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Estimated comparative integration hotspots identify different behaviors of retroviral gene transfer vectors.

Articolo
Data di Pubblicazione:
2011
Abstract:
Integration of retroviral vectors in the human genome follows non random patterns that favor insertional deregulation of gene expression and may cause risks of insertional mutagenesis when used in clinical gene therapy. Understanding how viral vectors integrate into the human genome is a key issue in predicting these risks. We provide a new statistical method to compare retroviral integration patterns. We identified the positions where vectors derived from the Human Immunodeficiency Virus (HIV) and the Moloney Murine Leukemia Virus (MLV) show different integration behaviors in human hematopoietic progenitor cells. Non-parametric density estimation was used to identify candidate comparative hotspots, which were then tested and ranked. We found 100 significative comparative hotspots, distributed throughout the chromosomes. HIV hotspots were wider and contained more genes than MLV ones. A Gene Ontology analysis of HIV targets showed enrichment of genes involved in antigen processing and presentation, reflecting the high HIV integration frequency observed at the MHC locus on chromosome 6. Four histone modifications/variants had a different mean density in comparative hotspots (H2AZ, H3K4me1, H3K4me3, H3K9me1), while gene expression within the comparative hotspots did not differ from background. These findings suggest the existence of epigenetic or nuclear three-dimensional topology contexts guiding retroviral integration to specific chromosome areas.
Tipologia CRIS:
1.1 Articolo in rivista
Keywords:
CHRONIC GRANULOMATOUS-DISEASE; LENTIVIRAL VECTOR; INTEGRATION SITES; DENSITY-ESTIMATION; STEM-CELLS; THERAPY; SCID-X1; IMMUNODEFICIENCY; SELECTION; ACTIVATION
Elenco autori:
Ambrosi, Alessandro; Glad, Ik; Pellin, D; Cattoglio, C; Mavilio, F; DI SERIO, Mariaclelia; Frigessi, A.
Autori di Ateneo:
AMBROSI ALESSANDRO
DI SERIO MARIACLELIA
Link alla scheda completa:
https://iris.unisr.it/handle/20.500.11768/47307
Pubblicato in:
PLOS COMPUTATIONAL BIOLOGY
Journal
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URL

http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002292
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