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van Steenbeek, F.

Publications and source records attributed to van Steenbeek, F..

3 recordsLinked to original sources

Reproducibility of the evaluation of genetic variant pathogenicity based on the animal variant classification guidelines

Until recently, due to the absence of standardized guidelines tailored for veterinary use, the evaluation of genetic variant pathogenicity for single-gene diseases was based on a personal interpretation of the presented evidence, which has led to ambiguous interpretation. With the publication of the animal variant classification guidelines (AVCG), a more objective approach became available. Variants are evaluated based on twenty-three criteria and labeled as pathogenic, likely pathogenic, variant of uncertain significance, likely benign or benign. While the accuracy was thoroughly tested in the original publication, the reproducibility of the various steps involved was only briefly checked, which is why the current study was performed. Each variant from a set of 150 published likely causal variants for single-gene diseases from three species (dog, cat, horse) was independently and blindly assessed by three different reviewers, each applying the same AVCG. An overall agreement of 93% for decisions on the scope, i.e. whether they fit the inclusion criteria to allow evaluation with AVCG, was found. More importantly, the reproducibility was 65% for the pathogenicity classification and this increased to 83% clinically relevant agreement. While a direct comparison of the reproducibility with human literature is not possible for the scope, the reproducibility on pathogenicity classification is in line with reports using the human American College for Medical Genetics and Genomics and Association for Molecular Pathology guidelines for human variants. Overall, based on the current study, the reproducibility of the guidelines in veterinary species is within current expectations.

genetics↗

Integrated mesenchymal and extracellular cues drive bioengineered liver tissue formation and function

Human liver tissue engineering holds promise for creating physiological in vitro models but faces challenges replicating liver complexity. In the present study, we created bioengineered liver tissues (BLTs) utilizing three different cell types; human intrahepatic cholangiocyte organoids (ICOs), hepatic stellate cells (HSCs), and mesenchymal stromal cells (MSCs). Co-culturing with HSCs and MSCs accelerated growth and spontaneous fusion, resulting in complex liver-like tissue structures. In a dynamic suspension culture, BLTs had a more compact morphology and higher expression of hepatic markers, including ALB, CYP3A4, and MRP2. We further showed that animal-derived Matrigel can be replaced by a synthetic polyisocyanide (PIC)-based hydrogel for BLTs. Importantly, PIC-based hydrogel further promoted the maturation of BLTs assessed by parameters as intracellular protein levels, morphological analysis, and metabolic activity. Transcriptomic analyses revealed mechanisms underlying tissue formation and function. To conclude, our strategy yields functional liver tissues suitable for disease modelling, drug screening, and toxicity tests, and forms an important basis for future development of larger liver tissues for in vivo transplantation.

bioengineering↗

Variant classification guidelines for animals to objectively evaluate genetic variant pathogenicity

Assessing the pathogenicity of a disease-associated variant in animals accurately is vital, both on a population and individual scale. At the population level, breeding decisions based on invalid DNA tests can lead to the incorrect exclusion of animals and compromise the long- term health of a population, and at the level of the individual animal, lead to incorrect treatment and even life-ending decisions. Criteria to determine pathogenicity are not standardized, hence no guidelines for animal variants are available. Here, we developed and optimized the animal variant classification guidelines, based on those developed for humans by The American College of Medical Genetics and Genomics, and demonstrated a superior classification in animals. We described methods to develop datasets for benchmarking the criteria and identified the most optimal in silico variant effect predictor tools. As the reproducibility was high, we classified 72 known disease-associated variants in cats and 40 other disease-associated variants in eight additional species.

genetics↗