Displaying publications 241 - 260 of 340 in total

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  1. Mienda BS, Shamsir MS, Md Illias R
    J Biomol Struct Dyn, 2016 Nov;34(11):2305-16.
    PMID: 26510527 DOI: 10.1080/07391102.2015.1113387
    Succinic acid is an important platform chemical that has broad applications and is been listed as one of the top twelve bio-based chemicals produced from biomass by the US Department of Energy. The metabolic role of Escherichia coli formate dehydrogenase-O (fdoH) under anaerobic conditions in relation to succinic acid production remained largely unspecified. Herein we report, what are to our knowledge, the first metabolic fdoH gene knockout that have enhanced succinate production using glucose and glycerol substrates in E. coli. Using the most recent E. coli reconstruction iJO1366, we engineered its host metabolism to enhance the anaerobic succinate production by deleting the fdoH gene, which blocked H(+) conduction across the mutant cell membrane for the enhanced succinate production. The engineered mutant strain BMS4 showed succinate production of 2.05 g l(-1) (41.2-fold in 7 days) from glycerol and .39 g l(-1) (6.2-fold in 1 day) from glucose. This work revealed that a single deletion of the fdoH gene is sufficient to increase succinate production in E. coli from both glucose and glycerol substrates.
    Matched MeSH terms: Computational Biology
  2. Zheng W, Mutha NV, Heydari H, Dutta A, Siow CC, Jakubovics NS, et al.
    PeerJ, 2016;4:e1698.
    PMID: 27017950 DOI: 10.7717/peerj.1698
    Background. The gram-negative Neisseria is associated with two of the most potent human epidemic diseases: meningococcal meningitis and gonorrhoea. In both cases, disease is caused by bacteria colonizing human mucosal membrane surfaces. Overall, the genus shows great diversity and genetic variation mainly due to its ability to acquire and incorporate genetic material from a diverse range of sources through horizontal gene transfer. Although a number of databases exist for the Neisseria genomes, they are mostly focused on the pathogenic species. In this present study we present the freely available NeisseriaBase, a database dedicated to the genus Neisseria encompassing the complete and draft genomes of 15 pathogenic and commensal Neisseria species. Methods. The genomic data were retrieved from National Center for Biotechnology Information (NCBI) and annotated using the RAST server which were then stored into the MySQL database. The protein-coding genes were further analyzed to obtain information such as calculation of GC content (%), predicted hydrophobicity and molecular weight (Da) using in-house Perl scripts. The web application was developed following the secure four-tier web application architecture: (1) client workstation, (2) web server, (3) application server, and (4) database server. The web interface was constructed using PHP, JavaScript, jQuery, AJAX and CSS, utilizing the model-view-controller (MVC) framework. The in-house developed bioinformatics tools implemented in NeisseraBase were developed using Python, Perl, BioPerl and R languages. Results. Currently, NeisseriaBase houses 603,500 Coding Sequences (CDSs), 16,071 RNAs and 13,119 tRNA genes from 227 Neisseria genomes. The database is equipped with interactive web interfaces. Incorporation of the JBrowse genome browser in the database enables fast and smooth browsing of Neisseria genomes. NeisseriaBase includes the standard BLAST program to facilitate homology searching, and for Virulence Factor Database (VFDB) specific homology searches, the VFDB BLAST is also incorporated into the database. In addition, NeisseriaBase is equipped with in-house designed tools such as the Pairwise Genome Comparison tool (PGC) for comparative genomic analysis and the Pathogenomics Profiling Tool (PathoProT) for the comparative pathogenomics analysis of Neisseria strains. Discussion. This user-friendly database not only provides access to a host of genomic resources on Neisseria but also enables high-quality comparative genome analysis, which is crucial for the expanding scientific community interested in Neisseria research. This database is freely available at http://neisseria.um.edu.my.
    Matched MeSH terms: Computational Biology
  3. Vignesvaran K, Alias Z
    Arch Insect Biochem Physiol, 2016 Jul;92(3):210-21.
