Tag Archives: cell cycle

Announcement: PTMs in Cell Signaling conference

Two years ago, I was one of the organizers of the 2nd Copenhagen Bioscience Conference entitled PTMs in Cell Signaling. I think it is fair to describe it as a highly successful meeting, and it is my great pleasure to announce that we will be organizing a second meeting on the topic September 14-18, 2014.

CBC6 poster

My co-chairs Jeremy Austin Daniel, Michael Lund Nielsen, and Amilcar Flores Morales have managed to put together the following excellent lineup of invited speakers:

Alfonso Valencia, Chris Sander, David Komander, Gary Nolan, Genevieve Almouzni, Guillermo Montoya, Hanno Steen, Henrik Daub, John Blenis, John Diffley, John Tainer, Karolin Luger, Marcus Bantscheff, Margaret Goodell, Matthias Mann, Michael Yaffe, Natalie Ahn, Pedro Beltrao, Stephen Elledge, Tanya Paull, Tony Hunter, Yang Shi, Yehudit Bergman, and Yosef Shiloh.

All conference expenses are covered, which means that there will be no registration fee and no expenses for accommodation or food. You will have to cover your own travel expenses, though.

Participants will be selected based on abstract submission, which is open until June 9, 2014. For more information please see the conference website.

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.

Live: Lecture by Nobel Laurate Avram Hershko

Today I am at the the symposium “Protein Chemistry ‐ Applications to Combat Diseases”, which takes place in Copenhagen a few minutes walk from where I work.

This morning started with a keynote lecture by Nobel Laurate Avram Hershko on regulation of the cell division cycle by ubiquitin‐mediated protein degradation. This post is just a very quick write-up and a few photos made during and immediately after his presentation.

Avram Hershko presenting in Copenhagen

Most of the early work on ubiquitylation was done on model proteins, most of which were extracellular. Interestingly, what spurred Avram Hershko on to study ubiquitylation of physiologically relevant proteins was the early work on cyclin degradation for which Tim Hunt received the Nobel Prize. Tim Hunt speculated speculated that there was a cyclin protease that would break down cyclins. However, Avram Hershko showed in 1991 that cyclins are in fact not degraded by a specific protease, but are rather targeted for proteasomal degradation by a specific ubiquitin ligase. Showed this in JBC papers in 1991 and 1994. One year later his group identified this ubiquitin ligase to be what is now known as the Anaphase Promoting Complex (APC) / Cyclosome (APC/C).

The role of APC/C in ubiquitylation and degradation of cyclins

In addition to being crucial for degradation of cyclins, APC/C is also required for entry into anaphase of the cell cycle (hence the name Anaphase Promoting Complex). This because it is responsible for targeting the securin protein for degradation, which in turns releases separase activity to degrade the cohesin rings that hold together sister chromatids.

Having worked on other cell-cycle proteins for many years, Avram Hershko has in recent years returned his interest to APC/C, more specifically to understand how the inhibition of APC/C is released, which in turn leads to the whole series of events described above.

Release of APC/C from checkpoint inhibition

Analysis: Limited agreement among lists of Cdc28p substrates

A collaboration between the Morgan lab at UCSF and the Gygi lab at Harvard has resulted in a paper by Holt et al. in Science, which reports the identification of several hundred substrates of the central cell-cycle kinase Cdc28p (also known as Cdk1) in the budding yeast Saccharomyces cerevisiae:

Global analysis of Cdk1 substrate phosphorylation sites provides insights into evolution.

To explore the mechanisms and evolution of cell-cycle control, we analyzed the position and conservation of large numbers of phosphorylation sites for the cyclin-dependent kinase Cdk1 in the budding yeast Saccharomyces cerevisiae. We combined specific chemical inhibition of Cdk1 with quantitative mass spectrometry to identify the positions of 547 phosphorylation sites on 308 Cdk1 substrates in vivo. Comparisons of these substrates with orthologs throughout the ascomycete lineage revealed that the position of most phosphorylation sites is not conserved in evolution; instead, clusters of sites shift position in rapidly evolving disordered regions. We propose that the regulation of protein function by phosphorylation often depends on simple nonspecific mechanisms that disrupt or enhance protein-protein interactions. The gain or loss of phosphorylation sites in rapidly evolving regions could facilitate the evolution of kinase-signaling circuits.

