Combining single-cell RNA sequencing data and transcriptomic data to unravel potential mechanisms and signature genes of the progression of idiopathic pulmonary fibrosis to lung adenocarcinoma and predict therapeutic agents.

Patients with idiopathic pulmonary fibrosis (IPF) have a significantly higher prevalence of lung adenocarcinoma (LUAD) than normal subjects, although the underlying association is unclear. The raw data involved were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis were used to screen for differentially expressed genes (DEGs) and modular signature genes (MSGs). Genes intersecting DEGs and MSGs were considered hub genes for IPF and LUAD. Machine learning algorithms were applied to capture epithelial cell-derived signature genes (EDSGs) shared. External cohort data were exploited to validate the robustness of EDSGs. Immunohistochemical staining and K-M plots were used to denote the prognostic value of EDSGs in LUAD. Based on EDSGs, we constructed a TF-gene-miRNA regulatory network. Molecular docking can validate the strength of action between candidate drugs and EDSGs. Epithelial cells, 650 DEGs, and 1773 MSGs were shared by IPF and LUAD. As for 379 hub genes, we performed pathway and functional enrichment analysis. By analyzing sc-RNA seq data, we identified 1234 marker genes of IPF epithelial cell-derived and 1481 of LUAD. And these genes shared 8 items with 379 hub genes. Through the machine learning algorithms, we further fished TRIM2, S100A14, CYP4B1, LMO7, and SFN. The ROC curves emphasized the significance of EDSGs in predicting the onset of LUAD and IPF. The TF-gene-miRNA network revealed regulatory relationships behind EDSGs. Finally, we predicted appropriate therapeutic agents. Our study preliminarily identified potential mechanisms between IPF and LUAD, which will inform subsequent studies.

Bioinformatically deciphering immune cell infiltration and signature genes in pelvic organ prolapse.

INTRODUCTION AND HYPOTHESIS: The study is aimed at bioinformatically deciphering immune cell infiltration, signature genes, and their correlations in POP. METHODS: Three microarray datasets were included. Matrixes representing the uterosacral ligament were merged as a test matrix and the others representing vaginal tissues were merged as a validation matrix. The single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm was performed to evaluate immune cell infiltration. Correlations among differential immune cells were revealed by Spearman's rank correlation. Differentially expressed genes (DEGs) were screened by both "Batch correction" and "RobustRankAggreg" methods. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes were conducted for functional analysis. Hub genes were identified through cytoHubba of Cytoscape, and further validated by a validation matrix and clinical samples as signature genes. Correlations of differential immune cells with signature genes were analyzed by Spearman's rank correlation. RESULTS: Five differential immune cells (macrophages, monocytes, regulatory T cell [Treg], type 1 T cell [Th1], and natural killer T cells [NKT]) were identified and eight pairs of immune cells had significant correlations. Screened 230 DEGs were extracellular matrix (ECM) and immune related. Eleven hub genes were initially identified and five of them (LOX, IL-6, SDC1, ICAM1, and CD38) were validated as signature genes. Significant correlations of differential immune cells with signature genes were shown in twelve pairs, especially Th1-IL6, NKT-IL6, Th1-ICAM1, macrophage-IL6, and macrophage-LOX pairs. CONCLUSIONS: Pelvic organ prolapse could be considered immune related. Significantly infiltrated immune cells may contribute to the development of POP through close involvement with ECM- and immune-related signature genes.

Spore-forming properties and enhanced oxygen tolerance of butyrate-producing Anaerostipes spp.

