Type 2 diabetes: genetic data sharing to advance complex disease research

Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data

In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.

Aged garlic extract preserves beta-cell functioning via modulation of nuclear factor kappa-B (NF-κB)/Toll-like receptor (TLR)-4 and sarco endoplasmic reticulum calcium ATPase (SERCA)/Ca2+ in diabetes mellitus

BACKGROUND

Peripheral insulin resistance and compromised insulin secretion from pancreatic β-cells are significant factors and pathogenic hallmarks of diabetes mellitus (DM). NF-κβ/TLR-4 and SERCA/Ca2+ pathways have been identified as potential pathways regulating insulin synthesis by preserving pancreatic β-cell functioning. The current study aimed to evaluate the therapeutic effect of aged garlic extract (AGE) against DM in a streptozotocin (STZ)-induced rat model with particular emphasis on pancreatic β-cell functioning.

METHODS

AGE was characterized by gas chromatography-mass spectrometry (GC-MS), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) to evaluate its physio-chemical characteristics followed by in-vitro anti-diabetic and antioxidant potential. This was followed by the induction of DM in laboratory animals for investigating the therapeutic action of AGE by evaluating the role of NF-κβ/TLR-4 and the SERCA/Ca2+ pathway. The parameters assessed in the present experimental setup encompassed antioxidant parameters, metabolic indicators, insulin concentration, intracellular calcium levels, apoptotic markers (CCK-8 and Caspase Glo-8), and protein expression (P-62 and APACHE-II).

RESULTS

AGE characterization by SEM, GC-MS, and X-ray diffraction (XRD) revealed the presence of phenylalanine, alliin, S-allylmercaptocysteine (SAMC), tryptophan, 1-methyl-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid as major bioactive constituents of AGE. Metabolic studies, including intraperitoneal glucose tolerance test (IPGTT), revealed significantly lower blood glucose levels in the AGE group compared to the disease control group. In contrast, the intraperitoneal insulin tolerance test (ITT) exhibited no significant difference in insulin sensitivity between the AGE supplementation group and the DM control group. Interestingly, AGE was found to have no significant effect on fasting glucose and serum insulin levels. In contrast, AGE supplementation was found to cause significant hypoglycaemia in postprandial blood glucose and insulin levels. Importantly, AGE causes restoration of intracellular Ca2+ levels by modulation of SERCA/Ca2 functioning and inhibition NF-κB/TLR-4 pathway. AGE was found to interact with and inhibit the DR-5/ caspase-8/3 apoptotic complex. Furthermore, microscopic studies revealed degeneration and apoptotic changes in pancreatic β-cells of the DM control group, while supplementation of AGE resulted in inhibition of apoptotic pathway and regeneration of pancreatic β-cells.

CONCLUSION

The current study suggests that AGE enhance glucose homeostasis by exerting their effects on pancreatic β-cells, without ameliorating peripheral sensitivity. Moreover, AGEs promote an increase in β-cell mass by mitigating the apoptosis of pancreatic β-cells. These findings suggest that AGE could aid in developing a viable alternative therapy for diabetes mellitus (DM).

Disrupting of family with sequence similarity 105, member A (Fam105a) deteriorates pancreatic β-cell physiology and insulin secretion in INS-1 cells

The role of "Family with sequence similarity 105, member A" (FAM105A) in pancreatic β-cell function in relation to type 2 diabetes mellitus (T2D) is not fully understood. To address this issue, various molecular and functional experiments were conducted on primary human islets and INS-1 cells. RNA-seq expression analysis showed that FAM105A is highly expressed in human islets and its expression is reduced in diabetic islets compared to healthy islets. FAM105A expression correlated negatively with HbA1c levels and body mass index (BMI). Co-expression analysis showed a significant correlation between FAM105A with PDX1, GCK, GLUT1 and INSR, but not the INS gene. Silencing of Fam105a impaired insulin release, content, glucose uptake, and mitochondria ATP content but did not affect cell viability, reactive oxygen species (ROS) or apoptosis levels. Silencing of Fam105a was associated with reduced Pdx1 and Glut2 expression at mRNA and protein levels. RNA-seq analysis of dysregulated genes in Fam105a-silenced cells showed an overall downregulation of gene expression in β-cells and insulin secretion pathway. Disrupting Pdx1 did not affect Fam105a expression in INS-1 cells. Overall, the results suggest that FAM105A plays an important role in pancreatic β-cells biology and may be involved in the development of T2D.

Transgenerational transmission of reproductive and metabolic dysfunction in the male progeny of polycystic ovary syndrome

The transgenerational maternal effects of polycystic ovary syndrome (PCOS) in female progeny are being revealed. As there is evidence that a male equivalent of PCOS may exists, we ask whether sons born to mothers with PCOS (PCOS-sons) transmit reproductive and metabolic phenotypes to their male progeny. Here, in a register-based cohort and a clinical case-control study, we find that PCOS-sons are more often obese and dyslipidemic. Our prenatal androgenized PCOS-like mouse model with or without diet-induced obesity confirmed that reproductive and metabolic dysfunctions in first-generation (F₁) male offspring are passed down to F₃. Sequencing of F₁-F₃ sperm reveals distinct differentially expressed (DE) small non-coding RNAs (sncRNAs) across generations in each lineage. Notably, common targets between transgenerational DEsncRNAs in mouse sperm and in PCOS-sons serum indicate similar effects of maternal hyperandrogenism, strengthening the translational relevance and highlighting a previously underappreciated risk of transmission of reproductive and metabolic dysfunction via the male germline.

The role of ChREBP in carbohydrate sensing and NAFLD development

Excessive sugar consumption and defective glucose sensing by hepatocytes contribute to the development of metabolic diseases including type 2 diabetes mellitus (T2DM) and nonalcoholic fatty liver disease (NAFLD). Hepatic metabolism of carbohydrates into lipids is largely dependent on the carbohydrate-responsive element binding protein (ChREBP), a transcription factor that senses intracellular carbohydrates and activates many different target genes, through the activation of de novo lipogenesis (DNL). This process is crucial for the storage of energy as triglycerides in hepatocytes. Furthermore, ChREBP and its downstream targets represent promising targets for the development of therapies for the treatment of NAFLD and T2DM. Although lipogenic inhibitors (for example, inhibitors of fatty acid synthase, acetyl-CoA carboxylase or ATP citrate lyase) are currently under investigation, targeting lipogenesis remains a topic of discussion for NAFLD treatment. In this Review, we discuss mechanisms that regulate ChREBP activity in a tissue-specific manner and their respective roles in controlling DNL and beyond. We also provide in-depth discussion of the roles of ChREBP in the onset and progression of NAFLD and consider emerging targets for NAFLD therapeutics.

Progress in genetics of type 2 diabetes and diabetic complications

Type 2 diabetes results from a complex interaction between genetic and environmental factors. Precision medicine for type 2 diabetes using genetic data is expected to predict the risk of developing diabetes and complications and to predict the effects of medications and life-style intervention more accurately for individuals. Genome-wide association studies (GWAS) have been conducted in European and Asian populations and new genetic loci have been identified that modulate the risk of developing type 2 diabetes. Novel loci were discovered by GWAS in diabetic complications with increasing sample sizes. Large-scale genome-wide association analysis and polygenic risk scores using biobank information is making it possible to predict the development of type 2 diabetes. In the ADVANCE clinical trial of type 2 diabetes, a multi-polygenic risk score was useful to predict diabetic complications and their response to treatment. Proteomics and metabolomics studies have been conducted and have revealed the associations between type 2 diabetes and inflammatory signals and amino acid synthesis. Using multi-omics analysis, comprehensive molecular mechanisms have been elucidated to guide the development of targeted therapy for type 2 diabetes and diabetic complications.

