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. response to RNAi knock-downs). 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. We want to understand the causes and effects of these changes and their relevance for age-associated diseases.
3. Possible projects:
How does transcription factor (TF) activity change with age and disease? TFs are key regulatory molecules controlling gene activity. TFs are important for responding to age-associated cellular stress and may causally contribute to the etiology of age-associated diseases. It is known that many lifespan extending treatments critically rely on specific TFs. Thus, we want to analyse systematically and across species how TFs change their activity during 'normal ageing' and under lifespan-extending conditions and disease conditions. Is there a common set of 'core' TFs that can be associated with ageing? Can their activity be modulated? We are going to monitor TF activity through integrating ChIP-seq data with (single-cell) transcriptome data and by modelling gene expression as a function of their regulators.
How do post-transcriptional processes influence protein levels during ageing? Increasing noise during transcription and transcription-coupled processes (such as splicing) is clearly contributing to ageing-associated phenotypes. However, protein levels are also affected by many other processes, such as translation and protein turnover (Liu et al. 2016). It is well known, that for example the proteasome plays an important role in ageing, but there is no systematic study about how post-transcriptional processes change during ageing and how much they contribute to protein abundance variation at higher age. Using publically available transcriptome- and proteome data this question shall be addressed.
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)
- 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 statistical skills are also important. Further, interest in and knowledge of molecular biology is important in order to aid the interpretation of the data.
- 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#. 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