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Bacterial social interactions and the emergence of community-intrinsic properties
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ScienceDirect Bacterial social interactions and the emergence of community-intrinsic properties Jonas Stenløkke Madsen, Søren Johannes Sørensen and Mette Burmølle Bacterial communities are dominated and shaped by social interactions, which facilitate the emergence of properties observed only in the community setting. Such communityintrinsic properties impact not only the phenotypes of cells in a community, but also community composition and function, and are thus likely to affect a potential host. Studying communityintrinsic properties is, therefore, important for furthering our understanding of clinical, applied and environmental microbiology. Here, we provide recent examples of research investigating community-intrinsic properties, focusing mainly on community composition and interactions in multispecies biofilms. We hereby wish to emphasize the importance of studying social interactions in settings where communityintrinsic properties are likely to emerge. Address Section of Microbiology, Department of Biology, University of Copenhagen, Denmark Corresponding author: Burmølle, Mette ([email protected])
Current Opinion in Microbiology 2018, 42:104–109 This review comes from a themed issue on Cell regulation Edited by Jan-Willem Veening and Rita Tamayo
http://dx.doi.org/10.1016/j.mib.2017.11.018 1369-5274/ã 2017 Elsevier Ltd. All rights reserved.
Introduction In line with other scholars, we are of the opinion that it is imperative to systematically study interspecies interactions in complex (e.g. multispecies) systems [1,2]. The primary goal of such research is to understand communityintrinsic properties: properties of bacteria that only arise in the context of a community, not in isolation (e.g. community-extrinsic properties). Bacteria have traditionally been perceived as phenotypically homogeneous and uncoordinated populations of cells, in which community-intrinsic properties were of little relevance. However, the more recent acknowledgement of bacterial social activities, such as biofilm formation and quorum sensing [3,4], has sparked greater interest in studying community-based social interactions. Thus, debating the importance of Current Opinion in Microbiology 2018, 42:104–109
community-intrinsic properties is particularly relevant now, as microbiology research moves from studying simpler, single species systems into examining more complex, multispecies systems [5,6].
Community-intrinsic properties The term community-intrinsic properties (see Figure 1) is inspired by the phenomenon of emergent properties of complex systems, but covers the diversely defined use of the phenomenon in the microbiology research literature [7,8–10], thus serving as an umbrella term for the many diverse definitions of emergent properties. Although scientists in other disciplines have acknowledged the connection between complexity and emergent properties in complex systems [11,12], the phenomenon has attracted little attention from microbiologists. One reason it has been overlooked is that emergent properties can be understood as almost any unexpected property of complex systems, that, in theory, could be identified through functional decomposition. The concept, therefore, tends to be disregarded because the term has acquired a definition that describes a lack of mechanistic understanding. This however seems shortsighted, as the acknowledgement that something is not understood should be a driver for exploration in itself. Another and rather distinctive definition of emergent properties posits that interactions in a complex system facilitate properties that cannot be identified through functional decomposition of their parts in isolation, that is, properties of a complex system that cannot be linearly extrapolated based on the sum of their parts [9,13]. These types of emergent properties are likely to occur in both biological and non-biological systems where multiple components interact and can result in complex or even chaotic behaviors [14]. Therefore, instead of disregarding emergent properties due to a lack of consensus of its definition, we advocate that the concept is of high practical value in, for example, applied microbiology, where such basic concepts, in our experience, are less often considered. Using communityintrinsic properties as a term that includes both of the aforementioned definitions of emergent properties (Figure 1) could be a way to move forward and focus this exciting and important line of research. Determining whether something is a community-intrinsic property requires a null-model (null-hypothesis) that www.sciencedirect.com
Community-intrinsic properties Madsen, Sørensen and Burmølle 105
Figure 1
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The concept of community-intrinsic properties. (a) Exemplifies a system where the output (red and blue colors) of components (representing e.g. bacterial species), illustrated as two differently shaped puzzle pieces, act the same when together as when in isolation. The output can, in terms of microbiology, for example, be a change of phenotype. The properties of this type of system are therefore predictable based on how the components act in isolation and no community-intrinsic properties thus emerge. (b) Is an example where community-intrinsic properties emerge. Here, an interaction (black arrow) between the two components results in a change in output of one of them (from red to green). The output change only occurs when the components are together, which is not predictable by studying the components in isolation and therefore a community-intrinsic property. See Figure 2 for an example relevant to microbiology resembling system B. (c) Is also an example where community-intrinsic properties emerge. Here the output of both components changes: First (1), the blue piece influences the red piece that therefore changes to green (as in B). Next (2), the now green piece influences the other piece to change from blue to purple. The output is therefore a community-intrinsic property that is less predictable than system B. More complex systems, such as bacterial communities consisting of many interacting species (pieces), will lead to diverse phenotypic responses (colors) resulting in the emergence of community-intrinsic properties.
allows one to asses if the system (inter-)acts as expected or not. Normally, such expectations are based on studies of the individual parts in isolation. Although the discussion of what constitutes a good null-model is not in the scope of this paper (see e.g. [15–17]), it is important to the concept of community-intrinsic properties because it enforces hypothesis driven research, as exemplified below.
