Yu-hui Qin

Bio: Yu-hui Qin is an academic researcher from Hunan University. The author has contributed to research in topics: Medicine & Gut flora. The author has an hindex of 6, co-authored 7 publications receiving 87 citations.

Integrated 16S rRNA Sequencing, Metagenomics, and Metabolomics to Characterize Gut Microbial Composition, Function, and Fecal Metabolic Phenotype in Non-obese Type 2 Diabetic Goto-Kakizaki Rats.

Abstract: Type 2 diabetes mellitus (T2DM) is one of the most prevalent endocrine diseases in the world. Recent studies have shown that dysbiosis of the gut microbiota may be an important contributor to T2DM pathogenesis. However, the mechanisms underlying the roles of the gut microbiome and fecal metabolome in T2DM have not been characterized. Recently, the Goto-Kakizaki (GK) rat model of T2DM was developed to study the clinical symptoms and characteristics of human T2DM. To further characterize T2DM pathogenesis, we combined multi-omics techniques, including 16S rRNA gene sequencing, metagenomic sequencing, and metabolomics, to analyze gut microbial compositions and functions, and further characterize fecal metabolomic profiles in GK rats. Our results showed that gut microbial compositions were significantly altered in GK rats, as evidenced by reduced microbial diversity, altered microbial taxa distribution, and alterations in the interaction network of the gut microbiome. Functional analysis based on the cluster of orthologous groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations suggested that 5 functional COG categories belonged to the metabolism cluster and 33 KEGG pathways related to metabolic pathways were significantly enriched in GK rats. Metabolomics profiling identified 53 significantly differentially abundant metabolites in GK rats, including lipids and lipid-like molecules. These lipids were enriched in the glycerophospholipid metabolic pathway. Moreover, functional correlation analysis showed that some altered gut microbiota families, such as Verrucomicrobiaceae and Bacteroidaceae, significantly correlated with alterations in fecal metabolites. Collectively, the results suggested that an altered gut microbiota is associated with T2DM pathogenesis.

Distinguishing the serum metabolite profiles differences in breast cancer by gas chromatography mass spectrometry and random forest method

Abstract: In this study, we proposed a metabolomics strategy to distinguish different metabolic characters of healthy controls, breast benign (BE) patients, and breast malignant (BC) patients by using the GC-MS and random forest method (RF). In the current study, the serum samples from healthy controls, BE patients, and BC patients were characterized by using GC-MS. Then, random forest (RF) models were established to visually discriminate the differences among three groups' metabolites profiles, and further investigate the progress of breast cancer from benign to malignant in patients based on these GC-MS profiles. We successfully discovered the differences between the healthy and breast cancer patients. And the metabolic changes from benign to malignant cancer were obviously visualized. The results suggested that combining GC-MS profiling with random forest method is a useful approach to analyze metabolites and to screen the potential biomarkers for exploring the serum metabolic profiles of breast cancer.

Short-chain fatty acids in control of energy metabolism.

Abstract: Short-chain fatty acids (SCFAs), especially acetate, propionate and butyrate, are the end products from the intestinal microbial fermentation of dietary fibers and resistant starch It has been well documented that plasma and colonic SCFAs are associated with metabolic syndromes Recently, the involvement of SCFAs in energy homeostasis regulation has been extensively studied The importance of SCFAs on energy metabolism has highlighted the potential of modulating SCFAs as a nutritional target to prevent and counteract metabolism disorders and its associated diseases such as obesity and type 2 diabetes Here, we summarize the current knowledge about the biological properties of SCFAs with their impact on the energy homeostasis

The early prediction of mortality in acute pancreatitis : a large population-based study. Commentary

Abstract: Background: Identification of patients at risk for mortality early in the course of acute pancreatitis (AP) is an important step in improving outcome. Methods: Using Classification and Regression Tree (CART) analysis, a clinical scoring system was developed for prediction of in-hospital mortality in AP. The scoring system was derived on data collected from 17 992 cases of AP from 212 hospitals in 2000-2001. The new scoring system was validated on data collected from 18 256 AP cases from 177 hospitals in 2004-2005. The accuracy of the scoring system for prediction of mortality was measured by the area under the receiver operating characteristic curve (AUC). The performance of the new scoring system was further validated by comparing its predictive accuracy with that of Acute Physiology and Chronic Health Examination (APACHE) II. Results: CART analysis identified five variables for prediction of in-hospital mortality. One point is assigned for the presence of each of the following during the first 24 h: blood urea nitrogen (BUN) >25 mg/dl; impaired mental status; systemic inflammatory response syndrome (SIRS); age >60 years; or the presence of a pleural effusion (BISAP). Mortality ranged from >20% in the highest risk group to <1% in the lowest risk group. In the validation cohort, the BISAP AUC was 0.82 (95% Cl 0.79 to 0.84) versus APACHE II AUC of 0.83 (95% Cl 0.80 to 0.85). Conclusions: A new mortality-based prognostic scoring system for use in AP has been derived and validated. The BISAP is a simple and accurate method for the early identification of patients at increased risk for in-hospital mortality.

Effects of Metformin on the Gut Microbiota in Obesity and Type 2 Diabetes Mellitus.

Abstract: Metformin is a first-line treatment for type 2 diabetes mellitus (T2DM); however, its underlying mechanism is not fully understood. Gut microbiota affect the development and progression of T2DM. In recent years, an increasing number of studies has focused on the relationship between metformin and gut microbiota, suggesting that metformin might exert part of its hypoglycemic effect through these microbes. However, most of these results were not consistent due to the complex composition of the microbiota, the differences between species, the large variation between individuals, and the differences in experimental design, bringing great obstacle for our better understanding of the effects of metformin on the gut microbiota. Here, we reviewed the published papers concerning about the impacts of metformin on the gut microbiota of mice, rats, and humans with obesity or T2DM, and summarized the changes of gut microbiota composition caused by metformin and the possible underlying hypoglycemic mechanism which is related to gut microbiota. It was found that the proportions of some microbiota, such as phyla Bacteroidetes and Verrucomicrobia and genera Akkermansia, Bacteroides and Escherichia, were significantly affected by metformin in several studies. Metformin may exert part of hypoglycemic effects by altering the gut microbiota in ways that maintain the integrity of the intestinal barrier, promote the production of short-chain fatty acids (SCFAs), regulate bile acid metabolism, and improve glucose homeostasis.