Analysis: Cell-cycle phenotypes and regulation, part 2

February 28, 2008

I have previously blogged about the relationship between cell-cycle phenotypes and regulation in human as well as budding yeast. I was thus excited to see the new RNAi study on cell-cycle phenotypes by Rines and coworkers that was published in Genome Biology two days ago. The title of their paper is “Whole genome functional analysis identifies novel components required for mitotic spindle integrity in human cells”, and the abstract reads as follows:


The mitotic spindle is a complex mechanical apparatus required for accurate segregation of sister chromosomes during mitosis. We designed a genetic screen using automated microscopy to discover factors essential for mitotic progression. Using a RNAi library of 49,164 double-stranded (ds)RNAs targeting 23,835 human genes, we performed a loss-of-function screen looking for siRNAs that arrest cells in metaphase.


Here we report the identification of genes that when suppressed result in structural defects in the mitotic spindle leading to bent, twisted, monopolar or multipolar spindles and cause a cell cycle arrest. We further described a novel analysis methodology for large-scale RNAi datasets which relies upon supervised clustering of these genes based on gene ontology (GO), protein families, tissue expression and protein-protein interactions.


This approach was utilized to functionally classify the identified genes in discrete mitotic processes. We confirmed the identity for a subset of these genes and examined more closely their mechanical role in spindle architecture.

The screen identified a set of 226 genes that when suppressed lead to spindle-related cell-cycle phenotypes. Using the name-mapping files from STRING, I was able to map 175 of them to the set of genes used in my other cell-cycle analyses. The results presented below are all based on this set of 175 genes.

To my surprise, Rines and coworkers did not compare their results to the earlier phenotypic screen published by Mukherji et al. in PNAS. Since I had already mapped this dataset onto the same gene set, it was easy to make a comparison of the new phenotype data from Rines et al. and the eight phenotypic categories defined by Mukherji et al.:

Category Description Overlap Significance
1 G1 small nuclear area 2/116 n.s.
2 G1 2/117 n.s.
3 S 1/61 n.s.
4 S + G2/M 4/59 P < 0.002; FDR < 1%
5 G2/M large nucleus 5/200 P < 0.019; FDR < 5%
6 G2/M 4/259 n.s.
7 G2/M + endoduplication 1/52 n.s.
8 Cytokinesis 3/36 P < 0.003; FDR < 1%

The statistical significance of the overlap was assessed using Fisher’s exact test and the false discovery rate (FDR) was calculated using the Benjamini-Hochberg method. As can be seen, the agreement between the two studies is very poor. Nonetheless, it is reassuring that the largest overlap (>8%) is observed for category 8, since spindle defects should be expected to result in problems during cytokinesis.

I also looked into the transcriptional and post-translational regulation of the 175 genes. The cell-cycle microarray study by Whitfield and coworkers covered 124 of the genes, 15 of which are periodically expressed (P < 0.002; Fisher’s exact test). Plotting the distribution of peak times for these genes confirms the observation by Rines et al. that the genes tend to be expressed around the G2/M transition and during M phase:

Peak time distributions for human genes identified by Rines et al. and Mukheriji et al.

As should be expected, the peak-time distribution for the genes identified by Rines et al. is in agreement with the corresponding distributions for categories 4, 5, and 8 from Mukherji and coworkers.

Comparison with a set of 985 phosphoproteins identified in low-throughput studies (obtained from Phospho.ELM) shows that the proteins products encoded by the 175 genes are preferentially phosphorylated (P < 0.001; Fisher’s exact test). This result is confirmed by comparisons with large mass-spectrometry studies (P < 0.03; Fisher’s exact test) and CDK substrates predicted by NetPhosK (P < 0.05; Fisher’s exact test).

Finally, I analyzed the protein products encoded by the 175 genes for degradation signals. 22 of them contain a strong D-box motif (P < 0.03; Fisher’s exact test) and 28 contain a KEN-box motif (P < 0.002; Fisher’s exact test). By contrast, the gene products identified by Rines et al. display no overrepresentation of PEST degradation signals. This makes sense since proteins with D-box and/or KEN-box motifs are polyubiquitinated by the anaphase-promoting complex (APC) during late M phase, which targets them for degradation by the proteasome.

In summary, Rines and coworkers has identified a set of genes that show weak but significant overlap with some of the phenotypic categories defined by Mukherji et al., with periodically expressed genes identified based on microarray data from Whitfield et al., with known and predicted phosphoproteins, and with predicted degradation signals. All of the results are consistent with the majority of the 175 genes functioning during G2/M and early M phase.

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