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VDJbase is a publicly available database that offers easy searching of data describing the complete sets of gene sequences (genotypes and haplotypes) inferred from adaptive immune receptor repertoire sequencing datasets. VDJbase is designed to act as a resource that will allow the scientific community to explore the genetic variability of the immunoglobulin (Ig) and T cell receptor (TR) gene loci. It can also assist in the investigation of Ig- and TR-related genetic predispositions to diseases. Our database includes web-based query and online tools to assist in visualization and analysis of the genotype and haplotype data. It enables users to detect those alleles and genes that are significantly over-represented in a particular population, in terms of genotype, haplotype and gene expression. The database website can be freely accessed at https://www.vdjbase.org/, and no login is required. The data and code use creative common licenses and are freely downloadable from https://bitbucket.org/account/user/yaarilab/projects/GPHP.

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.

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.

Analysis of antibody repertoires by high-throughput sequencing is of major importance in understanding adaptive immune responses. Our knowledge of variations in the genomic loci encoding immunoglobulin genes is incomplete, resulting in conflicting VDJ gene assignments and biased genotype and haplotype inference. Haplotypes can be inferred using IGHJ6 heterozygosity, observed in one third of the people. Here, we propose a robust novel method for determining VDJ haplotypes by adapting a Bayesian framework. Our method extends haplotype inference to IGHD- and IGHV-based analysis, enabling inference of deletions and copy number variations in the entire population. To test this method, we generated a multi-individual data set of naive B-cell repertoires, and found allele usage bias, as well as a mosaic, tiled pattern of deleted IGHD and IGHV genes. The inferred haplotypes may have clinical implications for genetic disease predispositions. Our findings expand the knowledge that can be extracted from antibody repertoire sequencing data., High-throughput sequencing and analyzes of antibody repertoire provide important information on immune responses, but current methodologies are limited in sequence assembly precision and haplotype inference validity. Here the authors propose a new Bayesian haplotyping method, and attest its broad application with a large, multi-individual dataset.

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.

The partial success of tumor immunotherapy induced by checkpoint blockade, which is not antigen-specific, suggests that the immune system of some patients contain antigen receptors able to specifically identify tumor cells. Here we focused on T-cell receptor (TCR) repertoires associated with spontaneous breast cancer. We studied the alpha and beta chain CDR3 domains of TCR repertoires of CD4 T cells using deep sequencing of cell populations in mice and applied the results to published TCR sequence data obtained from human patients. We screened peripheral blood T cells obtained monthly from individual mice spontaneously developing breast tumors by 5 months. We then looked at identical TCR sequences in published human studies; we used TCGA data from tumors and healthy tissues of 1,256 breast cancer resections and from 4 focused studies including sequences from tumors, lymph nodes, blood and healthy tissues, and from single cell dataset of 3 breast cancer subjects. We now report that mice spontaneously developing breast cancer manifest shared, Public CDR3 regions in both their alpha and beta and that a significant number of women with early breast cancer manifest identical CDR3 sequences. These findings suggest that the development of breast cancer is associated, across species, with biomarker, exclusive TCR repertoires., Author summary More than 1,100 cancer treatments are currently in clinical testing. Of these, 295 are immune-oncology medicines and vaccines. For Checkpoint Immunotherapy alone, 52,539 patients were enrolled in 2017. Of these treatments, 108 are for breast cancer, the leading cancer diagnosed in women. Although checkpoint immunotherapy has yet to be proven beneficial in most types of breast cancer, current observations of which specific T cell go into the tumor, and how these cells are connected to the response to treatment, have lead to a new understating of the role of the immune system. Yet, and in spite of this intense research into the role of T cells in tumors, very little is known about how clones of T cells behave during tumor formation. The paper we present here studies this clonal behavior over time. We study it during mammary tumor development in mice. We see that a set of T cell clones, which comes from a diversity of different nucleotide sequences, stands out in its behavior compared with other T cell clones. Importantly, we show that these mouse T cell clones have a strong connection to human breast cancer. The specific identification of a subset of T cell clones, together with the behavior of these clones over time, is critical to our understanding of the effect of modifications to the immune system, such as the modifications made by immunotherapy treatments, and to our understanding of how to better control the behavior of the immune system.

With the advent of high-throughput sequencing technologies, the fields of immunogenomics and adaptive immune receptor repertoire research are facing both opportunities and challenges. Adaptive immune receptor repertoire sequencing (AIRR-seq) has become an increasingly important tool to characterize T and B cell responses in settings of interest. However, the majority of AIRR-seq studies conducted so far were performed in individuals of European ancestry, restricting the ability to identify variation in human adaptive immune responses across populations and limiting their applications. As AIRR-seq studies depend on the ability to assign VDJ sequence reads to the correct germline gene segments, efforts to characterize the genomic loci that encode adaptive immune receptor genes in different populations are urgently needed. The availability of comprehensive germline gene databases and further applications of AIRR-seq studies to individuals of non-European ancestry will substantially enhance our understanding of human adaptive immune responses, promote the development of effective diagnostics and treatments, and eventually advance precision medicine., 22 pages,1 table

