COL1A1 as a potential new biomarker and therapeutic target for type 2 diabetes

Lin, Guopeng; Wan, Xuzhi; Liú, Dan; Wen, Yuxi; Yang, Chengfeng; Zhao, Chao

doi:10.1016/j.phrs.2021.105436

Cited by 28 publications

(21 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neutrophil degranulation [52], innate immune system [53], platelet degranulation [54], extracellular matrix organization [55], diseases of glycosylation [56], platelet activation, signaling and aggregation [57], hemostasis [58], secretion [59], secretory vesicle [60], transmembrane transporter activity [61], cell adhesion [62], localization of cell [63], extracellular matrix [55], intrinsic component of plasma membrane [64], structural molecule activity [65], signaling receptor binding [66], have been highly noted in T2DM. Reports indicate that HIF1A [67], HLA-DRB1 [68], CHI3L1 [69], ADORA2A [70], ADRB2 [71], CLU (clusterin) [72], AGT (angiotensinogen) [73], VCAM1 [74], PPARA (peroxisome proliferator activated receptor alpha) [75], APOL1 [76], ZFP36 [77], PPM1B [78], SOCS1 [79], SNCA (synuclein alpha) [80], CTSS (cathepsin S) [81], IL6R [82], CFB (complement factor B) [83], DEFB1 [84], VNN1 [85], RAB27A [86], DPP4 [87], RARRES2 [88], CASP1 [89], LCN2 [90], REG3A [91], CD74 [92], PCSK2 [93], CHGB (chromogranin B) [94], TTR (transthyretin) [95], LRG1 [96], ALB (albumin) [97], DPP7 [98], APOH (apolipoprotein H) [99], CTSD (cathepsin D) [100], GCG (glucagon) [101], KCNQ1 [102], NR4A1 [103], PLIN5 [104], ALDH2 [105], ANG (angiogenin) [106], CLDN7 [107], PRLR (prolactin receptor) [108], SOD2 [109], MLXIPL (MLX interacting protein like) [110], CTSD (cathepsin D) [111], PECAM1 [112], ADA (adenosine deaminase) [113], MFGE8 [114], COL1A1 [115], COL3A1 [116], NID2 [117], ARG1 [118], CD93 [119], IGF2 […”

Section: Discussionmentioning

confidence: 99%

“…A total of 925 DEGs (447 up regulated genes and 478 down regulated genes) were screened between T2DM and control samples. Emmens et al [39] found that PENK [110], CTSD (cathepsin D) [111], PECAM1 [112], ADA (adenosine deaminase) [113], MFGE8 [114], COL1A1 [115], COL3A1 [116], NID2 [117], ARG1 [118], CD93 [119], IGF2 [120], IL18 [121], LAMA1 [122], HPSE (heparanase) [123], BMP4 [124] [132], MMP3 [133], MMP9 [134], MMP11 [135], IL16 [136], TNFRSF11B [137], TIMP3 [138] and CAPN3 [139] were found in T2DM. HLA-A [140], HLA-B [141], HLA-C [142], HLA-DPB1 [143], HLA-E [144], ERBB3 [145], MFAP4 [146] and JAK3 [147] have been reported significantly expressed in type 1 diabetes mellitus, but these genes might be involved in T2DM progression.…”

Section: Discussionmentioning

confidence: 99%

“…Emmens et al [39] found that PENK (proenkephalin) expression was up regulated in cardiovascular diseases, but this gene might be responsible for progression of T2DM. Recent studies have proposed that the SAA1 [ [101], KCNQ1 [102], NR4A1 [103], PLIN5 [104], ALDH2 [105], ANG (angiogenin) [106], CLDN7 [107], PRLR (prolactin receptor) [108], SOD2 [109], MLXIPL (MLX interacting protein like) [110], CTSD (cathepsin D) [111], PECAM1 [112], ADA (adenosine deaminase) [113], MFGE8 [114], COL1A1 [115], COL3A1 [116], NID2 [117], ARG1 [118], CD93 [119], IGF2 [120], IL18 [121], LAMA1 [122], HPSE (heparanase) [123], BMP4 [124], CXCR4 [125], KDR (kinase insert domain receptor) [126], ESAM (endothelial cell adhesion molecule) [127], THBS1 [128], CD34 [129], SERPINE1 [130], WNT5B [131], MGP (matrix Gla protein) [132], MMP3 [133], MMP9 [134], MMP11 [135], IL16 [136], TNFRSF11B [137], TIMP3 [138] and CAPN3 [139] were found in T2DM. HLA-A [140], HLA-B [141], HLA-C [142], HLA-DPB1 [143], HLA-E [144], ERBB3 [145], MFAP4 [146] and JAK3…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Bioinformatics analysis of potential key genes and mechanisms in type 2 diabetes mellitus

