Evaluating Rrna As an Indicator of Microbial Activity in Environmental Communities: Limitations and Uses

The ISME Journal (2013) 7, 2061–2068 & 2013 International Society for Microbial Ecology All rights reserved 1751-7362/13 www.nature.com/ismej PERSPECTIVE Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses Steven J Blazewicz1,4, Romain L Barnard1,5, Rebecca A Daly2,3 and Mary K Firestone1,3 1The Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA; 2Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA and 3Ecology Department, Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Microbes exist in a range of metabolic states (for example, dormant, active and growing) and analysis of ribosomal RNA (rRNA) is frequently employed to identify the ‘active’ fraction of microbes in environmental samples. While rRNA analyses are no longer commonly used to quantify a population’s growth rate in mixed communities, due to rRNA concentration not scaling linearly with growth rate uniformly across taxa, rRNA analyses are still frequently used toward the more conservative goal of identifying populations that are currently active in a mixed community. Yet, evidence indicates that the general use of rRNA as a reliable indicator of metabolic state in microbial assemblages has serious limitations. This report highlights the complex and often contradictory relationships between rRNA, growth and activity. Potential mechanisms for confounding rRNA patterns are discussed, including differences in life histories, life strategies and non-growth activities. Ways in which rRNA data can be used for useful characterization of microbial assemblages are presented, along with questions to be addressed in future studies. The ISME Journal (2013) 7, 2061–2068; doi:10.1038/ismej.2013.102; published online 4 July 2013 Subject Category: Microbial population and community ecology Keywords: community rRNA; microbial activity; microbial growth; ribosomes; environmental samples; ecosystem processes Introduction this approach has limitations when attempting to assess current metabolic state. Ribosomal RNA Microorganisms have essential roles in shaping and genes (rRNA genes) are frequently used to identify controlling virtually all ecosystems including the microorganisms present in environmental samples atmosphere, oceans, soils and plant- and animal- regardless of metabolic state, while ribosomal RNA associated biomes. Microbes exist in different meta- (rRNA) has been widely applied to characterize the bolic states in these systems: growing, active, dormant growing or active microbes. We found 4100 studies and recently deceased (Figure 1). These metabolic that used rRNA for these purposes, including recent states correspond to different degrees of influence studies using rRNA to identify currently active that microbes can have on their environment. There- microbes (for example, Muttray and Mohn, 2000; fore, to understand the relationships between microbial community structure and ecosystem functions, it Duineveld et al., 2001; Mills et al., 2005; Schippers is important to accurately associate microbial identity et al., 2005; Gentile et al., 2006; DeAngelis et al., with concurrent metabolic state. Simultaneous 2010; Jones and Lennon, 2010; Brettar et al., 2011; identification of microbes and their metabolic states Egert et al., 2011; Gaidos et al., 2011; Wu¨ st et al., has been a longstanding goal in microbial ecology, 2011; Mannisto et al., 2012; Hunt et al., 2013). and methods to achieve this have recently been However, conflicting patterns between rRNA con- accumulating in our molecular toolboxes. tent and growth rate indicate that rRNA is not a Nucleic-acid analysis has proven to be effective reliable metric for growth or activity and in some for characterizing the phylogenetic, taxonomic and cases may be grossly misleading. Virtually all functional structure of microbial assemblages, but molecular characterization methods are imperfect, but we suggest that using rRNA analyses to evaluate microbial assemblages requires that limitations and Correspondence: SJ Blazewicz, US Geological Survey, 345 Mid- dlefield Road, MS 962, Menlo Park, CA 94025, USA. underlying assumptions be clearly identified and E-mail: [email protected] understood. Here, we explore critical limitations 4Current address: US Geological Survey, 345 Middlefield Road, and potential causes of inconsistent rRNA/activity MS 962, Menlo Park, CA 94025, USA. relationships. We then suggest employing rRNA 5Current address: INRA, UMR1347 Agroe´cologie, 17 rue Sully, BP 86510, Dijon, France. abundance data as an index of potential activity Received 31 October 2012; revised 2 May 2013; accepted 22 May and propose a framework for future application. 