The spillover of animal coronaviruses (aCoVs) to humans has caused SARS, MERS, and COVID-19. While antibody responses displaying cross-reactivity between SARS-CoV-2 and seasonal/common cold human coronaviruses (hCoVs) have been reported, potential cross-reactivity with aCoVs and the diagnostic implications are incompletely understood. Here, we probed for antibody binding against all seven hCoVs and 49 aCoVs represented as 12,924 peptides within a phage-displayed antigen library. Antibody repertoires of 269 recovered COVID-19 patients showed distinct changes compared to 260 unexposed pre-pandemic controls, not limited to binding of SARS-CoV-2 antigens but including binding to antigens from hCoVs and aCoVs with shared motifs to SARS-CoV-2. We isolated broadly reactive monoclonal antibodies from recovered COVID-19 patients that bind a shared motif of SARS-CoV-2, hCoV-OC43, hCoV-HKU1, and several aCoVs, demonstrating that interspecies cross-reactivity can be mediated by a single immunoglobulin. Employing antibody binding data against the entire CoV antigen library allowed accurate discrimination of recovered COVID-19 patients from unexposed individuals by machine learning. Leaving out SARS-CoV-2 antigens and relying solely on antibody binding to other hCoVs and aCoVs achieved equally accurate detection of SARS-CoV-2 infection. The ability to detect SARS-CoV-2 infection without knowledge of its unique antigens solely from cross-reactive antibody responses against other hCoVs and aCoVs suggests a potential diagnostic strategy for the early stage of future pandemics. Creating regularly updated antigen libraries representing the animal coronavirome can provide the basis for a serological assay already poised to identify infected individuals following a future zoonotic transmission event.

Germline variations in immunoglobulin genes influence the repertoire of B cell receptors and antibodies, and such polymorphisms may impact disease susceptibility. However, the knowledge of the genomic variation of the immunoglobulin loci is scarce. Here, we report 25 potential novel germline IGHV alleles as inferred from rearranged naïve B cell cDNA repertoires of 98 individuals. Thirteen novel alleles were selected for validation, out of which ten were successfully confirmed by targeted amplification and Sanger sequencing of non-B cell DNA. Moreover, we detected a high degree of variability upstream of the V-REGION in the 5′UTR, L-PART1 and L-PART2 sequences, and found that identical V-REGION alleles can differ in upstream sequences. Thus, we have identified a large genetic variation not only in the V-REGION but also in the upstream sequences of IGHV genes. Our findings provide a new perspective for annotating immunoglobulin repertoire sequencing data.

Cancer immunotherapy has made enormous progress in offering safer and more effective treatments for the disease. Specifically, programmed death ligand 1 antibody (αPDL1), designed to perform immune checkpoint blockade (ICB), is now considered a pillar in cancer immunotherapy. However, due to the complexity and heterogeneity of tumors, as well as the diversity in patient response, ICB therapy only has a 30% success rate, at most; moreover, the efficacy of ICB can be evaluated only two months after start of treatment. Therefore, early identification of potential responders and nonresponders to therapy, using noninvasive means, is crucial for improving treatment decisions. Here, we report a straightforward approach for fast, image-guided prediction of therapeutic response to ICB. In a colon cancer mouse model, we demonstrate that the combination of computed tomography imaging and gold nanoparticles conjugated to αPDL1 allowed prediction of therapeutic response, as early as 48 h after treatment. This was achieved by noninvasive measurement of nanoparticle accumulation levels within the tumors. Moreover, we show that the nanoparticles efficiently prevented tumor growth with only a fifth of the standard dosage of clinical care. This technology may be developed into a powerful tool for early and noninvasive patient stratification as responders or nonresponders.

The immune rejection of allografts is mediated by T cells via two distinct pathways: the direct and the indirect pathways. Direct alloresponse to intact donor MHC molecules is ensured by T cells which are polyclonal and directed toward a variety of antigens. This response is highly sensitive to treatment by immunosuppressive drugs including Cyclosporin A. Indirect alloresponse is oligoclonal and involves a few dominant antigen peptides on donor MHC. In contrast to its direct counterpart, indirect allorecognition is thought to be poorly sensitive to blockade by cyclosporin A. It is likely that indirect and direct types of alloresponses play different roles in the physiology of the rejection process. T cell responses occurring via direct allorecognition play a critical role during the early phase of acute graft rejection by sensitizing the host to graft antigens. Alternatively, once such sensitization has taken place, indirect type of alloresponse may become predominant and presumably represent the driving force in the actual destruction of transplanted tissues. In addition, we and others have provided strong circumstantial evidence indicating that secondary T cell responses via indirect allorecognition spread to new determinants on donor MHC and tissue-specific antigens. This phenomenon is likely to play an important role in late and chronic rejection, a major obstacle to long-term graft acceptance in clinical transplantation. Finally, a series of studies have demonstrated that early, pre-transplant treatment with tolerogenic donor-derived MHC peptides can protect the graft from rejection in rodents. Although the mechanisms involved in MHC-peptide-induced tolerance are ill defined, this strategy represents a promising approach for ensuring long-lasting graft acceptance in the absence of widespread immunosuppression. It is now crucial to further explore the mechanims involved in immunogenicity and tolerogenicity of MHC peptides and to initiate clinical studies to evaluate the efficacy of blocking indirect alloresponses in transplanted patients.

Small sample sizes combined with high person-to-person variability can make it difficult to detect significant gene expression changes from transcriptional profiling studies. Subtle, but coordinated, gene expression changes may be detected using gene set analysis approaches. Meta-analysis is another approach to increase the power to detect biologically relevant changes by integrating information from multiple studies. Here, we present a framework that combines both approaches and allows for meta-analysis of gene sets. QuSAGE meta-analysis extends our previously published QuSAGE framework, which offers several advantages for gene set analysis, including fully accounting for gene-gene correlations and quantifying gene set activity as a full probability density function. Application of QuSAGE meta-analysis to influenza vaccination response shows it can detect significant activity that is not apparent in individual studies.

