Displaying publications 41 - 60 of 119 in total

Abstract:
Sort:
  1. Application of Computational Techniques in Antibody Fc-Fused Molecule Design for Therapeutics
    Ng CL, Lim TS, Choong YS
    Mol Biotechnol, 2024 Apr;66(4):568-581.
    PMID: 37742298 DOI: 10.1007/s12033-023-00885-x
    Since the advent of hybridoma technology in the year 1975, it took a decade to witness the first approved monoclonal antibody Orthoclone OKT39 (muromonab-CD3) in the year 1986. Since then, continuous strides have been made to engineer antibodies for specific desired effects. The engineering efforts were not confined to only the variable domains of the antibody but also included the fragment crystallizable (Fc) region that influences the immune response and serum half-life. Engineering of the Fc fragment would have a profound effect on the therapeutic dose, antibody-dependent cell-mediated cytotoxicity as well as antibody-dependent cellular phagocytosis. The integration of computational techniques into antibody engineering designs has allowed for the generation of testable hypotheses and guided the rational antibody design framework prior to further experimental evaluations. In this article, we discuss the recent works in the Fc-fused molecule design that involves computational techniques. We also summarize the usefulness of in silico techniques to aid Fc-fused molecule design and analysis for the therapeutics application.
    Matched MeSH terms: Computational Biology/methods
  2. Computational epigenetic profiling of CpG islets in MTHFR
    Wei K, Sutherland H, Camilleri E, Haupt LM, Griffiths LR, Gan SH
    Mol Biol Rep, 2014 Dec;41(12):8285-92.
    PMID: 25213548 DOI: 10.1007/s11033-014-3729-x
    Computational epigenetics is a new area of research focused on exploring how DNA methylation patterns affect transcription factor binding that affect gene expression patterns. The aim of this study was to produce a new protocol for the detection of DNA methylation patterns using computational analysis which can be further confirmed by bisulfite PCR with serial pyrosequencing. The upstream regulatory element and pre-initiation complex relative to CpG islets within the methylenetetrahydrofolate reductase gene were determined via computational analysis and online databases. The 1,104 bp long CpG island located near to or at the alternative promoter site of methylenetetrahydrofolate reductase gene was identified. The CpG plot indicated that CpG islets A and B, within the island, contained 62 and 75 % GC content CpG ratios of 0.70 and 0.80-0.95, respectively. Further exploration of the CpG islets A and B indicates that the transcription start sites were GGC which were absent from the TATA boxes. In addition, although six PROSITE motifs were identified in CpG B, no motifs were detected in CpG A. A number of cis-regulatory elements were found in different regions within the CpGs A and B. Transcription factors were predicted to bind to CpGs A and B with varying affinities depending on the DNA methylation status. In addition, transcription factor binding may influence the expression patterns of the methylenetetrahydrofolate reductase gene by recruiting chromatin condensation inducing factors. These results have significant implications for the understanding of the architecture of transcription factor binding at CpG islets as well as DNA methylation patterns that affect chromatin structure.
    Matched MeSH terms: Computational Biology/methods*
  3. Isolation and cloning of microRNAs from recalcitrant plant tissues with small amounts of total RNA: a step-by step approach
    Yew CW, Kumar SV
    Mol Biol Rep, 2012 Feb;39(2):1783-90.
    PMID: 21625851 DOI: 10.1007/s11033-011-0919-7
    MicroRNAs (miRNAs) are small RNAs (sRNAs) with approximately 21-24 nucleotides in length. They regulate the expression of target genes through the mechanism of RNA silencing. Conventional isolation and cloning of miRNAs methods are usually technical demanding and inefficient. These limitations include the requirement for high amounts of starting total RNA, inefficient ligation of linkers, high amount of PCR artifacts and bias in the formation of short miRNA-concatamers. Here we describe in detail a method that uses 80 μg of total RNA as the starting material. Enhancement of the ligation of sRNAs and linkers with the use of polyethylene glycol (PEG8000) was described. PCR artifacts from the amplification of reverse-transcribed sRNAs were greatly decreased by using lower concentrations of primers and reducing the number of amplification cycles. Large concatamers with up to 1 kb in size with around 20 sRNAs/concatamer were obtained by using an optimized reaction condition. This protocol provide researchers with a rapid, efficient and cost-effective method for the construction of miRNA profiles from plant tissues containing low amounts of total RNA, such as fruit flesh and senescent leaves.
