Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex.

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

NEURD: automated proofreading and feature extraction for connectomics.

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows for state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

Functional connectomics reveals general wiring rule in mouse visual cortex.

To understand how the brain computes, it is important to unravel the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.

Origins of glycan selectivity in streptococcal Siglec-like adhesins suggest mechanisms of receptor adaptation.

Bacterial binding to host receptors underlies both commensalism and pathogenesis. Many streptococci adhere to protein-attached carbohydrates expressed on cell surfaces using Siglec-like binding regions (SLBRs). The precise glycan repertoire recognized may dictate whether the organism is a strict commensal versus a pathogen. However, it is currently not clear what drives receptor selectivity. Here, we use five representative SLBRs and identify regions of the receptor binding site that are hypervariable in sequence and structure. We show that these regions control the identity of the preferred carbohydrate ligand using chimeragenesis and single amino acid substitutions. We further evaluate how the identity of the preferred ligand affects the interaction with glycoprotein receptors in human saliva and plasma samples. As point mutations can change the preferred human receptor, these studies suggest how streptococci may adapt to changes in the environmental glycan repertoire.

Verteporfin is a substrate-selective γ-secretase inhibitor that binds the amyloid precursor protein transmembrane domain.

This work reports substrate-selective inhibition of a protease with broad substrate specificity based on direct binding of a small-molecule inhibitor to the substrate. The target for these studies was γ-secretase protease, which cleaves dozens of different single-span membrane protein substrates, including both the C99 domain of the human amyloid precursor protein and the Notch receptor. Substrate-specific inhibition of C99 cleavage is desirable to reduce production of the amyloid-ß polypeptide without inhibiting Notch cleavage, a major source of toxicity associated with broad specificity γ-secretase inhibitors. In order to identify a C99-selective inhibitors of the human γ-secretase, we conducted an NMR-based screen of FDA-approved drugs against C99 in model membranes. From this screen, we identified the small-molecule verteporfin with these properties. We observed that verteporfin formed a direct 1:1 complex with C99, with a KD of 15-47 µM (depending on the membrane mimetic used), and that it did not bind the transmembrane domain of the Notch-1 receptor. Biochemical assays showed that direct binding of verteporfin to C99 inhibits γ-secretase cleavage of C99 with IC50 values in the range of 15-164 µM, while Notch-1 cleavage was inhibited only at higher concentrations, and likely via a mechanism that does not involve binding to Notch-1. This work documents a robust NMR-based approach to discovery of small-molecule binders to single-span membrane proteins and confirmed that it is possible to inhibit γ-secretase in a substrate-specific manner.

A Model for the Signal Initiation Complex Between Arrestin-3 and the Src Family Kinase Fgr.

Arrestins regulate a wide range of signaling events, most notably when bound to active G protein-coupled receptors (GPCRs). Among the known effectors recruited by GPCR-bound arrestins are Src family kinases, which regulate cellular growth and proliferation. Here, we focus on arrestin-3 interactions with Fgr kinase, a member of the Src family. Previous reports demonstrated that Fgr exhibits high constitutive activity, but can be further activated by both arrestin-dependent and arrestin-independent pathways. We report that arrestin-3 modulates Fgr activity with a hallmark bell-shaped concentration-dependence, consistent with a role as a signaling scaffold. We further demonstrate using NMR spectroscopy that a polyproline motif within arrestin-3 interacts directly with the SH3 domain of Fgr. To provide a framework for this interaction, we determined the crystal structure of the Fgr SH3 domain at 1.9 Å resolution and developed a model for the GPCR-arrestin-3-Fgr complex that is supported by mutagenesis. This model suggests that Fgr interacts with arrestin-3 at multiple sites and is consistent with the locations of disease-associated Fgr mutations. Collectively, these studies provide a structural framework for arrestin-dependent activation of Fgr.

Evidence that the TRPV1 S1-S4 membrane domain contributes to thermosensing.

Sensing and responding to temperature is crucial in biology. The TRPV1 ion channel is a well-studied heat-sensing receptor that is also activated by vanilloid compounds, including capsaicin. Despite significant interest, the molecular underpinnings of thermosensing have remained elusive. The TRPV1 S1-S4 membrane domain couples chemical ligand binding to the pore domain during channel gating. Here we show that the S1-S4 domain also significantly contributes to thermosensing and couples to heat-activated gating. Evaluation of the isolated human TRPV1 S1-S4 domain by solution NMR, far-UV CD, and intrinsic fluorescence shows that this domain undergoes a non-denaturing temperature-dependent transition with a high thermosensitivity. Further NMR characterization of the temperature-dependent conformational changes suggests the contribution of the S1-S4 domain to thermosensing shares features with known coupling mechanisms between this domain with ligand and pH activation. Taken together, this study shows that the TRPV1 S1-S4 domain contributes to TRPV1 temperature-dependent activation.

