In the last two decades, significant progress has been made toward understanding the genetic basis of type 2 diabetes. An important supporter of this research has been the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), most recently through the Accelerating Medicines Partnership Program for Type 2 Diabetes (AMP T2D) and Accelerating Medicines Partnership Program for Common Metabolic Diseases (AMP CMD). These public-private partnerships of the National Institutes of Health, multiple biopharmaceutical and life sciences companies, and nonprofit organizations, facilitated and managed by the Foundation for the National Institutes of Health, were designed to improve understanding of therapeutically relevant biological pathways for type 2 diabetes. On the occasion of NIDDK's 75th anniversary, we review the history of NIDDK support for these partnerships, which saw the convergence of research directions prioritized by academic consortia, the pharmaceutical industry, and government funders. Although the NIDDK was not the sole originator or funder of these efforts, its support and leadership have been pivotal to the partnerships' success and have enabled their research to be broadly accessible through the AMP Common Metabolic Diseases Knowledge Portal (CMDKP) and the AMP Common Metabolic Diseases Genome Atlas (CMDGA). Findings from AMP CMD align with NIDDK's mission to conduct research and share results with the goal of improving health and quality of life.

The Accelerating Medicines Partnership Program for Type 2 Diabetes (AMP T2D) and Accelerating Medicines Partnership Program for Common Metabolic Diseases (AMP CMD) were created to accelerate the translation of genetic and genomic data into knowledge about the biology of disease. Their goal was to gain a better understanding of the mechanisms underlying types 1 and 2 diabetes and prediabetes, obesity, cardiovascular disease, kidney disease, and nonalcoholic steatohepatitis. This work identified multiple genes and pathways underlying these diseases. The findings of AMP T2D and AMP CMD have implications for drug development and improved risk prediction, diagnosis, and treatment for common metabolic diseases.

tRNA undergoes various post-transcriptional modifications in the anticodon loop. FTSJ1, a protein conserved among most eukaryotes, mediates 2'-O-methylations at position 32 (Nm32) or position 34 (Nm34), complexed with THADA or WDR6, respectively. These methylations are crucial for accurate translation and cellular growth. FTSJ1 mutations are associated with non-syndromic X-linked intellectual disability. Although the structure of the FTSJ1-WDR6 complex in yeast has been solved, the structural details of the FTSJ1-THADA complex formation and substrate recognition remain unclear. Herein, using cryo-electron microscopy, we solve the high-resolution structure of FTSJ1-THADA with or without a tRNA substrate. FTSJ1 binds to THADA via its C-terminal region, with a unique interaction mode distinct from the FTSJ1-WDR6 complex. The tRNA substrate is anchored inside THADA, and key THADA residues for THADA-tRNA interaction are identified via structural and biochemical analyses. These findings demonstrate how FTSJ1 and THADA form a complex to mediate Nm32 modification in various tRNAs.

A Disintegrin-like And Metalloprotease domain with Thrombospondin type I motifs (ADAMTS) 9 has essential, non-redundant roles during embryogenesis. Adamts9 null murine embryos die prior to completing gastrulation. Unusually for a protease, Adamts9 haploinsufficiency results in cardiovascular and ocular anomalies. ADAMTS9 is required for proteostasis of versican, a widely distributed large aggregating proteoglycan abundant in the provisional extracellular matrix during embryogenesis. Despite its importance, ADAMTS9 proteoglycanase activity has undergone limited characterization, especially in comparison to ADAMTS1, ADAMTS4, and ADAMTS5, due to difficulties in expressing and purifying the >200 kDa full-length form of ADAMTS9. Like ADAMTS1, ADAMTS4, and ADAMTS5, ADAMTS9 cleaves versican V1 isoform at E441-A442, but unlike them, cleavages at other sites are unknown. Here, we expressed a truncated ADAMTS9 construct (ADAMTS9 MDTCS) consisting of all ADAMTS 'core domains' present in ADAMTS1, ADAMTS4, and ADAMTS5, and characterized its activity against versican, aggrecan, and the small leucine-rich proteoglycan biglycan. We identified cleavages in versican (V1 and V2 isoforms) and biglycan using a z-score approach based on label-free quantitation of semi- and fully tryptic/GluC peptides. Moreover, using a quantitative assay, we established that ADAMTS9 MDTCS versicanase activity at the E441-A442 site is 175-fold lower than ADAMTS5, 9-fold lower than ADAMTS4, and 5.5-fold higher than ADAMTS1. Finally, we confirmed that ADAMTS9 MDTCS cleaves bovine aggrecan at E392-A393. This analysis of the proteoglycanase activity in the ADAMTS family highlights differences and similarities in cleavage site specificities which could be leveraged to develop selective small molecule inhibitors against current targets of interest, ADAMTS4, ADAMTS5, and ADAMTS7.

Type 2 diabetes mellitus (T2DM) remaina significant global health challenge, with its increasing prevalence and associated complications contributing to high morbidity and economic burden. Genetic factors play a crucial role in T2DM susceptibility, yet individual responses to dietary interventions vary widely, emphasizing the importance of gene-diet (G × D) interactions. This review synthesizes the current literature on the genetic basis of T2DM and the role of G × D interactions in shaping individual responses to diet. We examine the genetics implication in T2DM risk and modulation by dietary factors, with a focus on the potential of Nutrigenetics in guiding personalized nutrition (PN) strategies. Moreover, the clinical implications of these interactions for the personalized prevention and management of T2DM are explored, highlighting the promise of tailoring dietary recommendations based on genetic profiles. Critical research gaps, including the need for diverse and longitudinal studies, the integration of multi-omic data, and the inclusion of digital health technologies in PN are discussed. Finally, future directions for the field are outlined, advocating for more inclusive, large-scale studies to optimize PN approaches for diverse populations and improve the efficacy of T2DM prevention and management. This review underscores the potential of an individualized, genetically informed dietary approach in modulating the global burden of T2DM.

