Dataset describing the genome wide effects on transcription resulting from alterations in the relative levels of the bZIP transcription factors Atf1 and Pcr1 in Schizosaccharomyces pombe

Schizosaccharomyces pombe has been used as an excellent model for studying eukaryotic cell cycle regulation and stress responses. The bZIP transcription factors Atf1(ATF2 homolog) and Pcr1(CREB homolog) have been shown to be important for regulating the expression of genes related to both stress response and cell cycle. Pcr1 has in fact been implicated as a determining factor in the segregation of the cell cycle and stress response related functions of Atf1. Interestingly Atf1 and Pcr1 levels are known to vary during the cell cycle thus giving rise to the possibility that their relative levels can influence the periodic transcriptional program of the cell. Here we report our observations on the changes in transcriptome of S. pombe cells which have been genetically manipulated to create relative differences in the levels of Atf1 and Pcr1. These results highlight new information regarding the potential role of Atf1 and Pcr1 in orchestrating the integration of the transcriptional programs of cell cycle and stress response.


a b s t r a c t
Schizosaccharomyces pombe has been used as an excellent model for studying eukaryotic cell cycle regulation and stress responses. The bZIP transcription factors Atf1(ATF2 homolog) and Pcr1(CREB homolog) have been shown to be important for regulating the expression of genes related to both stress response and cell cycle. Pcr1 has in fact been implicated as a determining factor in the segregation of the cell cycle and stress response related functions of Atf1. Interestingly Atf1 and Pcr1 levels are known to vary during the cell cycle thus giving rise to the possibility that their relative levels can influence the periodic transcriptional program of the cell. Here we report our observations on the changes in transcriptome of S. pombe cells which have been genetically manipulated to create relative differences in the levels of Atf1 and Pcr1. These results highlight new information regarding the potential role of Atf1 and Pcr1 in orchestrating the integration of the transcriptional programs of cell cycle and stress response.  Table   Subject Biology Specific subject area Molecular biology Type of data Table  Venn diagram Graph How the data were acquired Data was acquired using Next Generation Sequencing TruSeq stranded mRNA preparation protocol was used to capture RNA, then the mRNA was purified and the cDNA library was prepared. The RNA sequence data were generated as a Fastq file. The quality of the data was checked. Read mapping to the reference genome was done using Cuffdiff. Gene ontology annotations were assigned using Uniprot, and the data analysis report was created.

Value of the Data
• The data reflects the gene expression landscape of S. pombe strains with altered levels of Atf1 and Pcr1, which are homologs of mammalian ATF2 and CREB, thus expanding our knowledge about individual functional roles of these two transcription factors in a living cell. Deregulation of both ATF2 and CREB is associated with multiple developmental disorders and tumorigenesis. Clear understanding of the interplay between these two transcription factors and its effect on the cell's transcription program is therefore very important. • The analysis of the data presented in this report identifies genes whose expression can be regulated by Pcr1 independently of Atf1. This is an important information as in earlier reports Pcr1 functions have been mostly characterized in the context of promoter specificity of Atf1. • Analysis of this dataset clearly shows the control exerted by Pcr1 on the expression of genes important for many important fundamental biological processes like stress response and cell cycle. • These data provide an entry point into investigations aimed at understanding how balance of the two transcription factors Atf1 and Pcr1 can regulate cell fate and proliferation. Extrapolation of these data can also facilitate studies aimed at understanding the contribution of ATF2 and CREB in disease progression.

Data Description
Studies done in our lab have established Pcr1 to be important in combating stress responses and to have contrasting outcomes on cell cycle progression [1] . In this study, we used genetic manipulations to vary the relative levels of Atf1 and Pcr1 in S. pombe cells. To study the effects of increase in Pcr1 levels, it was overexpressed in wt and atf1 cells and the transcriptional profiles of these cells were characterised. The effect of decrease in Atf1 levels was studied by comparing the gene expression profile of wt and atf1 cells. The effect of complete absence of both these transcription factors was studied by comparing the transcriptomes of wt and atf1 pcr1 cells.The group of genes identified to be induced and repressed in each set of experiments are reported in ( Tables 1-8 ). We performed a comparative analysis between the datasets obtained between different backgrounds, looking for unique genes . We found only 4 genes to be commonly upregulated by Pcr1 overexpression in both wt and atf1 cells ( Fig. 1 A). 8 genes were found to be downregulated only in the double mutant ( Fig. 1 B). Comparison of  these data revealed the identity of genes that can be positively regulated by Pcr1 independently of Atf1 ( Table 9 ). The genes found to be regulated independently by Pcr1 were then analyzed to identifiy the cellular processes associated with the gene expression changes using DAVID [2 , 3] . DAVID analysis classified the genes to be important in several biological processes( Fig. 1 C). The known expression changes of these genes during stress response [4] and cell division [5] was then looked up and the genes were then classified into Stress reponse and Cell cycle categories. We found that groups of genes are important during the stress response, the cell cycle, or both ( Fig. 1 D). 28 genes were found to be upregulated only in the atf1 pcr1 when compared to genes upregulated in atf1 cells ( Fig. 2 A). DAVID analysis identified several pathways that are downregulated by Pcr1 ( Fig. 2 B). These genes were also classified according to their previously known association with cell cycle and stress response ( Fig. 2 C). Genes that are downregulated by Pcr1 independently of Atf1 are listed in Table 10 . We compared the genes regulated by Pcr1 ( Tables 9 , 10 ) with those of the existing datasets of Atf1 dependent gene expression from studies previously conducted by us and other groups [4 , 6] . This comparison reveals that there are a few   genes whose expression is regulated in a contrasting manner by Atf1 and Pcr1 ( Tables 11 , 12 ). We compared our gene list obtained from this study with existing data for Atf1-dependent gene expression [4] and found 75 new genes that are upregulated by Atf1 and 34 new genes that are downregulated by it ( Fig. 3 A, B) in absence of stress. Genes upregulated and downregulated in each of the experimental backgrounds are mentioned in the tables below .  [7] to find out the overlaps between different datasets. (A) Overlap between Pcr1-OP in wt and Pcr1-OP in atf1 cells showed 36 genes to be upregulated by Pcr1, independent of regulation by Atf1. (B). Upon comparing atf1 and atf1 pcr1 , we found 8 genes to be uniquely downregulated in the latter, which could be considered as targets induced solely by Pcr1. (C) Genes found to be positively upregulated by Pcr1 independently of Atf1 were sorted into significant functional clusters obtained from DAVID based analysis of genes represented in Table 9 . (D) Graph represents the association of the genes positively upregulated by Pcr1 independently of Atf1 with cell cycle and/ or stress response or both.

