Behavioral impulsivity is associated with pupillary alterations and hyperactivity in CDKL5 mutant mice

Multi-site investigation of gut microbiota in CDKL5 deficiency disorder mouse models: Targeting dysbiosis to improve neurological outcomes

Cyclin-dependent kinase-like 5 (CDKL5) deficiency disorder (CDD) is a rare neurodevelopmental disorder often associated with gastrointestinal (GI) issues and subclinical immune dysregulation, suggesting a link to the gut microbiota. We analyze the fecal microbiota composition in two CDKL5 knockout (KO) mouse models at postnatal days (P) 25, 32 (youth), and 70 (adulthood), revealing significant microbial imbalances, particularly during juvenile stages. To investigate the role of the intestinal microbiota in CDD and assess causality, we administer antibiotics, which lead to improved visual cortical responses and reduce hyperactivity. Additionally, microglia morphology changes, indicative of altered surveillance and activation states, are reversed. Strikingly, fecal transplantation from CDKL5 KO to wild-type (WT) recipient mice successfully transfers both visual response deficits and hyperactive behavior. These findings show that gut microbiota alterations contribute to the severity of neurological symptoms in CDD, shedding light on the interplay between microbiota, microglia, and neurodevelopmental outcomes.

Independent genetic strategies define the scope and limits of CDKL5 deficiency disorder reversal

Cyclin-dependent kinase-like 5 (CDKL5) deficiency disorder (CDD) is a neurodevelopmental syndrome caused by mutations in the X-linked CDKL5 gene. The early onset of CDD suggests that CDKL5 is essential during development, but post-developmental re-expression rescues multiple CDD-related phenotypes in hemizygous male mice. Since most patients are heterozygous females, studies in clinically relevant female models are essential. Here, we systematically compare phenotype reversal across age and sex using two independent mouse models of CDD. We find that early re-activation of endogenous Cdkl5 in heterozygous females reverses most phenotypes, except working memory. Later re-expression improves several traits but has limited effects on cognitive function. Seizure prevention is more effective with early intervention in heterozygous females but becomes limited after seizure onset. These findings demonstrate the robust potential of CDKL5 re-expression to reverse CDD-related phenotypes in both sexes while underscoring the critical impact of age and disease stage in designing clinical trials.

A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity

Functional connectivity holds promise as a biomarker of schizophrenia. Yet, the high dimensionality of predictive models trained on functional connectomes, combined with small sample sizes in clinical research, increases the risk of overfitting. Recently, low-dimensional representations of the connectome such as macroscale cortical gradients and gradient dispersion have been proposed, with studies noting consistent gradient and dispersion differences in psychiatric conditions. However, it is unknown which of these derived measures has the highest predictive capacity and how they compare to raw functional connectivity specifically in the case of schizophrenia. Our study evaluates which connectome features derived from resting state functional MRI - functional connectivity, gradients, or gradient dispersion - best identify schizophrenia. To this end, we leveraged data of 936 individuals from three large open-access datasets: COBRE, LA5c, and SRPBS-1600. We developed a pipeline which allows us to aggregate over a million different features and assess their predictive potential in a single, computationally efficient experiment. We selected top 1% of features with the largest permutation feature importance and trained 13 classifiers on them using 10-fold cross-validation. Our findings indicate that functional connectivity outperforms its low-dimensional derivatives such as cortical gradients and gradient dispersion in identifying schizophrenia (Mann-Whitney test conducted on test accuracy: connectivity vs. 1st gradient: U = 142, p < 0.003; connectivity vs. neighborhood dispersion: U = 141, p = 0.004). Additionally, we demonstrated that the edges which contribute the most to classification performance are the ones connecting primary sensory regions. Functional connectivity within the primary sensory regions showed the highest discrimination capabilities between subjects with schizophrenia and neurotypical controls. These findings along with the feature selection pipeline proposed here will facilitate future inquiries into the prediction of schizophrenia subtypes and transdiagnostic phenomena.

A Survey on AI-Driven Mouse Behavior Analysis Applications and Solutions

The physiological similarities between mice and humans make them vital animal models in biological and medical research. This paper explores the application of artificial intelligence (AI) in analyzing mice behavior, emphasizing AI's potential to identify and classify these behaviors. Traditional methods struggle to capture subtle behavioral features, whereas AI can automatically extract quantitative features from large datasets. Consequently, this study aims to leverage AI to enhance the efficiency and accuracy of mice behavior analysis. The paper reviews various applications of mice behavior analysis, categorizes deep learning tasks based on an AI pyramid, and summarizes AI methods for addressing these tasks. The findings indicate that AI technologies are increasingly applied in mice behavior analysis, including disease detection, assessment of external stimuli effects, social behavior analysis, and neurobehavioral assessment. The selection of AI methods is crucial and must align with specific applications. Despite AI's promising potential in mice behavior analysis, challenges such as insufficient datasets and benchmarks remain. Furthermore, there is a need for a more integrated AI platform, along with standardized datasets and benchmarks, to support these analyses and further advance AI-driven mice behavior analysis.

