1. Research background:

A growing number of technologies allow for the genome-scale measurement of biological properties such as protein and mRNA concentrations or phenotypic changes. The genome-wide nature of the available data facilitates a systems perspective: it becomes possible to go beyond individual genes or pathways and to study regulatory processes of the entire system 'cell'. However, up to now the potential is by far not being fully exploited.

Our group adopts a network perspective by studying relationships between proteins and other biomolecules (e.g. DNA, RNA) in silico to reveal the regulatory context of relevant genes. Further, we are investigating how the production of RNA and proteins itself changes with age. During the past years we contributed new computational methods for large-scale data integration, network biology, and statistical genetics. This work is facilitated by a tight network of experimental collaborators, together with whom we develop experiments that support our computational analysis.

2. Research questions addressed by the group:

We wish to understand how aging impacts on the molecular networks in the cell and how these networks are utilized under conditions that extend lifespan. In collaboration with experimental partners (e.g. groups of Adam Antebi, Anne Schaefer, Thomas Benzing, Björn Schumacher) we investigate common molecular mechanisms affected under lifespan-extending conditions across model species (worm, mouse, human).

Further, we are investigating the loss of cellular fidelity with ageing. We have observed that biosynthetic processes get noisier with age. In particular, we could show that the elongation speed of RNA polymerase II (Pol-II) increases with age, which has detrimental consequences for RNA quality. We want to understand the causes and consequences of these changes and their relevance for age-associated diseases.

3. Possible projects:

1. How does cellular damage emerge and how does it spread in the tissue context? We have developed single-cell damage scores that utilize single-cell omics data to determine the physiological status of individual cells. By applying this concept to spatial transcriptomics data we want to investigate how a damaged cell interacts with neighboring cells and to what extent damage starts to spread from cell to cell. In particular we will apply this concept to podocytes, which are critical cells for the functioning of kidneys. As part of a newly funded research consortium (TRR422 PodoSigN) we will study these questions together with strong experimental and clinical partners using latest technologies.
2. What are the consequences of Pol-II speed changes for the ageing organism? Using bulk- and single-cell RNA-sequencing data we would like to explore how changes in Pol-II speed impact on transcript sequences and transcript structure (alternative splicing). We have developed an improved statistical framework for estimating RNA Pol-II elongation speed from total RNA sequencing data. In this project, we want to apply this method to a broad spectrum of existing total RNA sequencing data to identify factors determining Pol-II speed. Examples are gene and intron length (‘gene architecture’), chromatin structure, histone modifications, nucleosome density. Subsequently we aim to identify age-related epigenetic changes that likely contribute to Pol-II speed increase and other alterations of RNA production, including Pol-II stalling and cryptic transcription.

4. Applied methods and model organisms:

Our group works purely computational and uses published data and data from our collaborators. This gives us great flexibility with respect to the systems that we are studying. The data come from C. elegans, D. melanogasta, mouse, human and others.

Most important computational methods include:

• high-throughput data analysis (single-cell and bulk RNA-seq, ChIP-seq, proteomics, ...)
• statistical modelling (linear multi-dimensional regression, regularized regression)
• machine learning (e.g. Random Forest, Gradient Boosting)
• network biology methods (network propagation, network distance measures, information flow on networks)

5. Desirable skills and qualifications:

Proven background in computational biology and programming skills in particular are essential. Since we are analyzing real data, biostatistical skills are also important and prior experience with machine learning is certainly an asset. Further, interest in and knowledge of molecular biology is important in order to aid the interpretation of the data. We are solving real biological problems!

