Biology subjects

Jordan, C.

Publications and source records attributed to Jordan, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Characterisation of vaginal Lactobacillus isolates from South African women towards the development of a biotherapeutic to optimise the vaginal microbiome

HIV remains among the worlds most serious healthcare challenges, with adolescent girls and young women in sub-Saharan Africa at particularly high risk of infection. Bacterial vaginosis (BV) is a key risk factor for HIV acquisition, however current treatment strategies are limited. Optimal vaginal Lactobacillus spp. protect against BV and HIV, largely through immunoregulatory and antimicrobial activities mediated in part by lactic acid. Towards the development of a Lactobacillus-containing live biotherapeutic for African women, we sampled 181 vaginal Lactobacillus isolates from 25 BV-negative South African women. Fifty isolates were selected for evaluation of inflammatory responses using vaginal epithelial cells, D- and L-lactate and lactic acid production and culture acidification. Aside from a single Lactobacillus salivarius strain, L. crispatus isolates acidified the culture media the most and produced the most D- and L-lactic acid. Inflammatory cytokine responses to Lactobacillus strains were variable, with L. crispatus eliciting the lowest levels of cytokine production. When all properties were evaluated collectively, L. crispatus strains exhibited the most desirable biotherapeutic characteristics. Whole genome sequence analysis of ten L. crispatus isolates showed that the majority were more closely related to one another than to isolates from other geographical regions. This supports the need for live biotherapeutics to be tailored for the population of intended use. No antimicrobial resistance elements were detected, while putative bacteriocins and intact prophage sequences were identified in all isolates. L. crispatus isolates displayed characteristics essential for optimal live biotherapeutic performance, however additional analysis is required to determine the functionality of identified putative prophages. ImportanceHIV is a leading cause of morbidity and mortality in sub-Saharan Africa, where adolescent girls and young women are three times more likely to acquire HIV than their male counterparts. A key risk factor for HIV is bacterial vaginosis (BV), a condition characterised by the loss of beneficial Lactobacillus species and increased abundance of non-optimal, inflammatory bacteria. Although BV affects approximately 25% of women in sub-Saharan Africa, effective therapeutics are lacking. Live biotherapeutics containing optimal Lactobacillus spp. represent a promising strategy to improve BV treatment outcomes and reduce HIV infection risk. We isolated 181 vaginal Lactobacillus spp. from 25 BV-negative South African women and characterized 50 selected isolates. This led to the identification of live biotherapeutic candidates for African women with distinct genomes compared to isolates from other geographical regions. This study contributes to current knowledge of the characteristics that should be considered when screening novel isolates for this purpose.

microbiology↗

bioRxiv2026

Selective targeting of a histone-like silencer Sfx to the R6K conjugal transfer operon

Conjugative plasmids drive bacterial evolution and antibiotic resistance spread, yet their gene expression must be silenced to protect the host. A histone-like protein H-NS represses many mobile and sedentary xenogenes but fails to silence the conjugal transfer vir operon of R6K, a prototype IncX plasmid. Instead, R6K encodes its own H-NS homolog, Sfx, to repress the vir operon. Here, we show that, unlike other plasmid silencers that target promoters, Sfx cooperates with Rho factor to arrest transcription elongation. ChIP-seq reveals that despite sharing similar DNA motifs and a preference for negative supercoiling, Sfx and H-NS occupy distinct niches: Sfx binds weakly to the chromosome but is enriched on the R6K vir operon, from which H-NS is excluded. We hypothesize that this selective targeting is mediated by Sfx-vir interactions and phase separation. We show that Sfx binding to vir DNA critically depends on DNA topology but not on the target location. Our results suggest that Sfx phase separates with R6K to ensure its preferential recruitment to the plasmid DNA and forms stable nucleoprotein filaments that are impermeable to competitors. These findings reveal how histone-like proteins can partition the genome into distinct regulatory niches, a strategy likely mirrored across all life. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/707533v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1ee08a9org.highwire.dtl.DTLVardef@7ab423org.highwire.dtl.DTLVardef@1c89543org.highwire.dtl.DTLVardef@1bfa132_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2026

Decoding the ''bouba-kiki'' effect in early visual cortex

The "bouba-kiki" effect, or sound symbolism, is the non-arbitrary mapping between speech sounds and visual shapes, in which words like "bouba" are consistently associated with round shapes, and "kiki" with spiky shapes. Despite robust behavioral evidence for sound symbolic associations, it is still unclear whether they are a linguistic epiphenomenon or manifest themselves as distinct sensory representations in the human brain. Using fMRI and multivariate pattern analyses, we show that, in the absence of visual input, round and spiky sound symbolic words elicit distinct neural representations, not only in auditory and speech processing regions, but also in parietal and even early visual cortex including V1. These neural representations correlate with behavioural shape judgements of sound-symbolic words, as revealed by representational similarity analyses. Our findings suggest a potential functional mechanism for sound symbolism: phonological features of sound symbolic words are represented in auditory and speech processing regions and linked to cross-modal shape representations in parietal and early visual regions, which in turn lead to sound symbolic perceptual judgements. Thus, our results provide evidence for a neural mechanism underlying the "bouba-kiki" effect in the human brain.

