Salivary tannin‐binding proteins are a pervasive strategy used by the folivorous/frugivorous black howler monkey
Bibliographic record
Abstract
Dietary tannins can affect protein digestion and absorption, be toxic, and influence food selection by being astringent and bitter tasting. Animals that usually ingest tannins may regularly secrete tannin-binding salivary proteins (TBSPs) to counteract the negative effects of tannins or TBSPs production can be induced by a tannin-rich diet. In the wild, many primates regularly eat a diet that contains tannin-rich leaves and unripe fruit and it has been speculated that they have the physiological ability to cope with dietary tannins; however, details of their strategy remains unclear. Our research details the salivary protein composition of wild and zoo-living black howler monkeys (Alouatta pigra) feeding on natural versus manufactured low-tannin diets, and examines differences in TBSPs, mainly proline-rich proteins (PRPs), to determine whether production of these proteins is dependent on the tannin content of their food. We measured the pH, flow rate, and concentration of total protein and trichloroacetic acid soluble proteins (an index of PRPs) in saliva. Howler monkeys produced slightly alkaline saliva that may aid in the binding interaction between tannin and salivary proteins. We used gel electrophoresis to describe the salivary protein profile and this analysis along with a tannin-binding assay allowed us to detect several TBSPs in all individuals. We found no differences in the characteristics of saliva between wild and zoo-living monkeys. Our results suggest that black howler monkeys always secrete TBSPs even when fed on foods low in tannins. This strategy of constantly using this salivary anti-tannin defense enables them to obtain nutrients from plants that sometimes contain high levels of tannins and may help immediately to overcome the astringent sensation of their food allowing howler monkeys to eat tanniferous plants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".