Keystone Taxa As Drivers of Microbiome Structure and Functioning

PERSPECTIVES influence in the community33. On the basis OPINION of the available information, in addition to sea stars, other examples of keystone taxa Keystone taxa as drivers of include the Canadian beaver and African elephant in the animal kingdom and leguminous Trifolium in the plant kingdom. microbiome structure and In microbial communities, examples of such taxa are now available from a diverse functioning range of environments24–26,34–61, and their reports are continuously increasing (BOx 1), Samiran Banerjee, Klaus Schlaeppi and Marcel G. A. van der Heijden including Porphyromonas gingivalis and Bacteroides thetaiotaomicron in the human Abstract | Microorganisms have a pivotal role in the functioning of ecosystems. microbiome56,62–64. B. thetaiotaomicron, an Recent studies have shown that microbial communities harbour keystone taxa, anaerobic symbiont found in the human which drive community composition and function irrespective of their abundance. intestine, is considered a keystone taxon In this Opinion article, we propose a definition of keystone taxa in microbial based on empirical evidence56,58. Owing ecology and summarize over 200 microbial keystone taxa that have been identified to the complexity of microbial communities, the importance of connectedness and the in soil, plant and marine ecosystems, as well as in the human microbiome. We rapid turnover in both time and space, explore the importance of keystone taxa and keystone guilds for microbiome the definition of keystone taxa for structure and functioning and discuss the factors that determine their distribution microbiology needs to be adapted and activities. from the original concepts proposed in ecology30–32. In this Opinion article, we propose the following definition: microbial The role of microbial communities in demonstrated that the removal of sea stars keystone taxa are highly connected taxa ecosystem functioning is unequivocal1,2, (Pisaster ochraceus, which is a common that individually or in a guild exert a with microorganisms being key drivers predator of mussels) had a dramatic impact considerable influence on microbiome of many ecosystem processes, including on the shoreline ecosystem community structure and functioning irrespective of soil nutrient cycling, plant growth, marine and local biodiversity at Makah Bay, their abundance across space and time. biogeochemical processes and maintenance Washington, USA28. Since the term was These taxa have a unique and crucial role in of human health3–6. In recent years, first coined, the definition of keystone taxa microbial communities, and their removal microbial network analysis has been used has followed different lines of thought29–31. can cause a dramatic shift in microbiome to visualize co-occurrence among members The definition proposed by Paine in structure and functioning. in communities3,7–10. Microbial network 1969 mainly suggests that keystone taxa We briefly discuss how microbial analysis enables testing of ecological are important for community structure network analysis can be used to identify theories, the assessment of which was once and integrity, and their influence is non- keystone taxa and focus on recent evidence postulated to be a major impediment in redundant32. In 1996, a study defined of keystone taxa based on computational microbial ecology11,12. The concept of co- keystone taxa by introducing the concept inference and empirical evidence. We also occurrence and network thinking in ecology of ‘community importance’, which was discuss challenges in identifying keystone was proposed in 2005 (REF.13), and since then, calculated from proportional biomass taxa, including the characterization and microbial ecologists have shown particular and traits31. Subsequently, in 2012, other manipulation of such taxa, and explore their interest in network analysis7,14–19, resulting authors30 presented the evolution of the influence on microbiome functioning as in a large body of studies demonstrating term keystone taxa in ecology and how well as the factors that may determine their microbial co- occurrence patterns in a its overuse and misuse (for example, distribution and efficacy. diverse range of soil7, plant20 and marine21 keystone mutualist, keystone modifier ecosystems, as well as in the human and reverse keystone) have resulted in Microbial networks and keystone taxa microbiome22,23 (BOx 1). Reports are also considerable confusion about the actual With the advent of next-generation available from the Antarctic ecosystem24 meaning (readers are referred to their sequencing, millions of sequences are and Arctic ecosystem25,26. In addition to critical appraisal for further information now available from various environments. co-occurrence patterns, microbial