    PMID: 27075600 DOI: 10.1002/arch.21332
    Drosophila melanogaster glutathione S-transferase D3 (DmGSTD3) has a shorter amino acid sequence as compared to other GSTs known in the fruit flies. This is due to the 15 amino acid N-terminal truncation in which normally active amino acid residue is located. The work has made use of homology modeling to visualize the arrangement of amino acid side chains in the glutathione (GSH) substrate cavity. The identified amino acids were then replaced with amino acids without functional groups in the side chains and the mutants were analyzed kinetically. Homology modeling revealed that the side chains of Y89 and Y97 were shown facing toward the substrate cavity proposing their possible role in catalyzing the conjugation. Y97A and Y89A GSH gave large changes in Km (twofold increase), Vmax (fivefold reduction), and Kcat /Km values for GSH suggesting their significant role in the conjugation reaction. The replacement at either positions has not affected the affinity of the enzyme toward 1-chloro-2,4-dinitrobenzene as no significant change in values of Kmax was observed. The replacement, however, had significantly reduced the catalytic efficiency of both mutants with (Kcat /Km )(GSH) and (Kcat /Km )(CDNB) of eight- and twofold reduction. The recombinant DmGSTD3 has shown no activity toward 1,2-dichloro-4-nitrobenzene, 2,4-hexadienal, 2,4-heptadienal, p-nitrobenzyl chloride, ethacrynic acid, and sulfobromophthalein. Therefore, it was evident that DmGSTD3 has made use of distal amino acids Y97 and Y89 for GSH conjugation.
    Matched MeSH terms: Computational Biology
  4. Namazi H, Kulish VV
    Comput Math Methods Med, 2015;2015:148534.
    PMID: 26089955 DOI: 10.1155/2015/148534
    Human brain response is the result of the overall ability of the brain in analyzing different internal and external stimuli and thus making the proper decisions. During the last decades scientists have discovered more about this phenomenon and proposed some models based on computational, biological, or neuropsychological methods. Despite some advances in studies related to this area of the brain research, there were fewer efforts which have been done on the mathematical modeling of the human brain response to external stimuli. This research is devoted to the modeling and prediction of the human EEG signal, as an alert state of overall human brain activity monitoring, upon receiving external stimuli, based on fractional diffusion equations. The results of this modeling show very good agreement with the real human EEG signal and thus this model can be used for many types of applications such as prediction of seizure onset in patient with epilepsy.
    Matched MeSH terms: Computational Biology
  5. Yazdani S, Yusof R, Karimian A, Riazi AH, Bennamoun M
    Comput Math Methods Med, 2015;2015:829893.
    PMID: 26089978 DOI: 10.1155/2015/829893
    Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets.
    Matched MeSH terms: Computational Biology
  6. Gavai AK, Supandi F, Hettling H, Murrell P, Leunissen JA, van Beek JH
    PLoS One, 2015;10(3):e0119016.
    PMID: 25806817 DOI: 10.1371/journal.pone.0119016
    Predicting the distribution of metabolic fluxes in biochemical networks is of major interest in systems biology. Several databases provide metabolic reconstructions for different organisms. Software to analyze flux distributions exists, among others for the proprietary MATLAB environment. Given the large user community for the R computing environment, a simple implementation of flux analysis in R appears desirable and will facilitate easy interaction with computational tools to handle gene expression data. We extended the R software package BiGGR, an implementation of metabolic flux analysis in R. BiGGR makes use of public metabolic reconstruction databases, and contains the BiGG database and the reconstruction of human metabolism Recon2 as Systems Biology Markup Language (SBML) objects. Models can be assembled by querying the databases for pathways, genes or reactions of interest. Fluxes can then be estimated by maximization or minimization of an objective function using linear inverse modeling algorithms. Furthermore, BiGGR provides functionality to quantify the uncertainty in flux estimates by sampling the constrained multidimensional flux space. As a result, ensembles of possible flux configurations are constructed that agree with measured data within precision limits. BiGGR also features automatic visualization of selected parts of metabolic networks using hypergraphs, with hyperedge widths proportional to estimated flux values. BiGGR supports import and export of models encoded in SBML and is therefore interoperable with different modeling and analysis tools. As an application example, we calculated the flux distribution in healthy human brain using a model of central carbon metabolism. We introduce a new algorithm termed Least-squares with equalities and inequalities Flux Balance Analysis (Lsei-FBA) to predict flux changes from gene expression changes, for instance during disease. Our estimates of brain metabolic flux pattern with Lsei-FBA for Alzheimer's disease agree with independent measurements of cerebral metabolism in patients. This second version of BiGGR is available from Bioconductor.
