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  1. A Q-marker screening strategy based on ADME studies and systems biology for Chinese herbal medicine, taking Qianghuo Shengshi decoction in treating rheumatoid arthritis as an example
    Wang J, Tao C, Xu G, Ling J, Tong J, Goh BH, et al.
    Mol Omics, 2023 Dec 04;19(10):769-786.
    PMID: 37498608 DOI: 10.1039/d3mo00029j
    Chinese herbal medicine (CHM) exhibits a broad spectrum of clinical applications and demonstrates favorable therapeutic efficacy. Nonetheless, elucidating the underlying mechanism of action (MOA) of CHM in disease treatment remains a formidable task due to its inherent characteristics of multi-level, multi-linked, and multi-dimensional non-linear synergistic actions. In recent years, the concept of a Quality marker (Q-marker) proposed by Liu et al. has significantly contributed to the monitoring and evaluation of CHM products, thereby fostering the advancement of CHM research. Within this study, a Q-marker screening strategy for CHM formulas has been introduced, particularly emphasising efficacy and biological activities, integrating absorption, distribution, metabolism, and excretion (ADME) studies, systems biology, and experimental verification. As an illustrative case, the Q-marker screening of Qianghuo Shengshi decoction (QHSSD) for treating rheumatoid arthritis (RA) has been conducted. Consequently, from a pool of 159 compounds within QHSSD, five Q-markers exhibiting significant in vitro anti-inflammatory effects have been identified. These Q-markers encompass notopterol, isoliquiritin, imperatorin, cimifugin, and glycyrrhizic acid. Furthermore, by employing an integrated analysis of network pharmacology and metabolomics, several instructive insights into pharmacological mechanisms have been gleaned. This includes the identification of key targets and pathways through which QHSSD exerts its crucial roles in the treatment of RA. Notably, the inhibitory effect of QHSSD on AKT1 and MAPK3 activation has been validated through western blot analysis, underscoring its potential to mitigate RA-related inflammatory responses. In summary, this research demonstrates the proposed strategy's feasibility and provides a practical reference model for the systematic investigation of CHM formulas.
    Matched MeSH terms: Systems Biology
  2. Integrative Multi-Omics Through Bioinformatics
    Goh HH
    Adv Exp Med Biol, 2018 11 2;1102:69-80.
    PMID: 30382569 DOI: 10.1007/978-3-319-98758-3_5
    This chapter introduces different aspects of bioinformatics with a brief discussion in the systems biology context. Example applications in network pharmacology of traditional Chinese medicine, systems metabolic engineering, and plant genome-scale modelling are described. Lastly, this chapter concludes on how bioinformatics helps to integrate omics data derived from various studies described in previous chapters for a holistic understanding of secondary metabolite production in P. minus.
    Matched MeSH terms: Systems Biology*
  3. Proteomics in Systems Biology
    Aizat WM, Hassan M
    Adv Exp Med Biol, 2018 11 2;1102:31-49.
    PMID: 30382567 DOI: 10.1007/978-3-319-98758-3_3
    Proteomics is the study of proteins, the workhorses of cells. Proteins can be subjected to various post-translational modifications, making them dynamic to external perturbation. Proteomics can be divided into four areas: sequence, structural, functional and interaction and expression proteomics. These different areas used different instrumentations and have different focuses. For example, sequence and structural proteomics mainly focus on elucidating a particular protein sequence and structure, respectively. Meanwhile, functional and interaction proteomics concentrate on protein function and interaction partners, whereas expression proteomics allows the cataloguing of total proteins in any given samples, hence providing a holistic overview of various proteins in a cell. The application of expression proteomics in cancer and crop research is detailed in this chapter. The general workflow of expression proteomics consisting the use of mass spectrometry instrumentation has also been described, and some examples of proteomics studies are also presented.
    Matched MeSH terms: Systems Biology*
  4. Metabolomics: Challenges and Opportunities in Systems Biology Studies
    Mediani A, Baharum SN
    Methods Mol Biol, 2024;2745:77-90.
