Figure 2. Scoring of a PKA‐ and TOR‐signaling‐dependent network state.

1. Comparison of the effects of chemical inhibition of PKA (left) and TOR (right, both after treatment for 20 min, data from Kunkel et al, 2019) on transcripts to difference according to parental IRA2 allele on the corresponding protein abundances in the BYxRM collection. Assignment of transcripts to PT‐induced and PT‐reduced sets is indicated by green and blue dots, respectively. The PT score is calculated as the difference between the medians of marker protein abundance (scaled and centered across the BYxRM collection) in the two sets.
2. Comparison of a score based on transcript level changes during the diauxic shift (Brauer et al, 2005) to the PT score for all strains of the BYxRM collection.
3. PCA of proteomic data for BY4742 grown on different carbon sources (Paulo et al, 2016). Coloring by PT score for each sample, calculated based on protein abundances after scaling and centering across conditions, yielding a comparison to the global mean for the studied range of carbon sources. Fractions of total variance explained by each PC are indicated.
4. Comparison of sample scores on PC1 (panel C) to the PT score. Dashed red line shows the linear regression model.
5. Correlation of PT score with heat‐induced lag measurements per strain of the BYxRM collection. Dashed red line shows the linear regression model.
6. PCA for molecular variability between strains of the BYxRM collection based on transcript, protein, and phosphopeptide abundances as well as phospho‐residual levels (see Materials and Methods). Coloring by PT score. The PT score explained up to 86, 80, and 68% variability (adjusted R ²) between segregant scores along PC1 of PCAs based on protein, phosphopeptide, and phosphopeptide‐residual abundances, respectively. For the transcriptome, the PT score correlated more strongly with the second PC (adj. R ² = 0.44).

Source data are available online for this figure.