Recent advances and perspectives in next generation sequencing application to the genetic research of type 2 diabetes

Exploring the genetic basis of childhood monogenic diabetes

Monogenic diabetes is caused by one or even more genetic variations, which may be uncommon yet have a significant influence and cause diabetes at an early age. Monogenic diabetes affects 1% to 5% of children, and early detection and genetically focused treatment of neonatal diabetes and maturity-onset diabetes of the young can significantly improve long-term health and well-being. The etiology of monogenic diabetes in childhood is primarily attributed to genetic variations affecting the regulatory genes responsible for beta-cell activity. In rare instances, mutations leading to severe insulin resistance can also result in the development of diabetes. Individuals diagnosed with specific types of monogenic diabetes, which are commonly found, can transition from insulin therapy to sulfonylureas, provided they maintain consistent regulation of their blood glucose levels. Scientists have successfully devised materials and methodologies to distinguish individuals with type 1 or 2 diabetes from those more prone to monogenic diabetes. Genetic screening with appropriate findings and interpretations is essential to establish a prognosis and to guide the choice of therapies and management of these interrelated ailments. This review aims to design a comprehensive literature summarizing genetic insights into monogenetic diabetes in children and adolescents as well as summarizing their diagnosis and management.

Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets

Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors with T2D and non-diabetic controls. We exploited Machine Learning to perform multiOmics integration of DNA methylation, expression, SNPs, and phenotypes from pancreatic islets of 110 individuals, with ~ 30% being T2D cases. DNA methylation was analyzed using Infinium MethylationEPIC array, expression was analyzed using RNA-sequencing, and SNPs were analyzed using HumanOmniExpress arrays. Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ± 0.08 on the test dataset after cross-validation. Biomarkers identified by this multiOmics integration, including SACS and TXNIP DNA methylation, OPRD1 and RHOT1 expression and a SNP annotated to ANO1, provide novel insights into the interplay between different biological mechanisms contributing to T2D. This Machine Learning approach of multiOmics cross-sectional data from human pancreatic islets achieved a promising accuracy of T2D prediction, which may potentially find broad applications in clinical diagnostics. In addition, it delivered novel candidate biomarkers for T2D and links between them across the different Omics.

Genetic perspectives on childhood monogenic diabetes: Diagnosis, management, and future directions

Monogenic diabetes is caused by one or even more genetic variations, which may be uncommon yet have a significant influence and cause diabetes at an early age. Monogenic diabetes affects 1 to 5% of children, and early detection and gene-tically focused treatment of neonatal diabetes and maturity-onset diabetes of the young can significantly improve long-term health and well-being. The etiology of monogenic diabetes in childhood is primarily attributed to genetic variations affecting the regulatory genes responsible for beta-cell activity. In rare instances, mutations leading to severe insulin resistance can also result in the development of diabetes. Individuals diagnosed with specific types of monogenic diabetes, which are commonly found, can transition from insulin therapy to sulfonylureas, provided they maintain consistent regulation of their blood glucose levels. Scientists have successfully devised materials and methodologies to distinguish individuals with type 1 or 2 diabetes from those more prone to monogenic diabetes. Genetic screening with appropriate findings and interpretations is essential to establish a prognosis and to guide the choice of therapies and management of these interrelated ailments. This review aims to design a comprehensive literature summarizing genetic insights into monogenetic diabetes in children and adolescents as well as summarizing their diagnosis and mana-gement.

Recent Progress in the Diagnosis and Management of Type 2 Diabetes Mellitus in the Era of COVID-19 and Single Cell Multi-Omics Technologies

Type 2 diabetes mellitus (T2DM) is one of the world's leading causes of death and life-threatening conditions. Therefore, we review the complex vicious circle of causes responsible for T2DM and risk factors such as the western diet, obesity, genetic predisposition, environmental factors, and SARS-CoV-2 infection. The prevalence and economic burden of T2DM on societal and healthcare systems are dissected. Recent progress on the diagnosis and clinical management of T2DM, including both non-pharmacological and latest pharmacological treatment regimens, are summarized. The treatment of T2DM is becoming more complex as new medications are approved. This review is focused on the non-insulin treatments of T2DM to reach optimal therapy beyond glycemic management. We review experimental and clinical findings of SARS-CoV-2 risks that are attributable to T2DM patients. Finally, we shed light on the recent single-cell-based technologies and multi-omics approaches that have reached breakthroughs in the understanding of the pathomechanism of T2DM.

A 646C > G (rs41423247) polymorphism of the glucocorticoid receptor as a risk factor for hyperglycaemia diagnosed in pregnancy-data from an observational study

AIM

Hyperglycaemia diagnosed in pregnancy (HiP) is a serious and frequent complication of pregnancy, increasing the risk for adverse maternal and neonatal outcomes. Investigate whether allelic variations of the glucocorticoid receptor are related to an increased risk of HiP.

