Displaying publications 281 - 300 of 340 in total

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  1. Draft genome sequence of multidrug-resistant Salmonella enterica serovar Brancaster strain PS01 isolated from chicken meat, Malaysia
    Chin PS, Yu CY, Ang GY, Yin WF, Chan KG
    J Glob Antimicrob Resist, 2017 06;9:41-42.
    PMID: 28300643 DOI: 10.1016/j.jgar.2016.12.017
    OBJECTIVES: Salmonella spp. represent one of the main diarrhoeal pathogens that are transmitted via the food supply chain. Here we report the draft genome sequence of a multidrug-resistant Salmonella enterica serovar Brancaster (PS01) that was isolated from poultry meat in Malaysia.

    METHODS: Genomic DNA was extracted from Salmonella strain PS01 and was sequenced using an Illumina HiSeq 2000 platform. The generated reads were de novo assembled using CLC Genomics Workbench. The draft genome was annotated and the presence of antimicrobial resistance genes was identified.

    RESULTS: The 5 036 442bp genome contains various antimicrobial resistance genes conferring resistance to aminoglycosides, fluoroquinolones, fosfomycin, macrolides, phenicols, sulphonamides, tetracyclines and trimethoprim. The β-lactamase gene blaTEM-176 encoding TEM-176 was also found in this strain.

    CONCLUSIONS: The genome sequence will aid in the understanding of drug resistance mechanisms in foodborne Salmonella Brancaster and highlights the need to ensure the judicious use of antibiotics in animal husbandry as well as the importance of implementing proper food handling and preparation practices.

    Matched MeSH terms: Computational Biology
  2. Lupus Susceptibility Region Containing CDKN1B rs34330 Mechanistically Influences Expression and Function of Multiple Target Genes, Also Linked to Proliferation and Apoptosis
    Singh B, Maiti GP, Zhou X, Fazel-Najafabadi M, Bae SC, Sun C, et al.
    Arthritis Rheumatol, 2021 Dec;73(12):2303-2313.
    PMID: 33982894 DOI: 10.1002/art.41799
    OBJECTIVE: In a recent genome-wide association study, a significant genetic association between rs34330 of CDKN1B and risk of systemic lupus erythematosus (SLE) in Han Chinese was identified. This study was undertaken to validate the reported association and elucidate the biochemical mechanisms underlying the effect of the variant.

    METHODS: We performed an allelic association analysis in patients with SLE, followed by a meta-analysis assessing genome-wide association data across 11 independent cohorts (n = 28,872). In silico bioinformatics analysis and experimental validation in SLE-relevant cell lines were applied to determine the functional consequences of rs34330.

    RESULTS: We replicated a genetic association between SLE and rs34330 (meta-analysis P = 5.29 × 10-22 , odds ratio 0.84 [95% confidence interval 0.81-0.87]). Follow-up bioinformatics and expression quantitative trait locus analysis suggested that rs34330 is located in active chromatin and potentially regulates several target genes. Using luciferase and chromatin immunoprecipitation-real-time quantitative polymerase chain reaction, we demonstrated substantial allele-specific promoter and enhancer activity, and allele-specific binding of 3 histone marks (H3K27ac, H3K4me3, and H3K4me1), RNA polymerase II (Pol II), CCCTC-binding factor, and a critical immune transcription factor (interferon regulatory factor 1 [IRF-1]). Chromosome conformation capture revealed long-range chromatin interactions between rs34330 and the promoters of neighboring genes APOLD1 and DDX47, and effects on CDKN1B and the other target genes were directly validated by clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing. Finally, CRISPR/dead CRISPR-associated protein 9-based epigenetic activation/silencing confirmed these results. Gene-edited cell lines also showed higher levels of proliferation and apoptosis.

    CONCLUSION: Collectively, these findings suggest a mechanism whereby the rs34330 risk allele (C) influences the presence of histone marks, RNA Pol II, and IRF-1 transcription factor to regulate expression of several target genes linked to proliferation and apoptosis. This process could potentially underlie the association of rs34330 with SLE.

