A study on the association between gut microbiota, inflammation, and type 2 diabetes.

Type 2 diabetes mellitus (T2DM) was reported to be associated with impaired immune response and alterations in microbial composition and function. However, the underlying mechanism remains elusive. To investigate the association among retinoic acid-inducible gene-I-like receptors (RLRs) signaling pathway, intestinal bacterial microbiome, microbial tryptophan metabolites, inflammation, and a longer course of T2DM, 14 patients with T2DM and 7 healthy controls were enrolled. 16S rRNA amplicon sequencing and untargeted metabolomics were utilized to analyze the stool samples. RNA sequencing (RNA-seq) was carried out on the peripheral blood samples. Additionally, C57BL/6J specific pathogen-free (SPF) mice were used. It was found that the longer course of T2DM could lead to a decrease in the abundance of probiotics in the intestinal microbiome. In addition, the production of microbial tryptophan derivative skatole declined as a consequence of the reduced abundance of related intestinal microbes. Furthermore, low abundances of probiotics, such as Bacteroides and Faecalibacterium, could trigger the inflammatory response by activating the RLRs signaling pathway. The increased level of the member of TNF receptor-associated factors (TRAF) family, nuclear factor kappa-B (NF-κB) activator (TANK), in the animal colon activated nuclear factor kappa B subunit 2 (NFκB2), resulting in inflammatory damage. In summary, it was revealed that the low abundances of probiotics could activate the RLR signaling pathway, which could in turn activate its downstream signaling pathway, NF-κB, highlighting a relationship among gut microbes, inflammation, and a longer course of T2DM. KEY POINTS: Hyperglycemia may suppress tryptophanase activity. The low abundance of Bacteroides combined with the decrease of Dopa decarboxylase (DDC) activity may lead to the decrease of the production of tryptophan microbial derivative skatole, and the low abundance of Bacteroides or reduced skatole may further lead to the increase of blood glucose by downregulating the expression of glucagon-like peptide-1 (GLP1). A low abundance of anti-inflammatory bacteria may induce an inflammatory response by triggering the RLR signaling pathway and then activating its downstream NF-κB signaling pathway in prolonged T2DM.

Revealing the mechanism of quinoa on type 2 diabetes based on intestinal flora and taste pathways.

To investigate the antidiabetic effects and mechanisms of quinoa on type 2 diabetes mellitus (T2DM) mice model. In this context, we induced the T2DM mice model with a high-fat diet (HFD) combined with streptozotocin (STZ), followed by treatment with a quinoa diet. To explore the impact of quinoa on the intestinal flora, we predicted and validated its potential mechanism of hypoglycemic effect through network pharmacology, molecular docking, western blot, and immunohistochemistry (IHC). We found that quinoa could significantly improve abnormal glucolipid metabolism in T2DM mice. Further analysis showed that quinoa contributed to the improvement of gut microbiota composition positively. Moreover, it could downregulate the expression of TAS1R3 and TRPM5 in the colon. A total of 72 active components were identified by network pharmacology. Among them, TAS1R3 and TRPM5 were successfully docked with the core components of quinoa. These findings confirm that quinoa may exert hypoglycemic effects through gut microbiota and the TAS1R3/TRPM5 taste signaling pathway.

Moringa oleifera leaf supplementation relieves oxidative stress and regulates intestinal flora to ameliorate polycystic ovary syndrome in letrozole-induced rats.

This study investigated the effects of supplementation Moringa oleifera leaf (MOL) on relieving oxidative stress, anti-inflammation, changed the relative abundance of multiple intestinal flora and blood biochemical indices during letrozole-induced polycystic ovary syndrome (PCOS). Previous studies have shown that MOL has anti-inflammatory, anti-oxidation, insulin-sensitizing effects. However, whether MOL has beneficial effects on PCOS remains to be elucidated. In the current study, 10-week-old female Sprague-Dawley rats received letrozole to induce PCOS-like rats, and subsequently were treated with a MOL diet. Then, the body weight and estrus cycles were measured regularly in this period. Finally, the ovarian morphology, blood biochemical indices, anti-oxidative, intestinal flora, and anti-inflammation were observed at the end of the experiment. We found that MOL supplementation markedly decreased the body weight, significantly upregulated the expression of Sirt1, FoxO1, PGC-1α, IGF1, and substantially modulated the sex hormone level and improved insulin resistance, which may be associated with the relieves oxidative stress. Moreover, the supplementation of MOL changed the relative abundance of multiple intestinal flora, the relative abundance of Fusobacterium, Prevotella were decreased, and Blautia and Parabacteroides were increased. These results indicate that MOL is potentially a supplementary medication for the management of PCOS.

