In Silico Analysis of Mucor Circinelloides Genome-Scale Model for Enhancing Lipid Production
Authors: 
Amornpan Klanchui, 
Wanwipa Vongsangnak, Kobkul Laoteng, Asawin MeechaiAuthors Info & Claims
Pages 14 - 18
Abstract
Mucor circinelloides is an attractive oleaginous filamentous fungus because it can produce high content of triacylglycerol (TAG) rich in γ-linolenic acid (GLA). With the increasing attention in microbial oils, many experiments have been carried out in order to enhance storage lipid production in M. circinelloides. Availabilities of genome sequence, biotechnology and computational tools, and pathway databases have facilitated strain improvement through systems metabolic engineering. Genome-scale metabolic model (GEM) has become a useful tool for guiding the design of metabolic engineering strategies. In this study, the GEM of M. circinelloides, iWV1213, was used to investigate candidate genes and metabolites, which could drive metabolic fluxes for lipid production. To evaluate the metabolic capabilities related to growth and lipid formation of iWV1213, the model was firstly simulated and validated with experimental data. Using FBA and MOMA, a single gene deletion together with FVA were performed to identify candidate essential genes for growth and lipid production. Moreover, a single reaction deletion and shadow price analysis were also applied to determine the candidate essential metabolites. The results showed that the top 20 essential genes were involved in amino acid metabolism, TCA cycle, oxidative phosphorylation, and fatty acid oxidation, while the candidate metabolites were involved in lipid biosynthesis, such as malonyl-CoA and acetyl-CoA. These candidates could potentially be used as targets to guide future metabolic engineering strategies for enhancing TAG and GLA production in M. circinelloides.
References
[1]
Garay, L. A., Boundy-Mills, K. L., and German, J. B. 2014. Accumulation of high-value lipids in single-cell microorganisms: a mechanistic approach and future perspectives, J Agric Food Chem62, 2709--2727.
[2]
Tang, X., Zhang, H., Chen, H., Chen, Y. Q., Chen, W., and Song, Y. 2014. Effects of 20 standard amino acids on the growth, total fatty acids production, and gamma-linolenic acid yield in Mucor circinelloides, Curr Microbiol69, 899--908.
[3]
Tang, X., Zhao, L., Chen, H., Chen, Y. Q., Chen, W., Song, Y., and Ratledge, C. 2015. Complete Genome Sequence of a High Lipid-Producing Strain of Mucor circinelloides WJ11 and Comparative Genome Analysis with a Low Lipid-Producing Strain CBS 277.49, PloS one10, 1--11.
[4]
Vicente, G., Bautista, L. F., Rodríguez, R., Gutiérrez, F. J., Sádaba, I., Ruiz-Vázquez, R. M., Torres-Martínez, S., and Garre, V. 2009. Biodiesel production from biomass of an oleaginous fungus, Biochemical Engineering Journal48, 22--17.
[5]
Alabdulkarim, B., Bakeet, Z. A. N., and Arzoo, S. 2012. Role of some functional lipids in preventing diseases and promoting health, Journal of King Saud University - Science24, 319--329.
[6]
Wynn, J. P., Hamid, A. A., Li, Y., and Ratledge, C. 2001. Biochemical events leading to the diversion of carbon into storage lipids in the oleaginous fungi Mucor circinelloides and Mortierella alpina, Microbiology147, 2857--2864.
[7]
Xia, C., Zhang, J., Zhang, W., and Hu, B. 2011. A new cultivation method for microbial oil production: cell pelletization and lipid accumulation by Mucor circinelloides, Biotechnol Biofuels4, 15.
[8]
Tang, X., Zan, X., Zhao, L., Chen, H., Chen, Y. Q., Chen, W., Song, Y., and Ratledge, C. 2016. Proteomics analysis of high lipid-producing strain Mucor circinelloides WJ11: an explanation for the mechanism of lipid accumulation at the proteomic level, Microb Cell Fact.15, 1--16.
[9]
Zhao, L., Zhang, H., Wang, L., Chen, H., Chen, Y. Q., Chen, W., and Song, Y. 2015. 13C-metabolic flux analysis of lipid accumulation in the oleaginous fungus Mucor circinelloides, Bioresource Technology 197, 23--29.
[10]
Liang, M.-H., and Jiang, J.-G. 2013. Advancing oleaginous microorganisms to produce lipid via metabolic engineering technology, Progress in Lipid Research52, 395--408.
[11]
Thiele, I., and Palsson, B. O. 2010. A protocol for generating a high-quality genome-scale metabolic reconstruction, Nat Protoc5, 93--121.