2013; published online 4 July 2013 The reader should note that RNA extraction rRNA as indicator of microbial activity? SJ Blazewicz et al 2062 proportional to rRNA concentration and to the number of ribosomes in the cell, and has often been employed as a proxy for both (Kerkhof and Ward, 1993; Poulsen et al., 1993; Bremer and Dennis, 1996). In pure-culture experiments, cell counts can be done to determine RNA or ribosome concentration per cell. In mixed communities, other methods of normalization are necessary. Commonly, RNA or rRNA concentration is normalized to the number of cells using DNA concentration to calculate the RNA:DNA or an rRNA:rRNA gene ratio (for example, Kemp et al., 1993; Kerkhof and Ward, 1993; Poulsen et al., 1993; Muttray et al., 2001), since DNA concentration per cell is generally more stable than RNA concentration. Note, however, that while cell genome content commonly varies less than RNA Figure 1 Microorganism metabolic states and their contribution to ecosystem functioning. Viable microorganisms exist in one of content, genome abundance per cell can vary three general metabolic states that are all subject to mortality. significantly and therefore could influence Definitions of terms: Growing—cells are actively dividing, Active— RNA:DNA measurements (Schaechter et al., 1958; cells are measurably metabolizing (catabolic and/or anabolic Cooper and Helmstetter, 1968; Sukenik et al., 2012), processes) but are not necessarily dividing, Dormant—cells are not measurably dividing or metabolizing, Deceased—cells are not but this issue will not be addressed here. metabolically active or capable of becoming metabolically active in Historically, rRNA analyses have been used to the future, but intact macromolecules may persist. quantify populations’ growth rates in mixed microbial communities (for example, Poulsen et al., 1993; methods are important in interpreting the validity of Muttray et al., 2001), but recent application has any downstream RNA-based results. Often in the shifted toward the more qualitative approach using literature, purification and analytical methods for rRNA to identify currently active microbial popula- RNA differ and are not shown to be reproducible tions in a mixed community (for example, Jones and and quantitative. As techniques advance, methods Lennon, 2010; Kamke et al., 2010; Campbell et al., are continuously improved and new experimental 2011; DeAngelis et al., 2011; Gaidos et al., 2011; results are presented. From a technical point of Reid et al., 2011; Baldrian et al., 2012; Mannisto view, it is extremely arduous to re-interpret older et al., 2012; Mattila et al., 2012; Simister et al., 2012; results based on new methodological improvements Campbell and Kirchman, 2013; Hunt et al., 2013; and is beyond the scope of this review. However, Yarwood et al., 2013). Two principal lines of from an epistemological point of view, it is impor- evidence used to support rRNA as an indicator of tant to keep in mind potential methodological biases current activity originate from earlier studies testing to ensure that the assumptions of the relationship how rRNA scales with growth rate. First, total RNA between RNA and activity are clearly articulated, and rRNA content correlate well with growth rate and to recognize the specific limitations of applying for a handful of microbes in pure culture, over a a broad generalization for RNA content to environ- wide range of growth rates under balanced growth mental samples. With this in mind, we discuss conditions (that is, growing in an unchanging studies that utilized several different experimental environment) (Schaechter et al., 1958; Neidhardt approaches; thus, observed discrepancies between and Magasanik, 1960; Rosset et al., 1966; Koch, rRNA abundance and activity are very likely to be at 1970; Kemp et al., 1993; Kerkhof and Ward, 1993; least in part biological in origin and not simply Poulsen et al., 1993; Wagner, 1994; Bremer and methodological artifact. We focus on bacteria, which Dennis, 1996; Ramos et al., 2000). Second, have been extensively studied, but many of the decreased rRNA content is associated with limitations discussed here are likely relevant for decreased growth rate for some organisms growing other microbes, including archaea, fungi and algae. under specific nutrient-limiting conditions (Mandelstam and Halvorson, 1960; Davis et al., 1986; Kramer and Singleton, 1992; Tolker-Nielsen rRNA and its use in microbial ecology et al., 1997). Note that the relationship between rRNA concentration and growth rate is frequently The cell’s total RNA pool is mainly composed of coupled with the assumption that activity and rRNA (82–90%) (Tissieres and Watson, 1958; growth are synonymous. Here, we distinguish

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