Of bugs and men. Human beings, unlike their experimental counterparts (mice), do not live in a controlled environment. The immune repertoire of a human is shaped by exposures to infections and vaccinations from birth until death. While it is not possible to alter a history of prior infection, understanding cross-reactive immune responses in the context of such exposures could have important therapeutic implications for the design of human vaccines and immunotherapies.
Last week, the NIH announced that the first 178 genomes from the Human Microbiome Project were published (see May 20, 2010 News Release – National Institutes of Health (NIH) http://www.nih.gov/news/health/may2010/nhgri-20.htm). Until recently, the relationship between human commensal microbes, and the (human) self at the T-cell epitope level, also known as “heterologous immunity”, has been unexplored territory due to the imprecise nature of T-cell epitope mapping tools and the unavailability of the basic genomic information required for the comparison. Now that precision epitope mapping tools are available, high-throughput epitope comparison tools have been developed and genomic databases are becoming widely available (the human genome, the human microbiome, and the genomes of many human pathogens), the relationships between the T-cell epitopes of these proteomes are ripe for exploration.
The human immunome? The relevance of this exploration to the development of vaccines, to autoimmunity and human health, is quite clear. It has long been surmised that pre-exposure to some bacteria, viruses, or even commensal microbes might trigger auto-immunity, and such exposures could also affect the outcome of vaccination or infection with new pathogens. Heterologous immunity has now been shown to influence immune response to vaccines [,]. Cross-reactive T-cell responses may also alter immune response to self, leading to autoimmune disease, a health problem of steadily increasing proportions, affecting one in ten to one in twenty Americans . In-depth studies of autoimmunity have revealed HLA associations (and other genetic factors) and a number of environmental triggers [,].
Heterologous immunity. The term now used for cross-reactive immune responses to vaccine and pathogens is “heterologous immunity”. It was first used by collaborator Ray Welsh . Although often surmised by vaccine researchers and immunologists, the impact of heterologous immunity is now becoming evident. For example, as described by Welsh and Kornfeld, immune responses to BCG modify subsequent response to vaccinia challenge or vaccination [1,2]. And, in animal studies, after the Lyme vaccine component (OSP-A) was found to be cross-reactive with an autologous protein [–], the vaccine was withdrawn from the market out of concern about the potential for adverse events, even though no direct link between the vaccine and post-vaccination arthritis was made. Further, there is considerable evidence that in some individuals, chronic infections (such as HCV, or EBV) contribute to development of autoimmune disorders (such as multiple sclerosis, or reactive arthritis [,]). Responses to EBV and other pathogens may also affect the outcome of transplantation .
Regarding human immune response to commensals, human coronavirus has also been implicated in the development of autoimmune disease  and CMV may be implicated in the development of autoimmune diabetes . Thus, according to YC Manabe, “Antecedent or current infections can alter the immunopathologic outcome of a subsequent unrelated infection. Immunomodulation by co-infecting pathogens has been referred to as ‘heterologous immunity’ and has been postulated to play a role in host susceptibility to disease, tolerance to organ transplant, and autoimmune disease .
EpiVax, Inc., has developed a suite of computer algorithms that could be applied to the human microbiome; this suite includes EpiMatrix, ClustiMer, Conservatrix, BlastiMer, Aggregatrix, Optimatrix, and VaccineCAD. These tools may be of great usefulness when explorations of the human microbial immunome are considered.
The EpiMatrix algorithm, which rates the MHC binding capability for every 9 mer in a protein sequence, has been benchmarked using a set of “gold standard” epitopes published by the IEDB (Immune Epitope Database) (Zhang et al. 2008). Using this set of epitopes as an objective standard, EpiVax assessed the predictive accuracy of the EpiMatrix algorithm relative to eight well-known epitope-mapping tools (such as SYFPEITHI and BIMAS). The comparisons confirm that the EpiMatrix algorithm is the most accurate predictive tool currently available: http:// www.EpiVax.com/comps/ (Username: guest, Password: welcome) (Ardito 2009).
