Data-driven identification of inherent features of eukaryotic stress-responsive genes
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ABSTRACT:
PROVIDER: PRJNA719547 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR14139700.fastq.gz | Fastqsanger.gz | |||
| SRR14139701.fastq.gz | Fastqsanger.gz | |||
| SRR14139684.fastq.gz | Fastqsanger.gz | |||
| SRR14139685.fastq.gz | Fastqsanger.gz | |||
| SRR14139686.fastq.gz | Fastqsanger.gz |
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Project description:Living organisms are continuously challenged by changes in their environment that can propagate to stresses at the cellular level, such as rapid changes in osmolarity or oxygen tension. To survive these sudden changes, cells have developed stress-responsive mechanisms that tune cellular processes. The response of Saccharomyces cerevisiae to osmostress includes a massive reprogramming of gene expression. Identifying the inherent features of stress-responsive genes is of significant interest for understanding the basic principles underlying the rewiring of gene expression upon stress. Here, we generated a comprehensive catalog of osmostress-responsive genes from 5 independent RNA-seq experiments. We explored 30 features of yeast genes and found that 25 (83%) were distinct in osmostress-responsive genes. We then identified 13 non-redundant minimal osmostress gene traits and used statistical modeling to rank the most stress-predictive features. Intriguingly, the most relevant features of osmostress-responsive genes are the number of transcription factors targeting them and gene conservation. Using data on HeLa samples, we showed that the same features that define yeast osmostress-responsive genes can predict osmostress-responsive genes in humans, but with changes in the rank-ordering of feature-importance. Our study provides a holistic understanding of the basic principles of the regulation of stress-responsive gene expression across eukaryotes.
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Project description:Interactions between RNAs and RNA binding proteins (RBPs) regulate gene expression in eukaryotic cells. RNA-RBP affinities measured in vitro reveal diverse binding specificities, yet approaches to directly compare specificities across RBPs are lacking. Here, we introduce two quantitative metrics: inherent specificity, which measures how selectively an RBP distinguishes its strongest binding motif from all possible motifs, and mutational sensitivity, which assesses tolerance to single nucleotide variations within preferred motifs. Analyzing high-throughput sequencing datasets, we compared these metrics across 100 RBPs in vitro and 27 RBPs in cells, finding strong correlation between in vitro and cellular measurements for RBPs that bind RNA independently of a local structural context. Through CLIP experiments with swapped RNA recognition motifs between a low-specificity RBP (RBM25) and a high-specificity RBP (HNRNPC), we demonstrated that sequence specificity can be transferred between protein contexts. Using these insights, we developed mathematical models showing how RBPs with different specificity profiles compete for binding sites, revealing how variations in inherent specificity and mutational sensitivity influence target selection. Together, our results provide a quantitative framework for modeling RNA-RBP interactions and designing RBPs with targeted specificity.
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