DOI: 10.6086/d1q96z
title: Systematic characterization of gene function in the photosynthetic alga Chlamydomonas reinhardtii

description: 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.
description: 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
 CO<sub>2</sub> 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).
description: <b>Supplementary Table 1 - Source material used for
 screens</b> Table of information on the mutant
 libraries used in pooled mutant screening rounds.
 <b>Supplementary Table 2 - List of treatments and
 screens</b> 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.
 <b>Supplementary Table 3 - LATCA screen and dose titration
 validation</b> Initial screen of 3,650 LATCA
 chemicals to determine their effect on growth of cMJ030 and further
 validation of 1,140 of these chemicals.
 <b>Supplementary Table 4 - Mutant phenotypes across all
 screens</b> Table of phenotype data collected for
 94,461 mutants in 223 screens. <b>Supplementary
 Table 5 - FDRs for GO term enrichment</b> GO term
 enrichment in mutants that show a phenotype in screens.
 <b>Supplementary Table 6 - FDRs for all genes in all
 screens</b> Table of FDRs from our statistical
 framework to identify high-confidence gene-phenotype relationships  of
 15,582 genes in 223 screens. <b>Supplementary
 Table 7 - High-confidence gene-phenotype relationships</b>
 Table of the gene-phenotype relationships with an FDR
 &lt;0.3. <b>Supplementary Table 8 -
 Annotation of 50 gene-phenotype relationships</b>
 Table of 50 random gene-phenotype relationships that were
 annotated to determine novelty of gene-phenotype relationship.
 <b>Supplementary Table 9 - Suggested new gene
 names</b> List of new gene names assigned as a
 result of this study. <b>Supplementary Table 10 -
 Phenotypic and transcriptomic correlations of genes with high-confidence
 phenotypes</b> Table of the gene phenotypes,
 Pearson correlation coefficients of phenotype, and transcriptome
 correlation found in Extended Data Fig. 3a.
 <b>Supplementary Table 11 - Cluster annotations with yeast,
 mouse, and Arabidopsis orthologs</b> Annotations
 of clusters with yeast, mouse, and Arabidopsis orthologs.
 <b>Supplementary Table 12 - Mutant barcode read
 counts</b> Table of mutant read counts and
 normalized read counts. <b>Supplementary Table 13
 - List of samples that were averaged</b> List of
 which samples that were averaged for control treatments.
 <b>Supplementary Table 14 - Chlamydomonas and Arabidopsis
 strains used in this study</b> List of strains
 used in this study. <b>Supplementary Table 15 -
 Primers used in this study</b> List of primers
 used in this study. <b>Supplementary Data 1 -
 LATCA compound structures</b> Zip file that
 contains the chemical structure of all LATCA compounds used in this
 study. <b>Supplementary Data 2 - Java TreeView
 files of FDR less than 0.3 gene clusters</b> Zip
 file that contains the Java TreeView files of the FDR &lt; 0.3 gene
 cluster.