Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity.

BACKGROUND: The immune system undergoes a myriad of changes with age. While it is known that antibody-secreting plasma and long-lived memory B cells change with age, it remains unclear how the binding profile of the circulating antibody repertoire is impacted. RESULTS: To understand humoral immunity changes with respect to age, we characterized serum antibody binding to high density peptide microarrays in a diverse cohort of 1675 donors. We discovered thousands of peptides that bind antibodies in age-dependent fashion, many of which contain di-serine motifs. Peptide binding profiles were aggregated into an "immune age" by a machine learning regression model that was highly correlated with chronological age. Applying this regression model to previously-unobserved donors, we found that a donor's predicted immune age is longitudinally consistent over years, suggesting it could be a robust long-term biomarker of humoral immune ageing. Finally, we assayed serum from donors with autoimmune disease and found a significant association between "accelerated immune ageing" and autoimmune disease activity. CONCLUSIONS: The circulating antibody repertoire has increased binding to thousands of di-serine peptide containing peptides in older donors, which can be represented as an immune age. Increased immune age is associated with autoimmune disease, acute inflammatory disease severity, and may be a broadly relevant biomarker of immune function in health, disease, and therapeutic intervention.

A Simple Platform for the Rapid Development of Antimicrobials.

Recent infectious outbreaks highlight the need for platform technologies that can be quickly deployed to develop therapeutics needed to contain the outbreak. We present a simple concept for rapid development of new antimicrobials. The goal was to produce in as little as one week thousands of doses of an intervention for a new pathogen. We tested the feasibility of a system based on antimicrobial synbodies. The system involves creating an array of 100 peptides that have been selected for broad capability to bind and/or kill viruses and bacteria. The peptides are pre-screened for low cell toxicity prior to large scale synthesis. Any pathogen is then assayed on the chip to find peptides that bind or kill it. Peptides are combined in pairs as synbodies and further screened for activity and toxicity. The lead synbody can be quickly produced in large scale, with completion of the entire process in one week.

Scalable high-density peptide arrays for comprehensive health monitoring.

There is an increasing awareness that health care must move from post-symptomatic treatment to presymptomatic intervention. An ideal system would allow regular inexpensive monitoring of health status using circulating antibodies to report on health fluctuations. Recently, we demonstrated that peptide microarrays can do this through antibody signatures (immunosignatures). Unfortunately, printed microarrays are not scalable. Here we demonstrate a platform based on fabricating microarrays (~10 M peptides per slide, 330,000 peptides per assay) on silicon wafers using equipment common to semiconductor manufacturing. The potential of these microarrays for comprehensive health monitoring is verified through the simultaneous detection and classification of six different infectious diseases and six different cancers. Besides diagnostics, these high-density peptide chips have numerous other applications both in health care and elsewhere.

The immunosignature of canine lymphoma: characterization and diagnostic application.

BACKGROUND: Cancer diagnosis in both dogs and humans is complicated by the lack of a non-invasive diagnostic test. To meet this clinical need, we apply the recently developed immunosignature assay to spontaneous canine lymphoma as clinical proof-of-concept. Here we evaluate the immunosignature as a diagnostic for spontaneous canine lymphoma at both at initial diagnosis and evaluating the disease free interval following treatment. METHODS: Sera from dogs with confirmed lymphoma (B cell n = 38, T cell n = 11) and clinically normal dogs (n = 39) were analyzed. Serum antibody responses were characterized by analyzing the binding pattern, or immunosignature, of serum antibodies on a non-natural sequence peptide microarray. Peptides were selected and tested for the ability to distinguish healthy dogs from those with lymphoma and to distinguish lymphoma subtypes based on immunophenotype. The immunosignature of dogs with lymphoma were evaluated for individual signatures. Changes in the immunosignatures were evaluated following treatment and eventual relapse. RESULTS: Despite being a clonal disease, both an individual immunosignature and a generalized lymphoma immunosignature were observed in each dog. The general lymphoma immunosignature identified in the initial set of dogs (n = 32) was able to predict disease status in an independent set of dogs (n = 42, 97% accuracy). A separate immunosignature was able to distinguish the lymphoma based on immunophenotype (n = 25, 88% accuracy). The individual immunosignature was capable of confirming remission three months following diagnosis. Immunosignature at diagnosis was able to predict which dogs with B cell lymphoma would relapse in less than 120 days (n = 33, 97% accuracy). CONCLUSION: We conclude that the immunosignature can serve as a multilevel diagnostic for canine, and potentially human, lymphoma.

Could immunosignatures technology enable the development of a preventative cancer vaccine?

