Genetic Association, Post-translational Modification, and Protein-Protein Interactions in Type 2 Diabetes Mellitus

Unlocking the Potential: Amino Acids' Role in Predicting and Exploring Therapeutic Avenues for Type 2 Diabetes Mellitus

Diabetes mellitus, particularly type 2 diabetes mellitus (T2DM), imposes a significant global burden with adverse clinical outcomes and escalating healthcare expenditures. Early identification of biomarkers can facilitate better screening, earlier diagnosis, and the prevention of diabetes. However, current clinical predictors often fail to detect abnormalities during the prediabetic state. Emerging studies have identified specific amino acids as potential biomarkers for predicting the onset and progression of diabetes. Understanding the underlying pathophysiological mechanisms can offer valuable insights into disease prevention and therapeutic interventions. This review provides a comprehensive summary of evidence supporting the use of amino acids and metabolites as clinical biomarkers for insulin resistance and diabetes. We discuss promising combinations of amino acids, including branched-chain amino acids, aromatic amino acids, glycine, asparagine and aspartate, in the prediction of T2DM. Furthermore, we delve into the mechanisms involving various signaling pathways and the metabolism underlying the role of amino acids in disease development. Finally, we highlight the potential of targeting predictive amino acids for preventive and therapeutic interventions, aiming to inspire further clinical investigations and mitigate the progression of T2DM, particularly in the prediabetic stage.

Prediction and evaluation of deleterious and disease causing non-synonymous SNPs (nsSNPs) in human NF2 gene responsible for neurofibromatosis type 2 (NF2)

The majority of genetic variations in the human genome that lead to variety of different diseases are caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Neurofibromatosis type 2 (NF2) is a deadly disease caused by nsSNPs in the NF2 gene that encodes for a protein called merlin. This study used various in silico methods, SIFT, Polyphen-2, PhD-SNP and MutPred, to investigate the pathogenic effect of 14 nsSNPs in the merlin FERM domain. The G197C and L234R mutations were found to be two deleterious and disease mutations associated with the mild and severe forms of NF2, respectively. Molecular dynamics (MD) simulations were conducted to understand the stability, structure and dynamics of these mutations. Both mutant structures experienced larger flexibility compared to the wildtype. The L234R mutant suffered from more prominent structural instability, which may help to explain why it is associated with the more severe form of NF2. The intramolecular hydrogen bonding in L234R mutation decreased from the wildtype, while intermolecular hydrogen bonding of L234R mutation with solvent greatly increased. The native contacts were also found to be important. Protein-protein docking revealed that L234R mutation decreased the binding complementarity and binding affinity of LATS2 to merlin, which may have an impact on merlin's ability to regulate the Hippo signaling pathway. The calculated binding affinity of the LATS2 to L234R mutant and wildtype merlin protein is found to be 21.73 and -11 kcal/mol, respectively. The binding affinity of the wildtype merlin agreed very well with the experimental value, -8 kcal/mol.Communicated by Ramaswamy H. Sarma.

Genetic architecture for human aggression: A study of gene-phenotype relationship in OMIM

Genetic studies of human aggression have mainly focused on known candidate genes and pathways regulating serotonin and dopamine signaling and hormonal functions. These studies have taught us much about the genetics of human aggression, but no genetic locus has yet achieved genome-significance. We here present a review based on a paradoxical hypothesis that studies of rare, functional genetic variations can lead to a better understanding of the molecular mechanisms underlying complex multifactorial disorders such as aggression. We examined all aggression phenotypes catalogued in Online Mendelian Inheritance in Man (OMIM), an Online Catalog of Human Genes and Genetic Disorders. We identified 95 human disorders that have documented aggressive symptoms in at least one individual with a well-defined genetic variant. Altogether, we retrieved 86 causal genes. Although most of these genes had not been implicated in human aggression by previous studies, the most significantly enriched canonical pathways had been previously implicated in aggression (e.g., serotonin and dopamine signaling). Our findings provide strong evidence to support the causal role of these pathways in the pathogenesis of aggression. In addition, the novel genes and pathways we identified suggest additional mechanisms underlying the origins of human aggression. Genome-wide association studies with very large samples will be needed to determine if common variants in these genes are risk factors for aggression. © 2015 Wiley Periodicals, Inc.

