CMMC: Andreas Beyer / Argyris Papantonis

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.

Publications 2019 (January – August)

Ahuja, G., Bartsch, D., Yao, W., Geissen, S., Frank, S., Aguirre, A., Russ, N., Messling, J.E., Dodzian, J., Lagerborg, K.A., Vargas, N.E., Muck, J.S., Brodesser, S., Baldus, S., Sachinidis, A., Hescheler, J., Dieterich, C., Trifunovic, A., Papantonis, A., Petrascheck, M., Klinke, A., Jain, M., Valenzano, D.R., and Kurian, L. (2019). Loss of genomic integrity induced by lysosphingolipid imbalance drives ageing in the heart. EMBO Rep 20.

Frank, S., Ahuja, G., Bartsch, D., Russ, N., Yao, W., Kuo, J.C., Derks, J.P., Akhade, V.S., Kargapolova, Y., Georgomanolis, T., Messling, J.E., Gramm, M., Brant, L., Rehimi, R., Vargas, N.E., Kuroczik, A., Yang, T.P., Sahito, R.G.A., Franzen, J., Hescheler, J., Sachinidis, A., Peifer, M., Rada-Iglesias, A., Kanduri, M., Costa, I.G., Kanduri, C., Papantonis, A., and Kurian, L. (2019). yylncT Defines a Class of Divergently Transcribed lncRNAs and Safeguards the T-mediated Mesodermal Commitment of Human PSCs. Cell Stem Cell 24, 318-27 e8.

Gothe, H.J., Bouwman, B.A.M., Gusmao, E.G., Piccinno, R., Petrosino, G., Sayols, S., Drechsel, O., Minneker, V., Josipovic, N., Mizi, A., Nielsen, C.F., Wagner, E.M., Takeda, S., Sasanuma, H., Hudson, D.F., Kindler, T., Baranello, L., Papantonis, A., Crosetto, N., and Roukos, V. (2019). Spatial Chromosome Folding and Active Transcription Drive DNA Fragility and Formation of Oncogenic MLL Translocations. Mol Cell 75, 267-83 e12.

Lalioti, M.E., Arbi, M., Loukas, I., Kaplani, K., Kalogeropoulou, A., Lokka, G., Kyrousi, C., Mizi, A., Georgomanolis, T., Josipovic, N., Gkikas, D., Benes, V., Politis, P.K., Papantonis, A., Lygerou, Z., and Taraviras, S. (2019). GemC1 governs multiciliogenesis through direct interaction with and transcriptional regulation of p73. J Cell Sci 132.

Lalioti, M.E., Kaplani, K., Lokka, G., Georgomanolis, T., Kyrousi, C., Dong, W., Dunbar, A., Parlapani, E., Damianidou, E., Spassky, N., Kahle, K.T., Papantonis, A., Lygerou, Z., and Taraviras, S. (2019). GemC1 is a critical switch for neural stem cell generation in the postnatal brain. Glia10.1002/glia.23690.

Soste, M., Charmpi, K., Lampert, F., Gerez, J.A., van Oostrum, M., Malinovska, L., Boersema, P.J., Prymaczok, N.C., Riek, R., Peter, M., Vanni, S., Beyer, A., and Picotti, P. (2019). Proteomics-Based Monitoring of Pathway Activity Reveals that Blocking Diacylglycerol Biosynthesis Rescues from Alpha-Synuclein Toxicity. Cell Syst 9, 309-20 e8.

Publications 2018

Guo T, Li L, Zhong Q, Rupp NJ, Charmpi K, Wong CE, Wagner U, Rueschoff JH, Jochum W, Fankhauser CD, Saba K, Poyet C, Wild PJ, Aebersold R, and Beyer A (2018). Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers. Life Sci Alliance 1.

Hahn O, Stubbs TM, Reik W, Gronke S, Beyer A, and Partridge L (2018). Hepatic gene body hypermethylation is a shared epigenetic signature of murine longevity. PLoS Genet 14, e1007766.

