COPD selectively remodels microbe-B cell receptor interactions within compartmentalized airway immune niches
Description
The human airway hosts distinct immune-microbial ecosystems, yet how COPD and HIV modifies B cell receptor (BCR) repertoires across airway compartments remains poorly understood. Here, we integrated bulk RNA seq-derived BCR reconstruction with airway microbiome profiling to map clonotype-level microbe-BCR interactions across induced sputum, bronchoalveolar lavage (BAL), and peripheral blood in individuals with HIV infection and/or chronic obstructive pulmonary disease (COPD). Across compartments, anatomical location was the dominant determinant of BCR repertoire structure, with limited clonotype sharing between sputum, BAL, and blood. In contrast, disease effects emerged primarily at the level of microbe-clonotype coupling, rather than global repertoire architecture. Using outer membrane vesicle (OMV)-adjusted interaction modeling, we identified extensive, clonotype-resolved interaction remodeling in sputum for both HIV and COPD, attenuated and COPD-restricted effects in BAL, and selective COPD-associated interactions in blood, with complete absence of HIV-associated systemic effects. These patterns reveal a hierarchical, compartment-specific architecture in which disease selectively retunes microbe-BCR interactions without disrupting underlying repertoire organization. Together, our findings define a model of fine-scale ecological tuning of airway B cell immunity driven by disease and anatomical context.
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Steps to reproduce
TRUST4: Immune repertoire reconstruction from sputum bulk RNA-seq data. We reconstructed B cell receptor repertoires from bulk RNA-seq data using the TRUST4 algorithm 13. TRUST4 processes data in three main stages: candidate read extraction, de novo assembly, and annotation. During candidate extraction, reads mapping to known BCR V, J, or constant (C) gene regions, as well as reads with significant k-mer overlap to these loci, were retained. Candidate reads were then assembled de novo into contigs using an overlap-based greedy extension strategy that prioritizes highly expressed receptor sequences. Assembled contigs were annotated by alignment to the IMGT reference database to assign V, J, and C genes and to identify complementarity-determining region 3 (CDR3) sequences. TRUST4 outputs were generated in AIRR-compliant CSV format for downstream analysis. BCR clonotype data sources and preprocessing. Bulk RNA-seq data from induced sputum, bronchoalveolar lavage (BAL), and peripheral blood samples were independently processed with TRUST4 to reconstruct BCR repertoires for each compartment. Sample-level clinical metadata, including HIV status, COPD status, and combined dual status, were imported from accompanying metadata files and merged with repertoire outputs. Raw TRUST4 outputs were filtered to retain only productive BCR rearrangements from the IGH, IGK and IGL loci. Sequences lacking V gene, J gene, or junction amino acid (CDR3) annotations were excluded. Clonotypes were defined as unique combinations of V gene, J gene, and CDR3 amino acid sequence, and clonotype abundance was quantified using TRUST4-reported supporting read counts. Microbiome composition data were obtained from matched species- or genus-level abundance tables and aligned to BCR data at the sample level for integrative analyses. All downstream analyses were performed on filtered, AIRR-compliant clonotype tables with harmonized clinical and microbiome metadata. BCR clonotype quantification. For each sample, B cell receptor (BCR) clonotypes were reconstructed from bulk RNA sequencing data using TRUST4 and quantified separately for the IGH, and IGL chains. Clonotype abundance was defined as the number of supporting reads per unique CDR3 amino acid sequence and V-J gene combination. Group-level summaries were generated by anatomical compartment (sputum, bronchoalveolar lavage [BAL], and peripheral blood) and further stratified by HIV status, COPD status, and combined dual status. Total and unique clonotypes were enumerated at both the sample and compartment levels. Repertoire diversity and richness. Within-sample repertoire diversity was quantified using Shannon entropy and Simpson diversity indices, calculated from clonotype frequency distributions. Clonal richness was defined as the number of unique clonotypes detected per sample. Diversity and richness metrics were computed separately for the combined BCR repertoire (“ALL”) and for each individual IGH, IGL and IGK chain.
Institutions
- Makerere University College of Health SciencesKampala, Kampala