Diurnal Variations in Digestion and Flow Drive Microbial Dynamics in the Gut
Bibliographic record
Abstract
The human large intestine harbors a highly dynamic microbial ecosystem in which growing microbes regularly replenish biomass lost via feces. Elucidating these population dynamics is important as studies increasingly indicate a direct link between microbial density imbalances in the large intestine and host immune responses, colonic cell physiology, as well as disease development. However, given the strong changes in microbial growth, biomass, and intestinal fluid flow throughout the day, gaining a better understanding of these population dynamics remains a major challenge. Leveraging experimental data on fluid turnover, nutrient supply, and microbial growth, we here derive a biophysical model of microbial population dynamics in the proximal large intestine. We show how the digestion of meals in batches triggers strong fluctuations in fluid movement and bacteria growth. 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. As the microbial population undergoes several bottlenecks followed by rapid growth each day, the effective population size in the proximal large intestine is small, <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:mrow> <a:msub> <a:mi>N</a:mi> <a:mi>e</a:mi> </a:msub> <a:mo>∼</a:mo> <a:msup> <a:mrow> <a:mn>10</a:mn> </a:mrow> <a:mn>7</a:mn> </a:msup> <a:mo>−</a:mo> <a:msup> <a:mrow> <a:mn>10</a:mn> </a:mrow> <a:mn>11</a:mn> </a:msup> </a:mrow> </a:math> , promoting the rapid 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 complex interplay between population dynamics of the gut microbiota, fluid flow, and bacterial growth with important consequences for the microbiome and the host.
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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 | 0.001 | 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".