Methods paperUnderstanding alternative fluxes/effluxes through comparative metabolic pathway analysis of phylum actinobacteria using a simplified approach
Introduction
The availability of whole genome data has opened new realms in comparing genomes. While data from whole genome has been readily used for constructing phylogeny and comparing codon usage patterns, little effort has been made in comparing genomes based on metabolic pathways.
All organisms are variedly adapted according to their environments and defining their metabolism as the textbook pathway will be prejudice to them (Dandekar et al., 1999). Evolution of metabolism occurs through the acquisition and loss of genes whose products act as enzymes in metabolic reactions. There could be species-specific isozymes (hexokinase and glucokinase), or some alternative enzymes (pyruvate synthase and pyruvate dehydrogenase), or by-passes for utilizing the accumulated products (phosphoenol pyruvate (PEP) being utilized in amino sugar pathway) (Dandekar et al., 1999). Microorganisms have evolved anaerobic/aerobic/facultative mode; some can survive in extreme thermophilic conditions, whereas some are extreme halophiles (Averhoff and Müller, 2010). More complex pathways are seen in organisms to utilize various organic/inorganic substrates such as starch, iron sulfide etc. Some organisms (like Mycobacterium leprae) are endosymbiotic parasites that utilize host machinery. They utilize their own machinery only for some essential compounds (fatty acid metabolism). Therefore, pathogens are even found to have some degenerate pathways and they retain only those pathways that are essential for synthesis of certain compounds (Eiglmeier et al., 2001). In spite of this, there are still some basic pathways which are found in a large number of organisms (Bernhardsson et al., 2011, Fothergill-Gilmore, 1986).
Actinobacteria have diverse habitats, and therefore, they can use various complex compounds. Hence, their metabolic pathways may evolve according to the availability of substrates. Even for universal glycolytic pathway, high level of heterogeneity and plasticity has been reported based on whole genome comparison (Dandekar et al., 1999). In contrast to this universal pathway, many other pathways (like production of secondary metabolites) have been evolved in actinobacteria (Lechevalier and Lechevalier, 1967, Verma et al., 2011, Verma et al., 2013). Hence, for understanding the physiology of such a vivid phylum, the metabolic pathway comparisons based on available genomic data need to be performed.
A number of databases are available for studying metabolic pathways. These include WIT (Overbeek et al., 1997, Overbeek et al., 2000), KEGG (Kanehisa and Goto, 2000, Ogata et al., 1999), EcoCyc (http://bmb.med.miami.edu/EcoGene/index.html), ExPASy-Biochemical Pathways (http://expasy.ch/cgi-bin/searchbiochem-index), PathDB (http://www.ncgr.org/software/pathdb/), UM-BBD (Ellis et al., 1998, Ellis et al., 2000), CAMP (http://cbs.ym.edu.tw:8080/camp2/mpc.jsp) etc. Among these, KEGG is the database of choice as the pathways are classified according to the chemical structure of their main compounds. Each pathway depicts the step by step conversion and the enzymes involved that links the specific information about the compounds, enzymes and other related pathways. Till now, metabolic pathways have been studied by pathway alignment (Dandekar et al., 1999), by presence or absence of metabolic pathways (Liao et al., 2002), network comparisons (Tun et al., 2006) etc. But these analyses depend on complex algorithms. The approaches such as tracking presence or absence of key enzymes of metabolic pathways, presence of alternative enzymes and by-pass routes can be used for comparison. Such methodology not only helps to understand the metabolic networking within each organism but also helps in studying the relatedness of metabolic pathways within a group. Tracing the metabolic reactions and pathways of organisms (especially pathogens) can even help in medicine and drug designing by understanding the alternate substrate fluxes and key enzymes in vital metabolic pathway (Dandekar et al., 1999). Hence, four key metabolic pathways (glycolysis, pentose phosphate, pyruvate and citric acid cycle) of ninety actinobacteria were compared in the present study to know alternative fluxes/effluxes in these diversified organisms.
Section snippets
Genome selection
Actinobacteria with completely sequenced genomes were used for the present study. Ninety of such actinobacteria with complete genomes (which were deposited at NCBI by March 2010) were analyzed.
Metabolic pathway comparison
Metabolic pathways for ninety actinobacteria were obtained from KEGG pathway (http://www.genome.jp/kegg/pathway.html) and were compared manually. For this purpose, respective metabolic pathways, i.e., glycolysis, TCA, pentose phosphate pathway and pyruvate metabolism were selected using KEGG pathway
Results and discussion
Studies on comparative metabolic network have related the metabolism to the adaptation of organisms towards their natural habitat (Borenstein et al., 2008, Kreimer et al., 2008). In the present study, 4 key metabolic pathways (glycolysis, pentose phosphate pathway, pyruvate metabolism and citric acid cycle) were compared to answer the questions like: (i) Do differences exist between key metabolic pathways of closely related organisms? (ii) What are the alternative by-passes if key pathway
Conclusion
The present work defines a simple approach to explore the effluxes in four metabolic pathways within the phylum actinobacteria. The study clearly indicated that the analysis of metabolic network with large data sets may reveal important information about the actinobacteria and may help in understanding the adaptability of this diverse class to varied environmental conditions. The pathway comparison can help in finding the enzymes that can be used as drug targets for pathogens without effecting
Competing interests
The authors declare that they have no competing interests.
Author's contribution
M.V., D.L. and R.L. conceived and designed the experiments. M.V., D.L., A.S., S.A., J.K. and J.K. performed the experiments. M.V., D.L. and R.L. analyzed the data. D.L., M.V. and R.L. wrote the article. All authors read and approved the final article.
Acknowledgments
M.V., D.L., J.K. and S.A. acknowledge CSIR, Govt. of India; A.S. acknowledges UGC, Govt. of India and J.K. acknowledges DBT, Govt. of India for providing research fellowships, respectively.
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