Genes and Type 2 Diabetes Mellitus

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.

Prognostic interaction patterns in diabetes mellitus II: A random-matrix-theory relation

We analyze protein-protein interactions in diabetes mellitus II and its normal counterpart under the combined framework of random matrix theory and network biology. This disease is the fifth-leading cause of death in high-income countries and an epidemic in developing countries, affecting around 8% of the total adult population in the world. Treatment at the advanced stage is difficult and challenging, making early detection a high priority in the cure of the disease. Our investigation reveals specific structural patterns important for the occurrence of the disease. In addition to the structural parameters, the spectral properties reveal the top contributing nodes from localized eigenvectors, which turn out to be significant for the occurrence of the disease. Our analysis is time-efficient and cost-effective, bringing a new horizon in the field of medicine by highlighting major pathways involved in the disease. The analysis provides a direction for the development of novel drugs and therapies in curing the disease by targeting specific interaction patterns instead of a single protein.

Can data science inform environmental justice and community risk screening for type 2 diabetes?

Background

Having the ability to scan the entire country for potential “hotspots” with increased risk of developing chronic diseases due to various environmental, demographic, and genetic susceptibility factors may inform risk management decisions and enable better environmental public health policies.

Objectives

Develop an approach for community-level risk screening focused on identifying potential genetic susceptibility hotpots.

Methods

Our approach combines analyses of phenotype-genotype data, genetic prevalence of single nucleotide polymorphisms, and census/geographic information to estimate census tract-level population attributable risks among various ethnicities and total population for the state of California.

Results

We estimate that the rs13266634 single nucleotide polymorphism, a type 2 diabetes susceptibility genotype, has a genetic prevalence of 56.3%, 47.4% and 37.0% in Mexican Mestizo, Caucasian, and Asian populations. Looking at the top quintile for total population attributable risk, 16 California counties have greater than 25% of their population living in hotspots of genetic susceptibility for developing type 2 diabetes due to this single genotypic susceptibility factor.

Conclusions

This study identified counties in California where large portions of the population may bear additional type 2 diabetes risk due to increased genetic prevalence of a susceptibility genotype. This type of screening can easily be extended to include information on environmental contaminants of interest and other related diseases, and potentially enables the rapid identification of potential environmental justice communities. Other potential uses of this approach include problem formulation in support of risk assessments, land use planning, and prioritization of site cleanup and remediation actions.

Reduced plasma glucose by asparagine synthetase knockdown in the mouse liver

Expression of the asparagine synthetase gene is dependent on extracellular glucose concentration. This gene was knocked-down in livers of male Balb/c mice by a hydrodynamic tail vein injection of small interfering RNA (siRNA) against the gene. This knockdown resulted in a significant decrease in plasma glucose concentration. These results suggested that asparagine synthetase is a novel protein that regulates plasma glucose levels.

A nanocarrier system for the delivery of nucleic acids targeted to a pancreatic beta cell line

Pancreatic β cells secrete insulin in response to glucose levels and thus are involved in controlling blood glucose levels. A line of pancreatic β cells "MIN6" has been used in studies related to the function of β cells and diabetes therapy. Regulating gene expression in MIN6 cells could accelerate these studies, but an efficient method for the transfection of nucleic acids targeted to MIN6 cells is required. We report here on a liposome-based carrier targeted to pancreatic β cells (Multifunctional envelope-type nano device for pancreatic β cells, β-MEND). We identified a lipid composition for use in preparing the β-MEND, which permits the particles to be efficiently internalized into MIN6, as evidenced by flow cytometry analyses. Intracellular observation by confocal laser scanning microscopy showed that the β-MEND efficiently delivered the oligo nucleic acids to the cytosol of MIN6 cells. Moreover, using a β-MEND encapsulating a 2'-O-Methyl RNA complementary to a microRNA that suppresses insulin secretion, the knockdown of the targeted microRNA and an up-regulation of insulin secretion were observed in MIN6. Thus, the β-MEND holds promise as an efficient system for delivering nucleic acids targeted to MIN6 and can contribute to research and therapy aimed at diabetes.

The 'missing heritability' of common disorders: should health researchers care?