    Matched MeSH terms: Computational Biology
  3. Sequencing and analysis of chromosome 1 of Eimeria tenella reveals a unique segmental organization
    Ling KH, Rajandream MA, Rivailler P, Ivens A, Yap SJ, Madeira AM, et al.
    Genome Res, 2007 Mar;17(3):311-9.
    PMID: 17284678
    Eimeria tenella is an intracellular protozoan parasite that infects the intestinal tracts of domestic fowl and causes coccidiosis, a serious and sometimes lethal enteritis. Eimeria falls in the same phylum (Apicomplexa) as several human and animal parasites such as Cryptosporidium, Toxoplasma, and the malaria parasite, Plasmodium. Here we report the sequencing and analysis of the first chromosome of E. tenella, a chromosome believed to carry loci associated with drug resistance and known to differ between virulent and attenuated strains of the parasite. The chromosome--which appears to be representative of the genome--is gene-dense and rich in simple-sequence repeats, many of which appear to give rise to repetitive amino acid tracts in the predicted proteins. Most striking is the segmentation of the chromosome into repeat-rich regions peppered with transposon-like elements and telomere-like repeats, alternating with repeat-free regions. Predicted genes differ in character between the two types of segment, and the repeat-rich regions appear to be associated with strain-to-strain variation.
    Matched MeSH terms: Computational Biology
  4. Fulltext Image Processing for mHealth-Based Approach to Detect the Local Tissue Inflammation in Cutaneous Leishmaniasis: A Proof of Concept Study
    Silva H, Chellappan K, Karunaweera N
    Comput Math Methods Med, 2021;2021:4208254.
    PMID: 34873414 DOI: 10.1155/2021/4208254
    Skin lesions are a feature of many diseases including cutaneous leishmaniasis (CL). Ulcerative lesions are a common manifestation of CL. Response to treatment in such lesions is judged through the assessment of the healing process by regular clinical observations, which remains a challenge for the clinician, health system, and the patient in leishmaniasis endemic countries. In this study, image processing was initially done using 40 CL lesion color images that were captured using a mobile phone camera, to establish a technique to extract features from the image which could be related to the clinical status of the lesion. The identified techniques were further developed, and ten ulcer images were analyzed to detect the extent of inflammatory response and/or signs of healing using pattern recognition of inflammatory tissue captured in the image. The images were preprocessed at the outset, and the quality was improved using the CIE L∗a∗b color space technique. Furthermore, features were extracted using the principal component analysis and profiled using the signal spectrogram technique. This study has established an adaptive thresholding technique ranging between 35 and 200 to profile the skin lesion images using signal spectrogram plotted using Signal Analyzer in MATLAB. The outcome indicates its potential utility in visualizing and assessing inflammatory tissue response in a CL ulcer. This approach is expected to be developed further to a mHealth-based prediction algorithm to enable remote monitoring of treatment response of cutaneous leishmaniasis.
    Matched MeSH terms: Computational Biology
  5. Fulltext An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19
    Serena Low WC, Chuah JH, Tee CATH, Anis S, Shoaib MA, Faisal A, et al.
    Comput Math Methods Med, 2021;2021:5528144.
    PMID: 34194535 DOI: 10.1155/2021/5528144
    Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.
    Matched MeSH terms: Computational Biology
  6. Fulltext Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method
    Charoenkwan P, Chiangjong W, Lee VS, Nantasenamat C, Hasan MM, Shoombuatong W
    Sci Rep, 2021 Feb 04;11(1):3017.
    PMID: 33542286 DOI: 10.1038/s41598-021-82513-9
