Biology subjects

Stalder, L.

Publications and source records attributed to Stalder, L..

10 recordsLinked to original sources

bioRxiv2026

Contrastive learning for antibody-antigen sequence-to-specificity prediction

Predicting which antibodies bind to which antigens directly from primary amino acid sequences remains a major challenge, as no current method can reliably determine this specificity at both a repertoire and proteome scale. Structure-based protein design frameworks can propose antibody binders to specified antigenic epitopes, but they do not solve the "sequence-to-specificity" task of mapping antibodies to cognate epitopes, and vice versa. Here, we introduce CALM (Cross-attention Adaptive Immune Receptor-Antigen Language Model), a dual-encoder plus cross-attentive decoder architecture that treats antibody-antigen recognition as molecular translation. Using contrastive learning, antigen and antibody encoders learn a shared embedding space that aligns cognate epitope-paratope binding pairs. CALM-1.0 is trained and evaluated on 4,138 curated antibody-antigen pairs obtained from the PDB-derived structural antibody database (SAbDab). On a leakage-controlled test split drawn from sequence clusters at 80% identity and unseen during training, CALM-1.0 achieves a mean top-1 retrieval (R@1) of 7%, with consistent performance across both directions (Ab[->]Ag and Ag[->]Ab). CALM establishes a foundation for bidirectional antibody-antigen sequence-to-specificity prediction with the potential to unify retrieval and generative design.

immunology↗

bioRxiv2025

High-resolution microbial network analysis defines biocontrol consortia in the wheat phyllosphere

Plant-associated microbiomes comprise diverse microbial species that coexist and interact, influencing community structure and host plant health. However, our understanding of these interactions in field conditions and at strain-resolution remains limited. This hinders the development of effective biocontrol applications, as laboratory findings often fail to translate to field settings due to insufficient insights into in situ interaction network structures. This study addresses this limitation by employing taxon-specific high-resolution amplicons to resolve a cross-kingdom co-occurrence network within the wheat phyllosphere microbiome. We performed in-depth monitoring of strains from the hub genus Pseudomonas, revealing a high degree of strain-specificity of Pseudomonas interactions both within and across kingdoms. Through negative interaction modelling, we identified a consortium of ten biocontrol taxa with the potential to suppress seven fungal pathogens in field conditions. Additional stabilizer strains were found to likely enhance persistence. We validated the strain-specific interactions of Pseudomonas with the major fungal pathogen Zymoseptoria tritici using co-inoculation experiments with genotypes retrieved from the same field. Consistent with our prediction, we identified a P. poae isolate as the most antagonistic towards the pathogen both in vitro and in planta. Our study demonstrates that taxon-specific high-resolution network inference can effectively map microbial interaction networks and predict strain-specific interaction patterns of biocontrol genotypes with high persistence under field conditions. Our novel approach supports the design of more effective and sustainable biocontrol strategies.

microbiology↗

bioRxiv2025

Predicting TCR antigen specificity at proteome-scale with synthetic immune cells and machine learning

TCR specificity to peptide-HLA antigens is central to immunology, impacting responses in infection, autoimmunity and cancer. Achieving precise recognition while avoiding off-target reactivity is critical for effective immunity and safe therapeutic interventions. Comprehensive, proteome-wide specificity profiling of TCRs is challenging with current methods, which notably lack integrated machine learning for large-scale analysis. Here, we report a synthetic immune cell system coupled with machine learning to enable TCR functional and specificity mapping of peptide-HLA antigens at proteome-scale. Multi-step immunogenomic engineering of synthetic antigen-presenting cells (APCs) was performed to enable stable mono-allelic integration and precise display of peptide antigens and HLA class I from a defined genomic locus, ensuring genomically-encoded antigen presentation. Compatible with a synthetic TCR displaying T cell system, this platform incorporates a fluorescent reporter of cytokine-mediated signaling for real-time activation detection in both synthetic APCs and T cells. We combined this screening with diverse peptide antigen libraries and deep sequencing to train supervised machine learning models. These models were applied to predict TCR specificity to peptide-HLA antigens across the entire human proteome. Experimental validation confirmed novel off-targets for therapeutic TCR candidates, including for a clinically-approved TCR therapeutic. This integrated synthetic immune cell and machine learning approach provides unprecedented proteome-wide peptide-HLA specificity mapping to support the development of safer TCR-based therapies. One sentence summaryWe present a synthetic immune cell platform integrated with machine learning that enables prediction of TCR specificity to peptide-HLA antigens at proteome-scale.

