Background: Intestinal microbiota plays an important role in regulating metabolism, physiology, and immune response of the host. L-Tryptophan (Trp) are metabolized by several genera of bacteria. It remains largely unknown whether Trp can regulate the composition and diversity of the intestinal microbiota and contribute to intestinal homeostasis.Methods: A total of 126 weaning piglets were fed a corn- and soybean meal-based diet supplemented with 0, 0.2, or 0.4% Trp for 4 weeks. The intestinal microbiota was measured by using bacterial 16S rRNA gene-based high-throughput sequencing methods. Metabolites of Trp and short-chain fatty acids (SCFAs) in the hindgut were determined by high-performance liquid chromatography and gas chromatography, respectively. The mRNA levels for aromatic hydrocarbon receptor (AhR), tumor necrotic factor-α (TNF-α), interleukin-8 (IL-8), and protein abundances of tight junction proteins were determined.Results: Compared with the control group, Trp supplementation enhanced piglet growth performance and markedly altered the intestinal microbial composition as evidenced by enhanced alpha and beta diversity in the microbiome (P < 0.05). The abundances of Prevotella, Roseburia, and Succinivibrio genera were enriched, but those of Clostridium sensu stricto and Clostridium XI, opportunistic pathogens, were decreased with dietary Trp supplementation. Analysis of metabolic pathways indicated enhanced indole alkaloid biosynthesis and Trp metabolism, which was validated by elevated concentrations of 3-indoleacetic acid and indole in the intestinal contents of Trp-supplemented piglets (P < 0.05). These changes in Trp metabolites were correlated with activation of AhR and cytochrome p4501 A1 (CYP1A1) in cecum and colonic tissues, and with a decrease in the intestinal mucosal IL-8 mRNA level. Moreover, the protein abundances for zonula occluden (ZO)-1 and occludin were upregulated by Trp supplementation in colonic tissues.Conclusion: Dietary Trp supplementation altered intestinal microbial composition and diversity, improved intestinal mucosal barrier function, activated AhR signaling, and downregulated expression of inflammatory cytokines in the large intestine of weaned piglets. These results indicate a crosstalk between dietary Trp and intestine in nutrition, microbial metabolism, and mucosal immunity.

l-Tryptophan (Trp) is known to play an important role in the health of the large intestine. However, a role of dietary Trp in the small-intestinal mucosal barrier and microbiota remains poorly understood. The present study was conducted with weaned piglets to address this issue. Postweaning piglets were fed for 4 weeks a corn- and soybean meal-based diet supplemented with 0 (Control), 0.1, 0.2, or 0.4% Trp. The small-intestinal microbiota and serum amino acids were analyzed by bacterial 16S rRNA gene-based high-throughput sequencing methods and high-performance liquid chromatography, respectively. The mRNA levels for genes involved in host defense and the abundances of tight-junction proteins in jejunum and duodenum were measured by real time-PCR and Western blot techniques, respectively. The concentrations of Trp in the serum of Trp-supplemented piglets increased in a dose-dependent manner. Compared with the control group, dietary supplementation with 0.2–0.4% Trp reduced the abundances of Clostridium sensu stricto and Streptococcus in the jejunum, increased the abundances of Lactobacillus and Clostridium XI (two species of bacteria that can metabolize Trp) in the jejunum, and augmented the concentrations of secretory immunoglobulin A (sIgA) as well as mRNA levels for porcine β-defensins 2 and 3 in jejunal tissues. Moreover, dietary Trp supplementation activated the mammalian target of rapamycin signaling and increased the abundances of tight-junction proteins (zonula occludens (ZO)-1, ZO-3, and claudin-1) in jejunum and duodenum. We suggested that Trp-metabolizing bacteria in the small intestine of weaned pigs primarily mediated the beneficial effects of dietary Trp on its mucosal integrity, health, and function.

ObjectivesPrevious studies have demonstrated that microRNA-132 plays a vital part in and is actively associated with several cancers, with its tumor-suppressive role in hepatocellular carcinoma confirmed. The current study employed multiple bioinformatics techniques to establish gene signatures for hepatocellular carcinoma, microRNA-132 predicted target genes and the corresponding overlaps.MethodsVarious assays were performed to explore the role and cellular functions of miR-132 in HCC and a successive panel of tasks was completed, including NLP analysis, miR-132 target genes prediction, comprehensive analyses (gene ontology analysis, pathway analysis, network analysis and connectivity analysis), and analytical integration. Later, HCC-related and miR-132-related potential targets, pathways, networks and highlighted hub genes were revealed as well as those of the overlapped section.ResultsMiR-132 was effective in both impeding cell growth and boosting apoptosis in HCC cell lines. A total of fifty-nine genes were obtained from the analytical integration, which were considered to be both HCC- and miR-132-related. Moreover, four specific pathways were unveiled in the network analysis of the overlaps, i.e. adherens junction, VEGF signaling pathway, neurotrophin signaling pathway, and MAPK signaling pathway.ConclusionsThe tumor-suppressive role of miR-132 in HCC has been further confirmed by in vitro experiments. Gene signatures in the study identified the potential molecular mechanisms of HCC, miR-132 and their established associations, which might be effective for diagnosis, individualized treatments and prognosis of HCC patients. However, combined detections of miR-132 with other bio-indicators in clinical practice and further in vitro experiments are needed.

Guangxi, a province in southwestern China, has the second highest reported number of HIV/AIDS cases in China. This study aimed to develop an accurate and effective model to describe the tendency of HIV and to predict its incidence in Guangxi. HIV incidence data of Guangxi from 2005 to 2016 were obtained from the database of the Chinese Center for Disease Control and Prevention. Long short-term memory (LSTM) neural network models, autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models and exponential smoothing (ES) were used to fit the incidence data. Data from 2015 and 2016 were used to validate the most suitable models. The model performances were evaluated by evaluating metrics, including mean square error (MSE), root mean square error, mean absolute error and mean absolute percentage error. The LSTM model had the lowest MSE when the N value (time step) was 12. The most appropriate ARIMA models for incidence in 2015 and 2016 were ARIMA (1, 1, 2) (0, 1, 2)12 and ARIMA (2, 1, 0) (1, 1, 2)12, respectively. The accuracy of GRNN and ES models in forecasting HIV incidence in Guangxi was relatively poor. Four performance metrics of the LSTM model were all lower than the ARIMA, GRNN and ES models. The LSTM model was more effective than other time-series models and is important for the monitoring and control of local HIV epidemics.