    PMID: 38060180 DOI: 10.1007/978-1-0716-3577-3_5
    Metabolomics can provide diagnostic, prognostic, and therapeutic biomarker profiles of individual patients because a large number of metabolites can be simultaneously measured in biological samples in an unbiased manner. Minor stimuli can result in substantial alterations, making it a valuable target for analysis. Due to the complexity and sensitivity of the metabolome, studies must be devised to maintain consistency, minimize subject-to-subject variation, and maximize information recovery. This effort has been aided by technological advances in experimental design, rodent models, and instrumentation. Proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy of biofluids, such as plasma, urine, and faeces provide the opportunity to identify biomarker change patterns that reflect the physiological or pathological status of an individual patient. Metabolomics has the ultimate potential to be useful in a clinical context, where it could be used to predict treatment response and survival and for early disease diagnosis. During drug treatment, an individual's metabolic status could be monitored and used to predict deleterious effects. Therefore, metabolomics has the potential to improve disease diagnosis, treatment, and follow-up care. In this chapter, we demonstrate how a metabolomics study can be used to diagnose a disease by classifying patients as either healthy or pathological, while accounting for individual variation.
    Matched MeSH terms: Systems Biology
  5. Genome-scale metabolic models as platforms for strain design and biological discovery
    Mienda BS
    J Biomol Struct Dyn, 2017 Jul;35(9):1863-1873.
    PMID: 27251747 DOI: 10.1080/07391102.2016.1197153
    Genome-scale metabolic models (GEMs) have been developed and used in guiding systems' metabolic engineering strategies for strain design and development. This strategy has been used in fermentative production of bio-based industrial chemicals and fuels from alternative carbon sources. However, computer-aided hypotheses building using established algorithms and software platforms for biological discovery can be integrated into the pipeline for strain design strategy to create superior strains of microorganisms for targeted biosynthetic goals. Here, I described an integrated workflow strategy using GEMs for strain design and biological discovery. Specific case studies of strain design and biological discovery using Escherichia coli genome-scale model are presented and discussed. The integrated workflow presented herein, when applied carefully would help guide future design strategies for high-performance microbial strains that have existing and forthcoming genome-scale metabolic models.
    Matched MeSH terms: Systems Biology*
  6. Metabolomics in Systems Biology
    Baharum SN, Azizan KA
    Adv Exp Med Biol, 2018 11 2;1102:51-68.
    PMID: 30382568 DOI: 10.1007/978-3-319-98758-3_4
    Over the last decade, metabolomics has continued to grow rapidly and is considered a dynamic technology in envisaging and elucidating complex phenotypes in systems biology area. The advantage of metabolomics compared to other omics technologies such as transcriptomics and proteomics is that these later omics only consider the intermediate steps in the central dogma pathway (mRNA and protein expression). Meanwhile, metabolomics reveals the downstream products of gene and expression of proteins. The most frequently used tools are nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). Some of the common MS-based analyses are gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These high-throughput instruments play an extremely crucial role in discovery metabolomics to generate data needed for further analysis. In this chapter, the concept of metabolomics in the context of systems biology is discussed and provides examples of its application in human disease studies, plant responses towards stress and abiotic resistance and also microbial metabolomics for biotechnology applications. Lastly, a few case studies of metabolomics analysis are also presented, for example, investigation of an aromatic herbal plant, Persicaria minor metabolome and microbial metabolomics for metabolic engineering applications.
    Matched MeSH terms: Systems Biology*
  7. Fulltext A bioinformatics potpourri
    Schönbach C, Li J, Ma L, Horton P, Sjaugi MF, Ranganathan S
    BMC Genomics, 2018 01 19;19(Suppl 1):920.
    PMID: 29363432 DOI: 10.1186/s12864-017-4326-x
    The 16th International Conference on Bioinformatics (InCoB) was held at Tsinghua University, Shenzhen from September 20 to 22, 2017. The annual conference of the Asia-Pacific Bioinformatics Network featured six keynotes, two invited talks, a panel discussion on big data driven bioinformatics and precision medicine, and 66 oral presentations of accepted research articles or posters. Fifty-seven articles comprising a topic assortment of algorithms, biomolecular networks, cancer and disease informatics, drug-target interactions and drug efficacy, gene regulation and expression, imaging, immunoinformatics, metagenomics, next generation sequencing for genomics and transcriptomics, ontologies, post-translational modification, and structural bioinformatics are the subject of this editorial for the InCoB2017 supplement issues in BMC Genomics, BMC Bioinformatics, BMC Systems Biology and BMC Medical Genomics. New Delhi will be the location of InCoB2018, scheduled for September 26-28, 2018.
    Matched MeSH terms: Systems Biology/methods*
  8. Fulltext Getting the most out of RNA-seq data analysis
    Khang TF, Lau CY
    PeerJ, 2015;3:e1360.