Vastrad¹,

Vastrad²

2021

Preprint

View full text Add to dashboard Cite

Type 2 diabetes mellitus (T2DM) is etiologically related to metabolic disorder. The aim of our study was to screen out candidate genes of T2DM and to elucidate the underlying molecular mechanisms by bioinformatics methods. Expression profiling by high throughput sequencing data of GSE154126 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between T2DM and normal control were identified. And then, functional enrichment analyses of gene ontology (GO) and REACTOME pathway analysis was performed. Protein protein interaction (PPI) network and module analyses were performed based on the DEGs. Additionally, potential miRNAs of hub genes were predicted by miRNet database. Transcription factors (TFs) of hub genes were detected by NetworkAnalyst database. Further, validations were performed by receiver operating characteristic curve (ROC) analysis and real time polymerase chain reaction (RT PCR). In total, 925 DEGs were identified in T2DM, including 447 up regulated genes and 478 down-regulated genes. Functional enrichment analysis results showed that up regulated DEGs were significantly enriched in defense response, neutrophil degranulation, cell adhesion and extracellular matrix organization. The top 10 hub genes, JUN, VCAM1, RELA, U2AF2, ADRB2, FN1, CDK1, TK1, A2M and ACTA2 were identified from the PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network. Furthermore, ROC analysis and RT PCR revealed that JUN, VCAM1, RELA, U2AF2, ADRB2, FN1, CDK1, TK1, A2M and ACTA2 might serve as biomarkers in T2DM. Bioinformatics analysis is a useful tool to explore the molecular mechanism and pathogenesis of T2DM. The identified hub genes may participate in the onset and advancement of T2DM and serve as therapeutic targets.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Bioinformatics analysis of potential key genes and mechanisms in type 2 diabetes mellitus

Vastrad¹,

Vastrad²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Based on previous studies, CD248 is generally associated with progression of hypertension [34], but this gene might be linked with development of GDM. Lin et al [35] reported that expression of COL1A1 was essential for type 2 diabetes mellitus progression, but this gene might be involved in the development of GDM. Delfín et al [36] found that ABI3BP was responsible for progression of cardiovascular diseases, but this gene might be linked with development of GDM.…”

Section: Discussionmentioning

confidence: 99%

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

Vastrad¹,

Vastrad²,

Tengli

2021

Preprint

View full text Add to dashboard Cite

Gestational diabetes mellitus (GDM) is one of the metabolic diseases during pregnancy. The identification of the central molecular mechanisms liable for the disease pathogenesis might lead to the advancement of new therapeutic options. The current investigation aimed to identify central differentially expressed genes (DEGs) in GDM. The transcription profiling by array data (E-MTAB-6418) was obtained from the ArrayExpress database. The DEGs between GDM samples and non GDM samples were analyzed with limma package. Gene ontology (GO) and REACTOME enrichment analysis were performed using ToppGene. Then we constructed the protein-protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING) and module analysis was performed. Subsequently, we constructed the miRNA-hub gene network and TF-hub gene regulatory network by the miRNet database and NetworkAnalyst database. The validation of hub genes was performed through receiver operating characteristic curve (ROC). Finally, the candidate small molecules as potential drugs to treat GDM were predicted by using molecular docking. Through transcription profiling by array data, a total of 869 DEGs were detected including 439 up regulated and 430 down regulated genes. Biological process analysis of GO enrichment analysis showed these DEGs were mainly enriched in reproduction, nuclear outer membrane-endoplasmic reticulum membrane network, identical protein binding, cell adhesion, supramolecular complex and signaling receptor binding. Signaling pathway enrichment analysis indicated that these DEGs played a vital in cell surface interactions at the vascular wall and extracellular matrix organization. Ten genes, HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3, and PRKCA in the center of the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were associated with GDM, according to ROC analysis. Finally, the most significant small molecules were predicted based on molecular docking. Our results indicated that HSP90AA1, EGFR, RPS13, RBX1, PAK1, FYN, ABL1, SMAD3, STAT3, and PRKCA could be the potential novel biomarkers for GDM diagnosis, prognosis and the promising therapeutic targets. The current might be essential to understanding the molecular mechanism of GDM initiation and development.

