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ABSTRACT 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. SIMILAR CONTENT BEING VIEWED BY OTHERS PRIORITY EFFECTS IN MICROBIOME ASSEMBLY Article 27 August 2021 MICROBIOME DIFFERENTIAL ABUNDANCE METHODS PRODUCE DIFFERENT RESULTS

ACROSS 38 DATASETS Article Open access 17 January 2022 MIDAS 4: A GLOBAL CATALOGUE OF FULL-LENGTH 16S RRNA GENE SEQUENCES AND TAXONOMY FOR STUDIES OF BACTERIAL COMMUNITIES IN WASTEWATER

TREATMENT PLANTS Article Open access 07 April 2022 INTRODUCTION Microorganisms have essential roles in shaping and controlling virtually all ecosystems including the atmosphere, oceans,

soils and plant- and animal-associated biomes. Microbes exist in different metabolic states in these systems: _growing_, _active_, _dormant_ and recently _deceased_ (Figure 1). These

metabolic states correspond to different degrees of influence that microbes can have on their environment. Therefore, to understand the relationships between microbial community structure

and ecosystem functions, it is important to accurately associate microbial identity with concurrent metabolic state. Simultaneous identification of microbes and their metabolic states has

been a longstanding goal in microbial ecology, and methods to achieve this have recently been accumulating in our molecular toolboxes. Nucleic-acid analysis has proven to be effective for

characterizing the phylogenetic, taxonomic and functional structure of microbial assemblages, but this approach has limitations when attempting to assess current metabolic state. Ribosomal

RNA genes (rRNA genes) are frequently used to identify microorganisms present in environmental samples regardless of metabolic state, while ribosomal RNA (rRNA) has been widely applied to

characterize the growing or active microbes. We found >100 studies that used rRNA for these purposes, including recent studies using rRNA to identify currently active microbes (for

example, Muttray and Mohn, 2000; Duineveld et al., 2001; Mills et al., 2005; Schippers et al., 2005; Gentile et al., 2006; DeAngelis et al., 2010; Jones and Lennon, 2010; Brettar et al.,

2011; Egert et al., 2011; Gaidos et al., 2011; Wüst et al., 2011; Mannisto et al., 2012; Hunt et al., 2013). However, conflicting patterns between rRNA content and growth rate indicate that

rRNA is not a reliable metric for growth or activity and in some cases may be grossly misleading. Virtually all molecular characterization methods are imperfect, but we suggest that using

rRNA analyses to evaluate microbial assemblages requires that limitations and underlying assumptions be clearly identified and understood. Here, we explore critical limitations and potential

causes of inconsistent rRNA/activity relationships. We then suggest employing rRNA abundance data as an index of potential activity and propose a framework for future application. The

reader should note that RNA extraction methods are important in interpreting the validity of any downstream RNA-based results. Often in the literature, purification and analytical methods

for RNA differ and are not shown to be reproducible and quantitative. As techniques advance, methods are continuously improved and new experimental results are presented. From a technical

point of view, it is extremely arduous to re-interpret older results based on new methodological improvements and is beyond the scope of this review. However, from an epistemological point

of view, it is important to keep in mind potential methodological biases to ensure that the assumptions of the relationship between RNA and activity are clearly articulated, and to recognize

the specific limitations of applying a broad generalization for RNA content to environmental samples. With this in mind, we discuss studies that utilized several different experimental

approaches; thus, observed discrepancies between rRNA abundance and activity are very likely to be at least in part biological in origin and not simply methodological artifact. We focus on

bacteria, which have been extensively studied, but many of the limitations discussed here are likely relevant for other microbes, including archaea, fungi and algae. RRNA AND ITS USE IN

