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Background: Traumatic brain injury (TBI) is a central nervous system disease caused by external trauma, which has complex pathological and physiological mechanisms. The aim of this study was to explore the correlation between immune cell infiltration and ferroptosis post-TBI. Methods: This study utilized the GEO database to download TBI data and performed differentially expressed genes (DEGs) and ferroptosis-related differentially expressed genes (FRDEGs) analysis. DEGs were further analyzed for enrichment using the DAVID 6.8. Immunoinfiltration cell analysis was performed using the ssGSEA package and the Timer2.0 tool. The WGCNA analysis was then used to explore the gene modules in the data set associated with differential expression of immune cell infiltration and to identify the hub genes. The tidyverse package and corrplot package were used to calculate the correlations between hub genes and immune cell infiltration and ferroptosis-marker genes. The miRDB and TargetScan databases were used to predict complementary miRNAs for the Hub genes selected from the WGCNA analysis, and the DIANA-LncBasev3 tool was used to identify target lncRNAs for the miRNAs, constructing an mRNA-miRNA-lncRNA regulatory network. Results: A total of 320 DEGs and 21 FRDEGs were identified in GSE128543. GO and KEGG analyses showed that the DEGs after TBI were primarily associated with inflammation and immune response. Xcell and ssGSEA immune infiltration cell analysis showed significant infiltration of T cell CD4+ central memory, T cell CD4+ Th2, B cell memory, B cell naive, monocyte, macrophage, and myeloid dendritic cell activated. The WGCNA analysis identified two modules associated with differentially expressed immune cells and identified Lgmn as a hub gene associated with immune infiltrating cells. Lgmn showed significant correlation with immune cells and ferroptosis-marker genes, including Gpx4, Hspb1, Nfe2l2, Ptgs2, Fth1, and Tfrc. Finally, an mRNA-miRNA-lncRNA regulatory network was constructed using Lgmn. Conclusion: Our results indicate that there is a certain correlation between ferroptosis and immune infiltrating cells in brain tissue after TBI, and that Lgmn plays an important role in this process. [ABSTRACT FROM AUTHOR]

Background: Calcific aortic valve disease (CAVD) is a common valve disease with an increasing incidence, but no effective drugs as of yet. With the development of sequencing technology, non-coding RNAs have been found to play roles in many diseases as well as CAVD, but no circRNA/lncRNA–miRNA–mRNA interaction axis has been established. Moreover, valve interstitial cells (VICs) and valvular endothelial cells (VECs) play important roles in CAVD, and CAVD differed between leaflet phenotypes and genders. This work aims to explore the mechanism of circRNA/lncRNA–miRNA–mRNA network in CAVD, and perform subgroup analysis on the important characteristics of CAVD, such as key cells, leaflet phenotypes and genders. Results: We identified 158 differentially expressed circRNAs (DEcircRNAs), 397 DElncRNAs, 45 DEmiRNAs and 167 DEmRNAs, and constructed a hsa-circ-0073813/hsa-circ-0027587–hsa-miR-525-5p–SPP1/HMOX1/CD28 network in CAVD after qRT-PCR verification. Additionally, 17 differentially expressed genes (DEGs) in VICs, 9 DEGs in VECs, 7 DEGs between different leaflet phenotypes and 24 DEGs between different genders were identified. Enrichment analysis suggested the potentially important pathways in inflammation and fibro-calcification during the pathogenesis of CAVD, and immune cell patterns in CAVD suggest that M0 macrophages and memory B cells memory were significantly increased, and many genes in immune cells were also differently expressed. Conclusions: The circRNA/lncRNA–miRNA–mRNA interaction axis constructed in this work and the DEGs identified between different characteristics of CAVD provide a direction for a deeper understanding of CAVD and provide possible diagnostic markers and treatment targets for CAVD in the future. [ABSTRACT FROM AUTHOR]

