Biology subjects

Patel, S.

Publications and source records attributed to Patel, S..

At least 127 records · Page 7Linked to original sources

bioRxiv2023

TMPRSS2 activation of Omicron lineage Spike glycoproteins is regulated by TMPRSS2 cleavage of ACE2

Continued high-level spread of SARS-CoV-2 has enabled an accumulation of changes within the Spike glycoprotein, leading to resistance to neutralising antibodies and concomitant changes to entry requirements that increased viral transmission fitness. Herein, we demonstrate a significant change in angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) dependent entry by primary SARS-CoV-2 isolates that occurred upon arrival of Omicron lineages. Mechanistically we show this shift to be a function of two distinct ACE2 pools based on TMPRS22 association with the ACE2 Collectrin-Like Domain (CLD). In engineered cells overexpressing ACE2 and TMPRSS2, ACE2/TMPRSS2 complexes led to either augmentation or attenuation of viral infectivity of pre-Omicron and Omicron lineages, respectively. Mutagenesis of the ACE2-CLD TMPRSS2 cleavage site in ACE2 restored infectivity across all Omicron lineages through enabling ACE2 binding that facilitated TMPRSS2 activation of viral fusion. Our data supports the evolution of Omicron lineages towards the use of ACE2 unable to form complexes with TMPRSS2 and consistent with ACE2 structure and function as a chaperone for many tissue specific amino acid transport proteins. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/558930v4_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1c86cf0org.highwire.dtl.DTLVardef@16815feorg.highwire.dtl.DTLVardef@7be9e1org.highwire.dtl.DTLVardef@137de1c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO ACE2-TMPRSS2 pool model and evolution of SARS-CoV-2 tropism.A. &-B. ACE2-TMPRSS2 pool model to reconcile the evolving entry requirements of SARS-CoV-2 and changes in viral tropism in vivo. A. Both SARS-CoV-1 and early SARS-CoV-2 (pre-Omicron) lineages have molecular dual tropism, with efficient entry when ACE2 (blue protein) can form complexes with TMPRSS2 (green protein) and in settings where TMPRSS2 is excluded from ACE2 (C4-ACE2 CLD). In both settings ACE2 initially engages SARS-CoV-2 spike and fusion is then triggered through TMPRSS2 cleavage of the Spike S2 domain B. Over time, the dual tropism for two distinct pools of ACE2 (with and without TMPRSS2) has been lost, with consolidation towards ACE2 where TMPRSS2 is no longer in a complex. With the arrival of Omicron lineages, ACE2-TMPRSS2 complexes could no longer enable efficient Spike S2 cleavage and fusogenic activation (TMPRSS2 "off" confirmation"). Rather, only TMPRSS2 uncoupled from ACE2 could facilitate the latter cleavage of S2. C. Over time this has further consolidated over generations of omicron lineages from 2022 lineages (BA.1, BA.2 and BA.5) through to 2023 lineages (XBB.1.5) and now in 2024 JN.1 lineages such as KP.3. Overall, this supports the initial molecular tropism of early SARS-CoV-2 clades to be similar to that observed for SARS-CoV-1, with dual tropism across both ACE2 pools and replication proceeding in tissues where ACE2-TMPRSS2 complexes would be prevalent (e.g. Lung). The evolution away from ACE2-TMPRSS2 complexes towards ACE2 where TMPRSS2 is structurally uncoupled (e.g. ACE2 as a chaperone for solute carriers SLCA619 or SLCA620) is consistent selection of this ACE2 pool in a manner that has sustained transmission fitness within the human population. C_FIG

microbiology↗

bioRxiv2023

ZFP57 is a regulator of postnatal growth and life-long health

Early-life factors, including nutrition, shape long-term health outcomes. Despite the essential role of lactation in maternal nutritional support, the influence of epigenetic factors on lactation and postnatal growth remains poorly understood. Zinc-finger protein 57 (ZFP57), is an epigenetic regulator of genomic imprinting, a process that directs gene expression based on parental origin, playing a vital role in mammalian prenatal growth. Here, we identify a novel function of ZFP57 in regulating the mammary gland, where it serves as a key modulator of postnatal resource control, independently of imprinted genes. ZFP57 influences multiple aspects of mammary gland function, including ductal branching and cellular homeostasis. Its absence leads to significant differential gene expression, related to alveologenesis, lactogenesis and milk synthesis, associated with delayed lactation and altered milk composition. This results in life-long impacts on offspring including the development of metabolic syndrome. Cross-fostering reveals intricate dynamics between mother and offspring during lactation. Pups raised by a dam of a different genotype than their birth mother exhibit exacerbated metabolic features in adulthood, providing additional insight into the programming of offspring long-term health by maternal context. This study deepens our understanding of the interplay between epigenetic factors, lactation, and postnatal resource control and identifies ZFP57 as a major regulator of both pre and postnatal resource control in mammals.

