Back

Plant-derived, nodule-specific cysteine rich peptides inhibit growth and psyllid acquisition of 'Candidatus Liberibacter asiaticus', the citrus Huanglongbing bacterium

Higgins, S. A.; Igwe, D. O.; Ramsey, J. S.; DeBlasio, S. L.; Pitino, M.; Niedz, R.; Shatters, R. G.; Fleites, L.; Heck, M. L.

2023-06-18 plant biology
10.1101/2023.06.18.545457 bioRxiv
Show abstract

AbstractThe Asian citrus psyllid, Diaphorina citri, is a vector of Candidatus Liberibacter asiaticus (CLas), a gram-negative, obligate biotroph whose infection in Citrus species is associated with citrus greening disease, or Huanglongbing (HLB). Strategies to block CLas transmission by D. citri remain the best way to prevent the spread of the disease into new citrus growing regions. However, identifying control strategies to block HLB transmission poses significant challenges, such as the discovery and delivery of antimicrobial compounds targeting the bacterium and overcoming consumer hesitancy towards accepting the treatment. Here, we computationally identified and tested a series of 20-mer nodule-specific cysteine-rich peptides (NCRs) derived from the Mediterranean legume, Medicago truncatula Gaertn. (barrelclover) to identify those peptides that could effectively prevent or reduce CLas infection in citrus leaves and/or prevent CLas acquisition by the bacteriums insect vector, D. citri. A set of NCR peptides were tested in a screening pipeline involving three distinct assays: a bacterial culture assay, a CLas-infected excised citrus leaf assay, and a CLas-infected nymph acquisition assay that included D. citri nymphs, the only stage of D. citris life-cycle that can acquire CLas leading to the development of vector competent adult insects. We demonstrate that a subset of M. truncatula-derived NCRs inhibit both CLas growth in citrus leaves and CLas acquisition by D. citri from CLas-infected leaves. These findings reveal NCR peptides as a new class and source of biopesticide molecules to control CLas for the prevention and/or treatment of HLB.

Matching journals

The top 12 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.