BIMSB Quantitative Proteomik/Metabolomik Plattform

Overview

Within the past decades biochemical data of single processes, metabolic and signaling pathways were collected. Advances in technology led to improvements of sensitivity and resolution of bioanalytical techniques. These achievements build the basis of so called ‘genome wide’ analyses.

High throughput techniques are the basis for such large scale “-omics” studies” allowing the obtainment of a complete picture of a determinate cell state, concerning its metabolites, transcripts and proteins. For example two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GCxGC-TOF-MS) is a promising technique to overcome limits of complex metabolome analysis using one dimensional GC-TOF-MS (1). In a recent paper we applied this technique in combination with 13C labeling to generate a high dimensional data matrix for statistical analysis and comparison of two different growth conditions of the green alga C. reinhardtii.

Such unbiased approaches allow to compare multiple “snapshots” of a cell, in different conditions or, more valuably, in a time-course manner.  In the same stage they allow also the identification of still unknown compounds (metabolites or proteins), opening the door to targeted experiments, aimed to answer more specific questions, regarding regulatory networks, metabolic pathways or in a more traditional manner characterization of a specific protein of interest (2).

Moreover, the recent breakthroughs in a number of high-throughput analytical technologies do now allow also quantitative and comprehensive analyses of biological systems. For example, next generation sequencing technologies enable the analysis of billions of nucleotide sequences in parallel just in a few days (3), while recent advances in quantitative proteomics by mass spectrometry will make it possible to identify and quantify nearly all proteins in model systems.

However, single level study of a living organism (transcripts, proteins or metabolites) cannot give a complete understanding of the mechanism regulating biological functions.

The integration of transcriptomics, proteomics and metabolomics data in the newly emerging field of System Biology, combined with existing knowledge, allows connecting biological processes which where treated as independent so far (4, 5). Furthermore, absolute quantitative data generated by targeted approaches are used as input parameters for computational models of biological processes.

The recently founded Berlin Institute for Medical System Biology (BIMSB) will house and synergize a broad range of different expertise, all working on a diverse array of model systems amenable to a variety of high-throughput approaches as well as in disease models and medical applications in cardio-vascular/metabolic/neurodegenerative and cancer diseases.

In this context the aim of our group is to apply metabolomics and proteomics techniques for absolute quantification and analysis of turnover rates of proteins and metabolites using stable isotopes (1, 6); in addition, the further development of workflows for data analysis and integrative strategies will be in the focus of our interest.

 

References

1. Kempa S, Hummel J, Schwemmer T, Pietzke M, Strehmel N, Wienkoop S, Kopka J, Weckwerth W.  J Basic Microbiol. 2009; 49(1):82-91

2. Cavatorta V, Sforza S, Mastrobuoni G, Pieraccini G, Francese S, Moneti G, Dossena A, Pastorello EA, Marchelli R. J Mass Spectrom. 2009; [Epub ahead of print]

3. Chen W, Kalscheuer V, Tzschach A, Menzel C, Ullmann R, Schulz MH, Erdogan F, Li N, Kijas Z, Arkesteijn G, Pajares IL, Goetz-Sothmann M, Heinrich U, Rost I, Dufke A, Grasshoff U, Glaeser B, Vingron M, Ropers HH. Genome Res. 2008;18(7):1143-9.

4. Selbach M, Schwanhäusser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N. Nature. 2008; 455(7209):58-63.

5. Kempa S, May P, Wienkoop S,Usadel B, Christian N, Rupprecht J, Weiss J, Recuenco-Munoz L, Ebenhöh O, Weckwerth W, Walther D. Genetics. 2008; 179(1):157-66.

6. Schwanhäusser B, Gossen M, Dittmar G, Selbach M. Proteomics. 2009; 9(1):205-9