Comprehensive gene profiling of the metabolic landscape of humanized livers in mice

Abstract

The human liver transcriptome is complex and highly dynamic, e.g. one gene may produce multiple distinct transcripts, each with distinct posttranscriptional modifications. Direct knowledge of transcriptome dynamics, however, is largely obscured by the inaccessibility of the human liver to treatments and the insufficient annotation of the human liver transcriptome at transcript and RNA modification levels. We generated mice that carry humanized livers of identical genetic background and subjected them to representative metabolic treatments. We then analyzed the humanized livers with nanopore single-molecule direct RNA sequencing to determine the expression level, m6A modification and poly(A) tail length of all RNA transcript isoforms. Our system allows for the de novo annotation of human liver transcriptomes to reflect metabolic responses and for the study of transcriptome dynamics in parallel. Our analysis uncovered a vast number of novel genes and transcripts. Our transcript-level analysis of human liver transcriptomes also identified a multitude of regulated metabolic pathways that were otherwise invisible using conventional short-read RNA sequencing. We revealed for the first time the dynamic changes in m6A and poly(A) tail length of human liver transcripts, many of which are transcribed from key metabolic genes. Furthermore, we performed comparative analyses of gene regulation between humans and mice, and between two individuals using the liver-specific humanized mice, revealing that transcriptome dynamics are highly species- and genetic background-dependent. Our work revealed a complex metabolic response landscape of the human liver transcriptome and provides a novel resource to understand transcriptome dynamics of the human liver in response to physiologically relevant metabolic stimuli (https://caolab.shinyapps.io/human_hepatocyte_landscape/). Direct knowledge of the human liver transcriptome is currently very limited, hindering the overall understanding of human liver pathophysiology. We combined a liver-specific humanized mouse model and long-read direct RNA sequencing technology to establish a de novo annotation of the human liver transcriptome and identified a multitude of regulated metabolic pathways that were otherwise invisible using conventional technologies. The extensive regulatory information on human genes we provided could enable basic scientists to infer the pathological relevance of their genes of interest and physician scientists to better pinpoint the changes in metabolic networks underlying a specific pathophysiology.