KCC2: a novel therapeutic target to rescue GABAergic dysfunction and behavioral deficits induced by HIV and opiate use

With adherence to combined antiretroviral therapy (cART), HIV infection can be considered a controllable chronic condition, but quality of life issues remain. The preeminent of which, HIV-associated neurocognitive disorders (HAND), encompasses an array of neurological complications and has persisted despite cART implementation. The symptoms of HAND can be exacerbated by opiate use, a common comorbidity for people infected with HIV (PWH). While neurons are not infected by HIV, they incur sublethal damage, with γ-amino butyric acid- (GABA)ergic function being particularly vulnerable to viral and inflammatory factors released by infected/affected glia. This dissertation presents studies on novel organoid and dissociated primary human CNS models of HAND, the latter of which showed diminished levels of K+ - Cl- cotransporter 2 (KCC2), a neuronal transporter that maintains low intracellular Cl-, after exposure to HIV-1 and HIV proteins ± morphine. GABAAR-mediated hyperpolarization is predicated upon activity of KCC2 and functional examination of these neurons revealed decreased hyperpolarization and disinhibition in response to GABAAR activation due KCC2 loss. Additionally, we identified the mechanisms through which HIV-1 mediates KCC2 reduction: the HIV protein, transactivator of transcription (Tat), through activation of N-methyl-D-aspartate receptor (NMDAR), and the HIV protein, glycoprotein 120 (gp120), through a novel mechanism involving CCR5 activation. We also found that morphine acts through the µ opioid receptor (MOR) to dysregulate KCC2. Pharmacological maintenance of KCC2 with the KCC2 enhancer, CLP257, rescued HIV, Tat, and morphine effects on KCC2 and GABAAR activity. Common neurological deficits in PWH include memory and motor dysfunction which are likely the manifestations of HIV-induced hippocampal and striatal degeneration. Thus, we expanded our in vitro results to the glial fibrillary acidic protein (GFAP)-driven, doxycycline(DOX)-inducible Tat-transgenic mouse model of HAND. No changes in KCC2 in the hippocampus were seen, but we did find significant Tat-induced loss of KCC2 in the striatum which was associated with locomotor abnormalities in these mice. We also rescued phosphorylation of serine 940-KCC2 leading to increased KCC2 membrane localization and restoration of baseline motor activity with oral gavage of the prodrug of CLP257, CLP290. Overall, our in vitro and in vivo results demonstrate KCC2 as a promising, novel therapeutic target to alleviate the symptoms associated with HAND ± opiate use

Hiv-1 tat and morphine differentially disrupt pyramidal cell structure and function and spatial learning in hippocampal area ca1: Continuous versus interrupted morphine exposure

About half the people infected with human immunodeficiency virus (HIV) have neurocognitive deficits that often include memory impairment and hippocampal deficits, which can be exacerbated by opioid abuse. To explore the effects of opioids and HIV on hippocampal CA1 pyramidal neuron structure and function, we induced HIV-1 transactivator of transcription (Tat) expression in transgenic mice for 14 d and co-administered time-release morphine or vehicle subcutaneous implants during the final 5 d (days 9–14) to establish steady-state morphine levels. Morphine was withheld from some ex vivo slices during recordings to begin to assess the initial pharmacokinetic consequences of opioid withdrawal. Tat expression reduced hippocampal CA1 pyramidal neuronal excitability at lower stimulating currents. Pyramidal cell firing rates were unaffected by continuous morphine exposure. Behaviorally, exposure to Tat or high dosages of morphine impaired spatial memory. Exposure to Tat and steady-state levels of morphine appeared to have largely independent effects on pyramidal neuron structure and function, a response that is distinct from other vulnerable brain regions such as the striatum. By contrast, acutely withholding morphine (from morphine-tolerant ex vivo slices) revealed unique and selective neuroadaptive shifts in CA1 pyramidal neuronal excitability and dendritic plasticity, including some interactions with Tat. Collectively, the results show that opioid-HIV interactions in hippocampal area CA1 are more nuanced than previously assumed, and appear to vary depending on the outcome assessed and on the pharmacokinetics of morphine exposure

Effects of a Severe Cold Event on the Subtropical, Estuarine-Dependent Common Snook, Centropomus undecimalis

The effects of infrequent disturbance events on marine fishes are often difficult to determine, due largely to lack of sufficient pre- and post-disturbance event data. In January 2010, subtropical southwestern Florida (USA) experienced extreme cold for 13 days, which caused extensive mortality of many fish species. The effect of this severe cold event on common snook (Centropomus undecimalis), an economically important gamefish, was assessed using three years (2007-2009) of pre-event and one year (2010) of post-event data from a tag-recapture program conducted over 28 km of Gulf of Mexico barrier islands of Florida. All metrics pointed to a significant effect of the severe cold event: post-disturbance apparent survival of marked fish was 96-97% lower than pre-disturbance, and post-disturbance common snook abundance was 75.57% and 41.88% less than in 2008 and 2009, the two years immediately pre-event. Although severe cold events have impacted subtropical Florida in the past, these events are infrequent (the previous recorded event was \u3e30 years prior), and documentation of the impacts on common snook have not previously been published

Unicorn, hare, or tortoise? Using machine learning to predict working memory training performance

