Quantitative candidate gene association studies of metabolic traits in Han Chinese type 2 diabetes patients

Epigenome wide association study in peripheral blood of pregnant women identifies potential metabolic pathways related to gestational diabetes

Gestational diabetes mellitus (GDM) increases the risk of developing metabolic disorders in both pregnant women and their offspring. Factors such as nutrition or the intrauterine environment may play an important role, through epigenetic mechanisms, in the development of GDM. The aim of this work is to identify epigenetic marks involved in the mechanisms or pathways related to gestational diabetes. A total of 32 pregnant women were selected, 16 of them with GDM and 16 non-GDM. DNA methylation pattern was obtained from Illumina Methylation Epic BeadChip, from peripheral blood samples at the diagnostic visit (26-28 weeks). Differential methylated positions (DMPs) were extracted using ChAMP and limma package in R 2.9.10, with a threshold of FDR <0.05, deltabeta >|5|% and B >0. A total of 1.141 DMPs were found, and 714 were annotated in genes. A functional analysis was performed, and we found 23 genes significantly related to carbohydrate metabolism. Finally, a total of 27 DMPs were correlated with biochemical variables such as glucose levels at different points of oral glucose tolerance test, fasting glucose, cholesterol, HOMAIR and HbA1c, at different visits during pregnancy and postpartum. Our results show that there is a differentiated methylation pattern between GDM and non-GDM. Furthermore, the genes annotated to the DMPs could be implicated in the development of GDM as well as in alterations in related metabolic variables.

Genomic signatures of selection, local adaptation and production type characterisation of East Adriatic sheep breeds

BACKGROUND

The importance of sheep breeding in the Mediterranean part of the eastern Adriatic has a long tradition since its arrival during the Neolithic migrations. Sheep production system is extensive and generally carried out in traditional systems without intensive systematic breeding programmes for high uniform trait production (carcass, wool and milk yield). Therefore, eight indigenous Croatian sheep breeds from eastern Adriatic treated here as metapopulation (EAS), are generally considered as multipurpose breeds (milk, meat and wool), not specialised for a particular type of production, but known for their robustness and resistance to certain environmental conditions. Our objective was to identify genomic regions and genes that exhibit patterns of positive selection signatures, decipher their biological and productive functionality, and provide a "genomic" characterization of EAS adaptation and determine its production type.

RESULTS

We identified positive selection signatures in EAS using several methods based on reduced local variation, linkage disequilibrium and site frequency spectrum (eROHi, iHS, nSL and CLR). Our analyses identified numerous genomic regions and genes (e.g., desmosomal cadherin and desmoglein gene families) associated with environmental adaptation and economically important traits. Most candidate genes were related to meat/production and health/immune response traits, while some of the candidate genes discovered were important for domestication and evolutionary processes (e.g., HOXa gene family and FSIP2). These results were also confirmed by GO and QTL enrichment analysis.

CONCLUSIONS

Our results contribute to a better understanding of the unique adaptive genetic architecture of EAS and define its productive type, ultimately providing a new opportunity for future breeding programmes. At the same time, the numerous genes identified will improve our understanding of ruminant (sheep) robustness and resistance in the harsh and specific Mediterranean environment.

Discovery of type 2 diabetes mellitus with correlation and optimization driven hybrid deep learning approach

Diabetes mellitus is a severe condition that has the potential to impair strength. The disease known as diabetes mellitus, which is a chronic condition, is brought on by a significant rise in blood glucose levels. The diagnosis of this condition is made using a variety of chemical and physical testing. Diabetes, however, can harm the organs if it goes undetected. This study develops a hybrid deep-learning technique to recognize Type 2 diabetes mellitus. The data is cleaned up at the pre-processing stage using a data transformation technique based on the Yeo-Jhonson transformation. The tanimoto similarity is used in the feature selection process to select the best features from the data. To prepare data for future processing, data augmentation is performed. The Deep Residual Network and the Rider-based Neural Network are recommended and trained separately for the T2DM identification using the Competitive Multi-Verse Rider Optimizer. The outputs generated by the RideNN and DRN classifiers are blended using correlation-based fusion. The suggested CMVRO-based NN-DRN has shown improved performance with the highest accuracy of 91.4%, sensitivity of 94.8%, and specificity of 90.1%.