    Matched MeSH terms: Computational Biology/methods
  4. In silico 'fishing' using known small regulatory RNA (sRNA) candidates as the decoy from Escherichia coli, Salmonella typhi and Salmonella typhimurium manifested 14 novel sRNA candidates in the orthologous region of Proteus mirabilis
    KishanRaj S, Sumitha S, Siventhiran B, Thiviyaa O, Sathasivam KV, Xavier R, et al.
    Mol Biol Rep, 2018 Dec;45(6):2333-2343.
    PMID: 30284142 DOI: 10.1007/s11033-018-4397-z
    Proteus mirabilis, a gram-negative bacterium of the family Enterobacteriaceae, is a leading cause of urinary tract infection (UTI) with rapid development of multi-drug resistance. Identification of small regulatory RNAs (sRNAs), which belongs to a class of RNAs that do not translate into a protein, could permit the comprehension of the regulatory roles this molecules play in mediating pathogenesis and multi-drug resistance of the organism. In this study, comparative sRNA analysis across three different members of Enterobacteriaceae (Escherichia coli, Salmonella typhi and Salmonella typhimurium) was carried out to identify the sRNA homologs in P. mirabilis. A total of 232 sRNA genes that were reported in E. coli, S. typhi and S. typhimurium were subjected to comparative analysis against P. mirabilis HI4320 genome. We report the detection of 14 sRNA candidates, conserved in the orthologous regions of P. mirabilis, that are not included in Rfam database. Northern-blot analysis was carried out for selected three sRNA candidates from the current investigation and three known sRNA from Rfam of P. mirabilis. The expression pattern of the six sRNA candidates shows that they are growth stage-dependant. To the best of our knowledge, this is the first report on the identification of sRNA candidates in P. mirabilis.
    Matched MeSH terms: Computational Biology/methods
  5. The complete mitogenome of the endangered white-clawed freshwater crayfish Austropotamobius pallipes (Lereboullet, 1858) (Crustacea: Decapoda: Astacidae)
    Grandjean F, Tan MH, Gan HY, Gan HM, Austin CM
    Mitochondrial DNA A DNA Mapp Seq Anal, 2016 09;27(5):3329-30.
    PMID: 25738217 DOI: 10.3109/19401736.2015.1018207
    The Austropotamobius pallipes complete mitogenome has been recovered using Next-Gen sequencing. Our sample of A. pallipes has a mitogenome of 15,679 base pairs (68.44% A + T content) made up of 13 protein-coding genes, 2 ribosomal subunit genes, 22 transfer RNAs, and a 877 bp non-coding AT-rich region. This is the first mitogenome sequenced for a crayfish from the family Astacidae and the 4(th) for northern hemisphere genera.
    Matched MeSH terms: Computational Biology/methods
  6. The complete mitogenome of the giant clam Tridacna squamosa (Heterodonta: Bivalvia: Tridacnidae)
    Gan HM, Gan HY, Tan MH, Penny SS, Willan RC, Austin CM
    Mitochondrial DNA A DNA Mapp Seq Anal, 2016 09;27(5):3220-1.
    PMID: 25648928 DOI: 10.3109/19401736.2015.1007355
    The complete mitochondrial genome of the commercially and ecologically important and internationally vulnerable giant clam Tridacna squamosa was recovered by genome skimming using the MiSeq platform. The T. squamosa mitogenome has 20,930 base pairs (62.35% A+T content) and is made up of 12 protein-coding genes, 2 ribosomal subunit genes, 24 transfer RNAs, and a 2594 bp non-coding AT-rich region. The mitogenome has a relatively large insertion in the atp6 gene. This is the first mitogenome to be sequenced from the genus Tridacna, and the family Tridacnidae and represents a new gene order.
