Ferritin nanocage-enabled detection of pathological tau in living human retinal cells.

Tauopathies, including Alzheimer's disease and Frontotemporal Dementia, are debilitating neurodegenerative disorders marked by cognitive decline. Despite extensive research, achieving effective treatments and significant symptom management remains challenging. Accurate diagnosis is crucial for developing effective therapeutic strategies, with hyperphosphorylated protein units and tau oligomers serving as reliable biomarkers for these conditions. This study introduces a novel approach using nanotechnology to enhance the diagnostic process for tauopathies. We developed humanized ferritin nanocages, a novel nanoscale delivery system, designed to encapsulate and transport a tau-specific fluorophore, BT1, into human retinal cells for detecting neurofibrillary tangles in retinal tissue, a key marker of tauopathies. The delivery of BT1 into living cells was successfully achieved through these nanocages, demonstrating efficient encapsulation and delivery into retinal cells derived from human induced pluripotent stem cells. Our experiments confirmed the colocalization of BT1 with pathological forms of tau in living retinal cells, highlighting the method's potential in identifying tauopathies. Using ferritin nanocages for BT1 delivery represents a significant contribution to nanobiotechnology, particularly in neurodegenerative disease diagnostics. This method offers a promising tool for the early detection of tau tangles in retinal tissue, with significant implications for improving the diagnosis and management of tauopathies. This study exemplifies the integration of nanotechnology with biomedical science, expanding the frontiers of nanomedicine and diagnostic techniques.

The nature of non-phononic excitations in disordered systems.

The frequency scaling exponent of low-frequency excitations in microscopically small glasses, which do not allow for the existence of waves (phonons), has been in the focus of the recent literature. The density of states g(ω) of these modes obeys an ωs scaling, where the exponent s, ranging between 2 and 5, depends on the quenching protocol. The orgin of these findings remains controversal. Here we show, using heterogeneous-elasticity theory, that in a marginally-stable glass sample g(ω) follows a Debye-like scaling (s = 2), and the associated excitations (type-I) are of random-matrix type. Further, using a generalisation of the theory, we demonstrate that in more stable samples, other, (type-II) excitations prevail, which are non-irrotational oscillations, associated with local frozen-in stresses. The corresponding frequency scaling exponent s is governed by the statistics of small values of the stresses and, therefore, depends on the details of the interaction potential.

High-Throughput Interactome Determination via Sulfur Anomalous Scattering.

We propose a novel approach for detecting the binding between proteins making use of the anomalous diffraction of natively present heavy elements, e.g., sulfurs, inside molecular three-dimensional structures. In particular, we analytically and numerically show that the diffraction patterns produced by the anomalous scattering of the sulfur atoms in a given direction depend additively on the relative distances between all couples of sulfur atoms. Thus, the differences in the patterns produced by bound proteins with respect to their nonbonded states can be exploited to rapidly assess protein complex formation. On the basis of our results, we suggest a possible experimental procedure for detecting protein-protein binding. Overall, the completely label-free and rapid method we propose may be readily extended to probe interactions on a large scale, thus paving the way for the development of a novel field of research based on a synchrotron light source.

Biophysical modeling of the whole-cell dynamics of C. elegans motor and interneurons families.

The nematode Caenorhabditis elegans is a widely used model organism for neuroscience. Although its nervous system has been fully reconstructed, the physiological bases of single-neuron functioning are still poorly explored. Recently, many efforts have been dedicated to measuring signals from C. elegans neurons, revealing a rich repertoire of dynamics, including bistable responses, graded responses, and action potentials. Still, biophysical models able to reproduce such a broad range of electrical responses lack. Realistic electrophysiological descriptions started to be developed only recently, merging gene expression data with electrophysiological recordings, but with a large variety of cells yet to be modeled. In this work, we contribute to filling this gap by providing biophysically accurate models of six classes of C. elegans neurons, the AIY, RIM, and AVA interneurons, and the VA, VB, and VD motor neurons. We test our models by comparing computational and experimental time series and simulate knockout neurons, to identify the biophysical mechanisms at the basis of inter and motor neuron functioning. Our models represent a step forward toward the modeling of C. elegans neuronal networks and virtual experiments on the nematode nervous system.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Computational evidences of a misfolding event in an aggregation-prone light chain preceding the formation of the non-native pathogenic dimer.

