Search

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets.

IntroductionAnalysis of an individual’s immunoglobulin (IG) gene repertoire requires the use of high-quality germline gene reference sets. When sets only contain alleles supported by strong evidence, AIRR sequencing (AIRR-seq) data analysis is more accurate and studies of the evolution of IG genes, their allelic variants and the expressed immune repertoire is therefore facilitated.MethodsThe Adaptive Immune Receptor Repertoire Community (AIRR-C) IG Reference Sets have been developed by including only human IG heavy and light chain alleles that have been confirmed by evidence from multiple high-quality sources. To further improve AIRR-seq analysis, some alleles have been extended to deal with short 3’ or 5’ truncations that can lead them to be overlooked by alignment utilities. To avoid other challenges for analysis programs, exact paralogs (e.g. IGHV1-69*01 and IGHV1-69D*01) are only represented once in each set, though alternative sequence names are noted in accompanying metadata.Results and discussionThe Reference Sets include less than half the previously recognised IG alleles (e.g. just 198 IGHV sequences), and also include a number of novel alleles: 8 IGHV alleles, 2 IGKV alleles and 5 IGLV alleles. Despite their smaller sizes, erroneous calls were eliminated, and excellent coverage was achieved when a set of repertoires comprising over 4 million V(D)J rearrangements from 99 individuals were analyzed using the Sets. The version-tracked AIRR-C IG Reference Sets are freely available at the OGRDB website (https://ogrdb.airr-community.org/germline_sets/Human) and will be regularly updated to include newly observed and previously reported sequences that can be confirmed by new high-quality data.

Class switch recombination (CSR) is a process by which B cells switch their immunoglobulin isotype and develop pathogen-eliminating antibodies. Here, the authors show that a protein kinase DYRK1A is required for protection from viral infection through the regulation of CSR and effective clonal expansion.

Background & Aims: Hepatocellular carcinoma (HCC) is a model of a diverse spectrum of cancers because it is induced by well-known etiologies, mainly hepatitis C virus (HCV) and hepatitis B virus. Here, we aimed to identify HCV-specific mutational signatures and explored the link between the HCV-related regional variation in mutations rates and HCV-induced alterations in genome-wide chromatin organization. Methods: To identify an HCV-specific mutational signature in HCC, we performed high-resolution targeted sequencing to detect passenger mutations on 64 HCC samples from 3 etiology groups: hepatitis B virus, HCV, or other. To explore the link between the genomic signature and genome-wide chromatin organization we performed chromatin immunoprecipitation sequencing for the transcriptionally permissive H3K4Me3, H3K9Ac, and suppressive H3K9Me3 modifications after HCV infection. Results: Regional variation in mutation rate analysis showed significant etiology-dependent regional mutation rates in 12 genes: LRP2, KRT84, TMEM132B, DOCK2, DMD, INADL, JAK2, DNAH6, MTMR9, ATM, SLX4, and ARSD. We found an enrichment of C->T transversion mutations in the HCV-associated HCC cases. Furthermore, these cases showed regional variation in mutation rates associated with genomic intervals in which HCV infection dictated epigenetic alterations. This signature may be related to the HCV-induced decreased expression of genes encoding key enzymes in the base excision repair pathway. Conclusions: We identified novel distinct HCV etiology-dependent mutation signatures in HCC associated with HCV-induced alterations in histone modification. This study presents a link between cancer-causing mutagenesis and the increased predisposition to liver cancer in chronic HCV-infected individuals, and unveils novel etiology-specific mechanisms leading to HCC and cancer in general.

Summary: Neuroblastoma is a lethal childhood solid tumor of developing peripheral nerves. Two percent of children with neuroblastoma develop opsoclonus myoclonus ataxia syndrome (OMAS), a paraneoplastic disease characterized by cerebellar and brainstem-directed autoimmunity but typically with outstanding cancer-related outcomes. We compared tumor transcriptomes and tumor-infiltrating T and B cell repertoires from 38 OMAS subjects with neuroblastoma to 26 non-OMAS-associated neuroblastomas. We found greater B and T cell infiltration in OMAS-associated tumors compared to controls and showed that both were polyclonal expansions. Tertiary lymphoid structures (TLSs) were enriched in OMAS-associated tumors. We identified significant enrichment of the major histocompatibility complex (MHC) class II allele HLA-DOB∗01:01 in OMAS patients. OMAS severity scores were associated with the expression of several candidate autoimmune genes. We propose a model in which polyclonal auto-reactive B lymphocytes act as antigen-presenting cells and drive TLS formation, thereby supporting both sustained polyclonal T cell-mediated anti-tumor immunity and paraneoplastic OMAS neuropathology.