Immunoglobulins or antibodies are the main effector molecules of the B-cell lineage and are encoded by hundreds of variable (V), diversity (D), and joining (J) germline genes, which recombine to generate enormous IG diversity. Recently, high-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) of recombined V-(D)-J genes has offered unprecedented insights into the dynamics of IG repertoires in health and disease. Faithful biological interpretation of AIRR-seq studies depends upon the annotation of raw AIRR-seq data, using reference germline gene databases to identify the germline genes within each rearrangement. Existing reference databases are incomplete, as shown by recent AIRR-seq studies that have inferred the existence of many previously unreported polymorphisms. Completing the documentation of genetic variation in germline gene databases is therefore of crucial importance. Lymphocyte receptor genes and alleles are currently assigned by the Immunoglobulins, T cell Receptors and Major Histocompatibility Nomenclature Subcommittee of the International Union of Immunological Societies (IUIS) and managed in IMGT®, the international ImMunoGeneTics information system® (IMGT). In 2017, the IMGT Group reached agreement with a group of AIRR-seq researchers on the principles of a streamlined process for identifying and naming inferred allelic sequences, for their incorporation into IMGT®. These researchers represented the AIRR Community, a network of over 300 researchers whose objective is to promote all aspects of immunoglobulin and T-cell receptor repertoire studies, including the standardization of experimental and computational aspects of AIRR-seq data generation and analysis. The Inferred Allele Review Committee (IARC) was established by the AIRR Community to devise policies, criteria, and procedures to perform this function. Formalized evaluations of novel inferred sequences have now begun and submissions are invited via a new dedicated portal (https://ogrdb.airr-community.org). Here, we summarize recommendations developed by the IARC—focusing, to begin with, on human IGHV genes—with the goal of facilitating the acceptance of inferred allelic variants of germline IGHV genes. We believe that this initiative will improve the quality of AIRR-seq studies by facilitating the description of human IG germline gene variation, and that in time, it will expand to the documentation of TR and IG genes in many vertebrate species.

Recent advances in data acquiring technologies in biology have led to major challenges in mining relevant information from large datasets. For example, single-cell RNA sequencing technologies are producing expression and sequence information from tens of thousands of cells in every single experiment. A common task in analyzing biological data is to cluster samples or features (e.g. genes) into groups sharing common characteristics. This is an NP-hard problem for which numerous heuristic algorithms have been developed. However, in many cases, the clusters created by these algorithms do not reflect biological reality. To overcome this, a Networks Based Clustering (NBC) approach was recently proposed, by which the samples or genes in the dataset are first mapped to a network and then community detection (CD) algorithms are used to identify clusters of nodes.Here, we created an open and flexible python-based toolkit for NBC that enables easy and accessible network construction and community detection. We then tested the applicability of NBC for identifying clusters of cells or genes from previously published large-scale single-cell and bulk RNA-seq datasets.We show that NBC can be used to accurately and efficiently analyze large-scale datasets of RNA sequencing experiments.

The increasing worldwide prevalence of Hepatocellular carcinoma (HCC), characterized by resistance to conventional chemotherapy, poor prognosis and eventually mortality, place it as a prime target for new modes of prevention and treatment. Hepatitis C Virus (HCV) is the predominant risk factor for HCC in the US and Europe. Multiple epidemiological studies showed that sustained virological responses (SVR) following treatment with the powerful direct acting antivirals (DAAs), which have replaced interferon-based regimes, do not eliminate tumor development. We aimed to identify an HCV-specific pathogenic mechanism that persists post SVR following DAAs treatment. We demonstrate that HCV infection induces genome-wide epigenetic changes by performing chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) for histone post-translational modifications that are epigenetic markers for active and repressed chromatin. The changes in histone modifications correlate with reprogramed host gene expression and alter signaling pathways known to be associated with HCV life cycle and HCC. These epigenetic alterations require the presence of HCV RNA or/and expression of the viral proteins in the cells. Importantly, the epigenetic changes induced following infection persist as an "epigenetic signature" after virus eradication by DAAs treatment, as detected using in vitro HCV infection models. These observations led to the identification of an 8 gene signature that is associated with HCC development and demonstrate persistent epigenetic alterations in HCV infected and post SVR liver biopsy samples. The epigenetic signature was reverted in vitro by drugs that inhibit epigenetic modifying enzyme and by the EGFR inhibitor, Erlotinib. This epigenetic “scarring” of the genome, persisting following HCV eradication, suggest a novel mechanism for the persistent pathogenesis of HCV after its eradication by DAAs. Our study offers new avenues for prevention of the persistent oncogenic effects of chronic hepatitis infections using specific drugs to revert the epigenetic changes to the genome., Author summary Hepatitis C virus (HCV) is a leading cause of hepatocellular carcinoma (HCC) in western countries. While direct acting antivirals (DAAs) therapy for HCV efficiently eradicates the infection, sustained virological response (SVR) following anti-HCV treatment does not eliminate the risk for HCC development. With the 3–4 million newly infected cases each year, it is estimated that HCV-associated disease burden remains high in the next decade and the population of post-DAA-based SVR patients is expected to grow exponentially in the near future. Therefore, Post-SVR HCC is an emerging problem with an urgent unmet need for the elucidation of its molecular mechanisms for therapeutic target and biomarker discovery. We demonstrate that HCV-induce epigenetic missregulation. These HCV-induced epigenetic changes reprogram host gene expression and persist as an "epigenetic signature" following virus eradication. Treatment of HCV-cured cells with specific inhibitors reverted the epigenetic signature. These results suggest a "hit and run" scenario that may explain why some chronic HCV infected patients do proceed to develop HCC after HCV eradication. Our discoveries provide an insight into the outcomes of HCV infection and HCC development, also following SVR, and novel approaches for its prevention.