Insulin sensitivity (IS) is a key determinant of metabolic health and may share genetic factors with obesity-related traits. Previous large-scale genetic studies have identified variants associated with IS as well as obesity related traits like body mass index (BMI) and waist-to-hip ratio (WHR). Notably, many of these associations are shared across traits, indicating a potential genetic overlap. However, the genetic intersection between IS and obesity-related traits remains underexplored. To explore this gap, we investigated associations between six IS indices, including fasting and post-glucose load measures, and genetic variants linked to BMI and WHR to determine their influence on IS and related cardiometabolic traits. To achieve this, we calculated six IS indices using fasting and oral glucose tolerance test (OGTT) data from 5,007 non-diabetic individuals, grouping them into fasting, OGTT0,120, and OGTT0,30,120 categories. A total of 678 BMI-associated and 265 WHR-associated genetic variants were analysed using linear regression, adjusting for age and sex, with sex-specific analyses for WHR. Analyses were conducted with and without BMI adjustments and corrected for multiple testing (padj). Additionally, we explored the relationship between IS-linked variants and their associations with type 2 diabetes (T2D), coronary artery disease (CAD) and stroke. Among the 678 BMI-associated variants, 100 showed nominal associations (p < 0.05) with at least one IS index; and 20 remained significant after multiple testing correction (padj < 0.05) when not adjusting for BMI. After adjusting for BMI, 70 variants retained nominal associations, and six remained significant (padj < 0.05). In sex-specific analyses of the 265 WHR-associated variants, 12 variants were associated in females when adjusted for BMI, whereas no significant associations were observed in males. Furthermore, BMI- and WHR-associated variants linked to decreased IS, such as those in FTO and VPS13C loci, were also associated with increased T2D and stroke risk, whereas IS-increasing variants, including those in VPS13C and PPARG, were linked to lower T2D and stroke risk, with some, like THADA, showing opposing effects on CAD. This study offers insights into genetic variants that influence both IS and obesity-related traits, revealing BMI- and WHR-associated variants with both positive and negative effects on IS and their potential impact on cardiometabolic health.

Myokines are bioactive peptides released by skeletal muscle. Myokines exert auto-, para-, or endocrine effects, enabling them to regulate many aspects of metabolism in various tissues. However, the contribution of myokines to the dramatic changes in glucose homeostasis and muscle mass induced by bariatric surgery has not been established. Our review highlights that myokines such as brain-derived neurotrophic factor (BDNF), meteorin-like protein (Metrnl), secreted protein acidic and rich in cysteine (SPARC), apelin (APLN) and myostatin (MSTN) may mediate changes in glucose homeostasis and muscle mass after bariatric surgery. Our review also identifies myonectin as an interesting candidate for future studies, as this myokine may regulate lipid metabolism and muscle mass after bariatric surgery. These myokines may provide novel therapeutic targets and biomarkers for obesity, type 2 diabetes and sarcopenia.

Type 2 diabetes mellitus (T2D) is a significant health concern. Research using non-human primates, which develop T2D with similar symptoms and pancreatic changes as humans, is crucial but limited by long timelines and low success rates.

We targeted capture sequenced 61 normal and 81 T2D cynomolgus monkeys using a primer panel that captured 269 potential regulatory regions potentially associated with T2D in the cynomolgus monkey genome. 80 variants were identified to be associated with T2D and were used to construct a genetic prediction model. Among 8 machine learning algorithms tested, we found that the best prediction performance was achieve when the model using support vector machine with polynomial kernel as the machine learning algorithm (AUC = 0.933). Including age and sex in this model did not significantly improve the prediction performance. Using the genetic prediction model, we further screened 22 monkeys and found 13 were high risk while 9 were low risk. After feeding the 22 monkeys with high-energy food for 32 weeks, we found all the 9 low risk monkeys did not develop T2D while 4 out of 13 high risk monkeys (31 %) develop T2D.

This method greatly increased the success rate of establishing T2D monkey models while decreased the time needed compared to traditional methods. Therefore, we developed a new high-efficiency method to establish T2D monkey models by combining the genetic prediction model and high-energy diet, which will greatly contribute to the research on the clinical characteristics, pathogenesis, complications and potential new treatments.

Insulin responses to standardized meals differ between individuals. This variability may in part be explained by genotype. This systematic review evaluates associations between genotype and insulin response to an oral glucose tolerance test (OGTT) in terms of insulin area under the curve (AUC).

Three electronic databases (Web of Science, Embase, PubMed) were searched for studies investigating associations between insulin AUC after an OGTT and single nucleotide polymorphisms (SNPs) belonging to the transcription factor 7 like 2 (TCF7L2) gene, the peroxisome proliferator-activated receptor gamma (PPARγ) gene, or the potassium inwardly rectifying channel subfamily J member 11 (KCNJ11) gene in persons without diabetes. A total of 5199 articles were identified, of which 38 were included. Among them were family-based studies (9), twin studies (2), and studies with unrelated participants (27). Seventeen articles investigated TCF7L2 (7 SNPs), 14 investigated PPARγ (1 SNP), and 8 investigated KCNJ11 (5 SNPs). For all investigated SNPs, at least half of the reports indicated no statistically significant association with postprandial insulin AUC.

No evidence was found for associations between TCF7L2, PPARγ, and KCNJ11 genotypes and insulin AUC after an OGTT. Future studies should investigate the effect of genetic risk scores on postprandial insulin.

