Displaying publications 1 - 20 of 90 in total

  1. Ismail MA, Deris S, Mohamad MS, Abdullah A
    PLoS One, 2015;10(5):e0126199.
    PMID: 25961295 DOI: 10.1371/journal.pone.0126199
    This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved. To deal with this situation, this paper presents an in silico optimization method, namely the Newton Cooperative Genetic Algorithm (NCGA). The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark metabolic pathways, and the results showed that the NCGA achieved better results compared to the existing methods.
    Matched MeSH terms: Metabolic Networks and Pathways*
  2. Man MY, Mohamad MS, Choon YW, Ismail MA
    J Integr Bioinform, 2021 Aug 04;18(3).
    PMID: 34348418 DOI: 10.1515/jib-2020-0037
    Microorganisms commonly produce many high-demand industrial products like fuels, food, vitamins, and other chemicals. Microbial strains are the strains of microorganisms, which can be optimized to improve their technological properties through metabolic engineering. Metabolic engineering is the process of overcoming cellular regulation in order to achieve a desired product or to generate a new product that the host cells do not usually need to produce. The prediction of genetic manipulations such as gene knockout is part of metabolic engineering. Gene knockout can be used to optimize the microbial strains, such as to maximize the production rate of chemicals of interest. Metabolic and genetic engineering is important in producing the chemicals of interest as, without them, the product yields of many microorganisms are normally low. As a result, the aim of this paper is to propose a combination of the Bat algorithm and the minimization of metabolic adjustment (BATMOMA) to predict which genes to knock out in order to increase the succinate and lactate production rates in Escherichia coli (E. coli).
    Matched MeSH terms: Metabolic Networks and Pathways*
  3. Wong YM, Juan JC, Gan HM, Austin CM
    Genome Announc, 2014;2(2).
    PMID: 24604639 DOI: 10.1128/genomeA.00077-14
    Clostridium bifermentans strain WYM is an effective biohydrogen producer isolated from landfill leachate sludge. Here, we present the assembly and annotation of its genome, which may provide further insights into the metabolic pathways involved in efficient biohydrogen production.
    Matched MeSH terms: Metabolic Networks and Pathways
  4. Aziz FAA, Suzuki K, Moriuchi R, Dohra H, Tashiro Y, Futamata H
    Microbiol Resour Announc, 2020 Feb 13;9(7).
    PMID: 32054711 DOI: 10.1128/MRA.01478-19
    We report the draft genome sequence of Variovorax boronicumulans strain HAB-30, which was isolated from a phenol-degrading chemostat culture. This strain contains genes encoding a multicomponent type of phenol hydroxylase, with degradation pathways for catechol and other aromatic compounds. The genome sequence will be useful for understanding the metabolic pathways involved in phenol degradation.
    Matched MeSH terms: Metabolic Networks and Pathways
  5. Ramli NN, Othman AR, Kurniawan SB, Abdullah SRS, Hasan HA
    Microbiol Res, 2023 Mar;268:127288.
    PMID: 36571921 DOI: 10.1016/j.micres.2022.127288
    Heavy metal wastes, particularly hexavalent chromium [Cr(VI)], are generated from anthropogenic activities, and their increasing abundance has been a research concern due to their toxicity, genotoxicity, carcinogenicity and mutagenicity. Exposure to these dangerous pollutants could lead to chronic infections and even mortality in humans and animals. Bioremediation using microorganisms, particularly bacteria, has gained considerable interest because it can remove contaminants naturally and is safe to the surrounding environment. Bacteria, such as Pseudomonas putida and Bacillus subtilis, can reduce the toxic Cr(VI) to the less toxic trivalent chromium Cr(III) through mechanisms including biotransformation, biosorption and bioaccumulation. These mechanisms are mostly linked to chromium reductase and nitroreductase enzymes, which are involved in the Cr(VI) reduction pathway. However, relevant data on the nitroreductase route remain insufficient. Thus, this work proposes an alternative metabolic pathway of nitroreductase, wherein nitrate activates the reaction and indirectly reduces toxic chromium. This nitroreductase pathway occurs concurrently with the chromium reduction pathway.
    Matched MeSH terms: Metabolic Networks and Pathways
  6. Wang Y, Liu X, Dong L, Cheng KK, Lin C, Wang X, et al.
    Anal Chem, 2023 Apr 18;95(15):6203-6211.
    PMID: 37023366 DOI: 10.1021/acs.analchem.2c04603
    Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.
    Matched MeSH terms: Metabolic Networks and Pathways
  7. Lin G, Dong L, Cheng KK, Xu X, Wang Y, Deng L, et al.
    Anal Chem, 2023 Aug 22;95(33):12505-12513.
