Bioactive peptides are specific sequences of amino acids and remain inactive as long as they bind to other amino acids in the original structure of protein (Harnedy & Fitz Gerald, 2012). Bioactive peptides may be released in the body of a living organism during digestion, enzymatic hydrolysis of food, or processing of food products such as fermentation, germination, and ripening (Moller et al., 2008).Bioactive peptides are mainly derived from plant and animal protein sources. Plant sources of bioactive peptides include cereals, such as wheat, barley, rye and maize, and some legumes including soybeans, chickpeas, and peas. Animal sources of bioactive peptides also include milk, egg, red meat, and aquatic animals. Dairy products are important sources of bioactive peptides with antioxidant and antimicrobial activities. Bioactive properties of peptides depend on factors such as the length of peptide chain, type of amino acids, amino acid sequence, and location of C and N at or near the end of a chain (Giri & Ohshima, 2012). Type of protein substrate, raw material treatment, enzyme type and concentration, temperature, reaction duration, and protein density on peptide structure are factors influencing the function of bioactive peptides (Mills et al., 2011).The function of peptides with antioxidant activity has not been completely determined, but many researchers have suggested that peptides can prevent lipid oxidation (Sakanaka et al., 2004), scavenge
Background: Multiple sclerosis (MS), a non-contagious and chronic disease of the central nervous system, is an unpredictable and indirectly inherited disease affecting different people in different ways.Using Omics platforms data i.e. genomics, transcriptomics, proteomics, epigenomics, interactomics, and metabolomics it is now possible to construct sound systems biology models to extract full knowledge of the MS and path the way to likely construct personalized and therapeutic tools.Methods: In this study, we learned many Bayesian Networks in order to find the transcriptional gene regulation networks that drive MS disease. We used a set of BN algorithms using the R add-on package bnlearn. The BN results underwent further downstream analysis and were validated using a wide range of Cytoscape algorithms, web based computational tools and qPCR amplification of blood samples from 56 MS patients and 44 healthy controls. The results were semantically integrated to come up with improved understanding of the complex molecular architecture underlying MS, distinguishing distinct metabolic pathways and providing a valuable foundation for the discovery of deriven genes and possibly new treatments. Results: Results show that the LASP1, TUBA1C, and S100A6 genes were most significant and likely playing a biological role in MS development. Results from qPCR showed a significant increase (P <0.05) in LASP1 and S100A6 gene expression levels in MS patients compared to controls. However, a significant down regulation of TUBA1C was observed in the same comparison. Conclusion: This study provides potential diagnostic and therapeutic biomarkers for increasing understanding of gene regulation underlying MS.
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