Biology subjects

Bavencoffe, A. G.

Publications and source records attributed to Bavencoffe, A. G..

3 recordsLinked to original sources

bioRxiv2024

Widespread latent hyperactivity of nociceptors outlasts enhanced avoidance behavior following incision injury

Nociceptors with somata in dorsal root ganglia (DRGs) exhibit an unusual readiness to switch from an electrically silent state to a hyperactive state of tonic, nonaccommodating, low-frequency, irregular discharge of action potentials (APs). Ongoing activity (OA) during this state is present in vivo in rats months after spinal cord injury (SCI), and has been causally linked to SCI pain. OA induced by various neuropathic conditions in rats, mice, and humans is retained in nociceptor somata after dissociation and culturing, providing a powerful tool for investigating its mechanisms and functions. An important question is whether similar nociceptor OA is induced by painful conditions other than neuropathy. The present study shows that probable nociceptors dissociated from DRGs of rats subjected to postsurgical pain (induced by plantar incision) exhibit OA. The OA was most apparent when the soma was artificially depolarized to a level within the normal range of membrane potentials where large, transient depolarizing spontaneous fluctuations (DSFs) can approach AP threshold. This latent hyperactivity persisted for at least 3 weeks, whereas behavioral indicators of affective pain - hindpaw guarding and increased avoidance of a noxious substrate in an operant conflict test - persisted for 1 week or less. An unexpected discovery was latent OA in neurons from thoracic DRGs that innervate dermatomes distant from the injured tissue. The most consistent electrophysiological alteration associated with OA was enhancement of DSFs. Potential in vivo functions of widespread, low-frequency nociceptor OA consistent with these and other findings are to amplify hyperalgesic priming and to drive anxiety-related hypervigilance.

neuroscience↗

bioRxiv2023

Readiness of nociceptor cell bodies to generate spontaneous activity results from background activity of diverse ion channels and high input resistance

Nociceptor cell bodies generate "spontaneous" discharge that can promote ongoing pain in persistent pain conditions. Little is known about the underlying mechanisms. Recordings from nociceptor cell bodies (somata) dissociated from rodent and human dorsal root ganglia (DRGs) have shown that prior pain in vivo is associated with low-frequency discharge controlled by irregular depolarizing spontaneous fluctuations of membrane potential (DSFs), likely produced by transient inward currents across the somal input resistance. Here we show that DSFs are associated with high somal input resistance over a wide range of membrane potentials, including depolarized levels where DSFs approach action potential (AP) threshold. Input resistance and both the amplitude and frequency of DSFs were increased in neurons exhibiting spontaneous activity. Ion substitution experiments indicated that the depolarizing phase of DSFs is generated by spontaneous opening of channels permeable to Na+ and/or Ca2+, and that Ca2+-permeable channels are especially important for larger DSFs. Partial reduction of the amplitude and/or frequency of DSFs by perfusion of pharmacological inhibitors indicated small but significant contributions from Nav1.7, Nav1.8, TRPV1, TRPA1, TRPM4, and N-type Ca2+ channels. Less specific blockers suggested a contribution from NALCN channels, and global knockout suggested a role for Nav1.9. The combination of high somal input resistance plus background activity of diverse ion channels permeable to Na+ and/or Ca2+ produces DSFs that are poised to reach AP threshold if resting membrane potential (RMP) depolarizes, AP threshold decreases, and/or DSFs become enhanced -- all of which have been reported under painful neuropathic and inflammatory conditions.

neuroscience↗

bioRxiv2020

Frequency-independent biological signal identification (FIBSI): A free program that simplifies intensive analysis of non-stationary time series data

Extracting biological signals from non-linear, dynamic and stochastic experimental data can be challenging, especially when the signal is non-stationary. Many currently available methods make assumptions about the data structure (e.g., signal is periodic, sufficient recording time) and modify the raw data in pre-processing using filters and/or transformations. With an agnostic approach to biological data analysis as a goal, we implemented a signal detection algorithm in Python that quantifies the dimensional properties of waveform deviations from baseline via a running fit function. We call the resulting free program frequency-independent biological signal identification (FIBSI). We demonstrate the utility of FIBSI on two disparate types of experimental data: in vitro whole-cell current-clamp electrophysiological recordings of rodent sensory neurons (i.e., nociceptors) and in vivo fluorescence image time-lapse movies capturing gastrointestinal motility in larval zebrafish. In rodent nociceptors, depolarizing fluctuations in membrane potential are irregular in shape and difficult to distinguish from noise. Using FIBSI, we determined that nociceptors from naïve mice generate larger, more frequent fluctuations compared to naïve rats, suggesting species-specific specializations in rodent nociceptors. In zebrafish, measuring gut motility is a useful tool for addressing developmental and disease-related mechanisms associated with gut function. However, available methods are laborious, technically complex, and/or not cost-effective. We developed and tested a novel assay that can characterize intestinal peristalsis using imaging time series datasets. We used FIBSI to identify muscle contractions in the fluorescence signals and compared their frequencies in unfed and fed larvae. Additionally, FIBSI allowed us to discriminate between peristalsis and oscillatory sphincter-like movements in functionally distinct gut segments (foregut, midgut, and cloaca). We conclude that FIBSI, which is freely available via GitHub, is widely useful for the unbiased analysis of non-stationary signals and extraction of biologically meaningful information from experimental time series data and can be employed for both descriptive and hypothesis-driven investigations.Author Summary Biologists increasingly work with large, complex experimental datasets. Those datasets often encode biologically meaningful signals along with background noise that is recorded along with the biological data during experiments. Background noise masks the real signal but originates from other sources, for example from the equipment used to perform the measurements or environmental disturbances. When it comes to analyzing the data, distinguishing between the real biological signals and the background noise can be very challenging. Many existing programs designed to help scientists with this problem are either difficult to use, not freely available, or only appropriate to use on very specific types of datasets. The research presented here embodies our goal of helping others to analyze their data by employing a powerful but novice-friendly program that describes multiple features of biological activity in its raw form without abstract transformations. We show the program’s applicability using two different kinds of biological activity measured in our labs. It is our hope that this will help others to analyze complex datasets more easily, thoroughly, and rigorously.Competing Interest StatementThe authors have declared no competing interest.View Full Text

physiology↗