Differences in gene expression and endophytic bacterial diversity in <i>Atractylodes macrocephala</i> Koidz. rhizomes from different growth years
Bibliographic record
Abstract
Atractylodes macrocephala Koidz. (AMK) is widely used in traditional Chinese medicine owing to its pharmacological activity. Here, we aimed to characterize the differentially expressed genes (DEGs) of one- and three-year growth (OYG and TYG) rhizomes of AMK, combined with endophytic bacterial diversity analysis using high-throughput RNA sequencing. A total of 114 572 unigenes were annotated using six public databases. In all, 3570 DEGs revealed a clear difference, of which 936 and 2634 genes were upregulated and downregulated, respectively. The results of KEGG pathway analysis indicated that DEGs corresponding to terpenoid synthesis gene were downregulated in TYG rhizomes. In addition, 414 424 sequences corresponding to the 16S rRNA gene were divided into 1267 operational taxonomic units (OTUs). Moreover, the diversity of endophytic bacteria changed with species in the OYG (773) and TYG (1201) rhizomes at the OTU level, and Proteobacteria, Actinobacteria, and Bacteroidetes were the dominant phyla. A comparison of species differences among different growth years revealed that some species were significantly different, such as Actinomycetes, Variovorax, and Cloacibacterium. Interestingly, the decrease in the function-related metabolism of terpenoids and polyketides was correlated with the low expression of terpene synthesis genes in TYG rhizomes, as assessed using PICRUSt2. These data provide a scientific basis for elucidating the mechanisms underlying metabolite accumulation and endophytic bacterial diversity in relation to the growth years in AMK.
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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 | 0.001 |
| 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) | 0.000 | 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".