Gene-environment association of an ITGB2 sequence variant with obesity in ethnic Japanese

Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity

Background and Objectives: A subject with diabetes and obesity is a class of the metabolic disorder. The current investigation aimed to elucidate the potential biomarker and prognostic targets in subjects with diabetes and obesity. Materials and Methods: The next-generation sequencing (NGS) data of GSE132831 was downloaded from Gene Expression Omnibus (GEO) database. Functional enrichment analysis of DEGs was conducted with ToppGene. The protein-protein interactions network, module analysis, target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed. Furthermore, hub genes were validated by receiver operating characteristic (ROC) analysis. A total of 872 DEGs, including 439 up-regulated genes and 433 down-regulated genes were observed. Results: Second, functional enrichment analysis showed that these DEGs are mainly involved in the axon guidance, neutrophil degranulation, plasma membrane bounded cell projection organization and cell activation. The top ten hub genes (MYH9, FLNA, DCTN1, CLTC, ERBB2, TCF4, VIM, LRRK2, IFI16 and CAV1) could be utilized as potential diagnostic indicators for subjects with diabetes and obesity. The hub genes were validated in subjects with diabetes and obesity. Conclusion: This investigation found effective and reliable molecular biomarkers for diagnosis and prognosis by integrated bioinformatics analysis, suggesting new and key therapeutic targets for subjects with diabetes and obesity.

Transcriptomics analysis of differentially expressed genes in subcutaneous and perirenal adipose tissue of sheep as affected by their pre- and early postnatal malnutrition histories

BACKGROUND

Early life malnutrition is known to target adipose tissue with varying impact depending on timing of the insult. This study aimed to identify differentially expressed genes in subcutaneous (SUB) and perirenal (PER) adipose tissue of 2.5-years old sheep to elucidate the biology underlying differential impacts of late gestation versus early postnatal malnutrition on functional development of adipose tissues. Adipose tissues were obtained from 37 adult sheep born as twins to dams fed either NORM (fulfilling energy and protein requirements), LOW (50% of NORM) or HIGH (110% of protein and 150% of energy requirements) diets in the last 6-weeks of gestation. From day 3 to 6 months of age, lambs were fed high-carbohydrate-high-fat (HCHF) or moderate low-fat (CONV) diets, and thereafter the same moderate low-fat diet.

RESULTS

The gene expression profile of SUB in the adult sheep was not affected by the pre- or early postnatal nutrition history. In PER, 993 and 186 differentially expressed genes (DEGs) were identified in LOW versus HIGH and NORM, respectively, but no DEG was found between HIGH and NORM. DEGs identified in the mismatched pre- and postnatal nutrition groups LOW-HCHF (101) and HIGH-HCHF (192) were largely downregulated compared to NORM-CONV. Out of 831 DEGs, 595 and 236 were up- and downregulated in HCHF versus CONV, respectively. The functional enrichment analyses revealed that transmembrane (ion) transport activities, motor activities related to cytoskeletal and spermatozoa function (microtubules and the cytoskeletal motor protein, dynein), and responsiveness to the (micro) environmental extracellular conditions, including endocrine and nervous stimuli were enriched in the DEGs of LOW versus HIGH and NORM. We confirmed that mismatched pre- and postnatal feeding was associated with long-term programming of adipose tissue remodeling and immunity-related pathways. In agreement with phenotypic measurements, early postnatal HCHF feeding targeted pathways involved in kidney cell differentiation, and mismatched LOW-HCHF sheep had specific impairments in cholesterol metabolism pathways.

CONCLUSIONS

Both pre- and postnatal malnutrition differentially programmed (patho-) physiological pathways with implications for adipose functional development associated with metabolic dysfunctions, and PER was a major target.

Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies

Obesity is an excess accumulation of body fat. Its progression rate has remained high in recent years. Therefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. The gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Functional enrichment analysis was performed. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then protein-protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. The module analysis was performed based on the whole PPI network. We finally filtered out STAT3, CORO1C, SERPINH1, MVP, ITGB5, PCM1, SIRT1, EEF1G, PTEN and RPS2 hub genes. Hub genes were validated by ICH analysis, receiver operating curve (ROC) analysis and RT-PCR. Finally a molecular docking study was performed to find small drug molecules. The robust DEGs linked with the development of obesity were screened through the expression profile, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

Identification of gene coexpression modules, hub genes, and pathways related to spinal cord injury using integrated bioinformatics methods

