Evidence on the cusp between science, policy, and law

In his 2015 speech given in front of the Royal Society, the then President of the UK Supreme Court Lord Neuberger outlined a valuable parallel between Science and the Law. Both are systems produced by the human intellect, both are trying to identify laws that work---the former regarding natural phenomena, the latter human social behaviour---and ultimately to bring order into chaos. One could generalise even further this parallelism to public policy and organisational decision-making

A continuous model of physiological prion aggregation suggests a role for Orb2 in gating long-term synaptic information.

The regulation of mRNA translation at the level of the synapse is believed to be fundamental in memory and learning at the cellular level. The family of cytoplasmic polyadenylation element binding (CPEB) proteins emerged as an important RNA-binding protein family during development and in adult neurons. Drosophila Orb2 (homologue of mouse CPEB3 protein and of the neural isoform of Aplysia CPEB) has been found to be involved in the translation of plasticity-dependent mRNAs and has been associated with long-term memory. Orb2 protein presents two main isoforms, Orb2A and Orb2B, which form an activity-induced amyloid-like functional aggregate, thought to be the translation-inducing state of the RNA-binding protein. Here we present a first two-states continuous differential model for Orb2A-Orb2B aggregation. This model provides new working hypotheses for studying the role of prion-like CPEB proteins in long-term synaptic plasticity. Moreover, this model can be used as a first step to integrate translation- and protein aggregation-dependent phenomena in synaptic facilitation rules

Modelling of the functional amyloid aggregation at the synapse. New insights into the local computational properties of neurons.

The regulation of mRNA translation at synaptic level is believed to be fundamental in memory and learning at cellular level. A family of RNA binding proteins (RBPs) which emerged to be important during development and in adult neurons is the one of Cytoplasmic Polyadenylation Element Binding proteins (CPEBs). Drosophila Orb2 (homolog of vertebrate CPEB2 protein and of the neural isoform of Aplysia CPEB) has been found to be involved in the translation of plasticity-dependent mRNAs and has been associated to Long Term Memory (LTM). Orb2 protein presents two main isoforms, Orb2A and Orb2B, which form an activity induced amyloid-like functional aggregate, which is thought to be the translation-inducing state of the RBP. I present a two-states continuous differential model for Orb2A-Orb2B aggregation and I propose it, more generally, as a new synaptic facilitation rule for learning processes involving protein aggregation-dependent plasticity (PADP)

Complexity in Lipid Membrane Composition Induces Resilience to Aβ42 Aggregation.

The molecular origins of Alzheimer's disease are associated with the aggregation of the amyloid-β peptide (Aβ). This process is controlled by a complex cellular homeostasis system, which involves a variety of components, including proteins, metabolites, and lipids. It has been shown in particular that certain components of lipid membranes can speed up Aβ aggregation. This observation prompts the question of whether there are protective cellular mechanisms to counterbalance this effect. Here, to address this issue, we investigate the role of the composition of lipid membranes in modulating the aggregation process of Aβ. By adopting a chemical kinetics approach, we first identify a panel of lipids that affect the aggregation of the 42-residue form of Aβ (Aβ42), ranging from enhancement to inhibition. We then show that these effects tend to average out in mixtures of these lipids, as such mixtures buffer extreme aggregation behaviors as the number of components increases. These results indicate that a degree of quality control on protein aggregation can be achieved through a mechanism by which an increase in the molecular complexity of lipid membranes balances opposite effects and creates resilience to aggregation

Reduced proteasome activity in the aging brain results in ribosome stoichiometry loss and aggregation.

A progressive loss of protein homeostasis is characteristic of aging and a driver of neurodegeneration. To investigate this process quantitatively, we characterized proteome dynamics during brain aging in the short-lived vertebrate Nothobranchius furzeri combining transcriptomics and proteomics. We detected a progressive reduction in the correlation between protein and mRNA, mainly due to post-transcriptional mechanisms that account for over 40% of the age-regulated proteins. These changes cause a progressive loss of stoichiometry in several protein complexes, including ribosomes, which show impaired assembly/disassembly and are enriched in protein aggregates in old brains. Mechanistically, we show that reduction of proteasome activity is an early event during brain aging and is sufficient to induce proteomic signatures of aging and loss of stoichiometry in vivo. Using longitudinal transcriptomic data, we show that the magnitude of early life decline in proteasome levels is a major risk factor for mortality. Our work defines causative events in the aging process that can be targeted to prevent loss of protein homeostasis and delay the onset of age-related neurodegeneration

Cholesterol catalyses Aβ42 aggregation through a heterogeneous nucleation pathway in the presence of lipid membranes.