show abstract

“…Forty male ICR mice (SPF, 4 weeks age) were supplied by Wu Shi’s Experimental Center Laboratory (Fuzhou, China), and they were raised under a condition of 23 ± 2 °C, a humidity of 55 ± 5%, and 12 h light/dark cycle. After one week of acclimation with food and water ad libitum, eight mice fed a normal diet were assigned to the normal control (NC, n = 8) group, and the rest of the thirty-two mice were fed with a high-fat and high-sucrose (HFHS) diet (15% lard, 15% sucrose, 1% cholesterol, 10% yolk, 0.2% sodium deoxycholate, and 58.8% chow) [ 36 ]. After 4 weeks of diet intervention, the mice with HFHS diet were intraperitoneally injected streptozotocin (STZ, dissolved in 0.1 M citrate buffer, pH 4.5) at a dose of 45 mg/kg, whereas the NC group mice were injected with the same volume of citrate buffer for three times a week.…”

Section: Methodsmentioning

confidence: 99%

Anti-Diabetic Effects of Ethanol Extract from Sanghuangporous vaninii in High-Fat/Sucrose Diet and Streptozotocin-Induced Diabetic Mice by Modulating Gut Microbiota

Huang

Zhao

Zhu³

et al. 2022

Foods

View full text Add to dashboard Cite

Type 2 diabetes mellitus (T2DM) may lead to abnormally elevated blood glucose, lipid metabolism disorder, and low-grade inflammation. Besides, the development of T2DM is always accompanied by gut microbiota dysbiosis and metabolic dysfunction. In this study, the T2DM mice model was established by feeding a high-fat/sucrose diet combined with injecting a low dose of streptozotocin. Additionally, the effects of oral administration of ethanol extract from Sanghuangporous vaninii (SVE) on T2DM and its complications (including hypoglycemia, hyperlipidemia, inflammation, and gut microbiota dysbiosis) were investigated. The results showed SVE could improve body weight, glycolipid metabolism, and inflammation-related parameters. Besides, SVE intervention effectively ameliorated the diabetes-induced pancreas and jejunum injury. Furthermore, SVE intervention significantly increased the relative abundances of Akkermansia, Dubosiella, Bacteroides, and Parabacteroides, and decreased the levels of Lactobacillus, Flavonifractor, Odoribacter, and Desulfovibrio compared to the model group (LDA > 3.0, p < 0.05). Metabolic function prediction of the intestinal microbiota by PICRUSt revealed that glycerolipid metabolism, insulin signaling pathway, PI3K-Akt signaling pathway, and fatty acid degradation were enriched in the diabetic mice treated with SVE. Moreover, the integrative analysis indicated that the key intestinal microbial phylotypes in response to SVE intervention were strongly correlated with glucose and lipid metabolism-associated biochemical parameters. These findings demonstrated that SVE has the potential to alleviate T2DM and its complications by modulating the gut microbiota imbalance.

show abstract

COL1A1 as a potential new biomarker and therapeutic target for type 2 diabetes

Cited by 28 publications

References 42 publications

Bioinformatics analysis of potential key genes and mechanisms in type 2 diabetes mellitus

Bioinformatics analysis of potential key genes and mechanisms in type 2 diabetes mellitus

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gestational diabetes mellitus

Anti-Diabetic Effects of Ethanol Extract from Sanghuangporous vaninii in High-Fat/Sucrose Diet and Streptozotocin-Induced Diabetic Mice by Modulating Gut Microbiota

Contact Info

Product

Resources

About