Fig 1.
a) Gene expression contributes to organismal fitness. b) Rhythmic protein regulation presents a trade-off between the costs generated by integration into the rhythmic system (costs of complexity) and the advantages provided plus the costs saved over 24 hours. The middle exemplifies two extreme behaviors, while the right shows the distribution expected from populations of genes which follow these behaviors. c) The range of high fitness protein levels depends on the sensitivity of the function to deviations from an optimal level. We use the term “narrower” following Hausser et al. [20]. Noise sensitive genes have narrower fitness function, i.e. a small deviation from the optimum rapidly decreases the contribution to fitness. Precision is less important for genes with flat fitness functions. d) Mean or maximum expression level calculated from time-series datasets (see Methods). We assume that, in the absence of rhythmic regulation, the constant optimal level is included between the mean and the maximum expression level observed in rhythmic expression.
Fig 2.
Among the factors of expression costs, expression level is the main factor explaining the higher cost observed in rhythmic proteins.
a) The total cost of rhythmic proteins is higher than those of other proteins. b) With the exception of mouse liver, rhythmic proteins do not contain more expensive amino-acids than other proteins. c) Rhythmic proteins can be longer in some species. d-e) Mean or maximum expression level calculated from time-series datasets: rhythmic proteins are highly expressed proteins. Boxplots are log scaled except for the averaged AA synthesis cost. The first 15% of proteins from p-values ranking (from the most rhythmic to the most un-rhythmic genes) obtained from the rhythm detection algorithms were classified as rhythmic.
Table 1.
Simplified table showing the results of the Welch two sample t-test testing the hypothesis that the noise is equal between rhythmic versus non-rhythmic transcripts (a), or proteins (b), or between rhythmic versus non-rhythmic transcripts among rhythmic proteins (c) or among non-rhythmic proteins (d), and between rhythmic versus non-rhythmic proteins among genes with constant transcripts (e).
F* is an estimation of the noise based on Barroso et al. method [28]. Complete results are provided in S5 Table.
Table 2.
Simplified table showing the results of the Welch two sample t-test testing the hypothesis that dN/dS ratio is equal between rhythmic versus non-rhythmic transcripts (a), or proteins (b), or between rhythmic versus non-rhythmic transcripts among rhythmic proteins (c), and between rhythmic versus non-rhythmic proteins among genes with rhythmic transcripts (d).
In S8 Table, triangles give the result of the Welch two sample t-test without controlling for the effect of gene expression level. Complete results are provided in S6 Table.