Biology subjects

Gill, J. P.

Publications and source records attributed to Gill, J. P..

4 recordsLinked to original sources

bioRxiv2026

Release of BEC-1 from CED-9 stimulates lysosome tubulation in C. elegans by engaging Class III PI3K complex I

The anti-apoptotic protein CED-9 and the autophagy protein BEC-1 have been linked to autophagy regulation, but how their interaction modulates this process is not fully understood. Here, we find that the physical dissociation of BEC-1 from CED-9 influences lysosomal morphology in addition to autophagic activity in Caenorhabditis elegans. We present evidence that when autophagy is stimulated via dietary restriction, BEC-1 is released from CED-9 and assembles with VPS-34 and other members of the PI3K complex I to promote PI3P enrichment at lysosomal membranes and subsequent membrane tubulation. Mutation of two hydrophobic amino acids within a predicted BH-3 like motif of BEC-1 is sufficient to disrupt its interaction with CED-9, thereby constitutively inducing lysosomal tubulation and increasing autophagic cargo turnover. This phenotype is dependent on VPS-34 activity, linking the CED-9/BEC-1 dissociation to PI3K-dependent lysosomal remodeling. Collectively, our work identifies the CED-9/BEC-1 interaction as a molecular switch that governs apoptosis, autophagy, and lysosomal morphology.

cell biology↗

bioRxiv2024

Incorporating buccal mass planar mechanics and anatomical features improves neuromechanical modeling of Aplysia feeding behavior

To understand how behaviors arise in animals, it is necessary to investigate both the neural circuits and the biomechanics of the periphery. A tractable model system for studying multifunctional control is the feeding apparatus of the marine mollusk Aplysia californica. Previous in silico and in roboto models have investigated how the nervous and muscular systems interact in this system. However, these models are still limited in their ability to match in vivo data both qualitatively and quantitatively. We introduce a new neuromechanical model of Aplysia feeding that combines a modified version of a previously developed neural model with a novel biomechanical model that better reflects the anatomy and kinematics of Aplysia feeding. The model was calibrated using a combination of previously measured biomechanical parameters and hand-tuning to behavioral data. Using this model, simulation feeding experiments were conducted, and the resulting behavioral metrics were compared to animal data. The model successfully produces three key behaviors seen in Aplysia and demonstrates a good quantitative agreement with biting and swallowing behaviors. Additional work is needed to match rejection behavior quantitatively and to reflect qualitative observations related to the relative contributions of two key muscles, the hinge and I3. Future improvements will focus on incorporating the effects of deformable 3D structures in the simulated buccal mass. Author summaryAnimals need to produce a wide array of behaviors so that they can adapt to changes in their environment. To understand how behaviors are performed, we need to understand how the brain and the body work together in their environment. One tractable system in which to study this brain-body relationship is the feeding behavior of the sea slug Aplysia californica. Despite having a small fraction of the number of neurons that humans have, this animal can produce many behaviors, respond to a changing environment, and learn from previous experiences. We have create an improved computer model of the slugs mouthparts that simulates many of its key muscles and the forces they produce, together with a representation of the network of neurons that control them. With this model, we can recreate the feeding behaviors that we observe in the real animal, including biting, swallowing, and rejection, and use it to make quantitative predictions of how the animal will behave and respond to different stimuli. We found however that some aspects of the system were not well represented by simple 1-dimensional muscles, as has been done in most biomechanical models to date, but requires us to consider more complicated deformations of these soft bodies. Using this model as a tool, we aim to test hypotheses about brain-body interactions in the sea slug to better understand the behavior of small, slowly moving animals.

neuroscience↗

bioRxiv2022

Variational and phase response analysis for limit cycles with hard boundaries, with applications to neuromechanical control problems

Motor systems show an overall robustness, but because they are highly nonlinear, understanding how they achieve robustness is difficult. In many rhythmic systems, robustness against perturbations involves response of both the shape and the timing of the trajectory. This makes the study of robustness even more challenging. To understand how a motor system produces robust behaviors in a variable environment, we consider a neuromechanical model of motor patterns in the feeding apparatus of the marine mollusk Aplysia californica (Shaw et al., 2015; Lyttle et al., 2017). We established in (Wang et al., 2021) the tools for studying combined shape and timing responses of limit cycle systems under sustained perturbations and here apply them to study robustness of the neuromechanical model against increased mechanical load during swallowing. Interestingly, we discover that nonlinear biomechanical properties confer resilience by immediately increasing resistance to applied loads. In contrast, the effect of changed sensory feedback signal is significantly delayed by the firing rates hard boundary properties. Our analysis suggests that sensory feedback contributes to robustness in swallowing primarily by shifting the timing of neural activation involved in the power stroke of the motor cycle (retraction). This effect enables the system to generate stronger retractor muscle forces to compensate for the increased load, and hence achieve strong robustness. The approaches that we are applying to understanding a neuromechanical model in Aplysia, and the results that we have obtained, are likely to provide insights into the function of other motor systems that encounter changing mechanical loads and hard boundaries, both due to mechanical and neuronal firing properties.

neuroscience↗

bioRxiv2021

Carbon Fiber Electrodes for Intracellular Recording and Stimulation

To understand neural circuit dynamics, it is critical to manipulate and record from many neurons, ideally at the single neuron level. Traditional recording methods, such as glass microelectrodes, can only control a small number of neurons. More recently, devices with high electrode density have been developed, but few of them can be used for intracellular recording or stimulation in intact nervous systems, rather than on neuronal cultures. Carbon fiber electrodes (CFEs) are 8 micron-diameter electrodes that can be organized into arrays with pitches as low as 80 {micro}m. They have been shown to have good signal-to-noise ratios (SNRs) and are capable of stable extracellular recording during both acute and chronic implantation in vivo in neural tissue such as rat motor cortex. Given the small fiber size, it is possible that they could be used in arrays for intracellular stimulation. We tested this using the large identified and electrically compact neurons of the marine mollusk Aplysia californica. The cell bodies of neurons in Aplysia range in size from 30 to over 250 {micro}m. We compared the efficacy of CFEs to glass microelectrodes by impaling the same neurons cell body with both electrodes and connecting them to a DC coupled amplifier. We observed that intracellular waveforms were essentially identical, but the amplitude and SNR in the CFE were lower than in the glass microelectrode. CFE arrays could record from 3 to 8 neurons simultaneously for many hours, and many of these recordings were intracellular as shown by recording from the same neuron using a glass microelectrode. Stimulating through CFEs coated with platinum-iridium had stable impedances over many hours. CFEs not within neurons could record local extracellular activity. Despite the lower SNR, the CFEs could record synaptic potentials. Thus, the stability for multi-channel recording and the ability to stimulate and record intracellularly make CFEs a powerful new technology for studying neural circuit dynamics.

neuroscience↗