Transcriptomics of host-specific interactions in natural populations of the parasitic plant purple witchweed (<i>Striga hermonthica</i>)
Bibliographic record
Abstract
Abstract Host-specific interactions can maintain genetic and phenotypic diversity in parasites that attack multiple host species. Host diversity, in turn, may promote parasite diversity by selection for genetic divergence or plastic responses to host type. The parasitic weed purple witchweed [Striga hermonthica (Delile) Benth.] causes devastating crop losses in sub-Saharan Africa and is capable of infesting a wide range of grass hosts. Despite some evidence for host adaptation and host-by-Striga genotype interactions, little is known about intraspecific Striga genomic diversity. Here we present a study of transcriptomic diversity in populations of S. hermonthica growing on different hosts (maize [Zea mays L.] vs. grain sorghum [Sorghum bicolor (L.) Moench]). We examined gene expression variation and differences in allelic frequency in expressed genes of aboveground tissues from populations in western Nigeria parasitizing each host. Despite low levels of host-based genome-wide differentiation, we identified a set of parasite transcripts specifically associated with each host. Parasite genes in several different functional categories implicated as important in host–parasite interactions differed in expression level and allele on different hosts, including genes involved in nutrient transport, defense and pathogenesis, and plant hormone response. Overall, we provide a set of candidate transcripts that demonstrate host-specific interactions in vegetative tissues of the emerged parasite S. hermonthica. Our study shows how signals of host-specific processes can be detected aboveground, expanding the focus of host–parasite interactions beyond the haustorial connection.
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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".