Understanding how the genomic information is interpreted to yield a specific phenotype is perhaps the most important question in the post-genomic era. Proteins are central players in this process: On the one hand, they are the final product of most genes. On the other hand, proteins are also directly responsible for cellular phenotypes. Proteins thus represent the central link between the genome and the phenotype. We are using quantitative mass spectrometry-based proteomics as our central technology to investigate proteome dynamics on a global scale. The lab is interested in two major questions. First, how is the genomic information processed to yield a specific proteome? Second, how do proteins that are expressed at a specific cellular condition affect the phenotype? Answering the first question requires information about protein synthesis and degradation. Systematic analysis of protein-protein interactions and posttranslational modifications can help answering the second question.
Fig. 1: Different variants of SILAC for the analysis of cellular proteome dynamics.
Recently developed quantitative methods make it possible to obtain precise functional information and to monitor temporal changes in the proteome by mass spectrometry. In one approach, named SILAC (for stable-isotope labelling with amino acids in cell culture), cells are differentially labelled by cultivating them in the presence of either normal or a heavy isotope–substituted amino acids. Due to their mass difference, pairs of chemically identical peptides of different stable-isotope composition can be distinguished in a mass spectrometer. The ratio of intensities for such peptide pairs accurately reflects the abundance ratio for the corresponding proteins. SILAC can be used to quantify differences in steady-state protein levels (Fig. 1, left). In addition, pulsed SILAC can be employed to measure differences in protein synthesis (Fig. 1, middle). Finally, dynamic SILAC can reveal protein turnover (Fig. 1, right). Combined with high throughput mass spectrometry, the different variants of SILAC enable quantification of different aspects of proteome dynamics on a global scale.
The four fundamental cellular processes involved in gene expression are transcription, mRNA degradation, translation and protein degradation. Each of these four steps is controlled by gene-regulatory events. So far, little is known about how the combined effect of all regulatory events shapes gene expression. The fundamental question of how genomic information is processed to obtain a specific cellular proteome is therefore still largely unknown. We are using metabolic pulse labelling approaches to comprehensively quantify gene expression. Protein turnover can be quantified using dynamic SILAC. Similarly, newly synthesized RNA can be labelled with nucleoside analogues. Mass spectrometry and next generation sequencing (in collaboration with the lab of Wei Chen) then allows us to quantify the absolute abundance of mRNAs and proteins and their half-lives in parallel. These data can then be used to calculate synthesis rates of mRNAs and proteins by mathematical modeling (collaboration with the group of Jana Wolf). Our results indicate that gene expression in mouse fibroblasts is predominantly controlled at the level of translation (Schwanhausser, 2011). Consistently, we find that translation is actively regulated during Schwann cell development in vivo (collaboration with the lab of Carmen Birchmeier, Sheean et al., 2014). Currently, we are investigating how the different levels of gene expression change upon perturbation. In addition, we are using pulsed SILAC (pSILAC) to directly quantify changes in protein synthesis. For example, we are studying the impact of microRNAs and RNA-binding proteins on protein production.
Proteins typically interact with other proteins to exert a specific cellular function. Identifying interaction partners therefore provides direct insights into protein function and can reveal disease mechanisms. We are using quantitative mass spectrometry and to analyze protein-protein interactions (Paul et al., 2011). This approach has two unique advantages. First, accurate quantification allows us to distinguish specific interaction partners from background contaminants with very high confidence. Second, quantification reveals how interactions change in response to perturbation. We are using this method to study how disease-associated mutations and cell signaling events affect protein-protein interactions. We are also investigating how cellular interaction partners of influenza A virus proteins differ between strains with different pathogenic potential.
Cell culture-based experiments cannot recapitulate all of the complex interactions among different cell types and tissues that occur in vivo. Small animal models such as worms and fruit flies are attractive alternatives that are extensively used in many areas of biomedical research, especially in genetics and development. We have extended the SILAC technology to Caenorhabditis elegans (collaboration with the lab of Nikolaus Rajewsky) and Drosophila melanogaster (Sury et al., 2010; Grun et al., 2014). Currently, we are using these models to study protein-protein interactions in vivo.
- Sheean, M.E., McShane, E., Cheret, C., ..., Selbach, M., and Birchmeier, C. (2014). Activation of MAPK overrides the termination of myelin growth and replaces Nrg1/ErbB3 signals during Schwann cell development and myelination. Genes Dev 28, 290-303.
- Grun, D., Kirchner, M., Thierfelder, N., Stoeckius, M., Selbach, M., and Rajewsky, N. (2014). Conservation of mRNA and Protein Expression during Development of C. elegans. Cell Rep.
- Paul, F.E., Hosp, F., and Selbach, M. (2011). Analyzing protein-protein interactions by quantitative mass spectrometry. Methods 54, 387-395.
- Schwanhausser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., and Selbach, M. (2011). Global quantification of mammalian gene expression control. Nature 473, 337-342.
- Sury, M.D., Chen, J.X., and Selbach, M. (2010). The SILAC fly allows for accurate protein quantification in vivo. Mol Cell Proteomics 9, 2173-2183.