Higher-order interactions and emergent properties of microbial communities: The power of synthetic ecology

Abstract

Humans have long relied on microbial communities to create products, produce energy, and treat waste. The microbiota residing within our bodies directly impacts our health, while the soil and rhizosphere microbiomes influence the productivity of our crops. However, the complexity and diversity of microbial communities make them challenging to study and difficult to develop into applications, as they often exhibit the emergence of unpredictable higher-order phenomena. Synthetic ecology aims at simplifying complexity by constituting synthetic or semi-natural microbial communities with reduced diversity that become easier to study and analyze. This strategy combines methodologies that simplify existing complex systems (top-down approach) or build the system from its constituent components (bottom-up approach). Simplified communities are studied to understand how interactions among populations shape the behavior of the community and to model and predict their response to external stimuli. By harnessing the potential of synthetic microbial communities through a multidisciplinary approach, we can advance knowledge of ecological concepts and address critical public health, agricultural, and environmental issues more effectively.

Fig. 1

Frequency of publications on microbial synthetic ecology in the last decade. The data was obtained by searching the terms in the legends in Google Scholar.

Fig. 2

The Dynamic Process of Community Assembly. Community assembly is a dynamical process influenced by deterministic and stochastic factors. Some factors can be abiotic, such as pH, temperature, salinity, and oxygen; or biotic, such as species interactions, selection, and speciation. As these factors change, microbial communities change their structure in response, leading to a continuous cycle of assembly. In the case of gut microbiota, an unhealthy community structure has been associated with various illnesses, such as diabetes, gut cancer, and inflammatory bowel disease. However, treatments with prebiotics, probiotics, and postbiotics can induce assembly into a healthy structure, promoting overall gut health. Similarly, plants establish symbiotic relationships with microbes, and environmental factors such as intense land usage, nutrient depletion, and pollution can lead to poor functional soil and rhizosphere microbiomes, resulting in suboptimal conditions for plant growth. However, the addition of beneficial microbes to the soil, plant roots, and seeds can result in better crop growth and yield by restoring microbial communities. Growing natural microbial communities in the laboratory is challenging due to the potential changes in community composition and function that can occur when culturing samples under imposed artificial media and conditions. These changes can cause the resulting microbial community to differ significantly from the original community in its natural environment, making it difficult to accurately represent the structure and function found in nature.

Fig. 3

The Spectrum of Microbial Ecosystems. Microbial communities can be categorized into natural, semi-natural, and synthetic, forming a continuum of states in-between. Examples of natural communities include microbial mats and stromatolites, which are among the oldest examples of complex ecosystems on Earth. The human gut microbiome is extensively studied for its relevance to human health, while soil and rhizosphere communities have a significant impact on agriculture and every other life form on Earth. Semi-natural communities, that develop in mesocosms, Winogradsky columns, and host-associated microbiomes offer practical options for studying complex communities with limited accessibility. The construction of synthetic communities from axenic cultures, offer a systematic approach to study the emergence of community behavior and interactions networks in highly controlled setups with varying levels of complexity.

Fig. 4

Exploring the Complexity of Interactions: networks, properties, and patterns. A) Ecological interactions can be classified into six types: Mutualism, Competition, Commensalism, Ammensalism, Parasitism, and Neutralism. When taking directionality into account, nine possible interactions can occur between any two species. Positive effects are represented by green arrows while negative effects are represented by red arrows. B) As the number of interacting species in a community increases, the complexity of interaction networks also increases rapidly. This makes it challenging to design and characterize all possible interaction networks in synthetic communities. An example is shown for the possible interaction networks in communities with four, ten, and twenty species. C) Under stressful conditions, such as low nutrients or high toxicity, cooperation is promoted, while competition is observed under less stressful conditions. The prevalence of interference competition is thought to positively correlate with taxonomic relatedness. The strength of the antagonistic effect is cell density dependent. Pairwise antagonism assays between isolates from the same sampling site (referred to as “high sympatry”) show less antagonistic interactions than those from different sampling sites. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

Higher-Order Interactions Topologies Alter Species Dynamics. A) Indirect interactions between two species can be modified by the presence of a third interactor. B) The negative impact of an invasive species on two other organisms can be mitigated or avoided if those organisms are together. C) Adding more species to a community can increase the potential for coexistence in an environment where some species cannot survive alone. D) The addition of a third species can reverse a positive interaction, leading to a community with competition.

Key concepts and definitions [[205], [206], [207], [208], [209], [210], [211]].

Fig. 6

From Cultivation to Sequencing: Methods to Study Microbial Interactions and Community Dynamics. Microbial interactions and community dynamics can be studied using various techniques, which can be broadly classified into three categories: culture-based, nucleic acid-based, and function-based methods. Culture-based methods involve cultivating microorganisms in laboratory conditions to study their behavior and interactions. Nucleic acid-based methods utilize DNA and RNA molecular technologies to identify and quantify microorganisms. Function-based methods assess the metabolic activity of microorganisms in a community, such as measuring enzyme activity or substrate utilization. Examples of commonly used techniques within each category are shown.