Small RNA Sequencing Analysis of STZ-Injured Pancreas Reveals Novel MicroRNA and Transfer RNA-Derived RNA with Biomarker Potential for Diabetes Mellitus.

MicroRNAs (miRNAs) and transfer RNA-derived small RNAs (tsRNAs) play critical roles in the regulation of different biological processes, but their underlying mechanisms in diabetes mellitus (DM) are still largely unknown. This study aimed to gain a better understanding of the functions of miRNAs and tsRNAs in the pathogenesis of DM. A high-fat diet (HFD) and streptozocin (STZ)-induced DM rat model was established. Pancreatic tissues were obtained for subsequent studies. The miRNA and tsRNA expression profiles in the DM and control groups were obtained by RNA sequencing and validated with quantitative reverse transcription-PCR (qRT-PCR). Subsequently, bioinformatics methods were used to predict target genes and the biological functions of differentially expressed miRNAs and tsRNAs. We identified 17 miRNAs and 28 tsRNAs that were significantly differentiated between the DM and control group. Subsequently, target genes were predicted for these altered miRNAs and tsRNAs, including Nalcn, Lpin2 and E2f3. These target genes were significantly enriched in localization as well as intracellular and protein binding. In addition, the results of KEGG analysis showed that the target genes were significantly enriched in the Wnt signaling pathway, insulin pathway, MAPK signaling pathway and Hippo signaling pathway. This study revealed the expression profiles of miRNAs and tsRNAs in the pancreas of a DM rat model using small RNA-Seq and predicted the target genes and associated pathways using bioinformatics analysis. Our findings provide a novel aspect in understanding the mechanisms of DM and identify potential targets for the diagnosis and treatment of DM.

Prediction model for missed abortion of patients treated with IVF-ET based on XGBoost: a retrospective study.

Aim: In this study, we established a model based on XGBoost to predict the risk of missed abortion in patients treated with in vitro fertilization-embryo transfer (IVF-ET), evaluated its prediction ability, and compared the model with the traditional logical regression model. Methods: We retrospectively collected the clinical data of 1,017 infertile women treated with IVF-ET. The independent risk factors were screened by performing a univariate analysis and binary logistic regression analysis, and then, all cases were randomly divided into the training set and the test set in a 7:3 ratio for constructing and validating the model. We then constructed the prediction models by the traditional logical regression method and the XGBoost method and tested the prediction performance of the two models by resampling. Results: The results of the binary logistic regression analysis showed that several factors, including the age of men and women, abnormal ovarian structure, prolactin (PRL), anti-Müllerian hormone (AMH), activated partial thromboplastin time (APTT), anticardiolipin antibody (ACA), and thyroid peroxidase antibody (TPO-Ab), independently influenced missed abortion significantly (P < 0.05). The area under the receiver operating characteristic curve (AUC) score and the F1 score with the training set of the XGBoost model (0.877 ± 0.014 and 0.730 ± 0.019, respectively) were significantly higher than those of the logistic model (0.713 ± 0.013 and 0.568 ± 0.026, respectively). In the test set, the AUC and F1 scores of the XGBoost model (0.759 ± 0.023 and 0.566 ± 0.042, respectively) were also higher than those of the logistic model (0.695 ± 0.030 and 0.550 ± 049, respectively). Conclusions: We established a prediction model based on the XGBoost algorithm, which can accurately predict the risk of missed abortion in patients with IVF-ET. This model performed better than the traditional logical regression model.

Application of artificial neural networks in reproductive medicine.