A broad appeal of studying communityintrinsic properties If we, based on the above description, accept that community-intrinsic properties can emerge in complex microbial systems, then this allows us to decipher key parts of microbiology that cannot be uncovered otherwise. Importantly, this includes cases of community-intrinsic properties that are not decomposable in the frame of current knowledge and technology, and otherwise would have been marginalized due to a lack of data or conflicting results. Approaching experimental findings in complex multispecies systems by assessing whether the observed emergent properties may be understood by functional decomposition, will provide us with a way to identify and classify, for example, interactions in multispecies biofilms www.sciencedirect.com
[7]. Below, we will highlight examples of research that supports the notion that community-intrinsic properties are likely and important in complex microbial communities and that much can be gained from studying them. Bacterial social interactions are believed to shape community composition through cooperative integration [18] and competitive exclusion [19], as well as via priority effects influencing community assembly processes [20]. An indication of this is derived from the numerous microbiome studies published in recent years. Here, network analyses reveal a high degree of co-occurrences, that is, positive pairwise correlations of OTUs (operational taxonomical units) abundances across samples or over time [21,22,23]. Such co-occurrences are often interpreted as indicators of actual co-dependencies among different members of the microbiome [23]. Although niche overlap is often provided as an alternative explanation for these apparent co-occurrences, it seems likely that social interactions are widespread in microbiomes and influence community assembly and host– microbiome interactions. Hence, a microbiome’s effects on the host organism are most likely results of Current Opinion in Microbiology 2018, 42:104–109
106 Cell regulation
community-intrinsic properties, and should be investigated as such rather than being subjected to decomposition in the attempt to link the observed effect to specific, individual community members. It is becoming increasingly clear that factors shaping community diversity and composition are less known and the mechanisms are highly complex. It was recently shown that outcomes of bacterial competition in model communities could not be predicted from two-species pairwise bacterial competition [24]. Relatively accurate predictions (90%) of final community composition were, however, made based on competition experiments containing three species, further emphasizing the necessity of increasing complexity. Community-intrinsic properties may contribute hereto, as for example, a product of social interactions.
Interactions in multispecies biofilms — examples of community-intrinsic properties A simple parameter used for identification of communityintrinsic properties of model bacterial communities is production of biomass, either number of cells or biofilm biomass, which is composed of cells as well as matrix components. Regardless of the measure, more or less cell/ biomass than expected from that of the individual parts indicates interactions resulting in community-intrinsic properties. We have observed this among bacterial
consortia isolates from various environments, including both ‘natural’ bacterial habitats (marine, freshwater and soil [16]) and a food processing facility [25]. When coculturing isolates from these environments, we observed both biofilm induction and reduction, that is, more or less biofilm was formed than expected from the individual isolates. Intriguingly, induction and biofilm synergy were found to be common in the co-cultures, especially in cocultures that contained species that had been co-isolated [16]. Further exploration of one specific model consortium composed of four soil isolates has indicated that cooperative interactions underlie such community-intrinsic properties. Biofilm formation of these four strains was enhanced 3–4-fold in multi versus monospecies biofilms and cell numbers of all species were also significantly enhanced in the mixed biofilm, indicating that they all benefitted from joining this multispecies community [26]. In order to further understand the mechanisms shaping this community, we analyzed the gene expression in mono, dual and four-species biofilms, and identified 141 differentially expressed genes in the latter [27]. These could not be predicted from either monocultures or dual-species combinations, emphasizing that community-intrinsic properties in this system are based on up and down regulation of various functions that could not be predicted based on less complex versions of the system (Figure 2). We hypothesize that this is tightly linked to the spatial organization of cells in the biofilm [28].
