Vadim Alexandrov

SAHA Enhances Synaptic Function and Plasticity In Vitro but Has Limited Brain Availability In Vivo and Does Not Impact Cognition

Suberoylanilide hydroxamic acid (SAHA) is an inhibitor of histone deacetylases (HDACs) used for the treatment of cutaneous T cell lymphoma (CTCL) and under consideration for other indications. In vivo studies suggest reducing HDAC function can enhance synaptic function and memory, raising the possibility that SAHA treatment could have neurological benefits. We first examined the impacts of SAHA on synaptic function in vitro using rat organotypic hippocampal brain slices. Following several days of SAHA treatment, basal excitatory but not inhibitory synaptic function was enhanced. Presynaptic release probability and intrinsic neuronal excitability were unaffected suggesting SAHA treatment selectively enhanced postsynaptic excitatory function. In addition, long-term potentiation (LTP) of excitatory synapses was augmented, while long-term depression (LTD) was impaired in SAHA treated slices. Despite the in vitro synaptic enhancements, in vivo SAHA treatment did not rescue memory deficits in the Tg2576 mouse model of Alzheimer’s disease (AD). Along with the lack of behavioral impact, pharmacokinetic analysis indicated poor brain availability of SAHA. Broader assessment of in vivo SAHA treatment using high-content phenotypic characterization of C57Bl6 mice failed to demonstrate significant behavioral effects of up to 150 mg/kg SAHA following either acute or chronic injections. Potentially explaining the low brain exposure and lack of behavioral impacts, SAHA was found to be a substrate of the blood brain barrier (BBB) efflux transporters Pgp and Bcrp1. Thus while our in vitro data show that HDAC inhibition can enhance excitatory synaptic strength and potentiation, our in vivo data suggests limited brain availability may contribute to the lack of behavioral impact of SAHA following peripheral delivery. These results do not predict CNS effects of SAHA during clinical use and also emphasize the importance of analyzing brain drug levels when interpreting preclinical behavioral pharmacology.

Comprehensive Analysis of the 16p11.2 Deletion and Null Cntnap2 Mouse Models of Autism Spectrum Disorder

Autism spectrum disorder comprises several neurodevelopmental conditions presenting symptoms in social communication and restricted, repetitive behaviors. A major roadblock for drug development for autism is the lack of robust behavioral signatures predictive of clinical efficacy. To address this issue, we further characterized, in a uniform and rigorous way, mouse models of autism that are of interest because of their construct validity and wide availability to the scientific community. We implemented a broad behavioral battery that included but was not restricted to core autism domains, with the goal of identifying robust, reliable phenotypes amenable for further testing. Here we describe comprehensive findings from two known mouse models of autism, obtained at different developmental stages, using a systematic behavioral test battery combining standard tests as well as novel, quantitative, computer-vision based systems. The first mouse model recapitulates a deletion in human chromosome 16p11.2, found in 1% of individuals with autism. The second mouse model harbors homozygous null mutations in Cntnap2, associated with autism and Pitt-Hopkins-like syndrome. Consistent with previous results, 16p11.2 heterozygous null mice, also known as Del(7Slx1b-Sept1)4Aam weighed less than wild type littermates displayed hyperactivity and no social deficits. Cntnap2 homozygous null mice were also hyperactive, froze less during testing, showed a mild gait phenotype and deficits in the three-chamber social preference test, although less robust than previously published. In the open field test with exposure to urine of an estrous female, however, the Cntnap2 null mice showed reduced vocalizations. In addition, Cntnap2 null mice performed slightly better in a cognitive procedural learning test. Although finding and replicating robust behavioral phenotypes in animal models is a challenging task, such functional readouts remain important in the development of therapeutics and we anticipate both our positive and negative findings will be utilized as a resource for the broader scientific community.

