Multiomics resolution of molecular events during a day in the life of Chlamydomonas
Multiomics resolution of molecular events during a day in the life of Chlamydomonas
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DOI：
10.1073/pnas.1815238116
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发表时间：
2019-02-05
影响因子：
11.1
通讯作者：
Merchant, Sabeeha S.
中科院分区：
文献类型：
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作者：
Strenkert, Daniela;Schmollinger, Stefan;Merchant, Sabeeha S.
单细胞的绿色衣藻莱茵衣藻显示代谢的灵活性，以应对不断变化的环境。我们分析了其三个基因组在光暗周期下生长的细胞中的表达模式。近85%的转录基因表现出差异表达，不同的转录本在一天中被上调，以协调细胞分裂前的细胞生长。选择代谢物和色素，生理参数和蛋白质子集的平行测量使我们能够推断代谢事件，并评估转录组对蛋白质组的影响。其中的研究结果是观察，衣原体表现出较低的呼吸活动，在夜间与白天相比，多个发酵途径，一些氧敏感，在夜间通气的文化表达，我们建议，铁氧还蛋白，FDX9，是潜在的电子供体氢化酶。光胁迫响应基因PSBS、LHCSR1和LHCSR3在黎明突然的黑暗到光明的转换下显示出对光照的急性响应，而LHCSR3基因也在一天的中间表现出稍后的第二次表达爆发，这取决于光强度。每种对光的反应（急性和持续）都可以在特定条件下选择性地激活。我们的表达数据集，辅之以共表达网络和代谢产物分析，应该构成藻类和植物群落的优秀资源。
The unicellular green alga Chlamydomonas reinhardtii displays metabolic flexibility in response to a changing environment. We analyzed expression patterns of its three genomes in cells grown under light-dark cycles. Nearly 85% of transcribed genes show differential expression, with different sets of transcripts being up-regulated over the course of the day to coordinate cellular growth before undergoing cell division. Parallel measurements of select metabolites and pigments, physiological parameters, and a subset of proteins allow us to infer metabolic events and to evaluate the impact of the transcriptome on the proteome. Among the findings are the observations that Chlamydomonas exhibits lower respiratory activity at night compared with the day; multiple fermentation pathways, some oxygen-sensitive, are expressed at night in aerated cultures; we propose that the ferredoxin, FDX9, is potentially the electron donor to hydrogenases. The light stress-responsive genes PSBS, LHCSR1, and LHCSR3 show an acute response to lights-on at dawn under abrupt dark-to-light transitions, while LHCSR3 genes also exhibit a later, second burst in expression in the middle of the day dependent on light intensity. Each response to light (acute and sustained) can be selectively activated under specific conditions. Our expression dataset, complemented with coexpression networks and metabolite profiling, should constitute an excellent resource for the algal and plant communities.