    PMID: 26539333 DOI: 10.7717/peerj.1360
    Background. A common research goal in transcriptome projects is to find genes that are differentially expressed in different phenotype classes. Biologists might wish to validate such gene candidates experimentally, or use them for downstream systems biology analysis. Producing a coherent differential gene expression analysis from RNA-seq count data requires an understanding of how numerous sources of variation such as the replicate size, the hypothesized biological effect size, and the specific method for making differential expression calls interact. We believe an explicit demonstration of such interactions in real RNA-seq data sets is of practical interest to biologists. Results. Using two large public RNA-seq data sets-one representing strong, and another mild, biological effect size-we simulated different replicate size scenarios, and tested the performance of several commonly-used methods for calling differentially expressed genes in each of them. We found that, when biological effect size was mild, RNA-seq experiments should focus on experimental validation of differentially expressed gene candidates. Importantly, at least triplicates must be used, and the differentially expressed genes should be called using methods with high positive predictive value (PPV), such as NOISeq or GFOLD. In contrast, when biological effect size was strong, differentially expressed genes mined from unreplicated experiments using NOISeq, ASC and GFOLD had between 30 to 50% mean PPV, an increase of more than 30-fold compared to the cases of mild biological effect size. Among methods with good PPV performance, having triplicates or more substantially improved mean PPV to over 90% for GFOLD, 60% for DESeq2, 50% for NOISeq, and 30% for edgeR. At a replicate size of six, we found DESeq2 and edgeR to be reasonable methods for calling differentially expressed genes at systems level analysis, as their PPV and sensitivity trade-off were superior to the other methods'. Conclusion. When biological effect size is weak, systems level investigation is not possible using RNAseq data, and no meaningful result can be obtained in unreplicated experiments. Nonetheless, NOISeq or GFOLD may yield limited numbers of gene candidates with good validation potential, when triplicates or more are available. When biological effect size is strong, NOISeq and GFOLD are effective tools for detecting differentially expressed genes in unreplicated RNA-seq experiments for qPCR validation. When triplicates or more are available, GFOLD is a sharp tool for identifying high confidence differentially expressed genes for targeted qPCR validation; for downstream systems level analysis, combined results from DESeq2 and edgeR are useful.
    Matched MeSH terms: Systems Biology
  9. Fulltext Exploring Ayurvedic Knowledge on Food and Health for Providing Innovative Solutions to Contemporary Healthcare
    Payyappallimana U, Venkatasubramanian P
    Front Public Health, 2016;4:57.
    PMID: 27066472 DOI: 10.3389/fpubh.2016.00057
    Ayurveda, a traditional system of medicine that originated over three millennia ago in the South Asian region, offers extensive insights about food and health based on certain unique conceptual as well as theoretical positions. Health is defined as a state of equilibrium with one's self (svasthya) but which is inextricably linked to the environment. Ayurvedic principles, such as the tridosa (three humors) theory, provide the relationship between the microcosm and the macrocosm that can be applied in day-to-day practice. Classical Ayurveda texts cover an array of themes on food ranging from diversity of natural sources, their properties in relation to seasons and places and to their specific function both in physiological and pathological states. The epistemic perspective on health and nutrition in Ayurveda is very different from that of biomedicine and modern nutrition. However, contemporary knowledge is reinventing and advancing several of these concepts in an era of systems biology, personalized medicine, and the broader context of a more holistic transition in sciences in general. Trans-disciplinary research could be important not only for pushing the boundaries of food and health sciences but also for providing practical solutions for contemporary health conditions. This article briefly reviews the parallels in Ayurveda and biomedicine and draws attention to the need for a deeper engagement with traditional knowledge systems, such as Ayurveda. It points out that recreation of the methodologies that enabled the holistic view point about health in Ayurveda may unravel some of the complex connections with Nature.
    Matched MeSH terms: Systems Biology
  10. Fulltext Towards big data science in the decade ahead from ten years of InCoB and the 1st ISCB-Asia Joint Conference
    Ranganathan S, Schönbach C, Kelso J, Rost B, Nathan S, Tan TW
    BMC Bioinformatics, 2011;12 Suppl 13:S1.
