SPP1 in infliximab resistant ulcerative colitis and associated colorectal cancer: an analysis of differentially expressed genes

Transcriptomic signatures of classical monocytes reveal pro-inflammatory modules and heterogeneity in polyarticular juvenile idiopathic arthritis

Introduction

Polyarticular juvenile idiopathic arthritis (pJIA) is a childhood-onset autoimmune disease. Immune cells contribute to persistent inflammation observed in pJIA. Despite the crucial role of monocytes in arthritis, the precise involvement of classical monocytes in the pathogenesis of pJIA remains uncertain. Here, we aimed to uncover the transcriptomic patterns of classical monocytes in pJIA, focusing on their involvement in disease mechanism and heterogeneity.

Methods

A total of 17 healthy subjects and 18 premenopausal women with pJIA according to ILAR criteria were included. Classical monocytes were isolated, and RNA sequencing was performed. Differential expression analysis was used to compare pJIA patients and healthy control group. Differentially expressed genes (DEGs) were identified, and gene set enrichment analysis (GSEA) was performed. Using unsupervised learning approach, patients were clustered in two groups based on their similarities at transcriptomic level. Subsequently, these clusters underwent a comparative analysis to reveal differences at the transcriptomic level.

Results

We identified 440 DEGs in pJIA patients of which 360 were upregulated and 80 downregulated. GSEA highlighted TNF-α and IFN-γ response. Importantly, this analysis not only detected genes targeted by pJIA therapy but also identified new modulators of immuno-inflammation. PLAUR, IL1B, IL6, CDKN1A, PIM1, and ICAM1 were pointed as drivers of chronic hyperinflammation. Unsupervised learning approach revealed two clusters within pJIA, each exhibiting varying inflammation levels.

Conclusion

These findings indicate the pivotal role of immuno-inflammation driven by classical monocytes in pJIA and reveals the existence of two subclusters within pJIA, regardless the positivity of rheumatoid factor and anti-CCP, paving the way to precision medicine.

Gene Expression Changes of Murine Cortex Homeostasis in Response to Sleep Deprivation Hint Dysregulated Aging-like Transcriptional Responses

Sleep deprivation leads to the deterioration in the physiological functioning of the brain, cognitive decline, and many neurodegenerative diseases, all of which progress with advancing age. Sleep insufficiency and impairments in cognitive function are characterized by progressive neuronal losses in the cerebral cortex. In this study, we analyze gene expression profiles following sleep-deprived murine models and circadian matched controls to identify genes that might underlie cortical homeostasis in response to sleep deprivation. Screening of the literature resulted in three murine (Mus musculus) gene expression datasets (GSE6514, GSE78215, and GSE33491) that included cortical tissue biopsies from mice that are sleep deprived for 6 h (n = 15) and from circadian controls that are left undisturbed (n = 15). Cortical differentially expressed genes are used to construct a network of encoded proteins that are ranked based on their interactome according to 11 topological algorithms. The analysis revealed three genes-NFKBIA, EZR, and SGK1-which exhibited the highest multi-algorithmic topological significance. These genes are strong markers of increased brain inflammation, cytoskeletal aberrations, and glucocorticoid resistance, changes that imply aging-like transcriptional responses during sleep deprivation in the murine cortex. Their potential role as candidate markers of local homeostatic response to sleep loss in the murine cortex warrants further experimental validation.

Identification of Differentially Expressed Genes and miRNAs for Ulcerative Colitis Using Bioinformatics Analysis

Introduction: Ulcerative colitis (UC) is a chronic inflammatory disease of the intestine whose cause and underlying mechanisms are not fully understood. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in UC.

Materials and methods: Three UC datasets (GSE179285, GSE75214, GSE48958) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal and UC tissues were identified using the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed using Metascape. Protein-protein interaction network (PPI) analysis and visualization using STRING and Cytoscape. Finally, the miRNA gene regulatory network was constructed by Cytoscape to predict potential microRNAs (miRNAs) associated with DEGs.

Results: A total of 446 DEGs were identified, consisting of 309 upregulated genes and 137 downregulated genes. The enriched functions and pathways of the DEGs include extracellular matrix, regulation of cell adhesion, inflammatory response, response to cytokine, monocarboxylic acid metabolic process, response to toxic substance. The analysis of KEGG pathway indicates that the DEGs were significantly enriched in Complement and coagulation cascades, Amoebiasis, TNF signaling pathway, bile secretion, and Mineral absorption. Combining the results of the PPI network and CytoHubba, 9 hub genes including CXCL8, ICAM1, CXCR4, CD44, IL1B, MMP9, SPP1, TIMP1, and HIF1A were selected. Based on the DEG-miRNAs network construction, 7 miRNAs including miR-335-5p, mir-204-5p, miR-93-5p, miR106a-5p, miR-21-5p, miR-146a-5p, and miR-155-5p were identified as potential critical miRNAs.

Conclusion: In summary, we identified DEGs that may be involved in the progression or occurrence of UC. A total of 446 DEGs,9 hub genes and 7 miRNAs were identified, which may be considered as biomarkers of UC. Further studies, however, are needed to elucidate the biological functions of these genes in UC.

Gene Expression Profiles of the Aging Rat Hippocampus Imply Altered Immunoglobulin Dynamics

Aging is a process that leads to the deterioration in physiological functioning of the brain. Prior research has proposed that hippocampal aging is accompanied by genetic alterations in neural, synaptic, and immune functions. Nevertheless, interactome-based interrogations of gene alterations in hippocampal aging, remain scarce. Our study integrated gene expression profiles of the hippocampus from young and aged rats and functionally classified network-mapped genes based on their interactome. Hippocampal differentially expressed genes (DEGs) between young (5-8 months) and aged (21-26 months) male rats (Rattus norvegicus) were retrieved from five publicly available datasets (GSE14505, GSE20219, GSE14723, GSE14724, and GSE14725; 38 young and 29 aged samples). Encoded hippocampal proteins of age-related DEGs and their interactome were predicted. Clustered network DEGs were identified and the highest-ranked was functionally annotated. A single cluster of 19 age-related hippocampal DEGs was revealed, which was linked with immune response (biological process, P = 1.71E-17), immunoglobulin G binding (molecular function, P = 1.92E-08), and intrinsic component of plasma membrane (cellular component, P = 1.25E-06). Our findings revealed dysregulated hippocampal immunoglobulin dynamics in the aging rat brain. Whether a consequence of neurovascular perturbations and dysregulated blood-brain barrier permeability, the role of hippocampal immunoregulation in the pathobiology of aging warrants further investigation.