Effect of tissue-grouped regulatory variants associated to type 2 diabetes in related secondary outcomes

Genome-wide association studies have identified over five hundred loci that contribute to variation in type 2 diabetes (T2D), an established risk factor for many diseases. However, the mechanisms and extent through which these loci contribute to subsequent outcomes remain elusive. We hypothesized that combinations of T2D-associated variants acting on tissue-specific regulatory elements might account for greater risk for tissue-specific outcomes, leading to diversity in T2D disease progression. We searched for T2D-associated variants acting on regulatory elements and expression quantitative trait loci (eQTLs) in nine tissues. We used T2D tissue-grouped variant sets as genetic instruments to conduct 2-Sample Mendelian Randomization (MR) in ten related outcomes whose risk is increased by T2D using the FinnGen cohort. We performed PheWAS analysis to investigate whether the T2D tissue-grouped variant sets had specific predicted disease signatures. We identified an average of 176 variants acting in nine tissues implicated in T2D, and an average of 30 variants acting on regulatory elements that are unique to the nine tissues of interest. In 2-Sample MR analyses, all subsets of regulatory variants acting in different tissues were associated with increased risk of the ten secondary outcomes studied on similar levels. No tissue-grouped variant set was associated with an outcome significantly more than other tissue-grouped variant sets. We did not identify different disease progression profiles based on tissue-specific regulatory and transcriptome information. Bigger sample sizes and other layers of regulatory information in critical tissues may help identify subsets of T2D variants that are implicated in certain secondary outcomes, uncovering system-specific disease progression.

Molecular Genetics of Abnormal Redox Homeostasis in Type 2 Diabetes Mellitus

Numerous studies have shown that oxidative stress resulting from an imbalance between the production of free radicals and their neutralization by antioxidant enzymes is one of the major pathological disorders underlying the development and progression of type 2 diabetes (T2D). The present review summarizes the current state of the art advances in understanding the role of abnormal redox homeostasis in the molecular mechanisms of T2D and provides comprehensive information on the characteristics and biological functions of antioxidant and oxidative enzymes, as well as discusses genetic studies conducted so far in order to investigate the contribution of polymorphisms in genes encoding redox state-regulating enzymes to the disease pathogenesis.

Transcriptomic survey of key reproductive and metabolic tissues in mouse models of polycystic ovary syndrome

Excessive androgen production and obesity are key to polycystic ovary syndrome (PCOS) pathogenesis. Prenatal androgenized (PNA), peripubertal androgenized, and overexpression of nerve growth factor in theca cells (17NF) are commonly used PCOS-like mouse models and diet-induced maternal obesity model is often included for comparsion. To reveal the molecular features of these models, we have performed transcriptome survey of the hypothalamus, adipose tissue, ovary and metaphase II (MII) oocytes. The largest number of differentially expressed genes (DEGs) is found in the ovaries of 17NF and in the adipose tissues of peripubertal androgenized models. In contrast, hypothalamus is most affected in PNA and maternal obesity models suggesting fetal programming effects. The Ms4a6e gene, membrane-spanning 4-domains subfamily A member 6E, a DEG identified in the adipose tissue in all mouse models is also differently expressed in adipose tissue of women with PCOS, highlighting a conserved disease function. Our comprehensive transcriptomic profiling of key target tissues involved in PCOS pathology highlights the effects of developmental windows for androgen exposure and maternal obesity, and provides unique resource to investigate molecular mechanisms underlying PCOS pathogenesis.

The contribution of functional HNF1A variants and polygenic susceptibility to risk of type 2 diabetes in ancestrally diverse populations

AIMS/HYPOTHESIS

We examined the contribution of rare HNF1A variants to type 2 diabetes risk and age of diagnosis, and the extent to which their impact is affected by overall genetic susceptibility, across three ancestry groups.

METHODS

Using exome sequencing data of 160,615 individuals of the UK Biobank and 18,797 individuals of the BioMe Biobank, we identified 746 carriers of rare functional HNF1A variants (minor allele frequency ≤1%), of which 507 carry variants in the functional domains. We calculated polygenic risk scores (PRSs) based on genome-wide association study summary statistics for type 2 diabetes, and examined the association of HNF1A variants and PRS with risk of type 2 diabetes and age of diagnosis. We also tested whether the PRS affects the association between HNF1A variants and type 2 diabetes risk by including an interaction term.

RESULTS

Rare HNF1A variants that are predicted to impair protein function are associated with increased risk of type 2 diabetes in individuals of European ancestry (OR 1.46, p=0.049), particularly when the variants are located in the functional domains (OR 1.89, p=0.002). No association was observed for individuals of African ancestry (OR 1.10, p=0.60) or Hispanic-Latino ancestry (OR 1.00, p=1.00). Rare functional HNF1A variants were associated with an earlier age at diagnosis in the Hispanic-Latino population (β=-5.0 years, p=0.03), and this association was marginally more pronounced for variants in the functional domains (β=-5.59 years, p=0.03). No associations were observed for other ancestries (African ancestry β=-2.7 years, p=0.13; European ancestry β=-3.5 years, p=0.20). A higher PRS was associated with increased odds of type 2 diabetes in all ancestries (OR 1.61-2.11, p<10-5) and an earlier age at diagnosis in individuals of African ancestry (β=-1.4 years, p=3.7 × 10-6) and Hispanic-Latino ancestry (β=-2.4 years, p<2 × 10-16). Furthermore, a higher PRS exacerbated the effect of the functional HNF1A variants on type 2 diabetes in the European ancestry population (pinteraction=0.037).

CONCLUSIONS/INTERPRETATION

We show that rare functional HNF1A variants, in particular those located in the functional domains, increase the risk of type 2 diabetes, at least among individuals of European ancestry. Their effect is even more pronounced in individuals with a high polygenic susceptibility. Our analyses highlight the importance of the location of functional variants within a gene and an individual's overall polygenic susceptibility, and emphasise the need for more genetic data in non-European populations.

Vitamin C attenuates predisposition to high-fat diet-induced metabolic dysregulation in GLUT10-deficient mouse model

Background

The development of type 2 diabetes mellitus (T2DM) is highly influenced by complex interactions between genetic and environmental (dietary and lifestyle) factors. While vitamin C (ascorbic acid, AA) has been suggested as a complementary nutritional treatment for T2DM, evidence for the significance and beneficial effects of AA in T2DM is thus far inconclusive. We suspect that clinical studies on the topic might need to account for combination of genetic and dietary factors that could influence AA effects on metabolism. In this study, we tested this general idea using a mouse model with genetic predisposition to diet-induced metabolic dysfunction. In particular, we utilized mice carrying a human orthologous GLUT10^G128E variant (GLUT10^G128E mice), which are highly sensitive to high-fat diet (HFD)-induced metabolic dysregulation. The genetic variant has high relevance to human populations, as genetic polymorphisms in glucose transporter 10 (GLUT10) are associated with a T2DM intermediate phenotype in nondiabetic population.

Results

We investigated the impacts of AA supplementation on metabolism in wild-type (WT) mice and GLUT10^G128E mice fed with a normal diet or HFD. Overall, the beneficial effects of AA on metabolism were greater in HFD-fed GLUT10^G128E mice than in HFD-fed WT mice. At early postnatal stages, AA improved the development of compromised epididymal white adipose tissue (eWAT) in GLUT10^G128E mice. In adult animals, AA supplementation attenuated the predisposition of GLUT10^G128E mice to HFD-triggered eWAT inflammation, adipokine dysregulation, ectopic fatty acid accumulation, metabolic dysregulation, and body weight gain, as compared with WT mice.

Conclusions

Taken together, our findings suggest that AA has greater beneficial effects on metabolism in HFD-fed GLUT10^G128E mice than HFD-fed WT mice. As such, AA plays an important role in supporting eWAT development and attenuating HFD-induced metabolic dysregulation in GLUT10^G128E mice. Our results suggest that proper WAT development is essential for metabolic regulation later in life. Furthermore, when considering the usage of AA as a complementary nutrition for prevention and treatment of T2DM, individual differences in genetics and dietary patterns should be taken into account.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12263-022-00713-y.