Antibody light chain amyloidosis is a disorder in which protein aggregates, mainly composed of immunoglobulin light chains, deposit in diverse tissues impairing the correct functioning of organs. Interestingly, due to the high susceptibility of antibodies to mutations, AL amyloidosis appears to be strongly patient-specific. Indeed, every patient will display their own mutations that will make the proteins involved prone to aggregation thus hindering the study of this disease on a wide scale. In this framework, determining the molecular mechanisms that drive the aggregation could pave the way to the development of patient-specific therapeutics. Here, we focus on a particular patient-derived light chain, which has been experimentally characterized. We investigated the early phases of the aggregation pathway through extensive full-atom molecular dynamics simulations, highlighting a structural rearrangement and the exposure of two hydrophobic regions in the aggregation-prone species. Next, we moved to consider the pathological dimerization process through docking and molecular dynamics simulations, proposing a dimeric structure as a candidate pathological first assembly. Overall, our results shed light on the first phases of the aggregation pathway for a light chain at an atomic level detail, offering new structural insights into the corresponding aggregation process.

Progressive alteration of murine bladder elasticity in actinic cystitis detected by Brillouin microscopy.

Bladder mechanical properties are critical for organ function and tissue homeostasis. Therefore, alterations of tissue mechanics are linked to disease onset and progression. This study aims to characterize the tissue elasticity of the murine bladder wall considering its different anatomical components, both in healthy conditions and in actinic cystitis, a state characterized by tissue fibrosis. Here, we exploit Brillouin microscopy, an emerging technique in the mechanobiology field that allows mapping tissue mechanics at the microscale, in non-contact mode and free of labeling. We show that Brillouin imaging of bladder tissues is able to recognize the different anatomical components of the bladder wall, confirmed by histopathological analysis, showing different tissue mechanical properties of the physiological bladder, as well as a significant alteration in the presence of tissue fibrosis. Our results point out the potential use of Brillouin imaging on clinically relevant samples as a complementary technique to histopathological analysis, deciphering complex mechanical alteration of each tissue layer of an organ that strongly relies on mechanical properties to perform its function.

Photonic Stochastic Emergent Storage for deep classification by scattering-intrinsic patterns.

Disorder is a pervasive characteristic of natural systems, offering a wealth of non-repeating patterns. In this study, we present a novel storage method that harnesses naturally-occurring random structures to store an arbitrary pattern in a memory device. This method, the Stochastic Emergent Storage (SES), builds upon the concept of emergent archetypes, where a training set of imperfect examples (prototypes) is employed to instantiate an archetype in a Hopfield-like network through emergent processes. We demonstrate this non-Hebbian paradigm in the photonic domain by utilizing random transmission matrices, which govern light scattering in a white-paint turbid medium, as prototypes. Through the implementation of programmable hardware, we successfully realize and experimentally validate the capability to store an arbitrary archetype and perform classification at the speed of light. Leveraging the vast number of modes excited by mesoscopic diffusion, our approach enables the simultaneous storage of thousands of memories without requiring any additional fabrication efforts. Similar to a content addressable memory, all stored memories can be collectively assessed against a given pattern to identify the matching element. Furthermore, by organizing memories spatially into distinct classes, they become features within a higher-level categorical (deeper) optical classification layer.

Two Receptor Binding Strategy of SARS-CoV-2 Is Mediated by Both the N-Terminal and Receptor-Binding Spike Domain.

