Genetic variants including markers from the exome chip and metabolite traits of type 2 diabetes

A network analysis framework of genetic and nongenetic risks for type 2 diabetes

Both genetic and nongenetic factors have been found to be associated with type 2 diabetes, however, the correlation between them is still unclear. In the present study, we aimed to fully decipher the nongenetic and genetic factor association network for type 2 diabetes. We identified risk factors for type 2 diabetes by systematically searching for related meta-analyses and genome-wide association studies (GWAS) database. Among a total of 27,822 studies screened, 202 articles were eligible, from which 174 nongenetic factors and 210 genetic factors associated with type 2 diabetes were identified. Then, we obtained 584 associations between the nongenetic and genetic factors of type 2 diabetes, based on which a risk factor association network was conducted. The nongenetic factors could be classified into seven categories according to the Global Burden of Diseases (GBD). Of these seven categories of nongenetic factors, five were found to be correlated with genes associated with type 2 diabetes, including environmental risks, behavioral risks, metabolic risks, related disease of type 2 diabetes, and treatments. Specifically, air pollutants of environmental risks, alcohol using of behavioral risks, obesity of metabolic risks, rheumatoid arthritis of related disease risk, and simvastatin of treatment was correlated with the largest number of genes. In summary, the correlation between genetic factors and nongenetic factors identified in this study indicates that there is a common phenotype-genotype association in type 2 diabetes, with the combinations of genotypes ("genetic signature") clustering in phenotypes related to type 2 diabetes. Thus, we should take a systematic approach to explore the relationship of various factors for type 2 diabetes, as well as other noncommunicable diseases.

A Novel Metabolism-Related Signature as a Candidate Prognostic Biomarker for Hepatocellular Carcinoma

PURPOSE

Given that metabolic reprogramming has been recognized as an essential hallmark of cancer cells, this study sought to investigate the potential prognostic values of metabolism-related genes (MRGs) for the diagnosis and treatment of hepatocellular carcinoma (HCC).

METHODS

In total, 2752 metabolism-related gene sequencing data of HCC samples with clinical information were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). One hundred and seventy-eight the differentially expressed MRGs were identified from the ICGC cohort and TCGA cohort. Then, univariate Cox regression analysis was performed to identify these genes that were related to overall survival (OS). A novel metabolism-related prognostic signature was developed using the least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analyses in the ICGC dataset. The Broad Institute's Connectivity Map (CMap) was used in predicting which compounds on the basis of the prognostic MRGs. Furthermore, the signature was validated in the TCGA dataset. Finally, the expression levels of hub genes were validated in HCC cell lines by Western blotting (WB) and quantitative real-time PCR (qRT-PCR).

RESULTS

We found that 17 MRGs were most significantly associated with OS in HCC. Then, the Lasso and multivariate Cox regression analyses were applied to construct the novel metabolism-relevant prognostic signature, which consisted of six MRGs. The prognostic value of this prognostic model was further successfully validated in the TCGA dataset. Further analysis indicated that this particular signature could be an independent prognostic indicator after adjusting to other clinical factors. Six MRGs (FLVCR1, MOGAT2, SLC5A11, RRM2, COX7B2, and SCN4A) showed high prognostic performance in predicting HCC outcomes. Candidate drugs that aimed at hub ERGs were identified. Finally, hub genes were chosen for validation and the protein, mRNA expression of FLVCR1, SLC5A11, and RRM2 were significantly increased in human HCC cell lines compared to normal human hepatic cell lines, which were in agreement with the results of differential expression analysis.

CONCLUSION

Our data provided evidence that the metabolism-related signature could serve as a reliable prognostic and predictive tool for OS in patients with HCC.

Intake and metabolism of omega-3 and omega-6 polyunsaturated fatty acids: nutritional implications for cardiometabolic diseases

Prospective observational studies support the use of long-chain omega-3 polyunsaturated fatty acids (PUFAs) in the primary prevention of atherosclerotic cardiovascular disease; however, randomised controlled trials, have often reported neutral findings. There is a long history of debate about the potential harmful effects of a high intake of omega-6 PUFAs, although this idea is not supported by prospective observational studies or randomised controlled trials. Health effects of PUFAs might be influenced by Δ-5 and Δ-6 desaturases, the key enzymes in the metabolism of PUFAs. The activity of these enzymes and modulation by variants in encoding genes (FADS1-2-3 gene cluster) are linked to several cardiometabolic traits. This Review will further consider non-genetic determinants of desaturase activity, which have the potential to modify the availability of PUFAs to tissues. Finally, we discuss the consequences of altered desaturase activity in the context of PUFA intake, that is, gene-diet interactions and their clinical and public health implications.

