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Kossida, S.

Publications and source records attributed to Kossida, S..

15 recordsLinked to original sources

Ancestra: A lineage-explicit simulator for benchmarking B-cell receptor repertoire and lineage inference methods

AO_SCPLOWBSTRACTC_SCPLOWHigh-throughput B-cell receptor sequencing has transformed the analysis of adaptive immunity, but benchmarking clonal grouping and lineage reconstruction methods remains limited by the absence of datasets with known evolutionary histories. Here we present Ancestra, a lineage-explicit simulator of B-cell receptor heavy-chain affinity maturation. Ancestra models stochastic V(D)J recombination, context-dependent somatic hypermutation, affinity-based selection and clonal expansion while recording complete parent-child relationships and mutation events. The framework generates BCR heavy-chain sequence datasets together with their corresponding ground-truth lineage trees, enabling direct benchmarking of lineage-aware analytical methods. Across simulations, Ancestra recapitulates key properties of human repertoires, including complementarity-determining region 3 length distributions, amino-acid usage patterns, junctional mutation patterns consistent with IMGT criteria and heterogeneous branching topologies. Simulated lineages also reveal multi-label lineage trees, in which identical nucleotide sequences can arise independently along distinct evolutionary paths. Ancestra provides a practical foundation for rigorous benchmarking of lineage-aware immune repertoire analysis.

bioinformatics↗

UnivAIRRse: A Unified Framework for Organizing and Comparing Adaptive Immune Receptor Repertoire Simulators

Adaptive immune receptor repertoire sequencing (AIRR-seq) enables large-scale profiling of B- and T-cell receptor diversity and has become a cornerstone of modern computational immunology. However, AIRR-seq provides only a partial and lossy molecular snapshot of immune dynamics, lacking explicit ground truth for clonal ancestry, lineage trajectories, antigen specificity, and longitudinal immune evolution. This limitation complicates benchmarking, method validation, and mechanistic interpretation of repertoire analysis pipelines. Here, we introduce UnivAIRRse, a unified hierarchical framework that organizes AIRR simulators within a shared conceptual coordinate system spanning five operational levels, from observed sequence data to the theoretical generative potential of the adaptive immune system. By explicitly distinguishing sequence-, clonal-, specificity-, repertoire-, and generative-level representations, UnivAIRRse enables systematic comparison of simulator assumptions, biological scope, abstraction level, and application focus. To our knowledge, this is the first review to formalize such a unified structure across biological, computational, and functional layers of AIRR simulation. Using this framework, we review how simulation supports benchmarking, strengthens computational inference, and enables multi-scale investigation of immune repertoire formation and evolution. We identify persistent limitations in existing simulators, including incomplete biological context, limited modularity, restricted interoperability, and overreliance on AIRR-seq as a molecular proxy for complex spatiotemporal immune processes. To operationalize this framework, we provide an interactive web-based AIRR Simulation Landscape Explorer (publicly available at https://www.imgt.org/AIRR-Simulator/) that enables dynamic filtering and comparison of simulators across biological scope, abstraction level, output fidelity, and application focus. Finally, we outline emerging directions toward digital-twin-ready immune simulation, emphasizing modular architectures, longitudinal multi-omic integration, uncertainty quantification, and dynamic model updating. By providing a coherent conceptual and operational coordinate system, UnivAIRRse establishes a foundation for reproducible, interpretable, and clinically actionable modeling of adaptive immune repertoires, bridging current simulation practices with the next generation of predictive and personalized immunological modeling. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/706510v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@7a5a95org.highwire.dtl.DTLVardef@d127f1org.highwire.dtl.DTLVardef@19545c9org.highwire.dtl.DTLVardef@118cc74_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

The αβTCR repertoire at scale in the immgenT dataset

The immense T cell receptor (TCR) repertoire is shaped by VDJ combinatorial diversity, imprecise rearrangements, and clonal selection. The immgenT Project generated scRNA and TCRseq to map paired {beta}TCR repertoires across 734 mouse samples from diverse tissues and challenge conditions. Compositional analysis uncovered some extreme junctional architectures. Beyond probabilistic V and J pairing, over-represented joins suggested non-randomness in fine joining, broadening the precedent of quasi-invariant iNKT and MAIT TCRs. We charted public clonotypes linked to self or environmental antigens in the main lineages. Tissue analyses revealed compartmentalized tissue-specific expansions. Unproductive and productive rearrangements of a V gene appeared to interfere specifically with each other, at chromatin or RNA levels. Unexpectedly, allelic exclusion at TCR{beta} proved less stringent than thought, and we identified rearrangements of TCR in immature pre-T stages. This organism-wide look into the TCR repertoire offers novel insights on the evolutionary and immunological pressures on TCR repertoire selection.

immunology↗

Unraveling IGK Locus in Dog Breeds: IMGT(R) New Insights into Canine Immunogenetics.