developmental biology↗

bioRxiv2023

Discovery of a Well-Folded Protein Interaction Hub Within the Human Long Non-Coding RNA NORAD

The long non-coding RNA NORAD functions in maintaining genomic stability in humans via sequestering Pumilio proteins from the cytoplasm, and thereby modulating the gene expression of mRNA targets of Pumilio proteins. Despite its role in fundamental cellular pathways including chromosome segregation and DNA damage response, there have been limited structural and biophysical descriptions of NORAD. Here, using an integrative approach combining chemical probing coupled to high throughput sequencing, and RNA-pull downs coupled with mass spectrometry, we discovered a well-folded and structured protein interaction hub within the functional core of NORAD. Our in vitro biochemical reconstitutions using purified recombinant proteins and a NORAD repeat unit region within this hub reveal the assembly of a higher-order multimeric RNA-protein complex.

molecular biology↗

bioRxiv2023

PIEZO1 force sensing controls global lipid homeostasis

How cardiovascular activity beneficially regulates lipid homeostasis is unclear. Here we hypothesise a mechanism in which mechanical force sensed by PIEZO1 ion channels in endothelium links blood flow to lipid regulation. We engineered mice for conditional deletion of PIEZO1 in endothelium and determined consequences for lipid regulation. Prominent are upregulated expression of hepatic Cyp7a1 and intestinal Ldlr genes, which are pivotal in cholesterol catabolism and excretion. Consistent with such regulation is endothelial PIEZO1-dependence of hepatic, intestinal and whole body cholesterol and bile homeostasis. There is organ perfusion-dependent gene regulation via endothelial PIEZO1 and downstream nitric oxide synthase. Endothelial PIEZO1-deleted mice are protected against hyperlipidaemia and ectopic fat deposition. Human PIEZO1 gene variants and a recapitulated human PIEZO1 gain-of-function variant in mice associate with dyslipidaemia. The data suggest lipid-promoting effects of endothelial force sensing and new opportunity for understanding and addressing problems of hyperlipidaemia.

physiology↗

bioRxiv2023

Direct measurements of luminal Ca2+ with endo-lysosomal GFP-aequorin (ELGA) reveal functional IP3 receptors

Endo-lysosomes are considered acidic Ca2+ stores but direct measurements of luminal Ca2+ within them are limited. Here we report that the Ca2+-sensitive luminescent protein aequorin does not reconstitute with its cofactor at highly acidic pH but that a significant fraction of the probe is functional within a mildly acidic compartment when targeted to the endo-lysosomal system. We leveraged this probe (ELGA) to report Ca2+ dynamics in this compartment. We show that Ca2+ uptake is ATP-dependent and sensitive to blockers of endoplasmic reticulum Ca2+ pumps. We find that the Ca2+ mobilizing messenger IP3 which typically targets the endoplasmic reticulum evokes robust luminal responses in wild type cells, but not in IP3 receptor knock-out cells. Responses were comparable to those evoked by activation of the endo-lysosomal ion channel TRPML1. Stimulation with IP3-forming agonists also mobilized the store in intact cells. Super-resolution microscopy analysis confirmed the presence of IP3 receptors within the endo-lysosomal system, both in live and fixed cells. Our data reveal a physiologically-relevant, IP3-sensitive store of Ca2+ within the endo-lysosomal system.

physiology↗

bioRxiv2023

Including the Invisible Fraction in Whole Population Studies: a Guide to the Genetic Sampling of Unhatched Bird Eggs

Early embryo mortality has recently been proven to be a significant component of avian reproductive failure. Due to the difficulty in distinguishing eggs which have suffered early embryo mortality from unfertilised eggs, this cause of reproductive failure has historically been underestimated and overlooked. We describe methods for recognising and collecting undeveloped, unhatched eggs from wild bird populations, identifying and isolating embryonic material in unhatched eggs, and efficiently extracting DNA from those samples. We test these methods on unhatched eggs collected from the field which have undergone post-mortem incubation. We obtained DNA yields from early-stage embryos that are sufficient for a wide range of molecular techniques, including microsatellite genotyping for parentage analysis and sex-typing. The type of tissue sample taken from the egg affected downstream DNA yields and microsatellite amplification rates. Species-specific microsatellite markers had higher amplification success rates than cross-species markers. We make key recommendations for each stage of the sampling and extraction process and suggest improvements potential and protocol modifications. Genetic and possibly genomic analysis of embryos that die early in development has the potential to advance many fields. The methods described here will allow a more in-depth exploration of the previously overlooked causes of early embryo mortality in wild populations of birds, including threatened species.

