Antibody Repertoire Analysis of Hepatitis C Virus Infections Identifies Immune Signatures Associated With Spontaneous Clearance

Guidelines for reproducible analysis of adaptive immune receptor repertoire sequencing data

Enhancing the reproducibility and comprehension of adaptive immune receptor repertoire sequencing (AIRR-seq) data analysis is critical for scientific progress. This study presents guidelines for reproducible AIRR-seq data analysis, and a collection of ready-to-use pipelines with comprehensive documentation. To this end, ten common pipelines were implemented using ViaFoundry, a user-friendly interface for pipeline management and automation. This is accompanied by versioned containers, documentation and archiving capabilities. The automation of pre-processing analysis steps and the ability to modify pipeline parameters according to specific research needs are emphasized. AIRR-seq data analysis is highly sensitive to varying parameters and setups; using the guidelines presented here, the ability to reproduce previously published results is demonstrated. This work promotes transparency, reproducibility, and collaboration in AIRR-seq data analysis, serving as a model for handling and documenting bioinformatics pipelines in other research domains.

IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data

In adaptive immune receptor repertoire analysis, determining the germline variable (V) allele associated with each T- and B-cell receptor sequence is a crucial step. This process is highly impacted by allele annotations. Aligning sequences, assigning them to specific germline alleles, and inferring individual genotypes are challenging when the repertoire is highly mutated, or sequence reads do not cover the whole V region. Here, we propose an alternative naming scheme for the V alleles, as well as a novel method to infer individual genotypes. We demonstrate the strengths of the two by comparing their outcomes to other genotype inference methods. We validate the genotype approach with independent genomic long-read data. The naming scheme is compatible with current annotation tools and pipelines. Analysis results can be converted from the proposed naming scheme to the nomenclature determined by the International Union of Immunological Societies (IUIS). Both the naming scheme and the genotype procedure are implemented in a freely available R package (PIgLET https://bitbucket.org/yaarilab/piglet). To allow researchers to further explore the approach on real data and to adapt it for their uses, we also created an interactive website (https://yaarilab.github.io/IGHV_reference_book).

A comparison of the binding sites of antibodies and single-domain antibodies

Antibodies are the largest class of biotherapeutics. However, in recent years, single-domain antibodies have gained traction due to their smaller size and comparable binding affinity. Antibodies (Abs) and single-domain antibodies (sdAbs) differ in the structures of their binding sites: most significantly, single-domain antibodies lack a light chain and so have just three CDR loops. Given this inherent structural difference, it is important to understand whether Abs and sdAbs are distinguishable in how they engage a binding partner and thus, whether they are suited to different types of epitopes. In this study, we use non-redundant sequence and structural datasets to compare the paratopes, epitopes and antigen interactions of Abs and sdAbs. We demonstrate that even though sdAbs have smaller paratopes, they target epitopes of equal size to those targeted by Abs. To achieve this, the paratopes of sdAbs contribute more interactions per residue than the paratopes of Abs. Additionally, we find that conserved framework residues are of increased importance in the paratopes of sdAbs, suggesting that they include non-specific interactions to achieve comparable affinity. Furthermore, the epitopes of sdAbs are only marginally less accessible than those of Abs: we posit that this may be explained by differences in the orientation and compaction of sdAb and Ab CDR-H3 loops. Overall, our results have important implications for the engineering and humanization of sdAbs, as well as the selection of the best modality for targeting a particular epitope.

Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads

Deep parallel sequencing (NGS) is a viable tool for monitoring scFv and Fab library dynamics in many antibody engineering high-throughput screening efforts. Although very useful, the commonly used Illumina NGS platform cannot handle the entire sequence of scFv or Fab in a single read, usually focusing on specific CDRs or resorting to sequencing VH and VL variable domains separately, thus limiting its utility in comprehensive monitoring of selection dynamics. Here we present a simple and robust method for deep sequencing repertoires of full length scFv, Fab and Fv antibody sequences. This process utilizes standard molecular procedures and unique molecular identifiers (UMI) to pair separately sequenced VH and VL. We show that UMI assisted VH-VL matching allows for a comprehensive and highly accurate mapping of full length Fv clonal dynamics in large highly homologous antibody libraries, as well as identification of rare variants. In addition to its utility in synthetic antibody discovery processes, our method can be instrumental in generating large datasets for machine learning (ML) applications, which in the field of antibody engineering has been hampered by conspicuous paucity of large scale full length Fv data.