Over millennia, the selective breeding of dogs (Canis lupus familiaris) has generated remarkable genetic diversity among breeds, highlighting the need for comprehensive genomic and immunogenetic studies. This research provides detailed immunoglobulin kappa light chain locus (IGK) analysis across multiple dog breeds. It aims to uncover breed-specific genetic variations and their implications for immunology and veterinary medicine. The primary objectives were to do the biocuration of the IGK locus in nine canine genome assemblies, investigate structural variations, polymorphisms, and gene diversity, and to enrich the IMGT(R) database with comprehensive IGK data from diverse breeds, creating a more inclusive genetic resource. Our extensive annotation of breeds, including the Bernese Mountain Dog, Boxer, Cairn Terrier, Labrador Retriever, Great Dane, Basenji, and German Shepherd, identified 40 genes and 97 alleles, revealing both conserved genes and unique variants across these breeds, with in silico validation through Sanger sequencing. Notably, we analyzed discrepancies in the first reference assembly from the Boxer breed (Canfam3.1), highlighting potential errors in assembly, challenges in gene and allele nomenclature, and a low-density region within the canine IGK locus. This study not only refines the understanding of IGK locus diversity but also contributes to the IMGT(R) databases, advancing future research on immunogenetic variability, somatic mutations, and immune response dynamics in canine health and disease.

immunology↗

Characterization of Immunoglobulin Loci in Pongo abelii and Pongo pygmaeus: Insights from Multi-Genome Annotation and IMGT-Based Curation

Antibodies, or immunoglobulins (IG), are central to the vertebrate adaptive immune system, yet the genomic architecture of IG loci remains poorly characterized in many nonhuman primates. In this study we present the first comprehensive genomic analysis of the immunoglobulin (IG) loci (IGH, IGL, and IGK) in two critically endangered orangutan species; Pongo abelii (Sumatran orangutan) and Pongo pygmaeus (Bornean orangutan) across multiple genome assemblies. Using IMGT-standardized biocuration framework combined with read-level structural validation, we identified previously undocumented haplotype-specific variation, including multigene duplications, asymmetric gene absence, and species-specific expansions of variable gene families. Recombination signal sequence (RSS) and switch region analyses revealed conserved regulatory motifs with potential implications for V(D)J recombination and class-switch recombination. These findings underscore the complexity and evolutionary adaptability of IG loci in great apes and highlight the value of orangutans as key references for understanding immune system evolution in the Hominidae lineage.

immunology↗

Novel Genes and Polymorphisms in Human Immunoglobulin Light Chains Across Diverse Populations Through Comprehensive IMGT(R) Analysis

The human immunoglobulin light chain loci, kappa (IGK) and lambda (IGL), are structurally complex genomic regions with germline gene content that is not yet fully characterized. These loci are marked by extensive gene duplication, allelic diversity, and segmental duplications, features that contribute critically to the adaptive immune response. In this study, we present a comprehensive IMGT annotation of IGK and IGL using two high-quality human reference assemblies (GRCh38 and T2T-CHM13) along with 142 and 125 additional chromosomal-level haploid assemblies, respectively for each locus, from individuals representing all major human superpopulations. Detailed gene and allele annotation of the reference assemblies led to the identification of 5 novel IGKV genes and 8 new IGKV alleles, 16 new IGLV genes, and 22 novel IGLV alleles. These were confirmed through assembly read validation, presence in whole genome sequencing datasets, and recurrence in multiple assemblies. Gene-level identification across the broader dataset enabled assessment of structural variation (SV) at both loci. IGL displayed high conservation, with recurrent absence observed for only one gene. In contrast, IGK exhibited greater variability, including complete loss of the distal region in certain assemblies. This structural diversity was analyzed across superpopulations, allowing us to map potential patterns of gene presence and absence across different ancestral groups. All newly identified genes were consistently observed across individuals and genomic backgrounds. This work enhances the structural resolution of the IGK and IGL loci and expands the IMGT reference directory with newly described germline genes and alleles. The results provide a more complete view of light chain genomic diversity and serve as a valuable resource for studies of antibody gene repertoires, immunogenetic variation, monoclonal antibody development and population-level diversity.

