Seasonal shifts in the insect gut microbiome are concurrent with changes in cold tolerance and immunity
Bibliographic record
Abstract
Abstract Seasonal changes in the environment, such as varying temperature, have the potential to change the functional relationship between ectothermic animals, such as insects, and their microbiomes. Our objectives were to determine: (a) whether seasonal changes in temperature shift the composition of the insect gut microbiome, and (b) whether changes in the microbiome are concomitant with changes in the physiology of the host, including the immune system and response to cold. We exposed laboratory populations of the spring field cricket, Gryllus veletis (Orthoptera: Gryllidae), to simulated overwintering conditions in both a laboratory microcosm and a field‐like microcosm containing soil and leaves. In summer, autumn, winter and spring, we extracted and sequenced 16S bacterial genomic DNA from cricket guts, to capture seasonal variation in the composition of the microbiome. The composition of the gut microbiome was similar between microcosms, and overall highly anaerobic. In both microcosms, we captured similar seasonal variation in the composition of the microbiome, where overwintering resulted in permanent changes to these microbial communities. In particular, the abundance of Pseudomonas spp. decreased, and that of Wolbachia spp. increased, during overwintering. Concurrent with overwintering changes in the gut microbiome, G. veletis acquire freeze tolerance and immune function shifts temporarily, returning to summer levels of activity in the spring. In a specific manner, haemocyte concentrations increase but survival of fungal infection decreases in the winter, whereas the ability to clear bacteria from the haemolymph remains unchanged. Overall, we demonstrate that the gut microbiome does shift seasonally, and in concert with other physiological changes. We hypothesize that these changes may be linked, and suggest that it will next be important to determine whether these changes in the microbiome contribute to host overwintering success. A plain language summary is available for this article.
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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".