β-Strand mimetic foldamers rigidified through dipolar repulsion

Many therapeutically relevant protein-protein interactions contain hot-spot regions on secondary structural elements, which contribute disproportionately to binding enthalpy. Mimicry of such α-helical regions has met with considerable success, however the analogous approach for the β-strand has received less attention. Presented herein is a foldamer for strand mimicry in which dipolar repulsion is a central determinant of conformation. Computation as well as solution- and solid-phase data are consistent with an ensemble weighted almost exclusively in favor of the desired conformation.

Evidence of colorectal cancer-associated mutation in MCAK: a computational report

Computational prediction of disease-associated non-synonymous polymorphism (nsSNP) has provided a significant platform to filter out the pathological mutations from large pool of SNP datasets at a very low cost input. Several methodologies and complementary protocols have been previously implemented and has provided significant prediction results. Although the previously implicated prediction methods were capable of investigating the most likely deleterious nsSNPs, but due to the lack of genotype-phenotype association analysis, the prediction results lacked in accuracy level. In this work we implemented the computational compilation of protein conformational changes as well as the probable disease-associated phenotypic outcomes. Our result suggested E403K mutation in mitotic centromere-associated kinesin protein as highly damaging and showed strong concordance to the previously observed colorectal cancer mutations aggregation tendency and energy value changes. Moreover, the molecular dynamics simulation results showed major loss in conformation and stability of mutant N-terminal kinesin-like domain structure. The result obtained in this study will provide future prospect of computational approaches in determining the SNPs that may affect the native conformation of protein structure and lead to cancer-associated disorders.

Metabolomics in diabetes

Characterization of metabolic changes is key to early detection, treatment, and understanding molecular mechanisms of diabetes. Diabetes represents one of the most important global health problems. Approximately 90% of diabetics have type 2 diabetes. Identification of effective screening markers is critical for early treatment and intervention that can delay and/or prevent complications associated with this chronic disease. Fortunately, metabolomics has introduced new insights into the pathology of diabetes as well as to predict disease onset and revealed new biomarkers to improve diagnostics in a range of diseases. Small-molecule metabolites have an important role in biological systems and represent attractive candidates to understand T2D phenotypes. Characteristic patterns of metabolites can be revealed that broaden our understanding of T2D disorder. This technique-driven review aims to demystify the mechanisms of T2D, to provide updates on the applications of metabolomics in addressing T2D with a focus on metabolites based biomarker discovery.

INDICO: the development of a resource for epigenomic study of Indians undergoing socioeconomic transition

The INdian DIabetes COnsortium (INDICO) is an initiative to build a resource for genetic, epigenetic and clinical studies of type 2 diabetes (T2D) in socioeconomically transiting Indians. The consortium aims to follow selected communities which are undergoing rapid urbanization over a period of years to create a knowledge base for the better understanding of the gene-environment interaction in T2D. These resources can eventually be of value to not just the diabetes research community but would be able to contribute towards an improved understanding, diagnosis and prevention of numerous complex human disorders.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (doi:10.1007/s11568-011-9157-2) contains supplementary material, which is available to authorized users.

Travelling the world of gene-gene interactions

Over the last few years, main effect genetic association analysis has proven to be a successful tool to unravel genetic risk components to a variety of complex diseases. In the quest for disease susceptibility factors and the search for the 'missing heritability', supplementary and complementary efforts have been undertaken. These include the inclusion of several genetic inheritance assumptions in model development, the consideration of different sources of information, and the acknowledgement of disease underlying pathways of networks. The search for epistasis or gene-gene interaction effects on traits of interest is marked by an exponential growth, not only in terms of methodological development, but also in terms of practical applications, translation of statistical epistasis to biological epistasis and integration of omics information sources. The current popularity of the field, as well as its attraction to interdisciplinary teams, each making valuable contributions with sometimes rather unique viewpoints, renders it impossible to give an exhaustive review of to-date available approaches for epistasis screening. The purpose of this work is to give a perspective view on a selection of currently active analysis strategies and concerns in the context of epistasis detection, and to provide an eye to the future of gene-gene interaction analysis.