This article critiques the "missing heritability" position, which calls for greater efforts and funding to identify the genetic architecture of common disorders, even if this endeavor has yet to translate into tangible prevention, diagnosis, or treatment interventions. Supporters of the position contend that genetic variants "for" common disorders, which they argue must exist based on heritability estimates (hence their "missing heritability" position), have not been found because the current state of science and technology is not adequate to the task, yet they insist that this search warrants significant societal investments. We argue, instead, that these variants have not been found because they do not exist. The thrust of the problem with the "missing heritability" position, we propose, lies in its proponents' use of faulty concepts and research methods, including reliance on twin studies, plagued with environmental confounds; on the concept of heritability, a breeding statistic and not a measure of the importance of genetic influences on phenotypes; and on the belief that genetic variations are relevant to understanding, preventing, or treating common disorders, a belief that we argue is false. We elaborate on these problems, discuss their public health implications, and suggest future directions for a critical analysis of human genetics.

Pathobiochemical changes in diabetic skeletal muscle as revealed by mass-spectrometry-based proteomics

Insulin resistance in skeletal muscle tissues and diabetes-related muscle weakness are serious pathophysiological problems of increasing medical importance. In order to determine global changes in the protein complement of contractile tissues due to diabetes mellitus, mass-spectrometry-based proteomics has been applied to the investigation of diabetic muscle. This review summarizes the findings from recent proteomic surveys of muscle preparations from patients and established animal models of type 2 diabetes. The potential impact of novel biomarkers of diabetes, such as metabolic enzymes and molecular chaperones, is critically examined. Disease-specific signature molecules may be useful for increasing our understanding of the molecular and cellular mechanisms of insulin resistance and possibly identify new therapeutic options that counteract diabetic abnormalities in peripheral organ systems. Importantly, the biomedical establishment of biomarkers promises to accelerate the development of improved diagnostic procedures for characterizing individual stages of diabetic disease progression, including the early detection of prediabetic complications.

Cardiometabolic plasticity in response to a short-term diet and exercise intervention in young Hispanic and nonHispanic white adults

BACKGROUND

Young adult Mexican Americans (MA) exhibit lower insulin sensitivity (Si) than nonHispanic whites (NHW), even when controlling for fitness and adiposity. It is unclear if MA are as responsive to the same lifestyle intervention as NHW.

OBJECTIVE

We developed a model to examine cardiometabolic plasticity (i.e., changes in Si and plasma lipids) in MA compared to NHW adults in response to a diet-exercise intervention.

DESIGN

Sedentary subjects (20 NHW: 11F, 9M, 23.0 y, 25.5 kg/m(2); 17 MA: 13F, 4M, 22.7 y, 25.4 kg/m(2)) consumed their habitual diets and remained sedentary for 7 days, after which fasting blood samples were obtained, and a 3-h intravenous glucose tolerance test (IVGTT) was performed with the insulin area under the curve (IAUC) used to estimate Si. Subjects then completed a 7-day diet/exercise intervention (diet: low saturated fat, low added sugar, high fiber; exercise: cycling, six total sessions lasting 40-45 min/session at 65% VO(2) max). Pre-intervention tests were repeated.

RESULTS

Pre intervention IAUC was 28% higher (p<0.05) in MA (IAUC pre  =  2298 µU*180 min/mL) than in NHW (IAUC = 1795 µU*180 min/mL). Following the intervention, there was a significant reduction in IAUC in MA (29%) and NHW (32%), however, the IAUC remained higher (p<0.05) for MA (post  = 1635 µU*180 min/mL) than for NHW (post = 1211 µU*180 min/mL). Pre test plasma lipids were not different in MA compared to NHW. Plasma cholesterol and TG concentrations significantly improved in both groups, but concentrations of low density lipoprotein-cholesterol and small dense LDL particles significantly improved only in the NHW.

CONCLUSION

With a short-term diet-exercise intervention, the magnitude of improvements in Si and serum cholesterol and TG in Hispanics are similar to those in NHW. However, because at the outset MA were less insulin sensitive compared to NHW, within the short timeframe studied the ethnic gap in insulin sensitivity remained.

A systems biology approach to identify effective cocktail drugs

BACKGROUND

Complex diseases, such as Type 2 Diabetes, are generally caused by multiple factors, which hamper effective drug discovery. To combat these diseases, combination regimens or combination drugs provide an alternative way, and are becoming the standard of treatment for complex diseases. However, most of existing combination drugs are developed based on clinical experience or test-and-trial strategy, which are not only time consuming but also expensive.

