Cognitive behavioral phenotyping of DSCAM heterozygosity as a model for autism spectrum disorder.

It is estimated that 1 in 36 children are affected by autism spectrum disorder (ASD) in the United States, which is nearly a twofold increase from a decade ago. Recent genetic studies have identified de novo loss-of-function (dnLoF) mutations in the Down Syndrome Cell Adhesion Molecule (DSCAM) as a strong risk factor for ASD. Previous research has shown that DSCAM ablation confers social interaction deficits and perseverative behaviors in mouse models. However, it remains unknown to what extent DSCAM underexpression captures the full range of behaviors, specifically cognitive phenotypes, presented in ASD. Here, we conducted a comprehensive cognitive behavioral phenotyping which revealed that loss of one copy of DSCAM , as in the DSCAM 2J +/- mice, displayed hyperactivity, increased anxiety, and motor coordination impairments. Additionally, hippocampal-dependent learning and memory was affected, including working memory, long-term memory, and contextual fear learning. Interestingly, implicit learning processes remained intact. Therefore, DSCAM LoF produces autistic-like behaviors that are similar to human cases of ASD. These findings further support a role for DSCAM dnLoF mutations in ASD and suggest DSCAM 2J +/- as a suitable model for ASD research. Summary Statement: Autism spectrum disorder represents a growing patient population. Loss of one copy of the DSCAM gene provides a promising mouse model that reproduces autistic-like behaviors for research and therapeutic testing.

Human-Induced Pluripotent Stem Cell (iPSC)-Derived GABAergic Neuron Differentiation in Bipolar Disorder.

Bipolar disorder (BP) is a recurring psychiatric condition characterized by alternating episodes of low energy (depressions) followed by manias (high energy). Cortical network activity produced by GABAergic interneurons may be critical in maintaining the balance in excitatory/inhibitory activity in the brain during development. Initially, GABAergic signaling is excitatory; with maturation, these cells undergo a functional switch that converts GABAA channels from depolarizing (excitatory) to hyperpolarizing (inhibitory), which is controlled by the intracellular concentration of two chloride transporters. The earliest, NKCC1, promotes chloride entry into the cell and depolarization, while the second (KCC2) stimulates movement of chloride from the neuron, hyperpolarizing it. Perturbations in the timing or expression of NKCC1/KCC2 may affect essential morphogenetic events including cell proliferation, migration, synaptogenesis and plasticity, and thereby the structure and function of the cortex. We derived induced pluripotent stem cells (iPSC) from BP patients and undiagnosed control (C) individuals, then modified a differentiation protocol to form GABAergic interneurons, harvesting cells at sequential stages of differentiation. qRT-PCR and RNA sequencing indicated that after six weeks of differentiation, controls transiently expressed high levels of NKCC1. Using multi-electrode array (MEA) analysis, we observed that BP neurons exhibit increased firing, network bursting and decreased synchrony compared to C. Understanding GABA signaling in differentiation may identify novel approaches and new targets for treatment of neuropsychiatric disorders such as BP.

Biomarker evidence of early vision and rod energy-linked pathophysiology benefits from very low dose DMSO in 5xFAD mice.

Here, we test whether early visual and OCT rod energy-linked biomarkers indicating pathophysiology in nicotinamide nucleotide transhydrogenase (Nnt)-null 5xFAD mice also occur in Nnt-intact 5xFAD mice and whether these biomarkers can be pharmacologically treated. Four-month-old wild-type or 5xFAD C57BL/6 substrains with either a null (B6J) Nnt or intact Nnt gene (B6NTac) and 5xFAD B6J mice treated for one month with either R-carvedilol + vehicle or only vehicle (0.01% DMSO) were studied. The contrast sensitivity (CS), external limiting membrane-retinal pigment epithelium (ELM-RPE) thickness (a proxy for low pH-triggered water removal), profile shape of the hyperreflective band just posterior to the ELM (i.e., the mitochondrial configuration within photoreceptors per aspect ratio [MCP/AR]), and retinal laminar thickness were measured. Both wild-type substrains showed similar visual performance indices and dark-evoked ELM-RPE contraction. The lack of a light-dark change in B6NTac MCP/AR, unlike in B6J mice, is consistent with relatively greater mitochondrial efficiency. 5xFAD B6J mice, but not 5xFAD B6NTac mice, showed lower-than-WT CS. Light-adapted 5xFAD substrains both showed abnormal ELM-RPE contraction and greater-than-WT MCP/AR contraction. The inner retina and superior outer retina were thinner. Treating 5xFAD B6J mice with R-carvedilol + DMSO or DMSO alone corrected CS and ELM-RPE contraction but not supernormal MCP/AR contraction or laminar thinning. These results provide biomarker evidence for prodromal photoreceptor mitochondrial dysfunction/oxidative stress/oxidative damage, which is unrelated to visual performance, as well as the presence of the Nnt gene. This pathophysiology is druggable in 5xFAD mice.

Quantifying social roles in multi-animal videos using subject-aware deep-learning.

Analyzing social behaviors is critical for many fields, including neuroscience, psychology, and ecology. While computational tools have been developed to analyze videos containing animals engaging in limited social interactions under specific experimental conditions, automated identification of the social roles of freely moving individuals in a multi-animal group remains unresolved. Here we describe a deep-learning-based system - named LabGym2 - for identifying and quantifying social roles in multi-animal groups. This system uses a subject-aware approach: it evaluates the behavioral state of every individual in a group of two or more animals while factoring in its social and environmental surroundings. We demonstrate the performance of subject-aware deep-learning in different species and assays, from partner preference in freely-moving insects to primate social interactions in the field. Our subject-aware deep learning approach provides a controllable, interpretable, and efficient framework to enable new experimental paradigms and systematic evaluation of interactive behavior in individuals identified within a group.