Figure 2
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Community-intrinsic properties in a four-species biofilm model community. This figure is based on a subset of the data presented in Hansen et al. [27]. (Left) Heat-map showing differential gene transcription of species D when grown in four-species biofilms with A, B and C (A+B+C+D) and when grown in two-species biofilm with each of A, B or C individually (A+D, B+D, and C+D). (Right) Graphic illustration of how the gene transcription profile of species D is different in the four-species biofilm compared with the two species biofilms, exemplifying the emergence of community-intrinsic properties. Current Opinion in Microbiology 2018, 42:104–109
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Community-intrinsic properties Madsen, Sørensen and Burmølle 107
Recently we demonstrated distinct spatial organization of these four species in biofilms, visualized by fluorescent in situ hybridization combined with confocal microscopy. The distinct spatial organization was not predictable from that of any other combination (single, dual or threespecies) and was only observed in the presence of all four species [29]. An easily assessable community-intrinsic property of synthetic biofilm models is tolerance to chemical and physical stress, including antimicrobial agents [30,31,32], which is of high ecological, medical and industrial importance [33]. Lee et al. [31] demonstrated enhanced tobramycin and sodium dodecyl sulfate tolerance of a threespecies biofilm. When assessed individually, the sensitivity of the species varied to a high degree, whereas in the mixed biofilm, all species were protected and the composition was similar to that of the non-exposed mixed biofilm. This is likely attributed to sharing of public goods [34] resulting in ‘community-level resistance’. As discussed above, it should be noted that communityintrinsic properties do not necessarily result in higher expression of the specific trait or activity. Examples of particular interest illustrating this include reduced generation of phenotype variants in multispecies biofilms [35] and production of bacteriocins only in mixed communities, leading to lower cell numbers of susceptible community members than expected from studies of individual cell lineages [36]. Community-intrinsic properties with such diminishing effects should, however, not be confused with general competition, where nutrient limitation causes reduction in total cell numbers, which is readily extrapolated based on mono-culture studies and should be accounted for by the null-model used. Above, we have mostly focused on systems that are complex in terms of bacteria, but complexity also exists in the non-bacterial environment in which the bacteria reside — both biotic and abiotic. A recent and interesting example by Smith et al. [37] shows how two bacteria, Pseudomonas aeruginosa and Staphylococcus aureus that, in most environments, do not coexist because P. aeruginosa actively kills S. aureus, can do so in blood. This is caused by the presence of serum albumin that binds and sequesters the quorum sensing molecules produced by P. aeruginosa and thereby inhibits its production of the virulence factors that would otherwise kill S. aureus. Hence, complexity in terms of numbers of interacting abiotic and biotic factors, besides the bacteria, can promote the emergence of community-intrinsic properties.
Structured environments facilitate community-intrinsic properties Social bacterial activities are stabilized in structured environments, such as biofilms. The biofilm matrix provides key emergent properties, which has recently been www.sciencedirect.com
comprehensively reviewed by Flemming et al. [7] and we refer hereto for a detailed discussion. In short, there are several reasons why structured environments facilitate and stabilize social interactions, the main ones being the limited dispersal of interacting species, and the matrix’s physical retention of compounds which mediates social activities (e.g. public goods such as quorum sensing autoinducers and virulence factors [4,7,38]). This was demonstrated in a model system containing two different species, of which phenotypic variants were genetically engineered to become mutually dependent due to amino acid cross-deficiencies [39]. Experimental data showed that growth in liquid culture broth favored the noncooperative wild types, whereas the cooperative, interdependent mutants dominated in the structured environments [40]. Observations from other synthetic biofilm communities support this finding; in a four-species biofilm system (described above) we also observe enhanced biofilm biomass and cell numbers only when cultured in a biofilm setting [26]. Similarly, Lee et al. [31] reported community-level resistance only in biofilms, as opposed to planktonic settings. As most microbial growth in nature is associated with surfaces or bacteria flocs [41], this may help explain the prevalence and stability of social interactions, which facilitate community-intrinsic properties among natural bacterial isolates.
Conclusions and perspectives The above examples illustrate cases where the observed changes in phenotypes that occurred as a product of bacterial interactions were not predictable from analyzing the individual bacterial components; Had the strains not been exposed to relatively complex systems, then community-intrinsic properties would not have emerged. Yet, as illustrated by the examples, the level of complexity that may facilitate the emergence of community-intrinsic properties can be relatively low; the addition of just one factor (e.g. an additional species) is sometimes what triggers it. This, however, seems to be highly dependent on the system and what seems likely is that an increase of complexity enhances the probability of encountering community-intrinsic properties [13]. Our long-term goal is to move towards a mechanistic understanding of community-intrinsic properties by holistic system analyses, rather than decomposing in a reductionist fashion. Yet, we acknowledge that this might not be possible in light of current knowledge and technology (e.g. a lack of resolution). The recent breakthrough in sequencing technologies has, however, enabled meta studies, as described above, where network analyses may reveal social interactions among members of complex communities [1]. Such analyses are extremely useful for generating hypotheses about specific species interactions and identifying possible key members of communities. These hypotheses must, however, be experimentally validated, for example, in synthetic model Current Opinion in Microbiology 2018, 42:104–109
108 Cell regulation
communities where spatial and single cell analyses are possible, while maintaining an adequate level of biotic and abiotic complexity [42]. Only by combining community analyses and high-resolution model systems will we be able to direct research towards a better understanding of community-intrinsic properties.
Conflict of interest statement
14. Fussmann GF, Heber G: Food web complexity and chaotic population dynamics. Ecol Lett 2002, 5:394-401. 15. Foster KR, Bell T: Competition, not cooperation, dominates interactions among culturable microbial species. Curr Biol 2012, 22:1845-1850. 16. Madsen JS, Røder HL, Russel J, Sørensen H, Burmølle M, Sørensen SJ: Coexistence facilitates interspecific biofilm formation in complex microbial communities. Environ Microbiol 2016, 47:2565-2574.