Hohne M, Frese CK, Grahammer F, Dafinger C, Ciarimboli G, Butt L, Binz J, Hackl MJ, Rahmatollahi M, Kann M, Schneider S, Altintas MM, Schermer B, Reinheckel T, Gobel H, Reiser J, Huber TB, Kramann R, Seeger-Nukpezah T, Liebau MC, Beck BB, Benzing T, Beyer A, and Rinschen MM (2018). Single-nephron proteomes connect morphology and function in proteinuric kidney disease. Kidney Int 93, 1308-1319.

Michieletto, D., Chiang, M., Coli, D., Papantonis, A., Orlandini, E., Cook, P.R., and Marenduzzo, D. (2018). Shaping epigenetic memory via genomic bookmarking. Nucleic Acids Res 46, 83-93.

Rada-Iglesias, A., Grosveld, F.G., and Papantonis, A. (2018). Forces driving the three-dimensional folding of eukaryotic genomes. Mol Syst Biol 14, e8214.

Spath MR, Bartram MP, Palacio-Escat N, Hoyer KJR, Debes C, Demir F, Schroeter CB, Mandel AM, Grundmann F, Ciarimboli G, Beyer A, Kizhakkedathu JN, Brodesser S, Gobel H, Becker JU, Benzing T, Schermer B, Hohne M, Burst V, Saez-Rodriguez J, Huesgen PF, Muller RU, and Rinschen MM (2018). The proteome microenvironment determines the protective effect of preconditioning in cisplatin-induced acute kidney injury. Kidney Int10.1016/j.kint.2018.08.037.

Zirkel, A., and Papantonis, A. (2018). Detecting Circular RNAs by RNA Fluorescence In Situ Hybridization. Methods Mol Biol 1724, 69-75.

Zirkel, A., Nikolic, M., Sofiadis, K., Mallm, J.P., Brackley, C.A., Gothe, H., Drechsel, O., Becker, C., Altmuller, J., Josipovic, N., Georgomanolis, T., Brant, L., Franzen, J., Koker, M., Gusmao, E.G., Costa, I.G., Ullrich, R.T., Wagner, W., Roukos, V., Nurnberg, P., Marenduzzo, D., Rippe, K., and Papantonis, A. (2018). HMGB2 Loss upon Senescence Entry Disrupts Genomic Organization and Induces CTCF Clustering across Cell Types. Mol Cell 70, 730-44 e6.

Publications 2017

Franzen, J., Zirkel, A., Blake, J., Rath, B., Benes, V., Papantonis, A., and Wagner, W. (2017). Senescence-associated DNA methylation is stochastically acquired in subpopulations of mesenchymal stem cells. Aging Cell 16, 183-91.

Gabriel, E., Ramani, A., Karow, U., Gottardo, M., Natarajan, K., Gooi, L.M., Goranci-Buzhala, G., Krut, O., Peters, F., Nikolic, M., Kuivanen, S., Korhonen, E., Smura, T., Vapalahti, O., Papantonis, A., Schmidt-Chanasit, J., Riparbelli, M., Callaini, G., Kronke, M., Utermohlen, O., and Gopalakrishnan, J. (2017). Recent Zika Virus Isolates Induce Premature Differentiation of Neural Progenitors in Human Brain Organoids. Cell Stem Cell 20, 397-406 e5.

Hahn O, Gronke S, Stubbs TM, Ficz G, Hendrich O, Krueger F, Andrews S, Zhang Q, Wakelam MJ, Beyer A, Reik W, and Partridge L (2017). Dietary restriction protects from age-associated DNA methylation and induces epigenetic reprogramming of lipid metabolism. Genome Biol 18, 56.

Nikolic, M., Papantonis, A., and Rada-Iglesias, A. (2017). GARLIC: a bioinformatic toolkit for aetiologically connecting diseases and cell type-specific regulatory maps. Hum Mol Genet 26, 742-52.

Tain LS, Sehlke R, Jain C, Chokkalingam M, Nagaraj N, Essers P, Rassner M, Gronke S, Froelich J, Dieterich C, Mann M, Alic N, Beyer A, and Partridge L (2017). A proteomic atlas of insulin signalling reveals tissue-specific mechanisms of longevity assurance. Mol Syst Biol 13, 939.