Supervised machine learning of outbred mouse genotypes to predict hepatic immunological tolerance of individuals

Abstract

Abstract It is essential to elucidate the molecular mechanisms underlying liver transplant tolerance and rejection. In cases of mouse liver transplantation between inbred strains, immunological rejection of the allograft is reduced with spontaneous apoptosis without immunosuppressive drugs, which differs from the actual clinical result. This may be because inbred strains are genetically homogeneous and less heterogeneous than others. We exploited outbred CD1 mice, which show highly heterogeneous genotypes among individuals, to search for biomarkers related to immune responses and to construct a model for predicting the outcome of liver allografting. Of the 36 mice examined, 18 died within 3 weeks after transplantation, while the others survived for more than 6 weeks. Whole-exome sequencing of the 36 donors revealed more than 9 million variants relative to the C57BL/6 J reference. We selected 6517 single-nucleotide and indel variants and performed machine learning to determine whether or not we could predict the prognosis of each genotype. Models were built by both deep learning with a one-dimensional convolutional neural network and linear classification and evaluated by leave-one-out cross-validation. Given that one short-lived mouse died early in an accident, the models perfectly predicted the outcome of all individuals, suggesting the importance of genotype collection. In addition, linear classification models provided a list of loci potentially responsible for these responses. The present methods as well as results is likely to be applicable to liver transplantation in humans.

Keywords

• Transplantation tolerance
• Supervised machine learning
• Liver transplantation
• Outbred mouse