Background: Identifying reliable biomarkers could effectively predict esophagus carcinoma (EC) patients with poor prognosis. In this work, we constructed an immune-related gene pairs (IRGP) signature to evaluate the prognosis of EC. Results: The IRGP signature was trained by the TCGA cohort and validated by three GEO datasets, respectively. Cox regression model together with LASSO was applied to construct the overall survival (OS) associated IRGP. 21 IRGPs consisting of 38 immune-related genes were included in our signature, according to which patients were stratified into high- and low-risk groups. The results of Kaplan-Meier survival analyses indicated that high-risk EC patients had worse OS than low-risk group in the training set, meta-validation set and all independent validation datasets. After adjustment in multivariate Cox analyses, our signature continued to be an independent prognostic factor of EC and the signature-based nomogram could effectively predict the prognosis of EC sufferers. Besides, Gene Ontology analysis revealed this signature is related to immunity. 'CIBERSORT' analysis revealed the infiltration levels of plasma cells and activated CD4 memory T cells in two risk groups were significantly different. Ultimately, we validated the expression levels of six selected genes from IRGP index in KYSE-150 and KYSE-450. Conclusions: This IRGP signature could be applied to select EC patients with high mortality risk, thereby improving prospects for the treatment of EC. [ABSTRACT FROM AUTHOR]

Background: CRISPR-Cas (clustered regularly interspaced short palindromic repeats—CRISPR-associated proteins) systems are adaptive immune systems commonly found in prokaryotes that provide sequence-specific defense against invading mobile genetic elements (MGEs). The memory of these immunological encounters are stored in CRISPR arrays, where spacer sequences record the identity and history of past invaders. Analyzing such CRISPR arrays provide insights into the dynamics of CRISPR-Cas systems and the adaptation of their host bacteria to rapidly changing environments such as the human gut. Results: In this study, we utilized 601 publicly available Bacteroides fragilis genome isolates from 12 healthy individuals, 6 of which include longitudinal observations, and 222 available B. fragilis reference genomes to update the understanding of B. fragilis CRISPR-Cas dynamics and their differential activities. Analysis of longitudinal genomic data showed that some CRISPR array structures remained relatively stable over time whereas others involved radical spacer acquisition during some periods, and diverse CRISPR arrays (associated with multiple isolates) co-existed in the same individuals with some persisted over time. Furthermore, features of CRISPR adaptation, evolution, and microdynamics were highlighted through an analysis of host-MGE network, such as modules of multiple MGEs and hosts, reflecting complex interactions between B. fragilis and its invaders mediated through the CRISPR-Cas systems. Conclusions: We made available of all annotated CRISPR-Cas systems and their target MGEs, and their interaction network as a web resource at https://omics.informatics.indiana.edu/CRISPRone/Bfragilis. We anticipate it will become an important resource for studying of B. fragilis, its CRISPR-Cas systems, and its interaction with mobile genetic elements providing insights into evolutionary dynamics that may shape the species virulence and lead to its pathogenicity. [ABSTRACT FROM AUTHOR]

Background: Bi-specific T-cell engager (BiTE) antibody is a class of bispecific antibodies designed for cancer immunotherapy. Blinatumomab is the first approved BiTE to treat acute B cell lymphoblastic leukemia (B-ALL). It brings killer T and target B cells into close proximity, activating patient's autologous T cells to kill malignant B cells via mechanisms such as cytolytic immune synapse formation and inflammatory cytokine production. However, the activated T-cell subtypes and the target cell-dependent T cell responses induced by blinatumomab, as well as the mechanisms of resistance to blinatumomab therapy are largely unknown. Results: In this study, we performed single-cell sequencing analysis to identify transcriptional changes in T cells following blinatumomab-induced T cell activation using single cells from both, a human cell line model and a patient-derived model of blinatumomab-mediated cytotoxicity. In total, the transcriptome of 17,920 single T cells from the cell line model and 2271 single T cells from patient samples were analyzed. We found that CD8+ effector memory T cells, CD4+ central memory T cells, naïve T cells, and regulatory T cells were activated after blinatumomab treatment. Here, blinatumomab-induced transcriptional changes reflected the functional immune activity of the blinatumomab-activated T cells, including the upregulation of pathways such as the immune system, glycolysis, IFNA signaling, gap junctions, and IFNG signaling. Co-stimulatory (TNFRSF4 and TNFRSF18) and co-inhibitory (LAG3) receptors were similarly upregulated in blinatumomab-activated T cells, indicating ligand-dependent T cell functions. Particularly, B-ALL cell expression of TNFSF4, which encodes the ligand of T cell co-stimulatory receptor TNFRSF4, was found positively correlated with the response to blinatumomab treatment. Furthermore, recombinant human TNFSF4 protein enhanced the cytotoxic activity of blinatumomab against B-ALL cells. Conclusion: These results reveal a target cell-dependent mechanism of T-cell activation by blinatumomab and suggest that TNFSF4 may be responsible for the resistant mechanism and a potential target for combination therapy with blinatumomab, to treat B-ALL or other B-cell malignancies. [ABSTRACT FROM AUTHOR]