Challenges in microbial ecology: building predictive understanding of community function and dynamics

Widder, Stefanie; Allen, Rosalind J.; Pfeiffer, Thomas; Curtis, Thomas P.; Wiuf, Carsten; Sloan, William T.; Cordero, Otto X.; Brown, Sam P.; Momeni, Babak; Shou, Wenying; Kettle, Helen; Flint, Harry J.; Haas, Andreas F.; Laroche, Béatrice; Kreft, Jan‐Ulrich; Rainey, Paul B.; Freilich, Shiri; Schuster, Stefan; Milferstedt, Kim; Meer, Jan Roelof van der; Groβkopf, Tobias; Huisman, Jef; Free, Andrew; Picioreanu, Cristian; Quince, Christopher; Klapper, Isaac; Labarthe, Simon; Smets, Barth F.; Wang, Harris H.; Soyer, Orkun S.

doi:10.1038/ismej.2016.45

Cited by 624 publications

(533 citation statements)

References 77 publications

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“…Cocultures preserve core aspects of natural systems while offering greater practical experimental control (Shou et al, 2007;Hillesland and Stahl, 2010;Summers et al, 2010;Harcombe, 2010;Momeni et al, 2011;Hom and Murray, 2014;Mee et al, 2014). They are also more amenable to modeling than are natural systems and facilitate the development, experimental testing and refining of models for predicting community behavior (Zomorrodi and Segrè, 2015;Johns et al, 2016;Lindemann et al, 2016;Widder et al, 2016). As such, cocultures are very useful for defining the principles that underlie the stability and performance of microbial communities.…”

Section: Introductionmentioning

confidence: 99%

Microbial mutualism dynamics governed by dose-dependent toxicity of cross-fed nutrients

et al. 2016

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Microbial interactions, including mutualistic nutrient exchange (cross-feeding), underpin the flow of energy and materials in all ecosystems. Metabolic exchanges are difficult to assess within natural systems. As such, the impact of exchange levels on ecosystem dynamics and function remains unclear. To assess how cross-feeding levels govern mutualism behavior, we developed a bacterial coculture amenable to both modeling and experimental manipulation. In this coculture, which resembles an anaerobic food web, fermentative Escherichia coli and photoheterotrophic Rhodopseudomonas palustris obligately cross-feed carbon (organic acids) and nitrogen (ammonium). This reciprocal exchange enforced immediate stable coexistence and coupled species growth. Genetic engineering of R. palustris to increase ammonium cross-feeding elicited increased reciprocal organic acid production from E. coli, resulting in culture acidification. Consequently, organic acid function shifted from that of a nutrient to an inhibitor, ultimately biasing species ratios and decreasing carbon transformation efficiency by the community; nonetheless, stable coexistence persisted at a new equilibrium. Thus, disrupting the symmetry of nutrient exchange can amplify alternative roles of an exchanged resource and thereby alter community function. These results have implications for our understanding of mutualistic interactions and the use of microbial consortia as biotechnology.

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Section: Introductionmentioning

confidence: 99%

Microbial mutualism dynamics governed by dose-dependent toxicity of cross-fed nutrients

et al. 2016

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“…This resulted in a widespread application of the technology in longitudinal studies where shifts in community structure are related to environmental variables and functional outputs (Faust et al, 2015;Wilhelm et al, 2015). An inherent limitation of the sequencing technology is that the calculated taxon abundances comprise relative values (Widder et al, 2016). Hence, caution must be taken with the biological interpretation of these values, since inter-sample differences in cell density are not considered.…”

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confidence: 99%

Absolute quantification of microbial taxon abundances

et al. 2016

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High-throughput amplicon sequencing has become a well-established approach for microbial community profiling. Correlating shifts in the relative abundances of bacterial taxa with environmental gradients is the goal of many microbiome surveys. As the abundances generated by this technology are semi-quantitative by definition, the observed dynamics may not accurately reflect those of the actual taxon densities. We combined the sequencing approach (16S rRNA gene) with robust single-cell enumeration technologies (flow cytometry) to quantify the absolute taxon abundances. A detailed longitudinal analysis of the absolute abundances resulted in distinct abundance profiles that were less ambiguous and expressed in units that can be directly compared across studies. We further provide evidence that the enrichment of taxa (increase in relative abundance) does not necessarily relate to the outgrowth of taxa (increase in absolute abundance). Our results highlight that both relative and absolute abundances should be considered for a comprehensive biological interpretation of microbiome surveys. The ISME Journal (2017) 11, 584-587; doi:10.1038/ismej.2016 published online 9 September 2016 Recent advancements in high-throughput sequencing of marker genes, such as the 16S rRNA gene, have provided microbial ecologists the tools to accurately infer the relative composition of microbial communities (Franzosa et al., 2015). This resulted in a widespread application of the technology in longitudinal studies where shifts in community structure are related to environmental variables and functional outputs (Faust et al., 2015;Wilhelm et al., 2015). An inherent limitation of the sequencing technology is that the calculated taxon abundances comprise relative values (Widder et al., 2016). Hence, caution must be taken with the biological interpretation of these values, since inter-sample differences in cell density are not considered. To our knowledge, there are no descriptive studies that assess the extent to which relative abundances deliver a skewed image of the actual microbial community dynamics. In this study, we combined robust cell density measurements from flow cytometry (Prest et al., 2013;Van Nevel et al., 2013) with the relative abundances derived from 16S rRNA gene amplicon sequencing. We performed two extensive longitudinal surveys on the central water reservoir of a cooling water system. This engineered freshwater ecosystem was subjected to highly controlled operational phases (Supplementary Information and data set). We quantified the absolute taxon abundances and assessed whether additional insights could be attained with the combined approach.Based on the sample-specified total cell density, the absolute taxon abundances were calculated for each time point. Individual taxon densities ranged from 0.5 to 1 679 cells per μl. Several inter-taxon differences became apparent by performing ordinary least squares regression analysis between the relative and absolute abundances. We focused on the three most abundant taxa, which ...

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“…Black-box approaches apply empirical parameters to describe the basic kinetic function of community dynamics [30]. In general, kinetic models describe the growth of bacterial cultures through the use of empirically-derived equations that incorporate the concentration of the limiting substrate and the growth (or uptake) rates corresponding to that concentration [31].…”

Section: Kinetic Modelsmentioning

confidence: 99%

Modeling Microbial Communities: A Call for Collaboration between Experimentalists and Theorists

2017

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With our growing understanding of the impact of microbial communities, understanding how such communities function has become a priority. The influence of microbial communities is widespread. Human-associated microbiota impacts health, environmental microbes determine ecosystem sustainability, and microbe-driven industrial processes are expanding. This broad range of applications has led to a wide range of approaches to analyze and describe microbial communities. In particular, theoretical work based on mathematical modeling has been a steady source of inspiration for explaining and predicting microbial community processes. Here, we survey some of the modeling approaches used in different contexts. We promote classifying different approaches using a unified platform, and encourage cataloging the findings in a database. We believe that the synergy emerging from a coherent collection facilitates a better understanding of important processes that determine microbial community functions. We emphasize the importance of close collaboration between theoreticians and experimentalists in formulating, classifying, and improving models of microbial communities.

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Challenges in microbial ecology: building predictive understanding of community function and dynamics

Cited by 624 publications

References 77 publications

Microbial mutualism dynamics governed by dose-dependent toxicity of cross-fed nutrients

Microbial mutualism dynamics governed by dose-dependent toxicity of cross-fed nutrients

Absolute quantification of microbial taxon abundances

Modeling Microbial Communities: A Call for Collaboration between Experimentalists and Theorists

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