    As anticancer peptides (ACPs) have attracted great interest for cancer treatment, several approaches based on machine learning have been proposed for ACP identification. Although existing methods have afforded high prediction accuracies, however such models are using a large number of descriptors together with complex ensemble approaches that consequently leads to low interpretability and thus poses a challenge for biologists and biochemists. Therefore, it is desirable to develop a simple, interpretable and efficient predictor for accurate ACP identification as well as providing the means for the rational design of new anticancer peptides with promising potential for clinical application. Herein, we propose a novel flexible scoring card method (FSCM) making use of propensity scores of local and global sequential information for the development of a sequence-based ACP predictor (named iACP-FSCM) for improving the prediction accuracy and model interpretability. To the best of our knowledge, iACP-FSCM represents the first sequence-based ACP predictor for rationalizing an in-depth understanding into the molecular basis for the enhancement of anticancer activities of peptides via the use of FSCM-derived propensity scores. The independent testing results showed that the iACP-FSCM provided accuracies of 0.825 and 0.910 as evaluated on the main and alternative datasets, respectively. Results from comparative benchmarking demonstrated that iACP-FSCM could outperform seven other existing ACP predictors with marked improvements of 7% and 17% for accuracy and MCC, respectively, on the main dataset. Furthermore, the iACP-FSCM (0.910) achieved very comparable results to that of the state-of-the-art ensemble model AntiCP2.0 (0.920) as evaluated on the alternative dataset. Comparative results demonstrated that iACP-FSCM was the most suitable choice for ACP identification and characterization considering its simplicity, interpretability and generalizability. It is highly anticipated that the iACP-FSCM may be a robust tool for the rapid screening and identification of promising ACPs for clinical use.
    Matched MeSH terms: Computational Biology
  7. Structural vaccinology, molecular simulation and immune simulation approaches to design multi-epitopes vaccine against John Cunningham virus
    Suleman M, Khan TA, Ejaz H, Maroof S, Alshammari A, Albekairi NA, et al.
    Microb Pathog, 2024 Apr;189:106572.
    PMID: 38354987 DOI: 10.1016/j.micpath.2024.106572
    The JCV (John Cunningham Virus) is known to cause progressive multifocal leukoencephalopathy, a condition that results in the formation of tumors. Symptoms of this condition such as sensory defects, cognitive dysfunction, muscle weakness, homonosapobia, difficulties with coordination, and aphasia. To date, there is no specific and effective treatment to completely cure or prevent John Cunningham polyomavirus infections. Since the best way to control the disease is vaccination. In this study, the immunoinformatic tools were used to predict the high immunogenic and non-allergenic B cells, helper T cells (HTL), and cytotoxic T cells (CTL) epitopes from capsid, major capsid, and T antigen proteins of JC virus to design the highly efficient subunit vaccines. The specific immunogenic linkers were used to link together the predicted epitopes and subjected to 3D modeling by using the Robetta server. MD simulation was used to confirm that the newly constructed vaccines are stable and properly fold. Additionally, the molecular docking approach revealed that the vaccines have a strong binding affinity with human TLR-7. The codon adaptation index (CAI) and GC content values verified that the constructed vaccines would be highly expressed in E. coli pET28a (+) plasmid. The immune simulation analysis indicated that the human immune system would have a strong response to the vaccines, with a high titer of IgM and IgG antibodies being produced. In conclusion, this study will provide a pre-clinical concept to construct an effective, highly antigenic, non-allergenic, and thermostable vaccine to combat the infection of the John Cunningham virus.
    Matched MeSH terms: Computational Biology
  8. Fulltext Identification of the major allergen of Macrobrachium rosenbergii (giant freshwater prawn)
    Yadzir ZH, Misnan R, Abdullah N, Bakhtiar F, Arip M, Murad S
    Asian Pac J Trop Biomed, 2012 Jan;2(1):50-4.
    PMID: 23569834 DOI: 10.1016/S2221-1691(11)60189-5
    OBJECTIVE: To characterize the major allergens of Macrobrachium rosenbergii (giant freshwater prawn).