All authors declare no conflict of interests.
17. Mitri S, Foster KR: The genotypic view of social interactions in microbial communities. Annu Rev Genet 2013, 47:247-273.
Acknowledgements
18. Dethlefsen L, Eckburg PB, Bik EM, Relman DA: Assembly of the human intestinal microbiota. Trends Ecol Evol 2006, 21:517-523.
This study was partly funded by grants from The Danish Council for Independent Research, ref no.: DFF-1323-00235 (SIMICOM) and from the Villum Young Investigator Program (ref no. 10098).
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest 1. Faust K, Raes J: Microbial interactions: from networks to models. Nat Rev Microbiol 2012, 10:538-550. Ground breaking study using network analysis to reveal niche specialization in the human microbiome. 2.
Rivett DW, Scheuerl T, Culbert CT, Mombrikotb SB, Johnstone E, Barraclough TG, Bell T: Resource-dependent attenuation of species interactions during bacterial succession. ISME J 2016, 10:2259-2268. Elegant study of succession in aquatic bacterial communities and how initial antagonistic interactions over time decline towards a more neutral state. 3.
Parsek MR, Greenberg EP: Sociomicrobiology: the connections between quorum sensing and biofilms. Trends Microbiol 2005, 13:27-33.
4.
Xavier JB: Sociomicrobiology and pathogenic bacteria. Microbiol Spectr 2016, 4.
5.
Røder HL, Sørensen SJ, Burmølle M: Studying bacterial multispecies biofilms: where to start? Trends Microbiol 2016, 24:503-513.
6.
Tan CH, Lee KW, Burmølle M, Kjelleberg S, Rice SA: All together now: experimental multispecies biofilm model systems. Environ Microbiol 2017, 19:42-53.
7.
Flemming HC, Wingender J, Szewzyk U, Steinberg P, Rice SA, Kjelleberg S: Biofilms: an emergent form of bacterial life. Nat Rev Microbiol 2016, 14:563-575. Timely and comprehensive review of the emergent properties of biofilms with special focus on the structural and functional properties of matrix, and how these contribute to the characteristic features of biofilms. 8.
Friman VP, Dupont A, Bass D, Murrell DJ, Bell T: Relative importance of evolutionary dynamics depends on the composition of microbial predator–prey community. ISME J 2016, 10:1352-1362.
9.
Konopka A: What is microbial community ecology? ISME J 2009, 3:1223-1230.
10. Lindemann SR, Bernstein HC, Song HS, Fredrickson JK, Fields MW, Shou W, Johnson DR, Beliaev AS: Engineering microbial consortia for controllable outputs. ISME J 2016, 10:2077-2084. 11. Lusseau D: The emergent properties of a dolphin social network. Proc Biol Sci 2003, 270(Suppl 2):S186-S188. 12. Mattia E, Otto S: Supramolecular systems chemistry. Nat Nanotechnol 2015, 10:111-119. 13. Kallus Y, Miller JH, Libby E: Paradoxes in leaky microbial trade. Nat Commun 2017, 8:1361. Current Opinion in Microbiology 2018, 42:104–109
19. Coyte KZ, Schluter J, Foster KR: The ecology of the microbiome: networks, competition, and stability. Science 2015, 350:663-666. 20. Tucker CM, Fukami T: Environmental variability counteracts priority effects to facilitate species coexistence: evidence from nectar microbes. Proc Biol Sci 2014, 281:20132637. 21. Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J, Huttenhower C: Microbial co-occurrence relationships in the human microbiome. PLoS Comput Biol 2012, 8:e1002606. 22. Layeghifard M, Hwang DM, Guttman DS: Disentangling interactions in the microbiome: a network perspective. Trends Microbiol 2017, 25:217-228. Excellent review highlighting recent advances in network theory and their application to microbial populations. 23. Shi S, Nuccio EE, Shi ZJ, He Z, Zhou J, Firestone MK: The interconnected rhizosphere: high network complexity dominates rhizosphere assemblages. Ecol Lett 2016, 19:926-936. 24. Friedman J, Higgins LM, Gore J: Community structure follows simple assembly rules in microbial microcosms. Nat Ecol Evol 2017, 1:109. 25. Røder HL, Raghupathi PK, Herschend J, Brejnrod A, Knochel S, Sørensen SJ, Burmølle M: Interspecies interactions result in enhanced biofilm formation by co-cultures of bacteria isolated from a food processing environment. Food Microbiol 2015, 51:18-24. 26. Ren DW, Madsen JS, Sorensen SJ, Burmolle M: High prevalence of biofilm synergy among bacterial soil isolates in cocultures indicates bacterial interspecific cooperation. ISME J 2015, 9:81-89. 27. Hansen LBS, Ren DW, Burmølle M, Sørensen SJ: Distinct gene expression profile of Xanthomonas retroflexus engaged in synergistic multispecies biofilm formation. ISME J 2017, 11:300-303. Here we demonstrate differential gene expression in a bacterial consortium previously shown to interact synergistically in biofilm formation. 28. Liu W, Røder HL, Madsen JS, Bjarnsholt T, Sørensen SJ, Burmølle M: Interspecific bacterial interactions are reflected in multispecies biofilm spatial organization. Front Microbiol 2016, 7:1366. 