Identification of Human Intestinal Microbiota of 92 Men by Data Mining for 5 Characteristics, i.e., Age, BMI, Smoking Habit, Cessation Period of Previous Smokers and Drinking Habit

Effects of the prenatal and postnatal nurturing environment on the phenotype and gut microbiota of mice with polycystic ovary syndrome induced by prenatal androgen exposure: a cross-fostering study

The gut microbiome is implicated in the pathogenesis of polycystic ovary syndrome (PCOS), and prenatal androgen exposure is involved in the development of PCOS in later life. Our previous study of a mouse model of PCOS induced by prenatal dihydrotestosterone (DHT) exposure showed that the reproductive phenotype of PCOS appears from puberty, followed by the appearance of the metabolic phenotype after young adulthood, while changes in the gut microbiota was already apparent before puberty. To determine whether the prenatal or postnatal nurturing environment primarily contributes to these changes that characterize prenatally androgenized (PNA) offspring, we used a cross-fostering model to evaluate the effects of changes in the postnatal early-life environment of PNA offspring on the development of PCOS-like phenotypes and alterations in the gut microbiota in later life. Female PNA offspring fostered by normal dams (exposed to an abnormal prenatal environment only, fostered PNA) exhibited less marked PCOS-like phenotypes than PNA offspring, especially with respect to the metabolic phenotype. The gut microbiota of the fostered PNA offspring was similar to that of controls before adolescence, but differences between the fostered PNA and control groups became apparent after young adulthood. In conclusion, both prenatal androgen exposure and the postnatal early-life environment created by the DHT injection of mothers contribute to the development of PCOS-like phenotypes and the alterations in the gut microbiota that characterize PNA offspring. Thus, both the pre- and postnatal environments represent targets for the prevention of PCOS and the associated alteration in the gut microbiota in later life.

The potential of fecal microbiota and amino acids to detect and monitor patients with adenoma

The risk of recurrent dysplastic colonic lesions is increased following polypectomy. Yield of endoscopic surveillance after adenoma removal is low, while interval colorectal cancers occur. To longitudinally assess the dynamics of fecal microbiota and amino acids in the presence of adenomatous lesions and after their endoscopic removal. In this longitudinal case-control study, patients collected fecal samples prior to bowel preparation before scheduled colonoscopy and 3 months after this intervention. Based on colonoscopy outcomes, patients with advanced adenomas and nonadvanced adenomas (0.5-1.0 cm) who underwent polypectomy during endoscopy (n = 19) were strictly matched on age, body-mass index, and smoking habits to controls without endoscopic abnormalities (n = 19). Microbial taxa were measured by 16S RNA sequencing, and amino acids (AA) were measured by high-performance liquid chromatography (HPLC). Adenoma patients were discriminated from controls based on AA and microbial composition. Levels of proline (p = .001), ornithine (p = .02) and serine (p = .02) were increased in adenoma patients compared to controls but decreased to resemble those of controls after adenoma removal. These AAs were combined as a potential adenoma-specific panel (AUC 0.79(0.64-0.94)). For bacterial taxa, differences between patients with adenomas and controls were found (Bifidobacterium spp.↓, Anaerostipes spp.↓, Butyricimonas spp.↑, Faecalitalea spp.↑ and Catenibacterium spp.↑), but no alterations in relative abundance were observed after polypectomy. Furthermore, Faecalitalea spp. and Butyricimonas spp. were significantly correlated with adenoma-specific amino acids. We selected an amino acid panel specifically increased in the presence of adenomas and a microbial signature present in adenoma patients, irrespective of polypectomy. Upon validation, these panels may improve the effectiveness of the surveillance program by detection of high-risk individuals and determination of surveillance endoscopy timing, leading to less unnecessary endoscopies and less interval cancer.