High-throughput analysis of behavior for drug discovery

Drug testing with traditional behavioral assays constitutes a major bottleneck in the development of novel therapies. PsychoGenics developed three comprehensive highthroughtput systems, SmartCube®, NeuroCube® and PhenoCube® systems, to increase the efficiency of the drug screening and phenotyping in rodents. These three systems capture different domains of behavior, namely, cognitive, motor, circadian, social, anxiety-like, gait and others, using custom-built computer vision software and machine learning algorithms for analysis. This review exemplifies the use of the three systems and explains how they can advance drug screening with their applications to phenotyping of disease models, drug screening, selection of lead candidates, behavior-driven lead optimization, and drug repurposing.

Rapid, Computer Vision-Enabled Murine Screening System Identifies Neuropharmacological Potential of Two New Mechanisms

The lack of predictive in vitro models for behavioral phenotypes impedes rapid advancement in neuropharmacology and psychopharmacology. In vivo behavioral assays are more predictive of activity in human disorders, but such assays are often highly resource-intensive. Here we describe the successful application of a computer vision-enabled system to identify potential neuropharmacological activity of two new mechanisms. The analytical system was trained using multiple drugs that are used clinically to treat depression, schizophrenia, anxiety, and other psychiatric or behavioral disorders. During blinded testing the PDE10 inhibitor TP-10 produced a signature of activity suggesting potential antipsychotic activity. This finding is consistent with TP-10’s activity in multiple rodent models that is similar to that of clinically used antipsychotic drugs. The CK1ε inhibitor PF-670462 produced a signature consistent with anxiolytic activity and, at the highest dose tested, behavioral effects similar to that of opiate analgesics. Neither TP-10 nor PF-670462 was included in the training set. Thus, computer vision-based behavioral analysis can facilitate drug discovery by identifying neuropharmacological effects of compounds acting through new mechanisms.

Large-scale phenome analysis defines a behavioral signature for Huntington's disease genotype in mice.

Rapid technological advances for the frequent monitoring of health parameters have raised the intriguing possibility that an individuals genotype could be predicted from phenotypic data alone. Here we used a machine learning approach to analyze the phenotypic effects of polymorphic mutations in a mouse model of Huntingtons disease that determine disease presentation and age of onset. The resulting model correlated variation across 3,086 behavioral traits with seven different CAG-repeat lengths in the huntingtin gene (Htt). We selected behavioral signatures for age and CAG-repeat length that most robustly distinguished between mouse lines and validated the model by correctly predicting the repeat length of a blinded mouse line. Sufficient discriminatory power to accurately predict genotype required combined analysis of >200 phenotypic features. Our results suggest that autosomal dominant disease-causing mutations could be predicted through the use of subtle behavioral signatures that emerge in large-scale, combinatorial analyses. Our work provides an open data platform that we now share with the research community to aid efforts focused on understanding the pathways that link behavioral consequences to genetic variation in Huntingtons disease.

Comprehensive analysis of two Shank3 and the Cacna1c mouse models of autism spectrum disorder

To expand, analyze and extend published behavioral phenotypes relevant to autism spectrum disorder (ASD), we present a study of three ASD genetic mouse models: Fengs Shank3tm2Gfng model, hereafter Shank3/F, Jiangs Shank3tm1Yhj model, hereafter Shank3/J and the Cacna1c deletion model. The Shank3 models mimick gene mutations associated with Phelan–McDermid Syndrome and the Cacna1c model recapitulates the deletion underlying Timothy syndrome. This study utilizes both standard and novel behavioral tests with the same methodology used in our previously published companion report on the Cntnap2 null and 16p11.2 deletion models. We found that some but not all behaviors replicated published findings and those that did replicate, such as social behavior and overgrooming in Shank3 models, tended to be milder than reported elsewhere. The Shank3/F model, and to a much lesser extent, the Shank3/J and Cacna1c models, showed hypoactivity and a general anxiety‐like behavior triggered by external stimuli which pervaded social interactions. We did not detect deficits in a cognitive procedural learning test nor did we observe perseverative behavior in these models. We did, however, find differences in exploratory patterns of Cacna1c mutant mice suggestive of a behavioral effect in a social setting. In addition, only Shank3/F showed differences in sensory‐gating. Both positive and negative results from this study will be useful in identifying the most robust and replicable behavioral signatures within and across mouse models of autism. Understanding these phenotypes may shed light of which features to study when screening compounds for potential therapeutic interventions.