29. Liu W, Russel J, Røder HL, Madsen JS, Burmølle M, Sørensen SJ: Low-abundant species facilitates specific spatial organization that promotes multispecies biofilm formation. Environ Microbiol 2017, 19:2893-2905. 30. Burmølle M, Webb JS, Rao D, Hansen LH, Sørensen SJ, Kjelleberg S: Enhanced biofilm formation and increased resistance towards antimicrobial agents and bacterial invasion are caused by synergistic interactions in multispecies biofilms. Appl Environ Microbiol 2006, 72:3916-3923. 31. Lee KWK, Periasamy S, Mukherjee M, Xie C, Kjelleberg S, Rice SA: Biofilm development and enhanced stress resistance of a model, mixed-species community biofilm. ISME J 2014, 8:894-907. www.sciencedirect.com
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An excellent study of a three-species biofilm model system, reporting enhanced biofilm formation and tolerance to antimicrobials, ‘communitylevel resistance’, in mixed species biofilms. 32. Peters BM, Jabra-Rizk MA, O’May GA, Costerton JW, Shirtliff ME: Polymicrobial interactions: impact on pathogenesis and human disease. Clin Microbiol Rev 2012, 25:193-213. 33. Burmolle M, Ren DW, Bjarnsholt T, Sorensen SJ: Interactions in multispecies biofilms: do they actually matter? Trends Microbiol 2014, 22:84-91. 34. Bachmann H, Bruggeman FJ, Molenaar D, Branco Dos Santos F, Teusink B: Public goods and metabolic strategies. Curr Opin Microbiol 2016, 31:109-115. 35. Lee KWK, Hoong Yam JK, Mukherjee M, Periasamy S, Steinberg PD, Kjelleberg S, Rice SA: Interspecific diversity reduces and functionally substitutes for intraspecific variation in biofilm communities. ISME J 2016, 10:846-857. 36. Tyc O, van den Berg M, Gerards S, van Veen JA, Raaijmakers JM, de Boer W, Garbeva P: Impact of interspecific interactions on antimicrobial activity among soil bacteria. Front Microbiol 2014, 5:567. 37. Smith AC, Rice A, Sutton B, Gabrilska R, Wessel AK, Whiteley M, Rumbaugh KP: Albumin inhibits Pseudomonas aeruginosa
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quorum sensing and alters polymicrobial interactions. Infect Immun 2017:85. 38. Harrison F, Browning LE, Vos M, Buckling A: Cooperation and virulence in acute Pseudomonas aeruginosa infections. BMC Biol 2006, 4:21. 39. Pande S, Merker H, Bohl K, Reichelt M, Schuster S, de Figueiredo LF, Kaleta C, Kost C: Fitness and stability of obligate cross-feeding interactions that emerge upon gene loss in bacteria. ISME J 2014, 8:953-962. 40. Pande S, Kaftan F, Lang S, Svatos A, Germerodt S, Kost C: Privatization of cooperative benefits stabilizes mutualistic cross-feeding interactions in spatially structured environments. ISME J 2016, 10:1413-1423. 41. Katharios-Lanwermeyer S, Xi C, Jakubovics NS, Rickard AH: Minireview: microbial coaggregation: ubiquity and implications for biofilm development. Biofouling 2014, 30:1235-1251. 42. Mark Welch JL, Rossetti BJ, Rieken CW, Dewhirst FE, Borisy GG: Biogeography of a human oral microbiome at the micron scale. Proc Natl Acad Sci U S A 2016, 113:E791-E800. This impressive study describes the advancement of the fluorescent in situ hybridization (FISH) technology, CLASI-FISH, that enables visualization of numerous bacterial species in complex communities.
Current Opinion in Microbiology 2018, 42:104–109
ScienceDirect Bacterial social interactions and the emergence of community-intrinsic properties Jonas Stenløkke Madsen, Søren Johannes Sørensen and Mette Burmølle Bacterial communities are dominated and shaped by social interactions, which facilitate the emergence of properties observed only in the community setting. Such communityintrinsic properties impact not only the phenotypes of cells in a community, but also community composition and function, and are thus likely to affect a potential host. Studying communityintrinsic properties is, therefore, important for furthering our understanding of clinical, applied and environmental microbiology. Here, we provide recent examples of research investigating community-intrinsic properties, focusing mainly on community composition and interactions in multispecies biofilms. We hereby wish to emphasize the importance of studying social interactions in settings where communityintrinsic properties are likely to emerge. Address Section of Microbiology, Department of Biology, University of Copenhagen, Denmark Corresponding author: Burmølle, Mette ([email protected])
Current Opinion in Microbiology 2018, 42:104–109 This review comes from a themed issue on Cell regulation Edited by Jan-Willem Veening and Rita Tamayo
http://dx.doi.org/10.1016/j.mib.2017.11.018 1369-5274/ã 2017 Elsevier Ltd. All rights reserved.