    Matched MeSH terms: Computational Biology
  7. Mohd Yusoff MI
    Comput Math Methods Med, 2020;2020:9328414.
    PMID: 33224268 DOI: 10.1155/2020/9328414
    Researchers used a hybrid model (a combination of health resource demand model and disease transmission model), Bayesian model, and susceptible-exposed-infectious-removed (SEIR) model to predict health service utilization and deaths and mixed-effect nonlinear regression. Further, they used the mixture model to predict the number of confirmed cases and deaths or to predict when the curve would flatten. In this article, we show, through scenarios developed using system dynamics methodology, besides close to real-world results, the detrimental effects of ignoring social distancing guidelines (in terms of the number of people infected, which decreased as the percentage of noncompliance decreased).
    Matched MeSH terms: Computational Biology
  8. Chong YM, Sam IC, Chong J, Kahar Bador M, Ponnampalavanar S, Syed Omar SF, et al.
    PLoS Negl Trop Dis, 2020 11;14(11):e0008744.
    PMID: 33253226 DOI: 10.1371/journal.pntd.0008744
    Malaysia had 10,219 confirmed cases of COVID-19 as of September 20, 2020. About 33% were associated with a Tablighi Jamaat religious mass gathering held in Kuala Lumpur between February 27 and March 3, 2020, which drove community transmission during Malaysia's second wave. We analysed genome sequences of SARS-CoV-2 from Malaysia to better understand the molecular epidemiology and spread. We obtained 58 SARS-CoV-2 whole genome sequences from patients in Kuala Lumpur and performed phylogenetic analyses on these and a further 57 Malaysian sequences available in the GISAID database. Nine different SARS-CoV-2 lineages (A, B, B.1, B.1.1, B.1.1.1, B.1.36, B.2, B.3 and B.6) were detected in Malaysia. The B.6 lineage was first reported a week after the Tablighi mass gathering and became predominant (65.2%) despite being relatively rare (1.4%) globally. Direct epidemiological links between lineage B.6 viruses and the mass gathering were identified. Increases in reported total cases, Tablighi-associated cases, and community-acquired B.6 lineage strains were temporally linked. Non-B.6 lineages were mainly travel-associated and showed limited onward transmission. There were also temporally correlated increases in B.6 sequences in other Southeast Asian countries, India and Australia, linked to participants returning from this event. Over 95% of global B.6 sequences originated from Asia Pacific. We also report a nsp3-C6310A substitution found in 47.3% of global B.6 sequences which was associated with reduced sensitivity using a commercial diagnostic real-time PCR assay. Lineage B.6 became the predominant cause of community transmission in Malaysia after likely introduction during a religious mass gathering. This event also contributed to spikes of lineage B.6 in other countries in the Asia-Pacific. Mass gatherings can be significant causes of local and global spread of COVID-19. Shared genomic surveillance can be used to identify SARS-CoV-2 transmission chains to aid prevention and control, and to monitor diagnostic molecular assays. Clinical Trial Registration: COVID-19 paper.
    Matched MeSH terms: Computational Biology
  9. Walters K, Sarsenov R, Too WS, Hare RK, Paterson IC, Lambert DW, et al.
    BMC Genomics, 2019 Jun 03;20(1):454.