Hepatitis C virus (HCV) is a major public health concern, with over 70 million people infected worldwide, who are at risk for developing life-threatening liver disease. No vaccine is available, and immunity against the virus is not well-understood. Following the acute stage, HCV usually causes chronic infections. However, ~30% of infected individuals spontaneously clear the virus. Therefore, using HCV as a model for comparing immune responses between spontaneous clearer (SC) and chronically infected (CI) individuals may empower the identification of mechanisms governing viral infection outcomes. Here, we provide the first in-depth analysis of adaptive immune receptor repertoires in individuals with current or past HCV infection. We demonstrate that SC individuals, in contrast to CI patients, develop clusters of antibodies with distinct properties. These antibodies' characteristics were used in a machine learning framework to accurately predict infection outcome. Using combinatorial antibody phage display library technology, we identified HCV-specific antibody sequences. By integrating these data with the repertoire analysis, we constructed two antibodies characterized by high neutralization breadth, which are associated with clearance. This study provides insight into the nature of effective immune response against HCV and demonstrates an innovative approach for constructing antibodies correlating with successful infection clearance. It may have clinical implications for prognosis of the future status of infection, and the design of effective immunotherapies and a vaccine for HCV.

The interactions between antibodies, SARS-CoV-2 and immune cells contribute to the pathogenesis of COVID-19 and protective immunity. To understand the differences between antibody responses in mild versus severe cases of COVID-19, we analyzed the B cell responses in patients 1.5 months post SARS-CoV-2 infection. Severe, and not mild, infection correlated with high titers of IgG against Spike receptor binding domain (RBD) that were capable of ACE2:RBD inhibition. B cell receptor (BCR) sequencing revealed that VH3-53 was enriched during severe infection. Of the 22 antibodies cloned from two severe donors, six exhibited potent neutralization against authentic SARS-CoV-2, and inhibited syncytia formation. Using peptide libraries, competition ELISA and mutagenesis of RBD, we mapped the epitopes of the neutralizing antibodies (nAbs) to three different sites on the Spike. Finally, we used combinations of nAbs targeting different immune-sites to efficiently block SARS-CoV-2 infection. Analysis of 49 healthy BCR repertoires revealed that the nAbs germline VHJH precursors comprise up to 2.7% of all VHJHs. We demonstrate that severe COVID-19 is associated with unique BCR signatures and multi-clonal neutralizing responses that are relatively frequent in the population. Moreover, our data support the use of combination antibody therapy to prevent and treat COVID-19., Author summary The correlates of effective durable antibody response to SARS-CoV-2 infection are still unclear. In this study, we compared B cell receptor signatures in 8 Severe versus 10 Mild SARS-CoV-2 infected Israeli donors, at 1.5 months post infection using molecular and bioinformatic approaches. We found distinct features between the two groups with higher anti-SARS-CoV-2 receptor binding domain (RBD) plasma IgG titers and increased B cell expansion in donors with severe disease manifestations. We further isolated 22 monoclonal antibodies from these donors, 6 of which were highly potent neutralizing the live virus and inhibited the fusion of infected cells. Using mutagenesis and peptide libraries we mapped the binding sites of the neutralizing antibodies on the RBD of the SARS-CoV-2 Spike. We next demonstrated that combinations of different classes of neutralizing mAbs can completely block the live virus from spreading in culture. Lastly, we performed a bioinformatic search in 49 healthy BCR repertoires identifying precursors for these neutralizing antibodies in the top 30 most common precursors, suggesting that these antibodies can be readily produced by the majority of the uninfected population upon antigenic stimulation.

Classification of neurons into specific subtypes is essential for better understanding of brain function and information transmission. Despite continuous progress, there is still no consensus regarding categorizing neuron taxonomy into proper subtypes. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the general axonal projection layout. In this study, we support the use of a morphology-based classification approach, focusing on the axonal tree. We demonstrate that utilizing the geometrical parameters of axonal tree structures significantly improves neuronal classification compared to the dendritic tree classification. Furthermore, we used neuronal activity patterns to classify interneurons into subtypes as well. Simulations of the activity along ramified axonal trees indicate that the axonal branching geometry may yield diverse responses in different subtrees. The classification schemes introduced here can be utilized to robustly classify neuronal subtypes in a functionally relevant manner. Our results open the door for deducing functionality from anatomical data.

Recombinant antibodies serve as therapeutic molecules for a broad range of applications. High affinity antibodies are typically isolated following an active and effective immunization. Human-like antibodies may be obtained from immunized nonhuman primates (NHP), such as rhesus macaque, when immunized human origin is not available. For the isolation of such antibodies, strategies like phage and yeast display, are employed. These strategies are primarily based on the amplification of the rearranged variable (V) regions coded by mRNA, obtained from lymphatic source of immunized animals. To amplify these genomic sequences, designated set of primers are required, ideally covering the immune animal V-gene repertoire. Such primer sets are commonly designed based on the germline repertoire of specific animals according to immunoglobulin databases. However, In case of rhesus macaque, however, the known immunoglobulin germline V-gene database is still limited. The emergence and continuous improvements in high-throughput sequencing (HTS) technologies now enable the profiling of an immune repertoire for both basic and applicative studies, among which is the identification and expression of novel alleles. We report here on the profiling of non-immunized rhesus macaque (Macaca mulatta) expressed antibody repertoire, using HTS and advanced tailored bioinformatics tools. This analysis resulted in 32,480 and 73,354 complete heavy and light variable gene (VH and VL) sequences, respectively. Further analysis of these sequences, using the IgDiscover tool, resulted in the identification of 102, 214 and 48 inferred VH, Vκ and Vλ germline sequences, respectively, of which over 50% are novel alleles. This dataset, together with other recently published datasets, enabled the design of a comprehensive primer set (v2018), which demonstrated the broadest coverage of rhesus macaque germline genes identified up to date. The newly designed primer set was confirmed for its extent of coverage of the V-genes in various datasets of rhesus macaque germlines as well as the expressed repertoire mapped in this study. Among other things, an improvement of 28% and 50% in the coverage of the VH and VL expressed repertoire was demonstrated in comparison to a primer set we have previously designed. This primer set can be further used for various applications that require the complete coverage of the NHP V-gene repertoire.

