Writer: Roda Yaşar
Microbial communities are complex systems composed of various microorganisms that coexist and interact within shared environments. These communities include diverse groups such as bacteria, archaea, fungi, and viruses which play critical roles in ecosystem functioning. The interactions among microorganisms shape community dynamics and influence biogeochemical cycles. However, defining microbial communities can be difficult because of the microscopic scale on which these interactions often occur. This article aims to explore the definition of microbial communities, the types of interactions within them, and their impacts on ecosystems, addressing the existing gaps in our understanding of this vital area of study.
What is a microbial community?
The concept of community ecology emerged in plant and animal ecology. Communities are defined as multi-species assemblages, in which organisms live together in a contiguous environment and interact with each other. This discipline analyzes how biological assemblages are structured, what are their functional interactions and how community structure changes in space and time. Clements (1916) viewed the community as a ‘supra-organism’, which had a well-defined level of organization with tight interactions among organisms that comprise a causal system and give rise to emergent properties. The community's borders might not be defined by physical size but by the extent of strong interactions among populations rather than weak ones. (Levins and Lewontin, 1985). The alternative individualistic concept (Gleason, 1926) demonstrated that many species co-occur in the ecosystem because they tolerate similar physical and chemical conditions and do not necessarily need to interact with each other. The practical delineation of ‘community’ may then reflect the interests of the ecologist rather than any inherent characteristics.
The problems in rigorously defining community are heightened in the case of microbial ecology. In particular, delineating a ‘contiguous environment’ and the meaning of ‘interact’ may be problematic. Microorganisms react to and in turn influence conditions in their microenvironments, which usually have length scales of microns rather than millimeters (Young et al., 2008), except in cases of multicellular structures such as fungal hyphae. However, the consumption of substrates and the production of metabolic products in water-saturated sediments and density-stabilized aquatic water columns can generate chemical gradients over meters (Wakeham et al., 2007). The consequence is that these functional groups metabolically ‘interact’ over many meters. As a result, the strength of interaction among organisms and the defined spatial scale may vary substantially for investigator-defined microbial communities.
To increase rigor in the meaning of ‘microbial community,’ it would be valuable for microbial ecologists to explicitly articulate their meaning for each specific research effort. Microbes strongly interacting with each other in a microenvironment comprise a local community. However, the distribution of organisms and physicochemical properties within most habitats is patchy; even in well-mixed oligotrophic planktonic habitats or nutrient-rich foci of marine snow. (Azam and Malfatti, 2007). The patchwork of local communities has been termed a “phenomenological community” (Sterelny, 2006); for microbial ecology, this would represent a range of macroscale habitats delineated by the investigator, in which the assemblage of microbes persists in spatial association. The phenomenological community could be constrained to a smaller number of populations by defining an indexical community—the set of populations that directly interact with a key population or defined biogeochemical process, together with other local populations that affect the directly interacting populations (Sterelny, 2006).
Recent developments in community ecology have begun to recognize that the biological assemblage cannot be defined without reference to its abiotic environment. An appreciation for the tight interrelationship between their surroundings is significant for the delineation of microbial communities (O’Donnell et al., 2007). Defining microbial communities from a bottom-up perspective—based on the physicochemical characteristics of the microenvironment—can be instructive, rather than from a macroscale view (such as aquatic vs. terrestrial habitats). This approach involves analyzing the microenvironment's characteristics and then upscaling to define a spatial domain or ‘contiguous environment,’ which is the region where significant direct or indirect chemical interactions occur. This method focuses on the detailed interactions within a specific area to understand and delineate microbial communities more precisely. This approach presupposes an adequate analysis of the local physicochemical environment, but technical innovations are moving to the microscale level (Young et al., 2008). Konopka (2006) defined four ecosystems derived by considering the environment from the microbe's local perspective (Figure 1). Each has particular characteristics that define important selective forces in that habitat, but which also impact the spatial scale over which microbial interactions occur.
Figure 1 Microbial-scale ecosystems
Reproduced/adapted with permission from Konopka (2006).
The Impact of Microbial Communities on Ecosystems
Microorganisms play key roles in biogeochemical cycling, industry, and health and disease of humans, animals, and plants (Strom, 2008; Berendsen, Pieterse, & Bakker, 2012; Maukonen & Saarela, 2009; Gilbert et al., 2018; Fierer, 2017). The roughly 10^30 microbial cells on our planet contain 10 times more nitrogen than all plants combined and are responsible for half of the global production of O2 (Whitman et al., 1998). Almost all of these microorganisms reside in communities, which are community of multiple interacting species. Despite their importance, we are uncertain about how microbial communities form and function. However, knowing is essential if we aim to fully comprehend their characteristics and behavior, and also regulate the mechanisms that they facilitate (Widder et al., 2016; Zomorrodi & Segrè, 2016).
