Biology subjects

Yin, W.

Publications and source records attributed to Yin, W..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Precise amplification-free detection of highly structured RNA with an enhanced SCas12a assay

The CRISPR/Cas12a system has revolutionized molecular diagnostics; however, its application in directly detecting complex structured RNA remains challenging. Recently, we have developed a RNA detection method called SCas12a, which exhibits high sensitivity and efficiency in detecting RNA molecules devoid of intricate secondary structures. Here, we present an enhanced SCas12a assay (SCas12aV2) that facilitates precise amplification-free detection of highly structured RNA molecules. Our approach reengineers the split Cas12a system by optimizing the scaffold RNA length and targeting asymmetric RNA structures, thereby minimizing steric hindrance. We observe that utilization of a dsDNA-ssDNA hybrid DNA activator significantly enhances both the sensitivity and kinetics compared to those achieved using traditional ssDNA or dsDNA activators. The SCas12aV2 assay demonstrates exceptional sensitivity, with a limit of detection reaching 246 aM for pooled activators and 10 pM for single-site targeting. It also exhibits high specificity for single nucleotide polymorphisms (SNPs) and successfully identifies viable bacterial populations and SARS-CoV-2 infections from clinical samples. The assays versatility is further highlighted by its applicability to various Cas12a orthologs, including the thermostable CtCas12a. This work offers a significant advance in molecular diagnostics, enhancing the potential for accurate and efficient RNA detection.

molecular biology↗

bioRxiv2024

F-CPI: Prediction of activity changes induced by fluorine substitution using multimodal deep learning

There are a large number of fluorine (F)-containing compounds in approved drugs, and F substitution is a common method in drug discovery and development. However, F is difficult to form traditional hydrogen bonds and typical halogen bonds. As a result, accurate prediction of the activity after F substitution is still impossible using traditional drug design methods, whereas artificial intelligence driven activity prediction might offer a solution. Although more and more machine learning and deep learning models are being applied, there is currently no model specifically designed to study the effect of F on bioactivities. In this study, we developed a specialized deep learning model, F-CPI, to predict the effect of introducing F on drug activity, and tested its performance on a carefully constructed dataset. Comparison with traditional machine learning models and popular CPI task models demonstrated the superiority and necessity of F-CPI, achieving an accuracy of approximately 89% and a precision of approximately 67%. In the end, we utilized F-CPI for the structural optimization of hit compounds against SARS-CoV-2 3CLpro. Impressively, in one case, the introduction of only one F atom resulted in a more than 100-fold increase in activity (IC50: 22.99 nM vs. 28190 nM). Therefore, we believe that F-CPI is a helpful and effective tool in the context of drug discovery and design.

bioinformatics↗

bioRxiv2024

ERNIE-RNA: An RNA Language Model with Structure-enhanced Representations

With large amounts of unlabeled RNA sequences data produced by high-throughput sequencing technologies, pre-trained RNA language models have been developed to estimate semantic space of RNA molecules, which facilities the understanding of grammar of RNA language. However, existing RNA language models overlook the impact of structure when modeling the RNA semantic space, resulting in incomplete feature extraction and suboptimal performance across various downstream tasks. In this study, we developed a RNA pre-trained language model named ERNIE-RNA (Enhanced Representations with base-pairing restriction for RNA modeling) based on a modified BERT (Bidirectional Encoder Representations from Transformers) by incorporating base-pairing restriction with no MSA (Multiple Sequence Alignment) information. We found that the attention maps from ERNIE-RNA with no fine-tuning are able to capture RNA structure in the zero-shot experiment more precisely than conventional methods such as fine-tuned RNAfold and RNAstructure, suggesting that the ERNIE-RNA can provide comprehensive RNA structural representations. Furthermore, ERNIE-RNA achieved SOTA (state-of-the-art) performance after fine-tuning for various downstream tasks, including RNA structural and functional predictions. In summary, our ERNIE-RNA model provides general features which can be widely and effectively applied in various subsequent research tasks. Our results indicate that introducing key knowledge-based prior information in the BERT framework may be a useful strategy to enhance the performance of other language models.

bioinformatics↗

bioRxiv2024

Stable sequential dynamics in prefrontal cortex represents subjective estimation of time

