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بازسازی و آنالیز شبکه متابولیکی مبتنی بر ژنوم باکتری استرپتوکوکوس بویس B315 دخیل در تولید اسید لاکتیک در محیط شکمبه | ||
| علوم دامی | ||
| مقاله 9، دوره 31، شماره 121، اسفند 1397، صفحه 103-116 اصل مقاله (1.15 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/asj.2018.116808.1595 | ||
| نویسندگان | ||
| صابر جلوخانی نیارکی* 1؛ مجتبی طهمورث پور2؛ زرین مینوچهر3؛ احسان معتمدیان4؛ محمدرضا نصیری2 | ||
| 1دانش آموخته دکتری، گروه علوم دامی، دانشکده کشاورزی، دانشگاه فردوسی مشهد | ||
| 2استاد، گروه علوم دامی، دانشکده کشاورزی، دانشگاه فردوسی مشهد | ||
| 3دانشیار، گروه زیست فناوری سامانه ای، پژوهشگاه ملی مهندسی ژنتیک و زیست فناوری، تهران | ||
| 4استادیار، گروه بیوتکنولوژی، دانشکده مهندسی شیمی، دانشگاه تربیت مدرس، تهران | ||
| چکیده | ||
| استرپتوکوکوس بویس یکی از باکتری های مصرف کننده نشاسته و تولیدکننده اسید لاکتیک در شکمبه است. با توجه به نقش استرپتوکوکوس بویس در تولید اسید لاکتیک، اطلاعات زیستی وسیعی از این گونه منتشر شده است. اما تا به حال، خصوصیات و توانمندی های متابولیکی این باکتری در سطح یک سیستم تجزیه و تحلیل نشده است. در پژوهش حاضر، مدل متابولیکی مبتنی بر ژنوم باکتری استرپتوکوکوس بویس (iStr472) برای اولین بار بر اساس اطلاعات ژنومی بدست آمده از سویه استرپتوکوکوس بویس B315 ساخته شد. مدل iStr472 با اهداف مختلف نظیر استواری، توپولوژی و توانایی های مدل در مصرف سوبستراهای دیگر آنالیز گردید. مدل بازسازی شده تعداد 694 واکنش، 626 متابولیت و 472 ژن را شامل گردید. بیشترین واکنش ها در مسیر متابولیکی نوکلئوتیدها قرار داشتند. ژن های متابولیکی مدل 6/27 درصد از کل ژن های رمزگذارنده را شامل شد. مقایسه دو مدل (iStr472 و ModelSEED) نشان داد که مدل iStr472 از دقت و صحت بالاتری نسبت به مدل دیگر برخوردار است. شانزده متابولیت با درجه اتصال بالا در مدل شناسایی گردید. حذف واکنش ها نشان داد که مدل 126 واکنش حیاتی برای رشد را شامل می شود. بر اساس پیش بینی مدل، تولید زیست توده استرپتوکوکوس بویس تحت تأثیر لاکتات تولیدی قرار می گیرد. مدل همچنین قادر است از فروکتوز به عنوان منبع کربنی استفاده کند. روی هم رفته، از پیش بینی های مدل iStr472 می توان به عنوان ابزاری برای درک بهتر متابولیسم و همچنین مهندسی متابولیک استرپتوکوکوس بویس به منظور تولید لاکتات کمتر در محیط شکمبه بهره برد. | ||
| کلیدواژه‌ها | ||
| استرپتوکوکوس بویس؛ مدل متابولیکی مبتنی بر ژنوم؛ iStr472؛ شکمبه؛ COBRA | ||
| عنوان مقاله [English] | ||
| Reconstruction and analysis of the genome-scale metabolic network of Streptococcus bovis B315 involved in lactic acid production in the rumen | ||
| نویسندگان [English] | ||
| Saber Jelokhani-niaraki1؛ mojtaba Tahmoorespur2؛ Zarrin Minuchehr3؛ Ehsan Motamedian4؛ Mohammad Reza Nassiri2 | ||
| 1Faculty Member of Biotechnology Department-Animal Science Research Institute of Iran | ||
| 2Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran. | ||
| 3Department of Systems Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran. | ||
| 4Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran. | ||
| چکیده [English] | ||
| Streptococcus bovis has been considered to be one of the starch utilizers and lactate producers in the rumen. By considering the role of S. bovis as main lactic acid producer, a large amount of biological information about this strain has been published. But there has not been a systematic analysis of metabolic capabilities for S. bovis so far. In the present study, the first genome-scale metabolic model of S. bovis (iStr472) was reconstructed based on the genome annotation of S. bovis B315. The model was analyzed in terms of sensitivity, topology and capabilities for utilization of other substrates. Results revealed that iStr472 comprises 694 reactions, 626 metabolites and 472 genes. The majority of reactions were located on the nucleotides metabolic pathway. The metabolic genes of model estimated as 27.6 % of all coding genes. Comparison of two models (iStr472 and ModelSEED) indicated that iStr472 has a higher