Achievements, prospects and challenges in precision care for monogenic insulin-deficient and insulin-resistant diabetes

Integration of genomic and other data has begun to stratify type 2 diabetes in prognostically meaningful ways, but this has yet to impact on mainstream diabetes practice. The subgroup of diabetes caused by single gene defects thus provides the best example to date of the vision of 'precision diabetes'. Monogenic diabetes may be divided into primary pancreatic beta cell failure, and primary insulin resistance. In both groups, clear examples of genotype-selective responses to therapy have been advanced. The benign trajectory of diabetes due to pathogenic GCK mutations, and the sulfonylurea-hyperresponsiveness conferred by activating KCNJ11 or ABCC8 mutations, or loss-of-function HNF1A or HNF4A mutations, often decisively guide clinical management. In monogenic insulin-resistant diabetes, subcutaneous leptin therapy is beneficial in some severe lipodystrophy. Increasing evidence also supports use of 'obesity therapies' in lipodystrophic people even without obesity. In beta cell diabetes the main challenge is now implementation of the precision diabetes vision at scale. In monogenic insulin-resistant diabetes genotype-specific benefits are proven in far fewer patients to date, although further genotype-targeted therapies are being evaluated. The conceptual paradigm established by the insulin-resistant subgroup with 'adipose failure' may have a wider influence on precision therapy for common type 2 diabetes, however. For all forms of monogenic diabetes, population-wide genome sequencing is currently forcing reappraisal of the importance assigned to pathogenic mutations when gene sequencing is uncoupled from prior suspicion of monogenic diabetes.

Association of cluster determinant 36, scavenger receptor class B type 1, and major facilitator superfamily domain containing the 2a genetic polymorphism with serum lipid profile in aging population with type 2 diabetes mellitus

Background

Lipid metabolism disorder commonly happens in subjects with Type 2 diabetes mellitus (T2DM) which may be linked to genetic variants of lipid metabolism-related genes. However, few studies have explored the relationship between lipid metabolism-related gene polymorphism and serum lipid profile in aging subjects with T2DM. The present study was designed to explore the impact of genetic polymorphism of cluster determinant 36 (CD36) (rs1049673, rs1054516, rs2151916), scavenger receptor class B type 1 (SCARB1) (rs5888), and major facilitator superfamily domain containing the 2a (MFSD2A) (rs12083239, rs4233508, rs12072037) on the relationship between circulating lipids in aging subjects with T2DM.

Methods

205 T2DM patients and 205 age and gender matched control subjects were recruited. Information on demographic characteristics was collected by using a self-administered questionnaire. Fasting venous blood samples were taken for lipid-related gene genotyping and serum lipid profile measurement. The Chi-square test was used to compare percentage differences and to calculate P-value for Hardy-Weinberg equilibrium. Logistic regression and multiple linear regression were used to explore the risk or correlation between variables, and general linear model (GLM) was used to compare the means of serum lipids between the groups.

Results

In T2DM group, CD36 rs1054516 and MFSD2A rs12072037 were correlated with serum TC level. In control group, CD36 rs1049673 was correlated with serum HDL-C level. Meanwhile, T2DM subjects with MFSD2A rs12083239 (CG), MFSD2A rs4233508 (TT), and MFSD2A rs12072037 (AA) had higher TG level than control subjects. T2DM subjects with CD36 rs1049673 (CG, GG), CD36 rs1054516 (CT), CD36 rs2151916 (TT, CT), SCARB1 rs5888 (GG), MFSD2A rs12083239 (GG, CG), MFSD2A rs4233508 (TT), and MFSD2A rs12072037 (CA, AA) had lower HDL-C level than control subjects. T2DM subjects with MFSD2A rs12072037 (AA) had lower LDL-C level than control subjects. In dominant model, major genotype (GG) of SCARB1 gene was associated with the risk of T2DM (OR = 0.636, P = 0.032).

Conclusion

The genetic polymorphism of CD36 (rs1049673, rs1054516, rs2151916), SCARB1 (rs5888), and MFSD2A (rs12083239, rs4233508, rs12072037) were associated with serum lipids in T2DM subjects. The SCARB1 rs5888 major genotype (GG) was a protective factor for T2DM. Large scale cohort study is required to determine the relationship between lipid metabolism-related gene polymorphism, serum lipid profile and T2DM in aging subjects.

Genetics of Type 2 Diabetes: Past, Present, and Future

Diabetes has reached epidemic proportions worldwide. Currently, approximately 537 million adults (20–79 years) have diabetes, and the total number of people with diabetes is continuously increasing. Diabetes includes several subtypes. About 80% of all cases of diabetes are type 2 diabetes (T2D). T2D is a polygenic disease with an inheritance ranging from 30 to 70%. Genetic and environment/lifestyle factors, especially obesity and sedentary lifestyle, increase the risk of T2D. In this review, we discuss how studies on the genetics of diabetes started, how they expanded when genome-wide association studies and exome and whole-genome sequencing became available, and the current challenges in genetic studies of diabetes. T2D is heterogeneous with respect to clinical presentation, disease course, and response to treatment, and has several subgroups which differ in pathophysiology and risk of micro- and macrovascular complications. Currently, genetic studies of T2D focus on these subgroups to find the best diagnoses and treatments for these patients according to the principles of precision medicine.

Pathophysiology, phenotypes and management of type 2 diabetes mellitus in Indian and Chinese populations

Nearly half of all adults with type 2 diabetes mellitus (T2DM) live in India and China. These populations have an underlying predisposition to deficient insulin secretion, which has a key role in the pathogenesis of T2DM. Indian and Chinese people might be more susceptible to hepatic or skeletal muscle insulin resistance, respectively, than other populations, resulting in specific forms of insulin deficiency. Cluster-based phenotypic analyses demonstrate a higher frequency of severe insulin-deficient diabetes mellitus and younger ages at diagnosis, lower β-cell function, lower insulin resistance and lower BMI among Indian and Chinese people compared with European people. Individuals diagnosed earliest in life have the most aggressive course of disease and the highest risk of complications. These characteristics might contribute to distinctive responses to glucose-lowering medications. Incretin-based agents are particularly effective for lowering glucose levels in these populations; they enhance incretin-augmented insulin secretion and suppress glucagon secretion. Sodium-glucose cotransporter 2 inhibitors might also lower blood levels of glucose especially effectively among Asian people, while α-glucosidase inhibitors are better tolerated in east Asian populations versus other populations. Further research is needed to better characterize and address the pathophysiology and phenotypes of T2DM in Indian and Chinese populations, and to further develop individualized treatment strategies.

Evaluating human genetic support for hypothesized metabolic disease genes

We investigate the extent to which human genetic data are incorporated into studies that hypothesize novel links between genes and metabolic disease. To lower the barriers to using genetic data, we present an approach to enable researchers to evaluate human genetic support for experimentally determined hypotheses.

Genetics of Type 2 Diabetes: Implications from Large-Scale Studies

PURPOSE OF REVIEW

Type 2 diabetes (T2D) is a multifactorial, heritable syndrome characterized by dysregulated glucose homeostasis that results from impaired insulin secretion and insulin resistance. Genetic association studies have successfully identified hundreds of T2D risk loci implicating many genes in disease pathogenesis. In this review, we provide an overview of the recent T2D genetic studies from the past 3 years with particular focus on the effects of sample size and ancestral diversity on genetic discovery as well as discuss recent work on the use and limitations of genetic risk scores (GRS) for T2D risk prediction.

RECENT FINDINGS

Recent large-scale, multi-ancestry genetic studies of T2D have identified over 500 novel risk loci. The genetic variants (i.e., single nucleotide polymorphisms (SNPs)) marking these novel loci in general have smaller effect sizes than previously discovered loci. Inclusion of samples from diverse ancestral backgrounds shows a few ancestry specific loci marked by common variants, but overall, the majority of loci discovered are common across ancestries. Inclusion of common variant GRS, even with hundreds of loci, does not substantially increase T2D risk prediction over standard clinical risk factors such as age and family history. Common variant association studies of T2D have now identified over 700 T2D risk loci, half of which have been discovered in the past 3 years. These recent studies demonstrate that inclusion of ancestrally diverse samples can enhance locus discovery and improve accuracy of GRS for T2D risk prediction. GRS based on common variants, however, only minimally enhances risk prediction over standard clinical risk factors.