Reprint of: Highthroughtput analysis of behavior for drug discovery.

Drug testing with traditional behavioral assays constitutes a major bottleneck in the development of novel therapies. PsychoGenics developed three comprehensive highthroughtput systems, SmartCube(®), NeuroCube(®) and PhenoCube(®) systems, to increase the efficiency of the drug screening and phenotyping in rodents. These three systems capture different domains of behavior, namely, cognitive, motor, circadian, social, anxiety-like, gait and others, using custom-built computer vision software and machine learning algorithms for analysis. This review exemplifies the use of the three systems and explains how they can advance drug screening with their applications to phenotyping of disease models, drug screening, selection of lead candidates, behavior-driven lead optimization, and drug repurposing.

In vivo phenotypic drug discovery: applying a behavioral assay to the discovery and optimization of novel antipsychotic agents

Phenotypic drug discovery (PDD) is increasingly being recognized as a viable compliment to target-based drug discovery (TDD). By measuring functional changes, typically at a systems level, PDD can facilitate the identification of compounds having a desirable pharmacology. This capability is particularly important when studying CNS diseases where drug efficacy may require modulation of multiple targets in order to overcome a robust, adaptive biological system. Here, we report the application of a mouse-based high-dimensional behavioral assay to the discovery and optimization of a structurally and mechanistically novel antipsychotic. Lead optimization focused on optimizing complex behavioral features and no explicit effort was made to identify the target (or targets) involved.

EFFECTS OF TREATMENT WITH DOXYCYCLINE ON BEHAVIOR, NEUROINFLAMMATION AND BRAIN PATHOLOGY IN THE RTG-4510 MOUSE MODEL

rTG 4510 mice express h4R0N P301L Tau downstream of a tetracycline-operon-responder construct under control of Ca2+-calmodulin kinase II promoter, that allows control of gene expression by feeding of doxycycline (tet-off; Ramsded et al, 2005). The mice express transgenic tau mainly in forebrain structures. The pathology forms very early, showing pretangles consisting of hyper-phosphorylated tau already at 2.5m and agyrophilic NFTs at 4m in cortex and at 5.5m in hippocampus (Spires et al, 2006). NFTs are furthermore congophilic and Thioflavin S positive. These mice also display significant degeneration of CA1 hippocampal neurons at 5.5m and gross atrophy of the forebrain and hippocampus being present at 4mo. This is also the age to develop significant behavioral deficits (learning/memory). It is important to note that transgene suppression by doxycycline stops neuronal death, decreases levels of tau and p-tau, and reverses the cognitive disturbance (Santacruz et al., 2005), which provides a robust control for any treatment study. Only little is known to the effect of Dox treatment on total Tau levels and related gliosis from histological quantifications, the here presented data shall fill this gap.

DIFFERENCES BETWEEN TG2576- AND APP/PS1 MICE IN HIGH-THROUGHPUT BEHAVIORAL SCREENING CORRELATES WITH DIFFERENCES IN BRAIN PATHOLOGY

The APP/PS1 double transgenic mice were created by cross between Tg2576 (APP695sw) and a mutant PS1 (M146L) mouse line (Holcomb et al 1998; 1999). These mice show increased amyloid-β 40 and 42 levels and develop early amyloid pathology in the cerebral cortex and hippocampus, accompanied by signs of neuro-inflammation (Jimenez et al., 2008), characterized by significant microglia activation and significant astrogliosis in cortex and hippocampus. The mice also show increased brain and peripheral inflammatory markers. Behavioral deficits correlate to brain pathology (Gordon et al., 2001), resulting in a significant memory deficit as early as 12 weeks of age in spontaneous alternation, fear conditioning and spatial learning. This behavioral abnormality seems to persist at later ages (6 to 9 months). Spatial reference memory as measured by Morris water maze is altered around 5-6 months of age (Gong et al., 2004). Also, sensorimotor functions, including hearing vestibular functions and motor coordination, are intact in the APP/PS1 mice (Holcomb et al., 1999).