Differential Profiles of Gut Microbiota and Metabolites Associated with Host Shift of Plutella xylostella
Bibliographic record
Abstract
Evolutionary and ecological forces are important factors that shape gut microbial profiles in hosts, which can help insects adapt to different environments through modulating their metabolites. However, little is known about how gut microbes and metabolites are altered when lepidopteran pest species switch hosts. In the present study, using 16S-rDNA sequencing and mass spectrometry-based metabolomics, we analyzed the gut microbiota and metabolites of three populations of Plutella xylostella: one feeding on radish (PxR) and two feeding on peas (PxP; with PxP-1 and PxP-17 being the first and 17th generations after host shift from radish to peas, respectively). We found that the diversity of gut microbes in PxP-17 was significantly lower than those in PxR and PxP-1, which indicates a distinct change in gut microbiota after host shift. Kyoto Encyclopedia of Genes and Genomes analysis revealed that the functions of energy metabolism, signal transduction, and xenobiotics biodegradation and metabolism were increased in PxP-17, suggesting their potential roles in host adaptation. Metabolic profiling showed a significant difference in the abundance of gut metabolites between PxR and PxP-17, and significant correlations of gut bacteria with gut metabolites. These findings shed light on the interaction among plants, herbivores, and symbionts, and advance our understanding of host adaptation associated with gut bacteria and metabolic activities in P. xylostella.
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 | 0.001 | 0.001 |
| 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".