The paper makes several interested in analyses and observations. However, I found the comparison to the previous study of Cdc28p substrates by Ubersax et al. from the Morgan lab to be less detailed than I had hoped for:

Phosphorylation of Cdk1 consensus sites was observed on 67% (122 of 181) of proteins previously identified as Cdk1 substrates in vitro (4). Sixty-six percent (80 of 122) of these proteins contained sites at which phosphorylation decreased (log2 H/L < –1) after inhibition of Cdk1 (only 45 of 122 are expected if there is no correlation between the experiments in vitro and in vivo; χ2 test, P < 10-10).

In other words, 44% (80 of 181) of Cdc28p substrates identified in the old study were confirmed by the new study, and only 26% (80 of 308) of the Cdc28p substrates identified in the new study are supported by the old study. There are many possible explanations for this discrepancy

Depth of the mass spectrometry

It is notoriously difficult to identify peptides from low-abundance proteins in mass spectrometry. In the new mass spectrometry study, the authors were able to map 8710 precise phosphorylation sites on 1957 proteins. However, budding yeast is estimated to express in the order of 4500 distinct proteins during exponential growth (Gavin et al., 2006). Assuming that the majority of these proteins contain sites that are phosphorylated during at least part of the mitotic cell cycle, it is likely that a considerable number of low-abundance Cdc28p substrates identified in the old study have been missed in the new study.

Biases in phosphopeptide enrichment

When doing phosphoproteomics, it is necessary to first enrich for phosphopeptides to improve the coverage. To this end, Holt et al. used immobilized metal affinity chromatography (IMAC). In 2007, the Aebersold group at ETH published a paper showing that different purification methods lead to isolation of different, partially overlapping segments of the phosphoproteome. Specifically, they showed that IMAC enrichment biases the data towards isolation of multiply phosphorylated peptides. Given that only a single purification method was used, it is likely that in vivo Cdc28p substrates may have been missed in the new study, in particular if the peptides contain only a single phosphorylation site.

In vitro vs. in vivo conditions

The old study by Ubersax et al. was done performed on cell lysate, which is an in vitro strategy (although all other proteins expressed during the cell cycle are present). It is thus likely that some of the proteins that are phosphorylated by Cdc28p under these conditions are nonetheless not in vivo Cdc28p substrates.

Can we do better?

As always, it is easy to point out potential flaws in other people’s data sets; however, it is much more constructive to do something about the problems. The challenge is thus to construct a larger and more reliable set of Cdc28p substrates by combining the data from the two studies.

To check the feasibility of assigning confidence scores to different putative Cdc28p substrates, I tested if the fold change observed in the new study correlates with the chance that the substrate was also identified in the old study. To this end, I divided the 308 Cdc28p substrates from the new studies into two groups and constructed histograms of the fold changes for each group:

Phosphorylation ratios from Holt et al.

The fold changes are clearly skewed towards larger negative values for the Cdc28p substrates also identified by the old study relative to the proteins that were not previously identified as Cdc28p substrates. This difference is statistically significant at P < 1% according to the Kolmogorov-Smirnov test. This suggests that the observed fold changes in the new mass spectrometry study correlates with the likelihood that the proteins are true Cdc28p substrates.

The old study gave rise to so-called P-score for the individual proteins (not to be confused with P-values). I decided to test if these too can be used as quality scores, I constructed an equivalent histogram in which the Cdc28p substrates found in the old study were divided into two groups based on whether or not they were also found in the new study:

P-scores from Ubersax et al.

In this case, no obvious trend is seen and a Kolmogorov-Smirnov test indeed reveals no statistically significant difference between the two distributions. Surprisingly, the P-scores do thus not appear to be useful quality scores for the putative Cdc28p substrates.

