Disrupted Neurogenesis in Germ-Free Mice: Effects of Age and Sex
Bibliographic record
Abstract
The gut microbiome has profound effects on development and function of the nervous system. Recent evidence indicates that disruption of the gut microbiome leads to altered hippocampal neurogenesis. Here, we examined whether the effects of gut microbiome disruption on neurogenesis are age-dependent, given that both neurogenesis and the microbiome show age-related changes. Additionally, we examined memory induced functional connectivity of hippocampal networks. Control and germ-free mice at three different ages (4, 8, and 12 weeks) were trained in contextual fear-conditioning, then subsequently tested the following day. Hippocampal neurogenesis, quantified via BrdU and doublecortin, exhibited age-dependent changes relative to controls, with the established age-dependent decrease in neurogenesis being delayed in germ-free mice. Moreover, we found sex-dependent effects of germ-free status on neurogenesis, with 4 week old male germ-free mice having decreased neurogenesis and 8 week old female germ-free mice having increased neurogenesis. To assess systems-level consequences of disrupted neurogenesis, we assessed functional connectivity of hippocampal networks by inducing c-Fos expression with contextual memory retrieval and applying a previously described network analysis. Our results indicate impaired connectivity of the dentate gyrus in germ-free mice in a pattern highly correlated with adult neurogenesis. In control but not germ-free mice, functional connectivity became more refined with age, indicating that age dependent network refinement is disrupted in germ-free mice. Overall, the results show that disruption of the gut microbiome affects hippocampal neurogenesis in an age- and sex-dependent manner and that these changes are also related to changes in the dentate gyrus functional network.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".