Control of biotin biosynthesis in mycobacteria by a pyruvate carboxylase dependent metabolic signal.

Biotin is an essential cofactor utilized by all domains of life, but only synthesized by bacteria, fungi and plants, making biotin biosynthesis a target for antimicrobial development. To understand biotin biosynthesis in mycobacteria, we executed a genetic screen in Mycobacterium smegmatis for biotin auxotrophs and identified pyruvate carboxylase (Pyc) as required for biotin biosynthesis. The biotin auxotrophy of the pyc::tn strain is due to failure to transcriptionally induce late stage biotin biosynthetic genes in low biotin conditions. Loss of bioQ, the repressor of biotin biosynthesis, in the pyc::tn strain reverted biotin auxotrophy, as did reconstituting the last step of the pathway through heterologous expression of BioB and provision of its substrate DTB. The role of Pyc in biotin regulation required its catalytic activities and could be supported by M. tuberculosis Pyc. Quantitation of the kinetics of depletion of biotinylated proteins after biotin withdrawal revealed that Pyc is the most rapidly depleted biotinylated protein and metabolomics revealed a broad metabolic shift in wild type cells upon biotin withdrawal which was blunted in cell lacking Pyc. Our data indicate that mycobacterial cells monitor biotin sufficiency through a metabolic signal generated by dysfunction of a biotinylated protein of central metabolism.

Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network.

Bacteria of many species rely on a simple molecule, the intracellular secondary messenger c-di-GMP (Bis-(3'-5')-cyclic dimeric guanosine monophosphate), to make a vital choice: whether to stay in one place and form a biofilm, or to leave it in search of better conditions. The c-di-GMP network has a bow-tie shaped architecture that integrates many signals from the outside world-the input stimuli-into intracellular c-di-GMP levels that then regulate genes for biofilm formation or for swarming motility-the output phenotypes. How does the 'uninformed' process of evolution produce a network with the right input/output association and enable bacteria to make the right choice? Inspired by new data from 28 clinical isolates of Pseudomonas aeruginosa and strains evolved in laboratory experiments we propose a mathematical model where the c-di-GMP network is analogous to a machine learning classifier. The analogy immediately suggests a mechanism for learning through evolution: adaptation though incremental changes in c-di-GMP network proteins acquires knowledge from past experiences and enables bacteria to use it to direct future behaviors. Our model clarifies the elusive function of the ubiquitous c-di-GMP network, a key regulator of bacterial social traits associated with virulence. More broadly, the link between evolution and machine learning can help explain how natural selection across fluctuating environments produces networks that enable living organisms to make sophisticated decisions.

Metabolism and the Evolution of Social Behavior.

How does metabolism influence social behavior? This fundamental question at the interface of molecular biology and social evolution is hard to address with experiments in animals, and therefore, we turned to a simple microbial system: swarming in the bacterium Pseudomonas aeruginosa. Using genetic engineering, we excised a locus encoding a key metabolic regulator and disrupted P. aeruginosa's metabolic prudence, the regulatory mechanism that controls expression of swarming public goods and protects this social behavior from exploitation by cheaters. Then, using experimental evolution, we followed the joint evolution of the genome, the metabolome and the social behavior as swarming re-evolved. New variants emerged spontaneously with mutations that reorganized the metabolome and compensated in distinct ways for the disrupted metabolic prudence. These experiments with a unicellular organism provide a detailed view of how metabolism-currency of all physiological processes-can determine the costs and benefits of a social behavior and ultimately influence how an organism behaves towards other organisms of the same species.

Integration of Metabolic and Quorum Sensing Signals Governing the Decision to Cooperate in a Bacterial Social Trait.