Proteomic analysis for antiobesity potential of capsaicin on white adipose tissue in rats fed with a high fat diet

It is well recognized that capsaicin increases thermogenesis through enhancement of catecholamine secretion from the adrenal medulla. In the present study of the antiobesity effect of capsaicin, rats (5-week old) received capsaicin (10 mg/kg) along with a high-fat diet (HFD). In comparison with saline-treated rats, body weight of those in the capsaicin-treated group decreased by 8%. We performed differential proteomic analysis using two-dimensional electrophoresis (2-DE) combined with MALDI-TOF mass spectrometry to elucidate the molecular action of capsaicin on the antiobesity effect in epididymal white adipose tissue (WAT). Protein mapping of WAT homogenates using 2-DE revealed significant alterations to a number of proteins: 10 spots were significantly up-regulated and 10 spots were remarkably down-regulated in HFD fed rats treated with capsaicin. Among them, significant down-regulation of heat shock protein 27 (Hsp27) and Steap3 protein, as well as up-regulation of olfactory receptor (Olr1434) in obese WAT was reported for the first time in association with obesity. Most of the identified proteins are associated with lipid metabolism and redox regulation, in which levels of vimentin, peroxiredoxin, and NAD(P)H:quinone oxidoreductase 1 (NQO1) were significantly reduced (>2-fold), whereas aldo-keto reductase, flavoprotein increased with capsaicin treatment. These data demonstrate that thermogenesis and lipid metabolism related proteins were markedly altered upon capsaicin treatment in WAT, suggesting that capsaicin may be a useful phytochemical for attenuation of obesity.

Gene prioritization in Type 2 Diabetes using domain interactions and network analysis

Background

Identification of disease genes for Type 2 Diabetes (T2D) by traditional methods has yielded limited success. Based on our previous observation that T2D may result from disturbed protein-protein interactions affected through disrupting modular domain interactions, here we have designed an approach to rank the candidates in the T2D linked genomic regions as plausible disease genes.

Results

Our approach integrates Weight value (Wv) method followed by prioritization using clustering coefficients derived from domain interaction network. Wv for each candidate is calculated based on the assumption that disease genes might be functionally related, mainly facilitated by interactions among domains of the interacting proteins. The benchmarking using a test dataset comprising of both known T2D genes and non-T2D genes revealed that Wv method had a sensitivity and specificity of 0.74 and 0.96 respectively with 9 fold enrichment. The candidate genes having a Wv > 0.5 were called High Weight Elements (HWEs). Further, we ranked HWEs by using the network property-the clustering coefficient (C_i). Each HWE with a C_i < 0.015 was prioritized as plausible disease candidates (HWEc) as previous studies indicate that disease genes tend to avoid dense clustering (with an average C_i of 0.015). This method further prioritized the identified disease genes with a sensitivity of 0.32 and a specificity of 0.98 and enriched the candidate list by 6.8 fold. Thus, from the dataset of 4052 positional candidates the method ranked 435 to be most likely disease candidates. The gene ontology sharing for the candidates showed higher representation of metabolic and signaling processes. The approach also captured genes with unknown functions which were characterized by network motif analysis.

Conclusions

Prioritization of positional candidates is essential for cost-effective and an expedited discovery of disease genes. Here, we demonstrate a novel approach for disease candidate prioritization from numerous loci linked to T2D.

Genetic variants of FOXA2: risk of type 2 diabetes and effect on metabolic traits in North Indians

