An ImmunoSignature test distinguishes Trypanosoma cruzi, hepatitis B, hepatitis C and West Nile virus seropositivity among asymptomatic blood donors

Highly heterogenous humoral immune response in Lyme disease patients revealed by broad machine learning-assisted antibody binding profiling with random peptide arrays

Introduction

Lyme disease (LD), a rapidly growing public health problem in the US, represents a formidable challenge due to the lack of detailed understanding about how the human immune system responds to its pathogen, the Borrelia burgdorferi bacterium. Despite significant advances in gaining deeper insight into mechanisms the pathogen uses to evade immune response, substantial gaps remain. As a result, molecular tools for the disease diagnosis are lacking with the currently available tests showing poor performance. High interpersonal variability in immune response combined with the ability of the pathogen to use a number of immune evasive tactics have been implicated as underlying factors for the limited test performance.

Methods

This study was designed to perform a broad profiling of the entire repertoire of circulating antibodies in human sera at the single-individual level using planar arrays of short linear peptides with random sequences. The peptides sample sparsely, but uniformly the entire combinatorial sequence space of the same length peptides for profiling the humoral immune response to a B.burg. infection and compare them with other diseases with etiology similar to LD and healthy controls.

Results

The study revealed substantial variability in antibody binding profiles between individual LD patients even to the same antigen (VlsE protein) and strong similarity between individuals diagnosed with Lyme disease and healthy controls from the areas endemic to LD suggesting a high prevalence of seropositivity in endemic healthy control.

Discussion

This work demonstrates the utility of the approach as a valuable analytical tool for agnostic profiling of humoral immune response to a pathogen.

Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease

Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a variety of infectious diseases. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. This "immunosignature" approach, is based on a statistical evaluation of the binding pattern for each sample but it ignores the information contained in the amino acid sequences that the antibodies are binding to. Here, similar array-based antibody profiles are instead used to train a neural network to model the sequence dependence of molecular recognition involved in the immune response of each sample. The binding profiles used resulted from incubating serum from 5 infectious disease cohorts (Hepatitis B and C, Dengue Fever, West Nile Virus and Chagas disease) and an uninfected cohort with 122,926 peptide sequences on an array. These sequences were selected quasi-randomly to represent an even but sparse sample of the entire possible combinatorial sequence space (~1012). This very sparse sampling of combinatorial sequence space was sufficient to capture a statistically accurate representation of the humoral immune response across the entire space. Processing array data using the neural network not only captures the disease-specific sequence-binding information but aggregates binding information with respect to sequence, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared with the raw binding data. Because the neural network model is trained on all samples simultaneously, a highly condensed representation of the differential information between samples resides in the output layer of the model, and the column vectors from this layer can be used to represent each sample for classification or unsupervised clustering applications.

Predicting response and toxicity to immune checkpoint inhibitors in lung cancer using antibodies to frameshift neoantigens

PURPOSE

To evaluate a new class of blood-based biomarkers, anti-frameshift peptide antibodies, for predicting both tumor responses and adverse immune events to immune checkpoint inhibitor (ICI) therapies in advanced lung cancer patients.

EXPERIMENTAL DESIGN

Serum samples were obtained from 74 lung cancer patients prior to palliative PD-(L)1 therapies with subsequently recorded tumor responses and immune adverse events (irAEs). Pretreatment samples were assayed on microarrays of frameshift peptides (FSPs), representing ~ 375,000 variant peptides that tumor cells can be informatically predicted to produce from translated mRNA processing errors. Serum-antibodies specifically recognizing these ligands were measured. Binding activities preferentially associated with best-response and adverse-event outcomes were determined. These antibody bound FSPs were used in iterative resampling analyses to develop predictive models of tumor response and immune toxicity.

RESULTS

Lung cancer serum samples were classified based on predictive models of ICI treatment outcomes. Disease progression was predicted pretreatment with ~ 98% accuracy in the full cohort of all response categories, though ~ 30% of the samples were indeterminate. This model was built with a heterogeneous sample cohort from patients that (i) would show either clear response or stable outcomes, (ii) would be administered either single or combination therapies and (iii) were diagnosed with different lung cancer subtypes. Removing the stable disease, combination therapy or SCLC groups from model building increased the proportion of samples classified while performance remained high. Informatic analyses showed that several of the FSPs in the all-response model mapped to translations of variant mRNAs from the same genes. In the predictive model for treatment toxicities, binding to irAE-associated FSPs provided 90% accuracy pretreatment, with no indeterminates. Several of the classifying FSPs displayed sequence similarity to self-proteins.