The adaptive immune receptor repertoire (AIRR) contains information on an individuals’ immune past, present and potential in the form of the evolving sequences that encode the B cell receptor (BCR) repertoire. AIRR sequencing (AIRR-seq) studies rely on databases of known BCR germline variable (V), diversity (D) and joining (J) genes to detect somatic mutations in AIRR-seq data via comparison to the best-aligning database alleles. However, it has been shown that these databases are far from complete, leading to systematic misidentification of mutated positions in subsets of sample sequences. We previously presented TIgGER, a computational method to identify subject-specific V gene genotypes, including the presence of novel V gene alleles, directly from AIRR-seq data. However, the original algorithm was unable to detect alleles that differed by more than 5 single nucleotide polymorphisms (SNPs) from a database allele. Here we present and apply an improved version of the TIgGER algorithm which can detect alleles that differ by any number of SNPs from the nearest database allele, and can construct subject-specific genotypes with minimal prior information. TIgGER predictions are validated both computationally (using a leave-one-out strategy) and experimentally (using genomic sequencing), resulting in the addition of three new immunoglobulin heavy chain V (IGHV) gene alleles to the IMGT repertoire. Finally, we develop a Bayesian strategy to provide a confidence estimate associated with genotype calls. All together, these methods allow for much higher accuracy in germline allele assignment, an essential step in AIRR-seq studies.

Computational modeling of signal propagation in neurons is critical to our understanding of basic principles underlying brain organization and activity. Exploring these models is used to address basic neuroscience questions as well as to gain insights for clinical applications. The seminal Hodgkin Huxley model is a common theoretical framework to study brain activity. It was mainly used to investigate the electrochemical and physical properties of neurons. The influence of neuronal structure on activity patterns was explored, however, the rich dynamics observed in neurons with different morphologies is not yet fully understood. Here, we study signal propagation in fundamental building blocks of neuronal branching trees, unbranched and branched axons. We show how these simple axonal elements can code information on spike trains, and how asymmetric responses can emerge in axonal branching points. This asymmetric phenomenon has been observed experimentally but until now lacked theoretical characterization. Together, our results suggest that axonal morphological parameters are instrumental in activity modulation and information coding. The insights gained from this work lay the ground for better understanding the interplay between function and form in real-world complex systems. It may also supply theoretical basis for the development of novel therapeutic approaches to damaged nervous systems.

The cancer genome is abnormal genome, and the ability to monitor its sequence had undergone a technological revolution. Yet prognosis and diagnosis remain an expert-based decision, with only limited abilities to provide machine-based decisions. We introduce a heterogeneity-based method for stratifying and visualizing whole-genome sequencing (WGS) reads. This method uses the heterogeneity within WGS reads to markedly reduce the dimensionality of next-generation sequencing data; it is available through the tool HiBS (Heterogeneity-Based Subclassification) that allows cancer sample classification. We validated HiBS using >200 WGS samples from nine different cancer types from The Cancer Genome Atlas (TCGA). With HiBS, we show progress with two WGS related issues: (i) differentiation between normal (NB) and tumor (TP) samples based solely on the information structure of their WGS data, and (ii) identification of specific regions of chromosomal amplification/deletion and their association with tumor stage. By comparing results to those obtained through available WGS analyses tools, we demonstrate some of the novelties obtained by the approach implemented in HiBS and also show nearly perfect normal/tumor classification, used to identify known and unknown chromosomal aberrations. Finally, the HiBS index has been associated with breast cancer tumor stage.

High-throughput sequencing of B-cell immunoglobulin repertoires is increasingly being applied to gain insights into the adaptive immune response in healthy individuals and in those with a wide range of diseases. Recent applications include the study of autoimmunity, infection, allergy, cancer and aging. As sequencing technologies continue to improve, these repertoire sequencing experiments are producing ever larger datasets, with tens- to hundreds-of-millions of sequences. These data require specialized bioinformatics pipelines to be analyzed effectively. Numerous methods and tools have been developed to handle different steps of the analysis, and integrated software suites have recently been made available. However, the field has yet to converge on a standard pipeline for data processing and analysis. Common file formats for data sharing are also lacking. Here we provide a set of practical guidelines for B-cell receptor repertoire sequencing analysis, starting from raw sequencing reads and proceeding through pre-processing, determination of population structure, and analysis of repertoire properties. These include methods for unique molecular identifiers and sequencing error correction, V(D)J assignment and detection of novel alleles, clonal assignment, lineage tree construction, somatic hypermutation modeling, selection analysis, and analysis of stereotyped or convergent responses. The guidelines presented here highlight the major steps involved in the analysis of B-cell repertoire sequencing data, along with recommendations on how to avoid common pitfalls.

During the several-week course of an immune response, B cells undergo a process of clonal expansion, somatic hypermutation of the immunoglobulin (Ig) genes and affinity-dependent selection. Over a lifetime, each B cell may participate in multiple rounds of affinity maturation as part of different immune responses. These two time-scales for selection are apparent in the structure of B-cell lineage trees, which often contain a ‘trunk’ consisting of mutations that are shared across all members of a clone, and several branches that form a ‘canopy’ consisting of mutations that are shared by a subset of clone members. The influence of affinity maturation on the B-cell population can be inferred by analysing the pattern of somatic mutations in the Ig. While global analysis of mutation patterns has shown evidence of strong selection pressures shaping the B-cell population, the effect of different time-scales of selection and diversification has not yet been studied. Analysis of B cells from blood samples of three healthy individuals identifies a range of clone sizes with lineage trees that can contain long trunks and canopies indicating the significant diversity introduced by the affinity maturation process. We here show that observed mutation patterns in the framework regions (FWRs) are determined by an almost purely purifying selection on both short and long time-scales. By contrast, complementarity determining regions (CDRs) are affected by a combination of purifying and antigen-driven positive selection on the short term, which leads to a net positive selection in the long term. In both the FWRs and CDRs, long-term selection is strongly dependent on the heavy chain variable gene family.

