Multidimensional Analysis of a Social Behavior Identifies Regression and Phenotypic Heterogeneity in a Female Mouse Model for Rett Syndrome.

Regression is a key feature of neurodevelopmental disorders such as autism spectrum disorder, Fragile X syndrome, and Rett syndrome (RTT). RTT is caused by mutations in the X-linked gene methyl-CpG-binding protein 2 (MECP2). It is characterized by an early period of typical development with subsequent regression of previously acquired motor and speech skills in girls. The syndromic phenotypes are individualistic and dynamic over time. Thus far, it has been difficult to capture these dynamics and syndromic heterogeneity in the preclinical Mecp2-heterozygous female mouse model (Het). The emergence of computational neuroethology tools allows for robust analysis of complex and dynamic behaviors to model endophenotypes in preclinical models. Toward this first step, we utilized DeepLabCut, a marker-less pose estimation software to quantify trajectory kinematics and multidimensional analysis to characterize behavioral heterogeneity in Het in the previously benchmarked, ethologically relevant social cognition task of pup retrieval. We report the identification of two distinct phenotypes of adult Het: Het that display a delay in efficiency in early days and then improve over days like wild-type mice and Het that regress and perform worse in later days. Furthermore, regression is dependent on age and behavioral context and can be detected in the initial days of retrieval. Together, the novel identification of two populations of Het suggests differential effects on neural circuitry, opens new avenues to investigate the underlying molecular and cellular mechanisms of heterogeneity, and designs better studies for stratifying therapeutics.

Wild-type MECP2 expression coincides with age-dependent sensory phenotypes in a female mouse model for Rett syndrome.

Rett syndrome is characterized by an early period of typical development and then, regression of learned motor and speech skills in girls. Loss of MECP2 protein is thought to cause Rett syndrome phenotypes. The specific underlying mechanisms from typical developmental trajectory to regression features throughout life are unclear. Lack of established timelines to study the molecular, cellular, and behavioral features of regression in female mouse models is a major contributing factor. Due to random X-chromosome inactivation, female patients with Rett syndrome and female mouse models for Rett syndrome (Mecp2Heterozygous , Het) express a functional copy of wild-type MECP2 protein in approximately half of all cells. As MECP2 expression is regulated during early postnatal development and experience, we characterized the expression of wild-type MECP2 in the primary somatosensory cortex of female Het mice. Here, we report increased MECP2 levels in non-parvalbumin-positive neurons of 6-week-old adolescent Het relative to age-matched wild-type controls, while also displaying typical levels of perineuronal net expression in the barrel field subregion of the primary somatosensory cortex, mild tactile sensory perception deficits, and efficient pup retrieval behavior. In contrast, 12-week-old adult Het express MECP2 at levels similar to age-matched wild-type mice, show increased perineuronal net expression in the cortex, and display significant tactile sensory perception deficits. Thus, we have identified a set of behavioral metrics and the cellular substrates to study regression during a specific time in the female Het mouse model, which coincides with changes in wild-type MECP2 expression. We speculate that the precocious increase in MECP2 expression within specific cell types of adolescent Het may provide compensatory benefits at the behavioral level, while the inability to further increase MECP2 levels leads to regressive behavioral phenotypes over time.

Barrels XXXV: barrels in-person.

PURPOSE OF THE STUDY: After two years of virtual meetings, the Barrels Meeting resumed to an in-person format on 10 and 11 November 2022 in La Jolla California. MATERIALS AND METHODS: The meeting focused on the rodent sensorimotor system, with a focus on integrated information from the cellular to the systems level. A series of invited and selected oral presentations were delivered in addition to a poster session. RESULTS: The latest results in the whisker-to-barrel pathway were discussed. Presentations included how the system encodes peripheral information, motor planning, and is disrupted in neurodevelopmental disorders. CONCLUSION: The 36th Annual Barrels Meeting brought together the research community to effectively discuss the latest advances in the field.

Landscape-Based Protein Stability Analysis and Network Modeling of Multiple Conformational States of the SARS-CoV-2 Spike D614G Mutant: Conformational Plasticity and Frustration-Induced Allostery as Energetic Drivers of Highly Transmissible Spike Variants.

