H3K4me1 recruits DNA repair proteins in plants.

DNA repair proteins can be recruited by their histone reader domains to specific epigenomic features, with consequences on intragenomic mutation rate variation. Here, we investigated H3K4me1-associated hypomutation in plants. We first examined 2 proteins which, in plants, contain Tudor histone reader domains: PRECOCIOUS DISSOCIATION OF SISTERS 5 (PDS5C), involved in homology-directed repair, and MUTS HOMOLOG 6 (MSH6), a mismatch repair protein. The MSH6 Tudor domain of Arabidopsis (Arabidopsis thaliana) binds to H3K4me1 as previously demonstrated for PDS5C, which localizes to H3K4me1-rich gene bodies and essential genes. Mutations revealed by ultradeep sequencing of wild-type and msh6 knockout lines in Arabidopsis show that functional MSH6 is critical for the reduced rate of single-base substitution (SBS) mutations in gene bodies and H3K4me1-rich regions. We explored the breadth of these mechanisms among plants by examining a large rice (Oryza sativa) mutation data set. H3K4me1-associated hypomutation is conserved in rice as are the H3K4me1-binding residues of MSH6 and PDS5C Tudor domains. Recruitment of DNA repair proteins by H3K4me1 in plants reveals convergent, but distinct, epigenome-recruited DNA repair mechanisms from those well described in humans. The emergent model of H3K4me1-recruited repair in plants is consistent with evolutionary theory regarding mutation modifier systems and offers mechanistic insight into intragenomic mutation rate variation in plants.

Harnessing Deep Learning Methods for Voltage-Gated Ion Channel Drug Discovery.

Voltage-gated ion channels (VGICs) are pivotal in regulating electrical activity in excitable cells and are critical pharmaceutical targets for treating many diseases including cardiac arrhythmia and neuropathic pain. Despite their significance, challenges such as achieving target selectivity persist in VGIC drug development. Recent progress in deep learning, particularly diffusion models, has enabled the computational design of protein binders for any clinically relevant protein based solely on its structure. These developments coincide with a surge in experimental structural data for VGICs, providing a rich foundation for computational design efforts. This review explores the recent advancements in computational protein design using deep learning and diffusion methods, focusing on their application in designing protein binders to modulate VGIC activity. We discuss the potential use of these methods to computationally design protein binders targeting different regions of VGICs, including the pore domain, voltage-sensing domains, and interface with auxiliary subunits. We provide a comprehensive overview of the different design scenarios, discuss key structural considerations, and address the practical challenges in developing VGIC-targeting protein binders. By exploring these innovative computational methods, we aim to provide a framework for developing novel strategies that could significantly advance VGIC pharmacology and lead to the discovery of effective and safe therapeutics.

Dorsal and median raphe neuronal firing dynamics characterized by nonlinear measures.

The dorsal (DRN) and median (MRN) raphe are important nuclei involved in similar functions, including mood and sleep, but playing distinct roles. These nuclei have a different composition of neuronal types and set of neuronal connections, which among other factors, determine their neuronal dynamics. Most works characterize the neuronal dynamics using classic measures, such as using the average spiking frequency (FR), the coefficient of variation (CV), and action potential duration (APD). In the current study, to refine the characterization of neuronal firing profiles, we examined the neurons within the raphe nuclei. Through the utilization of nonlinear measures, our objective was to discern the redundancy and complementarity of these measures, particularly in comparison with classic methods. To do this, we analyzed the neuronal basal firing profile in both nuclei of urethane-anesthetized rats using the Shannon entropy (Bins Entropy) of the inter-spike intervals, permutation entropy of ordinal patterns (OP Entropy), and Permutation Lempel-Ziv Complexity (PLZC). Firstly, we found that classic (i.e., FR, CV, and APD) and nonlinear measures fail to distinguish between the dynamics of DRN and MRN neurons, except for the OP Entropy. We also found strong relationships between measures, including the CV with FR, CV with Bins entropy, and FR with PLZC, which imply redundant information. However, APD and OP Entropy have either a weak or no relationship with the rest of the measures tested, suggesting that they provide complementary information to the characterization of the neuronal firing profiles. Secondly, we studied how these measures are affected by the oscillatory properties of the firing patterns, including rhythmicity, bursting patterns, and clock-like behavior. We found that all measures are sensitive to rhythmicity, except for the OP Entropy. Overall, our work highlights OP Entropy as a powerful and useful quantity for the characterization of neuronal discharge patterns.

Consciousness is supported by near-critical slow cortical electrodynamics.

Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, we empirically identify a mathematically specific critical point near which waking cortical oscillatory dynamics operate, which is known as the edge-of-chaos critical point, or the boundary between stability and chaos. We do so by applying the recently developed modified 0-1 chaos test to electrocorticography (ECoG) and magnetoencephalography (MEG) recordings from the cortices of humans and macaques across normal waking, generalized seizure, anesthesia, and psychedelic states. Our evidence suggests that cortical information processing is disrupted during unconscious states because of a transition of low-frequency cortical electric oscillations away from this critical point; conversely, we show that psychedelics may increase the information richness of cortical activity by tuning low-frequency cortical oscillations closer to this critical point. Finally, we analyze clinical electroencephalography (EEG) recordings from patients with disorders of consciousness (DOC) and show that assessing the proximity of slow cortical oscillatory electrodynamics to the edge-of-chaos critical point may be useful as an index of consciousness in the clinical setting.

Filtration evolution of hypergraphs: A novel approach to studying multidimensional datasets.

The rapid growth of large datasets has led to a demand for novel approaches to extract valuable insights from intricate information. Graph theory provides a natural framework to model these relationships, but standard graphs may not capture the complex interdependence between components. Hypergraphs are a powerful extension of graphs that can represent higher-order relationships in the data. In this paper, we propose a novel approach to studying the structure of a dataset using hypergraph theory and a filtration method. Our method involves building a set of hypergraphs based on a variable distance parameter, enabling us to infer qualitative and quantitative information about the data structure. We apply our method to various sets of points, dynamical systems, signal models, and real electrophysiological data. Our results show that the proposed method can effectively differentiate between varying datasets, demonstrating its potential utility in a range of scientific applications.

Structural modeling of peptide toxin-ion channel interactions using RosettaDock.

Voltage-gated ion channels play essential physiological roles in action potential generation and propagation. Peptidic toxins from animal venoms target ion channels and provide useful scaffolds for the rational design of novel channel modulators with enhanced potency and subtype selectivity. Despite recent progress in obtaining experimental structures of peptide toxin-ion channel complexes, structural determination of peptide toxins bound to ion channels in physiologically important states remains challenging. Here we describe an application of RosettaDock approach to the structural modeling of peptide toxins interactions with ion channels. We tested this approach on 10 structures of peptide toxin-ion channel complexes and demonstrated that it can sample near-native structures in all tested cases. Our approach will be useful for improving the understanding of the molecular mechanism of natural peptide toxin modulation of ion channel gating and for the structural modeling of novel peptide-based ion channel modulators.

Rao-Burbea centroids applied to the statistical characterization of time series and images through ordinal patterns.

Divergences or similarity measures between probability distributions have become a very useful tool for studying different aspects of statistical objects, such as time series, networks, and images. Notably, not every divergence provides identical results when applied to the same problem. Therefore, it seems convenient to have the widest possible set of divergences to be applied to the problems under study. Besides this choice, an essential step in the analysis of every statistical object is the mapping of each one of their representing values into an alphabet of symbols conveniently chosen. In this work, we choose the family of divergences known as the Burbea-Rao centroids (BRCs). For the mapping of the original time series into a symbolic sequence, we work with the ordinal pattern scheme. We apply our proposals to analyze simulated and real time series and to real textured images. The main conclusion of our work is that the best BRC, at least in the studied cases, is the Jensen-Shannon divergence, besides the fact that it verifies some interesting formal properties.

Urethane anaesthesia exhibits neurophysiological correlates of unconsciousness and is distinct from sleep.

Urethane is a general anaesthetic widely used in animal research. The state of urethane anaesthesia is unique because it alternates between macroscopically distinct electrographic states: a slow-wave state that resembles non-rapid eye movement (NREM) sleep and an activated state with features of both REM sleep and wakefulness. Although it is assumed that urethane produces unconsciousness, this has been questioned because of states of cortical activation during drug exposure. Furthermore, the similarities and differences between urethane anaesthesia and physiological sleep are still unclear. In this study, we recorded the electroencephalogram (EEG) and electromyogram in chronically prepared rats during natural sleep-wake states and during urethane anaesthesia. We subsequently analysed the power, coherence, directed connectivity and complexity of brain oscillations and found that EEG under urethane anaesthesia has clear signatures of unconsciousness, with similarities to other general anaesthetics. In addition, the EEG profile under urethane is different in comparison with natural sleep states. These results suggest that consciousness is disrupted during urethane. Furthermore, despite similarities that have led others to conclude that urethane is a model of sleep, the electrocortical traits of depressed and activated states during urethane anaesthesia differ from physiological sleep states.