Diabetes is a leading cause of global mortality and disability, and its economic burden is substantial. This Review focuses on type 2 diabetes, which makes up 90-95% of all diabetes cases. Type 2 diabetes involves a progressive loss of insulin secretion often alongside insulin resistance and metabolic syndrome. Although obesity and a sedentary lifestyle are considerable contributors, research over the last 25 years has shown that type 2 diabetes develops on a predisposing genetic background, with family and twin studies indicating considerable heritability (ie, 31-72%). This Review explores type 2 diabetes from a genetic perspective, highlighting insights into its pathophysiology and the implications for precision medicine. More specifically, the traditional understanding of type 2 diabetes genetics has focused on a dichotomy between monogenic and polygenic forms. However, emerging evidence suggests a continuum that includes monogenic, oligogenic, and polygenic contributions, revealing their complementary roles in type 2 diabetes pathophysiology. Recent genetic studies provide deeper insights into disease mechanisms and pave the way for precision medicine approaches that could transform type 2 diabetes management. Additionally, the effect of environmental factors on type 2 diabetes, particularly from epigenetic modifications, adds another layer of complexity to understanding and addressing this multifaceted disease.

Gene networks encapsulate biological knowledge, often linked to polygenic diseases. While model system experiments generate many plausible gene networks, validating their role in human phenotypes requires evidence from human genetics. Rare variants provide the most straightforward path for such validation. While single-gene analyses often lack power due to rare variant sparsity, expanding the unit of association to networks offers a powerful alternative, provided it integrates network connections. Here, we introduce NERINE, a hierarchical model-based association test that integrates gene interactions that integrates gene interactions while remaining robust to network inaccuracies. Applied to biobanks, NERINE uncovers compelling network associations for breast cancer, cardiovascular diseases, and type II diabetes, undetected by single-gene tests. For Parkinson's disease (PD), NERINE newly substantiates several GWAS candidate loci with rare variant signal and synergizes human genetics with experimental screens targeting cardinal PD pathologies: dopaminergic neuron survival and alpha-synuclein pathobiology. CRISPRi-screening in human neurons and NERINE converge on PRL, revealing an intraneuronal α-synuclein/prolactin stress response that may impact resilience to PD pathologies.

Over the past three decades, significant advancements have been made in mutation enrichment methods, driven by the increasing need for precise and efficient identification of rare genetic variants associated with diseases. Mutation-enrichment methods have emerged to boost sensitivity and enable easy detection of low-frequency mutations. These methods are crucial in genomics research and clinical diagnostics, allowing for the detection of low-frequency mutations within large genomic datasets. This review presents a summary of technological developments in rare mutation enrichment and emphasizes their mechanisms and applications in liquid biopsies.

Adaptation to environmental conditions occurs over diverse evolutionary timescales. In multi-cellular organisms, adaptive traits are often studied in tissues/organs relevant to the environmental challenge. We argue for the importance of an underappreciated layer of evolutionary adaptation manifesting at the cellular level. Cell-autonomous adaptations (CAAs) are inherited traits that boost organismal fitness by enhancing individual cell function. For instance, the cell-autonomous enhancement of mitochondrial oxygen utilization in hypoxic environments differs from an optimized erythropoiesis response, which involves multiple tissues. We explore the breadth of CAAs across challenges and highlight their counterparts in unicellular organisms. Applying these insights, we mine selection signals in Andean highlanders, revealing novel candidate CAAs. The conservation of CAAs across species may reveal valuable insights into multi-cellular evolution.

Although diabetes is now a global epidemic, China has the highest number of affected people, presenting profound public health and socioeconomic challenges. In China, rapid ecological and lifestyle shifts have dramatically altered diabetes epidemiology and risk factors. In this Review, we summarize the epidemiological trends and the impact of traditional and emerging risk factors on Chinese diabetes prevalence. We also explore recent genetic, metagenomic and metabolomic studies of diabetes in Chinese, highlighting their role in pathogenesis and clinical management. Although heterogeneity across these multidimensional areas poses major analytic challenges in classifying patterns or features, they have also provided an opportunity to increase the accuracy and specificity of diagnosis for personalized treatment and prevention. National strategies and ongoing research are essential for improving diabetes detection, prevention and control, and for personalizing care to alleviate societal impacts and maintain quality of life.

Detecting genome-wide SNP-SNP interactions (epistasis) efficiently is essential to harnessing the vast data now available from modern biobanks. With millions of SNPs and genetic information from hundreds of thousands of individuals, researchers are positioned to uncover new insights into complex disease pathways. However, this data scale brings significant computational and statistical challenges. To address these, recent approaches leverage GPU-based parallel computing for high-throughput, cost-effective analysis and refine algorithms to improve time and memory efficiency. In this survey, we systematically review GPU-accelerated methods for exhaustive epistasis detection, detailing the statistical models used and the computational strategies employed to enhance performance. Our findings indicate substantial speedups with GPU implementations over traditional CPU approaches. We conclude that while GPU-based solutions hold promise for advancing genomic research, continued innovation in both algorithm design and hardware optimization is necessary to meet future data challenges in the field.

Lifestyle intervention prevents or delays type 2 diabetes (T2D) in subjects at a high risk of T2D. However, it is not known whether genetic variants modify the effect on incident T2D during lifestyle intervention.

To investigate whether a low or high genetic risk has effects on incident T2D in a group-based lifestyle intervention study.

The T2D-GENE trial involved 973 men from the Metabolic Syndrome in Men (METSIM) cohort, aged 50-75 years, body mass index ≥25 kg/m2, fasting plasma glucose 5.6-6.9 mmol/L, hemoglobin A1c < 48 mmol/mol, and either a low or high genetic risk score for T2D. There were 2 intervention groups, a low (n = 315) and high genetic risk for T2D (n = 313). They were provided with a 3-year group-based intervention with access to a web portal focused on healthy diet and physical activity. There were also corresponding population-based control groups at low (n = 196) and high (n = 149) genetic risk for T2D who had two laboratory visits (0 and 3 years) and general health advice as a part of their METSIM cohort protocol. The primary outcome was incident T2D, and a secondary outcome was glycemia.

