Metagenomics and Faecal Metabolomics Integrative Analysis towards the Impaired Glucose Regulation and Type 2 Diabetes in Uyghur-Related Omics

Nuli, Rebiya; Azhati, Jureti; Cai, Junxiu; Kadeer, Aizhatiguli; Zhang, Bing; Mohemaiti, Patamu

doi:10.1155/2019/2893041

Cited by 24 publications

(20 citation statements)

References 41 publications

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“…Moreover, differences in β -diversity (PLS-DA) were observed in the three groups. Our results were consistent with those of previous studies, which reported that lower α -diversity indices (Shannon and Chao) of gut microbiota and different β -diversity were observed in T2DM patients compared with non-diabetic individuals ( Nuli et al., 2019 ; Zhao et al., 2019 ; Reitmeier et al., 2020 ). Therefore, we postulate that the onset and progression of diabetes and its retinal complications are related to alterations in the gut microbiota.…”

Section: Discussionsupporting

confidence: 93%

Dysbiosis and Implication of the Gut Microbiota in Diabetic Retinopathy

Huang

Wang

Ma³

et al. 2021

Front. Cell. Infect. Microbiol.

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The pathogenesis of type 2 diabetes mellitus (T2DM) is commonly associated with altered gut bacteria. However, whether the microbial dysbiosis that exists in human diabetic patients with or without retinopathy is different remains largely unknown. Here, we collected clinical information and fecal samples from 75 participants, including 25 diabetic patients without retinopathy (DM), 25 diabetic patients with retinopathy (DR), and 25 healthy controls (HC). The gut microbial composition in the three groups was analyzed using 16S ribosomal RNA (rRNA) gene sequencing. Microbial structure and composition differed in the three groups. The α and β diversities in both the DM and DR groups were reduced compared with those in the HC group. Blautia was the most abundant genus, especially in the DM group. In addition, increased levels of Bifidobacterium and Lactobacillus and decreased levels of Escherichia-Shigella, Faecalibacterium, Eubacterium_hallii_group and Clostridium genera were observed in the DM and DR groups compared with the HC group. Furthermore, a biomarker set of 25 bacterial families, which could distinguish patients in the DR group from those in the DM and HC groups was identified, with the area under the curve values ranging from 0.69 to 0.85. Of note, Pasteurellaceae, which was increased in DM and decreased in DR compared with HC, generated a high AUC (0.74) as an individual predictive biomarker. Moreover, 14 family biomarkers were associated with fasting blood glucose levels or diabetes, with most of them being negatively correlated. In summary, our study establishes compositional alterations of gut microbiota in DM and DR, suggesting the potential use of gut microbiota as a non-invasive biomarker for clinical and differential diagnosis, as well as identifying potential therapeutic targets of diabetic retinopathy.

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Section: Discussionsupporting

confidence: 93%

Dysbiosis and Implication of the Gut Microbiota in Diabetic Retinopathy

Huang

Wang

Ma³

et al. 2021

Front. Cell. Infect. Microbiol.

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“…However, Fusobacteria were highly abundant in the control group and Actinobacteria were highly abundant in SARS-CoV-2 diabetic group (Figure s4). The dominance of Firmicutes in diabetic patients were also described in other studies 27, 28 .…”

Section: Discussionsupporting

confidence: 75%

Metagenomic diagnosis and pathogenic network profile of SARS-CoV-2 in patients co-morbidly affected by type 2 diabetes

Al‐Emran¹,

Rahman

Hasan³

et al. 2021

Preprint

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Background: The mortality of COVID-19 disease is very high among males or elderly or individuals having comorbidities with obesity, cardiovascular diseases, lung infections, hypertension, and/or diabetes. Our study characterizes the metagenomic feature of SARS-CoV-2 infected patients with or without type 2 diabetes to identify the microbial interactions associated with its fatal consequences. Method: This study compared the baseline nasopharyngeal microbiome of SARS-CoV-2 infected diabetic and non-diabetic patients with controls adjusted with age and gender. The mNGS were performed using Ion GeneStudio S5 Series and the data were analyzed by the Vegan-package in R. Results: All three groups possessed significant bacterial diversity and dissimilarity indexes (p<0.05). Spearmans correlation coefficient network analysis illustrated 183 significant positive correlations and 13 negative correlations of pathogenic bacteria (r=0.6-1.0, p<0.05), and 109 positive correlations among normal-flora and probiotic bacteria (r>0.6, p<0.05). The SARS-CoV-2 diabetic group exhibited a significant increase of pathogens (p<0.05) and opportunistic pathogens (p<0.05) with a simultaneous decrease of normal-flora (p<0.05). The molecular docking analysis of Salivaricin, KLD4 (alpha), and enterocin produced by several enriched probiotic strains presented strong binding affinity with Shiga toxin, outer membrane proteins (ompA, omp33) or hemolysin. Conclusion: The dysbiosis of the bacterial community might be linked with severe consequences of COVID-19 infected diabetic patients, although few probiotic strains inhibited numerous pathogens in the same pathological niches. This study suggested that the promotion of normal-flora and probiotics through dietary changes and reduction of excessive pro-inflammatory states by preventing pathogenic environment might lead to a better outcome for those co-morbid patients.