Consciousness as an Emergent Phenomenon: A Tale of Different Levels of Description.

One of the biggest queries in cognitive sciences is the emergence of consciousness from matter. Modern neurobiological theories of consciousness propose that conscious experience is the result of interactions between large-scale neuronal networks in the brain, traditionally described within the realm of classical physics. Here, we propose a generalized connectionist framework in which the emergence of "conscious networks" is not exclusive of large brain areas, but can be identified in subcellular networks exhibiting nontrivial quantum phenomena. The essential feature of such networks is the existence of strong correlations in the system (classical or quantum coherence) and the presence of an optimal point at which the system's complexity and energy dissipation are maximized, whereas free-energy is minimized. This is expressed either by maximization of the information content in large scale functional networks or by achieving optimal efficiency through the quantum Goldilock effect.

Unifying biophysical consciousness theories with MaxCon: maximizing configurations of brain connectivity.

There is such a vast proliferation of scientific theories of consciousness that it is worrying some scholars. There are even competitions to test different theories, and the results are inconclusive. Consciousness research, far from converging toward a unifying framework, is becoming more discordant than ever, especially with respect to theoretical elements that do not have a clear neurobiological basis. Rather than dueling theories, an integration across theories is needed to facilitate a comprehensive view on consciousness and on how normal nervous system dynamics can develop into pathological states. In dealing with what is considered an extremely complex matter, we try to adopt a perspective from which the subject appears in relative simplicity. Grounded in experimental and theoretical observations, we advance an encompassing biophysical theory, MaxCon, which incorporates aspects of several of the main existing neuroscientific consciousness theories, finding convergence points in an attempt to simplify and to understand how cellular collective activity is organized to fulfill the dynamic requirements of the diverse theories our proposal comprises. Moreover, a computable index indicating consciousness level is presented. Derived from the level of description of the interactions among cell networks, our proposal highlights the association of consciousness with maximization of the number of configurations of neural network connections -constrained by neuroanatomy, biophysics and the environment- that is common to all consciousness theories.

Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold.

Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (NaV) channel - NaV1.8, human voltage-gated calcium (CaV) channel - CaV1.1, and human voltage-gated potassium (KV) channel - KV1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of NaV1.8, CaV1.1, and KV1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research.

Elucidating molecular mechanisms of protoxin-II state-specific binding to the human NaV1.7 channel.

Human voltage-gated sodium (hNaV) channels are responsible for initiating and propagating action potentials in excitable cells, and mutations have been associated with numerous cardiac and neurological disorders. hNaV1.7 channels are expressed in peripheral neurons and are promising targets for pain therapy. The tarantula venom peptide protoxin-II (PTx2) has high selectivity for hNaV1.7 and is a valuable scaffold for designing novel therapeutics to treat pain. Here, we used computational modeling to study the molecular mechanisms of the state-dependent binding of PTx2 to hNaV1.7 voltage-sensing domains (VSDs). Using Rosetta structural modeling methods, we constructed atomistic models of the hNaV1.7 VSD II and IV in the activated and deactivated states with docked PTx2. We then performed microsecond-long all-atom molecular dynamics (MD) simulations of the systems in hydrated lipid bilayers. Our simulations revealed that PTx2 binds most favorably to the deactivated VSD II and activated VSD IV. These state-specific interactions are mediated primarily by PTx2's residues R22, K26, K27, K28, and W30 with VSD and the surrounding membrane lipids. Our work revealed important protein-protein and protein-lipid contacts that contribute to high-affinity state-dependent toxin interaction with the channel. The workflow presented will prove useful for designing novel peptides with improved selectivity and potency for more effective and safe treatment of pain.

Is the tendency to maximise energy distribution an optimal collective activity for biological purposes? A proposal for a global principle of biological organization.

Our purpose is to address the biological problem of finding foundations of the organization in the collective activity among cell networks in the nervous system, at the meso/macroscale, giving rise to cognition and consciousness. But in doing so, we encounter another problem related to the interpretation of methods to assess the neural interactions and organization of the neurodynamics, because thermodynamic notions, which have precise meaning only under specific conditions, have been widely employed in these studies. The consequence is that apparently contradictory results appear in the literature, but these contradictions diminish upon the considerations of the specific circumstances of each experiment. After clarifying some of these controversial points and surveying some experimental results, we propose that a necessary condition for cognition/consciousness to emerge is to have available enough energy, or cellular activity; and a sufficient condition is the multiplicity of configurations in which cell networks can communicate, resulting in non-uniform energy distribution, the generation and dissipation of energy gradients due to the constant activity. The diversity of sensorimotor processing of higher animals needs a flexible, fluctuating web on neuronal connections, and we review results supporting such multiplicity of configurations among brain regions associated with conscious awareness and healthy brain states. These ideas may reveal possible fundamental principles of brain organization that could be extended to other natural phenomena and how healthy activity may derive to pathological states.