    PMID: 22372736 DOI: 10.1186/1471-2105-12-S13-S1
    The 2011 International Conference on Bioinformatics (InCoB) conference, which is the annual scientific conference of the Asia-Pacific Bioinformatics Network (APBioNet), is hosted by Kuala Lumpur, Malaysia, is co-organized with the first ISCB-Asia conference of the International Society for Computational Biology (ISCB). InCoB and the sequencing of the human genome are both celebrating their tenth anniversaries and InCoB's goalposts for the next decade, implementing standards in bioinformatics and globally distributed computational networks, will be discussed and adopted at this conference. Of the 49 manuscripts (selected from 104 submissions) accepted to BMC Genomics and BMC Bioinformatics conference supplements, 24 are featured in this issue, covering software tools, genome/proteome analysis, systems biology (networks, pathways, bioimaging) and drug discovery and design.
    Matched MeSH terms: Systems Biology
  11. Multiobjective optimization to reconstruct biological networks
    Naef A, Abdullah R, Abdul Rashid N
    Biosystems, 2018 Sep 17;174:22-36.
    PMID: 30236951 DOI: 10.1016/j.biosystems.2018.09.003
    Automated methods for reconstructing biological networks are becoming increasingly important in computational systems biology. Public databases containing information on biological processes for hundreds of organisms are assisting in the inference of such networks. This paper proposes a multiobjective genetic algorithm method to reconstruct networks related to metabolism and protein interaction. Such a method utilizes structural properties of scale-free networks and known biological information about individual genes and proteins to reconstruct metabolic networks represented as enzyme graph and protein interaction networks. We test our method on four commonly-used protein networks in yeast. Two are networks related to the metabolism of the yeast: KEGG and BioCyc. The other two datasets are networks from protein-protein interaction: Krogan and BioGrid. Experimental results show that the proposed method is capable of reconstructing biological networks by combining different omics data and structural characteristics of scale-free networks. However, the proposed method to reconstruct the network is time-consuming because several evaluations must be performed. We parallelized this method on GPU to overcome this limitation by parallelizing the objective functions of the presented method. The parallel method shows a significant reduction in the execution time over the GPU card which yields a 492-fold speedup.
    Matched MeSH terms: Systems Biology
  12. Functional Genomics
    Goh HH, Ng CL, Loke KK
    Adv Exp Med Biol, 2018 11 2;1102:11-30.
    PMID: 30382566 DOI: 10.1007/978-3-319-98758-3_2
    Functional genomics encompasses diverse disciplines in molecular biology and bioinformatics to comprehend the blueprint, regulation, and expression of genetic elements that define the physiology of an organism. The deluge of sequencing data in the postgenomics era has demanded the involvement of computer scientists and mathematicians to create algorithms, analytical software, and databases for the storage, curation, and analysis of biological big data. In this chapter, we discuss on the concept of functional genomics in the context of systems biology and provide examples of its application in human genetic disease studies, molecular crop improvement, and metagenomics for antibiotic discovery. An overview of transcriptomics workflow and experimental considerations is also introduced. Lastly, we present an in-house case study of transcriptomics analysis of an aromatic herbal plant to understand the effect of elicitation on the biosynthesis of volatile organic compounds.
    Matched MeSH terms: Systems Biology
  13. Recent Development in Omics Studies
    Aizat WM, Ismail I, Noor NM
    Adv Exp Med Biol, 2018 11 2;1102:1-9.
    PMID: 30382565 DOI: 10.1007/978-3-319-98758-3_1
    The central dogma of molecular biology (DNA, RNA, protein and metabolite) has engraved our understanding of genetics in all living organisms. While the concept has been embraced for many decades, the development of high-throughput technologies particularly omics (genomics, transcriptomics, proteomics and metabolomics) has revolutionised the field to incorporate big data analysis including bioinformatics and systems biology as well as synthetic biology area. These omics approaches as well as systems and synthetic biology areas are now increasingly popular as seen by the growing numbers of publication throughout the years. Several journals which have published most of these related fields are also listed in this chapter to overview their impact and target journals.
    Matched MeSH terms: Systems Biology
  14. Fulltext The Use of System Dynamics Methodology in Building a COVID-19 Confirmed Case Model
    Mohd Yusoff MI
    Comput Math Methods Med, 2020;2020:9328414.
