Abstract
The unicellular green alga Chlamydomonas reinhardtii is a choice reference system for the study of photosynthesis and chloroplast metabolism, cilium assembly and function, lipid and starch metabolism, and metal homeostasis. Despite decades of research, the functions of thousands of genes remain largely unknown, and new approaches are needed to categorically assign genes to cellular pathways. Growing collections of transcriptome and proteome data now allow a systematic approach based on integrative co-expression analysis. We used a dataset comprising 518 deep transcriptome samples derived from 58 independent experiments to identify potential co-expression relationships between genes. We visualized co-expression potential with the R package corrplot, to easily assess co-expression and anti-correlation between genes. We extracted several hundred high-confidence genes at the intersection of multiple curated lists involved in cilia, cell division, and photosynthesis, illustrating the power of our method. Surprisingly, Chlamydomonas experiments retained a significant rhythmic component across the transcriptome, suggesting an underappreciated variable during sample collection, even in samples collected in constant light. Our results therefore document substantial residual synchronization in batch cultures, contrary to assumptions of asynchrony. We provide step-by-step protocols for the analysis of co-expression across transcriptome data sets from Chlamydomonas and other species to help foster gene function discovery.
Highlights
Discovering the functions of genes has driven biology for over a century, using a multitude of tools to determine the factors associated with a given cellular process
First by X- or gamma rays, paved the way to classical genetic screens in multiple species, including the Jimson weed (Datura stramonium), the fruit fly, the green unicellular alga Chlamydomonas (Chlamydomonas reinhardtii) and barley (Hordeum vulgare), the latter creating the field of radiation breeding (Gager and Blakeslee, 1927; Muller, 1928; Stadler, 1928; Birch et al, 1953)
We initially set out to analyze multiple RNAseq datasets to prioritize genes whose expression responded to changes in iron status in Chlamydomonas and Arabidopsis
Summary
Discovering the functions of genes has driven biology for over a century, using a multitude of tools to determine the factors associated with a given cellular process. First by X- or gamma rays, paved the way to classical genetic screens in multiple species, including the Jimson weed (Datura stramonium), the fruit fly, the green unicellular alga Chlamydomonas (Chlamydomonas reinhardtii) and barley (Hordeum vulgare), the latter creating the field of radiation breeding (Gager and Blakeslee, 1927; Muller, 1928; Stadler, 1928; Birch et al, 1953) These mutations fueled a very thorough phenotypic dissection of the processes affected by the absence of a gene product, but it is only in the 1970s that the nature of the mutated genes began to be unraveled. The development of transformation protocols to introduce transgenes into model systems further opened new possibilities for dissecting the role of a gene in situ by over-expression of a wild-type or mutated copy (Leutwiler et al, 1986; Hinnen et al, 1978; Rubin and Spradling, 1982; Kindle et al., 1989; Rochaix et al, 1984)
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