Mouse Model of Alagille Syndrome and Mechanisms of Jagged1 Missense Mutations.

BACKGROUND & AIMS: Alagille syndrome is a genetic disorder characterized by cholestasis, ocular abnormalities, characteristic facial features, heart defects, and vertebral malformations. Most cases are associated with mutations in JAGGED1 (JAG1), which encodes a Notch ligand, although it is not clear how these contribute to disease development. We aimed to develop a mouse model of Alagille syndrome to elucidate these mechanisms. METHODS: Mice with a missense mutation (H268Q) in Jag1 (Jag1+/Ndr mice) were outbred to a C3H/C57bl6 background to generate a mouse model for Alagille syndrome (Jag1Ndr/Ndr mice). Liver tissues were collected at different timepoints during development, analyzed by histology, and liver organoids were cultured and analyzed. We performed transcriptome analysis of Jag1Ndr/Ndr livers and livers from patients with Alagille syndrome, cross-referenced to the Human Protein Atlas, to identify commonly dysregulated pathways and biliary markers. We used species-specific transcriptome separation and ligand-receptor interaction assays to measure Notch signaling and the ability of JAG1Ndr to bind or activate Notch receptors. We studied signaling of JAG1 and JAG1Ndr via NOTCH 1, NOTCH2, and NOTCH3 and resulting gene expression patterns in parental and NOTCH1-expressing C2C12 cell lines. RESULTS: Jag1Ndr/Ndr mice had many features of Alagille syndrome, including eye, heart, and liver defects. Bile duct differentiation, morphogenesis, and function were dysregulated in newborn Jag1Ndr/Ndr mice, with aberrations in cholangiocyte polarity, but these defects improved in adult mice. Jag1Ndr/Ndr liver organoids collapsed in culture, indicating structural instability. Whole-transcriptome sequence analyses of liver tissues from mice and patients with Alagille syndrome identified dysregulated genes encoding proteins enriched at the apical side of cholangiocytes, including CFTR and SLC5A1, as well as reduced expression of IGF1. Exposure of Notch-expressing cells to JAG1Ndr, compared with JAG1, led to hypomorphic Notch signaling, based on transcriptome analysis. JAG1-expressing cells, but not JAG1Ndr-expressing cells, bound soluble Notch1 extracellular domain, quantified by flow cytometry. However, JAG1 and JAG1Ndr cells each bound NOTCH2, and signaling from NOTCH2 signaling was reduced but not completely inhibited, in response to JAG1Ndr compared with JAG1. CONCLUSIONS: In mice, expression of a missense mutant of Jag1 (Jag1Ndr) disrupts bile duct development and recapitulates Alagille syndrome phenotypes in heart, eye, and craniofacial dysmorphology. JAG1Ndr does not bind NOTCH1, but binds NOTCH2, and elicits hypomorphic signaling. This mouse model can be used to study other features of Alagille syndrome and organ development

Maatschappelijke kosten voor astma, COPD en respiratoire allergie.

Volgens schattingen van het Rijksinstituut voor Volksgezondheid en Milieu (RIVM) stijgt het aantal mensen in Nederland met astma en COPD de komende 25 jaar sterk, met respectievelijk 28% en 70%. Dit komt vooral door de bevolkingsgroei en de vergrijzing. Het aantal patiënten met respiratoire allergie (zoals hooikoorts) blijft in deze periode ongeveer gelijk. Deze aandoening komt namelijk bij ouderen minder voor. De verwachting is dat de totale medische kosten voor alle drie de aandoeningen over 25 jaar (fors) zijn gestegen. Voor respiratoire allergie zal dat met 73% zijn; voor astma stijgen de kosten met 150%, voor COPD met 220%. Deze percentages zijn inclusief de jaarlijkse stijging van zorguitgaven door onder andere technologische veranderingen (zoals nieuwe medicijnen) en prijsstijgingen, volgens de trendanalyse van het Centraal Planbureau. Het RIVM heeft deze schattingen gemaakt op verzoek van het Longfonds. De cijfers zijn gebaseerd op nieuwe analyses van de kosten in 2007. Voor astma bedroegen de medische kosten in totaal 287 miljoen euro, gemiddeld 530 euro per patiënt. Dit bedrag bestaat voor bijna driekwart uit kosten voor medicijnen. Bij werknemers komt daar nog gemiddeld 1200 euro per persoon per jaar bovenop vanwege extra ziekteverzuim door astma. Van hen verzuimen werknemers die ouder zijn dan 55 jaar het meest. De medische kosten voor COPD in Nederland in 2007 waren 415 miljoen euro, gemiddeld 1400 euro per patiënt. Hierbij waren geneesmiddelen, ziekenhuisopnames en langdurige zorg (zoals thuiszorg en in verzorgingshuizen) de belangrijke kostenposten. Kosten van arbeidsongeschiktheid waren voor werkenden met COPD gemiddeld 1200 euro per persoon. Voor ziekteverzuim waren deze gemiddeld 1900 euro per werkende met COPD. Deze kosten overtreffen veruit de kosten van het zorggebruik voor COPD. De medische kosten voor respiratoire allergie waren 102 miljoen euro, gemiddeld 170 euro per patiënt. Medicatiekosten vormden hierin het grootste deel, 90%. Er waren te weinig data om de ziekteverzuimkosten betrouwbaar te schatten. De gepresenteerde cijfers over de verwachte stijging van het aantal mensen met deze drie aandoeningen en de kosten die hiermee gemoeid zijn, leveren belangrijke informatie op voor het beleid. Preventie en behandeling zijn daarbij onverminderd belangrijk, zoals stoppen met roken en doelmatiger gebruik van geneesmiddelen. Aangezien er steeds meer oudere patiënten met astma en COPD komen, is specifieke ondersteuning ook voor hen van belang