Epilepsy-linked kinase CDKL5 phosphorylates voltage-gated calcium channel Cav2.3, altering inactivation kinetics and neuronal excitability

Developmental and epileptic encephalopathies (DEEs) are a group of rare childhood disorders characterized by severe epilepsy and cognitive deficits. Numerous DEE genes have been discovered thanks to advances in genomic diagnosis, yet putative molecular links between these disorders are unknown. CDKL5 deficiency disorder (CDD, DEE2), one of the most common genetic epilepsies, is caused by loss-of-function mutations in the brain-enriched kinase CDKL5. To elucidate CDKL5 function, we looked for CDKL5 substrates using a SILAC-based phosphoproteomic screen. We identified the voltage-gated Ca2+ channel Cav2.3 (encoded by CACNA1E) as a physiological target of CDKL5 in mice and humans. Recombinant channel electrophysiology and interdisciplinary characterization of Cav2.3 phosphomutant mice revealed that loss of Cav2.3 phosphorylation leads to channel gain-of-function via slower inactivation and enhanced cholinergic stimulation, resulting in increased neuronal excitability. Our results thus show that CDD is partly a channelopathy. The properties of unphosphorylated Cav2.3 closely resemble those described for CACNA1E gain-of-function mutations causing DEE69, a disorder sharing clinical features with CDD. We show that these two single-gene diseases are mechanistically related and could be ameliorated with Cav2.3 inhibitors.

Voluntary Running Improves Behavioral and Structural Abnormalities in a Mouse Model of CDKL5 Deficiency Disorder

Cyclin-dependent kinase-like 5 (CDKL5) deficiency disorder (CDD) is a rare neurodevelopmental disease caused by mutations in the X-linked CDKL5 gene. CDD is characterized by a broad spectrum of clinical manifestations, including early-onset refractory epileptic seizures, intellectual disability, hypotonia, visual disturbances, and autism-like features. The Cdkl5 knockout (KO) mouse recapitulates several features of CDD, including autistic-like behavior, impaired learning and memory, and motor stereotypies. These behavioral alterations are accompanied by diminished neuronal maturation and survival, reduced dendritic branching and spine maturation, and marked microglia activation. There is currently no cure or effective treatment to ameliorate the symptoms of the disease. Aerobic exercise is known to exert multiple beneficial effects in the brain, not only by increasing neurogenesis, but also by improving motor and cognitive tasks. To date, no studies have analyzed the effect of physical exercise on the phenotype of a CDD mouse model. In view of the positive effects of voluntary running on the brain of mouse models of various human neurodevelopmental disorders, we sought to determine whether voluntary daily running, sustained over a month, could improve brain development and behavioral defects in Cdkl5 KO mice. Our study showed that long-term voluntary running improved the hyperlocomotion and impulsivity behaviors and memory performance of Cdkl5 KO mice. This is correlated with increased hippocampal neurogenesis, neuronal survival, spine maturation, and inhibition of microglia activation. These behavioral and structural improvements were associated with increased BDNF levels. Given the positive effects of BDNF on brain development and function, the present findings support the positive benefits of exercise as an adjuvant therapy for CDD.

From pupil to the brain: New insights for studying cortical plasticity through pupillometry

Pupil size variations have been associated with changes in brain activity patterns related with specific cognitive factors, such as arousal, attention, and mental effort. The locus coeruleus (LC), a key hub in the noradrenergic system of the brain, is considered to be a key regulator of cognitive control on pupil size, with changes in pupil diameter corresponding to the release of norepinephrine (NE). Advances in eye-tracking technology and open-source software have facilitated accurate pupil size measurement in various experimental settings, leading to increased interest in using pupillometry to track the nervous system activation state and as a potential biomarker for brain disorders. This review explores pupillometry as a non-invasive and fully translational tool for studying cortical plasticity starting from recent literature suggesting that pupillometry could be a promising technique for estimating the degree of residual plasticity in human subjects. Given that NE is known to be a critical mediator of cortical plasticity and arousal, the review includes data revealing the importance of the LC-NE system in modulating brain plasticity and pupil size. Finally, we will review data suggesting that pupillometry could provide a quantitative and complementary measure of cortical plasticity also in pre-clinical studies.