Whole-exome sequencing in maya indigenous families: variant in PPP1R3A is associated with type 2 diabetes

Sex differences in the influence of type 2 diabetes (T2D)-related genes, parental history of T2D, and obesity on T2D development: a case-control study

BACKGROUND

This study investigated the effect of sex and age at type 2 diabetes (T2D) diagnosis on the influence of T2D-related genes, parental history of T2D, and obesity on T2D development.

METHODS

In this case-control study, 1012 T2D cases and 1008 healthy subjects were selected from the Diabetes in Mexico Study database. Participants were stratified by sex and age at T2D diagnosis (early, ≤ 45 years; late, ≥ 46 years). Sixty-nine T2D-associated single nucleotide polymorphisms were explored and the percentage contribution (R²) of T2D-related genes, parental history of T2D, and obesity (body mass index [BMI] and waist-hip ratio [WHR]) on T2D development was calculated using univariate and multivariate logistic regression models.

RESULTS

T2D-related genes influenced T2D development most in males who were diagnosed early (R² = 23.5%; females, R² = 13.5%; males and females diagnosed late, R² = 11.9% and R² = 7.3%, respectively). With an early diagnosis, insulin production-related genes were more influential in males (76.0% of R²) while peripheral insulin resistance-associated genes were more influential in females (52.3% of R²). With a late diagnosis, insulin production-related genes from chromosome region 11p15.5 notably influenced males while peripheral insulin resistance and genes associated with inflammation and other processes notably influenced females. Influence of parental history was higher among those diagnosed early (males, 19.9%; females, 17.5%) versus late (males, 6.4%; females, 5,3%). Unilateral maternal T2D history was more influential than paternal T2D history. BMI influenced T2D development for all, while WHR exclusively influenced males.

CONCLUSIONS

The influence of T2D-related genes, maternal T2D history, and fat distribution on T2D development was greater in males than females.

Analysis of the different characteristics between omental preadipocytes and differentiated white adipocytes using bioinformatics methods

Obesity is emerging as an epidemiological issue, being associated with the onset and progress of various metabolism-related disorders. Obesity is characterized by the white adipose expansion, which encounters white adipocyte hypertrophy and hyperplasia. White adipocyte hyperplasia is defined as adipogenesis with the increase in the number of the white adipocytes from the preadipocytes. Adipogenesis contributes to distributing excess triglycerides among the smaller newly formed adipocytes, reducing the number of hypertrophic adipocytes and secreting anti-inflammatory factor. Therefore, adipogenesis is emerging as a new therapeutic target for the treatment of obesity. In the present study, for a better understanding of the contribution of the alteration of the omental differentiated white adipocytes to the systemic metabolic disorders, we downloaded the mRNA expression profiles from GEO database GSE1657, 328 differentially expressed genes (DEGs) were screened between the undifferentiated preadipocytes (UNDIF) and omental differentiated white adipocytes (DIF). The contributions of the upregulated and downregulated DEGs to the system were performed via the Gene Ontology (GO) analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Protein-Protein Interaction (PPI) network, respectively. The potential contribution of the whole altered genes in the differentiated white adipocytes was explored with the performance of Gene Set Enrichment Analysis (GSEA), especially on the GO analysis, KEGG analysis, hallmark analysis, oncogenic analysis and related miRNA analysis. The output of the current study will shed light on the new targets for the treatment of obesity and obesity-related disorders.

Combined analysis of whole-exome sequencing and RNA sequencing in type 2 diabetes mellitus patients with thirst and fatigue

BACKGROUND

The principal objective of this study was to gain a better understanding of the mechanisms of type 2 diabetes mellitus (T2DM) patients with fatigue (D-T2DM) through exome and transcriptome sequencing.

METHODS

After whole-exome sequencing on peripheral blood of 6 D-T2DM patients, the consensus mutations were screen out and analyzed by a series of bioinformatics analyses. Then, we combined whole-exome sequencing and transcriptome sequencing results to find the important genes that changed at both the DNA and RNA levels.

RESULTS

The results showed that a total of 265,393 mutation sites were found in D-T2DM patients compared with normal individuals, 235 of which were consensus mutations shared with D-T2DM patients. These genes significantly enriched in HIF-1 signaling pathway and sphingolipid signaling pathway. At the RNA level, a total of 375 genes were identified to be differentially expressed. After the DNA-RNA joint analysis, eight genes were screened that changed at both DNA and RNA levels. Among these genes, FUS and LMNA were related to carbohydrate metabolism, energy metabolism, and mitochondrial function. Subsequently, we predicted the herbs, including Qin Pi and Hei Zhi Ma, that might play a therapeutic role in D-T2DM through the SymMap database.