The structural and functional studies of the SARS-CoV-2 spike protein variants revealed an important role of the D614G mutation that is shared across many variants of concern (VOCs), suggesting the effect of this mutation on the enhanced virus infectivity and transmissibility. The recent structural and biophysical studies provided important evidence about multiple conformational substates of the D614G spike protein. The development of a plausible mechanistic model that can explain the experimental observations from a more unified thermodynamic perspective is an important objective of the current work. In this study, we employed efficient and accurate coarse-grained simulations of multiple structural substates of the D614G spike trimers together with the ensemble-based mutational frustration analysis to characterize the dynamics signatures of the conformational landscapes. By combining the local frustration profiling of the conformational states with residue-based mutational scanning of protein stability and network analysis of allosteric interactions and communications, we determine the patterns of mutational sensitivity in the functional regions and sites of variants. We found that the D614G mutation may induce a considerable conformational adaptability of the open states in the SARS-CoV-2 spike protein without compromising the folding stability and integrity of the spike protein. The results suggest that the D614G mutant may employ a hinge-shift mechanism in which the dynamic couplings between the site of mutation and the interprotomer hinge modulate the interdomain interactions, global mobility change, and the increased stability of the open form. This study proposes that mutation-induced modulation of the conformational flexibility and energetic frustration at the interprotomer interfaces may serve as an efficient mechanism for allosteric regulation of the SARS-CoV-2 spike proteins.

Frustration-driven allosteric regulation and signal transmission in the SARS-CoV-2 spike omicron trimer structures: a crosstalk of the omicron mutation sites allosterically regulates tradeoffs of protein stability and conformational adaptability.

Dissecting the regulatory principles underlying function and activity of the SARS-CoV-2 spike protein at the atomic level is of paramount importance for understanding the mechanisms of virus transmissibility and immune escape. In this work, we introduce a hierarchical computational approach for atomistic modeling of allosteric mechanisms in the SARS-CoV-2 Omicron spike proteins and present evidence of a frustration-based allostery as an important energetic driver of the conformational changes and spike activation. By examining conformational landscapes and the residue interaction networks in the SARS-CoV-2 Omicron spike protein structures, we have shown that the Omicron mutational sites are dynamically coupled and form a central engine of the allosterically regulated spike machinery that regulates the balance and tradeoffs between conformational plasticity, protein stability, and functional adaptability. We have found that the Omicron mutational sites at the inter-protomer regions form regulatory hotspot clusters that control functional transitions between the closed and open states. Through perturbation-based modeling of allosteric interaction networks and diffusion analysis of communications in the closed and open spike states, we have quantified the allosterically regulated activation mechanism and uncover specific regulatory roles of the Omicron mutations. Atomistic reconstruction of allosteric communication pathways and kinetic modeling using Markov transient analysis reveal that the Omicron mutations form the inter-protomer electrostatic bridges that operate as a network of coupled regulatory switches that could control global conformational changes and signal transmission in the spike protein. The results of this study have revealed distinct and yet complementary roles of the Omicron mutation sites as a network of hotspots that enable allosteric modulation of structural stability and conformational changes which are central for spike activation and virus transmissibility.

Probing conformational landscapes and mechanisms of allosteric communication in the functional states of the ABL kinase domain using multiscale simulations and network-based mutational profiling of allosteric residue potentials.

In the current study, multiscale simulation approaches and dynamic network methods are employed to examine the dynamic and energetic details of conformational landscapes and allosteric interactions in the ABL kinase domain that determine the kinase functions. Using a plethora of synergistic computational approaches, we elucidate how conformational transitions between the active and inactive ABL states can employ allosteric regulatory switches to modulate intramolecular communication networks between the ATP site, the substrate binding region, and the allosteric binding pocket. A perturbation-based network approach that implements mutational profiling of allosteric residue propensities and communications in the ABL states is proposed. Consistent with biophysical experiments, the results reveal functionally significant shifts of the allosteric interaction networks in which preferential communication paths between the ATP binding site and substrate regions in the active ABL state become suppressed in the closed inactive ABL form, which in turn features favorable allosteric coupling between the ATP site and the allosteric binding pocket. By integrating the results of atomistic simulations with dimensionality reduction methods and Markov state models, we analyze the mechanistic role of macrostates and characterize kinetic transitions between the ABL conformational states. Using network-based mutational scanning of allosteric residue propensities, this study provides a comprehensive computational analysis of long-range communications in the ABL kinase domain and identifies conserved regulatory hotspots that modulate kinase activity and allosteric crosstalk between the allosteric pocket, ATP binding site, and substrate binding regions.

