Biology subjects

Willmore, L.

Publications and source records attributed to Willmore, L..

6 recordsLinked to original sources

bioRxiv2023

Differences in an aversive teaching signal produce brainwide and behavioral substrates of susceptibility

Some individuals are susceptible to the experience of chronic stress and others are more resilient. While many brain regions implicated in learning are dysregulated after stress, little is known about whether and how neural teaching signals during stress differ between susceptible and resilient individuals. Here, we seek to determine if activity in the lateral habenula (LHb), which encodes a negative teaching signal, differs between susceptible and resilient mice during stress to produce different outcomes. After, but not before, chronic social defeat stress (CSDS), the LHb is active when susceptible mice are in the proximity of the aggressor strain. During stress itself, LHb activity is higher in susceptible mice during aggressor proximity, and activation of the LHb during stress biases mice towards susceptibility. This manipulation generates a persistent and widespread increase in the balance of subcortical versus cortical activity in susceptible mice. Taken together, our results indicate that heightened activity in the LHb during stress produces lasting brainwide and behavioral substrates of susceptibility.

neuroscience↗

bioRxiv2023

Overlapping representations of food and social stimuli in VTA dopamine neurons

Dopamine neurons of the ventral tegmental area (VTADA) respond to food and social stimuli and contribute to both forms of motivation. However, it is unclear if the same or different VTADA neurons encode these different stimuli. To address this question, we performed 2-photon calcium imaging in mice presented with food and conspecifics, and found statistically significant overlap in the populations responsive to both stimuli. Both hunger and opposite-sex social experience further increased the proportion of neurons that respond to both stimuli, implying that modifying motivation for one stimulus affects responses to both stimuli. In addition, single-nucleus RNA sequencing revealed significant co-expression of feeding- and social-hormone related genes in individual VTADA neurons. Taken together, our functional and transcriptional data suggest overlapping VTADA populations underlie food and social motivation.

neuroscience↗

bioRxiv2023

A conserved local structural motif controls the kinetics of PTP1B catalysis

Protein tyrosine phosphatase 1B (PTP1B) is a negative regulator of the insulin and leptin signaling pathways, making it a highly attractive target for the treatment of type II diabetes. For PTP1B to perform its enzymatic function, a loop referred to as the "WPD loop" must transition between open (catalytically incompetent) and closed (catalytically competent) conformations, which have both been resolved by X-ray crystallography. Although prior studies have established this transition as the rate-limiting step for catalysis, the transition mechanism for PTP1B and other PTPs has been unclear. Here we present an atomically detailed model of WPD-loop transitions in PTP1B based on unbiased, long-timescale molecular dynamics simulations and weighted ensemble simulations. We found that a specific WPD-loop region-- the PDFG motif--acted as the key conformational switch, with structural changes to the motif being necessary and sufficient for transitions between long-lived open and closed states of the loop. Simulations starting from the closed state repeatedly visited open states of the loop that quickly closed again unless the infrequent conformational switching of the motif stabilized the open state. The functional role of the PDFG motif is supported by the fact that it (or the similar PDHG motif) is conserved across all PTPs. Bioinformatic analysis shows that the PDFG motif is also conserved, and adopts two distinct conformations, in deiminases, and the related DFG motif is known to function as a conformational switch in many kinases, suggesting that PDFG-like motifs may control transitions between structurally distinct, long-lived conformational states in multiple protein families.

biophysics↗

bioRxiv2022

Discovery and validation of the binding poses of allosteric fragment hits to PTP1b: From molecular dynamics simulations to X-ray crystallography

Fragment-based drug discovery has led to six approved drugs, but the small size of the chemical fragments used in such methods typically results in only weak interactions between the fragment and its target molecule, which makes it challenging to experimentally determine the three-dimensional poses fragments assume in the bound state. One computational approach that could help address this difficulty is long-timescale molecular dynamics (MD) simulation, which has been used in retrospective studies to recover experimentally known binding poses of fragments. Here, we present the results of long-timescale MD simulations that we used to prospectively discover binding poses for two series of fragments in allosteric pockets on a difficult and important pharmaceutical target, protein-tyrosine phosphatase 1b (PTP1b). Our simulations reversibly sampled the fragment association and dissociation process. One of the binding pockets found in the simulations has not to our knowledge been previously observed with a bound fragment, and the other pocket adopted a very rare conformation. We subsequently obtained high-resolution crystal structures of members of each fragment series bound to PTP1b, and the experimentally observed poses confirmed the simulation results. To the best of our knowledge, our findings provide the first demonstration that MD simulations can be used prospectively to determine fragment binding poses to previously unidentified pockets.

biophysics↗

bioRxiv2022

Behavioral and dopaminergic signatures of resilience

Chronic stress can have lasting adverse consequences in some individuals, yet others are resilient to the same stressor1,2. While previous work found differences in the intrinsic properties of mesolimbic dopamine (DA) neurons in susceptible and resilient individuals after stress was over;3-10 the causal links between DA activity during stress, dynamic stress-evoked behavior, and individual differences in susceptibility and resilience are not known. Here, we record behavior and neural activity in DA projections to the nucleus accumbens (NAc, signals reward11-14) and to the tail striatum (TS, signals threat15-18) during a multiday chronic social defeat paradigm and discover behavioral and neural signatures of resilience. Using supervised and unsupervised behavioral quantification, we find that resilient and susceptible individuals employ different behavioral strategies during stress. In addition, NAc-DA (but not TS-DA) activity is higher in the proximity of the aggressor in resilient mice, consistent with a greater subjective value of the aggressor. Moreover, NAc-DA tends to be elevated at the onset of fighting back in resilient mice and at the offset of attacks in susceptible mice. To test whether DA activation during defeat can generate resilience, and if its timing with respect to behavior is critical, we performed optogenetic stimulation of NAc-DA in open-loop (randomly timed) during defeat or timed to specific behaviors using real-time pose-tracking and behavioral classification. We find that both open-loop DA activation and fighting-back-timed activation promote resilience, in both cases reorganizing behavior during defeat toward resilience-associated patterns. Attack offset-timed activation promotes avoidance during defeat but does not promote susceptibility afterwards. Together, these data suggest a model whereby, during stress, DA in the NAc can increase resilience primarily by elevating the subjective value of the stressor rather than by reinforcing particular stress-responsive behaviors.

neuroscience↗

bioRxiv2018

Fast animal pose estimation using deep neural networks

Recent work quantifying postural dynamics has attempted to define the repertoire of behaviors performed by an animal. However, a major drawback to these techniques has been their reliance on dimensionality reduction of images which destroys information about which parts of the body are used in each behavior. To address this issue, we introduce a deep learning-based method for pose estimation, LEAP (LEAP Estimates Animal Pose). LEAP automatically predicts the positions of animal body parts using a deep convolutional neural network with as little as 10 frames of labeled data for training. This framework consists of a graphical interface for interactive labeling of body parts and software for training the network and fast prediction on new data (1 hr to train, 185 Hz predictions). We validate LEAP using videos of freely behaving fruit flies (Drosophila melanogaster) and track 32 distinct points on the body to fully describe the pose of the head, body, wings, and legs with an error rate of <3% of the animals body length. We recapitulate a number of reported findings on insect gait dynamics and show LEAPs applicability as the first step in unsupervised behavioral classification. Finally, we extend the method to more challenging imaging situations (pairs of flies moving on a mesh-like background) and movies from freely moving mice (Mus musculus) where we track the full conformation of the head, body, and limbs.

animal behavior and cognition↗