Biology subjects

Greiff, V.

Publications and source records attributed to Greiff, V..

49 records · Page 3Linked to original sources

bioRxiv2021

Alternative splice variants and germline polymorphisms in human immunoglobulin light chain genes

Immunoglobulin loci are rich in germline polymorphisms and identification of novel polymorphic variants can be facilitated by germline inference of B cell receptor repertoires. Germline gene inference is complicated by somatic hypermutations, errors arising from PCR amplification, and DNA sequencing as well as from the varying length of reference alleles. Inference of light chain genes is even more challenging than inference of heavy chain genes due to large gene duplication events on the kappa locus as well as absence of D genes in the rearranged light chain transcripts. Here, we analyzed the light chain cDNA sequences from naive BCR repertoires of a Norwegian cohort of 100 individuals. We optimized light chain allele inference by tweaking parameters within TIgGER functions, extending the germline reference sequences, and establishing mismatch frequency patterns at polymorphic positions to filter out false positive candidates. As a result, we identified 48 previously unreported variants of light chain variable genes. Altogether, we selected 14 candidates for novel light chain polymorphisms for validation and successfully validated 11 by Sanger sequencing. Additional clustering of light chain 5UTR, L-PART1 and L-PART2 revealed partial intron retention in alternative splice variants in 11 kappa and 9 lambda V alleles. The alternatively spliced transcripts were only observed in genes with low expression levels, suggesting a possible role in expression regulation. Our results provide novel insight into germline variation in human light chain immunoglobulin loci.

genetics↗

bioRxiv2021

Single-cell sequencing reveals clonally expanded plasma cells during chronic viral infection produce virus-specific and cross-reactive antibodies

Plasma cells and their secreted antibodies play a central role in the long-term protection against chronic viral infection. However, due to experimental limitations, a comprehensive description of linked genotypic, phenotypic, and antibody repertoire features of plasma cells (gene expression, clonal frequency, virus specificity, and affinity) has been challenging to obtain. To address this, we performed single-cell transcriptome and antibody repertoire sequencing of the murine bone marrow plasma cell population following chronic lymphocytic choriomeningitis virus infection. Our single-cell sequencing approach recovered full-length and paired heavy and light chain sequence information for thousands of plasma cells and enabled us to perform recombinant antibody expression and specificity screening. Antibody repertoire analysis revealed that, relative to protein immunization, chronic infection led to increased levels of clonal expansion, class-switching, and somatic variants. Furthermore, antibodies from the highly expanded and class-switched (IgG) plasma cells were found to be specific for multiple viral antigens and a subset of clones exhibited cross-reactivity to non-viral- and auto-antigens. Integrating single-cell transcriptome data with antibody specificity suggested that plasma cell transcriptional phenotype was correlated to viral antigen specificity. Our findings demonstrate that chronic viral infection can induce and sustain plasma cell clonal expansion, combined with significant somatic hypermutation, and can generate cross-reactive antibodies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=69 SRC="FIGDIR/small/428852v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@9d0852org.highwire.dtl.DTLVardef@18d46fcorg.highwire.dtl.DTLVardef@2f7f80org.highwire.dtl.DTLVardef@5be3fd_HPS_FORMAT_FIGEXP M_FIG Graphical abstract.Single-cell sequencing reveals clonally expanded plasma cells during chronic viral infection produce virus-specific and cross-reactive antibodies. C_FIG

immunology↗

bioRxiv2020

Single cell BCR and transcriptome analysis after respiratory virus infection reveals spatiotemporal dynamics of antigen-specific B cell responses.

B cell responses are a critical component of anti-viral immunity. However, a comprehensive picture of antigen-specific B cell responses, differentiation, clonal proliferation and dynamics in different organs after infection is lacking. Here, we combined single-cell RNA sequencing with single-cell B cell receptor (BCR) characterization of antigen-specific cells in the draining lymph nodes, spleen and lungs after influenza infection. We identify several novel B cell subpopulations forming after infection and find organ-specific differences that persist over the course of the response. We discover important transcriptional differences between memory cells in lungs and lymphoid organs and describe organ-restricted clonal expansion. Strikingly, by combining BCR mutational analysis, monoclonal antibody expression and affinity measurements we find no differences between germinal center (GC)-derived memory and plasmacells, at odds with an affinity-based selection model. By linking antigen-recognition with transcriptional programming, clonal-proliferation and differentiation, these finding provide important advances in our understanding of antiviral B cell immunity.

immunology↗

bioRxiv2020

DeepRC: Immune repertoire classification with attention-based deep massive multiple instance learning