CONCLUSION

These findings have significant implications for understanding the mechanisms of D-T2DM and provide potential targets for D-T2DM diagnosis and treatment.

Dietary Diversification and Specialization in Neotropical Bats Facilitated by Early Molecular Evolution

Dietary adaptation is a major feature of phenotypic and ecological diversification, yet the genetic basis of dietary shifts is poorly understood. Among mammals, Neotropical leaf-nosed bats (family Phyllostomidae) show unmatched diversity in diet; from a putative insectivorous ancestor, phyllostomids have radiated to specialize on diverse food sources including blood, nectar, and fruit. To assess whether dietary diversification in this group was accompanied by molecular adaptations for changing metabolic demands, we sequenced 89 transcriptomes across 58 species and combined these with published data to compare ∼13,000 protein coding genes across 66 species. We tested for positive selection on focal lineages, including those inferred to have undergone dietary shifts. Unexpectedly, we found a broad signature of positive selection in the ancestral phyllostomid branch, spanning genes implicated in the metabolism of all major macronutrients, yet few positively selected genes at the inferred switch to plantivory. Branches corresponding to blood- and nectar-based diets showed selection in loci underpinning nitrogenous waste excretion and glycolysis, respectively. Intriguingly, patterns of selection in metabolism genes were mirrored by those in loci implicated in craniofacial remodeling, a trait previously linked to phyllostomid dietary specialization. Finally, we show that the null model of the widely-used branch-site test is likely to be misspecified, with the implication that the test is too conservative and probably under-reports true cases of positive selection. Our findings point to a complex picture of adaptive radiation, in which the evolution of new dietary specializations has been facilitated by early adaptations combined with the generation of new genetic variation.

A network-based machine-learning framework to identify both functional modules and disease genes

Disease gene identification is a critical step towards uncovering the molecular mechanisms of diseases and systematically investigating complex disease phenotypes. Despite considerable efforts to develop powerful computing methods, candidate gene identification remains a severe challenge owing to the connectivity of an incomplete interactome network, which hampers the discovery of true novel candidate genes. We developed a network-based machine-learning framework to identify both functional modules and disease candidate genes. In this framework, we designed a semi-supervised non-negative matrix factorization model to obtain the functional modules related to the diseases and genes. Of note, we proposed a disease gene-prioritizing method called MapGene that integrates the correlations from both functional modules and network closeness. Our framework identified a set of functional modules with highly functional homogeneity and close gene interactions. Experiments on a large-scale benchmark dataset showed that MapGene performs significantly better than the state-of-the-art algorithms. Further analysis demonstrates MapGene can effectively relieve the impact of the incompleteness of interactome networks and obtain highly reliable rankings of candidate genes. In addition, disease cases on Parkinson's disease and diabetes mellitus confirmed the generalization of MapGene for novel candidate gene identification. This work proposed, for the first time, an integrated computing framework to predict both functional modules and disease candidate genes. The methodology and results support that our framework has the potential to help discover underlying functional modules and reliable candidate genes in human disease.

Whole exome sequencing identifies the novel putative gene variants related with type 2 diabetes in Mizo population, northeast India

The contribution of genes towards T2D development varies among different population groups across the world. It has been reported that a number of loci involved in T2D susceptibility are common across certain population groups, but ethnicity specific variants are also observed. The population of Mizoram has an independent ethnic identity and there are no scientific records about the history of the Mizo people; which makes this ethnic group unique and interesting to study. The aim of the study focuses on the identification of the gene variants which may contribute to T2D susceptibility in Mizo-Mongloid ethnic tribe of North east India through whole exome sequencing. The variants like 328G > C (KRT18), 997G > T (CYP4A11), 2368 T > C (SLC4A3), 508G > A (SLC26A5), 1659C > T (KCNS1), 650C > A (ABCD1) 821A > T (YTHDC2), 931G > T (PINX1), 3280C > A (TNRC6A), 48C > A(TACO1), 6035A > T(LAMA1), 805C > A(ACP7) and 806A > G(ACP7) variants were not reported for any disease in the database and were found to be pathogenic in different insilico analysis softwares. The changes in protein stability upon mutation has been predicted where 35.71% increases the stability of the protein, while 64.28% of the variants decrease the stability of the protein. These findings present the population specific variants which might involve in the susceptibility to T2D in Mizo population. Further, in this study some gene variants have contribution as a possible diagnostic or prognostic marker for other diseases as well, which suggests the need for performing association analysis for different disease manifestations in Mizo population in the near future.