    PMID: 31159744 DOI: 10.1186/s12864-019-5850-7
    BACKGROUND: Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of cellular processes in diseases such as cancer, although the functions of most remain poorly understood. To address this, here we apply a novel strategy to integrate gene expression profiles across 32 cancer types, and cluster human lncRNAs based on their pan-cancer protein-coding gene associations. By doing so, we derive 16 lncRNA modules whose unique properties allow simultaneous inference of function, disease specificity and regulation for over 800 lncRNAs.

    RESULTS: Remarkably, modules could be grouped into just four functional themes: transcription regulation, immunological, extracellular, and neurological, with module generation frequently driven by lncRNA tissue specificity. Notably, three modules associated with the extracellular matrix represented potential networks of lncRNAs regulating key events in tumour progression. These included a tumour-specific signature of 33 lncRNAs that may play a role in inducing epithelial-mesenchymal transition through modulation of TGFβ signalling, and two stromal-specific modules comprising 26 lncRNAs linked to a tumour suppressive microenvironment and 12 lncRNAs related to cancer-associated fibroblasts. One member of the 12-lncRNA signature was experimentally supported by siRNA knockdown, which resulted in attenuated differentiation of quiescent fibroblasts to a cancer-associated phenotype.

    CONCLUSIONS: Overall, the study provides a unique pan-cancer perspective on the lncRNA functional landscape, acting as a global source of novel hypotheses on lncRNA contribution to tumour progression.

    Matched MeSH terms: Computational Biology
  10. Salleh SM, Mazzoni G, Løvendahl P, Kadarmideen HN
    BMC Bioinformatics, 2018 Dec 17;19(1):513.
    PMID: 30558534 DOI: 10.1186/s12859-018-2553-z
    BACKGROUND: Selection for feed efficiency is crucial for overall profitability and sustainability in dairy cattle production. Key regulator genes and genetic markers derived from co-expression networks underlying feed efficiency could be included in the genomic selection of the best cows. The present study identified co-expression networks associated with high and low feed efficiency and their regulator genes in Danish Holstein and Jersey cows. RNA-sequencing data from Holstein and Jersey cows with high and low residual feed intake (RFI) and treated with two diets (low and high concentrate) were used. Approximately 26 million and 25 million pair reads were mapped to bovine reference genome for Jersey and Holstein breed, respectively. Subsequently, the gene count expressions data were analysed using a Weighted Gene Co-expression Network Analysis (WGCNA) approach. Functional enrichment analysis from Ingenuity® Pathway Analysis (IPA®), ClueGO application and STRING of these modules was performed to identify relevant biological pathways and regulatory genes.

    RESULTS: WGCNA identified two groups of co-expressed genes (modules) significantly associated with RFI and one module significantly associated with diet. In Holstein cows, the salmon module with module trait relationship (MTR) = 0.7 and the top upstream regulators ATP7B were involved in cholesterol biosynthesis, steroid biosynthesis, lipid biosynthesis and fatty acid metabolism. The magenta module has been significantly associated (MTR = 0.51) with the treatment diet involved in the triglyceride homeostasis. In Jersey cows, the lightsteelblue1 (MTR = - 0.57) module controlled by IFNG and IL10RA was involved in the positive regulation of interferon-gamma production, lymphocyte differentiation, natural killer cell-mediated cytotoxicity and primary immunodeficiency.

    CONCLUSION: The present study provides new information on the biological functions in liver that are potentially involved in controlling feed efficiency. The hub genes and upstream regulators (ATP7b, IFNG and IL10RA) involved in these functions are potential candidate genes for the development of new biomarkers. However, the hub genes, upstream regulators and pathways involved in the co-expressed networks were different in both breeds. Hence, additional studies are required to investigate and confirm these findings prior to their use as candidate genes.