People differ considerably in the extent to which they benefit from working memory (WM) training. Although there is increasing research focusing on individual differences associated with WM training outcomes, we still lack an understanding of which specific individual differences, and in what combination, contribute to inter-individual variations in training trajectories. In the current study, 568 undergraduates completed one of several N-back intervention variants over the course of two weeks. Participants\u27 training trajectories were clustered into three distinct training patterns (high performers, intermediate performers, and low performers). We applied machine-learning algorithms to train a binary tree model to predict individuals\u27 training patterns relying on several individual difference variables that have been identified as relevant in previous literature. These individual difference variables included pre-existing cognitive abilities, personality characteristics, motivational factors, video game experience, health status, bilingualism, and socioeconomic status. We found that our classification model showed good predictive power in distinguishing between high performers and relatively lower performers. Furthermore, we found that openness and pre-existing WM capacity to be the two most important factors in distinguishing between high and low performers. However, among low performers, openness and video game background were the most significant predictors of their learning persistence. In conclusion, it is possible to predict individual training performance using participant characteristics before training, which could inform the development of personalized interventions

Distributional Latent Variable Models with an Application in Active Cognitive Testing

Cognitive modeling commonly relies on asking participants to complete a battery of varied tests in order to estimate attention, working memory, and other latent variables. In many cases, these tests result in highly variable observation models. A near-ubiquitous approach is to repeat many observations for each test, resulting in a distribution over the outcomes from each test given to each subject. In this paper, we explore the usage of latent variable modeling to enable learning across many correlated variables simultaneously. We extend latent variable models (LVMs) to the setting where observed data for each subject are a series of observations from many different distributions, rather than simple vectors to be reconstructed. By embedding test battery results for individuals in a latent space that is trained jointly across a population, we are able to leverage correlations both between tests for a single participant and between multiple participants. We then propose an active learning framework that leverages this model to conduct more efficient cognitive test batteries. We validate our approach by demonstrating with real-time data acquisition that it performs comparably to conventional methods in making item-level predictions with fewer test items.Comment: 9 pages, 6 figure

Contrast response function estimation with nonparametric Bayesian active learning

Multidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning tools can be leveraged to provide an adjustable balance between accuracy and efficiency. Contrast sensitivity functions (CSFs) are behaviorally estimated curves that provide insight into both peripheral and central visual function. Because estimation can be impractically long, current clinical workflows must make compromises such as limited sampling across spatial frequency or strong assumptions on CSF shape. This article describes the development of the machine learning contrast response function (MLCRF) estimator, which quantifies the expected probability of success in performing a contrast detection or discrimination task. A machine learning CSF can then be derived from the MLCRF. Using simulated eyes created from canonical CSF curves and actual human contrast response data, the accuracy and efficiency of the machine learning contrast sensitivity function (MLCSF) was evaluated to determine its potential utility for research and clinical applications. With stimuli selected randomly, the MLCSF estimator converged slowly toward ground truth. With optimal stimulus selection via Bayesian active learning, convergence was nearly an order of magnitude faster, requiring only tens of stimuli to achieve reasonable estimates. Inclusion of an informative prior provided no consistent advantage to the estimator as configured. MLCSF achieved efficiencies on par with quickCSF, a conventional parametric estimator, but with systematically higher accuracy. Because MLCSF design allows accuracy to be traded off against efficiency, it should be explored further to uncover its full potential

PIT tag antennae arrays as fishery monitoring tools in tropical environments

ABSTRACT Long-term monitoring of marine and estuarine fishes is labor intensive and subject to varying environmental conditions and spatio-temporal constraints. To better understand fish populations and increase predictive capabilities, scientists and managers need reliable long-term monitoring systems that collect data on populations through all environmental conditions and reduce the labor required for data collection. To collect long-term data on fish survival and movements, we adapted autonomous passive integrated transponder (PIT) tag antennae for use in tropical environments. These antennae function through all environmental conditions, accurately recording the unique identification number of each PIT tagged individual passing over an antenna, and only require labor for construction, data download, maintenance, and the marking of fish. Antennae have long life spans and function continuously, and PIT tags have a lifespan measured in decades, making this system ideal for long-term studies. The utility of this recapture system was demonstrated during a nursery habitat study in Charlotte Harbor, Florida USA. From November 2008 to February 2010, we marked 1,446 juvenile common snook (Centropomus undecimalis) with PIT tags (and a total of 3,810 snook since 2004). Between August 2008 and August 2010, the 11 antennae we constructed throughout four mangrove creeks recorded 362,880 PIT tag detections of 1,594 individual fish. The antenna array recaptured 83.7% of fish marked after antenna construction was complete. The detailed recapture information allowed for highly precise calculation of apparent survival and examination of long-term habitat use. In addition to discussing the data we have collected, this paper details how to design and construct customized PIT tag antenna systems and covers the issues and limitations associated with adapting these systems to tropical marine and estuarine environments. These systems may be especially useful in the tropics for monitoring juveniles of other species that use near shore nursery habitats such as mangroves

The critical role of the linear plasmid lp36 in the infectious cycle of Borrelia burgdorferi

Borrelia burgdorferi, the aetiological agent of Lyme disease, follows a life cycle that involves passage between the tick vector and the mammalian host. To investigate the role of the 36 kb linear plasmid, lp36 (also designated the B. burgdorferi K plasmid), in the infectious cycle of B. burgdorferi, we examined a clone lacking this plasmid, but containing all other plasmids known to be required for infectivity. Our results indicated that lp36 was not required for spirochete survival in the tick, but the clone lacking lp36 demonstrated low infectivity in the mammal. Restoration of lp36 to the mutant strain confirmed that the infectivity defect was due to loss of lp36. Moreover, spirochetes lacking lp36 exhibited a nearly 4-log increase in ID50 relative to the isogenic lp36+ clone. The infectivity defect of lp36-minus spirochetes was localized, in part, to loss of the bbk17 (adeC) gene, which encodes an adenine deaminase. This work establishes a vital role for lp36 in the infectious cycle of B. burgdorferi and identifies the bbk17 gene as a component of this plasmid that contributes to mammalian infectivity