The vexing complexity of the amyloidogenic pathway.

The role of the amyloidogenic pathway in the etiology of Alzheimer's disease (AD), particularly the common sporadic late onset forms of the disease, is controversial. To some degree, this is a consequence of the failure of drug and therapeutic antibody trials based either on targeting the proteases in this pathway or its amyloid end products. Here, we explore the formidable complexity of the biochemistry and cell biology associated with this pathway. For example, we review evidence that the immediate precursor of amyloid-ß, the C99 domain of the amyloid precursor protein (APP), may itself be toxic. We also review important new results that appear to finally establish a direct genetic link between mutations in APP and the sporadic forms of AD. Based on the complexity of amyloidogenesis, it seems possible that a major contributor to the failure of related drug trials is that we have an incomplete understanding of this pathway and how it is linked to Alzheimer's pathogenesis. If so, this highlights a need for further characterization of this pathway, not its abandonment.

Conducting clinical research in community mental health settings: Opportunities and challenges.

Tremendous progress has been made in the past decade surrounding the underlying mechanisms and treatment of neuropsychiatric disease. Technological advancements and a broadened research paradigm have contributed to the understanding of the neurochemistry, brain function and brain circuitry involved in neuropsychiatric disorders. The predominant area of unmet medical need in the United States is major psychiatric disorders, and major depressive disorder is the leading cause of disability for ages 15-44. Total spending on research and development by the pharmaceutical industry has grown exponentially during the past decade, but fewer new molecular entities (NME) for the treatment of major psychiatric disorders have received regulatory approvals compared to other therapeutic areas. Though significant expansion has occurred during the "decade of the brain", the translation of clinical trials outcomes into the community mental health setting is deficient. Randomized controlled trials (RCTs) have been the standard approach to clinical evaluation of the safety and efficacy of NMEs for the past 60 years; however, there are significant barriers and skepticism in the implementation of evidence-based outcomes into clinical practice. Recruitment of patients, shortages of experienced clinical researchers, regulatory requirements and later translation of outcomes into clinical practice are ever growing problems faced by investigators. The community mental health setting presents particular barriers in the replication of therapeutic outcomes from RCTs. The diagnostic complexity of major psychiatric diseases and the highly selective patient populations involved in clinical trials lend to the gap in translation from the "bench to the bedside". The community mental health setting lends to a diverse patient population with numerous co-morbidities and environmental factors that are unaccounted in the average RCT. While we acknowledge the enormous complexity in developing novel and innovative treatments for major psychiatric disorders, we must continue to improve the translatability of clinical trials to real world settings. Progress has been rather slow but as the gap in treatment effectiveness is reduced, so will costs and barriers in community mental health.

Targeting cognitive deficits in schizophrenia: a review of the development of a new class of medicines from the perspective of community mental health researchers.

INTRODUCTION: Schizophrenia is a chronic disorder associated with positive and negative symptoms and wide-ranging deficits in neurocognitive function. Neurocognitive deficits are considered to be the core pathophysiological symptoms of the illness. Neurocognitive deficits are also closely associated with functional outcome. At present, cognitive deficits remain one of the most important unmet therapeutic needs in schizophrenia. AREAS COVERED: Neuroscientific discoveries over the past decades have enriched our understanding of the neurobiological mechanism underlying cognitive deficits in schizophrenia. This research has identified new molecular mechanisms and processes as promising pharmacological targets. However, in spite of extensive efforts to develop a new class of cognitive-enhancing medicines for the treatment of schizophrenia over the past 5 years, no novel pharmacological agents have received the regulatory approvals required by the Food and Drug Administration. The efficacy and safety outcomes from selective Phase II clinical trials are reviewed. EXPERT OPINION: The evolving concept of neurocognition and the current guidelines for the design and methodology of clinical trials of cognitive-enhancing drugs for the treatment of individuals with schizophrenia are critically examined. The future directions in the development of cognitive-enhancing medicines for the treatment of schizophrenia from the perspective of clinicians and researchers from community mental health settings are discussed.