    Matched MeSH terms: Computational Biology
  11. Calero R, Mirabal M, Bouza J, Guzmán MV, Carrillo H, López Y, et al.
    BMC Immunol, 2013;14 Suppl 1:S9.
    PMID: 23458073 DOI: 10.1186/1471-2172-14-S1-S9
    TB, caused by Mycobacterium tuberculosis (MTB), is one of the major global infectious diseases. For the pandemic control, early diagnosis with sensitive and specific methods is fundamental. With the advent of bioinformatics' tools, the identification of several proteins involved in the pathogenesis of TB (TB) has been possible. In the present work, the MTB genome was explored to look for molecules with possible antigenic properties for their evaluation as part of new generation diagnostic kits based on the release of cytokines. Seven proteins from the MTB proteome and some of their combinations suited the computational test and the results suggested their potential use for the diagnosis of infection in the following population groups: Cuba, Mexico, Malaysia and sub-Saharan Africa. Our predictions were performed using public bioinformatics tools plus three computer programs, developed by our group, to facilitate information retrieval and processing.
    Matched MeSH terms: Computational Biology
  12. Yap FC, Yan YJ, Loon KT, Zhen JL, Kamau NW, Kumaran JV
    Anim Biotechnol, 2010 Oct;21(4):226-40.
    PMID: 20967642 DOI: 10.1080/10495398.2010.506334
    The present investigation was carried out in an attempt to study the phylogenetic analysis of different breeds of domestic chickens in Peninsular Malaysia inferred from partial cytochrome b gene information and random amplified polymorphic DNA (RAPD) markers. Phylogenetic analysis using both neighbor-joining (NJ) and maximum parsimony (MP) methods produced three clusters that encompassed Type-I village chickens, the red jungle fowl subspecies and the Japanese Chunky broilers. The phylogenetic analysis also revealed that majority of the Malaysian commercial chickens were randomly assembled with the Type-II village chickens. In RAPD assay, phylogenetic analysis using neighbor-joining produced six clusters that were completely distinguished based on the locality of chickens. High levels of genetic variations were observed among the village chickens, the commercial broilers, and between the commercial broilers and layer chickens. In this study, it was found that Type-I village chickens could be distinguished from the commercial chickens and Type-II village chickens at the position of the 27th nucleotide of the 351 bp cytochrome b gene. This study also revealed that RAPD markers were unable to differentiate the type of chickens, but it showed the effectiveness of RAPD in evaluating the genetic variation and the genetic relationships between chicken lines and populations.
    Matched MeSH terms: Computational Biology
  13. Sandya Menon Prabhakaran Menon, Asita Elengoe
    MyJurnal
    Introduction: Colorectal cancer is one of the top three most commonly occurring cancer worldwide with more than 1.8 million cases in 2018. In Malaysia, colorectal cancer is the most common cancer in males and the second most common cancer in females. Albeit being the second most common form of cancer in Malaysia, there is a lack of a formal or structured national colorectal cancer screening programme in Malaysia and it remains a low priority in healthcare planning and expenditure in Malaysia. The risk of developing colon cancer is greatly influenced by factors such as lifestyle habits, genetic inheritance, diet, weight, and exercise. Kras, the most frequently mutated oncogene in cancer, occurs in about 50 percent of colorectal cancers. Methods: This study maps the kras gene involved in colon cancer pathway, using bioinformatics applications such as STRING version 11.0 and Cytoscape version 3.7.0 to provide a clear visualisation of all the related and involved proteins and genes that interact with this kras gene in the pathway. Results: The 3391 protein interactions were assembled and visualized in y organic form. Six spe-cific non-overlapping clusters of various sizes, which emerged from the huge network of protein-interactors using MCODE version 1.32 clustering algorithm were found. Biological Networks Gene Ontology (BiNGO) was used to determine two ontologies (molecular function and biological process) involved in the protein network. Based on the resulting protein-protein network interaction map, each interaction plays an important role in the cell cycle, meta-bolic pathways and signal transduction. Conclusion: Understanding these interactions provide insight into cellular activities and thus assist in the understanding of the aetiology of disease.