Here, we examined the association of genetic variants of FOXA2, an upstream activator of the beta-cell transcription factor network, with type 2 diabetes and related phenotypes in North India. We genotyped three SNPs (rs1212275, rs1055080, rs6048205) and the (TCC)( n ) repeat polymorphism in 1,656 participants comprising 1,031 patients with type 2 diabetes and 625 controls. SNPs rs1212275 and rs6048205 were uncommon (MAF < 5%) with similar distribution among patients and controls. We found a strong association of (TCC)( n ) common allele A5 with type 2 diabetes [OR = 1.66 (95% CI 1.36-2.04, p = 5.9 x 10(-7)) for A5 homozygotes]. Obese individuals with A5A5 genotype had enhanced risk when segregated from normal-weight subjects [OR = 1.92 (95% CI 1.47-2.51), p = 1.6 x 10(-6)]. A5 was also nominally associated with higher fasting glucose (p = 0.02) and lower fasting insulin (p = 0.0028) and C-peptide (p = 0.036) levels among controls. At the rs1055080 locus, GG was found to provide reduced risk among normal-weight subjects [OR = 0.59 (95% CI 0.40-0.88), p = 0.011]. Combination of protective GG and non-risk genotypes of (TCC)( n ) showed reduced risk of type 2 diabetes both among normal-weight [OR = 0.43 (95% CI 0.29-0.65), p = 1.2 x 10(-6)] and obese individuals [0.47 (95% CI 0.34-0.64), p = 4.3 x 10(-5)]. For the first time we demonstrated that FOXA2 variants may affect risk of type 2 diabetes and metabolic traits in North India, however replication analyses in other cohorts are required to confirm the findings.

Intron 4 a/b polymorphism of the endothelial nitric oxide synthase gene is associated with both type 1 and type 2 diabetes in a genetically homogeneous population

Current classifications of diabetes distinguish between type 1 diabetes (T1D) and type 2 diabetes (T2D), however recent evidence highlights overlap between T1D and T2D. Earlier studies have suggested altered nitric oxide (NO) metabolism in both T1D and T2D. In the present case-control study, we investigated whether the endothelial NO synthase gene intron 4 a/b polymorphism is associated with T1D and T2D in the island of Crete, a well-defined area with genetically homogeneous population. Mutated allele "a" was more common in individuals with both T1D and T2D than in controls (odds ratio [OR] = 1.71, 95% confidence interval [CI] = 1.06-2.77, p = 0.013; and OR = 1.50, 95% CI = 0.930-2.42, p = 0.047, respectively). Mutated genotype (a/a or a/b) was more common in individuals with T1D than in nondiabetic individuals (OR = 1.93, 95% CI = 1.12-3.32, p = 0.008); this increased frequency was also observed for T2D, although not at a significant level (OR = 1.38, 95% CI = 0.802-2.37). No difference was found in the frequency of mutated allele a or mutated genotype (a/a or a/b) between T1D and T2D populations. In conclusion, our results indicate that allele a of the intron 4 endothelial NO synthase gene is associated with susceptibility to both T1D and T2D and may represent a common genetic factor for diabetes.

Disease proteomics of high-molecular-mass proteins by two-dimensional gel electrophoresis with agarose gels in the first dimension (Agarose 2-DE)

Agarose gel is the preferred electrophoretic medium currently used for separating high molecular mass (HMM) proteins (MW>100 kDa). Agarose gels are widely used for both SDS-agarose gel electrophoresis and agarose isoelectric focusing (IEF). A two-dimensional gel electrophoresis method employing agarose gels in the first dimension (agarose 2-DE) that is sufficiently good at separating up to 1.5mg of HMM proteins with molecular masses as large as 500 kDa has been used to separate proteins from various diseased tissues and cells. Although resolution of the agarose 2-DE pattern always depends on the tissue being analyzed, sample preparation procedures including (i) protein extraction with an SDS sample buffer; (ii) ultracentrifugation of a tissue homogenate; and (iii) 1% SDS in both stacking and separation gels of the second-dimension SDS-PAGE gel, are generally effective for HMM protein detection. In a comprehensive prostate cancer proteome study using agarose 2-DE, the HMM region of the gel was rich in proteins of particular gene/protein expression groups (39.1% of the HMM proteins but only 28.4% of the LMM ones were classified as transcription/translation-related proteins). Examples include transcription factors, DNA or RNA binding proteins, and ribosomal proteins. To understand oxidative stress-induced cellular damage at the protein level, a novel proteomic method, in which protein carbonyls were derivatized with biotin hydrazide followed by agarose 2-DE, was useful for detecting HMM protein carbonyls in tissues of both a diabetes model Ostuka Long-Evans Tokushima Fatty (OLETF) rat and a control Long-Evans Tokushima Otsuka (LETO) rat. In this paper, we review the use of agarose gels for separation of HMM proteins and disease proteomics of HMM proteins in general, with particular attention paid to our proteome analyzes based on the use of agarose 2-DE for protein separation followed by the use of mass spectrometry for protein identification.