genomics↗

Therapeutic Monoclonal Antibodies Repurposing in Oncology via IMGT/mAb-KG Embeddings

BackgroundCancer remains one of the leading causes of mortality world-wide, accounting for approximately 9.7 million deaths in 2022. Faced with this significant public health challenge, therapeutic monoclonal antibodies (mAbs) have emerged as promising alternatives that may minimize the side effects associated with conventional treatments such as radiotherapy and chemotherapy. To support mAb research and development, IMGT(R), the international ImMuno-GeneTics information system, has established two standardized data sources namely IMGT/mAb-DB, a comprehensive database for mAbs, and, more recently, IMGT/mAb-KG, a dedicated knowledge graph for mAbs. Despite these advances, the development of therapeutic mAbs remains both time-consuming and financially burdensome--costs can reach up to $2.8 billion. To address this challenge and accelerate cancer treatment, mAb repurposing represents a promising alternative. ResultsIn this study, we leveraged a subset of IMGT/mAb-KG, dedicated to the oncology domain, to develop a scientific hypothesis generation application for mAb repurposing. This application, based on knowledge graph embedding techniques, is designed to suggest potential mAb candidates for novel oncology applications. A user-friendly web interface provides access to the tool, incorporating visual support to facilitate the interpretation of generated hypotheses. This application is a decision support tool aiming to accelerate the discovery of new therapeutic applications for existing mAbs. ConclusionOur application demonstrates the potential of knowledge graph embedding techniques in the oncology domain by enabling the repurposing of existing mAbs for new therapeutic uses. Using this tool, we have identified two novel mAbs, loncastuximab tesirine and glofitamab, both currently undergoing clinical trials for the treatment of chronic lymphocytic leukemia. This decision-support tool thus facilitates the discovery of new therapeutic opportunities by effectively repositioning existing mAbs for oncological indications, potentially accelerating the development of cancer therapies and addressing critical public health needs.

bioinformatics↗

Identification of Engineered IMGT Fc Variants in IMGT/mAb-DB Therapeutic Antibodies and Fusion proteins

Monoclonal antibodies (mAbs) and fusion proteins for immune applications (FPIA) play a crucial role in treating autoimmune diseases and cancers by targeting cell-surface proteins and triggering multiple immune mechanisms. These functions are mediated by the fragment crystallizable (Fc) region of mAbs and fusion proteins, whose interaction with Fc gamma receptors (Fc{gamma}Rs) can be modulated through Fc amino acid (AA) engineering. To address this, we developed the IMGT/FcVariantsExplorer tool (https://www.imgt.org/fcvariantsexplorer/) to identify AA changes within the Fc region in mAb and fusion proteins sequences from IMGT/2Dstructure-DB, the AA sequence database of IMGT(R), the international ImMunoGeneTics information system(R). We used the IMGT(R) nomenclature of engineered Fc variants involved in antibody effector properties and formats, applying a standardized classification in five categories: Effector, Half-life, Physicochemical properties, Structure, and Hybrid. We analyzed sequences of 1,107 mAbs and fusion proteins, identifying 483 entries with Fc AA changes, resulting in 211 unique Fc variants in the dataset. We also used web scraping to retrieve associated biological data from literature. All data have been integrated into IMGT/mAb-DB, with links to sequences in IMGT/2Dstructure-DB, enabling users to query Fc variants by their Category or Effect. This curated dataset reveals key trends in antibody engineering.

