Salari, A. | Search bioRxiv

bioRxiv2024

Diurnal variations in microbial population dynamics along the human large intestine

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, Ne [~] 107 - 1011, 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 statementThe 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.

microbiology↗

bioRxiv2020

Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics

Machine learning models are commonly applied to human brain imaging datasets in an effort to associate function or structure with behaviour, health, or other individual phenotypes. Such models often rely on low-dimensional maps generated by complex processing pipelines. However, the numerical instabilities inherent to pipelines limit the fidelity of these maps and introduce computational bias. Monte Carlo Arithmetic, a technique for introducing controlled amounts of numerical noise, was used to perturb a structural connectome estimation pipeline, ultimately producing a range of plausible networks for each sample. The variability in the perturbed networks was captured in an augmented dataset, which was then used for an age classification task. We found that resampling brain networks across a series of such numerically perturbed outcomes led to improved performance in all tested classifiers, preprocessing strategies, and dimensionality reduction techniques. Importantly, we find that this benefit does not hinge on a large number of perturbations, suggesting that even minimally perturbing a dataset adds meaningful variance which can be captured in the subsequently designed models. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=65 SRC="FIGDIR/small/423084v2_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@bccd23org.highwire.dtl.DTLVardef@13fcfe1org.highwire.dtl.DTLVardef@68e987org.highwire.dtl.DTLVardef@1bbada9_HPS_FORMAT_FIGEXP M_FIG This paper demonstrates how Monte Carlo Arithmetic, a dataagnostic perturbation technique, can be used for dataset augmentation during the generation of structural connectomes and improve downstream phenotypic prediction. C_FIG

neuroscience↗