Bottom-Up Approaches to Synthetic Cooperation in Microbial Communities

life Review Bottom-Up Approaches to Synthetic Cooperation in Microbial Communities Daniel Rodríguez Amor * and Martina Dal Bello Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; * Correspondence: Received: 3 December 2018; Accepted: 14 February 2019; Published: 26 February 2019



 Abstract: Microbial cooperation pervades ecological scales, from single-species populations to host-associated microbiomes. Understanding the mechanisms promoting the stability of cooperation against potential threats by cheaters is a major question that only recently has been approached experimentally. Synthetic biology has helped to uncover some of these basic mechanisms, which were to some extent anticipated by theoretical predictions. Moreover, synthetic cooperation is a promising lead towards the engineering of novel functions and enhanced productivity of microbial communities. Here, we review recent progress on engineered cooperation in microbial ecosystems. We focus on bottom-up approaches that help to better understand cooperation at the population level, progressively addressing the challenges of tackling higher degrees of complexity: spatial structure, multispecies communities, and host-associated microbiomes. We envisage cooperation as a key ingredient in engineering complex microbial ecosystems. Keywords: synthetic microbial communities; mutualism; cheaters; host-microbiome interactions; synthetic ecology 1. Introduction Cooperation emerges at multiple scales of complexity in microbial ecosystems. Clonal populations are among the simplest microbial ecosystems that can be studied, and yet, they provide a convenient laboratory arena to analyze several cooperative behaviors in microbes [1,2]. These include extracellular digestion of resources [3,4], protection against antibiotics [5], and even the formation of fruiting bodies, a much rarer event that enhances the fitness of a small fraction of the population at the expense of the majority [6]. Inspection of natural communities reveals widespread cooperative interactions occurring not only within cells sharing a genotype [7], but also between different strains or species; see Table 1. Such heterotypic interactions are commonly known as mutualisms, and they can give rise to a variety of behaviors in microbial consortia, e.g., cross-feeding [8], cross-protection [9], and division of labor [? ]. These behaviors can be influenced by specific lifestyles that microbial communities adopt, such as the formation of spatially-structured biofilms [10]. In a way, microbial cooperative skills can even transcend the small size of unicellular organisms as, for example, in microbiomes, where microbes engage in symbiotic relationships with their hosts. Despite the progresses made in the past decades in disentangling microbe–microbe and host–microbe interactions, we still have limited understanding of microbial cooperation in natural communities [11]. What mechanisms promote cooperative interactions? How does cooperation shape the dynamics of complex microbial communities, or more generally, how can cooperators endure exploitation by cheaters? Since The Origin of Species was published, this question has puzzled evolutionary scientists [12–17], Charles Darwin included. Assuming a simple scenario in which cooperators pay a fitness cost in order to help their neighbors altruistically, cheaters could easily beat cooperators by exploiting any available public Life 2019, 9, 22; doi:10.3390/life9010022 www.mdpi.com/journal/life Life 2019, 9, 22 2 of 17 good while avoiding its costs. Hence, for cooperation to be an evolutionarily-stable strategy, additional mechanisms promoting cooperation have to be at play [17,18]. The recent blooming of synthetic biology [19] has provided a convenient platform to interrogate cooperation in microbial systems. Editing wild strain genomes has allowed manipulating microbial strategies within a population in order to understand how microbes face social dilemmas [1,20–24]. Moreover, engineering mutualisms between multiple genotypes recently provided insights into heterotypic partnerships such as cross-feeding interactions [8], collective resistance to antibiotics [9], and spatial self-organization [25]. Engineered symbiosis is progressively opening new avenues to explore interactions that benefit both microbial consortia and their associated hosts. Beyond improving our understanding of microbial interactions, a major goal in synthetic biology is to engineer complex microbial ecosystems for industrial [26,27], bioremediation [28], or therapeutic purposes [29,30]. To this aim, a better understanding of how cooperative feedbacks could be used to enhance the productivity and stability of different engineered consortia is needed. Here, we review recent advances on synthetic cooperation in microbial ecosystems. We focus on cooperative and parasitic interactions (see Table 1) in synthetic microbial ecosystems from an ecological perspective, rather than focusing on the specific genetic circuits to engineer these systems, which were reviewed, e.g., by McCarty et al. [31] and by Brophy et al. [32]. In the following, we start by discussing low complexity systems in simple laboratory environments, progressively moving on to more complex microbial ecosystems (see Figure 1). Each of the following sections covers a specific scale of complexity: well-mixed populations, spatially-structured environments, multispecies communities, and host-associated microbial communities. While reviewing several key drivers of microbial cooperation at these different scales, we highlight the potential impact of cheaters that exploit collective benefits. We discuss several mechanisms that promote cooperation against cheaters in microbial ecosystems, as well as how transitions between cooperators and cheaters could be used in microbial community engineering. Figure 1. The complexity of microbial ecosystems classified according to two different components: the structure of the community and the structure of the environment. As the number of species in the community increases, community structure (and hence, the network of interactions) becomes more complex. As the complexity of the environment increases, the ecosystem becomes more heterogeneous, very often unfolding new outcomes for the community. A well-mixed culture with two strains (bottom-left) provides one of the simplest ways to study microbial interactions, yet the outcome of interactions can change if spatial structure is at play (agar surface at the bottom-right). The complexity of the interaction network can increase with the number of community members (top-left), and again, complex environments such a spatially-structured animal gut can interfere with both microbial interactions and community composition (top-right). Life 2019, 9, 22 3 of 17 Table 1. Social interactions in microbes. This table presents co (...truncated)