MICROBIAL ECOLOGY The cell’s total RNA pool is mainly composed of rRNA (82–90%) (Tissieres and Watson, 1958; Neidhardt and Magasanik, 1960; Neidhardt, 1987). As an integral structural

component of ribosomes, rRNA is a fundamental constituent of all known microorganisms and most rRNA found in a cell is ribosome associated (Lindahl, 1975; Nomura et al., 1984). Total RNA

concentration is generally 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 content, genome abundance per cell can vary significantly and therefore could influence RNA:DNA measurements (Schaechter

et al., 1958; Cooper and Helmstetter, 1968; Sukenik et al., 2012), but this issue will not be addressed here. Historically, rRNA analyses have been used to quantify populations’ growth

rates in mixed microbial communities (for example, Poulsen et al., 1993; Muttray et al., 2001), but recent application has shifted toward the more qualitative approach using rRNA to identify

currently active microbial populations in a mixed community (for example, Jones and Lennon, 2010; Kamke et al., 2010; Campbell et al., 2011; DeAngelis et al., 2011; Gaidos et al., 2011;

Reid et al., 2011; Baldrian et al., 2012; Mannisto et al., 2012; Mattila et al., 2012; Simister et al., 2012; Campbell and Kirchman, 2013; Hunt et al., 2013; Yarwood et al., 2013). Two

principal lines of evidence used to support rRNA as an indicator of current activity originate from earlier studies testing how rRNA scales with growth rate. First, total RNA and rRNA

content correlate well with growth rate for a handful of microbes in pure culture, over a wide range of growth rates under balanced growth conditions (that is, growing in an unchanging

environment) (Schaechter et al., 1958; Neidhardt and Magasanik, 1960; Rosset et al., 1966; Koch, 1970; Kemp et al., 1993; Kerkhof and Ward, 1993; Poulsen et al., 1993; Wagner, 1994; Bremer

and Dennis, 1996; Ramos et al., 2000). Second, decreased rRNA content is associated with decreased growth rate for some organisms growing under specific nutrient-limiting conditions

(Mandelstam and Halvorson, 1960; Davis et al., 1986; Kramer and Singleton, 1992; Tolker-Nielsen et al., 1997). Note that the relationship between rRNA concentration and growth rate is

frequently coupled with the assumption that activity and growth are synonymous. Here, we distinguish growth from activity; while all growing organisms are active, not all active organisms

are growing (Figure 1). Experimental evidence demonstrates numerous limitations to use rRNA to quantify population growth rates in mixed communities, many of which have been addressed in

methodological reviews (for example, Molin and Givskov, 1999). However, while most of these limitations are also pertinent when attempting to identify which microbes are currently active in

a community, these limitations are frequently overlooked or ignored in practice. Here, we provide a summary of limitations (Box 1) that pertain to the relationship between rRNA and current

activity, and discuss relevant examples to assess the information that rRNA data can actually provide. BOX 1: LIMITATIONS OF RRNA AS AN INDICATOR OF CURRENT MICROBIAL ACTIVITY (REFERENCES

INCLUDE THE SEMINAL STUDIES THAT WERE LATER OFTEN OVERLY GENERALIZED TO SUPPORT RRNA–ACTIVITY RELATIONSHIP) * 1 Concentration of rRNA and growth rate are not always simply correlated;

therefore, the relationship between rRNA and activity is not likely consistent (Schaechter et al., 1958; Mandelstam and Halvorson, 1960; Flärdh et al., 1992, Kemp et al., 1993;

Tolker-Nielsen et al., 1997; Binder and Liu, 1998; Lepp and Schmidt, 1998; McKillip et al., 1998; Kerkhof and Kemp, 1999; Morgenroth et al., 2000; Oda et al., 2000; Schmid et al., 2001;

Worden and Binder, 2003). * 2 The relationship between rRNA concentration and growth rate can differ significantly among taxa; therefore, relative rRNA abundance will likely not provide

robust information regarding which taxa are relatively more active in a community (Mandelstam and Halvorson, 1960; Wade and Robinson, 1965; Rosset et al., 1966; Kemp et al., 1993; Pang and

