Tag Archives: regulation

Announcement: PTMs In Cell Signaling

It is my great pleasure to announce the 2nd Copenhagen Bioscience conference “PTMs In Cell Signaling”, which will take place in Helsingør, Denmark on December 3-5, 2012.

The conference will feature a truly excellent lineup of speakers: Philippe Bastiaens, Søren Brunak, Ivan Dikic, Gerald Hart, Tim Hunt, Steve Jackson, Doug Lauffenburger, Jiri Lukas, Matthias Mann, Andre Nussenzweig, Brenda Schulman, Henrik Semb, Eric Verdin, Forest White, Michael Yaffe, and Juleen Zierath.

The conferences is limited to 220 participants. It is fully sponsored by the Novo Nordisk Foundation who covers the conference fee, hotel, transport and meals during the conference. Participants cover their own travel expenses.

To find out more, please check the conference web site.

Analysis: On the evolution of protein length and phosphorylation sites

It has been much too long since I have last written a blog post. Part of the reason has been that I have been busy moving back to Denmark, starting up a research group, and co-founding a company. More on that in other blog posts. The main reason, however, has been a lack of papers that inspired me to do the simple follow-up analyses that I usually blog about.

This has thankfully changed now. Pedro Beltrao and coworkers recently published an interesting paper in PLoS Biology on the evolution of regulation through protein phosphorylation. The paper presents several interesting analyses and comparisoins of phosphoproteomics data from three yeast species; the abstract summarizes the findings better than I can do:

Evolution of Phosphoregulation: Comparison of Phosphorylation Patterns across Yeast Species
The extent by which different cellular components generate phenotypic diversity is an ongoing debate in evolutionary biology that is yet to be addressed by quantitative comparative studies. We conducted an in vivo mass-spectrometry study of the phosphoproteomes of three yeast species (Saccharomyces cerevisiae, Candida albicans, and Schizosaccharomyces pombe) in order to quantify the evolutionary rate of change of phosphorylation. We estimate that kinase–substrate interactions change, at most, two orders of magnitude more slowly than transcription factor (TF)–promoter interactions. Our computational analysis linking kinases to putative substrates recapitulates known phosphoregulation events and provides putative evolutionary histories for the kinase regulation of protein complexes across 11 yeast species. To validate these trends, we used the E-MAP approach to analyze over 2,000 quantitative genetic interactions in S. cerevisiae and Sc. pombe, which demonstrated that protein kinases, and to a greater extent TFs, show lower than average conservation of genetic interactions. We propose therefore that protein kinases are an important source of phenotypic diversity.

Figure 1a in the paper shows the intriguing observation that, despite rapid evolution of individual phosphorylation sites, the relative number of phosphorylation sites within proteins from different functional classes (Gene Ontology categories) remains remarkably constant between species:

Beltrao et al., PLoS Biology, 2009, Figure 1a

However, it occurred to me that this could potentially be a consequence of longer proteins having more phosphorylation sites, and protein length being conserved through evolution. I thus counted the number of unique phosphorylation sites identified in each protein (thanks to Pedro Beltrao for providing the data) and correlated it with the length of the proteins. In the two plots below, I have pooled the proteins so that each dot corresponds to 100 proteins. The upper and lower panels show the results for S. cerevisiae and S. pombe, respectively:

Number of phosphorylation sites vs. protein lengh for S. cerevisiae

Number of phosphorylation sites vs. protein length for S. pombe

As should be evident from the plots, the average number of phosphorylation sites in a protein correlates strongly with its length, which is by no means surprisings. It is unclear to me why the intercept with the y-axis appears to differ from zero in both plots; suggestions are welcome.

The next question was whether the Gene Ontology terms that correspond to proteins with many phosphorylation sites are indeed assigned to proteins that are longer than average. I thus examined the terms “Cell budding”, “Morphogenesis”, and “Signal transduction”.

