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Systematic characterization of gene function in the photosynthetic alga Chlamydomonas reinhardtii

Cite this dataset

Jinkerson, Robert (2021). Systematic characterization of gene function in the photosynthetic alga Chlamydomonas reinhardtii [Dataset]. Dryad.


Most genes in photosynthetic organisms remain functionally uncharacterized. Here, using a barcoded mutant library of the model eukaryotic alga Chlamydomonas reinhardtii, we determined the phenotypes of more than 58,000 mutants under more than 121 different environmental growth conditions and chemical treatments. 59% of genes are represented by at least one mutant that showed a phenotype, providing clues to the functions of thousands of genes. Mutant phenotypic profiles place uncharacterized genes into functional pathways such as DNA repair, photosynthesis, the CO2-concentrating mechanism, and ciliogenesis. We illustrate the value of this resource by validating novel phenotypes and gene functions, including three novel components of an actin cytoskeleton defense pathway. The data also inform phenotype discovery in land plants: mutants in Arabidopsis thaliana genes exhibit similar phenotypes to those we observed in their Chlamydomonas homologs. We anticipate that this resource will guide the functional characterization of genes across the tree of life.


To connect genotypes to phenotypes, we measured the growth of 58,101 Chlamydomonas mutants representing 14,695 genes (83% of all genes encoded in the Chlamydomonas genome, based on the current genome annotation, v5.6) under 121 environmental and chemical stress conditions (both control and experimental conditions are given in Supplementary Table 1 and Supplementary Table 2). We pooled the entire Chlamydomonas mutant collection from plates into a liquid culture and used molecular barcodes to quantify the relative abundance of each mutant after competitive growth (Fig. 1a-f). Growth conditions included heterotrophic, mixotrophic, and photoautotrophic growth under different photon flux densities and CO2 concentrations, as well as abiotic stress conditions such as various pH and temperatures. We also subjected the library to known chemical stressors, including DNA damaging agents, reactive oxygen species, antimicrobial drugs such as paromomycin and spectinomycin, as well as the actin-depolymerizing drug latrunculin B (LatB). To further expand the range of stressors in the dataset, we identified 1,222 small molecules from the Library of AcTive Compounds on Arabidopsis (LATCA) that negatively influence Chlamydomonas growth (Supplementary Table 3, Supplementary Data 1), and performed competitive growth experiments in the presence of 52 of the most potent compounds. We chose to screen the LATCA library for active compounds in Chlamydomonas because we believed that these compounds would be more likely to impact pathways both in Chlamydomonas and plants, thus providing more general insights into gene functions in the green lineage. Taken together, this effort represents, to the best of our knowledge, the largest mutant-by-phenotype dataset to date for any photosynthetic organism, with 16.8 million data points (Supplementary Table 4).

Usage notes

Supplementary Table 1 - Source material used for screens

Table of information on the mutant libraries used in pooled mutant screening rounds.

Supplementary Table 2 - List of treatments and screens

List of treatments mutant pools were subjected to and list of screens. All treatments were inoculated with 2x10^4 mutant cells per ml unless otherwise specified.

Supplementary Table 3 - LATCA screen and dose titration validation

Initial screen of 3,650 LATCA chemicals to determine their effect on growth of cMJ030 and further validation of 1,140 of these chemicals.

Supplementary Table 4 - Mutant phenotypes across all screens

Table of phenotype data collected for 94,461 mutants in 223 screens.

Supplementary Table 5 - FDRs for GO term enrichment

GO term enrichment in mutants that show a phenotype in screens.

Supplementary Table 6 - FDRs for all genes in all screens

Table of FDRs from our statistical framework to identify high-confidence gene-phenotype relationships  of 15,582 genes in 223 screens.

Supplementary Table 7 - High-confidence gene-phenotype relationships

Table of the gene-phenotype relationships with an FDR <0.3.

Supplementary Table 8 - Annotation of 50 gene-phenotype relationships

Table of 50 random gene-phenotype relationships that were annotated to determine novelty of gene-phenotype relationship.

Supplementary Table 9 - Suggested new gene names

List of new gene names assigned as a result of this study.

Supplementary Table 10 - Phenotypic and transcriptomic correlations of genes with high-confidence phenotypes

Table of the gene phenotypes, Pearson correlation coefficients of phenotype, and transcriptome correlation found in Extended Data Fig. 3a.

Supplementary Table 11 - Cluster annotations with yeast, mouse, and Arabidopsis orthologs

Annotations of clusters with yeast, mouse, and Arabidopsis orthologs.

Supplementary Table 12 - Mutant barcode read counts

Table of mutant read counts and normalized read counts.

Supplementary Table 13 - List of samples that were averaged

List of which samples that were averaged for control treatments.

Supplementary Table 14 - Chlamydomonas and Arabidopsis strains used in this study

List of strains used in this study.

Supplementary Table 15 - Primers used in this study

List of primers used in this study.

Supplementary Data 1 - LATCA compound structures

Zip file that contains the chemical structure of all LATCA compounds used in this study.

Supplementary Data 2 - Java TreeView files of FDR less than 0.3 gene clusters

Zip file that contains the Java TreeView files of the FDR < 0.3 gene cluster.