Association of glucokinase gene and glucokinase regulatory protein gene polymorphisms with gestational diabetes mellitus: A case-control study

OBJECTIVES

The aim of this study was to investigate the association of glucokinase (GCK) gene, glucokinase regulatory protein (GCKR) gene polymorphisms with the susceptibility to GDM in Chinese population.

RESEARCH DESIGN AND METHODS

This case-control study included 835 GDM patients and 870 non-diabetic pregnant women who had their prenatal examinations at 24-28 gestational weeks at the Maternal and Child Health Hospital of Hubei Province from January 15, 2018 to March 31, 2019. The nurses were trained to collect clinical information and blood samples. The candidate single nucleotide polymorphism (SNPs, GCK rs1799884, rs4607517, rs10278336, rs2268574, rs730497 and GCKR rs780094, rs1260326) were genotyped on Sequenom Massarray platform. Statistical analysis including independent sample t test, chi-square test, logistic regression and one-way ANOVA were performed to evaluate the differences in allele and genotype distributions and their correlations with the odds of GDM.

RESULTS

There were statistically significant differences in age, pre-gestational BMI, education level and family history of diabetes between case and control group (P < 0.05). After adjusting for these confounders, GCK rs1799884 was still significantly associated with GDM (P < 0.05), but there were no significant associations between rs4607517, rs10278336 and rs2268574, rs780094 and rs1260326 polymorphisms and GDM odds (P > 0.05). In addition, the pregnant women with rs4607517 TT genotype had the significantly higher fasting blood glucose level than CC genotype (P < 0.05).

CONCLUSION

GCK rs1799884 mutation is associated with higher GDM odds in Chinese population. Further larger studies are needed to explore the association between GCK and GCKR polymorphisms and GDM susceptibility.

Glucose-Related Traits and Risk of Migraine—A Potential Mechanism and Treatment Consideration

Migraine and glucose-related (glycaemic) traits (fasting glucose, fasting insulin, and type 2 diabetes) are common and complex comorbid disorders that cause major economic and social burdens on patients and their families. Studies on the relationship between migraine and glucose-related traits have yielded inconsistent results. The purpose of this review is to synthesise and discuss the information from the available literature on the relationship between fasting glucose, fasting insulin, and type 2 diabetes (T2D) with migraine. Publications on migraine and fasting glucose, migraine and fasting insulin, and migraine and T2D were identified from a PubMed and Google Scholar database search and reviewed for this article. Multiple publications have suggested that the comorbidity of migraine and glucose-related traits may have a similar complex pathogenic mechanism, including impaired glucose homeostasis, insulin resistance, reduced cerebrovascular reactivity, abnormal brain metabolism, shared genetic factors, neurotransmitters, and sex hormones. Furthermore, several studies have found a bi-directional link between migraine with insulin resistance and T2D. There is strong evidence for a biological association between migraine headache and glucose-related traits, and burgeoning evidence for shared genetic influences. Therefore, genetic research into these comorbid traits has the potential to identify new biomarkers and therapeutic targets and provide biological insight into their relationships. We encourage healthcare professionals to consider the co-occurrence of migraine with glucose-related traits in the evaluation and treatment of their patients.

Catch-up growth in juvenile rats, fat expansion, and dysregulation of visceral adipose tissue

BACKGROUND

Accelerated catch-up growth following intrauterine restriction increases the risk of developing visceral adiposity and metabolic abnormalities. However, the underlying molecular mechanisms of such metabolic programming are still poorly understood.

METHODS

A Wistar rat model of catch-up growth following intrauterine restriction was used. A gene expression array was performed in the retroperitoneal adipose tissue sampled at postnatal day (PD) 42.

RESULTS

Five hundred and forty-six differentially expressed genes (DEGs) were identified (adjusted p value < 0.05). Gene ontology enrichment analysis identified pathways related to immune and lipid metabolic processes, brown fat cell differentiation, and regulation of PI3K. Ccl21, Npr3, Serpina3n, Pnpla3, Slc2a4, and Serpina12 were validated to be upregulated in catch-up pups (all p < 0.01) and related to several fat expansion and metabolic parameters, including body weight at PD42, postnatal body weight gain, white and brown adipose tissue mass, plasma triglycerides, and insulin resistance index (all p < 0.05).