accuracy and validity than the ModelSEED. The 16 highly connected metabolites were found in the model. The results of reaction deletion presented that the model has 126 vital reactions essential for the organism's growth. According to the model prediction, production of biomass was inversely influenced by lactate production. The iStr472 is also capable of utilizing fructose as carbon source. Taken together, it would be possible to use the predictions of iStr472 as a tool for better understanding the metabolism and metabolic engineering of S. bovis for reduced lactate production in the rumen. | ||
| کلیدواژه‌ها [English] | ||
| Streptococcus bovis, genome-scale metabolic model, iStr472, Rumen, COBRA | ||
| مراجع | ||
|
Ates, O., Oner, E.T. and Arga, K.Y. (2011). Genome-scale reconstruction of metabolic network for a halophilic extremophile, Chromohalobacter salexigens DSM 3043. BMC Systems Biology. 5(1), 12. Barabasi, A.L. and Oltvai, Z.N. (2004). Network biology: understanding the cell's functional organization. Nature reviews. Genetics. 5: 101-113. Becker, S.A. and Palsson, B.O. (2005). Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation. BMC microbiology. 5: 8. Becker, S.A., Feist, A.M., Mo, M.L., Hannum, G., Palsson, B.O. and Herrgard, M.J. (2007). Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nature protocols. 2: 727-738. Chung, B.K., Selvarasu, S., Andrea, C., Ryu, J., Lee, H., Ahn, J., Lee, H. and Lee, D.Y. (2010). Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement. Microbial Cell Factories. 9: 50. Dauner, M. and Sauer, U. (2001). Stoichiometric growth model for riboflavin-producing Bacillus subtilis. Biotechnology and Bioengineering. 76: 132-143. Del Rio, G., Koschutzki, D. and Coello, G. (2009). How to identify essential genes from molecular networks?. BMC systems biology. 3: 102. El-Semman, I.E., Karlsson, F.H., Shoaie, S., Nookaew, I., Soliman, T.H. and Nielsen, J. (2014). Genome-scale metabolic reconstructions of Bifidobacterium adolescentis L2-32 and Faecalibacterium prausnitzii A2-165 and their interaction. BMC systems biology. 8: 41. Feist, A.M. and Palsson, B.O. (2008). The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli. Nature biotechnology. 26: 659-667. Feist, A.M., Henry, C.S., Reed, J.L., Krummenacker, M., Joyce, A.R., Karp, P.D., Broadbelt, L.J., Hatzimanikatis, V. and Palsson, B.O. (2007). A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Molecular Systems Biology. 3, 121.
Gianchandani, E.P., Chavali, A.K. and Papin, J.A. (2010). The application of flux balance analysis in systems biology. Wiley interdisciplinary reviews, Systems biology and medicine. 2: 372-382. Henry, C.S., DeJongh, M., Best, A.A., Frybarger, P.M., Linsay, B. and Stevens, R.L. (2010). High-throughput generation, optimization and analysis of genome-scale metabolic models. Nature biotechnology. 28: 977-982. Hungate, R.E., Dougherty, R.W., Bryant, M.P. and Cello, R.M. (1952). Microbiological and physiological changes associated with acute indigestion in sheep. The Cornell Veterinarian. 42(4): 423-449. Jouany, J.P. (2006). Optimizing rumen functions in the close-up transition period and early lactation to drive dry matter intake and energy balance in cows. Animal Reproduction Science. 96: 250-264. Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y. and Hattori, M. (2004). The KEGG resource for deciphering the genome. Nucleic Acids Research. 32: D277-280. Kelly, W., Huntemann, M., Han, J., Chen, A., Kyrpides, N., Mavromatis, K., Markowitz, V., Palaniappan, K., Ivanova, N., Schaumberg, A., Pati, A., Liolios, K., Nordberg, H.P., Cantor, M.N., Hua, S.X. and Woyke, T. (2013). Streptococcus equinus B315, whole genome shotgun sequencing. Submitted (02-JUL-2013) DOE Joint Genome Institute, 2800 MitchellDrive, Walnut Creek, CA 94598-1698, USA.