    Matched MeSH terms: Computational Biology/methods
  7. Computational Analysis of Protein-Protein Interactions in Motile T-Cells
    Kumar S, Fazil MHUT, Ahmad K, Tripathy M, Rajapakse JC, Verma NK
    Methods Mol Biol, 2019;1930:149-156.
    PMID: 30610609 DOI: 10.1007/978-1-4939-9036-8_18
    Analysis of protein-protein interactions is important for better understanding of molecular mechanisms involved in immune regulation and has potential for elaborating avenues for drug discovery targeting T-cell motility. Currently, only a small fraction of protein-protein interactions have been characterized in T-lymphocytes although there are several detection methods available. In this regard, computational approaches garner importance, with the continued explosion of genomic and proteomic data, for handling protein modeling and protein-protein interactions in large scale. Here, we describe a computational method to identify protein-protein interactions based on in silico protein design.
    Matched MeSH terms: Computational Biology/methods*
  8. Shotgun Proteomics and Mass Spectrometry as a Tool for Protein Identification and Profiling of Bio-Carrier-Based Therapeutics on Human Cancer Cells
    Abidin SAZ, Othman I, Naidu R
    Methods Mol Biol, 2021;2211:233-240.
    PMID: 33336281 DOI: 10.1007/978-1-0716-0943-9_16
    Shotgun proteomics has been widely applied to study proteins in complex biological samples. Combination of high-performance liquid chromatography with mass spectrometry has allowed for comprehensive protein analysis with high resolution, sensitivity, and mass accuracy. Prior to mass spectrometry analysis, proteins are extracted from biological samples and subjected to in-solution trypsin digestion. The digested proteins are subjected for clean-up and injected into the liquid chromatography-mass spectrometry system for peptide mass identification. Protein identification is performed by analyzing the mass spectrometry data on a protein search engine software such as PEAKS studio loaded with protein database for the species of interest. Results such as protein score, protein coverage, number of peptides, and unique peptides identified will be obtained and can be used to determine proteins identified with high confidence. This method can be applied to understand the proteomic changes or profile brought by bio-carrier-based therapeutics in vitro. In this chapter, we describe methods in which proteins can be extracted for proteomic analysis using a shotgun approach. The chapter outlines important in vitro techniques and data analysis that can be applied to investigate the proteome dynamics.
    Matched MeSH terms: Computational Biology/methods
  9. CATH-Gene3D: Generation of the Resource and Its Use in Obtaining Structural and Functional Annotations for Protein Sequences
    Dawson NL, Sillitoe I, Lees JG, Lam SD, Orengo CA
    Methods Mol Biol, 2017;1558:79-110.
    PMID: 28150234 DOI: 10.1007/978-1-4939-6783-4_4
    This chapter describes the generation of the data in the CATH-Gene3D online resource and how it can be used to study protein domains and their evolutionary relationships. Methods will be presented for: comparing protein structures, recognizing homologs, predicting domain structures within protein sequences, and subclassifying superfamilies into functionally pure families, together with a guide on using the webpages.
    Matched MeSH terms: Computational Biology/methods*
  10. In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
    Ealam Selvan M, Lim KS, Teo CH, Lim YY
    J Vis Exp, 2022 Oct 21.
    PMID: 36342167 DOI: 10.3791/64565
    Circular RNAs (circRNAs) are a class of non-coding RNAs that are formed via back-splicing. These circRNAs are predominantly studied for their roles as regulators of various biological processes. Notably, emerging evidence demonstrates that host circRNAs can be differentially expressed (DE) upon infection with pathogens (e.g., influenza and coronaviruses), suggesting a role for circRNAs in regulating host innate immune responses. However, investigations on the role of circRNAs during pathogenic infections are limited by the knowledge and skills required to carry out the necessary bioinformatic analysis to identify DE circRNAs from RNA sequencing (RNA-seq) data. Bioinformatics prediction and identification of circRNAs is crucial before any verification, and functional studies using costly and time-consuming wet-lab techniques. To solve this issue, a step-by-step protocol of in silico prediction and characterization of circRNAs using RNA-seq data is provided in this manuscript. The protocol can be divided into four steps: 1) Prediction and quantification of DE circRNAs via the CIRIquant pipeline; 2) Annotation via circBase and characterization of DE circRNAs; 3) CircRNA-miRNA interaction prediction through Circr pipeline; 4) functional enrichment analysis of circRNA parental genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). This pipeline will be useful in driving future in vitro and in vivo research to further unravel the role of circRNAs in host-pathogen interactions.
