David Botstein’s group at Princeton recently published a paper in Molecular Biology of the Cell with the title “Coordination of Growth Rate, Cell Cycle, Stress Response, and Metabolic Activity in Yeast”. As described in their abstract, they found interesting several correlations between the transcriptional responses to changes in growth rate and the regulation in response to stress and during the metabolic cycle:
We studied the relationship between growth rate and genome-wide gene expression, cell cycle progression, and glucose metabolism in 36 steady-state continuous cultures limited by one of six different nutrients (glucose, ammonium, sulfate, phosphate, uracil, or leucine). The expression of more than one quarter of all yeast genes is linearly correlated with growth rate, independent of the limiting nutrient. The subset of negatively growth-correlated genes is most enriched for peroxisomal functions, whereas positively correlated genes mainly encode ribosomal functions. Many (not all) genes associated with stress response are strongly correlated with growth rate, as are genes that are periodically expressed under conditions of metabolic cycling. We confirmed a linear relationship between growth rate and the fraction of the cell population in the G0/G1 cell cycle phase, independent of limiting nutrient. Cultures limited by auxotrophic requirements wasted excess glucose, whereas those limited on phosphate, sulfate, or ammonia did not; this phenomenon (reminiscent of the “Warburg effect” in cancer cells) was confirmed in batch cultures. Using an aggregate of gene expression values, we predict (in both continuous and batch cultures) an “instantaneous growth rate”. This concept is useful in interpreting the system-level connections among growth rate, metabolism, stress, and the cell cycle.
Because of my interest in cell cycle, their results regarding growth rate and cell-cycle regulation caught my attention. In Figure 6 of their paper, Brauer et al. show the slope distribution for the genes belonging to each of the phase-specific clusters defined by Spellman et al. (1998). The only trend they observe is that genes expressed at the G1/M transition.
I decided to redo the cell-cycle part of their analysis in a slightly different manner, hoping that I would be able to get a stronger signal than they did. Rather than using the 800 periodically expressed genes proposed by Spellman et al. (1998), I thus made use of the list of 600 periodically expressed genes from de Lichtenberg et al. (2005). Like Brauer et al., I found no difference in growth-rate response between cell-cycle-regulated genes and other genes. To analyze the phase-specific expression, I chose to plot the peak time distributions for genes that are up- and down-regulated in response to increasing growth rate:
In agreement with Brauer et al., genes that are down-regulated at high growth rates appear to have a striking preference for being expressed at the G1/M transition. However, manual inspection of these genes revealed that more than half of them belong to the Y’ family of DNA helicases, which are encoded by the sub-telomeric regions (striped blue bars). The trend observed by Brauer et al. is thus presumably not due to slower growing cells spending more time in M-G1 phase as suggested by the authors, Instead, it is likely an artifact of the many Y’ helicase genes found in the sub-telomeric regions of budding yeast, which are so highly homologous that they can cross hybridize on microarrays and hence all appear to be periodically expressed with identical peak times.
After correcting for this the down-regulated genes show a weak preference for being expressed during M phase whereas the up-regulated genes tend to be expressed in late G1 and S phase. However, the peak-time distributions of up- and down-regulated do not differ significantly from that of all cell-cycle-regulated genes (Kolmogorov-Smirnov test).
In summary, my reanalysis suggests that there is no correlation between the transcriptional response to changes in growth rate and transcriptional cell-cycle regulation. It also reiterates the importance of manually inspecting the results from statistical analyses – they may be highly significant for all the wrong reasons.
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