The intervention significantly lowered the risk of T2D among the participants with a high genetic risk for T2D [hazards ratio (HR) 0.30, 95% confidence interval (CI) 0.16-0.56, P < .001) whereas in the low genetic risk group the effect was not significant (HR 0.69, 95% CI 0.36-1.32, P = .262). The intervention effect was not significantly different between the high and low genetic risk groups (P = .135). The intervention significantly ameliorated the worsening of glycemia and decreased weight both in the low and high genetic risk groups.

Our results showed that individuals with a high genetic risk for T2D benefitted from a low-cost group-based intervention focusing on healthy diet and physical activity. Therefore, all individuals at risk of T2D should be encouraged to make lifestyle changes regardless of genetic risk.

Although sex differences in coronary artery disease (CAD) risk have been observed, little is known about the role of sex hormones in CAD genetics. Accounting for sex hormone levels may help identify CAD-risk loci and extend our knowledge of its genetic architecture.

A total of 365 662 individuals of European ancestry enrolled in the UK Biobank were considered. Genetic interaction of total testosterone, bioavailable testosterone, and SHBG (sex hormone-binding globulin) were evaluated. Gene-environment interactions in millions of samples software was used to conduct sex-stratified genome-wide interaction analysis with prevalent CAD as the outcome. Participant age at enrollment and principal components 1 to 10 were adjusted as covariates. We identified 45 loci in men and 8 loci in women that reached genome-wide significance (P < 5 × 10-8) for CAD. Ten of the loci identified (5 loci in both men and women) were through joint effects and would not have been picked up using a traditional genome-wide association study. Two of the joint effect loci in women were independently identified with significant single nucleotide polymorphism-total testosterone interactions.

This genome-wide gene-sex hormone interaction study identified genomic-risk loci that may contribute to the differential CAD risk between men and women, which otherwise would not have been discovered in a traditional genome-wide association study solely including marginal genetic effects.

Ghrelin is a hormone consisting of 28 amino acids. Growth hormone secretagogue receptor (GHSR) is a receptor for ghrelin, which is expressed in the brain, pituitary gland, and adrenal glands, especially in the hypothalamus. The binding of ghrelin to the receptor 1a subtype mediates most of the biological effects of ghrelin. Ghrelin has a close relationship with the onset of psychosis. Ghrelin can affect the onset of psychosis by regulating neurotransmitters such as dopamine, γ-aminobutyric acid (GABA), and 5-hydroxytryptamine (5-HT) through the hypothalamus-pituitary-adrenal (HPA) axis, brain-gut axis, the mesolimbic dopamine system, and other ways. Ghrelin activates neuropeptide Y (NPY) in the hypothalamic arcuate nucleus (ARC) through the GHSR. Ghrelin binds to neurons in the ventral tegmental area (VTA), where it promotes the activity of dopamine neurons in the nucleus accumbens (NAcs) in a GHSR-dependent way, increasing dopamine levels and the reward system. This article summarized the recent research progress of ghrelin in depression, anxiety, schizophrenia, anorexia nervosa (AN), and bulimia nervosa (BN), and emphasized its potential application for psychiatric disorders treatment.

Diabetes and its complications are major diseases that affect human health. Diabetic cardiovascular complications such as cardiovascular diseases (CVDs) are the major complications of diabetes, which are associated with the loss of cardiovascular cells. Pathogenically the role of ferroptosis, an iron-dependent cell death, and cuproptosis, a copper-dependent cell death has recently been receiving attention for the pathogenesis of diabetes and its cardiovascular complications. How exposure to environmental metals affects these two metal-dependent cell deaths in cardiovascular pathogenesis under diabetic and nondiabetic conditions remains largely unknown. As an omnipresent environmental metal, cadmium exposure can cause oxidative stress in the diabetic cardiomyocytes, leading to iron accumulation, glutathione depletion, lipid peroxidation, and finally exacerbate ferroptosis and disrupt the cardiac. Moreover, cadmium-induced hyperglycemia can enhance the circulation of advanced glycation end products (AGEs). Excessive AGEs in diabetes promote the upregulation of copper importer solute carrier family 31 member 1 through activating transcription factor 3/transcription factor PU.1, thereby increasing intracellular Cu+ accumulation in cardiomyocytes and disturbing Cu+ homeostasis, leading to a decline of Fe-S cluster protein and reactive oxygen species accumulation in cardiomyocytes mitochondria. In this review, we summarize the available evidence and the most recent advances exploring the underlying mechanisms of ferroptosis and cuproptosis in CVDs and diabetic cardiovascular complications, to provide critical perspectives on the potential pathogenic roles of ferroptosis and cuproptosis in cadmium-induced or exacerbated cardiovascular complications in diabetic individuals.

Hyperglycemia is affected by lifestyle and genetic factors. We investigated if dietary patterns associate with glycemia in individuals with high or low genetic risk for type 2 diabetes (T2D).

Men (n = 1577, 51-81 years) without T2D from the Metabolic Syndrome in Men (METSIM) cohort filled a food-frequency questionnaire and participated in a 2-hour oral glucose tolerance test. Polygenetic risk score (PRS) including 76 genetic variants was used to stratify participants into low or high T2D risk groups. We established two data-driven dietary patterns, termed healthy and unhealthy, and investigated their association with plasma glucose concentrations and hyperglycemia risk.

Healthy dietary pattern was associated with lower fasting and 2-hour plasma glucose, glucose area under the curve, and better insulin sensitivity (Matsuda insulin sensitivity index) and insulin secretion (disposition index) in unadjusted and adjusted models, whereas the unhealthy pattern was not. No interaction was observed between the patterns and PRS on glycemic measures. Healthy dietary pattern was negatively associated with the risk for hyperglycemia in an adjusted model (OR 0.69, 95% CI 0.51-0.95, in the highest tertile), whereas unhealthy pattern was not (OR 1.08, 95% CI 0.79-1.47, in the highest tertile). No interaction was found between diet and PRS on the risk for hyperglycemia (p = 0.69 for healthy diet, p = 0.54 for unhealthy diet).

