Author response: Resilience of small intestinal beneficial bacteria to the toxicity of soybean oil fatty acids
Bibliographic record
Abstract
Though “you are what you eat” may just be a figure of speech, it is clear that what we eat does affect our own cells and the microbes that live in our gut. During the 20th century, the American diet changed dramatically and now includes a lot more vegetable oil from soybeans, the main component of which – a fat called linoleic acid – is toxic to many microbes. Among the microbes inhibited by linoleic acid is a beneficial bacterium called Lactobacillus reuteri. This microbe has become less common in Western populations, and the timing of its decline approximately follows when the consumption of soybean oil began increasing. However, L. reuteri and other related microbes still exist in people who eat a Western diet. This suggests that these bacteria must be protected from linoleic acid in the gut, or that they can become resistant to this toxic molecule. Now, Di Rienzi et al. report that both protection in the gut and resistance could explain how L. reuteri can persist in the presence of linoleic acid. First, experiments in the laboratory showed that these microbes could indeed become resistant to linoleic acid, either by gaining mutations in genes involved in creating fats, by growing in an acid, and by forming a cell wall. Further experiments involving mice then showed that the gut protects also L. reuteri from this molecule: linoleic acid did not inhibit L. reuteri within the mouse, but those same L. reuteri were inhibited when grown outside of a mouse. Di Rienzi et al. went on to recover some resistant L. reuteri from mice, implying that there is a mix of resistant and non-resistant strains in the mouse gut. However, notably, the resistant bacteria recovered from the mice did not have mutations in the genes that had been identified from the earlier experiments. Together these findings show that gut bacteria have several means of surviving the high levels of potentially toxic fat molecules. Also, the specific finding that linoleic acid does not inhibit L. reuteri within the gut may help scientists to understand how a high fat diet affects microbes; for example, it is possible that the decrease in carbohydrates or protein that occurs in high fat diets may explain why such diets cause microbes to be lost. Lastly, and on a practical level, linoleic acid-resistant L. reuteri may in the future be used as a probiotic in foods rich in vegetable oil.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.117 | 0.046 |
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".