Undergraduate laboratory series that employs a complete polymerase chain reaction-restriction fragment length polymorphism experiment for determination of a single nucleotide polymorphism in CYP2R1 gene

Nowadays, novel Biochemistry lab techniques are being introduced at a very fast pace in scientific research. This requires development of new labs for undergraduate Biochemistry courses to equip the students with up-to-date techniques. However, the time limit of Biochemistry labs for undergraduate students represents a major obstacle. This article presents a clear set of laboratory exercises designed to introduce students to the use of polymerase chain reaction-restriction-fragment length polymorphism (PCR-RFLP) as a means of detection of genetic variants. Three consecutive lab experiments have been designed for the undergraduate students to serve this purpose. The first session was performed in a computer lab (dry lab) where students were taught how to obtain a specific gene sequence, identify an exact single nucleotide polymorphism location, choose the target sequence for amplification, design specific primers for this particular sequence and choose the most suitable restriction enzyme from web tools. The second and third lab sessions were performed as wet labs where in the second lab session, students optimized PCR conditions and performed a successful PCR. The PCR products were kept for use in the third lab session where they utilized the selected restriction enzyme and carried out gel electrophoresis to determine the exact genotype.

Validation and genetic heritability estimation of known type 2 diabetes related variants in the Korean population

Genome-wide association studies (GWASs) facilitated the discovery of countless disease-associated variants. However, GWASs have mostly been conducted in European ancestry samples. Recent studies have reported that these European-based association results may reduce disease prediction accuracy when applied in non-Europeans. Therefore, previously reported variants should be validated in non-European populations to establish reliable scientific evidence for precision medicine. In this study, we validated known associations with type 2 diabetes (T2D) and related metabolic traits in 125,850 samples from a Korean population genotyped by the Korea Biobank Array (KBA). At the end of December 2020, there were 8,823 variants associated with glycemic traits, lipids, liver enzymes, and T2D in the GWAS catalog. Considering the availability of imputed datasets in the KBA genome data, publicly available East-Asian T2D summary statistics, and the linkage disequilibrium among the variants (r2 < 0.2), 2,900 independent variants were selected for further analysis. Among these, 1,837 variants (63.3%) were statistically significant (p ≤ 0.05). Most of the non-replicated variants (n = 1,063) showed insufficient statistical power and decreased minor allele frequencies compared with the replicated variants. Moreover, most of known variants showed <10% genetic heritability. These results could provide valuable scientific evidence for future study designs, the current power of GWASs, and future applications in precision medicine in the Korean population.

The Association between Fasting Glucose and Sugar Sweetened Beverages Intake Is Greater in Latin Americans with a High Polygenic Risk Score for Type 2 Diabetes Mellitus

Chile is one of the largest consumers of sugar-sweetened beverages (SSB) world-wide. However, it is unknown whether the effects from this highly industrialized food will mimic those reported in industrialized countries or whether they will be modified by local lifestyle or population genetics. Our goal is to evaluate the interaction effect between SSB intake and T2D susceptibility on fasting glucose. We calculated a weighted genetic risk score (GRSw) based on 16 T2D risk SNPs in 2828 non-diabetic participants of the MAUCO cohort. SSB intake was categorized in four levels using a food frequency questionnaire. Log-fasting glucose was regressed on SSB and GRSw tertiles while accounting for socio-demography, lifestyle, obesity, and Amerindian ancestry. Fasting glucose increased systematically per unit of GRSw (β = 0.02 ± 0.006, p = 0.00002) and by SSB intake (β[cat4] = 0.04 ± 0.01, p = 0.0001), showing a significant interaction, where the strongest effect was observed in the highest GRSw-tertile and in the highest SSB consumption category (β = 0.05 ± 0.02, p = 0.02). SNP-wise, SSB interacted with additive effects of rs7903146 (TCF7L2) (β = 0.05 ± 0.01, p = 0.002) and with the G/G genotype of rs10830963 (MTNRB1B) (β = 0.19 ± 0.05, p = 0.001). Conclusions: The association between SSB intake and fasting glucose in the Chilean population without diabetes is modified by T2D genetic susceptibility.

Multi-Trait Genomic Risk Stratification for Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) is continuously rising with more disease cases every year. T2DM is a chronic disease with many severe comorbidities and therefore remains a burden for the patient and the society. Disease prevention, early diagnosis, and stratified treatment are important elements in slowing down the increase in diabetes prevalence. T2DM has a substantial genetic component with an estimated heritability of 40-70%, and more than 500 genetic loci have been associated with T2DM. Because of the intrinsic genetic basis of T2DM, one tool for risk assessment is genome-wide genetic risk scores (GRS). Current GRS only account for a small proportion of the T2DM risk; thus, better methods are warranted for more accurate risk assessment. T2DM is correlated with several other diseases and complex traits, and incorporating this information by adjusting effect size of the included markers could improve risk prediction. The aim of this study was to develop multi-trait (MT)-GRS leveraging correlated information. We used phenotype and genotype information from the UK Biobank, and summary statistics from two independent T2DM studies. Marker effects for T2DM and seven correlated traits, namely, height, body mass index, pulse rate, diastolic and systolic blood pressure, smoking status, and information on current medication use, were estimated (i.e., by logistic and linear regression) within the UK Biobank. These summary statistics, together with the two independent training summary statistics, were incorporated into the MT-GRS prediction in different combinations. The prediction accuracy of the MT-GRS was improved by 12.5% compared to the single-trait GRS. Testing the MT-GRS strategy in two independent T2DM studies resulted in an elevated accuracy by 50-94%. Finally, combining the seven information traits with the two independent T2DM studies further increased the prediction accuracy by 34%. Across comparisons, body mass index and current medication use were the two traits that displayed the largest weights in construction of the MT-GRS. These results explicitly demonstrate the added benefit of leveraging correlated information when constructing genetic scores. In conclusion, constructing GRS not only based on the disease itself but incorporating genomic information from other correlated traits as well is strongly advisable for obtaining improved individual risk stratification.

LncRNA NEAT1 regulated diabetic retinal epithelial-mesenchymal transition through regulating miR-204/SOX4 axis

AIM

Epithelial-mesenchymal transition (EMT) of retinal pigment epithelium (RPE) cells is the key of the development of diabetic retinopathy (DR), and lncRNA NEAT1 could accelerate EMT in diabetic nephropathy. Meanwhile, as a diabetes susceptibility gene, whether sex-determining region Y-related (SRY) high-mobility group box 4 (SOX4) has relationship with lncRNA NEAT1 in DR remains unclear.

METHODS

Firstly, NEAT1, SOX4 and miR-204 were evaluated by qRT-PCR (quantitative reverse-transcriptase PCR) under high glucose condition. Then, cell viability, proliferation, migration and invasion were respectively detected by MTT, BrdU staining, wound healing and transwell assay after NEAT1 knockdown or miR-204 overexpression. Also, the EMT-related proteins were examined by western blot and cell immunofluorescence assay. In order to confirm the relationship between miR-204 and NEAT1 or SOX4, dual luciferase reporter gene assay was conducted. At the same time, the protein levels of SOX4 and EMT-related proteins were investigated by immunohistochemistry in vivo.

RESULTS

High glucose upregulated NEAT1 and SOX4 and downregulated miR-204 in ARPE19 cells. NEAT1 knockdown or miR-204 overexpression inhibited the proliferation and EMT progression of ARPE19 cells induced by high glucose. NEAT1 was identified as a molecular sponge of miR-204 to increase the level of SOX4. The effect of NEAT1 knockdown on the progression of EMT under high glucose condition in ARPE19 cells could be reversed by miR-204 inhibitor. Also, NEAT1 knockdown inhibited retinal EMT in diabetic mice.

CONCLUSION

NEAT1 regulated the development of EMT in DR through miR-204/SOX4 pathway, which could provide reference for clinical prevention and treatment.

Re-Programming Autoreactive T Cells Into T-Regulatory Type 1 Cells for the Treatment of Autoimmunity

Systemic delivery of peptide-major histocompatibility complex (pMHC) class II-based nanomedicines can re-program cognate autoantigen-experienced CD4+ T cells into disease-suppressing T-regulatory type 1 (TR1)-like cells. In turn, these TR1-like cells trigger the formation of complex regulatory cell networks that can effectively suppress organ-specific autoimmunity without impairing normal immunity. In this review, we summarize our current understanding of the transcriptional, phenotypic and functional make up of TR1-like cells as described in the literature. The true identity and direct precursors of these cells remain unclear, in particular whether TR1-like cells comprise a single terminally-differentiated lymphocyte population with distinct transcriptional and epigenetic features, or a collection of phenotypically different subsets sharing key regulatory properties. We propose that detailed transcriptional and epigenetic characterization of homogeneous pools of TR1-like cells will unravel this conundrum.