Introduction In line with other scholars, we are of the opinion that it is imperative to systematically study interspecies interactions in complex (e.g. multispecies) systems [1,2]. The primary goal of such research is to understand communityintrinsic properties: properties of bacteria that only arise in the context of a community, not in isolation (e.g. community-extrinsic properties). Bacteria have traditionally been perceived as phenotypically homogeneous and uncoordinated populations of cells, in which community-intrinsic properties were of little relevance. However, the more recent acknowledgement of bacterial social activities, such as biofilm formation and quorum sensing [3,4], has sparked greater interest in studying community-based social interactions. Thus, debating the importance of Current Opinion in Microbiology 2018, 42:104–109
community-intrinsic properties is particularly relevant now, as microbiology research moves from studying simpler, single species systems into examining more complex, multispecies systems [5,6].
Community-intrinsic properties The term community-intrinsic properties (see Figure 1) is inspired by the phenomenon of emergent properties of complex systems, but covers the diversely defined use of the phenomenon in the microbiology research literature [7,8–10], thus serving as an umbrella term for the many diverse definitions of emergent properties. Although scientists in other disciplines have acknowledged the connection between complexity and emergent properties in complex systems [11,12], the phenomenon has attracted little attention from microbiologists. One reason it has been overlooked is that emergent properties can be understood as almost any unexpected property of complex systems, that, in theory, could be identified through functional decomposition. The concept, therefore, tends to be disregarded because the term has acquired a definition that describes a lack of mechanistic understanding. This however seems shortsighted, as the acknowledgement that something is not understood should be a driver for exploration in itself. Another and rather distinctive definition of emergent properties posits that interactions in a complex system facilitate properties that cannot be identified through functional decomposition of their parts in isolation, that is, properties of a complex system that cannot be linearly extrapolated based on the sum of their parts [9,13]. These types of emergent properties are likely to occur in both biological and non-biological systems where multiple components interact and can result in complex or even chaotic behaviors [14]. Therefore, instead of disregarding emergent properties due to a lack of consensus of its definition, we advocate that the concept is of high practical value in, for example, applied microbiology, where such basic concepts, in our experience, are less often considered. Using communityintrinsic properties as a term that includes both of the aforementioned definitions of emergent properties (Figure 1) could be a way to move forward and focus this exciting and important line of research. Determining whether something is a community-intrinsic property requires a null-model (null-hypothesis) that www.sciencedirect.com
Community-intrinsic properties Madsen, Sørensen and Burmølle 105
Figure 1
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The concept of community-intrinsic properties. (a) Exemplifies a system where the output (red and blue colors) of components (representing e.g. bacterial species), illustrated as two differently shaped puzzle pieces, act the same when together as when in isolation. The output can, in terms of microbiology, for example, be a change of phenotype. The properties of this type of system are therefore predictable based on how the components act in isolation and no community-intrinsic properties thus emerge. (b) Is an example where community-intrinsic properties emerge. Here, an interaction (black arrow) between the two components results in a change in output of one of them (from red to green). The output change only occurs when the components are together, which is not predictable by studying the components in isolation and therefore a community-intrinsic property. See Figure 2 for an example relevant to microbiology resembling system B. (c) Is also an example where community-intrinsic properties emerge. Here the output of both components changes: First (1), the blue piece influences the red piece that therefore changes to green (as in B). Next (2), the now green piece influences the other piece to change from blue to purple. The output is therefore a community-intrinsic property that is less predictable than system B. More complex systems, such as bacterial communities consisting of many interacting species (pieces), will lead to diverse phenotypic responses (colors) resulting in the emergence of community-intrinsic properties.
allows one to asses if the system (inter-)acts as expected or not. Normally, such expectations are based on studies of the individual parts in isolation. Although the discussion of what constitutes a good null-model is not in the scope of this paper (see e.g. [15–17]), it is important to the concept of community-intrinsic properties because it enforces hypothesis driven research, as exemplified below.
