Forward genetics combined with unsupervised classifications identified zebrafish mutants affecting biliary system formation

Abstract

AbstractImpaired formation of the biliary network can lead to congenital cholestatic liver diseases; however, the genes responsible for proper biliary system formation and maintenance have not been fully identified. Combining computational network structure analysis algorithms with a zebrafish forward genetic screen, we identified 24 new zebrafish mutants that display impaired intrahepatic biliary network formation. Complementation tests suggested that these 24 mutants affect 24 different genes. We applied unsupervised clustering algorithms to classify the recovered mutants into three classes unbiasedly. Further computational analyses revealed that each of the recovered mutations in these three classes shows a unique effect on node subtype composition and connection property distribution of the intrahepatic biliary network. Besides, we found that most recovered mutations are viable. In those mutant fish, biliary network phenotypes persist into adulthood, which themselves are good animal models to study chronic cholestatic liver diseases. Altogether, this study provides unique genetic and computational toolsets that advance our understanding of the molecular pathways leading to biliary system malformation and cholestatic liver diseases.