FMRFamide-Like Peptide 22 Influences the Head Movement, Host Finding, and Infection of Heterodera glycines
Bibliographic record
Abstract
The FMRFamide-like peptides (FLPs) represent the largest family of nematode neuropeptides and are involved in multiple parasitic activities. The immunoreactivity to FMRFamide within the nervous system of Heterodera glycines, the most economically damaging parasite of soybean [Glycine max L. (Merr)], has been reported in previous research. However, the family of genes encoding FLPs of H. glycines were not identified and functionally characterized. In this study, an FLP encoding gene Hg-flp-22 was cloned from H. glycines, and its functional characterization was uncovered by using in vitro RNA interference and application of synthetic peptides. Bioinformatics analysis showed that flp-22 is widely expressed in multiple nematode species, where they encode the highly conserved KWMRFamide motifs. Quantitative real-time (qRT)-PCR results revealed that Hg-flp-22 was highly expressed in the infective second-stage juveniles (J2s) and adult males. Silencing of Hg-flp-22 resulted in the reduced movement of J2s to the host root and reduced penetration ability, as well as a reduction in their subsequent number of females. Behavior and infection assays demonstrated that application of synthetic peptides Hg-FLP-22b (TPQGKWMRFa) and Hg-FLP-22c (KMAIEGGKWVRFa) significantly increased the head movement frequency and host invasion abilities in H. glycines but not in Meloidogyne incognita. In addition, the number of H. glycines females on the host roots was found to be significantly higher in Hg-FLP-22b treated nematodes than the ddH2O-treated control J2s. These results presented in this study elucidated that Hg-flp-22 plays a role in regulating locomotion and infection of H. glycines. This suggests the potential of FLP signaling as putative control targets for H. glycines in soybean production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".