The exciting prospect of developing a universal prophylactic cancer vaccine now seems more possible due to advances in technology and basic knowledge. However, the problem of testing the efficacy of such a vaccine in a clinical trial seems daunting. The low incidence and long lead-time to diagnosis of cancer would make a standard clinical trial long and expensive. Recently, we demonstrated that the immunosignatures diagnostic technology could be useful in evaluating vaccines. The technology is based on profiling the antibody diversity in an individual on a peptide chip platform. Here we propose that this technology may also enable a clinical trial of a preventative vaccine. Preliminary evidence supports the prospect of immunosignatures detecting cancer at very early stages, well before conventional diagnosis. Because the technology is simple and inexpensive, it could be used to monitor the occurrence of cancer in participants and shorten the clinical trial.

Immunosignatures can predict vaccine efficacy.

The development of new vaccines would be greatly facilitated by having effective methods to predict vaccine performance. Such methods could also be helpful in monitoring individual vaccine responses to existing vaccines. We have developed "immunosignaturing" as a simple, comprehensive, chip-based method to display the antibody diversity in an individual on peptide arrays. Here we examined whether this technology could be used to develop correlates for predicting vaccine effectiveness. By using a mouse influenza infection, we show that the immunosignaturing of a natural infection can be used to discriminate a protective from nonprotective vaccine. Further, we demonstrate that an immunosignature can determine which mice receiving the same vaccine will survive. Finally, we show that the peptides comprising the correlate signatures of protection can be used to identify possible epitopes in the influenza virus proteome that are correlates of protection.

Autoreactive antibodies raised by self derived de novo peptides can identify unrelated antigens on protein microarrays. Are autoantibodies really autoantibodies?

The development of arrays of human proteins has been a huge boon to the search for autoantibody diagnostics. Typically, slides with thousands of recombinant human proteins arrayed in an addressable fashion are incubated with sera from diseased or normal people. If an antibody binds a protein more in the diseased than in the normal cohort it is considered an autoantibody response. It is usually presumed that the autoantibody was elicited by the protein bound on the array. However, our studies using human protein and random peptide arrays indicate that antibody specificity may not be as high as commonly thought. Therefore we have tested the assumption of the source of autoantibodies. One test was to generate antibodies to two totally random peptides and bind these antibodies to a human protein array. One of the antibodies generated bound two human proteins. A second test was to generate an antibody to a frameshift peptide occurring in cancers. This antibody also bound several proteins on the array. We conclude that one should be cautious about assuming a particular autoantibody target on an array which elicited the original immune response.

Evaluation of biological sample preparation for immunosignature-based diagnostics.

To address the need for a universal system to assess health status, we previously described a method termed "immunosignaturing" which splays the entire humoral antibody repertoire across a peptide microarray. Two important issues relative to the potential broad use of immunosignatures are sample preparation and stability. In the present study, we compared the immunosignatures developed from serum, plasma, saliva, and antibodies eluted from blood dried onto filter paper. We found that serum and plasma provide identical immunosignatures. Immunosignatures derived from dried blood also correlated well with those from nondried serum from the same individual. Immunosignatures derived from dried blood were capable of distinguishing naïve mice from those infected with influenza virus. Saliva was applied to the arrays, and the IgA immunosignature correlated strongly with that from dried blood. Finally, we demonstrate that dried blood retains immunosignature information even when exposed to high temperature. This work expands the potential diagnostic uses for immunosignatures. These features suggest that different forms of archival samples can be used for diagnosis development and that in prospective studies samples can be easily procured.

A general method for characterization of humoral immunity induced by a vaccine or infection.

A universal system to diagnose disease, characterize infection or evaluate the response to a vaccine would be useful. Towards this end we introduce a machine-readable platform that we term "Immunosignaturing". Rather than attempt to identify antibodies one by one, we splay the entire immune response across an array of 10,000 random sequence peptides. This segregates serum antibodies sufficiently to group and characterize responses caused by disease or vaccination. In the present study, we explore in detail the murine immunosignature to influenza A/PR/8/34 immunization and subsequent challenge. Even though the peptides are random sequence, the response to immunization and challenge is quite apparent. We find that the immunosignatures contained information not evident in whole virus ELISA. Antibody recognition of 283 influenza-specific peptides increased upon immunization and remained elevated for 211 days post-challenge. A set of 65 peptides, which overlapped 39 of the peptides that were consistent across time, was capable of distinguishing mice based on infectious dose, while whole virus ELISA could not. These peptide populations are consistently recognized in independent biological replicates of infection and are largely, but not solely, composed of virus reactive antibodies. The immunosignaturing analysis was expanded to analysis of human recipients of the 2006/2007 seasonal influenza vaccine. We find that 30 peptides are significantly recognized by all donors 21 days post-immunization and have the power to distinguish immune from pre-immune samples. Taken together the data suggest that immunosignaturing on a random peptide array can serve as a universal platform to assess antibody status in ways that cannot be replicated by conventional immunological assays.