Background: Obesity is a global epidemic in the industrialized and developing world, and many children suffer from obesity-related complications. Gut microbiota dysbiosis might have significant effect on the development of obesity. The microbiota continues to develop through childhood and thus childhood may be the prime time for microbiota interventions to realize health promotion or disease prevention. Therefore, it is crucial to understand the structure and function of pediatric gut microbiota. Methods: According to the inclusion criteria and exclusion criteria, twenty-three normal weight and twenty-eight obese children were recruited from Nanjing, China. Genomic DNA was extracted from fecal samples. The V4 region of the bacterial 16S rDNA was amplified by PCR, and sequencing was applied to analyze the gut microbiota diversity and composition using the Illumina HiSeq 2500 platform. Results: The number of operational taxonomic units (OTUs) showed a decrease in the diversity of gut microbiota with increasing body weight. The alpha diversity indices showed that the normal weight group had higher abundance and observed species than the obese group (Chao1: P < 0.001; observed species: P < 0.001; PD whole tree: P < 0.001; Shannon index: P = 0.008). Principal coordinate analysis (PCoA) and Nonmetric multidimensional scaling (NMDS) revealed significant differences in gut microbial community structure between the normal weight group and the obese group. The liner discriminant analysis (LDA) effect size (LEfSe) analysis showed that fifty-five species of bacteria were abundant in the fecal samples of the normal weight group and forty-five species of bacteria were abundant in the obese group. In regard to phyla, the gut microbiota in the obese group had lower proportions of Bacteroidetes (51.35%) compared to the normal weight group (55.48%) (P = 0.030). There was no statistical difference in Firmicutes between the two groups (P = 0.436), and the Firmicutes/Bacteroidetes between the two groups had no statistical difference (P = 0.983). At the genus level, Faecalibacterium, Phascolarctobacterium, Lachnospira, Megamonas, and Haemophilus were significantly more abundant in the obese group than in the normal weight group (P = 0.048, P = 0.018, P < 0.001, P = 0.040, and P = 0.003, respectively). The fecal microbiota of children in the obese group had lower proportions of Oscillospira and Dialister compared to the normal weight group (P = 0.002 and P = 0.002, respectively). Conclusions: Our results showed a decrease in gut microbiota abundance and diversity as the BMI increased. Variations in the bacterial community structure were associated with obesity. Gut microbiota dysbiosis might play a crucial part in the development of obesity in Chinese children.

Background: Depression and anxiety are common comorbid diseases of constipation. Fecal microbiota transplantation (FMT) significantly relieves gastrointestinal-related symptoms, but its impact on psychiatric symptoms remains uncharted. Methods: We collected fecal and serum samples before and after FMT from 4 functional constipation patients with psychiatric symptoms and corresponding donor stool samples. We categorized the samples into two groups: before FMT (Fb) and after FMT (Fa). Parameters associated with constipation, depression, and anxiety symptoms were evaluated. Metagenomics and targeted neurotransmitter metabolomics were performed to investigate the gut microbiota and metabolites. 5-hydroxytryptamine (5-HT) biosynthesis was detected in patients’ fecal supernatants exposed to the QGP-1 cell model in vitro. Results: Our study demonstrated that patient’s constipation, depression, and anxiety were improved after FMT intervention. At the genus level, relative abundance of g_Bacteroides and g_Klebsiella decreased in the Fa group, while g_Lactobacillus, and g_Selenomonas content increased in the same group. These observations suggest a potential involvement of these genera in the pathogenesis of constipation with psychiatric symptoms. Metabolomics analysis showed that FMT intervention decreased serum 5-HT levels. Additionally, we found that species, including s_Klebsiella sp. 1_1_55, s_Odoribacter splanchnicus, and s_Ruminococcus gnavus CAG:126, were positively correlated with 5-HT levels. In contrast, s_Acetobacterium bakii, s_Enterococcus hermanniensis, s_Prevotella falsenii, s_Propionispira arboris, s_Schwartzia succinivorans, s_Selenomonas artemidis, and s_Selenomonas sp. FC4001 were negatively correlated with 5-HT levels. Furthermore, we observed that patients’ fecal supernatants increased 5-HT biosynthesis in QGP-1 cells. Conclusion: FMT can relieve patients’ constipation, depression, and anxiety symptoms by reshaping gut microbiota. The 5-HT level was associated with an altered abundance of specific bacteria or metabolites. This study provides specific evidence for FMT intervention in constipation patients with psychiatric symptoms.

