Francesco Biagini

In vitro models for culturing complex microbial communities are progressively being used to study the effects of different factors on the modeling of in vitro-cultured microorganisms. In previous work, we validated a 3D in vitro model of the human gut microbiota based on electrospun gelatin scaffolds covered with mucins. The aim of this study was to evaluate the effect of Bacillus cereus, a pathogen responsible for food poisoning diseases in humans, on the gut microbiota grown in the model. Real-time quantitative PCR and 16S ribosomal RNA-gene sequencing were performed to obtain information on microbiota composition after introducing B. cereus ATCC 14579 vegetative cells or culture supernatants. The adhesion of B. cereus to intestinal mucins was also tested. The presence of B. cereus induced important modifications in the intestinal communities. Notably, levels of Proteobacteria (particularly Escherichia coli), Lactobacillus, and Akkermansia were reduced, while abundances of Bifidobacterium and Mitsuokella increased. In addition, B. cereus was able to adhere to mucins. The results obtained from our in vitro model stress the hypothesis that B. cereus is able to colonize the intestinal mucosa by stably adhering to mucins and impacting intestinal microbial communities as an additional pathogenetic mechanism during gastrointestinal infection.

Although the adhesion of bacteria on surfaces is a widely studied process, to date, most of the works focus on a single species of microorganisms and are aimed at evaluating the antimicrobial properties of biomaterials. Here, we describe how a complex microbial community, i.e., the human gut microbiota, adheres to a surface to form stable biofilms. Two electrospun structures made of natural, i.e., gelatin, and synthetic, i.e., polycaprolactone, polymers were used to study their ability to both promote the adhesion of the human gut microbiota and support microbial growth in vitro. Due to the different wettabilities of the two surfaces, a mucin coating was also added to the structures to decouple the effect of bulk and surface properties on microbial adhesion. The developed biofilm was quantified and monitored using live/dead imaging and scanning electron microscopy. The results indicated that the electrospun gelatin structure without the mucin coating was the optimal choice for developing a 3D in vitro model of the human gut microbiota.

The present research aimed at shedding light on the interplay between the composition of the human gut microbiota and bone cells.

Variations and fluctuations are characteristic features of biological systems and are also manifested in cell cultures. Here, we describe a computational pipeline for identifying the range of three-dimensional (3D) cell-aggregate sizes in which nonisometric scaling emerges in the presence of joint mass and metabolic rate fluctuations. The 3D cell-laden spheroids with size and single-cell metabolic rates described by probability density functions were randomly generated in silico. The distributions of the resulting metabolic rates of the spheroids were computed by modeling oxygen diffusion and reaction. Then, a method for estimating scaling exponents of correlated variables through statistically significant data collapse of joint probability distributions was developed. The method was used to identify a physiologically relevant range of spheroid sizes, where both nonisometric scaling and a minimum oxygen concentration (0.04 mol⋅m⁻³) is maintained. The in silico pipeline described enables the prediction of the number of experiments needed for an acceptable collapse and, thus, a consistent estimate of scaling parameters. Using the pipeline, we also show that scaling exponents may be significantly different in the presence of joint mass and metabolic-rate variations typically found in cells. Our study highlights the importance of incorporating fluctuations and variability in size and metabolic rates when estimating scaling exponents. It also suggests the need for taking into account their covariations for better understanding and interpreting experimental observations both in vitro and in vivo and brings insights for the design of more predictive and physiologically relevant in vitro models. Significance Allometric scaling has many applications, from the prediction of pharmacokinetics in animals and humans to the probing of ecosystem dynamics. Most studies have neglected to account for variations and fluctuations, although they are intrinsic features of all biological systems. To understand how metabolic scaling emerges in the presence of variations, we developed computer-generated models of cell-laden spheroids to define the experimental size range of cell cultures with quantifiable similitudes in terms of fluctuations and metabolic scaling with living organisms. We show that the estimates of scaling exponents may change with increasing variability in both mass and metabolic rate. The computational pipeline described underpins the sound design of statistically meaningful cell-based models, with impacts in both biomedical science and ecology.