Diurnal variations in digestion and luminal flow determine microbial population dynamics along the human large intestine
Bibliographic record
Abstract
Abstract The human large intestine harbors a highly dynamic microbial ecosystem in which growing microbes regularly replenish biomass lost via feces. Understanding this population dynamics is biomedically important but remains a significant challenge due to rapid changes in microbial biomass and intestinal fluid flows. Leveraging experimental data on fluid turnover, nutrient supply, and microbial growth, we here derive a biophysical model of population dynamics. We show how the digestion of meals in batches triggers strong fluctuations in fluid movement and bacteria growth along the proximal large intestine. Comparing different model scenarios, we further analyze how the expandable nature of the proximal large intestine, the presence of a pouch-like cecum off major flow paths, and the periodic exit of luminal content via ‘mass movements’ are required in combination to maintain the high microbial population observed in the proximal large intestine. Since the microbial population undergoes several bottlenecks followed by rapid growth each day, the effective population size in the proximal large intestine is small, N e ∼ 10 7 − 10 11 , promoting the fast evolution of microbes. The diurnal fluctuations in flow also hamper the accumulation of slower-growing bacteria and lead to substantial variations in the uptake of fermentation products by the host. Our findings underscore the highly intertwined population dynamics of the gut microbiota, determined by the physics of fluid flow and growth with strong consequences for the microbiome and the host. Significance statement The density of microbial biomass within the large intestine is a key determinant of microbiome-host interactions and their impact on the human body. Studies with animal models have suggested highly intertwined population dynamics with strong variations of microbial densities over time and space. To elucidate the physiological drivers of these spatiotemporal dynamics along the human large intestine, we introduce a biophysical modeling framework that considers at its core the diurnal variation of digestion and fluid flow. Our analysis reveals how nutrient supply and the rapid movement of luminal content cause strong fluctuations in microbial biomass turnover throughout the day. The intertwined dynamics provide the host with ample mechanisms to control the microbiota, suppressing, for example, the emergence of slow-growing cross-feeding microbes.
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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 | 0.001 |
| 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 | 0.000 | 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".