Displaying publications 121 - 140 of 340 in total

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  1. Dzaki N, Ramli KN, Azlan A, Ishak IH, Azzam G
    Sci Rep, 2017 03 16;7:43618.
    PMID: 28300076 DOI: 10.1038/srep43618
    The mosquito Aedes aegypti (Ae. aegypti) is the most notorious vector of illness-causing viruses such as Dengue, Chikugunya, and Zika. Although numerous genetic expression studies utilizing quantitative real-time PCR (qPCR) have been conducted with regards to Ae. aegypti, a panel of genes to be used suitably as references for the purpose of expression-level normalization within this epidemiologically important insect is presently lacking. Here, the usability of seven widely-utilized reference genes i.e. actin (ACT), eukaryotic elongation factor 1 alpha (eEF1α), alpha tubulin (α-tubulin), ribosomal proteins L8, L32 and S17 (RPL8, RPL32 and RPS17), and glyceraldeyde 3-phosphate dehydrogenase (GAPDH) were investigated. Expression patterns of the reference genes were observed in sixteen pre-determined developmental stages and in cell culture. Gene stability was inferred from qPCR data through three freely available algorithms i.e. BestKeeper, geNorm, and NormFinder. The consensus rankings generated from stability values provided by these programs suggest a combination of at least two genes for normalization. ACT and RPS17 are the most dependably expressed reference genes and therefore, we propose an ACT/RPS17 combination for normalization in all Ae. aegypti derived samples. GAPDH performed least desirably, and is thus not a recommended reference gene. This study emphasizes the importance of validating reference genes in Ae. aegypti for qPCR based research.
    Matched MeSH terms: Computational Biology/methods
  2. 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*
  3. Low TY, Mohtar MA, Ang MY, Jamal R
    Proteomics, 2019 05;19(10):e1800235.
    PMID: 30431238 DOI: 10.1002/pmic.201800235
    Understanding the relationship between genotypes and phenotypes is essential to disentangle biological mechanisms and to unravel the molecular basis of diseases. Genes and proteins are closely linked in biological systems. However, genomics and proteomics have developed separately into two distinct disciplines whereby crosstalk among scientists from the two domains is limited and this constrains the integration of both fields into a single data modality of useful information. The emerging field of proteogenomics attempts to address this by building bridges between the two disciplines. In this review, how genomics and transcriptomics data in different formats can be utilized to assist proteogenomics application is briefly discussed. Subsequently, a much larger part of this review focuses on proteogenomics research articles that are published in the last five years that answer two important questions. First, how proteogenomics can be applied to tackle biological problems is discussed, covering genome annotation and precision medicine. Second, the latest developments in analytical technologies for data acquisition and the bioinformatics tools to interpret and visualize proteogenomics data are covered.
    Matched MeSH terms: Computational Biology*
  4. Mat-Sharani S, Firdaus-Raih M
    BMC Bioinformatics, 2019 Feb 04;19(Suppl 13):551.
    PMID: 30717662 DOI: 10.1186/s12859-018-2550-2
    BACKGROUND: Small open reading frames (smORF/sORFs) that encode short protein sequences are often overlooked during the standard gene prediction process thus leading to many sORFs being left undiscovered and/or misannotated. For many genomes, a second round of sORF targeted gene prediction can complement the existing annotation. In this study, we specifically targeted the identification of ORFs encoding for 80 amino acid residues or less from 31 fungal genomes. We then compared the predicted sORFs and analysed those that are highly conserved among the genomes.

    RESULTS: A first set of sORFs was identified from existing annotations that fitted the maximum of 80 residues criterion. A second set was predicted using parameters that specifically searched for ORF candidates of 80 codons or less in the exonic, intronic and intergenic sequences of the subject genomes. A total of 1986 conserved sORFs were predicted and characterized.

