arXiv subjects

Georg Meisl

Publications and source records attributed to Georg Meisl.

8 recordsLinked to original sources

arXiv2025

A universal phase-plane model for in vivo protein aggregation

Neurodegenerative diseases are driven by the accumulation of protein aggregates in the brain of affected individuals. The aggregation behaviour in vitro is well understood and driven by the equilibration of a super-saturated protein solution to its aggregated equilibrium state. However, the situation is altered fundamentally in living systems where active processes consume energy to remove aggregates. It remains unclear how and why cells transition from a state with predominantly monomeric protein, which is stable over decades, to one dominated by aggregates. Here, we develop a simple but universal theoretical framework to describe cellular systems that include both aggregate formation and removal. Using a two-dimensional phase-plane representation, we show that the interplay of aggregate formation and removal generates cell-level bistability, with a bifurcation structure that explains both the emergence of disease and the effects of therapeutic interventions. We explore a wide range of aggregate formation and removal mechanisms and show that phenomena such as seeding arise robustly when a minimal set of requirements on the mechanism are satisfied. By connecting in vitro aggregation mechanisms to changes in cell state, our framework provides a general conceptual link between molecular-level therapeutic interventions and their impact on disease progression.

q-bio.BM↗

arXiv2025

A cell-level model to predict the spatiotemporal dynamics of neurodegenerative disease

A central challenge in modeling neurodegenerative diseases is connecting cellular-level mechanisms to tissue-level pathology, in particular to determine whether pathology is driven primarily by cell-autonomous triggers or by propagation from cells that are already in a pathological, runaway aggregation state. To bridge this gap, we here develop a bottom-up physical model that explicitly incorporates these two fundamental cell-level drivers of protein aggregation dynamics. We show that our model naturally explains the characteristic long, slow development of pathology followed by a rapid acceleration, a hallmark of many neurodegenerative diseases. Furthermore, the model reveals the existence of a critical switch point at which the system's dynamics transition from being dominated by slow, spontaneous formation of diseased cells to being driven by fast propagation. This framework provides a robust physical foundation for interpreting pathological data and offers a method to predict which class of therapeutic strategies is best matched to the underlying drivers of a specific disease.

q-bio.QM↗

arXiv2025

Kinetics of seeded protein aggregation: theory and application

``Seeding'' is the addition of preformed fibrils to a solution of monomeric protein to accelerate its aggregation into new fibrils. It is a versatile and widely-used tool for scientists studying protein aggregation kinetics, as it enables the isolation and separate study of discrete reaction steps contributing to protein aggregation, specifically elongation and secondary nucleation. However, the seeding levels required to achieve dominating effects on each of these steps separately have been established largely by trial-and-error, due in part to the lack of availability of integrated rate laws valid for moderate to high seeding levels and generally applicable to all common underlying reaction mechanisms. Here, we improve on a recently developed mathematical method based on Lie symmetries for solving differential equations, and with it derive such an integrated rate law. We subsequently develop simple expressions for the amounts of seed required to isolate each step. We rationalize the empirical observation that fibril seeds must often be broken up into small pieces to successfully isolate elongation. We also derive expressions for average fibril lengths at different times in the aggregation reaction, and explore different methods to break up fibrils. This paper will provide an invaluable reference for future experimental and theoretical studies in which seeding techniques are employed, and should enable more sophisticated analyses than have been performed to date.

physics.chem-ph↗

arXiv2024

Competing addition processes give distinct growth regimes in the assembly of 1D filaments

We present a model to describe the concentration-dependent growth of protein filaments. Our model contains two states, a low entropy/high affinity ordered state and a high entropy/low affinity disordered state. Consistent with experiments, our model shows a diffusion-limited linear growth regime at low concentration, followed by a concentration independent plateau at intermediate concentrations, and rapid disordered precipitation at the highest concentrations. We show that growth in the linear and plateau regions is the result of two processes that compete amid the rapid binding and unbinding of non-specific states. The first process is the addition of ordered molecules during the periods where the end of the filament is free of incorrectly bound molecules. The second process is the capture of defects, which occurs when consecutive ordered additions occur on top of incorrectly bound molecules. We show that a key molecular property is the probability that a diffusive collision results in a correctly bound state. Small values of this probability suppress the defect capture growth mode, resulting in a plateau in the growth rate when incorrectly bound molecules become common enough to poison ordered growth. We show that conditions that non-specifically suppress or enhance intermolecular interactions, such as the addition of depletants or osmolytes, have opposite effects on the growth rate in the linear and plateau regimes. In the linear regime stronger interactions promote growth by reducing dissolution events, but in the plateau regime stronger interactions inhibit growth by stabilizing incorrectly bound molecules.