With the emergence of the age of information, the data on reproductive medicine has improved immensely. Nonetheless, healthcare workers who wish to utilise the relevance and implied value of the various data available to aid clinical decision-making encounter the difficulty of statistically analysing such large data. The application of artificial intelligence becoming widespread in recent years has emerged as a turning point in this regard. Artificial neural networks (ANNs) exhibit beneficial characteristics of comprehensive analysis and autonomous learning, owing to which these are being applied to disease diagnosis, embryo quality assessment, and prediction of pregnancy outcomes. The present report aims to summarise the application of ANNs in the field of reproduction and analyse its further application potential.

Combined analysis of whole-exome sequencing and RNA sequencing in type 2 diabetes mellitus patients with thirst and fatigue.

BACKGROUND: The principal objective of this study was to gain a better understanding of the mechanisms of type 2 diabetes mellitus (T2DM) patients with fatigue (D-T2DM) through exome and transcriptome sequencing. METHODS: After whole-exome sequencing on peripheral blood of 6 D-T2DM patients, the consensus mutations were screen out and analyzed by a series of bioinformatics analyses. Then, we combined whole-exome sequencing and transcriptome sequencing results to find the important genes that changed at both the DNA and RNA levels. RESULTS: The results showed that a total of 265,393 mutation sites were found in D-T2DM patients compared with normal individuals, 235 of which were consensus mutations shared with D-T2DM patients. These genes significantly enriched in HIF-1 signaling pathway and sphingolipid signaling pathway. At the RNA level, a total of 375 genes were identified to be differentially expressed. After the DNA-RNA joint analysis, eight genes were screened that changed at both DNA and RNA levels. Among these genes, FUS and LMNA were related to carbohydrate metabolism, energy metabolism, and mitochondrial function. Subsequently, we predicted the herbs, including Qin Pi and Hei Zhi Ma, that might play a therapeutic role in D-T2DM through the SymMap database. CONCLUSION: These findings have significant implications for understanding the mechanisms of D-T2DM and provide potential targets for D-T2DM diagnosis and treatment.

Naringenin regulates gut microbiota and SIRT1/ PGC-1ɑ signaling pathway in rats with letrozole-induced polycystic ovary syndrome.

PURPOSE: To evaluate the effect of naringenin on improving PCOS and explore the mechanism. METHODS: Firstly, we carried out differential gene expression analysis from transcriptome sequencing data of human oocyte to screen the KEGG pathway, then the PCOS-like rat model was induced by letrozole. They were randomly divided into four groups: Normal group (N), PCOS group (P), Diane-35 group (D), and Naringenin group (Nar). The changes of estrus cycle, body weight, ovarian function, serum hormone levels, glucose metabolism, along with the expression of SIRT1, PGC-1ɑ, claudin-1 and occludin of the ovary and colon were investigated. Furthermore, the composition of the gut microbiome of fecal was tested. RESULTS: By searching the KEGG pathway in target genes, we found that at least 15 KEGG pathways are significantly enriched in the ovarian function, such as AMPK signaling pathway, insulin secretion, and ovarian steroidogenesis. Interestingly, naringenin supplementation significantly reduced body weight, ameliorated hormone levels, improved insulin resistance, and mitigated pathological changes in ovarian tissue, up-regulated the expression of PGC-1ɑ, SIRT1, occludin and claudin-1 in colon. In addition, we also found that the abundance of Prevotella and Gemella was down-regulated, while the abundance of Butyricimonas, Lachnospira, Parabacteroides, Butyricicoccus, Streptococcus, Coprococcus was up-regulated. CONCLUSION: Our data suggest that naringenin exerts a treatment PCOS effect, which may be related to the modulation of the gut microbiota and SIRT1/PGC-1ɑ signaling pathway. Our research may provide a new perspective for the treatment of PCOS and related diseases.

Quinoa Reduces High-Fat Diet-Induced Obesity in Mice via Potential Microbiota-Gut-Brain-Liver Interaction Mechanisms.