Time estimation is an essential prerequisite underlying various cognitive functions. Previous studies identified "sequential firing" and "activity ramps" as the primary neuron activity patterns in the medial frontal cortex (mPFC) that could convey information regarding time. However, the relationship between these patterns and the timing behavior has not been fully understood. In this study, we utilized in vivo calcium imaging of mPFC in rats performing a timing task. We observed cells that showed selective activation at trial start, end, or during the timing interval. By aligning long-term time-lapse datasets, we discovered that sequential patterns of time coding were stable over weeks, while cells coding for trial start or end showed constant dynamism. Furthermore, with a novel behavior design that allowed the animal to determine individual trial interval, we were able to demonstrate that real-time adjustment in the sequence procession speed closely tracked the trial-to-trial interval variations. And errors in the rats timing behavior can be primarily attributed to the premature ending of the time sequence. Together, our data suggest that sequential activity maybe a stable neural substrate thatrepresentstime under physiological conditions.Furthermore, our results imply the existence of a unique cell type in the mPFC that participates in the time-related sequences. Future characterization of this cell type could provide important insights in the neural mechanism of timing and related cognitive functions.

neuroscience↗

bioRxiv2024

Decoration of Burkholderia Hcp1 protein to virus-like particles as a vaccine delivery platform.

Virus-like particles (VLPs) are protein-based nanoparticles frequently used as carrier in conjugate vaccine platforms. VLPs have been used to display foreign antigens for vaccination and to deliver immunotherapeutic against diseases. Hemolysin-coregulated proteins 1 (Hcp1) is a protein component of the Burkholderia type 6 secretion system which participates in intracellular invasion and dissemination. This protein has been reported as a protective antigen and is used in multiple vaccine candidates with various platforms against melioidosis, a severe infectious disease caused by the intracellular pathogen B. pseudomallei. In this study, we used P22 VLPs as a surface platform for decoration with Hcp1 using chemical conjugation. C57BL/6 mice were intranasally immunized with three doses of either PBS, VLPs or conjugated Hcp1-VLPs. Immunization with Hcp1-VLPs formulation induced Hcp1-specific-IgG, IgG1, IgG2c and IgA antibody responses. Furthermore, the serum from Hcp1-VLPs immunized mice enhanced the bacterial uptake and opsonophagocytosis by macrophages in the presence of complement. This study demonstrated an alternative strategy to develop a VLPs-based vaccine platform against Burkholderia species.

microbiology↗

bioRxiv2024

A split crRNA with CRISPR-Cas12a enables highly sensitive, selective, and multiplexed detection of RNA and DNA

The complete 40-nt CRISPR RNA (crRNA) of Cas12a can be artificially divided into two parts, including the 20-nt scaffold RNA with fixed sequences and the 20-nt spacer RNA with variable sequences, respectively. Herein, we found that Cas12a protein, scaffold RNA, and spacer RNA can be reassembled into an active ribonucleoprotein (RNP) which has a trans-cleavage activity comparable to that of wild-type Cas12a RNP. By leveraging such split CRISPR-Cas12a system (SCas12a), we devised fast fluorescence and lateral flow assays for highly sensitive, selective, and multiplexed detection of miRNAs without the need for reverse transcription and pre-amplification, achieving a limit of detection (LoD) of 10 fM. Additionally, our SCas12a assay enables detecting long-stranded RNA without secondary structure, as well as distinguishing mature miRNA from its precursor (pre-miRNA) that comprises the same sequence of miRNA. Beyond RNA detection, SCas12a outperforms wild-type Cas12a in specificity towards DNA point mutations. In combination with recombinase polymerase amplification (RPA), we set up a one-pot assay to detect attomolar concentrations of human papillomavirus (HPV) in patient samples. In conclusion, this work provides a simple, cost-effective, yet powerful SCas12a-based rapid nucleic acid detection platform in various diagnostic settings. Significance StatementConventional Cas12a-based diagnostic methods cannot directly detect RNA targets. Here, we develop rapid fluorescence and lateral flow assays based on a split Cas12a system (called SCas12a), achieving amplification-free detection of RNA with high sensitivity and specificity. By supplying pooled activators, our method enables multiplexed detection of miRNA and DNA targets. In addition, SCas12a can discriminate mature miRNA from its pre-miRNA, which was achieved for the first time with CRISPR detection technology. Moreover, SCas12a outperforms wild-type Cas12a in specificity towards point mutation and can be combined with RPA to detect attomolar concentration of HPV in patient samples. Overall, this work offers a generic "split crRNA-activator" strategy for developing new CRISPR diagnostic tools.

