Liraglutide-induced structural modulation of the gut microbiota in patients with type 2 diabetes mellitus.

Accumulating evidence has suggested the importance of gut microbiota in the development of type 2 diabetes mellitus (T2DM). In the present study, 40 patients with T2DM were treated with liraglutide for 4 months. Feces samples and clinical characteristics were collected from these 40 T2DM patients before and after the liraglutide treatment. The diversity and composition of gut microbiota in the two groups were determined by sequencing the V4 region of bacterial 16S rRNA genes. Meanwhile, blood glucose, insulin, hemoglobin A1c (HbA1c), and lipid metabolism were also measured in the pre- and post-liraglutide-treatment groups. We find that Baseline HbA1c was associated with liraglutide treatment response (R 2 = 0.527, ß =  - 0.726, p < 0.0001). After adjusted for baseline HbA1c, blood urea nitrogen was associated with liraglutide treatment response. Besides, our results showed reduced gut microbial alpha diversity, different community structure distribution and altered microbial interaction network in patients treated with liraglutide. The liner discriminant analysis (LDA) effect size (LEfSe) analysis showed that 21 species of bacteria were abundant in the pre-liraglutide-treatment group and 15 species were abundant in the post-liraglutide-treatment group. In addition, we also find that Megamonas were significantly correlated with older age, diabetes duration and diabetic retinopathy, Clostridum were significantly correlated with family history of diabetes and Oscillospira were significantly correlated with both diabetic retinopathy and diabetic peripheral neuropathy. Functional analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) and cluster of orthologous groups (COG) annotations enriched three KEGG metabolic pathways and six functional COG categories in the post-liraglutide-treatment group. In conclusion, our research suggests that baseline HbA1c, blood urea nitrogen and gut microbiota are associated with the liraglutide treatment applied on patients with T2DM. These findings may contribute to the beneficial effects of liraglutide against diabetes.

ATP-binding cassette transporter 13 mRNA expression level in schizophrenia patients.

The objective of this study was to investigate the expression and clinical role of ATP-binding cassette transporter 13 (ABCA13) gene previously shown to be associated with schizophrenia (SZ) through Genome-wide association studies studies. Thirty-two first-episode drug-naive SZ patients and forty-eight age and gender-matched healthy controls were enrolled in this study. We measured ABCA13 mRNA expression levels using quantitative real-time PCR at baseline and 12 weeks after antipsychotic therapy. Moreover, clinical symptoms were measured by the Positive and Negative Syndrome Scale (PANSS) at baseline and 12-week follow-up. We found that ABCA13 mRNA levels were significantly lower in SZ patients compared with healthy controls at baseline. SZ patients' symptoms were decreased, but ABCA13 mRNA levels were increased after 12 weeks antipsychotic therapy. In addition, there was a significant difference in ABCA13 mRNA levels among SZ patients at baseline and 12-week follow-up. The ABCA13 mRNA levels were not associated with age, BMI, years of education. Of the clinical symptoms measured, the ABCA13 mRNA levels were negatively associated with the PANSS scores at baseline and 12-week follow-up. The results indicated that the ABCA13 mRNA expression level is of interest, and upon further studies, it could be used as a biomarker for SZ treatment outcome.

An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder.

Studies to date have reported hundreds of genes connected to bipolar disorder (BP). However, many studies identifying candidate genes have lacked replication, and their results have, at times, been inconsistent with one another. This paper, therefore, offers a computational workflow that can curate and evaluate BP-related genetic data. Our method integrated large-scale literature data and gene expression data that were acquired from both postmortem human brain regions (BP case/control: 45/50) and peripheral blood mononuclear cells (BP case/control: 193/593). To assess the pathogenic profiles of candidate genes, we conducted Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analyses, with 4 metrics proposed and validated for each gene. Our approach developed a scalable BP genetic database (BP_GD), including BP related genes, drugs, pathways, diseases and supporting references. The 4 metrics successfully identified frequently-studied BP genes (e.g. GRIN2A, DRD1, DRD2, HTR2A, CACNA1C, TH, BDNF, SLC6A3, P2RX7, DRD3, and DRD4) and also highlighted several recently reported BP genes (e.g. GRIK5, GRM1 and CACNA1A). The computational biology approach and the BP database developed in this study could contribute to a better understanding of the current stage of BP genetic research and assist further studies in the field.