networks on keystone taxa in ecology). Thus, there Network analysis can help disentangle can be used to statistically identify is no uniformly accepted operational microbial co- abundance and provide keystone taxa27. definition of keystone taxa in ecology, comprehensive insight into the microbial The tenet of keystone taxa was originally especially in microbial ecology. Keystone community structure and assembly proposed by ecologist Robert T. Paine taxa have also been frequently referred to as patterns3,65. Several algorithms are available in 1966. In a classic experiment, he ‘ecosystem engineers’ owing to their large to construct microbial networks, and NATURE REVIEWS | MICROBIOLOGY © 2018 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. PERSPECTIVES Box 1 | Microbial co-occurrence and network analysis keystone taxa belong to the Pezizomycotina subdivision38. Keystone taxa that were The number of papers (based on the Web of Science database) reporting microbial network not numerically dominant in the analyses and keystone taxa is increasing exponentially (see the figure; data are based on the Web of communities have also been identified in Science database, time span 2004–2016, keywords ‘microbial network analysis’ and ‘microbial the Arctic ecosystem25,26,44,46 and Antarctic keystone’). A wide range of methods and algorithms are available to construct microbial networks. 24 Starting with basic Pearson or Spearman rank correlation-based approaches7,14, microbial networks ecosystem . Similar reports of numerically quickly evolved to incorporate more robust methods. For example, the maximal information inconspicuous keystone taxa are also coefficient (MIC) relies on equitability and generality of relationships15 and can yield a variety of available for microbial communities in linear and nonlinear associations among microorganisms that can be interpreted similarly to the contaminated soils47,48, roots71 and aquatic coefficient of determination (denoted as r2). Local similarity analysis (LSA) that detects change in systems24,55,72. Interestingly, our literature abundance of operational taxonomic units (OTUs) over time in an environment is particularly useful review revealed that various members of for analysing microbial temporal variability the Rhizobiales and Burkholderiales orders data16, whereas sparse correlation for were consistently identified as keystone 0GVYQTMCPCN[UGU compositional data (SparCC) is especially suited taxa in different studies and across different for compositionally diverse microbial data17. ecosystems (TABle 1; see Supplementary Moreover, the ensemble approach (CoNet) can use multiple measures (similarity, correlation Table 1). Rhizobiales comprises not only and mutual information) with the generalized nitrogen- fixing bacteria, such as Rhizobium boosted linear model to generate spp. and Bradyrhizobium spp., but also comprehensive networks18. A recent study19 members of the genus Methylobacterium, U reported that LSA, MIC and SparCC are well which is known to be endosymbiotic suited for both count and compositional data and abundant in the phyllosphere73. and are less sensitive to how data are By contrast, Burkholderiales includes distributed, whereas the CoNet ensemble important genera such as Bordetella, /KETQDKCNMG[UVQPGVCZC approach performs better for data with high 0WODGTQHRCRGT Ralstonia and Oxalobacter, which are well- scatteredness or sparsity. However, it was also known pathogens, as well as Burkholderia, noted that different approaches may yield different results and significance levels for the which is one of the most versatile and same data set, and although the scores obtained diverse terrestrial microbial groups. The from thousands of pairwise correlations are computational identification does not typically corrected for type I error using a mean that all members of the Rhizobiales Bonferroni correction or the false discovery rate, and Burholderiales can be considered extremely rare OTUs or OTUs with a large keystone taxa (for example, many taxa number of zeros should be avoided for network in those orders are subordinate taxa in 2WDNKECVKQP[GCT construction. microbial communities and have no major influence on community composition or functioning). Computational inference these algorithms have been reviewed these scores have been used to find putative of Rhizobiales and Burkholderiales as previously19,65,66; thus, for brevity, we only keystone taxa in microbial networks in keystone taxa can also be due to their present a brief overview (BOx 1). Perhaps recent studies12,41,42. The importance of sheer abundance in various environments. one of the most useful features of network a quantifiable threshold for consistent Nonetheless, the likelihood

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