Identification of novel non-synonymous variants associated with type 2 diabetes-related metabolites in Korean population

Metabolome-genome wide association studies (mGWASs) are useful for understanding the genetic regulation of metabolites in complex diseases, including type 2 diabetes (T2D). Numerous genetic variants associated with T2D-related metabolites have been identified in previous mGWASs; however, these analyses seem to have difficulty in detecting the genetic variants with functional effects. An exome array focussed on potentially functional variants is an alternative platform to obtain insight into the genetics of biochemical conversion processes. In the present study, we performed an mGWAS using 27,140 non-synonymous variants included in the Illumina HumanExome BeadChip and nine T2D-related metabolites identified by a targetted metabolomics approach to evaluate 2,338 Korean individuals from the Korea Association REsource (KARE) cohort. A linear regression analysis controlling for age, sex, BMI, and T2D status as covariates was performed to identify novel non-synonymous variants associated with T2D-related metabolites. We found significant associations between glycine and CPS1 (rs1047883) and PC ae C36:0 and CYP4F2 (rs2108622) variants (P<2.05 × 10⁻⁷, after the Bonferroni correction for multiple testing). One of the two significantly associated variants, rs1047883 was newly identified whereas rs2108622 had been previously reported to be associated with T2D-related traits. These findings expand our understanding of the genetic determinants of T2D-related metabolites and provide a basis for further functional validation.

The Emerging Role and Promise of Circular RNAs in Obesity and Related Metabolic Disorders

Circular RNAs (circRNAs) are genome transcripts that are produced from back-splicing of specific regions of pre-mRNA. These single-stranded RNA molecules are widely expressed across diverse phyla and many of them are stable and evolutionary conserved between species. Growing evidence suggests that many circRNAs function as master regulators of gene expression by influencing both transcription and translation processes. Mechanistically, circRNAs are predicted to act as endogenous microRNA (miRNA) sponges, interact with functional RNA-binding proteins (RBPs), and associate with elements of the transcriptional machinery in the nucleus. Evidence is mounting that dysregulation of circRNAs is closely related to the occurrence of a range of diseases including cancer and metabolic diseases. Indeed, there are several reports implicating circRNAs in cardiovascular diseases (CVD), diabetes, hypertension, and atherosclerosis. However, there is very little research addressing the potential role of these RNA transcripts in the occurrence and development of obesity. Emerging data from in vitro and in vivo studies suggest that circRNAs are novel players in adipogenesis, white adipose browning, obesity, obesity-induced inflammation, and insulin resistance. This study explores the current state of knowledge on circRNAs regulating molecular processes associated with adipogenesis and obesity, highlights some of the challenges encountered while studying circRNAs and suggests some perspectives for future research directions in this exciting field of study.

Identification of novel biomarkers affecting the metastasis of colorectal cancer through bioinformatics analysis and validation through qRT-PCR

Background

Tumor progression and distant metastasis are the main causes of deaths in colorectal cancer (CRC) patients, and the molecular mechanisms in CRC metastasis have not been completely discovered.

Methods

We identified differentially expressed genes (DEGs) and lncRNAs (DELs) of CRC from The Cancer Genome Atlas (TCGA) database. Then we conducted the weighted gene co-expression network analysis (WGCNA) to investigate co-expression modules related with CRC metastasis. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, DEG-DEL co-expression network and survival analyses of significant modules were also conducted. Finally, the expressions of selected biomarkers were validated in cell lines by quantitative real-time PCR (qRT-PCR).