The average S. cerevisiae protein is 450 aa long. Proteins annotated with “Cell budding”, “Morphogenesis”, and “Signal transduction” are on average 1.6 (739 aa), 2.1 (945 aa), and 1.5 (679 aa) times longer, respectively. By comparison, the corresponding ratios observed for phosphorylation sites are approximately 2.3, 2.6, and 2.4. It would thus appear that differences in protein length between functional classes of proteins account for much, but not all, of the signal that was observed by Beltrao et al. when comparing the number phosphorylation sites.

Edit: Make sure to read Pedro Beltrao’s follow-up blog post, which nicely confirms that whereas protein length does play a role, it is not the full story.

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Analysis: Cell-cycle-regulated proteins are more abundant in haploid relative to diploid cells

Two days ago, Matthias Mann’s group published a paper in Nature in which they compare the level of individual proteins in haploid relative to diploid budding yeast cells:

Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast

Mass spectrometry is a powerful technology for the analysis of large numbers of endogenous proteins. However, the analytical challenges associated with comprehensive identification and relative quantification of cellular proteomes have so far appeared to be insurmountable. Here, using advances in computational proteomics, instrument performance and sample preparation strategies, we compare protein levels of essentially all endogenous proteins in haploid yeast cells to their diploid counterparts. Our analysis spans more than four orders of magnitude in protein abundance with no discrimination against membrane or low level regulatory proteins. Stable-isotope labelling by amino acids in cell culture (SILAC) quantification was very accurate across the proteome, as demonstrated by one-to-one ratios of most yeast proteins. Key members of the pheromone pathway were specific to haploid yeast but others were unaltered, suggesting an efficient control mechanism of the mating response. Several retrotransposon-associated proteins were specific to haploid yeast. Gene ontology analysis pinpointed a significant change for cell wall components in agreement with geometrical considerations: diploid cells have twice the volume but not twice the surface area of haploid cells. Transcriptome levels agreed poorly with proteome changes overall. However, after filtering out low confidence microarray measurements, messenger RNA changes and SILAC ratios correlated very well for pheromone pathway components. Systems-wide, precise quantification directly at the protein level opens up new perspectives in post-genomics and systems biology.

Although the paper focuses on the larger amount of cell-wall proteins and proteins involved in pheromone response in haploid cells, the supplementary tables reveal similar biases for many other functional classes, including nucleosomes and cyclin-dependent kinase inhibitors. As many of these proteins are regulated during the cell cycle, I suspected that cell-cycle-regulated proteins might be more abundant in haploid cells relative to diploid cells.

To test this hypothesis, I divided the proteins quantified by the Mann group into two classes: dynamic proteins, which are encoded by genes that are periodically expressed during the cell cycle, and static proteins, which are encoded by genes that are expressed at a constant level (de Lichtenberg et al., 2005). For each class, I plotted the log2-ratios of the protein levels in haploid and diploid cells:

The plot reeals a quite strong shift of dynamic proteins toward higher log-ratios; this difference is highly significant according to the Mann-Whitney U test (P < 10-12). Proteins encoded by cell-cycle-regulated genes are thus in general more abundant in haploid budding yeast cells than in diploid cells.

Full disclosure: I currently collaborate with Matthias Mann and members of his group, and we will soon be colleagues a the Novo Nordisk Foundation Center for Protein Research.

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Analysis: Degradation signals correlate with protein half-life

I yesterday blogged about how the protein half-life data from the O’Shea lab fit well with my earlier analyses of transcriptional regulation during the budding yeast cell cycle and with the just-in-time assembly hypothesis. However, I have now realized that the same data set can be used to test the validity of the sequence-based predictions of protein degradation signals that I relied on for the cell-cycle study.

To this end, I divided the budding yeast proteome into six groups: proteins with a D-box, proteins without a D-box, proteins with a KEN-box, proteins without a KEN-box, proteins with a PEST region, and proteins without a PEST region. For each of these six groups of proteins, I simply plotted the distribution of protein half-lives as a histogram:

The figure shows that for all three degradation signals, proteins with the sequence motif tend to have shorter half-lives than proteins without the motif. These differences are all statistically significant according to the Mann-Whitney U test (D-box, P < 10-6; KEN-box, P < 0.02; PEST region, P < 10-15). It is noteworthy that the KEN-box motif gives a far weaker correlation with protein half-live than the two other degradation signals, as it was also the only degradation signal that did not correlate with transcriptional cell-cycle regulation in budding yeast (see supplementary information of Jensen et al., 2006).

