From biosynthesis and beyond—Loss or overexpression of the cytokinin synthesis gene, <i>iptA</i> , alters cytokinesis and mitochondrial and amino acid metabolism in <i>Dictyostelium discoideum</i>
Bibliographic record
Abstract
Cytokinins (CKs) are a class of growth-promoting signaling molecules that affect multiple cellular and developmental processes. These phytohormones are well studied in plants, but their presence continues to be uncovered in organisms spanning all kingdoms, which poses new questions about their roles and functions outside of plant systems. Cytokinin production can be initiated by one of two different biosynthetic enzymes, adenylate isopentenyltransfases (IPTs) or tRNA isopentenyltransferases (tRNA-IPTs). In this study, the social amoeba, Dictyostelium discoideum, was used to study the role of CKs by generating deletion and overexpression strains of its single adenylate-IPT gene, iptA. The life cycle of D. discoideum is unique and possesses both single- and multicellular stages. Vegetative amoebae grow and divide while food resources are plentiful, and multicellular development is initiated upon starvation, which includes distinct life cycle stages. CKs are produced in D. discoideum throughout its life cycle and their functions have been well studied during the later stages of multicellular development of D. discoideum. To investigate potential expanded roles of CKs, this study focused on vegetative growth and early developmental stages. We found that iptA-deficiency results in cytokinesis defects, and both iptA-deficiency and overexpression results in dysregulated tricarboxylic acid (TCA) cycle and amino acid metabolism, as well as increased levels of adenosine monophosphate (AMP). Collectively, these findings extend our understanding of CK function in amoebae, indicating that iptA loss and overexpression alter biological processes during vegetative growth that are distinct from those reported during later development.
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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 | 0.000 | 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 | 0.001 |
| 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".