CLR-Seq: a pipeline to identify bacterial microbiota species with immunomodulatory potential through human C-type lectin receptor interaction

AbstractBacterial microbiota permanently colonize host barrier sites such as the gastrointestinal tract. Select members of the microbiota contribute to antibody-based barrier homeostasis and host defense by inducing local and systemic antibody responses, respectively. However, the molecular mechanisms underlying the selection of these immunologically-relevant microbiota are still unknown. Barrier-resident antigen-presenting cells (APCs) are critical to initiate local and systemic immune responses upon bacterial invasion. Furthermore, APCs sample the environment across intact epithelial barriers during homeostasis by extending their dendrites, which are densely covered with glycan-binding C-type lectin receptors (CLRs). We hypothesized that APCs are thereby ideally positioned and equipped to induce and sustain local and systemic immunity by probing surface-expressed bacterial glycans. To identify the repertoire of microbiota species that could be recognized by human CLRs and thereby sampled by APCs, we have developed CLR-sequencing (CLR-seq), combining CLR-based bacterial cell sorting and 16S rRNA gene sequencing. We established our experimental pipeline using the fluorescently-labeled CLRs langerin (CD207) and the macrophage galactose C-type lectin (MGL, CD301), and fecal microbiota samples from healthy donors. The proportion of langerin- and MGL-positive microbiota populations showed inter-individual variation but shared enrichment of specific genera. Moreover, CLR-enriched genera correlated with IgA-enriched genera, suggesting a link between glycan-based recognition of bacterial species and the induction of local antibody production. Finally, we validated CLR interaction for several species with high CLR enrichment in monoculture. In summary, CLR-seq allows identification of human microbiota species based on CLR-interacting glycans with easy expansion to include additional CLRs or patient microbiota samples.