METHOD

The following polymorphisms of the glucocorticoid receptor (GR) were investigated in the cohort study of N = 197 pregnant women with HiP and N = 133 normoglycemic pregnant controls: 646C > G (rs41423247), N363S (rs6195), ER23/22EK (rs6190, rs6189).

RESULTS

A GG variant of the rs41423247 polymorphism was associated with a significantly higher risk for HiP: OR 1.94 (1.18; 3.18), p = 0.009. The relationship remained significant after controlling for maternal age and prepregnancy BMI: OR 3.09 (1.25; 7.64), p = 0.014.

CONCLUSIONS

The allelic GG variant of the 646C > G (rs41423247) polymorphism is associated with an increased risk for hyperglycaemia in pregnancy.

Guidelines for genetic testing of healthy adults who deposit samples and related data in bioresource collections and biobanks

Currently, a significant part of research in the fields of human and medical genetics is carried out using tissue samples, genealogical, population, medical and personal data. Their use is of particular relevance in the “genome era”, since only joint analysis of genomic data and health status of the population is crucial for understanding how genes are associated with health and disease. Genetic studies of adults without symptoms of diseases are carried out to obtain data on a possible predisposition to multifactorial diseases, to establish the carrier status of autosomal recessive mutations as part of preconception care and to assess individual sensitivity to drugs. In addition, healthy individuals can be tested to detect an inherited disease at presymptomatic stage. This situation increasingly emphasizes the importance of storing data on genome sequencing or any other patient tests for subsequent data reanalysis, as well as their safety, including biosamples from an individual and one’s family. The review article, based on international experience, summarizes guidelines for genetic testing of healthy individuals. The options for storing biological samples and related data are considered.

Diabetes: discovery of insulin, genetic, epigenetic and viral infection mediated regulation

Diabetes mellitus, commonly referred to as diabetes, is a combination of many metabolic diseases. Insulin deficiency in our body is the main cause of diabetes. Insulin is one of the most well studied proteins, yet the genesis of its discovery was not getting much attention so far. Nevertheless, the history of the discovery of insulin is an exemplary of solving observational and scientific riddles, drudgery, patience and even professional turmoil. It is an inspiration for all medical personnel and scientists who are practising in the field of molecular medicine. Additionally, the genetic and epigenetic regulation of different types of diabetes needs to be addressed because of the widespread nature of the disease. Diabetes not only involves genetic predisposition but environmental factors, lifestyle etc. can be the major contributor for its inception. Nonetheless, viral infections at an early age are also found to trigger the onset of type I diabetes. In this review article, the history of the discovery of insulin is detailed along with the justification for the genetic and epigenetic regulatory mechanisms of diabetes and explained how viral infections can also trigger the onset of diabetes.

Microbial driven genetic variation in holobionts

Genetic variation in holobionts, (host and microbiome), occurring by changes in both host and microbiome genomes, can be observed from two perspectives: observable variations and the processes that bring about the variation. The observable includes the enormous genetic diversity of prokaryotes, which gave rise to eukaryotic organisms. Holobionts then evolved a rich microbiome with a stable core containing essential genes, less so common taxa, and a more diverse non-core enabling considerable genetic variation. The result being that, the human gut microbiome, for example, contains 1,000 times more unique genes than are present in the human genome. Microbial driven genetic variation processes in holobionts include: (1) Acquisition of novel microbes from the environment, which bring in multiple genes in one step, (2) amplification/reduction of certain microbes in the microbiome, that contribute to holobiont` s adaptation to changing conditions, (3) horizontal gene transfer between microbes and between microbes and host, (4) mutation, which plays an important role in optimizing interactions between different microbiota and between microbiota and host. We suggest that invertebrates and plants, where microbes can live intracellularly, have a greater chance of genetic exchange between microbiota and host, thus a greater chance of vertical transmission and a greater effect of microbiome on evolution of host than vertebrates. However, even in vertebrates the microbiome can aid in environmental fluctuations by amplification/reduction and by acquisition of novel microorganisms.

The Gut Microbial Endocrine Organ in Type 2 Diabetes

Historically, the focus of type II diabetes mellitus (T2DM) research has been on host metabolism and hormone action. However, emerging evidence suggests that the gut microbiome, commensal microbes that colonize the gastrointestinal tract, also play a significant role in T2DM pathogenesis. Specifically, gut microbes metabolize what is available to them through the host diet to produce small molecule metabolites that can have endocrine-like effects on human cells. In fact, the meta-organismal crosstalk between gut microbe-generated metabolites and host receptor systems may represent an untapped therapeutic target for those at risk for or suffering from T2DM. Recent evidence suggests that gut microbe-derived metabolites can impact host adiposity, insulin resistance, and hormone secretion to collectively impact T2DM progression. Here we review the current evidence that structurally diverse gut microbe-derived metabolites, including short chain fatty acids, secondary bile acids, aromatic metabolites, trimethylamine-N-oxide, polyamines, and N-acyl amides, that can engage with host receptors in an endocrine-like manner to promote host metabolic disturbance associated with T2DM. Although these microbe-host signaling circuits are not as well understood as host hormonal signaling, they hold untapped potential as new druggable targets to improve T2DM complications. Whether drugs that selectively target meta-organismal endocrinology will be safe and efficacious in treating T2DM is a key new question in the field of endocrinology. Here we discuss the opportunities and challenges in targeting the gut microbial endocrine organ for the treatment of diabetes and potentially many other diseases where diet-microbe-host interactions play a contributory role.