RESULTS

In this paper, we presented a novel network-based systems biology approach to identify effective drug combinations by exploiting high throughput data. We assumed that a subnetwork or pathway will be affected in the networked cellular system after a drug is administrated. Therefore, the affected subnetwork can be used to assess the drug's overall effect, and thereby help to identify effective drug combinations by comparing the subnetworks affected by individual drugs with that by the combination drug. In this work, we first constructed a molecular interaction network by integrating protein interactions, protein-DNA interactions, and signaling pathways. A new model was then developed to detect subnetworks affected by drugs. Furthermore, we proposed a new score to evaluate the overall effect of one drug by taking into account both efficacy and side-effects. As a pilot study we applied the proposed method to identify effective combinations of drugs used to treat Type 2 Diabetes. Our method detected the combination of Metformin and Rosiglitazone, which is actually Avandamet, a drug that has been successfully used to treat Type 2 Diabetes.

CONCLUSIONS

The results on real biological data demonstrate the effectiveness and efficiency of the proposed method, which can not only detect effective cocktail combination of drugs in an accurate manner but also significantly reduce expensive and tedious trial-and-error experiments.

Addressing the need to tailor treatment to the spectrum of type 2 diabetes: new perspectives

Type 2 diabetes mellitus is characterized by the progressive loss of beta cell function, which occurs after many years of insulin resistance. Within this definition, clinicians may see a diverse array of presentations, suggesting different proportions of these two pathogenic factors and a complex etiology. There are also differences in the rate of type 2 diabetes progression in each patient, so treatments must be reviewed frequently to respond to changing severity of pathophysiologies. This article first considers some of the heritable factors and the pathogenic heterogeneity of type 2 diabetes. Relevant socioeconomic and demographic factors influencing disease development are reviewed after that, while emphasizing how a patient's treatment requires changes over time.

Major dietary patterns, ethnicity, and prevalence of type 2 diabetes in rural Hawaii

OBJECTIVE

The association of type 2 diabetes (T2DM) with the overall dietary pattern and its relation with ethnicity was examined.

METHODS

A cross-sectional study with 1257 participants with four ethnicities (Caucasian, Filipino, Native Hawaiian, and Japanese) in the North Kohala region of Hawaii was conducted. Participants 18-95 y of age were surveyed for their ethnic and demographic backgrounds, dietary intakes, and biochemical indexes of glucose intolerance between 1997 and 2000.

RESULTS

Three dietary patterns from the food-frequency questionnaire were identified by factor analysis. Factor 1 was characterized by a healthy diet with a frequent intake of vegetables and fruits, and factor 2 was dominated by animal foods and local ethnic dishes. Factor 3 was characterized by a Western diet, which was dominated by French fries, fast-food hamburgers, pizza, and chips. Multivariate logistic regression model for T2DM prevalence included ethnicity and three dietary factors after adjustment for age, sex, income, physical activity, smoking status, and energy intake. Ethnicity was significantly associated with T2DM, with an odds ratio of 1.83 (95% confidence interval [CI] 1.12-3.00) for Native Hawaiians and 1.92 (95% CI 1.12-3.29) for Filipinos compared with Caucasians 1.92 (95% CI 1.12-3.29). Among the three dietary factors, factor 2 was positively associated with T2DM (odds ratio 1.30, 95% CI 1.03-1.68), but the significance disappeared after adjustment for energy intake.

CONCLUSION

The findings show that ethnicity is a stronger risk factor for T2DM than dietary patterns when energy intake is adjusted for. Reducing energy intake to prevent T2DM deserves more attention during health promotion for the multiethnic population of Hawaii.

Endophenotypes, dimensions, risks: is psychosis analogous to common inherited medical illnesses?

Psychiatric illnesses are perceived as fundamentally different from common medical disorders, a view arising from the mind-body problem and difficulties relating the brain's emergent properties to its physiological substrates. However, schizophrenia and many common medical illnesses are heritable and result from the influence of both genetic and environmental sources. Unlike illnesses such as Huntington's disease, which are caused by a fully penetrant dominant mutation, no single "schizophrenia gene" has been identified. Instead, schizophrenia is likely caused by common variants of many genes, each contributing a subtle effect. Schizophrenia genetically resembles common medical illnesses such as type 2 diabetes, ischemic heart disease, and familial hypercholesterolemia, that have an associated genetic variant, but that are also influenced by other factors such as diet, culture and habits. Just as these illnesses operate through complex gene/environment interaction, schizophrenia is likely caused by several gene variants, neurodevelopmental processes, and learned behavioral response biases. These clinical diseases, however, represent severe forms of the phenotype for both psychiatric and medical illnesses. From a dimensional perspective, individuals possessing the same genotype could express milder forms of the clinical disorder along a spectrum of related traits. We discuss this perspective in the context of an endophenotypic and biological marker approach to understanding schizophrenia and present a research strategy to compare schizophrenia endophenotypes to risk for common medical illnesses.

