Profiling the IgOme: meeting the challenge

Serological Profiling of Group A Streptococcus Infections in Acute Rheumatic Fever

Rheumatic fever is a serious post-infectious sequela of group A Streptococcus (GAS). Prior GAS exposures were mapped in sera using a large panel of M-type specific peptides. Rheumatic fever patients had serological evidence of significantly more GAS exposures than matched controls suggesting immune priming by repeat infections contributes to pathogenesis.

Domain-Scan: Combinatorial Sero-Diagnosis of Infectious Diseases Using Machine Learning

The presence of pathogen-specific antibodies in an individual's blood-sample is used as an indication of previous exposure and infection to that specific pathogen (e.g., virus or bacterium). Measurement of the diagnostic antibodies is routinely achieved using solid phase immuno-assays such as ELISA tests and western blots. Here, we describe a sero-diagnostic approach based on phage-display of epitope arrays we term "Domain-Scan". We harness Next-generation sequencing (NGS) to measure the serum binding to dozens of epitopes derived from HIV-1 and HCV simultaneously. The distinction of healthy individuals from those infected with either HIV-1 or HCV, is modeled as a machine-learning classification problem, in which each determinant ("domain") is considered as a feature, and its NGS read-out provides values that correspond to the level of determinant-specific antibodies in the sample. We show that following training of a machine-learning model on labeled examples, we can very accurately classify unlabeled samples and pinpoint the domains that contribute most to the classification. Our experimental/computational Domain-Scan approach is general and can be adapted to other pathogens as long as sufficient training samples are provided.

Diagnostic Profiling of the Human Public IgM Repertoire With Scalable Mimotope Libraries

Specific antibody reactivities are routinely used as biomarkers, but the antibody repertoire reactivity (igome) profiles are still neglected. Here, we propose rationally designed peptide arrays as efficient probes for these system level biomarkers. Most IgM antibodies are characterized by few somatic mutations, polyspecificity, and physiological autoreactivity with housekeeping function. Previously, probing this repertoire with a set of immunodominant self-proteins provided a coarse analysis of the respective repertoire profiles. In contrast, here, we describe the generation of a peptide mimotope library that reflects the common IgM repertoire of 10,000 healthy donors. In addition, an appropriately sized subset of this quasi-complete mimotope library was further designed as a potential diagnostic tool. A 7-mer random peptide phage display library was panned on pooled human IgM. Next-generation sequencing of the selected phage yielded 224,087 sequences, which clustered in 790 sequence clusters. A set of 594 mimotopes, representative of the most significant sequence clusters, was shown to probe symmetrically the space of IgM reactivities in patients' sera. This set of mimotopes can be easily scaled including a greater proportion of the mimotope library. The trade-off between the array size and the resolution can be explored while preserving the symmetric sampling of the mimotope sequence and reactivity spaces. BLAST search of the non-redundant protein database with the mimotopes sequences yielded significantly more immunoglobulin J region hits than random peptides, indicating a considerable idiotypic connectivity of the targeted igome. The proof of principle predictors for random diagnoses was represented by profiles of mimotopes. The number of potential reactivity profiles that can be extracted from this library is estimated at more than 10⁷⁰. Thus, a quasi-complete IgM mimotope library and a scalable representative subset thereof are found to address very efficiently the dynamic diversity of the human public IgM repertoire, providing informationally dense and structurally interpretable IgM reactivity profiles.

