SWI/SNF senses carbon starvation with a pH-sensitive low complexity sequence

Purpose: It is increasingly appreciated that intracellular pH changes are important biological signals. This motivates the elucidation of molecular mechanisms of pH-sensing. We determined that a nucleocytoplasmic pH oscillation was required for the transcriptional response to carbon starvation in S. cerevisiae. Methods: We performed RNA sequencing analysis to determine the extent of the requirement for theSNF5 QLC in the activation of glucose-repressed genes. Total RNA was extracted from WT, ΔQ-snf5 and HtoA-snf5 strains during exponentially growth (+Glu) and after 4 hours of glucose starvation. Next, Poly-A selection was performed using Dynabeads and libraries were performed following manufactures indications. Sequencing of the 32 samples was performed on an Illumina Hi-seq on two lanes. RNA-seq data were aligned to the University of California, Santa Cruz (UCSC), sacCer2 genome using Kallisto (0.43.0,http://www.nature.com/nbt/journal/v34/n5/full/nbt.3519.html) and downstream visualization and analysis was done using R (3.2.2). Differential gene expression analysis, heat maps and volcano plots were created using DESeq2 where a wald test was used to determine differentially expressed genes and Euclidean distance to calculate clustering for heat maps. Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 16,014 transcripts in the retinas of WT and Nrl−/− mice with BWA workflow and 34,115 transcripts with TopHat workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 12 of these were validated with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR for more than four orders of magnitude and a goodness of fit (R2) of 0.8798. Approximately 10% of the transcripts showed differential expression between the WT and Nrl−/− retina, with a fold change ≥1.5 and p value <0.05. Altered expression of 25 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to retinal function. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: We found that pH changes and the SNF5 QLC are required for correct transcriptional reprogramming upon carbon starvation, but the dependencies are nuanced.