OBJECTIVES: Butyrate producing bacteria are promising candidates for next-generation probiotics. However, they are extremely sensitive to oxygen, which is a significant obstacle to their inclusion in food matrices in a viable form. The present study characterized the spore-forming properties and stress tolerance of human gut butyrate-producing Anaerostipes spp. METHODS: Spore formation properties in six species of Anaerostipes spp. were studied by in vitro and in silico tests. RESULTS: Spores were observed from the cells of three species using microscopic analyses, while the remaining three did not form spores under the tested conditions. Spore-forming properties were confirmed by an ethanol treatment. The spores of Anaerostipes caccae were tolerant to oxygen and survived for 15 weeks under atmospheric conditions. Spores tolerated heat stress at 70 °C, but not at 80 °C. An in silico analysis of the conservation of potential sporulation signature genes revealed that the majority of human gut butyrate-producing bacteria were classified as potential spore formers. Comparative genomics revealed that three spore-forming Anaerostipes spp. specifically possessed the spore formation-related genes of bkdR, sodA, and splB, which may be key genes for different sporulation properties in Anaerostipes spp. CONCLUSIONS: The present study demonstrated the enhanced stress tolerance of butyrate producing Anaerostipes spp. for future probiotic application. Presence of specific gene(s) are possibly keys for sporulation in Anaerostipes spp.

Genomic diversity and comprehensive taxonomical classification of 61 Bacillus subtilis group member infecting bacteriophages, and the identification of ortholog taxonomic signature genes.

BACKGROUND: Despite the applications of Bacillus subtilis group species in various sectors, limited information is available regarding their phages. Here, 61 B. subtilis group species-infecting phages (BSPs) were studied for their taxonomic classification considering the genome-size, genomic diversity, and the host, followed by the identification of orthologs taxonomic signature genes. RESULTS: BSPs have widely ranging genome sizes that can be bunched into groups to demonstrate correlations to family and subfamily classifications. Comparative analysis re-confirmed the existing, BSPs-containing 14 genera and 21 species and displayed inter-genera similarities within existing subfamilies. Importantly, it also revealed the need for the creation of new taxonomic classifications, including 28 species, nine genera, and two subfamilies (New subfamily1 and New subfamily2) to accommodate inter-genera relatedness. Following pangenome analysis, no ortholog shared by all BSPs was identified, while orthologs, namely, the tail fibers/spike proteins and poly-gamma-glutamate hydrolase, that are shared by more than two-thirds of the BSPs were identified. More importantly, major capsid protein (MCP) type I, MCP type II, MCP type III and peptidoglycan binding proteins that are distinctive orthologs for Herelleviridae, Salasmaviridae, New subfamily1, and New subfamily2, respectively, were identified and analyzed which could serve as signatures to distinguish BSP members of the respective taxon. CONCLUSIONS: In this study, we show the genomic diversity and propose a comprehensive classification of 61 BSPs, including the proposition for the creation of two new subfamilies, followed by the identification of orthologs taxonomic signature genes, potentially contributing to phage taxonomy.

CD69+ memory T lymphocytes of the bone marrow and spleen express the signature transcripts of tissue-resident memory T lymphocytes.

It is a matter of current debate whether the bone marrow is a hub for circulating memory T lymphocytes and/or the home of resident memory T lymphocytes. Here we demonstrate for CD69+ murine CD8+ , and CD69+ murine and human CD4+ memory T lymphocytes of the bone marrow, making up between 30 and 60% of bone marrow memory T lymphocytes, that they express the gene expression signature of tissue-resident memory T lymphocytes. This suggests that a substantial proportion of bone marrow memory T lymphocytes are resident. It adds to previous evidence that bone marrow memory T cells are resting in terms of mobility and proliferation, and maintain exclusive long-term memory to distinct, systemic antigens.

Establishment and characterization of five immortalized human scalp dermal papilla cell lines.

Dermal papilla (DP) regulates the growth and cycling of hair follicles. Cultured DP cells are useful for the study of their role in relation to hair growth and regeneration. However, cultivation of human DP cells is tedious and difficult. In addition, cultured DP cells possess a relatively short replicative life span, requiring immortalized human DP cell lines. We previously established an immortalized human DP cell line, SV40T-hTERT-DPC, by introducing human telomerase reverse transcriptase (hTERT) gene into the transformed cell line, SV40T-DPC. In this study, we co-transfected the simian virus 40 large T antigen (SV40T-Ag) and hTERT into DP cells from scalp hair follicles from a male with androgenetic alopecia and established five immortalized DP cell lines and named KNU-101, KNU-102, KNU-103, KNU-201 and KNU-202. We then evaluated tumorigenicity, expression of DP markers, responses to androgen, Wnt3a and BMP4, and expression of DP signature genes. These cell lines displayed early passage morphology and maintained responses to androgen, Wnt and BMP. Furthermore, these cell lines expressed DP markers and DP signature genes. KNU cell lines established in this study are potentially useful sources for hair research.

