Spiking burstiness and working memory in the human medial temporal lobe

Persistent activity has commonly been considered to be a hallmark of working memory (WM). Recent evidence indicates that neuronal discharges in the medial temporal lobe (MTL) are compatible with WM neural patterns observed in cortical areas. However, the characterization of this activity rarely consists of measurements other than firing rates of single neurons. Moreover, a varied repertoire of firing dynamics has been reported in the MTL regions, which motivate the more detailed examination of the relationships between WM processes and discharge patterns undertaken here. Specifically, we investigate' at different resolution levels, firing irregularities in electrode recordings from the hippocampus, amygdala, and the entorhinal cortex of epileptic patients during a WM task. We show that some types of (ir)regularities predict response times of the patients depending on the trial periods under consideration. Prominent burst activity at the population level is observed in the amygdala and entorhinal cortex during memory retrieval. In general, regular and bursty neurons contribute to the decoding of the memory load, yet they display important differences across the three anatomical areas. Our results suggest that nonrandom (non-Poisson) patterns are relevant for WM, which calls for the development and use of statistics complementary to mere spike counts

Probing the Early Stages of Polyglutamine Aggregation with Computational Methods

Exonic CAG repeat diseases are a class of neurodegenerative age-of-onset diseases caused by an unstable trinucleotide expansion in a coding region of a gene. The most prominent example is Huntington\u27s disease: HD) whose symptoms are characterized by loss of motor control and cognitive deficits. For all nine of the known CAG repeat diseases, pathology is ascribed to the mutant proteins which carry expanded stretches of glutamine residues: polyglutamine). The length of the polyglutamine segment is inversely correlated with the disease age-of-onset. Protein aggregates are routinely found in postmortem tissue samples of brains of HD patients. These findings suggest a prominent role for polyglutamine-mediated protein aggregation in disease pathogenesis. Subsequent studies characterized the intracellular aggregates as amyloid-like. In amyloids, the polypeptide backbone predominantly adopts conformations in the β-basin of the Ramachandran map, i.e., the aggregates have high net β-content. This has led to the hypothesis that β-rich conformers play a prominent role in mediating the aggregation process; specifically, it has been postulated that a β-rich form of polyglutamine acts as the monomeric nucleus from which fibrillar aggregates grow via a downhill elongation mechanism. This thesis investigates the intrinsic properties of polyglutamine during early stages of aggregation. We employ computer simulations to obtain a qualitative picture of the process at an atomistic level. Our results suggest the following: soluble polyglutamine is intrinsically disordered and forms collapsed globules in aqueous solution. These globules associate readily and randomly to form disordered dimers. We identified no structural requirements for association to occur. The conversion of monomeric polyglutamine to a conformation high in β-content, i.e., to a putative aggregation nucleus, is associated with a high free energy penalty. We detect no coupling between structure and associativity, but find a profound modulation of polyglutamine\u27s intrinsic properties in the presence of wild-type flanking sequences. From our results, we postulate a model where polyglutamine forms large soluble and disordered oligomers which undergo a rate-limiting conformational conversion to a fibrillar precipitate. We conclude that structure-based drug designs may not prove a viable strategy for interfering with the early stages of polyglutamine aggregation and hence with disease pathology

