Mechanism of Secondary Nucleation at the Single Fibril Level from Direct Observations of Aß42 Aggregation.

The formation of amyloid fibrils and oligomers is a hallmark of several neurodegenerative disorders, including Alzheimer's disease (AD), and contributes to the disease pathway. To progress our understanding of these diseases at a molecular level, it is crucial to determine the mechanisms and rates of amyloid formation and replication. In the context of AD, the self-replication of aggregates of the Aß42 peptide by secondary nucleation, leading to the formation of new aggregates on the surfaces of existing ones, is a major source of both new fibrils and smaller toxic oligomeric species. However, the core mechanistic determinants, including the presence of intermediates, as well as the role of heterogeneities in the fibril population, are challenging to determine from bulk aggregation measurements. Here, we obtain such information by monitoring directly the time evolution of individual fibrils by TIRF microscopy. Crucially, essentially all aggregates have the ability to self-replicate via secondary nucleation, and the amplification of the aggregate concentration cannot be explained by a small fraction of "superspreader" fibrils. We observe that secondary nucleation is a catalytic multistep process involving the attachment of soluble species to the fibril surface, followed by conversion/detachment to yield a new fibril in solution. Furthermore, we find that fibrils formed by secondary nucleation resemble the parent fibril population. This detailed level of mechanistic insights into aggregate self-replication is key in the rational design of potential inhibitors of this process.

The catalytic nature of protein aggregation.

The formation of amyloid fibrils from soluble peptide is a hallmark of many neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Characterization of the microscopic reaction processes that underlie these phenomena have yielded insights into the progression of such diseases and may inform rational approaches for the design of drugs to halt them. Experimental evidence suggests that most of these reaction processes are intrinsically catalytic in nature and may display enzymelike saturation effects under conditions typical of biological systems, yet a unified modeling framework accounting for these saturation effects is still lacking. In this paper, we therefore present a universal kinetic model for biofilament formation in which every fundamental process in the reaction network can be catalytic. The single closed-form expression derived is capable of describing with high accuracy a wide range of mechanisms of biofilament formation and providing the first integrated rate law of a system in which multiple reaction processes are saturated. Moreover, its unprecedented mathematical simplicity permits us to very clearly interpret the effects of increasing saturation on the overall kinetics. The effectiveness of the model is illustrated by fitting it to the data of in vitro Aß40 aggregation. Remarkably, we find that primary nucleation becomes saturated, demonstrating that it must be heterogeneous, occurring at interfaces and not in solution.

All that Glitters Is not Gold: Type-I Error Controlled Variable Selection from Clinical Trial Data.

Clinical trials are primarily conducted to estimate causal effects, but the data collected can also be invaluable for additional research, such as identifying prognostic measures of disease or biomarkers that predict treatment efficacy. However, these exploratory settings are prone to false discoveries (type-I errors) due to the multiple comparisons they entail. Unfortunately, many methods fail to address this issue, in part because the algorithms used are generally designed to optimize predictions and often only provide the measures used for variable selection, such as machine learning model importance scores, as a byproduct. To address the resulting unclear uncertainty in the selection sets, the knockoff framework offers a model-agnostic, robust approach to variable selection with guaranteed type-I error control. Here, we review the knockoff framework in the setting of clinical data, highlighting main considerations using simulation studies. We also extend the framework by introducing a novel knockoff generation method that addresses two main limitations of previously suggested methods relevant for clinical development settings. With this new method, we empirically obtain tighter bounds on type-I error control and gain an order of magnitude in computational efficiency in mixed data settings. We demonstrate comparable selections to those of the competing method for identifying prognostic biomarkers for C-reactive protein levels in patients with psoriatic arthritis in four clinical trials. Our work increases access to the knockoff framework for variable selection from clinical trial data. Hereby, this paper helps to address the current replicability crisis which can result in unnecessary research efforts, increased patient burden, and avoidable costs.

Perphenazine-Macrocycle Conjugates Rapidly Sequester the Aß42 Monomer and Prevent Formation of Toxic Oligomers and Amyloid.

Alzheimer's disease is imposing a growing social and economic burden worldwide, and effective therapies are urgently required. One possible approach to modulation of the disease outcome is to use small molecules to limit the conversion of monomeric amyloid (Aß42) to cytotoxic amyloid oligomers and fibrils. We have synthesized modulators of amyloid assembly that are unlike others studied to date: these compounds act primarily by sequestering the Aß42 monomer. We provide kinetic and nuclear magnetic resonance data showing that these perphenazine conjugates divert the Aß42 monomer into amorphous aggregates that are not cytotoxic. Rapid monomer sequestration by the compounds reduces fibril assembly, even in the presence of pre-formed fibrillar seeds. The compounds are therefore also able to disrupt monomer-dependent secondary nucleation, the autocatalytic process that generates the majority of toxic oligomers. The inhibitors have a modular design that is easily varied, aiding future exploration and use of these tools to probe the impact of distinct Aß42 species populated during amyloid assembly.

Microfluidic characterisation reveals broad range of SARS-CoV-2 antibody affinity in human plasma.

The clinical outcome of SARS-CoV-2 infections, which can range from asymptomatic to lethal, is crucially shaped by the concentration of antiviral antibodies and by their affinity to their targets. However, the affinity of polyclonal antibody responses in plasma is difficult to measure. Here we used microfluidic antibody affinity profiling (MAAP) to determine the aggregate affinities and concentrations of anti-SARS-CoV-2 antibodies in plasma samples of 42 seropositive individuals, 19 of which were healthy donors, 20 displayed mild symptoms, and 3 were critically ill. We found that dissociation constants, K d, of anti-receptor-binding domain antibodies spanned 2.5 orders of magnitude from sub-nanomolar to 43 nM. Using MAAP we found that antibodies of seropositive individuals induced the dissociation of pre-formed spike-ACE2 receptor complexes, which indicates that MAAP can be adapted as a complementary receptor competition assay. By comparison with cytopathic effect-based neutralisation assays, we show that MAAP can reliably predict the cellular neutralisation ability of sera, which may be an important consideration when selecting the most effective samples for therapeutic plasmapheresis and tracking the success of vaccinations.

Kinetic and Thermodynamic Driving Factors in the Assembly of Phenylalanine-Based Modules.

The formation of ordered protein and peptide assemblies is a phenomenon related to a wide range of human diseases. However, the mechanism of assembly at the molecular level remains largely unknown. Minimal models enable the exploration of the underlying interactions that are at the core of such self-assembly processes. In particular, the ability of phenylalanine, a single aromatic amino acid, to form an amyloid-like structure has challenged the previous dogma viewing a peptide backbone as a prerequisite for assembly. The driving forces controlling the nucleation and assembly in the absence of a peptide backbone remain to be identified. Here, aiming to unravel these forces, we explored the kinetics and thermodynamics of three phenylalanine-containing molecules during their assembly process: the amino acid phenylalanine, which accumulates in phenylketonuria patients, the diphenylalanine core-motif of the amyloid ß peptide related to Alzheimer's disease, and the extended triphenylalanine peptide which forms a range of distinct nanostructures in vitro. We found that the aggregation propensity, regarding the critical monomer concentration, strongly increases with size, with triphenylalanine being the most aggregation-prone species under our experimental conditions. In the context of classical nucleation theory, this increase in aggregation propensity can be attributed to the larger free energy decrease upon aggregation of larger peptides and is not due to the presence/absence of a peptide bond per se. Taken together, this work provides insights into the aggregation processes of chemically simple systems and suggests that both backbone-containing peptides and backbone-lacking amino acids assemble through a similar mechanism, thus supporting the classification of amino acids in the continuum of amyloid-forming building blocks.

Effects of sedimentation, microgravity, hydrodynamic mixing and air-water interface on α-synuclein amyloid formation.

The formation of amyloid fibrils is a characterizing feature of a range of protein misfolding diseases, including Parkinson's disease. The propensity of native proteins to form such amyloid fibril, both in vitro and in vivo, is highly sensitive to the surrounding environment, which can alter the aggregation kinetics and fibrillization mechanisms. Here, we investigate systematically the influence of several representative environmental stimuli on α-synuclein aggregation, including hydrodynamic mixing, the presence of an air-water interface and sedimentation. Our results show that hydrodynamic mixing and interfacial effects are critical in promoting several microscopic steps of α-synuclein aggregation and amyloid fibril formation. The presence of an air-water interface under agitation significantly promoted primary nucleation. Secondary processes were facilitated by hydrodynamic mixing, produced by 3D rotation and shaking either in the presence or in the absence of an air-water interface. Effects of sedimentation, as investigated in a microgravity incubator, of α-synuclein lead only to minor changes on the aggregation kinetics rates in comparison to static conditions. These results forward the understanding of α-synuclein fibrillization, paving the way for the development of high-throughput assays for the screening of pharmacological approaches targeting Parkinson's disease.