Diversity of ABC transporter genes across the plant kingdom and their potential utility in biotechnology
Bibliographic record
Abstract
BACKGROUND: The ATP-binding cassette (ABC) transporter gene superfamily is ubiquitous among extant organisms and prominently represented in plants. ABC transporters act to transport compounds across cellular membranes and are involved in a diverse range of biological processes. Thus, the applicability to biotechnology is vast, including cancer resistance in humans, drug resistance among vertebrates, and herbicide and other xenobiotic resistance in plants. In addition, plants appear to harbor the highest diversity of ABC transporter genes compared with any other group of organisms. This study applied transcriptome analysis to survey the kingdom-wide ABC transporter diversity in plants and suggest biotechnology applications of this diversity. RESULTS: We utilized sequence similarity-based informatics techniques to infer the identity of ABC transporter gene candidates from 1295 phylogenetically-diverse plant transcriptomes. A total of 97,149 putative (approximately 25 % were full-length) ABC transporter gene members were identified; each RNA-Seq library (plant sample) had 88 ± 30 gene members. As expected, simpler organisms, such as algae, had fewer unique members than vascular land plants. Differences were also noted in the richness of certain ABC transporter subfamilies. Land plants had more unique ABCB, ABCC, and ABCG transporter gene members on average (p < 0.005), and green algae, red algae, and bryophytes had significantly more ABCF transporter gene members (p < 0.005). Ferns had significantly fewer ABCA transporter gene members than all other plant groups (p < 0.005). CONCLUSIONS: We present a transcriptomic overview of ABC transporter gene members across all major plant groups. An increase in the number of gene family members present in the ABCB, ABCC, and ABCD transporter subfamilies may indicate an expansion of the ABC transporter superfamily among green land plants, which include all crop species. The striking difference between the number of ABCA subfamily transporter gene members between ferns and other plant taxa is surprising and merits further investigation. Discussed is the potential exploitation of ABC transporters in plant biotechnology, with an emphasis on crops.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".