In Silico Investigations Of Virulent Gene Transfers In Xanthomonas Oryzae: A Study On Rice Bacterial Leaf Blight Disease.
Bibliographic record
Abstract
Rice (Oryza sativa L.) is a major global food crop, providing nutrition for nearly half the world's population. India ranks second in both rice production and acreage, with rice contributing nearly 70% of calories in the Indian diet. The rice is susceptible to various diseases caused by fungi, bacteria, nematodes, and viruses, leading to significant crop losses. Bacterial leaf blight (BLB), is a widespread disease caused by the plant pathogenic bacterium Xanthomonas oryzae pv. oryzae, with reports of frequently gaining genes from non-ancestral origins through methods like conjugation and transduction from other species and genera. These laterally transmitted genes (LTGs) enhance the bacterium's adaptability, pathogenicity, and ability to resist host defences. The present study integrates multiple computational methods to find and analyze genes with potential lateral transfer and abnormal properties, providing insights into the evolution and adaptability of Xanthomonas oryzae pv. oryzae. In the present study, a workflow of computational algorithms to identify horizontally transferred genes (HTGs) in bacterial chromosomes was employed. The SeqWord Gene Island Sniffer program predicted 12 genomic islands (GIs) containing genes with non-ancestral features, characterized by decreased GC content and potentially fast-evolving DNA regions. The DFAST server annotated 248 protein-coding sequences from the identified islands, and NCBI BLAST+ executables matched 225 of these proteins with those of Xanthomonas oryzae PXO99A proteome. MP3 tool predicted 80 pathogenic proteins using the SVM method for analysis. A locally created database of putative horizontally transmitted proteins consisting of nearly 1.3 lakh sequences revealed 20 proteins potentially involved in lateral transfer. Dark Horse web server validated 13 genes of it, and CodonW software assessed anomalous gene nature by correspondence analysis, examining G+C, GC3, and ENc values for 13 anticipated genes compared to overall organism values.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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".