Chih-Min Chiu

Some previous studies have identified bacteria in semen as being a potential factor in male infertility. However, only few types of bacteria were taken into consideration while using PCR-based or culturing methods. Here we present an analysis approach using next-generation sequencing technology and bioinformatics analysis to investigate the associations between bacterial communities and semen quality. Ninety-six semen samples collected were examined for bacterial communities, measuring seven clinical criteria for semen quality (semen volume, sperm concentration, motility, Kruger's strict morphology, antisperm antibody (IgA), Atypical, and leukocytes). Computer-assisted semen analysis (CASA) was also performed. Results showed that the most abundant genera among all samples were Lactobacillus (19.9%), Pseudomonas (9.85%), Prevotella (8.51%) and Gardnerella (4.21%). The proportion of Lactobacillus and Gardnerella was significantly higher in the normal samples, while that of Prevotella was significantly higher in the low quality samples. Unsupervised clustering analysis demonstrated that the seminal bacterial communities were clustered into three main groups: Lactobacillus, Pseudomonas, and Prevotella predominant group. Remarkably, most normal samples (80.6%) were clustered in Lactobacillus predominant group. The analysis results showed seminal bacteria community types were highly associated with semen health. Lactobacillus might not only be a potential probiotic for semen quality maintenance, but also might be helpful in countering the negative influence of Prevotella and Pseudomonas. In this study, we investigated whole seminal bacterial communities and provided the most comprehensive analysis of the association between bacterial community and semen quality. The study significantly contributes to the current understanding of the etiology of male fertility.

Eighty-one stool samples from Taiwanese were collected for analysis of the association between the gut flora and obesity. The supervised analysis showed that the most, abundant genera of bacteria in normal samples (from people with a body mass index (BMI) ≤ 24) were Bacteroides (27.7%), Prevotella (19.4%), Escherichia (12%), Phascolarctobacterium (3.9%), and Eubacterium (3.5%). The most abundant genera of bacteria in case samples (with a BMI ≥ 27) were Bacteroides (29%), Prevotella (21%), Escherichia (7.4%), Megamonas (5.1%), and Phascolarctobacterium (3.8%). A principal coordinate analysis (PCoA) demonstrated that normal samples were clustered more compactly than case samples. An unsupervised analysis demonstrated that bacterial communities in the gut were clustered into two main groups: N-like and OB-like groups. Remarkably, most normal samples (78%) were clustered in the N-like group, and most case samples (81%) were clustered in the OB-like group (Fisher's P value = 1.61E - 07). The results showed that bacterial communities in the gut were highly associated with obesity. This is the first study in Taiwan to investigate the association between human gut flora and obesity, and the results provide new insights into the correlation of bacteria with the rising trend in obesity.

BACKGROUND: Gastrointestinal microbiota, particularly gut microbiota, is associated with human health. The biodiversity of gut microbiota is affected by ethnicities and environmental factors such as dietary habits or medicine intake, and three enterotypes of the human gut microbiome were announced in 2011. These enterotypes are not significantly correlated with gender, age, or body weight but are influenced by long-term dietary habits. However, to date, only two enterotypes (predominantly consisting of Bacteroides and Prevotella) have shown these characteristics in previous research; the third enterotype remains ambiguous. Understanding the enterotypes can improve the knowledge of the relationship between microbiota and human health. RESULTS: We obtained 181 human fecal samples from adults in Taiwan. Microbiota compositions were analyzed using next-generation sequencing (NGS) technology, which is a culture-independent method of constructing microbial community profiles by sequencing 16S ribosomal DNA (rDNA). In these samples, 17,675,898 sequencing reads were sequenced, and on average, 215 operational taxonomic units (OTUs) were identified for each sample. In this study, the major bacteria in the enterotypes identified from the fecal samples were Bacteroides, Prevotella, and Enterobacteriaceae, and their correlation with dietary habits was confirmed. A microbial interaction network in the gut was observed on the basis of the amount of short-chain fatty acids, pH value of the intestine, and composition of the bacterial community (enterotypes). Finally, a decision tree was derived to provide a predictive model for the three enterotypes. The accuracies of this model in training and independent testing sets were 97.2 and 84.0%, respectively. CONCLUSIONS: We used NGS technology to characterize the microbiota and constructed a predictive model. The most significant finding was that Enterobacteriaceae, the predominant subtype, could be a new subtype of enterotypes in the Asian population.

BACKGROUND: One of the most common and recurrent vaginal infections is bacterial vaginosis (BV). The diagnosis is based on changes to the "normal" vaginal microbiome; however, the normal microbiome appears to differ according to reproductive status and ethnicity, and even among individuals within these groups. The Amsel criteria and Nugent score test are widely used for diagnosing BV; however, these tests are based on different criteria, and so may indicate distinct changes in the vaginal microbial community. Nevertheless, few studies have compared the results of these test against metagenomics analysis. METHODS: Vaginal flora samples from 77 participants were classified according to the Amsel criteria and Nugent score test. The microbiota composition was analyzed using 16S ribosome RNA gene amplicon sequencing. Bioinformatics analysis and multivariate statistical analysis were used to evaluate the microbial diversity and function. RESULTS: Only 3 % of the participants diagnosed BV negative using the Amsel criteria (A-) were BV-positive according to the Nugent score test (N+), while over half of the BV-positive patients using the Amsel criteria (A+) were BV-negative according to the Nugent score test (N-). Thirteen genera showed significant differences in distribution among BV status defined by BV tests (e.g., A - N-, A + N- and A + N+). Variations in the four most abundant taxa, Lactobacillus, Gardnerella, Prevotella, and Escherichia, were responsible for most of this dissimilarity. Furthermore, vaginal microbial diversity differed significantly among the three groups classified by the Nugent score test (N-, N+, and intermediate flora), but not between the Amsel criteria groups. Numerous predictive microbial functions, such as bacterial chemotaxis and bacterial invasion of epithelial cells, differed significantly among multiple BV test, but not between the A- and A+ groups. CONCLUSIONS: Metagenomics analysis can greatly expand our current understanding of vaginal microbial diversity in health and disease. Metagenomics profiling may also provide more reliable diagnostic criteria for BV testing.

BACKGROUND: The human body plays host to a vast array of bacteria, found in oral cavities, skin, gastrointestinal tract and the vagina. Some bacteria are harmful while others are beneficial to the host. Despite the availability of many methods to identify bacteria, most of them are only applicable to specific and cultivable bacteria and are also tedious. Based on high throughput sequencing technology, this work derives 16S rRNA sequences of bacteria and analyzes probiotics and pathogens species. RESULTS: We constructed a database that recorded the species of probiotics and pathogens from literature, along with a modified Smith-Waterman algorithm for assigning the taxonomy of the sequenced 16S rRNA sequences. We also constructed a bacteria disease risk model for seven diseases based on 98 samples. Applicability of the proposed platform is demonstrated by collecting the microbiome in human gut of 13 samples. CONCLUSIONS: The proposed platform provides a relatively easy means of identifying a certain amount of bacteria and their species (including uncultivable pathogens) for clinical microbiology applications. That is, detecting how probiotics and pathogens inhabit humans and how affect their health can significantly contribute to develop a diagnosis and treatment method.