Displaying publications 1 - 20 of 39 in total

Abstract:
Sort:
  1. Rosilan NF, Jamali MAM, Sufira SA, Waiho K, Fazhan H, Ismail N, et al.
    PLoS One, 2024;19(1):e0297759.
    PMID: 38266027 DOI: 10.1371/journal.pone.0297759
    Shrimp aquaculture contributes significantly to global economic growth, and the whiteleg shrimp, Penaeus vannamei, is a leading species in this industry. However, Vibrio parahaemolyticus infection poses a major challenge in ensuring the success of P. vannamei aquaculture. Despite its significance in this industry, the biological knowledge of its pathogenesis remains unclear. Hence, this study was conducted to identify the interaction sites and binding affinity between several immune-related proteins of P. vannamei with V. parahaemolyticus proteins associated with virulence factors. Potential interaction sites and the binding affinity between host and pathogen proteins were identified using molecular docking and dynamics (MD) simulation. The P. vannamei-V. parahaemolyticus protein-protein interaction of Complex 1 (Ferritin-HrpE/YscL family type III secretion apparatus protein), Complex 2 (Protein kinase domain-containing protein-Chemotaxis CheY protein), and Complex 3 (GPCR-Chemotaxis CheY protein) was found to interact with -4319.76, -5271.39, and -4725.57 of the docked score and the formation of intermolecular bonds at several interacting residues. The docked scores of Complex 1, Complex 2, and Complex 3 were validated using MD simulation analysis, which revealed these complexes greatly contribute to the interactions between P. vannamei and V. parahaemolyticus proteins, with binding free energies of -22.50 kJ/mol, -30.20 kJ/mol, and -26.27 kJ/mol, respectively. This finding illustrates the capability of computational approaches to search for molecular binding sites between host and pathogen, which could increase the knowledge of Vibrio spp. infection on shrimps, which then can be used to assist in the development of effective treatment.
  2. Tieng FYF, Abdullah-Zawawi MR, Md Shahri NAA, Mohamed-Hussein ZA, Lee LH, Mutalib NA
    Brief Bioinform, 2023 Nov 22;25(1).
    PMID: 38040490 DOI: 10.1093/bib/bbad421
    RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.
  3. Rosilan NF, Waiho K, Fazhan H, Sung YY, Zakaria NH, Afiqah-Aleng N, et al.
    Fish Shellfish Immunol, 2023 Nov;142:109171.
    PMID: 37858788 DOI: 10.1016/j.fsi.2023.109171
    Protein-protein interactions (PPIs) are essential for understanding cell physiology in normal and pathological conditions, as they might involve in all cellular processes. PPIs have been widely used to elucidate the pathobiology of human and plant diseases. Therefore, they can also be used to unveil the pathobiology of infectious diseases in shrimp, which is one of the high-risk factors influencing the success or failure of shrimp production. PPI network analysis, specifically host-pathogen PPI (HP-PPI), provides insights into the molecular interactions between the shrimp and pathogens. This review quantitatively analyzed the research trends within this field through bibliometric analysis using specific keywords, countries, authors, organizations, journals, and documents. This analysis has screened 206 records from the Scopus database for determining eligibility, resulting in 179 papers that were retrieved for bibliometric analysis. The analysis revealed that China and Thailand were the driving forces behind this specific field of research and frequently collaborated with the United States. Aquaculture and Diseases of Aquatic Organisms were the prominent sources for publications in this field. The main keywords identified included "white spot syndrome virus," "WSSV," and "shrimp." We discovered that studies on HP-PPI are currently quite scarce. As a result, we further discussed the significance of HP-PPI by highlighting various approaches that have been previously adopted. These findings not only emphasize the importance of HP-PPI but also pave the way for future researchers to explore the pathogenesis of infectious diseases in shrimp. By doing so, preventative measures and enhanced treatment strategies can be identified.
  4. Jantan I, Arshad L, Septama AW, Haque MA, Mohamed-Hussein ZA, Govender NT
    Phytother Res, 2023 Mar;37(3):1036-1056.
    PMID: 36343627 DOI: 10.1002/ptr.7671
    The worldwide spreading of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed a serious threat to health, economic, environmental, and social aspects of human lives. Currently, there are no approved treatments that can effectively block the virus although several existing antimalarial and antiviral agents have been repurposed and allowed use during the pandemic under the emergency use authorization (EUA) status. This review gives an updated overview of the antiviral effects of phytochemicals including alkaloids, flavonoids, and terpenoids against the COVID-19 virus and their mechanisms of action. Search for natural lead molecules against SARS-CoV-2 has been focusing on virtual screening and in vitro studies on phytochemicals that have shown great promise against other coronaviruses such as SARS-CoV. Until now, there is limited data on in vivo investigations to examine the antiviral activity of plants in SARS-CoV-2-infected animal models and the studies were performed using crude extracts. Further experimental and preclinical investigations on the in vivo effects of phytochemicals have to be performed to provide sufficient efficacy and safety data before clinical studies can be performed to develop them into COVID-19 drugs. Phytochemicals are potential sources of new chemical leads for the development of safe and potent anti-SARS-CoV-2 agents.
  5. Abdullah-Zawawi MR, Govender N, Karim MB, Altaf-Ul-Amin M, Kanaya S, Mohamed-Hussein ZA
    Plant Methods, 2022 Nov 05;18(1):118.
    PMID: 36335358 DOI: 10.1186/s13007-022-00951-6
    BACKGROUND: Phytochemicals or secondary metabolites are low molecular weight organic compounds with little function in plant growth and development. Nevertheless, the metabolite diversity govern not only the phenetics of an organism but may also inform the evolutionary pattern and adaptation of green plants to the changing environment. Plant chemoinformatics analyzes the chemical system of natural products using computational tools and robust mathematical algorithms. It has been a powerful approach for species-level differentiation and is widely employed for species classifications and reinforcement of previous classifications.