Experimental design
Differential gene expression studies based on RNA sequencing were carried out following overexpression experiments in a series of S. pombe transformants and mutants. All samples were processed in duplicates.

Strains, media and growth conditions
S. pombe strains used in this study are listed in ( Table 13 ). Cells were grown as described in [8] . For overexpression experiments, cells were grown overnight in Edinburgh Minimal Medium, EMM (Leu-) supplemented with 20 μM thiamine, harvested, washed, resuspended in EMM (Leu-) and incubated for 24 h at 30 °C. Cells were thereafter harvested, washed and resuspended in RNAlater Stabilization Solution (Thermo Scientific). ). 5μl of denatured salmon sperm DNA (10 mg/ml) was added to it. 1 μg of the purified plasmid DNA was then added to this mixture and allowed to stand overnight at room temperature, after which the cells were resuspended in 150 μl YES and spread onto appropriate selection plates.

RNA isolation
TRIzol TM Reagent (Invitrogen) was used for RNA isolation. After homogenizing the sample with TRIzol TM reagent, chloroform was added, and the homogenate was allowed to separate into a clear upper aqueous layer (containing RNA), an interphase, and a red lower organic layer (containing the DNA and proteins). RNA was thereafter precipitated from the aqueous layer with isopropanol. Furthermore, the steps of cDNA library preparation and Next Generation Sequencing and Analysis were done by Agrigenome.

Library preparation
TruSeqstranded mRNA sample preparation protocol was used to capture coding RNA and multiple forms of noncoding polyadenylated RNA using poly-T oligo attached magnetic beads. After fragmentation of mRNA, first-strand cDNA was done using reverse transcriptase (strand specificity was obtained by replacing dTTP with dUTP, followed by second-strand cDNA synthesis using DNA Polymerase I and RNase H. Then adenylation of the 3' ends are done following ligation of adapters. The products are then purified and enriched with PCR to create the final cDNA library. Finally, quality control analysis and quantification of the DNA library templates were performed to create optimum cluster densities across every lane of flow cell.

Data analysis
Raw sequence data generation was done using Fastq [9] file followed by data quality check.
Mapping is done to the reference genome using Kim et al [10] . to evaluate sample quality, followed by differential expression analysis using cuffdiff [11 , 12] Gene Ontology Annotations were assigned using Uniprot [13] and the report of the analysis was produced. Correlation analyses were performed to check the variability between replicates and across samples The box plot was used to show the distribution of data based on the five number summary. Log transformation is performed to make the variation similar across orders of magnitude (See Supplementary Figure S1 ). The correlation between the samples being compared was revealed by the scatter plot. The samples being compared are said to be highly correlated if the data falls in a straight line (See Supplementary Figure S2 ). The distance matrix plot showed the correlation between the samples being compared. (See Supplementary Figure S3 ). The matrix plot describes the number of significant genes at 5% FDR for each pairwise interaction tested. It gives a quick view of the number of significant features at a given q value cutoff < = 0.05 (See Supplementary Figure S4 ). The Volcano plot helps visualize the statistically significant differentially expressed genes. The plot is constructed by plotting -log10 ( p -value) on the y-axis, and the log2 fold change between the two samples on the X-axis. Genes that pass the filtering of q -value < 0.05 are indicated on the plot in red (See Supplementary Figure S5 ). Further analysis was performed in lab. Genes with significant fold changes were taken for analysis and a cut off of ≥1.5 fold for up-regulated genes and ≤0.75 fold for down-regulated genes was set for further analysis of the differential expression in the gene sets. Gene clusters and functions were generated using DAVID Functional Annotation Bioinformatics tool (David v6.8) [2 , 3] . Lock et al [14] . was used to assign and verify specific functions of the respective genes. Gene expression profiles during cell cycle and stress were explored using Chen Lab Resources [4 , 5] . Hulsen et al [7] . application was used for the comparison and visualization of gene lists using area proportional Venn diagrams.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.