Dataset Information

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling.

ABSTRACT: 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.

SUBMITTER: Chaiboonchoe A

PROVIDER: S-EPMC4261833 | biostudies-other | 2014

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling.

Chaiboonchoe Amphun A Dohai Bushra Saeed BS Cai Hong H Nelson David R DR Jijakli Kenan K Salehi-Ashtiani Kourosh K

Frontiers in bioengineering and biotechnology 20141210

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, ...[more]

PMID: 25540776

Similar Datasets

Project description:Glycoproteins play key roles in various molecular and cellular functions and are among the most difficult to analyze biomolecules on account of their microheterogeneity, non-template-driven synthesis, and low abundances. The stability, serum half-life, immunogenicity, and biological activity of therapeutic glycoproteins, including antibodies, vaccines, and biomarkers, are regulated by their glycosylation profile. Thus, there is increasing demand for the qualitative and quantitative characterization and validation of glycosylation on glycoproteins. One of the most important derivatization processes for the structural characterization of released glycans by mass spectrometry (MS) is permethylation. We have recently developed a permethylation strategy in microscale that allows facile permethylation of glycans and permits the processing of large sample sets in nanogram amounts through high-throughput sample handling. Here, we are reporting the wide potential of micropermethylation-based high-throughput structural analysis of glycans from various sources, including human plasma, mammalian cells, and purified glycoproteins, through an automated tandem electrospray ionization-mass spectrometry (ESI-MSn) platform. The glycans released from the plasma, cells, and glycoproteins are permethylated in microscale in a 96-well plate or microcentrifuge tube and isolated by a C18 tip-based cleanup through a shorter and simple process. We have developed a workflow to accomplish an in-depth automated structural characterization MS program for permethylated N/O-glycans through an automated high-throughput multistage tandem MS acquisition. We have demonstrated the utility of this workflow using the examples of sialic acid linkages and bisecting GlcNAc (N-acetylglucosamine) on the glycans. This approach can automate the high-throughput screening of glycosylation on large sample sets of glycoproteins, including clinical glycan biomarkers and glycoprotein therapeutics.

Dataset Information

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling.

Publications

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure