Salt concentration in substrate modulates the composition of bacterial and yeast microbiomes of <i>Drosophila melanogaster</i>
Bibliographic record
Abstract
Aim: Microbiomes influence the physiology and behavior of multicellular organisms and contribute to their adaptation to changing environmental conditions. However, yeast and bacterial microbiota have usually been studied separately; therefore, the interaction between bacterial and yeast communities in the gut of Drosophila melanogaster (D. melanogaster ) is often overlooked. In this study, we investigate the correlation between bacterial and yeast communities in the gut of D. melanogaster . Methods: We studied the shifts in the joint microbiome of Drosophila melanogaster , encompassing both yeasts and bacteria, during adaptation to substrate with varying salt concentrations (0%, 2%, 4%, and 7%) using plating for both yeasts and bacteria and NGS-sequencing of variable 16S rRNA gene regions for bacteria. Results: The microbiome of flies and their substrates was gradually altered at moderate NaCl concentrations (2% and 4% compared with the 0% control) and completely transformed at high salt concentrations (7%). The relative abundance of Acetobacter , potentially beneficial to D. melanogaster , decreased as NaCl concentration increased, whereas the relative abundance of the more halotolerant lactobacilli first increased, peaking at 4% NaCl, and then declined dramatically at 7%. At this salinity level, potentially pathogenic bacteria of the genera Leuconostoc and Providencia were dominant. The yeast microbiome of D. melanogaster also undergoes significant changes with an increase in salt concentration in the substrate. The total yeast abundance undergoes nonlinear changes: it is lowest at 0% salt concentration and highest at 2%-4%. At a 7% concentration, the yeast abundance in flies and their substrate is lower than at 2%-4% but significantly higher than at 0%. Conclusions: The abundance and diversity of bacteria that are potentially beneficial to the flies decreased, while the proportion of potential pathogens, Leuconostoc and Providencia , increased with an increase in salt concentration in the substrate. In samples with a relatively high abundance and/or diversity of yeasts, the corresponding indicators for bacteria were often lowered, and vice versa . This may be due to the greater halotolerance of yeasts compared to bacteria and may also indicate antagonism between these groups of microorganisms.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".