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Prediction of Functional Profiles of Gut Microbiota from 16S Rrna

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Prediction of Functional Profiles of Gut Microbiota from 16S Rrna Original Article Advance Publication PredictionJCBNJournal0912-00091880-5086theKyoto,jcbn17-4410.3164/jcbn.17-44Original Society Japanof Article Clinical for Free Biochemistry Radical of Research andfunctional Nutrition Japan profiles of gut microbiota from 16S rRNA metagenomic data provides a more robust evaluation of gut dysbiosis occurring in Japanese type 2 diabetic patients Ryo Inoue,1,† Ryuji OhueKitano,2,† Takamitsu Tsukahara,3 Masashi Tanaka,2 Shinya Masuda,2 Takayuki Inoue,2 Hajime Yamakage,2 Toru Kusakabe,2 Koji Hasegawa,4 Akira Shimatsu5 and Noriko SatohAsahara2,* 1Laboratory of Animal Science, Kyoto Prefectural University, 15 Shimogamo Hangicho, Sakyoku, Kyoto 6068522, Japan 2Department of Endocrinology, Metabolism, and Hypertension, 4Division of Translational Research and 5Clinical Research Institute, National Hospital Organization Kyoto Medical Center, 11 Fukakusa Mukaihatacho, Fushimiku, Kyoto 6128555, Japan 3Kyoto Institute of Nutrition & Pathology, 72 Furuikedani, Tachikawa, Ujitawara, Kyoto 6100231, Japan (Received?? 12 May, 2017; Accepted 3 July, 2017) CreativestrictedvidedWeCopyright2017This assessed the isuse, originalanCommons opendistribution,©whether 2017 workaccess JCBNAttribution gut is article and properlymicrobial reproduction distributed License, cited. functional underwhichin anyprofiles the permitsmedium, terms predicted ofunre- pro- the genomic sequencing across many samples and laboratory work.(3) from 16S rRNA metagenomics differed in Japanese type 2 diabetic To gain insight into the pathophysiological roles of gut patients. A total of 22 Japanese subjects were recruited from our microbial functions in type 2 diabetes, we examined the relation- outpatient clinic in an observational study. Fecal samples were ship between gut bacterial compositions, predictive functional obtained from 12 control and 10 type 2 diabetic subjects. 16S profiles of gut microbial communities, and anthropometric/ rRNA metagenomic data were generated and functional profiles metabolic parameters in Japanese subjects with or without type 2 predicted using “Phylogenetic Investigation of Communities by diabetes. Reconstruction of Unobserved States” software. We measured the parameters of glucose metabolism, gut bacterial taxonomy and Materials and Methods functional profile, and examined the associations in a cross sectional manner. Eleven of 288 “Kyoto Encyclopedia of Genes Subjects. A total of 22 Japanese subjects (13 men and 9 and Genomes” pathways were significantly enriched in diabetic women; mean age 63.1 ± 7.6 years) were recruited from our patients compared with control subjects (p<0.05, q<0.1). The outpatient clinic from January 2016 to February 2016. There were relative abundance of almost all pathways, including the Insulin 12 non-diabetic control subjects and 10 type 2 diabetic patients. signaling pathway and Glycolysis/Gluconeogenesis, showed strong, Details of the subjects recruiting this study were mentioned at positive correlations with hemoglobin A1c (HbA1c) and fasting Table 1. Patients treated with glucose-lowering agents were plasma glucose (FPG) levels. Bacterial taxonomic analysis showed excluded. We measured the parameters of glucose metabolism, that genus Blautia significantly differed between groups and had gut bacterial taxonomy and functional profile in all subjects negative correlations with HbA1c and FPG levels. Our findings without missing data, and examined the associations in a cross- suggest a novel pathophysiological relationship between gut sectional manner. microbial communities and diabetes, further highlighting the Next generation sequencing. Fecal samples were collected significance and utility of combining prediction of functional from the subjects with a stool collection brush and storage tube profiles with ordinal bacterial taxonomic analysis (UMIN Clinical (Wako Pure Chemicals, Osaka, Japan), followed by DNA extrac- Trails Registry number: UMIN000026592). tion and 16S rRNA metagenomic sequencing using the MiSeq platform (Illumina, CA) as previously described.(5) Gut bacterial Key Words: diabetes, dysbiosis, genus Blautia, glucose metabolism, composition analysis was performed as previously reported,(5) prediction of functional profiles except that singletons were removed in the present study. Data analyses. Prediction of functional profiles from 16S IntroductionThe prevalence of type 2 diabetes has increased worldwide, rRNA datasets was conducted using Phylogenetic Investigation of T including in Japan. Accumulating evidence has implicated Communities by Reconstruction of Unobserved States (PICRUSt) dysbiosis of the gut microbiota in the development of type 2 software and the Kyoto Encyclopedia of Genes and Genomes diabetes.