It is not well understood why severe acute respiratory syndrome (SARS)-CoV-2 spreads much faster than other ß-coronaviruses such as SARS-CoV and Middle East respiratory syndrome (MERS)-CoV. In a previous publication, we predicted the binding of the N-terminal domain (NTD) of SARS-CoV-2 spike to sialic acids (SAs). Here, we experimentally validate this interaction and present simulations that reveal a second possible interaction between SAs and the spike protein via a binding site located in the receptor-binding domain (RBD). The predictions from molecular-dynamics simulations and the previously-published 2D-Zernike binding-site recognition approach were validated through flow-induced dispersion analysis (FIDA)─which reveals the capability of the SARS-CoV-2 spike to bind to SA-containing (glyco)lipid vesicles, and flow-cytometry measurements─which show that spike binding is strongly decreased upon inhibition of SA expression on the membranes of angiotensin converting enzyme-2 (ACE2)-expressing HEK cells. Our analyses reveal that the SA binding of the NTD and RBD strongly enhances the infection-inducing ACE2 binding. Altogether, our work provides in silico, in vitro, and cellular evidence that the SARS-CoV-2 virus utilizes a two-receptor (SA and ACE2) strategy. This allows the SARS-CoV-2 spike to use SA moieties on the cell membrane as a binding anchor, which increases the residence time of the virus on the cell surface and aids in the binding of the main receptor, ACE2, via 2D diffusion.

A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG.

The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.

Heterogeneous-elasticity theory of instantaneous normal modes in liquids.

Since decades, the concept of vibrational density of states in glasses has been mirrored in liquids by the instantaneous-normal-mode spectrum. In glasses instantaneous configurations are believed to be situated close to minima of the potential-energy hypersurface and all eigenvalues of the associated Hessian matrix are positive. In liquids this is no longer true, and modes corresponding to both positive and negative eigenvalues exist. The instantaneous-normal-mode spectrum has been numerically investigated in the past, and it has been demonstrated to bring important information on the liquid dynamics and transport properties. A systematic deeper theoretical understanding is now needed. Heterogeneous-elasticity theory has proven to be particularly successful in explaining many details of the low-frequency excitations in glasses, ranging from the thoroughly studied boson peak, to other anomalies related to the crossover between wave-like and random-matrix-like excitations. Here we present an extension of heterogeneous-elasticity theory to the liquid state, and show that the outcome of the theory agrees well to the results of extensive molecular-dynamics simulations of a model liquid at different temperatures. We find that the spectrum of eigenvalues [Formula: see text] has a sharp maximum close to (but not at) [Formula: see text], and decreases monotonically with [Formula: see text] on both its stable and unstable side. We show that the spectral shape strongly depends on temperature, being symmetric at high temperatures and becoming rather asymmetric at low temperatures, close to the dynamical critical temperature. Most importantly, we demonstrate that the theory naturally reproduces a surprising phenomenon, a zero-energy spectral singularity with a cusp-like character developing in the vibrational spectra upon cooling. This feature, known from a few previous numerical studies, has been generally overlooked in the past due to a misleading representation of the data. We provide a thorough analysis of this issue, based on both very accurate predictions of our theory, and computational studies of model liquid systems with extended size.

Molecular dynamics analysis of superoxide dismutase 1 mutations suggests decoupling between mechanisms underlying ALS onset and progression.

Mutations in the superoxide dismutase 1 (SOD1) gene are the second most common known cause of ALS. SOD1 variants express high phenotypic variability and over 200 have been reported in people with ALS. It was previously proposed that variants can be broadly classified in two groups, 'wild-type like' (WTL) and 'metal binding region' (MBR) variants, based on their structural location and biophysical properties. MBR variants, but not WTL variants, were associated with a reduction of SOD1 enzymatic activity. In this study we used molecular dynamics and large clinical datasets to characterise the differences in the structural and dynamic behaviour of WTL and MBR variants with respect to the wild-type SOD1, and how such differences influence the ALS clinical phenotype. Our study identified marked structural differences, some of which are observed in both variant groups, while others are group specific. Moreover, collecting clinical data of approximately 500 SOD1 ALS patients carrying variants, we showed that the survival time of patients carrying an MBR variant is generally longer (∼6 years median difference, p < 0.001) with respect to patients with a WTL variant. In conclusion, our study highlighted key differences in the dynamic behaviour between WTL and MBR SOD1 variants, and between variants and wild-type SOD1 at an atomic and molecular level, that could be further investigated to explain the associated phenotypic variability. Our results support the hypothesis of a decoupling between mechanisms of onset and progression of SOD1 ALS, and an involvement of loss-of-function of SOD1 with the disease progression.

Stress fluctuations and adiabatic speed of sound in liquids: a simple way to estimate it from ab initio simulations.