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.

Analysis and Interpretation of the Impact of Missense Variants in Cancer

Large scale genome sequencing allowed the identification of a massive number of genetic variations, whose impact on human health is still unknown. In this review we analyze, by an in silico-based strategy, the impact of missense variants on cancer-related genes, whose effect on protein stability and function was experimentally determined. We collected a set of 164 variants from 11 proteins to analyze the impact of missense mutations at structural and functional levels, and to assess the performance of state-of-the-art methods (FoldX and Meta-SNP) for predicting protein stability change and pathogenicity. The result of our analysis shows that a combination of experimental data on protein stability and in silico pathogenicity predictions allowed the identification of a subset of variants with a high probability of having a deleterious phenotypic effect, as confirmed by the significant enrichment of the subset in variants annotated in the COSMIC database as putative cancer-driving variants. Our analysis suggests that the integration of experimental and computational approaches may contribute to evaluate the risk for complex disorders and develop more effective treatment strategies.

Progress in Defining the Genetic Contribution to Type 2 Diabetes in Individuals of East Asian Ancestry

PURPOSE OF REVIEW

Prevalence of type 2 diabetes (T2D) and progression of complications differ between worldwide populations. While obesity is a major contributing risk factor, variations in physiological manifestations, e.g., developing T2D at lower body mass index in some populations, suggest other contributing factors. Early T2D genetic associations were mostly discovered in European ancestry populations. This review describes the progression of genetic discoveries associated with T2D in individuals of East Asian ancestry in the last 10 years and highlights the shared genetic susceptibility between the population groups and additional insights into genetic contributions to T2D.

RECENT FINDINGS

Through increased sample size and power, new genetic associations with T2D were discovered in East Asian ancestry populations, often with higher allele frequencies than European ancestry populations. As we continue to generate maps of T2D-associated variants across diverse populations, there will be a critical need to expand and diversify other omics resources to enable integration for clinical translation.

Searching Far and Genome-Wide: The Relevance of Association Studies in Amyotrophic Lateral Sclerosis

Genome-wide association studies (GWAS) and rare variant association studies (RVAS) are applied across many areas of complex disease to analyze variation in whole genomes of thousands of unrelated patients. These approaches are able to identify variants and/or biological pathways which are associated with disease status and, in contrast to traditional linkage studies or candidate gene approaches, do so without requiring multigenerational affected families, prior hypotheses, or known genes of interest. However, the novel associations identified by these methods typically have lower effect sizes than those found in classical family studies. In the motor neuron disease amyotrophic lateral sclerosis (ALS), GWAS, and RVAS have been used to identify multiple disease-associated genes but have not yet resulted in novel therapeutic interventions. There is significant urgency within the ALS community to identify additional genetic markers of disease to uncover novel biological mechanisms, stratify genetic subgroups of disease, and drive drug development. Given the widespread and increasing application of genetic association studies of complex disease, it is important to recognize the strengths and limitations of these approaches. Here, we review ALS gene discovery via GWAS and RVAS.

Association of HLA-B Gene Polymorphisms with Type 2 Diabetes in Pashtun Ethnic Population of Khyber Pakhtunkhwa, Pakistan

Human leukocyte antigen (HLA) system is the most polymorphic and gene dense region of human DNA that has shown many disease associations. It has been further divided into HLA classes I, II, and III. Polymorphism in HLA class II genes has been reported to play an important role in the pathogenesis of type 1 diabetes (T1D). It also showed association with T2D in different ethnic populations. However, a little is known about the relationship of HLA class I gene polymorphism and T2D. This study has evaluated the association of HLA-B (class I gene) variants with T2D in Pashtun ethnic population of Khyber Pakhtunkhwa. In the first phase of the study, whole exome sequencing (WES) of 2 pooled DNA samples was carried out, and DNA pools used were constructed from 100 diabetic cases and 100 control subjects. WES results identified a total of n = 17 SNPs in HLA-B gene. In the next phase, first 5 out of n = 17 reported SNPs were genotyped using MassARRAY® system in order to validate WES results and to confirm association of selected SNPs with T2D. Minor allele frequencies (MAFs) and selected SNPs×T2D association were determined using chi-square test and logistic regression analysis. The frequency of minor C allele was significantly higher in the T2D group as compared to control group (45.0% vs. 13.0%) (p = 0.006) for rs2308655 in HLA-B gene. No significant difference in MAF distribution between cases and controls was observed for rs1051488, rs1131500, rs1050341, and rs1131285 (p > 0.05). Binary logistic regression analyses showed significant results for SNP rs2308655 (OR = 2.233, CI (95%) = 1.223-4.077, and p = 0.009), while no considerable association was observed for the other 4 SNPs. However, when adjusted for these variants, the association of rs2308655 further strengthened significantly (adjusted OR = 7.485, CI (95%) = 2.353-23.812, and p = 0.001), except for rs1131500, which has no additive effect. In conclusion, the finding of this study suggests rs2308655 variant in HLA-B gene as risk variant for T2D susceptibility in Pashtun population.

The Liability Threshold Model for Predicting the Risk of Cardiovascular Disease in Patients with Type 2 Diabetes: A Multi-Cohort Study of Korean Adults

Personalized risk prediction for diabetic cardiovascular disease (DCVD) is at the core of precision medicine in type 2 diabetes (T2D). We first identified three marker sets consisting of 15, 47, and 231 tagging single nucleotide polymorphisms (tSNPs) associated with DCVD using a linear mixed model in 2378 T2D patients obtained from four population-based Korean cohorts. Using the genetic variants with even modest effects on phenotypic variance, we observed improved risk stratification accuracy beyond traditional risk factors (AUC, 0.63 to 0.97). With a cutoff point of 0.21, the discrete genetic liability threshold model consisting of 231 SNPs (GLT₂₃₁) correctly classified 87.7% of 2378 T2D patients as high or low risk of DCVD. For the same set of SNP markers, the GLT and polygenic risk score (PRS) models showed similar predictive performance, and we observed consistency between the GLT and PRS models in that the model based on a larger number of SNP markers showed much-improved predictability. In silico gene expression analysis, additional information was provided on the functional role of the genes identified in this study. In particular, HDAC4, CDKN2B, CELSR2, and MRAS appear to be major hubs in the functional gene network for DCVD. The proposed risk prediction approach based on the liability threshold model may help identify T2D patients at high CVD risk in East Asian populations with further external validations.

Unsupervised Clustering of Missense Variants in HNF1A Using Multidimensional Functional Data Aids Clinical Interpretation

Exome sequencing in diabetes presents a diagnostic challenge because depending on frequency, functional impact, and genomic and environmental contexts, HNF1A variants can cause maturity-onset diabetes of the young (MODY), increase type 2 diabetes risk, or be benign. A correct diagnosis matters as it informs on treatment, progression, and family risk. We describe a multi-dimensional functional dataset of 73 HNF1A missense variants identified in exomes of 12,940 individuals. Our aim was to develop an analytical framework for stratifying variants along the HNF1A phenotypic continuum to facilitate diagnostic interpretation. HNF1A variant function was determined by four different molecular assays. Structure of the multi-dimensional dataset was explored using principal component analysis, k-means, and hierarchical clustering. Weights for tissue-specific isoform expression and functional domain were integrated. Functionally annotated variant subgroups were used to re-evaluate genetic diagnoses in national MODY diagnostic registries. HNF1A variants demonstrated a range of behaviors across the assays. The structure of the multi-parametric data was shaped primarily by transactivation. Using unsupervised learning methods, we obtained high-resolution functional clusters of the variants that separated known causal MODY variants from benign and type 2 diabetes risk variants and led to reclassification of 4% and 9% of HNF1A variants identified in the UK and Norway MODY diagnostic registries, respectively. Our proof-of-principle analyses facilitated informative stratification of HNF1A variants along the continuum, allowing improved evaluation of clinical significance, management, and precision medicine in diabetes clinics. Transcriptional activity appears a superior readout supporting pursuit of transactivation-centric experimental designs for high-throughput functional screens.