bioinformatics↗

IMGT(R) at scale: FAIR, Dynamic and Automated Tools for Immune Locus Analysis

IMGT(R), the international ImMunoGeneTics information system(R), has advanced its comprehensive platform for the analysis of immunoglobulin (IG) and T cell receptor (TR) genes through the development of new automated and scalable tools. This article presents major updates aligned with IMGTs three axes of research. Axis I introduces dynamic resources such as IMGT/GeneTables, IMGT/AssemblyComparison, and IMGT/StatAssembly, enabling real-time access to annotated genomic data and quality assessment of assemblies. Axis II enhances repertoire analysis with a redesigned IMGT/GeneFrequency tool and new customization features in IMGT/V-QUEST, supporting flexible exploration of IG and TR gene expression. Axis III improves the accurate prediction of peptide-MHC thanks to IMGT/RobustpMHC. Additionally, the IMGT Knowledge Graph (IMGT-KG) and its therapeutic extension, IMGT/mAb-KG, provide semantically structured access to more than 100 million immunogenetic triplets, integrating IMGT databases and linking IMGT content to external biomedical resources. These developments promote standardization, interoperability, and integrative analysis across immunogenetics and clinical applications, reinforcing IMGTs role as a core reference in the era of FAIR data and personalized medicine.

bioinformatics↗

IMGT(R) Analysis of the Human IGH Locus: Unveiling Novel Polymorphisms and Copy Number Variations in Genome Assemblies from Diverse Ancestral Backgrounds

Unraveling the genetic complexity of the human immunoglobulin heavy chain (IGH) locus provides valuable insights into the mechanisms underlying the efficacy and specificity of the adaptive immune response. Despite its crucial role, the IGH locus remains insufficiently characterized, with its allelic diversity and polymorphisms inadequately investigated. In this study, we present an analysis of the human IGH locus, incorporating 15 human genome assemblies from diverse ancestries, including African, European, Asian, Saudi, and mixed backgrounds. Through our examination of both maternal and paternal assemblies, we uncover novel IGH alleles, copy number variations (CNV), and polymorphisms, particularly within the variable (IGHV) region. Our findings reveal extensive and previously uncharacterized genetic variability in the constant (IGHC) region and distinct IMGT CNV forms across individuals. This research contributes to a significant enrichment of the IMGT(R) IGH reference directory, databases, tools and web resources and lays the groundwork for a comprehensive IMGT(R) haplotype database which can be progressively enriched to support future studies in population-specific immune profiles and adaptive immune related disease susceptibility, as comprehensive datasets become available. Such a resource promises to propel personalized immunogenomics forward, with exciting applications in cancer immunotherapy, COVID-19, and other immune-related diseases. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/665479v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@17afc2corg.highwire.dtl.DTLVardef@141d785org.highwire.dtl.DTLVardef@1ac7500org.highwire.dtl.DTLVardef@13558fc_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

Development of ferret immune repertoire reference resources and single-cell-based high-throughput profiling assays

Domestic ferrets (Mustela putorius furo) are important for modeling human respiratory diseases. However, ferret B and T cell receptors have not been completely identified or annotated, limiting immune repertoire studies. Here we performed long read transcriptome sequencing of ferret splenocyte and lymph node samples to obtain over 120,000 high-quality full-length immunoglobin (Ig) and T cell receptor (TCR) transcripts. We constructed a complete reference set of the constant regions of ferret Ig and TCR isotypes and chain types. We also systematically annotated germline Ig and TCR variable (V), diversity (D), joining (J), and constant (C) genes on a recent ferret reference genome assembly. We designed new ferret-specific immune repertoire profiling assays by targeting positions in constant regions without allelic diversity across 11 ferret genome assemblies, and experimentally validated them using a commercially compatible single-cell-based platform. These improved resources and assays will enable future studies to fully capture ferret immune repertoire diversity.

genomics↗

Genomic Specificity of Anti-TCR mAbs determined by single-cell RNAseq

T cells play a pivotal role in the immune system, relying on their somatically rearranged T cell receptor (TCR) to recognize peptide-MHC complexes. A comprehensive and extensively used set of monoclonal antibodies (mAbs) against TCR Variable regions was generated in the previous century. The separate identification of mAb-specific TCR-V proteins and TRV genes has resulted in multiple nomenclatures, making their relationships unclear. To formally re-establish this link and determine patterns of reactivity within TRV subfamilies, we sorted T cells positive for any one of a panel of 22 anti-V mAbs and determined their TRV genes by single-cell TCRseq. RNAseq data revealed consistently higher expression of repeated elements from the ERV1-family LTR RLTR6Mm (mapping to Gm20400) in cells utilizing TRBV segments encoded within a 66kb genomic region between TRBV23 and TRBV30. Our findings provide a comprehensive resource for anti-TCR mAb specificity and insight into V-gene usage biases and T cell function.