Background: With characteristic self-renewal and multipotent differentiation, cancer stem cells (CSCs) have a crucial influence on the metastasis, relapse and drug resistance of gastric cancer (GC). However, the genes that participates in the stemness of GC stem cells have not been identified. Methods: The mRNA expression-based stemness index (mRNAsi) was analyzed with differential expressions in GC. The weighted gene co-expression network analysis (WGCNA) was utilized to build a co-expression network targeting differentially expressed genes (DEG) and discover mRNAsi-related modules and genes. We assessed the association between the key genes at both the transcription and protein level. Gene Expression Omnibus (GEO) database was used to validate the expression levels of the key genes. The risk model was established according to the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Furthermore, we determined the prognostic value of the model by employing Kaplan-Meier (KM) plus multivariate Cox analysis. Results: GC tissues exhibited a substantially higher mRNAsi relative to the healthy non-tumor tissues. Based on WGCNA, 17 key genes (ARHGAP11A, BUB1, BUB1B, C1orf112, CENPF, KIF14, KIF15, KIF18B, KIF4A, NCAPH, PLK4, RACGAP1, RAD54L, SGO2, TPX2, TTK, and XRCC2) were identified. These key genes were clearly overexpressed in GC and validated in the GEO database. The protein-protein interaction (PPI) network as assessed by STRING indicated that the key genes were tightly connected. After LASSO analysis, a nine-gene risk model (BUB1B, NCAPH, KIF15, RAD54L, KIF18B, KIF4A, TTK, SGO2, C1orf112) was constructed. The overall survival in the high-risk group was relatively poor. The area under curve (AUC) of risk score was higher compared to that of clinicopathological characteristics. According to the multivariate Cox analysis, the nine-gene risk model was a predictor of disease outcomes in GC patients (HR, 7.606; 95% CI, 3.037–19.051; P < 0.001). We constructed a prognostic nomogram with well−fitted calibration curve based on risk score and clinical data. Conclusion: The 17 mRNAsi-related key genes identified in this study could be potential treatment targets in GC treatment, considering that they can inhibit the stemness properties. The nine-gene risk model can be employed to predict the disease outcomes of the patients.

Background: Childhood obesity constitutes a worldwide health problem, and the gut microbiota play extremely important roles in obesity. Herein, we aimed to characterize the gut microbiota in children of normal weight, overweight, and obesity. Methods: Thirty children of normal weight, 35 who were overweight, and 35 with obesity were enrolled from Nanjing, China. We isolated DNA from fecal samples, and employed 16S rRNA gene sequencing to explore the diversity and composition of gut microbiota. Results: The operational taxonomic unit (OTU) numbers exhibited a reduction in the gut microbiota abundance with the increase in the body weight. Alpha diversity analysis revealed a sharp decrease in the mean microbial abundance among the three groups (Chao1: F = 5.478, P = 0.006; observed species: F = 7.271, P = 0.001; PD whole tree: F = 8.735, P < 0.001). Beta diversity analysis indicated notable differences in the gut microbial composition between children of normal weight and obesity. However, overweight children had little difference in gut microbiota compared to either children of normal weight or obesity. At the genus level, Oscillospira decreased among the three groups (χ² = 10.062, P = 0.001), and Sutterella increased (F = 4.052, P = 0.020). There were many remarkably increased species of gut bacteria in the comparison among three groups, 31 in the normal weight group, 32 in the obese group, and only three species of bacteria were identified in the overweight group. These significantly increased species of gut bacteria may have a close relationship with the progression of obesity. Conclusions: The abundance of species decreased significantly as the BMI increased. Although the gut microbial composition between children of normal weight and obesity was notably different, due to the changing ratio of some microbial communities, gut microbiota in overweight children showed similarities to that of children with normal weight and obesity.

Background: Cardiovascular disease, which is mainly caused by coronary atherosclerosis, is one of the leading causes of death and disability worldwide. Gut microbiota likely play an important role in coronary atherosclerosis. This study aims to investigate the microbiota profile of adults with coronary atherosclerosis to provide a theoretical basis for future research. Methods: Fecal samples were collected from 35 adult patients diagnosed with coronary atherosclerosis and 32 healthy adults in Nanjing, China, and the V3-V4 region of 16S rDNA genes was sequenced using high-throughput sequencing. Differences in alpha diversity, beta diversity, and gut microbiota composition between the two groups were then compared. Results: A beta diversity analysis revealed significant differences between adults with coronary atherosclerosis and controls, but there was no statistical difference in alpha diversity between the two groups. There were also differences in the composition of the gut microbiota between the two groups. The genera, Megamonas, Streptococcus, Veillonella, Ruminococcus_torques_group, Prevotella_2, Tyzzerella_4, were identified as potential biomarkers for coronary atherosclerosis. Conclusion: There are some differences in the gut microbiota of adults with coronary atherosclerosis compared to healthy adults. The insights from this study could be used to explore microbiome-based mechanisms for coronary atherosclerosis.

Gastric cancer is a malignant tumor in the world and circRNA has a close connection with it. However, the effects of circRNA on gastric cancer is still not clear. A comprehensive search of PubMed, Web of Science and Embase for published experimental studies about circRNA from 2013 to June 2018 was conducted with two investigators. Diagnostic OR (DOR) was calculated to evaluate the diagnostic efficacy by STATA 12.0. Total 11 studies were found, including 12 kinds of circRNAs, 11 in tissues, 5 in plasma, and all down regulated. The combined DOR and AUC (Area Under the Curve of Receiver Operating Characteristic) with their 95%CI were 8.778 [6.108, 12.614] and 0.81 [0.78, 0.84] respectively, indicating that circRNAs can reflect gastric cancer as well. Subgroup analysis revealed that circRNAs in plasma were higher than tissues as well as in subgroup with different sample sizes. We speculated that the heterogeneity of the literatures was mainly due to the different backgrounds of gastric cancer and the differences in experimental design and operation process. And the Deeks' funnel map revealed there was no obvious public biasness in the literature. CircRNAs have high sensitivity and specificity in the diagnosis of gastric cancer, and it may become an auxiliary diagnostic biomarker of gastric cancer.