    CONCLUSIONS: It is evident that numerous open reading frames that could potentially encode for polypeptides consisting of 80 amino acid residues or less are overlooked during standard gene prediction and annotation. From our results, additional targeted reannotation of genomes is clearly able to complement standard genome annotation to identify sORFs. Due to the lack of, and limitations with experimental validation, we propose that a simple conservation analysis can provide an acceptable means of ensuring that the predicted sORFs are sufficiently clear of gene prediction artefacts.

    Matched MeSH terms: Computational Biology/methods*
  5. Ashkani S, Yusop MR, Shabanimofrad M, Azady A, Ghasemzadeh A, Azizi P, et al.
    Curr Issues Mol Biol, 2015;17:57-73.
    PMID: 25706446
    Allele mining is a promising way to dissect naturally occurring allelic variants of candidate genes with essential agronomic qualities. With the identification, isolation and characterisation of blast resistance genes in rice, it is now possible to dissect the actual allelic variants of these genes within an array of rice cultivars via allele mining. Multiple alleles from the complex locus serve as a reservoir of variation to generate functional genes. The routine sequence exchange is one of the main mechanisms of R gene evolution and development. Allele mining for resistance genes can be an important method to identify additional resistance alleles and new haplotypes along with the development of allele-specific markers for use in marker-assisted selection. Allele mining can be visualised as a vital link between effective utilisation of genetic and genomic resources in genomics-driven modern plant breeding. This review studies the actual concepts and potential of mining approaches for the discovery of alleles and their utilisation for blast resistance genes in rice. The details provided here will be important to provide the rice breeder with a worthwhile introduction to allele mining and its methodology for breakthrough discovery of fresh alleles hidden in hereditary diversity, which is vital for crop improvement.
    Matched MeSH terms: Computational Biology/methods
  6. Dzayee SA, Khudhur PK, Mahmood A, Markov A, Maseleno A, Ebrahimpour Gorji A
    Anim Biotechnol, 2022 Nov;33(6):1359-1370.
    PMID: 33761829 DOI: 10.1080/10495398.2021.1899937
    Mastitis disease causes significant economic losses in dairy farms by reducing milk production, increasing production costs, and reducing milk quality. Streptococcus agalactiae continues to be a major cause of mastitis in dairy cattle. To date, there has been no approved multi-epitope vaccine against this pathogen in the market. In the present study, an efficient multi-epitope vaccine against S. agalactiae, the causative agent of mastitis, was designed using various immonoinformtics approaches. Potential epitopes were selected from Sip protein to improve vaccine immunogenicity. The designed vaccine is more antigenic in nature. Then, linkers and profilin adjuvant were added to enhance the immunity of vaccines. The designed vaccine was evaluated in terms of molecular weight, PI, immunogenicity, Toxicity, and allergenicity. Prediction of three-dimensional (3 D) structure of multi-epitope vaccine, followed by refinement and validation, was conducted to obtain a high-quality 3 D structure of the designed multi-epitope vaccine. The designed vaccine was then subjected to molecular docking with Toll-like receptor 11 (TLR11) receptor to evaluate its binding efficiency followed by dynamic simulation for stable interaction. In silico cloning approach was carried out to improve the expression of the vaccine construct. These analyses indicate that the designed multi-epitope vaccine may produce particular immune responses against S. agalactiae and may be further helpful to control mastitis after in vitro and in vivo immunological assays.
    Matched MeSH terms: Computational Biology/methods
  7. Pipatchartlearnwong K, Swatdipong A, Vuttipongchaikij S, Apisitwanich S
    BMC Genet, 2017 10 12;18(1):88.
    PMID: 29025415 DOI: 10.1186/s12863-017-0554-y
    BACKGROUND: Borassus flabellifer or Asian Palmyra palm is an important crop for local economies in the South and Southeast Asia for its fruit and palm sugar production. Archeological and historical evidence indicated the presence of this species in Southeast Asia dating back at least 1500 years. B. flabellifer is believed to be originated in Africa, spread to South Asia and introduced into Southeast Asia through commercial routes and dissemination of cultures, however, the nature of its invasion and settlement in Thailand is unclear.

