Joon Yoon

Gut microbiota Modulated by Probiotics and Garcinia cambogia Extract Correlate with Weight Gain and Adipocyte Sizes in High Fat-Fed Mice

Results of recent studies on gut microbiota have suggested that obesogenic bacteria exacerbate obesity and metabolic dysfunction in the host when fed a high fat diet (HFD). In order to explore obesity-associated bacterial candidates and their response to diet, the composition of faecal bacterial communities was investigated by analyzing 16S rRNA gene sequences in mice. Dietary intervention with probiotics and Garcinia cambogia extract attenuated weight gain and adipocyte size in HFD-fed mice. To identify obesity-causative microbiota, two statistical analyses were performed. Forty-eight bacterial species were found to overlap between the two analyses, indicating the commonly identified species as diet-driven and obesity-associated, which would make them strong candidates for host-microbiome interaction on obesity. Finally, correlation based network analysis between diet, microbe, and host revealed that Clostridium aminophilum, a hyper-ammonia-producing bacterium, was highly correlated with obesity phenotypes and other associated bacteria, and shown to be suppressed by the combination of probiotics and Garcinia cambogia extract. Results of the present study suggest that probiotics and Garcinia cambogia extract alleviate weight gain and adiposity, in part via differentially modulating the composition of gut microbiota in HFD fed mice.

Genome sequence of pacific abalone (Haliotis discus hannai): the first draft genome in family Haliotidae

Abstract Background: Abalones are large marine snails in the family Haliotidae and the genus Haliotis belonging to the class Gastropoda of the phylum Mollusca. The family Haliotidae contains only one genus, Haliotis, and this single genus is known to contain several species of abalone. With 18 additional subspecies, the most comprehensive treatment of Haliotidae considers 56 species valid [1]. Abalone is an economically important fishery and aquaculture animal that is considered a highly prized seafood delicacy. The total global supply of abalone has increased 5-fold since the 1970s and farm production increased explosively from 50 mt to 103 464 mt in the past 40 years. Additionally, researchers have recently focused on abalone given their reported tumor suppression effect. However, despite the valuable features of this marine animal, no genomic information is available for the Haliotidae family and related research is still limited. To construct the H. discus hannai genome, a total of 580-G base pairs using Illumina and Pacbio platforms were generated with 322-fold coverage based on the 1.8-Gb estimated genome size of H. discus hannai using flow cytometry. The final genome assembly consisted of 1.86 Gb with 35 450 scaffolds (>2 kb). GC content level was 40.51%, and the N50 length of assembled scaffolds was 211 kb. We identified 29 449 genes using Evidence Modeler based on the gene information from ab initio prediction, protein homology with known genes, and transcriptome evidence of RNA-seq. Here we present the first Haliotidae genome, H. discus hannai, with sequencing data, assembly, and gene annotation information. This will be helpful for resolving the lack of genomic information in the Haliotidae family as well as providing more opportunities for understanding gastropod evolution.

RNA-seq analysis for detecting quantitative trait-associated genes.

Many recent RNA-seq studies were focused mainly on detecting the differentially expressed genes (DEGs) between two or more conditions. In contrast, only a few attempts have been made to detect genes associated with quantitative traits, such as obesity index and milk yield, on RNA-seq experiment with large number of biological replicates. This study illustrates the linear model application on trait associated genes (TAGs) detection in two real RNA-seq datasets: 89 replicated human obesity related data and 21 replicated Holsteins’ milk production related RNA-seq data. Based on these two datasets, the performance between suggesting methods, such as ordinary regression and robust regression, and existing methods: DESeq2 and Voom, were compared. The results indicate that suggesting methods have much lower false discoveries compared to the precedent two group comparisons based approaches in our simulation study and qRT-PCR experiment. In particular, the robust regression outperforms existing DEG finding method as well as ordinary regression in terms of precision. Given the current trend in RNA-seq pricing, we expect our methods to be successfully applied in various RNA-seq studies with numerous biological replicates that handle continuous response traits.