Winkler, 1994; Oda et al., 2000; Binnerup et al., 2001; Worden and Binder, 2003). * 3 Dormant cells can contain high numbers of ribosomes; therefore, in environments that could likely

contain dormant cells, employing rRNA to identify current activity is highly problematic (Chaloupecky, 1964; Bishop and Doi, 1966; Chambon et al., 1968; Filion et al., 2009; Sukenik et al.,

2012). * 4 The relationship between non-growth activities and concentration of rRNA has not yet been investigated. CRITICAL ANALYSIS OF RRNA AS AN INDICATOR OF CURRENT ACTIVITY CONCENTRATION

OF RRNA AND GROWTH RATE ARE NOT ALWAYS SIMPLY CORRELATED The first line of evidence that has been used to support a predictable relationship between the presence of rRNA and current

activity is based on pure-culture studies assessing growth under balanced growth conditions. However, even under constrained conditions (balanced growth) the correlation between growth rate

and rRNA concentration is commonly not straightforward and in some cases breaks down altogether. For example, the relationship between growth rate and rRNA content is not linear or

consistent across all measured growth rates. Under balanced growth conditions, _Synechococcus_ and _Prochlorococcus_ strains can have a three-phase relationship between growth and rRNA

concentration: (1) at low growth rates, rRNA concentration remains constant, (2) at intermediate growth rates, rRNA concentration increases linearly with growth rate and (3) at higher growth

rates, rRNA content decreases as growth rate increases (Binder and Liu, 1998; Worden and Binder, 2003). For these organisms, rRNA concentration is not a robust proxy for growth rate. We

argue that rRNA will also not be a robust measure of current activity, since changes in growth-associated activity must impact total activity. Additionally, balanced growth conditions are

unlikely in most environments. Little work has characterized how rRNA concentration varies with growth rate under more environmentally realistic non-steady state conditions. Kerkhof and Kemp

(1999) identified three different relationship patterns between rRNA concentration and growth rate for Proteobacteria strains under non-steady state conditions: a direct linear

relationship, an indirect relationship in which cell growth rate consistently lagged behind rRNA concentration or no discernible relationship. The latter was observed in _Vibrio fischeri_,

and included periods during which growth rate decreased while rRNA content increased. Again, since growth activity likely accounts for much of total activity, these results indicate that

using rRNA concentration to assess current activity or changes in activity over time is problematic. Further evidence showing potential for misleading environmental interpretations includes

significant increase in cellular rRNA in _Aphanizomenon ovalisporum_ cells transitioning from vegetative to dormant state (Sukenik et al., 2012). These results indicate that a measurable

increase in rRNA abundance does not necessarily indicate an increase in activity. A second line of evidence cited to support rRNA as an indicator of current activity arises from studies on

RNA stability under different growth-limiting conditions (for example, carbon or nutrient limitations). Several studies have reported that exponentially growing cells subjected to nutrient

starvation degrade much of their rRNA in a relatively short time. However, the dynamics of cellular rRNA may be strongly tied to previous growth conditions. For example, _Azotobacter agilis_

was grown on different substrates, then starved for 72 h (Sobek et al., 1966). When grown on glucose, RNA did not decrease during the starvation period, but O2 consumption dramatically

dropped, indicating that cell activity (that is, respiration) declined. In another study, _Rhodopseudomonas palustris_ cells were grown at different growth rates, then carbon-starved (Oda et

al., 2000). The rRNA concentration of _R. palustris_ cells grown at maximum growth rate decreased by ∼50% within a week of starvation; however, cells grown at lower rates before the

starvation period were able to maintain near pre-starvation rRNA concentrations for more than a week of starvation. These results indicate that measurable rRNA concentration can be

influenced not only by current conditions, but also by life history (that is, the sequence of events that impacted an organism up to a given time point, and the resulting physiological

response to these events). THE RELATIONSHIP BETWEEN RRNA CONCENTRATION AND GROWTH RATE CAN DIFFER SIGNIFICANTLY AMONG TAXA Relating rRNA concentration and growth rate becomes even more