    Matched MeSH terms: Computational Biology
  14. Sorribes-Dauden R, Peris D, Martínez-Pastor MT, Puig S
    Comput Struct Biotechnol J, 2020;18:3712-3722.
    PMID: 33304466 DOI: 10.1016/j.csbj.2020.10.044
    Iron is an essential micronutrient for most living beings since it participates as a redox active cofactor in many biological processes including cellular respiration, lipid biosynthesis, DNA replication and repair, and ribosome biogenesis and recycling. However, when present in excess, iron can participate in Fenton reactions and generate reactive oxygen species that damage cells at the level of proteins, lipids and nucleic acids. Organisms have developed different molecular strategies to protect themselves against the harmful effects of high concentrations of iron. In the case of fungi and plants, detoxification mainly occurs by importing cytosolic iron into the vacuole through the Ccc1/VIT1 iron transporter. New sequenced genomes and bioinformatic tools are facilitating the functional characterization, evolution and ecological relevance of metabolic pathways and homeostatic networks across the Tree of Life. Sequence analysis shows that Ccc1/VIT1 homologs are widely distributed among organisms with the exception of animals. The recent elucidation of the crystal structure of a Ccc1/VIT1 plant ortholog has enabled the identification of both conserved and species-specific motifs required for its metal transport mechanism. Moreover, recent studies in the yeast Saccharomyces cerevisiae have also revealed that multiple transcription factors including Yap5 and Msn2/Msn4 contribute to the expression of CCC1 in high-iron conditions. Interestingly, Malaysian S. cerevisiae strains express a partially functional Ccc1 protein that renders them sensitive to iron. Different regulatory mechanisms have been described for non-Saccharomycetaceae Ccc1 homologs. The characterization of Ccc1/VIT1 proteins is of high interest in the development of biofortified crops and the protection against microbial-derived diseases.
    Matched MeSH terms: Computational Biology
  15. Eskandari A, Leow TC, Rahman MBA, Oslan SN
    Biomolecules, 2020 12 09;10(12).
    PMID: 33317024 DOI: 10.3390/biom10121649
    Antifreeze proteins (AFPs) are specific proteins, glycopeptides, and peptides made by different organisms to allow cells to survive in sub-zero conditions. AFPs function by reducing the water's freezing point and avoiding ice crystals' growth in the frozen stage. Their capability in modifying ice growth leads to the stabilization of ice crystals within a given temperature range and the inhibition of ice recrystallization that decreases the drip loss during thawing. This review presents the potential applications of AFPs from different sources and types. AFPs can be found in diverse sources such as fish, yeast, plants, bacteria, and insects. Various sources reveal different α-helices and β-sheets structures. Recently, analysis of AFPs has been conducted through bioinformatics tools to analyze their functions within proper time. AFPs can be used widely in various aspects of application and have significant industrial functions, encompassing the enhancement of foods' freezing and liquefying properties, protection of frost plants, enhancement of ice cream's texture, cryosurgery, and cryopreservation of cells and tissues. In conclusion, these applications and physical properties of AFPs can be further explored to meet other industrial players. Designing the peptide-based AFP can also be done to subsequently improve its function.
    Matched MeSH terms: Computational Biology
  16. Leow CY, Willis C, Leow CH, Hofmann A, Jones M
    Mol Biochem Parasitol, 2019 12;234:111231.