CONCLUSIONS

Genes related to immune and metabolic processes were upregulated in retroperitoneal adipose tissue following catch-up growth in juvenile rats and were found to be associated with fat expansion and metabolic parameters. Our results provide evidence for several dysregulated genes in white adipose tissue that could help develop novel strategies to prevent the metabolic abnormalities associated with catch-up growth.

IMPACT

Catch-up growth presents several dysregulated genes in white adipose tissue related to metabolic abnormalities. Ccl21, Npr3, Serpina3n, Pnpla3, Slc2a4, and Serpina12 were validated to be upregulated in catch-up pups and related to visceral fat expansion and metabolic parameters. Profiling and validation of these dysregulated genes in visceral adipose tissue could help develop novel strategies to prevent the metabolic abnormalities associated with catch-up growth.

Potential role of the ABCG2-Q141K polymorphism in type 2 diabetes

Type 2 diabetes mellitus (T2DM) is a complex metabolic disease and variations in multispecific membrane transporter functions may affect T2DM development, complications or treatment. In this work we have analyzed the potential effects of a major polymorphism, the Q141K variant of the ABCG2 transporter in T2DM. The ABCG2 protein is a multispecific xeno- and endobiotic transporter, affecting drug metabolism and playing a key role in uric acid extrusion. The ABCG2-Q141K variant, with reduced expression level and function, is present in 15-35% of individuals, depending on the genetic background of the population, and has been shown to significantly affect gout development. Several other diseases, including hypertension, chronic renal failure, and T2DM have also been reported to be associated with high serum uric acid levels, suggesting that ABCG2 may also play a role in these conditions. In this work we have compared relatively small cohorts (n = 203) of T2DM patients (n = 99) and healthy (n = 104) individuals regarding the major laboratory indicators of T2DM and determined the presence of the SNP rs2231142 (C421A), resulting the ABCG2-Q141K protein variant. We found significantly higher blood glucose and HbA1c levels in the T2DM patients carrying the ABCG2-Q141K variant. These findings may emphasize the potential metabolic role of ABCG2 in T2DM and indicate that further research should explore how prevention and treatment of this disease may be affected by the frequent polymorphism of ABCG2.

Integrated analysis of probability of type 2 diabetes mellitus with polymorphisms and methylation of KCNQ1 gene: A nested case-control study

BACKGROUND

To estimate the associations between single-nucleotide polymorphisms (SNPs) and methylation of KCNQ1 gene and type 2 diabetes mellitus (T2DM) risk and the interactions among SNPs, methylation, and environmental factors on T2DM risk.

METHODS

We genotyped five SNPs and tested methylation at 39 CpG loci of KCNQ1 in 290 T2DM cases and 290 matched controls nested in the Rural Chinese Cohort Study. Conditional logistic regression model was used to estimate the associations between SNPs and KCNQ1 methylation and T2DM risk. Multifactor dimensionality reduction (MDR) analysis was used to estimate the effect of the interactions SNPs-SNPs, SNPs-methylation, methylation-methylation and SNPs, and methylation-environment on T2DM risk.

RESULTS

Probability of T2DM was decreased with rs2283228 of KCNQ1 (CA vs AA, odds ratio [OR] = 0.65, 95% confidence interval [CI] 0.42-0.99). T2DM probability was significantly increased with rs2237895 combined with hypertriglyceridemia (OR_eg  = 2.76, 95% CI 1.35-5.62), with hypertension (OR_eg  = 2.23, 95% CI 1.25-3.98), and with body mass index (BMI; OR_eg  = 1.93, 95% CI 1.12-3.34). T2DM probability was associated with methylation of CG11 and CG41 (OR = 1.89, 95% CI 1.23-2.89, P = .003). It was significantly associated with the interaction between BMI, hypertriglyceridemia, and CG5 methylation (P = .028 and .028), and the combined effects of CG11 with hypertriglyceridemia and hypertension. On MDR analysis, no significant interaction was observed.

