Spinner, A. | Search bioRxiv

bioRxiv2024

Pooled overexpression screening identifies PIPPI as a novel microprotein involved in the ER stress response

Microproteins encoded by short open reading frames (sORFs) of less than 100 codons have been predicted to constitute a substantial fraction of the eukaryotic proteome. However, relevance and roles of the majority of microproteins remain undefined because only a small fraction of these intriguing cellular players have been in-depth characterized so far. Here we use pooled overexpression screens with a library of 11338 sORFs to overcome the challenge of elucidating which of the thousands of putative translated sORFs are biologically functional. As a proof-of-concept, we performed a phenotypic screen to identify sORFs protecting cells from treatment with the nucleotide analogue 6-thioguanine. With this approach, we identified two cytoprotective microproteins: altDDIT3 and PIPPI. PIPPI is encoded as part of the LC16a core duplicon/Morpheus gene cluster, a highly duplicated region of the human genome, which is undergoing rapid positive selection in primates. Our data show that PIPPI interacts with proteins of the endoplasmic reticulum, including protein disulfide isomerase ERp44. Besides providing mechanistic insights on a new microprotein, this study highlights the power of using pooled overexpression screens to identify functional microproteins.

cell biology↗

bioRxiv2024

LOL-EVE: Predicting Promoter VariantEffects from Evolutionary Sequences

Disease-associated genetic variants occur extensively in noncoding regions like promoters, but current methods focus primarily on single nucleotide variants (SNVs) that typically have small regulatory effect sizes. Expanding beyond single nucleotide events is essential with insertions and deletions (indels) representing the logical next step as they are readily identifiable in population data and more likely to disrupt regulatory elements. However, existing methods struggle with indel prediction, and clinical interpretation often requires assessing complete promoter haplotypes rather than individual variants. We present LOL-EVE (Language Of Life for Evolutionary Variant Effects), a conditional autoregressive transformer trained on 13.6 million mammalian promoter sequences that enables both zero-shot indel prediction and complete promoter sequence scoring. We introduce three benchmarks for promoter indel prediction: ultra rare variant prioritization, causal eQTL identification, and transcription factor binding site disruption analysis. LOL-EVEs superior performance demonstrates that evolutionary patterns learned from indels enable accurate assessment of broader promoter function. Application to Genomics England clinical data shows that LOL-EVE can prioritize promoter haplotypes in known developmental disorder genes, suggesting potential utility for clinical variant assessment. LOL-EVE bridges individual variant prediction with haplotype-level analysis, demonstrating how evolution-based genomic language models may assist in evaluating regulatory variants in complex genetic cases.

genomics↗