    RESULTS: This study attempts to classify Angiosperms using plant sulfur-containing compound (SCC) or sulphated compound information. The SCC dataset of 692 plant species were collected from the comprehensive species-metabolite relationship family (KNApSAck) database. The structural similarity score of metabolite pairs under all possible combinations (plant species-metabolite) were determined and metabolite pairs with a Tanimoto coefficient value > 0.85 were selected for clustering using machine learning algorithm. Metabolite clustering showed association between the similar structural metabolite clusters and metabolite content among the plant species. Phylogenetic tree construction of Angiosperms displayed three major clades, of which, clade 1 and clade 2 represented the eudicots only, and clade 3, a mixture of both eudicots and monocots. The SCC-based construction of Angiosperm phylogeny is a subset of the existing monocot-dicot classification. The majority of eudicots present in clade 1 and 2 were represented by glucosinolate compounds. These clades with SCC may have been a mixture of ancestral species whilst the combinatorial presence of monocot-dicot in clade 3 suggests sulphated-chemical structure diversification in the event of adaptation during evolutionary change.

    CONCLUSIONS: Sulphated chemoinformatics informs classification of Angiosperms via machine learning technique.

  6. Govender N, Zulkifli NS, Badrul Hisham NF, Ab Ghani NS, Mohamed-Hussein ZA
    PeerJ, 2022;10:e14168.
    PMID: 36518265 DOI: 10.7717/peerj.14168
    BACKGROUND: Pea eggplant (Solanum torvum Swartz) commonly known as turkey berry or 'terung pipit' in Malay is a vegetable plant widely consumed by the local community in Malaysia. The shrub bears pea-like turkey berry fruits (TBFs), rich in phytochemicals of medicinal interest. The TBF phytochemicals hold a wide spectrum of pharmacological properties. In this study, the TBF phytochemicals' potential inhibitory properties were evaluated against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) of the Coronavirus disease 2019 (COVID-19). The TBF polyphenols were screened against SARS-CoV receptors via molecular docking and the best receptor-ligand complex was validated further by molecular dynamics (MD) simulation.