Pathophysiology of Type 2 Diabetes Mellitus

Type 2 Diabetes Mellitus (T2DM), one of the most common metabolic disorders, is caused by a combination of two primary factors: defective insulin secretion by pancreatic β-cells and the inability of insulin-sensitive tissues to respond appropriately to insulin. Because insulin release and activity are essential processes for glucose homeostasis, the molecular mechanisms involved in the synthesis and release of insulin, as well as in its detection are tightly regulated. Defects in any of the mechanisms involved in these processes can lead to a metabolic imbalance responsible for the development of the disease. This review analyzes the key aspects of T2DM, as well as the molecular mechanisms and pathways implicated in insulin metabolism leading to T2DM and insulin resistance. For that purpose, we summarize the data gathered up until now, focusing especially on insulin synthesis, insulin release, insulin sensing and on the downstream effects on individual insulin-sensitive organs. The review also covers the pathological conditions perpetuating T2DM such as nutritional factors, physical activity, gut dysbiosis and metabolic memory. Additionally, because T2DM is associated with accelerated atherosclerosis development, we review here some of the molecular mechanisms that link T2DM and insulin resistance (IR) as well as cardiovascular risk as one of the most important complications in T2DM.

Commutative regulation between endothelial NO synthase and insulin receptor substrate 2 by microRNAs

Endothelial NO synthase (eNOS) expression is regulated by a number of transcriptional and post-transcriptional mechanisms, but the effects of competing endogenous RNAs (ceRNAs) on eNOS mRNA and the underlying mechanisms are still unknown. Our bioinformatic analysis revealed three highly expressed eNOS-targeting miRNAs (miR-15b, miR-16, and miR-30b) in human endothelial cells (ECs). Among the 1103 mRNA targets of these three miRNAs, 15 mRNAs share a common disease association with eNOS. Gene expression and correlation analysis in patients with cardiovascular diseases identified insulin receptor substrate 2 (IRS2) as the most correlated eNOS-ceRNA. The expression levels of eNOS and IRS2 were coincidentally increased by application of laminar shear but reduced with eNOS or IRS2 siRNA transfection in human ECs, which was impeded by Dicer siRNA treatment. Moreover, luciferase reporter assay showed that these three miRNAs directly target the 3'UTR of eNOS and IRS2. Overexpression of these three miRNAs decreased, whereas inhibition of them increased, both mRNA and protein levels of eNOS and IRS2. Functionally, silencing eNOS suppressed the Akt signal pathway, while IRS2 knockdown reduced NO production in ECs. Thus, we identified eNOS and IRS2 as ceRNAs and revealed a novel mechanism explaining the coincidence of metabolic and cardiovascular diseases.

Identification of 5 Gene Signatures in Survival Prediction for Patients with Lung Squamous Cell Carcinoma Based on Integrated Multiomics Data Analysis

Background

Lung squamous cell carcinoma (LSCC) is a frequently diagnosed cancer worldwide, and it has a poor prognosis. The current study is aimed at developing the prediction of LSCC prognosis by integrating multiomics data including transcriptome, copy number variation data, and mutation data analysis, so as to predict patients' survival and discover new therapeutic targets.

Methods

RNASeq, SNP, CNV data, and LSCC patients' clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and the samples were randomly divided into two groups, namely, the training set and the validation set. In the training set, the genes related to prognosis and those with different copy numbers or with different SNPs were integrated to extract features using random forests, and finally, robust biomarkers were screened. In addition, a gene-related prognostic model was established and further verified in the test set and GEO validation set.

Results

We obtained a total of 804 prognostic-related genes and 535 copy amplification genes, 621 copy deletions genes, and 388 significantly mutated genes in genomic variants; noticeably, these genomic variant genes were found closely related to tumor development. A total of 51 candidate genes were obtained by integrating genomic variants and prognostic genes, and 5 characteristic genes (HIST1H2BH, SERPIND1, COL22A1, LCE3C, and ADAMTS17) were screened through random forest feature selection; we found that many of those genes had been reported to be related to LSCC progression. Cox regression analysis was performed to establish 5-gene signature that could serve as an independent prognostic factor for LSCC patients and can stratify risk samples in training set, test set, and external validation set (p < 0.01), and the 5-year survival areas under the curve (AUC) of both training set and validation set were > 0.67.

Conclusion

In the current study, 5 gene signatures were constructed as novel prognostic markers to predict the survival of LSCC patients. The present findings provide new diagnostic and prognostic biomarkers and therapeutic targets for LSCC treatment.

Glucose transporter 10 modulates adipogenesis via an ascorbic acid-mediated pathway to protect mice against diet-induced metabolic dysregulation

The development of type 2 diabetes mellitus (T2DM) depends on interactions between genetic and environmental factors, and a better understanding of gene-diet interactions in T2DM will be useful for disease prediction and prevention. Ascorbic acid has been proposed to reduce the risk of T2DM. However, the links between ascorbic acid and metabolic consequences are not fully understood. Here, we report that glucose transporter 10 (GLUT10) maintains intracellular levels of ascorbic acid to promote adipogenesis, white adipose tissue (WAT) development and protect mice from high-fat diet (HFD)-induced metabolic dysregulation. We found genetic polymorphisms in SLC2A10 locus are suggestively associated with a T2DM intermediate phenotype in non-diabetic Han Taiwanese. Additionally, mice carrying an orthologous human Glut10^G128E variant (Glut10^G128E mice) with compromised GLUT10 function have reduced adipogenesis, reduced WAT development and increased susceptibility to HFD-induced metabolic dysregulation. We further demonstrate that GLUT10 is highly expressed in preadipocytes, where it regulates intracellular ascorbic acid levels and adipogenesis. In this context, GLUT10 increases ascorbic acid-dependent DNA demethylation and the expression of key adipogenic genes, Cebpa and Pparg. Together, our data show GLUT10 regulates adipogenesis via ascorbic acid-dependent DNA demethylation to benefit proper WAT development and protect mice against HFD-induced metabolic dysregulation. Our findings suggest that SLC2A10 may be an important HFD-associated susceptibility locus for T2DM.

Environmental triggers may amplify genetically determined disease susceptibility, especially for carriers of rare variants with relatively large individual effect sizes, making these polymorphisms highly informative for predicting individualized clinical risk and preventing disease. Since transitions in dietary pattern have greatly contributed to the increased prevalence of obesity and accelerated the spread of the T2DM epidemic worldwide, a better understanding of gene-diet interactions in T2DM will be useful for disease prediction and prevention. Here, we demonstrate that polymorphisms in the gene encoding GLUT10 are associated with a T2DM intermediate phenotype in non-diabetic human subjects. Additionally, mice that carry a GLUT10 rare variant have reduced WAT development and are susceptible for HFD-induced T2DM. We further demonstrate that GLUT10 is highly expressed in preadipocytes, where it regulates intracellular ascorbic acid levels and ascorbic acid-dependent DNA demethylation to control adipogenesis. Preadipocytes carrying the GLUT10 rare variant or with knockdown of GLUT10 expression have reduced the adipogenesis. Thus, we are able to conclude that GLUT10 regulates adipogenesis via ascorbic acid-dependent DNA demethylation to affect WAT development and contribute to the sensitivity of HFD-induced metabolic dysregulation.

Joint analysis of individual-level and summary-level GWAS data by leveraging pleiotropy

MOTIVATION

A large number of recent genome-wide association studies (GWASs) for complex phenotypes confirm the early conjecture for polygenicity, suggesting the presence of large number of variants with only tiny or moderate effects. However, due to the limited sample size of a single GWAS, many associated genetic variants are too weak to achieve the genome-wide significance. These undiscovered variants further limit the prediction capability of GWAS. Restricted access to the individual-level data and the increasing availability of the published GWAS results motivate the development of methods integrating both the individual-level and summary-level data. How to build the connection between the individual-level and summary-level data determines the efficiency of using the existing abundant summary-level resources with limited individual-level data, and this issue inspires more efforts in the existing area.

