Monoclonal antibodies from humans with Mycobacterium tuberculosis exposure or latent infection recognize distinct arabinomannan epitopes
Cite this dataset
Achkar, Jacqueline M.; Ishida, Elise (2021). Monoclonal antibodies from humans with Mycobacterium tuberculosis exposure or latent infection recognize distinct arabinomannan epitopes [Dataset]. Dryad. https://doi.org/10.5061/dryad.dv41ns200
The surface polysacharide arabinomannan (AM) and related glycolipid lipoarabinomannan (LAM) play critical roles in tuberculosis pathogenesis. Human antibody responses to AM/LAM are heterogenous and knowledge of reactivity to specific glycan epitopes at the monoclonal level is limited, especially in individuals who can control M. tuberculosis infection. We generated human IgG mAbs to AM/LAM from B cells of two asymptomatic individuals exposed to or latently infected with M. tuberculosis. We here show that two of these mAbs have high affinity to AM/LAM, are non-competing, and recognize different glycan epitopes distinct from other anti-AM/LAM mAbs reported. Both mAbs recognize virulent M. tuberculosis and nontuberculous mycobacteria with marked differences, can be used for the detection of urinary LAM, and can detect M. tuberculosis and LAM in infected lungs. These mAbs enhance our understanding of the spectrum of antibodies to AM/LAM epitopes in humans and are valuable for tuberculosis diagnostic and research applications.
ELISA data contain optical densities read at 450nm from three experiments.
Glycan array raw data have been provided and additional details can also be found on the NCBI Gene Expression Omnibus (GEO) database with accession number GSE180517.
The BioLayer Interferometry (BLI) kinetics data provided were used to estimate values for the kon (association rate constant), koff (dissociation rate constant), and KD,app (apparent equilibrium dissociation constant), we used a global data fitting 2:1 binding model.
The BioLayer Interferometry (BLI) epitope binning data provided were used to estimate mAb competition in a two-phase experiment.
National Institutes of Health