biochemistry↗

bioRxiv2023

Human microbiota is a reservoir of SARS-CoV-2 advantageous mutations

SARS-CoV-2 mutations are rapidly emerging, in particular advantageous mutations in the spike (S) protein, which either increase transmissibility or lead to immune escape, are posing a major challenge to pandemic prevention and treatment. However, how the virus acquires a high number of advantageous mutations in a short time remains a mystery. Here, we show that the human microbiota may contribute to mutations in variants of concern (VOCs). We identified a mutation and adjacent 6 amino acids (aa) in a viral mutation fragment (VMF) and searched for homologous fragments (HFs) in the National Center for Biotechnology Information (NCBI) database. Among the approximate 8000 HFs obtained, 61 mutations in S and other outer membrane proteins were found in bacteria, accounting for 62% of all mutation sources, which is a 12-fold higher than the natural variable proportion. Approximately 70% of these bacterial species belong to the human microbiota, are primarily distributed in the gut or lung and exhibit a composition pattern similar to that of COVID-19 patients. Importantly, SARS- CoV-2 RNA-dependent RNA polymerase (RdRp) replicates corresponding bacterial mRNAs harboring mutations, producing chimeric RNAs. Collectively, SARS-CoV-2 may acquire mutations from the human microbiota, resulting in alterations in the binding sites or antigenic determinants of the original virus. Our study sheds light on the evolving mutational mechanisms of SARS-CoV-2.

microbiology↗

bioRxiv2023

The Synaptic Architecture of Layer 5 Thick Tufted Excitatory Neurons in the Visual Cortex of Mice

The neocortex is one of the most critical structures that makes us human, and it is involved in a variety of cognitive functions from perception to sensory integration and motor control. Composed of repeated modules, or microcircuits, the neocortex relies on distinct cell types as its fundamental building blocks. Despite significant progress in characterizing these cell types 1-5, an understanding of the complete synaptic partners associated with individual excitatory cell types remain elusive. Here, we investigate the connectivity of arguably the most well recognized and studied excitatory neuron in the neocortex: the thick tufted layer 5 pyramidal cell 6-10 also known as extra telencephalic (ET) 11 neurons. Although the synaptic interactions of ET neurons have been extensively explored, a comprehensive characterization of their local connectivity remains lacking. To address this knowledge gap, we leveraged a 1 mm3 electron microscopic (EM) dataset. We found that ET neurons primarily establish connections with inhibitory cells in their immediate vicinity. However, when they extend their axons to other cortical regions, they tend to connect more with excitatory cells. We also find that the inhibitory cells targeted by ET neurons are a specific group of cell types, and they preferentially inhibit ET cells. Finally, we observed that the most common excitatory targets of ET neurons are layer 5 IT neurons and layer 6 pyramidal cells, whereas synapses with other ET neurons are not as common. These findings challenge current views of the connectivity of ET neurons and suggest a circuit design that involves local competition among ET neurons and collaboration with other types of excitatory cells. Our results also highlight a specific circuit pattern where a subclass of excitatory cells forms a network with specific inhibitory cell types, offering a framework for exploring the connectivity of other types of excitatory cells.

neuroscience↗

bioRxiv2023

Bio-inspired mineralization collagen induce fibrocartilage regeneration after tendon-bone injury by activating Gli1+Dkk3+ progenitor cells

A fibrocartilaginous connection between the tendon and bone, plays a critical role in transferring force from muscle to bone to enable joint movement. However, due to the high mechanical stress it experiences, the enthesis is vulnerable to injury and incapable of regenerating. The spatial relationship and functional basis of the principal components of the fibrocartilage - mineral and collagen - have not been clearly elucidated, which is a significant remaining gap in reconstructing complex architectures for promoting interface tissue regeneration. Here, using three-dimensional electron tomography imaging and high-resolution two-dimensional electron microscopy, we discover that mineral particles form a continuous cross-fibrillar phase within the fibrocartilage region. By developing a "floating mineralization" system, we fabricate a three-layer hydrogel that mimics the hierarchical nano- to micro-scale structure of tendon-bone interface (TBI). The middle layer is noteworthy for its resemblance to the nanostructure of fibrocartilage and its superior ability to induce mineralized fibrochondrogenesis in vitro. Based on motor function analysis, imaging diagnosis, histological staining, immunofluorescence staining, and biomechanics performance, we demonstrate that in situ transplantation of the gradient hydrogel achieved tendon-fibrocartilage-bone synchronous regeneration and result in 68% maximum mechanical recovery at 8-week postoperation. Single-cell RNA sequencing analysis reveals that a unique atlas of in situ stem/progenitor cells is generated during the TBI healing in vivo. Notably, the bio-inspired hydrogel microenvironment drived endogenous Gli1+Dkk3+ progenitor cells, playing a key role in TBI regeneration. Therefore, we have successfully decoded and reconstructed the nanostructure of fibrocartilage, which has great potential in TBI regeneration.