neuroscience↗

bioRxiv2024

Reporting and justification of sample size in translational chronic variable stress procedures: A systematic review

All in vivo studies using laboratory animals should be guided by the Three Rs: Replacement, Reduction and Refinement. The concept of Reduction is important in sample size estimation; the sample size used should allow the detection of a biologically meaningful effect size using appropriate statistical tests, but not at the expense of animal suffering. Because studies using chronic variable stress (CVS) procedures deliberately impose suffering, we reasoned that Three Rs principles would be a strong consideration in experimental design. To explore this, we conducted a systematic review of CVS studies to ask whether a biologically meaningful effect size was used to determine the sample size. Only one article in our sample of 385 reported doing this. Accordingly, it is questionable whether most of these studies align strongly with the principle of Reduction. While determining a biologically meaningful effect size is not always straightforward, we believe it is central to making biologically informed decisions about study design and interpretation, and we discuss possible ways forward.

physiology↗

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

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

Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes using Mellon

Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive cellular differentiation, regeneration, and disease. Here, we present Mellon, a novel computational algorithm for high-resolution estimation of cell-state densities from single-cell data. We demonstrate Mellons efficacy by dissecting the density landscape of various differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. Utilizing hematopoietic stem cell fate specification to B-cells as a case study, we present evidence implicating enhancer priming and the activation of master regulators in the emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during the inherently continuous developmental processes. Scalable and adaptable, Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide new insights into the regulatory mechanisms guiding cellular fate decisions.

bioinformatics↗

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

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

Large-scale unsupervised discovery of excitatory morphological cell types in mouse visual cortex

Neurons in the neocortex exhibit astonishing morphological diversity which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological "bar code" describing more than 30,000 excitatory neurons in mouse visual areas V1, AL and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2-3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5 which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons morphological diversity is better understood by considering axes of variation than using distinct m-types.

neuroscience↗

bioRxiv2022

Development of a multi-sensor integrated midbrain organoid-on-a-chip platform for studying Parkinson's disease

Due to its ability to recapitulate key pathological processes in vitro, midbrain organoid technology has significantly advanced the modeling of Parkinsons disease over the last few years. However, some limitations such as insufficient tissue differentiation and maturation, deficient nutrient supply, and low analytical accessibility persist, altogether restricting the technology from reaching its full potential. To overcome these drawbacks, we have developed a multi-sensor integrated organ-on-a-chip platform capable of monitoring the electrophysiological, respiratory, and dopaminergic activity of human midbrain organoids. Our study showed that microfluidic cultivation resulted in a marked reduction in necrotic core formation, improved tissue differentiation as well as the recapitulation of key pathological hallmarks. Non-invasive monitoring employing an orthogonal sensing strategy revealed a clear time dependency in the onset of Parkinsons disease-related phenotypes, reflecting the complex progression of the neurodegenerative disorder. Furthermore, drug-mediated rescue effects were observed after treatment with the repurposed compound 2-hydroxypropyl {beta}-cyclodextrin, highlighting the platforms potential in the context of drug screening applications as well as personalized medicine.

bioengineering↗

bioRxiv2022

Quantitative Census of Local Somatic Features in Mouse Visual Cortex

Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches. Each modality offers new perspectives on the variation of biological processes underlying cell type specialization. Cellular scale electron microscopy (EM) provides dense ultrastructural examination and an unbiased perspective into the subcellular organization of brain cells, including their synaptic connectivity and nanometer scale morphology. It also presents a clear challenge for analysis to identify cell-types in data that contains tens of thousands of neurons, most of which have incomplete reconstructions. To address this challenge, we present the first systematic survey of the somatic region of all cells within a cubic millimeter of cortex using quantitative features obtained from EM. This analysis demonstrates a surprising sufficiency of the perisomatic region to identify cell-types, including types defined primarily based on their connectivity patterns. We then describe how this classification facilitates cell type specific connectivity characterization and locating cells with rare connectivity patterns in the dataset.

neuroscience↗

bioRxiv2021

Petascale neural circuit reconstruction: automated methods

3D electron microscopy (EM) has been successful at mapping invertebrate nervous systems, but the approach has been limited to small chunks of mammalian brains. To scale up to larger volumes, we have built a computational pipeline for processing petascale image datasets acquired by serial section EM, a popular form of 3D EM. The pipeline employs convolutional nets to compute the nonsmooth transformations required to align images of serial sections containing numerous cracks and folds, detect neuronal boundaries, label voxels as axon, dendrite, soma, and other semantic categories, and detect synapses and assign them to presynaptic and postsynaptic segments. The output of neuronal boundary detection is segmented by mean affinity agglomeration with semantic and size constraints. Pipeline operations are implemented by leveraging distributed and cloud computing. Intermediate results of the pipeline are held in cloud storage, and can be effortlessly viewed as images, which aids debugging. We applied the pipeline to create an automated reconstruction of an EM image volume spanning four visual cortical areas of a mouse brain. Code for the pipeline is publicly available, as is the reconstructed volume.