(1) However, the functional profiles of the gut microbiota, (KEGG) database release 70.0, according to the tutorial on the including activated/inactivated biological pathways in diabetic official website (http://picrust.github.io/). The pathways involved patients, are yet to be fully elucidated. in Human Diseases were removed, as these pathways apply to Currently, there are two major approaches to evaluate the func- human cells or tissues. tions of gut microbiota by metagenomics: 1) whole-genome Differences in the abundance of bacterial genera or KEGG shotgun metagenomics and 2) computational prediction using data pathways between groups were analyzed using STAMP software of 16S rRNA metagenomics,(2) the former being more accurate but (http://kiwi.cs.dal.ca/Software/STAMP) by Welch’s t test with expensive than the latter, as it requires many more sequence the false detection rate correction.(5) Correlations of the bacterial reads.(3,4) The latter is a recently developed approach that can predict the functions of microbial communities based solely on †These authors contributed equally to this work. the computational analysis of 16S rRNA datasets, effectively *To whom correspondence should be addressed. eliminating the need for both prohibitively expensive meta- Email: [email protected] doi: 10.3164/jcbn.1744 J. Clin. Biochem. Nutr. | Published online: 11 October 2017 | 1–5 Table 1. Patient characteristics Results Type 2 diabetic Variable Control patients A total of 22 Japanese subjects were recruited, and all of N (male/female) 12 (8/4) 10 (5/5) them were included in the entire cross-sectional analyses. The ± ± mean ± SD of BMI, fasting plasma glucose (FPG), and hemo- Age (years) 61.7 6.9 64.9 8.4 2 2 ± ± globin A1c (HbA1c) in diabetic patients were 29.9 ± 6.6 kg/m BMI (kg/m ) 25.6 3.3 29.9 6.6 2 ± ± (control: 25.6 ± 3.3 kg/m ), 6.9 ± 1.6 mM (control: 5.5 ± 0.6 mM), SBP (mmHg) 133 12 132 15 ± ± ± ± ± and 6.8 0.8% or 50.7 8.8 mmol/mol) (control: 5.6 0.2% or DBP (mmHg) 81 68310 ± ± ± 38.1 1.9 mmol/mol), respectively (Table 1). FPG and HbA1c but FPG (mM) 5.5 0.6 6.9 1.6 not BMI was significantly higher in diabetic patients than control HbA1c (%) 5.6 ± 0.2 6.8 ± 0.8 ± ± subjects (p<0.01). HbA1c (mmol/mol) 38.1 1.9 50.7 8.8 In functional analysis, the abundance of 12 KEGG pathways ± ± Total cholesterol (mM) 4.7 0.9 4.7 0.8 was significantly different between groups (Fig. 1). The abundance Triglycerides (mM) 0.9 [0.6, 1.2] 1.0 [0.8, 1.5] of 11 pathways; Glycolysis/Gluconeogenesis (ko00010), Tyrosine HDLcholesterol (mM) 1.8 ± 0.4 1.8 ± 1.0 metabolism (ko00350), Naphthalene degradation (ko00626), LDLcholesterol (mM) 2.5 ± 0.6 2.6 ± 0.7 Insulin signaling pathway (ko04910), Phenylalanine metabolism Proportion (n, %) (ko00360), Butirosin and neomycin biosynthesis (ko00524), taking antidiabetic medication 0, 0.0% 5, 50.0% Drug metabolism-cytochrome P450 (ko00982), Metabolism of taking calcium antagonist 4, 33.3% 3, 30.0% xenobiotics by cytochrome P450 (ko00980), Retinol metabolism taking ACE/ARB 3, 25.0% 2, 20.0% (ko00830), Ethylbenzene degradation (ko00642), and Proximal tubule bicarbonate reclamation (ko04964), were significantly taking statins 1, 8.3% 2, 20.0% ± higher in diabetic patients than in control subjects (p<0.05 and Data are expressed as the mean SD or median (interquartile range). q<0.1). Conversely, the KEGG pathway Oxidative phosphoryla- BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c (NGSP); tion (ko00190) was the only pathway of which abundance was HDL, highdensity lipoprotein; LDL, lowdensity lipoprotein. significantly lower in diabetic patients than in control subjects (p<0.05 and q<0.1). We evaluated the correlation between the abundance of these 12 KEGG pathways and the parameters of glucose metabolism (Fig. 2). The abundance of almost all path- ways, especially the Insulin signaling pathway and Glycolysis/ abundance or KEGG pathways with anthropometric/metabolic Gluconeogenesis, showed significantly strong positive correlations parameters were examined by the Spearman’s test using R soft- with HbA1c (R = 0.73 and 0.73, respectively; Fig. 3). Furthermore, ware (https://www.r-project.org/). P<0.05 and q-value (q) <0.1 the Insulin signaling pathway showed a significantly strong posi- were considered significant. tive correlation with FPG (R = 0.59), and Glycolysis/Gluconeo- Ethics. The study protocol was approved by the Ethics genesis tended to be positively correlated with FPG (R = 0.39) Committee for Clinical Research at the National Hospital Organi- (Fig. 3). zation Kyoto Medical Center, and written informed consents were In bacterial taxonomic analysis, only the abundance of the obtained from all participants (UMIN000026592). genus Blautia was significantly
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