    RESULTS: Here, we analyzed genetic data of 230 B. flabellifer accessions across Thailand using 17 EST-SSR and 12 gSSR polymorphic markers. Clustering analysis revealed that the population consisted of two genetic clusters (STRUCTURE K = 2). Cluster I is found mainly in southern Thailand, while Cluster II is found mainly in the northeastern. Those found in the central are of an extensive mix between the two. These two clusters are in moderate differentiation (F ST = 0.066 and N M = 3.532) and have low genetic diversity (HO = 0.371 and 0.416; AR = 2.99 and 3.19, for the cluster I and II respectively). The minimum numbers of founders for each genetic group varies from 3 to 4 individuals, based on simulation using different allele frequency assumptions. These numbers coincide with that B. flabellifer is dioecious, and a number of seeds had to be simultaneously introduced for obtaining both male and female founders.

    CONCLUSIONS: From these data and geographical and historical evidence, we hypothesize that there were at least two different invasive events of B. flabellifer in Thailand. B. flabellifer was likely brought through the Straits of Malacca to be propagated in the southern Thailand as one of the invasive events before spreading to the central Thailand. The second event likely occurred in Khmer Empire, currently Cambodia, before spreading to the northeastern Thailand.

    Matched MeSH terms: Computational Biology/methods*
  8. Adamu A, Wahab RA, Shamsir MS, Aliyu F, Huyop F
    Comput Biol Chem, 2017 Oct;70:125-132.
    PMID: 28873365 DOI: 10.1016/j.compbiolchem.2017.08.007
    The l-2-haloacid dehalogenases (EC 3.8.1.2) specifically cleave carbon-halogen bonds in the L-isomers of halogenated organic acids. These enzymes have potential applications for the bioremediation and synthesis of various industrial products. One such enzyme is DehL, the l-2-haloacid dehalogenase from Rhizobium sp. RC1, which converts the L-isomers of 2-halocarboxylic acids into the corresponding D-hydroxycarboxylic acids. However, its catalytic mechanism has not been delineated, and to enhance its efficiency and utility for environmental and industrial applications, knowledge of its catalytic mechanism, which includes identification of its catalytic residues, is required. Using ab initio fragment molecular orbital calculations, molecular mechanics Poisson-Boltzmann surface area calculations, and classical molecular dynamic simulation of a three-dimensional model of DehL-l-2-chloropropionic acid complex, we predicted the catalytic residues of DehL and propose its catalytic mechanism. We found that when Asp13, Thr17, Met48, Arg51, and His184 were individually replaced with an alanine in silico, a significant decrease in the free energy of binding for the DehL-l-2-chloropropionic acid model complex was seen, indicating the involvement of these residues in catalysis and/or structural integrity of the active site. Furthermore, strong inter-fragment interaction energies calculated for Asp13 and L-2-chloropropionic acid, and for a water molecule and His184, and maintenance of the distances between atoms in the aforementioned pairs during the molecular dynamics run suggest that Asp13 acts as the nucleophile and His184 activates the water involved in DehL catalysis. The results of this study should be important for the rational design of a DehL mutant with improved catalytic efficiency.
    Matched MeSH terms: Computational Biology*
  9. 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*
  10. Kazi A, Chuah C, Majeed ABA, Leow CH, Lim BH, Leow CY
    Pathog Glob Health, 2018 05;112(3):123-131.
    PMID: 29528265 DOI: 10.1080/20477724.2018.1446773
    Immunoinformatics plays a pivotal role in vaccine design, immunodiagnostic development, and antibody production. In the past, antibody design and vaccine development depended exclusively on immunological experiments which are relatively expensive and time-consuming. However, recent advances in the field of immunological bioinformatics have provided feasible tools which can be used to lessen the time and cost required for vaccine and antibody development. This approach allows the selection of immunogenic regions from the pathogen genomes. The ideal regions could be developed as potential vaccine candidates to trigger protective immune responses in the hosts. At present, epitope-based vaccines are attractive concepts which have been successfully trailed to develop vaccines which target rapidly mutating pathogens. In this article, we provide an overview of the current progress of immunoinformatics and their applications in the vaccine design, immune system modeling and therapeutics.
