Dynamic insulin‐stimulated mTOR/GSK3 signaling in peripheral immune cells: Preliminary evidence for an association with lithium response in bipolar disorder
AbstractIntroductionA key mechanism of lithium is the inhibition of glycogen synthase kinase‐3β (GSK3β) and activation of mammalian target of rapamycin (mTOR), two contributors to insulin signaling. We explored the relationship between these markers and clinical response to lithium in bipolar disorder (BD).MethodsThirty‐four subjects with BD who had been taking lithium for ≥2 years and had a maintenance lithium Alda score defined as either high (≥7; n = 20) or low (≤2; n = 14) were included in the study. Baseline protein expression of GSK3β and mTOR (total and phosphorylated (p)) was obtained from a buffy coat. Peripheral blood mononuclear cells (PBMCs) from a subset of each group (n = 11) were stimulated with insulin (10 µg) and change in protein expression was determined using Western blot.ResultsIn buffy coat samples, significantly higher levels of pmTOR were present in subjects with an Alda score ≤2 (lithium non‐responsive), relative to those with scores ≥7 (lithium‐responsive). No differences were observed for pGSK3β. In contrast, functional PBMC responses to 5 min of insulin stimulation demonstrated robust increases in pGSK3β (87.05 ± 43.41%) and pmTOR (105.7 ± 66.48%) in the lithium responsive group only. This contrasted observed decreases in pGSK3β (34.08 ± 16.12%) and pmTOR (37.84 ± 14.39%) 5 mins post‐insulin in non‐responders.ConclusionsDynamic increases in pmTOR and pGSK3β post‐insulin stimulation may reflect an immunometabolic state that facilitates lithium response. Further prospective analyses are needed to replicate and extend these preliminary findings and further investigate the role of insulin signaling in lithium response in BD.
Automatic and Reliable Quantification of Tonic Dopamine Concentrations In Vivo Using a Novel Probabilistic Inference Method
Abstract Neurochemical recording techniques have expanded our understanding of the pathophysiology of neurological disorders, as well as the mechanisms of action of treatment modalities like deep brain stimulation (DBS). DBS is used to treat diseases such as Parkinson’s disease, Tourette syndrome, and obsessive-compulsive disorder, among others. Although DBS is effective at alleviating symptoms related to these diseases and improving the quality of life of these patients, the mechanism of action of DBS is currently not fully understood. A leading hypothesis is that DBS modulates the electrical field potential by modifying neuronal firing frequencies to non-pathological rates thus providing therapeutic relief. To address this gap in knowledge, recent advances in electrochemical sensing techniques have given insight into the importance of neurotransmitters, such as dopamine, serotonin, glutamate, and adenosine, in disease pathophysiology. These studies have also highlighted their potential use in tandem with electrophysiology to serve as biomarkers in disease diagnosis and progression monitoring, as well as characterize response to treatment. Here, we provide an overview of disease-relevant neurotransmitters and their roles and implications as biomarkers, as well as innovations to the biosensors used to record these biomarkers. Furthermore, we discuss currently available neurochemical and electrophysiological recording devices, and discuss their viability to be implemented into the development of a closed-loop DBS system.
Clinical applications of neurochemical and electrophysiological measurements for closed-loop neurostimulation
The development of closed-loop deep brain stimulation (DBS) systems represents a significant opportunity for innovation in the clinical application of neurostimulation therapies. Despite the highly dynamic nature of neurological diseases, open-loop DBS applications are incapable of modifying parameters in real time to react to fluctuations in disease states. Thus, current practice for the designation of stimulation parameters, such as duration, amplitude, and pulse frequency, is an algorithmic process. Ideal stimulation parameters are highly individualized and must reflect both the specific disease presentation and the unique pathophysiology presented by the individual. Stimulation parameters currently require a lengthy trial-and-error process to achieve the maximal therapeutic effect and can only be modified during clinical visits. The major impediment to the development of automated, adaptive closed-loop systems involves the selection of highly specific disease-related biomarkers to provide feedback for the stimulation platform. This review explores the disease relevance of neurochemical and electrophysiological biomarkers for the development of closed-loop neurostimulation technologies. Electrophysiological biomarkers, such as local field potentials, have been used to monitor disease states. Real-time measurement of neurochemical substances may be similarly useful for disease characterization. Thus, the introduction of measurable neurochemical analytes has significantly expanded biomarker options for feedback-sensitive neuromodulation systems. The potential use of biomarker monitoring to advance neurostimulation approaches for treatment of Parkinson’s disease, essential tremor, epilepsy, Tourette syndrome, obsessive-compulsive disorder, chronic pain, and depression is examined. Further, challenges and advances in the development of closed-loop neurostimulation technology are reviewed, as well as opportunities for next-generation closed-loop platforms.