Computer Simulations and Network-Based Profiling of Binding and Allosteric Interactions of SARS-CoV-2 Spike Variant Complexes and the Host Receptor: Dissecting the Mechanistic Effects of the Delta and Omicron Mutations.

In this study, we combine all-atom MD simulations and comprehensive mutational scanning of S-RBD complexes with the angiotensin-converting enzyme 2 (ACE2) host receptor in the native form as well as the S-RBD Delta and Omicron variants to (a) examine the differences in the dynamic signatures of the S-RBD complexes and (b) identify the critical binding hotspots and sensitivity of the mutational positions. We also examined the differences in allosteric interactions and communications in the S-RBD complexes for the Delta and Omicron variants. Through the perturbation-based scanning of the allosteric propensities of the SARS-CoV-2 S-RBD residues and dynamics-based network centrality and community analyses, we characterize the global mediating centers in the complexes and the nature of local stabilizing communities. We show that a constellation of mutational sites (G496S, Q498R, N501Y and Y505H) correspond to key binding energy hotspots and also contribute decisively to the key interfacial communities that mediate allosteric communications between S-RBD and ACE2. These Omicron mutations are responsible for both favorable local binding interactions and long-range allosteric interactions, providing key functional centers that mediate the high transmissibility of the virus. At the same time, our results show that other mutational sites could provide a "flexible shield" surrounding the stable community network, thereby allowing the Omicron virus to modulate immune evasion at different epitopes, while protecting the integrity of binding and allosteric interactions in the RBD-ACE2 complexes. This study suggests that the SARS-CoV-2 S protein may exploit the plasticity of the RBD to generate escape mutants, while engaging a small group of functional hotspots to mediate efficient local binding interactions and long-range allosteric communications with ACE2.

Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration.

In the current study, we introduce an integrative machine learning strategy for the autonomous molecular design of protein kinase inhibitors using variational autoencoders and a novel cluster-based perturbation approach for exploration of the chemical latent space. The proposed strategy combines autoencoder-based embedding of small molecules with a cluster-based perturbation approach for efficient navigation of the latent space and a feature-based kinase inhibition likelihood classifier that guides optimization of the molecular properties and targeted molecular design. In the proposed generative approach, molecules sharing similar structures tend to cluster in the latent space, and interpolating between two molecules in the latent space enables smooth changes in the molecular structures and properties. The results demonstrated that the proposed strategy can efficiently explore the latent space of small molecules and kinase inhibitors along interpretable directions to guide the generation of novel family-specific kinase molecules that display a significant scaffold diversity and optimal biochemical properties. Through assessment of the latent-based and chemical feature-based binary and multiclass classifiers, we developed a robust probabilistic evaluator of kinase inhibition likelihood that is specifically tailored to guide the molecular design of novel SRC kinase molecules. The generated molecules originating from LCK and ABL1 kinase inhibitors yielded ~40% of novel and valid SRC kinase compounds with high kinase inhibition likelihood probability values (p > 0.75) and high similarity (Tanimoto coefficient > 0.6) to the known SRC inhibitors. By combining the molecular perturbation design with the kinase inhibition likelihood analysis and similarity assessments, we showed that the proposed molecular design strategy can produce novel valid molecules and transform known inhibitors of different kinase families into potential chemical probes of the SRC kinase with excellent physicochemical profiles and high similarity to the known SRC kinase drugs. The results of our study suggest that task-specific manipulation of a biased latent space may be an important direction for more effective task-oriented and target-specific autonomous chemical design models.

