Canine Olfactory Detection of a Non-Systemic Phytobacterial Citrus Pathogen of International Quarantine Significance.

For millennia humans have benefitted from application of the acute canine sense of smell to hunt, track and find targets of importance. In this report, canines were evaluated for their ability to detect the severe exotic phytobacterial arboreal pathogen Xanthomonas citri pv. citri (Xcc), which is the causal agent of Asiatic citrus canker (Acc). Since Xcc causes only local lesions, infections are non-systemic, limiting the use of serological and molecular diagnostic tools for field-level detection. This necessitates reliance on human visual surveys for Acc symptoms, which is highly inefficient at low disease incidence, and thus for early detection. In simulated orchards the overall combined performance metrics for a pair of canines were 0.9856, 0.9974, 0.9257 and 0.9970, for sensitivity, specificity, precision, and accuracy, respectively, with 1-2 s/tree detection time. Detection of trace Xcc infections on commercial packinghouse fruit resulted in 0.7313, 0.9947, 0.8750, and 0.9821 for the same performance metrics across a range of cartons with 0-10% Xcc-infected fruit despite the noisy, hot and potentially distracting environment. In orchards, the sensitivity of canines increased with lesion incidence, whereas the specificity and overall accuracy was >0.99 across all incidence levels; i.e., false positive rates were uniformly low. Canines also alerted to a range of 1-12-week-old infections with equal accuracy. When trained to either Xcc-infected trees or Xcc axenic cultures, canines inherently detected the homologous and heterologous targets, suggesting they can detect Xcc directly rather than only volatiles produced by the host following infection. Canines were able to detect the Xcc scent signature at very low concentrations (10,000× less than 1 bacterial cell per sample), which implies that the scent signature is composed of bacterial cell volatile organic compound constituents or exudates that occur at concentrations many fold that of the bacterial cells. The results imply that canines can be trained as viable early detectors of Xcc and deployed across citrus orchards, packinghouses, and nurseries.

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

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.

Comparison of FTIR spectra between Huanglongbing (citrus greening) and other citrus maladies.

Fourier transform infrared (FTIR) spectroscopy has the ability to quickly identify the presence of specific carbohydrates in plant materials. The presence of the disease huanglongbing (HLB) in the leaves of infected citrus plants has a distinctive spectrum that can be used to distinguish an infected plant from a healthy plant. However, many citrus diseases display similar visible symptoms and are of concern to citrus growers. In this study several citrus diseases (citrus leaf rugose virus, citrus tristeza virus, citrus psorosis virus, and Xanthomonas axonopodis ) and nutrient deficiencies (iron, copper, zinc, manganese, and magnesium) were compared with HLB using FTIR spectroscopy to determine if the spectra alone can be used to identify plants that are infected with HLB instead of another disease. The results indicate that the spectra of some diseases and deficiencies more closely resemble those of apparently healthy plants and some share the carbohydrate transformation that has been seen in the spectra of HLB-infected plants.

Detection of citrus Huanglongbing by Fourier transform infrared-attenuated total reflection spectroscopy.

Citrus Huanglongbing (HLB, also known as citrus greening disease) was discovered in Florida in 2005 and is spreading rapidly amongst the citrus growing regions of the state. Detection via visual symptoms of the disease is not a long-term viable option. New techniques are being developed to test for the disease in its earlier presymptomatic stages. Fourier transform infrared-attenuated total reflection (FT-IR-ATR) spectroscopy is a candidate for rapid, inexpensive, early detection of the disease. The mid-infrared region of the spectrum reveals dramatic changes that take place in the infected leaves when compared to healthy non-infected leaves. The carbohydrates that give rise to peaks in the 900-1180 cm(-1) range are reliable in distinguishing leaves from infected plants versus non-infected plants. A model based on chemometrics was developed using the spectra from 179 plants of known disease status. This model then correctly predicted the status of >95% of the plants tested.