Protein Structures as Biomarkers of Disease Progression to Support Personalized Patient Care
Short Summary
This project aims at assessing the capability of a new mass spectrometric technology (LiP-MS) to generate molecular data that comprehensively describe physiological and pathological states of an organism. The technology enables the detection of aberrant protein structures from minute amounts of biological specimens. To illustrate the capabilities of the approach, we will apply LiP-MS to the identification of novel biomarkers for Parkinson’s disease (PD) and for the stratification of PD patients.
Goals
This project aims at assessing the power of a new technology based on mass spectrometry (LiP-MS) to detect aberrant protein structures as a novel type of disease biomarkers. LiP-MS generates a novel type of molecular data that comprehensively and quantitatively describe physiological and pathological states of an organism. To showcase the potential of the technology, the project focuses on the use of LiP-MS for the identification of novel PD biomarkers for early disease detection and predictors of cognitive decline.
Significance
This project will generate a new type of molecular data from a cohort of PD patients and age-matched controls and has the potential to yield novel PD biomarkers. Further, it will translate the LiP-MS technology to a clinical setting. Upon completion of the project, we plan to make the technology available for clinical applications through the mass spectrometric (MS) platform recently established by the PHRT executive committee. LiP-MS analyses can be applied to generate molecular data for a variety of other diseases, such as cancer.
Background
Parkinson disease (PD) is is a progressive neurodegenerative disorder characterized by the appearance of aberrant proteinaceous structures in the brain tissue. The main symptoms of PD are tremor and bradichinesia. A substantial fraction of PD patients however suffers from non-motor symptoms including cognitive dysfunction. The early diagnosis of PD and the stratification of patients based on PD subtypes would help physicians to take informed decisions on the best course of management and would increase the chance of success of disease-modifying compounds. At present, there are no reliable biochemical biomarkers for PD diagnosis and for patient stratification.
Publications
Patents / Startups
Publications
- Cappelletti V et al. Cell 184 (2), 545-559 (2020). Dynamic 3D proteomes reveal protein functional alterations at high resolution in situ. https://doi.org/10.1016/j.cell.2020.12.021
- Melnik A et al. J Proteomics 225, 103862 (2020). Comparative analysis of the intracellular responses to disease-related aggregation-prone proteins. https://doi.org/10.1016/j.jprot.2020.103862
- Pepelnjak M, de Souza N, Picotti P. TIBS 45 (10), 919-920 (2020). Detecting protein-small molecule interactions using limited proteolysis-mass spectrometry. https://doi.org/10.1016/j.tibs.2020.05.006
- Piazza, I et al. A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes. Nat Commun 11, 4200 (2020). https://doi.org/10.1038/s41467-020-18071-x
- Tognetti M et al. Cell Systems 12 (5), 401-418 (2021). Deciphering the signaling network of breast cancer improves drug sensitivity prediction. 1016/j.cels.2021.04.002
Patents / Startups
Technology Translation
Prof. Dr. Paola Picotti
Institute of Molecular Systems Biology, ETH Zurich
Co-Investigators
- Dr. Wilma D.J. van de Berg, VU University Medical Centre (VUmC), Amsterdam, The Netherlands
- Prof. Dr. Andreas Beyer, University of Cologne, Germany
Consortium
Status
Completed
- 1st Call, Tech Trans
Funded by