In summary, proteins that contain putative degradation signals have significantly shorter half-lives than proteins that do not contain such signals. The only caveat is that long sequences are more likely to match the sequence motifs, and that O’Shea and colleagues found a negative correlation between sequence length and protein half-life. The correlations described here could thus be a secondary effect; however, it is also possible that the presence of degradation signals in long sequences is the missing explanation for their short half-lives.

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Analysis: Cell-cycle-regulated genes encode short-lived proteins

In relation to an entirely different analysis than the one I will describe here, I downloaded the protein half-life data for budding yeast that was published in PNAS by the O’Shea lab about two years ago:

Quantification of protein half-lives in the budding yeast proteome

A complete description of protein metabolism requires knowledge of the rates of protein production and destruction within cells. Using an epitope-tagged strain collection, we measured the half-life of >3,750 proteins in the yeast proteome after inhibition of translation. By integrating our data with previous measurements of protein and mRNA abundance and translation rate, we provide evidence that many proteins partition into one of two regimes for protein metabolism: one optimized for efficient production or a second optimized for regulatory efficiency. Incorporation of protein half-life information into a simple quantitative model for protein production improves our ability to predict steady-state protein abundance values. Analysis of a simple dynamic protein production model reveals a remarkable correlation between transcriptional regulation and protein half-life within some groups of coregulated genes, suggesting that cells coordinate these two processes to achieve uniform effects on protein abundances. Our experimental data and theoretical analysis underscore the importance of an integrative approach to the complex interplay between protein degradation, transcriptional regulation, and other determinants of protein metabolism.

The idea that transcriptional regulation goes hand-in-hand with protein degradation is fully consistent with the just-in-time assembly hypothesis. I thus examined the distributions of protein half-lives for dynamic (i.e. periodically expressed) and static (i.e. not periodically expressed) proteins:

The histogram suggests that dynamic proteins are shifted towards shorter half-lives relative to static proteins. The difference is indeed statistically significant according to the Mann-Whitney U test (P < 10-4). This result supports the sequence-based observation that dynamic proteins contain more D-box, KEN-box, and PEST degradation signals than static proteins.

I next tested if the half-life of the dynamic proteins varies during the cell cycle by make scatter plot of the protein half-life as function of the time of peak expression for the corresponding mRNA:

There appears to be no correlation. Together, these analyses indicate that dynamic proteins have shorter half-lives than static proteins, irrespective of when in the cell cycle they are expressed.

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Analysis: A democratic approach to identification of cell-cycle-regulated genes

Over the years several microarray time-course experiments have been performed to identify the genes that are transcriptionally regulated during the mitotic cell cycle, i.e the periodically expressed genes. Moreover, bioinformaticians have developed many different computational methods for identifying the periodically expressed genes from microarray time-course data.

Below is a list of the experimental and computational analyses of the budding yeast cell cycle that I am aware of (please notify me if you know of other microarray experiments or computational methods):

