Biology subjects

Dempsey, G.

Publications and source records attributed to Dempsey, G..

5 recordsLinked to original sources

bioRxiv2025

Bioenergetic responses to β-adrenergic stimulation in beige adipocyte depend on actomyosin driven forces

Beige (BeAT) and white adipoctyes (WAT) reside within the same fat depots but differ in their responses to {beta}-adrenergic stimulation. While both activate classical PKA-mediated pathways, only beige adipocytes show induction of UCP1 expression and enhanced mitochondrial respiration, suggesting additional mechanisms that distinguish their responses. Here we uncover a parallel, non-canonical biomechanical pathway specific to beige adipocytes that is essential for thermogenic activation. Mechanistically, {beta}-AR stimulation induces a rapid Myh9-mediated actomyosin contraction and stiffening in beige, but not white, adipocytes that activates focal adhesion kinase (FAK), which we show to be critical for the expression of oxidative and thermogenic genes including UCP1 and PLIN5. These findings reveal a new biomechanical Myh9-FAK signaling arm downstream of b-adrenergic activation that differentiates thermogenic from white adipocytes.

cell biology↗

bioRxiv2025

Connectomics Reveals a Feed-Forward Swallowing Circuit Driving Protein Appetite

Nutrient state shapes not only what animals eat, but how they eat it. We lack circuit-level mechanisms explaining how gustatory and internal state information regulates feeding at the motor control level. In Drosophila, protein deprivation prolongs protein-specific feeding bursts, yet the detailed motor mechanisms underlying this change remain unknown. Using EM connectomics, we identified a feed-forward pathway from proteinsensitive gustatory receptor neurons to swallowing motor neurons. At its core is the Sustain neuron, which coordinates multiple swallowing motor neurons to move food efficiently through the cibarium and pharynx. This nutrient-dependent facilitation of swallowing sustains long feeding bursts, directly linking internal state to the temporal structure of the feeding motor program. Our findings reveal how a dedicated sensorimotor circuit translates physiological need into precise motor control to drive nutrient-specific feeding appetite and highlights the power of combining EM connectomics with experimental circuit dissection.

neuroscience↗

bioRxiv2020

The connectome of the adult Drosophila mushroom body: implications for function

Making inferences about the computations performed by neuronal circuits from synapse-level connectivity maps is an emerging opportunity in neuroscience. The mushroom body (MB) is well positioned for developing and testing such an approach due to its conserved neuronal architecture, recently completed dense connectome, and extensive prior experimental studies of its roles in learning, memory and activity regulation. Here we identify new components of the MB circuit in Drosophila, including extensive visual input and MB output neurons (MBONs) with direct connections to descending neurons. We find unexpected structure in sensory inputs, in the transfer of information about different sensory modalities to MBONs, and in the modulation of that transfer by dopaminergic neurons (DANs). We provide insights into the circuitry used to integrate MB outputs, connectivity between the MB and the central complex and inputs to DANs, including feedback from MBONs. Our results provide a foundation for further theoretical and experimental work.

neuroscience↗

bioRxiv2020

Input connectivity reveals additional heterogeneity of dopaminergic reinforcement in Drosophila

Different types of Drosophila dopaminergic neurons (DANs) reinforce memories of unique valence and provide state-dependent motivational control [1]. Prior studies suggest that the compartment architecture of the mushroom body (MB) is the relevant resolution for distinct DAN functions [2, 3]. Here we used a recent electron microscope volume of the fly brain [4] to reconstruct the fine anatomy of individual DANs within three MB compartments. We find the 20 DANs of the {gamma}5 compartment, at least some of which provide reward teaching signals, can be clustered into 5 anatomical subtypes that innervate different regions within {gamma}5. Reconstructing 821 upstream neurons reveals input selectivity, supporting the functional relevance of DAN sub-classification. Only one PAM-{gamma}5 DAN subtype {gamma}5(fb) receives direct recurrent input from {gamma}5{beta}2a mushroom body output neurons (MBONs) and behavioral experiments distinguish a role for these DANs in memory revaluation from those reinforcing sugar memory. Other DAN subtypes receive major, and potentially reinforcing, inputs from putative gustatory interneurons or lateral horn neurons, which can also relay indirect feedback from MBONs. We similarly reconstructed the single aversively reinforcing PPL1-{gamma}1pedc DAN. The {gamma}1pedc DAN inputs mostly differ from those of {gamma}5 DANs and they cluster onto distinct dendritic branches, presumably separating its established roles in aversive reinforcement and appetitive motivation [5, 6]. Tracing also identified neurons that provide broad input to {gamma}5, {beta}2a and {gamma}1pedc DANs suggesting that distributed DAN populations can be coordinately regulated. These connectomic and behavioral analyses therefore reveal further complexity of dopaminergic reinforcement circuits between and within MB compartments. HighlightsO_LINanoscale anatomy reveals additional subtypes of rewarding dopaminergic neurons. C_LIO_LIConnectomics reveals extensive input specificity to subtypes of dopaminergic neurons. C_LIO_LIAxon morphology implies dopaminergic neurons provide subcompartment-level function. C_LIO_LIUnique dopaminergic subtypes serve aversive memory extinction and sugar learning. C_LI

neuroscience↗

bioRxiv2018

Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data

Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution. The resulting datasets are quite large (with scales of TB/hour in some cases), which has presented a barrier to routine open sharing of this data, slowing progress in reproducible research. State of the art methods for analyzing this data are based on non-negative matrix factorization (NMF); these approaches solve a non-convex optimization problem, and are highly effective when good initializations are available, but can break down e.g. in low-SNR settings where common initialization approaches fail.\n\nHere we introduce an improved approach to compressing and denoising functional imaging data. The method is based on a spatially-localized penalized matrix decomposition (PMD) of the data to separate (low-dimensional) signal from (temporally-uncorrelated) noise. This approach can be applied in parallel on local spatial patches and is therefore highly scalable, does not impose non-negativity constraints or require stringent identifiability assumptions (leading to significantly more robust results compared to NMF), and estimates all parameters directly from the data, so no hand-tuning is required. We have applied the method to a wide range of functional imaging data (including one-photon, two-photon, three-photon, widefield, somatic, axonal, dendritic, calcium, and voltage imaging datasets): in all cases, we observe ~2-4x increases in SNR and compression rates of 20-300x with minimal visible loss of signal, with no adjustment of hyperparameters; this in turn facilitates the process of demixing the observed activity into contributions from individual neurons. We focus on two challenging applications: dendritic calcium imaging data and voltage imaging data in the context of optogenetic stimulation. In both cases, we show that our new approach leads to faster and much more robust extraction of activity from the video data.

neuroscience↗