Displaying publications 1 - 20 of 35 in total

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  1. Azizan KA, Ressom HW, Mendoza ER, Baharum SN
    PeerJ, 2017;5:e3451.
    PMID: 28695065 DOI: 10.7717/peerj.3451
    Lactococcus lactis subsp. cremoris MG1363 is an important starter culture for dairy fermentation. During industrial fermentations, L. lactis is constantly exposed to stresses that affect the growth and performance of the bacterium. Although the response of L. lactis to several stresses has been described, the adaptation mechanisms at the level of in vivo fluxes have seldom been described. To gain insights into cellular metabolism, 13C metabolic flux analysis and gas chromatography mass spectrometry (GC-MS) were used to measure the flux ratios of active pathways in the central metabolism of L. lactis when subjected to three conditions varying in temperature (30°C, 37°C) and agitation (with and without agitation at 150 rpm). Collectively, the concentrations of proteinogenic amino acids (PAAs) and free fatty acids (FAAs) were compared, and Pearson correlation analysis (r) was calculated to measure the pairwise relationship between PAAs. Branched chain and aromatic amino acids, threonine, serine, lysine and histidine were correlated strongly, suggesting changes in flux regulation in glycolysis, the pentose phosphate (PP) pathway, malic enzyme and anaplerotic reaction catalysed by pyruvate carboxylase (pycA). Flux ratio analysis revealed that glucose was mainly converted by glycolysis, highlighting the stability of L. lactis' central carbon metabolism despite different conditions. Higher flux ratios through oxaloacetate (OAA) from pyruvate (PYR) reaction in all conditions suggested the activation of pyruvate carboxylate (pycA) in L. lactis, in response to acid stress during exponential phase. Subsequently, more significant flux ratio differences were seen through the oxidative and non-oxidative pentose phosphate (PP) pathways, malic enzyme, and serine and C1 metabolism, suggesting NADPH requirements in response to environmental stimuli. These reactions could play an important role in optimization strategies for metabolic engineering in L. lactis. Overall, the integration of systematic analysis of amino acids and flux ratio analysis provides a systems-level understanding of how L. lactis regulates central metabolism under various conditions.
    Matched MeSH terms: Metabolic Engineering
  2. 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 Engineering
  3. Daud KM, Mohamad MS, Zakaria Z, Hassan R, Shah ZA, Deris S, et al.
    Comput Biol Med, 2019 10;113:103390.
    PMID: 31450056 DOI: 10.1016/j.compbiomed.2019.103390
    Metabolic engineering is defined as improving the cellular activities of an organism by manipulating the metabolic, signal or regulatory network. In silico reaction knockout simulation is one of the techniques applied to analyse the effects of genetic perturbations on metabolite production. Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. However, the final goal is to increase the production rate. Furthermore, they produce one single solution, though in reality, cells do not focus on one objective and they need to consider various different competing objectives. In this work, a method, termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis), has been developed to find the reaction knockouts involved in maximising the production rate and growth rate of the mutant, by incorporating Pareto dominance concepts. The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. We obtained a set of non-dominated solutions, with each solution representing a different mutant strain. The results obtained were compared with the single objective optimisation (SOO) and multi-objective optimisation (MOO) methods. The results demonstrate that ndsDSAFBA is better than the other methods in terms of production rate and growth rate.
    Matched MeSH terms: Metabolic Engineering*
  4. Song AA, In LLA, Lim SHE, Rahim RA
    Microb Cell Fact, 2017 04 04;16(1):55.
    PMID: 28376880 DOI: 10.1186/s12934-017-0669-x
    Lactococcus lactis has progressed a long way since its discovery and initial use in dairy product fermentation, to its present biotechnological applications in genetic engineering for the production of various recombinant proteins and metabolites that transcends the heterologous species barrier. Key desirable features of this gram-positive lactic acid non-colonizing gut bacteria include its generally recognized as safe (GRAS) status, probiotic properties, the absence of inclusion bodies and endotoxins, surface display and extracellular secretion technology, and a diverse selection of cloning and inducible expression vectors. This have made L. lactis a desirable and promising host on par with other well established model bacterial or yeast systems such as Escherichia coli, Saccharomyces [corrected] cerevisiae and Bacillus subtilis. In this article, we review recent technological advancements, challenges, future prospects and current diversified examples on the use of L. lactis as a microbial cell factory. Additionally, we will also highlight latest medical-based applications involving whole-cell L. lactis as a live delivery vector for the administration of therapeutics against both communicable and non-communicable diseases.
    Matched MeSH terms: Metabolic Engineering/methods*
  5. Baradaran A, Sieo CC, Foo HL, Illias RM, Yusoff K, Rahim RA
    Biotechnol Lett, 2013 Feb;35(2):233-8.
