Characterization of microRNAs in the cyst nematode <i>Heterodera glycines</i> identifies possible candidates involved in cross-kingdom interactions with its host <i>Glycine max</i>
Bibliographic record
Abstract
) is one of the most damaging pests to the cultivated soybean worldwide. Using a wide array of stylet-secreted effector proteins, this nematode can restructure its host cells into a complex and highly active feeding structure called the syncytium. Tight regulation of these proteins is thought to be essential to the successful formation of this syncytium. To date, multiple mechanisms have been proposed to regulate the expression of these proteins including through post-transcriptional regulation. MicroRNAs (miRNAs) are a class of small, roughly 22-nucleotide-long, non-coding RNA shown to regulate gene expression through its interaction with the 3' untranslated region of genes. These same small RNAs have also been hypothesized to be able to cross over kingdom barriers and regulate genes in other species in a process called cross-kingdom interactions. In this study, we characterized the miRNome of the SCN via sequencing of small-RNAs isolated from whole nematodes and exosomes representing all developmental stages. We identified 121 miRNA loci encoding 96 distinct miRNA families including multiple lineage- and species-specific candidates. Using a combination of plant- and animal-specific miRNA target predictors, we generated a unique repertoire of miRNA:mRNA interacting partners in the nematode and its host plant leading to the identification of a set of nine probable cross-kingdom miRNA candidates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".