Phage display peptide libraries: deviations from randomness and correctives

Peptide-expressing phage display libraries are widely used for the interrogation of antibodies. Affinity selected peptides are then analyzed to discover epitope mimetics, or are subjected to computational algorithms for epitope prediction. A critical assumption for these applications is the random representation of amino acids in the initial naïve peptide library. In a previous study, we implemented next generation sequencing to evaluate a naïve library and discovered severe deviations from randomness in UAG codon over-representation as well as in high G phosphoramidite abundance causing amino acid distribution biases. In this study, we demonstrate that the UAG over-representation can be attributed to the burden imposed on the phage upon the assembly of the recombinant Protein 8 subunits. This was corrected by constructing the libraries using supE44-containing bacteria which suppress the UAG driven abortive termination. We also demonstrate that the overabundance of G stems from variant synthesis-efficiency and can be corrected using compensating oligonucleotide-mixtures calibrated by mass spectroscopy. Construction of libraries implementing these correctives results in markedly improved libraries that display random distribution of amino acids, thus ensuring that enriched peptides obtained in biopanning represent a genuine selection event, a fundamental assumption for phage display applications.

2016 International meeting of the Global Virus Network

The Global Virus Network (GVN) was established in 2011 in order to strengthen research and responses to current viral causes of human disease and to prepare against new viral pandemic threats. There are now 38 GVN Centers of Excellence and 6 Affiliate laboratories in 24 countries. GVN scientists meet annually to learn about each other's current research, address collaborative priorities and plan future programs. The 2016 meeting was held from October 23–25 in Hokkaido, Japan, in partnership with the Japanese Society for Virology, the National Institute of Infectious Diseases of Japan and the Research Center for Zoonosis Control of Hokkaido University. This report highlights the accomplishments of GVN researchers in many priority areas of medical virology, including the current Zika epidemic, infections by human papillomavirus, influenza, Ebola, Lassa, dengue, HIV, hepatitis C, and chikungunya viruses, and the development of improved diagnostics and new vaccines.

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The GVN is an international research network comprised of 38 Centers of Excellence and 6 Affiliates in 24 countries.

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The 2016 Global Virus Network (GVN) Meeting was held in Sapporo, Japan from October 23–25.

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New data were presented on all various aspects of medical virology, therapy, and vaccines were presented.

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International collaboration is needed to develop effective viral vaccines and therapeutics.

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The 2017 international GVN meeting will be held on September 25–28 in Melbourne, Australia.

Serological Evidence of Immune Priming by Group A Streptococci in Patients with Acute Rheumatic Fever

Acute rheumatic fever (ARF) is an autoimmune response to Group A Streptococcus (GAS) infection. Repeated GAS exposures are proposed to ‘prime’ the immune system for autoimmunity. This notion of immune-priming by multiple GAS infections was first postulated in the 1960s, but direct experimental evidence to support the hypothesis has been lacking. Here, we present novel methodology, based on antibody responses to GAS T-antigens, that enables previous GAS exposures to be mapped in patient sera. T-antigens are surface expressed, type specific antigens and GAS strains fall into 18 major clades or T-types. A panel of recombinant T-antigens was generated and immunoassays were performed in parallel with serum depletion experiments allowing type-specific T-antigen antibodies to be distinguished from cross-reactive antibodies. At least two distinct GAS exposures were detected in each of the ARF sera tested. Furthermore, no two sera had the same T-antigen reactivity profile suggesting that each patient was exposed to a unique series of GAS T-types prior to developing ARF. The methods have provided much-needed experimental evidence to substantiate the immune-priming hypothesis, and will facilitate further serological profiling studies that explore the multifaceted interactions between GAS and the host.

Serology in the 21st century: the molecular-level analysis of the serum antibody repertoire

The ensemble of antibodies found in serum and secretions represents the key adaptive component of B-cell mediated humoral immunity. The antibody repertoire is shaped by the historical record of exposure to exogenous factors such as pathogens and vaccines, as well as by endogenous host-intrinsic factors such as genetics, self-antigens, and age. Thanks to very recent technology advancements it is now becoming possible to identify and quantify the individual antibodies comprising the serological repertoire. In parallel, the advent of high throughput methods for antigen and immunosignature discovery opens up unprecedented opportunities to transform our understanding of numerous key questions in adaptive humoral immunity, including the nature and dynamics of serological memory, the role of polyspecific antibodies in health and disease and how protective responses to infections or vaccine challenge arise. Additionally, these technologies also hold great promise for therapeutic antibody and biomarker discovery in a variety of settings.