In addition to the EpiMatrix algorithm for T-cell epitope identification, the EpiMatrix toolset also includes a set of analysis and design tools directly applicable to the vaccine design process. ClustiMer, an ancillary algorithm used with EpiMatrix, maps MHC motif matches along the length of a protein and calculates the density of motifs for eight common class II HLA alleles: DRB1*0101, DRB1*0301, DRB1*0401, DRB1*0701, DRB1*0801, DRB1*1101, DRB1*1301, and DRB1*1501. Typical T-cell epitope “clusters” range from 9 to roughly 25 amino acids in length, and considering their affinity to multiple alleles and across multiple frames, they can contain anywhere from 4 to 40 binding motifs, also known as promiscuous epitopes. The Conservatrix algorithm identifies conserved segments from among any given set of variable protein isolates. Pairing EpiMatrix with Conservatrix allows users to identify peptides, which are both potentially antigenic and conserved in circulating disease strains.
BlastiMer is a blast tool that enables researchers to examine conservation between predicted epitopes and the human proteome.
In the short term, a project to explore the human microbiome for cross-reactive epitopes would capitalize on the mass of data provided by the human microbiome project to understand the diversity of human immune responses to vaccination and generate profiles of protective as well as ineffective immune responses, contributing to better understanding of the biological phenomenon of “heterologous immunity” as it relates to human immune response to vaccines, pathogens, and commensal microbes. In the long term, the Human Microbiome Immunome Project could contribute to the development of more highly focused, safer immunotherapies (vaccines and biologics) for human use, and may elucidate the pathogenesis of some forms of autoimmune disease.
. Mathurin KS, Martens GW, Kornfeld H, Welsh RM. CD4 T-cell-mediated heterologous immunity between mycobacteria and poxviruses. J Virol. 2009 Apr;83(8):3528-39. Epub 2009 Feb 4
. Aarnisalo J, Veijola R, Vainionpää R, Simell O, Knip M, Ilonen J.Cytomegalovirus infection in early infancy: risk of induction and progression of autoimmunity associated with type 1 diabetes.Diabetologia. 2008 May;51(5):769-72. Epub 2008 Feb 16.
. Steere, A.C., Klitz, W., Drouin, E. E., Falk, B. A., Kwok, W. W., Nepom, G. T., and Baxter-Lowe, L. A. 2006. Antibiotic-refractory Lyme arthritis is associated with HLA-DR molecules that bind a Borrelia burgdorferi peptide. J Exp Med. 203:961-971.
. Ausubel LJ, O’Connor KC, Baecher-Allen C, Trollmo C, Kessler B, Hekking B, Merritt D, Meyer AL, Kwok B, Ploegh H, Huber BT, Hafler DA. Characterization of in vivo expanded OspA-specific human T-cell clones. Clin Immunol. 2005 Jun;115(3):313-22
. Sundström P, Nyström L, Jidell E, Hallmans G.EBNA-1 reactivity and HLA DRB1*1501 as statistically independent risk factors for multiple sclerosis: a case-control study. Mult Scler. 2008 Sep;14(8):1120-2. Epub 2008 Jun 23.
. Kasprowicz V, Ward SM, Turner A, Grammatikos A, Nolan BE, Lewis-Ximenez L, Sharp C, Woodruff J, Fleming VM, Sims S, Walker BD, Sewell AK, Lauer GM, Klenerman P. Defining the directionality and quality of influenza virus-specific CD8+ T cell cross-reactivity in individuals infected with hepatitis C virus. J Clin Invest. 2008 Mar;118(3):1143-53.
. Adams AB, Williams MA, Jones TR, Shirasugi N, Durham MM, Kaech SM, Wherry J, Onami T, Lanier JG, Kokko KE, Pearson TC, Ahmed R, Larsen CP. Heterologous immunity provides a potent barrier to transplantation tolerance. J. Clin. Invest. 2003 Jun;111(2):1887-1895.
. Hiemstra HS, Schloot NC, can Ceelen PA, Willemen SJM, Franken KSLM, van Rood JJ, de Vries RRP, Chaudrhuri A, Behan PO, Drijfhout JW, Roep BO. Cytomegalovirus in autoimmunity: T cell crossreactivity to viral antigen and autoantigen glutamic acid decarboxylase. Proc Natl Acad Sci. 2001Mar;98(7):3988-91.