Boldina, G. | Search bioRxiv

bioRxiv2022

Uncoupling from transcription protects polyadenylation site cleavage from inhibition by DNA damage

Pre-mRNA 3-end processing by cleavage and polyadenylation (CPA) is a nuclear process in which RNA polymerase II (Pol II) transcripts are cleaved at the polyadenylation site (PAS cleavage) before addition of a poly(A) tail. While PAS cleavage is usually coupled to transcription termination, for some pre-mRNAs it occurs post-transcriptionally, i.e. after pre-mRNA release from chromatin to nucleoplasm through a downstream co-transcriptional cleavage (CoTC) event. DNA-damaging agents such as ultraviolet-C (UV) irradiation trigger rapid shutdown of pre-mRNA 3-end processing. However, specific compensatory mechanisms exist to ensure efficient 3-end processing for some pre-mRNAs encoding proteins involved in the DNA damage response (DDR), such as the p53 tumor suppressor protein. Here, we show that PAS cleavage of the p53 pre-mRNA occurs in part post-transcriptionally, in a PCF11-independent manner, in the nucleoplasm, following a CoTC-type event. Upon UV-irradiation, cells with an engineered deletion of the p53 CoTC site exhibit impaired 3-end processing of the p53 pre-mRNA, decreased mRNA and protein levels of p53 and its transcriptional target, p21, and altered cell cycle progression. Finally, using a transcriptome-wide analysis of PAS cleavage, we identified additional-including DDR related-pre-mRNAs whose PAS cleavage is maintained in response to UV and occurs post-transcriptionally. These findings indicate that CoTC-type cleavage of pre-mRNAs, followed by PAS cleavage in the nucleoplasm, allows specific pre-mRNAs to escape 3-end processing inhibition in response to UV-induced DNA damage.

molecular biology↗

bioRxiv2021

ASIGNTF: AGNOSTIC SIGNATURE USING NTF: A UNIVERSAL AGNOSTIC STRATEGY TO ESTIMATE CELL-TYPES ABUNDANCE FROM TRANSCRIPTOMIC DATASETS

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSMolecular signatures for deconvolution of immune cell types have been proposed, based on a methodology that relies on the biological classification of the cell types being studied. When working with less known biological material, a data-driven approach is needed to uncover the underlying classes and construct ad hoc signatures. ResultsWe introduce a new approach, ASigNTF: Agnostic Signature using Non-negative Tensor Factorization, to perform the deconvolution of cell types from transcriptomics data (RNAseq and microarray). ASigNTF, which is based on two complementary statistical/mathematical tools: non-negative tensor factorization (for dimensionality reduction) and the Herfindahl-Hirschman index (for signature selection), can be applied to any type of tissue as long as transcriptomic data on isolated cells is available. As a direct result of the new method, we propose two new signatures for the deconvolution of immune cell types, one consisting of a relatively small set of 415 genes, which is more compatible with microarray platforms, and a larger set of 915 genes. Using external datasets, our two signatures outperform the CIBERSORT LM22 signature in deconvolution of RNA-seq data. Our signature with 415 genes allows to recognize a larger number of cell types compared to the ABIS microarray signature. ConclusionsThe paper proposes a new method, ASigNTF; applies the method, and also provides a software implementation that allows to identify molecular signatures for deconvolution of complex tissues and specifically up to 16 immune cell types from micro-array or RNA-seq data. HO_SCPLOWIGHLIGHTSC_SCPLOWO_LISeveral signatures of immune cell types have been proposed, which follow a methodology deeply rooted in the known biological classification of the investigated cell types. C_LIO_LIWhen working with less known biological material, a more agnostic, data-driven approach is required to uncover the underlying classes and construct ad hoc signatures. C_LIO_LIWe present ASigNTF, a new agnostic approach to cell type classification and signature selection supported by an application software. C_LIO_LIWe discuss the results of benchmarking our proposed signatures, ABIS-seq and CIBERSORT on external datasets. C_LI

bioinformatics↗