The gut microbiota is important in the occurrence and development of obesity. It can not only via its metabolites, but also through microbiota-gut-brain-liver interactions, directly or indirectly, influence obesity. Quinoa, known as one kind of pseudocereals and weight loss food supplements, has been high-profile for its high nutritional value and broad applications. In this context, we produced high-fat diet-induced (HFD) obese mouse models and assessed the efficacy of quinoa with saponin and quinoa without saponin on obesity. We explored the potential therapeutic mechanisms of quinoa using methods such as 16S rRNA, Western blotting, Immunohistochemical (IHC). Our results indicated that quinoa can improve the obese symptoms significantly on HFD mice, as well as aberrant glucose and lipid metabolism. Further analyses suggest that quinoa can regulate microbiota in the colon and have predominantly regulation on Bacteroidetes, Actinobacteria and Desulfovibrio, meanwhile can decrease the F/B ratio and the abundance of Blautia. Contemporaneously, quinoa can upregulate the expression of TGR5 in the colon and brain, as well as GLP-1 in the colon, liver and brain. while downregulate the expression of TLR4 in the colon and liver, as well as markers of ER stress and oxidative stress in livers and serums. Beyond this, tight junctional proteins in colons and brains are also increased in response to quinoa. Therefore, quinoa can effectively reduce obesity and may possibly exert through microbiota-gut-brain-liver interaction mechanisms. IMPORTANCE Gut microbiota has been investigated extensively, as a driver of obesity as well as a therapeutic target. Studies of its mechanisms are predominantly microbiota-gut-brain axis or microbiota-gut-liver axis. Recent studies have shown that there is an important correlation between the gut-brain-liver axis and the energy balance of the body. Our research focus on microbiota-gut-brain-liver axis, as well as influences of quinoa in intestinal microbiota. We extend this study to the interaction between microbiota and brains, and the result shows obvious differences in the composition of the microbiome between the HFD group and others. These observations infer that besides the neurotransmitter and related receptors, microbiota itself may be a mediator for regulating bidirectional communication, along the gut-brain-liver axis. Taken together, these results also provide strong evidence for widening the domain of applicability of quinoa.

Establishment of a nomogram model for predicting adverse outcomes in advanced-age pregnant women with preterm preeclampsia.

AIM: To establish a model for predicting adverse outcomes in advanced-age pregnant women with preterm preeclampsia in China. METHODS: We retrospectively collected the medical records of 896 pregnant women with preterm preeclampsia who were older than 35 years and delivered at the Affiliated Hospital of Qingdao University from June 2018 to December 2020. The pregnant women were divided into an adverse outcome group and a non-adverse outcome group according to the occurrence of adverse outcomes. The data were divided into a training set and a verification set at a ratio of 8:2. A nomogram model was developed according to a binary logistic regression model created to predict the adverse outcomes in advanced-age pregnant women with preterm preeclampsia. ROC curves and their AUCs were used to evaluate the predictive ability of the model. The model was internally verified by using 1000 bootstrap samples, and a calibration diagram was drawn. RESULTS: Binary logistic regression analysis showed that platelet count (PLT), uric acid (UA), blood urea nitrogen (BUN), prothrombin time (PT), and lactate dehydrogenase (LDH) were the factors that independently influenced adverse outcomes (P < 0.05). The AUCs of the internal and external verification of the model were 0.788 (95% CI: 0.737 ~ 0.764) and 0.742 (95% CI: 0.565 ~ 0.847), respectively. The calibration curve was close to the diagonal. CONCLUSIONS: The model we constructed can accurately predict the risk of adverse outcomes of pregnant women of advanced age with preterm preeclampsia, providing corresponding guidance and serving as a basis for preventing adverse outcomes and improving clinical treatment and maternal and infant prognosis.

An early model to predict the risk of gestational diabetes mellitus in the absence of blood examination indexes: application in primary health care centres.