Microbial communities, like all complex systems, are more than the sum of their parts: they are characterized by a multitude of often complex interactions between their constituent members (figure 2). At any given time, microbes may compete for shared resources such as metabolites and space, inhibit each other via the secretion of antibiotics and other toxic compounds, and even kill each other upon direct cell-cell contact (Hibbing et al., 2010; Basler et al., 2013). Yet, not all is bleak in the microbial world: some organisms may—accidentally or actively—excrete enzymes or molecules that others can use, and even commit suicide for others (Nadell, Drescher, & Foster, 2016; Estrela et al., 2019; Riley & Gordon, 1999). The overall sign and strength of an interaction between two organisms is the net result of all such processes and can be characterized as anything from competition to parasitism to mutualism (Dolinšek, Goldschmidt, & Johnson, 2016; Foster & Bell, 2012).
Interspecific interactions have a crucial role to play in microbial communities.This is the major characteristic of microbial community—rather than a random set of species—is precisely these interactions, because they give rise to properties at the level of the community that we cannot understand by considering each species in isolation. For instance, it might be challenging to anticipate the combined reproductive yield of a group or the group's resilience to outside influences, solely based on the individual reproductive capabilities of each species. Microbial communities can also perform chemical transformations that would be impossible for one individual species to achieve (Morris et al., 2013), and some communities even display complex behaviors such as collective motion and electrochemical signaling, which have generally been associated with higher organisms (Srinivasan et al., 2019; Martinez-Corral et al., 2019).
Over the past decades, an impressive amount of effort has been dedicated to obtaining an ever-more detailed and realistic picture of a wide range of different microbial systems. The advance of -omics technologies has led to an incredible leap forward in terms of available data that can be integrated to extract and analyze patterns that inform us about the lives of microbes in their natural surroundings. -Omics technologies encompass genomics, transcriptomics, proteomics, and metabolomics, which involve the comprehensive analysis of an organism's genetic material, RNA transcripts, proteins, and metabolites, respectively. This method facilitates the identification and classification of microbial species, helping to construct phylogenetic trees that illustrate evolutionary relationships among different microorganisms. It has significantly advanced our understanding of microbial diversity, allowing for the detection of previously uncultured organisms that play critical roles in various ecosystems. However, if we want to arrive at a more fundamental understanding of the current and future properties of microbial communities, we have to uncover general principles of how such communities typically change over time. Since interactions are often mediated by whole suites of different chemicals, interactions may significantly change in strength and even sign over ecological timescales. For example, bacteria may alter the pH of their environment during growth, which may then change the sign of their interactions with other species from positive to negative, or vice versa (Ratzke & Gore, 2018).
Extrapolating such findings to more general principles of community dynamics is challenging. Nonetheless, an increasing number of studies find that ecological community dynamics are remarkably repeatable across experimental and biological replicates, and may be understood from a combination of metabolic properties of the environment and species' functional traits (Enke et al., 2019; Goldford et al., 2018; Friedman, Higgins, & Gore, 2017). The transformation of communities over long periods of evolution is not well understood, even though these changes can have greater and longer-lasting impacts due to being less reversible, as they result from genetic mutations. Considering that interactions are what characterize a community, we suggest that the evolution of interspecific interactions is paramount in this regard.
Here, we reviewed the empirical evidence that evolution was an important driver of microbial community properties and dynamics on timescales that had traditionally been regarded as purely ecological. Next, we briefly discussed different modeling approaches to this problem, emphasizing the similarities and differences between evolutionary and ecological perspectives. We then proposed a simple conceptual model for the evolution of communities, which we explored using simulations. Finally, we discussed experimental approaches that might help to test our framework and thus improve our understanding of this fascinating process.
Figure 2 Microbial communities are characterized by a multitude of often complex interactions between their members. For example, three bacterial species that can co-occur in dairy products engage in both positive and negative interactions, which are mediated by metabolic compounds and toxins (Villani et al., 1995; Sieuwerts et al., 2008).
Methods for Studying Microbial Communities
Studying microbial communities and their interactions involves a variety of advanced techniques and methodologies that provide insights into their composition, function, and ecological roles. One of the most significant methods is metagenomics, which allows researchers to sequence the collective DNA from all microorganisms present in a given environment. This approach enables the identification of microbial diversity and functional potential without the need for culturing individual organisms. By analyzing the metagenomic data, researchers can gain insights into the metabolic capabilities of these communities and their contributions to ecosystem processes.