6. Key publications:

• Papantonis A, Antebi A, Partridge L, Beyer A. (2024) Age-associated changes in transcriptional elongation and their effects on homeostasis. (Review) Trends Cell Biol. S0962-8924(24)00247-2. doi: 10.1016/j.tcb.2024.11.005.
• Leote AC, Lopes F, Beyer A. (2024) Loss of coordination between basic cellular processes in human aging. Nat. Aging 4(10):1432-1445. doi: 10.1038/s43587-024-00696-y.
• Unger Avila P, Padvitski T, Leote AC, Chen H, Saez-Rodriguez J, Kann M, Beyer A. (2024) Gene regulatory networks in disease and aging (Review). Nat. Review Nephrology. 20(9):616-633. doi: 10.1038/s41581-024-00849-7
• Weith M, Großbach J, Clement-Ziza M, Gillet L, Rodríguez-López M, Marguerat S, Workman CT, Picotti P, Bähler J, Aebersold R, Beyer A (2023) Genetic effects on molecular network states explain complex traits. Mol Syst Biol. e11493. doi: 10.15252/msb.202211493.
• Debès C*, Papadakis A*, Grönke S*, Karalay Ö*, Tain LS, Mizi A, Nakamura S, Hahn O, Weigelt C, Josipovic N, Zirkel A, Brusius I, Sofiadis K, Lamprousi M, Lu YX, Huang W, Esmaillie R, Kubacki T, Späth MR, Schermer B, Benzing T, Müller RU, Antebi A^#, Partridge L^#, Papantonis A^#, Beyer A^#. (2023) Ageing-associated changes in transcriptional elongation influence longevity. Nature. 616(7958):814-821. doi: 10.1038/s41586-023-05922-y.
• Grossbach J*, Gillet L*, Clément-Ziza M*, Schmalohr CL, Schubert OT, Schütter M, Mawer JSP, Barnes CA, Bludau I, Weith M, Tessarz P, Graef M, Aebersold R^#, Beyer A^#. (2022) The impact of genomic variation on protein phosphorylation states and regulatory networks. Mol Syst Biol. 18(5):e10712. doi: 10.15252/msb.202110712. (Cover story)
• Fraser HC, Kuan V, Johnen R, Zwierzyna M, Hingorani AD, Beyer A^#, Partridge L^#. (2022) Biological mechanisms of aging predict age-related disease co-occurrence in patients. Aging Cell. e13524. doi: 10.1111/acel.13524.
• Leote AC, Wu X, Beyer A (2022) Regulatory network-based imputation of dropouts in single-cell RNA sequencing data. PLoS Comput Biol. 18(2):e1009849.
• Charmpi K, Chokkalingam M, Johnen R, Beyer A. (2021) Optimizing network propagation for multi-omics data integration. PLoS Comput Biol. 17(11):e1009161. doi: 10.1371/journal.pcbi.1009161.
• Lackner A*, Sehlke R*, Garmhausen M*, Giuseppe Stirparo G*, Huth M, Titz-Teixeira F, van der Lelij P, Ramesmayer J, Thomas HF, Ralser M, Santini L, Galimberti E, Sarov M, Stewart AF, Smith A, Beyer A^#, Leeb M^#. (2021) Cooperative genetic networks drive embryonic stem cell transition from naïve to formative pluripotency. EMBO J. 40(8):e105776. doi: 10.15252/embj.2020105776.
• Cappelletti V*, Hauser T*, Piazza I*, Pepelnjak M, Malinovska L, Fuhrer T, Li Y, Dörig C, Boersema P, Gillet L, Grossbach J, Dugourd A, Saez-Rodriguez J, Beyer A, Zamboni N, Caflisch A, de Souza N, Picotti P (2021) Dynamic 3D proteomes reveal protein functional alterations at high resolution in situ. Cell (20)31691-3. doi: 10.1016/j.cell.2020.12.021.
• Charmpi K^*, Guo T^#,*, Zhong Q^*, Wagner U^*, Sun R, Toussaint NC, Fritz CE, Yuan C, Chen H, Rupp NJ, Christiansen A, Rutishauser D, Rüschoff JH, Fankhauser C, Saba K, Poyet C, Hermanns T, Oehl K, Moore AL, Beisel C, Calzone L, Martignetti L, Zhang Q, Zhu Y, Rodríguez Martínez M, Manica M, Haffner MC, Aebersold R^#, Wild PJ^#, Beyer A^# (2020) Convergent network effects along the axis of gene expression during prostate cancer progression. Genome Biology 21(1):302. doi: 10.1186/s13059-020-02188-9.
• Hahn O, Stubbs TM, Reik W, Grönke S, Beyer A^#, Partridge L^#. (2018) Hepatic gene body hypermethylation is a shared epigenetic signature of murine longevity. PLoS Genet. 14(11):e1007766.
• Tain LS, Sehlke R, Jain C, Chokkalingam M, Nagarajuna N, Essers P, Rassner M, Grönke S, Froelich J, Dieterich C, Mann M, Alic N, Beyer A^#, Partridge L^#. (2017) A proteomic atlas of insulin signaling reveals tissue-specific mechanisms of longevity-assurance. Mol. Syst. Biol. 13(9):939. doi: 10.15252/msb.20177663.
• Liu Y*, Beyer A*^#, Aebersold R^#. (2016) On the dependency of cellular protein levels on mRNA abundance. (Review)Cell 165(3):535-50.

* equal contribution

# corresponding author