CONCLUSION

T2DM probability was reduced 35% with rs2283228 polymorphism. It was associated with rs2237895 combined with hypertension, with BMI and with hypertriglyceridemia. The methylation at two CpG loci of KCNQ1 significantly increased T2DM risk by 89%.

Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis

Objectives Adipocytes and adipocyte lipid metabolism are closely related with obesity and type 2 diabetes, but the molecular mechanism still needs further investigation. The aim of this study is to discover the adipocyte genes and pathways involved in obesity and type 2 diabetes using bioinformatics analysis.

Methods The GSE27951 gene expression profile was obtained. Software and online tools (STRING, Cytoscape, BioGPS, CTD, and FunRich) were used to identify core genes.21 human subcutaneous adipose samples, with 10 from type 2 diabetic patients and 11 from normal controls, were included in these analyses.

Results 184 differentially expressed genes (DEGs) including 42 up-regulated genes and 142 down-regulated genes were found to be enriched in metabolism, receptor activity, collagen type IV and glutamine biosynthesis I pathway by using the enrichment analysis. Seven hub genes were identified from the PPI network using various software (Cytoscape, STRING, BioGPS, and CTD). Four core genes (COL4A2, ACACB, GLUL, and CD36) were found to be highly expressed in subcutaneous adipose tissue of obese patients accompanying type 2 diabetes.

Conclusion COL4A2, ACACB, GLUL and CD36 might be the core molecular biomarkers of obesity in patients with or without type 2 diabetes.

Omics Approaches in Adipose Tissue and Skeletal Muscle Addressing the Role of Extracellular Matrix in Obesity and Metabolic Dysfunction

Extracellular matrix (ECM) remodeling plays important roles in both white adipose tissue (WAT) and the skeletal muscle (SM) metabolism. Excessive adipocyte hypertrophy causes fibrosis, inflammation, and metabolic dysfunction in adipose tissue, as well as impaired adipogenesis. Similarly, disturbed ECM remodeling in SM has metabolic consequences such as decreased insulin sensitivity. Most of described ECM molecular alterations have been associated with DNA sequence variation, alterations in gene expression patterns, and epigenetic modifications. Among others, the most important epigenetic mechanism by which cells are able to modulate their gene expression is DNA methylation. Epigenome-Wide Association Studies (EWAS) have become a powerful approach to identify DNA methylation variation associated with biological traits in humans. Likewise, Genome-Wide Association Studies (GWAS) and gene expression microarrays have allowed the study of whole-genome genetics and transcriptomics patterns in obesity and metabolic diseases. The aim of this review is to explore the molecular basis of ECM in WAT and SM remodeling in obesity and the consequences of metabolic complications. For that purpose, we reviewed scientific literature including all omics approaches reporting genetic, epigenetic, and transcriptomic (GWAS, EWAS, and RNA-seq or cDNA arrays) ECM-related alterations in WAT and SM as associated with metabolic dysfunction and obesity.

Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis

Type 2 diabetes mellitus (T2DM) is a multifactorial and multigenetic disease, and its pathogenesis is complex and largely unknown. In the present study, microarray data (GSE201966) of β-cell enriched tissue obtained by laser capture microdissection were downloaded, including 10 control and 10 type 2 diabetic subjects. A comprehensive bioinformatics analysis of microarray data in the context of protein-protein interaction (PPI) networks was employed, combined with subcellular location information to mine the potential candidate genes for T2DM and provide further insight on the possible mechanisms involved. First, differential analysis screened 108 differentially expressed genes. Then, 83 candidate genes were identified in the layered network in the context of PPI via network analysis, which were either directly or indirectly linked to T2DM. Of those genes obtained through literature retrieval analysis, 27 of 83 were involved with the development of T2DM; however, the rest of the 56 genes need to be verified by experiments. The functional analysis of candidate genes involved in a number of biological activities, demonstrated that 46 upregulated candidate genes were involved in ‘inflammatory response’ and ‘lipid metabolic process’, and 37 downregulated candidate genes were involved in ‘positive regulation of cell death’ and ‘positive regulation of cell proliferation’. These candidate genes were also involved in different signaling pathways associated with ‘PI3K/Akt signaling pathway’, ‘Rap1 signaling pathway’, ‘Ras signaling pathway’ and ‘MAPK signaling pathway’, which are highly associated with the development of T2DM. Furthermore, a microRNA (miR)-target gene regulatory network and a transcription factor-target gene regulatory network were constructed based on miRNet and NetworkAnalyst databases, respectively. Notably, hsa-miR-192-5p, hsa-miR-124-5p and hsa-miR-335-5p appeared to be involved in T2DM by potentially regulating the expression of various candidate genes, including procollagen C-endopeptidase enhancer 2, connective tissue growth factor and family with sequence similarity 105, member A, protein phosphatase 1 regulatory inhibitor subunit 1 A and C-C motif chemokine receptor 4. Smad5 and Bcl6, as transcription factors, are regulated by ankyrin repeat domain 23 and transmembrane protein 37, respectively, which might also be used in the molecular diagnosis and targeted therapy of T2DM. Taken together, the results of the present study may offer insight for future genomic-based individualized treatment of T2DM and help determine the underlying molecular mechanisms that lead to T2DM.