    METHODS: Raw and cooked extracts of the giant freshwater prawn were prepared. The IgE reactivity pattern was identified by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and immunoblotting technique with the sera of 20 skin prick test (SPT) positive patients. The major allergen identified was then characterized using the proteomics approach involving a combination of two-dimensional (2-DE) electrophoresis, mass spectrometry and bioinformatics tools.

    RESULTS: SDS-PAGE of the raw extract showed 23 protein bands (15-250 kDa) but those ranging from 40 to 100 kDa were not found in the cooked extract. From immunoblotting experiments, raw and cooked extracts demonstrated 11 and 5 IgE-binding proteins, respectively, with a molecular mass ranging from 15 to 155 kDa. A heat-resistant 36 kDa protein was identified as the major allergen of both extracts. In addition, a 42 kDa heat-sensitive protein was shown to be a major allergen of the raw extract. The 2-DE gel fractionated the prawn proteins to more than 50 different protein spots. Of these, 10 spots showed specific IgE reactivity with patients' sera. Matrix assisted laser desorption/ionization-time of flight (MALDI-TOF) analysis led to identification of 2 important allergens, tropomyosin and arginine kinase.

    CONCLUSIONS: It can be concluded that the availability of such allergens would help in component-based diagnosis and therapy of prawn allergies.

    Matched MeSH terms: Computational Biology
  9. Fulltext Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks
    Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A
    Comput Biol Med, 2020 Jun;121:103795.
    PMID: 32568676 DOI: 10.1016/j.compbiomed.2020.103795
    Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19. However, the performance of radiologists in diagnosis of COVID-19 was moderate. Accordingly, additional investigations are needed to improve the performance in diagnosing COVID-19. In this study is suggested a rapid and valid method for COVID-19 diagnosis using an artificial intelligence technique based. 1020 CT slices from 108 patients with laboratory proven COVID-19 (the COVID-19 group) and 86 patients with other atypical and viral pneumonia diseases (the non-COVID-19 group) were included. Ten well-known convolutional neural networks were used to distinguish infection of COVID-19 from non-COVID-19 groups: AlexNet, VGG-16, VGG-19, SqueezeNet, GoogleNet, MobileNet-V2, ResNet-18, ResNet-50, ResNet-101, and Xception. Among all networks, the best performance was achieved by ResNet-101 and Xception. ResNet-101 could distinguish COVID-19 from non-COVID-19 cases with an AUC of 0.994 (sensitivity, 100%; specificity, 99.02%; accuracy, 99.51%). Xception achieved an AUC of 0.994 (sensitivity, 98.04%; specificity, 100%; accuracy, 99.02%). However, the performance of the radiologist was moderate with an AUC of 0.873 (sensitivity, 89.21%; specificity, 83.33%; accuracy, 86.27%). ResNet-101 can be considered as a high sensitivity model to characterize and diagnose COVID-19 infections, and can be used as an adjuvant tool in radiology departments.
    Matched MeSH terms: Computational Biology
  10. Fulltext Human non-small cell lung cancer expresses putative cancer stem cell markers and exhibits the transcriptomic profile of multipotent cells
    Zakaria N, Yusoff NM, Zakaria Z, Lim MN, Baharuddin PJ, Fakiruddin KS, et al.
    BMC Cancer, 2015;15:84.
    PMID: 25881239 DOI: 10.1186/s12885-015-1086-3
    Despite significant advances in staging and therapies, lung cancer remains a major cause of cancer-related lethality due to its high incidence and recurrence. Clearly, a novel approach is required to develop new therapies to treat this devastating disease. Recent evidence indicates that tumours contain a small population of cells known as cancer stem cells (CSCs) that are responsible for tumour maintenance, spreading and resistant to chemotherapy. The genetic composition of CSCs so far is not fully understood, but manipulation of the specific genes that maintain their integrity would be beneficial for developing strategies to combat cancer. Therefore, the goal of this study isto identify the transcriptomic composition and biological functions of CSCs from non-small cell lung cancer (NSCLC).
    Matched MeSH terms: Computational Biology/methods
  11. Fulltext Genome-Wide Transcription and Functional Analyses Reveal Heterogeneous Molecular Mechanisms Driving Pyrethroids Resistance in the Major Malaria Vector Anopheles funestus Across Africa
    Riveron JM, Ibrahim SS, Mulamba C, Djouaka R, Irving H, Wondji MJ, et al.
    G3 (Bethesda), 2017 06 07;7(6):1819-1832.
    PMID: 28428243 DOI: 10.1534/g3.117.040147
    Pyrethroid resistance in malaria vector, An. funestus is increasingly reported across Africa, threatening the sustainability of pyrethroid-based control interventions, including long lasting insecticidal nets (LLINs). Managing this problem requires understanding of the molecular basis of the resistance from different regions of the continent, to establish whether it is being driven by a single or independent selective events. Here, using a genome-wide transcription profiling of pyrethroid resistant populations from southern (Malawi), East (Uganda), and West Africa (Benin), we investigated the molecular basis of resistance, revealing strong differences between the different African regions. The duplicated cytochrome P450 genes (CYP6P9a and CYP6P9b) which were highly overexpressed in southern Africa are not the most upregulated in other regions, where other genes are more overexpressed, including GSTe2 in West (Benin) and CYP9K1 in East (Uganda). The lack of directional selection on both CYP6P9a and CYP6P9b in Uganda in contrast to southern Africa further supports the limited role of these genes outside southern Africa. However, other genes such as the P450 CYP9J11 are commonly overexpressed in all countries across Africa. Here, CYP9J11 is functionally characterized and shown to confer resistance to pyrethroids and moderate cross-resistance to carbamates (bendiocarb). The consistent overexpression of GSTe2 in Benin is coupled with a role of allelic variation at this gene as GAL4-UAS transgenic expression in Drosophila flies showed that the resistant 119F allele is highly efficient in conferring both DDT and permethrin resistance than the L119. The heterogeneity in the molecular basis of resistance and cross-resistance to insecticides in An. funestus populations throughout sub-Saharan African should be taken into account in designing resistance management strategies.
    Matched MeSH terms: Computational Biology/methods
  12. Fulltext Identification of independent association signals and putative functional variants for breast cancer risk through fine-scale mapping of the 12p11 locus
    Zeng C, Guo X, Long J, Kuchenbaecker KB, Droit A, Michailidou K, et al.
    Breast Cancer Res, 2016 06 21;18(1):64.
    PMID: 27459855 DOI: 10.1186/s13058-016-0718-0
    BACKGROUND: Multiple recent genome-wide association studies (GWAS) have identified a single nucleotide polymorphism (SNP), rs10771399, at 12p11 that is associated with breast cancer risk.

