There have been few large-scale studies on the relationship between smoking and gut microbiota. We investigated the relationship between smoking status and the composition of gut microbiota. This was a population-based cross-sectional study using Healthcare Screening Center cohort data. A total of 758 men were selected and divided into three groups: never (n = 288), former (n = 267), and current smokers (n = 203). Among the three groups, there was no difference in alpha diversity, however, Jaccard-based beta diversity showed significant difference (p = 0.015). Pairwise permutational multivariate analysis of variance (PERMANOVA) tests between never and former smokers did not show a difference; however, there was significant difference between never and current smokers (p = 0.017) and between former and current smokers (p = 0.011). Weighted UniFrac-based beta diversity also showed significant difference among the three groups (p = 0.038), and pairwise PERMANOVA analysis of never and current smokers showed significant difference (p = 0.01). In the analysis of bacterial composition, current smokers had an increased proportion of the phylum Bacteroidetes with decreased Firmicutes and Proteobacteria compared with never smokers, whereas there were no differences between former and never smokers. In conclusion, gut microbiota composition of current smokers was significantly different from that of never smokers. Additionally, there was no difference in gut microbiota composition between never and former smokers.

Gut microbiota plays an important role in the harvesting, storage, and expenditure of energy obtained from one’s diet. Our cross-sectional study aimed to identify differences in gut microbiota according to body mass index (BMI) in a Korean population. 16S rRNA gene sequence data from 1463 subjects were categorized by BMI into normal, overweight, and obese groups. Fecal microbiotas were compared to determine differences in diversity and functional inference analysis related with BMI. The correlation between genus-level microbiota and BMI was tested using zero-inflated Gaussian mixture models, with or without covariate adjustment of nutrient intake. We confirmed differences between 16Sr RNA gene sequencing data of each BMI group, with decreasing diversity in the obese compared with the normal group. According to analysis of inferred metagenomic functional content using PICRUSt algorithm, a highly significant discrepancy in metabolism and immune functions (P < 0.0001) was predicted in the obese group. Differential taxonomic components in each BMI group were greatly affected by nutrient adjustment, whereas signature bacteria were not influenced by nutrients in the obese compared with the overweight group. We found highly significant statistical differences between normal, overweight and obese groups using a large sample size with or without diet confounding factors. Our informative dataset sheds light on the epidemiological study on population microbiome.

Several recent studies showed that next-generation sequencing (NGS)-based human leukocyte antigen (HLA) typing is a feasible and promising technique for variant calling of highly polymorphic regions. To date, however, no method with sufficient read depth has completely solved the allele phasing issue. In this study, we developed a new method (HLAscan) for HLA genotyping using NGS data. HLAscan performs alignment of reads to HLA sequences from the international ImMunoGeneTics project/human leukocyte antigen (IMGT/HLA) database. The distribution of aligned reads was used to calculate a score function to determine correctly phased alleles by progressively removing false-positive alleles. Comparative HLA typing tests using public datasets from the 1000 Genomes Project and the International HapMap Project demonstrated that HLAscan could perform HLA typing more accurately than previously reported NGS-based methods such as HLAreporter and PHLAT. In addition, the results of HLA-A, −B, and -DRB1 typing by HLAscan using data generated by NextGen were identical to those obtained using a Sanger sequencing–based method. We also applied HLAscan to a family dataset with various coverage depths generated on the Illumina HiSeq X-TEN platform. HLAscan identified allele types of HLA-A, −B, −C, −DQB1, and -DRB1 with 100% accuracy for sequences at ≥ 90× depth, and the overall accuracy was 96.9%. HLAscan, an alignment-based program that takes read distribution into account to determine true allele types, outperformed previously developed HLA typing tools. Therefore, HLAscan can be reliably applied for determination of HLA type across the whole-genome, exome, and target sequences.

Pathophysiological background in different phenotypes of nonalcoholic fatty liver disease (NAFLD) remains to be elucidated. The aim was to investigate the association between fecal and blood microbiota profiles and the presence of NAFLD in obese versus lean subjects. Demographic and clinical data were reviewed in 268 health checkup examinees, whose fecal and blood samples were available for microbiota analysis. NAFLD was diagnosed with ultrasonography, and subjects with NAFLD were further categorized as obese (body mass index (BMI) ≥25) or lean (BMI <25). Fecal and blood microbiota communities were analyzed by sequencing of the V3-V4 domains of the 16S rRNA genes. Correlation between microbiota taxa and NAFLD was assessed using zero-inflated Gaussian mixture models, with adjustment of age, sex, and BMI, and Bonferroni correction. The NAFLD group (n = 76) showed a distinct bacterial community with a lower biodiversity and a far distant phylotype compared with the control group (n = 192). In the gut microbiota, the decrease in Desulfovibrionaceae was associated with NAFLD in the lean NAFLD group (log2 coefficient (coeff.) = -2.107, P = 1.60E-18), but not in the obese NAFLD group (log2 coeff. = 1.440, P = 1.36E-04). In the blood microbiota, Succinivibrionaceae showed opposite correlations in the lean (log2 coeff. = -1.349, P = 5.34E-06) and obese NAFLD groups (log2 coeff. = 2.215, P = 0.003). Notably, Leuconostocaceae was associated with the obese NAFLD in the gut (log2 coeff. = -1.168, P = 0.041) and blood (log2 coeff. = -2.250, P = 1.28E-10). In conclusion, fecal and blood microbiota profiles showed different patterns between subjects with obese and lean NAFLD, which might be potential biomarkers to discriminate diverse phenotypes of NAFLD.

Personality affects fundamental behavior patterns and has been related with health outcomes and mental disorders. Recent evidence has emerged supporting a relationship between the microbiota and behavior, referred to as brain-gut relationships. Here, we first report correlations between personality traits and gut microbiota. This research was performed using the Revised NEO Personality Inventory and the sequencing data of the 16S rRNA gene in 672 adults. The diversity and the composition of the human gut microbiota exhibited significant difference when stratified by personality traits. We found that personality traits were significantly correlated with diversity of gut microbiota, while their differences were extremely subtle. High neuroticism and low conscientiousness groups were correlated with high abundance of Gammaproteobacteria and Proteobacteria, respectively when covariates, including age, sex, BMI and nutrient intake, were controlled. Additionally, high conscientiousness group also showed increased abundance of some universal butyrate-producing bacteria including Lachnospiraceae. This study was of observational and cross-sectional design and our findings must be further validated through metagenomic or metatranscriptomic methodologies, or metabolomics-based analyses. Our findings will contribute to elucidating potential links between the gut microbiota and personality, and provide useful insights toward developing and testing personality- and microbiota-based interventions for promoting health.