Characterization of the vaginal microbiota of Japanese women

The composition of vaginal microbiota changes throughout life in response to health status, sexual activity, and pregnancy. Here the constitution of the vaginal microbiota among non-pregnant women, pregnant woman, and commercial sex workers (CSWs) in Japan were compared. Vaginal samples were obtained from 54 women between January 2014 and February 2015 and the microbiota of each was analyzed by 16S metagenomics as well as cluster and diversity analyses to identify differences. In addition, vaginal Lactobacillus spp. were isolated for comparison. Furthermore, data regarding the use of ritodrine hydrochloride by pregnant women was collected from medical charts. The vaginal microbiota were clustered into three groups. Group 1 was most often dominated by Lactobacillus spp., whereas groups 2 and 3 included not only Lactobacillus spp. but also Bifidobacterium, Atopobium, Prevotella, and Gardnerella spp., in addition to a few other taxa. In non-pregnant women, the proportions of microbes in groups 1, 2, and 3 were 31.8%, 36.4%, and 31.8%, respectively. In pregnant women, the abundance of group 1 microbes was notably greater than that of groups 2 and 3 (66.7% vs. 12.5% and 20.8%, respectively). In CSWs, the prevalence of group 3 microbes was far greater than that of group 1 (87.5% vs. 12.5%, respectively). The alpha diversity of non-pregnant women was significantly greater than that of pregnant women. The detection rate of live Lactobacillus spp. in CSWs was lower than in pregnant and non-pregnant women (25% vs. 50% and 68.2%, respectively). The vaginal microbiota of most pregnant women (60%) who received ritodrine hydrochloride was not dominated by Lactobacillus spp. These results suggest that there were clear differences in the colonization rate of Lactobacillus spp. among non-pregnant, pregnant, and CSW women groups. In addition, the dominance of Lactobacillus may influence the risk of preterm birth among women who received ritodrine hydrochloride during pregnancy.

Numerical analyses of intestinal microbiota by data mining

The human intestinal microbiota has a close relationship with health control and causes of diseases, and a vast number of scientific papers on this topic have been published recently. Some progress has been made in identifying the causes or species of related microbiota, and successful results of data mining are reviewed here. Humans who are targets of a disease have their own individual characteristics, including various types of noise because of their individual life style and history. The quantitatively dominant bacterial species are not always deeply connected with a target disease. Instead of conventional simple comparisons of the statistical record, here the Gini-coefficient (i.e., evaluation of the uniformity of a group) was applied to minimize the effects of various types of noise in the data. A series of results were reviewed comparatively for normal daily life, disease and technical aspects of data mining. Some representative cases (i.e., heavy smokers, Crohn’s disease, coronary artery disease and prediction accuracy of diagnosis) are discussed in detail. In conclusion, data mining is useful for general diagnostic applications with reasonable cost and reproducibility.

Characterization of gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism: gut microbiota could be a diagnostic marker of coronary artery disease

The association between atherosclerosis and gut microbiota has been attracting increased attention. We previously demonstrated a possible link between gut microbiota and coronary artery disease. Our aim of this study was to clarify the gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism (T-RFLP). This study included 39 coronary artery disease (CAD) patients and 30 age- and sex- matched no-CAD controls (Ctrls) with coronary risk factors. Bacterial DNA was extracted from their fecal samples and analyzed by T-RFLP and data mining analysis using the classification and regression algorithm. Five additional CAD patients were newly recruited to confirm the reliability of this analysis. Data mining analysis could divide the composition of gut microbiota into 2 characteristic nodes. The CAD group was classified into 4 CAD pattern nodes (35/39 = 90 %), while the Ctrl group was classified into 3 Ctrl pattern nodes (28/30 = 93 %). Five additional CAD samples were applied to the same dividing model, which could validate the accuracy to predict the risk of CAD by data mining analysis. We could demonstrate that operational taxonomic unit 853 (OTU853), OTU657, and OTU990 were determined important both by the data mining method and by the usual statistical comparison. We classified the gut microbiota profiles in coronary artery disease patients using data mining analysis of T-RFLP data and demonstrated the possibility that gut microbiota is a diagnostic marker of suffering from CAD.

Use of T-RFLP and seven restriction enzymes to compare the faecal microbiota of obese and lean Japanese healthy men

The composition of the intestinal microbiota of 92 healthy Japanese men was measured following consumption of identical meals for 3 days; terminal restriction fragment length polymorphisms were then used to analyse the DNA content of their faeces. The obtained operational taxonomic units (OTUs) were further analysed using seven restriction enzymes: 516f-BslI and -HaeIII, 27f-MspI and -AluI, and 35f-HhaI, -MspI and -AluI. Subjects were classified by their body mass index (BMI) as lean (<18.5) or obese (>25.0). OTUs were then analysed using data mining software. Pearson correlation coefficients on data mining results indicated only a weak relationship between BMI and OTU diversity. Specific OTUs attributed to lean and obese subjects were further examined by data mining with six groups of enzymes and closely related accession numbers for lean and obese subjects were successfully narrowed down. 16S rRNA sequences showed Bacillus spp., Erysipelothrix spp. and Holdemania spp. to be present among 30 bacterial candidates related to the lean group. Fifteen candidates were classified Firmicutes, one was classified as Chloroflexi, and the others were not classified. 45 Microbacteriaceae, 11 uncultured Actinobacterium, and 3 other families were present among the 119 candidate OTUs related to obesity. We conclude that the presence of Firmicutes and Actinobacteria may be related to the BMI of the subject.