genetics↗

bioRxiv2023

Energy Guided Diffusion for Generating Neurally Exciting Images

In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal activity have become an established method to study tuning properties of biological and artificial visual systems. However, as we move up the visual hierarchy, the complexity of neuronal computations increases. Consequently, it becomes more challenging to model neuronal activity, requiring more complex models. In this study, we introduce a new attention readout for a convolutional data-driven core for neurons in macaque V4 that outperforms the state-of-the-art task-driven ResNet model in predicting neuronal responses. However, as the predictive network becomes deeper and more complex, synthesizing MEIs via straightforward gradient ascent (GA) can struggle to produce qualitatively good results and overfit to idiosyncrasies of a more complex model, potentially decreasing the MEIs model-to-brain transferability. To solve this problem, we propose a diffusion-based method for generating MEIs via Energy Guidance (EGG). We show that for models of macaque V4, EGG generates single neuron MEIs that generalize better across architectures than the state-of-the-art GA while preserving the within-architectures activation and requiring 4.7x less compute time. Furthermore, EGG diffusion can be used to generate other neurally exciting images, like most exciting natural images that are on par with a selection of highly activating natural images, or image reconstructions that generalize better across architectures. Finally, EGG is simple to implement, requires no retraining of the diffusion model, and can easily be generalized to provide other characterizations of the visual system, such as invariances. Thus EGG provides a general and flexible framework to study coding properties of the visual system in the context of natural images.1

neuroscience↗

bioRxiv2023

Deep learning-driven characterization of single cell tuning in primate visual area V4 unveils topological organization

Deciphering the brains structure-function relationship is key to understanding the neuronal mechanisms underlying perception and cognition. The cortical column, a vertical organization of neurons with similar functions, is a classic example of primate neocortex structure-function organization. While columns have been identified in primary sensory areas using parametric stimuli, their prevalence across higher-level cortex is debated, particularly regarding complex tuning in natural image space. However, a key hurdle in identifying columns is characterizing the complex, nonlinear tuning of neurons to high-dimensional sensory inputs. Building on prior findings of topological organization for features like color and orientation, we investigate functional clustering in macaque visual area V4 in non-parametric natural image space, using large-scale recordings and deep learning-based analysis. We combined linear probe recordings with deep learning methods to systematically characterize the tuning of >1,200 V4 neurons using in silico synthesis of most exciting images (MEIs), followed by in vivo verification. Single V4 neurons exhibited MEIs containing complex features, including textures and shapes, and even high-level attributes with eye-like appearance. Neurons recorded on the same silicon probe, inserted orthogonal to the cortical surface, often exhibited similarities in their spatial feature selectivity, suggesting a degree of functional organization along the cortical depth. We quantified MEI similarity using human psychophysics and distances in a contrastive learning-derived embedding space. Moreover, the selectivity of the V4 neuronal population showed evidence of clustering into functional groups of shared feature selectivity. These functional groups showed parallels with the feature maps of units in artificial vision systems, suggesting potential shared encoding strategies. These results demonstrate the feasibility and scalability of deep learning-based functional characterization of neuronal selectivity in naturalistic visual contexts, offering a framework for quantitatively mapping cortical organization across multiple levels of the visual hierarchy.

neuroscience↗

bioRxiv2023

SINGLE CELL RESOLUTION SPATIAL MAPPING OF HUMAN HEMATOPOIESIS REVEALS AGING-ASSOCIATED TOPOGRAPHIC REMODELING

The spatial anatomy of hematopoiesis in bone marrow has been extensively studied in mice and other preclinical models, but technical challenges have precluded a commensurate exploration in humans. Institutional pathology archives contain thousands of paraffinized bone marrow core biopsy tissue specimens, providing a rich resource for studying the intact human bone marrow topography in a variety of physiologic states. Thus, we developed an end-to-end pipeline involving multiparameter whole tissue staining, in situ imaging at single-cell resolution, and artificial intelligence (AI)-based digital image analysis, and then applied it to a cohort of disease-free samples to survey alterations in the hematopoietic topography associated with aging. Our data indicate heterogeneity in marrow adipose tissue (MAT) content within each age group, and an inverse correlation between MAT content and proportions of early myeloid and erythroid precursors, irrespective of age. We identify consistent endosteal and perivascular positioning of hematopoietic stem and progenitor cells (HSPCs) with medullary localization of more differentiated elements and, importantly, uncover new evidence of aging-associated changes in cellular and vascular morphologies, microarchitectural alterations suggestive of inflammaging, and diminution of a potentially active megakaryocytic niche. Overall, our findings suggest that there is topographic remodeling of human hematopoiesis associated with aging. More generally, we demonstrate the potential to deeply unravel the spatial biology of normal and pathologic human bone marrow states using intact archival tissue specimens.