    PMID: 33224268 DOI: 10.1155/2020/9328414
    Researchers used a hybrid model (a combination of health resource demand model and disease transmission model), Bayesian model, and susceptible-exposed-infectious-removed (SEIR) model to predict health service utilization and deaths and mixed-effect nonlinear regression. Further, they used the mixture model to predict the number of confirmed cases and deaths or to predict when the curve would flatten. In this article, we show, through scenarios developed using system dynamics methodology, besides close to real-world results, the detrimental effects of ignoring social distancing guidelines (in terms of the number of people infected, which decreased as the percentage of noncompliance decreased).
    Matched MeSH terms: Systems Biology/methods
  15. Converting differential-equation models of biological systems to membrane computing
    Muniyandi RC, Zin AM, Sanders JW
    Biosystems, 2013 Dec;114(3):219-26.
    PMID: 24120990 DOI: 10.1016/j.biosystems.2013.09.008
    This paper presents a method to convert the deterministic, continuous representation of a biological system by ordinary differential equations into a non-deterministic, discrete membrane computation. The dynamics of the membrane computation is governed by rewrite rules operating at certain rates. That has the advantage of applying accurately to small systems, and to expressing rates of change that are determined locally, by region, but not necessary globally. Such spatial information augments the standard differentiable approach to provide a more realistic model. A biological case study of the ligand-receptor network of protein TGF-β is used to validate the effectiveness of the conversion method. It demonstrates the sense in which the behaviours and properties of the system are better preserved in the membrane computing model, suggesting that the proposed conversion method may prove useful for biological systems in particular.
    Matched MeSH terms: Systems Biology/methods*
  16. Fulltext An improved swarm optimization for parameter estimation and biological model selection
    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: Systems Biology/methods*
  17. Fulltext Challenges of the next decade for the Asia Pacific region: 2010 International Conference in Bioinformatics (InCoB 2010)
    Ranganathan S, Schönbach C, Nakai K, Tan TW
    BMC Genomics, 2010;11 Suppl 4:S1.
    PMID: 21143792 DOI: 10.1186/1471-2164-11-S4-S1
    The 2010 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation formed in 1998, was organized as the 9th International Conference on Bioinformatics (InCoB), Sept. 26-28, 2010 in Tokyo, Japan. Initially, APBioNet created InCoB as forum to foster bioinformatics in the Asia Pacific region. Given the growing importance of interdisciplinary research, InCoB2010 included topics targeting scientists in the fields of genomic medicine, immunology and chemoinformatics, supporting translational research. Peer-reviewed manuscripts that were accepted for publication in this supplement, represent key areas of research interests that have emerged in our region. We also highlight some of the current challenges bioinformatics is facing in the Asia Pacific region and conclude our report with the announcement of APBioNet's 100 BioDatabases (BioDB100) initiative. BioDB100 will comply with the database criteria set out earlier in our proposal for Minimum Information about a Bioinformatics and Investigation (MIABi), setting the standards for biocuration and bioinformatics research, on which we will report at the next InCoB, Nov. 27 - Dec. 2, 2011 at Kuala Lumpur, Malaysia.
    Matched MeSH terms: Systems Biology
  18. Fulltext Understanding Host-Pathogen Interactions in Brassica napus in the Omics Era
    Neik TX, Amas J, Barbetti M, Edwards D, Batley J
    Plants (Basel), 2020 Oct 10;9(10).
    PMID: 33050509 DOI: 10.3390/plants9101336
    Brassica napus (canola/oilseed rape/rapeseed) is an economically important crop, mostly found in temperate and sub-tropical regions, that is cultivated widely for its edible oil. Major diseases of Brassica crops such as Blackleg, Clubroot, Sclerotinia Stem Rot, Downy Mildew, Alternaria Leaf Spot and White Rust have caused significant yield and economic losses in rapeseed-producing countries worldwide, exacerbated by global climate change, and, if not remedied effectively, will threaten global food security. To gain further insights into the host-pathogen interactions in relation to Brassica diseases, it is critical that we review current knowledge in this area and discuss how omics technologies can offer promising results and help to push boundaries in our understanding of the resistance mechanisms. Omics technologies, such as genomics, proteomics, transcriptomics and metabolomics approaches, allow us to understand the host and pathogen, as well as the interaction between the two species at a deeper level. With these integrated data in multi-omics and systems biology, we are able to breed high-quality disease-resistant Brassica crops in a more holistic, targeted and accurate way.
    Matched MeSH terms: Systems Biology
  19. Fulltext Why metabonomics?
    Yap, Ivan K.S.
    International e-Journal of Science, Medicine & Education, 2011;5(1):17-26.
