ROS networks: designs, aging, Parkinson’s disease and precision therapies

Alexey N. Kolodkin*, Raju Prasad Sharma, Anna Maria Colangelo, Andrew Ignatenko, Francesca Martorana, Danyel Jennen, Jacco J. Briedé, Nathan Brady, Matteo Barberis, Thierry D.G.A. Mondeel, Michele Papa, Vikas Kumar, Bernhard Peters, Alexander Skupin, Lilia Alberghina, Rudi Balling, Hans V. Westerhoff

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

How the network around ROS protects against oxidative stress and Parkinson’s disease (PD), and how processes at the minutes timescale cause disease and aging after decades, remains enigmatic. Challenging whether the ROS network is as complex as it seems, we built a fairly comprehensive version thereof which we disentangled into a hierarchy of only five simpler subnetworks each delivering one type of robustness. The comprehensive dynamic model described in vitro data sets from two independent laboratories. Notwithstanding its five-fold robustness, it exhibited a relatively sudden breakdown, after some 80 years of virtually steady performance: it predicted aging. PD-related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the collapse, while antioxidants or caffeine retarded it. Introducing a new concept (aging-time-control coefficient), we found that as many as 25 out of 57 molecular processes controlled aging. We identified new targets for “life-extending interventions”: mitochondrial synthesis, KEAP1 degradation, and p62 metabolism.

Original languageEnglish
Article number34
Pages (from-to)1-20
Number of pages20
Journalnpj Systems Biology and Applications
Volume6
Issue number1
Early online date26 Oct 2020
DOIs
Publication statusPublished - 1 Dec 2020

Funding

We cordially thank Nathan Price and Evangelos Simeonidis from ISB (Seattle), Nilgün Sahin from the VU University Amsterdam, and Ewelina Weglarz-Tomczak of the University of Amsterdam for highly influential discussions and support. We thank Stephan Gebel for his great help with the PD map and Olga Krebs for her help with FAIRDOMHub. A.K. acknowledges funding from the Luxembourg BioTech Initiative and LCSB; A.K. and A.I. acknowledge the financial support from an FNR grant for the Ph.D. project ROSIM. H.V.W. thanks the EU, the BBSRC, EPSRC, and NWO for extensive research support throughout many years, such as in grants BB/F003528/1, BB/ C008219/1, BB/F003528/1, BB/G530225/1, BB/I004696/1, BB/I017186/1, BB/I00470X/1, BB/I004688/1, BB/J500422/1, BB/J003883/1, BB/J020060/1, and the EU-FP7 projects SYNPOL, EC-MOAN, NUCSYS, UNI-CELLSYS, ITFoM, BioSiM, and EPIPredict. M.B. acknowledges the Systems Biology Grants of the University of Surrey and of the Swammerdam Institute for Life Science Starting Grant of the University of Amsterdam. This work was further supported by the Corbel EU-H2020 through Hans V. Westerhoff and Anna M Colangelo; and grants from the Italian Ministry of University and Research (MIUR): SYSBIO-Italian ROADMAP ESFRI Infrastructures to L.A., A.M.C., and M.P.; MIUR ALISEI-IVASCOMAR-Italian National Cluster to A.M.C., and grant Dipartimenti di Eccellenza-2017 to the University of Milano-Bicocca Department of Biotechnology and Biosciences.

FundersFunder number
University of Surrey
Biotechnology and Biological Sciences Research Council
European Commission
University of Milano-Bicocca Department of Biotechnology and Biosciences
EU-H2020
EU-FP7
Engineering and Physical Sciences Research Council
LCSB
Universiteit van Amsterdam
Ministero dell’Istruzione, dell’Università e della RicercaSYSBIO-Italian ROADMAP ESFRI Infrastructures
Nederlandse Organisatie voor Wetenschappelijk OnderzoekBB/J500422/1, BB/F003528/1, BB/ C008219/1, BB/I017186/1, BB/I00470X/1, BB/I004688/1, BB/G530225/1, BB/J003883/1, BB/I004696/1
FP7 HealthITFoM, BioSiM, UNI-CELLSYS, NUCSYS, EPIPredict, SYNPOL, EC-MOAN
UK Research and InnovationBB/J020060/1
Fonds De La Recherche Scientifique - FNRSROSIM
Fonds National de la Recherche LuxembourgLuxembourg BioTech Initiative

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