    METHOD: The SARS-CoV receptor structure files (viral structural components) were retrieved from the Protein Data Bank (PDB) database: membrane protein (PDB ID: 3I6G), main protease (PDB ID: 5RE4), and spike glycoproteins (PDB ID: 6VXX and 6VYB). The receptor binding pocket regions were identified by Discovery Studio (BIOVIA) for targeted docking with TBF polyphenols (genistin, kaempferol, mellein, rhoifolin and scutellarein). The ligand and SARS-CoV family receptor structure files were pre-processed using the AutoDock tools. Molecular docking was performed with the Lamarckian genetic algorithm using AutoDock Vina 4.2 software. The best pose (ligand-receptor complex) from the molecular docking analysis was selected based on the minimum binding energy (MBE) and extent of structural interactions, as indicated by BIOVIA visualization tool. The selected complex was validated by a 100 ns MD simulation run using the GROMACS software. The dynamic behaviour and stability of the receptor-ligand complex were evaluated by the root mean square displacement (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), solvent accessible surface volume (SASV) and number of hydrogen bonds.

    RESULTS: At RMSD = 0, the TBF polyphenols showed fairly strong physical interactions with SARS-CoV receptors under all possible combinations. The MBE of TBF polyphenol-bound SARS CoV complexes ranged from -4.6 to -8.3 kcal/mol. Analysis of the structural interactions showed the presence of hydrogen bonds, electrostatic and hydrophobic interactions between the receptor residues (RR) and ligands atoms. Based on the MBE values, the 3I6G-rhoifolin (MBE = -8.3 kcal/mol) and 5RE4-genistin (MBE = -7.6 kcal/mol) complexes were ranked with the least value. However, the latter showed a greater extent of interactions between the RRs and the ligand atoms and thus was further validated by MD simulation. The MD simulation parameters of the 5RE4-genistin complex over a 100 ns run indicated good structural stability with minimal flexibility within genistin binding pocket region. The findings suggest that S. torvum polyphenols hold good therapeutics potential in COVID-19 management.