Given the two sets of putative Cdc28 substrates, only one of which can be ranked by reliability, how can we create a better combined set? If one aims for the high accuracy at the price of low coverage, one could obviously choose to trust only the substrates identified by both screens. However, given the caveats regarding depth of mass spectrometry and biases arising from the enrichment procedure, I would be hesitant to use this approach. Alternatively, one could aim for maximal coverage at the price of accuracy by trusting all sites identified by either study. However, seeing the large fraction of novel substrates identified by Holt et al. with a log2-ratio only slightly below -1, I would personally tend to apply a more stringent threshold to the data from the new study by Holt et al., for example requiring log2-ratio below -2, before merging the sets of substrates from the two studies.

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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: Transcriptional and posttranslational regulation of cell-cycle kinases

Daub and coworkers from Matthias Mann’s group recently published a paper in Molecular Cell, describing a phosphoproteomics study of kinases during S and M phase of the mitotic cell cycle:

Kinase-selective enrichment enables quantitative phosphoproteomics of the kinome across the cell cycle.

Protein kinases are pivotal regulators of cell signaling that modulate each other’s functions and activities through site-specific phosphorylation events. These key regulatory modifications have not been studied comprehensively, because low cellular abundance of kinases has resulted in their underrepresentation in previous phosphoproteome studies. Here, we combine kinase-selective affinity purification with quantitative mass spectrometry to analyze the cell-cycle regulation of protein kinases. This proteomics approach enabled us to quantify 219 protein kinases from S and M phase-arrested human cancer cells. We identified more than 1000 phosphorylation sites on protein kinases. Intriguingly, half of all kinase phosphopeptides were upregulated in mitosis. Our data reveal numerous unknown M phase-induced phosphorylation sites on kinases with established mitotic functions. We also find potential phosphorylation networks involving many protein kinases not previously implicated in mitotic progression. These results provide a vastly extended knowledge base for functional studies on kinases and their regulation through site-specific phosphorylation.

In the study, they identified phosphorylation sites for 219 protein kinases, of which 159 showed differential phosphorylation (at least two-fold induction for at least one site) in S and/or M phase.

My collaborators at CBS and I have previously shown that transcriptional and posttranslational regulation (for example, phosphorylation by cyclin-dependent kinases) tend to target the same proteins (de Lichtenberg et al., 2005; Jensen et al., 2006). One should thus expect that the differentially regulated kinases have a tendency to be encoded by periodically expressed genes.

To test this hypothesis, I compared the phosphoproteomics data of Daub et al. to the cell-cycle microarray expression study by Whitfield et al. (2002). I was able to map 132 of the 159 kinases to the microarrays and found that 17 of them are encoded by the top-600 cycling genes. This corresponds to a significant (P < 0.001) two-fold overrepresentation of transcriptional cell-cycle regulation among the genes encoding kinases that are differentially phosphorylated during S and/or M phase.

One could imagine that this trend is not specific to kinases that are differentially phosphorylated during the cell cycle, but that it instead applies to kinases in general. To test this, I also mapped the 60 non-modulated kinases found by Daub et al. to the microarrays (Whitfield et al., 2002). Of the 54 kinases that could be mapped, only 3 are encoded by periodically expressed genes, which is almost exactly what is expected by random chance.

I next examined if timing of phosphorylation correlates with the timing of expression of the 17 kinases mentioned above. The kinases can be divided into three classes: phosphorylated in S phase, phosphorylated in M phase, and phosphorylated in both S and M phase. Notably, 13 of the 17 kinases fall in to the M phase class. Looking at the peak times of expression for these (that is when in the cell-cycle the corresponding mRNAs are most highly expressed) reveals that 8 of the 13 kinases are presumably synthesized in M phase only shortly before they become phosphorylated.

In summary, comparison of the phosphoproteomics data from Daub et al. (2008) and the microarray expression data from Whitfield et al. (2002) supports the view that transcriptional and posttranslational regulation tend to target the same proteins during the mitotic cell cycle. Moreover, it shows that for most of the kinases that are subject to such dual cell-cycle control, both expression and phosphorylation takes place during M phase when the cyclin-dependent kinase activity is maximal.

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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