Ori Research Group
Functional Proteomics of Aging
Mammalian cells are made up of >10,000 different protein species. The abundance of these proteins is tightly regulated in a cell-type specific manner so that specialized cells such as muscle cells and neurons can be made using the same genetic information. Measuring protein abundance is therefore crucial for understanding the molecular alterations that lead to the dysfunction of a specific organ or cell type. We employ state-of-the-art mass spectrometry based proteomics to obtain proteome profiles of tissues and cell types across age groups and genetic backgrounds as well as to evaluate the consequences of environmental factors such as stress, calorie restriction and exercise.
Protein complexes with variable composition
Proteins do not work alone but they engage in physical interactions with other proteins to form protein complexes. Protein complexes are the molecular machines that carry out essential functions inside our cells such as production of energy, replication of genetic material and transport of molecules. We have previously shown that certain protein complexes, such as the nuclear pore complex, adapt their composition to fulfill cell-type specific needs (Ori et al. MSB 2013). This is a clever way that cells use to switch the function of a protein complex by changing only few, critical components. We are interested in studying how age and age-associated mutations affect the structure and function of protein complexes. For this purpose, we use computational approaches that allow us to compare the composition of protein complexes across different proteome profiles. In addition, we use biochemical approaches to isolate protein complexes with a definite composition and study their interactions with other proteins.
Novel approaches for proteomic data analysis and integration
Protein abundance can be regulated by different means including transcription, translation and degradation. In order to understand at which level of regulation the impact of aging manifests, it is necessary to integrate proteomic data with complementary information such as measurements of transcript abundance or translation rate (Ori, Toyama et al. Cell Systems 2015). We work in close collaboration with leading computational biologists to develop tools for the analysis of large proteomic dataset and their integration with genomic information.
|Alessandro Ori||+49 3641 email@example.com||Group Leader|
|Aleksandar Bartolomefirstname.lastname@example.org||Doctoral Student|
|Simone Di Sanzo||+49 3641 email@example.com||Doctoral Student|
|Nadja Gebert||+49 3641 firstname.lastname@example.org||Doctoral Student|
|Svenja Schüler||+49 3641 email@example.com||Doctoral Student|
|Ivonne Heinze||+49 3641 firstname.lastname@example.org||Technical Assistant|
|Yanhui Zhangemail@example.com||Master Student|
|Domenico Di Fraia||---||Guest|