• Twitter Round

© 2014 by Tomer Hertz. Proudly created with Wix.com

Co-Eevolution of Viruses and the Immune System

Inside our body, a battle rages between the immune system and disease-causing pathogens. Striving for an advantage, pathogens constantly evolve to evade detection by the immune system. When viruses infect a cell, they bring their own genetic material into the cell and use cellular resources to propagate. As a result, HLA molecules present viral proteins on the infected cell’s surface, spurring an immune attack on the "odd" cells. However, viruses often mutate to evade detection, altering the protein segments that HLA molecules are most likely to present. 
 

On the other side, the distribution of the thousands of HLA variants present in human populations can change over many generations. This sets up an evolutionary game: viruses on one side, our immune system on the other. To analyze this contest, the researchers quantified HLA-binding preferences according to targeting efficiency, a novel measure that captures the correlation between HLA-binding affinities and the genetic conservation in the targeted regions. In theory, HLA molecules should draw attention to protein segments that are shared across related viral species, as such regions should be functionally important and thus immutable. 
 

Analysis of targeting efficiencies indicated that HLA molecules do indeed prefer to target such conserved regions. The magnitude of this preference varies in a way that shows evidence of target splitting, where two different HLA loci focus on different viral families. This phenomenon is consistent with theoretical biology predictions for predator-prey models and indicates that targeting efficiency as a measure of the HLA-virus links will be useful in analyzing viral evolution. Furthermore, in many cases the host’s total targeting efficiency scores for various viruses correlate with clinical outcomes, offering a potentially useful system of measures for analyzing infection outcomes in individual patients or entire human populations under different conditions, such as post-vaccination or following a previous viral infection. 

 

publications:

 

 

Heat map distribution of allele efficiencies for human viruses and human proteins (x axis) by HLA supertype families (y axis). A matrix of efficiency scores computed for each of the 95 HLA alleles studied for 52 human viruses and a set of human proteins. Each entry in this efficiency matrix represents the efficiency score of a specific HLA allele (y axis) for a specific viral proteome. HLA alleles were grouped by supertypes, and human viruses were grouped by viral families and by Baltimore classification. Average efficiency scores over a large set of human proteins are presented in the bar to the left of the matrix. Distinct patterns of targeting efficiency can be observed for both HLA alleles (grouped by supertype or loci) and for different viral groups and families. UC, unclassified alleles that have not been assigned to supertypes; HSV-1, herpes simplex virus type 1; EBV, Epstein-Barr virus; CMV, cytomegalovirus; KSHV, Kaposi’s sarcoma-associated herpesvirus; SARs-CoV, severe acute respiratory syndrome coronavirus; HTLV-1, human T-cell leukemia virus type 1; ssRNA, single-stranded RNA; RT, reverse transcriptase.