Rapidly regulated genes are intron poor

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1 Supplementary material Rapidly regulated genes are intron poor Daniel C. Jeffares 1 *, Christopher J. Penkett 1,2 * and Jürg Bähler 1,3 1 Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK 2 Current address: European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK 3 Current address: UCL Cancer Institute and Department of Genetics, Evolution and Environment, University College London, London WC1E 6BT, UK Corresponding author: Jeffares, D.C. (dcj@sanger.ac.uk). *These authors contributed equally to this work. 1

2 Supplementary Methods Genome data The following genome data were used: Saccharomyces cerevisiae, 26/5/2007 ( Schizosaccharomyces pombe, 31/3/2007 ( Arabidopsis thaliana, 4/11/2005 ( Mus musculus, 30/11/2006 ( Expression data S. cerevisiae data are from [1]. The median Affymetrix signals of all control data points at t = 0 were used to estimate the total expression levels. All data are available here: S. pombe data are from [2]. Affymetrix signals from unstressed cells in minimal medium were used to estimate the total expression levels [3]. All data are available here: A. thaliana data are from Nottingham Arabidopsis Stock Centre's microarray database [4] ; using all Stress Series data from the AtGenExpress Project (cold, osmotic, salt, drought, genotoxic, oxidative, UV- B, wounding and heat stress) [5]. The median signals of all control plant data points were used to estimate the total expression level for each gene. M. musculus data are from The NCBI Gene Expression Omnibus (GEO, data sets GDS1015 (serum response factor SRF induction in embryonic stem cells) [6] and GDS683 (oxidative stress in liver cells induced by 3-nitropropionic acid, using wild-type data only) [7]. The mean of t = 0 time points from both experiments was used estimate the total expression level. The ranges of the 20 intron density bins are based on the maximum intron density. The first density bin is 0-1/20th of the maximum intron density, the second density bin is 1/20th-2/20th of the maximum intron density, etc. R max calculation The maximum rate of transcriptional change per unit time for each gene was estimated as follows. Where E is the array signal for a time point (t) following a stress stimulus, f = 2

3 log 2 (E), then R i = ( f/ t) = (f i+1 - f i )/(t i+1 - t i ) and R stress = max i ( R i ). R max is the highest R stress value over all experiments. Analysis of robustness in R max estimates A possible artefact of the R max estimates is that low-expression genes will have a higher experimental variance, which would result in greater error in R max estimates. To show that correlations with R max estimates are not primarily due to low-expression genes, we conducted our analysis using only those genes with expression levels greater than the median for each species. We examined all correlations between R max and intron number or intron density, and the Mann-Whitney test for a difference between R max in intron-less genes and introncontaining genes. All data resulting from these tests were very similar to the data obtained using all genes, correlations remained significant (P <0.05) and in the same direction (data not shown). Plot sub-sampling For Figure 2, we used a sub-sampling technique to eliminate the potential for misleading plots resulting from unequal numbers of genes in each category (intron number or intron density bin); that is, categories that contain more members are more likely to conain higher outliers. We selected 300 genes at random from each intron category, with replacement, which allowed for consistent treatment of all genomes shown. For each category, a median, lower (25th percentile) and upper (75th percentile) quartile were calculated using the statistics package R. One thousand such trials were performed, and the means of the medians as well as lower and upper quartiles of each bin were calculated and used for the box plots. Plot whiskers were the data point furthest from the box up to 1.5x the interquartile range away from the box in both directions. The bold curved lines were calculated as best fit of exponential models (including a positive constant coefficient) for medians of intron categories from the entire dataset (rather than the sub-sampled categories). 3

4 Supplementary Table 1. Spearman rank correlations between intron and geneexpression properties. Budding yeast Intron length Intron number Intron density Expression level R max Intron length Intron number Intron density Expression level R max 1.00 Fission yeast Intron length Intron number Intron density Expression level R max Intron length Intron number Intron density Expression level R max 1.00 Thale cress Intron length Intron number Intron density Expression level R max Intron length Intron number Intron density Expression level R max 1.00 Mouse Intron length Intron number Intron density Expression level R max Intron length (ns) 0.15 Intron number (ns) 0.12 Intron density (9x10-3 ) Expression level (10-4 ) R max 1.00 All budding yeast p-values are <5 x10-9, all fission yeast p-values are <2.2 x10-16 except where indicated (ns, not significant P >0.05), all thale cress p-values are <2.2 x 10-16, all 4

5 mouse p-values are <2 x The principle correlations that are discussed in the main text are indicated in bold type. 5

6 Supplementary Figures 6

7 Supplementary Figure 1. Box plots of R max vs intron number or density as described in Figure 2, but showing all data for the four organisms analyzed. Since larger categories are more likely to contain outliers with high R max values, outliers in these plots can be misleading, but means and quartiles are not. Spearman rank correlations are indicated in Supplementary Table 1. (a) Budding yeast stress data; R max values inversely correlate with intron number and density, and intron-less genes have significantly higher R max than intron-containing genes (Mann- Whitney, P = 4.6x10-9, median R max intron-less genes 0.17, intron-containing genes 0.13). (b) Fission yeast stress data; R max values inversely correlate with intron number and density, and intron-less genes have significantly higher R max than intron-containing genes (median R max of intron-less genes = 0.071, intron-containing genes = 0.047, Mann-Whitney, P <2.2x10-16 ). (c) Thale cress stress data; R max values inversely correlate with intron number and density, and intron-less genes have significantly higher R max than intron-containing genes (Mann- Whitney, P <2.2x10-16 ). (d) Mouse stress data; R max values positively correlate with intron number, but inversely correlate with intron density, and genes in the lowest two density bins have significantly higher R max than genes in the other bins (median Rmax of the lowest two density bin genes = 0.13, all other genes = 0.11, Mann-Whitney, P = 2.6x10-11 ). (e) Fission yeast cell cycle data; R max values inversely correlate with intron number and density, and intron-less genes have higher significantly higher R max than intron-containing genes (Mann-Whitney, P = 3.9x10-5 ). 7

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9 Supplementary Figure 2. Principal components analysis of introns, transcripts and gene expression. Plots show the relationship between unspliced transcript length, intron number, intron density, inverse log R max and total gene expression level. (a) S. cerevisiae, (b), S. pombe (c), A. thaliana, (d), M. musculus. References 1. Causton, H.C., et al. (2001) Remodeling of yeast genome expression in response to environmental changes. Molecular biology of the cell 12, Chen, D., et al. (2003) Global transcriptional responses of fission yeast to environmental stress. Molecular biology of the cell 14, Lackner, D.H., et al. (2007) A network of multiple regulatory layers shapes gene expression in fission yeast. Molecular cell 26, Craigon, D.J., et al. (2004) NASCArrays: a repository for microarray data generated by NASC's transcriptomics service. Nucleic acids research 32, D Schmid, M., et al. (2005) A gene expression map of Arabidopsis thaliana development. Nature genetics 37, Philippar, U., et al. (2004) The SRF target gene Fhl2 antagonizes RhoA/MALdependent activation of SRF. Molecular cell 16, Madsen, M.A., et al. (2004) Altered oxidative stress response of the long-lived Snell dwarf mouse. Biochemical and biophysical research communications 318,