RESULTS

In this study, we propose a novel statistical approach, LEP, which provides a novel way of modeling the connection between the individual-level data and summary-level data. LEP integrates both types of data by LEveraging Pleiotropy to increase the statistical power of risk variants identification and the accuracy of risk prediction. The algorithm for parameter estimation is developed to handle genome-wide-scale data. Through comprehensive simulation studies, we demonstrated the advantages of LEP over the existing methods. We further applied LEP to perform integrative analysis of Crohn's disease from WTCCC and summary statistics from GWAS of some other diseases, such as Type 1 diabetes, Ulcerative colitis and Primary biliary cirrhosis. LEP was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.39% (±0.58%) to 68.33% (±0.32%) using about 195 000 variants.

AVAILABILITY AND IMPLEMENTATION

The LEP software is available at https://github.com/daviddaigithub/LEP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Integrative Phenotype-Genotype Approach Using Phenotypic Characteristics from the UAE National Diabetes Study Identifies HSD17B12 as a Candidate Gene for Obesity and Type 2 Diabetes

The United Arab Emirates National Diabetes and Lifestyle Study (UAEDIAB) has identified obesity, hypertension, obstructive sleep apnea, and dyslipidemia as common phenotypic characteristics correlated with diabetes mellitus status. As these phenotypes are usually linked with genetic variants, we hypothesized that these phenotypes share single nucleotide polymorphism (SNP)-clusters that can be used to identify causal genes for diabetes. We explored the National Human Genome Research Institute-European Bioinformatics Institute Catalog of Published Genome-Wide Association Studies (NHGRI-EBI GWAS) to list SNPs with documented association with the UAEDIAB-phenotypes as well as diabetes. The shared chromosomal regions affected by SNPs were identified, intersected, and searched for Enriched Ontology Clustering. The potential SNP-clusters were validated using targeted DNA next-generation sequencing (NGS) in two Emirati diabetic patients. RNA sequencing from human pancreatic islets was used to study the expression of identified genes in diabetic and non-diabetic donors. Eight chromosomal regions containing 46 SNPs were identified in at least four out of the five UAEDIAB-phenotypes. A list of 34 genes was shown to be affected by those SNPs. Targeted NGS from two Emirati patients confirmed that the identified genes have similar SNP-clusters. ASAH1, LRP4, FES, and HSD17B12 genes showed the highest SNPs rate among the identified genes. RNA-seq analysis revealed high expression levels of HSD17B12 in human islets and to be upregulated in type 2 diabetes (T2D) donors. Our integrative phenotype-genotype approach is a novel, simple, and powerful tool to identify clinically relevant potential biomarkers in diabetes. HSD17B12 is a novel candidate gene for pancreatic β-cell function.

Role of inflammasomes in the pathogenesis of periodontal disease and therapeutics

Inflammasomes are a group of multimolecular intracellular complexes assembled around several innate immune proteins. Recognition of a diverse range of microbial, stress and damage signals by inflammasomes results in direct activation of caspase‐1, which subsequently induces the only known form of secretion of active interleukin‐1β and interleukin‐18. Although the importance of interleukin‐1β in the periodontium is not questioned, the impact of inflammasomes in periodontal disease and its potential for therapeutics in periodontology is still in its very early stages. Increasing evidence in preclinical models and human data strongly implicate the involvement of inflammasomes in a number of inflammatory, autoinflammatory and autoimmune disorders. Here we review: (a) the currently known inflammasome functions, (b) clinical/preclinical data supporting inflammasome involvement in the context of periodontal and comorbid diseases and (c) potential therapies targeting inflammasomes. To clarify further the inflammasome involvement in periodontitis, we present analyses of data from a large clinical study (n = 5809) that measured the gingival crevicular fluid‐interleukin‐1β and grouped the participants based on current periodontal disease classifications. We review data on 4910 European‐Americans that correlate 16 polymorphisms in the interleukin‐1B region with high gingival crevicular fluid‐interleukin‐1β levels. We show that inflammasome components are increased in diseased periodontal tissues and that the caspase‐1 inhibitor, VX‐765, inhibits ~50% of alveolar bone loss in experimental periodontitis. The literature review further supports that although patients clinically present with the same phenotype, the disease that develops probably has different underlying biological pathways. The current data indicate that inflammasomes have a role in periodontal disease pathogenesis. Understanding the contribution of different inflammasomes to disease development and distinct patient susceptibility will probably translate into improved, personalized therapies.

Tai Chi is Effective in Delaying Cognitive Decline in Older Adults with Mild Cognitive Impairment: Evidence from a Systematic Review and Meta-Analysis

To determine whether Tai Chi (TC) is effective in slowing cognitive decline in older populations with mild cognitive impairment (MCI), we performed a systematic review and meta-analysis of randomized controlled trials (RCTs) on Tai Chi and MCI. We searched eight electronic databases (PubMed, PsycINFO, Wanfang, Web of Science, MEDLINE, CNKI, EBSCO, and the Cochrane Central Register of Controlled Trials) for appropriate RCTs published up to August 2019. For those studies included, the data were extracted, methodological quality was evaluated, and then meta-analysis was performed using Review Manager software (version 5.3). A total of 11 of the studies were available for systematic review, which together included 1061 participants, met the inclusion criteria, and ten of these were included in the meta-analysis. For most RCTs, the methodological quality was moderate. The meta-analysis revealed that Tai Chi could significantly improve global cognitive function; memory and learning; mental speed and attention; ideas, abstraction, figural creations, and mental flexibility; and visuospatial perception. The present review adds to the evidence showing that Tai Chi is potentially beneficial in improving cognitive functions among elderly people with MCI. However, strictly designed and well-reported RCTs are required.

The Importance of Precision Medicine in Type 2 Diabetes Mellitus (T2DM): From Pharmacogenetic and Pharmacoepigenetic Aspects

BACKGROUND

Type 2 Diabetes Mellitus (T2DM) is a worldwide disorder as the most important challenges of health-care systems. Controlling the normal glycaemia greatly profit long-term prognosis and gives explanation for early, effective, constant, and safe intervention.

MATERIAL AND METHODS

Finding the main genetic and epigenetic profile of T2DM and the exact molecular targets of T2DM medications can shed light on its personalized management. The comprehensive information of T2DM was earned through the genome-wide association study (GWAS) studies. In the current review, we represent the most important candidate genes of T2DM like CAPN10, TCF7L2, PPAR-γ, IRSs, KCNJ11, WFS1, and HNF homeoboxes. Different genetic variations of a candidate gene can predict the efficacy of T2DM personalized strategy medication.

RESULTS

SLCs and AMPK variations are considered for metformin, CYP2C9, KATP channel, CDKAL1, CDKN2A/2B and KCNQ1 for sulphonylureas, OATP1B, and KCNQ1 for repaglinide and the last but not the least ADIPOQ, PPAR-γ, SLC, CYP2C8, and SLCO1B1 for thiazolidinediones response prediction.

CONCLUSION

Taken everything into consideration, there is an extreme need to determine the genetic status of T2DM patients in some known genetic region before planning the medication strategies.

Big Data and diabetes: the applications of Big Data for diabetes care now and in the future

We review current applications of Big Data in diabetes care and consider the future potential by carrying out a scoping study of the academic literature on Big Data and diabetes care. Healthcare data are being produced at ever-increasing rates, and this information has the potential to transform the provision of diabetes care. Big Data is beginning to have an impact on diabetes care through data research. The use of Big Data for routine clinical care is still a future application. Vast amounts of healthcare data are already being produced, and the key is harnessing these to produce actionable insights. Considerable development work is required to achieve these goals.

Benefits and limitations of genome-wide association studies

Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.