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“…Gene variants or polymorphisms of IL1B and ENP1 have been found to correlate with T2DM in many human studies [ 39 – 41 ]. Moreover, starch and sucrose metabolism and MAPK signaling pathways have been implicated in the development of T2DM in multiple animal models and human studies [ 42 , 43 ]. The findings of the present study suggested that lnc-HIST1H2AG-6 and lnc-AIM1-3 play an important role in T2DM; thus, further investigation should be performed.…”

Section: Discussionmentioning

confidence: 99%

Deregulation of lncRNA HIST1H2AG-6 and AIM1-3 in peripheral blood mononuclear cells is associated with newly diagnosed type 2 diabetes

Jiang

Lou²,

Chen

et al. 2021

BMC Med Genomics

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Background Type 2 diabetes mellitus (T2DM) is mainly affected by genetic and environmental factors; however, the correlation of long noncoding RNAs (lncRNAs) with T2DM remains largely unknown. Methods Microarray analysis was performed to identify the differentially expressed lncRNAs and messenger RNAs (mRNAs) in patients with T2DM and healthy controls, and the expression of two candidate lncRNAs (lnc-HIST1H2AG-6 and lnc-AIM1-3) were further validated using quantitative real-time polymerase chain reaction (qRT-PCR). Spearman’s rank correlation coefficient was used to measure the degree of association between the two candidate lncRNAs and differentially expressed mRNAs. Furthermore, the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and GO (Gene Ontology) enrichment analysis were used to reveal the biological functions of the two candidate lncRNAs. Additionally, multivariate logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed. Results The microarray analysis revealed that there were 55 lncRNAs and 36 mRNAs differentially expressed in patients with T2DM compared with healthy controls. Notably, lnc-HIST1H2AG-6 was significantly upregulated and lnc-AIM1-3 was significantly downregulated in patients with T2DM, which was validated in a large-scale qRT-PCR examination (90 controls and 100 patients with T2DM). Spearman’s rank correlation coefficient revealed that both lncRNAs were correlated with 36 differentially expressed mRNAs. Furthermore, functional enrichment (KEGG and GO) analysis demonstrated that the two lncRNA-related mRNAs might be involved in multiple biological functions, including cell programmed death, negative regulation of insulin receptor signal, and starch and sucrose metabolism. Multivariate logistic regression analysis revealed that lnc-HIST1H2AG-6 and lnc-AIM1-3 were significantly correlated with T2DM (OR = 5.791 and 0.071, respectively, both P = 0.000). Furthermore, the ROC curve showed that the expression of lnc-HIST1H2AG-6 and lnc-AIM1-3 might be used to differentiate patients with T2DM from healthy controls (area under the ROC curve = 0.664 and 0.769, respectively). Conclusion The profiles of lncRNA and mRNA were significantly changed in patients with T2DM. The expression levels of lnc-HIST1H2AG-6 and lnc-AIM1-3 genes were significantly correlated with some features of T2DM, which may be used to distinguish patients with T2DM from healthy controls and may serve as potential novel biomarkers for diagnosis in the future.

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Metagenomics and Faecal Metabolomics Integrative Analysis towards the Impaired Glucose Regulation and Type 2 Diabetes in Uyghur-Related Omics

Cited by 24 publications

References 41 publications

Dysbiosis and Implication of the Gut Microbiota in Diabetic Retinopathy

Dysbiosis and Implication of the Gut Microbiota in Diabetic Retinopathy

Metagenomic diagnosis and pathogenic network profile of SARS-CoV-2 in patients co-morbidly affected by type 2 diabetes

Deregulation of lncRNA HIST1H2AG-6 and AIM1-3 in peripheral blood mononuclear cells is associated with newly diagnosed type 2 diabetes

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