BACKGROUND: Gestational diabetes mellitus (GDM) is one of the critical causes of adverse perinatal outcomes. A reliable estimate of GDM in early pregnancy would facilitate intervention plans for maternal and infant health care to prevent the risk of adverse perinatal outcomes. This study aims to build an early model to predict GDM in the first trimester for the primary health care centre. METHODS: Characteristics of pregnant women in the first trimester were collected from eastern China from 2017 to 2019. The univariate analysis was performed using SPSS 23.0 statistical software. Characteristics comparison was applied with Mann-Whitney U test for continuous variables and chi-square test for categorical variables. All analyses were two-sided with p < 0.05 indicating statistical significance. The train_test_split function in Python was used to split the data set into 70% for training and 30% for test. The Random Forest model and Logistic Regression model in Python were applied to model the training data set. The 10-fold cross-validation was used to assess the model's performance by the areas under the ROC Curve, diagnostic accuracy, sensitivity, and specificity. RESULTS: A total of 1,139 pregnant women (186 with GDM) were included in the final data analysis. Significant differences were observed in age (Z=-2.693, p=0.007), pre-pregnancy BMI (Z=-5.502, p<0.001), abdomen circumference in the first trimester (Z=-6.069, p<0.001), gravidity (Z=-3.210, p=0.001), PCOS (χ2=101.024, p<0.001), irregular menstruation (χ2=6.578, p=0.010), and family history of diabetes (χ2=15.266, p<0.001) between participants with GDM or without GDM. The Random Forest model achieved a higher AUC than the Logistic Regression model (0.777±0.034 vs 0.755±0.032), and had a better discrimination ability of GDM from Non-GDMs (Sensitivity: 0.651±0.087 vs 0.683±0.084, Specificity: 0.813±0.075 vs 0.736±0.087). CONCLUSIONS: This research developed a simple model to predict the risk of GDM using machine learning algorithm based on pre-pregnancy BMI, abdomen circumference in the first trimester, age, PCOS, gravidity, irregular menstruation, and family history of diabetes. The model was easy in operation, and all predictors were easily obtained in the first trimester in primary health care centres.

Supplementation of quinoa regulates glycolipid metabolism and endoplasmic reticulum stress in the high-fat diet-induced female obese mice.

OBJECTIVE: To explore the effects of the quinoa diet on glycolipid metabolism and endoplasmic reticulum (ER) stress in an obese mouse model. METHODS: Six-week-old C57BL/6J female mice have received a high-fat diet (HFD) to induce obesity and subsequently were treated with a quinoa diet for 12 weeks. During this period, fasting blood glucose, body fat and insulin resistance were measured regularly. At the end of the experiment, mouse serum and liver tissue were collected. The differences in glucose and lipid metabolism were analyzed, and liver tissue pathological morphology, liver endoplasmic reticulum stress-related mRNA and protein levels, and serum oxidative stress levels were measured. RESULTS: Quinoa diet could significantly reduce the level of blood glucose, triglyceride, cholesterol, low-density lipoprotein, improve glucose tolerance, as well as improve histological changes of liver tissues in obese mice (P < 0.05 or < 0.01). Besides, quinoa could improve oxidative stress indicators such as GSH, and MDA (P < 0.05 or < 0.01). Furthermore, quinoa can down-regulate mRNA expression of ER stress markers eIF2α, GRP78, and CHOP in the liver of obese mice (P < 0.05 or < 0.01). CONCLUSIONS: Quinoa supplementation can improve glycolipid metabolism, regulate ER stress, and alleviate obesity in HFD-induced mice.

Establishment of a nomogram model to predict macrosomia in pregnant women with gestational diabetes mellitus.

AIM: To establish a nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus in China. METHODS: We retrospectively collected the medical records of 783 pregnant women with gestational diabetes who underwent prenatal examinations and delivered at the Affiliated Hospital of Qingdao University from October 2019 to October 2020. The pregnant women were randomly divided into two groups in a 4:1 ratio to generate and validate the model. The independent risk factors for macrosomia in pregnant women with gestational diabetes mellitus were analyzed by multivariate logistic regression, and the nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus was established and verified by R software. RESULTS: Logistic regression analysis showed that prepregnancy body mass index, weight gain during pregnancy, fasting plasma glucose, triglycerides, biparietal diameter and amniotic fluid index were independent risk factors for macrosomia (P < 0.05). The areas under the ROC curve for internal and external validation of the model were 0.813 (95 % confidence interval 0.754-0.862) and 0.903 (95 % confidence interval 0.588-0.967), respectively. The calibration curve was a straight line with a slope close to 1. CONCLUSIONS: In this study, we constructed a nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus. The model has good discrimination and calibration abilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women with gestational diabetes mellitus.

Effects of Salvianolic acid B on RNA expression and co-expression network of lncRNAs in brown adipose tissue of obese mice.

