ERVmap analysis reveals genome-wide transcription of human endogenous retroviruses.
ERVmap analysis reveals genome-wide transcription of human endogenous retroviruses.
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DOI：
10.1073/pnas.1814589115
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发表时间：
2018-12-11
影响因子：
11.1
通讯作者：
Iwasaki A
中科院分区：
文献类型：
--
作者：
Tokuyama M;Kong Y;Song E;Jayewickreme T;Kang I;Iwasaki A
Endogenous retrovirus (ERV) sequences make up a large fraction of our genome, yet little is understood about their function and biological relevance. Deep-sequencing data contain valuable information on a genome-wide scale. Yet, due to their highly repetitive nature, analysis of ERVs has been computationally challenging. We describe a bioinformatics tool called ERVmap to analyze transcription of unique sets of human ERVs in a range of cell types in health and disease settings. Our open-source code and accompanied web tool should facilitate researchers in all fields to study the expression patterns of ERVs in sequencing data and should lead to significant advancement in understanding the biological relevance of ERVs in health and disease. Endogenous retroviruses (ERVs) are integrated retroviral elements that make up 8% of the human genome. However, the impact of ERVs on human health and disease is not well understood. While select ERVs have been implicated in diseases, including autoimmune disease and cancer, the lack of tools to analyze genome-wide, locus-specific expression of proviral autonomous ERVs has hampered the progress in the field. Here we describe a method called ERVmap, consisting of an annotated database of 3,220 human proviral ERVs and a pipeline that allows for locus-specific genome-wide identification of proviral ERVs that are transcribed based on RNA-sequencing data, and provide examples of the utility of this tool. Using ERVmap, we revealed cell-type–specific ERV expression patterns in commonly used cell lines as well as in primary cells. We identified 124 unique ERV loci that are significantly elevated in the peripheral blood mononuclear cells of patients with systemic lupus erythematosus that represent an IFN-independent signature. Finally, we identified additional tumor-associated ERVs that correlate with cytolytic activity represented by granzyme and perforin expression in breast cancer tissue samples. The open-source code of ERVmap and the accompanied web tool are made publicly available to quantify proviral ERVs in RNA-sequencing data with ease. Use of ERVmap across a range of diseases and experimental conditions has the potential to uncover novel disease-associated antigens and effectors involved in human health that is currently missed by focusing on protein-coding sequences.
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通讯作者：
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