STAT1 and STAT6 Act as Antagonistic Regulators of PPARγ in Diabetic Patients with and without Cardiovascular Diseases

Bendaya, Imen; Riahi, Aouatef; Kharat, Maher; Kahla, Saloua; Sdiri, Wissem; Oueslati, Ridha

doi:10.7754/clin.lab.2017.171013

Cited by 10 publications

(6 citation statements)

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“…Increased PPARγ expression has been reported in activated alveolar macrophages (AMs), a primary host cell in Mycobacterium tuberculosis infection. Impaired activity of PPARγ has been found in the setting of diabetes with and without cardiovascular diseases, PD, and LC cells (Mirza et al, 2015;Kwon et al, 2017;Bendaya et al, 2018;Lecca et al, 2018;Sippel et al, 2019). Higher levels of TGF-beta1 have been reported in positive than negative tuberculin reactors in tuberculosis patients (Jang et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

An Integrative Network Approach to Identify Common Genes for the Therapeutics in Tuberculosis and Its Overlapping Non-Communicable Diseases

et al. 2022

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Tuberculosis (TB) is the leading cause of death from a single infectious agent. The estimated total global TB deaths in 2019 were 1.4 million. The decline in TB incidence rate is very slow, while the burden of noncommunicable diseases (NCDs) is exponentially increasing in low- and middle-income countries, where the prevention and treatment of TB disease remains a great burden, and there is enough empirical evidence (scientific evidence) to justify a greater research emphasis on the syndemic interaction between TB and NCDs. The current study was proposed to build a disease-gene network based on overlapping TB with NCDs (overlapping means genes involved in TB and other/s NCDs), such as Parkinson’s disease, cardiovascular disease, diabetes mellitus, rheumatoid arthritis, and lung cancer. We compared the TB-associated genes with genes of its overlapping NCDs to determine the gene-disease relationship. Next, we constructed the gene interaction network of disease-genes by integrating curated and experimentally validated interactions in humans and find the 13 highly clustered modules in the network, which contains a total of 86 hub genes that are commonly associated with TB and its overlapping NCDs, which are largely involved in the Inflammatory response, cellular response to cytokine stimulus, response to cytokine, cytokine-mediated signaling pathway, defense response, response to stress and immune system process. Moreover, the identified hub genes and their respective drugs were exploited to build a bipartite network that assists in deciphering the drug-target interaction, highlighting the influential roles of these drugs on apparently unrelated targets and pathways. Targeting these hub proteins by using drugs combination or drug repurposing approaches will improve the clinical conditions in comorbidity, enhance the potency of a few drugs, and give a synergistic effect with better outcomes. Thus, understanding the Mycobacterium tuberculosis (Mtb) infection and associated NCDs is a high priority to contain its short and long-term effects on human health. Our network-based analysis opens a new horizon for more personalized treatment, drug-repurposing opportunities, investigates new targets, multidrug treatment, and can uncover several side effects of unrelated drugs for TB and its overlapping NCDs.

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Section: Discussionmentioning

confidence: 99%

An Integrative Network Approach to Identify Common Genes for the Therapeutics in Tuberculosis and Its Overlapping Non-Communicable Diseases

et al. 2022

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“…The decrease of blood glucose content in the body can also inhibit inflammation and reduce atherosclerosis. With the pathological changes caused by diabetes, the level of PPARγ in the body is significantly reduced, and the expression of the immune system is inhibited so that all tissues and organs are involved, and the development of diabetes is aggravated (19). Aβ1-42 is A substance deposited in the vascular wall and connective tissue, which is easy to accumulate and become the precipitation center.…”

Section: Predictive Efficacy Of Pparγ Aβ1-42 and Mir-155 Levels On The Occurrence And Development Of Diabetes Mellitusmentioning

confidence: 99%

Study on the correlation between PPARγ, Aβ1-42, miR-155 and the occurrence and development of diabetes

Wang²,

Yu³

2022

Cell Mol Biol (Noisy-le-grand)

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The objective of this research was to study the correlation between PPARγ, Aβ1-42, miR-155 and the occurrence and development of diabetes. For this purpose, 52 patients with diabetes who were hospitalized from September 2019 to May 2021 were selected as the research objects. They were grouped according to the severity of the disease, which was pre-diabetes (n=16), mild (n=25), and moderate (n=16). =11), another 20 healthy subjects were taken as the control group, and the levels of PPARγ, Aβ1-42, miR-155 in each group were measured, and the correlation between changes in the levels of various indicators and the occurrence and development of diabetes was explored. Results showed that comparison of age, gender, course of disease, BMI, living habits, comorbidities, and high-density lipoprotein among the groups of diabetic patients (P>0.05); the levels of total cholesterol, triglycerides and low-density cholesterol decreased with the development of diabetes (P<0.05) ); Compared with the healthy group, the levels of PPARγ and miR-155 were significantly reduced, and the levels of Aβ1-42 were significantly increased (P<0.05); compared with the prediabetes, the levels of mild and moderate PPARγ and miR-155 showed a downward trend, and Aβ1 -42 level showed an upward trend (P<0.05); PPARγ and miR-155 levels were negatively correlated with pre-diabetes, mild, and moderate; Aβ1-42 was positively correlated with pre-diabetes, mild, and moderate (P<0.05) ; PPARγ, Aβ1-42, miR-155 levels can effectively predict the occurrence and development of diabetes, and the sensitivity, specificity and accuracy of the detection of diabetes by various indicators, positive predictive value and negative predictive value are significantly higher. It is concluded that PPARγ, Aβ1-42, miR-155 are closely related to the occurrence and development of diabetes, and there are many influencing factors of diabetes. Clinical intervention measures can be taken according to specific conditions.