    PMID: 23076361 DOI: 10.1007/s10529-012-1059-4
    Fifty signal peptides of Pediococcus pentosaceus were characterized by in silico analysis and, based on the physicochemical analysis, (two potential signal peptides Spk1 and Spk3 were identified). The coding sequences of SP were amplified and fused to the gene coding for green fluorescent protein (GFP) and cloned into Lactococcus lactis pNZ8048 and pMG36e vectors, respectively. Western blot analysis indicated that the GFP proteins were secreted using both heterologous SPs. ELISA showed that the secretion efficiency of GFP using Spk1 (0.64 μg/ml) was similar to using Usp45 (0.62 μg/ml) and Spk3 (0.58 μg/ml).
    Matched MeSH terms: Metabolic Engineering*
  6. 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 Engineering
  7. Ting TY, Li Y, Bunawan H, Ramzi AB, Goh HH
    J Biosci Bioeng, 2023 Apr;135(4):259-265.
    PMID: 36803862 DOI: 10.1016/j.jbiosc.2023.01.010
    Saccharomyces cerevisiae has a long-standing history of biotechnological applications even before the dawn of modern biotechnology. The field is undergoing accelerated advancement with the recent systems and synthetic biology approaches. In this review, we highlight the recent findings in the field with a focus on omics studies of S. cerevisiae to investigate its stress tolerance in different industries. The latest advancements in S. cerevisiae systems and synthetic biology approaches for the development of genome-scale metabolic models (GEMs) and molecular tools such as multiplex Cas9, Cas12a, Cpf1, and Csy4 genome editing tools, modular expression cassette with optimal transcription factors, promoters, and terminator libraries as well as metabolic engineering. Omics data analysis is key to the identification of exploitable native genes/proteins/pathways in S. cerevisiae with the optimization of heterologous pathway implementation and fermentation conditions. Through systems and synthetic biology, various heterologous compound productions that require non-native biosynthetic pathways in a cell factory have been established via different strategies of metabolic engineering integrated with machine learning.
    Matched MeSH terms: Metabolic Engineering
  8. Ku Nurul Aqmar Ku Bahaudin, Ahmad Bazli Ramzi, Syarul Nataqain Baharum, Suriana Sabri, Adam Leow Thean Chor, Tewin Tencomnao
    Sains Malaysiana, 2018;47:3077-3084.
    Flavonoid is an industrially-important compound due to its high pharmaceutical and cosmeceutical values. However,
    conventional methods in extracting and synthesizing flavonoids are costly, laborious and not sustainable due to small
    amount of natural flavonoids, large amounts of chemicals and space used. Biotechnological production of flavonoids
    represents a viable and sustainable route especially through the use of metabolic engineering strategies in microbial
    production hosts. In this review, we will highlight recent strategies for the improving the production of flavonoids
    using synthetic biology approaches in particular the innovative strategies of genetically-encoded biosensors for in
    vivo metabolite analysis and high-throughput screening methods using fluorescence-activated cell sorting (FACS).
    Implementation of transcription factor based-biosensor for microbial flavonoid production and integration of systems
    and synthetic biology approaches for natural product development will also be discussed.
    Matched MeSH terms: Metabolic Engineering
  9. Leong YK, Show PL, Ooi CW, Ling TC, Lan JC
    J Biotechnol, 2014 Jun 20;180:52-65.
    PMID: 24698847 DOI: 10.1016/j.jbiotec.2014.03.020
    Pursuing the current trend, the "green-polymers", polyhydroxyalkanoates (PHAs) which are degradable and made from renewable sources have been a potential substitute for synthetic plastics. Due to the increasing concern towards escalating crude oil price, depleting petroleum resource and environmental damages done by plastics, PHAs have gained more and more attractions, both from industry and research. From the view point of Escherichia coli, a microorganism that used in the biopolymer large scale production, this paper describes the backgrounds of PHA and summarizes the current advances in PHA developments. In the short-chain-length (scl) PHAs section, the study of poly[(R)-3-hydroxybutyrate] [P(3HB)] as model polymer, ultra-high-molecular-weight P(3HB) which rarely discussed, and P(3HB-co-3HV), another commercialized PHA polymer are included. Other than that, this review also shed some light on the new members of PHA family, lactate-based PHAs and P(3HP) with topics such as block copolymers and invention of novel biopolymers. Flexibility of microorganisms in utilizing different carbon sources to accumulate medium-chain-length (mcl) PHAs and lastly, the promising scl-mcl-PHAs with interesting properties are also discussed.