A Systematically Assembled Signature of Genes to be Deep-Sequenced for Their Associations with the Blood Pressure Response to Exercise

: Background: Exercise is one of the best nonpharmacologic therapies to treat hypertension. The blood pressure (BP) response to exercise is heritable. Yet, the genetic basis for the antihypertensive effects of exercise remains elusive. Methods: To assemble a prioritized gene signature, we performed a systematic review with a series of Boolean searches in PubMed (including Medline) from earliest coverage. The inclusion criteria were human genes in major BP regulatory pathways reported to be associated with: (1) the BP response to exercise; (2) hypertension in genome-wide association studies (GWAS); (3) the BP response to pharmacotherapy; (4a) physical activity and/or obesity in GWAS; and (4b) BP, physical activity, and/or obesity in non-GWAS. Included GWAS reports disclosed the statistically significant thresholds used for multiple testing. Results: The search yielded 1422 reports. Of these, 57 trials qualified from which we extracted 11 genes under criteria 1, 18 genes under criteria 2, 28 genes under criteria 3, 27 genes under criteria 4a, and 29 genes under criteria 4b. We also included 41 genes identified from our previous work. Conclusions: Deep-sequencing the exons of this systematically assembled signature of genes represents a cost and time efficient approach to investigate the genomic basis for the antihypertensive effects of exercise.

Identification of Key Candidate Genes and Pathways for Relationship between Ovarian Cancer and Diabetes Mellitus Using Bioinformatical Analysis

Ovarian cancer is one of the three major gynecologic cancers in the world. The aim of this study is to find the relationship between ovarian cancer and diabetes mellitus by using the genetic screening technique. By GEO database query and related online tools of analysis, we analyzed 185 cases of ovarian cancer and 10 control samples from GSE26712, and a total of 379 different genes were identified, including 104 up-regulated genes and 275 down-regulated genes. The up-regulated genes were mainly enriched in biological processes, including cell adhesion, transcription of nucleic acid and biosynthesis, and negative regulation of cell metabolism. The down-regulated genes were enriched in cell proliferation, migration, angiogenesis and macromolecular metabolism. Protein-protein interaction was analyzed by network diagram and module synthesis analysis. The top ten hub genes (CDC20, H2AFX, ENO1, ACTB, ISG15, KAT2B, HNRNPD, YWHAE, GJA1 and CAV1) were identified, which play important roles in critical signaling pathways that regulate the process of oxidation-reduction reaction and carboxylic acid metabolism. CTD analysis showed that the hub genes were involved in 1,128 distinct diseases (bonferroni-corrected P<0.05). Further analysis by drawing the Kaplan-Meier survival curve indicated that CDC20 and ISG15 were statistically significant (P<0.05). In conclusion, glycometabolism was related to ovarian cancer and genes and proteins in glycometabolism could serve as potential targets in ovarian cancer treatment.

