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

Methods
For Chlamydomonas, the data set consists of 518 RNA-seq samples derived from 58 independent experiments, most published.

For Arabidospis, the data sets were downloaded from the AtGenExpress project site (http://jsp.weigelworld.org/AtGenExpress/resources/), and collated into a single file that consisted of 34 Arabidopsis accessions, 16 sets of etiolated seedlings exposed to various light treatments, 36 sets of seedlings exposed to pathogens, 13 cell culture samples, 68 sets each for shoots and roots exposed to various abiotic stresses, 79 developmental samples (72 from shoots or leaves, 7 from roots), and 18 sets each for leaves and roots subjected to iron deficiency, with controls included.

单细胞绿藻莱茵衣藻（Chlamydomonas reinhardtii）是研究光合作用与叶绿体代谢、纤毛组装与功能、脂质与淀粉代谢以及金属稳态的首选参考研究体系。尽管已有数十年的研究积累，仍有数千个基因的功能尚未明确，亟需全新方法以实现基因与细胞通路的精准归类。日益丰富的转录组（transcriptome）与蛋白质组（proteome）数据集合，为基于整合共表达分析的系统性研究提供了可能。本研究使用涵盖58项独立实验的518例深度转录组样本数据集，以鉴定基因间潜在的共表达关联。我们借助R语言工具包corrplot可视化基因共表达潜力，从而能够直观评估基因间的共表达与负相关关联。我们从纤毛、细胞分裂及光合作用相关的多组经人工整理的基因列表的交集中，筛选出数百个高置信度基因，以此验证了本研究方法的有效性。令人意外的是，莱茵衣藻实验的转录组数据中仍存在显著的节律性成分，这表明即使是在恒光条件下采集的样本，样本收集过程中仍存在未被充分重视的干扰变量。因此本研究结果证实，批量培养的莱茵衣藻仍存在显著的残余同步性，这与此前认为其培养物不同步的假设相悖。我们提供了针对莱茵衣藻及其他物种转录组数据集的共表达分析分步实验方案，以助力基因功能的发掘研究。

方法
针对莱茵衣藻：本数据集包含来自58项独立实验的518例RNA-seq样本，其中绝大多数已发表。

针对拟南芥（Arabidopsis）：数据集从AtGenExpress项目官网（http://jsp.weigelworld.org/AtGenExpress/resources/）下载并整合为单一文件，涵盖34份拟南芥生态型、16组经不同光处理的黄化幼苗样本、36组受病原体侵染的幼苗样本、13份细胞培养样本、分别针对地上组织与根系的68组非生物胁迫处理样本、79份发育阶段样本（其中72份来自地上组织或叶片，7份来自根系），以及分别针对叶片与根系的18组缺铁处理样本，所有分组均设置对照。