Resumen de Understanding interactions between EBV and human genomic variation
The coexistence of the ubiquitous EBV in human B-cell is the best example of balanced interaction between human and EBV, under normal circumstances. Although the virus persists in asymptomatic conditions, with the exceptional cases, an unusual interaction of human and EBV can result in the development of multiple cancers and other disorders with marked geographical variation in prevalence. An impact of host genetic variation on EBV infection was recognized previously in fragments providing a clue to the idea that differences in genetic architecture would confer differential susceptibility to EBV-associated diseases. However, there is still insufficient knowledge available at genomic scale on the genetic factors that can influence EBV biology, infection or the prevalence of EBV-associated disease due their large focus on analysis of a particular gene, typically centered on particular populations. This enforces us to shift our paradigm from single gene and/or population analysis to the large-scale genome-wide analysis considering worldwide populations at a time, to link the genetic polymorphism pinpointed throughout the genomes in different healthy populations, that could provide hints to ultimately link genomic variants with EBV associated diseases.
This PhD work is essentially a one step forward in understanding the interaction of human host and EBV to uncover the role of genetic variation in EBV infection, disease susceptibility and immune recognition. The combination of NGS methods (human and EBV whole genome sequences) and genome-wide association studies (GWAS) impelled us to perform a whole-genome level study to detect the genetic variants influencing EBV copy number (EBV load) of diverse genome samples from African, American, European, and Asian populations, ultimately providing clues to disease susceptibility. To detect the genetic variants from these 1000 Genome Project populations, first we conformed that EBV copy number is a stable phenotype over a time period within the lymphoblastoid cell lines (LCLs) and subsequently we developed an in silico algorithm to estimate the EBV copy number from LCLs. With this measurement, we have demonstrated that there is substantial variation in EBV copy number across the 1000 Genome populations suggesting a role of human genetic variation that can interact with EBV.
In another aspect of the PhD work, to extend our understanding on the immune response to EBV copy number, we have assessed the interaction of the human leukocyte antigen (HLA) alleles with EBV copy number as a phenotypic consequence controlling EBV infection in different population samples. Lastly, an extensive analysis was carried out to mine the EBV genome sequence polymorphisms in order to report on how natural variations (antigenic variations) might help the virus to escape the immune system and support lifelong persistent infection.
