The optimization of microbial functions through rational environmental manipulations.

Microorganisms play a central role in biotechnology and it is key that we develop strategies to engineer and optimize their functionality. To this end, most efforts have focused on introducing genetic manipulations in microorganisms which are then grown either in monoculture or in mixed-species consortia. An alternative strategy to optimize microbial processes is to rationally engineer the environment in which microbes grow. The microbial environment is multidimensional, including factors such as temperature, pH, salinity, nutrient composition, etc. These environmental factors all influence the growth and phenotypes of microorganisms and they generally "interact" with one another, combining their effects in complex, non-additive ways. In this piece, we overview the origins and consequences of these "interactions" between environmental factors and discuss how they have been built into statistical, bottom-up predictive models of microbial function to identify optimal environmental conditions for monocultures and microbial consortia. We also overview alternative "top-down" approaches, such as genetic algorithms, to finding optimal combinations of environmental factors. By providing a brief summary of the state of this field, we hope to stimulate further work on the rational manipulation and optimization of the microbial environment.

Diversity begets diversity during community assembly until ecological limits impose a diversity ceiling.

Microbial communities are hugely diverse, but we do not yet understand how species invasions and extinctions drive and limit their diversity. On the one hand, the ecological limits hypothesis posits that diversity is primarily limited by environmental resources. On the other hand, the diversity-begets-diversity hypothesis posits that such limits can be easily lifted when new ecological niches are created by biotic interactions. To find out which hypothesis better explains the assembly of microbial communities, we used metabolic modelling. We represent each microbial species by a metabolic network that harbours thousands of biochemical reactions. Together, these reactions determine which carbon and energy sources a species can use, and which metabolic by-products-potential nutrients for other species-it can excrete in a given environment. We assemble communities by modelling thousands of species invasions in a chemostat-like environment. We find that early during the assembly process, diversity begets diversity. By-product excretion transforms a simple environment into one that can sustain dozens of species. During later assembly stages, the creation of new niches slows down, existing niches become filled, successful invasions become rare, and species diversity plateaus. Thus, ecological limitations dominate the late assembly process. We conclude that each hypothesis captures a different stage of the assembly process. Species interactions can raise a community's diversity ceiling dramatically, but only within limits imposed by the environment.

Acetate and glycerol are not uniquely suited for the evolution of cross-feeding in E. coli.

The evolution of cross-feeding among individuals of the same species can help generate genetic and phenotypic diversity even in completely homogeneous environments. Cross-feeding Escherichia coli strains, where one strain feeds on a carbon source excreted by another strain, rapidly emerge during experimental evolution in a chemically minimal environment containing glucose as the sole carbon source. Genome-scale metabolic modeling predicts that cross-feeding of 58 carbon sources can emerge in the same environment, but only cross-feeding of acetate and glycerol has been experimentally observed. Here we use metabolic modeling to ask whether acetate and glycerol cross-feeding are especially likely to evolve, perhaps because they require less metabolic change, and thus perhaps also less genetic change than other cross-feeding interactions. However, this is not the case. The minimally required metabolic changes required for acetate and glycerol cross feeding affect dozens of chemical reactions, multiple biochemical pathways, as well as multiple operons or regulons. The complexity of these changes is consistent with experimental observations, where cross-feeding strains harbor multiple mutations. The required metabolic changes are also no less complex than those observed for multiple other of the 56 cross feeding interactions we study. We discuss possible reasons why only two cross-feeding interactions have been discovered during experimental evolution and argue that multiple new cross-feeding interactions may await discovery.

An enormous potential for niche construction through bacterial cross-feeding in a homogeneous environment.

Microorganisms modify their environment by excreting by-products of metabolism, which can create new ecological niches that can help microbial populations diversify. A striking example comes from experimental evolution of genetically identical Escherichia coli populations that are grown in a homogeneous environment with the single carbon source glucose. In such experiments, stable communities of genetically diverse cross-feeding E. coli cells readily emerge. Some cells that consume the primary carbon source glucose excrete a secondary carbon source, such as acetate, that sustains other community members. Few such cross-feeding polymorphisms are known experimentally, because they are difficult to screen for. We studied the potential of bacterial metabolism to create new ecological niches based on cross-feeding. To do so, we used genome scale models of the metabolism of E. coli and metabolisms of similar complexity, to identify unique pairs of primary and secondary carbon sources in these metabolisms. We then combined dynamic flux balance analysis with analytical calculations to identify which pair of carbon sources can sustain a polymorphic cross-feeding community. We identified almost 10,000 such pairs of carbon sources, each of them corresponding to a unique ecological niche. Bacterial metabolism shows an immense potential for the construction of new ecological niches through cross feeding.

Source and regulation of flux variability in Escherichia coli.

BACKGROUND: Metabolic responses are essential for the adaptation of microorganisms to changing environmental conditions. The repertoire of flux responses that the metabolic network can display in different external conditions may be quantified applying flux variability analysis to genome-scale metabolic reconstructions. RESULTS: A procedure is developed to classify and quantify the sources of flux variability. We apply the procedure to the latest Escherichia coli metabolic reconstruction, in glucose minimal medium, with an additional constraint to account for the mechanism coordinating carbon and nitrogen utilization mediated by α-ketoglutarate. Flux variability can be decomposed into three components: internal, external and growth variability. Unexpectedly, growth variability is the only significant component of flux variability in the physiological ranges of glucose, oxygen and ammonia uptake rates. To obtain substantial increases in metabolic flexibility, E. coli must decrease growth rate to suboptimal values. This growth-flexibility trade-off gives a straightforward interpretation to recent work showing that most overall cell-to-cell flux variability in a population of E. coli can be attained sampling a small number of enzymes most likely to constrain cell growth. Importantly, it provides an explanation for the global reorganization occurring in metabolic networks during adaptations to environmental challenges. The calculations were repeated with a pathogenic strain and an old reconstruction of the commensal strain, having less than 50% of the reactions of the latest reconstruction, obtaining the same general conclusions. CONCLUSIONS: In E. coli growing on glucose, growth variability is the only significant component of flux variability for all physiological conditions explored. Increasing flux variability requires reducing growth to suboptimal values. The growth-flexibility trade-off operates in physiological and evolutionary adaptations, and provides an explanation for the global reorganization occurring during adaptations to environmental challenges. The results obtained do not rely on the knowledge of kinetic and regulatory details of the system and are highly robust to incomplete or incorrect knowledge of the reaction network.