    Matched MeSH terms: Computational Biology/methods
  11. Analysis and construction of pathogenicity island regulatory pathways in Salmonella enterica serovar Typhi
    Ong SY, Ng FL, Badai SS, Yuryev A, Alam M
    J Integr Bioinform, 2010;7(1).
    PMID: 20861532 DOI: 10.2390/biecoll-jib-2010-145
    Signal transduction through protein-protein interactions and protein modifications are the main mechanisms controlling many biological processes. Here we described the implementation of MedScan information extraction technology and Pathway Studio software (Ariadne Genomics Inc.) to create a Salmonella specific molecular interaction database. Using the database, we have constructed several signal transduction pathways in Salmonella enterica serovar Typhi which causes Typhoid Fever, a major health threat especially in developing countries. S. Typhi has several pathogenicity islands that control rapid switching between different phenotypes including adhesion and colonization, invasion, intracellular survival, proliferation, and biofilm formation in response to environmental changes. Understanding of the detailed mechanism for S. Typhi survival in host cells is necessary for development of efficient detection and treatment of this pathogen. The constructed pathways were validated using publically available gene expression microarray data for Salmonella.
    Matched MeSH terms: Computational Biology/methods*
  12. Random forests for predicting species identity of forensically important blow flies (Diptera: Calliphoridae) and flesh flies (Diptera: Sarcophagidae) using geometric morphometric data: Proof of concept
    Khang TF, Mohd Puaad NAD, Teh SH, Mohamed Z
    J Forensic Sci, 2021 May;66(3):960-970.
    PMID: 33438785 DOI: 10.1111/1556-4029.14655
    Wing shape variation has been shown to be useful for delineating forensically important fly species in two Diptera families: Calliphoridae and Sarcophagidae. Compared to DNA-based identification, the cost of geometric morphometric data acquisition and analysis is relatively much lower because the tools required are basic, and stable softwares are available. However, to date, an explicit demonstration of using wing geometric morphometric data for species identity prediction in these two families remains lacking. Here, geometric morphometric data from 19 homologous landmarks on the left wing of males from seven species of Calliphoridae (n = 55), and eight species of Sarcophagidae (n = 40) were obtained and processed using Generalized Procrustes Analysis. Allometric effect was removed by regressing centroid size (in log10 ) against the Procrustes coordinates. Subsequently, principal component analysis of the allometry-adjusted Procrustes variables was done, with the first 15 principal components used to train a random forests model for species prediction. Using a real test sample consisting of 33 male fly specimens collected around a human corpse at a crime scene, the estimated percentage of concordance between species identities predicted using the random forests model and those inferred using DNA-based identification was about 80.6% (approximate 95% confidence interval = [68.9%, 92.2%]). In contrast, baseline concordance using naive majority class prediction was 36.4%. The results provide proof of concept that geometric morphometric data has good potential to complement morphological and DNA-based identification of blow flies and flesh flies in forensic work.
    Matched MeSH terms: Computational Biology/methods*
  13. Thermodynamic heuristics with case-based reasoning: combined insights for RNA pseudoknot secondary structure
    Al-Khatib RM, Rashid NA, Abdullah R
    J Biomol Struct Dyn, 2011 Aug;29(1):1-26.
    PMID: 21696223
    The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.
    Matched MeSH terms: Computational Biology/methods*
  14. iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions
    Naseer S, Ali RF, Khan YD, Dominic PDD
    J Biomol Struct Dyn, 2022;40(22):11691-11704.
