ABSTRACT
Metabolic models are reconstructed based on an organism's available genome annotation and provide predictive tools to study metabolic processes at a systems-level. Genome-scale metabolic models may include gaps as well as reactions that are unverified experimentally. Reconstructed models of newly isolated microalgal species will result in weaknesses due to these gaps, as there is usually sparse biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology is an effective, high-throughput method that functionally determines cellular metabolic activities in response to a wide array of entry metabolites. Combining the high throughput phenotypic assays with metabolic modeling can allow existing metabolic network models to be rapidly reconstructed or optimized by providing biochemical evidence to support and expand genomic evidence. This work will show the use of PM assays for the study of microalgae by using the green microalgal model species Chlamydomonas reinhardtii as an example. Experimental evidence for over 254 reactions obtained by PM was used in this study to expand and refine a genome-scale C. reinhardtii metabolic network model, iRC1080, by approximately 25 percent. The protocol created here can be used as a basis for functionally profiling the metabolism of other microalgae, including known microalgae mutants and new isolates.
Subject(s)
Chlamydomonas reinhardtii , Microalgae , Chlamydomonas reinhardtii/genetics , Genome , Genomics , Metabolic Networks and PathwaysABSTRACT
Diatoms are a major group of microalgae that initiate biofouling by surface colonization of human-made underwater structures; however, the involved regulatory pathways remain uncharacterized. Here, we describe a protocol for identifying and validating regulatory genes involved in the morphology shift of the model diatom species Phaeodactylum tricornutum during surface colonization. We also provide a workflow for characterizing biofouling transformants. By using this protocol, gene targets such as GPCR signaling genes could be identified and manipulated to turn off diatom biofouling. For complete information on the generation and use of this protocol, please refer to Fu et al. (2020).