Our findings suggest that healthy diet is associated with lower glucose concentrations and lower risk for hyperglycemia in men with no interaction with the genetic risk.

Genome-wide association studies (GWAS) have significantly advanced the identification of genetic susceptibility variants associated with complex diseases. As of 2023, approximately 800 variants predisposing individuals to the risk of type 2 diabetes (T2D) were identified through GWAS, and the majority of studies were predominantly conducted in European populations. Despite the shared nature of the majority of genetic susceptibility loci across diverse ethnic populations, GWAS in non-European populations, including Japanese and East Asian populations, have revealed population-specific T2D loci. Currently, polygenic risk scores (PRSs), encompassing millions of associated variants, can identify individuals with a higher T2D risk than the general population. However, GWAS focusing on microvascular complications of diabetes have identified a limited number of disease-susceptibility loci. Ongoing efforts are crucial to enhance the applicability of PRS for all ethnic groups and unravel the genetic architecture of microvascular complications of diabetes.

Diabetic kidney disease (DKD) is the main cause of chronic kidney disease worldwide. While injury to the podocytes, visceral epithelial cells that comprise the glomerular filtration barrier, drives albuminuria, proximal tubule (PT) dysfunction is the critical mediator of DKD progression. Here, we report that the podocyte-specific induction of human KLF6, a zinc-finger binding transcription factor, attenuates podocyte loss, PT dysfunction, and eventual interstitial fibrosis in a male murine model of DKD. Utilizing combination of snRNA-seq, snATAC-seq, and tandem mass spectrometry, we demonstrate that podocyte-specific KLF6 triggers the release of secretory ApoJ to activate calcium/calmodulin dependent protein kinase 1D (CaMK1D) signaling in neighboring PT cells. CaMK1D is enriched in the first segment of the PT, proximal to the podocytes, and is critical to attenuating mitochondrial fission and restoring mitochondrial function under diabetic conditions. Targeting podocyte-PT signaling by enhancing ApoJ-CaMK1D might be a key therapeutic strategy in attenuating the progression of DKD.

Cattle are regarded as highly valuable animals because of their milk, beef, dung, fur, and ability to draft. The scientific community has tried a number of strategies to improve the genetic makeup of bovine germplasm. To ensure higher returns for the dairy and beef industries, researchers face their greatest challenge in improving commercially important traits. One of the biggest developments in the last few decades in the creation of instruments for cattle genetic improvement is the discovery of the genome. Breeding livestock is being revolutionized by genomic selection made possible by the availability of medium- and high-density single nucleotide polymorphism (SNP) arrays coupled with sophisticated statistical techniques. It is becoming easier to access high-dimensional genomic data in cattle. Continuously declining genotyping costs and an increase in services that use genomic data to increase return on investment have both made a significant contribution to this. The field of genomics has come a long way thanks to groundbreaking discoveries such as radiation-hybrid mapping, in situ hybridization, synteny analysis, somatic cell genetics, cytogenetic maps, molecular markers, association studies for quantitative trait loci, high-throughput SNP genotyping, whole-genome shotgun sequencing to whole-genome mapping, and genome editing. These advancements have had a significant positive impact on the field of cattle genomics. This manuscript aimed to review recent advances in genomic technologies for cattle breeding and future prospects in this field.

This study aimed to evaluate the efficacy of a purification method developed for isolating alpha, beta, and delta cells from pancreatic islets of adult mice, extending its application to islets from newborn and aged mice. Furthermore, it sought to examine transcriptome dynamics in mouse pancreatic endocrine islet cells throughout postnatal development and to validate age-related alterations within these cell populations.

We leveraged the high surface expression of CD71 on beta cells and CD24 on delta cells to FACS-purify alpha, beta, and delta cells from newborn (1-week-old), adult (12-week-old), and old (18-month-old) mice. Bulk RNA sequencing was conducted on these purified cell populations, and subsequent bioinformatic analyses included differential gene expression, overrepresentation, and intersection analysis.

Alpha, beta, and delta cells from newborn and aged mice were successfully FACS-purified using the same method employed for adult mice. Our analysis of the age-related transcriptional changes in alpha, beta, and delta cell populations revealed a decrease in cell cycling and an increase in neuron-like features processes during the transition from newborn to adult mice. Progressing from adult to old mice, we identified an inflammatory gene signature related to aging (inflammaging) encompassing an increase in β-2 microglobulin and major histocompatibility complex (MHC) Class I expression.

Our study demonstrates the effectiveness of our cell sorting technique in purifying endocrine subsets from mouse islets at different ages. We provide a valuable resource for better understanding endocrine pancreas aging and identified an inflammaging gene signature with increased β-2 microglobulin and MHC Class I expression as a common hallmark of old alpha, beta, and delta cells, with potential implications for immune response regulation and age-related diabetes.

In Māori and Pacific adults, the CREBRF rs373863828 minor (A) allele is associated with increased body mass index (BMI) but reduced incidence of type-2 and gestational diabetes mellitus. In this prospective cohort study of Māori and Pacific infants, nested within a nutritional intervention trial for pregnant women with obesity and without pregestational diabetes, we investigated whether the rs373863828 A allele is associated with differences in growth and body composition from birth to 12-18 months' corrected age. Infants with and without the variant allele were compared using generalised linear models adjusted for potential confounding by gestation length, sex, ethnicity and parity, and in a secondary analysis, additionally adjusted for gestational diabetes. Carriage of the rs373863828 A allele was not associated with altered growth and body composition from birth to 6 months. At 12-18 months, infants with the rs373863828 A allele had lower whole-body fat mass [FM 1.4 (0.7) vs. 1.7 (0.7) kg, aMD -0.4, 95% CI -0.7, 0.0, P = 0.05; FM index 2.2 (1.1) vs. 2.6 (1.0) kg/m2 aMD -0.6, 95% CI -1.2,0.0, P = 0.04]. However, this association was not significant after adjustment for gestational diabetes, suggesting that it may be mediated, at least in part, by the beneficial effect of CREBRF rs373863828 A allele on maternal glycemic status.

Genome-wide association studies (GWAS) have facilitated a substantial and rapid increase in the number of confirmed genetic susceptibility variants for complex diseases. Approximately 700 variants predisposing individuals to the risk for type 2 diabetes have been identified through GWAS until 2023. From 2018 to 2022, hundreds of type 2 diabetes susceptibility loci with smaller effect sizes were identified through large-scale GWAS with sample sizes of 200,000 to >1 million. The clinical translation of genetic information for type 2 diabetes includes the development of novel therapeutics and risk predictions. Although drug discovery based on loci identified in GWAS remains challenging owing to the difficulty of functional annotation, global efforts have been made to identify novel biological mechanisms and therapeutic targets by applying multi-omics approaches or searching for disease-associated coding variants in isolated founder populations. Polygenic risk scores (PRSs), comprising up to millions of associated variants, can identify individuals with higher disease risk than those in the general population. In populations of European descent, PRSs constructed from base GWAS data with a sample size of approximately 450,000 have predicted the onset of diseases well. However, European GWAS-derived PRSs have limited predictive performance in non-European populations. The predictive accuracy of a PRS largely depends on the sample size of the base GWAS data. The results of GWAS meta-analyses for multi-ethnic groups as base GWAS data and cross-population polygenic prediction methodology have been applied to establish a universal PRS applicable to small isolated ethnic populations.

Cardiac glycosides, known as inhibitors of Na+,K+-ATPase, have anti-cancer effects such as suppression of cancer cell proliferation and induction of cancer cell death. Here, we examined the signaling pathway elicited by cardiac glycosides in the human hepatocellular carcinoma HepG2 cells and human epidermoid carcinoma KB cells. Three kinds of cardiac glycosides (ouabain, oleandrin, and digoxin) inhibited the cancer cell proliferation and decreased the expression level of thyroid adenoma-associated protein (THADA). Interestingly, the knockdown of THADA inhibited cancer cell proliferation, and the proliferation was significantly rescued by re-expression of THADA in the THADA-knockdown cells. In addition, the THADA-knockdown markedly decreased the expression level of L-type amino acid transporter LAT1. Cardiac glycosides also reduced the LAT1 expression. The LAT1 inhibitor, JPH203, significantly weakened the cancer cell proliferation. These results suggest that the binding of cardiac glycosides to Na+,K+-ATPase negatively regulates the THADA-LAT1 pathway, exerting the anti-proliferative effect in cancer cells.

Limited studies investigated the association between high-level fine particulate matter (PM2.5) pollution and early-onset diabetes, leaving the possible metabolic mechanisms unclear. We assessed the association of cumulative PM2.5 exposure with diabetes, including early-onset, in high-pollution areas of China and explored whether metabolic factors mediated this association.

124,204 participants (≥18 years) from 121 counties in Hunan province, China, were enrolled between 2005 and 2020, with follow-up until 2021. The ground-level air pollution concentrations at each participant's residence were calculated using a high-quality dataset in China. The independent association of PM2.5 with incident diabetes and early-onset diabetes was assessed by Cox proportional hazards models. Restricted cubic splines were utilized to establish the exposure-response relationships. The role of metabolism-related mediators was estimated by mediation analysis.

During a median follow-up of 8.47 (IQR, 6.65-9.82) years, there were 3650 patients with new-onset diabetes. Each 1 μg/m3 increase in the level of cumulative PM2.5 exposure was positively related to an increased incidence of diabetes (HR 1.177, 95 % CI 1.172-1.181) among individuals in the PM2.5 > 50 μg/m3 group after adjusting for multiple variables. The relationship of the PM2.5 dose-response curve for diabetes was non-linear. Significant associations between PM2.5 exposure and early-onset diabetes risk were observed, with this risk showing an increase with the earlier age of early diabetes onset. Males, young individuals (≤45 years), and those with a lower body mass index (BMI <24 kg/m2) appeared to be more susceptible to diabetes. Moreover, change in BMI significantly mediated 31.06 % of the PM2.5-diabetes relationship.

Long-term cumulative PM2.5 exposure increased the risk of early-onset diabetes, which is partially mediated by BMI. Sustained air pollution control measures, priority protection of vulnerable individuals, and effective management of BMI should be taken to reduce the burden of diabetes.

The cell proliferation protein 123 (CDC123) is involved in the synthesis of the eukaryotic initiation factor 2 (eIF2), which regulates eukaryotic translation. Although CDC123 is considered a candidate oncogene in breast cancer, its expression and role in Hepatocellular Carcinoma (HCC) remain unknown. Herein, we obtained the CDC123 RNA-seq and clinical prognostic data from the TCGA database. The mRNA level revealed that CDC123 was highly expressed in HCC patients, and Kaplan-Meier analysis implied better prognoses in HCC patients with low CDC123 expression (P < 0.001). The multivariate Cox analysis revealed that the CDC123 level was an independent prognostic factor (P < 0.001). We further confirmed a high CDC123 expression in HCC cell lines. Additionally, we found that CDC123 knockdown in HCC cell lines significantly inhibited cellular proliferation, invasion, and migration. Moreover, CDC123 was co-expressed with the CDK5 Regulatory Subunit-Associated Protein 1 Like 1 (CDKAL1), whose mRNA level was decreased after silencing CDC123. Therefore, we hypothesized that CDC123 promotes HCC progression by regulating CDKAL1.

