Co-expression networks in Chlamydomonas reveal significant rhythmicity in batch cultures and empower gene function discovery

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