Individual variation in germline and expressed B-cell immunoglobulin (Ig) repertoires has been associated with aging, disease susceptibility, and differential response to infection and vaccination. Repertoire properties can now be studied at large-scale through next-generation sequencing of rearranged Ig genes. Accurate analysis of these repertoire-sequencing (Rep-Seq) data requires identifying the germline variable (V), diversity (D), and joining (J) gene segments used by each Ig sequence. Current V(D)J assignment methods work by aligning sequences to a database of known germline V(D)J segment alleles. However, existing databases are likely to be incomplete and novel polymorphisms are hard to differentiate from the frequent occurrence of somatic hypermutations in Ig sequences. Here we develop a Tool for Ig Genotype Elucidation via Rep-Seq (TIgGER). TIgGER analyzes mutation patterns in Rep-Seq data to identify novel V segment alleles, and also constructs a personalized germline database containing the specific set of alleles carried by a subject. This information is then used to improve the initial V segment assignments from existing tools, like IMGT/HighV-QUEST. The application of TIgGER to Rep-Seq data from seven subjects identified 11 novel V segment alleles, including at least one in every subject examined. These novel alleles constituted 13% of the total number of unique alleles in these subjects, and impacted 3% of V(D)J segment assignments. These results reinforce the highly polymorphic nature of human Ig V genes, and suggest that many novel alleles remain to be discovered. The integration of TIgGER into Rep-Seq processing pipelines will increase the accuracy of V segment assignments, thus improving B-cell repertoire analyses.

Summary: Advances in high-throughput sequencing technologies now allow for large-scale characterization of B cell immunoglobulin (Ig) repertoires. The high germline and somatic diversity of the Ig repertoire presents challenges for biologically meaningful analysis, which requires specialized computational methods. We have developed a suite of utilities, Change-O, which provides tools for advanced analyses of large-scale Ig repertoire sequencing data. Change-O includes tools for determining the complete set of Ig variable region gene segment alleles carried by an individual (including novel alleles), partitioning of Ig sequences into clonal populations, creating lineage trees, inferring somatic hypermutation targeting models, measuring repertoire diversity, quantifying selection pressure, and calculating sequence chemical properties. All Change-O tools utilize a common data format, which enables the seamless integration of multiple analyses into a single workflow. Availability and implementation: Change-O is freely available for non-commercial use and may be downloaded from http://clip.med.yale.edu/changeo. Contact: steven.kleinstein@yale.edu

A hallmark of celiac disease is autoantibodies to transglutaminase 2 (TG2). By visualizing TG2-specific antibodies by antigen staining of affected gut tissue, we identified TG2-specific plasma cells in the lamina propria as well as antibodies in the subepithelial layer, inside the epithelium, and at the brush border. The frequency of TG2-specific plasma cells were found not to correlate with serum antibody titers, suggesting that antibody production at other sites may contribute to serum antibody levels. Upon commencement of a gluten-free diet, the frequency of TG2-specific plasma cells in the lesion dropped dramatically within 6 months, yet some cells remained. The frequency of TG2-specific plasma cells in the celiac lesion is thus dynamically regulated in response to gluten exposure. Laser microdissection of plasma cell patches, followed by antibody gene sequencing, demonstrated that clonal cells were seeded in distinct areas of the mucosa. This was confirmed by immunoglobulin heavy chain repertoire analysis of plasma cells isolated from individual biopsies of two untreated patients, both for TG2-specific and non-TG2-specific cells. Our results shed new light on the processes underlying the B-cell response in celiac disease, and the approach of staining for antigen-specific antibodies should be applicable to other antibody-mediated diseases.

Autoreactive IgA plasma cells (PCs) specific for the enzyme transglutaminase 2 (TG2) are abundant in the small intestine of patients with active celiac disease (CD), and their number drops in patients treated by dietary gluten elimination. Little is known about their characteristics and their role in the disease. In this study, using high-throughput sequencing of the IgH V region (IGHV) genes, we have studied features of TG2-specific PCs and their related B cell clones in peripheral blood. We found that TG2-specific PCs from both untreated and treated patients have acquired lower number of somatic hypermutation and used focused IGHV repertoire with overrepresentation of the IGHV3-48, IGHV4-59, IGHV5-10-1, and IGHV5-51 gene segments. Furthermore, these PCs were clonally expanded and showed signs of affinity maturation. Lineage trees demonstrated shared clones between gut PCs and blood memory B cells, primarily IgAs. Some trees also involved IgG cells, suggesting that anti-TG2 IgA and IgG responses are related. Similarly to TG2-specific PCs, clonally related memory IgA B cells of blood showed lower mutation rates with biased usage of IGHV3-48 and IGHV5-51. Such memory cells were rare in peripheral blood, yet detectable in most patients assessed by production of anti-TG2 Abs in vitro following stimulation of cells from patients who had been on a long-term gluten-free diet. Thus, the Ab response to TG2 in CD, while maintaining its IGHV gene usage, is dynamically regulated in response to gluten exposure with a low degree of maintenance at both PC and memory B cell levels in patients in remission.