Association of the SLC1A1 glutamate transporter gene and obsessive-compulsive disorder

CONTEXT

Obsessive-Compulsive Disorder (OCD) is a debilitating illness with putative glutamatergic abnormalities. Two separate proximal haplotypes in the glutamate transporter gene, SLC1A1, were recently reported to be associated with OCD among males, but replication is required.

OBJECTIVES

This study examines SLC1A1 as a candidate gene for OCD and explores gender influences. It was hypothesized that a significant association between SLC1A1 and OCD would be replicated in an independent sample of males but not females.

DESIGN

Family-based association candidate gene study.

SETTING

Participants were recruited from tertiary care OCD specialty clinics.

PARTICIPANTS

OCD probands and their first degree relatives.

MAIN OUTCOMES MEASURES

Association of OCD with genotypes of single nucleotide polymorphism (SNP) markers and related haplotypes.

RESULTS

Association between OCD and the three-marker haplotype rs12682807/ rs2072657/ rs301430, with overtransmission of A/T/T, was observed in both genders combined (global P = 0.0015) and in males (global P = 0.0031). Single-marker associations with OCD in the region (rs3780412 and rs2228622) demonstrated modest significance (permuted P = 0.045).

CONCLUSIONS

This study identifies a significant association between the SLC1A1 glutamate transporter gene and OCD in a haplotype overlapping with that recently reported.

Network-based analysis of affected biological processes in type 2 diabetes models

Type 2 diabetes mellitus is a complex disorder associated with multiple genetic, epigenetic, developmental, and environmental factors. Animal models of type 2 diabetes differ based on diet, drug treatment, and gene knockouts, and yet all display the clinical hallmarks of hyperglycemia and insulin resistance in peripheral tissue. The recent advances in gene-expression microarray technologies present an unprecedented opportunity to study type 2 diabetes mellitus at a genome-wide scale and across different models. To date, a key challenge has been to identify the biological processes or signaling pathways that play significant roles in the disorder. Here, using a network-based analysis methodology, we identified two sets of genes, associated with insulin signaling and a network of nuclear receptors, which are recurrent in a statistically significant number of diabetes and insulin resistance models and transcriptionally altered across diverse tissue types. We additionally identified a network of protein–protein interactions between members from the two gene sets that may facilitate signaling between them. Taken together, the results illustrate the benefits of integrating high-throughput microarray studies, together with protein–protein interaction networks, in elucidating the underlying biological processes associated with a complex disorder.

Type 2 diabetes mellitus currently affects millions of people. It is clinically characterized by insulin resistance in addition to an impaired glucose response and associated with numerous complications including heart disease, stroke, neuropathy, and kidney failure, among others. Accurate identification of the underlying molecular mechanisms of the disease or its complications is an important research problem that could lead to novel diagnostics and therapy. The main challenge stems from the fact that insulin resistance is a complex disorder and affects a multitude of biological processes, metabolic networks, and signaling pathways. In this report, the authors develop a network-based methodology that appears to be more sensitive than previous approaches in detecting deregulated molecular processes in a disease state. The methodology revealed that both insulin signaling and nuclear receptor networks are consistently and differentially expressed in many models of insulin resistance. The positive results suggest such network-based diagnostic technologies hold promise as potentially useful clinical and research tools in the future.

How much can a large population study on genes, environments, their interactions and common diseases contribute to the health of the American people?

I offer a critical perspective on a large-scale population study on gene-environment interactions and common diseases proposed by the US Secretary of Health and Human Services' Advisory Committee on Genetics, Health, and Society (SACGHS). I argue that for scientific and policy reasons this and similar studies have little to add to current knowledge about how to prevent, treat, or decrease inequalities in common diseases, all of which are major claims of the proposal. I use diabetes as an exemplar of the diseases that the study purports to illuminate. I conclude that the question is not whether the study will meet expectations or whether the current emphasis on a genetic paradigm is real or imagined, desirable or not. Rather, the question is why, given the flaws of the science underwriting the study, its assumptions remain unchallenged. Future research should investigate the reasons for this immunity from criticism and for the popularity of this and similar projects among laypersons as well as among intellectuals.