SAIC: an iterative clustering approach for analysis of single cell RNA-seq data.

BACKGROUND: Research interests toward single cell analysis have greatly increased in basic, translational and clinical research areas recently, as advances in whole-transcriptome amplification technique allow scientists to get accurate sequencing result at single cell level. An important step in the single-cell transcriptome analysis is to identify distinct cell groups that have different gene expression patterns. Currently there are limited bioinformatics approaches available for single-cell RNA-seq analysis. Many studies rely on principal component analysis (PCA) with arbitrary parameters to identify the genes that will be used to cluster the single cells. RESULTS: We have developed a novel algorithm, called SAIC (Single cell Analysis via Iterative Clustering), that identifies the optimal set of signature genes to separate single cells into distinct groups. Our method utilizes an iterative clustering approach to perform an exhaustive search for the best parameters within the search space, which is defined by a number of initial centers and P values. The end point is identification of a signature gene set that gives the best separation of the cell clusters. Using a simulated data set, we showed that SAIC can successfully identify the pre-defined signature gene sets that can correctly separated the cells into predefined clusters. We applied SAIC to two published single cell RNA-seq datasets. For both datasets, SAIC was able to identify a subset of signature genes that can cluster the single cells into groups that are consistent with the published results. The signature genes identified by SAIC resulted in better clusters of cells based on DB index score, and many genes also showed tissue specific expression. CONCLUSIONS: In summary, we have developed an efficient algorithm to identify the optimal subset of genes that separate single cells into distinct clusters based on their expression patterns. We have shown that it performs better than PCA method using published single cell RNA-seq datasets.

Comparative gene co-expression network analysis of epithelial to mesenchymal transition reveals lung cancer progression stages.

BACKGROUND: The epithelial to mesenchymal transition (EMT) plays a key role in lung cancer progression and drug resistance. The dynamics and stability of gene expression patterns as cancer cells transition from E to M at a systems level and relevance to patient outcomes are unknown. METHODS: Using comparative network and clustering analysis, we systematically analyzed time-series gene expression data from lung cancer cell lines H358 and A549 that were induced to undergo EMT. We also predicted the putative regulatory networks controlling EMT expression dynamics, especially for the EMT-dynamic genes and related these patterns to patient outcomes using data from TCGA. Example EMT hub regulatory genes were validated using RNAi. RESULTS: We identified several novel genes distinct from the static states of E or M that exhibited temporal expression patterns or 'periods' during the EMT process that were shared in different lung cancer cell lines. For example, cell cycle and metabolic genes were found to be similarly down-regulated where immune-associated genes were up-regulated after middle EMT stages. The presence of EMT-dynamic gene expression patterns supports the presence of differential activation and repression timings at the transcriptional level for various pathways and functions during EMT that are not detected in pure E or M cells. Importantly, the cell line identified EMT-dynamic genes were found to be present in lung cancer patient tissues and associated with patient outcomes. CONCLUSIONS: Our study suggests that in vitro identified EMT-dynamic genes capture elements of gene EMT expression dynamics at the patient level. Measurement of EMT dynamic genes, as opposed to E or M only, is potentially useful in future efforts aimed at classifying patient's responses to treatments based on the EMT dynamics in the tissue.

Discovery of signature genes in gastric cancer associated with prognosis.

