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Obesity is an important component of metabolic syndrome X and predisposes to the development of type 2 diabetes mellitus. The incidence of obesity, type 2 diabetes mellitus and metabolic syndrome X is increasing, and the cause(s) for this increasing incidence is not clear. Although genetics could play an important role in the higher prevalence of these diseases, it is not clear how genetic factors interact with environmental and dietary factors to increase their incidence. We performed gene expression profile in subjects with obesity and type 2 diabetes mellitus with and without family history of these diseases. It was noted that genes involved in carbohydrate, lipid and amino acid metabolism pathways, glycan of biosynthesis, metabolism of cofactors and vitamin pathways, ubiquitin mediated proteolysis, signal transduction pathways, neuroactive ligandreceptor interaction, nervous system pathways, neurodegenerative disorders pathways are upregulated in obesity compared to healthy subjects. In contrast genes involved in cell adhesion molecules, cytokine-cytokine receptor interaction, insulin signaling and immune system pathways are downregulated in obese. Genes involved in signal transduction, regulation of actin cytoskeleton, antigen processing and presentation, complement and coagulation cascades, axon guidance and neurodegenerative disorders pathways are upregulated in subjects with type 2 diabetes with family history of diabetes compared to those who are diabetic but with no family history. Genes involved in oxidative phosphorylation, immune, nervous system, and metabolic disorders pathways are upregulated in those with diabetes with family history of diabetes compared to those with diabetes but with no family history. In contrast, genes involved in lipid and amino acid pathways, ubiquitin mediated proteolysis, signal transduction, insulin signaling and PPAR signaling pathways are downregulated in subjects with diabetes with family history of diabetes. It was noted that genes involved in inflammatory pathway are differentially expressed both in obesity and type 2 diabetes. These results suggest that genes concerned with carbohydrate, lipid and amino acid metabolic pathways, neuronal function and inflammation play a significant role in the pathobiology of obesity and type 2 diabetes.

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Multiple approaches have been devised and evaluated to computationally estimate binding free energies. Results using a recently developed Quantum Mechanics (QM)/Molecular Mechanics (MM) based Free Energy Perturbation (FEP) method suggest that this method has the potential to provide the most accurate estimation of binding affinities to date. The method treats ligands/inhibitors using QM while using MM for the rest of the system. The method has been applied and validated for a structurally diverse set of fructose 1,6- bisphosphatase (FBPase) inhibitors suggesting that the approach has the potential to be used as an integral part of drug discovery for both lead identification lead optimization, where there is a structure available. In addition, this QM/MM-based FEP method was shown to accurately replicate the anomalous hydration behavior exhibited by simple amines and amides suggesting that the method may also prove useful in predicting physical properties of molecules. While the method is about 5-fold more computationally demanding than conventional FEP, it has the potential to be less demanding on the end user since it avoids development of MM force field parameters for novel ligands and thereby eliminates this time-consuming step that often contributes significantly to the inaccuracy of binding affinity predictions using conventional FEP methods. The QM/MM-based FEP method has been extensively tested with respect to important considerations such as the length of the simulation required to obtain satisfactory convergence in the calculated relative solvation and binding free energies for both small and large structural changes between ligands. Future automation of the method and parallelization of the code is expected to enhance the speed and increase its use for drug design and lead optimization.

Development of type 2 diabetes mellitus is influenced by built environment, which is, ‘the environments that are modified by humans, including homes, schools, workplaces, highways, urban sprawls, accessibility to amenities, leisure, and pollution.’ Built environment contributes to diabetes through access to physical activity and through stress, by affecting the sleep cycle. With globalization, there is a possibility that western environmental models may be replicated in developing countries such as India, where the underlying genetic predisposition makes them particularly susceptible to diabetes. Here we review published information on the relationship between built environment and diabetes, so that appropriate modifications can be incorporated to reduce the risk of developing diabetes mellitus.

Cluster analysis of DNA microarray data that uses statistical algorithms to arrange the genes according to similarity in patterns of gene expression and the output displayed graphically is described in this article. Hierarchical clustering is a multivariate tool often used in phylogenetics, comparative genomics to relate the evolution of species. The patterns seen in microarray expression data can be interpreted as indications of the status of the genes responsible for nephropathy in peripheral blow cells of type 2 diabetes (T2DN). Out of 415 genes totally expressed in the 3 DNA chips it was concluded that only 116 genes expressed in T2DN and in that only 50 are functional genes. These 50 functional genes are responsible for diabetic nephropathy; of these 50, some of the genes which are more expressed and responsible are AGXT: Alanine-glyoxylate aminotransferase, RHOD: Ras homolog gene family, CAPN6: Calpain 6, EFNB2: Ephrin-B2, ANXA7: Annexin A7, PEG10: Paternally expressed 10, DPP4: Dipeptidyl-peptidase 4 (CD26, adenosine deaminase complexing protein 2), ENSA: Endosulfine alpha, IGFBP2: Insulin-like growth factor binding protein 2, 36kDa, CENPB: Centromere protein B, 80kDa, MLL3: Myeloid/lymphoid or mixed-lineage leukemia 3, BDNF: Brain-derived neurotrophic factor, EIF4A2: Eukaryotic translation initiation factor 4A, isoform 2, PPP2R1A: Protein phosphatase 2 (formerly 2A), regulatory subunit A, alpha isoform. Fifty genes and their nucleotide sequences are taken from NCBI and a phylogenetic tree is constructed using CLUSTAL W and the distances are closer to each other concluding that based on the sequence similarity and evolution the genes are expressed similarly. Literature survey is done for each gene in OMIM and the genes responsible for diabetic nephropathy are listed.

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