Person:

Gutman, Roee

Logistic regression is commonly used to test for treatment effects in observational studies. If the distribution of a continuous covariate differs between treated and control populations, logistic regression yields an invalid hypothesis test even in an uncounfounded study if the link is not logistic. This flaw is not corrected by the commonly used technique of discretizing the covariate into intervals. A valid test can be obtained by discretization followed by regression adjustment within each interval.

Background: Fission yeast Schizosaccharomyces pombe and budding yeast Saccharomyces cerevisiae are among the original model organisms in the study of the cell-division cycle. Unlike budding yeast, no large-scale regulatory network has been constructed for fission yeast. It has only been partially characterized. As a result, important regulatory cascades in budding yeast have no known or complete counterpart in fission yeast. Results: By integrating genome-wide data from multiple time course cell cycle microarray experiments we reconstructed a gene regulatory network. Based on the network, we discovered in addition to previously known regulatory hubs in M phase, a new putative regulatory hub in the form of the HMG box transcription factor SPBC19G7.04. Further, we inferred periodic activities of several less known transcription factors over the course of the cell cycle, identified over 500 putative regulatory targets and detected many new phase-specific and conserved cis-regulatory motifs. In particular, we show that SPBC19G7.04 has highly significant periodic activity that peaks in early M phase, which is coordinated with the late G2 activity of the forkhead transcription factor fkh2. Finally, using an enhanced Bayesian algorithm to co-cluster the expression data, we obtained 31 clusters of co-regulated genes 1) which constitute regulatory modules from different phases of the cell cycle, 2) whose phase order is coherent across the 10 time course experiments, and 3) which lead to identification of phase-specific control elements at both the transcriptional and post-transcriptional levels in S. pombe. In particular, the ribosome biogenesis clusters expressed in G2 phase reveal new, highly conserved RNA motifs. Conclusion: Using a systems-level analysis of the phase-specific nature of the S. pombe cell cycle gene regulation, we have provided new testable evidence for post-transcriptional regulation in the G2 phase of the fission yeast cell cycle. Based on this comprehensive gene regulatory network, we demonstrated how one can generate and investigate plausible hypotheses on fission yeast cell cycle regulation which can potentially be explored experimentally.

Background: Many genes oscillate in their level of expression through the cell division cycle. Previous studies have identified such genes by applying Fourier analysis to cell cycle time course experiments. Typically, such analyses generate p-values; i.e., an oscillating gene has a small p-value, and the observed oscillation is unlikely due to chance. When multiple time course experiments are integrated, p-values from the individual experiments are combined using classical meta-analysis techniques. However, this approach sacrifices information inherent in the individual experiments, because the hypothesis that a gene is regulated according to the time in the cell cycle makes two independent predictions: first, that an oscillation in expression will be observed; and second, that gene expression will always peak in the same phase of the cell cycle, such as S-phase. Approaches that simply combine p-values ignore the second prediction. Results: Here, we improve the detection of cell cycle oscillating genes by systematically taking into account the phase of peak gene expression. We design a novel meta-analysis measure based on vector addition: when a gene peaks or troughs in all experiments in the same phase of the cell cycle, the representative vectors add to produce a large final vector. Conversely, when the peaks in different experiments are in various phases of the cycle, vector addition produces a small final vector. We apply the measure to ten genome-wide cell cycle time course experiments from the fission yeast Schizosaccharomyces pombe, and detect many new, weakly oscillating genes. Conclusion: A very large fraction of all genes in S. pombe, perhaps one-quarter to one-half, show some cell cycle oscillation, although in many cases these oscillations may be incidental rather than adaptive.