Proteome-Wide Analysis and Surface Protein Isolation for Secretome Characterization Reveal Insights into the Biology of the Leaf-Cutter Ant <i>Acromyrmex echinatior</i>
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Characterizing the proteome of an organism can provide critical insights into the proteins that regulate key biological processes such as development, physiology, and environmental interactions. While proteome-wide analyses reveal broad protein dynamics, spatially resolved approaches can uncover specific, localized functions. For example, the leaf-cutter ant Acromyrmex echinatior secretes a unique protein layer that coats its exoskeleton and interacts with biotic and abiotic factors, including its symbiotic bacterium Pseudonocardia . In this study, to characterize both the whole-body proteome and the externally secreted cuticular protein layer of A. echinatior, we utilize a dual-layered proteomic approach. Using diaPASEF, we quantified 4,428 proteins across four early adult ages, uncovering distinct age-dependent protein clusters enriched in muscle development, lipid metabolism, and immune-related responses. We then developed an acid-based extraction method to isolate the externally secreted protein layer, identifying 323 secreted proteins via the ddaPASEF acquisition. Many of these proteins exhibited temporal abundance changes and were associated with functions, such as environmental stress response, microbial defense, and cuticle sclerotization. Notably, tropomyosin-family proteins were highly enriched in the external secretome and exhibited significant changes across early adult time points, potentially linking these ion-binding molecules to metal-enrichment processes occurring during this crucial stage. Together, this work reveals dynamic changes in the internal and surface proteomes of young adult A. echinatior ants and provides a methodological framework for further probing localized extra-cuticular protein function in complex biological systems.
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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 | 0.000 |
| 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".