  1. Cho et al., Mol. Cell, 1998
  2. Spellman et al., Mol. Biol. Cell, 1998
  3. Zhao et al., Proc. Natl. Acad. Sci. USA, 2001
  4. Langmead et al., Proc. IEEE Comput. Soc. Bioinformatics Conf., 2002
  5. Langmead et al.,RECOMB, 2002
  6. Langmead et al., J. Comput. Biol., 2003
  7. de Lichtenberg et al., J. Mol. Biol., 2003
  8. Johansson et al., Bioinformatics, 2003
  9. Wichert et al., Bioinformatics, 2004
  10. Lu et al., Nucleic Acids Res., 2004
  11. Luan and Li, Bioinformatics, 2004
  12. de Lichtenberg et al., Bioinformatics, 2005
  13. de Lichtenberg et al., Yeast, 2005
  14. Willbrand et al., Bioinformatics, 2005
  15. Ahdesmäki et al., BMC Bioinformatics, 2005
  16. Chen, BMC Bioinformatics, 2005
  17. Qiu et al., Conf. Proc. IEEE Eng. Med. Biol. Soc., 2005
  18. Qiu et al., Bioinformatics, 2006
  19. Andersson et al., BMC Bioinformatics, 2006
  20. Gan et al., Int. Conf. Pattern Recog., 2006
  21. Glynn et al., Bioinformatics, 2006
  22. Ahnert et al., Bioinformatics, 2006
  23. Lu et al., Bioinformatics, 2006
  24. Xu et al., LSS Comput. Syst. Bioinformatics Conf., 2006
  25. Pramilla et al., Genes Dev., 2006
  26. Liew et al, BMC Bioinformatics, 2007
  27. Lu et al., Genome Biol., 2007
  28. Morton et al., Stat. Appl. Genet. Mol. Biol., 2007
  29. Rowicka et al., Proc. Natl. Acad. Sci. USA, 2007
  30. Gauthier et al., Nucleic Acids Res., 2008
  31. Orlando et al., Nature, 2008

These studies have reported a mixture of ranked and unranked lists of periodically expressed genes. By that I mean that some studies provided a list of genes sorted according to how periodic the expression profiles appear, whereas others simply provide a list of the genes deemed periodic. For the ranked lists, I first checked the publications to see if the authors suggested a cutoff for the number of periodically expressed genes, in which case I followed their recommendations. If the authors suggested multiple lists of varying confidence, I used the highest-confidence list. If no cutoff was proposed, I selected the top-300 genes if the list was based on a single time course and the top-500 genes if the list was based on three or more time courses. It should be noted that both of these cutoffs are on the conservative side since most studies propose 800 or more periodically expressed genes when combining multiple expression time courses.

This meta-analysis resulted in a list of more than 4200 budding yeast genes that are periodically expressed according to at least one of the methods listed above; that is more than two-thirds of all genes encoded by the budding yeast genome!

To investigate further how such a large number of genes can have been proposed to be periodically expressed, I plotted how many of these genes are on how many of the lists of periodically expressed genes:

The histogram reveals that the majority of the over 4200 genes have been proposed by only one or two analyses. It seems reasonable to assume that the genes that have been proposed as periodically expressed by only one or a few methods are less likely to be correct than the ones that many methods agree on. Also, one could expect that taking the consensus of many methods would yield a more reliable answer than using just a single method.

To test these two hypotheses, I compared two different ways of identifying the periodically expressed genes:

  1. Ranking the genes based on a single scoring scheme that combines all the available experimental data (Gauthier et al., Nucleic Acids Res., 2008)
  2. Ranking the genes based on vote among 30 different methods (not 31; the analysis by Orlando and coworkers was left out of the voting as this dataset is not included in Cyclebase.org)

To benchmark the two methods, I compared the ranked lists to a set of target genes for cell-cycle transcrition factors identified in genome-wide ChIP-on-chip experiments and plotted the fraction of these that were identified as function of the number of genes proposed to be periodically expressed:

The plot confirms that genes proposed to be periodically by multiple methods are more likely to be targets of cell-cycle transcription factors, and are hence more likely to truly be subject to transcriptional cell-cycle regulation. However, it also shows that the list obtained by voting among 30 methods is a bit worse than what is obtained by using the single best method.

This result may come as a surprise to many since meta-servers that combine multiple prediction methods have in the past proven very successful for many other bioinformatics tasks. I suspect that the approach fails in this case for two reasons: first, many of the analyses included perform considerably worse than the best one, and second, most of the methods make use of only half of the available experimental data. It may thus be possible to obtain better results by selecting only a subset of the methods and rerunning each of them on all the available data. So far, however, dictatorship seems to work better than democracy for identification of periodically expressed genes.