    PMID: 31628972 DOI: 10.1016/j.molbiopara.2019.111231
    Schistosomes are parasitic blood flukes that infect approximately 250 million people worldwide. The disease known as schistosomiasis, is the second most significant tropical parasitic disease after malaria. Praziquantel is the only effective drug currently licensed for schistosomiasis and there are concerns about resistance to the drug. There has been much effort to develop vaccines against schistosomiasis to produce long-term protection in endemic regions. Surface-associated proteins, and in particular, those expressed in the body wall, or tegument, have been proposed as potential vaccine targets. Of these, annexins are thought to be of integral importance for the stability of this apical membrane system. Here, we present the structural and immunobiochemical characterization of four homologous annexins namely annexin B30, annexin B5a, annexin B7a and annexin B5b from S. mansoni. Bioinformatics analysis showed that there was no signal peptide predicted for any annexin in this study. Further analysis showed that each of all four annexin protein possesses a primary structure consisting of a short but variable N-terminal region and a long C-terminal core containing four homologous annexin repeats (I-IV), which contain five alpha-helices. The life cycle expression profile of each annexin was assessed using quantitative PCR. The results showed that the overall transcript levels of the each of four homologous annexins were relatively low in the egg stage, but increased gradually after the transition of cercariae (the invasive schistosome larvae) to schistosomula (the post-invasive larvae). Circular dichroism (CD) demonstrated that rAnnexin B30, rAnnexin B5a and rAnnexin 7a were folded, showing a secondary structure content rich in alpha-helices. The membrane binding affinity was enhanced when rAnnexin B30, rAnnexin B5a and rAnnexin 7a was incubated in the presence of Ca2+. All annexin members evaluated in this study were immunolocalized to the tegument, with immunoreactivity also occurring in cells and in muscle of adult parasites. All four recombinant annexins were immunoreactive and they were recognized by the sera of mice infected with S. mansoni. In conclusion, the overall results present the molecular characterization of annexin B30, annexin B5a, annexin B7a and annexin B5b from S. mansoni in host-parasite interactions and strongly suggest that the molecules could be useful candidates for vaccine or diagnostic development.
    Matched MeSH terms: Computational Biology
  17. Fazal F, Anwar T, Waheed Y, Parvaiz F
    Trop Biomed, 2020 Sep 01;37(3):566-577.
    PMID: 33612772 DOI: 10.47665/tb.37.3.566
    This study is focused towards developing a global consensus sequence of nonstructural protein 2 (NSP2), a protease of Chikungunya Virus (CHIKV) and predict immunogenic promiscuous T-cell epitopes based on various bioinformatics tools. To date, no epitope data is available for the Chikungunya virus in the IEDB database. In this study, 100 available nucleotide sequences of NSP2-CHIKV belonging to different strains were downloaded from the National Centre for Biotechnology Information (NCBI) database. The nucleotide sequences were subjected to translated sequencing using the EXPASY tool followed by protein alignment using the CLC workbench and a global consensus sequence for the respective protein was developed. IEDB tool was used to predict HLA-I and HLA-II binding promiscuous epitopes from the consensus sequence of NSP2-CHIKV. Thirty-four B-cell based epitopes are predicted and the promiscuous epitope is VVDTTGSTKPDPGD at position 341-354. Twenty-six MHC-I short peptide epitopes are predicted to bind with HLA-A. The promiscuous epitopes predicted to bind with HLA-A*01:01 are VTAIVSSLHY, SLSESATMVY, FSKPLVYY, QPTDHVVGEY at positions 317-326, 84-93, 535-544 and 15-24 with percentile ranks 0.17, 0.39, 0.51 and 0.81, respectively. Twenty-four MHC-II short peptide epitopes are predicted for HLA-DRB. The promiscuous epitope predicted to bind with HLA-DRB*01:01 is VVGEYLVLSPQTVLRS from 20-35 with a lowest percentile rank of 0.01. These predicted epitopes can be effective targets towards development of vaccine against CHIKV. Epitopes predicted in this study displayed good binding affinity, antigenicity and promiscuity for the HLA classes. These predicted epitopes can prove to be translationally important towards the development of CHIKV.
    Matched MeSH terms: Computational Biology
  18. Ung CY, Teoh TC
    J Biosci, 2014 Jun;39(3):493-504.
