful inflammation. The contribution of RNA-binding proteins (RBPs) to shaping the macrophage response to pathogens remains poorly understood. Transcriptomic analysis reveals that a member of the serine/arginine-rich (SR) family of mRNA processing factors, SRSF7, is required for optimal expression of a cohort of interferon-stimulated genes in macrophages. Using genetic and biochemical assays, we discover that in addition to its canonical role in regulating alternative splicing, SRSF7 drives transcription of interferon regulatory transcription factor 7 (IRF7) to promote antiviral immunity. At the Irf7 promoter, SRSF7 maximizes STAT1 transcription factor binding and RNA polymerase II elongation via cooperation with the H4K20me1 histone methyltransferase KMT5a (SET8). These studies define a role for an SR protein in activating transcription and reveal an RBP-chromatin network that orchestrates macrophage antiviral gene expression.
It is now understood that brain metastases do not occur randomly but have distinct spatial patterns depending on the origin of the cancer. According to the "seed and soil" hypothesis, the final colonization of metastatic cells is the result of their adaptation to the altered environment. To investigate the most favorable microenvironment for brain metastasis, we analyzed neuroimaging data from 177 patients with breast cancer brain metastasis and 548 patients with lung cancer brain metastasis to create a replicable probabilistic map of metastatic locations.
Introduction: Typhoid fever is an infectious disease primarily caused by sv. Typhi ( Typhi), a bacterium that causes as many as 20 million infections and 600,000 deaths annually. Asymptomatic chronic carriers of S.
Background: Asthma is a global chronic respiratory disease with complex pathogenesis. While current therapies offer some relief, they often fall short in effectively managing symptoms and preventing exacerbations for numerous patients. Thus, understanding its mechanisms and discovering new drug targets remains a pressing need for better treatment.
This paper develops a method for cancer classification from microRNA data using a convolutional neural network (CNN)-based model optimized by genetic algorithm. The convolutional neural network has performed well in various recognition and perception tasks. This paper contributes to the cancer classification using a union of two CNNs.
Department of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, China.
Head and neck squamous carcinoma (HNSC), characterized by a high degree of malignancy, develops in close association with the tumor immune microenvironment (TIME). Therefore, identifying effective targets related to HNSC and TIME is of paramount importance. Here, we employed the ESTIMATE algorithm to compute immune and stromal cell scores for HNSC samples from the TCGA database and identified differentially expressed genes (DEGs) based on these scores.