Gene expression profiles of gastric cancer (GC) were analyzed with bioinformatics tools to identify signature genes associated with prognosis. Four gene expression data sets (accession number: GSE2685, GSE30727, GSE38932 and GSE26253) were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) were screened out using significance analysis of microarrays (SAM) algorithm. P-value 1 were set as the threshold. A co-expression network was constructed for the GC-related genes with package WGCNA of R. Modules were disclosed with WGCNA algorithm. Survival-related signature genes were screened out via COX single-variable regression.A total of 3210 GC-related genes were identified from the 3 data sets. Significantly enriched GO biological process terms included cell death, cell proliferation, apoptosis, response to hormone and phosphorylation. Pathways like viral carcinogenesis, metabolism, EBV viral infection, and PI3K-AKT signaling pathway were significantly over-represented in the DEGs. A gene co-expression network including 2414 genes was constructed, from which 7 modules were revealed. A total of 17 genes were identified as signature genes, such as DAB2, ALDH2, CD58, CITED2, BNIP3L, SLC43A2, FAU and COL5A1.Many signature genes associated with prognosis of GC were identified in present study, some of which have been implicated in the pathogenesis of GC. These findings could not only improve the knowledge about GC, but also provide clues for clinical treatments.

Cell culture of retinal pigment epithelium: Special Issue.

This series of review articles highlights how in vitro models of RPE can be effectively used to understand essential functions of the RPE that are not only fundamental to epithelial biology, but also have direct relevance to the visual system. The issue contains reviews from experts in the field covering aspects of basic cell and epithelial biology, namely: the barrier properties of the RPE (Rizzolo, 2014), epithelial polarity (Lehmann et al., 2014), cytoskeleton (Bonilha, 2014), and lysosomes (Guha et al., 2014), as well as properties more unique to the RPE, e.g., vitamin A metabolism (Hu and Bok, 2014), bioenergetics (Adijanto and Philp, 2014), phagocytosis (Mazzoni et al., 2014), ion transport (Reichhart and Strauß, 2014), and melanin/lipofuscin (Boulton, 2014).

Exploring Pyroptosis-related Signature Genes and Potential Drugs in Ulcerative Colitis by Transcriptome Data and Animal Experimental Validation.

Ulcerative colitis (UC) is an idiopathic, relapsing inflammatory disorder of the colonic mucosa. Pyroptosis contributes significantly to UC. However, the molecular mechanisms of UC remain unexplained. Herein, using transcriptome data and animal experimental validation, we sought to explore pyroptosis-related molecular mechanisms, signature genes, and potential drugs in UC. Gene profiles (GSE48959, GSE59071, GSE53306, and GSE94648) were selected from the Gene Expression Omnibus (GEO) database, which contained samples derived from patients with active and inactive UC, as well as health controls. Gene Set Enrichment Analysis (GSEA), Weighted Gene Co-expression Network Analysis (WGCNA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on microarrays to unravel the association between UC and pyroptosis. Then, differential expressed genes (DEGs) and pyroptosis-related DEGs were obtained by differential expression analyses and the public database. Subsequently, pyroptosis-related DEGs and their association with the immune infiltration landscape were analyzed using the CIBERSORT method. Besides, potential signature genes were selected by machine learning (ML) algorithms, and then validated by testing datasets which included samples of colonic mucosal tissue and peripheral blood. More importantly, the potential drug was screened based on this. And these signature genes and the drug effect were finally observed in the animal experiment. GSEA and KEGG enrichment analyses on key module genes derived from WGCNA revealed a close association between UC and pyroptosis. Then, a total of 20 pyroptosis-related DEGs of UC and 27 pyroptosis-related DEGs of active UC were screened. Next, 6 candidate genes (ZBP1, AIM2, IL1ß, CASP1, TLR4, CASP11) in UC and 2 candidate genes (TLR4, CASP11) in active UC were respectively identified using the binary logistic regression (BLR), least absolute shrinkage and selection operator (LASSO), random forest (RF) analysis and artificial neural network (ANN), and these genes also showed high diagnostic specificity for UC in testing sets. Specially, TLR4 was elevated in UC and further elevated in active UC. The results of the drug screen revealed that six compounds (quercetin, cyclosporine, resveratrol, cisplatin, paclitaxel, rosiglitazone) could target TLR4, among which the effect of quercetin on intestinal pathology, pyroptosis and the expression of TLR4 in UC and active UC was further determined by the murine model. These findings demonstrated that pyroptosis may promote UC, and especially contributes to the activation of UC. Pyroptosis-related DEGs offer new ideas for the diagnosis of UC. Besides, quercetin was verified as an effective treatment for pyroptosis and intestinal inflammation. This study might enhance our comprehension on the pathogenic mechanism and diagnosis of UC and offer a treatment option for UC.