Probing the early stages of polyglutamine aggregation with computational methods

Exonic CAG repeat diseases are a class of neurodegenerative age-of-onset diseases caused by an unstable trinucleotide expansion in a coding region of a gene. The most prominent example is Huntington's disease (HD) whose symptoms are characterized by loss of motor control and cognitive deficits. For all nine of the known CAG repeat diseases, pathology is ascribed to the mutant proteins which carry expanded stretches of glutamine residues (polyglutamine). The length of the polyglutamine segment is inversely correlated with the disease age-of-onset. Protein aggregates are routinely found in postmortem tissue samples of brains of HD patients. These findings suggest a prominent role for polyglutamine-mediated protein aggregation in disease pathogenesis. Subsequent studies characterized the intracellular aggregates as amyloid-like. In amyloids, the polypeptide backbone predominantly adopts conformations in the β-basin of the Ramachandran map, i.e., the aggregates have high net β-content. This has led to the hypothesis that β-rich conformers play a prominent role in mediating the aggregation process; specifically, it has been postulated that a β-rich form of polyglutamine acts as the monomeric nucleus from which fibrillar aggregates grow via a downhill elongation mechanism. This thesis investigates the intrinsic properties of polyglutamine during early stages of aggregation. We employ computer simulations to obtain a qualitative picture of the process at an atomistic level. Our results suggest the following: soluble polyglutamine is intrinsically disordered and forms collapsed globules in aqueous solution. These globules associate readily and randomly to form disordered dimers. We identified no structural requirements for association to occur. The conversion of monomeric polyglutamine to a conformation high in β-content, i.e., to a putative aggregation nucleus, is associated with a high free energy penalty. We detect no coupling between structure and associativity, but find a profound modulation of polyglutamine's intrinsic properties in the presence of wild-type flanking sequences. From our results, we postulate a model where polyglutamine forms large soluble and disordered oligomers which undergo a rate-limiting conformational conversion to a fibrillar precipitate. We conclude that structure-based drug designs may not prove a viable strategy for interfering with the early stages of polyglutamine aggregation and hence with disease pathology

Unsupervised Methods for Detection of Neural States: Case Study of Hippocampal-Amygdala Interactions

The hippocampus and amygdala are functionally coupled brain regions that play a crucial role in processes involving memory and learning. Because interareal communication has been reported both during specific sleep stages and in awake, behaving animals, these brain regions can serve as an archetype to establish that measuring functional interactions is important for comprehending neural systems. To this end, we analyze here a public dataset of local field potentials (LFPs) recorded in rats simultaneously from the hippocampus and amygdala during different behaviors. Employing a specific, time-lagged embedding technique, named topological causality (TC), we infer directed interactions between the LFP band powers of the two regions across six frequency bands in a time-resolved manner. The combined power and interaction signals are processed with our own unsupervised tools developed originally for the analysis of molecular dynamics simulations to effectively visualize and identify putative, neural states that are visited by the animals repeatedly. Our proposed methodology minimizes impositions onto the data, such as isolating specific epochs, or averaging across externally annotated behavioral stages, and succeeds in separating internal states by external labels such as sleep or stimulus events. We show that this works better for two of the three rats we analyzed, and highlight the need to acknowledge individuality in analyses of this type. Importantly, we demonstrate that the quantification of functional interactions is a significant factor in discriminating these external labels, and we suggest our methodology as a general tool for large, multisite recordings

On the removal of initial state bias from simulation data

Classical atomistic simulations of biomolecules play an increasingly important role in molecular life science. The structure of current computing architectures favors methods that run multiple trajectories at once without requiring extensive communication between them. Many advanced sampling strategies in the field fit this mold. These approaches often rely on an adaptive logic and create ensembles of comparatively short trajectories whose starting points are not distributed according to the correct Boltzmann weights. This type of bias is notoriously difficult to remove, and Markov state models (MSMs) are one of the few strategies available for recovering the correct kinetics and thermodynamics from these ensembles of trajectories. In this contribution, we analyze the performance of MSMs in the thermodynamic reweighting task for a hierarchical set of systems. We show that MSMs can be rigorous tools to recover the correct equilibrium distribution for systems of sufficiently low dimensionality. This is conditional upon not tampering with local flux imbalances found in the data. For a real-world application, we find that a pure likelihood-based inference of the transition matrix produces the best results. The removal of the bias is incomplete, however, and for this system, all tested MSMs are outperformed by an alternative albeit less general approach rooted in the ideas of statistical resampling. We conclude by formulating some recommendations for how to address the reweighting issue in practice