immunology↗

Deciphering of Gorilla gorilla gorilla Immunoglobulin Loci in Multiple Genome Assemblies and Enrichment of IMGT Resources

Through the analysis of immunoglobulin genes at the IGH, IGK, and IGL loci from four Gorilla gorilla gorilla genome assemblies, IMGT(R) provides an in-depth overview of these loci and their individual variations in a species closely related to humans. The similarity between gorilla and human IG gene organization allowed the assignment of gorilla IG gene names based on their human counterparts. This study revealed significant findings, including variability in the IGH locus, the presence of known and new copy number variations (CNVs), and the accurate estimation of IGHG genes. The IGK locus displayed remarkable homogeneity and lacked the gene duplication seen in humans, while the IGL locus showed a previously unconfirmed CNV in the J-C cluster. The curated data from these analyses, available on the IMGT website, enhance our understanding of gorilla immunogenetics and provide valuable insights into primate evolution.

genomics↗

IMGT/RobustpMHC: Robust Training for class-I MHCPeptide Binding Prediction

The accurate prediction of peptide-MHC class I binding probabilities is a critical endeavor in immunoinformatics, with broad implications for vaccine development and immunotherapies. While recent deep neural network based approaches have showcased promise in peptide-MHC prediction, they have two shortcomings: (i) they rely on hand-crafted pseudo-sequence extraction, (ii) they do not generalise well to different datasets, which limits the practicality of these approaches. In this paper, we present PerceiverpMHC that is able to learn accurate representations on full-sequences by leveraging efficient transformer based architectures. Additionally, we propose IMGT/RobustpMHC that harnesses the potential of unlabeled data in improving the robustness of peptide-MHC binding predictions through a self-supervised learning strategy. We extensively evaluate RobustpMHC on 8 different datasets and showcase the improvements over the state-of-the-art approaches. Finally, we compile CrystalIMGT, a crystallography verified dataset that presents a challenge to existing approaches due to significantly different peptide-MHC distributions.

bioinformatics↗

PickPocket : Pocket binding prediction for specific ligands family using neural networks.

Most of the protein biological functions occur through contacts with other proteins or ligands. The residues that constitute the contact surface of a ligand-binding pocket are usually located far away within its sequence. Therefore, the identification of such motifs is more challenging than the linear protein domains. To discover new binding sites, we developed a tool called PickPocket that focuses on a small set of user-defined ligands and uses neural networks to train a ligand-binding prediction model. We tested PickPocket on fatty acid-like ligands due to their structural similarities and their under-representation in the ligand-pocket binding literature. Our results show that for fatty acid-like molecules, pocket descriptors and secondary structures are enough to obtain predictions with accuracy >90% using a dataset of 1740 manually curated ligand-binding pockets. The trained model could also successfully predict the ligand-binding pockets using unseen structural data of two recently reported fatty acid-binding proteins. We think that the PickPocket tool can help to discover new protein functions by investigating the binding sites of specific ligand families. The source code and all datasets contained in this work are freely available at https://github.com/benjaminviart/PickPocket. Author SummaryMost of the protein biological functions are defined by its interactions with other proteins or ligands. The cavity of the protein structure that receives a ligand, also called a pocket, is made of residues that are usually located far away within its sequence. Therefore understanding the omplementarity of pocket and ligand is a real challenge. To discover new binding sites, we developed a tool called PickPocket that focuses on a small set of user-defined ligands to train a prediction model. Our results show that for fatty acid-like molecules, pocket descriptors (such as volume, shape, hydrophobicity...) and secondary structures are enough to obtain predictions with accuracy >90% using a dataset of 1740 manually curated ligand-binding pockets. The trained model could also successfully predict the ligand-binding pockets using unseen structural data of two recently reported fatty acid-binding proteins. We think that the PickPocket tool can help to discover new protein functions by investigating the binding sites of specific ligand families.

bioinformatics↗