Standardizing the analysis of conditioned fear in rodents: a multidimensional software approach

Data comparability between different laboratories strongly depends on the individually applied analysis method. This factor is often a critical source of variation in rodent phenotyping and has never been systematically investigated in Pavlovian fear conditioning paradigms. In rodents, fear is typically quantified in terms of freezing duration via manual observation or automated systems. While manual analysis includes biases such as tiredness or inter‐personal scoring variability, computer‐assisted systems are unable to distinguish between freezing and immobility. Consequently, the novel software called MOVE follows a semi‐automatized approach that prefilters video sequences of interest for the final human judgment. Furthermore, MOVE allows integrating additional data sources (e.g. force‐sensitive platform, EEG) to reach the most accurate and precise results. MOVE directly supports multi‐angle video recordings with webcams or standard laboratory equipment. The integrated manual key logger and internal video player complement this all‐in‐one software solution. Calculating the interlaboratory variability of manual freezing evaluation revealed significantly different freezing scores in two out of six laboratories. This difference was minimized when all experiments were analyzed with MOVE. Applied to a genetically modified mouse model, MOVE revealed higher fear responses of CB1 deficient mice compared to their wild‐type littermates after foreground context fear conditioning. Multi‐angle video analysis compared to the single‐camera approach reached up to 15% higher accuracy and two fold higher precision. Multidimensional analysis provided by integration of additional data sources further improved the overall result. We conclude that the widespread usage of MOVE could substantially improve the comparability of results from different laboratories.     

Analysis of POFUT1 Gene Mutation in a Chinese Family with Dowling-Degos Disease

Dowling-Degos disease (DDD) is an autosomal dominant genodermatosis characterized by reticular pigmented anomaly mainly affecting flexures. Though KRT5 has been identified to be the causal gene of DDD, the heterogeneity of this disease was displayed: for example, POFUT1 and POGLUT1 were recently identified and confirmed to be additional pathogenic genes of DDD. To identify other DDD causative genes, we performed genome-wide linkage and exome sequencing analyses in a multiplex Chinese DDD family, in which the KRT5 mutation was absent. Only a novel 1-bp deletion (c.246+5delG) in POFUT1 was found. No other novel mutation or this deletion was detected in POFUT1 in a second DDD family and a sporadic DDD case by Sanger Sequencing. The result shows the genetic-heterogeneity and complexity of DDD and will contribute to the further understanding of DDD genotype/phenotype correlations and to the pathogenesis of this disease.     

Targeted next-generation sequencing to diagnose disorders of HDL cholesterol

A low level of HDL cholesterol (HDL-C) is a common clinical scenario and an important marker for increased cardiovascular risk. Many patients with very low or very high HDL-C have a rare mutation in one of several genes, but identification of the molecular abnormality in patients with extreme HDL-C is rarely performed in clinical practice. We investigated the accuracy and diagnostic yield of a targeted next-generation sequencing (NGS) assay for extreme levels of HDL-C. We developed a targeted NGS panel to capture the exons, intron/exon boundaries, and untranslated regions of 26 genes with highly penetrant effects on plasma lipid levels. We sequenced 141 patients with extreme HDL-C levels and prioritized variants in accordance with medical genetics guidelines. We identified 35 pathogenic and probably pathogenic variants in HDL genes, including 21 novel variants, and performed functional validation on a subset of these. Overall, a molecular diagnosis was established in 35.9% of patients with low HDL-C and 5.2% with high HDL-C, and all prioritized variants identified by NGS were confirmed by Sanger sequencing. Our results suggest that a molecular diagnosis can be identified in a substantial proportion of patients with low HDL-C using targeted NGS.