    PMID: 37557184 DOI: 10.1021/acs.analchem.3c02246
    Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.
    Matched MeSH terms: Metabolic Networks and Pathways
  8. Abd Algfoor Z, Shahrizal Sunar M, Abdullah A, Kolivand H
    Brief Funct Genomics, 2017 03 01;16(2):87-98.
    PMID: 26969656 DOI: 10.1093/bfgp/elw002
    Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches.
    Matched MeSH terms: Metabolic Networks and Pathways*
  9. Lee MK, Mohamad MS, Choon YW, Mohd Daud K, Nasarudin NA, Ismail MA, et al.
    J Integr Bioinform, 2020 May 06;17(1).
    PMID: 32374287 DOI: 10.1515/jib-2019-0073
    The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite's production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.
    Matched MeSH terms: Metabolic Networks and Pathways
  10. Deng L, Ma L, Cheng KK, Xu X, Raftery D, Dong J
    J Proteome Res, 2021 06 04;20(6):3204-3213.
    PMID: 34002606 DOI: 10.1021/acs.jproteome.1c00064
    Metabolite set enrichment analysis (MSEA) has gained increasing research interest for identification of perturbed metabolic pathways in metabolomics. The method incorporates predefined metabolic pathways information in the analysis where metabolite sets are typically assumed to be mutually exclusive to each other. However, metabolic pathways are known to contain common metabolites and intermediates. This situation, along with limitations in metabolite detection or coverage leads to overlapping, incomplete metabolite sets in pathway analysis. For overlapping metabolite sets, MSEA tends to result in high false positives due to improper weights allocated to the overlapping metabolites. Here, we proposed an extended partial least squares (PLS) model with a new sparse scheme for overlapping metabolite set enrichment analysis, named overlapping group PLS (ogPLS) analysis. The weight vector of the ogPLS model was decomposed into pathway-specific subvectors, and then a group lasso penalty was imposed on these subvectors to achieve a proper weight allocation for the overlapping metabolites. Two strategies were adopted in the proposed ogPLS model to identify the perturbed metabolic pathways. The first strategy involves debiasing regularization, which was used to reduce inequalities amongst the predefined metabolic pathways. The second strategy is stable selection, which was used to rank pathways while avoiding the nuisance problems of model parameter optimization. Both simulated and real-world metabolomic datasets were used to evaluate the proposed method and compare with two other MSEA methods including Global-test and the multiblock PLS (MB-PLS)-based pathway importance in projection (PIP) methods. Using a simulated dataset with known perturbed pathways, the average true discovery rate for the ogPLS method was found to be higher than the Global-test and the MB-PLS-based PIP methods. Analysis with a real-world metabolomics dataset also indicated that the developed method was less prone to select pathways with highly overlapped detected metabolite sets. Compared with the two other methods, the proposed method features higher accuracy, lower false-positive rate, and is more robust when applied to overlapping metabolite set analysis. The developed ogPLS method may serve as an alternative MSEA method to facilitate biological interpretation of metabolomics data for overlapping metabolite sets.
    Matched MeSH terms: Metabolic Networks and Pathways
  11. Thiagarajan SK, Mok SY, Ogawa S, Parhar IS, Tang PY
    Int J Mol Sci, 2023 Feb 17;24(4).
    PMID: 36835497 DOI: 10.3390/ijms24044088
    Several theories have been proposed to explain the mechanisms of substance use in schizophrenia. Brain neurons pose a potential to provide novel insights into the association between opioid addiction, withdrawal, and schizophrenia. Thus, we exposed zebrafish larvae at 2 days post-fertilization (dpf) to domperidone (DPM) and morphine, followed by morphine withdrawal. Drug-induced locomotion and social preference were assessed, while the level of dopamine and the number of dopaminergic neurons were quantified. In the brain tissue, the expression levels of genes associated with schizophrenia were measured. The effects of DMP and morphine were compared to vehicle control and MK-801, a positive control to mimic schizophrenia. Gene expression analysis revealed that α1C, α1Sa, α1Aa, drd2a, and th1 were up-regulated after 10 days of exposure to DMP and morphine, while th2 was down-regulated. These two drugs also increased the number of positive dopaminergic neurons and the total dopamine level but reduced the locomotion and social preference. The termination of morphine exposure led to the up-regulation of th2, drd2a, and c-fos during the withdrawal phase. Our integrated data implicate that the dopamine system plays a key role in the deficits in social behavior and locomotion that are common in the schizophrenia-like symptoms and opioid dependence.