IntroductionThe 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.MethodsWe 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.ResultsIn 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.DiscussionThese 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.

Abstract 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 immunoglobulin genes of inbred mouse strains that are commonly used in models of antibody-mediated human diseases are poorly characterized. This compromises data analysis. To infer the immunoglobulin genes of BALB/c mice, we used long-read SMRT sequencing to amplify VDJ-C sequences from F1 (BALB/c x C57BL/6) hybrid animals. Strain variations were identified in the Ighm and Ighg2b genes, and analysis of VDJ rearrangements led to the inference of 278 germline IGHV alleles. 169 alleles are not present in the C57BL/6 genome reference sequence. To establish a set of expressed BALB/c IGHV germline gene sequences, we computationally retrieved IGHV haplotypes from the IgM dataset. Haplotyping led to the confirmation of 162 BALB/c IGHV gene sequences. A musIGHV398 pseudogene variant also appears to be present in the BALB/cByJ substrain, while a functional musIGHV398 gene is highly expressed in the BALB/cJ substrain. Only four of the BALB/c alleles were also observed in the C57BL/6 haplotype. The full set of inferred BALB/c sequences has been used to establish a BALB/c IGHV reference set, hosted at https://ogrdb.airr-community.org. We assessed whether assemblies from the Mouse Genome Project (MGP) are suitable for the determination of the genes of the IGH loci. Only 37 (43.5%) of the 85 confirmed IMGT-named BALB/c IGHV and 33 (42.9%) of the 77 confirmed non-IMGT IGHV were found in a search of the MGP BALB/cJ genome assembly. This suggests that current MGP assemblies are unsuitable for the comprehensive documentation of germline IGHVs and more efforts will be needed to establish strain-specific reference sets.

Summary: Inference of germline polymorphisms in immunoglobulin genes from B cell receptor repertoires is complicated by somatic hypermutations, sequencing/PCR errors, and by varying length of reference alleles. The light chain inference is particularly challenging owing to large gene duplications and absence of D genes. We analyzed the light chain cDNA sequences from naïve B cell receptor repertoires from 100 individuals. We optimized light chain allele inference by tweaking parameters of the TIgGER functions, extending the germline reference sequences, and establishing mismatch frequency patterns at polymorphic positions to filter out false-positive candidates. We identified 48 previously unreported variants of light chain variable genes. We selected 14 variants for validation and successfully validated 11 by Sanger sequencing. Clustering of light chain 5′UTR, L-PART1, and L-PART2 revealed partial intron retention in 11 kappa and 9 lambda V alleles. Our results provide insight into germline variation in human light chain immunoglobulin loci.

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.

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.

Summary: Antibodies secreted within the intestinal tract provide protection from the invasion of microbes into the host tissues. Germinal center (GC) formation in lymph nodes and spleen strictly requires SLAM-associated protein (SAP)-mediated T cell functions; however, it is not known whether this mechanism plays a similar role in mucosal-associated lymphoid tissues. Here, we find that in Peyer’s patches (PPs), SAP-mediated T cell help is required for promoting B cell selection in GCs, but not for clonal diversification. PPs of SAP-deficient mice host chronic GCs that are absent in T cell-deficient mice. GC B cells in SAP-deficient mice express AID and Bcl6 and generate plasma cells in proportion to the GC size. Single-cell IgA sequencing analysis reveals that these mice host few diversified clones that were subjected to mild selection forces. These findings demonstrate that T cell-derived help to B cells in PPs includes SAP-dependent and SAP-independent functions. : SAP is required for proper T cell help in germinal centers (GCs). Biram et al. show that SAP-independent GCs are formed within Peyer’s patches. These GCs host highly diversified clones that are subjected to mild selection forces, demonstrating that clonal diversification can be uncoupled from clonal selection in chronic GCs. Keywords: germinal center, antibody, B cells, SAP, T follicular helper cells, Peyer’s patches, clonal diversification, IgA, plasma cells

A sports multi-bet is a bet on the results of a set of N games. One type of multi-bet offered by the Israeli government is WINNER 16, where participants guess the results of a set of 16 soccer games. The prizes in WINNER 16 are determined by the accumulated profit in previous rounds, and are split among all winning forms. When the reward increases beyond a certain threshold, a profitable strategy can be devised. Here, we present a machine-learning algorithm scheme to play WINNER 16. Our proposed algorithm is marginally profitable on average in a range of hyper-parameters, indicating inefficiencies in this game. To make a better prize-pricing mechanism we suggest a generalization of the single-bet approach. We studied the expected profit and risk of WINNER 16 after applying our suggestion. Our proposal can make the game more fair and more appealing without reducing the profitability.

Abstract 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 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.