Hepatitis C Virus prediction based on machine learning framework: a real-world case study in Egypt

Prediction and classification of diseases are essential in medical science, as it attempts to immune the spread of the disease and discover the infected regions from the early stages. Machine learning (ML) approaches are commonly used for predicting and classifying diseases that are precisely utilized as an efficient tool for doctors and specialists. This paper proposes a prediction framework based on ML approaches to predict Hepatitis C Virus among healthcare workers in Egypt. We utilized real-world data from the National Liver Institute, founded at Menoufiya University (Menoufiya, Egypt). The collected dataset consists of 859 patients with 12 different features. To ensure the robustness and reliability of the proposed framework, we performed two scenarios: the first without feature selection and the second after the features are selected based on sequential forward selection (SFS). Furthermore, the feature subset selected based on the generated features from SFS is evaluated. Naïve Bayes, random forest (RF), K-nearest neighbor, and logistic regression are utilized as induction algorithms and classifiers for model evaluation. Then, the effect of parameter tuning on learning techniques is measured. The experimental results indicated that the proposed framework achieved higher accuracies after SFS selection than without feature selection. Moreover, the RF classifier achieved 94.06% accuracy with a minimum learning elapsed time of 0.54 s. Finally, after adjusting the hyperparameter values of the RF classifier, the classification accuracy is improved to 94.88% using only four features.

Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity

Introduction

The success of the human body in fighting SARS-CoV2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance.

Methods

We report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV2 compared with uninfected controls.

Results

In contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients.

Discussion

These features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges.

A somatic hypermutation-based machine learning model stratifies individuals with Crohn's disease and controls

Crohn's disease (CD) is a chronic relapsing-remitting inflammatory disorder of the gastrointestinal tract that is characterized by altered innate and adaptive immune function. Although massively parallel sequencing studies of the T cell receptor repertoire identified oligoclonal expansion of unique clones, much less is known about the B cell receptor (BCR) repertoire in CD. Here, we present a novel BCR repertoire sequencing data set from ileal biopsies from pediatric patients with CD and controls, and identify CD-specific somatic hypermutation (SHM) patterns, revealed by a machine learning (ML) algorithm trained on BCR repertoire sequences. Moreover, ML classification of a different data set from blood samples of adults with CD versus controls identified that V gene usage, clusters, or mutation frequencies yielded excellent results in classifying the disease (F1 > 90%). In summary, we show that an ML algorithm enables the classification of CD based on unique BCR repertoire features with high accuracy.

A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction methods

Affinity maturation (AM) of B cells through somatic hypermutations (SHMs) enables the immune system to evolve to recognize diverse pathogens. The accumulation of SHMs leads to the formation of clonal lineages of antibody-secreting b cells that have evolved from a common naïve B cell. Advances in high-throughput sequencing have enabled deep scans of B cell receptor repertoires, paving the way for reconstructing clonal trees. However, it is not clear if clonal trees, which capture microevolutionary time scales, can be reconstructed using traditional phylogenetic reconstruction methods with adequate accuracy. In fact, several clonal tree reconstruction methods have been developed to fix supposed shortcomings of phylogenetic methods. Nevertheless, no consensus has been reached regarding the relative accuracy of these methods, partially because evaluation is challenging. Benchmarking the performance of existing methods and developing better methods would both benefit from realistic models of clonal lineage evolution specifically designed for emulating B cell evolution. In this paper, we propose a model for modeling B cell clonal lineage evolution and use this model to benchmark several existing clonal tree reconstruction methods. Our model, designed to be extensible, has several features: by evolving the clonal tree and sequences simultaneously, it allows modeling selective pressure due to changes in affinity binding; it enables scalable simulations of large numbers of cells; it enables several rounds of infection by an evolving pathogen; and, it models building of memory. In addition, we also suggest a set of metrics for comparing clonal trees and measuring their properties. Our results show that while maximum likelihood phylogenetic reconstruction methods can fail to capture key features of clonal tree expansion if applied naively, a simple post-processing of their results, where short branches are contracted, leads to inferences that are better than alternative methods.