cond-mat.soft↗

arXiv2023

General integrated rate law for complex self-assembly reactions reveals the mechanism of amyloid-beta co-aggregation

Analyzing kinetic experiments on protein aggregation using integrated rate laws has led to numerous advances in our understanding of the fundamental chemical mechanisms behind amyloidogenic disorders such as Alzheimer's and Parkinson's diseases. However, the description of biologically relevant processes may require rate equations that are too complex to solve using existing methods, hindering mechanistic insights into these processes. An example of significance is co-aggregation in environments containing multiple amyloid-beta (Abeta) peptide alloforms, which may play a crucial role in the biochemistry of Alzheimer's disease but whose mechanism is still poorly understood. Here, we use the mathematics of symmetry to derive a general integrated rate law valid for most plausible linear self-assembly reactions. We use it in conjunction with experimental data to determine the mechanism of co-aggregation of the most physiologically abundant Abeta alloforms: Abeta42, Abeta40, Abeta38 and Abeta37 peptides. We find that Abeta42 fibril surfaces catalyze the formation of co-oligomers, which accelerate new Abeta40, Abeta38 and Abeta37 fibril formation whilst inhibiting secondary nucleation of new Abeta42 fibrils. The simplicity, accuracy and broad applicability of our general integrated rate law will enable kinetic analysis of more complex filamentous self-assembly reactions, both with and without co-aggregation.

physics.chem-ph↗

arXiv2020

The role of clearance mechanisms in the kinetics of toxic protein aggregates involved in neurodegenerative diseases

Protein aggregates in the brain play a central role in cognitive decline and structural damage associated with neurodegenerative diseases. For instance, in Alzheimer's disease the formation of Amyloid-beta plaques and tau proteins neurofibrillary tangles follows from the accumulation of different proteins into large aggregates through specific mechanisms such as nucleation and elongation. These mechanisms have been studied in vitro where total protein mass is conserved. However, in vivo, clearance mechanisms may play an important role in limiting the formation of aggregates. Here, we generalise classical models of protein aggregation to take into account both production of monomers and the clearance of protein aggregates. Depending on the clearance model, we show that there may be a critical clearance value above which aggregation does not take place. Our result offers further evidence in support of the hypotheses that clearance mechanisms play a potentially crucial role in neurodegenerative disease initiation and progression; and as such, are a possible therapeutic target.

q-bio.BM↗

arXiv2020

Mechanism, scaling and rates of protein aggregation from in vivo measurements

The formation and proliferation of protein aggregates play a central role in a number of devastating neuro-degenerative diseases. Many experimental studies indicate that the ability of existing aggregates to replicate is a key property in generating their pathogenic effect across a range of diseases. However, given the complexity of the process in vivo, no principled general approach currently exists to obtain the rates of the fundamental steps that underlie aggregate formation from measurements in living systems. In order to address this challenge, here we present a general approach for analysing aggregation kinetics that considers broad classes of processes that can be described by a family of scaling solutions. Our approach is not limited only to fibrillar aggregates, but applies to any aggregate shape. We show that the rates can reliably be extracted by fitting of a simple logistic function, even from experimental data in living systems, and give a very general analytical expression that relates the scaling of the exponential rate with monomer concentration to the microscopic details of the underlying reaction. This approach can thus be used to infer the microscopic mechanism driving the aggregation process from macroscopic measurements in complex systems.

physics.bio-ph↗

arXiv2016

A general reaction network unifies the aggregation behaviour of the A$\beta$42 peptide and its variants

The amyloid $\beta$ peptide (A$\beta$42), whose aggregation is associated with Alzheimer's disease, is an amphiphatic peptide with a high propensity to self-assemble. A$\beta$42 has a net negative charge at physiological pH and modulations of intermolecular electrostatic interactions can significantly alter its aggregation behaviour. Variations in sequence and solution conditions lead to varied macroscopic behaviour, often resulting in a number of different mechanistic explanations for the aggregation of these closely related systems. Here we alter the electrostatic interactions governing the fibril aggregation kinetics by varying the ionic strength over an order of magnitude, which allows us to sample the space of different reaction mechanisms, and develop a minimal reaction network that explains the experimental kinetics under all the different conditions. We find that an increase in the ionic strength leads to an increased rate of surface catalysed nucleation over fragmentation and eventually to a saturation of this nucleation process. More generally, this reaction network connects previously separate systems, such as mutants of A$\beta$42 and the wild type, on a continuous mechanistic landscape, thereby providing a unified picture of the aggregation mechanism of A$\beta$42 and the means of directly comparing the effects of intrinsic modifications of the peptide to those of simple electrostatic shielding.

q-bio.MN↗