Behavioral impulsivity is associated with pupillary alterations and hyperactivity in CDKL5 mutant mice. Issue 23 (21st July 2022)

• Record Type: Journal Article
• Authors: Viglione, Aurelia; Sagona, Giulia; Carrara, Fabio; Amato, Giuseppe; Totaro, Valentino; Lupori, Leonardo; Putignano, Elena; Pizzorusso, Tommaso; Mazziotti, Raffaele
• Is Part Of: Human molecular genetics. Volume 31:Issue 23(2022)
• Page Start: 4107
• Page End: 4120
• Publication Date: 2022-07-21
• Subjects: Human molecular genetics -- Periodicals; Human chromosome abnormalities -- Periodicals; 572.8
• DOI: 10.1093/hmg/ddac164
• Languages: English
• ISSNs: 0964-6906

Abstract

Abstract: Cyclin-dependent kinase-like 5 ( Cdkl5 ) deficiency disorder (CDD) is a severe neurodevelopmental condition caused by mutations in the X-linked Cdkl5 gene. CDD is characterized by early-onset seizures in the first month of life, intellectual disability, motor and social impairment. No effective treatment is currently available and medical management is only symptomatic and supportive. Recently, mouse models of Cdkl5 disorder have demonstrated that mice lacking Cdkl5 exhibit autism-like phenotypes, hyperactivity and dysregulations of the arousal system, suggesting the possibility to use these features as translational biomarkers. In this study, we tested Cdkl5 male and female mutant mice in an appetitive operant conditioning chamber to assess cognitive and motor abilities, and performed pupillometry to assess the integrity of the arousal system. Then, we evaluated the performance of artificial intelligence models to classify the genotype of the animals from the behavioral and physiological phenotype. The behavioral results show that CDD mice display impulsivity, together with low levels of cognitive flexibility and perseverative behaviors. We assessed arousal levels by simultaneously recording pupil size and locomotor activity. Pupillometry reveals in CDD mice a smaller pupil size and an impaired response to unexpected stimuli associated with hyperlocomotion, demonstrating a global defect in arousal modulation. Finally, machine learning reveals that both behavioral and pupillometry parameters can be considered good predictors of CDD. Since early diagnosis is essential to evaluate treatment outcomes and pupillary measures can be performed easily, we proposed the monitoring of pupil size as a promising biomarker for CDD.