  7. Abdullah-Zawawi MR, Ahmad-Nizammuddin NF, Govender N, Harun S, Mohd-Assaad N, Mohamed-Hussein ZA
    Sci Rep, 2021 10 04;11(1):19678.
    PMID: 34608238 DOI: 10.1038/s41598-021-99206-y
    Transcription factors (TFs) form the major class of regulatory genes and play key roles in multiple plant stress responses. In most eukaryotic plants, transcription factor (TF) families (WRKY, MADS-box and MYB) activate unique cellular-level abiotic and biotic stress-responsive strategies, which are considered as key determinants for defense and developmental processes. Arabidopsis and rice are two important representative model systems for dicot and monocot plants, respectively. A comprehensive comparative study on 101 OsWRKY, 34 OsMADS box and 122 OsMYB genes (rice genome) and, 71 AtWRKY, 66 AtMADS box and 144 AtMYB genes (Arabidopsis genome) showed various relationships among TFs across species. The phylogenetic analysis clustered WRKY, MADS-box and MYB TF family members into 10, 7 and 14 clades, respectively. All clades in WRKY and MYB TF families and almost half of the total number of clades in the MADS-box TF family are shared between both species. Chromosomal and gene structure analysis showed that the Arabidopsis-rice orthologous TF gene pairs were unevenly localized within their chromosomes whilst the distribution of exon-intron gene structure and motif conservation indicated plausible functional similarity in both species. The abiotic and biotic stress-responsive cis-regulatory element type and distribution patterns in the promoter regions of Arabidopsis and rice WRKY, MADS-box and MYB orthologous gene pairs provide better knowledge on their role as conserved regulators in both species. Co-expression network analysis showed the correlation between WRKY, MADs-box and MYB genes in each independent rice and Arabidopsis network indicating their role in stress responsiveness and developmental processes.
  8. Jantan I, Haque MA, Arshad L, Harikrishnan H, Septama AW, Mohamed-Hussein ZA
    J Nutr Biochem, 2021 07;93:108634.
    PMID: 33794330 DOI: 10.1016/j.jnutbio.2021.108634
    The high failure rate of the reductionist approach to discover effective and safe drugs to treat chronic inflammatory diseases has led scientists to seek alternative ways. Recently, targeting cell signaling pathways has been utilized as an innovative approach to discover drug leads from natural products. Cell signaling mechanisms have been identified playing key role in diverse diseases by inducing proliferation, cell survival and apoptosis. Phytochemicals are known to be able to modulate the cellular and molecular networks which are associated to chronic diseases including cancer-associated inflammation. In this review, the roles of dietary polyphenols (apigenin, kaempferol, quercetin, curcumin, genistein, isoliquiritigenin, resveratrol and gallic acid) in modulating multiple inflammation-associated cell signaling networks are deliberated. Scientific databases on suppressive effects of the polyphenols on chronic inflammation via modulation of the pathways especially in the recent five years are gathered and critically analyzed. The polyphenols are able to modulate several inflammation-associated cell signaling pathways, namely nuclear factor-kappa β, mitogen activated protein kinases, Wnt/β-catenin and phosphatidylinositol 3-kinase and protein kinase B via selective actions on various components of the networks. The suppressive effects of the polyphenols on the multiple cell signaling pathways reveal their potential use in prevention and treatment of chronic inflammatory disorders. Understanding the mechanistic effects involved in modulation of the signaling pathways by the polyphenols is necessary for lead identification and development of future functional foods for prevention and treatment of chronic inflammatory diseases.
  9. Zifruddin AN, Mohamad-Khalid KA, Suhaimi SA, Mohamed-Hussein ZA, Hassan M
    Biosci Biotechnol Biochem, 2021 Jun 24;85(7):1628-1638.
    PMID: 33890631 DOI: 10.1093/bbb/zbab072
    Juvenile hormone III (JH III) plays an important role in insect reproduction, development, and behavior. The second branch of JH III production includes oxidation of farnesol to farnesal by farnesol dehydrogenase. This study reported the identification and characterization of Plutella xylostella farnesol dehydrogenase (PxFoLDH). Our results showed that PxFoLDH belongs to the short-chain dehydrogenase/reductase superfamily, consisting of a single domain with a structurally conserved Rossman fold, an NAD(P) (H)-binding region and a structurally diverse C-terminal region. The purified enzyme displayed maximum activity at 55$\ $°C with pH 9.5 and was stable in the temperature below 70$\ ^\circ $C. PxFoLDH was determined to be a monomer with a relative molecular weight of 27 kDa and highly specific for trans, trans-farnesol, and NADP+. Among analog inhibitors tested, farnesyl acetate was the most effective inhibitor with the lowest Ki value of 0.02 µm. Our findings showed this purified enzyme may represent as NADP+-farnesol dehydrogenase.
  10. Harun S, Rohani ER, Ohme-Takagi M, Goh HH, Mohamed-Hussein ZA
    J Plant Res, 2021 Mar;134(2):327-339.
    PMID: 33558947 DOI: 10.1007/s10265-021-01257-9
    Glucosinolates (GSLs) are plant secondary metabolites consisting of sulfur and nitrogen, commonly found in Brassicaceae crops, such as Arabidopsis thaliana. These compounds are known for their roles in plant defense mechanisms against pests and pathogens. 'Guilt-by-association' (GBA) approach predicts genes encoding proteins with similar function tend to share gene expression pattern generated from high throughput sequencing data. Recent studies have successfully identified GSL genes using GBA approach, followed by targeted verification of gene expression and metabolite data. Therefore, a GSL co-expression network was constructed using known GSL genes obtained from our in-house database, SuCComBase. DPClusO was used to identify subnetworks of the GSL co-expression network followed by Fisher's exact test leading to the discovery of a potential gene that encodes the ARIA-interacting double AP2-domain protein (ADAP) transcription factor (TF). Further functional analysis was performed using an effective gene silencing system known as CRES-T. By applying CRES-T, ADAP TF gene was fused to a plant-specific EAR-motif repressor domain (SRDX), which suppresses the expression of ADAP target genes. In this study, ADAP was proposed as a negative regulator in aliphatic GSL biosynthesis due to the over-expression of downstream aliphatic GSL genes (UGT74C1 and IPMI1) in ADAP-SRDX line. The significant over-expression of ADAP gene in the ADAP-SRDX line also suggests the behavior of the TF that negatively affects the expression of UGT74C1 and IPMI1 via a feedback mechanism in A. thaliana.
  11. Afiqah-Aleng N, Mohamed-Hussein ZA
    Methods Mol Biol, 2021;2189:119-132.
    PMID: 33180298 DOI: 10.1007/978-1-0716-0822-7_10
    In this post-genomic era, protein network can be used as a complementary way to shed light on the growing amount of data generated from current high-throughput technologies. Protein network is a powerful approach to describe the molecular mechanisms of the biological events through protein-protein interactions. Here, we describe the computational methods used to construct the protein network using expression data. We provide a list of available tools and databases that can be used in constructing the network.
  12. Harun S, Afiqah-Aleng N, Karim MB, Altaf Ul Amin M, Kanaya S, Mohamed-Hussein ZA
    PeerJ, 2021;9:e11876.
    PMID: 34430080 DOI: 10.7717/peerj.11876
    Background: Glucosinolates (GSLs) are plant secondary metabolites that contain nitrogen-containing compounds. They are important in the plant defense system and known to provide protection against cancer in humans. Currently, increasing the amount of data generated from various omics technologies serves as a hotspot for new gene discovery. However, sometimes sequence similarity searching approach is not sufficiently effective to find these genes; hence, we adapted a network clustering approach to search for potential GSLs genes from the Arabidopsis thaliana co-expression dataset.