    PMID: 24845512
    DARPP-32 (dopamine and adenosine 3', 5'-monophosphate-regulated phosphoprotein of 32 kDa), which belongs to PPP1R1 gene family, is known to act as an important integrator in dopamine-mediated neurotransmission via the inhibition of protein phosphatase-1 (PP1). Besides its neuronal roles, this protein also behaves as a key player in pathological and pharmacological aspects. Use of bioinformatics and phylogenetics approaches to further characterize the molecular features of DARPP-32 can guide future works. Predicted phosphorylation sites on DARPP-32 show conservation across vertebrates. Phylogenetics analysis indicates evolutionary strata of phosphorylation site acquisition at the C-terminus, suggesting functional expansion of DARPP-32, where more diverse signalling cues may involve in regulating DARPP-32 in inhibiting PP1 activity. Moreover, both phylogenetics and synteny analyses suggest de novo origination of PPP1R1 gene family via chromosomal rearrangement and exonization.
    Matched MeSH terms: Computational Biology
  19. Lau YL, Lee WC, Gudimella R, Zhang G, Ching XT, Razali R, et al.
    PLoS One, 2016;11(6):e0157901.
    PMID: 27355363 DOI: 10.1371/journal.pone.0157901
    Toxoplasmosis is a widespread parasitic infection by Toxoplasma gondii, a parasite with at least three distinct clonal lineages. This article reports the whole genome sequencing and de novo assembly of T. gondii RH (type I representative strain), as well as genome-wide comparison across major T. gondii lineages. Genomic DNA was extracted from tachyzoites of T. gondii RH strain and its identity was verified by PCR and LAMP. Subsequently, whole genome sequencing was performed, followed by sequence filtering, genome assembly, gene annotation assignments, clustering of gene orthologs and phylogenetic tree construction. Genome comparison was done with the already archived genomes of T. gondii. From this study, the genome size of T. gondii RH strain was found to be 69.35Mb, with a mean GC content of 52%. The genome shares high similarity to the archived genomes of T. gondii GT1, ME49 and VEG strains. Nevertheless, 111 genes were found to be unique to T. gondii RH strain. Importantly, unique genes annotated to functions that are potentially critical for T. gondii virulence were found, which may explain the unique phenotypes of this particular strain. This report complements the genomic archive of T. gondii. Data obtained from this study contribute to better understanding of T. gondii and serve as a reference for future studies on this parasite.
    Matched MeSH terms: Computational Biology
  20. Hartini Yusof, Mohamad Shafiq Aazmi, Teh Lay Kek, Mohd Zaki Salleh, Ili Ng Abdullah, Aminuddin Ahmad, et al.
    MyJurnal
    Obesity is a growing epidemic due to an accelerated phase of industrialization and urbanization with the overfed people
    now outnumbered the underfed. It is the major public health problem with a lot of research interest as it is associated
    with many complicated chronic disorders such as type-2 diabetes, cardiovascular diseases (CVD) and cancers. A global
    estimation of 2.8 million deaths per year is due to obesity and there are tremendous on-going efforts to identify hosts
    and environmental factors that infl uence the cause and pathogenesis of obesity. Concerted efforts from different research
    groups had successfully shown that obese subjects have altered composition of gut microbiota and transplantation of this
    microbiota infl uences body weight in the germ-free recipient mice. The advancement of technology had made possible
    the study of gut microbiota which was unculturable for better understanding of their impact to human health. Rapid
    deep sequencing of DNA at reasonable cost through various options of platforms followed by data analysis using robust
    bioinformatic tools are an important way of analysing the gut microbiome. Here we review the role of gut microbiota
    which modulates host’s metabolic functions and gene expression, facilitating the extraction and storage of energy from the
    ingested dietary substances and leading to body-weight gain. We will discuss on the different techniques used, focusing
    on the high-defi nition technologies for the determination of the composition, function and ecology of gut microbiota. This
    allows the appropriate selection of platform which becomes the key for success of subsequent research.
    Matched MeSH terms: Computational Biology
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