    Matched MeSH terms: Metabolic Engineering/trends*
  10. Subramaniam M, Baradaran A, Rosli MI, Rosfarizan M, Khatijah Y, Raha AR
    J. Mol. Microbiol. Biotechnol., 2012;22(6):361-72.
    PMID: 23295307 DOI: 10.1159/000343921
    Cyclodextrin glucanotransferase (CGTase) is an extracellular enzyme which catalyzes the formation of cyclodextrin from starch. The production of CGTase using lactic acid bacterium is an attractive alternative and safer strategy to produce CGTase. In this study, we report the construction of genetically modified Lactococcus lactis strains harboring plasmids that secrete the Bacillus sp. G1 β-CGTase, with the aid of the signal peptides (SPs) SPK1, USP45 and native SP (NSP). Three constructed vectors, pNZ:NSP:CGT, pNZ:USP:CGT and pNZ:SPK1:CGT, were developed in this study. Each vector harbored a different SP fused to the CGTase. The formation of halo zones on starch plates indicated the production and secretion of β-CGTase by the recombinants. The expression of this enzyme is shown by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and zymogram analysis. A band size of ∼75 kDa corresponding to β-CGTase is identified in the intracellular and the extracellular environments of the host after medium modification. The replacement of glucose by starch in the medium was shown to induce β-CGTase production in L. lactis. Although β-CGTase production is comparatively low in NZ:SPK1:CGT, the SP SPK1 was shown to have higher secretion efficiency compared to the other SPs used in this study.
    Matched MeSH terms: Metabolic Engineering
  11. Xu L, Kaopong R, Nualkaew S, Chullasara A, Phongdara A
    Sains Malaysiana, 2017;46:1491-1498.
    soflavonoids are the main compound in White Kwao Krua (Pueraria mirifica), which is an effective folk medicinal plant endemic to Thailand. It has been widely used for improving human physical and treating diseases. There are substances with estrogenic activities have been isolated from P. mirifica, such as puerarin, daidzein and genistein. Isoflavone synthase (IFS) is one of the key enzymes in Leguminous plants to convert liquiritigenin, liquiritigenin C-glucoside and naringenin chalcone to isoflavonoids. The aim of this research was to enhance the production of isoflavonoids by metabolic engineering. Transgenic plants were constructed by introducing P450 gene (EgP450) which is similar to IFS from oil palm (Elaeis guineensis), into P. mirifica by a biolistic method. After the transgenic plants had proved successfully, isoflavonoids of each group plants were determined by HPLC. The contents of daidzein and genistein in transgenic plants were higher than the control plants
    Matched MeSH terms: Metabolic Engineering
  12. 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: Metabolic Engineering
  13. Kato T, Azegami J, Yokomori A, Dohra H, El Enshasy HA, Park EY
    BMC Genomics, 2020 Apr 23;21(1):319.
    PMID: 32326906 DOI: 10.1186/s12864-020-6709-7
    BACKGROUND: Ashbya gossypii naturally overproduces riboflavin and has been utilized for industrial riboflavin production. To improve riboflavin production, various approaches have been developed. In this study, to investigate the change in metabolism of a riboflavin-overproducing mutant, namely, the W122032 strain (MT strain) that was isolated by disparity mutagenesis, genomic analysis was carried out.

    RESULTS: In the genomic analysis, 33 homozygous and 1377 heterozygous mutations in the coding sequences of the genome of MT strain were detected. Among these heterozygous mutations, the proportion of mutated reads in each gene was different, ranging from 21 to 75%. These results suggest that the MT strain may contain multiple nuclei containing different mutations. We tried to isolate haploid spores from the MT strain to prove its ploidy, but this strain did not sporulate under the conditions tested. Heterozygous mutations detected in genes which are important for sporulation likely contribute to the sporulation deficiency of the MT strain. Homozygous and heterozygous mutations were found in genes encoding enzymes involved in amino acid metabolism, the TCA cycle, purine and pyrimidine nucleotide metabolism and the DNA mismatch repair system. One homozygous mutation in AgILV2 gene encoding acetohydroxyacid synthase, which is also a flavoprotein in mitochondria, was found. Gene ontology (GO) enrichment analysis showed heterozygous mutations in all 22 DNA helicase genes and genes involved in oxidation-reduction process.

    CONCLUSION: This study suggests that oxidative stress and the aging of cells were involved in the riboflavin over-production in A. gossypii riboflavin over-producing mutant and provides new insights into riboflavin production in A. gossypii and the usefulness of disparity mutagenesis for the creation of new types of mutants for metabolic engineering.

    Matched MeSH terms: Metabolic Engineering/methods
  14. Norhafini H, Huong KH, Amirul AA
    Int J Biol Macromol, 2019 Mar 15;125:1024-1032.
