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The modulation of fragile X behaviors by the muscarinic M4 antagonist, tropicamide

Surabi Veeraragavan, Nghiem Bui, Jennie R. Perkins, Lisa A. Yuva-Paylor, Richard Paylor
2011 Behavioral Neuroscience  
Previously, we had shown that dicyclomine reduces AGS in a mouse model of FXS (Veeraragavan et al., 2011) .  ...  Each animal received a single injection only on the training day 1 as described (Veeraragavan et al., 2011) .  ... 
doi:10.1037/a0025202 pmid:21942438 pmcid:PMC3183989 fatcat:dewzjn6q4bgndomhevstnlcdlu

Rigor and reproducibility in rodent behavioral research

Maria Gulinello, Heather A. Mitchell, Qiang Chang, W. Timothy O'Brien, Zhaolan Zhou, Ted Abel, Li Wang, Joshua G. Corbin, Surabi Veeraragavan, Rodney C. Samaco, Nick A. Andrews, Michela Fagiolini (+3 others)
2018 Neurobiology of Learning and Memory  
Unpublished photo contributed by Surabi Veeraragavan, Baylor College of Medicine IDDRC Neurobehavioral Core.  ... 
doi:10.1016/j.nlm.2018.01.001 pmid:29307548 pmcid:PMC6034984 fatcat:ft4lombhafg6jcj3dhtvmm37i4

Genetic reduction of muscarinic M4 receptor modulates analgesic response and acoustic startle response in a mouse model of fragile X syndrome (FXS)

Surabi Veeraragavan, Deanna Graham, Nghiem Bui, Lisa A. Yuva-Paylor, Jürgen Wess, Richard Paylor
2012 Behavioural Brain Research  
Introduction-The G-protein coupled muscarinic acetylcholine receptors, widely expressed in the CNS, have been implicated in Fragile × Syndrome (F×S). Recent studies have reported an overactive signaling through the muscarinic receptors in the Fmr1KO mouse model. Hence, it was hypothesized that reducing muscarinic signaling might modulate behavioral phenotypes in the Fmr1KO mice. Pharmacological studies from our lab have provided evidence for this hypothesis, with subtype-preferring muscarinic M
more » ... 1 and M 4 receptor antagonists modulating select behaviors in the Fmr1KO mice. Since the pharmacological antagonists were not highly specific, we investigated the specific role of M 4 receptors in the Fmr1KO mouse model, using a genetic approach. Methods-We created a double mutant heterozygous for the M 4 receptor gene and hemizygous for the Fmr1 gene and examined the mutants on various behaviors. Each animal was tested on a behavior battery comprising of open-field activity (activity), light-dark (anxiety), marble burying (perseverative behavior), prepulse inhibition (sensorimotor gating), rotarod (motor coordination), passive avoidance (learning and memory) and hotplate (analgesia). Animals were also tested on the audiogenic seizure protocol and testis weights were measured. Results-Reduction of M 4 receptor expression in the heterozygotes completely rescued the analgesic response and partly rescued the acoustic startle response phenotype in the Fmr1KO mice. However, no modulation was observed in a number of behaviors including learning and memory, activity, perseverative behavior and audiogenic seizures. Conclusion-Reducing M 4 receptor signaling altered only select behavioral phenotypes in the Fmr1KO mouse model, suggesting that other targets are involved in the modulation of fragile × behaviors.
doi:10.1016/j.bbr.2011.11.018 pmid:22123412 pmcid:PMC3264832 fatcat:xnv2specbbd3heweppyjejoai4

A truncating mutation of Magel2 in the rat modelled for the study of Schaaf-Yang and Prader-Willi syndromes alters select behavioral and physiological outcomes [article]

Derek L Reznik, Mingxiao V Yang, Pedro Albelda de la Haza, Antrix Jain, Melanie Spanjaard, Susanne Theiss, Christian P Schaaf, Anna Malovannaya, Theresa V Strong, Surabi Veeraragavan, Rodney C Samaco
2022 bioRxiv   pre-print
This observation has been previously reported in other monogenic rat models with normal levels of transcript in the presence of altered protein expression (Veeraragavan et al., 2016) .  ...  In the three chamber test for social approach (Ku et al., 2016; Veeraragavan et al., 2016; Yang et al., 2011) , the typical expected pattern of social approach behavior was observed by spending significantly  ... 
doi:10.1101/2022.08.09.503377 fatcat:4gtcwswkvzgepgjjtrl7ehlf3a

Loss of MeCP2 in the rat models regression, impaired sociability and transcriptional deficits of Rett syndrome

Surabi Veeraragavan, Ying-Wooi Wan, Daniel R. Connolly, Shannon M. Hamilton, Christopher S. Ward, Sirena Soriano, Meagan R. Pitcher, Christopher M. McGraw, Sharon G. Huang, Jennie R. Green, Lisa A. Yuva, Agnes J. Liang (+6 others)
2016 Human Molecular Genetics  
Mouse models of the transcriptional modulator Methyl-CpG-Binding Protein 2 (MeCP2) have advanced our understanding of Rett syndrome (RTT). RTT is a 'prototypical' neurodevelopmental disorder with many clinical features overlapping with other intellectual and developmental disabilities (IDD). Therapeutic interventions for RTT may therefore have broader applications. However, the reliance on the laboratory mouse to identify viable therapies for the human condition may present challenges in
more » ... ting findings from the bench to the clinic. In addition, the need to identify outcome measures in well-chosen animal models is critical for preclinical trials. Here, we report that a novel Mecp2 rat model displays high face validity for modelling psychomotor regression of a learned skill, a deficit that has not been shown in Mecp2 mice. Juvenile play, a behavioural feature that is uniquely present in rats and not mice, is also impaired in female Mecp2 rats. Finally, we demonstrate that evaluating the molecular consequences of the loss of MeCP2 in both mouse and rat may result in higher predictive validity with respect to transcriptional changes in the human RTT brain. These data underscore the similarities and differences caused by the loss of MeCP2 among divergent rodent species which may have important implications for the treatment of individuals with disease-causing MECP2 mutations. Taken together, these findings demonstrate that the Mecp2 rat model is a complementary tool with unique features for the study of RTT and highlight the potential benefit of cross-species analyses in identifying potential disease-relevant preclinical outcome measures. † Present address:
doi:10.1093/hmg/ddw178 pmid:27365498 pmcid:PMC5179927 fatcat:uzob7hhngrg3tmmbuxamj4svtq

Soft Windowing Application to Improve Analysis of High-throughput Phenotyping Data [article]

Hamed Haselimashhadi, Mason C. Jeremy, Violeta Munoz-Fuentes, Federico López-Gómez, Kolawole Babalola, Elif F. Acar, Vivek Kumar, Jacqui White, Ann M. Flenniken, Ruairidh King, Ewan Straiton, John Richard Seavitt (+52 others)
2019 bioRxiv   pre-print
AbstractMotivationHigh-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximises analytic power while minimising noise from unspecified environmental factors.ResultsHere we introduce "soft windowing", a methodological approach that selects a window of time that includes the most appropriate controls for
more » ... alysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant p-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft windowed and non-windowed approaches, respectively, from a set of 2,082 mutant mouse lines. Our method is generalisable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources.Availability and ImplementationThe method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin.
doi:10.1101/656678 fatcat:bx337qzovfa2zp42px4sap44ri