Computational Drug Design of Novel Agonists of the µ -Opioid Receptor to Inhibit Pain Signaling.

Opioids such as Morphine, Codeine, Hydrocodone, and Oxycodone target the µ-opioid receptor, a G-protein-coupled receptor (GPCR), blocking the transmission of nociceptive signals. In this study, four opioids were analyzed for ADMET properties and molecular interactions with a GPCR crystal structure (PDB ID: 8EF6). This aided in the computational design of two novel drug candidates with improved docking scores and ADMET properties when compared to Hydrocodone. Homology analysis indicated that a Mus musculus (house mouse) animal model could be used in the preclinical studies of these drug candidates in the development of safer and more effective opioid drugs for pain management with reduced side effects.

Electrostatic Screening, Acidic pH and Macromolecular Crowding Increase the Self-Assembly Efficiency of the Minute Virus of Mice Capsid In Vitro.

The hollow protein capsids from a number of different viruses are being considered for multiple biomedical or nanotechnological applications. In order to improve the applied potential of a given viral capsid as a nanocarrier or nanocontainer, specific conditions must be found for achieving its faithful and efficient assembly in vitro. The small size, adequate physical properties and specialized biological functions of the capsids of parvoviruses such as the minute virus of mice (MVM) make them excellent choices as nanocarriers and nanocontainers. In this study we analyzed the effects of protein concentration, macromolecular crowding, temperature, pH, ionic strength, or a combination of some of those variables on the fidelity and efficiency of self-assembly of the MVM capsid in vitro. The results revealed that the in vitro reassembly of the MVM capsid is an efficient and faithful process. Under some conditions, up to ~40% of the starting virus capsids were reassembled in vitro as free, non aggregated, correctly assembled particles. These results open up the possibility of encapsidating different compounds in VP2-only capsids of MVM during its reassembly in vitro, and encourage the use of virus-like particles of MVM as nanocontainers.

Elucidating Molecular Mechanisms of Protoxin-2 State-specific Binding to the Human NaV1.7 Channel.

Human voltage-gated sodium (hNaV) channels are responsible for initiating and propagating action potentials in excitable cells and mutations have been associated with numerous cardiac and neurological disorders. hNaV1.7 channels are expressed in peripheral neurons and are promising targets for pain therapy. The tarantula venom peptide protoxin-2 (PTx2) has high selectivity for hNaV1.7 and serves as a valuable scaffold to design novel therapeutics to treat pain. Here, we used computational modeling to study the molecular mechanisms of the state-dependent binding of PTx2 to hNaV1.7 voltage-sensing domains (VSDs). Using Rosetta structural modeling methods, we constructed atomistic models of the hNaV1.7 VSD II and IV in the activated and deactivated states with docked PTx2. We then performed microsecond-long all-atom molecular dynamics (MD) simulations of the systems in hydrated lipid bilayers. Our simulations revealed that PTx2 binds most favorably to the deactivated VSD II and activated VSD IV. These state-specific interactions are mediated primarily by PTx2's residues R22, K26, K27, K28, and W30 with VSD as well as the surrounding membrane lipids. Our work revealed important protein-protein and protein-lipid contacts that contribute to high-affinity state-dependent toxin interaction with the channel. The workflow presented will prove useful for designing novel peptides with improved selectivity and potency for more effective and safe treatment of pain.

Effects of optogenetic and visual stimulation on gamma activity in the visual cortex.

Studying brain functions and activity during gamma oscillations can be a challenge because it requires careful planning to create the necessary conditions for a controlled experiment. Such an experiment consists of placing the brain into a gamma state and investigating cognitive processing with a careful design. Cortical oscillations in the gamma frequency range (30-80 Hz) play an essential role in a variety of cognitive processes, including visual processing and cognition. The present study aims to investigate the effects of a visual stimulus on the primary visual cortex under gamma oscillations. Specifically, we sought to explore the behavior of gamma oscillations triggered by optogenetic stimulation in the II and IV layers of the visual cortex, both with and without concurrent visual stimulation. Our results show that optogenetic stimulation increases the power of gamma oscillation in both layers of the visual cortex. However, the combined stimuli resulted in a reduction of gamma power in layer II and an increase and reinforcement in gamma power in layer IV. Modelling the results with the Wilson-Cowan model suggests changes in the input of the excitatory population due to the combined stimuli. In addition, our analysis of the data using the Lempel-Ziv complexity method supports our interpretations from the modeling. Thus, our results suggest that optogenetic stimulation enhances low gamma power in both layers of the visual cortex, while simultaneous visual stimulation has differing effects on the two layers, reducing gamma power in layer II and increasing it in layer IV.