    Methods: We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher's exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher's exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters.

    Results: The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.

  13. Zainal-Abidin RA, Zainal Z, Mohamed-Hussein ZA, Sew YS, Simoh S, Ab Razak S, et al.
    Data Brief, 2020 Aug;31:105806.
    PMID: 32566707 DOI: 10.1016/j.dib.2020.105806
    The genomics and genetic data of pigmented and non-pigmented Malaysian rice varieties are still limited. Hence, we performed the genome resequencing of two black rice varieties (Bali, Pulut Hitam 9), two red rice varieties (MRM16, MRQ100) and two white rice varieties (MR297 and MRQ76) using Illumina HiSeq 4000 platform with 30x sequencing coverage. We aimed to identify and annotate single nucleotide polymorphisms (SNPs) from the genome of these four pigmented and two non-pigmented rice varieties. The potential SNPs will be used in developing the functional SNP markers related to nutritional (i.e. antioxidant, folate, amylose) and quality (i.e. aromatic) traits. Raw data of the pigmented and non-pigmented rice varieties have been deposited into the European Nucleotide Archive (ENA) database with accession number PRJEB29070 and PRJEB32344, respectively.
  14. Harun S, Abdullah-Zawawi MR, Goh HH, Mohamed-Hussein ZA
    J Agric Food Chem, 2020 Jul 15;68(28):7281-7297.
    PMID: 32551569 DOI: 10.1021/acs.jafc.0c01916
    Glucosinolates (GSLs) are plant secondary metabolites comprising sulfur and nitrogen mainly found in plants from the order of Brassicales, such as broccoli, cabbage, and Arabidopsis thaliana. The activated forms of GSL play important roles in fighting against pathogens and have health benefits to humans. The increasing amount of data on A. thaliana generated from various omics technologies can be investigated more deeply in search of new genes or compounds involved in GSL biosynthesis and metabolism. This review describes a comprehensive inventory of A. thaliana GSLs identified from published literature and databases such as KNApSAcK, KEGG, and AraCyc. A total of 113 GSL genes encoding for 23 transcription components, 85 enzymes, and five protein transporters were experimentally characterized in the past two decades. Continuous efforts are still on going to identify all molecules related to the production of GSLs. A manually curated database known as SuCCombase (http://plant-scc.org) was developed to serve as a comprehensive GSL inventory. Realizing lack of information on the regulation of GSL biosynthesis and degradation mechanisms, this review also includes relevant information and their connections with crosstalk among various factors, such as light, sulfur metabolism, and nitrogen metabolism, not only in A. thaliana but also in other crucifers.
  15. Zainal-Abidin RA, Zainal Z, Mohamed-Hussein ZA, Abu-Bakar N, Ab Razak MSF, Simoh S, et al.
    Data Brief, 2020 Jun;30:105432.
    PMID: 32280737 DOI: 10.1016/j.dib.2020.105432
    Pigmented rice is enriched with antioxidants, macro- and micronutrients. A comprehensive investigation of the gene expression patterns among the pigmented rice varieties would help to understand the cellular mechanism and biological processes of rice grain pigmentation. Hence, we performed RNA sequencing and analysis on the whole grain of dehusked mature seeds of selected six Malaysian rice varieties with varying grain pigmentations. These varieties were black rice (BALI and Pulut Hitam 9), red rice (MRM16 and MRQ100) and white rice (MR297 and MRQ76). Illumina HiSeq™ 4000 sequencer was used to generate total raw nucleotides of approximately 53 Gb in size. From 353,937,212 total paired-end raw reads, 340,131,496 total clean reads were obtained. The raw reads were deposited into European Nucleotide Archive (ENA) database and can be accessed via accession number PRJEB34340. This dataset allows us to identify and profile all expressed genes with functions related to nutritional traits (i.e. antioxidants, folate and amylose content) and quality trait (i.e. aroma) across both pigmented and non-pigmented rice varieties. In addition, the transcriptome data obtained will be valuable for discovery of potential gene markers and functional SNPs related to functional traits to assist in rice breeding programme.
  16. Afiqah-Aleng N, Altaf-Ul-Amin M, Kanaya S, Mohamed-Hussein ZA
    Reprod Biomed Online, 2020 Feb;40(2):319-330.
    PMID: 32001161 DOI: 10.1016/j.rbmo.2019.11.012
    RESEARCH QUESTION: Polycystic ovary syndrome (PCOS) is a complex endocrine disorder with diverse clinical implications, such as infertility, metabolic disorders, cardiovascular diseases and psychological problems among others. The heterogeneity of conditions found in PCOS contribute to its various phenotypes, leading to difficulties in identifying proteins involved in this abnormality. Several studies, however, have shown the feasibility in identifying molecular evidence underlying other diseases using graph cluster analysis. Therefore, is it possible to identify proteins and pathways related to PCOS using the same approach?