The Neutralizing Antibody Responses of Individuals That Spontaneously Resolve Hepatitis C Virus Infection

Hepatitis C virus (HCV) infection is a major global health problem. In the majority of cases the virus is not cleared by the host immune response and progresses to chronic infection. Studies of the neutralizing antibody responses in individuals that naturally clear infection are limited. Understanding what constitutes a successful antibody response versus one that has 'failed' and resulted in chronic infection is important to understand what type of antibody response would need to be elicited by a protective vaccine. Samples from spontaneous clearers are difficult to obtain therefore studies are often limited. In our study through HCV Research UK, we had access to a cohort of over 200 samples. We identified the samples that contained HCV neutralizing antibodies using ELISA and HCV pseudoparticle (HCVpp) assays. We then utilised mutagenesis and cross-competition analysis to determine the profile of the neutralizing antibody responses. In addition, we analysed a cohort of samples from chronic infection using the same techniques to enable direct comparison of the antibody profiles observed in both cohorts. We conclude that similar profiles are present in both cohorts indicating that it is not the neutralizing antibody response per se that determines the outcome of infection. These data will provide useful information for future HCV vaccine design.

A Deep Learning Model for Accurate Diagnosis of Infection Using Antibody Repertoires

The adaptive immune receptor repertoire consists of the entire set of an individual's BCRs and TCRs and is believed to contain a record of prior immune responses and the potential for future immunity. Analyses of TCR repertoires via deep learning (DL) methods have successfully diagnosed cancers and infectious diseases, including coronavirus disease 2019. However, few studies have used DL to analyze BCR repertoires. In this study, we collected IgG H chain Ab repertoires from 276 healthy control subjects and 326 patients with various infections. We then extracted a comprehensive feature set consisting of 10 subsets of repertoire-level features and 160 sequence-level features and tested whether these features can distinguish between infected individuals and healthy control subjects. Finally, we developed an ensemble DL model, namely, DL method for infection diagnosis (https://github.com/chenyuan0510/DeepID), and used this model to differentiate between the infected and healthy individuals. Four subsets of repertoire-level features and four sequence-level features were selected because of their excellent predictive performance. The DL method for infection diagnosis outperformed traditional machine learning methods in distinguishing between healthy and infected samples (area under the curve = 0.9883) and achieved a multiclassification accuracy of 0.9104. We also observed differences between the healthy and infected groups in V genes usage, clonal expansion, the complexity of reads within clone, the physical properties in the α region, and the local flexibility of the CDR3 amino acid sequence. Our results suggest that the Ab repertoire is a promising biomarker for the diagnosis of various infections.

T cell receptor beta germline variability is revealed by inference from repertoire data

BACKGROUND

T and B cell receptor (TCR, BCR) repertoires constitute the foundation of adaptive immunity. Adaptive immune receptor repertoire sequencing (AIRR-seq) is a common approach to study immune system dynamics. Understanding the genetic factors influencing the composition and dynamics of these repertoires is of major scientific and clinical importance. The chromosomal loci encoding for the variable regions of TCRs and BCRs are challenging to decipher due to repetitive elements and undocumented structural variants.

METHODS

To confront this challenge, AIRR-seq-based methods have recently been developed for B cells, enabling genotype and haplotype inference and discovery of undocumented alleles. However, this approach relies on complete coverage of the receptors' variable regions, whereas most T cell studies sequence a small fraction of that region. Here, we adapted a B cell pipeline for undocumented alleles, genotype, and haplotype inference for full and partial AIRR-seq TCR data sets. The pipeline also deals with gene assignment ambiguities, which is especially important in the analysis of data sets of partial sequences.

RESULTS

From the full and partial AIRR-seq TCR data sets, we identified 39 undocumented polymorphisms in T cell receptor Beta V (TRBV) and 31 undocumented 5 ' UTR sequences. A subset of these inferences was also observed using independent genomic approaches. We found that a single nucleotide polymorphism differentiating between the two documented T cell receptor Beta D2 (TRBD2) alleles is strongly associated with dramatic changes in the expressed repertoire.

CONCLUSIONS

We reveal a rich picture of germline variability and demonstrate how a single nucleotide polymorphism dramatically affects the composition of the whole repertoire. Our findings provide a basis for annotation of TCR repertoires for future basic and clinical studies.

Immune2vec: Embedding B/T Cell Receptor Sequences in ℝ N Using Natural Language Processing

The adaptive branch of the immune system learns pathogenic patterns and remembers them for future encounters. It does so through dynamic and diverse repertoires of T- and B- cell receptors (TCR and BCRs, respectively). These huge immune repertoires in each individual present investigators with the challenge of extracting meaningful biological information from multi-dimensional data. The ability to embed these DNA and amino acid textual sequences in a vector-space is an important step towards developing effective analysis methods. Here we present Immune2vec, an adaptation of a natural language processing (NLP)-based embedding technique for BCR repertoire sequencing data. We validate Immune2vec on amino acid 3-gram sequences, continuing to longer BCR sequences, and finally to entire repertoires. Our work demonstrates Immune2vec to be a reliable low-dimensional representation that preserves relevant information of immune sequencing data, such as n-gram properties and IGHV gene family classification. Applying Immune2vec along with machine learning approaches to patient data exemplifies how distinct clinical conditions can be effectively stratified, indicating that the embedding space can be used for feature extraction and exploratory data analysis.

Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics

Dengue virus (DENV) poses a serious threat to global health as the causative agent of dengue fever. The virus is endemic in more than 128 countries resulting in approximately 390 million infection cases each year. Currently, there is no approved therapeutic for treatment nor a fully efficacious vaccine. The development of therapeutics is confounded and hampered by the complexity of the immune response to DENV, in particular to sequential infection with different DENV serotypes (DENV1-5). Researchers have shown that the DENV envelope (E) antigen is primarily responsible for the interaction and subsequent invasion of host cells for all serotypes and can elicit neutralizing antibodies in humans. The advent of high-throughput sequencing and the rapid advancements in computational analysis of complex data, has provided tools for the deconvolution of the DENV immune response. Several types of complex statistical analyses, machine learning models and complex visualizations can be applied to begin answering questions about the B- and T-cell immune responses to multiple infections, antibody-dependent enhancement, identification of novel therapeutics and advance vaccine research.

Hepatitis C Virus Epitope Immunodominance and B Cell Repertoire Diversity

Despite the advent of effective, curative treatments for hepatitis C virus (HCV), a preventative vaccine remains essential for the global elimination of HCV. It is now clear that the induction of broadly neutralising antibodies (bNAbs) is essential for the rational design of such a vaccine. This review details the current understanding of epitopes on the HCV envelope, characterising the potency, breadth and immunodominance of antibodies induced against these epitopes, as well as describing the interactions between B-cell receptors and HCV infection, with a particular focus on bNAb heavy and light chain variable gene usage. Additionally, we consider the importance of a public repertoire for antibodies against HCV, compiling current knowledge and suggesting that further research in this area may be critical to the rational design of an effective HCV vaccine.

Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls

Celiac disease (CeD) is a common autoimmune disorder caused by an abnormal immune response to dietary gluten proteins. The disease has high heritability. HLA is the major susceptibility factor, and the HLA effect is mediated via presentation of deamidated gluten peptides by disease-associated HLA-DQ variants to CD4+ T cells. In addition to gluten-specific CD4+ T cells the patients have antibodies to transglutaminase 2 (autoantigen) and deamidated gluten peptides. These disease-specific antibodies recognize defined epitopes and they display common usage of specific heavy and light chains across patients. Interactions between T cells and B cells are likely central in the pathogenesis, but how the repertoires of naïve T and B cells relate to the pathogenic effector cells is unexplored. To this end, we applied machine learning classification models to naïve B cell receptor (BCR) repertoires from CeD patients and healthy controls. Strikingly, we obtained a promising classification performance with an F1 score of 85%. Clusters of heavy and light chain sequences were inferred and used as features for the model, and signatures associated with the disease were then characterized. These signatures included amino acid (AA) 3-mers with distinct bio-physiochemical characteristics and enriched V and J genes. We found that CeD-associated clusters can be identified and that common motifs can be characterized from naïve BCR repertoires. The results may indicate a genetic influence by BCR encoding genes in CeD. Analysis of naïve BCRs as presented here may become an important part of assessing the risk of individuals to develop CeD. Our model demonstrates the potential of using BCR repertoires and in particular, naïve BCR repertoires, as disease susceptibility markers.

Antibody Responses in Hepatitis C Infection

Antibody responses in hepatitis C virus (HCV) have been a rather mysterious research topic for many investigators working in the field. Chronic HCV infection is often associated with dysregulation of immune functions particularly in B cells, leading to abnormal lymphoproliferation or the production of autoantibodies that exacerbate inflammation and extrahepatic diseases. When considering the antiviral function of antibody, it was difficult to endorse its role in HCV protection, whereas T-cell response has been shown unequivocally critical for natural recovery. Recent breakthroughs in the study of HCV and antigen-specific antibody responses provide important insights into viral vulnerability to antibodies and the immunogenetic and structural properties of the neutralizing antibodies. The new knowledge reinvigorates HCV vaccine research by illuminating a new path for the rational design of vaccine antigens to elicit broadly neutralizing antibodies for protection.