A broad appeal of studying communityintrinsic properties If we, based on the above description, accept that community-intrinsic properties can emerge in complex microbial systems, then this allows us to decipher key parts of microbiology that cannot be uncovered otherwise. Importantly, this includes cases of community-intrinsic properties that are not decomposable in the frame of current knowledge and technology, and otherwise would have been marginalized due to a lack of data or conflicting results. Approaching experimental findings in complex multispecies systems by assessing whether the observed emergent properties may be understood by functional decomposition, will provide us with a way to identify and classify, for example, interactions in multispecies biofilms www.sciencedirect.com
[7]. Below, we will highlight examples of research that supports the notion that community-intrinsic properties are likely and important in complex microbial communities and that much can be gained from studying them. Bacterial social interactions are believed to shape community composition through cooperative integration [18] and competitive exclusion [19], as well as via priority effects influencing community assembly processes [20]. An indication of this is derived from the numerous microbiome studies published in recent years. Here, network analyses reveal a high degree of co-occurrences, that is, positive pairwise correlations of OTUs (operational taxonomical units) abundances across samples or over time [21,22,23]. Such co-occurrences are often interpreted as indicators of actual co-dependencies among different members of the microbiome [23]. Although niche overlap is often provided as an alternative explanation for these apparent co-occurrences, it seems likely that social interactions are widespread in microbiomes and influence community assembly and host– microbiome interactions. Hence, a microbiome’s effects on the host organism are most likely results of Current Opinion in Microbiology 2018, 42:104–109
106 Cell regulation
community-intrinsic properties, and should be investigated as such rather than being subjected to decomposition in the attempt to link the observed effect to specific, individual community members. It is becoming increasingly clear that factors shaping community diversity and composition are less known and the mechanisms are highly complex. It was recently shown that outcomes of bacterial competition in model communities could not be predicted from two-species pairwise bacterial competition [24]. Relatively accurate predictions (90%) of final community composition were, however, made based on competition experiments containing three species, further emphasizing the necessity of increasing complexity. Community-intrinsic properties may contribute hereto, as for example, a product of social interactions.
Interactions in multispecies biofilms — examples of community-intrinsic properties A simple parameter used for identification of communityintrinsic properties of model bacterial communities is production of biomass, either number of cells or biofilm biomass, which is composed of cells as well as matrix components. Regardless of the measure, more or less cell/ biomass than expected from that of the individual parts indicates interactions resulting in community-intrinsic properties. We have observed this among bacterial
consortia isolates from various environments, including both ‘natural’ bacterial habitats (marine, freshwater and soil [16]) and a food processing facility [25]. When coculturing isolates from these environments, we observed both biofilm induction and reduction, that is, more or less biofilm was formed than expected from the individual isolates. Intriguingly, induction and biofilm synergy were found to be common in the co-cultures, especially in cocultures that contained species that had been co-isolated [16]. Further exploration of one specific model consortium composed of four soil isolates has indicated that cooperative interactions underlie such community-intrinsic properties. Biofilm formation of these four strains was enhanced 3–4-fold in multi versus monospecies biofilms and cell numbers of all species were also significantly enhanced in the mixed biofilm, indicating that they all benefitted from joining this multispecies community [26]. In order to further understand the mechanisms shaping this community, we analyzed the gene expression in mono, dual and four-species biofilms, and identified 141 differentially expressed genes in the latter [27]. These could not be predicted from either monocultures or dual-species combinations, emphasizing that community-intrinsic properties in this system are based on up and down regulation of various functions that could not be predicted based on less complex versions of the system (Figure 2). We hypothesize that this is tightly linked to the spatial organization of cells in the biofilm [28].
Figure 2
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Community-intrinsic properties in a four-species biofilm model community. This figure is based on a subset of the data presented in Hansen et al. [27]. (Left) Heat-map showing differential gene transcription of species D when grown in four-species biofilms with A, B and C (A+B+C+D) and when grown in two-species biofilm with each of A, B or C individually (A+D, B+D, and C+D). (Right) Graphic illustration of how the gene transcription profile of species D is different in the four-species biofilm compared with the two species biofilms, exemplifying the emergence of community-intrinsic properties. Current Opinion in Microbiology 2018, 42:104–109
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Recently we demonstrated distinct spatial organization of these four species in biofilms, visualized by fluorescent in situ hybridization combined with confocal microscopy. The distinct spatial organization was not predictable from that of any other combination (single, dual or threespecies) and was only observed in the presence of all four species [29]. An easily assessable community-intrinsic property of synthetic biofilm models is tolerance to chemical and physical stress, including antimicrobial agents [30,31,32], which is of high ecological, medical and industrial importance [33]. Lee et al. [31] demonstrated enhanced tobramycin and sodium dodecyl sulfate tolerance of a threespecies biofilm. When assessed individually, the sensitivity of the species varied to a high degree, whereas in the mixed biofilm, all species were protected and the composition was similar to that of the non-exposed mixed biofilm. This is likely attributed to sharing of public goods [34] resulting in ‘community-level resistance’. As discussed above, it should be noted that communityintrinsic properties do not necessarily result in higher expression of the specific trait or activity. Examples of particular interest illustrating this include reduced generation of phenotype variants in multispecies biofilms [35] and production of bacteriocins only in mixed communities, leading to lower cell numbers of susceptible community members than expected from studies of individual cell lineages [36]. Community-intrinsic properties with such diminishing effects should, however, not be confused with general competition, where nutrient limitation causes reduction in total cell numbers, which is readily extrapolated based on mono-culture studies and should be accounted for by the null-model used. Above, we have mostly focused on systems that are complex in terms of bacteria, but complexity also exists in the non-bacterial environment in which the bacteria reside — both biotic and abiotic. A recent and interesting example by Smith et al. [37] shows how two bacteria, Pseudomonas aeruginosa and Staphylococcus aureus that, in most environments, do not coexist because P. aeruginosa actively kills S. aureus, can do so in blood. This is caused by the presence of serum albumin that binds and sequesters the quorum sensing molecules produced by P. aeruginosa and thereby inhibits its production of the virulence factors that would otherwise kill S. aureus. Hence, complexity in terms of numbers of interacting abiotic and biotic factors, besides the bacteria, can promote the emergence of community-intrinsic properties.