problematic when considering microbial assemblages. rRNA concentration may correlate well with growth rate in some strains of bacteria, but correlations can differ significantly between

strains (Wade and Robinson, 1965; Kemp et al., 1993; Pang and Winkler, 1994; Binnerup et al., 2001; Worden and Binder, 2003). Even at the ‘species’ level of bacteria, the relationship

between rRNA and growth rate can differ significantly between subpopulations (Rosset et al., 1966; Licht et al., 1999). Hence, using rRNA to compare relative activity or changes in activity

between taxa will likely provide misleading information. DORMANT CELLS CAN CONTAIN HIGH NUMBERS OF RIBOSOMES Dormant organisms contain measurable amounts of rRNA (Chambon et al., 1968) and

in some cases can contain significantly more rRNA in dormancy than in a vegetative state (Sukenik et al., 2012). Detectability of rRNA in dormant cells can be affected more by methodology

(due to changes in cell structure) than by low levels of rRNA (Filion et al., 2009). The issue of dormant cells containing measurable rRNA concentrations can be especially problematic when

using rRNA data to identify currently active organisms in environments likely to contain many dormant organisms such as soil, deep subsurface, frozen environments or the atmosphere. One

approach to discounting rRNA in dormant cells is to estimate the rRNA concentration per cell for specific taxa by calculating rRNA:rRNA gene ratios, then defining a minimum cutoff value for

activity (for example, DeAngelis et al., 2011; Jones and Lennon, 2010). However, rRNA:rRNA gene ratios have been characterized for very few bacteria in dormant state. The limited available

evidence demonstrates the difficulties in establishing a suitable universal cutoff value for rRNA:rRNA gene ratio. For example, an RNA:DNA ratio of around 5 was found both in dormant

_Bacillus megaterium_ (Chambon et al., 1968) and in bacteria growing at the rapid pace of ∼0.5 h−1 (Kerkhof and Ward, 1993). THE RELATIONSHIP BETWEEN NON-GROWTH ACTIVITIES AND CONCENTRATION

OF RRNA HAS NOT BEEN INVESTIGATED Finally, the relationship between rRNA concentration and growth rate is commonly considered to be equivalent to that between rRNA and activity. However,

many microbial activities are not necessarily related to growth, including those associated with maintenance, such as cell motility, osmoregulation, defense against oxidative stress,

communication, exopolysaccharide production or conjugation (van Bodegom, 2007). To our knowledge, no published work has investigated the relationship between non-growth activities and rRNA

concentration. It has been hypothesized that under certain stress conditions, microbes can dramatically increase the portion of metabolism geared toward non-growth maintenance activities

(Schimel et al., 2007), indicating that, under appropriate conditions, non-growth activities may contribute significantly to ecosystem processes. RELATIONSHIP BETWEEN RRNA, GROWTH AND

ACTIVITY: PHYSIOLOGICAL LINKS The multi-level modulation and regulation of most cell functions may easily invalidate simple correlations between current metabolic state and rRNA abundance.

For example, the relationship between microbial activity and measurable rRNA can be influenced by heterogeneity of cell physiology within a population (Licht et al., 1999), changes in the

ratio of non-growth to growth-specific metabolic activity, life history (Oda et al., 2000), life strategy (Flärdh et al., 1992; Lepp and Schmidt, 1998; Mitchell et al., 2009; Sukenik et al.,

2012), sample heterogeneity, changing environmental conditions and of course fundamental enzyme kinetics (that is, substrate concentration). Additionally, the concentration of rRNA in a

cell at a given point in time is the net result of rRNA synthesis (that is, transcription) and degradation rates (Gausing, 1977), each of which may be under distinct controls. All of these

factors can affect the relationship between ribosome turnover and microbial activity at multiple levels (Figure 2) and should be considered when analyzing rRNA data from environmental

samples. RRNA ANALYSES IN COMMUNITY ECOLOGY rRNA-based measurements can provide meaningful insight into microbial community dynamics. rRNA directly relates to a population’s potential to

catalyze the specific function of protein synthesis, and can therefore document the relative expression of this function. rRNA-based measurements provide a specific piece of information in

the spectrum of molecular approaches (including metagenomics, metatranscriptomics, metaproteomics and community proteogenomics) that are increasingly applied to study microbial communities.

