PREDICTING THE P-EIF2Α TRANSLATOME: A COMPUTATIONAL FRAMEWORK REVEALS KEY DRIVERS OF SYNAPTIC PLASTICITY AND NEURODEGENERATION

We focused on the integrated stress response (ISR), governed by phosphorylation of eukaryotic initiation factor 2 alpha (p-eIF2α) that reprograms protein synthesis. Given its role in neuronal plasticity, a systematic identification of its target mRNAs was needed. Our goal was to develop a computational predictor for mRNAs whose 5’ untranslated regions (UTRs) confer sensitivity to p-eIF2α.
We compiled a human 5’UTR database from Ensembl. Using published ribosome and RNA sequencing data, we generated a training dataset. A logistic regression model was trained on 5’UTR features: length, GC content, upstream open reading frames (uORFs), and similarity to ATF4. The model was validated.
The tool pinpointed p-eIF2α–dependent mRNAs, predicting 473 high-confidence targets from the transcriptome. Functional analysis revealed these transcripts are associated with neural development, synapse formation, and ion transport. Key proteins include synaptic scaffolds SHANK1 and LRRC4, ion channels GRIN2A and KCNC4, and heparan sulphate proteoglycans. Interactome analysis connected these targets to pathways underlying synaptic plasticity and Alzheimer's disease pathology.
This resource provides a tool for advancing ISR research and supports discovering novel therapeutic strategies.
This work was supported by Spanish Ministry of Science and Innovation and Agencia Estatal de Investigación plus FEDER Funds through grants PID2023-149767OB-I00 (FJM) and ‘Unidad de Excelencia María de Maeztu’ CEX2024-001431-M funded by MICIU/AEI/10.13039/501100011033 and by ’ERDF A way of making Europe’.