Structured environments facilitate community-intrinsic properties Social bacterial activities are stabilized in structured environments, such as biofilms. The biofilm matrix provides key emergent properties, which has recently been www.sciencedirect.com
comprehensively reviewed by Flemming et al. [7] and we refer hereto for a detailed discussion. In short, there are several reasons why structured environments facilitate and stabilize social interactions, the main ones being the limited dispersal of interacting species, and the matrix’s physical retention of compounds which mediates social activities (e.g. public goods such as quorum sensing autoinducers and virulence factors [4,7,38]). This was demonstrated in a model system containing two different species, of which phenotypic variants were genetically engineered to become mutually dependent due to amino acid cross-deficiencies [39]. Experimental data showed that growth in liquid culture broth favored the noncooperative wild types, whereas the cooperative, interdependent mutants dominated in the structured environments [40]. Observations from other synthetic biofilm communities support this finding; in a four-species biofilm system (described above) we also observe enhanced biofilm biomass and cell numbers only when cultured in a biofilm setting [26]. Similarly, Lee et al. [31] reported community-level resistance only in biofilms, as opposed to planktonic settings. As most microbial growth in nature is associated with surfaces or bacteria flocs [41], this may help explain the prevalence and stability of social interactions, which facilitate community-intrinsic properties among natural bacterial isolates.
Conclusions and perspectives The above examples illustrate cases where the observed changes in phenotypes that occurred as a product of bacterial interactions were not predictable from analyzing the individual bacterial components; Had the strains not been exposed to relatively complex systems, then community-intrinsic properties would not have emerged. Yet, as illustrated by the examples, the level of complexity that may facilitate the emergence of community-intrinsic properties can be relatively low; the addition of just one factor (e.g. an additional species) is sometimes what triggers it. This, however, seems to be highly dependent on the system and what seems likely is that an increase of complexity enhances the probability of encountering community-intrinsic properties [13]. Our long-term goal is to move towards a mechanistic understanding of community-intrinsic properties by holistic system analyses, rather than decomposing in a reductionist fashion. Yet, we acknowledge that this might not be possible in light of current knowledge and technology (e.g. a lack of resolution). The recent breakthrough in sequencing technologies has, however, enabled meta studies, as described above, where network analyses may reveal social interactions among members of complex communities [1]. Such analyses are extremely useful for generating hypotheses about specific species interactions and identifying possible key members of communities. These hypotheses must, however, be experimentally validated, for example, in synthetic model Current Opinion in Microbiology 2018, 42:104–109
108 Cell regulation
communities where spatial and single cell analyses are possible, while maintaining an adequate level of biotic and abiotic complexity [42]. Only by combining community analyses and high-resolution model systems will we be able to direct research towards a better understanding of community-intrinsic properties.
Conflict of interest statement
14. Fussmann GF, Heber G: Food web complexity and chaotic population dynamics. Ecol Lett 2002, 5:394-401. 15. Foster KR, Bell T: Competition, not cooperation, dominates interactions among culturable microbial species. Curr Biol 2012, 22:1845-1850. 16. Madsen JS, Røder HL, Russel J, Sørensen H, Burmølle M, Sørensen SJ: Coexistence facilitates interspecific biofilm formation in complex microbial communities. Environ Microbiol 2016, 47:2565-2574.
All authors declare no conflict of interests.
17. Mitri S, Foster KR: The genotypic view of social interactions in microbial communities. Annu Rev Genet 2013, 47:247-273.
Acknowledgements
18. Dethlefsen L, Eckburg PB, Bik EM, Relman DA: Assembly of the human intestinal microbiota. Trends Ecol Evol 2006, 21:517-523.
This study was partly funded by grants from The Danish Council for Independent Research, ref no.: DFF-1323-00235 (SIMICOM) and from the Villum Young Investigator Program (ref no. 10098).
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