Biology subjects

Fagre, C.

Publications and source records attributed to Fagre, C..

3 recordsLinked to original sources

bioRxiv2026

Identification of rare pseudouridylated microRNA by comprehensive small RNA bisulfite sequencing of mouse and human tissues

Pseudouridine ({Psi}) is an important post-transcriptional modification of many noncoding RNAs that is under-characterized in microRNA (miRNA) due to historical limitations in pseudouridine mapping methods. {Psi} modification stabilizes RNA duplex structures and could therefore play an important role in miRNA target binding and repression. To investigate the extent to which mammalian miRNAs are modified with {Psi}, we profiled the modification landscape of short (<30 nt) RNA in human cells and mouse tissues using bisulfite sequencing. Our approach was powered to detect small RNA pseudouridylation based on robust detection of known {Psi} positions in tRNA fragments (tRFs), some of which show tissue-specific patterns of modification. In contrast with tRFs, we find that miRNA pseudouridylation is exceedingly rare, with a single modified miRNA (miR-3068-5p) identified in mouse tissues. Pseudouridylated miR-3068-5p diSerentially repressed predicted miRNA targets with less stable miRNA:mRNA pairing modes. This study fills a long-standing gap in transcriptome-wide {Psi} profiling and reveals a new potential function for {Psi} as a modulator of activity of small regulatory RNAs.

molecular biology↗

bioRxiv2020

Massively parallel phenotyping of variant impact in cancer with Perturb-seq reveals a shift in the spectrum of cell states induced by somatic mutations

Genome sequencing studies have identified millions of somatic variants in cancer, but their phenotypic impact remains challenging to predict. Current experimental approaches to distinguish between functionally impactful and neutral variants require customized phenotypic assays that often report on average effects, and are not easily scaled. Here, we develop a generalizable, high-dimensional, and scalable approach to functionally assess variant impact in single cells by pooled Perturb-seq. Specifically, we assessed the impact of 200 TP53 and KRAS variants in >300,000 single lung cancer cells, and used the profiles to categorize variants into phenotypic subsets to distinguish gain-of-function, loss-of-function and dominant negative variants, which we validated by comparison to orthogonal assays. Surprisingly, KRAS variants did not merely fit into discrete functional categories, but rather spanned a continuum of gain-of-function phenotypes driven by quantitative shifts in cell composition at the single cell level. We further discovered novel gain-of-function KRAS variants whose impact could not have been predicted solely by their occurrence in patient samples. Our work provides a scalable, gene-agnostic method for coding variant impact phenotyping, which can be applied in cancer and other diseases driven by somatic or germline coding mutations.

genomics↗

bioRxiv2020

Using protein interaction networks to identify cancer dependencies from tumor genome data

Genes required for tumor proliferation and survival (dependencies) are challenging to predict from cancer genome data, but are of high therapeutic value. We developed an algorithm (network purifying selection [NPS]) that aggregates weak signals of purifying selection across a genes first order protein-protein interaction network. We applied NPS to 4,742 tumor genomes to show that a genes NPS score is predictive of whether it is a dependency and validated 58 NPS-predicted dependencies in six cancer cell lines. Importantly, we demonstrate that leveraging NPS predictions to execute targeted CRISPR screens is a powerful, highly cost-efficient approach for identifying and validating dependencies quickly, because it eliminates the substantial experimental overhead required for whole-genome screening.

cancer biology↗