Results

2032 DEGs and 487 DELs were involved the construction of WGCNA network, and greenyellow, turquoise and brown module were identified to have more significant correlation with CRC metastasis. GO and KEGG pathway analysis of these three modules have proven that the functions of DEGs were closely involved in many important processes in cancer pathogenesis. Through the DEG-DEL co-expression network, 12 DEGs and 2 DELs were considered as hub nodes. Besides, survival analysis showed that 30 DEGs were associated with the overall survival of CRC. Then 10 candidate biomarkers were chosen for validation and the expression of CA2, CHP2, SULT1B1, MOGAT2 and C1orf115 were significantly decreased in CRC cell lines when compared to normal human colonic epithelial cells, which were consistent with the results of differential expression analysis. Especially, low expression of SULT1B1, MOGAT2 and C1orf115 were closely correlated with poorer survival of CRC.

Conclusion

This study identified 5 genes as new biomarkers affecting the metastasis of CRC. Besides, SULT1B1, MOGAT2 and C1orf115 might be implicated in the prognosis of CRC patients.

Relationships of Non-coding RNA with diabetes and depression

In order to study the molecular differences between type 2 diabetes mellitus (T2DM) and T2DM with depression (DD), we aimed to screen the differential expression of lncRNA, mRNA, and circRNA in the blood of patients with T2DM and DD. Based on the self-rating depression scale (SDS), patient health questionnaire 9 (PHQ9), blood glucose and HbA1c, we divided the patients into T2DM and DD group. Peripheral blood was collected from the two groups of patients to perform lncRNA, mRNA, and circRNA expression profiling and screening DD-related specific molecules. Subsequently, bioinformatics analysis was performed to investigate the functions of differentially expressed genes (DEgenes). Finally, RT-PCR and lncRNA-mRNA regulatory network was performed to verify the expressions of lncRNAs and mRNAs related to the occurrence and development of DD. 28 lncRNAs, 107 circRNAs, and 89 mRNAs were identified in DD differential expression profiles. GO and pathway analysis found that 20 biological process (BP) related entities and 20 pathways associated with DD. The analysis shows that the genes that are differentially expressed in the DD group involved in the development of the neuropsychiatric system, immunity, and inflammation. Then, we screening for the important DElncRNA and mRNA associated with DD were verified by RT-PCR experiments and the results of RT-PCR were consistent with the sequencing results. LncRNA, circRNA, and mRNA differential expression profiles exist in DD patients compared with T2DM. The lncRNA-mRNA regulatory network analysis confirmed the crosslinking and complex regulation patterns of lncRNA and mRNA expression and verified the authenticity of the regulatory network.

Identification of novel alleles associated with insulin resistance in childhood obesity using pooled-DNA genome-wide association study approach

Background:

Recently, we witnessed great progress in the discovery of genetic variants associated with obesity and type 2 diabetes (T2D), especially in adults. Much less is known regarding genetic variants associated with insulin resistance (IR). We hypothesized that novel IR genes could be efficiently detected in a population of obese children and adolescents who may not exhibit comorbidities and other confounding factors.

Objectives:

This study aimed to determine whether a genome-wide association study (GWAS), using a DNA-pooling approach, could identify novel genes associated with IR.

Subjects:

The pooled-DNA GWAS analysis included Slovenian obese children and adolescents with and without IR matched for body mass index, gender and age. A replication study was conducted in another independent cohort with or without IR.

Methods:

For the pooled-DNA GWAS, we used HumanOmni5-Quad SNP array (Illumina). Allele frequency distributions were compared with modified t-tests and χ²-tests and ranked using PLINK. Top single nucleotide polymorphisms (SNPs) were validated using individual genotyping by high-resolution melting analysis and TaqMan assay.

Results:

We identified five top-ranking SNPs from the pooled-DNA GWAS analysis within the ECE1, IL1R2, GNPDA1, HLA-J and PYGB loci. All except SNP rs9261108 (HLA-J locus) were confirmed in the validation phase using individual genotyping. The SNP rs2258617 within PYGB remained statistically significant for both recessive and additive models in both cohorts and in a merged analysis of both cohorts and present the strongest novel candidate gene for IR.

Conclusion:

We report for the first time a pooled-DNA GWAS approach to identify five novel SNPs or genes for IR in a paediatric population. The four loci confirmed in the second validation phase study warrant further studies, especially the strongest SNP rs2258617 within PYGB, and provide targets for further basic research of IR mechanisms and for the development of potential new IR and T2D therapies.