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Analysis: Cell-cycle expression of cancer genes

I have long used a data integration approach to obtain a global picture of eukaryotic cell-cycle regulation. The cell cycle is a popular research topic in part because of its importance for cancer research. I thus recently compared microarray expression data on the human cell cycle to genes with mutations that have been causally implicated in various forms of cancer.

From the Cancer Genome Project website, I downloaded a list of 353 human genes that are implicated in cancer. Using the identifier mapping file from STRING, I was able to automatically map 338 of these genes to the set of human genes from Ensembl that I used in earlier cell-cycle studies. 295 of the 338 genes were present on the microarrays used in the cell-cycle expression study by Whitfield et al. (2002). However, only 23 of these are among the 600 periodically expressed genes identified in the reanalysis by Jensen et al. (2006). The many numbers are illustrated in the diagram below:

By random chance, 295*600/12097 = 15 of the 295 genes would be expected to be periodically expressed, and the enrichment is thus only a bit over 1.5 fold. Although this enrichment is statistically significantly (P < 3%, Fisher’s exact test), the correlation is clearly not strong enough to allow prediction of novel cancer genes.

My step was to look at the evolutionary conservation of the 23 periodically expressed cancer genes. Only 12 of them belong to an orthologous group. Half of them do thus not appear to have orthologs in budding yeast, fission yeast, or Arabidopsis thaliana. Only three periodically expressed cancer genes have orthologs in all of these organisms. One of these genes is periodically expressed onlt in human, one in human and fission yeast, and one in all four organisms (a histone subunit).

In summary, it seems that one cannot say much about cancer based on cell-cycle mRNA expression data. This is perhaps not surprising considering that the transcriptional regulation does not seem to vary much between cancer cells and normal cells.

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Analysis: Cancer or not, cell-cycle expression stays the same

The groups of Ziv Bar-Joseph and Itamar Simon recently published a paper in PNAS on a new microarray study of the cell cycle of primary human fibroblasts:

Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells

Characterization of the transcriptional regulatory network of the normal cell cycle is essential for understanding the perturbations that lead to cancer. However, the complete set of cycling genes in primary cells has not yet been identified. Here, we report the results of genome-wide expression profiling experiments on synchronized primary human foreskin fibroblasts across the cell cycle. Using a combined experimental and computational approach to deconvolve measured expression values into ‘‘single-cell’’ expression profiles, we were able to overcome the limitations inherent in synchronizing nontransformed mammalian cells. This allowed us to identify 480 periodically expressed genes in primary human foreskin fibroblasts. Analysis of the reconstructed primary cell profiles and comparison with published expression datasets from synchronized transformed cells reveals a large number of genes that cycle exclusively in primary cells. This conclusion was supported by both bioinformatic analysis and experiments performed on other cell types. We suggest that this approach will help pinpoint genetic elements contributing to normal cell growth and cellular transformation.

In contrast to the earlier study by Whitfield et al. (2002), which was performed on HeLa cells, Ziv Bar-Joseph et al. worked on non-transformed fibroblasts. The dataset thus offers a first global view of the differences between the cell cycle of normal human cells and that of cancer cells.

To compare their list of cell-cycle-regulated human genes to the one the I have used so far, I mapped their 480 genes to Ensembl using the mapping file from the STRING database. This resulted in a list of 410 genes, that is 70 genes could not be mapped by the automatic procedure. Whereas this is far from a perfect mapping, it is sufficient to judge the quality of the list.

The plots below show the fraction of a benchmark set that is identified as function of the number of genes that is proposed to be periodically expressed during the cell cycle. In each plot, I compare the results for the list of 410 obtained from the new study by Bar-Joseph et al., the analysis by Whitfield et al., and the reanalysis of the latter dataset by Jensen et al. (2006) (available from Cyclebase.org). To make the comparison as fair as possible, I only considered the subset of genes that were present in both microarray designs. The first plot uses as benchmark a set of 63 genes that have been identified as periodically expressed in targeted small-scale studies:

Three sets of cell-cycle-regulated human genes compared to benchmark set B1

I also benchmarked the three gene lists against a second benchmark set, which consists of predicted target genes of E2F cell-cycle transcription factors:

Three sets of cell-cycle-regulated human genes compared to benchmark set B2

Both benchmarks suggest that the three lists are of very comparable quality, but that the list by Whitfield and coworkers is much more inclusive than the one from Bar-Joseph and coworkers. In other words, the former list has better sensitivity whereas the latter has better specificity. This is consistent with the results presented by Bar-Joseph et al., who conclude that their list is more reliable than the previously published list. However, this is probably not due to better quality of the raw expression data, since reanalysis of the data by Whitfield et al. yielded a list with almost identical sensitivity and specificity (that is the red curve is very close to the blue cross in both plots).

Although the two lists of periodically expressed are of comparable quality, they may still contain very different sets of genes. I therefore decided to compare the list of genes that are periodically expressed in the time course on primary fibroblasts and in each of the four time courses on HeLa cells. To make this comparison as easy as possible, I selected the top-364 cycling genes from each of the four HeLa time courses based on the reanalysis by Jensen et al. (2006). The ten Venn diagrams below show all pairwise comparisons of the five lists of 364 genes each:

The average overlap between the list by Bar-Joseph et al. and an experiment from Whitfield et al. is 114 genes. By comparison, the average overlap between the top-364 lists from two individual experiments from Whitfield et al. is 123 genes. Although the overlap may seem low, I thus believe that it is due to the poor reproducibility between microarray time courses rather than due to genuine differences between primary fibroblasts and HeLa cells as suggested by Bar-Joseph and colleagues.

Although cancer cells have to circumvent the regulatory mechanisms that would normally prevent cell proliferation, the cell cycle itself appears to function the same way as in normal cells. In other words, the difference does not lie in the “engine” but in the “brakes”, which have been sabotaged in cancer cells.

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Commentary: Viewing the cell cycle in a new light

Atsushi Miyawaki’s lab from RIKEN has recently published a Cell paper that describes a novel approach for how to monitor cell-cycle progression of individual cells:

Visualizing spatiotemporal dynamics of multicellular cell-cycle progression

The cell-cycle transition from G1 to S phase has been difficult to visualize. We have harnessed antiphase oscillating proteins that mark cell-cycle transitions in order to develop genetically encoded fluorescent probes for this purpose. These probes effectively label individual G1 phase nuclei red and those in S/G2/M phases green. We were able to generate cultured cells and transgenic mice constitutively expressing the cell-cycle probes, in which every cell nucleus exhibits either red or green fluorescence. We performed time-lapse imaging to explore the spatiotemporal patterns of cell-cycle dynamics during the epithelial-mesenchymal transition of cultured cells, the migration and differentiation of neural progenitors in brain slices, and the development of tumors across blood vessels in live mice. These mice and cell lines will serve as model systems permitting unprecedented spatial and temporal resolution to help us better understand how the cell cycle is coordinated with various biological events.

The clever idea was to fuse a red- and a green-emitting fluorescent protein to Cdt1 and Geminin, respectively. Cdt1 is ubiquitinated by SCFSkp2 at the onset of S phase, which causes it to be rapidly degraded by the proteasome, whereas Geminin is targeted for proteolytic degradation by APCCdh1 in late M phase. By fluorescent labeling of two proteins, Miyawaki and colleagues managed to make mouse cells that become increasingly red during G1 phase, yellow around the G1/S transition, and increasingly green through S, G2, and M phase. It is thus possible to monitor the cell-cycle states of individual cells with a microscope.

The movie below follows a few HeLa cells for 3-4 cell cycles:

The authors also show how their construct can be used for imaging the cell-cycle state of the cells in a slice of a mouse brain or a mouse embryo. I expect that this will become an indispensable tool for unraveling the links between cell-cycle control and developmental processes.

For more details, I strongly recommend that you read Jake Young’s post at Pure Pedantry.

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Analysis: The transcriptional response to growth rate is unrelated to cell-cycle regulation

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:

Peak-time distribution for genes that are up- or 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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