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.

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.

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 T cell receptor (TCR) controls the cellular adaptive immune response to antigens, but our understanding of TCR repertoire diversity and response to challenge is still incomplete. For example, TCR clones shared by different individuals with minimal alteration to germline gene sequences (public clones) are detectable in all vertebrates, but their significance is unknown. Although small in size, the zebrafish TCR repertoire is controlled by processes similar to those operating in mammals. Thus, we studied the zebrafish TCR repertoire and its response to stimulation with self and foreign antigens. We found that cross-reactive public TCRs dominate the T cell response, endowing a limited TCR repertoire with the ability to cope with diverse antigenic challenges. These features of vertebrate public TCRs might provide a mechanism for the rapid generation of protective T cell immunity, allowing a short temporal window for the development of more specific private T cell responses.

Objectives: The study of sequential effects in aiming tasks might shed light on the organization of repetitive motor performances over time. To date, investigations of such effects in sports have been limited and yielded mixed results. Given the relatively short time intervals between successive attempts, and the absence of defensive interventions, dart throwing provides a potentially fruitful testing ground for examining the presence of sequential performance effects in the motor domain.Methods and Results: A total of 80 competitive darts matches of 10 of the world’s best players were scored from publicly available video footage in terms of sequences of hits and misses of triple 20. In darts, throws are organized in legs, i.e., a rapid succession of three throws by the same player, allowing us to investigate various transitions in performance (throw 1 → 2, 2 → 3, and 3 → 1). The resulting binary sequences were analyzed statistically in terms of independence and stationarity. Across players significant statistical evidence was found for sequential dependence from the first throw in a leg to the second throw, but not for the other transitions. As regards to stationarity, a significant decline in performance was observed in the course of the match.Conclusions: In professional darts, evidence can be found for both sequential dependence as well as for non-stationarity, implying that performance does not, or at least not always, constitute a stationary random independent process. More research is needed on the motor control mechanisms underlying the observed carry-over effects within triplets as well as the possible causes of non-stationarity.

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.

Recently, the "hot hand" phenomenon regained interest due to the availability and accessibility of large scale data sets from the world of sports. In support of common wisdom and in contrast to the original conclusions of the seminal paper about this phenomenon by Gilovich, Vallone and Tversky in 1985, solid evidences were supplied in favor of the existence of this phenomenon in different kinds of data. This came after almost three decades of ongoing debates whether the "hot hand" phenomenon in sport is real or just a mis-perception of human subjects of completely random patterns present in reality. However, although this phenomenon was shown to exist in different sports data including basketball free throws and bowling strike rates, a somehow deeper question remained unanswered: are these non random patterns results of causal, short term, feedback mechanisms or simply time fluctuations of athletes performance. In this paper, we analyze large amounts of data from the Professional Bowling Association(PBA). We studied the results of the top 100 players in terms of the number of available records (summed into more than 450,000 frames). By using permutation approach and dividing the analysis into different aggregation levels we were able to supply evidence for the existence of the "hot hand" phenomenon in the data, in agreement with previous studies. Moreover, by using this approach, we were able to demonstrate that there are, indeed, significant fluctuations from game to game for the same player but there is no clustering of successes (strikes) and failures (non strikes) within each game. Thus we were lead to the conclusion that bowling results show correlation to recent past results but they are not influenced by them in a causal manner.

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.

The persistence of a spatially structured population is determined by the rate of dispersal among habitat patches. If the local dynamic at the subpopulation level is extinction-prone, the system viability is maximal at intermediate connectivity where recolonization is allowed, but full synchronization that enables correlated extinction is forbidden. Here we developed and used an algorithm for agent-based simulations in order to study the persistence of a stochastic metapopulation. The effect of noise is shown to be dramatic, and the dynamics of the spatial population differs substantially from the predictions of deterministic models. This has been validated for the stochastic versions of the logistic map, the Ricker map and the Nicholson-Bailey host-parasitoid system. To analyze the possibility of extinction, previous studies were focused on the attractiveness (Lyapunov exponent) of stable solutions and the structure of their basin of attraction (dependence on initial population size). Our results suggest that these features are of secondary importance in the presence of stochasticity. Instead, optimal sustainability is achieved when decoherence is maximal. Individual-based simulations of metapopulations of different sizes, dimensions and noise types, show that the system's lifetime peaks when it displays checkerboard spatial patterns. This conclusion is supported by the results of a recently published Drosophila experiment. The checkerboard strategy provides a technique for the manipulation of migration rates (e.g., by constructing corridors) in order to affect the persistence of a metapopulation. It may be used in order to minimize the risk of extinction of an endangered species, or to maximize the efficiency of an eradication campaign.