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A principled method to identify individual differences and behavioral shifts in signaled active avoidance.

ABSTRACT: Signaled active avoidance (SigAA) is the key experimental procedure for studying the acquisition of instrumental responses toward conditioned threat cues. Traditional analytic approaches (e.g., general linear model) often obfuscate important individual differences, although individual differences in learned responses characterize both animal and human learning data. However, individual differences models (e.g., latent growth curve modeling) typically require large samples and onerous computational methods. Here, we present an analytic methodology that enables the detection of individual differences in SigAA performance at a high accuracy, even when a single animal is included in the data set (i.e., n = 1 level). We further show an online software that enables the easy application of our method to any SigAA data set.

SUBMITTER: Krypotos AM

PROVIDER: S-EPMC6191017 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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A principled method to identify individual differences and behavioral shifts in signaled active avoidance.

Krypotos Angelos-Miltiadis AM Moscarello Justin M JM Sears Robert M RM LeDoux Joseph E JE Galatzer-Levy Isaac I

Learning & memory (Cold Spring Harbor, N.Y.) 20181015 11

Signaled active avoidance (SigAA) is the key experimental procedure for studying the acquisition of instrumental responses toward conditioned threat cues. Traditional analytic approaches (e.g., general linear model) often obfuscate important individual differences, although individual differences in learned responses characterize both animal and human learning data. However, individual differences models (e.g., latent growth curve modeling) typically require large samples and onerous computation ...[more]

PMID: 30322888

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Project description:Individuals exposed to traumatic stressors follow divergent patterns including resilience and chronic stress. However, researchers utilizing animal models that examine learned or instrumental threat responses thought to have translational relevance for Posttraumatic Stress Disorder (PTSD) and resilience typically use central tendency statistics that assume population homogeneity. This approach potentially overlooks fundamental differences that can explain human diversity in response to traumatic stressors. The current study tests this assumption by identifying and replicating common heterogeneous patterns of response to signaled active avoidance (AA) training. In this paradigm, rats are trained to prevent an aversive outcome (shock) by performing a learned instrumental behavior (shuttling between chambers) during the presentation of a conditioned threat cue (tone). We test the hypothesis that heterogeneous trajectories of threat avoidance provide more accurate model fit compared to a single mean trajectory in two separate studies. Study 1 conducted 3 days of signaled AA training (n = 81 animals) and study 2 conducted 5 days of training (n = 186 animals). We found that four trajectories in both samples provided the strongest model fit. Identified populations included animals that acquired and retained avoidance behavior on the first day (Rapid Avoiders: 22 and 25%); those who never successfully acquired avoidance (Non-Avoiders; 20 and 16%); a modal class who acquired avoidance over 3 days (Modal Avoiders; 37 and 50%); and a population who demonstrated a slow pattern of avoidance, failed to fully acquire avoidance in study 1 and did acquire avoidance on days 4 and 5 in study 2 (Slow Avoiders; 22.0 and 9%). With the exception of the Slow Avoiders in Study 1, populations that acquired demonstrated rapid step-like increases leading to asymptotic levels of avoidance. These findings indicate that avoidance responses are heterogeneous in a way that may be informative for understanding both resilience and PTSD as well as the nature of instrumental behavior acquisition. Characterizing heterogeneous populations based on their response to threat cues would increase the accuracy and translatability of such models and potentially lead to new discoveries that explain diversity in instrumental defensive responses.

Project description:Understanding the mechanisms behind chemical susceptibility differences is key to protecting sensitive populations. However, elucidating gene-environment interactions (GxE) presents a daunting challenge. While mammalian models have proven useful, problems with scalability to an enormous chemical exposome and clinical translation faced by all models remain; therefore, alternatives are needed. Zebrafish (Danio rerio) have emerged as an excellent model for investigating GxE. This study used a combined bioinformatic and experimental approach to probe the mechanisms underlying chemical susceptibility differences in a genetically diverse zebrafish population. Starting from high-throughput screening (HTS) data, a genome-wide association study (GWAS) using embryonic fish exposed to 0.6 μM Abamectin revealed significantly different effects between individuals. A hypervariable region with two distinct alleles-one with G at the SNP locus (GG) and one with a T and the 16 bp deletion (TT)-associated with differential susceptibility was found. Sensitive fish had significantly lower sox7 expression. Due to their location and the observed expression differences, we hypothesized that these sequences differentially regulate sox7. A luciferase reporter gene assay was used to test if these sequences, alone, could lead to expression differences. The TT allele showed significantly lower expression than the GG allele in MCF-7 cells. To better understand the mechanism behind these expression differences, predicted transcription factor binding differences between individuals were compared in silico, and several putative binding differences were identified. EMSA was used to test for binding differences in whole embryo protein lysate to investigate these TF binding predictions. We confirmed that the GG sequence is bound to protein in zebrafish. Through a competition EMSA using an untagged oligo titration, we confirmed that the GG oligo had a higher binding affinity than the TT oligo, explaining the observed expression differences. This study identified differential susceptibility to chemical exposure in a genetically diverse population, then identified a plausible mechanism behind those differences from a genetic to molecular level. Thus, an HTS-compatible zebrafish model is valuable and adaptable in identifying GxE mechanisms behind susceptibility differences to chemical exposure.

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A principled method to identify individual differences and behavioral shifts in signaled active avoidance.

Publications

A principled method to identify individual differences and behavioral shifts in signaled active avoidance.

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