Exploring shared molecular signatures and regulatory mechanisms in nonalcoholic steatohepatitis and inflammatory bowel disease using integrative bioinformatics analysis.

The co-existence of inflammatory bowel disease (IBD) and non-alcoholic steatohepatitis (NASH) has raised interest in identifying shared molecular mechanisms and potential therapeutic targets. However, the relationship between these two diseases remains unclear and effective medical treatments are still lacking. Through the bioinformatics analysis in this study, 116 shared differentially expressed genes (SDEGs) were identified between IBD and NASH datasets. GO and KEGG pathway analyses revealed significant involvement of SDEGs in apoptotic processes, cell death, defense response, cytokine and chemokine activity, and signaling pathways. Furthermore, weighted gene co-expression network analysis (WGCNA) identified five shared signature genes associated specifically with IBD and NASH, they were CXCL9, GIMAP2, ADAMTS5, GRAP, and PRF1. These five genes represented potential diagnostic biomarkers for distinguishing patients with diseases from healthy individuals by using two classifier algorithms and were positively related to autophagy, ferroptosis, angiogenesis, and immune checkpoint factors in the two diseases. Additionally, single-cell analysis of IBD and NASH samples highlighted the expression of regulatory genes in various immune cell subtypes, emphasizing their significance in disease pathogenesis. Our work elucidated the shared signature genes and regulatory mechanisms of IBD and NASH, which could provide new potential therapies for patients with IBD and NASH.

Development of a 32-gene signature using machine learning for accurate prediction of inflammatory bowel disease.

Inflammatory bowel disease (IBD) is a chronic inflammatory condition caused by multiple genetic and environmental factors. Numerous genes are implicated in the etiology of IBD, but the diagnosis of IBD is challenging. Here, XGBoost, a machine learning prediction model, has been used to distinguish IBD from healthy cases following elaborative feature selection. Using combined unsupervised clustering analysis and the XGBoost feature selection method, we successfully identified a 32-gene signature that can predict IBD occurrence in new cohorts with 0.8651 accuracy. The signature shows enrichment in neutrophil extracellular trap formation and cytokine signaling in the immune system. The probability threshold of the XGBoost-based classification model can be adjusted to fit personalized lifestyle and health status. Therefore, this study reveals potential IBD-related biomarkers that facilitate an effective personalized diagnosis of IBD.

A long-awaited taxogenomic investigation of the family Halomonadaceae.

The family Halomonadaceae is the largest family composed of halophilic bacteria, with more than 160 species with validly published names as of July 2023. Several classifications to circumscribe this family are available in major resources, such as those provided by the List of Prokaryotic names with Standing in Nomenclature (LPSN), NCBI Taxonomy, Genome Taxonomy Database (GTDB), and Bergey's Manual of Systematics of Archaea and Bacteria (BMSAB), with some degree of disagreement between them. Moreover, regardless of the classification adopted, the genus Halomonas is not phylogenetically consistent, likely because it has been used as a catch-all for newly described species within the family Halomonadaceae that could not be clearly accommodated in other Halomonadaceae genera. In the past decade, some taxonomic rearrangements have been conducted on the Halomonadaceae based on ribosomal and alternative single-copy housekeeping gene sequence analysis. High-throughput technologies have enabled access to the genome sequences of many type strains belonging to the family Halomonadaceae; however, genome-based studies specifically addressing its taxonomic status have not been performed to date. In this study, we accomplished the genome sequencing of 17 missing type strains of Halomonadaceae species that, together with other publicly available genome sequences, allowed us to re-evaluate the genetic relationship, phylogeny, and taxonomy of the species and genera within this family. The approach followed included the estimate of the Overall Genome Relatedness Indexes (OGRIs) such as the average amino acid identity (AAI), phylogenomic reconstructions using amino acid substitution matrices customized for the family Halomonadaceae, and the analysis of clade-specific signature genes. Based on our results, we conclude that the genus Halovibrio is obviously out of place within the family Halomonadaceae, and, on the other hand, we propose a division of the genus Halomonas into seven separate genera and the transfer of seven species from Halomonas to the genus Modicisalibacter, together with the emendation of the latter. Additionally, data from this study demonstrate the existence of various synonym species names in this family.