    Matched MeSH terms: Computational Biology/methods*
  11. Shahab M, Aiman S, Alshammari A, Alasmari AF, Alharbi M, Khan A, et al.
    Int J Biol Macromol, 2023 Dec 31;253(Pt 2):126678.
    PMID: 37666399 DOI: 10.1016/j.ijbiomac.2023.126678
    Jamestown Canyon virus (JCV) is a deadly viral infection transmitted by various mosquito species. This mosquito-borne virus belongs to Bunyaviridae family, posing a high public health threat in the in tropical regions of the United States causing encephalitis in humans. Common symptoms of JCV include fever, headache, stiff neck, photophobia, nausea, vomiting, and seizures. Despite the availability of resources, there is currently no vaccine or drug available to combat JCV. The purpose of this study was to develop an epitope-based vaccine using immunoinformatics approaches. The vaccine aimed to be secure, efficient, bio-compatible, and capable of stimulating both innate and adaptive immune responses. In this study, the protein sequence of JCV was obtained from the NCBI database. Various bioinformatics methods, including toxicity evaluation, antigenicity testing, conservancy analysis, and allergenicity assessment were utilized to identify the most promising epitopes. Suitable linkers and adjuvant sequences were used in the design of vaccine construct. 50s ribosomal protein sequence was used as an adjuvant at the N-terminus of the construct. A total of 5 CTL, 5 HTL, and 5 linear B cell epitopes were selected based on non-allergenicity, immunological potential, and antigenicity scores to design a highly immunogenic multi-peptide vaccine construct. Strong interactions between the proposed vaccine and human immune receptors, i.e., TLR-2 and TLR-4, were revealed in a docking study using ClusPro software, suggesting their possible relevance in the immunological response to the vaccine. Immunological and physicochemical properties assessment ensured that the proposed vaccine demonstrated high immunogenicity, solubility and thermostability. Molecular dynamics simulations confirmed the strong binding affinities, as well as dynamic and structural stability of the proposed vaccine. Immune simulation suggest that the vaccine has the potential to effectively stimulate cellular and humoral immune responses to combat JCV infection. Experimental and clinical assays are required to validate the results of this study.
    Matched MeSH terms: Computational Biology/methods
  12. Abdullah A, Deris S, Mohamad MS, Anwar S
    PLoS One, 2013;8(4):e61258.
    PMID: 23593445 DOI: 10.1371/journal.pone.0061258
    One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.
    Matched MeSH terms: Computational Biology/methods*
  13. da Fonseca RR, Couto A, Machado AM, Brejova B, Albertin CB, Silva F, et al.
    Gigascience, 2020 Jan 01;9(1).
    PMID: 31942620 DOI: 10.1093/gigascience/giz152
    BACKGROUND: The giant squid (Architeuthis dux; Steenstrup, 1857) is an enigmatic giant mollusc with a circumglobal distribution in the deep ocean, except in the high Arctic and Antarctic waters. The elusiveness of the species makes it difficult to study. Thus, having a genome assembled for this deep-sea-dwelling species will allow several pending evolutionary questions to be unlocked.

    FINDINGS: We present a draft genome assembly that includes 200 Gb of Illumina reads, 4 Gb of Moleculo synthetic long reads, and 108 Gb of Chicago libraries, with a final size matching the estimated genome size of 2.7 Gb, and a scaffold N50 of 4.8 Mb. We also present an alternative assembly including 27 Gb raw reads generated using the Pacific Biosciences platform. In addition, we sequenced the proteome of the same individual and RNA from 3 different tissue types from 3 other species of squid (Onychoteuthis banksii, Dosidicus gigas, and Sthenoteuthis oualaniensis) to assist genome annotation. We annotated 33,406 protein-coding genes supported by evidence, and the genome completeness estimated by BUSCO reached 92%. Repetitive regions cover 49.17% of the genome.