A bioinformatic framework for immune repertoire diversity profiling enables detection of immunological status

BACKGROUND

Lymphocyte receptor repertoires are continually shaped throughout the lifetime of an individual in response to environmental and pathogenic exposure. Thus, they may serve as a fingerprint of an individual's ongoing immunological status (e.g., healthy, infected, vaccinated), with far-reaching implications for immunodiagnostics applications. The advent of high-throughput immune repertoire sequencing now enables the interrogation of immune repertoire diversity in an unprecedented and quantitative manner. However, steadily increasing sequencing depth has revealed that immune repertoires vary greatly among individuals in their composition; correspondingly, it has been reported that there are few shared sequences indicative of immunological status ('public clones'). Disconcertingly, this means that the wealth of information gained from repertoire sequencing remains largely unused for determining the current status of immune responses, thereby hampering the implementation of immune-repertoire-based diagnostics.

METHODS

Here, we introduce a bioinformatics repertoire-profiling framework that possesses the advantage of capturing the diversity and distribution of entire immune repertoires, as opposed to singular public clones. The framework relies on Hill-based diversity profiles composed of a continuum of single diversity indices, which enable the quantification of the extent of immunological information contained in immune repertoires.

RESULTS

We coupled diversity profiles with unsupervised (hierarchical clustering) and supervised (support vector machine and feature selection) machine learning approaches in order to correlate patients' immunological statuses with their B- and T-cell repertoire data. We could predict with high accuracy (greater than or equal to 80 %) a wide range of immunological statuses such as healthy, transplantation recipient, and lymphoid cancer, suggesting as a proof of principle that diversity profiling can recover a large amount of immunodiagnostic fingerprints from immune repertoire data. Our framework is highly scalable as it easily allowed for the analysis of 1000 simulated immune repertoires; this exceeds the size of published immune repertoire datasets by one to two orders of magnitude.

CONCLUSIONS

Our framework offers the possibility to advance immune-repertoire-based fingerprinting, which may in the future enable a systems immunogenomics approach for vaccine profiling and the accurate and early detection of disease and infection.

Stochastic particle barcoding for single-cell tracking and multiparametric analysis

This study presents stochastic particle barcoding (SPB), a method for tracking cell identity across bioanalytical platforms. In this approach, single cells or small collections of cells are co-encapsulated within an enzymatically-degradable hydrogel block along with a random collection of fluorescent beads, whose number, color, and position encode the identity of the cell, enabling samples to be transferred in bulk between single-cell assay platforms without losing the identity of individual cells. The application of SPB is demonstrated for transferring cells from a subnanoliter protein secretion/phenotyping array platform into a microtiter plate, with re-identification accuracies in the plate assay of 96±2%. Encapsulated cells are recovered by digesting the hydrogel, allowing subsequent genotyping and phenotyping of cell lysates. Finally, a model scaling is developed to illustrate how different parameters affect the accuracy of SPB and to motivate scaling of the method to thousands of unique blocks.

Interaction analysis through proteomic phage display

Phage display is a powerful technique for profiling specificities of peptide binding domains. The method is suited for the identification of high-affinity ligands with inhibitor potential when using highly diverse combinatorial peptide phage libraries. Such experiments further provide consensus motifs for genome-wide scanning of ligands of potential biological relevance. A complementary but considerably less explored approach is to display expression products of genomic DNA, cDNA, open reading frames (ORFs), or oligonucleotide libraries designed to encode defined regions of a target proteome on phage particles. One of the main applications of such proteomic libraries has been the elucidation of antibody epitopes. This review is focused on the use of proteomic phage display to uncover protein-protein interactions of potential relevance for cellular function. The method is particularly suited for the discovery of interactions between peptide binding domains and their targets. We discuss the largely unexplored potential of this method in the discovery of domain-motif interactions of potential biological relevance.