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“…As a result, a total of 955 DEGs were identified. [99], AZGP1 [100], CYP1B1 [101], PRRX1 [102], CD34 [103], A2M [104], CDKN2A [105], SERPINE1 [106], CD44 [107], FABP4 [108], ITGB3 [109], ALOX5AP [110], DAND5 [111], SFRP4 [112], RUNX2 [113], TACR3 [114], MYD88 [115], CYBA (cytochrome b-245 alpha chain) [116], STAT6 [117], FOXC1 [118], FN1 [119], TLR6 [120], CAV1 [121], RGS4 [122], TPM2 [123], TNFSF4 [124], LOX (lysyl oxidase) [125], SMOC2 [126], SPHK1 [127], FOLH1 [128], CYP2C8 [129], CD163 [130], DIRAS3 [131], OSMR (oncostatin M receptor) [132], POSTN (periostin) [133], SELL (selectin L) [134], TMPRSS2 [135], FLNC (filamin C) [136], CXCL16 [137], APOBR (apolipoprotein B receptor) [138], COL6A2 [139], LTBP2 [140], SPARCL1 [141], FOSL2 [142], ISL1 [143], HTR2C [144], TNNT2 [145], HGF (hepatocyte growth factor) [146], IL33 [147], SYK (spleen associated tyrosine kinase) [148], ADRB1 [149], CMKLR1 [150], SHOX2 [151], MEG3 [152], SCUBE1 [153...…”

Section: Discussionmentioning

confidence: 99%

“…GPCR ligand binding [60], extracellular matrix organization [61], cytokine signaling in immune system [62], interferon signaling [63], signaling by GPCR [64], neuronal system [65] and platelet activation, signaling and aggregation [66] plays an important role in the schizophrenia. Studies have revealed that SIX1 [67], VIP (vasoactive intestinal peptide) [68], GATA6 [69], FRZB (frizzled related protein) [70], CD40 [71], WT1 [72], PCDHGA3 [73], TFAP2B [74], HFE (homeostatic iron regulator) [75], NKX2-5 [76], IGFBP7 [77], HLA-F [78], CCL2 [79], COL1A2 [80], RUNX1 [81], TFF3 [82], IRX4 [83], NOS1 [84], DKK2 [85], IL18R1 [86], ADAM12 [87], NPPC (natriuretic peptide C) [88], COL1A1 [89], ABCG2 [90], SIX2 [91], CSRP1 [92], MR1 [93], NINJ2 [94], ACE (angiotensin I converting enzyme) [95], TBX1 [96], CTSC (cathepsin C) [97], DLX6 [98], KCNE1 [99], AZGP1 [100], CYP1B1 [101], PRRX1 [102], CD34 [103], A2M [104], CDKN2A [105], SERPINE1 [106], CD44 [107], FABP4 [108], ITGB3 [109], ALOX5AP [110], DAND5 [111], SFRP4 [112], RUNX2 [113], TACR3 [114], MYD88 [115], CYBA (cytochrome b-245 alpha chain) [116], STAT6 [117], FOXC1 [118], FN1 [119], TLR6 [120], CAV1 [121], RGS4 [122], TPM2 [123], TNFSF4 [124], LOX (lysyl oxidase) [125], SMOC2 [126], SPHK1 [127], FOLH1 [128], CYP2C8 [129], CD163 [130], DIRAS3 [131], OSMR (oncostatin M receptor) [132], POSTN (peri...…”

Section: Discussionmentioning

confidence: 99%

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad,

Vastrad

2023

Preprint

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Schizophrenia is thought to be the most prevalent chronic psychiatric disorder. Numerous proteins have been identified that are associated with the occurrence and development of schizophrenia. This study aimed to identify potential core genes and pathways involved in schizophrenia, through exhaustive bioinformatic and next generation sequencing (NGS) data analyses using GSE20966 NGS data of neural progenitor cells and neurons obtained from healthy controls and patients with schizophrenia. The NGS data were downloaded from the Gene Expression Omnibus database. NGS data was processed by the DESeq2 package in R software and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment analysis and REACTOME pathway enrichment analysis were carried out to identify potential biological functions and pathways of the DEGs. Protein-protein interaction (PPI) network, module, miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to identify the hub genes, miRNA and TFs. Potential hub genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). In this investigation, an overall 955 DEGs were identified: 478 genes were remarkably up regulated and 477 genes were distinctly down regulated. These genes were enriched for GO terms and pathways mainly involved in the multicellular organismal process, GPCR ligand binding, regulation of cellular process and amine ligand-binding receptors. MYC, FN1, CDKN2A, EEF1G, CAV1, ONECUT1, SYK, MAPK13, TFAP2A and BTK were considered the potential hub genes. miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed successfully. On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of schizophrenia and provide potential targets for further investigation.

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STAT1 and STAT6 Act as Antagonistic Regulators of PPARγ in Diabetic Patients with and without Cardiovascular Diseases

Abstract: Our results suggest that the expression and activity of PPARγ mediates CD36 in PBMCs and varies with respect to STAT6 and STAT1 trafficking in diabetic patients with and without cardiovascular diseases.

Cited by 10 publications

References 0 publications

An Integrative Network Approach to Identify Common Genes for the Therapeutics in Tuberculosis and Its Overlapping Non-Communicable Diseases

An Integrative Network Approach to Identify Common Genes for the Therapeutics in Tuberculosis and Its Overlapping Non-Communicable Diseases

Study on the correlation between PPARγ, Aβ1-42, miR-155 and the occurrence and development of diabetes

Screening of the Key Genes and Signaling Pathways for Schizophrenia Using Bioinformatics and Next Generation Sequencing Data Analysis

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