Epigenetic reprogramming and imprinting in origins of disease

The traditional view that gene and environment interactions control disease susceptibility can now be expanded to include epigenetic reprogramming as a key determinant of origins of human disease. Currently, epigenetics is defined as heritable changes in gene expression that do not alter DNA sequence but are mitotically and transgenerationally inheritable. Epigenetic reprogramming is the process by which an organism's genotype interacts with the environment to produce its phenotype and provides a framework for explaining individual variations and the uniqueness of cells, tissues, or organs despite identical genetic information. The main epigenetic mediators are histone modification, DNA methylation, and non-coding RNAs. They regulate crucial cellular functions such as genome stability, X-chromosome inactivation, gene imprinting, and reprogramming of non-imprinting genes, and work on developmental plasticity such that exposures to endogenous or exogenous factors during critical periods permanently alter the structure or function of specific organ systems. Developmental epigenetics is believed to establish "adaptive" phenotypes to meet the demands of the later-life environment. Resulting phenotypes that match predicted later-life demands will promote health, while a high degree of mismatch will impede adaptability to later-life challenges and elevate disease risk. The rapid introduction of synthetic chemicals, medical interventions, environmental pollutants, and lifestyle choices, may result in conflict with the programmed adaptive changes made during early development, and explain the alarming increases in some diseases. The recent identification of a significant number of epigenetically regulated genes in various model systems has prepared the field to take on the challenge of characterizing distinct epigenomes related to various diseases. Improvements in human health could then be redirected from curative care to personalized, preventive medicine based, in part, on epigenetic markings etched in the "margins" of one's genetic make-up.

Techniques used in studies of epigenome dysregulation due to aberrant DNA methylation: an emphasis on fetal-based adult diseases

Epigenetic changes are heritable modifications that do not involve alterations in the primary DNA sequence. They regulate crucial cellular functions such as genome stability, X-chromosome inactivation, and gene imprinting. Epidemiological and experimental observations now suggest that such changes may also explain the fetal basis of adult diseases such as cancer, obesity, diabetes, cardiovascular disorders, neurological diseases, and behavioral modifications. The main molecular events known to initiate and sustain epigenetic modifications are histone modification and DNA methylation. This review specifically focuses on existing and emerging technologies used in studying DNA methylation, which occurs primarily at CpG dinucleotides in the genome. These include standard exploratory tools used for global profiling of DNA methylation and targeted gene investigation: methylation sensitive restriction fingerprinting (MSRF), restriction landmark genomic scanning (RLGS), methylation CpG island amplification-representational difference analysis (MCA-RDA), differential methylation hybridization (DMH), and cDNA microarrays combined with treatment with demethylating agents and inhibitors of histone deacetylase. The basic operating principals, resource requirements, applications, and benefits and limitations of each methodology are discussed. Validation methodologies and functional assays needed to establish the role of a CpG-rich sequence in regulating the expression of a target or candidate gene are outlined. These include in silico database searches, methylation status studies (bisulfite genomic sequencing, COBRA, MS-PCR, MS-SSCP), gene expression studies, and promoter activity analyses. Our intention is to give readers a starting point for choosing methodologies and to suggest a workflow to follow during their investigations. We believe studies of epigenetic changes such as DNA methylation hold great promise in understanding the early origins of adult diseases and in advancing their diagnosis, prevention, and treatment.

G72/G30 in schizophrenia and bipolar disorder: review and meta-analysis

Association of the G72/G30 locus with schizophrenia and bipolar disorder has now been reported in several studies. The G72/G30 locus may be one of several that account for the evidence of linkage that spans a broad region of chromosome 13q. However, the story of G72/G30 is complex. Our meta-analysis of published association studies shows highly significant evidence of association between nucleotide variations in the G72/G30 region and schizophrenia, along with compelling evidence of association with bipolar disorder. But the associated alleles and haplotypes are not identical across studies, and some strongly associated variants are located approximately 50 kb telomeric of G72. Interestingly, G72 and G30 are transcribed in opposite directions; hence, their transcripts could cross-regulate translation. A functional native protein and functional motifs for G72 or G30 remain to be demonstrated. The interaction of G72 with d-amino acid oxidase, itself of interest as a modulator of N-methyl-d-aspartate receptors through regulation of d-serine levels, has been reported in one study and could be a key functional link that deserves further investigation. The association findings in the G72/G30 region, among the most compelling in psychiatry, may expose an important molecular pathway involved in susceptibility to schizophrenia and bipolar disorder.