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Nunez-Iglesias, J.

Publications and source records attributed to Nunez-Iglesias, J..

3 recordsLinked to original sources

Identification of a stress-sensitive anorexigenic neurocircuit from medial prefrontal cortex to lateral hypothalamus in humans and mice

By modeling neural network dynamics related to homeostatic state and BMI, we identified a novel pathway projecting from the medial prefrontal cortex (mPFC) to the lateral hypothalamus (LH) in humans. We then assessed the physiological role and dissected the function of this mPFC-LH circuit in mice. In vivo recordings of population calcium activity revealed that this glutamatergic mPFC-LH pathway is activated in response to acute stressors and inhibited during food consumption, suggesting a role in stress-related control over food intake. Consistent with this role, inhibition of this circuit increased feeding and sucrose seeking during mild stressors, but not under non-stressful conditions. Finally, chemogenetic or optogenetic activation of the mPFC-LH pathway is sufficient to suppress food intake and sucrose-seeking in mice. These studies identify a glutamatergic mPFC-LH as a novel stress-sensitive anorexigenic neural pathway involved in the cortical control of food intake.

neuroscience

Glucose-sensing in AgRP neurons integrates homeostatic energy state with dopamine signalling in the striatum.

Hunger increases the motivation of an organism to seek out and consume highly palatable energy dense foods. While hunger-sensing Agouti-related peptide (AgRP) neurons influence this process, whether metabolic detection of homeostatic state via metabolic sensing in AgRP neurons potentiates motivation through the midbrain dopamine system is unexplored. Here, we used the AgRP-specific deletion of carnitine acetyltransferase (Crat), a metabolic enzyme regulating glucose and fatty acid oxidation, as a model of impaired metabolic-sensing in AgRP neurons. We then tested the hypothesis that appropriate metabolic-sensing in AgRP neurons is required to increase food reward motivation by modulating accumbal or striatal dopamine release. Electrophysiological studies confirm that Crat deletion in AgRP neurons (KO) impairs normal ex vivo glucose-sensing, and in vivo photometry experiments show that AgRP neurons in KO mice do not exhibit normal responses to repeated palatable food presentation and consumption, highlighting that this model is appropriate to test the hypothesis. Fiber photometry experiments, using the dopamine sensor GRAB-DA, revealed that impaired metabolic-sensing reduces acute dopamine release (seconds) in the nucleus accumbens, but not the dorsal striatum, to palatable food consumption and during operant responding. Positron electron tomography (PET) methods indicated that impaired metabolic-sensing in AgRP neurons suppressed radiolabelled 18F-fDOPA accumulation after [~]30 minutes in the dorsal striatum but not the ventral striatum, suggesting a role for AgRP neurons to restrict a long term post-ingestive dopamine response in the dorsal striatum. Finally, impaired metabolic-sensing in AgRP neurons suppresses motivated operant responding for sucrose rewards. Notably, these behavioural effects are potentiated in the hungry state and therefore highlight that metabolic-sensing in AgRP neurons is required for the appropriate temporal integration and transmission of homeostatic hunger-sensing to dopamine signalling in the striatum.

neuroscience

Interactive design of GPU-accelerated Image Data Flow Graphs and cross-platform deployment using multi-lingual code generation

Modern life science relies heavily on fluorescent microscopy and subsequent quantitative bio-image analysis. The current rise of graphics processing units (GPUs) in the context of image processing enables batch processing large amounts of image data at unprecedented speed. In order to facilitate adoption of this technology in daily practice, we present an expert system based on the GPU-accelerated image processing library CLIJ: The CLIJ-assistant keeps track of which operations formed an image and suggests subsequent operations. It enables new ways of interaction with image data and image processing operations because its underlying GPU-accelerated image data flow graphs (IDFGs) allow changes to parameters of early processing steps and instantaneous visualization of their final results. Operations, their parameters and connections in the IDFG are stored at any point in time enabling the CLIJ-assistant to offer an undo-function for virtually unlimited rewinding parameter changes. Furthermore, to improve reproducibility of image data analysis workflows and interoperability with established image analysis platforms, the CLIJ-assistant can generate code from IDFGs in programming languages such as ImageJ Macro, Java, Jython, JavaScipt, Groovy, Python and C++ for later use in ImageJ, Fiji, Icy, Matlab, QuPath, Jupyter Notebooks and Napari. We demonstrate the CLIJ-assistant for processing image data in multiple scenarios to highlight its general applicability. The CLIJ-assistant is open source and available online: https://clij.github.io/assistant/

bioinformatics