    METHODS: Known PCOS-related proteins (PCOSrp) from PCOSBase and DisGeNET were integrated with protein-protein interactions (PPI) information from Human Integrated Protein-Protein Interaction reference to construct a PCOS PPI network. The network was clustered with DPClusO algorithm to generate clusters, which were evaluated using Fisher's exact test. Pathway enrichment analysis using gProfileR was conducted to identify significant pathways.

    RESULTS: The statistical significance of the identified clusters has successfully predicted 138 novel PCOSrp with 61.5% reliability and, based on Cronbach's alpha, this prediction is acceptable. Androgen signalling pathway and leptin signalling pathway were among the significant PCOS-related pathways corroborating the information obtained from the clinical observation, where androgen signalling pathway is responsible in producing male hormones in women with PCOS, whereas leptin signalling pathway is involved in insulin sensitivity.

    CONCLUSIONS: These results show that graph cluster analysis can provide additional insight into the pathobiology of PCOS, as the pathways identified as statistically significant correspond to earlier biological studies. Therefore, integrative analysis can reveal unknown mechanisms, which may enable the development of accurate diagnosis and effective treatment in PCOS.

  17. Remali J, Aizat WM, Ng CL, Lim YC, Mohamed-Hussein ZA, Fazry S
    PeerJ, 2020;8:e9197.
    PMID: 32509463 DOI: 10.7717/peerj.9197
    Background: DNA double strand break repair is important to preserve the fidelity of our genetic makeup after DNA damage. Rad50 is one of the components in MRN complex important for DNA repair mechanism. Rad50 mutations can lead to microcephaly, mental retardation and growth retardation in human. However, Rad50 mutations in human and other organisms have never been gathered and heuristically compared for their deleterious effects. It is important to assess the conserved region in Rad50 and its homolog to identify vital mutations that can affect functions of the protein.

