Hanuman Thota

Alzheimer's disease and Type 2 diabetes mellitus: the cholinesterase connection?

Alzheimers disease and type 2 diabetes mellitus tend to occur together. We sought to identify protein(s) common to both conditions that could suggest a possible unifying pathogenic role. Using human neuronal butyrylcholinesterase (AAH08396.1) as the reference protein we used BLAST Tool for protein to protein comparison in humans. We found three groups of sequences among a series of 12, with an E-value between 0–12, common to both Alzheimers disease and diabetes: butyrylcholinesterase precursor K allele (NP_000046.1), acetylcholinesterase isoform E4-E6 precursor (NP_000656.1), and apoptosis-related acetylcholinesterase (1B41|A). Butyrylcholinesterase and acetylcholinesterase related proteins were found common to both Alzheimers disease and diabetes; they may play an etiological role via influencing insulin resistance and lipid metabolism.

Techniques for integrating -omics data.

The challenge for -omics research is to tackle the problem of fragmentation of knowledge by integrating several sources of heterogeneous information into a coherent entity. It is widely recognized that successful data integration is one of the keys to improve productivity for stored data. Through proper data integration tools and algorithms, researchers may correlate relationships that enable them to make better and faster decisions. The need for data integration is essential for present ‐omics community, because ‐omics data is currently spread world wide in wide variety of formats. These formats can be integrated and migrated across platforms through different techniques and one of the important techniques often used is XML. XML is used to provide a document markup language that is easier to learn, retrieve, store and transmit. It is semantically richer than HTML. Here, we describe bio warehousing, database federation, controlled vocabularies and highlighting the XML application to store, migrate and validate -omics data.

Proteomic Analysis in Diabetic Cardiomyopathy using Bioinformatics Approach

Diabetic cardiomyopathy is a distinct clinical entity that produces asymptomatic heart failure in diabetic patients without evidence of coronary artery disease and hypertension. Abnormalities in diabetic cardiomyopathy include: myocardial hypertrophy, impairment of contractile proteins, accumulation of extracellular matrix proteins, formation of advanced glycation end products, and decreased left ventricular compliance. These abnormalities lead to the most common clinical presentation of diabetic cardiomyopathy in the form of diastolic dysfunction. We evaluated the role of various proteins that are likely to be involved in diabetic cardiomyopathy by employing multiple sequence alignment using ClustalW tool and constructed a Phylogenetic tree using functional protein sequences extracted from NCBI. Phylogenetic tree was constructed using Neighbour—Joining Algorithm in bioinformatics approach. These results suggest a causal relationship between altered calcium homeostasis and diabetic cardiomyopathy that implies that efforts directed to normalize calcium homeostasis could form a novel therapeutic approach.

Alzheimer's disease care and management: role of information technology.

Alzheimers disease (AD) an ailment that is supposed to affect people in old age. There are evidences that it might affect others also. The number of elders is increasing as the average life expectancy is increasing. AD afflicts its patients with the dementia and AD might increase in malignance over time. People with cognitive disabilities can be overwhelmed through cognitive prosthetics. With the help of information technology we can enhance the quality of life. Significant achievements are possible with an interdisciplinary approach that includes genomic, genetic, technological and therapeutic measures. The combination and coordination of Bioinformatics facilitates generation of various diagnostic tools for the people who are suffering from Alzheimers disease. These tools help the care providers also. In this article, we emphasize the literature regarding the use of technology and its methodologies to improve the quality of care for the people with Alzheimers disease.

Identification of Protein Biomarkers for Diabetic Retinopathy using Sequence Mining Techniques

Bioinformatics and sequence mining are the application and development of data mining techniques to solve problems by comprehending biological data. Sequence analysis is the most primitive operation in sequence mining techniques. Modern sequence mining research is specialized in analyzing sequential patterns which are relevant and distinct from one another and utilizing retrieved sequences similarity and distance between different protein sequences can be analyzed. Diabetic retinopathy is the major cause of blindness in individuals mostly adults with diabetes and is it is the common problem of diabetes mellitus across the world. Various research analyses stated that there are many proteins which are found to take part in diabetic retinopathy. In this paper, we have evaluated certain proteins which are closely related with diabetic retinopathy with the help of multiple alignment tool viz. Clustal Omega and obtained a phylogenetic tree of 28 protein sequences gathered from National Center for Biotechnology Information (NCBI). In this work data mining technique called sequence mining plays a significant role in providing phylogram obtained with Neighbor-Joining algorithm. From the phylogenetic tree it was recognized that cortistatin, vitamin-D receptor and somatostatin proteins has close connection with diabetic retinopathy. Molecular docking studies have also been performed which is the most extensively used method for the calculation of protein-ligand interactions. In silico docking studies indicated that four inhibitory compounds i.e. Quercetin, Kaempferol, Naringenin and Melicitrin interact with aldose reductase which also found to have role in diabetic retinopathy. Outcomes infer that techniques intended to standardize cortistatin, vitamin-D receptor and somatostatin activities be of huge advantage and provide benefit in inhibiting diabetic retinopathy.

Bioinformatics Analysis of Functional Protein Sequences Reveals a Role for Tumor Necrosis Factor-α and Nitric Oxide in Insulin Resistance Syndrome

Using bioinformatics techniques and sequence analyses algorithms, we identified that tumor necrosis factor-α (TNF-α) and nitric oxide (NO) have a significant role in the pathobiology of insulin resistance syndrome, a condition that is common in subjects with abdominal obesity, hypertension, dyslipidemia, atherosclerosis, and coronary heart disease and are accompanied by endothelial dysfunction due to reduced endothelial nitric oxide generation. TNF-α has neurotoxic actions, stimulates inducible NO synthase activity, and modulates the expression of neurotransmitters involved in the control of feeding and thermogenesis. NO is a neurotransmitter and influences secretion and actions of various hypothalamic peptides and neuropeptides. Insulin suppresses the production of TNF-α but stimulates that of endothelial NO. This close interaction between TNF-α, NO, hypothalamic peptides, and insulin suggests that regulation of TNF-α and NO production and action could be critical in the management of insulin resistance syndrome and its associated conditions.