Alzheimer's disease is a neurodegenerative disorder associated with the aberrant aggregation of the amyloid-β peptide. Although increasing evidence implicates cholesterol in the pathogenesis of Alzheimer's disease, the detailed mechanistic link between this lipid molecule and the disease process remains to be fully established. To address this problem, we adopt a kinetics-based strategy that reveals a specific catalytic role of cholesterol in the aggregation of Aβ42 (the 42-residue form of the amyloid-β peptide). More specifically, we demonstrate that lipid membranes containing cholesterol promote Aβ42 aggregation by enhancing its primary nucleation rate by up to 20-fold through a heterogeneous nucleation pathway. We further show that this process occurs as a result of cooperativity in the interaction of multiple cholesterol molecules with Aβ42. These results identify a specific microscopic pathway by which cholesterol dramatically enhances the onset of Aβ42 aggregation, thereby helping rationalize the link between Alzheimer's disease and the impairment of cholesterol homeostasis

Effects of system complexity on protein aggregation in neurodegeneration

Alzheimer's disease (AD) is a neurodegenerative disorder, the leading cause of dementia worldwide, and is one of the most pressing challenges in ageing societies. The disease is characterised by extensive brain atrophy, synaptic loss, and the accumulation of two types of protein aggregates: amyloid β (Aβ) plaques and tau neurofibrillary tangles. The deposition of Aβ can be causally linked to familial and early onset AD, however Aβ-targeting treatments against the more common sporadic form of the disease have so far shown limited to no efficacy. If early onset AD can be considered an autosomal dominant disease, sporadic AD is multi-factorial, and it might not have a unique cause. Indeed, the two largest risk factors for late-onset AD are not linked directly with Aβ processing: the first one being age and the second being the isoform APOE4 of the lipoprotein APOE—which is involved in lipid and cholesterol metabolism. In light of the high-dimensionality of this problem, in my thesis I will follow a bottom-up (biophysics to systems biology) approach in dealing with the influence of systemic complexity in protein aggregation across different components of cellular homoeostasis, in particular lipid membranes and the proteostasis network. In the first chapter of the thesis, I will present a selection of the scientific literature on AD, with a particular focus on systemic aspects, such as cellular networks of quality control, protein co-aggregation, and ageing. The second chapter will address the relationship between protein aggregation and the complexity of the lipid environment in which it occurs. This will be done by studying the aggregation of recombinant Aβ (rAβ) peptide in vitro in presence of model lipid membranes. After describing the theoretical foundations of the study of protein aggregation via means of chemical kinetics, I will show how different lipid species affect the aggregation kinetics of rAβ42 and highlight on how lipid-peptide interactions affect the ability to apply standard models of protein aggregation at the molecular scale. Then, I will present results demonstrating the acceleration of rAβ aggregation by cholesterol via a mechanism of heterogeneous primary nucleation. Finally, I will show the effect that mixtures of lipids of increasing complexity have on rAβ42 and introduce the concept of resilience in complexity—where complex lipid mixtures show an overall neutral kinetic contribution to amyloid aggregation and induce resistance to the onset of cooperative phenomena such as heterogeneous primary nucleation. The third chapter will analyse the role of a second critical component in protein aggregation: the proteostasis network. Combining mRNA and protein data from the turquoise killifish, an animal model for molecular ageing, with predictors of biophysical properties such as chaperon requirement for folding and intrinsic disorder, I will demonstrate strong links between the proteostasis network, protein stability, and protein aggregation upon ageing. In the final chapter, I will summarise and discuss some potential lines of research to expand and further generalise the work described in this thesis. AD is a complex disease, so adopting a systemic approach to the study of protein aggregation processes underlying its aetiology is of crucial importance from a conceptual point of view. Moreover, this framework has the potential to suggest network-wide therapeutic solutions for this and other neurodegenerative disorders

Figure 3 data

Source file for Figure 3

Supplementary File 1

This is a Python 3.5 script of the ODE model described in Sanguanini and Cattaneo 2018. This file contains a model which uses only one stimulation pattern and was used to generate Figure 2

Figure 4A data

Source file Figure 4 panel