    METHOD: We performed a fine-scale mapping study of a 700 kb region including 441 genotyped and more than 1300 imputed genetic variants in 48,155 cases and 43,612 controls of European descent, 6269 cases and 6624 controls of East Asian descent and 1116 cases and 932 controls of African descent in the Breast Cancer Association Consortium (BCAC; http://bcac.ccge.medschl.cam.ac.uk/ ), and in 15,252 BRCA1 mutation carriers in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Stepwise regression analyses were performed to identify independent association signals. Data from the Encyclopedia of DNA Elements project (ENCODE) and the Cancer Genome Atlas (TCGA) were used for functional annotation.

    RESULTS: Analysis of data from European descendants found evidence for four independent association signals at 12p11, represented by rs7297051 (odds ratio (OR) = 1.09, 95 % confidence interval (CI) = 1.06-1.12; P = 3 × 10(-9)), rs805510 (OR = 1.08, 95 % CI = 1.04-1.12, P = 2 × 10(-5)), and rs1871152 (OR = 1.04, 95 % CI = 1.02-1.06; P = 2 × 10(-4)) identified in the general populations, and rs113824616 (P = 7 × 10(-5)) identified in the meta-analysis of BCAC ER-negative cases and BRCA1 mutation carriers. SNPs rs7297051, rs805510 and rs113824616 were also associated with breast cancer risk at P 