TRB CDR3-cancer testis antigen chemical complementarity scoring for identifying productive immune responses in renal cell carcinoma.

BACKGROUND: Immunogenomics approaches to the characterization of renal cell carcinoma (RCC) have helped to better our understanding of the features of RCC immune dysfunction. However, much is still unknown with regard to specific immune interactions and their impact in the tumor microenvironment. OBJECTIVE: This study applied chemical complementarity scoring for the TRB complementarity determining region-3 (CDR3) amino acid sequences and cancer testis antigens (CTAs) to determine whether such complementarity correlated with survival and the expression of immune marker genes. METHODS: TRB recombination reads from RCC tumor samples from RNAseq files obtained from two separate databases, Moffitt Cancer Center and The Cancer Genome Atlas (TCGA), were evaluated. Chemical complementarity scores (CSs) were calculated for TRB CDR3-CTA pairs and survival assessments based on those CSs were performed. RESULTS: Moffitt Cancer Center and TCGA cases representing the upper 50th percentile of chemical CSs for TRB CDR3 amino acid sequences and the CTA POTEA were found to be associated with a better overall survival (OS) Also, greater tumor RNA expression of multiple immune signature genes, including granzyme A, granzyme B, and interferon-gamma were correlated with the higher chemical CSs. CONCLUSIONS: These results indicate that TRB CDR3-CTA chemical complementarity scoring may be useful in distinguishing RCC cases with a productive, anti-tumor immune response from cases where basic immune parameter assessments are inconsistent with a productive immune response.

In silico-in vitro modeling to uncover cues involved in establishing microglia identity: TGF-ß3 and laminin can drive microglia signature gene expression.

Microglia are the resident macrophages of the central nervous system (CNS) and play a key role in CNS development, homeostasis, and disease. Good in vitro models are indispensable to study their cellular biology, and although much progress has been made, in vitro cultures of primary microglia still only partially recapitulate the transcriptome of in vivo microglia. In this study, we explored a combination of in silico and in vitro methodologies to gain insight into cues that are involved in the induction or maintenance of the ex vivo microglia reference transcriptome. First, we used the in silico tool NicheNet to investigate which (CNS-derived) cues could underlie the differences between the transcriptomes of ex vivo and in vitro microglia. Modeling on basis of gene products that were found to be upregulated in vitro, predicted that high mobility group box 2 (HMGB2)- and interleukin (IL)-1ß-associated signaling pathways were driving their expression. Modeling on basis of gene products that were found to be downregulated in vitro, did not lead to predictions on the involvement of specific signaling pathways. This is consistent with the idea that in vivo microenvironmental cues that determine microglial identity are for most part of inhibitory nature. In a second approach, primary microglia were exposed to conditioned medium from different CNS cell types. Conditioned medium from spheres composed of microglia, oligodendrocytes, and radial glia, increased the mRNA expression levels of the microglia signature gene P2RY12. NicheNet analyses of ligands expressed by oligodendrocytes and radial glia predicted transforming growth factor beta 3 (TGF-ß3) and LAMA2 as drivers of microglia signature gene expression. In a third approach, we exposed microglia to TGF-ß3 and laminin. In vitro exposure to TGF-ß3 increased the mRNA expression levels of the microglia signature gene TREM2. Microglia cultured on laminin-coated substrates were characterized by reduced mRNA expression levels of extracellular matrix-associated genes MMP3 and MMP7, and by increased mRNA expression levels of the microglia signature genes GPR34 and P2RY13. Together, our results suggest to explore inhibition of HMGB2- and IL-1ß-associated pathways in in vitro microglia. In addition, exposure to TGF-ß3 and cultivation on laminin-coated substrates are suggested as potential improvements to current in vitro microglia culture protocols.