    Matched MeSH terms: Metabolic Networks and Pathways
  12. Shanti Dwita Lestari, Shobirin Meor Hussin A, Mustafa S, Yun Shin Sew, Ming Gan H, Hashim AM, et al.
    Food Chem, 2023 Nov 15;426:136568.
    PMID: 37437500 DOI: 10.1016/j.foodchem.2023.136568
    The fermentation of Malaysian fish sauce (budu) varies from one to twelve months depending on the producer, resulting in inconsistent quality. The microbiota, their predicted metabolic pathways and volatile metabolites profiles were determined at different stages of budu fermentation. Budu fermented for 1 and 3 months were characterized by the presence of Gram negative Enterobacterales, Gammaproteobacteria, and Fusobacteriaceae, which continuously decrease in abundance over fermentation time. The metabolic pathways prediction grouped 1- and 3- month budu in a cluster enriched with degradation reactions. 6-month budu were dominated by Halanaerobium and Staphylococcus, while the 12-month were dominated by Lentibacillus, Bacilli, and Halomonas. Biosynthesis-type predicted pathways involving protein and lipid derivatives were enriched in 6- and 12-month fermented budu, accumulating 2,6-dimethylpyrazine, methyl 2-ethyldecanoate, 2-phenylacetaldehyde, 3-methylbutanal, and 3-methylbutanoic acid. These compounds may indicate budu maturity and quality. This result may assist as a reference for quality control and fermentation monitoring.
    Matched MeSH terms: Metabolic Networks and Pathways
  13. Lu SJ, Salleh AH, Mohamad MS, Deris S, Omatu S, Yoshioka M
    Comput Biol Chem, 2014 12;53PB:175-183.
    PMID: 25462325 DOI: 10.1016/j.compbiolchem.2014.09.008
    Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker's yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms.
    Matched MeSH terms: Metabolic Networks and Pathways
  14. Kalam S, Basu A, Ahmad I, Sayyed RZ, El-Enshasy HA, Dailin DJ, et al.
    Front Microbiol, 2020;11:580024.
    PMID: 33193209 DOI: 10.3389/fmicb.2020.580024
    Acidobacteria represents an underrepresented soil bacterial phylum whose members are pervasive and copiously distributed across nearly all ecosystems. Acidobacterial sequences are abundant in soils and represent a significant fraction of soil microbial community. Being recalcitrant and difficult-to-cultivate under laboratory conditions, holistic, polyphasic approaches are required to study these refractive bacteria extensively. Acidobacteria possesses an inventory of genes involved in diverse metabolic pathways, as evidenced by their pan-genomic profiles. Because of their preponderance and ubiquity in the soil, speculations have been made regarding their dynamic roles in vital ecological processes viz., regulation of biogeochemical cycles, decomposition of biopolymers, exopolysaccharide secretion, and plant growth promotion. These bacteria are expected to have genes that might help in survival and competitive colonization in the rhizosphere, leading to the establishment of beneficial relationships with plants. Exploration of these genetic attributes and more in-depth insights into the belowground mechanics and dynamics would lead to a better understanding of the functions and ecological significance of this enigmatic phylum in the soil-plant environment. This review is an effort to provide a recent update into the diversity of genes in Acidobacteria useful for characterization, understanding ecological roles, and future biotechnological perspectives.
    Matched MeSH terms: Metabolic Networks and Pathways
  15. Ee Uli J, Yong CS, Yeap SK, Alitheen NB, Rovie-Ryan JJ, Mat Isa N, et al.
    BMC Res Notes, 2018 Dec 22;11(1):923.
    PMID: 30577850 DOI: 10.1186/s13104-018-4014-1
    OBJECTIVE: Using high-throughput RNA sequencing technology, this study aimed to sequence the transcriptome of kidney and liver tissues harvested from Peninsular Malaysia cynomolgus macaque (Macaca fascicularis). M. fascicularis are significant nonhuman primate models in the biomedical field, owing to the macaque's biological similarities with humans. The additional transcriptomic dataset will supplement the previously described Peninsular Malaysia M. fascicularis transcriptomes obtained in a past endeavour.

    RESULTS: A total of 75,350,240 sequence reads were obtained via Hi-seq 2500 sequencing technology. A total of 5473 significant differentially expressed genes were called. Gene ontology functional categorisation showed that cellular process, catalytic activity, and cell part categories had the highest number of expressed genes, while the metabolic pathways category possessed the highest number of expressed genes in the KEGG pathway analysis. The additional sequence dataset will further enrich existing M. fascicularis transcriptome assemblies, and provide a dataset for further downstream studies.

    Matched MeSH terms: Metabolic Networks and Pathways
  16. Deng L, Guo F, Cheng KK, Zhu J, Gu H, Raftery D, et al.
    J Proteome Res, 2020 05 01;19(5):1965-1974.
