Abstract
Transcription is necessary for the synthesis of new proteins, often leading to the assumption that changes in transcript levels lead to changes in protein levels which directly impact a cell’s phenotype. Using a synchronized biological rhythm, we show that despite genome-wide partitioning of transcription, transcripts and translation levels into two phase-shifted expression clusters related to metabolism, detectable protein levels remain constant over time. This disconnect between cycling translation and constant protein levels can be explained by slow protein turnover rates, with overall protein levels maintained by low level pulses of new protein synthesis. Instead, rhythmic post-translational regulation of the activities of different proteins, influenced by the metabolic state of the cells, appears to be key to coordinating the physiology of the biological rhythm with cycling transcription. Thus, transcriptional and translational cycling reflects, rather than drives, metabolic and biosynthetic changes during biological rhythms. We propose that transcriptional changes are often the consequence, rather than the cause, of changes in cellular physiology and that caution is needed when inferring the activity of biological processes from transcript data.
Changes in protein levels do not explain the changing states of a biological rhythm
Slow protein turnover rates decouple proteins levels from a rhythmic transcriptome
Metabolites determine protein activity via rhythmic post-translational modifications
Cycling protein activity explains rhythmic transcription and ribosome biogenesis
A cycling transcriptome is a consequence, not a cause, of physiological changes
Competing Interest Statement
JM holds stock in Oxford Biodynamcs plc, Chronos Therapeutics Ltd. and Sibelius Natural Products Ltd., and acts an as advisor to OBD and Sibelius.
Footnotes
The abstract has been modified, additional supporting data has been included, and the discussion refined. Otherwise the results and conclusions remain unchanged. All the authors have been added to the author list. The methods have been extended and the proteomics data submitted to the PRIDE data base.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138023