Genetic, Functional, and Immunological Study of ZnT8 in Diabetes

Zinc level in the body is finely regulated to maintain cellular function. Dysregulation of zinc metabolism may induce a variety of diseases, e.g., diabetes. Zinc participates in insulin synthesis, storage, and secretion by functioning as a "cellular second messenger" in the insulin signaling pathway and glucose homeostasis. The highest zinc concentration is in the pancreas islets. Zinc accumulation in cell granules is manipulated by ZnT8, a zinc transporter expressed predominately in pancreatic α and β cells. A common ZnT8 gene (SLC30A8) polymorphism increases the risk of type 2 diabetes mellitus (T2DM), and rare mutations may present protective effects. In type 1 diabetes mellitus (T1DM), autoantibodies show specificity for binding two variants of ZnT8 (R or W at amino acid 325) dictated by a polymorphism in SLC30A8. In this review, we summarize the structure, feature, functions, and polymorphisms of ZnT8 along with its association with diabetes and explore future study directions.

Management of hyperuricemia and gout in obese patients undergoing bariatric surgery

BACKGROUND

Hyperuricemia and gout represent important issues in the obese patients. Considering the epidemic trend of overweight and obesity in developed countries, the impact of these conditions is likely to increase. At present, bariatric surgery represents the most effective treatment for the management of severe obesity for reducing weight and the impact of associated comorbidities, but its effects on hyperuricemia and gout have not been fully elucidated.

METHODS

In this narrative review, we discuss the current knowledge about hyperuricemia and gout in obese patients undergoing bariatric surgery. We also suggest a useful approach to prevent gouty attacks in the perioperative period.

RESULTS

Weight loss seems to reduce hyperuricemia in the long-term follow-up, but there is evidence also of a high frequency of acute attacks early after surgery in patients with a diagnosis of gout.

CONCLUSION

Bariatric surgery has a high impact on hyperuricemia and gout. A perioperative approach is suggested, based on appropriate hydration, early physical resumption, urate lowering drugs and nonsteroidal anti-inflammatory drugs (NSAIDs), or colchicine and corticosteroids if NSAIDs are ineffective or not tolerated.

Impact of KCNQ1, CDKN2A/2B, CDKAL1, HHEX, MTNR1B, SLC30A8, TCF7L2, and UBE2E2 on risk of developing type 2 diabetes in Thai population

BACKGROUND

Several type 2 diabetes (T2D) susceptibility loci identified via genome-wide association studies were found to be replicated among various populations. However, the influence of these loci on T2D in Thai population is unknown. The aim of this study was to investigate the influence of eight single nucleotide polymorphisms (SNPs) reported in GWA studies on T2D and related quantitative traits in Thai population.

METHODS

Eight SNPs in or near the KCNQ1, CDKN2A/2B, SLC30A8, HHEX, CDKAL1, TCF7L2, MTNR1B, and UBE2E2 genes were genotyped. A case-control association study comprising 500 Thai patients with T2D and 500 ethnically-matched control subjects was conducted. Associations between SNPs and T2D were examined by logistic regression analysis. The impact of these SNPs on quantitative traits was examined by linear regression among case and control subjects.

RESULTS

Five SNPs in KCNQ1 (rs2237892), CDK2A/2B (rs108116610, SLC30A8 (rs13266634), TCF7L2 (rs7903146) and MTNR1B (rs1387153) were found to be marginally associated with risk of developing T2D, with odds ratios ranging from 1.43 to 2.02 (p = 0.047 to 3.0 × 10-4) with adjustments for age, sex, and body mass index. Interestingly, SNP rs13266634 of SLC30A8 gene reached statistical significance after correcting for multiple testing (p = 0.0003) (p < 0.006 after Bonferroni correction). However, no significant association was detected between HHEX (rs1111875), CDKAL1 (rs7756992), or UBE2E2 (rs7612463) and T2D. We also observed association between rs10811661 and both waist circumference and waist-hip ratio (p = 0.007 and p = 0.023, respectively). In addition, rs13266634 in SLC30A8 was associated with glycated hemoglobin (p = 0.018), and rs7903146 in TCF7L2 was associated with high-density lipoprotein cholesterol level (p = 0.023).

CONCLUSION

Of the eight genes included in our analysis, significant association was observed between KCNQ1, CDKN2A/2B, SLC30A8, TCF7L2, and MTNR1B loci and T2D in our Thai study population. Of these, CDKN2A/2B, SLC30A8, and TCF7L2 genes were also significantly associated with anthropometric, glycemic and lipid characteristics. Larger cohort studies and meta-analyses are needed to further confirm the effect of these variants in Thai population.