    PMID: 32174118 DOI: 10.1021/acs.jproteome.9b00793
    In metabolomics, identification of metabolic pathways altered by disease, genetics, or environmental perturbations is crucial to uncover the underlying biological mechanisms. A number of pathway analysis methods are currently available, which are generally based on equal-probability, topological-centrality, or model-separability methods. In brief, prior identification of significant metabolites is needed for the first two types of methods, while each pathway is modeled separately in the model-separability-based methods. In these methods, interactions between metabolic pathways are not taken into consideration. The current study aims to develop a novel metabolic pathway identification method based on multi-block partial least squares (MB-PLS) analysis by including all pathways into a global model to facilitate biological interpretation. The detected metabolites are first assigned to pathway blocks based on their roles in metabolism as defined by the KEGG pathway database. The metabolite intensity or concentration data matrix is then reconstructed as data blocks according to the metabolite subsets. Then, a MB-PLS model is built on these data blocks. A new metric, named the pathway importance in projection (PIP), is proposed for evaluation of the significance of each metabolic pathway for group separation. A simulated dataset was generated by imposing artificial perturbation on four pre-defined pathways of the healthy control group of a colorectal cancer study. Performance of the proposed method was evaluated and compared with seven other commonly used methods using both an actual metabolomics dataset and the simulated dataset. For the real metabolomics dataset, most of the significant pathways identified by the proposed method were found to be consistent with the published literature. For the simulated dataset, the significant pathways identified by the proposed method are highly consistent with the pre-defined pathways. The experimental results demonstrate that the proposed method is effective for identification of significant metabolic pathways, which may facilitate biological interpretation of metabolomics data.
    Matched MeSH terms: Metabolic Networks and Pathways
  17. 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: Metabolic Networks and Pathways
  18. Arif MA, Mohamad MS, Abd Latif MS, Deris S, Remli MA, Mohd Daud K, et al.
    Comput Biol Med, 2018 11 01;102:112-119.
    PMID: 30267898 DOI: 10.1016/j.compbiomed.2018.09.015
    Metabolic engineering involves the modification and alteration of metabolic pathways to improve the production of desired substance. The modification can be made using in silico gene knockout simulation that is able to predict and analyse the disrupted genes which may enhance the metabolites production. Global optimization algorithms have been widely used for identifying gene knockout strategies. However, their productions were less than theoretical maximum and the algorithms are easily trapped into local optima. These algorithms also require a very large computation time to obtain acceptable results. This is due to the complexity of the metabolic models which are high dimensional and contain thousands of reactions. In this paper, a hybrid algorithm of Cuckoo Search and Minimization of Metabolic Adjustment is proposed to overcome the aforementioned problems. The hybrid algorithm searches for the near-optimal set of gene knockouts that leads to the overproduction of metabolites. Computational experiments on two sets of genome-scale metabolic models demonstrate that the proposed algorithm is better than the previous works in terms of growth rate, Biomass Product Couple Yield, and computation time.
    Matched MeSH terms: Metabolic Networks and Pathways
  19. Ghosh P, Kumar M, Kapoor R, Kumar SS, Singh L, Vijay V, et al.
    Bioresour Technol, 2020 Jan;296:122275.
    PMID: 31683109 DOI: 10.1016/j.biortech.2019.122275
    The present study intends to evaluate the potential of co-digestion for utilizing Organic fraction of Municipal Solid Waste (OFMSW) and sewage sludge (SS) for enhanced biogas production. Metagenomic analysis was performed to identify the dominant bacteria, archaea and fungi, changes in their communities with time and their functional roles during the course of anaerobic digestion (AD). The cumulative biogas yield of 586.2 mL biogas/gVS with the highest methane concentration of 69.5% was observed under an optimum ratio of OFMSW:SS (40:60 w/w). Bacteria and fungi were found to be majorly involved in hydrolysis and initial stages of AD. Probably, the most common archaea Methanosarsina sp. primarily followed the acetoclastic pathway. The hydrogenotrophic pathway was less followed as indicated by the reduction in abundance of syntrophic acetate oxidizers. An adequate understanding of microbial communities is important to manipulate and inoculate the specific microbial consortia to maximize CH4 production through AD.
    Matched MeSH terms: Metabolic Networks and Pathways
  20. Tan PL, Liong MT
    Trends Biotechnol, 2014 Dec;32(12):599-601.
    PMID: 25457386 DOI: 10.1016/j.tibtech.2014.09.011
    Matched MeSH terms: Metabolic Networks and Pathways/genetics*
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