bioengineering↗

bioRxiv2023

CAVE: Connectome Annotation Versioning Engine

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets ([~]1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVEs data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

neuroscience↗

bioRxiv2023

Host-microbiota interactions contributing to the heterogeneous tumor microenvironment in colorectal cancer

BackgroundColorectal cancer (CRC) is a highly heterogeneous cancer with four consensus molecular subtypes (CMSs) characterized by distinct tumor microenvironment (TME). We aimed to depict the characteristics of host-microbiota interactions and their contributions to TME in each CMS. MethodsHost transcriptome and intratumoral microbiome profiles of 594 CRC samples were derived from RNA-Seq data from TCGA. Differential host genes and microbes among CMSs were identified. Immune microenvironments were assessed by CIBERSORTx and ESTIMATE and microbial co-abundance analyses were performed by FastSpar. Host-microbiota associations were evaluated by LASSO penalized regression in each CMS. ResultsAlong with distinct host gene signatures, including ferroptosis-related genes and immune microenvironments, 293, 153, 66 and 109 intratumoral differential microbial genera were identified within each of the four CMSs, respectively. Furthermore, the host-microbiota interactions contributed to distinct TME in each CMS, represented by 829, 1,270, 634 and 1,882 robust gene-microbe associations, respectively. The TME in CMS1 was featured with inflammation-related HSF1 activation and interactions between genes of endothelin pathway and Flammeovirga. Integrins-related genes positively correlated with Sutterella in CMS2 while CMS3 displayed microbial associations with biosynthetic and metabolic pathways. Genes in collagens biosynthesis positively correlated with Sutterella, contributing to homeostasis disturbance in CMS4. Besides, ferroptosis dysregulation was more remarkable in immune-high subtypes, which might partly result from the colonization of tissue microbes. ConclusionsWe systematically profiled the landscapes of TME, of each CMS in CRC, encompassing host genes, intratumoral microbiome and their interactions, which could illuminate novel mechanisms for the heterogeneity in CRC and potential therapeutic targets.

microbiology↗

bioRxiv2023

Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types

Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types1. Neural cell types have previously been defined by morphology 2, 3, electrophysiology 4, 5, transcriptomic expression 6-8, connectivity 9-13, or even a combination of such modalities 14-16. More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells 17-20. Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex 21. It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type 22, 23 known to be Somatostatin positive (Sst+) 24, 25, were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.

neuroscience↗

bioRxiv2023

NEURD: A mesh decomposition framework forautomated proofreading and morphologicalanalysis of neuronal EM reconstructions

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 produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting 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 meshed neurons into compact and extensively-annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state of the art automated proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

neuroscience↗

bioRxiv2023

Functional connectomics reveals general wiring rule in mouse visual cortex

Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain implements computation. In the mouse primary visual cortex (V1), excitatory neurons with similar response properties are more likely to be synaptically connected, but previous studies have been limited to within V1, leaving much unknown about broader connectivity rules. In this study, we leverage the millimeter-scale MICrONS dataset to analyze synaptic connectivity and functional properties of individual neurons across cortical layers and areas. Our results reveal that neurons with similar responses are preferentially connected both within and across layers and areas -- including feedback connections -- suggesting the universality of the like-to-like connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections, beyond what could be explained by the physical proximity of axons and dendrites. We also found a higher-order rule where postsynaptic neuron cohorts downstream of individual presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Notably, recurrent neural networks (RNNs) trained on a simple classification task develop connectivity patterns mirroring both pairwise and higher-order rules, with magnitude similar to those in the MICrONS data. Lesion studies in these RNNs reveal that disrupting like-to-like connections has a significantly greater impact on performance compared to lesions of random connections. These findings suggest that these connectivity principles may play a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

neuroscience↗

bioRxiv2023

Spatial transcriptomic analysis of virtual prostate biopsy reveals confounding effect of heterogeneity on genomic signature scoring