One of the fundamental quantities in dynamics of the liquid state, the adiabatic speed of sound [Formula: see text], is extremely difficult to predict from computer simulations, especially in ab initio simulations. Here we derive an expression for the instantaneous correlator of fluctuations of longitudinal component of stress tensor, which contains [Formula: see text] along with others quantities easy accessible via classical and ab initio computer simulations. We show that the proposed methodology works well in the case of Lennard-Jones and soft-sphere simple fluids, Kr-Ar liquid mixture in connection with simulations with effective pair interactions as well as for liquid Sb, fluid Hg and molten NaCl from ab initio simulations.

Reference-less wavefront shaping in a Hopfield-like rough intensity landscape.

This study introduces a new digital-micromirror based binary-phase wavefront shaping technique, which allows the measurement of the full coupling matrix of a disordered medium without a reference and enables to focusing transmitted light. The coupling matrix takes on a bi-dyadic structure, similar to a Hopfield memory matrix containing two memory patterns. Sequential wavefront optimization in this configuration often stalls due to a rough intensity landscape, resulting in a non-optimal state. To overcome this issue, we propose the Complete Couplings Mapping method, which consistently reaches the theoretically expected maximum intensity.

Modeling of olfactory transduction in AWCON neuron via coupled electrical-calcium dynamics.

Amphid wing "C" (AWC) neurons are among the most important and studied neurons of the nematode Caenorhabditis elegans. In this work, we unify the existing electrical and intracellular calcium dynamics descriptions to obtain a biophysically accurate model of olfactory transduction in AWCON neurons. We study the membrane voltage and the intracellular calcium dynamics at different exposure times and odorant concentrations to grasp a complete picture of AWCON functioning. Moreover, we investigate the complex cascade of biochemical processes that allow AWC activation upon odor removal. We analyze the behavior of the different components of the models and, by suppressing them selectively, we extrapolate their contribution to the overall neuron response and study the resilience of the dynamical system. Our results are all in agreement with the available experimental data. Therefore, we provide an accurate mathematical and biophysical model for studying olfactory signal processing in C. elegans.

Collective behavior and self-organization in neural rosette morphogenesis.

Neural rosettes develop from the self-organization of differentiating human pluripotent stem cells. This process mimics the emergence of the embryonic central nervous system primordium, i.e., the neural tube, whose formation is under close investigation as errors during such process result in severe diseases like spina bifida and anencephaly. While neural tube formation is recognized as an example of self-organization, we still do not understand the fundamental mechanisms guiding the process. Here, we discuss the different theoretical frameworks that have been proposed to explain self-organization in morphogenesis. We show that an explanation based exclusively on stem cell differentiation cannot describe the emergence of spatial organization, and an explanation based on patterning models cannot explain how different groups of cells can collectively migrate and produce the mechanical transformations required to generate the neural tube. We conclude that neural rosette development is a relevant experimental 2D in-vitro model of morphogenesis because it is a multi-scale self-organization process that involves both cell differentiation and tissue development. Ultimately, to understand rosette formation, we first need to fully understand the complex interplay between growth, migration, cytoarchitecture organization, and cell type evolution.

Differences in the organization of interface residues tunes the stability of the SARS-CoV-2 spike-ACE2 complex.

The continuous emergence of novel variants represents one of the major problems in dealing with the SARS-CoV-2 virus. Indeed, also due to its prolonged circulation, more than ten variants of concern emerged, each time rapidly overgrowing the current viral version due to improved spreading features. As, up to now, all variants carry at least one mutation on the spike Receptor Binding Domain, the stability of the binding between the SARS-CoV-2 spike protein and the human ACE2 receptor seems one of the molecular determinants behind the viral spreading potential. In this framework, a better understanding of the interplay between spike mutations and complex stability can help to assess the impact of novel variants. Here, we characterize the peculiarities of the most representative variants of concern in terms of the molecular interactions taking place between the residues of the spike RBD and those of the ACE2 receptor. To do so, we performed molecular dynamics simulations of the RBD-ACE2 complexes of the seven variants of concern in comparison with a large set of complexes with different single mutations taking place on the RBD solvent-exposed residues and for which the experimental binding affinity was available. Analyzing the strength and spatial organization of the intermolecular interactions of the binding region residues, we found that (i) mutations producing an increase of the complex stability mainly rely on instaurating more favorable van der Waals optimization at the cost of Coulombic ones. In particular, (ii) an anti-correlation is observed between the shape and electrostatic complementarities of the binding regions. Finally, (iii) we showed that combining a set of dynamical descriptors is possible to estimate the outcome of point mutations on the complex binding region with a performance of 0.7. Overall, our results introduce a set of dynamical observables that can be rapidly evaluated to probe the effects of novel isolated variants or different molecular systems.