Modeling ZNF408-Associated FEVR in Zebrafish Results in Abnormal Retinal Vasculature

PURPOSE. Familial exudative vitreoretinopathy (FEVR) is an inherited retinal disease in which the retinal vasculature is affected. Patients with FEVR typically lack or have abnormal vasculature in the peripheral retina, the outcome of which can range from mild visual impairment to complete blindness. A missense mutation (p.His455Tyr) in ZNF408 was identified in an autosomal dominant FEVR family. Little, however, is known about the molecular role of ZNF408 and how its defect leads to the clinical features of FEVR. METHODS. Using CRISPR/Cas9 technology, two homozygous mutant zebrafish models with truncated znf408 were generated, as well as one heterozygous and one homozygous missense znf408 model in which the human p.His455Tyr mutation is mimicked. RESULTS. Intriguingly, all three znf408-mutant zebrafish strains demonstrated progressive retinal vascular pathology, initially characterized by a deficient hyaloid vessel development at 5 days postfertilization (dpf) leading to vascular insufficiency in the retina. The generation of stable mutant lines allowed long-term follow up studies, which showed ectopic retinal vascular hyper-sprouting at 90 dpf and extensive vascular leakage at 180 dpf. CONCLUSIONS. Together, our data demonstrate an important role for znf408 in the development and maintenance of the vascular system within the retina.Funding Agencies|Radboudumc PhD grant; Svenska Sallskapet for Medicinsk Forskning; Linkoping University; Loo och Hans Ostermans Stiftelse; Eva och Oscar Ahrens Stiftelse; Stiftelsen Sigurd och Elsa Goljes Minne; Magnus Bergvalls Stiftelse; Ogonfonden; Jeanssons Stiftelser; VetenskapsradetSwedish Research Council</p

Machine learning to improve false-positive results in the Dutch newborn screening for congenital hypothyroidism

Objective: The Dutch Congenital hypothyroidism (CH) Newborn Screening (NBS) algorithm for thyroidal and central congenital hypothyroidism (CH-T and CH-C, respectively) is primarily based on determination of thyroxine (T4) concentrations in dried blood spots, followed by thyroid-stimulating hormone (TSH) and thyroxine-binding globulin (TBG) measurements enabling detection of both CH-T and CH-C, with a positive predictive value (PPV) of 21%. A calculated T4/TBG ratio serves as an indirect measure for free T4. The aim of this study is to investigate whether machine learning techniques can help to improve the PPV of the algorithm without missing the positive cases that should have been detected with the current algorithm. Design & methods: NBS data and parameters of CH patients and false-positive referrals in the period 2007–2017 and of a healthy reference population were included in the study. A random forest model was trained and tested using a stratified split and improved using synthetic minority oversampling technique (SMOTE). NBS data of 4668 newborns were included, containing 458 CH-T and 82 CH-C patients, 2332 false-positive referrals and 1670 healthy newborns. Results: Variables determining identification of CH were (in order of importance) TSH, T4/TBG ratio, gestational age, TBG, T4 and age at NBS sampling. In a Receiver-Operating Characteristic (ROC) analysis on the test set, current sensitivity could be maintained, while increasing the PPV to 26%. Conclusions: Machine learning techniques have the potential to improve the PPV of the Dutch CH NBS. However, improved detection of currently missed cases is only possible with new, better predictors of especially CH-C and a better registration and inclusion of these cases in future models

Machine learning to improve false-positive results in the Dutch newborn screening for congenital hypothyroidism

Objective: The Dutch Congenital hypothyroidism (CH) Newborn Screening (NBS) algorithm for thyroidal and central congenital hypothyroidism (CH-T and CH-C, respectively) is primarily based on determination of thyroxine (T4) concentrations in dried blood spots, followed by thyroid-stimulating hormone (TSH) and thyroxine-binding globulin (TBG) measurements enabling detection of both CH-T and CH-C, with a positive predictive value (PPV) of 21%. A calculated T4/TBG ratio serves as an indirect measure for free T4. The aim of this study is to investigate whether machine learning techniques can help to improve the PPV of the algorithm without missing the positive cases that should have been detected with the current algorithm. Design & methods: NBS data and parameters of CH patients and false-positive referrals in the period 2007–2017 and of a healthy reference population were included in the study. A random forest model was trained and tested using a stratified split and improved using synthetic minority oversampling technique (SMOTE). NBS data of 4668 newborns were included, containing 458 CH-T and 82 CH-C patients, 2332 false-positive referrals and 1670 healthy newborns. Results: Variables determining identification of CH were (in order of importance) TSH, T4/TBG ratio, gestational age, TBG, T4 and age at NBS sampling. In a Receiver-Operating Characteristic (ROC) analysis on the test set, current sensitivity could be maintained, while increasing the PPV to 26%. Conclusions: Machine learning techniques have the potential to improve the PPV of the Dutch CH NBS. However, improved detection of currently missed cases is only possible with new, better predictors of especially CH-C and a better registration and inclusion of these cases in future models