Sapphire-based clustering

Molecular dynamics simulations are a popular means to study biomolecules, but it is often difficult to gain insights from the trajectories due to their large size, in both time and number of features. The Sapphire (States And Pathways Projected with HIgh REsolution) plot allows a direct visual inference of the dominant states visited by high-dimensional systems and how they are interconnected in time. Here, we extend this visual inference into a clustering algorithm. Specifically, the automatic procedure derives from the Sapphire plot states that are kinetically homogeneous, structurally annotated, and of tunable granularity. We provide a relative assessment of the kinetic fidelity of the Sapphire-based partitioning in comparison to popular clustering methods. This assessment is carried out on trajectories of n-butane, a β-sheet peptide, and the small protein BPTI. We conclude with an application of our approach to a recent 100 μs trajectory of the main protease of SARS-CoV-2

Equilibrium sampling approach to the interpretation of electron density maps

The derivation of molecular models from spatial density data generated by X-ray crystallography or electron microscopy is an active field of research. Here, we introduce and evaluate an approach relying on the equilibrium sampling of energy landscapes describing restraints to experimental input data. Our procedure combines density restraints with replica exchange methodologies in the parameter space of the restraints, and we demonstrate its applicability to both flexible polymers and the assembly of protein complexes from rigid components. For the most difficult system studied, we highlight the importance of advanced data analysis techniques in mining poorly converged data further. Successful and unbiased interpretation of input density maps is a prerequisite for using this approach as an auxiliary restraint term in molecular simulations. Because these simulations will also utilize physical interaction potentials, we hope that they will contribute to deriving families of structural models for input data that are ambiguous per se

Precise estimation of transfer free energies for ionic species between similar media

Transfer properties and partition coefficients for individual ions are relevant in a variety of scientific and engineering contexts, such as predicting the effects of different electrolytes on biomacromolecules in a preferential interaction sense or predicting the distribution of heavy metal ions in soils, rivers, etc. Computer simulations allow free energies of transfer to be estimated by considering single ions explicitly. When the two media under consideration are similar to each other regarding ion solvation, the resultant free energies are small in absolute magnitude. In these cases, it is advisable to simulate the transfer process directly. Here, we demonstrate how this can be achieved using two-dimensional umbrella sampling in conjunction with canonical ensemble molecular dynamics simulations where two liquid media are in direct contact. By calculating full two-dimensional potentials of mean force, these simulations allow the estimation of single-ion transfer free energies by integrating this surface accordingly. We report statistical accuracies to highlight that very high precision is achieved and needed to make even just qualitative statements about the transfer process. We close by discussing implications of our results for the specific case considered: the transfer of polypeptide side chain analogs from water to aqueous denaturant solutions

50 Years of Lifson–Roig Models: Application to Molecular Simulation Data

Simple helix–coil transition theories have been indispensable tools in the analysis of data reporting on the reversible folding of α-helical polypeptides. They provide a transferable means to not only characterize different systems but to also compare different techniques, viz., experimental probes monitoring helix–coil transitions in vitro or biomolecular force fields in silico. This article addresses several issues with the application of Lifson–Roig theory to helix–coil transition data. We use computer simulation to generate two sets of ensembles for the temperature-controlled, reversible folding of the 21-residue, alanine-rich FS peptide. Ensembles differ in the rigidity of backbone bond angles and are analyzed using two distinct descriptors of helicity. The analysis unmasks an underlying phase diagram that is surprisingly complex. The complexities give rise to fitted nucleation and propagation parameters that are difficult to interpret and that are inconsistent with the distribution of isolated residues in the α-helical basin. We show that enthalpies of helix formation are more robustly determined using van’t Hoff analysis of simple measures of helicity rather than fitted propagation parameters. To overcome some of these issues, we design a simple variant of the Lifson–Roig model that recovers physical interpretability of the obtained parameters by allowing bundle formation to be described in simple fashion. The relevance of our results is discussed in relation to the applicability of Lifson–Roig models to both in silico and in vitro data