Electrostatic complementarity at the interface drives transient protein-protein interactions.

Understanding the mechanisms driving bio-molecules binding and determining the resulting complexes' stability is fundamental for the prediction of binding regions, which is the starting point for drug-ability and design. Characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions, and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on the role of electrostatic. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. Probing the amino acid composition, the disposition of the charges, and the electrostatic potential they generated on the protein molecular surfaces, we found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. Interestingly, (iii) shape and electrostatic complementarity, for patches defined on short-range interactions, behave oppositely when one stratifies the complexes by their binding affinity: complexes with higher binding affinity present high values of shape complementarity (the role of the Lennard-Jones potential predominates) while electrostatic tends to be randomly distributed. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a novel, fast, and efficient method, based on the 2D Zernike polynomial formalism, to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of [Formula: see text] 0.8. Ultimately, our work helps shedding light on the non-trivial relationship between the hydrophobic and electrostatic contributions in the binding interfaces, thus favoring the development of new predictive methods for binding affinity characterization.

Computational structural-based GPCR optimization for user-defined ligand: Implications for the development of biosensors.

Organisms have developed effective mechanisms to sense the external environment. Human-designed biosensors exploit this natural optimization, where different biological machinery have been adapted to detect the presence of user-defined molecules. Specifically, the pheromone pathway in the model organism Saccharomyces cerevisiae represents a suitable candidate as a synthetic signaling system. Indeed, it expresses just one G-Protein Coupled Receptor (GPCR), Ste2, able to recognize pheromone and initiate the expression of pheromone-dependent genes. To date, the standard procedure to engineer this system relies on the substitution of the yeast GPCR with another one and on the modification of the yeast G-protein to bind the inserted receptor. Here, we propose an innovative computational procedure, based on geometrical and chemical optimization of protein binding pockets, to select the amino acid substitutions required to make the native yeast GPCR able to recognize a user-defined ligand. This procedure would allow the yeast to recognize a wide range of ligands, without a-priori knowledge about a GPCR recognizing them or the corresponding G protein. We used Monte Carlo simulations to design on Ste2 a binding pocket able to recognize epinephrine, selected as a test ligand. We validated Ste2 mutants via molecular docking and molecular dynamics. We verified that the amino acid substitutions we identified make Ste2 able to accommodate and remain firmly bound to epinephrine. Our results indicate that we sampled efficiently the huge space of possible mutants, proposing such a strategy as a promising starting point for the development of a new kind of S.cerevisiae-based biosensors.

Retinal fingerprints of ALS in patients: Ganglion cell apoptosis and TDP-43/p62 misplacement.

Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor neuron function. Although ophthalmic deficits are not considered a classic symptom of ALS, recent studies suggest that changes in retinal cells, similar to those in the spinal cord motor neurons, have been observed in postmortem human tissues and animal models. Methods: In this study, we examined by immunofluorescence analysis the retinal cell layers of sporadic ALS patients in post-mortem retinal slices. We evaluated the presence of cytoplasmic TDP-43 and SQSTM1/p62 aggregates, activation of the apoptotic pathway, and microglia and astrocytes reactivity. Results: We found in the retinal ganglion cell layer of ALS patients the increase of mislocalized TDP-43, SQSTM1/p62 aggregates, activation of cleaved caspase-3, and microglia density, suggesting that retinal changes can be used as an additional diagnostic tool for ALS. Discussion: The retina is considered part of the central nervous system, and neurodegenerative changes in the brain may be accompanied by structural and possibly functional changes in the neuroretina and ocular vasculature. Therefore, using in vivo retinal biomarkers as an additional diagnostic tool for ALS may provide an opportunity to longitudinally monitor individuals and therapies over time in a noninvasive and cost-effective manner.