Complex carbohydrate utilization by gut bacteria modulates host food preference.

The gut microbiota interacts directly with dietary nutrients and has the ability to modify host feeding behavior, but the underlying mechanisms remain poorly understood. Select gut bacteria digest complex carbohydrates that are non-digestible by the host and liberate metabolites that serve as additional energy sources and pleiotropic signaling molecules. Here we use a gnotobiotic mouse model to examine how differential fructose polysaccharide metabolism by commensal gut bacteria influences host preference for diets containing these carbohydrates. Bacteroides thetaiotaomicron and Bacteroides ovatus selectively ferment fructans with different glycosidic linkages: B. thetaiotaomicron ferments levan with ß2-6 linkages, whereas B. ovatus ferments inulin with ß2-1 linkages. Since inulin and levan are both fructose polymers, inulin and levan diet have similar perceptual salience to mice. We find that mice colonized with B. thetaiotaomicron prefer the non-fermentable inulin diet, while mice colonized with B. ovatus prefer the non-fermentable levan diet. Knockout of bacterial fructan utilization genes abrogates this preference, whereas swapping the fermentation ability of B. thetaiotaomicron to inulin confers host preference for the levan diet. Bacterial fructan fermentation and host behavioral preference for the non-fermentable fructan are associated with increased neuronal activation in the arcuate nucleus of the hypothalamus, a key brain region for appetite regulation. These results reveal that selective nutrient metabolism by gut bacteria contributes to host associative learning of dietary preference, and further informs fundamental understanding of the biological determinants of food choice.

Microplastics retained in stormwater control measures: Where do they come from and where do they go?

Stormwater control measures (SCM) can remove and accumulate microplastics and may serve as a long-term source of microplastics for groundwater pollution because of their potential for downward mobility in subsurface. Furthermore, the number of microplastics accumulated in SCM may have been underestimated as the calculation typically only accounts for microplastics accumulated via episodic stormwater loading and ignores microplastic accumuation via continuous atmospheric deposition. To evaluate the source pathways of accumulated microplastics and their potential for downward mobility to groundwater, we analyzed spatial distributions of microplastics above ground on the canopy around SCM and below ground in the subsurface in and outside the boundaries of fourteen SCM in Los Angeles. Using an exponential model, we link subsurface retardation of microplastics to the median particle size of soil (D50) and land use. Despite receiving significantly more stormwater, microplastic concentrations in SCM at surface depth or subsurface depth were not significantly different from the concentration at the same depth outside the SCM. Similar concentration in and outside of SCM indicates that stormwater is not the sole source of microplastics accumulated in SCM. The high concentration of microplastics on leaves of vegetation in SCM confirms that the contribution of atmospheric deposition is significant. Within and outside the SCM boundary, microplastics are removed within the top 5 cm of the subsurface, and their concentration decreases exponentially with depth, indicating limited potential for groundwater pollution from the microplastics accumulated in SCM. Outside the SCM boundary, the subsurface retardation coefficient decreases with increases in D50, indicating straining of microplastics as the dominant removal mechanism. Inside the boundary of SCM, however, the retardation coefficient was independent of D50, implying that microplastics could have either moved deeper into the filter layer in SCM or that compost, mulch, or organic amendments used in the filter media were pre-contaminated with microplastics. Overall, these results provide insights on microplastics source, accumulation, and downward mobility in SCM.