Pyruvate dehydrogenase complex-enzyme 2, a new target for Listeria spp. detection identified using combined phage display technologies

The genus Listeria comprises ubiquitous bacteria, commonly present in foods and food production facilities. In this study, three different phage display technologies were employed to discover targets, and to generate and characterize novel antibodies against Listeria: antibody display for biomarker discovery and antibody generation; ORFeome display for target identification; and single-gene display for epitope characterization. With this approach, pyruvate dehydrogenase complex—enzyme 2 (PDC-E2) was defined as a new detection target for Listeria, as confirmed by immunomagnetic separation-mass spectrometry (IMS-MS). Immunoblot and fluorescence microscopy showed that this protein is accessible on the bacterial cell surface of living cells. Recombinant PDC-E2 was produced in E. coli and used to generate 16 additional antibodies. The resulting set of 20 monoclonal scFv-Fc was tested in indirect ELISA against 17 Listeria and 16 non-Listeria species. Two of them provided 100% sensitivity (CI 82.35–100.0%) and specificity (CI 78.20–100.0%), confirming PDC-E2 as a suitable target for the detection of Listeria. The binding region of 18 of these antibodies was analyzed, revealing that ≈ 90% (16/18) bind to the lipoyl domains (LD) of the target. The novel target PDC-E2 and highly specific antibodies against it offer new opportunities to improve the detection of Listeria.

COVID-19: A Review on Diagnosis, Treatment, and Prophylaxis

Coronavirus 2 (CoV) Severe Acute Respiratory Syndrome (SARS-CoV2) is causing a highly infectious pandemic pneumonia. Coronaviruses are positive sense single-stranded RNA viruses that infect several animal species, causing symptoms that range from those similar to the common cold to severe respiratory syndrome. The Angiotensin Converting Enzyme 2 (ACE2) is the SARS-CoV2 functional receptor. Measures are currently undertaken worldwide to control the infection to avoid disruption of the social and economic equilibrium, especially in countries with poor healthcare resources. In a guarded optimistic view, we hope that the undertaken preventive and treatment measures will at least contribute to contain viral diffusion, attenuate activity, or even eliminate SARS-CoV2. In this review, we discuss emerging perspectives for prevention/treatment of COVID-19 infection. In addition to vaccines under development, passive immunization is an open opportunity since patients develop neutralizing antibodies. A full spectrum of potential drugs for COVID-19 infections could in turn affect virus binding or enzymatic activities involved in viral replication and transcription. Furthermore, clinical trials are currently evaluating the safety and efficacy of anti-inflammatory drugs, such as tocilizumab. Bioinformatics may allow characterization of specific CD8+ and CD4+ T cell responses; thus, CoV2 T cells' frequency can be correlated with the disease severity and outcome. Combinatorial antibody phage display may be empowered to identify the immune repertoire of CoV2-specific neutralizing antibodies.

Association of the Sialylation of Antibodies Specific to the HCV E2 Envelope Glycoprotein with Hepatic Fibrosis Progression and Antiviral Therapy Efficacy

The E2 envelope glycoprotein of the hepatitis C virus (HCV) is a major target of broadly neutralizing antibodies that are closely related to a spontaneous cure of HCV infection. There is still no data about the diversity of E2-specific antibodies (Abs) glycosylation. The aim of this study was to analyze the level and sialylation of E2 IgG Abs, the relation of the respective changes to hepatic fibrosis (F) progression and their possible association with the efficacy of interferon-α-2a plus ribavirin (IFN-RBV) antiviral therapy. One hundred three HCV infected treatment-naive patients were examined using ELISA with E2 recombinant protein as antigen and sialic acid-specific Sambucus nigra agglutinin. The efficacy of the IFN-RBV treatment of patients with HCV dominant 1b and 3a genotypes (GT) was evaluated. A significant decrease of E2 Abs sialylation in the late stages of fibrosis was found irrespective of HCV genotype. On this basis, the F4 stage of fibrosis can be discriminated from its F0 or F1-3 stage by an about 75-79% accuracy. HCV infection of 1b genotype is associated with the production of lower sialylated E2 Abs, a higher frequency of F4 stage fibrosis, and a worse response to antiviral therapy. The increased SNA reactivity of E2 Abs was observed in patients with a sustained virological response (SVR). The proportion of SVR responders was significantly higher among patients with 3a genotype. However, for both dominant HCV genotypes (3a and 1b), an increased sialylation of E2 IgG was associated with a higher rate of patients with sustained virological response to antiviral therapy. Thus, the association of alterations of anti-E2 IgG Abs sialylation with hepatic fibrosis stage, HCV genotype, and the efficacy of antiviral therapy enables using these changes as novel noninvasive predictive biomarkers. The clinical potential of these findings is discussed.