Spinal cord injury (SCI) is characterized by dramatic neurons loss and axonal regeneration suppression. The underlying mechanism associated with SCI-induced immune suppression is still unclear. Weighted gene coexpression network analysis (WGCNA) is now widely applied for the identification of the coexpressed modules, hub genes, and pathways associated with clinic traits of diseases. We performed this study to identify hub genes associated with SCI development. Gene Expression Omnibus (GEO) data sets GSE45006 and GSE20907 were downloaded and the significant correlativity and connectivity between them were detected using WGCNA. Three significant consensus modules, including 567 eigengenes, were identified from the master GSE45006 data following the preconditions of approximate scale-free topology for WGCNA. Further bioinformatics analysis showed these eigengenes were involved in inflammatory and immune responses in SCI. Three hub genes Rac2, Itgb2, and Tyrobp and one pathway "natural killer cell-mediated cytotoxicity" were identified following short time-series expression miner, protein-protein interaction network, and functional enrichment analysis. Gradually upregulated expression patterns of Rac2, Itgb2, and Tyrobp genes at 0, 3, 7, and 14 days after SCI were confirmed based on GSE45006 and GSE20907 data set. Finally, we found that Rac2, Itgb2, and Tyrobp genes might take crucial roles in SCI development through the "natural killer cell-mediated cytotoxicity" pathway.

CD18 deficiency improves liver injury in the MCD model of steatohepatitis

Neutrophils and macrophages are important constituents of the hepatic inflammatory infiltrate in non-alcoholic steatohepatitis. These innate immune cells express CD18, an adhesion molecule that facilitates leukocyte activation. In the context of fatty liver, activation of infiltrated leukocytes is believed to enhance hepatocellular injury. The objective of this study was to determine the degree to which activated innate immune cells promote steatohepatitis by comparing hepatic outcomes in wild-type and CD18-mutant mice fed a methionine-choline-deficient (MCD) diet. After 3 weeks of MCD feeding, hepatocyte injury, based on serum ALT elevation, was 40% lower in CD18-mutant than wild-type mice. Leukocyte infiltration into the liver was not impaired in CD18-mutant mice, but leukocyte activation was markedly reduced, as shown by the lack of evidence of oxidant production. Despite having reduced hepatocellular injury, CD18-mutant mice developed significantly more hepatic steatosis than wild-type mice after MCD feeding. This coincided with greater hepatic induction of pro-inflammatory and lipogenic genes as well as a modest reduction in hepatic expression of adipose triglyceride lipase. Overall, the data indicate that CD18 deficiency curbs MCD-mediated liver injury by limiting the activation of innate immune cells in the liver without compromising intrahepatic cytokine activation. Reduced liver injury occurs at the expense of increased hepatic steatosis, which suggests that in addition to damaging hepatocytes, infiltrating leukocytes may influence lipid homeostasis in the liver.

An integrative systems genetics approach reveals potential causal genes and pathways related to obesity

Background

Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach.

Methods

Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data.

Results

Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate < 0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B.

Conclusions

To our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-015-0229-0) contains supplementary material, which is available to authorized users.

Low high-density lipoprotein cholesterol level is a significant risk factor for development of type 2 diabetes: Data from the Hawaii-Los Angeles-Hiroshima study

Aims/Introduction

A low level of high‐density lipoprotein cholesterol (HDLC) is a common feature of metabolic syndrome. We have reported that Japanese–Americans who share a virtually identical genetic makeup with native Japanese, but who have lived Westernized lifestyles for decades, have lower HDLC levels and a high prevalence of type 2 diabetes compared with native Japanese. However, the impact of low HDLC level on type 2 diabetes is unclear. The aims of the present study were to evaluate whether serum HDLC level was associated with development of type 2 diabetes and if the effect might be modified by lifestyle.

Materials and Methods

We examined 1,133 non‐diabetic Japanese–Americans and 1,072 non‐diabetic Japanese, who underwent the 75‐g oral glucose tolerance test (OGTT) and were followed for an average of 8.8 and 7.0 years, respectively. We analyzed whether serum HDLC level is a risk factor for development of type 2 diabetes based on the Cox proportional hazards model.

Results

After adjustment for age and sex, hazard ratios for development of type 2 diabetes per unit of serum HDLC level (mmol/L) were 0.292 (95% confidence interval [CI] 0.186–0.458, P < 0.0001) among Japanese–Americans and 0.551 (95% CI 0.375–0.88, P = 0.0023) among native Japanese. Comparable hazard ratios after further adjustment for category of OGTT and body mass index were 0.981 (95% CI 0.970–0.993, P = 0.0018) and 0.991 (95% CI 0.980–1.002, P = 0.112), respectively.

Conclusions

HDLC level was associated with development of type 2 diabetes in both Japanese–Americans and native Japanese. However, these results suggest that the impact of high‐density lipoprotein on glucose metabolism might be affected by lifestyle.

Network graph analysis of gene-gene interactions in genome-wide association study data

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene (GxG) interactions. However, the biological interpretation of GxG interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified GxG interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.