immunology↗

bioRxiv2025

Spatial Transcriptomics and Single-Nucleus RNA Sequencing Reveal rAAV2- and rAAV9-Specific Transduction Signatures in the Mouse Liver

The liver is a primary target for recombinant adeno-associated viral (rAAV) vectors, yet the influence of serotype, sex, and liver zonation on transduction and transcriptomic changes remain incompletely understood. This proof-of-concept study employs spatial transcriptomics alongside single-nucleus RNA sequencing to map the spatial distribution and impacts of rAAV2- and rAAV9-CMV-EGFP vectors in male and female mouse livers. Spatial transcriptomics provided precise transgene mapping and highlighted that rAAV vectors deregulate hepatocellular lipid metabolism, the circadian clock, and the immune/stress response with sex specific differences. Lipid metabolism genes (Elovl3, Chka, Irs2, Ppard), were deregulated independent of zonation, serotype, and sex, while Srebf1, Tlcd4, Cpt2, and Acot1 exhibited sex-specific patterns. Circadian clock modulators (Dbp, Tef, Arntl, Nfil3, Nr1d1/Nr1d2) were altered independent of zonation. The study found sex-specific downregulation of immune and stress-response genes and pathways, including Gadd45g and hypoxia pathways. TGF-{beta} and EGFR pathways were upregulated sex-independently. Spatial transcriptomics further enabled examination of transgene and rAAV entry factor co-expression, identifying known and novel factors like Rpsa, Dpp4, Sdc1, and solute carrier proteins highlighting its role in supporting targeted screening. Our findings demonstrate spatial transcriptomics as a powerful tool in gene therapy research and reveal novel rAAV vector effects on liver biology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/643011v2_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@af87b9org.highwire.dtl.DTLVardef@1f79165org.highwire.dtl.DTLVardef@6aa2ccorg.highwire.dtl.DTLVardef@e6274c_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2025

Delineating inter- and intra-antibody repertoire evolution with AntibodyForests

MotivationThe rapid advancements in immune repertoire sequencing, powered by single-cell technologies and artificial intelligence, have created unprecedented opportunities to study B cell evolution at a novel scale and resolution. However, fully leveraging these data requires specialized software capable of performing inter- and intra-repertoire analyses to unravel the complex dynamics of B cell repertoire evolution during immune responses. ResultsHere, we present AntibodyForests, software to infer B cell lineages, quantify inter- and intra-antibody repertoire evolution, and analyze somatic hypermutation using protein language models and protein structure. Availability and implementationThis R package is available on CRAN (1) and Github at https://github.com/alexyermanos/AntibodyForests, a vignette is available at https://cran.case.edu/web/packages/AntibodyForests/vignettes/AntibodyForests_vignette.html

bioinformatics↗

bioRxiv2024

Clonally expanded IgG antibody-secreting cells preferentially target influenza nucleoprotein following homologous and heterologous infections

Infection with influenza virus remains a significant global health concern due to its ability to acquire mutations at key antigenic sites to escape antibody recognition. While germinal center (GC) and memory B cells have been well studied following influenza infection, the clonal dynamics of antibody secreting cells (ASCs), particularly those within the bone marrow (BM) niche that are responsible for serum immune protection, remain poorly understood. Here, we combine single-cell RNA (scRNA) and B cell receptor (BCR) sequencing to characterize individual ASCs following various Influenza exposure histories. We find that BM repertories are populated by highly expanded and class-switched ASCs following Influenza infection with similar transcriptional and repertoire characteristics regardless of homologous or heterologous infection histories. By combining single-cell analysis with monoclonal antibody expression and characterization, we find that a large proportion of the expanded IgG-, but not IgA-, ASC repertoire demonstrates specificity to influenza nucleoprotein (NP). Together, our data reveal the complex relationship between BM ASC repertoires, mucosal humoral immune responses, and BCR antigen specificity during influenza infection.

immunology↗

bioRxiv2023

High-resolution profiling of bacterial and fungal communities using pangenome-informed taxon-specific amplicons and long-read sequence