    PMID: 34396935 DOI: 10.1080/07391102.2021.1962738
    Lysine glutarylation is a post-translation modification which plays an important regulatory role in a variety of physiological and enzymatic processes including mitochondrial functions and metabolic processes both in eukaryotic and prokaryotic cells. This post-translational modification influences chromatin structure and thereby results in global regulation of transcription, defects in cell-cycle progression, DNA damage repair, and telomere silencing. To better understand the mechanism of lysine glutarylation, its identification in a protein is necessary, however, experimental methods are time-consuming and labor-intensive. Herein, we propose a new computational prediction approach to supplement experimental methods for identification of lysine glutarylation site prediction by deep neural networks and Chou's Pseudo Amino Acid Composition (PseAAC). We employed well-known deep neural networks for feature representation learning and classification of peptide sequences. Our approach opts raw pseudo amino acid compositions and obsoletes the need to separately perform costly and cumbersome feature extraction and selection. Among the developed deep learning-based predictors, the standard neural network-based predictor demonstrated highest scores in terms of accuracy and all other performance evaluation measures and outperforms majority of previously reported predictors without requiring expensive feature extraction process. iGluK-Deep:Computational Identification of lysine glutarylationsites using deep neural networks with general Pseudo Amino Acid Compositions Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, P.D.D DominicCommunicated by Ramaswamy H. Sarma.
    Matched MeSH terms: Computational Biology/methods
  15. Samira-VP: A simple protein alignment method with rechecking the alphabet vector positions
    Fotoohifiroozabadi S, Mohamad MS, Deris S
    J Bioinform Comput Biol, 2017 Apr;15(2):1750004.
    PMID: 28274174 DOI: 10.1142/S0219720017500044
    Protein structure alignment and comparisons that are based on an alphabetical demonstration of protein structure are more simple to run with faster evaluation processes; thus, their accuracy is not as reliable as three-dimension (3D)-based tools. As a 1D method candidate, TS-AMIR used the alphabetic demonstration of secondary-structure elements (SSE) of proteins and compared the assigned letters to each SSE using the [Formula: see text]-gram method. Although the results were comparable to those obtained via geometrical methods, the SSE length and accuracy of adjacency between SSEs were not considered in the comparison process. Therefore, to obtain further information on accuracy of adjacency between SSE vectors, the new approach of assigning text to vectors was adopted according to the spherical coordinate system in the present study. Moreover, dynamic programming was applied in order to account for the length of SSE vectors. Five common datasets were selected for method evaluation. The first three datasets were small, but difficult to align, and the remaining two datasets were used to compare the capability of the proposed method with that of other methods on a large protein dataset. The results showed that the proposed method, as a text-based alignment approach, obtained results comparable to both 1D and 3D methods. It outperformed 1D methods in terms of accuracy and 3D methods in terms of runtime.
    Matched MeSH terms: Computational Biology/methods*
  16. Fulltext Molecular study of wound healing after using biosynthesized BNC/Fe3O4 nanocomposites assisted with a bioinformatics approach
    Moniri M, Boroumand Moghaddam A, Azizi S, Abdul Rahim R, Zuhainis Saad W, Navaderi M, et al.
    Int J Nanomedicine, 2018;13:2955-2971.
    PMID: 29861630 DOI: 10.2147/IJN.S159637
    Background: Molecular investigation of wound healing has allowed better understanding about interaction of genes and pathways involved in healing progression.

    Objectives: The aim of this study was to prepare magnetic/bacterial nanocellulose (Fe3O4/BNC) nanocomposite films as ecofriendly wound dressing in order to evaluate their physical, cytotoxicity and antimicrobial properties. The molecular study was carried out to evaluate expression of genes involved in healing of wounds after treatment with BNC/Fe3O4 films.

    Study design materials and methods: Magnetic nanoparticles were biosynthesized by using Aloe vera extract in new isolated bacterial nanocellulose (BNC) RM1. The nanocomposites were characterized using X-ray diffraction, Fourier transform infrared, and field emission scanning electron microscopy. Moreover, swelling property and metal ions release profile of the nanocomposites were investigated. The ability of nanocomposites to promote wound healing of human dermal fibroblast cells in vitro was examined. Bioinformatics databases were used to identify genes with important healing effect. Key genes which interfered with healing were studied by quantitative real time PCR.