    Matched MeSH terms: Computational Biology/methods
  13. Fulltext Molecular phylogeny and predicted 3D structure of plant beta-D-N-acetylhexosaminidase
    Hossain MA, Roslan HA
    ScientificWorldJournal, 2014;2014:186029.
    PMID: 25165734 DOI: 10.1155/2014/186029
    beta-D-N-Acetylhexosaminidase, a family 20 glycosyl hydrolase, catalyzes the removal of β-1,4-linked N-acetylhexosamine residues from oligosaccharides and their conjugates. We constructed phylogenetic tree of β-hexosaminidases to analyze the evolutionary history and predicted functions of plant hexosaminidases. Phylogenetic analysis reveals the complex history of evolution of plant β-hexosaminidase that can be described by gene duplication events. The 3D structure of tomato β-hexosaminidase (β-Hex-Sl) was predicted by homology modeling using 1now as a template. Structural conformity studies of the best fit model showed that more than 98% of the residues lie inside the favoured and allowed regions where only 0.9% lie in the unfavourable region. Predicted 3D structure contains 531 amino acids residues with glycosyl hydrolase20b domain-I and glycosyl hydrolase20 superfamily domain-II including the (β/α)8 barrel in the central part. The α and β contents of the modeled structure were found to be 33.3% and 12.2%, respectively. Eleven amino acids were found to be involved in ligand-binding site; Asp(330) and Glu(331) could play important roles in enzyme-catalyzed reactions. The predicted model provides a structural framework that can act as a guide to develop a hypothesis for β-Hex-Sl mutagenesis experiments for exploring the functions of this class of enzymes in plant kingdom.
    Matched MeSH terms: Computational Biology
  14. In vitro expression of Candida albicans alcohol dehydrogenase genes involved in acetaldehyde metabolism
    Bakri MM, Rich AM, Cannon RD, Holmes AR
    Mol Oral Microbiol, 2015 Feb;30(1):27-38.
    PMID: 24975985 DOI: 10.1111/omi.12064
    Alcohol consumption is a risk factor for oral cancer, possibly via its conversion to acetaldehyde, a known carcinogen. The oral commensal yeast Candida albicans may be one of the agents responsible for this conversion intra-orally. The alcohol dehydrogenase (Adh) family of enzymes are involved in acetaldehyde metabolism in yeast but, for C. albicans it is not known which family member is responsible for the conversion of ethanol to acetaldehyde. In this study we determined the expression of mRNAs from three C. albicans Adh genes (CaADH1, CaADH2 and CaCDH3) for cells grown in different culture media at different growth phases by Northern blot analysis and quantitative reverse transcription polymerase chain reaction. CaADH1 was constitutively expressed under all growth conditions but there was differential expression of CaADH2. CaADH3 expression was not detected. To investigate whether CaAdh1p or CaAdh2p can contribute to alcohol catabolism in C. albicans, each gene from the reference strain C. albicans SC5314 was expressed in Saccharomyces cerevisiae. Cell extracts from an CaAdh1p-expressing S. cerevisiae recombinant, but not an CaAdh2p-expressing recombinant, or an empty vector control strain, possessed ethanol-utilizing Adh activity above endogenous S. cerevisiae activity. Furthermore, expression of C. albicans Adh1p in a recombinant S. cerevisiae strain in which the endogenous ScADH2 gene (known to convert ethanol to acetaldehyde in this yeast) had been deleted, conferred an NAD-dependent ethanol-utilizing, and so acetaldehyde-producing, Adh activity. We conclude that CaAdh1p is the enzyme responsible for ethanol use under in vitro growth conditions, and may contribute to the intra-oral production of acetaldehyde.
    Matched MeSH terms: Computational Biology
  15. Manipulation of the conformation and enzymatic properties of T1 lipase by site-directed mutagenesis of the protein core
    Wahab RA, Basri M, Rahman RN, Salleh AB, Rahman MB, Chor LT
    Appl Biochem Biotechnol, 2012 Jun;167(3):612-20.
    PMID: 22581079 DOI: 10.1007/s12010-012-9728-2