Metagenomic data provide information about the functional potential of a sample, without providing insight into current metabolic state. Metatranscriptomic data come one step closer to

current metabolic state, without providing direct evidence of translation or enzyme activity. Metaproteomic data come an additional step closer to current metabolic state, by identifying

enzymes expressed in a community, but without providing direct evidence of enzyme activity. While rRNA is a product of transcription, community rRNA data are more analogous to metaproteomic

than to metatranscriptomic (mRNA) data; rRNA is generally much more stable than mRNA (Snyder and Champness, 2007), and is not translated to protein but instead acts as a structural component

of housekeeping catalysts (ribosomes). Therefore, rRNA data can provide evidence of the relative expression of an enzyme, with the explicit function of protein synthesis, for different

populations in a community. Analogously, in metaproteomics, environmental proteins are characterized to provide information about specific enzymatic functions that are expressed (Wilmes and

Bond, 2004). Taking this analogy one step further, the community proteogenomics approach can be used to map the expressed function of a community (metaproteomic data) onto the available

template of potential functions (metagenomic data) (Verberkmoes et al., 2009), to provide valuable information about how environmental changes correspond to changes in community expression

in the context of community composition. Similarly, rRNA data can be mapped onto rRNA gene data to illuminate relative ribosomal expression of the total community. However, it is important

to recognize that while enzyme/protein data come closer than gene and transcript data to identifying real-time activity, the presence of an enzyme does not unequivocally denote current

activity for a given function, because many factors control enzymatic activity _in vivo_ (Nannipieri et al., 2002). Similarly, the presence of rRNA is indicative of protein synthesis

_potential_, not of _realized_ protein synthesis (Figure 2). The number of ribosomes present at a given time limits the maximum protein synthesis activity for a population, but does not

directly inform about realized protein synthesis activity. The distinction between actual activity and potential activity is critical when attempting to identify and characterize the

dynamics of organisms that drive ecosystem functions (Figure 1). APPLICATIONS IN MICROBIAL ECOLOGY: FUTURE DIRECTIONS What does measuring ‘protein synthesis potential’ tell us about

microbial populations? The relationship between the number of ribosomes and the ability to synthesize proteins links the quantity of rRNA in a population with its potential for growth and

acclimation (that is, to upregulate or change currently expressed metabolic functions). rRNA can represent potential future activity, in addition to reflecting historical activity and

conditions (as discussed above). For example, some microorganisms increase ribosome concentration as they enter a dormant state, a life strategy that provides them with higher protein

synthesis potential, and therefore potentially higher fitness, as they return to a vegetative state when environmental conditions improve (Sukenik et al., 2012). Similarly, non-dormant

populations maintaining ribosome levels above current protein synthesis demands likely have the ability to rapidly shift metabolic functions to adapt to changing conditions, thereby becoming

better competitors (Koch, 1971; Alton and Koch, 1974; Flärdh et al., 1992). Recognizing that rRNA concentration reflects past, current and future activities in addition to different life

strategies restricts its utility as a metric of real-time activity, but provides the basis for generating and testing important hypotheses. Several studies show that under repeated temporal

patterns of changing environmental conditions, microbes may develop an anticipatory life strategy, enduring one phase of the cycle while preparing for a more favorable phase that regularly

follows. Further, accumulating or maintaining rRNA during periods of low metabolic activity may confer a competitive advantage during a favorable phase of the cycle. In _Synechococcus_ sp.