systems biology↗

bioRxiv2023

Inosine Induces Stemness Features in CAR T cells and Enhances Potency

Adenosine (Ado) mediates immune suppression in the tumor microenvironment and exhausted CD8+ CAR T cells mediate Ado-induced immunosuppression through CD39/73-dependent Ado production. Knockout of CD39, CD73 or A2aR had modest effects on exhausted CAR T cells, whereas overexpression of Ado deaminase (ADA), which metabolizes Ado to inosine (INO), induced stemness features and potently enhanced functionality. Similarly, and to a greater extent, exposure of CAR T cells to INO augmented CAR T cell function and induced hallmark features of T cell stemness. INO induced a profound metabolic reprogramming, diminishing glycolysis and increasing oxidative phosphorylation, glutaminolysis and polyamine synthesis, and modulated the epigenome toward greater stemness. Clinical scale manufacturing using INO generated enhanced potency CAR T cell products meeting criteria for clinical dosing. These data identify INO as a potent modulator of T cell metabolism and epigenetic stemness programming and deliver a new enhanced potency platform for immune cell manufacturing. Statement of SignificanceAdenosine is well known to inhibit T cell function and substantial effort has focused on inhibiting adenosine generation and signaling. Here, we show that exhausted T cells are suppressed by adenosine, which is only modestly impacted by inhibiting adenosine generation or signaling. In contrast, metabolism of adenosine to inosine augmented T cell function and culture of T cells with inosine induced multi-level reprogramming leading to stemness and improved anti-tumor potency. We demonstrate the feasibility of introducing inosine during GMP cell manufacturing as a novel strategy to generate enhanced CAR-T cells.

immunology↗

bioRxiv2023

Towards a Foundation Model of the Mouse Visual Cortex

The complexity of neural circuits makes it challenging to decipher the brains algorithms of intelligence. Recent break-throughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brains computational objectives and neural coding. However, these models struggle to generalize beyond their training distribution, limiting their utility. The emergence of foundation models, trained on vast datasets, has introduced a new AI paradigm with remarkable generalization capabilities. We collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. It could also be adapted to new tasks beyond neural prediction, accurately predicting anatomical cell types, dendritic features, and neuronal connectivity within the MICrONS functional connectomics dataset. Our work is a crucial step toward building foundation brain models. As neuroscience accumulates larger, multi-modal datasets, foundation models will uncover statistical regularities, enabling rapid adaptation to new tasks and accelerating research.

neuroscience↗

bioRxiv2023

Bipartite invariance in mouse primary visual cortex

A primary goal of sensory systems is to extract robust and meaningful features that are invariant to variations in the sensory input. Characterizing these invariances at the neuronal level is crucial for understanding how the visual system supports generalization, but the high-dimensional nature of ecological stimuli poses major challenges. Consequently, our understanding of how the brain represents invariances has historically depended on a few examples, such as phase invariance to grating stimuli in V1 complex cells. Here, we leverage the inception loop paradigm --iterating between large-scale recordings, deep learning neuronal predictive models, and in silico experiments with in vivo verification--to characterize neuronal invariances in mouse V1. Using a neuronal predictive model, we synthesized Diverse Exciting Inputs (DEIs) that strongly drive target neurons while differing substantially in image space. These DEIs revealed a novel bipartite invariance: one portion of the receptive field encodes shift-invariant, high-frequency textures, while the other encodes a fixed, low-frequency spatial pattern. This subfield division aligned with object boundaries defined by spatial frequency differences in highly activating stimuli, suggesting bi-partite invariance contributes to segmentation. Our analysis of computational models and anatomical data from the MICrONS dataset revealed a hierarchical organization of excitatory neurons in mouse V1 Layers 2/3: We found that postsynaptic neurons exhibited greater invariance than their presynaptic inputs, while neurons with lower invariance formed more connections. These findings suggest a synaptic-level hierarchy that progressively increases neural invariance within the primary visual cortex. Intriguingly, similar high-low frequency bipartite patterns strongly activate certain units in artificial neural networks, suggesting that universal visual representations govern both biological and artificial systems, potentially aiding in the extraction of visual features from complex backgrounds.

