The interactions between interleukin-1 family genes: IL1A, IL1B, IL1RN, and obesity parameters

Maculewicz, Ewelina; Antkowiak, Bożena; Antkowiak, Oktawiusz; Borecka, Anna; Mastalerz, Andrzej; Leońska-Duniec, Agata; Humińska-Lisowska, Kinga; Michałowska–Sawczyn, Monika; Garbacz, Aleksandra; Lorenz, Katarzyna; Szarska, Ewa; Dziuda, Łukasz; Cywińska, Anna

doi:10.1186/s12864-021-08258-x

Cited by 18 publications

(9 citation statements)

References 41 publications

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“…C-X-C motif chemokine 10 (CXCL10) is a small cytokine belonging to the CXC chemokine family that is correlated and responsible for the greater susceptibility of patients with severe portal hypertension, diabetes, obesity, and lung cancer [ 101 – 104 ]. Interleukin 1 alpha (IL1A) is a member of the interleukin 1 cytokine family that is involved in the molecular mechanisms of hypertension, diabetes, obesity, and lung cancer [ 88 , 105 – 107 ]. Interferon regulatory factor 7 (IRF7) is a member of the interferon regulatory transcription factor family and is involved in the pathogenesis of diabetes, obesity, and lung cancer [ 108 – 110 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID‐19 Using Bioinformatics Approaches

Omit

Akhter

Rana

et al. 2023

BioMed Research International

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Several studies have been done to identify comorbidities of COVID-19. In this work, we developed an analytical bioinformatics framework to reveal COVID-19 comorbidities, their genomic associations, and molecular mechanisms accomplishing transcriptomic analyses of the RNA-seq datasets provided by the Gene Expression Omnibus (GEO) database, where normal and infected tissues were evaluated. Using the framework, we identified 27 COVID-19 correlated diseases out of 7,092 collected diseases. Analyzing clinical and epidemiological research, we noticed that our identified 27 diseases are associated with COVID-19, where hypertension, diabetes, obesity, and lung cancer are observed several times in COVID-19 patients. Therefore, we selected the above four diseases and performed assorted analyses to demonstrate the association between COVID-19 and hypertension, diabetes, obesity, and lung cancer as comorbidities. We investigated genomic associations with the cross-comparative analysis and Jaccard’s similarity index, identifying shared differentially expressed genes (DEGs) and linking DEGs of COVID-19 and the comorbidities, in which we identified hypertension as the most associated illness. We also revealed molecular mechanisms by identifying statistically significant ten pathways and ten ontologies. Moreover, to understand cellular physiology, we did protein-protein interaction (PPI) analyses among the comorbidities and COVID-19. We also used the degree centrality method and identified ten biomarker hub proteins (IL1B, CXCL8, FN1, MMP9, CXCL10, IL1A, IRF7, VWF, CXCL9, and ISG15) that associate COVID-19 with the comorbidities. Finally, we validated our findings by searching the published literature. Thus, our analytical approach elicited interconnections between COVID-19 and the aforementioned comorbidities in terms of remarkable DEGs, pathways, ontologies, PPI, and biomarker hub proteins.

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Section: Discussionmentioning

confidence: 99%

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID‐19 Using Bioinformatics Approaches

Omit

Akhter

Rana

et al. 2023

BioMed Research International

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“…Furthermore, monocytes and macrophages recruit neutrophils through the interaction of CXCL3 and CXCL8 with CXCR1 and CXCR2. Neutrophils express many IL-1 family genes that have been shown to play a key role in in ammation, brosis, and autoimmune reactions 46,47 . Therefore, IL-1 family interaction may be a reason for promoting collagen deposition in the POP sacral ligament.…”

Section: Discussionmentioning

confidence: 99%

Single-cell analysis of uterosacral ligament revealed cellular heterogeneity in women with pelvic organ prolapse

Liu

Sue²,

Wei³

et al. 2023

Preprint

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Pelvic organ prolapse (POP) markedly affects the quality of life of women, including significant financial burden. Different cell types and corresponding marker genes were identified in the vaginal wall of women with POP; however, no study has explored gene expression and cellular heterogeneity in the uterosacral ligament. Using single-cell RNA sequencing, we constructed a transcriptional profile of the uterosacral ligament and control samples, and identified 10 major cell types. We performed subpopulation analysis of POP primary cells, explored differentially expressed genes, and performed pseudo-time and transcription factor analyses. We verified previous cell clusters of human fibroblasts, neutrophils, and found new clusters of uterosacral ligaments. Additionally, we dissected pattern changes of cell–cell interaction between stromal and immune cells and transcription factors related to the extracellular matrix, development, and immunity in POP. Here we provide insight into the molecular mechanisms of POP and valuable information for future research directions.

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“…Signaling pathways include GPCR ligand binding [89], neutrophil degranulation [90], immune system [91], metabolism of lipids [92] and signal transduction [93] made great contribution to the development of IPF. MAP3K15 [94], PRTN3 [95], CX3CR1 [96], AGRP (agouti related neuropeptide) [97], MPO (myeloperoxidase) [98], CD5L [99], S100A8 [100], NPR3 [101], VEGFD (vascular endothelial growth factor D) [102], CXCL11 [103], IL1A [104], CBS (cystathionine beta-synthase) [105], WNT7A [106], SCD (stearoyl-CoA desaturase) [107], LRP2 [108], SLC6A4 [109], BDNF (brain derived neurotrophic factor) [110], CXCL10 [111], ANGPTL7 [112], S100A9 [113], NPY1R [114], IL1B [115], GPIHBP1 [116], CYP1B1 [117], CD36 [118], MACROD2 [119], TRIB3 [120], SPX (spexin hormone) [121], PCSK9 [122], GPD1 [123], CDH13 [124], FFAR4 [125], FGF2 [126], FASN (fatty acid synthase) [127], DGAT2 [128], DACH1 [129], PNPLA3 [130], FGF9 [131], SLC7A11 [132], CLIC5 [133], VIP (vasoactive intestinal peptide) [134], SMAD6 [135], BMPR2 [136], APOA1 [137], INSIG1 [138], TLR3 [139], NLRP12 [140], ADRB1 [141], TLR8 [142], GATA3 [143], CCR2 [144], TLR7 [145], CCRL2 [146], BMPER (BMP binding endothelial regulator) [147], CAV1 [148], TFPI (tissue factor pathway inhibitor) [149], FADS1 [150], SUCNR1 [151], CADM2 [152], SLC19A3 [153], SGCG (sarcoglycan gamma) [154], ADH1B [155], NEGR1 [156], HSD17B12 [157], OXTR (oxytocin receptor) [158] and ANKK1 […”

Section: Discussionmentioning

confidence: 99%

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

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Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analysis of DEGs were performed. Then, a protein protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst database was used to construct the targeted microRNAs (miRNAs) and transcription factors (TFs) of the hub genes. Finally receiver operating characteristic (ROC) curve analysis was used to validates the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8 and EFHC2 were selected. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

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The interactions between interleukin-1 family genes: IL1A, IL1B, IL1RN, and obesity parameters

Cited by 18 publications

References 41 publications

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID‐19 Using Bioinformatics Approaches

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID‐19 Using Bioinformatics Approaches

Single-cell analysis of uterosacral ligament revealed cellular heterogeneity in women with pelvic organ prolapse

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

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