Prof. Dr. Andreas Beyer

Research Area: Computational Biology, Systems Biology

Website: CellNet Group 

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 (e.g. through CRISPR-Cas9 or transposon screens). 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. 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 addresses 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 (groups of Adam Antebi, Linda Partridge, Dario Valenzano, Thomas Benzing, Björn Schumacher) we investigate common molecular mechanisms affected under lifespan-extending conditions across model species (worm, fly, mouse, human). Together with Linda Partridge we are currently investigating the tissue specificity of these changes, e.g. when repressing insulin signalling or under dietary restriction.

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 effects of these changes and their relevance for age-associated diseases.

3. Possible projects:

  1. How does gene expression regulation change during ageing? While many published studies focus on the differential expression of specific genes during ageing (old versus young), we want to better understand how the expression regulation in general changes during ageing. We were able to develop a transcriptome-wide model describing the expression regulation of tens of thousands of genes in human and mouse cells. This work revealed that the expression regulation (i.e. the ‘wiring’ of the network itself) is surprisingly invariant across tissues and cell types. Thus, whereas gene expression levels are highly cell type-specific their regulatory interactions are static. Now we would like to explore the changes in this network occurring during ageing. Using large compendia of bulk- and single-cell RNA-sequencing datasets from mice and humans at different ages, the project will explore changes in this network as a function of age and explain those changes using wide variety of other ‘omics’ data, such as transcription factor binding data, epigenetic maps (e.g. histone modifications) and chromatin accessibility data (e.g. ATAC-seq).
  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 a computational framework to accurately quantify sequence mistakes in RNAs using single-cell RNA-sequencing data. Next, we would like to use this approach to quantify age-associated increases in sequence ‘mistakes’. This project will explore which genes and gene groups are possibly more affected than others. E.g. are longer genes more affected than shorter genes? Are genes that are generally transcribed at a greater speed subject to more mistakes than slowly transcribed genes? What is the impact of chromatin structure and DNA methylation on the rate of RNA sequence ‘mistakes’?

4. Applied Methods and model organisms:

Our group works purely computational and uses 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 (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:

  • 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.
  • Soste M, Charmpi K, Lampert F, Gerez JA, van Oostrum M, Malinovska L, Boersema PJ, Prymaczok NC, Riek R, Peter M, Vanni S, Beyer A#, Picotti P#. (2019) Proteomics-based monitoring of pathway activity reveals that blocking diacylglycerol biosynthesis rescues from alpha-synuclein toxicity. Cell Systems 9(3):309-320.e8; doi: 10.1016/j.cels.2019.07.010.
  • 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; doi.org/10.1016/j.ddmod.2019.03.003
  • 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.
  • Seifert M, Friedrich B, Beyer A. (2016) Importance of rare gene copy number alterations for personalized tumor characterization and survival analysis. Genome Biol. 3;17(1):204.

* equal contribution

# corresponding author