Controlling Ser/Thr protein phosphatase PP1 activity and function through interaction with regulatory subunits

Protein phosphatase 1 is a major Ser/Thr protein phosphatase activity in eukaryotic cells. It is composed of a catalytic polypeptide (PP1C), with little substrate specificity, that interacts with a large variety of proteins of diverse structure (regulatory subunits). The diversity of holoenzymes that can be formed explain the multiplicity of cellular functions under the control of this phosphatase. In quite a few cases, regulatory subunits have an inhibitory role, downregulating the activity of the phosphatase. In this chapter we shall introduce PP1C and review the most relevant families of PP1C regulatory subunits, with particular emphasis in describing the structural basis for their interaction.

Variation in Actionable Pharmacogenetic Markers in Natives and Mestizos From Mexico

The identification and characterization of pharmacogenetic variants in Latin American populations is still an ongoing endeavor. Here, we investigated SNVs on genes listed by the Pharmacogenomics Knowledge Base in 1284 Mestizos and 94 Natives from Mexico. Five institutional cohorts with NGS data were retrieved from different research projects at INMEGEN, sequencing files were filtered for 55 pharmacogenes present in all cohorts to identify novel and known variation. Bioinformatic tools VEP, PROVEAN, and FATHMM were used to assess, in silico, the functional impact of this variation. Next, we focused on 17 genes with actionable variants that have been clinically implemented. Allele frequencies were compared with major continental groups and differences discussed in the scope of a pharmacogenomic impact. We observed a wide genetic variability for known and novel SNVs, the largest variation was on UGT1A > ACE > COMT > ABCB1 and the lowest on APOE and NAT2. Although with allele frequencies around 1%, novel variation was observed in 16 of 17 PGKB genes. In Natives we identified 59 variants and 58 in Mestizos. Several genes did not show novel variation, on CYP2B6, CYP2D6, and CYP3A4 in Natives; and APOE, UGT1A, and VKORC1 in Mestizos. Similarities in allele frequency, comparing major continental groups for VIP pharmacogenes, hint towards a comparable PGx for drugs metabolized by UGT1A1, DPYD, ABCB1, CBR3, COMT, and TPMT; in contrast to variants on CYP3A5 and CYP2B6 for which significant MAF differences were identified. Our observations offer some discernment into the extent of pharmacogenetic variation registered up-to-date in Mexicans and contribute to quantitatively dissect actionable pharmacogenetic variants in Natives and Mestizos.

GWAS for Meat and Carcass Traits Using Imputed Sequence Level Genotypes in Pooled F2-Designs in Pigs

In order to gain insight into the genetic architecture of economically important traits in pigs and to derive suitable genetic markers to improve these traits in breeding programs, many studies have been conducted to map quantitative trait loci. Shortcomings of these studies were low mapping resolution, large confidence intervals for quantitative trait loci-positions and large linkage disequilibrium blocks. Here, we overcome these shortcomings by pooling four large F2 designs to produce smaller linkage disequilibrium blocks and by resequencing the founder generation at high coverage and the F1 generation at low coverage for subsequent imputation of the F2 generation to whole genome sequencing marker density. This lead to the discovery of more than 32 million variants, 8 million of which have not been previously reported. The pooling of the four F2 designs enabled us to perform a joint genome-wide association study, which lead to the identification of numerous significantly associated variant clusters on chromosomes 1, 2, 4, 7, 17 and 18 for the growth and carcass traits average daily gain, back fat thickness, meat fat ratio, and carcass length. We could not only confirm previously reported, but also discovered new quantitative trait loci. As a result, several new candidate genes are discussed, among them BMP2 (bone morphogenetic protein 2), which we recently discovered in a related study. Variant effect prediction revealed that 15 high impact variants for the traits back fat thickness, meat fat ratio and carcass length were among the statistically significantly associated variants.

Whole Genome Sequence, Variant Discovery and Annotation in Mapuche-Huilliche Native South Americans

Whole human genome sequencing initiatives help us understand population history and the basis of genetic diseases. Current data mostly focuses on Old World populations, and the information of the genomic structure of Native Americans, especially those from the Southern Cone is scant. Here we present annotation and variant discovery from high-quality complete genome sequences of a cohort of 11 Mapuche-Huilliche individuals (HUI) from Southern Chile. We found approximately 3.1 × 10⁶ single nucleotide variants (SNVs) per individual and identified 403,383 (6.9%) of novel SNVs events. Analyses of large-scale genomic events detected 680 copy number variants (CNVs) and 4,514 structural variants (SVs), including 398 and 1,910 novel events, respectively. Global ancestry composition of HUI genomes revealed that the cohort represents a sample from a marginally admixed population from the Southern Cone, whose main genetic component derives from Native American ancestors. Additionally, we found that HUI genomes contain variants in genes associated with 5 of the 6 leading causes of noncommunicable diseases in Chile, which may have an impact on the risk of prevalent diseases in Chilean and Amerindian populations. Our data represents a useful resource that can contribute to population-based studies and for the design of early diagnostics or prevention tools for Native and admixed Latin American populations.