Transcriptome of Arabidopsis thaliana Plants Exposed to Human Parasites Cryptosporidium parvum and Giardia lamblia
Bibliographic record
Abstract
Pathogen infection in animals and plants is recognized in a relatively similar manner by the interaction of pattern recognition receptors on the host cell surface with pathogen-associated molecular patterns on the pathogen surface. Previous work demonstrates that animal pathogenic bacteria can be recognized by plant receptors and alter transcriptome. In this work, we have hypothesized that exposure to human parasites, Cryptosporidium parvum and Giardia lamblia, would also trigger pathogen response in plants, leading to changes in transcriptome. Detached Arabidopsis leaves were exposed for one hour to heat-inactivated Cryptosporidia or Giardia. The transcriptome profile showed large changes in gene expression with significant overlap between two parasites, including upregulated GO terms “cellular response to chitin”, “response to wounding”, “response to oomycetes”, “defense response to fungus”, “incompatible interaction”, and “activation of innate immune response”, and downregulated GO terms “positive regulation of development”, “cell surface”, “regulation of organ growth”, “wax biosynthetic process”, “leaf and shoot morphogenesis”. Uniquely downregulated GO terms in response to Cryptosporidia were GO terms related to chromatin remodelling, something that was not reported before. To conclude, it appears that while Cryptosporidia or Giardia are not pathogens of Arabidopsis, this plant possesses various mechanisms of recognition of pathogenic components of parasites.
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) | 0.001 | 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".