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
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
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
Streptomycetes remain as one of the important sources for bioactive products. Isolated from the mangrove forest, Streptomyces gilvigriseus MUSC 26T was previously characterised as a novel streptomycete. The high quality draft genome of MUSC 26T contained 5,213,277bp with G+C content of 73.0%. Through genome mining, several gene clusters associated with secondary metabolites production were revealed in the genome of MUSC 26T. These findings call for further investigations into the potential exploitation of the strain for production of pharmaceutically important compounds.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
The growing number of multidrug-resistant pathogenic bacteria is becoming a world leading challenge for the scientific community and for public health. However, advances in high-throughput technologies and whole-genome sequencing of bacterial pathogens make the construction of bacterial genome-scale metabolic models (GEMs) increasingly realistic. The use of GEMs as an alternative platforms will expedite identification of novel unconditionally essential genes and enzymes of target organisms with existing and forthcoming GEMs. This approach will follow the existing protocol for construction of high-quality GEMs, which could ultimately reduce the time, cost and labor-intensive processes involved in identification of novel antimicrobial drug targets in drug discovery pipelines. We discuss the current impact of existing GEMs of selected multidrug-resistant pathogenic bacteria for identification of novel antimicrobial drug targets and the challenges of closing the gap between genome-scale metabolic modeling and conventional experimental trial-and-error approaches in drug discovery pipelines.
Matched MeSH terms: Metabolic Networks and Pathways/genetics*
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 Networks and Pathways/genetics
Pasteurella multocida causes respiratory infectious diseases in a multitude of birds and mammals. A number of virulence-associated genes were reported across different strains of P. multocida, including those involved in the iron transport and metabolism. Comparative iron-associated genes of P. multocida among different animal hosts towards their interaction networks have not been fully revealed. Therefore, this study aimed to identify the iron-associated genes from core- and pan-genomes of fourteen P. multocida strains and to construct iron-associated protein interaction networks using genome-scale network analysis which might be associated with the virulence. Results showed that these fourteen strains had 1587 genes in the core-genome and 3400 genes constituting their pan-genome. Out of these, 2651 genes associated with iron transport and metabolism were selected to construct the protein interaction networks and 361 genes were incorporated into the iron-associated protein interaction network (iPIN) consisting of nine different iron-associated functional modules. After comparing with the virulence factor database (VFDB), 21 virulence-associated proteins were determined and 11 of these belonged to the heme biosynthesis module. From this study, the core heme biosynthesis module and the core outer membrane hemoglobin receptor HgbA were proposed as candidate targets to design novel antibiotics and vaccines for preventing pasteurellosis across the serotypes or animal hosts for enhanced precision agriculture to ensure sustainability in food security.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
The genus Meridianimaribacter is one of the least-studied genera within Cytophaga-Flavobacteria. To date, no genomic analysis of Meridianimaribacter has been reported. In this study, Meridianimaribacter sp. strain CL38, a lignocellulose degrading halophile was isolated from mangrove soil. The genome of strain CL38 was sequenced and analyzed. The assembled genome contains 17 contigs with 3.33 Mbp, a GC content of 33.13% and a total of 2982 genes predicted. Lignocellulose degrading enzymes such as cellulases (GH3, 5, 9, 16, 74 and 144), xylanases (GH43 and CE4) and mannanases (GH5, 26, 27 and 130) are encoded in the genome. Furthermore, strain CL38 demonstrated its ability to decompose empty fruit bunch, a lignocellulosic waste residue arising from palm oil industry. The genome information coupled with experimental studies confirmed the ability of strain CL38 to degrade lignocellulosic biomass. Therefore, Meridianimaribacter sp. strain CL38, with its halotolerance, could be useful for seawater based lignocellulosic biorefining.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
Evaluation of transcriptome data in combination with QTL information has been applied in many crops to study the expression of genes responsible for specific phenotypes. In oil palm, the mesocarp oil extracted from E. oleifera × E. guineensis interspecific hybrids is known to have lower palmitic acid (C16:0) content compared to pure African palms. The present study demonstrates the effectiveness of transcriptome data in revealing the expression profiles of genes in the fatty acid (FA) and triacylglycerol (TAG) biosynthesis processes in interspecific hybrids. The transcriptome assembly yielded 43,920 putative genes of which a large proportion were homologous to known genes in the public databases. Most of the genes encoding key enzymes involved in the FA and TAG synthesis pathways were identified. Of these, 27, including two candidate genes located within the QTL associated with C16:0 content, showed differential expression between developmental stages, populations and/or palms with contrasting C16:0 content. Further evaluation using quantitative real-time PCR revealed that differentially expressed patterns are generally consistent with those observed in the transcriptome data. Our results also suggest that different isoforms are likely to be responsible for some of the variation observed in FA composition of interspecific hybrids.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
