bioRxiv ScienceSearch

Biology subjects

Vähärautio, A.

Publications and source records attributed to Vähärautio, A..

2 recordsLinked to original sources

Myc-dependent cell competition and proliferative response requires induction of the ribosome biogenesis regulator Peter Pan

The transcription factor Myc is activated in most major forms of human cancer. Myc regulates a large set of target genes, and drives cell growth across animal phyla. However, it has not been clear which target genes are required for Myc-induced growth, and whether the targets are individually necessary or act in an additive fashion. Here, we have used comparative functional genomics to identify a core set of Myc target genes whose regulation is conserved between humans and Drosophila melanogaster. Most of these targets are essential genes involved in ribosome biogenesis and ribonucleotide metabolism. To identify Drosophila genes whose upregulation is necessary for Myc induced growth, we deleted the Myc binding sites (E-boxes) in the promoter regions of four genes using CRISPR/Cas9. All mutant flies were homozygous viable, indicating that E-box sequences are not required for basal expression of the Myc target genes. E-Box deletions in RpS20, RpS24 and Nop56 did not cause strong growth phenotypes. However, deletion of the E-box in the rRNA processing factor Peter Pan (ppan) made the flies resistant to Myc-induced cell growth, without affecting Myc-induced apoptosis. Despite their failure to respond to Myc, the ppanEbox-/- flies are healthy and display only a minor developmental delay, suggesting that it may be possible to treat or prevent tumorigenesis by targeting individual downstream targets of Myc.

cell biology

PRISM: Recovering cell type specific expression profilesfrom composite RNA-seq data

A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell type specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell type specific expression through whole-genome sequencing and RNA in situ hybridization experiments. PRISM is freely available with full source code and documentation.

bioinformatics