[12]
Simeonidis, E., and Price, N. D. 2015. Genome-scale modeling for metabolic engineering, J Ind Microbiol Biotechnol42, 327--338.
[13]
Loira, N., Dulermo, T., Nicaud, J. M., and Sherman, D. J. 2012. A genome-scale metabolic model of the lipid-accumulating yeast Yarrowia lipolytica, BMC Syst Biol6, 35.
[14]
Pan, P., and Hua, Q. 2012. Reconstruction and in silico analysis of metabolic network for an oleaginous yeast, Yarrowia lipolytica, PLoS One7, e51535.
[15]
Ye, C., Xu, N., Chen, H., Chen, Y. Q., Chen, W., and Liu, L. 2015. Reconstruction and analysis of a genome-scale metabolic model of the oleaginous fungus Mortierella alpina, BMC Syst Biol9, 1.
[16]
Vongsangnak, W., Klanchui, A., Tawornsamretkit, I., Tatiyaborwornchai, W., Laoteng, K., and Meechai, A. 2016. Genome-scale metabolic modeling of Mucor circinelloides and comparative analysis with other oleaginous species, Gene583, 121--129.
[17]
Tang, X., Chen, H., Chen, Y. Q., Chen, W., Garre, V., Song, Y., and Ratledge, C. 2015. Comparison of biochemical activities between high and low lipid-producing strains of Mucor circinelloides: An explanation for the high oleaginicity of strain WJ11, PLoS One10, e0128396.
[18]
Orth, J. D., Thiele, I., and Palsson, B. O. 2010. What is flux balance analysis?, Nat Biotechnol28, 245--248.
[19]
Shlomi, T., Berkman, O., and Ruppin, E. 2005. Regulatory on/off minimization of metabolic flux changes after genetic perturbations, Proc Natl Acad Sci U S A102, 7695--7700.
[20]
Gudmundsson, S., and Thiele, I. 2010. Computationally efficient flux variability analysis, BMC Bioinformatics11, 489.
[21]
Schellenberger, J., Que, R., Fleming, R. M., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., Kang, J., Hyduke, D. R., and Palsson, B. O. 2011. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0, Nat Protoc6, 1290--1307.
[22]
Laoteng, K., Čertík, M., and Cheevadhanark, S. 2011. Mechanisms controlling lipid accumulation and polyunsaturated fatty acid synthesis in oleaginous fungi, Chemical Papers65, 97--103.
[23]
Boulton, C. A., and Ratledge, C. 1981. Correlation of lipid accumulation in yeasts with possession of ATP:citrate lyase, Microbiology127, 169--176.
[24]
Vorapreeda, T., Thammarongtham, C., Cheevadhanarak, S., and Laoteng, K. 2012. Alternative routes of acetyl-CoA synthesis identified by comparative genomic analysis: involvement in the lipid production of oleaginous yeast and fungi, Microbiology158, 217--228.
[25]
Qiao, K., Imam Abidi, S.H., Liu, H.,Zhang, H., Chakraborty, S., Watson, N., Kumaran Ajikumar, P., and Stephanopoulos, G. 2015. Engineering lipid overproduction in the oleaginous yeast Yarrowia lipolytica, Metab Eng29, 56--65.
- In Silico Analysis of Mucor Circinelloides Genome-Scale Model for Enhancing Lipid Production
Recommendations
Investigating metabolite essentiality through genome-scale analysis of Escherichia coli production capabilities
Motivation: A phenotype mechanism is classically derived through the study of a set of mutants and comparison of their biochemical capabilities. One method of comparing mutant capabilities is to characterize producible and knocked out metabolites. ...
Genome-scale community modeling for deciphering the inter-microbial metabolic interactions in fungus-farming termite gut microbiome
AbstractSpecialized microbial communities in the fungus-farming termite gut and fungal comb microbiome help maintain host nutrition through interactive biochemical activities of complex carbohydrate degradation. Numerous research studies have ...
Graphical abstractDisplay Omitted
Highlights- Genome-scale community metabolic model of O. badius gut and fungal comb microbiota.
A hybrid of Cuckoo Search and Minimization of Metabolic Adjustment to optimize metabolites production in genome-scale models
AbstractMetabolic 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 ...
Highlights- A hybrid method to optimize metabolites production in genome-scale models is proposed.
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
December 2016
68 pages
ISBN:9781450347945
DOI:10.1145/3029375
Copyright © 2016 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 December 2016
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
CSBio '16
CSBio '16: 7th International Conference on Computational Systems-Biology and Bioinformatics
December 19 - 22, 2016
Macau, Macao
Acceptance Rates
Overall Acceptance Rate 23 of 37 submissions, 62%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 63Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)1
Reflects downloads up to 22 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in