Functional and mechanistic studies of human-specific long noncoding RNAs using a humanized mouse model

National Heart, Lung, And Blood Institute

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Abstract

We have recently demonstrated that human lncRNAs robustly regulate systemic energy metabolism and their dysregulation could potentially contribute to the pathogenesis of metabolic disease (Nature Communications, 2020 and Journal of Clinical Investigation, 2021). The annotation of human liver lncRNAs, however, is far from complete and accurate impeding systematic identification and characterization of disease-associated human lncRNAs. This void is rooted in a paradox that is caused by the inaccessibility of human liver to treatments and the insufficient annotation of liver transcriptome. LncRNA expressions are often tissue- and condition-dependent. Establishing an inclusive annotation of human lncRNA transcriptomes reflecting metabolic responses thus requires human liver samples of diverse conditions, which are paradoxically unavailable due to the inaccessibility of human liver to treatments. We addressed this challenge by coupling an isogenic humanized mouse model with Nanopore single-molecule direct RNA sequencing (DRS). We first generated mice that carried humanized livers of identical genetic background, and then subjected the mice to representative metabolic treatments. We then analyzed the humanized livers with Nanopore DRS, which directly reads full-length native RNAs to determine the expression level of all lncRNA transcript isoforms. Thus, our system allows for constructing a de novo annotation of human liver lncRNA transcriptomes reflecting metabolic responses and studying transcriptome dynamics in conjunction. Our analysis uncovered a vast number of novel lncRNA genes and transcripts that have not been previously reported. 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-seq. Hence our work revealed a complex metabolic responsive landscape of human liver lncRNA transcriptomes and also provided a framework to understand transcriptome dynamics of human liver in response to physiologically relevant metabolic stimuli.