Proteomic changes in maize as a response to heavy metal (lead) stress revealed by iTRAQ quantitative proteomics
Bibliographic record
Abstract
Lead (Pb), a heavy metal, has become a crucial pollutant in soil and water, causing not only permanent and irreversible health problems, but also substantial reduction in crop yields. In this study, we conducted proteome analysis of the roots of the non-hyperaccumulator inbred maize line 9782 at four developmental stages (0, 12, 24, and 48 h) under Pb pollution using isobaric tags for relative and absolute quantification technology. A total of 252, 72 and 116 proteins were differentially expressed between M12 (after 12-h Pb treatment) and CK (water-mocked treatment), M24 (after 24-h Pb treatment) and CK, and M48 (after 48-h Pb treatment) and CK, respectively. In addition, 14 differentially expressed proteins were common within each comparison group. Moreover, Cluster of Orthologous Groups enrichment analysis revealed predominance of the proteins involved in posttranslational modification, protein turnover, and chaperones. Additionally, the changes in protein profiles showed a lower concordance with corresponding alterations in transcript levels, indicating important roles for transcriptional and posttranscriptional regulation in the response of maize roots to Pb pollution. Furthermore, enriched functional categories between the successive comparisons showed that the proteins in functional categories of stress, redox, signaling, and transport were highly up-regulated, while those in the functional categories of nucleotide metabolism, amino acid metabolism, RNA, and protein metabolism were down-regulated. This information will help in furthering our understanding of the detailed mechanisms of plant responses to heavy metal stress by combining protein and mRNA profiles.
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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 | 0.001 | 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".