Previous studies on genetic diseases predominantly focused on protein-coding variations, overlooking the vast noncoding regions in the human genome. The development of high-throughput sequencing technologies and functional genomics tools has enabled the systematic identification of functional noncoding variants. These variants can impact gene expression, regulation, and chromatin conformation, thereby contributing to disease pathogenesis. Understanding the mechanisms that underlie the impact of noncoding variants on genetic diseases is indispensable for the development of precisely targeted therapies and the implementation of personalized medicine strategies. The intricacies of noncoding regions introduce a multitude of challenges and research opportunities. In this review, we introduce a spectrum of noncoding variants involved in genetic diseases, along with research strategies and advanced technologies for their precise identification and in-depth understanding of the complexity of the noncoding genome. We will delve into the research challenges and propose potential solutions for unraveling the genetic basis of rare and complex diseases.

Studies demonstrated the associations of cadmium (Cd) with lipid levels and dyslipidemia risk, but the mechanisms involved need further exploration.

We aimed to explore the role of DNA methylation (DNAM) in the relationship of Cd with lipid levels and dyslipidemia risk.

Urinary cadmium levels (UCd) were measured by inductively coupled plasma mass spectrometry, serum high-density lipoprotein (HDL), total cholesterol, triglyceride, and low-density lipoprotein were measured with kits, and DNAM was measured using the Infinium MethylationEPIC BeadChip. Robust linear regressions were conducted for epigenome-wide association study. Multivariate linear and logistic regressions were performed to explore the associations of UCd with lipid levels and dyslipidemia risk, respectively. Mediation analyses were conducted to explore potential mediating role of DNAM in the associations of Cd with lipid levels and dyslipidemia risk.

UCd was negatively associated with HDL levels (p = 0.01) and positively associated with dyslipidemia (p < 0.01). There were 92/11 DMPs/DMRs (FDR<0.05) associated with UCd. Cd-associated DNAM and pathways were connected with cardiometabolic diseases and immunity. Cg07829377 (LINC01060) mediated 42.05%/22.88% of the UCd-HDL/UCd-dyslipidemia associations (p = 0.02 and 0.01, respectively).

Cadmium caused site-specific DNAM alterations and the associations of UCd with lipid levels and dyslipidemia risk may be partially mediated by DNAM.

This review discusses the landscape of personalized prevention and management of obesity from a nutrigenetics perspective. Focusing on macronutrient tailoring, we discuss the impact of genetic variation on responses to carbohydrate, lipid, protein, and fiber consumption. Our bioinformatic analysis of genomic variants guiding macronutrient intake revealed enrichment of pathways associated with circadian rhythm, melatonin metabolism, cholesterol and lipoprotein remodeling and PPAR signaling as potential targets of macronutrients for the management of obesity in relevant genetic backgrounds. Notably, our data-based in silico predictions suggest the potential of repurposing the SYK inhibitor fostamatinib for obesity treatment in relevant genetic profiles. In addition to dietary considerations, we address genetic variations guiding lifestyle changes in weight management, including exercise and chrononutrition. Finally, we emphasize the need for a refined understanding and expanded research into the complex genetic landscape underlying obesity and its management.

Among the premenopausal women, Polycystic Ovary Syndrome (PCOS) is the most prevalent endocrinopathy affecting the reproductive system and metabolic rhythms leading to disrupted menstrual cycle. Being heterogeneous in nature it is characterized by complex symptomology of oligomennorhoea, excess of androgens triggering masculine phenotypic appearance and/or multiple follicular ovaries. The etiology of this complex disorder remains somewhat doubtful and the researchers hypothesize multisystem links in the pathogenesis of this disease. In this review, we attempt to present several hypotheses that tend to contribute to the etiology of PCOS. Metabolic inflexibility, aberrant pattern of gonadotropin signaling along with the evolutionary, genetic and environmental factors have been discussed. Considered a lifelong endocrinological implication, no universal treatment is available for PCOS so far however; multiple drug therapy is often advised along with simple life style intervention is mainly advised to manage its cardinal symptoms. Here we aimed to present a summarized view of pathophysiological links of PCOS with potential therapeutic strategies.

Folk medicines are attractive therapeutic agents for treating type 2 diabetes mellitus (T2DM). Most plant extracts that have been suggested to restore β-cells function were tested in vivo. Some only have been tested in vitro to determine whether they have a direct effect on β-cells islets of Langerhans. Currently, there are no defined criteria for screening of β-cell-directed plant-based remedies as potential antidiabetic agents.

In this review, we have identified certain criteria/characteristics that can be used to generate a "screening portfolio" to identify plant extracts as potential β-cell-directed agents for the treatment of T2DM. To validate our screening method, we studied the potential therapeutic efficacy of a Gymnema sylvestre (GS) extract using the screening criteria detailed in the review. Six criteria have been identified and validated using OSA®, a GS extract. By using this screening method, we show that OSA® fulfilled most of the criteria identified for an effective β-cell-directed antidiabetic therapy, being an effective insulin-releasing agent at nontoxic concentrations; maintaining β-cell insulin content by stimulating a concomitant increase in insulin gene transcription; maintaining β-cell mass by protecting against apoptosis; and being effective at maintaining normoglycemia in vivo in a mouse model and a human cohort with T2DM.

The present review has highlighted the importance of having a screening portfolio for plant extracts that have potential antidiabetic effects in the treatment of T2DM. We propose that this screening method should be adopted for future studies to identify new β-cell-directed antidiabetic plant derived agents.

Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or 'fill-in' missing phenotypes in population-scale biobank datasets. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods. On three traits with notable amounts of missingness, we show that AutoComplete yields imputed phenotypes that are genetically similar to the originally observed phenotypes while increasing the effective sample size by about twofold on average. Further, genome-wide association analyses on the resulting imputed phenotypes led to a substantial increase in the number of associated loci. Our results demonstrate the utility of deep learning-based phenotype imputation to increase power for genetic discoveries in existing biobank datasets.