neuroscience↗

bioRxiv2021

Functional connectomics spanning multiple areas of mouse visual cortex

To understand the brain we must relate neurons functional responses to the circuit architecture that shapes them. Here, we present a large functional connectomics dataset with dense calcium imaging of a millimeter scale volume. We recorded activity from approximately 75,000 neurons in primary visual cortex (VISp) and three higher visual areas (VISrl, VISal and VISlm) in an awake mouse viewing natural movies and synthetic stimuli. The functional data were co-registered with a volumetric electron microscopy (EM) reconstruction containing more than 200,000 cells and 0.5 billion synapses. Subsequent proofreading of a subset of neurons in this volume yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Here, we release this dataset as an open-access resource to the scientific community including a set of tools that facilitate data retrieval and downstream analysis. In accompanying papers we describe our findings using the dataset to provide a comprehensive structural characterization of cortical cell types1-3 and the most detailed synaptic level connectivity diagram of a cortical column to date2, uncovering unique cell-type specific inhibitory motifs that can be linked to gene expression data4. Functionally, we identify new computational principles of how information is integrated across visual space5, characterize novel types of neuronal invariances6 and bring structure and function together to decipher a general principle that wires excitatory neurons within and across areas7, 8.

neuroscience↗

bioRxiv2021

Microvasculature-on-a-Chip: Bridging the interstitial blood-lymph interface via mechanobiological stimuli

After decades of simply being referred to as the bodys sewage system, the lymphatic system has recently been recognized as a key player in numerous physiological and pathological processes. As an essential site of immune cell interactions, the lymphatic system is a potential target for next-generation drug delivery approaches in treatments for cancer, infections, and inflammatory diseases. However, the lack of cell-based assays capable of recapitulating the required biological complexity combined with unreliable in vivo animal models currently hamper scientific progress in lymph-targeted drug delivery. To gain more in-depth insight into the blood-lymph interface, we established an advanced chip-based microvascular model to study mechanical stimulations importance on lymphatic sprout formation. Our microvascular models key feature is the co-cultivation of spatially separated 3D blood and lymphatic vessels under controlled, unidirectional interstitial fluid flow while allowing signaling molecule exchange similar to the in vivo situation. We demonstrate that our microphysiological model recreates biomimetic interstitial fluid flow, mimicking the route of fluid in vivo, where shear stress within blood vessels pushes fluid into the interstitial space, which is subsequently transported to the nearby lymphatic capillaries. Results of our cell culture optimization study clearly show an increased vessel sprouting number, length, and morphological characteristics under dynamic cultivation conditions and physiological relevant mechanobiological stimulation. For the first time, a microvascular on-chip system incorporating microcapillaries of both blood and lymphatic origin in vitro recapitulates the interstitial blood-lymph interface.

bioengineering↗

bioRxiv2020

Zinc potentiates dopamine neurotransmission and cocaine seeking

Cocaine binds to the dopamine (DA) transporter (DAT) to regulate cocaine reward and seeking behavior. Zinc (Zn2+) also binds to the DAT, but the in vivo relevance of this interaction is unknown. We found that Zn2+ concentrations in postmortem brain (caudate) tissue from humans who died of cocaine overdose were significantly lower than in control subjects. Moreover, the level of striatal Zn2+ content in these subjects negatively correlated with plasma levels of benzoylecgonine, a cocaine metabolite indicative of recent use. In mice, repeated cocaine exposure increased synaptic Zn2+ concentrations in the caudate putamen (CPu) and nucleus accumbens (NAc). Cocaine-induced increases in Zn2+ were dependent on the Zn2+ transporter 3 (ZnT3), a neuronal Zn2+ transporter localized to synaptic vesicle membranes, as ZnT3 knockout (KO) mice were insensitive to cocaine-induced increases in striatal Zn2+. ZnT3 KO mice showed significantly lower electrically-evoked DA release and greater DA clearance when exposed to cocaine compared to controls. ZnT3 KO mice also displayed significant reductions in cocaine locomotor sensitization, conditioned place preference (CPP), self-administration, and reinstatement compared to control mice and were insensitive to cocaine-induced increases in striatal DAT binding. Finally, dietary Zn2+ deficiency in mice resulted in decreased striatal Zn2+ content, cocaine locomotor sensitization, CPP, and striatal DAT binding. These results indicate that cocaine increases synaptic Zn2+ release and turnover/metabolism in the striatum, and that synaptically-released Zn2+ potentiates the effects of cocaine on striatal DA neurotransmission and behavior and is required for cocaine-primed reinstatement. In sum, these findings reveal new insights into cocaines pharmacological mechanism of action and suggest that Zn2+ may serve as an environmentally-derived regulator of DA neurotransmission, cocaine pharmacodynamics, and vulnerability to cocaine use disorders.

neuroscience↗