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

Pattern completion and disruption characterize contextual modulation in mouse visual cortex

Vision is fundamentally context-dependent, with neuronal responses influenced not just by local features but also by surrounding contextual information. In the visual cortex, studies using simple grating stimuli indicate that congruent stimuli--where the center and surround share the same orientation--are more inhibitory than when orientations are orthogonal, potentially serving redundancy reduction and predictive coding. Understanding these center-surround interactions in relation to natural image statistics is challenging due to the high dimensionality of the stimulus space, yet crucial for deciphering the neuronal code of real-world sensory processing. Utilizing large-scale recordings from mouse V1, we trained convolutional neural networks (CNNs) to predict and synthesize surround patterns that either optimally suppressed or enhanced responses to center stimuli, confirmed by in v ivo experiments. Contrary to the notion that congruent stimuli are suppressive, we found that surrounds that completed patterns based on natural image statistics were facilitatory, while disruptive surrounds were suppressive. Applying our CNN image synthesis method in macaque V1, we discovered that pattern completion within the near surround occurred more frequently with excitatory than with inhibitory surrounds, suggesting that our results in mice are conserved in macaques. Further, experiments and model analyses confirmed previous studies reporting the opposite effect with grating stimuli in both species. Using the MICrONS functional connectomics dataset, we observed that neurons with similar feature selectivity formed excitatory connections regardless of their receptive field overlap, aligning with the pattern completion phenomenon observed for excitatory surrounds. Finally, our empirical results emerged in a normative model of perception implementing Bayesian inference, where neuronal responses are modulated by prior knowledge of natural scene statistics. In summary, our findings identify a novel relationship between contextual information and natural scene statistics and provide evidence for a role of contextual modulation in hierarchical inference.

neuroscience↗

bioRxiv2023

Development of a human glioblastoma model using humanized DRAG mice for immunotherapy

Glioblastoma (GBM) is the most common and lethal primary brain tumor with high mortality rates and a short median survival rate of about 15 months despite intensive multimodal treatment of maximal surgical resection, radiotherapy, and chemotherapy. Although immunotherapies have been successful in the treatment of various cancers, disappointing results from clinical trials for GBM immunotherapy represent our incomplete understanding. The development of alternative humanized mouse models with fully functional human immune cells will potentially accelerate the progress of GBM immunotherapy. In this study, we developed a humanized DRAG (NOD.Rag1KO.IL2R{gamma}cKO) mouse model, in which the human hematopoietic stem cells (HSCs) were well-engrafted and subsequently differentiated into a full lineage of immune cells. Using this humanized DRAG mouse model, GBM patient-derived tumorsphere lines were successfully engrafted to form xenografted tumors, which can recapitulate the pathological features and the immune cell composition of human GBM. Importantly, the administration of anti-human PD-1 antibodies in these DRAG mice bearing a GBM patient-derived tumorsphere line resulted in decreasing the major tumor-infiltrating immunosuppressive cell populations, including CD4+PD-1+ and CD8+PD-1+ T cells, CD11b+CD14+HLA-DR+ macrophages, CD11b+CD14+HLA-DR-CD15- and CD11b+CD14- CD15+ myeloid-derived suppressor cells, indicating the humanized DRAG mouse model as a useful model to test the efficacy of immune checkpoint inhibitors in GBM immunotherapy. Together, these results suggest that humanized DRAG mouse models are a reliable preclinical platform for brain cancer immunotherapy and beyond.

cancer biology↗

bioRxiv2023

Development of a multifunctional toolkit of intrabody-based biosensors recognizing the V5 peptide tag: highlighting applications with G protein-coupled receptors

ABSTRACT/SUMMARYProtein-protein interactions (PPIs) form the underpinnings of any cellular signaling network. PPIs are highly dynamic processes and often, cell-based assays can be essential for their study as they closely mimic the biological intricacies of cellular environments. Since no sole platform can perform all needed experiments to gain a thoroughly comprehensive understanding into these processes, developing a versatile toolkit is much needed to address this longstanding gap. The use of small peptide tags, such as the V5-tag, has been extensively used in biological and biomedical research, including labeling the C-termini of one of the largest human genome-wide open-reading frame collections. However, these small peptide tags have been primarily used in vitro and lack the in vivo traceability and functionality of larger specialized tags. In this study, we combined structural studies and computer-aided maturation to generate an intracellular nanobody, interacting with the V5-tag. Suitable for assays commonly used to study protein-protein interactions, our nanobody has been applied herein to interrogate G protein-coupled receptor signalling. This novel serviceable intrabody is the cornerstone of a multipurpose intracellular nanobody-based biosensors toolkit, named iBodyV5, which will be available for the scientific community at large.

molecular biology↗

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↗