ETHNOPHARMACOLOGICAL RELEVANCE: Salvianolic acid B (SalB) is a polyphenolic compound in Salvia miltiorrhiza Bunge ("Danshen"), which has been largely used in Traditional Chinese Medicine for the treatment of metabolic syndrome, obesity, diabetes, among others. AIM OF STUDY: This study was to investigate the effects of Salvianolic acid B (SalB) on mRNA, lncRNA and circRNA's expression profile in brown adipose tissue (BAT) of obese mice. MATERIALS AND METHODS: High-fat-diet induced obese C57BL/6J mice were treated with SalB (100 mg/kg/day) for 8 weeks. Then, BAT was harvested for RNA-Seq analysis. Differentially expressed mRNAs, lncRNAs and circRNAs were analyzed using the Illumina Hiseq 4000. Following this procedure, bioinformatic tools including Gene ontology (GO), KEGG pathway and lncRNA-mRNA co-network analysis were utilized. Finally, RT-qPCR was performed to validate the differentially expressed RNAs. RESULTS: Compared with control group, 2532 mRNAs, 774 lncRNAs and 25 circRNAs were differentially expressed in SalB group. Additionally, 40 upregulated and 109 downregulated gene-related pathways were identified in the SalB group. Among them, metabolic pathways showed the highest enrichment coefficient in upregulated genes. Moreover, 54 up-regulated and 626 down-regulated coding mRNAs associated with lncRNA-Hsd11b1 and lncRNA-Vmp1. CONCLUSIONS: SalB may play an anti-obesity role by adjusting the expression of mRNAs correlated with inflammatory response and energy metabolism through regulating the expression of lncRNA-Hsd11b1. The findings of this research provide new directions to study the mechanisms of SalB, and would open therapeutic avenues for the treatment of obesity.

Transcriptome Sequencing Analysis of Peripheral Blood of Type 2 Diabetes Mellitus Patients With Thirst and Fatigue.

Purpose: The purpose of this study is to explore the differences in transcriptome expression profiles between healthy subjects and type 2 diabetes mellitus patients with thirst and fatigue (D-T2DM) and, in addition, to investigate the possible role of noncoding ribonucleic acids (RNAs) in the pathogenesis of D-T2DM. Methods: We constructed the expression profiles of RNAs by RNA sequencing in the peripheral blood of D-T2DM patients and healthy subjects and analyzed differentially expressed RNAs. Results: Compared with healthy subjects, a total of 469 mRNAs, 776 long non-coding RNAs (lncRNAs), and 21 circular RNAs (circRNAs) were differentially expressed in D-T2DM patients. Furthermore, several genes associated with insulin resistance, inflammation, and mitochondrial dysfunction were identified within the differentially expressed mRNAs. Differentially expressed lncRNAs were primarily involved in biological processes associated with immune responses. In addition, differentially expressed circRNAs may target miRNAs associated with glucose metabolism and mitochondrial function. Conclusions: Our results may bring a new perspective on differential RNA expression involved in the pathogenesis of D-T2DM and promote the development of novel treatments for this disease.

Microarray analysis of aberrant microRNA expression patterns in spinal cord gliomas of different grades.

MicroRNAs (miRNAs) are involved in the development of several types of tumor; however, their role in spinal gliomas remains unknown. The present study aimed to identify potentially novel spinal cord gliomas (SCG)-associated miRNAs and to characterize their roles in the development and progression of SCG. miRNA expression levels in low-grade SCG (classed as stage I-II SCG based on the World Health Organization grading system), high-grade SCG (classed as stage IV SCG based on the World Health Organization grading system) and 5 control cases were measured using a miRNA expression microarray. Subsequently, blood samples from the spinal cord of patients with differing grades of SCG were screened for differentially expressed miRNAs (DEmiRNAs). Compared with the control group, 7 upregulated and 36 downregulated miRNAs were identified in the low-grade SCG group and a total of 70 upregulated and 20 downregulated miRNAs were identified in the high-grade SCG group (P≤0.05, fold change >2). Gene Ontology analysis revealed that the regulation of cellular metabolic processes, negative regulation of biological processes and axon guidance were primarily involved. Moreover, pathway analysis showed that the target genes of DEmiRNAs were enriched in tumor-related signaling pathways, such as the MAPK and Wnt signaling pathway. The results suggest that DEmiRNAs in peripheral blood may serve as novel target markers with high specificity and sensitivity for the diagnosis of SCG.