    Method: In this study, Rad50 mutations were retrieved from SNPeffect 4.0 database and literature. Each of the mutations was analyzed using various bioinformatic analyses such as PredictSNP, MutPred, SNPeffect 4.0, I-Mutant and MuPro to identify its impact on molecular mechanism, biological function and protein stability, respectively.

    Results: We identified 103 mostly occurred mutations in the Rad50 protein domains and motifs, which only 42 mutations were classified as most deleterious. These mutations are mainly situated at the specific motifs such as Walker A, Q-loop, Walker B, D-loop and signature motif of the Rad50 protein. Some of these mutations were predicted to negatively affect several important functional sites that play important roles in DNA repair mechanism and cell cycle signaling pathway, highlighting Rad50 crucial role in this process. Interestingly, mutations located at non-conserved regions were predicted to have neutral/non-damaging effects, in contrast with previous experimental studies that showed deleterious effects. This suggests that software used in this study may have limitations in predicting mutations in non-conserved regions, implying further improvement in their algorithm is needed. In conclusion, this study reveals the priority of acid substitution associated with the genetic disorders. This finding highlights the vital roles of certain residues such as K42E, C681A/S, CC684R/S, S1202R, E1232Q and D1238N/A located in Rad50 conserved regions, which can be considered for a more targeted future studies.

  18. 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.
  19. Harun S, Abdullah-Zawawi MR, A-Rahman MRA, Muhammad NAN, Mohamed-Hussein ZA
    Database (Oxford), 2019 01 01;2019.
    PMID: 30793170 DOI: 10.1093/database/baz021
    Plants produce a wide range of secondary metabolites that play important roles in plant defense and immunity, their interaction with the environment and symbiotic associations. Sulfur-containing compounds (SCCs) are a group of important secondary metabolites produced in members of the Brassicales order. SCCs constitute various groups of phytochemicals, but not much is known about them. Findings from previous studies on SCCs were scattered in published literatures, hence SuCComBase was developed to store all molecular information related to the biosynthesis of SCCs. Information that includes genes, proteins and compounds that are involved in the SCC biosynthetic pathway was manually identified from databases and published scientific literatures. Sets of co-expression data was analyzed to search for other possible (previously unknown) genes that might be involved in the biosynthesis of SCC. These genes were named as potential SCC-related encoding genes. A total of 147 known and 92 putative Arabidopsis thaliana SCC-related genes from literatures were used to identify other potential SCC-related encoding genes. We identified 778 potential SCC-related encoding genes, 4026 homologs to the SCC-related encoding genes and 116 SCCs as shown on SuCComBase homepage. Data entries are searchable from the Main page, Search, Browse and Datasets tabs. Users can easily download all data stored in SuCComBase. All publications related to SCCs are also indexed in SuCComBase, which is currently the first and only database dedicated to plant SCCs. SuCComBase aims to become a manually curated and au fait knowledge-based repository for plant SCCs.
  20. Zainal-Abidin RA, Abu-Bakar N, Sew YS, Simoh S, Mohamed-Hussein ZA
    Int J Genomics, 2019;2019:4168045.
    PMID: 31687375 DOI: 10.1155/2019/4168045
    Recently, rice breeding program has shown increased interests on the pigmented rice varieties due to their benefits to human health. However, the genetic variation of pigmented rice varieties is still scarce and remains unexplored. Hence, we performed genome-wide SNP analysis from the genome resequencing of four Malaysian pigmented rice varieties, representing two black and two red rice varieties. The genome of four pigmented varieties was mapped against Nipponbare reference genome sequences, and 1.9 million SNPs were discovered. Of these, 622 SNPs with polymorphic sites were identified in 258 protein-coding genes related to metabolism, stress response, and transporter. Comparative analysis of 622 SNPs with polymorphic sites against six rice SNP datasets from the Ensembl Plants variation database was performed, and 70 SNPs were identified as novel SNPs. Analysis of SNPs in the flavonoid biosynthetic genes revealed 40 nonsynonymous SNPs, which has potential as molecular markers for rice seed colour identification. The highlighted SNPs in this study show effort in producing valuable genomic resources for application in the rice breeding program, towards the genetic improvement of new and improved pigmented rice varieties.
Filters
Contact Us

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

External Links