    PMID: 30557643 DOI: 10.1016/j.ijbiomac.2018.12.121
    P(3HB-co-4HB) with a high 4HB monomer composition was previously successfully produced using the transformant Cupriavidus malaysiensis USMAA1020 containing an additional copy of the PHA synthase gene. In this study, high PHA density fed-batch cultivation strategies were developed for such 4HB-rich P(3HB-co-4HB). The pulse, constant and mixed feeding strategies resulted in high PHA accumulation, with a PHA content of 74-92 wt% and 4HB monomer composition of 92-99 mol%. The pulse-feed of carbon and nitrogen resulted in higher PHA concentration (30.7 g/L) than carbon alone (22.3 g/L), suggesting that a trace amount of nitrogen is essential to support cell density for PHA accumulation. Constant feeding was found to be a more feasible strategy than mixed feeding, since the latter caused a drastic fluctuation in the C/N ratio, as evidenced by higher biomass formation indicating more carbon flux towards the competitive TCA pathway. A two-times carbon and nitrogen pulse feeding was the most optimal strategy achieving 92 wt% accommodation of the total biomass, with the highest PHA concentration (46 g/L) and yield (Yp/x) of 11.5 g/g. The strategy has kept the C/N at optimal ratio during the active PHA-producing phase. This is the first report of the production of high PHA density for 4HB-rich P(3HB-co-4HB).
    Matched MeSH terms: Metabolic Engineering/methods
  15. 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 Engineering
  16. Mienda BS, Shamsir MS
    J Biomol Struct Dyn, 2015;33(11):2380-9.
    PMID: 25921851 DOI: 10.1080/07391102.2015.1036461
    Systems metabolic engineering and in silico analyses are necessary to study gene knockout candidate for enhanced succinic acid production by Escherichia coli. Metabolically engineered E. coli has been reported to produce succinate from glucose and glycerol. However, investigation on in silico deletion of ptsG/b1101 gene in E. coli from glycerol using minimization of metabolic adjustment algorithm with the OptFlux software platform has not yet been elucidated. Herein we report what is to our knowledge the first direct predicted increase in succinate production following in silico deletion of the ptsG gene in E. coli GEM from glycerol with the OptFlux software platform. The result indicates that the deletion of this gene in E. coli GEM predicts increased succinate production that is 20% higher than the wild-type control model. Hence, the mutant model maintained a growth rate that is 77% of the wild-type parent model. It was established that knocking out of the ptsG/b1101 gene in E. coli using glucose as substrate enhanced succinate production, but the exact mechanism of this effect is still obscure. This study informs other studies that the deletion of ptsG/b1101 gene in E. coli GEM predicted increased succinate production, enabling a model-driven experimental inquiry and/or novel biological discovery on the underground metabolic role of this gene in E. coli central metabolism in relation to increasing succinate production when glycerol is the substrate.
    Matched MeSH terms: Metabolic Engineering
  17. 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 Engineering
  18. 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: Metabolic Engineering*
  19. Hatti-Kaul R, Chen L, Dishisha T, Enshasy HE
    FEMS Microbiol Lett, 2018 10 01;365(20).
    PMID: 30169778 DOI: 10.1093/femsle/fny213
    Lactic acid bacteria constitute a diverse group of industrially significant, safe microorganisms that are primarily used as starter cultures and probiotics, and are also being developed as production systems in industrial biotechnology for biocatalysis and transformation of renewable feedstocks to commodity- and high-value chemicals, and health products. Development of strains, which was initially based mainly on natural approaches, is also achieved by metabolic engineering that has been facilitated by the availability of genome sequences and genetic tools for transformation of some of the bacterial strains. The aim of this paper is to provide a brief overview of the potential of lactic acid bacteria as biological catalysts for production of different organic compounds for food and non-food sectors based on their diversity, metabolic- and stress tolerance features, as well as the use of genetic/metabolic engineering tools for enhancing their capabilities.
    Matched MeSH terms: Metabolic Engineering/methods
  20. Ramzi AB
    Adv Exp Med Biol, 2018 11 2;1102:81-95.
    PMID: 30382570 DOI: 10.1007/978-3-319-98758-3_6
    In the modern era of next-generation genomics and Fourth Industrial Revolution, there is a growing demand for translational research that brings about not only impactful research but also potential commercialisation of R- and D-based products. Advancement of metabolic engineering and synthetic biology has put forward a viable and innovative biotechnological platform for bioproduct development especially using microbial chassis. In this chapter, readers will be introduced on the concepts of metabolic engineering, synthetic biology and microbial chassis and the applications of these biological engineering (BioE) components in the advancement of industrial and agricultural biotechnology. Main strategies in employing BioE platform are discussed especially for waste bioconversion and value-added product development. More importantly, this chapter will also discuss current endeavours in integrating systems and synthetic biology for microbial production of natural products by introducing flavonoid biosynthesis genes of Polygonum minus, a medicinally important tropical plant in engineered yeast.
    Matched MeSH terms: Metabolic Engineering*
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