    Results: Spherical magnetic nanoparticles (15-30 nm) were formed and immobilized within the structure of BNC. The BNC/Fe3O4 was nontoxic (IC50>500 μg/mL) with excellent wound healing efficiency after 48 hours. The nanocomposites showed good antibacterial activity ranging from 6±0.2 to 13.40±0.10 mm against Staphylococcus aureus, Staphylococcus epidermidis and Pseudomonas aeruginosa. The effective genes for the wound healing process were TGF-B1, MMP2, MMP9, Wnt4, CTNNB1, hsa-miR-29b, and hsa-miR-29c with time dependent manner. BNC/Fe3O4 has an effect on microRNA by reducing its expression and therefore causing an increase in the gene expression of other genes, which consequently resulted in wound healing.

    Conclusion: This eco-friendly nanocomposite with excellent healing properties can be used as an effective wound dressing for treatment of cutaneous wounds.

    Matched MeSH terms: Computational Biology/methods
  17. Fulltext Mapping a Circular RNA-microRNA-mRNA-Signaling Regulatory Axis That Modulates Stemness Properties of Cancer Stem Cell Populations in Colorectal Cancer Spheroid Cells
    Rengganaten V, Huang CJ, Tsai PH, Wang ML, Yang YP, Lan YT, et al.
    Int J Mol Sci, 2020 Oct 23;21(21).
    PMID: 33114016 DOI: 10.3390/ijms21217864
    Spheroidal cancer cell cultures have been used to enrich cancer stem cells (CSC), which are thought to contribute to important clinical features of tumors. This study aimed to map the regulatory networks driven by circular RNAs (circRNAs) in CSC-enriched colorectal cancer (CRC) spheroid cells. The spheroid cells established from two CRC cell lines acquired stemness properties in pluripotency gene expression and multi-lineage differentiation capacity. Genome-wide sequencing identified 1503 and 636 circRNAs specific to the CRC parental and spheroid cells, respectively. In the CRC spheroids, algorithmic analyses unveiled a core network of mRNAs involved in modulating stemness-associated signaling pathways, driven by a circRNA-microRNA (miRNA)-mRNA axis. The two major circRNAs, hsa_circ_0066631 and hsa_circ_0082096, in this network were significantly up-regulated in expression levels in the spheroid cells. The two circRNAs were predicted to target and were experimentally shown to down-regulate miR-140-3p, miR-224, miR-382, miR-548c-3p and miR-579, confirming circRNA sponging of the targeted miRNAs. Furthermore, the affected miRNAs were demonstrated to inhibit degradation of six mRNA targets, viz. ACVR1C/ALK7, FZD3, IL6ST/GP130, SKIL/SNON, SMAD2 and WNT5, in the CRC spheroid cells. These mRNAs encode proteins that are reported to variously regulate the GP130/Stat, Activin/Nodal, TGF-β/SMAD or Wnt/β-catenin signaling pathways in controlling various aspects of CSC stemness. Using the CRC spheroid cell model, the novel circRNA-miRNA-mRNA axis mapped in this work forms the foundation for the elucidation of the molecular mechanisms of the complex cellular and biochemical processes that determine CSC stemness properties of cancer cells, and possibly for designing therapeutic strategies for CRC treatment by targeting CSC.
    Matched MeSH terms: Computational Biology/methods
  18. Dissecting Candida albicans Infection from the Perspective of C. albicans Virulence and Omics Approaches on Host-Pathogen Interaction: A Review
    Chin VK, Lee TY, Rusliza B, Chong PP
    Int J Mol Sci, 2016 Oct 18;17(10).