CONCLUSIONS

Anti-FSP antibodies may serve as biomarkers for predicting ICI outcomes when tested against ligands corresponding to mRNA-error derived FSPs. Model performances suggest this approach might provide a single test to predict treatment response to ICI and identify patients at high risk for immunotherapy toxicities.

Protective antigenic epitopes revealed by immunosignatures after three doses of inactivated SARS-CoV-2 vaccine

Background

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has infected millions of people around the world. Vaccination is a pillar in the strategy to control transmission of the SARS-CoV-2 spread. Immune responses to vaccination require elucidation.

Methods

The immune responses to vaccination with three doses of inactivated SARS-CoV-2 vaccine were followed in a cohort of 37 healthy adults (18–59 years old). Blood samples were collected at multiple time points and submitted to peptide array, machine learning modeling, and sequence alignment analyses, the results of which were used to generate vaccine-induced antibody-binding region (VIABR) immunosignatures (Registration number: ChiCTR2200058571).

Results

Antibody spectrum signals showed vaccination stimulated antibody production. Sequence alignment analyses revealed that a third vaccine dose generated a new highly represented VIABR near the A570D mutation, and the whole process of inoculation enhanced the VIABR near the N501Y mutation. In addition, the antigen conformational epitopes varied between short- and long-term samples. The amino acids with the highest scores in the short-term samples were distributed primarily in the receptor binding domain (RBD) and N-terminal domain regions of spike (S) protein, while in the long-term samples (12 weeks after the 2^nd dose), some new conformational epitopes (CEs) were localized to crevices within the head of the S protein trimer.

Conclusion

Protective antigenic epitopes were revealed by immunosignatures after three doses of inactivated SARS-CoV-2 vaccine inoculation. A third dose results in a new top-10 VIABR near the A570D mutation site of S protein, and the whole process of inoculation enhanced the VIABR near the N501Y mutation, thus potentially providing protection from strains that have gained invasion and immune escape abilities through these mutation.

Serological Approaches for Trypanosoma cruzi Strain Typing

Trypanosoma cruzi, the protozoan agent of Chagas' disease, displays a complex population structure made up of multiple strains showing a diverse ecoepidemiological distribution. Parasite genetic variability may be associated with disease outcome, hence stressing the need to develop methods for T. cruzi typing in vivo. Serological typing methods that exploit the presence of host antibodies raised against polymorphic parasite antigens emerge as an appealing approach to address this issue. These techniques are robust, simple, cost-effective, and are not curtailed by methodological/biological limitations intrinsic to available genotyping methods. Here, we critically assess the progress towards T. cruzi serotyping and discuss the opportunity provided by high-throughput immunomics to improve this field.

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.

Production of high-complexity frameshift neoantigen peptide microarrays

Parallel measurement of large numbers of antigen-antibody interactions are increasingly enabled by peptide microarray technologies. Our group has developed an in situ synthesized peptide microarray of >400 000 frameshift neoantigens using mask-based photolithographic peptide synthesis, to profile patient specific neoantigen reactive antibodies in a single assay. The system produces 208 replicate mircoarrays per wafer and is capable of producing multiple wafers per synthetic lot to routinely synthesize over 300 million peptides simultaneously. In this report, we demonstrate the feasibility of the system for detecting peripheral-blood antibody binding to frameshift neoantigens across multiple synthetic lots.

Antibody characterization using immunosignatures

Therapeutic monoclonal antibodies have the potential to work as biological therapeutics. OKT3, Herceptin, Keytruda and others have positively impacted healthcare. Antibodies evolved naturally to provide high specificity and high affinity once mature. These characteristics can make them useful as therapeutics. However, we may be missing characteristics that are not obvious. We present a means of measuring antibodies in an unbiased manner that may highlight therapeutic activity. We propose using a microarray of random peptides to assess antibody properties. We tested twenty-four different commercial antibodies to gain some perspective about how much information can be derived from binding antibodies to random peptide libraries. Some monoclonals preferred to bind shorter peptides, some longer, some preferred motifs closer to the C-term, some nearer the N-term. We tested some antibodies with clinical activity but whose function was blinded to us at the time. We were provided with twenty-one different monoclonal antibodies, thirteen mouse and eight human IgM. These antibodies produced a variety of binding patterns on the random peptide arrays. When unblinded, the antibodies with polyspecific binding were the ones with the greatest therapeutic activity. The protein target to these therapeutic monoclonals is still unknown but using common sequence motifs from the peptides we predicted several human and mouse proteins. The same five highest proteins appeared in both mouse and human lists.