    CONCLUSIONS: This annotated draft genome of A. dux provides a critical resource to investigate the unique traits of this species, including its gigantism and key adaptations to deep-sea environments.

    Matched MeSH terms: Computational Biology/methods
  14. Yong HS, Song SL, Chua KO, Wayan Suana I, Eamsobhana P, Tan J, et al.
    Sci Rep, 2021 May 21;11(1):10680.
    PMID: 34021208 DOI: 10.1038/s41598-021-90162-1
    Spiders of the genera Nephila and Trichonephila are large orb-weaving spiders. In view of the lack of study on the mitogenome of these genera, and the conflicting systematic status, we sequenced (by next generation sequencing) and annotated the complete mitogenomes of N. pilipes, T. antipodiana and T. vitiana (previously N. vitiana) to determine their features and phylogenetic relationship. Most of the tRNAs have aberrant clover-leaf secondary structure. Based on 13 protein-coding genes (PCGs) and 15 mitochondrial genes (13 PCGs and two rRNA genes), Nephila and Trichonephila form a clade distinctly separated from the other araneid subfamilies/genera. T. antipodiana forms a lineage with T. vitiana in the subclade containing also T. clavata, while N. pilipes forms a sister clade to Trichonephila. The taxon vitiana is therefore a member of the genus Trichonephila and not Nephila as currently recognized. Studies on the mitogenomes of other Nephila and Trichonephila species and related taxa are needed to provide a potentially more robust phylogeny and systematics.
    Matched MeSH terms: Computational Biology/methods
  15. Parsons MT, Tudini E, Li H, Hahnen E, Wappenschmidt B, Feliubadaló L, et al.
    Hum Mutat, 2019 Sep;40(9):1557-1578.
    PMID: 31131967 DOI: 10.1002/humu.23818
    The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification.
    Matched MeSH terms: Computational Biology/methods*
  16. Zhang Y, Liu W, Lin Y, Ng YK, Li S
    BMC Genomics, 2019 Apr 04;20(Suppl 2):186.
    PMID: 30967119 DOI: 10.1186/s12864-019-5470-2
    BACKGROUND: Recent advances in genome analysis have established that chromatin has preferred 3D conformations, which bring distant loci into contact. Identifying these contacts is important for us to understand possible interactions between these loci. This has motivated the creation of the Hi-C technology, which detects long-range chromosomal interactions. Distance geometry-based algorithms, such as ChromSDE and ShRec3D, have been able to utilize Hi-C data to infer 3D chromosomal structures. However, these algorithms, being matrix-based, are space- and time-consuming on very large datasets. A human genome of 100 kilobase resolution would involve ∼30,000 loci, requiring gigabytes just in storing the matrices.

    RESULTS: We propose a succinct representation of the distance matrices which tremendously reduces the space requirement. We give a complete solution, called SuperRec, for the inference of chromosomal structures from Hi-C data, through iterative solving the large-scale weighted multidimensional scaling problem.

    CONCLUSIONS: SuperRec runs faster than earlier systems without compromising on result accuracy. The SuperRec package can be obtained from http://www.cs.cityu.edu.hk/~shuaicli/SuperRec .

