Integration and Fixation Preferences of Human and Mouse Endogenous Retroviruses Uncovered with Functional Data Analysis.
Endogenous retroviruses (ERVs), the remnants of retroviral infections in the germ line, occupy ~8% and ~10% of the human and mouse genomes, respectively, and affect their structure, evolution, and function. Yet we still have a limited understanding of how the genomic landscape influences integration and fixation of ERVs. Here we conducted a genome-wide study of the most recently active ERVs in the human and mouse genome. We investigated 826 fixed and 1,065 in vitro HERV-Ks in human, and 1,624 fixed and 242 polymorphic ETns, as well as 3,964 fixed and 1,986 polymorphic IAPs, in mouse. We quantitated >40 human and mouse genomic features (e.g., non-B DNA structure, recombination rates, and histone modifications) in ±32 kb of these ERVs' integration sites and in control regions, and analyzed them using Functional Data Analysis (FDA) methodology. In one of the first applications of FDA in genomics, we identified genomic scales and locations at which these features display their influence, and how they work in concert, to provide signals essential for integration and fixation of ERVs. The investigation of ERVs of different evolutionary ages (young in vitro and polymorphic ERVs, older fixed ERVs) allowed us to disentangle integration vs. fixation preferences. As a result of these analyses, we built a comprehensive model explaining the uneven distribution of ERVs along the genome. We found that ERVs integrate in late-replicating AT-rich regions with abundant microsatellites, mirror repeats, and repressive histone marks. Regions favoring fixation are depleted of genes and evolutionarily conserved elements, and have low recombination rates, reflecting the effects of purifying selection and ectopic recombination removing ERVs from the genome. In addition to providing these biological insights, our study demonstrates the power of exploiting multiple scales and localization with FDA. These powerful techniques are expected to be applicable to many other genomic investigations.
Genetics Graduate Program, The Huck Institutes of the Life Sciences, Penn State University, University Park, Pennsylvania, United States of America.
Rebeca
R
Campos-Sánchez
MOX-Modeling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Milano, Italy.|Department of Statistics, Penn State University, University Park, Pennsylvania, United States of America.
Marzia A
0000-0001-8899-7316
MA
Cremona
MOX-Modeling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Milano, Italy.
Alessia
A
Pini
Department of Statistics, Penn State University, University Park, Pennsylvania, United States of America.|Center for Medical Genomics, The Huck Institutes of the Life Sciences, Penn State University, University Park, Pennsylvania, United States of America.
Francesca
F
Chiaromonte
Center for Medical Genomics, The Huck Institutes of the Life Sciences, Penn State University, University Park, Pennsylvania, United States of America.|Department of Biology, Penn State University, University Park, Pennsylvania, United States of America.
Kateryna D
KD
Makova
D000818:Animals; D002874:Chromosome Mapping; D019295:Computational Biology; D004261:DNA Replication; D003627:Data Interpretation, Statistical; D020077:Endogenous Retroviruses / Q000235:genetics*; D044127:Epigenesis, Genetic; D015894:Genome, Human; D006801:Humans; D016015:Logistic Models; D051379:Mice; D008954:Models, Biological; D011995:Recombination, Genetic; D012091:Repetitive Sequences, Nucleic Acid; D012641:Selection, Genetic; D016662:Virus Integration / Q000235:genetics*