The Open Challenge of in vitro Modeling Complex and Multi-Microbial Communities in Three-Dimensional Niches

A novel on-a-chip system with a 3D-bioinspired gut mucus suitable to investigate bacterial endotoxins dynamics

The possible pathogenic impact of pro-inflammatory molecules produced by the gut microbiota is one of the hypotheses considered at the basis of the biomolecular dialogue governing the microbiota-gut-brain axis. Among these molecules, lipopolysaccharides (LPS) produced by Gram-negative gut microbiota strains may have a potential key role due to their toxic effects in both the gut and the brain. In this work, we engineered a new dynamic fluidic system, the MINERVA device (MI-device), with the potential to advance the current knowledge of the biological mechanisms regulating the microbiota-gut molecular crosstalk. The MI-device supported the growth of bacteria that are part of the intestinal microbiota under dynamic conditions within a 3D moving mucus model, with features comparable to the physiological conditions (storage modulus of 80 ± 19 Pa, network mesh size of 41 ± 3 nm), without affecting their viability (∼ 109 bacteria/mL). The integration of a fluidically optimized and user-friendly design with a bioinspired microenvironment enabled the sterile extraction and quantification of the LPS produced within the mucus by bacteria (from 423 ± 34 ng/mL to 1785 ± 91 ng/mL). Compatibility with commercially available Transwell-like inserts allows the user to precisely control the transport phenomena that occur between the two chambers by selecting the pore density of the insert membrane without changing the design of the system. The MI-device is able to provide the flow of sterile medium enriched with LPS directly produced by bacteria, opening up the possibility of studying the effects of bacteria-derived molecules on cells in depth, as well as the assessment and characterization of their effects in a physiological or pathological scenario.

Microbial interactions from a new perspective: reinforcement learning reveals new insights into microbiome evolution

MOTIVATION

Microbes are essential part of all ecosystems, influencing material flow and shaping their surroundings. Metabolic modeling has been a useful tool and provided tremendous insights into microbial community metabolism. However, current methods based on flux balance analysis (FBA) usually fail to predict metabolic and regulatory strategies that lead to long-term survival and stability especially in heterogenous communities.

RESULTS

Here, we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, which treats microbial metabolism as a decision-making process, allowing individual microbial agents to evolve by learning and adapting metabolic strategies for enhanced long-term fitness. This algorithm predicts what microbial flux regulation policies will stabilize in the dynamic ecosystem of interest in the presence of other microbes with minimal reliance on predefined strategies. Throughout this article, we present several scenarios wherein our algorithm outperforms existing methods in reproducing outcomes, and we explore the biological significance of these predictions.

AVAILABILITY AND IMPLEMENTATION

The source code for this article is available at: https://github.com/chan-csu/SPAM-DFBA.