GRACOMICS: software for graphical comparison of multiple results with omics data

BackgroundAnalysis of large-scale omics data has become more and more challenging due to high dimensionality. More complex analysis methods and tools are required to handle such data. While many methods already exist, those methods often produce different results. To help users obtain more appropriate results (i.e. candidate genes), we propose a tool, GRACOMICS that compares numerous analysis results visually in a more systematic way; this enables the users to easily interpret the results more comfortably.ResultsGRACOMICS has the ability to visualize multiple analysis results interactively. We developed GRACOMICS to provide instantaneous results (plots and tables), corresponding to user-defined threshold values, since there are yet no other up-to-date omics data visualization tools that provide such features. In our analysis, we successfully employed two types of omics data: transcriptomic data (microarray and RNA-seq data) and genomic data (SNP chip and NGS data).ConclusionsGRACOMICS is a graphical user interface (GUI)-based program written in Java for cross-platform computing environments, and can be applied to compare analysis results for any type of large-scale omics data. This tool can be useful for biologists to identify genes commonly found by intersected statistical methods, for further experimental validation.

Genome-wide analysis identifies colonic genes differentially associated with serum leptin and insulin concentrations in C57BL/6J mice fed a high-fat diet

Obesity-induced chronic inflammation is known to increase the risk of ulcerative colitis, Crohn’s disease, and colorectal cancer. Accumulating evidence suggests that leptin and insulin are key molecules linking obesity with diseases of the lower intestine. Here, we identified serum phenotype-associated genes in the colon of diet-induced obese mice as early biomarkers of obesity-associated colonic diseases. C57BL/6J mice were fed with either normal diet (ND, 15% of fat calories) or high-fat diet (HFD, 45% of fat calories) for 8 weeks. Serum concentrations of insulin, insulin-like growth factor-1 (IGF-1), leptin, and adiponectin were measured as obesity-related phenotypic markers. Genome-wide gene expression profiles of colon tissue were determined, followed by statistical analyses to detect differentially expressed and serum phenotype-associated genes. HFD-fed mice showed higher serum concentrations of leptin (P < 0.001) and insulin (P < 0.01) than those in the ND group, whereas serum IGF-1 and adiponectin concentrations did not differ between the two dietary groups. Among differentially expressed genes affected by HFD, 135, 128, 110, and 341 genes were associated with serum levels of leptin, insulin, IGF-1, and adiponectin, respectively. We identified 17 leptin-associated genes and 4 insulin-associated genes that inversely responded to HFD and ND. Among these, leptin-associated Peli3 (Pellino E3 ubiquitin protein ligase family member 3), Creb1 (cAMP responsive element binding protein 1), and Enpp2 (ectonucleotide pyrophosphatase/phosphodiesterase 2, autotaxin) and insulin-associated Centg1 (AGAP2, ArfGAP with GTPase domain) are reported to play a role either in obesity or colonic diseases. mRNA expression of these genes was validated by RT-qPCR. Our data suggest Peli3, Creb1, Enpp2, and Centg1 as potential early biomarker candidates for obesity-induced pathophysiological changes in the colon. Future studies verifying the function of these candidates are needed for the prevention, early detection, and treatment of colon diseases.

Multivariate genome-wide association studies on tenderness of Berkshire and Duroc pig breeds

Genome-wide association studies (GWAS) have been steadily used for identification of genomic links to disease and various economical traits. Of those traits, a tenderness of pork is one of the most important factors in quality evaluation of consumers. In this study, we use two pig breed populations; Berkshire is known for its excellent meat quality and Duroc which is known for its high intramuscular fat content in meat. Multivariate genome-wide association studies (MV-GWAS) was executed to compare SNPs of two pigs to find out what genetic variants occur the tenderness of pork. Through MV-GWAS, we have identified candidate genes and the association of biological pathways involved in the tenderness of pork. From these direct and indirect associations, we displayed the usefulness of simple statistical models and their potential contribution to improving the meat quality of pork. We identified a candidate gene related to the tenderness in only Berkshire. Furthermore, several of the biological pathways involved in tenderness in both Berkshire and Duroc were found. The candidate genes identified in this study will be helpful to use them in breeding programs for improving pork quality.

