Our lab aims at understanding how protein sequences can become toxic upon mutation. We are particularly interested in amino acid sequences that can adopt different conformations and undergo a process of self-assembly which results in distinct physical states. The concept of protein aggregation has mainly been associated to the formation of insoluble amyloid fibrils, best known for their implication in the pathogenesis of a number of neurodegenerative conditions, such as Parkinson’s disease or Amyotrophic Lateral Sclerosis. However, examples of functional amyloid are also widespread, especially across bacteria and fungi. Recently, it has become clear that proteins can assemble also into a more dynamic and reversible state. Liquid condensates, for example, are formed by proteins containing intrinsically disordered regions, through a process of liquid de-mixing in the cytoplasm. The self-assembly of these proteins results in a distinct liquid phase and it’s key to the formation of many membrane-less organelles, hence contributing to the organisation of the intracellular space. However, also for proteins undergoing liquid de-mixing, the balance between function and dysfunction is far from clear. It is also unknown if, in vivo, liquid de-mixed states are precursors of insoluble amyloid-like states, and to which extent proteins are structured once in the liquid state.
In order to understand how mutations affect these delicate equilibria and to elucidate when and why a sequence becomes toxic for the cell, our lab integrates experimental and computational approaches in different model systems. Recently, we developed a deep mutational scanning strategy that allows to quantify the toxicity of thousands of mutations in a disordered protein sequence. The idea behind this type of approach is that by portraying the full landscape of the effect of mutations in a specific protein domain we can reach a more systematic and comprehensive understanding of the determinants of toxicity. Besides developing high-throughput methods to measure the toxicity of thousands of mutations in parallel, we are also interested in developing similar high-throughput strategies to measure in vivo the effect of mutations on the physical state the proteins acquire upon mutation (diffuse, liquid de-mixed, insoluble). We couple these approaches to a high-resolution investigation of the mechanisms underlying toxicity, which we normally carry out by standard biophysical and biochemical methods. Overall, we aim at generating exhaustive datasets that will give insights into the specific conformations and mechanisms leading to toxicity.
Currently we are focusing mainly on classical amyloids, such as the amyloid-beta peptide, the main component of the plaques found in Alzheimer’s disease patients, but we are also exploring a less characterised part of the proteome: prion-like domains. Prion-like domains are intrinsically disordered domains able to populate multiple physical states and to take part in membrane-less organelles.
Importantly, prion-like domains are frequently mutated in a number of neurodegenerative conditions. Pathological mutations affect the equilibria among different states in ways we cannot yet fully understand or predict. Just like most disordered protein regions, prion-like domains are particularly difficult to study in vitro. In this perspective, in vivo approaches such as the ones we develop, can provide a unique opportunity to investigate these sequences in a systematic way.
|PRIOMUT Escaneado exhaustivo de mutaciones en un dominio priónico para entender la toxicidad inducida por proteínas (2019-2021)||MICIU, Retos investigación: Proyectos I+D||Benedetta Bolognesi|
Bolognesi, Benedetta, Faure, Andre J., Seuma, Mireia, Schmiedel, Jörrn M., Tartaglia, Gian Gaetano, Lehner, Ben, (2019). The mutational landscape of a prion-like domain Nature Communications 10, (1), 4162
Bolognesi, Benedetta, Lehner, Ben, (2018). Reaching the limit eLife 7, e39804
- Ben Lehner
- Sofia Giorgetti
University of Pavia, Italy
- Xavier Salvatella
- Ina Vorberg
DZNE Bonn, Germany
- Broder Schmidt
University of Stanford