Probing Mechanisms of Binding and Allostery in the SARS-CoV-2 Spike Omicron Variant Complexes with the Host Receptor: Revealing Functional Roles of the Binding Hotspots in Mediating Epistatic Effects and Communication with Allosteric Pockets.

In this study, we performed all-atom MD simulations of RBD-ACE2 complexes for BA.1, BA.1.1, BA.2, and BA.3 Omicron subvariants, conducted a systematic mutational scanning of the RBD-ACE2 binding interfaces and analysis of electrostatic effects. The binding free energy computations of the Omicron RBD-ACE2 complexes and comprehensive examination of the electrostatic interactions quantify the driving forces of binding and provide new insights into energetic mechanisms underlying evolutionary differences between Omicron variants. A systematic mutational scanning of the RBD residues determines the protein stability centers and binding energy hotpots in the Omicron RBD-ACE2 complexes. By employing the ensemble-based global network analysis, we propose a community-based topological model of the Omicron RBD interactions that characterized functional roles of the Omicron mutational sites in mediating non-additive epistatic effects of mutations. Our findings suggest that non-additive contributions to the binding affinity may be mediated by R493, Y498, and Y501 sites and are greater for the Omicron BA.1.1 and BA.2 complexes that display the strongest ACE2 binding affinity among the Omicron subvariants. A network-centric adaptation model of the reversed allosteric communication is unveiled in this study, which established a robust connection between allosteric network hotspots and potential allosteric binding pockets. Using this approach, we demonstrated that mediating centers of long-range interactions could anchor the experimentally validated allosteric binding pockets. Through an array of complementary approaches and proposed models, this comprehensive and multi-faceted computational study revealed and quantified multiple functional roles of the key Omicron mutational site R493, R498, and Y501 acting as binding energy hotspots, drivers of electrostatic interactions as well as mediators of epistatic effects and long-range communications with the allosteric pockets.

Maternal Experience-Dependent Cortical Plasticity in Mice Is Circuit- and Stimulus-Specific and Requires MECP2.

The neurodevelopmental disorder Rett syndrome is caused by mutations in the gene Mecp2 Misexpression of the protein MECP2 is thought to contribute to neuropathology by causing dysregulation of plasticity. Female heterozygous Mecp2 mutants (Mecp2het ) failed to acquire a learned maternal retrieval behavior when exposed to pups, an effect linked to disruption of parvalbumin-expressing inhibitory interneurons (PV) in the auditory cortex. Nevertheless, how dysregulated PV networks affect the neural activity dynamics that underlie auditory cortical plasticity during early maternal experience is unknown. Here we show that maternal experience in WT adult female mice (WT) triggers suppression of PV auditory responses. We also observe concomitant disinhibition of auditory responses in deep-layer pyramidal neurons that is selective for behaviorally relevant pup vocalizations. These neurons further exhibit sharpened tuning for pup vocalizations following maternal experience. All of these neuronal changes are abolished in Mecp2het , suggesting that they are an essential component of maternal learning. This is further supported by our finding that genetic manipulation of GABAergic networks that restores accurate retrieval behavior in Mecp2het also restores maternal experience-dependent plasticity of PV. Our data are consistent with a growing body of evidence that cortical networks are particularly vulnerable to mutations of Mecp2 in PV neurons. Moreover, our work links, for the first time, impaired in vivo cortical plasticity in awake Mecp2 mutant animals to a natural, ethologically relevant behavior.SIGNIFICANCE STATEMENT Rett syndrome is a genetic disorder that includes language communication problems. Nearly all Rett syndrome is caused by mutations in the gene that produces the protein MECP2, which is important for changes in brain connectivity believed to underlie learning. We previously showed that female Mecp2 mutants fail to learn a simple maternal care behavior performed in response to their pups' distress cries. This impairment appeared to critically involve inhibitory neurons in the auditory cortex called parvalbumin neurons. Here we record from these neurons before and after maternal experience, and we show that they adapt their response to pup calls during maternal learning in nonmutants, but not in mutants. This adaptation is partially restored by a manipulation that improves learning.