incubated under light and dark diurnal cycles, rRNA content increased during dark periods compared with light periods; in contrast, growth occurred during the light periods and ceased during

the dark periods (Lepp and Schmidt, 1998). Similar results were found for a strain of _Prochlorococcus_ in which expression of ribosomal genes was higher during a dark cycle than during a

light cycle (Zinser et al., 2009). Further evidence for anticipatory behavior in bacteria was found in _E. coli_ manifesting a Pavlovian-type response to a primary stimulus by preemptively

modifying genetic expression for a secondary stimulus before it occurred (Tagkopoulos et al., 2008; Mitchell et al., 2009). Anticipatory strategies may also take place on a seasonal scale:

at the end of a summer dry-down period, Mediterranean soil communities showed almost no measurable microbial activity (based on CO2 production), yet total extractable bacterial 16S rRNA was

similar to that found after the microbes become activated by the first wet-up event (Placella et al., 2012), which could reflect anticipation for the upcoming annual rainy season (Barnard et

al., 2013). If anticipatory life strategies reflected in rRNA concentrations are common in microbial populations experiencing repeated cyclic patterns, then can this information be

meaningfully applied to predict future changes in ecosystem function? To utilize rRNA data to characterize microbial assemblages, we need to better our understanding of how these data relate

to environmental conditions and community interactions; this understanding could be furthered by several experimental approaches: * a) _Coupling direct measurements of metabolic activity to

rRNA data_. * b) _Explicitly testing the relationship between non-growth activities and rRNA concentrations_. * c) _Characterizing ribosome turnover under different environmental

conditions._ CONCLUSION A number of pure-culture studies have shown a correlation between growth rate and rRNA concentration. This relationship makes intuitive and biological sense, since

rRNA is a critical component of ribosomes, and ribosomes are necessary to synthesize protein. However, the correlation between real-time activity and rRNA in environmental samples is

inconsistent due to differences in life histories, life strategies and non-growth activities. Using rRNA analysis as a general indicator of currently active microbes in environmental samples

is not valid under many circumstances, and may actually hinder progress connecting microbial activities to ecosystem functions. Considering rRNA measurements as indicators of protein

synthesis potential provides microbial ecologists with a robust framework, facilitating a more prudent yet comprehensive understanding of the complex dynamics at play in microbial

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Prochlorococcus. _PLoS ONE_ 4: e5135. Article  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We thank Jim Prosser, Josh Schimel, Eoin Brodie and Laurent

Philippot for constructive comments. SJB was supported by a National Science Foundation Graduate Research Fellowship. RLB was funded by the European Community’s Seventh Framework Programme

under grant agreement PIOF-GA-2008-219357. DOE Genomic Science Program grant (FOA DE-PS02-09ER09-25 award #0016377) to MKF. AUTHOR INFORMATION Author notes * Steven J Blazewicz Present

address: 4Current address: US Geological Survey, 345 Middlefield Road, MS 962, Menlo Park, CA 94025, USA., * Romain L Barnard Present address: 5Current address: INRA, UMR1347 Agroécologie,

17 rue Sully, BP 86510, Dijon, France., AUTHORS AND AFFILIATIONS * The Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA Steven J

Blazewicz, Romain L Barnard & Mary K Firestone * Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA Rebecca A Daly * Ecology Department, Earth

Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Rebecca A Daly & Mary K Firestone Authors * Steven J Blazewicz View author publications You can also search

for this author inPubMed Google Scholar * Romain L Barnard View author publications You can also search for this author inPubMed Google Scholar * Rebecca A Daly View author publications You

can also search for this author inPubMed Google Scholar * Mary K Firestone View author publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHOR

Correspondence to Steven J Blazewicz. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no conflict of interest. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE

CITE THIS ARTICLE Blazewicz, S., Barnard, R., Daly, R. _et al._ Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. _ISME J_ 7,

2061–2068 (2013). https://doi.org/10.1038/ismej.2013.102 Download citation * Received: 31 October 2012 * Revised: 02 May 2013 * Accepted: 22 May 2013 * Published: 04 July 2013 * Issue Date:

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shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative KEYWORDS * community rRNA * microbial

activity * microbial growth * ribosomes * environmental samples * ecosystem processes