Many unicellular organisms live in multicellular communities that rely on cooperation between cells. However, cooperative traits are vulnerable to exploitation by non-cooperators (cheaters). We expand our understanding of the molecular mechanisms that allow multicellular systems to remain robust in the face of cheating by dissecting the dynamic regulation of cooperative rhamnolipids required for swarming in Pseudomonas aeruginosa. We combine mathematical modeling and experiments to quantitatively characterize the integration of metabolic and population density signals (quorum sensing) governing expression of the rhamnolipid synthesis operon rhlAB. The combined computational/experimental analysis reveals that when nutrients are abundant, rhlAB promoter activity increases gradually in a density dependent way. When growth slows down due to nutrient limitation, rhlAB promoter activity can stop abruptly, decrease gradually or even increase depending on whether the growth-limiting nutrient is the carbon source, nitrogen source or iron. Starvation by specific nutrients drives growth on intracellular nutrient pools as well as the qualitative rhlAB promoter response, which itself is modulated by quorum sensing. Our quantitative analysis suggests a supply-driven activation that integrates metabolic prudence with quorum sensing in a non-digital manner and allows P. aeruginosa cells to invest in cooperation only when the population size is large enough (quorum sensing) and individual cells have enough metabolic resources to do so (metabolic prudence). Thus, the quantitative description of rhlAB regulatory dynamics brings a greater understating to the regulation required to make swarming cooperation stable.

Multilevel selection analysis of a microbial social trait.

The study of microbial communities often leads to arguments for the evolution of cooperation due to group benefits. However, multilevel selection models caution against the uncritical assumption that group benefits will lead to the evolution of cooperation. We analyze a microbial social trait to precisely define the conditions favoring cooperation. We combine the multilevel partition of the Price equation with a laboratory model system: swarming in Pseudomonas aeruginosa. We parameterize a population dynamics model using competition experiments where we manipulate expression, and therefore the cost-to-benefit ratio of swarming cooperation. Our analysis shows that multilevel selection can favor costly swarming cooperation because it causes population expansion. However, due to high costs and diminishing returns constitutive cooperation can only be favored by natural selection when relatedness is high. Regulated expression of cooperative genes is a more robust strategy because it provides the benefits of swarming expansion without the high cost or the diminishing returns. Our analysis supports the key prediction that strong group selection does not necessarily mean that microbial cooperation will always emerge.

Convergent evolution of hyperswarming leads to impaired biofilm formation in pathogenic bacteria.

Most bacteria in nature live in surface-associated communities rather than planktonic populations. Nonetheless, how surface-associated environments shape bacterial evolutionary adaptation remains poorly understood. Here, we show that subjecting Pseudomonas aeruginosa to repeated rounds of swarming, a collective form of surface migration, drives remarkable parallel evolution toward a hyperswarmer phenotype. In all independently evolved hyperswarmers, the reproducible hyperswarming phenotype is caused by parallel point mutations in a flagellar synthesis regulator, FleN, which locks the naturally monoflagellated bacteria in a multiflagellated state and confers a growth rate-independent advantage in swarming. Although hyperswarmers outcompete the ancestral strain in swarming competitions, they are strongly outcompeted in biofilm formation, which is an essential trait for P. aeruginosa in environmental and clinical settings. The finding that evolution in swarming colonies reliably produces evolution of poor biofilm formers supports the existence of an evolutionary trade-off between motility and biofilm formation.

Exploiting social evolution in biofilms.

Bacteria are highly social organisms that communicate via signaling molecules, move collectively over surfaces and make biofilm communities. Nonetheless, our main line of defense against pathogenic bacteria consists of antibiotics-drugs that target individual-level traits of bacterial cells and thus, regrettably, select for resistance against their own action. A possible solution lies in targeting the mechanisms by which bacteria interact with each other within biofilms. The emerging field of microbial social evolution combines molecular microbiology with evolutionary theory to dissect the molecular mechanisms and the evolutionary pressures underpinning bacterial sociality. This exciting new research can ultimately lead to new therapies against biofilm infections that exploit evolutionary cheating or the trade-off between biofilm formation and dispersal.