    MyJurnal
    Metabonomics can be used to quantitatively measure dynamic biochemical responses of living organisms to physiological or pathological stimuli. A range of analytical tools such as high-resolution nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) combined with multivariate statistical analysis can be employed to create comprehensive metabolic signatures of biological samples including urine, plasma, faecal water and tissue extracts. These metabolic signatures can reflect the physiological or pathological condition of the organism and indicate imbalances in the homeostatic regulation of tissues and extracellular fluids. This technology has been employed in a diverse range of application areas including investigation of disease mechanisms, diagnosis/prognosis of pathologies, nutritional interventions and drug toxicity. Metabolic profiling is becoming increasingly important in identifying biomarkers of disease progression and drug intervention, and can provide additional information to support or aid the interpretation of genomic and proteomic data. With the new generation of postgenomic technologies, the paradigm in many biological fields has shifted to either top down systems biology approaches, aiming to achieve a general understanding of the global and integrated response of an organism or to bottom up modelling of specific pathways and networks using a priori knowledge based on mining large bodies of literature. Whilst metabolic profiling lends itself to either approach, using it in an exploratory and hypothesis generating capacity clearly allows new mechanisms to be uncovered.
    Matched MeSH terms: Systems Biology
  20. Fulltext Insight into plant cell wall degradation and pathogenesis of Ganoderma boninense via comparative genome analysis
    Ramzi AB, Che Me ML, Ruslan US, Baharum SN, Nor Muhammad NA
    PeerJ, 2019;7:e8065.
    PMID: 31879570 DOI: 10.7717/peerj.8065
    Background: G. boninense is a hemibiotrophic fungus that infects oil palms (Elaeis guineensis Jacq.) causing basal stem rot (BSR) disease and consequent massive economic losses to the oil palm industry. The pathogenicity of this white-rot fungus has been associated with cell wall degrading enzymes (CWDEs) released during saprophytic and necrotrophic stage of infection of the oil palm host. However, there is a lack of information available on the essentiality of CWDEs in wood-decaying process and pathogenesis of this oil palm pathogen especially at molecular and genome levels.

    Methods: In this study, comparative genome analysis was carried out using the G. boninense NJ3 genome to identify and characterize carbohydrate-active enzyme (CAZymes) including CWDE in the fungal genome. Augustus pipeline was employed for gene identification in G. boninense NJ3 and the produced protein sequences were analyzed via dbCAN pipeline and PhiBase 4.5 database annotation for CAZymes and plant-host interaction (PHI) gene analysis, respectively. Comparison of CAZymes from G. boninense NJ3 was made against G. lucidum, a well-studied model Ganoderma sp. and five selected pathogenic fungi for CAZymes characterization. Functional annotation of PHI genes was carried out using Web Gene Ontology Annotation Plot (WEGO) and was used for selecting candidate PHI genes related to cell wall degradation of G. boninense NJ3.

    Results: G. boninense was enriched with CAZymes and CWDEs in a similar fashion to G. lucidum that corroborate with the lignocellulolytic abilities of both closely-related fungal strains. The role of polysaccharide and cell wall degrading enzymes in the hemibiotrophic mode of infection of G. boninense was investigated by analyzing the fungal CAZymes with necrotrophic Armillaria solidipes, A. mellea, biotrophic Ustilago maydis, Melampsora larici-populina and hemibiotrophic Moniliophthora perniciosa. Profiles of the selected pathogenic fungi demonstrated that necrotizing pathogens including G. boninense NJ3 exhibited an extensive set of CAZymes as compared to the more CAZymes-limited biotrophic pathogens. Following PHI analysis, several candidate genes including polygalacturonase, endo β-1,3-xylanase, β-glucanase and laccase were identified as potential CWDEs that contribute to the plant host interaction and pathogenesis.

    Discussion: This study employed bioinformatics tools for providing a greater understanding of the biological mechanisms underlying the production of CAZymes in G. boninense NJ3. Identification and profiling of the fungal polysaccharide- and lignocellulosic-degrading enzymes would further facilitate in elucidating the infection mechanisms through the production of CWDEs by G. boninense. Identification of CAZymes and CWDE-related PHI genes in G. boninense would serve as the basis for functional studies of genes associated with the fungal virulence and pathogenicity using systems biology and genetic engineering approaches.

    Matched MeSH terms: Systems Biology
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