Identification of mitochondria-related key gene and association with immune cells infiltration in intervertebral disc degeneration.

Intervertebral disc (IVD) degeneration and its inflammatory microenvironment can result in discogenic pain, which has been shown to stem from the nucleus pulposus (NP). Increasing evidence suggests that mitochondrial related genes are strictly connected to cell functionality and, importantly, it can regulate cell immune activity in response to damaged associated signals. Therefore, identification of mitochondria related genes might offer new diagnostic markers and therapeutic targets for IVD degeneration. In this study, we identified key genes involved in NP tissue immune cell infiltration during IVD degeneration by bioinformatic analysis. The key modules were screened by weighted gene co-expression network analysis (WCGNA). Characteristic genes were identified by random forest analysis. Then gene set enrichment analysis (GSEA) was used to explore the signaling pathways associated with the signature genes. Subsequently, CIBERSORT was used to classify the infiltration of immune cells. Function of the hub gene was confirmed by PCR, Western blotting and ELISA. Finally, we identified MFN2 as a crucial molecule in the process of NP cell pyroptosis and NLRP3 inflammasome activation. We speculate that the increased MFN2 expression in NP tissue along with the infiltration of CD8+ T cells, NK cell and neutrophils play important roles in the pathogenesis of IVD degeneration.

Construction of an Immunity and Ferroptosis-Related Risk Score Model to Predict Ovarian Cancer Clinical Outcomes and Immune Microenvironment.

BACKGROUND: Ovarian cancer (OV) is a severe and common gynecological disease. Ferroptosis can regulate the progression and invasion of tumors. The immune system is a decisive factor in cancer. The present study aimed to use gene expression data to establish an immunity and ferroptosis-related risk score model as a prognostic biomarker to predict clinical outcomes and the immune microenvironment of OV. METHODS: Common gene expression data were searched from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Immunity-related genes and ferroptosis-related genes were searched and downloaded from the ImmPort and FerrDb databases, followed by the analysis of the overall survival of patients with OV and the identification of genes. Subsequently, the status of the infiltration of immune cells and the association between immune checkpoints and risk score were assessed. RESULTS: A total of 10 prognostic genes (C5AR1, GZMB, IGF2R, ISG20, PPP3CA, STAT1, TRIM27, TSHR, RB1, and EGFR) were included in the immunity and ferroptosis-related risk score model. The high-risk group had a higher infiltration of immune cells. The risk score, an independent prognostic feature of OV was negatively associated with each immune checkpoint. The risk score may thus help to predict the response to immunotherapy. CONCLUSIONS: The immunity and ferroptosis-related risk score model is an independent prognostic factor for OV. The established risk score may help to predict the response of patients to immunotherapy.

Metabolite Effect on Angiogenesis: Insights from Transcriptome Analysis.

Metabolic status of the cells is important in the expression of the angiogenic phenotype in endothelial cells. Our earlier studies demonstrated the effects of metabolites such as lactate, citrate and lipoxygenase products, on VEGFA-VEGFR2 signaling pathway. Though this link between metabolite status and molecular mechanisms of angiogenesis is becoming evident, it is not clear how it affects genome-level expression in endothelial cells, critical to angiogenesis. In the present study, computational analysis was carried out on the transcriptome data of 4 different datasets where HUVECs were exposed to low and high glucose, both in vitro and in vivo, and the expression of a key enzyme involved in glucose metabolism is altered. The differentially expressed genes belonging to both VEGFA-VEGFR2 signaling pathway, as well as several VEGF signature genes as hub genes were also identified. These findings suggest the metabolite dependence, particularly glucose dependence, of angiogenesis, involving modulation of genome-level expression of angiogenesis- functional genome. This is important in tumor angiogenesis where reprogramming of metabolism is critical.