Sleep and cognition in aging dogs. A polysomnographic study.

Introduction: Sleep is fundamental for cognitive homeostasis, especially in senior populations since clearance of amyloid beta (key in the pathophysiology of Alzheimer's disease) occurs during sleep. Some electroencephalographic characteristics of sleep and wakefulness have been considered a hallmark of dementia. Owners of dogs with canine cognitive dysfunction syndrome (a canine analog to Alzheimer's disease) report that their dogs suffer from difficulty sleeping. The aim of this study was to quantify age-related changes in the sleep-wakefulness cycle macrostructure and electroencephalographic features in senior dogs and to correlate them with their cognitive performance. Methods: We performed polysomnographic recordings in 28 senior dogs during a 2 h afternoon nap. Percentage of time spent in wakefulness, drowsiness, NREM, and REM sleep, as well as latency to the three sleep states were calculated. Spectral power, coherence, and Lempel Ziv Complexity of the brain oscillations were estimated. Finally, cognitive performance was evaluated by means of the Canine Dementia Scale Questionnaire and a battery of cognitive tests. Correlations between age, cognitive performance and sleep-wakefulness cycle macrostructure and electroencephalographic features were calculated. Results: Dogs with higher dementia scores and with worse performance in a problem-solving task spent less time in NREM and REM sleep. Additionally, quantitative electroencephalographic analyses showed differences in dogs associated with age or cognitive performance, some of them reflecting shallower sleep in more affected dogs. Discussion: Polysomnographic recordings in dogs can detect sleep-wakefulness cycle changes associated with dementia. Further studies should evaluate polysomnography's potential clinical use to monitor the progression of canine cognitive dysfunction syndrome.

Convergent validity, academic correlates and age- and SES-based normative data for the d2 Test of attention in children.

The d2, test of attention is one of the most used neuropsychological tests to measure attention in clinical and research settings. To date, no studies have examined neither its convergent and divergent validity in children nor its relationship with academic skills at school age. The aims of the present study were: (1) to examine the convergent and divergent validity of the d2 Test in a non-clinical pediatric population, (2) to explore the relationship between d2 task performance and academic skills (i.e., math, reading and writing abilities) and (3) to develop normative data for Spanish-speaking children (n = 360 8- to 12-year-old children) stratified by age and socioeconomic status (SES). Pearson's correlation and Structural Equation Models (SEM) were used to analyze the d2 Test validity and its relationship with academic skills. A between-subjects factorial MANOVA was used to examine differences among SES (Middle, Low), age (8-10, 11-12), and sex (male, female). Findings revealed a significant relationship between d2 task performance and all attention and executive functions (EF) measures under analysis providing evidence of good convergent validity. Furthermore, SEM results showed that attention has direct effects on math and reading and writing skills. Finally, our study confirms the influence of age and SES on d2 task performance and provides normative data for middle- and low-SES children. These results have important implications for the assessment of attention functions in clinical and research settings in children with typical and atypical development.

Complexity of Brain Dynamics as a Correlate of Consciousness in Anaesthetized Monkeys.

The use of anaesthesia is a fundamental tool in the investigation of consciousness. Anesthesia procedures allow to investigate different states of consciousness from sedation to deep anesthesia within controlled scenarios. In this study we use information quantifiers to measure the complexity of electrocorticogram recordings in monkeys. We apply these metrics to compare different stages of general anesthesia for evaluating consciousness in several anesthesia protocols. We find that the complexity of brain activity can be used as a correlate of consciousness. For two of the anaesthetics used, propofol and medetomidine, we find that the anaesthetised state is accompanied by a reduction in the complexity of brain activity. On the other hand we observe that use of ketamine produces an increase in complexity measurements. We relate this observation with increase activity within certain brain regions associated with the ketamine used doses. Our measurements indicate that complexity of brain activity is a good indicator for a general evaluation of different levels of consciousness awareness, both in anesthetized and non anesthetizes states.