Another widely used technique is 16S rRNA gene sequencing, which focuses on a highly conserved region of the ribosomal RNA gene that is present in all bacteria and archaea. This method facilitates the identification and classification of microbial species, helping to construct phylogenetic trees that illustrate evolutionary relationships among different microorganisms. It has significantly advanced our understanding of microbial diversity, allowing for the detection of previously uncultured organisms that play critical roles in various ecosystems.
Stable isotope probing (SIP) is also a valuable technique used in studying microbial communities. SIP involves incorporating isotopically labeled substrates into microbial biomass to trace the flow of nutrients within these communities. By analyzing which microorganisms assimilate these labeled compounds, researchers can identify specific metabolic pathways and the microorganisms responsible for key biochemical processes. This method provides valuable information about microbial interactions and their roles in nutrient cycling.
Additionally, fluorescence in situ hybridization (FISH) is a powerful method for visualizing
and identifying microbes in their natural environments. FISH utilizes fluorescently labeled probes that bind to specific DNA or RNA sequences within microbial cells. This technique allows researchers to observe microbial communities without disturbing their natural structure, providing a more accurate assessment of community dynamics and spatial distributions.
Finally, functional profiling techniques such as metatranscriptomics, metaproteomics, and metabolomics are essential for understanding the functional activities of microbial communities. Metatranscriptomics analyzes gene expression patterns to determine which genes are actively expressed under specific environmental conditions. Metaproteomics examines the entire protein content of a microbial community, revealing the functional capacities of the microbes present. Metabolomics, on the other hand, focuses on the small molecules produced by these communities, providing insights into their metabolic activities and responses to environmental changes. Together, these methods reveal how microbial communities adapt to their surroundings and interact with one another, thereby illuminating their roles in biogeochemical cycles.
The integration of these diverse methodologies is essential for gaining a comprehensive understanding of microbial communities. Each technique contributes unique information about microbial diversity, interactions, and their significant roles in maintaining ecosystem health. As research progresses, these methodologies continue to evolve, offering new insights into the complex dynamics of microbial life (Miller, 2018; Tiedje, 1999; Szalay et al, 2020; Lueders, 2000; Radajewski et al, 2000; Pernthaler, 2005; Liu et al, 2012).
Conclusion
This article discusses the concept of microbial communities, which are defined as assemblages of multiple interacting species within a shared environment. The definition of a community in microbial ecology presents challenges due to the unique characteristics of microorganisms, including their interactions in microenvironments. These communities can be understood from various perspectives, emphasizing the importance of both biotic and abiotic factors in defining their structure and function.
Microbial communities play critical roles in biogeochemical cycling, influencing ecosystem health and functioning. They consist of diverse microorganisms, including bacteria, archaea, fungi, and viruses, each contributing to essential ecological processes. Interactions within these communities range from symbiosis to competition and parasitism, shaping community dynamics and stability.
The article also highlights advanced methodologies for studying microbial communities, such as metagenomics, 16S rRNA gene sequencing, stable isotope probing, and fluorescence in situ hybridization. These techniques provide insights into microbial diversity, metabolic functions, and interactions, enhancing our understanding of microbial life and its implications for ecosystem health.
References
Azam F., & Malfatti F. (2007). Microbial structuring of marine ecosystems. Nature Reviews Microbiology. pp. 782–791.
Basler M., Ho B. T., & Mekalanos J. J. (2013). Tit-for-tat: type VI secretion system counterattack during bacterial cell-cell interactions. Cell. pp. 884–894.
Berendsen R. L., Pieterse C. M. J., & Bakker P. A. H. M. (2012). The rhizosphere microbiome and plant health. Trends in Plant Science. pp. 478–486.
Clements F. E. (1916). Plant Succession: An Analysis of the Development of Vegetation. Carnegie Institution: Washington, D.C
.
Estrela S., Libby E., Van Cleve J., Débarre F., Deforet M., Harcombe W. R., Peña J., Brown S. P., & Hochberg M. E. (2019). Environmentally mediated social dilemmas. Trends in Ecology & Evolution. pp. 6–18.
Fierer N. (2017). Embracing the unknown: disentangling the complexities of the soil microbiome. Nature Reviews Microbiology. pp. 579–590.
Foster K. R., & Bell T. (2012). Competition, not cooperation, dominates interactions among culturable microbial species. Current Biology. pp. 1845–1850.