Anoxybacillus sp. SK 3-4 is a Gram-positive, rod-shaped bacterium and a member of family Bacillaceae. We had previously reported that the strain is an aluminum resistant thermophilic bacterium. This is the first report to provide a detailed analysis of the global transcriptional response of Anoxybacillus when the cells were exposed to 600 mg L(-1) of aluminum. The transcriptome was sequenced using Illumina MiSeq sequencer. Total of 708 genes were differentially expressed (fold change >2.00) with 316 genes were up-regulated while 347 genes were down-regulated, in comparing to control with no aluminum added in the culture. Based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, the majority of genes encoding for cell metabolism such as glycolysis, sulfur metabolism, cysteine and methionine metabolism were up-regulated; while most of the gene associated with tricarboxylic acid cycle (TCA cycle) and valine, leucine and isoleucine metabolism were down-regulated. In addition, a significant number of the genes encoding ABC transporters, metal ions transporters, and some stress response proteins were also differentially expressed following aluminum exposure. The findings provide further insight and help us to understand on the resistance of Anoxybacillus sp. SK 3-4 toward aluminium.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
Staphylococcus aureus has become a serious concern in hospitals and community due to rapid adaptation to existing antimicrobial agents. Betulinaldehyde [3β-hydroxy-20(29)-lupen-28-al (BE)] belongs to pentacyclic triterpenoids that are based on a 30-carbon skeleton comprising four six-membered rings and one five-membered ring. In a preliminary study, BE exhibited antimicrobial activity against reference strains of methicillin-resistant and methicillin-sensitive S. aureus. However, the response mechanism of S. aureus to this compound is not known. In this study, the global gene expression patterns of both the reference strains in response to sub-inhibitory concentrations of BE were analyzed using DNA microarray to identify gene targets, particularly essential targets in novel pathways, i.e. not targeted by currently used antibiotics, or novel targets in existing pathways. The transcriptome analysis revealed repression of genes in the aminoacyl-tRNA synthetase and ribosome pathways in both the reference strains. Other pathways such as cell division, two-component systems, ABC transporters, fatty acid biosynthesis and peptidoglycan biosynthesis were affected only in the reference strain of methicillin-resistant S. aureus. The findings suggest that BE regulates multiple desirable targets which could be further explored in the development of therapeutic agents for the treatment of S. aureus infections.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
Predicting the distribution of metabolic fluxes in biochemical networks is of major interest in systems biology. Several databases provide metabolic reconstructions for different organisms. Software to analyze flux distributions exists, among others for the proprietary MATLAB environment. Given the large user community for the R computing environment, a simple implementation of flux analysis in R appears desirable and will facilitate easy interaction with computational tools to handle gene expression data. We extended the R software package BiGGR, an implementation of metabolic flux analysis in R. BiGGR makes use of public metabolic reconstruction databases, and contains the BiGG database and the reconstruction of human metabolism Recon2 as Systems Biology Markup Language (SBML) objects. Models can be assembled by querying the databases for pathways, genes or reactions of interest. Fluxes can then be estimated by maximization or minimization of an objective function using linear inverse modeling algorithms. Furthermore, BiGGR provides functionality to quantify the uncertainty in flux estimates by sampling the constrained multidimensional flux space. As a result, ensembles of possible flux configurations are constructed that agree with measured data within precision limits. BiGGR also features automatic visualization of selected parts of metabolic networks using hypergraphs, with hyperedge widths proportional to estimated flux values. BiGGR supports import and export of models encoded in SBML and is therefore interoperable with different modeling and analysis tools. As an application example, we calculated the flux distribution in healthy human brain using a model of central carbon metabolism. We introduce a new algorithm termed Least-squares with equalities and inequalities Flux Balance Analysis (Lsei-FBA) to predict flux changes from gene expression changes, for instance during disease. Our estimates of brain metabolic flux pattern with Lsei-FBA for Alzheimer's disease agree with independent measurements of cerebral metabolism in patients. This second version of BiGGR is available from Bioconductor.