A central mechanism in machine learning is to identify, store, and recognize patterns. How to learn, access, and retrieve such patterns is crucial in Hopfield networks and the more recent transformer architectures. We show that the attention mechanism of transformer architectures is actually the update rule of modern Hop-field networks that can store exponentially many patterns. We exploit this high storage capacity of modern Hopfield networks to solve a challenging multiple instance learning (MIL) problem in computational biology: immune repertoire classification. Accurate and interpretable machine learning methods solving this problem could pave the way towards new vaccines and therapies, which is currently a very relevant research topic intensified by the COVID-19 crisis. Immune repertoire classification based on the vast number of immunosequences of an individual is a MIL problem with an unprecedentedly massive number of instances, two orders of magnitude larger than currently considered problems, and with an extremely low witness rate. In this work, we present our novel method DeepRC that integrates transformer-like attention, or equivalently modern Hopfield networks, into deep learning architectures for massive MIL such as immune repertoire classification. We demonstrate that DeepRC outperforms all other methods with respect to predictive performance on large-scale experiments, including simulated and real-world virus infection data, and enables the extraction of sequence motifs that are connected to a given disease class. Source code and datasets: https://github.com/ml-jku/DeepRC

bioinformatics↗

bioRxiv2020

Mouse-specific but infection-unspecific IgM repertoire fingerprint following viral infection

Antibody repertoire sequencing provides a molecular fingerprint of current and past pathogens encountered by the immune system. Most repertoire studies in humans require measuring the B cell response in the blood, resulting in a large bias to the IgM isotype. The extent to which the circulating IgM antibody repertoire correlates to lymphoid tissue-resident B cells in the setting of viral infection remains largely uncharacterized. Therefore, we compared the IgM repertoires from both blood and bone marrow (BM) plasma cells (PCs) following acute or chronic lymphocytic choriomeningitis virus (LCMV) infection in mice. Despite previously reported serum alterations between acute and chronic infection, IgM repertoire signatures based on clonal diversity metrics, public clones, network and phylogenetic analysis were largely unable to distinguish infection cohorts. Our findings, however, revealed mouse-specific congruence between the blood and PC repertoires irrespective of infection status. Our study reveals that IgM repertoire analyses may be unsuitable for providing a fingerprint of current or previous immune challenges.

immunology↗

bioRxiv2020

The influence of the phylogenetic inference pipeline on murine antibody repertoire sequencing data following viral infection

Understanding B cell evolution following vaccination or infection is crucial for instructing targeted immunotherapies when searching for potential therapeutic or virus-neutralizing antibodies. Antibody phylogenetics holds the potential to quantify both clonal selection and somatic hypermutation, two key players shaping B cell evolution. A wide range of bioinformatic pipelines and phylogenetic inference methods have been utilized on antibody repertoire sequencing datasets to delineate B cell evolution. Although the majority of B cell repertoire studies incorporate some aspect of antibody evolution, how the chosen computational methods affect the results is largely ignored. Therefore, we performed an extensive computational analysis on time-resolved antibody repertoire sequencing data to better characterize how commonly employed bioinformatic practices influence conclusions regarding antibody selection and evolution. Our findings reveal that different combinations of clonal lineage assignment strategies, phylogenetic inference methods, and biological sampling affect the inferred size, mutation rates, and topologies of B cell lineages in response to virus infection.

bioinformatics↗

bioRxiv2019

Discrete populations of isotype-switched memory B lymphocytes are maintained in murine spleen and bone marrow

Here we describe tissue-resident memory B lymphocytes of spleen and bone marrow. Single cell transcriptomes and B cell receptor repertoires identify several exclusive populations of isotype-switched memory B cells (Bsm) in murine spleen and bone marrow, and one interconnected population of 10-20%. A population of marginal zone-like Bsm is located exclusively in the spleen while a novel population of quiescent Bsm is located exclusively in the bone marrow. Cells of two further populations, present in both, spleen and bone marrow, differ in repertoire between the two organs, i.e. are resident as well. Finally, another interconnected population of Bsm of the B1 lineage is present in spleen and bone marrow. In the bone marrow, all Bsm individually dock onto VCAM1+ stromal cells, resting in terms of activation, proliferation and mobility. The discrete B cell memory of bone marrow may be key to rapid secondary humoral responses to systemic antigens.

immunology↗

bioRxiv2019

A finite vocabulary of antibody-antigen interaction enables predictability of paratope-epitope binding

Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In the largest set of non-redundant antibody-antigen structures, we identified structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (i) is compact, less than 104 motifs, (ii) distinct from non-immune protein-protein interactions, and (iii) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work successfully leveraged combined structure- and sequence-based learning showing that machine-learning-driven predictive paratope and epitope engineering is feasible.

immunology↗

bioRxiv2019

immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking

SummaryB- and T-cell receptor repertoires of the adaptive immune system have become a key target for diagnostics and therapeutics research. Consequently, there is a rapidly growing number of bioinformatics tools for immune repertoire analysis. Benchmarking of such tools is crucial for ensuring reproducible and generalizable computational analyses. Currently, however, it remains challenging to create standardized ground truth immune receptor repertoires for immunoinformatics tool benchmarking. Therefore, we developed immuneSIM, an R package that allows the simulation of native-like and aberrant synthetic full length variable region immune receptor sequences. ImmuneSIM enables the tuning of the immune receptor features: (i) species and chain type (BCR, TCR, single, paired), (ii) germline gene usage, (iii) occurrence of insertions and deletions, (iv) clonal abundance, (v) somatic hypermutation, and (vi) sequence motifs. Each simulated sequence is annotated by the complete set of simulation events that contributed to its in silico generation. immuneSIM permits the benchmarking of key computational tools for immune receptor analysis such as germline gene annotation, diversity and overlap estimation, sequence similarity, network architecture, clustering analysis, and machine learning methods for motif detection.\n\nAvailabilityThe package is available via https://github.com/GreiffLab/immuneSIM and will also be available at CRAN (submitted). The documentation is hosted at https://immuneSIM.readthedocs.io.\n\nContactvictor.greiff@medisin.uio.no, sai.reddy@ethz.ch