    PMID: 27763544
    Candida bloodstream infections remain the most frequent life-threatening fungal disease, with Candida albicans accounting for 70% to 80% of the Candida isolates recovered from infected patients. In nature, Candida species are part of the normal commensal flora in mammalian hosts. However, they can transform into pathogens once the host immune system is weakened or breached. More recently, mortality attributed to Candida infections has continued to increase due to both inherent and acquired drug resistance in Candida, the inefficacy of the available antifungal drugs, tedious diagnostic procedures, and a rising number of immunocompromised patients. Adoption of animal models, viz. minihosts, mice, and zebrafish, has brought us closer to unraveling the pathogenesis and complexity of Candida infection in human hosts, leading towards the discovery of biomarkers and identification of potential therapeutic agents. In addition, the advancement of omics technologies offers a holistic view of the Candida-host interaction in a non-targeted and non-biased manner. Hence, in this review, we seek to summarize past and present milestone findings on C. albicans virulence, adoption of animal models in the study of C. albicans infection, and the application of omics technologies in the study of Candida-host interaction. A profound understanding of the interaction between host defense and pathogenesis is imperative for better design of novel immunotherapeutic strategies in future.
    Matched MeSH terms: Computational Biology/methods*
  19. Fulltext Identification of NRAS Diagnostic Biomarkers and Drug Targets for Endometrial Cancer-An Integrated in Silico Approach
    Alessandro L, Low KE, Abushelaibi A, Lim SE, Cheng WH, Chang SK, et al.
    Int J Mol Sci, 2022 Nov 18;23(22).
    PMID: 36430761 DOI: 10.3390/ijms232214285
    The diagnosis of endometrial cancer involves sequential, invasive tests to assess the thickness of the endometrium by a transvaginal ultrasound scan. In 6−33% of cases, endometrial biopsy results in inadequate tissue for a conclusive pathological diagnosis and 6% of postmenopausal women with non-diagnostic specimens are later discovered to have severe endometrial lesions. Thus, identifying diagnostic biomarkers could offer a non-invasive diagnosis for community or home-based triage of symptomatic or asymptomatic women. Herein, this study identified high-risk pathogenic nsSNPs in the NRAS gene. The nsSNPs of NRAS were retrieved from the NCBI database. PROVEAN, SIFT, PolyPhen-2, SNPs&GO, PhD-SNP and PANTHER were used to predict the pathogenicity of the nsSNPs. Eleven nsSNPs were identified as “damaging”, and further stability analysis using I-Mutant 2.0 and MutPred 2 indicated eight nsSNPs to cause decreased stability (DDG scores < −0.5). Post-translational modification and protein−protein interactions (PPI) analysis showed putative phosphorylation sites. The PPI network indicated a GFR-MAPK signalling pathway with higher node degrees that were further evaluated for drug targets. The P34L, G12C and Y64D showed significantly lower binding affinity towards GTP than wild-type. Furthermore, the Kaplan−Meier bioinformatics analyses indicated that the NRAS gene deregulation affected the overall survival rate of patients with endometrial cancer, leading to prognostic significance. Findings from this could be considered novel diagnostic and therapeutic markers.
    Matched MeSH terms: Computational Biology/methods
  20. Fulltext Protein-Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome
    Ramly B, Afiqah-Aleng N, Mohamed-Hussein ZA
    Int J Mol Sci, 2019 Jun 18;20(12).
    PMID: 31216618 DOI: 10.3390/ijms20122959
    Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein-protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related proteins (PCOSrp) obtained from PCOSBase. MCODE was used to identify highly connected regions in the PCOS network, known as subnetworks. These subnetworks represent protein families, where their molecular information is used to explain the association between PCOS and other diseases. Fisher's exact test and comorbidity data were used to identify PCOS-disease subnetworks. Pathway enrichment analysis was performed on the PCOS-disease subnetworks to identify significant pathways that are highly involved in the PCOS-disease associations. Migraine, schizophrenia, depressive disorder, obesity, and hypertension, along with twelve other diseases, were identified to be highly associated with PCOS. The identification of significant pathways, such as ribosome biogenesis, antigen processing and presentation, and mitophagy, suggest their involvement in the association between PCOS and migraine, schizophrenia, and hypertension.
    Matched MeSH terms: Computational Biology/methods
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links