Functional Screening of Candidate Causal Genes for Insulin Resistance in Human Preadipocytes and Adipocytes

Rationale: Genome-wide association studies have identified genetic loci associated with insulin resistance (IR) but pinpointing the causal genes of a risk locus has been challenging. Objective: To identify candidate causal genes for IR, we screened regional and biologically plausible genes (16 in total) near the top 10 IR-loci in risk-relevant cell types, namely preadipocytes and adipocytes. Methods and Results: We generated 16 human Simpson-Golabi-Behmel syndrome preadipocyte knockout lines each with a single IR-gene knocked out by lentivirus-mediated CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 system. We evaluated each gene knockout by screening IR-relevant phenotypes in the 3 insulin-sensitizing mechanisms, including adipogenesis, lipid metabolism, and insulin signaling. We performed genetic analyses using data on the genotype-tissue expression portal expression quantitative trait loci database and accelerating medicines partnership type 2 diabetes mellitus Knowledge Portal to evaluate whether candidate genes prioritized by our in vitro studies were expression quantitative trait loci genes in human subcutaneous adipose tissue, and whether expression of these genes is associated with risk of IR, type 2 diabetes mellitus, and cardiovascular diseases. We further validated the functions of 3 new adipose IR genes by overexpression-based phenotypic rescue in the Simpson-Golabi-Behmel syndrome preadipocyte knockout lines. Twelve genes, PPARG, IRS-1, FST, PEPD, PDGFC, MAP3K1, GRB14, ARL15, ANKRD55, RSPO3, COBLL1, and LYPLAL1, showed diverse phenotypes in the 3 insulin-sensitizing mechanisms, and the first 7 of these genes could affect all the 3 mechanisms. Five out of 6 expression quantitative trait loci genes are among the top candidate causal genes and the abnormal expression levels of these genes (IRS-1, GRB14, FST, PEPD, and PDGFC) in human subcutaneous adipose tissue could be associated with increased risk of IR, type 2 diabetes mellitus, and cardiovascular disease. Phenotypic rescue by overexpression of the candidate causal genes (FST, PEPD, and PDGFC) in the Simpson-Golabi-Behmel syndrome preadipocyte knockout lines confirmed their function in adipose IR. Conclusions: Twelve genes showed diverse phenotypes indicating differential roles in insulin sensitization, suggesting mechanisms bridging the association of their genomic loci with IR. We prioritized PPARG, IRS-1, GRB14, MAP3K1, FST, PEPD, and PDGFC as top candidate genes. Our work points to novel roles for FST, PEPD, and PDGFC in adipose tissue, with consequences for cardiometabolic diseases.

LEP: A Statistical Method Integrating Individual-Level and Summary-Level Data of the Same Trait From Different Populations

Statistical approaches for integrating multiple data sets in genome-wide association studies (GWASs) are increasingly important. Proper utilization of more relevant information is expected to improve statistical efficiency in the analysis. Among these approaches, LEP was proposed for joint analysis of individual-level data and summary-level data in the same population by leveraging pleiotropy. The key idea of LEP is to explore correlation of the association status among different data sets while accounting for the heterogeneity. In this commentary, we show that LEP is applicable to integrate individual-level data and summary-level data of the same trait from different populations, providing new insights into the genetic architecture of different populations.

Debutant iOS app and gene-disease complexities in clinical genomics and precision medicine

Background

The last decade has seen a dramatic increase in the availability of scientific data, where human-related biological databases have grown not only in count but also in volume, posing unprecedented challenges in data storage, processing, analysis, exchange, and curation. Next generation sequencing (NGS) advancements have facilitated and accelerated the process of identifying genetic variations. Adopting NGS with Whole-Genome and RNA sequencing in a diagnostic context has the potential to improve disease-risk detection in support of precision medicine and drug discovery. Several bioinformatics pipelines have been developed to strengthen variant interpretation by efficiently processing and analyzing sequence data, whereas many published results show how genomics data can be proactively incorporated into medical practices and improve utilization of clinical information. To utilize the wealth of genomics and health, there is a crucial need to generate appropriate gene-disease annotation repositories accessed through modern technology.

Results

Our focus here is to create a comprehensive database with mobile access to actionable genes and classified diseases, considered the foundation for clinical genomics and precision medicine. We present a publicly available iOS app, PAS-Gen, which invites global users to freely download it on iPhone and iPad devices, quickly adopt its easy to use interface, and search for genes and related diseases. PAS-Gen was developed using Swift, XCODE, and PHP scripting that uses Web and MySQL database servers, which includes over 59,000 protein-coding and non-coding genes, and over 90,000 classified gene-disease associations. PAS-Gen is founded on the clinical and scientific premise that easier healthcare and genomics data sharing will accelerate future medical discoveries.

Conclusions

We present a cutting-edge gene-disease database with a smart phone application, integrating information on classified diseases and related genes. The PAS-Gen app will assist researchers, medical practitioners, and pharmacists by providing a broad and view of genes that may be implicated in the likelihood of developing certain diseases. This tool with accelerate users’ abilities to understand the genetic basis of human complex diseases and by assimilating genomic and phenotypic data will support future work to identify gene-specific designer drugs, target precise molecular fingerprints for tumors, suggest appropriate drug therapies, predict individual susceptibility to disease, and diagnose and treat rare illnesses.

Genes associated with Type 2 Diabetes and vascular complications

Type 2 Diabetes (T2D) is a chronic disease associated with a number of micro- and macrovascular complications that increase the morbidity and mortality of patients. The risk of diabetic complications has a strong genetic component. To this end, we sought to evaluate the association of 40 single nucleotide polymorphisms (SNPs) in 21 candidate genes with T2D and its vascular complications in 503 T2D patients and 580 healthy controls. The genes were chosen because previously reported to be associated with T2D complications and/or with the aging process. We replicated the association of T2D risk with IGF2BP rs4402960 and detected novel associations with TERT rs2735940 and rs2736098. The addition of these SNPs to a model including traditional risk factors slightly improved risk prediction. After stratification of patients according to the presence/absence of vascular complications, we found significant associations of variants in the CAT, FTO, and UCP1 genes with diabetic retinopathy and nephropathy. Additionally, a variant in the ADIPOQ gene was found associated with macrovascular complications. Notably, these genes are involved in some way in mitochondrial biology and reactive oxygen species regulation. Hence, our findings strongly suggest a potential link between mitochondrial oxidative homeostasis and individual predisposition to diabetic vascular complications.

Therapeutic Silencing of Centromere Protein X Ameliorates Hyperglycemia in Zebrafish and Mouse Models of Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) is characterized by persistent hyperglycemia and is influenced by genetic and environmental factors. Optimum T2DM management involves early diagnosis and effective glucose-lowering therapies. Further research is warranted to improve our understanding of T2DM pathophysiology and reveal potential roles of genetic predisposition. We have previously developed an obesity-induced diabetic zebrafish model that shares common pathological pathways with humans and may be used to identify putative pharmacological targets of diabetes. Additionally, we have previously identified several candidate genes with altered expression in T2DM zebrafish. Here, we performed a small-scale zebrafish screening for these genes and discovered a new therapeutic target, centromere protein X (CENPX), which was further validated in a T2DM mouse model. In zebrafish, cenpx knockdown by morpholino or knockout by CRISPR/Cas9 system ameliorated overfeeding-induced hyperglycemia and upregulated insulin level. In T2DM mice, small-interfering RNA-mediated Cenpx knockdown decreased hyperglycemia and upregulated insulin synthesis in the pancreas. Gene expression analysis revealed insulin, mechanistic target of rapamycin, leptin, and insulin-like growth factor 1 pathway activation following Cenpx silencing in pancreas tissues. Thus, CENPX inhibition exerted antidiabetic effects via increased insulin expression and related pathways. Therefore, T2DM zebrafish may serve as a powerful tool in the discovery of new therapeutic gene targets.

The Genetic Epidemiology of Type 2 Diabetes: Opportunities for Health Translation

PURPOSE OF REVIEW

Genome-wide association studies have delineated the genetic architecture of type 2 diabetes. While functional studies to identify target transcripts are ongoing, new genetic knowledge can be translated directly to health applications. The review covers several translation directions but focuses on genomic polygenic scores for screening and prevention.

RECENT FINDINGS

Over 400 genomic variants associated with T2D and its related quantitative traits are now known. Genetic scores comprising dozens to millions of associated variants can predict incident T2D. However, measurement of body mass index is more efficient than genetic scores to detect T2D risk groups, and knowledge of T2D genetic risk alone seems insufficient to improve health. Genetically determined metabolic sub-phenotypes can be identified by clustering variants associated with physiological axes like insulin resistance. Genetic sub-phenotyping may be a way forward to identify specific individual phenotypes for prevention and treatment. Genomic polygenic scores for T2D can predict incident diabetes but may not be useful to improve health overall. Genetic detection of T2D sub-phenotypes could be useful to personalize screening and care.