Cross-species analysis uncovers the mitochondrial stress response in the hippocampus as a shared mechanism in mouse early life stress and human depression.

Depression, or major depressive disorder, poses a significant burden for both individuals and society, affecting approximately 10.8% of the general population. This psychiatric disorder leads to approximately 800,000 deaths per year. A combination of genetic and environmental factors such as early life stress (ELS) increase the risk for development of depression in humans, and a clear role for the hippocampus in the pathophysiology of depression has been shown. Nevertheless, the underlying mechanisms of depression remain poorly understood, resulting in a lack of effective treatments. To better understand the core mechanisms underlying the development of depression, we used a cross-species design to investigate shared hippocampal pathophysiological mechanisms in mouse ELS and human depression. Mice were subjected to ELS by a maternal separation paradigm, followed by RNA sequencing analysis of the adult hippocampal tissue. This identified persistent transcriptional changes linked to mitochondrial stress response pathways, with oxidative phosphorylation and protein folding emerging as the main mechanisms affected by maternal separation. Remarkably, there was a significant overlap between the pathways involved in mitochondrial stress response we observed and publicly available RNAseq data from hippocampal tissue of depressive patients. This cross-species conservation of changes in gene expression of mitochondria-related genes suggests that mitochondrial stress may play a pivotal role in the development of depression. Our findings highlight the potential significance of the hippocampal mitochondrial stress response as a core mechanism underlying the development of depression. Further experimental investigations are required to expand our understanding of these mechanisms.

EEG-based visual deviance detection in freely behaving mice.

The mouse is widely used as an experimental model to study visual processing. To probe how the visual system detects changes in the environment, functional paradigms in freely behaving mice are strongly needed. We developed and validated the first EEG-based method to investigate visual deviance detection in freely behaving mice. Mice with EEG implants were exposed to a visual deviant detection paradigm that involved changes in light intensity as standard and deviant stimuli. By subtracting the standard from the deviant evoked waveform, deviant detection was evident as bi-phasic negativity (starting around 70 ms) in the difference waveform. Additionally, deviance-associated evoked (beta/gamma) and induced (gamma) oscillatory responses were found. We showed that the results were stimulus-independent by applying a "flip-flop" design and the results showed good repeatability in an independent measurement. Together, we put forward a validated, easy-to-use paradigm to measure visual deviance processing in freely behaving mice.

Introduction to the EQIPD quality system.

While high risk of failure is an inherent part of developing innovative therapies, it can be reduced by adherence to evidence-based rigorous research practices. Supported through the European Union's Innovative Medicines Initiative, the EQIPD consortium has developed a novel preclinical research quality system that can be applied in both public and private sectors and is free for anyone to use. The EQIPD Quality System was designed to be suited to boost innovation by ensuring the generation of robust and reliable preclinical data while being lean, effective and not becoming a burden that could negatively impact the freedom to explore scientific questions. EQIPD defines research quality as the extent to which research data are fit for their intended use. Fitness, in this context, is defined by the stakeholders, who are the scientists directly involved in the research, but also their funders, sponsors, publishers, research tool manufacturers, and collaboration partners such as peers in a multi-site research project. The essence of the EQIPD Quality System is the set of 18 core requirements that can be addressed flexibly, according to user-specific needs and following a user-defined trajectory. The EQIPD Quality System proposes guidance on expectations for quality-related measures, defines criteria for adequate processes (i.e. performance standards) and provides examples of how such measures can be developed and implemented. However, it does not prescribe any pre-determined solutions. EQIPD has also developed tools (for optional use) to support users in implementing the system and assessment services for those research units that successfully implement the quality system and seek formal accreditation. Building upon the feedback from users and continuous improvement, a sustainable EQIPD Quality System will ultimately serve the entire community of scientists conducting non-regulated preclinical research, by helping them generate reliable data that are fit for their intended use.

Behavioral signatures related to genetic disorders in autism.

BACKGROUND: Autism spectrum disorder (ASD) is well recognized to be genetically heterogeneous. It is assumed that the genetic risk factors give rise to a broad spectrum of indistinguishable behavioral presentations. METHODS: We tested this assumption by analyzing the Autism Diagnostic Interview-Revised (ADI-R) symptom profiles in samples comprising six genetic disorders that carry an increased risk for ASD (22q11.2 deletion, Down's syndrome, Prader-Willi, supernumerary marker chromosome 15, tuberous sclerosis complex and Klinefelter syndrome; total n = 322 cases, groups ranging in sample sizes from 21 to 90 cases). We mined the data to test the existence and specificity of ADI-R profiles using a multiclass extension of support vector machine (SVM) learning. We subsequently applied the SVM genetic disorder algorithm on idiopathic ASD profiles from the Autism Genetics Resource Exchange (AGRE). RESULTS: Genetic disorders were associated with behavioral specificity, indicated by the accuracy and certainty of SVM predictions; one-by-one genetic disorder stratifications were highly accurate leading to 63% accuracy of correct genotype prediction when all six genetic disorder groups were analyzed simultaneously. Application of the SVM algorithm to AGRE cases indicated that the algorithm could detect similarity of genetic behavioral signatures in idiopathic ASD subjects. Also, affected sib pairs in the AGRE were behaviorally more similar when they had been allocated to the same genetic disorder group. CONCLUSIONS: Our findings provide evidence for genotype-phenotype correlations in relation to autistic symptomatology. SVM algorithms may be used to stratify idiopathic cases of ASD according to behavioral signature patterns associated with genetic disorders. Together, the results suggest a new approach for disentangling the heterogeneity of ASD.