The role of B cells and posttranslational modifications in pathogenesis of organ-specific immune diseases is increasingly envisioned but remains poorly understood, particularly in human disorders. In celiac disease, transglutaminase 2–modified (TG2-modified; deamidated) gluten peptides drive disease-specific T cell and B cell responses, and antibodies to deamidated gluten peptides are excellent diagnostic markers. Here, we substantiate by high-throughput sequencing of IGHV genes that antibodies to a disease-specific, deamidated, and immunodominant B cell epitope of gluten (PLQPEQPFP) have biased and stereotyped usage of IGHV3-23 and IGHV3-15 gene segments with modest somatic mutations. X-ray crystal structures of 2 prototype IGHV3-15/IGKV4-1 and IGHV3-23/IGLV4-69 antibodies reveal peptide interaction mainly via germline-encoded residues. In-depth mutational analysis showed restricted selection and substitution patterns at positions involved in antigen binding. While the IGHV3-15/IGKV4-1 antibody interacts with Glu5 and Gln6, the IGHV3-23/IGLV4-69 antibody interacts with Gln3, Pro4, Pro7, and Phe8 — residues involved in substrate recognition by TG2. Hence, both antibodies, despite different interaction with the epitope, recognize signatures of TG2 processing that facilitates B cell presentation of deamidated gluten peptides to T cells, thereby providing a molecular framework for the generation of these clinically important antibodies. The study provides essential insight into the pathogenic mechanism of celiac disease. © 2018 American Society for Clinical Investigation

The adaptive immune system confers protection by generating a diverse repertoire of antibody receptors that are rapidly expanded and contracted in response to specific targets. Next-generation DNA sequencing now provides the opportunity to survey this complex and vast repertoire. In the present work, we describe a set of tools for the analysis of antibody repertoires and their application to elucidating the dynamics of the response to viral vaccination in human volunteers. By analyzing data from 38 separate blood samples across 2 y, we found that the use of the germ-line library of V and J segments is conserved between individuals over time. Surprisingly, there appeared to be no correlation between the use level of a particular VJ combination and degree of expansion. We found the antibody RNA repertoire in each volunteer to be highly dynamic, with each individual displaying qualitatively different response dynamics. By using combinatorial phage display, we screened selected VH genes paired with their corresponding VL library for affinity against the vaccine antigens. Altogether, this work presents an additional set of tools for profiling the human antibody repertoire and demonstrates characterization of the fast repertoire dynamics through time in multiple individuals responding to an immune challenge.

Broadly neutralizing HIV antibodies (bnAbs) are typically highly somatically mutated, raising doubts as to whether they can be elicited by vaccination. We used 454 sequencing and designed a novel phylogenetic method to model lineage evolution of the bnAbs PGT121–134 and found a positive correlation between the level of somatic hypermutation (SHM) and the development of neutralization breadth and potency. Strikingly, putative intermediates were characterized that show approximately half the mutation level of PGT121–134 but were still capable of neutralizing roughly 40–80% of PGT121–134 sensitive viruses in a 74-virus panel at median titers between 15- and 3-fold higher than PGT121–134. Such antibodies with lower levels of SHM may be more amenable to elicitation through vaccination while still providing noteworthy coverage. Binding characterization indicated a preference of inferred intermediates for native Env binding over monomeric gp120, suggesting that the PGT121–134 lineage may have been selected for binding to native Env at some point during maturation. Analysis of glycan-dependent neutralization for inferred intermediates identified additional adjacent glycans that comprise the epitope and suggests changes in glycan dependency or recognition over the course of affinity maturation for this lineage. Finally, patterns of neutralization of inferred bnAb intermediates suggest hypotheses as to how SHM may lead to potent and broad HIV neutralization and provide important clues for immunogen design., National Institutes of Health (U.S.) (Bioinformatics and Integrative Genomics Fellowship), National Institutes of Health (U.S.) (Center for Excellence in Genomic Science)

Meningiomas are brain tumors that arise from the meninges surrounding the brain and spinal cord and are among the most common adult neoplasms of the CNS.1 Although 80% of meningiomas are slow-growing nonmalignant World Health Organization (WHO) grade I tumors,2 they may still cause harm via mass effect on neighboring structures. Favorable outcomes for those WHO grade I tumors can often be achieved by complete surgical resection. However, the atypical meningiomas, WHO grades II and III, can have more devastating effects. Despite optimal management that includes surgical resection and radiation treatment, patients with grade II and grade III tumors have 5-year survival rates of 78.5% and 44%, respectively, and corresponding 10-year survival rates of 53.3% and 14.2%.3 Unfortunately, there is no consensus for treatment strategies, and after several surgeries, treatment options are often limited. Thus, treatment strategies beyond radiation and surgery for these patients are needed.4,5 One such strategy that is being explored in other primary brain tumors, such as gliomas, is immunotherapy.6,7 Currently, our understanding of the relationship between meningiomas and the immune system is limited. By further exploring these interactions, we aim to determine whether immunotherapeutic strategies could be used to target meningiomas. Several studies in non-CNS tumors, such as lung cancer, melanoma, ovarian cancer, and colon cancer, indicate that the presence of lymphocytes can be a critical determinant of clinical prognosis.7–11 Furthermore, it has been suggested that the types and distribution patterns of these infiltrating lymphocytes can be more powerful prognostic indicators than the previously used pathological criteria for tumor staging12 or oncogene expression.13 For example, a high tumor infiltrating CD8+/regulatory T cell (Treg) ratio is often correlated with long-term survival.14 However, high levels of T cell response and antibody production can also be present in progressively growing tumors, indicating that an immune response does not necessarily result in immune protection.15 The interaction between tumors and the immune system is complex, and its outcome cannot be predicted from the size of the infiltrate alone. Immune cell infiltrates of meningiomas can include variable numbers of T cells, B cells, plasma cells, and macrophages.16–20 Although the presence of immune cells in meningiomas has been well delineated at a descriptive level, many of the characteristics of these cells remain unknown. Understanding whether such cells demonstrate antigen experience, have developed into activated effector cells, or display exhausted phenotypes is one of the first steps toward understanding their role in tumor biology. In the present work, we began to determine the role of the immune cells that populate the tumor parenchyma in meningiomas.