A compendium and functional characterization of mammalian genes involved in adaptation to Arctic or Antarctic environments

Background

Many mammals are well adapted to surviving in extremely cold environments. These species have likely accumulated genetic changes that help them efficiently cope with low temperatures. It is not known whether the same genes related to cold adaptation in one species would be under selection in another species. The aims of this study therefore were: to create a compendium of mammalian genes related to adaptations to a low temperature environment; to identify genes related to cold tolerance that have been subjected to independent positive selection in several species; to determine promising candidate genes/pathways/organs for further empirical research on cold adaptation in mammals.

Results

After a search for publications containing keywords: “whole genome”, “transcriptome or exome sequencing data”, and “genome-wide genotyping array data” authors looked for information related to genetic signatures ascribable to positive selection in Arctic or Antarctic mammalian species. Publications related to Human, Arctic fox, Yakut horse, Mammoth, Polar bear, and Minke whale were chosen. The compendium of genes that potentially underwent positive selection in >1 of these six species consisted of 416 genes. Twelve of them showed traces of positive selection in three species. Gene ontology term enrichment analysis of 416 genes from the compendium has revealed 13 terms relevant to the scope of this study. We found that enriched terms were relevant to three major groups: terms associated with collagen proteins and the extracellular matrix; terms associated with the anatomy and physiology of cilium; terms associated with docking. We further revealed that genes from compendium were over-represented in the lists of genes expressed in the lung and liver.

Conclusions

A compendium combining mammalian genes involved in adaptation to cold environment was designed, based on the intersection of positively selected genes from six Arctic and Antarctic species. The compendium contained 416 genes that have been positively selected in at least two species. However, we did not reveal any positively selected genes that would be related to cold adaptation in all species from our list. But, our work points to several strong candidate genes involved in mechanisms and biochemical pathways related to cold adaptation response in different species.

Electronic supplementary material

The online version of this article (10.1186/s12863-017-0580-9) contains supplementary material, which is available to authorized users.

Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information

BACKGROUND

Diabetes mellitus characterized by hyperglycemia as a result of insufficient production of or reduced sensitivity to insulin poses a growing threat to the health of people. It is a heterogeneous disorder with multiple etiologies consisting of type 1 diabetes, type 2 diabetes, gestational diabetes and so on. Diabetes-associated protein/gene prediction is a key step to understand the cellular mechanisms related to diabetes mellitus. Compared with experimental methods, computational predictions of candidate proteins/genes are cheaper and more effortless. Protein-protein interaction (PPI) data produced by the high-throughput technology have been used to prioritize candidate disease genes/proteins. However, the false interactions in the PPI data seriously hurt computational methods performance. In order to address that particular question, new methods are developed to identify candidate disease genes/proteins via integrating biological data from other sources.

RESULTS

In this study, a new framework called PDMG is proposed to predict candidate disease genes/proteins. First, the weighted networks are building in terms of the combination of the subcellular localization information and PPI data. To form the weighted networks, the importance of each compartment is evaluated based on the number of interacted proteins in this compartment. This is because the very different roles played by different compartments in cell activities. Besides, some compartments are more important than others. Based on the evaluated compartments, the interactions between proteins are scored and the weighted PPI networks are constructed. Second, the known disease genes are extracted from OMIM database as the seed genes to expand disease-specific networks based on the weighted networks. Third, the weighted values between a protein and its neighbors in the disease-related networks are added together and the sum is as the score of the protein. Last but not least, the proteins are ranked based on descending order of their scores. The candidate proteins in the top are considered to be associated with the diseases and are potential disease-related proteins. Various types of data, such as type 2 diabetes-associated genes, subcellular localizations and protein interactions, are used to test PDMG method.

CONCLUSIONS

The results show that the proteins/genes functionally exerting a direct influence over diabetes are consistently placed at the head of the queue. PDMG expands and ranks 445 candidate proteins from the seed set including original 27 type 2 diabetes proteins. Out of the top 27 proteins, 14 proteins are the real type 2 diabetes proteins. The literature extracted from the PubMed database has proved that, out of 13 novel proteins, 8 proteins are associated with diabetes.