Friedman J., Higgins L. M., & Gore J. (2017). Community structure follows simple assembly rules in microbial microcosms. Nature Ecology & Evolution.
Gilbert J. A., Blaser M. J., Caporaso J. G., Jansson J. K., Lynch S. V., & Knight R. (2018). Current understanding of the human microbiome. Nature Medicine. pp. 392–400.
Gleason H. A. (1926). The individualistic concept of the plant association. Bulletin of the Torrey Botanical Club. pp. 7–26.
Gorter A. F., Manhart M., & Ackermann M. (Yıl). Understanding the evolution of interspecies interactions in microbial communities. pp. 2–3.
Goldford J. E., Lu N., Bajić D., Estrela S., Tikhonov M., Sanchez-Gorostiaga A., Segrè D., Mehta P., & Sanchez A. (2018). Emergent simplicity in microbial community assembly. Science. pp. 469–474.
Hibbing M. E., Fuqua C., Parsek M. R., & Peterson S. B. (2010). Bacterial competition: surviving and thriving in the microbial jungle. Nature Reviews Microbiology. pp. 15–25.
Konopka A. (2006). Microbial ecology: searching for principles. Microbe. pp. 175–179.
Levins R., & Lewontin R. C. (1985). Dialectics and reduction in ecology. In R. Levins & R. C.
Lewontin (Eds.), The Dialectical Biologist. Cambridge: Harvard University Press. pp. 132–160.
Lueders T., & Friedrich M. W. (2000). Determination of Bacterial Community Structures in
Environmental Samples by 16S rRNA Gene Analysis. Applied and Environmental Microbiology. pp. 3728–3733.
Maukonen J., & Saarela M. (2009). Microbial communities in industrial environment. Current Opinion in Microbiology. pp. 238–243
.
Martinez-Corral R., Liu J., Prindle A., Süel G. M., & Garcia-Ojalvo J. (2019). Metabolic basis of brain-like electrical signaling in bacterial communities. Philosophical Transactions of the Royal Society B.
Miller S. R., & Harlow J. (2018). Metagenomics: A Primer for Microbial Ecology. Microbial Ecology. pp. 1–12.
Morris B. E. L., Henneberger R., Huber H., & Moissl-Eichinger C. (2013). Microbial syntrophy: interaction for the common good. FEMS Microbiology Reviews. pp. 384–406.
Nadell C. D., Drescher K., & Foster K. R. (2016). Spatial structure, cooperation and competition in biofilms. Philosophical Transactions of the Royal Society B. pp. 589–600.
O’Donnell A. G., Young I. M., Rushton S. P., Shirley M. D., & Crawford J. W. (2007). Visualization, modelling, and prediction in soil microbiology. Nature Reviews Microbiology. pp. 689–699.
Pernthaler A., & Pernthaler J. (2005). Fluorescence In Situ Hybridization (FISH) for the
Identification of Microorganisms in Environmental Samples. Methods in Microbiology. pp. 207–218.
Radajewski S., et al. (2000). Stable Isotope Probing as a Tool for Identifying Active Microbial Communities. Applied and Environmental Microbiology. pp. 5054–5064.
Riley M. A., & Gordon D. M. (1999). The ecological role of bacteriocins in bacterial competition. Trends in Microbiology. pp. 129–133.
Sieuwerts S., De Bok F. A. M., Hugenholtz J., & Van Hylckama Vlieg J. E. T. (2008). Unraveling microbial interactions in food fermentations: from classical to genomics approaches. Applied and Environmental Microbiology. pp. 4997–5007.
Sterelny K. (2006). Local ecological communities. Philosophy of Science. pp. 215–231.
Strom S. L. (2008). Microbial ecology of ocean biogeochemistry: a community perspective. Science. pp. 1043–1045.
Tiedje J. M., et al. (1999). Concepts and Applications of Metagenomics in Environmental Microbiology. Environmental Microbiology. pp. 17–27.
Whitman W. B., Coleman D. C., & Wiebe W. J. (1998). Prokaryotes: the unseen majority. Proceedings of the National Academy of Sciences USA. pp. 6578–6583.
Widder S., et al. (2016). Challenges in microbial ecology: building predictive understanding of community function and dynamics. ISME Journal. pp. 2557–2568.
Young I. M., Crawford J. W., Nunan N., Otten W., & Spiers A. (2008). Microbial distribution in soils: physics and scaling. Advances in Agronomy. pp. 81–121.
Zomorrodi A. R., & Segrè D. (2016). Synthetic ecology of microbes: mathematical models and applications. Journal of Molecular Biology. pp. 837–861.
Kommentare