MinION Nanopore-based detection of Clavibacter nebraskensis, the corn Goss’s wilt pathogen, and bacteriomic profiling of necrotic lesions of naturally-infected leaf samples
Bibliographic record
Abstract
The Goss's bacterial wilt pathogen, Clavibacter nebraskensis, of corn is a candidate A1 quarantine organism; and its recent re-emergence and spread in the USA and Canada is a potential biothreat to the crop. We developed and tested an amplicon-based Nanopore detection system for C. nebraskensis (Cn), targeting a purine permease gene. The sensitivity (1 pg) of this system in mock bacterial communities (MBCs) spiked with serially diluted DNA of C. nebraskensis NCPPB 2581T is comparable to that of real-time PCR. Average Nanopore reads increased exponentially from 125 (1pg) to about 6000 reads (1000 pg) after a 3-hr run-time, with 99.0% of the reads accurately assigned to C. nebraskensis. Three run-times were used to process control MBCs, Cn-spiked MBCs, diseased and healthy leaf samples. The mean Nanopore reads doubled as the run-time is increased from 3 to 6 hrs while from 6 to 12 hrs, a 20% increment was recorded in all treatments. Cn-spiked MBCs and diseased corn leaf samples averaged read counts of 5,100, 11,000 and 14,000 for the respective run-times, with 99.8% of the reads taxonomically identified as C. nebraskensis. The control MBCs and healthy leaf samples had 47 and 14 Nanopore reads, respectively. 16S rRNA bacteriomic profiles showed that Sphingomonas (22.7%) and Clavibacter (21.2%) were dominant in diseased samples while Pseudomonas had only 3.5% relative abundance. In non-symptomatic leaf samples, however, Pseudomonas (20.0%) was dominant with Clavibacter at 0.08% relative abundance. This discrepancy in Pseudomonas abundance in the samples was corroborated by qPCR using EvaGreen chemistry. Our work outlines a new useful tool for diagnosis of the Goss's bacterial wilt disease; and provides the first insight on Pseudomonas community dynamics in necrotic leaf lesions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".