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.

One of the gravest dangers facing cancer patients is an extended symptom-free lull between tumor initiation and the first diagnosis. Detection of tumors is critical for effective intervention. Using the body’s immune system to detect and amplify tumor-specific signals may enable detection of cancer using an inexpensive immunoassay. Immunosignatures are one such assay: they provide a map of antibody interactions with random-sequence peptides. They enable detection of disease-specific patterns using classic train/test methods. However, to date, very little effort has gone into extracting information from the sequence of peptides that interact with disease-specific antibodies. Because it is difficult to represent all possible antigen peptides in a microarray format, we chose to synthesize only 330,000 peptides on a single immunosignature microarray. The 330,000 random-sequence peptides on the microarray represent 83% of all tetramers and 27% of all pentamers, creating an unbiased but substantial gap in the coverage of total sequence space. We therefore chose to examine many relatively short motifs from these random-sequence peptides. Time-variant analysis of recurrent subsequences provided a means to dissect amino acid sequences from the peptides while simultaneously retaining the antibody–peptide binding intensities. We first used a simple experiment in which monoclonal antibodies with known linear epitopes were exposed to these random-sequence peptides, and their binding intensities were used to create our algorithm. We then demonstrated the performance of the proposed algorithm by examining immunosignatures from patients with Glioblastoma multiformae (GBM), an aggressive form of brain cancer. Eight different frameshift targets were identified from the random-sequence peptides using this technique. If immune-reactive antigens can be identified using a relatively simple immune assay, it might enable a diagnostic test with sufficient sensitivity to detect tumors in a clinically useful way.

Background: An accurate method that can diagnose and predict lupus and its neuropsychiatric manifestations is essential since currently there are no reliable methods. Autoantibodies to a varied panel of antigens in the body are characteristic of lupus. In this study we investigated whether serum autoantibody binding patterns on random-sequence peptide microarrays (immunosignaturing) can be used for diagnosing and predicting the onset of lupus and its central nervous system (CNS) manifestations. We also tested the techniques for identifying potentially pathogenic autoantibodies in CNS-Lupus. We used the well-characterized MRL/lpr lupus animal model in two studies as a first step to develop and evaluate future studies in humans.

Results: In study one we identified possible diagnostic peptides for both lupus and altered behavior in the forced swim test. When comparing the results of study one to that of study two (carried out in a similar manner), we further identified potential peptides that may be diagnostic and predictive of both lupus and altered behavior in the forced swim test. We also characterized five potentially pathogenic brain-reactive autoantibodies, as well as suggested possible brain targets.

Conclusions: These results indicate that immunosignaturing could predict and diagnose lupus and its CNS manifestations. It can also be used to characterize pathogenic autoantibodies, which may help to better understand the underlying mechanisms of CNS-Lupus.

We have previously shown that the diversity of antibodies in an individual can be displayed on chips on which 130,000 peptides chosen from random sequence space have been synthesized. This immunosignature technology is unbiased in displaying antibody diversity relative to natural sequence space, and has been shown to have diagnostic and prognostic potential for a wide variety of diseases and vaccines. Here we show that a global measure such as Shannon’s entropy can be calculated for each immunosignature. The immune entropy was measured across a diverse set of 800 people and in 5 individuals over 3 months. The immune entropy is affected by some population characteristics and varies widely across individuals. We find that people with infections or breast cancer, generally have higher entropy values than non-diseased individuals. We propose that the immune entropy as measured from immunosignatures may be a simple method to monitor health in individuals and populations.

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.

Objective: Progressive accumulation of α-synuclein (α-syn) has been associated with Parkinson's disease (PD) and Dementia with Lewy body (DLB). The mechanisms through which α-syn leads to neurodegeneration are not completely clear; however, the formation of various oligomeric species have been proposed to play a role. Antibody therapy has shown effectiveness at reducing α-syn accumulation in the central nervous system (CNS); however, most of these studies have been conducted utilizing antibodies that recognize both monomeric and higher molecular weight α-syn. In this context, the main objective of this study was to investigate the efficacy of immunotherapy with single-chain antibodies (scFVs) against specific conformational forms of α-syn fused to a novel brain penetrating sequence.

Method: We screened various scFVs against α-syn expressed from lentiviral vectors by intracerebral injections in an α-syn tg model. The most effective scFVs were fused to the cell-penetrating peptide penetratin to enhance transport across the blood–brain barrier, and lentiviral vectors were constructed and tested for efficacy following systemic delivery intraperitoneal into α-syn tg mice.

Result: Two scFVs (D5 and 10H) selectively targeted different α-syn oligomers and reduced the accumulation of α-syn and ameliorated functional deficits when delivered late in disease development; however, only one of the antibodies (D5) was also effective when delivered early in disease development. These scFVs were also utilized in an enzyme-linked immunosorbent assay (ELISA) assay to monitor the effects of immunotherapy on α-syn oligomers in brain and plasma.

Interpretation: The design and targeting of antibodies for specific species of α-syn oligomers is crucial for therapeutic immunotherapy and might be of relevance for the treatment of Lewy body disease.

Misfolding and aggregation of α-synuclein into toxic soluble oligomeric α-synuclein aggregates has been strongly correlated with the pathogenesis of Parkinson’s disease (PD). Here, we show that two different morphologically distinct oligomeric α-synuclein aggregates are present in human post-mortem PD brain tissue and are responsible for the bulk of α-synuclein induced toxicity in brain homogenates from PD samples. Two antibody fragments that selectively bind the different oligomeric α-synuclein variants block this α-synuclein induced toxicity and are useful tools to probe how various cell models replicate the α-synuclein aggregation pattern of human PD brain. Using these reagents, we show that mammalian cell type strongly influences α-synuclein aggregation, where neuronal cells best replicate the PD brain α-synuclein aggregation profile. Overexpression of α-synuclein in the different cell lines increased protein aggregation but did not alter the morphology of the oligomeric aggregates generated. Differentiation of the neuronal cells into a cholinergic-like or dopaminergic-like phenotype increased the levels of oligomeric α-synuclein where the aggregates were localized in cell neurites and cell bodies.