Matched MeSH terms: Metabolic Networks and Pathways*
Pandoraea vervacti DSM 23571(T) is an oxalate metabolizing bacterium isolated from an uncultivated field soil in Mugla, Turkey. Here, we present the first complete genome sequence of P. vervacti DSM 23571(T). A complete pathway for degradation of oxalate was revealed from the genome analysis. These data are important to path new opportunities for genetic engineering in the field of biotechnology.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
Helicobacter pylori colonizes almost half of the human population worldwide. H. pylori strains are genetically diverse, and the specific genotypes are associated with various clinical manifestations including gastric adenocarcinoma, peptic ulcer disease (PUD), and nonulcer dyspepsia (NUD). However, our current knowledge of the H. pylori metabolism is limited. To understand the metabolic differences among H. pylori strains, we investigated four Malaysian H. pylori clinical strains, which had been previously sequenced, and a standard strain, H. pylori J99, at the phenotypic level.
Matched MeSH terms: Metabolic Networks and Pathways*
Glycogenes regulate a large number of biological processes such as cancer and development. In this work, we created an interaction network of 923 glycogenes to detect potential hubs from different mouse tissues using RNA-Seq data. DAVID functional cluster analysis revealed enrichment of immune response, glycoprotein and cholesterol metabolic processes. We also explored nsSNPs that may modify the expression and function of identified hubs using computational methods. We observe that the number of nsSNPs predicted by any two methods to affect protein function is 4, 7 and 2 for FLT1, NID2 and TNFRSF1B. Residues in the native and mutant proteins were analyzed for solvent accessibility and secondary structure change. Analysis of hubs can help in determining their degree of conservation and understanding their functions in biological processes. The nsSNPs proposed in this work may be further targeted through experimental methods for understanding structural and functional relationships of hub mutants.
Matched MeSH terms: Metabolic Networks and Pathways/genetics
Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.
Matched MeSH terms: Metabolic Networks and Pathways
Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Early diagnosis and accurate staging of the disease is vital to improve the prognosis. Metabolomics has been used to identify changes in metabolite profiles in the different stages of cancer in order to introduce new non-invasive molecular tools for staging. In this systematic review, we aim to identify the common metabolite changes in human biological samples and the dominant metabolic pathways associated with CRC progression. A broad systematic search was carried out from selected databases. Four reviewers screened and reviewed the titles, abstracts, and full-text articles according to the inclusion and exclusion criteria. Quality assessment was conducted on the eight articles which met the criteria. Data showed that the metabolites involved with redox status, energy metabolism and intermediates of amino acids, choline and nucleotides metabolism were the most affected during CRC progression. However, there were differences in the levels of individual metabolites detected between the studies, and this might be due to the study population, sample preparation, analytical platforms used and statistical tools. In conclusion, this systematic review highlights the changes in metabolites from early to late stages of CRC. Moreover, biomarkers for prognosis are important to reduce CRC-related mortality.
Matched MeSH terms: Metabolic Networks and Pathways
The subterranean termite Globitermus sulphureus is an important Southeast Asian pest with limited genomic resources that causes damages to agriculture crops and building structures. Therefore, the main goal of this study was to survey the G. sulphureus transcriptome composition. Here, we performed de novo transcriptome for G. sulphureus workers' heads using Illumina HiSeq paired-end sequencing technology. A total of 88, 639, 408 clean reads were collected and assembled into 243, 057 transcripts and 193, 344 putative genes. The transcripts were annotated with the Trinotate pipeline. In total, 27, 061 transcripts were successfully annotated using BLASTX against the SwissProt database and 17, 816 genes were assigned to 47, 598 GO terms. We classified 14, 223 transcripts into COG classification, resulting in 25 groups of functional annotations. Next, a total of 12, 194 genes were matched in the KEGG pathway and 392 metabolic pathways were predicted based on the annotation. Moreover, we detected two endogenous cellulases in the sequences. The RT-qPCR analysis showed that there were significant differences in the expression levels of two genes β-glucosidase and endo-β-1,4-glucanase between worker and soldier heads of G. sulphureus. This is the first study to characterize the complete head transcriptome of a higher termite G. sulphureus using a high-throughput sequencing. Our study may provide an overview and comprehensive molecular resource for comparative studies of the transcriptomics and genomics of termites.
Matched MeSH terms: Metabolic Networks and Pathways