High-throughput sequencing technologies have greatly advanced our understanding of microbiomes, but resolving microbial communities at species and strain levels remains challenging. Here, we developed and validated a pipeline for designing, multiplexing, and sequencing highly polymorphic taxon-specific long-read amplicons. We focused on the wheat microbiome as a proof-of-principle and demonstrate unprecedented resolution for the wheat-associated Pseudomonas microbiome and the ubiquitous fungal pathogen Zymoseptoria tritici. We achieved an order of magnitude higher phylogenetic resolution compared to existing ribosomal amplicons. The designed amplicons accurately capture species and strain diversity outperforming full-length 16S and ITS amplicons. Furthermore, we tracked microbial communities in the wheat phyllosphere across time and space to establish fine-grained species and strain-specific dynamics. To expand the utility of our approach, we generated pangenome-informed amplicon templates for additional key bacterial and fungal genera. Pangenome-informed microbiome profiling enables the tracking of microbial community dynamics in complex environments and overcomes limitations in phylogenetic resolution.

microbiology↗

bioRxiv2022

ePlatypus: an ecosystem for computational analysis of immunogenomics data

The maturation of systems immunology methodologies requires novel and transparent computational frameworks capable of integrating diverse data modalities in a reproducible manner. Here, we present the ePlatypus computational immunology ecosystem for immunogenomics data analysis, with a focus on adaptive immune repertoires and single-cell sequencing. ePlatypus is a web-based platform and provides programming tutorials and an integrative database that elucidates selection patterns of adaptive immunity. Furthermore, the ecosystem links novel and established bioinformatics pipelines relevant for single-cell immune repertoires and other aspects of computational immunology such as predicting ligand-receptor interactions, structural modeling, simulations, machine learning, graph theory, pseudotime, spatial transcriptomics and phylogenetics. The ePlatypus ecosystem helps extract deeper insight in computational immunology and immunogenomics and promote open science. Accessibilityhttps://alexyermanos.github.io/Platypus/index.html

immunology↗

bioRxiv2021

The population genetics of adaptation through copy-number variation in a fungal plant pathogen

Microbial pathogens can rapidly adapt to changing environments such as the application of pesticides or host resistance. Copy number variations (CNV) are a major source of adaptive genetic variation for recent adaptation. Here, we analyze how a major fungal pathogen of barley, Rhynchosporium commune, has adapted to host environment, fungicide and temperature challenges. We screen the genomes of 126 isolates sampled across a worldwide set of populations and identify a total of 7879 gene duplications and 116 gene deletions. Most gene duplications result from segmental chromosomal duplications. We find that genes showing recent gains or losses are enriched in functions related to host exploitation (i.e. effectors and cell wall degrading enzymes). We perform a phylogeny-informed genome-wide association study (GWAS) and identify 191 copy-number variants associated with different pathogenesis and temperature related traits, including a large segmental duplication of CYP51A that has contributed to the emergence of azole resistance. Additionally, we use a genome-wide SNP dataset to replicate the GWAS and contrast it with the CNV-focused analysis. We find that frequencies of adaptive CNV alleles show high variation among populations for traits under strong selection such as fungicide resistance. In contrast, adaptive CNV alleles underpinning temperature adaptation tend to be near fixation. Finally, we show that transposable elements are important drivers of recent gene copy-number variation. Loci showing signatures of recent positive selection are enriched in miniature inverted repeat transposons. Our findings show how extensive segmental duplications create the raw material for recent adaptation in global populations of a fungal pathogen.

evolutionary biology↗

bioRxiv2020

SWATH-MS co-expression profiles reveal paralogue interference in protein complex evolution

Understanding the conservation and evolution of protein complexes is of critical value to decode their function in physiological and pathological processes. One prominent proposal posits gene duplication as a potential mechanism for protein complex evolution. In this study we take advantage of large-scale proteome expression datasets to systematically investigate the role of paralogues, and specifically self-interacting paralogues, in shaping the evolutionary trajectories of protein complexes. First, we show that protein co-expression derived from quantitative proteomic matrices is a good indicator for complex membership and is conserved across species. Second, we suggest that paralogues are commonly strongly co-expressed and that for the subset of paralogues that show diverging co-expression patterns, the divergent co-expression patterns reflect both sequence and functional divergence. Finally, on this basis, we show that homomeric paralogues known to be part of protein complexes display a unique co-expression pattern distribution, with a subset of them being highly diverging. These findings support the idea that homomeric paralogues can avoid cross-interference by diversifying their expression patterns, and corroborates the role of this mechanism as a force shaping protein complex evolution and specialization.

evolutionary biology↗