    In silico and experimental investigations were conducted to explore the effects of substituting hydrophobic residues, Val, Met, Leu, Ile, Trp, and Phe into Gln 114 of T1 lipase. The in silico investigations accurately predicted the enzymatic characteristics of the mutants in the experimental studies and provided rationalization for some of the experimental observations. Substitution with Leu successfully improved the conformational stability and enzymatic characteristics of T1 lipase. However, replacement of Gln114 with Trp negatively affected T1 lipase and resulted in the largest disruption of protein stability, diminished lipase activity and inferior enzymatic characteristics. These results suggested that the substitution of a larger residue in a densely packed area of the protein core can have considerable effects on the structure and function of an enzyme. This is especially true when the residue is next to the catalytic serine as demonstrated with the Phe and Trp mutation.
    Matched MeSH terms: Computational Biology
  16. Enhanced optical flow field of left ventricular motion using quasi-Gaussian DCT filter
    Riyadi S, Mustafa MM, Hussain A, Maskon O, Nor IF
    Adv Exp Med Biol, 2011;696:461-9.
    PMID: 21431586 DOI: 10.1007/978-1-4419-7046-6_46
    Left ventricular motion estimation is very important for diagnosing cardiac abnormality. One of the popular techniques, optical flow technique, promises useful results for motion quantification. However, optical flow technique often failed to provide smooth vector field due to the complexity of cardiac motion and the presence of speckle noise. This chapter proposed a new filtering technique, called quasi-Gaussian discrete cosine transform (QGDCT)-based filter, to enhance the optical flow field for myocardial motion estimation. Even though Gaussian filter and DCT concept have been implemented in other previous researches, this filter introduces a different approach of Gaussian filter model based on high frequency properties of cosine function. The QGDCT is a customized quasi discrete Gaussian filter in which its coefficients are derived from a selected two-dimensional DCT. This filter was implemented before and after the computation of optical flow to reduce the speckle noise and to improve the flow field smoothness, respectively. The algorithm was first validated on synthetic echocardiography image that simulates a contracting myocardium motion. Subsequently, this method was also implemented on clinical echocardiography images. To evaluate the performance of the technique, several quantitative measurements such as magnitude error, angular error, and standard error of measurement are computed and analyzed. The final motion estimation results were in good agreement with the physician manual interpretation.
    Matched MeSH terms: Computational Biology
  17. Microbial biodiversity in a Malaysian oil field and a systematic comparison with oil reservoirs worldwide
    Li D, Midgley DJ, Ross JP, Oytam Y, Abell GC, Volk H, et al.
    Arch Microbiol, 2012 Jun;194(6):513-23.
    PMID: 22245906 DOI: 10.1007/s00203-012-0788-z
    Microbial diversity within formation water and oil from two compartments in Bokor oil reservoir from a Malaysian petroleum oil field was examined. A total of 1,056 16S rRNA gene clones were screened from each location by amplified ribosomal DNA restriction analysis. All samples were dominated by clones affiliated with Marinobacter, some novel Deferribacteraceae genera and various clones allied to the Methanococci. In addition, either Marinobacterium- or Pseudomonas-like operational taxonomic units were detected from either compartment. A systematic comparison with the existing pertinent studies was undertaken by analysing the microbial amplicons detected and the PCR primers used. The analyses demonstrated that bacterial communities were site specific, while Archaea co-occurred more frequently. Amplicons related to Marinobacter, Marinobacterium and Pseudomonas were detected in a number of the studies examined, suggesting they may be ubiquitous members in oil reservoirs. Further analysis of primers used in those studies suggested that most primer pairs had fairly broad but low matches across the bacterial and archaeal domains, while a minority had selective matches to certain taxa or low matches to all the microbial taxa tested. Thus, it indicated that primers may play an important role in determining which taxa would be detected.