Genetic signatures have added a molecular dimension to prognostics and therapeutic decision-making. However, tumour heterogeneity in prostate cancer and current sampling methods could confound accurate assessment. Based on previously published spatial transcriptomic data from multifocal prostate cancer, we created virtual biopsy models that mimic conventional biopsy placement and core size. We then analysed the gene expression of different prognostic signatures (OncotypeDx(R), Decipher(R), Prostadiag(R)) using a step-wise approach increasing resolution from pseudo-bulk analysis of the whole biopsy, to differentiation by tissue subtype (benign, stroma, tumour), followed by distinct tumour grade and finally clonal resolution. The gene expression profile of virtual tumour biopsies revealed clear differences between grade groups and tumour clones, compared to a benign control, which were not reflected in bulk analyses. This suggests that bulk analyses of whole biopsies or tumour-only areas, as used in clinical practice, may provide an inaccurate assessment of gene profiles. The type of tissue, the grade of the tumour and the clonal composition all influence the gene expression in a biopsy. Clinical decision making based on biopsy genomics should be made with caution while we await more precise targeting and cost-effective spatial analyses. Patient summaryProstate cancers are very variable, including within a single tumour. Current genetic scoring systems, which are sometimes used to make decisions for how to treat patients with prostate cancer, are based on sampling methods which do not reflect these variations. We found, using state-of-the-art spatial genetic technology to simulate accurate assessment of variation in biopsies, that the current approaches miss important details which could negatively impact clinical decisions. Take home messageVirtual biopsies from spatial transcriptomic analysis of a whole prostate reveal that current genomic risk scores potentially deliver misleading results as they are based on bulk analysis of prostate biopsies and ignore tumour heterogeneity.

genomics↗

bioRxiv2023

Clonal phylogenies inferred from bulk, single cell, and spatial transcriptomic analysis of cancer

Epithelial cancers are typically heterogeneous with primary prostate cancer being a typical example of histological and genomic variation. Prostate cancer is the second most common male cancer in western industrialized countries. Prior studies of primary prostate cancer tumor genetics revealed extensive inter and intra-patient tumor heterogeneity. Recent advances have enabled extensive single-cell and spatial transcriptomic profiling of tissue specimens. The ability to resolve accurate prostate cancer tumor phylogenies at high spatial resolution would provide tools to address questions in tumorigenesis, disease progression, and metastasis. Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data. The inferred SNV and CNV states can be used to resolve clonal phylogenies, however, it is still unknown how faithfully transcript-based tumor phylogenies reconstruct ground truth DNA-based tumor phylogenies. We sought to study the accuracy of inferred-transcript to recapitulate DNA-based tumor phylogenies. We first performed in-silico comparisons of inferred and directly resolved SNV and CNV status, from single cancer cells, from three different cell lines. We found that inferred SNV phylogenies accurately recapitulate DNA phylogenies (entanglement = 0.097). We observed similar results in iCNV and CNV based phylogenies (entanglement = 0.11). Analysis of published prostate cancer DNA phylogenies and inferred CNV, SNV and transcript based phylogenies demonstrated phylogenetic concordance. Finally, a comparison of pseudo-bulked spatial transcriptomic data to adjacent sections with WGS data also demonstrated recapitulation of ground truth (entanglement = 0.35). These results suggest that transcript-based inferred phylogenies recapitulate conventional genomic phylogenies. Further work will need to be done to increase accuracy, genomic, and spatial resolution.

bioinformatics↗

bioRxiv2023

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.

neuroscience↗

bioRxiv2022

Structure of a G protein-coupled receptor with GRK2 and a biased ligand

Phosphorylation of G protein-coupled receptors (GPCR) by GPCR kinases (GRKs) desensitizes G protein signaling and promotes arrestin signaling, which is also modulated by biased ligands1-6. Molecular assembly of GRKs to GPCRs and the basis of GRK-mediated biased signaling remain largely unknown due to the weak GPCR-GRK interactions. Here we report the complex structure of neurotensin receptor 1 (NTSR1) bound to GRK2, Gq, and an arrestin-biased ligand, SBI-5537, at a resolution of 2.92 [A]. The high-quality density map reveals the clear arrangement of the intact GRK2 with the receptor, with the N-terminal helix of GRK2 docking into the open cytoplasmic pocket formed by the outward movement of the receptor TM6, analogous of the binding of G protein to the receptor. Strikingly, the arrestin-biased ligand is found at the interface between GRK2 and NTSR1 to enhance GRK2 binding. The binding mode of the biased ligand is compatible with arrestin binding but is clashed with the binding of a G protein, thus provide an unambiguous mechanism for its arrestin-biased signaling capability. Together, our structure provides a solid model for understanding the details of GPCR-GRK interactions and biased signaling.

cell biology↗