Microalgal Metabolic Network Model Refinement through High Throughput Functional Metabolic Profiling
Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstru...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-12-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fbioe.2014.00068/full |
| _version_ | 1811269698584576000 |
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| author | Amphun eChaiboonchoe Bushra Saeed Dohai Hong eCai David R Nelson Kenan eJijakli Kenan eJijakli Kourosh eSalehi-Ashtiani |
| author_facet | Amphun eChaiboonchoe Bushra Saeed Dohai Hong eCai David R Nelson Kenan eJijakli Kenan eJijakli Kourosh eSalehi-Ashtiani |
| author_sort | Amphun eChaiboonchoe |
| collection | DOAJ |
| description | Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The Phenotype Microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi but it has not been reported for the phenotyping of microalgae. Here we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of D-amino acids, L-dipeptides, and L-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates. |
| first_indexed | 2024-04-12T21:46:47Z |
| format | Article |
| id | doaj.art-b36b83d3bd7c4233b8e216a6d4ca4189 |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-04-12T21:46:47Z |
| publishDate | 2014-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-b36b83d3bd7c4233b8e216a6d4ca41892022-12-22T03:15:36ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852014-12-01210.3389/fbioe.2014.00068123427Microalgal Metabolic Network Model Refinement through High Throughput Functional Metabolic ProfilingAmphun eChaiboonchoe0Bushra Saeed Dohai1Hong eCai2David R Nelson3Kenan eJijakli4Kenan eJijakli5Kourosh eSalehi-Ashtiani6New York University Abu DhabiNew York University Abu DhabiNew York University Abu DhabiNew York University Abu DhabiNew York University Abu DhabiBiofineryNew York University Abu DhabiMetabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The Phenotype Microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi but it has not been reported for the phenotyping of microalgae. Here we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of D-amino acids, L-dipeptides, and L-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.http://journal.frontiersin.org/Journal/10.3389/fbioe.2014.00068/fullChlamydomonas reinhardtiiMetabolic Networks and PathwaysMicroalgaeFlux balance analysisphenotype microarray |
| spellingShingle | Amphun eChaiboonchoe Bushra Saeed Dohai Hong eCai David R Nelson Kenan eJijakli Kenan eJijakli Kourosh eSalehi-Ashtiani Microalgal Metabolic Network Model Refinement through High Throughput Functional Metabolic Profiling Frontiers in Bioengineering and Biotechnology Chlamydomonas reinhardtii Metabolic Networks and Pathways Microalgae Flux balance analysis phenotype microarray |
| title | Microalgal Metabolic Network Model Refinement through High Throughput Functional Metabolic Profiling |
| title_full | Microalgal Metabolic Network Model Refinement through High Throughput Functional Metabolic Profiling |
| title_fullStr | Microalgal Metabolic Network Model Refinement through High Throughput Functional Metabolic Profiling |
| title_full_unstemmed | Microalgal Metabolic Network Model Refinement through High Throughput Functional Metabolic Profiling |
| title_short | Microalgal Metabolic Network Model Refinement through High Throughput Functional Metabolic Profiling |
| title_sort | microalgal metabolic network model refinement through high throughput functional metabolic profiling |
| topic | Chlamydomonas reinhardtii Metabolic Networks and Pathways Microalgae Flux balance analysis phenotype microarray |
| url | http://journal.frontiersin.org/Journal/10.3389/fbioe.2014.00068/full |
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