    Matched MeSH terms: Computational Biology/methods*
  17. 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
  18. Chee CW, Mohd Hashim N, Abdullah I, Nor Rashid N
    Appl Biochem Biotechnol, 2024 Jun;196(6):3216-3233.
    PMID: 37642925 DOI: 10.1007/s12010-023-04690-9
    Morindone, a natural anthraquinone compound, has been reported to have significant pharmacological properties in different cancers. However, its anticancer effects in colorectal cancer (CRC) and the underlying molecular mechanisms remain obscure. In this study, RNA sequencing was used to assess the differentially expressed genes (DEGs) following morindone treatment in two CRC cell lines, HCT116 and HT29 cells. Functional enrichment analysis of overlapping DEGs revealed that negative regulation of cell development from biological processes and the MAPK signalling pathway were the most significant Gene Ontology terms and Kyoto Encyclopaedia of Genes and Genome pathway, respectively. Seven hub genes were identified among the overlapping genes, including MCM5, MCM6, MCM10, GINS2, POLE2, PRIM1, and WDHD1. All hub genes were found downregulated and involved in DNA replication fork. Among these, GINS2 was identified as the most cancer-dependent gene in both cells with better survival outcomes. Validation was performed on seven hub genes with rt-qPCR, and the results were consistent with the RNA sequencing findings. Collectively, this study provides corroboration of the potential therapeutic benefits and suitable pharmacological targets of morindone in the treatment of CRC.
    Matched MeSH terms: Computational Biology*
  19. Garrido-Palazuelos LI, Almanza-Orduño AA, Waseem M, Basheer A, Medrano-Félix JA, Mukthar M, et al.
    J Mol Graph Model, 2024 Nov;132:108848.
    PMID: 39182254 DOI: 10.1016/j.jmgm.2024.108848
    Staphylococcus aureus is a common bacterium that causes a variety of infections in humans. This microorganism produces several virulence factors, including hemolysins, which contribute to its disease-causing ability. The treatment of S. aureus infections typically involves the use of antibiotics. However, the emergence of antibiotic-resistant strains has become a major concern. Therefore, vaccination against S. aureus has gained attention as an alternative approach. Vaccination has the advantage of stimulating the immune system to produce specific antibodies that can neutralize bacteria and prevent infection. However, developing an effective vaccine against S. aureus has proven to be challenging. This study aimed to use in silico methods to design a multi-epitope vaccine against S. aureus infection based on hemolysin proteins. The designed vaccine contained four B-cell epitopes, four CTL epitopes, and four HTL epitopes, as well as the ribosomal protein L7/L12 and pan-HLA DR-binding epitope, included as adjuvants. Furthermore, the vaccine was non-allergenic and non-toxic with the potential to stimulate the TLR2-, TLR-4, and TLR-6 receptors. The predicted vaccine exhibited a high degree of antigenicity and stability, suggesting potential for further development as a viable vaccine candidate. The population coverage of the vaccine was 94.4 %, indicating potential widespread protection against S. aureus. Overall, these findings provide valuable insights into the design of an effective multi-epitope vaccine against S. aureus infection and pave the way for future experimental validations.
    Matched MeSH terms: Computational Biology/methods
  20. Ng CY, Wickneswari R, Choong CY
    Genet. Mol. Res., 2014;13(3):6037-49.
    PMID: 25117361 DOI: 10.4238/2014.August.7.18
    Calamus palustris Griff. is an economically important dioecious rattan species in Southeast Asia. However, dioecy and onset of flowering at 3-4 years old render uncertainties in desired female:male seedling ratios to establish a productive seed orchard for this rattan species. We constructed a subtractive library for male floral tissue to understand the genetic mechanism for gender determination in C. palustris. The subtractive library produced 1536 clones with 1419 clones of high quality. Reverse Northern screening showed 313 clones with differential expression, and sequence analyses clustered them into 205 unigenes, including 32 contigs and 173 singletons. The subtractive library was further validated with reverse transcription-quantitative polymerase chain reaction analysis. Homology identification classified the unigenes into 12 putative functional proteins with 83% unigenes showing significant match to proteins in databases. Functional annotations of these unigenes revealed genes involved in male flower development, including MADS-box genes, pollen-related genes, phytohormones for flower development, and male flower organ development. Our results showed that the male floral genes may play a vital role in sex determination in C. palustris. The identified genes can be exploited to understand the molecular basis of sex determination in C. palustris.
    Matched MeSH terms: Computational Biology
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