Deciphering signature of selection affecting beef quality traits in Angus cattle

Artificial selection towards a desired phenotype/trait has modified the genomes of livestock dramatically that generated breeds that greatly differ in morphology, production and environmental adaptation traits. Angus cattle are among the famous cattle breeds developed for superior beef quality. This paper aimed at exploring genomic regions under selection in Angus cattle that are associated with meat quality traits and other associated phenotypes. The whole genome of 10 Angus cattle was compared with 11 Hanwoo (A-H) and 9 Jersey (A-J) cattle breeds using a cross-population composite likelihood ratio (XP-CLR) statistical method. The top 1% of the empirical distribution was taken as significant and annotated using UMD3.1. As a result, 255 and 210 genes were revealed under selection from A–H and A–J comparisons, respectively. The WebGestalt gene ontology analysis resulted in sixteen (A–H) and five (A–J) significantly enriched KEGG pathways. Several pathways associated with meat quality traits (insulin signaling, type II diabetes mellitus pathway, focal adhesion pathway, and ECM-receptor interaction), and feeding efficiency (olfactory transduction, tight junction, and metabolic pathways) were enriched. Genes affecting beef quality traits (e.g., FABP3, FTO, DGAT2, ACS, ACAA2, CPE, TNNI1), stature and body size (e.g., PLAG1, LYN, CHCHD7, RPS20), fertility and dystocia (e.g., ESR1, RPS20, PPP2R1A, GHRL, PLAG1), feeding efficiency (e.g., PIK3CD, DNAJC28, DNAJC3, GHRL, PLAG1), coat color (e.g., MC1-R) and genetic disorders (e.g., ITGB6, PLAG1) were found to be under positive selection in Angus cattle. The study identified genes and pathways that are related to meat quality traits and other phenotypes of Angus cattle. The findings in this study, after validation using additional or independent dataset, will provide useful information for the study of Angus cattle in particular and beef cattle in general.

The transcriptome of early chicken embryos reveals signaling pathways governing rapid asymmetric cellularization and lineage segregation

The phylogenomics and comparative functional genomics of avian species were investigated in the Bird 10,000 Genomes (B10K) project because of the important evolutionary position of birds and their value as a research model. However, the systematic profiling of transcriptional changes prior to oviposition has not been investigated in avian species because of the practical difficulties in obtaining pre-oviposited eggs. In this study, a total of 137 pre-oviposited embryos were collected from hen ovaries and oviducts and subjected to RNA-sequencing analyses. Two waves of chicken zygotic genome activation (ZGA) were observed. Functionally distinct developmental programs involving Notch, MAPK, Wnt and TGFβ signaling were separately detected during cleavage and area pellucida formation. Furthermore, the early stages of chicken development were compared with the human and mouse counterparts, highlighting chicken-specific signaling pathways and gradually analogous gene expression via ZGA. These findings provide a genome-wide understanding of avian embryogenesis and comparisons among amniotes. Summary: Transcriptomic analysis of pre-oviposited avian embryos reveals two distinct programs during early development and a gradual increase in similarity of gene expression in mammalian embryos at equivalent stages.

GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data

BackgroundWith the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants.ResultsHere, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes.ConclusionThe proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X.

Methanobrevibacter attenuation via probiotic intervention reduces flatulence in adult human: A non-randomised paired-design clinical trial of efficacy

Trial design The aim of this study was to investigate which of the gut microbes respond to probiotic intervention, as well as study whether they are associated with gastrointestinal symptoms in a healthy adult human. For the experimental purpose, twenty-one healthy adults were recruited and received probiotic mixture, which is composed of five Lactobacilli strains and two Bifidobacteria strains, once a day for 60 days. Defecation survey and Bioelectrical Impedance Analysis were conducted pre- and post-administration to measure phenotypic differences. Stool samples of the subjects were collected twice. Methods The statistical analysis was performed for pair designed metagenome data with 11 phenotypic records of the bioelectrical impedance body composition analyzer and 6 responses of the questionnaires about gastrointestinal symptom. Furthemore, correlation-based network analysis was conducted for exploring complex relationships among microbiome communities. Results The abundances of Citrobacter, Klebsiella, and Methanobrevibacter were significantly reduced, which are strong candidates to be highly affected by the probiotic administration. In addition, interaction effects were observed between flatulence symptom attenuation and decreasing patterns of the Methanobrevibacter abundance. Conclusions These results reveal that probiotic intervention modulated the composition of gut microbiota and reduced the abundance of potential pathogens (i.e. Citrobacter and Klebsiella). In addition, methanogens (i.e. Methanobrevibacter) associated with the gastrointestinal symptom in an adult human.