Kjeldsen, K. R. and Nielsen, J. (2009). In silico genome-scale reconstruction and validation of the Corynebacterium glutamicum metabolic network. Biotechnology and Bioengineering. 102(2): 583-597. Klanchui, A., Khannapho, C., Phodee, A., Cheevadhanarak, S. and Meechai, A. (2012). iAK692: a genome-scale metabolic model of Spirulina platensis C1. BMC systems biology. 6: 71. Lee, N.R., Lakshmanan, M., Aggarwal, S., Song, J.W., Karimi, I.A., Lee, D.Y. and Park, J.B. (2014). Genome-scale metabolic network reconstruction and in silico flux analysis of the thermophilic bacterium Thermus thermophilus HB27. Microbial cell factories. 13: 61. Liu, L., Agren, R., Bordel, S. and Nielsen, J. (2010). Use of genome-scale metabolic models for understanding microbial physiology. FEBS Letters. 584(12): 2556-2564. Markowitz, V.M., Chen, I.M., Palaniappan, K., Chu, K., Szeto, E., Grechkin, Y., Ratner, A., Jacob, B., Huang, J., Williams, P., Huntemann, M., Anderson, I., Mavromatis, K., Ivanova, N.N. and Kyrpides, N.C. (2012). IMG: the Integrated Microbial Genomes database and comparative analysis system. Nucleic Acids Research. 40(Database issue): D115-122. Marounek, M. and Bartos, S. (1987). Interactions between rumen amylolytic and lactate-utilizing bacteria in growth on starch. Journal of Applied Bacteriology. 63: 233-238. Martelli, C., De Martino, A., Marinari, E., Marsili, M. and Perez Castillo, I. (2009). Identifying essential genes in Escherichia coli from a metabolic optimization principle. Proceedings of the National Academy of Sciences of the United States of America. 106: 2607-2611. McCloskey, D., Palsson, B.O. and Feist, A.M. (2013). Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Molecular systems biology. 9: 661. Mo, M.L., Palsson, B.O. and Herrgard, M. J. (2009). Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Systems Biology. 3: 37. Navid, A. (2011). Applications of system-level models of metabolism for analysis of bacterial physiology and identification of new drug targets. Briefings in functional genomics. 10: 354-364. Nielsen, J. and Vidal, M. (2010). Systems biology of microorganisms. Current opinion in microbiology. 13: 335-336. Oberhardt, M.A., Puchalka, J., Fryer, K.E., Martins dos Santos, V.A. and Papin, J.A. (2008). Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. Journal of Bacteriology. 190(8): 2790-2803. Oh, Y.K., Palsson, B.O., Park, S.M., Schilling, C.H. and Mahadevan, R. (2007). Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. Journal of Biological Chemistry. 282(39): 28791-28799. Oliveira, A.P., Nielsen, J. and Forster, J. (2005). Modeling Lactococcus lactis using a genome-scale flux model. BMC microbiology. 5: 39-39. Orth, J.D., Thiele, I. and Palsson, B.O. (2010). What is flux balance analysis? Nature biotechnology. 28: 245-248. Palsson, B.O. (2006). Systems Biology: Properties of Reconstructed Networks: Cambridge University Press. Puchalka, J., Oberhardt, M.A., Godinho, M., Bielecka, A., Regenhardt, D., Timmis, K.N., Papin, J.A. and Martins dos Santos, V.A. (2008). Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology. PLoS computational biology. 4: e1000210. Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K.S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., Yamada, T., Mende, D.R., Li, J., Xu, J., Li, S., Li, D., Cao, J., Wang, B., Liang, H., Zheng, H., Xie, Y., Tap, J., Lepage, P., Bertalan, M., Batto, J.M., Hansen, T., Le Paslier, D., Linneberg, A., Nielsen, H.B., Pelletier, E. et al. (2010). A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 464: 59-65. Romero-Hernández, B., del Campo, R. and Canton, R. (2013). Streptococcus bovis, situación taxonómica, relevancia clínica y sensibilidad antimicrobiana. Enfermedades Infecciosas y Microbiología Clínica. 31: 14-19. Russell, J.B. and Hino, T. (1985). Regulation of lactate production in Streptococcus bovis: A spiraling effect that contributes to rumen acidosis. Journal of Dairy Science. 68(7): 1712-1721. Stelling, J., Klamt, S., Bettenbrock, K., Schuster, S. and Gilles, E.D. (2002). Metabolic network structure determines key aspects of functionality and regulation. Nature. 420: 190-193. Teusink, B., Bachmann, H. and Molenaar, D. (2011). Systems biology of lactic acid bacteria: a critical review. Microbial cell factories. 10 Suppl 1: S11. Thiele, I. and Palsson, B.O. (2010). A protocol for generating a high-quality genome-scale metabolic reconstruction. Nature protocols. 5: 93-121. Triana, J., Montagud, A., Siurana, M., Fuente, D., Urchueguia, A., Gamermann, D., Torres, J., Tena, J., de Cordoba, P.F. and Urchueguia, J.F. (2014). Generation and evaluation of a genome-scale metabolic network model of Synechococcus elongatus PCC7942. Metabolites. 4: 680-698. Woelders, H., Te Pas, M.F., Bannink, A., Veerkamp, R.F. and Smits, M.A. (2011). Systems biology in animal sciences. Animal. 5: 1036-1047. Wooley, J.C., Godzik, A. and Friedberg, I. (2010). A primer on metagenomics. Plos Computational Biology. 6(2): e1000667. Xue, y. (2011). Development of Microecologic Preparation for Subacute Ruminal Acidosis of Beef Cattle and Its Effects. Jilin University. | ||
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