Enrichment analysis of gene sets is a popular approach that provides a functional interpretation of genome-wide expression data. Existing tests are affected by inter-gene correlations, resulting in a high Type I error. The most widely used test, Gene Set Enrichment Analysis, relies on computationally intensive permutations of sample labels to generate a null distribution that preserves gene–gene correlations. A more recent approach, CAMERA, attempts to correct for these correlations by estimating a variance inflation factor directly from the data. Although these methods generate P-values for detecting gene set activity, they are unable to produce confidence intervals or allow for post hoc comparisons. We have developed a new computational framework for Quantitative Set Analysis of Gene Expression (QuSAGE). QuSAGE accounts for inter-gene correlations, improves the estimation of the variance inflation factor and, rather than evaluating the deviation from a null hypothesis with a P-value, it quantifies gene-set activity with a complete probability density function. From this probability density function, P-values and confidence intervals can be extracted and post hoc analysis can be carried out while maintaining statistical traceability. Compared with Gene Set Enrichment Analysis and CAMERA, QuSAGE exhibits better sensitivity and specificity on real data profiling the response to interferon therapy (in chronic Hepatitis C virus patients) and Influenza A virus infection. QuSAGE is available as an R package, which includes the core functions for the method as well as functions to plot and visualize the results.

Objective: To profile the reactivity of CSF-derived immunoglobulin from patients with multiple sclerosis (MS) against a large panel of antigens, to identify disease-specific reactivities. Methods: CSF from subjects with MS with elevated immunoglobulin G and CSF from control subjects presenting with other inflammatory neurologic disease were screened against a protein array consisting of 9,393 proteins. Reactivity to a candidate protein identified using these arrays was confirmed with ELISA and immunocytochemistry. Results: Autoantibodies against one protein on the array, recombination signal binding protein for immunoglobulin kappa J region (RBPJ), discriminated between patients with MS and controls (p = 0.0052). Using a large validation cohort, we found a higher prevalence of autoantibodies against RBPJ in the CSF of patients with MS (12.5%) compared with the CSF of patients with other neurologic diseases (1.6%; p = 0.02) by ELISA. This difference in reactivity was restricted to the CSF as serum reactivity against RBPJ did not differ between patients and controls. The presence of CSF autoantibodies against RBPJ was further confirmed by immunocytochemistry. Conclusions: These data indicate that RBPJ, a ubiquitous protein of the Notch signaling pathway that plays an important role in Epstein-Barr virus infection, is a novel MS autoantigen candidate that is recognized by CSF-derived immunoglobulin G in a subset of patients with MS.

The long lasting debate initiated by Gilovich, Vallone and Tversky in [Formula: see text] is revisited: does a "hot hand" phenomenon exist in sports? Hereby we come back to one of the cases analyzed by the original study, but with a much larger data set: all free throws taken during five regular seasons ([Formula: see text]) of the National Basketball Association (NBA). Evidence supporting the existence of the "hot hand" phenomenon is provided. However, while statistical traces of this phenomenon are observed in the data, an open question still remains: are these non random patterns a result of "success breeds success" and "failure breeds failure" mechanisms or simply "better" and "worse" periods? Although free throws data is not adequate to answer this question in a definite way, we speculate based on it, that the latter is the dominant cause behind the appearance of the "hot hand" phenomenon in the data.

High-throughput immunoglobulin sequencing promises new insights into the somatic hypermutation and antigen-driven selection processes that underlie B-cell affinity maturation and adaptive immunity. The ability to estimate positive and negative selection from these sequence data has broad applications not only for understanding the immune response to pathogens, but is also critical to determining the role of somatic hypermutation in autoimmunity and B-cell cancers. Here, we develop a statistical framework for Bayesian estimation of Antigen-driven SELectIoN (BASELINe) based on the analysis of somatic mutation patterns. Our approach represents a fundamental advance over previous methods by shifting the problem from one of simply detecting selection to one of quantifying selection. Along with providing a more intuitive means to assess and visualize selection, our approach allows, for the first time, comparative analysis between groups of sequences derived from different germline V(D)J segments. Application of this approach to next-generation sequencing data demonstrates different selection pressures for memory cells of different isotypes. This framework can easily be adapted to analyze other types of DNA mutation patterns resulting from a mutator that displays hot/cold-spots, substitution preference or other intrinsic biases.

The tumor microenvironment hosts antibody-secreting cells (ASCs) associated with a favorable prognosis in several types of cancer. Patient-derived antibodies have diagnostic and therapeutic potential; yet, it remains unclear how antibodies gain autoreactivity and target tumors. Here, we found that somatic hypermutations (SHMs) promote antibody antitumor reactivity against surface autoantigens in high-grade serous ovarian carcinoma (HGSOC). Patient-derived tumor cells were frequently coated with IgGs. Intratumoral ASCs in HGSOC were both mutated and clonally expanded and produced tumor-reactive antibodies that targeted MMP14, which is abundantly expressed on the tumor cell surface. The reversion of monoclonal antibodies to their germline configuration revealed two types of classes: one dependent on SHMs for tumor binding and a second with germline-encoded autoreactivity. Thus, tumor-reactive autoantibodies are either naturally occurring or evolve through an antigen-driven selection process. These findings highlight the origin and potential applicability of autoantibodies directed at surface antigens for tumor targeting in cancer patients.