Higher-order interaction patterns among proteins have the potential to reveal mechanisms behind molecular processes and diseases. While clustering methods are used to identify functional groups within molecular interaction networks, these methods largely focus on edge density and do not explicitly take into consideration higher-order interactions. Disease genes in these networks have been shown to exhibit rich higher-order structure in their vicinity, and considering these higher-order interaction patterns in network clustering have the potential to reveal new disease-associated modules.

We propose a higher-order community detection method which identifies community structure in networks with respect to specific higher-order connectivity patterns beyond edges. Higher-order community detection on four different protein-protein interaction networks identifies biologically significant modules and disease modules that conventional edge-based clustering methods fail to discover. Higher-order clusters also identify disease modules from genome-wide association study data, including new modules that were not discovered by top-performing approaches in a Disease Module DREAM Challenge. Our approach provides a more comprehensive view of community structure that enables us to predict new disease-gene associations.

https://github.com/Reed-CompBio/graphlet-clustering.

The identification of myokines susceptible to improve glucose homeostasis following bariatric surgery could lead to new therapeutic approaches for type 2 diabetes.

Changes in the homeostasis model assessment (HOMA) test were assessed in patients before and 3 months after bariatric surgery. Changes in myokines expression and circulating levels were assessed using real-time quantitative polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA). Myokines known to regulate glucose homeostasis were identified using literature (targeted study) and putative myokines using RNA-sequencing (untargeted study). A linear regression analysis adjusted for age and sex was used to search for associations between changes in the HOMA test and changes in myokines.

In the targeted study, brain-derived neurotrophic factor (BDNF) expression was upregulated (+30%, P = .006) while BDNF circulating levels were decreased (-12%, P = .001). Upregulated BDNF expression was associated with decreased HOMA of insulin resistance (HOMA-IR) (adjusted estimate [95% confidence interval {CI}]: -0.51 [-0.88 to -0.13], P = .010). Decreased BDNF serum levels were associated with decreased HOMA of beta-cell function (HOMA-B) (adjusted estimate [95% CI] = 0.002 [0.00002-0.0031], P = .046). In the untargeted study, upregulated putative myokines included XYLT1 (+64%, P < .001), LGR5 (+57, P< .001), and SPINK5 (+46%, P < .001). Upregulated LGR5 was associated with decreased HOMA-IR (adjusted estimate [95% CI] = -0.50 [-0.86 to -0.13], P = .009). Upregulated XYLT1 and SPINK5 were associated with increased HOMA of insulin sensitivity (HOMA-S) (respectively, adjusted estimate [95% CI] = 109.1 [28.5-189.8], P = .009 and 16.5 [0.87-32.19], P = .039).

Improved glucose homeostasis following bariatric surgery is associated with changes in myokines expression and circulating levels. In particular, upregulation of BDNF, XYLT1, SPINK5, and LGR5 is associated with improved insulin sensitivity. These results suggest that these myokines could contribute to improved glucose homeostasis following bariatric surgery.

NCT03341793 on ClinicalTrials.gov (https://clinicaltrials.gov/).

The co-occurrence of migraine and glycemic traits has long been reported in observational epidemiological studies, but it has remained unknown how they are linked genetically. We used large-scale GWAS summary statistics on migraine, headache, and nine glycemic traits in European populations to perform cross-trait analyses to estimate genetic correlation, identify shared genomic regions, loci, genes, and pathways, and test for causal relationships. Out of the nine glycemic traits, significant genetic correlation was observed for fasting insulin (FI) and glycated haemoglobin (HbA1c) with both migraine and headache, while 2-h glucose was genetically correlated only with migraine. Among 1703 linkage disequilibrium (LD) independent regions of the genome, we found pleiotropic regions between migraine and FI, fasting glucose (FG), and HbA1c, and pleiotropic regions between headache and glucose, FI, HbA1c, and fasting proinsulin. Cross-trait GWAS meta-analysis with glycemic traits, identified six novel genome-wide significant lead SNPs with migraine, and six novel lead SNPs with headache (Pmeta < 5.0 × 10-8 and Psingle-trait < 1 × 10-4), all of which were LD-independent. Genes with a nominal gene-based association (Pgene ≤ 0.05) were significantly enriched (overlapping) across the migraine, headache, and glycemic traits. Mendelian randomisation analyses produced intriguing, but inconsistent, evidence for a causal relationship between migraine and headache with multiple glycemic traits; and consistent evidence suggesting increased fasting proinsulin levels may causally decrease the risk of headache. Our findings indicate that migraine, headache, and glycemic traits share a common genetic etiology and provide genetic insights into the molecular mechanisms contributing to their comorbid relationship.

Pediatric endocrinology will undergo an extraordinary revolution this century [...].

Agonist-mediated stimulated pathway of mucin and insulin release are biphasic in which rapid fusion of pre-docked granules is followed by slow docking and fusion of granules from the reserve pool. Here, based on a cell-culture system, we show that plasma membrane-located tetraspanin-8 sequesters syntaxin-2 to control mucin release. Tetraspanin-8 affects fusion of granules during the second phase of stimulated mucin release. The tetraspanin-8/syntaxin-2 complex does not contain VAMP-8, which functions with syntaxin-2 to mediate granule fusion. We suggest that by sequestering syntaxin-2, tetraspanin-8 prevents docking of granules from the reserve pool. In the absence of tetraspanin-8, more syntaxin-2 is available for docking and fusion of granules and thus doubles the quantities of mucins secreted. This principle also applies to insulin release and we suggest a cell type specific Tetraspanin/Syntaxin combination is a general mechanism regulating the fusion of dense core granules.