Science Spotlight

Our pathogenic pasts can’t hide

From the Bradley Lab, Public Health Sciences Division

The leading job of the adaptive immune system is to form a memory of encountered pathogens such that future exposures can be met with swift immune system-directed removal of the repeat invader. The formation of an immunological memory and removal of a pathogen requires the coordination of several cellular and molecular events. At the center of this task are T cell receptors (TCRs), proteins anchored in the plasma membrane of T cells that detect and bind foreign molecules referred to as antigens. The antigens are bound to and presented by major histocompatibility complex (MHC) proteins on the surface of antigen-presenting cells. Upon formation of a TCR-antigen-MHC complex, signaling cascades prompt the T cell to proliferate and generate cells with identical TCR antigen specificity. This fundamental process of the adaptive immune system forms the basis of immunological memory essential for the successful control of pathogenic invasions.  

The MHC proteins are encoded by the human leukocyte antigen (HLA) genes, a group of genes famous for their significant allelic variation. With such extensive variation, one’s “HLA type”, the composition of six major MHC proteins, is unique and highly unlikely to mirror that of non-relatives. The binding capability of any one MHC protein determines which antigens it can bind and present on the cell surface, influencing which TCRs can bind a presented antigen. Taken together, the suite of TCRs and HLA alleles presents an intricate puzzle for scientists to decipher in order to understand the relationship between TCRs and previous pathogen exposure. A recently published paper in the journal eLife, from Dr. Phil Bradley and colleagues in the Division of Public Health Sciences, reports that statistical analyses of genetic sequences of these major cellular components actively involved in immunological memory formation, TCRs and MHC, can uncover information about previous pathogenic exposure.

Earlier work in the field took advantage of high-throughput sequencing techniques to reveal that one’s TCR repertoire itself can provide clues about pathogenic history. One such past study focused on what are referred to as public TCRs, sequences that are observed in several individuals, and associated them with known cytomegalovirus (CMV) serostatus in 666 healthy individuals (Emerson et al.). Graduate student William DeWitt, first author of the new study, was also involved in that research: “Will helped to lay the groundwork for this study through his work on the project that generated the amazing set of T cell receptor sequence repertoires that we analyzed,” said Dr. Bradley.

Matrix of T cell receptors (TCR; rows) across human subjects (columns) with a crystal structure of a TCR:peptide:MHC co-complex overlaid.
Matrix of T cell receptors (TCR; rows) across human subjects (columns). A white dot indicates that the TCR corresponding to that row is present in the T cell repertoire of the subject corresponding to that column. A crystal structure of a TCR:peptide:MHC co-complex is overlaid. Image provided by Dr. Phil Bradley

The new study used the same TCR sequencing data from the 666 individuals and increased the predictive power of that dataset by adding high-resolution HLA typing data. The authors aimed to determine if they could use the TCR sequence data to identify unknown immune exposures. As a first step, the authors investigated TCR co-occurrence patterns—TCRs that correlate with each other—among the entire study cohort (see example in Figure). The authors reasoned that a co-occurring set of TCRs may be indicative of a common pathogenic exposure. However, they realized it could also be possible for TCR co-occurrence patterns to be driven by HLA type. Indeed, their initial analyses identified two classes of co-occurring TCR pairs: one class demonstrated strong association with a shared HLA allele while the other class had a much weaker HLA allele association.

The authors used a clustering algorithm to link co-occurring TCRs together that were strongly correlated, this analysis identified 28 clusters and the number of TCRs contained within each cluster ranged anywhere from 7 to 386. From there, the authors found that nearly all of the clusters were strongly associated with at least one HLA allele. This finding prompted the authors to then analyze co-occurrence patterns within smaller subsets of individuals with specific HLA alleles. For example, in looking at the TCR associations with the A*02.01 HLA allele, they found that eight of the top ten TCRs are known to be responsive to exposure to viral antigens. In this analysis overall, they found that the more common HLA alleles are associated with greater numbers of TCRs as compared to those that are less common.

The next goal of the study was to determine if TCR clusters within HLA alleles are indicative of common immune exposure. This analysis was conducted for individual HLA alleles and included only the subset of individuals that were positive for a given HLA allele. The results were very encouraging when they found that several of the most highly significant TCR clusters contained TCRs that had previously been independently associated with various pathogens. Among the top five most significant clusters, associations with pathogens such as influenza virus, parvovirus B19, CMV, and Epstein-Barr virus were found.

This study generated a number of new insights into how to unlock the immunological history stored in our immune cells. When asked about the most significant contributions, Dr. Bradley commented, “In this paper, we introduce new tools for analysis of immune repertoires and we apply these tools to make some interesting observations about the impact of common pathogens (e.g., flu, EBV, and CMV) and immune genotype (HLA) on repertoire structure. These observations support the idea that T cell receptor (TCR) repertoire sequences carry interpretable information about our past immune exposures.”

The authors have exciting follow-up studies planned. “We want to understand which features of a person's immune exposure history and/or genotype may correlate with these TCR clusters. For some of the clusters we have hypotheses based on the literature; for others we don't have a clue,” said Dr. Bradley. New types of data will be incorporated into these future studies, which may increase the ability to identify triggers of immune response, as described by Dr. Bradley, “One plan is to generate data on antibody responses present in these samples, which could identify infectious or autoimmune exposures as correlates. We also plan to perform a genome-wide covariation analysis that could identify genetic correlates outside the HLA region.”

Cancer Consortium authors include Philip Bradley and Erick Matsen.

This research was supported by the National Institutes of Health.

DeWitt WS, Smith A, Schoch G, Hansen JA, Matsen FA, Bradley P. 2018. Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity. eLife. doi: 10.7554/eLife.38358

Additional citation:

Emerson RO, DeWitt WS, Vignali M, Gravley J, Hu JK, Osborne EJ, Desmarais C, Klinger M, Carlson CS, Hansen JA, Rieder M, Robins HS. 2017. Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire. Nature Genetics. doi: 10.1038/ng.3822.

Science Spotlight Editors
From the left: Science Spotlight editors Yiting Lim (Basic Sciences), Kyle Woodward (Clinical Research), Nicolas Chuvin (Human Biology), Maggie Burhans (Public Health Sciences) and Brianna Traxinger (Vaccine and Infectious Disease) Photo by Robert Hood / Fred Hutch


Yiting Lim
Basic Sciences Division

Nicolas Chuvin
Human Biology Division

Maggie Burhans, Ph.D.
Public Health Sciences Division

Brianna Traxinger
Vaccine and Infectious Disease Division

Kyle Woodward
Clinical Research Division

Julian Simon, Ph.D.
Faculty Mentor
Clinical Research Division
and Human Biology Division

Allysha Eyler
Publication Tracking
Arnold Digital Library

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