| Chapter title |
High-Throughput Genetics Strategies for Identifying New Components of Lipid Metabolism in the Green Alga Chlamydomonas reinhardtii.
|
|---|---|
| Chapter number | 10 |
| Book title |
Lipids in Plant and Algae Development
|
| Published in |
Sub cellular biochemistry, January 2016
|
| DOI | 10.1007/978-3-319-25979-6_10 |
| Pubmed ID | |
| Book ISBNs |
978-3-31-925977-2, 978-3-31-925979-6
|
| Authors |
Xiaobo Li, Martin C. Jonikas |
| Editors |
Yuki Nakamura, Yonghua Li-Beisson |
| Abstract |
Microalgal lipid metabolism is of broad interest because microalgae accumulate large amounts of triacylglycerols (TAGs) that can be used for biodiesel production (Durrett et al Plant J 54(4):593-607, 2008; Hu et al Plant J 54(4):621-639, 2008). Additionally, green algae are close relatives of land plants and serve as models to understand conserved lipid metabolism pathways in the green lineage. The green alga Chlamydomonas reinhardtii (Chlamydomonas hereafter) is a powerful model organism for understanding algal lipid metabolism. Various methods have been used to screen Chlamydomonas mutants for lipid amount or composition, and for identification of the mutated loci in mutants of interest. In this chapter, we summarize the advantages and caveats for each of these methods with a focus on screens for mutants with perturbed TAG content. We also discuss technical opportunities and new tools that are becoming available for screens of mutants altered in TAG content or perturbed in other processes in Chlamydomonas. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 29% |
| Student > Master | 2 | 14% |
| Student > Postgraduate | 2 | 14% |
| Professor | 1 | 7% |
| Other | 1 | 7% |
| Other | 2 | 14% |
| Unknown | 2 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 9 | 64% |
| Agricultural and Biological Sciences | 4 | 29% |
| Unknown | 1 | 7% |