    Matched MeSH terms: Computational Biology
  18. Fulltext Adaptive localization of focus point regions via random patch probabilistic density from whole-slide, Ki-67-stained brain tumor tissue
    Alomari YM, Sheikh Abdullah SN, MdZin RR, Omar K
    Comput Math Methods Med, 2015;2015:673658.
    PMID: 25793010 DOI: 10.1155/2015/673658
    Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.
    Matched MeSH terms: Computational Biology
  19. Fulltext Insights into the ecological roles and evolution of methyl-coenzyme M reductase-containing hot spring Archaea
    Hua ZS, Wang YL, Evans PN, Qu YN, Goh KM, Rao YZ, et al.
    Nat Commun, 2019 10 08;10(1):4574.
    PMID: 31594929 DOI: 10.1038/s41467-019-12574-y
    Several recent studies have shown the presence of genes for the key enzyme associated with archaeal methane/alkane metabolism, methyl-coenzyme M reductase (Mcr), in metagenome-assembled genomes (MAGs) divergent to existing archaeal lineages. Here, we study the mcr-containing archaeal MAGs from several hot springs, which reveal further expansion in the diversity of archaeal organisms performing methane/alkane metabolism. Significantly, an MAG basal to organisms from the phylum Thaumarchaeota that contains mcr genes, but not those for ammonia oxidation or aerobic metabolism, is identified. Together, our phylogenetic analyses and ancestral state reconstructions suggest a mostly vertical evolution of mcrABG genes among methanogens and methanotrophs, along with frequent horizontal gene transfer of mcr genes between alkanotrophs. Analysis of all mcr-containing archaeal MAGs/genomes suggests a hydrothermal origin for these microorganisms based on optimal growth temperature predictions. These results also suggest methane/alkane oxidation or methanogenesis at high temperature likely existed in a common archaeal ancestor.
    Matched MeSH terms: Computational Biology
  20. Differential Analysis of Mycelial Proteins and Metabolites From Rigidoporus Microporus During In Vitro Interaction With Hevea Brasiliensis
    Fisol AFBC, Saidi NB, Al-Obaidi JR, Lamasudin DU, Atan S, Razali N, et al.
    Microb Ecol, 2021 Apr 22.
    PMID: 33890145 DOI: 10.1007/s00248-021-01757-0
    Rigidoporus microporus is the fungus accountable for the white root rot disease that is detrimental to the rubber tree, Hevea brasiliensis. The pathogenicity mechanism of R. microporus and the identity of the fungal proteins and metabolites involved during the infection process remain unclear. In this study, the protein and metabolite profiles of two R. microporus isolates, Segamat (SEG) and Ayer Molek (AM), were investigated during an in vitro interaction with H. brasiliensis. The isolates were used to inoculate H. brasiliensis clone RRIM 2025, and mycelia adhering to the roots of the plant were collected for analysis. Transmission electron microscope (TEM) images acquired confirms the hyphae attachment and colonization of the mycelia on the root of the H. brasiliensis clones after 4 days of inoculation. The protein samples were subjected to 2-DE analysis and analyzed using MALDI-ToF MS/MS, while the metabolites were extracted using methanol and analyzed using LC/MS-QTOF. Based on the differential analyses, upregulation of proteins that are essential for fungal evolution such as malate dehydrogenase, fructose 1,6-biphosphate aldolase, and glyceraldehyde-3-phosphate dehydrogenase hints an indirect role in fungal pathogenicity, while metabolomic analysis suggests an increase in acidic compounds which may lead to increased cell wall degrading enzyme activity. Bioinformatics analyses revealed that the carbohydrate and amino acid metabolisms were prominently affected in response to the fungal pathogenicity. In addition to that, other pathways that were significantly affected include "Protein Ubiquitination Pathway," Unfolded Protein Response," "HIFα Signaling," and "Sirtuin Signaling Pathway." The identification of responsive proteins and metabolites from this study promotes a better understanding of mechanisms underlying R. microporus pathogenesis and provides a list of potential biological markers for early recognition of the white root rot disease.
    Matched MeSH terms: Computational Biology
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