Technical Aspects of Nominal Partitions on Accuracy of Data Mining Classification of Intestinal Microbiota - Comparison between 7 Restriction Enzymes

The application of data mining analyses (DM) is effective for the quantitative classification of human intestinal microbiota (HIM). However, there remain various technical problems that must be overcome. This paper deals with the number of nominal partitions (NP) of the target dataset, which is a major technical problem. We used here terminal restriction fragment length polymorphism data, which was obtained from the feces of 92 Japanese men. Data comprised operational taxonomic units (OTUs) and subject smoking and drinking habits, which were effectively classified by two NP (2-NP; Yes or No). Using the same OTU data, 3-NP and 5-NP were examined here and results were obtained, focusing on the accuracies of prediction, and the reliability of the selected OTUs by DM were compared to the former 2-NP. Restriction enzymes for PCR were further affected by the accuracy and were compared with 7 enzymes. There were subjects who possess HIM at the border zones of partitions, and the greater the number of partitions, the lower the obtained DM accuracy. The application of balance nodes boosted and duplicated the data, and was able to improve accuracy. More accurate and reliable DM operations are applicable to the classification of unknown subjects for identifying various characteristics, including disease.

Applying Data Mining to Classify Age by Intestinal Microbiota in 92 Healthy Men Using a Combination of Several Restriction Enzymes for T-RFLP Experiments

The composition of the intestinal microbiota was measured following consumption of identical meals for 3 days in 92 Japanese men, and terminal restriction fragment length polymorphism (T-RFLP) was used to analyze their feces. The obtained operational taxonomic units (OTUs) and the subjects' ages were classified by using Data mining (DM) software that compared these data with continuous data and for 5 partitions for age divided at 5 years intervals between the ages of 30 and 50. The DM provided Decision trees in which the selected OTUs were closely related to the ages of the subjects. DM was also used to compare the OTUs from the T-RFLP data with seven restriction enzymes (two enzymes of 516f-BslI and 516f-HaeIII, two enzymes of 27f-MspI and 27f-AluI, three enzymes of 35f-HhaI, 35f-MspI and 35f-AluI) and their various combinations. The OTUs delivered from the five enzyme-digested partitions were analyzed to classify their age clusters. For use in future DM processing, we discussed the enzymes that were effective for accurate classification. We selected two OTUs (HA624 and HA995) that were useful for classifying the subject's ages. Depending on the 16S rRNA sequences of the OTUs, Ruminicoccus obeum clones 1-4 were present in 18 of 36 bacterial candidates in the older age group-related OTU (HA624). On the other hand, Ruminicoccus obeum clones 1-33 were present in 65 of 269 candidates in the younger age group-related OUT (HA995).

Comparison of the accuracy and mechanism of data mining identification of the intestinal microbiota with 7 restriction enzymes

The intestinal microbiota compositions of 92 Japanese men were identified following consumption of identical meals for 3 days, and collected feces were analyzed through terminal restriction fragment length polymorphism. The obtained operational taxonomic units (OTUs) and subjects’ smoking and drinking habits, which had 2 nominal partitions, yes or no, were analyzed by Data mining software. Identification of subjects for each habit was successfully performed and reported previously, but the identification accuracy was closely dependent on the species of the applied restriction enzymes for PCR. For the sake of better selection of enzymes and understanding the mechanisms of Data mining analysis, 516f-BslI and 516f-HaeIII, 27f-MspI and 27f-AluI and 35f-HhaI, 35f-MspI and 35f-AluI, altogether 7 enzymes, were examined comparatively. Data mining analysis provides a Decision tree for identification of subjects and their dividing pathways that is produced using a limited number of OTUs, which affects the accuracy of the results. The present report discusses not only a global comparison of accuracies for characteristics, but also the detailed mechanisms that result in better or worse results and the practical roles and functions of OTUs. The OTU at the 1st step of the constructed Decision tree was the most important for any identification, and for all cases, the combination of subsequent OTUs, which formed later in the Decision tree, was also unignorable. Detailed dividing pathways were traced and compared for the 7 enzymes and the future supporting ideas were provided for better Data mining analysis of the human intestinal microbiota.