Long-term treatment of polysaccharides-based hydrogel microparticles as oral insulin delivery in streptozotocin-induced type 2 diabetic mice

To develop a more effective and safer drug for the treatment of type 2 diabetes mellitus (T2DM), polysaccharides-based hydrogel microparticles as oral insulin delivery was prepared and explored. This study was aimed to evaluate the antidiabetic effects and hypoglycemic mechanism with long-term administration(four weeks) of oral insulin hydrogel microparticles in type 2 diabetic mice on a model of diabetes using a high fat diet combined with streptozotocin. The results revealed that the long-term treatment of oral insulin polysaccharides-based hydrogel microparticles could significantly alleviate the symptoms of polyphagia, polydipsia, polyuria and weight loss in diabetic mice. Also, oral administration of insulin hydrogel microparticles could significantly reduce fasting blood glucose levels, ameliorate insulin resistance and increase insulin sensitivity in the mice with T2DM. The concentration of plasma TG, TC, LDL-C, FFA, BUN, CRE significantly decreased and the levels of HDL-C increased showed that insulin polysaccharides-based hydrogel microparticles were effective in regulating lipid metabolism and prevent diabetic nephropathy complication in diabetic mice. In addition, the supplementation of insulin hydrogel microparticles could significant improve the antioxidant capacity by increasing the level of SOD, CAT and decreasing the level of MDA, GPT, NO, TNF-α, and reverse histological deterioration of kidney and pancreas in diabetic mice. The above outcome concluded that insulin polysaccharides-based hydrogel microparticles may exhibit promising anti-diabetic activity and the potential to be a drug candidate for T2DM.

Pharmacogenetics of Type 2 Diabetes-Progress and Prospects

Type 2 diabetes mellitus (T2D) is a chronic metabolic disease resulting from insulin resistance and progressively reduced insulin secretion, which leads to impaired glucose utilization, dyslipidemia and hyperinsulinemia and progressive pancreatic beta cell dysfunction. The incidence of type 2 diabetes mellitus is increasing worldwide and nowadays T2D already became a global epidemic. The well-known interindividual variability of T2D drug actions such as biguanides, sulfonylureas/meglitinides, DPP-4 inhibitors/GLP1R agonists and SGLT-2 inhibitors may be caused, among other things, by genetic factors. Pharmacogenetic findings may aid in identifying new drug targets and obtaining in-depth knowledge of the causes of disease and its physiological processes, thereby, providing an opportunity to elaborate an algorithm for tailor or precision treatment. The aim of this article is to summarize recent progress and discoveries for T2D pharmacogenetics and to discuss the factors which limit the furthering accumulation of genetic variability knowledge in patient response to therapy that will allow improvement the personalized treatment of T2D.

Precision and Personalized Medicine: How Genomic Approach Improves the Management of Cardiovascular and Neurodegenerative Disease

Life expectancy has gradually grown over the last century. This has deeply affected healthcare costs, since the growth of an aging population is correlated to the increasing burden of chronic diseases. This represents the interesting challenge of how to manage patients with chronic diseases in order to improve health care budgets. Effective primary prevention could represent a promising route. To this end, precision, together with personalized medicine, are useful instruments in order to investigate pathological processes before the appearance of clinical symptoms and to guide physicians to choose a targeted therapy to manage the patient. Cardiovascular and neurodegenerative diseases represent suitable models for taking full advantage of precision medicine technologies applied to all stages of disease development. The availability of high technology incorporating artificial intelligence and advancement progress made in the field of biomedical research have been substantial to understand how genes, epigenetic modifications, aging, nutrition, drugs, microbiome and other environmental factors can impact health and chronic disorders. The aim of the present review is to address how precision and personalized medicine can bring greater clarity to the clinical and biological complexity of these types of disorders associated with high mortality, involving tremendous health care costs, by describing in detail the methods that can be applied. This might offer precious tools for preventive strategies and possible clues on the evolution of the disease and could help in predicting morbidity, mortality and detecting chronic disease indicators much earlier in the disease course. This, of course, will have a major effect on both improving the quality of care and quality of life of the patients and reducing time efforts and healthcare costs.