bioinformatics↗

bioRxiv2019

Resident memory CD8 T cells persist for years in human small intestine

In human small intestine, most CD8 T cells in the lamina propria and epithelium express a resident memory (Trm) phenotype and persist for at least one year in transplanted tissue. Intestinal CD8 Trm cells have a clonally expanded immune repertoire that is stable over time and exhibit enhanced protective capabilities. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/553792v1_ufig1.gif" ALT="Figure 1"> View larger version (98K): org.highwire.dtl.DTLVardef@10c19aaorg.highwire.dtl.DTLVardef@168e86forg.highwire.dtl.DTLVardef@73f0f5org.highwire.dtl.DTLVardef@1d08d10_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThe vast majority of CD8 T cells in the human small intestine are Trm cells C_LIO_LICD8 Trm cells persist for >1 year in transplanted duodenum C_LIO_LIIntraepithelial and lamina propria CD8 Trm cells show highly similar TCR repertoire C_LIO_LIIntestinal CD8 Trm cells efficiently produce cytokines and cytotoxic mediators C_LI

immunology↗

bioRxiv2018

High-throughput antibody engineering in mammalian cells by CRISPR/Cas9-mediated homology-directed mutagenesis

Antibody engineering is performed to improve therapeutic properties by directed evolution, usually by high-throughput screening of phage or yeast display libraries. Engineering antibodies in mammalian cells offers advantages associated with expression in their final therapeutic format (full-length glycosylated IgG), however, the inability to express large and diverse libraries severely limits their potential throughput. To address this limitation, we have developed homology-directed mutagenesis (HDM), a novel method which extends the concept of CRISPR/Cas9-mediated homology-directed repair (HDR). HDM leverages oligonucleotides with degenerate codons to generate site-directed mutagenesis libraries in mammalian cells. By improving HDM efficiency (>35-fold) and combining mammalian display screening with next-generation sequencing (NGS), we validated this approach can be used for key applications in antibody engineering at high-throughput: rational library construction, novel variant discovery, affinity maturation, and deep mutational scanning (DMS). We anticipate that HDM will be a valuable tool for engineering and optimizing antibodies in mammalian cells, and eventually enable directed evolution of other complex proteins and cellular therapeutics.

bioengineering↗

bioRxiv2017

Learning The High-Dimensional Immunogenomic Features That Predict Public And Private Antibody Repertoires

Recent studies have revealed that immune repertoires contain a substantial fraction of public clones, which are defined as antibody or T-cell receptor (TCR) clonal sequences shared across individuals. As of yet, it has remained unclear whether public clones possess predictable sequence features that separate them from private clones, which are believed to be generated largely stochastically. This knowledge gap represents a lack of insight into the shaping of immune repertoire diversity. Leveraging a machine learning approach capable of capturing the high-dimensional compositional information of each clonal sequence (defined by the complementarity determining region 3, CDR3), we detected predictive public- and private-clone-specific immunogenomic differences concentrated in the CDR3s N1-D-N2 region, which allowed the prediction of public and private status with 80% accuracy in both humans and mice. Our results unexpectedly demonstrate that not only public but also private clones possess predictable high-dimensional immunogenomic features. Our support vector machine model could be trained effectively on large published datasets (3 million clonal sequences) and was sufficiently robust for public clone prediction across studies prepared with different library preparation and high-throughput sequencing protocols. In summary, we have uncovered the existence of high-dimensional immunogenomic rules that shape immune repertoire diversity in a predictable fashion. Our approach may pave the way towards the construction of a comprehensive atlas of public clones in immune repertoires, which may have applications in rational vaccine design and immunotherapeutics.

systems biology↗

bioRxiv2017

The fundamental principles of antibody repertoire architecture revealed by large-scale network analysis

The antibody repertoire is a vast and diverse collection of B-cell receptors and antibodies that confer protection against a plethora of pathogens. The architecture of the antibody repertoire, defined by the network similarity landscape of its sequences, is unknown. Here, we established a novel high-performance computing platform to construct large-scale networks from high-throughput sequencing data (>100000 unique antibodies), in order to uncover the architecture of antibody repertoires. We identified three fundamental principles of antibody repertoire architecture across B-cell development: reproducibility, robustness and redundancy. Reproducibility of network structure explains clonal expansion and selection. Robustness ensures a functional immune response even under extensive loss of clones (50%). Redundancy in mutational pathways suggests that there is a pre-programmed evolvability in antibody repertoires. Our analysis provides guidelines for a quantitative network analysis of antibody repertoires, which can be applied to other facets of adaptive immunity (e.g., T cell receptors), and may direct the construction of synthetic repertoires for biomedical applications.

systems biology↗