A comparative study of microbial community and functions of type 2 diabetes mellitus patients with obesity and healthy people.

The gut microbiota is crucial in the pathogenesis of type 2 diabetes mellitus (T2DM). However, the metabolism of T2DM patients is not well-understood. We aimed to identify the differences on composition and function of gut microbiota between T2DM patients with obesity and healthy people. In this study, 6 T2DM patients with obesity and 6 healthy volunteers were recruited, and metagenomic approach and bioinformatics analysis methods were used to understand the composition of the gut microbiota and the metabolic network. We found a decrease in the abundance of Firmicutes, Oribacterium, and Paenibacillus; this may be attributed to a possible mechanism and biological basis of T2DM; moreover, we identified three critical bacterial taxa, Bacteroides plebeius, Phascolarctobacterium sp. CAG207, and the order Acidaminococcales that can potentially be used for T2DM treatment. We also revealed the composition of the microbiota through functional annotation based on multiple databases and found that carbohydrate metabolism contributed greatly to the pathogenesis of T2DM. This study helps in elucidating the different metabolic roles of microbes in T2DM patients with obesity.

Publisher Correction: Comparison of Alterations in miRNA Expression in Matched Tissue and Blood Samples during Spinal Cord Glioma Progression.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Relationships of Non-coding RNA with diabetes and depression.

In order to study the molecular differences between type 2 diabetes mellitus (T2DM) and T2DM with depression (DD), we aimed to screen the differential expression of lncRNA, mRNA, and circRNA in the blood of patients with T2DM and DD. Based on the self-rating depression scale (SDS), patient health questionnaire 9 (PHQ9), blood glucose and HbA1c, we divided the patients into T2DM and DD group. Peripheral blood was collected from the two groups of patients to perform lncRNA, mRNA, and circRNA expression profiling and screening DD-related specific molecules. Subsequently, bioinformatics analysis was performed to investigate the functions of differentially expressed genes (DEgenes). Finally, RT-PCR and lncRNA-mRNA regulatory network was performed to verify the expressions of lncRNAs and mRNAs related to the occurrence and development of DD. 28 lncRNAs, 107 circRNAs, and 89 mRNAs were identified in DD differential expression profiles. GO and pathway analysis found that 20 biological process (BP) related entities and 20 pathways associated with DD. The analysis shows that the genes that are differentially expressed in the DD group involved in the development of the neuropsychiatric system, immunity, and inflammation. Then, we screening for the important DElncRNA and mRNA associated with DD were verified by RT-PCR experiments and the results of RT-PCR were consistent with the sequencing results. LncRNA, circRNA, and mRNA differential expression profiles exist in DD patients compared with T2DM. The lncRNA-mRNA regulatory network analysis confirmed the crosslinking and complex regulation patterns of lncRNA and mRNA expression and verified the authenticity of the regulatory network.

Comparison of Alterations in miRNA Expression in Matched Tissue and Blood Samples during Spinal Cord Glioma Progression.

Abnormal expression of microRNAs (miRNAs) contributes to glioma initiation. However, the expression of miRNAs in tumour tissue or blood of spinal cord glioma (SCG) patients, particularly in high-grade spinal gliomas (Grade IV) known as glioblastoma (GBM), remains largely unknown. In this study we aimed to determine differentially expressed miRNAs (DEmiRNAs) in the tissue and blood between spinal cord glioblastoma (SC-GBM) patients and low grade SCG (L-SCG) patients. Additionally, we predicted key miRNA targets and pathways that may be critical in glioma development using pathway and gene ontology analysis. A total of 74 miRNAs were determined to be differentially expressed (25 upregulated and 49 downregulated) in blood, while 207 miRNAs (20 up-regulated and 187 down-regulated) were identified in tissue samples. Gene ontology analysis revealed multicellular organism development and positive regulation of macromolecule metabolic process to be primarily involved. Pathway analysis revealed "Glioma", "Signalling pathways regulating pluripotency of stem cells" to be the most relevant pathways. miRNA-mRNA analysis revealed that hsa-miRNA3196, hsa-miR-27a-3p, and hsa-miR-3664-3p and their target genes are involved in cancer progression. Our study provides a molecular basis for SCG pathological grading based on differential miRNA expression.