Canine olfactory detection of a vectored phytobacterial pathogen, Liberibacter asiaticus, and integration with disease control

Abstract

Early detection and rapid response are crucial to avoid severe epidemics of exotic pathogens. However, most detection methods (molecular, serological, chemical) are logistically limited for large-scale survey of outbreaks due to intrinsic sampling issues and laboratory throughput. Evaluation of 10 canines trained for detection of a severe exotic phytobacterial arboreal pathogen, Candidatus Liberibacter asiaticus (CLas), demonstrated 0.9905 accuracy, 0.8579 sensitivity, and 0.9961 specificity. In a longitudinal study, cryptic CLas infections that remained subclinical visually were detected within 2 wk postinfection compared with 1 to 32 mo for qPCR. When allowed to interrogate a diverse range of in vivo pathogens infecting an international citrus pathogen collection, canines only reacted to Liberibacter pathogens of citrus and not to other bacterial, viral, or spiroplasma pathogens. Canines trained to detect CLas-infected citrus also alerted on CLas-infected tobacco and periwinkle, CLas-bearing psyllid insect vectors, and CLas cocultured with other bacteria but at CLas titers below the level of molecular detection. All of these observations suggest that canines can detect CLas directly rather than only host volatiles produced by the infection. Detection in orchards and residential properties was real time, ∼2 s per tree. Spatiotemporal epidemic simulations demonstrated that control of pathogen prevalence was possible and economically sustainable when canine detection was followed by intervention (i.e., culling infected individuals), whereas current methods of molecular (qPCR) and visual detection failed to contribute to the suppression of an exponential trajectory of infection.

Keywords: canine detection; direct assay; early detection; epidemic simulation; huanglongbing.

Fig. 1.

Canine performance detecting CLas in citrus trees. Canines interrogated a 10 × 10 grid of potted citrus trees in which zero to five CLas-positive trees were randomly placed. For each of 10 replications (trial) by a canine, the placement of the infected trees was rerandomized. (A) White to red gradient indicates the total error (FN + FP; range = 0 to 4) associated with each canine for CLas detection over the 10 trials of 100 trees each. Numerical values in each cell indicate the number of FN errors (first number) + FP errors (second number) detections per replicate by individual canine. White blocks indicate that no errors occurred for that trial/canine. The canines with the fewest total errors were Akim, Boby, and Mira with four, six, and six errors, respectively. Canine–handler team performance assessment: (B) FN error, (C) FP error, and (D) overall accuracy. CLas detection metrics for consensus of two or more canines: (E) sensitivity, (F) specificity, and (G) overall accuracy.

Fig. 2.

Spatial heterogeneity of CLas detection. (A) Cumulative randomized placement of CLas-infected trees within the 100-tree test grid over all trials. (B) Frequency of TP CLas-infected tree identification by canine detection. (C) Spatial distribution of canine detection FN and FP errors.

Fig. 3.

Temporal assessment of canine subclinical detection of CLas infection (black) compared with qPCR detection/confirmation at two accepted regulatory qPCR thresholds for 30 psyllid-inoculated Valencia orange trees; 4 to 10 canines assayed each tree on each assay date (resulting in 0 to 10 detections indicated by variance bars). One sample for qPCR assay was selected from each tree consisting of four leaves split into two samples of two leaves; each was processed via qPCR (any positive subsample denoted a positive qPCR detection). CT, or cycle threshold, is the number of PCR cycles required for the fluorescent signal to cross the threshold and exceed background level and thereby denote a positive/negative assay.

Fig. 4.

SECIR simulation model comparison. Scenario-based simulations integrating disease control strategies of HLB via canine, PCR, and visual survey to detect CLas infections over a 10-y period in a 16.2-ha (40-acre) orchard with 10 initial edge infections. Fiscal outcomes are based on actual orchard management costs for 180-d survey intervals and removal of infected trees postdetection (SI Appendix, Figs. S19–S22 shows full 10-y simulations). (A) Five- and (B) 10-y grove snapshots from a single simulation run where the hosts are color coded (susceptible [S; green], exposed [E; blue], cryptic [C; orange], infected [I; red], and removed [R; black]) dots indicating the spatial location and individual tree disease status within each of six contiguous citrus planting blocks. Cryptic denotes infectious CLas individuals that are asymptomatic, while infected denotes CLas hosts that are both infectious and showing symptoms. (C) Disease dynamics and resulting tree numbers for SECIR partition for each detection method when integrated into control over 10 y. (D) Predicted dynamics of operating profit per acre for additional scenarios involving initial introduction settings (edge or random) and removal protocols (within 30 d or delayed removal up to 90 d postdetection). Profits decline steadily when deploying PCR or visual detection methods, leading to losses early in all scenarios. PCR detection is infeasible throughout due to the high cost of assays, whereas canine detection sustains both viable plantings and long-term profits when deployed twice a year, particularly when paired with prompt removal (SI Appendix, Figs. S23 and S24 shows additional removal delay and replanting effects when deploying canines).