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

Abstract

Impaired 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 these 24 mutations affect 24 different genes. We applied unsupervised clustering algorithms to unbiasedly classify the recovered mutants into three classes. Further computational analysis revealed that each of the recovered mutations in these three classes has a unique phenotype on node-subtype composition and distribution within the intrahepatic biliary network. In addition, we found most of the recovered mutations are viable. In those mutant fish, which are already good animal models to study chronic cholestatic liver diseases, the biliary network phenotypes persist into adulthood. 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.

Keywords: Biliary atresia; Branching morphogenesis; Cholestatic liver disease; Forward genetics; Intrahepatic biliary network; Machine learning.