Andreas Beyer / Argyris Papantonis - C 4

Contribution of deteriorating RNA biosynthesis to cellular ageing

Using transcriptome data from 5 animal species we have shown that the speed of RNA polymerase II(Pol-2) elongation increases with ageing. In this project we plan to further analyse this phenomenon and to better elucidate molecular mechanisms. Using additional molecular profiling data and integration with published data we are going to elucidate the mechanisms underlying the Pol-2 speed changes.

Introduction

Multiple cellular processes have been implicated in age-associated degeneration of tissues, such as cellular senescence, depletion of stem cells, or declining cell-cell interactions. However, we know surprisingly little about the molecular causes leading to these changes. Whereas molecular pathways responsible for some of these changes are well studied (such as DNA repair, TOR signalling, apoptosis, and others), we lack sufficient understanding of the factors impacting on or altering these pathways.
Previous research has been focussing on transcriptional programs that either trigger ageing or which are activated in response to ageing. Little attention has been payed to the process of transcription itself and its role in ageing-associated decline of cellular function and tissue integrity. We recently showed that the quality of transcription itself declines during aging, which has substantial impact on the quality of the transcripts generated from e.g. RNA polymerase II (Pol-2). By analysing transcriptome data from 5 species (C. elegans, D. melanogaster, M. musculus, R. novegicus, H. sapiens) we showed that the elongation speed of Pol-2 increases with age, thereby reducing the quality of splicing. Although the absolute effects were subtle, they affected hundreds of genes and they were strikingly consistent across all of the species studied. Importantly, we could show that lifespan-extending conditions such as dietary restriction or inhibition of insulin signalling reduced Pol-2 elongation speed and improved splicing efficiency. We hypothesize that these changes (which affected particularly regulatory proteins such as transcriptional regulators) have widespread impact on tissue integrity and therefore contribute to age-associated phenotypes.

Bulk-seq-based analysis

As part of this project we have conducted extensive nucleosome profiling and transcriptome profiling of human cell models (HUVEC and IMR90). In particular, we have now extended the analysis by performing total RNA-seq (‘ribozero’) and nascent RNA profiling (‘factory-seq’) on both cell models. The factory-seq protocol was developed by us (A. P.) and it enables a more direct profiling of nascent transcripts than total RNA-seq. The dual profiling of the two models with both protocols allowed us to compare the two protocols. Our analysis confirmed our conclusions with both protocols, which adds robustness to our findings. The resulting manuscript has now been submitted. Details about the nucleosome profiling were already provided in the last report and are also contained in the manuscript.

Single cell transcriptome profiling

Profiling Pol-II at the level of single cells is challenging. Our Pol-II elongation speed measure relies on the detection of a sufficient number of reads from intronic regions in nascent (unspliced) transcripts. Standard single cell sequencing protocols rely on poly-A enrichment of mature transcripts, which precludes nascent transcripts. We therefore devised a different protocol: our protocol (developed by A. P.) extracts single nuclei, rather than single whole cells. Subsequently, all transcripts in the nucleus are poly-A labelled, i.e. even immature nascent transcripts can subsequently be processed with standard protocols. We have performed a first test of this protocol using proliferating (‘young’) and senescent (‘old’) HUVEC cells as a model. After filtering for quality we retrieved sufficient data for 86 young cells and 139 senescent cells.After processing the data with ZINB-WaVE we obtained a clear clustering distinguishing ‘old’ from ‘young’ cells, which underlines the quality and utility of the data (Figure 1). Further, we could confirm that on average, the Pol-II speed was higher in the senescent cells compared to the proliferating cells (Figure 2). Unfortunately, it was impossible to quantify Pol-II speed at the level of single cells (i.e. for each cell individually) despite intense efforts to develop tailored computational methods.

Perspectives

We are currently working on improving the single nucleus sequencing protocol. Further, we are integrating other (published) chromatin information with the RNA-seq data.

Selected publications (CMMC-project related)

1. Sofiadis K, […], Beyer A, Papantonis A. HMGB1 as a rheostat of chromatin topology and RNA homeostasis on the path to senescence. https://doi.org/10.1101/540146 (Submitted).

2. Debès C, […], Papantonis A, […], Beyer A. Aging-associated changes in transcriptional elongation influence metazoan longevity. (Submitted).

3. Debès C*, Leote AC*, Beyer A(2019) Computational approaches for the systematic analysis of ageing-associated molecular alterations (Review).Drug Discov. Tod.: Disease Mod. 27:51-59;

4. Zirkel A, […], Papantonis A: HMGB2 Loss upon Senescence Entry Disrupts Genomic Organization and Induces CTCF Clustering across Cell Types. Molecular Cell (2018) 70(4):730-744.


Prof. Dr. Andreas Beyer

CECAD Cologne / RG location

Prof. Dr. Andreas Beyer

Principal Investigator C 4

andreas.beyer@uni-koeln.de

Publications - Andreas Beyer

Link to PubMed


Prof. Dr. Argyris Papantonis

Center for Molecular Medicine Cologne

Prof. Dr. Argyris Papantonis

Co-Principal Investigator C 4 /
Principal Investigator CMMC JRG VIII
(08/2013-10/2018)

argyris.papantonis@uni-koeln.de

Publications - Argyris Papantonis

Link to PubMed

Group Members

Antonios Papadakis (doctoral student)

Figure 1

CMMC Research Odenthal

Figure 1: Single-cell transcriptome analysis. Clustering of single cells based on expression similarity using ZINB-WaVE. Proliferating (‘young’) and senescent (‘old’) cells are clearly separated.

Figure 2

CMMC Research Odenthal

Figure 2: Median (top) and mean (bottom) slopes of intronic read distributions. More shallow slopes reflect faster Pol-II elongation. I.e. senescent HUVEC cells are characterised by faster elongating polymerases compared to proliferating cells.