The neurovascular unit (NVU) regulates metabolic homeostasis as well as drug pharmacokinetics and pharmacodynamics in the central nervous system. Metabolic fluxes and conversions over the NVU rely on interactions between brain microvascular endothelium, perivascular pericytes, astrocytes and neurons, making it difficult to identify the contributions of each cell type. Here we model the human NVU using microfluidic organ chips, allowing analysis of the roles of individual cell types in NVU functions. Three coupled chips model influx across the blood-brain barrier (BBB), the brain parenchymal compartment and efflux across the BBB. We used this linked system to mimic the effect of intravascular administration of the psychoactive drug methamphetamine and to identify previously unknown metabolic coupling between the BBB and neurons. Thus, the NVU system offers an in vitro approach for probing transport, efficacy, mechanism of action and toxicity of neuroactive drugs.

Metabolic exchange mediates interactions among microbes, helping explain diversity in microbial communities. As these interactions often involve a fitness cost, it is unclear how stable cooperation can emerge. Here we use genome-scale metabolic models to investigate whether the release of “costless” metabolites (i.e. those that cause no fitness cost to the producer), can be a prominent driver of intermicrobial interactions. By performing over 2 million pairwise growth simulations of 24 species in a combinatorial assortment of environments, we identify a large space of metabolites that can be secreted without cost, thus generating ample cross-feeding opportunities. In addition to providing an atlas of putative interactions, we show that anoxic conditions can promote mutualisms by providing more opportunities for exchange of costless metabolites, resulting in an overrepresentation of stable ecological network motifs. These results may help identify interaction patterns in natural communities and inform the design of synthetic microbial consortia.

1Metabolic exchange can mediate beneficial interactions among microbes, helping 2 explain diversity in microbial communities. These interactions are often assumed to 3 involve a fitness cost, prompting questions on how cooperative phenotypes can be 4 stable and withstand the emergence of cheaters. Here we use genome-scale models of 5 metabolism to investigate whether a radically different scenario, the pervasive release 6 of "costless" metabolites (i.e. those that cause no fitness cost to the producing 7 organism), can serve as a prominent mechanism for inter-microbial interactions. By 8 carrying out over 1 million pairwise growth simulations for 14 microbial species in a 9 combinatorial assortment of environmental conditions, we find that there is indeed a 10 large space of metabolites that can be secreted at no cost, which can generate ample 11 cross-feeding opportunities. In addition to providing an atlas of putative costless 12interdependencies, our modeling also demonstrates that oxygen availability significantly 13 enhances mutualistic interactions by providing more opportunities for metabolic 14 exchange through costless metabolites, resulting in an over-representation of specific 15 ecological network motifs. In addition to helping explain natural diversity, we show how 16the exchange of costless metabolites can facilitate the engineering of stable synthetic 17 microbial consortia. 18 19

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Beyond being simply positive or negative, beneficial or inhibitory, microbial interactions can involve a diverse set of mechanisms, dependencies and dynamical properties. These more nuanced features have been described in great detail for some specific types of interactions, (e.g. pairwise metabolic cross-feeding, quorum sensing or antibiotic killing), often with the use of quantitative measurements and insight derived from modeling. With a growing understanding of the composition and dynamics of complex microbial communities for human health and other applications, we face the challenge of integrating information about these different interactions into comprehensive quantitative frameworks. Here, we review the literature on a wide set of microbial interactions, and explore the potential value of a formal categorization based on multidimensional vectors of attributes. We propose that such an encoding can facilitate systematic, direct comparisons of interaction mechanisms and dependencies, and we discuss the relevance of an atlas of interactions for future modeling and rational design efforts.