Shift from dormancy to microbial growth revealed by RNA:DNA ratio

Ecological Indicators 85 (2018) 603–612

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Research paper

Shift from dormancy to microbial growth revealed by RNA:DNA ratio Sebastian Loeppmann

a,e,⁎

b,c

, Mikhail Semenov , Yakov Kuzyakov

d,e

, Evgeniya Blagodatskaya

MARK c,e

a

Dept. of Biogeochemistry of Agroecosystems, University of Göttingen, Germany V.V. Dokuchaev Soil Science Institute, Moscow, Russian Federation Institute of Physical chemical and Biological Problems in Soil Science, Pushchino, Russian Federation d Dept. of Agricultural Soil Science, University of Göttingen, Germany e Dept. of Soil Science of Temperate Ecosystems, University of Göttingen, Germany b c

A R T I C L E I N F O

A B S T R A C T

Keywords: dsDNA yield RNA yield Nucleic acids RNase Particle size distribution Metabolic quotient Microbial growth

Although soil microorganisms spend most of their lifetime at dormant or resting states, they are quickly activated by substrate input and easily switch to growth. Under steady-state, the double-stranded DNA (dsDNA) content is considered as a measure of total microbial biomass while the RNA content mainly indicates the active microbial fraction. In growing population, however, an increase in DNA is solely related to replication of microbial cells, while the RNA, besides growth, is also involved in non-growth processes. Therefore, the dsDNA and RNA content increase differently during microbial growth, applying the RNA:dsDNA ratio as promising indicator of growth-related and non-growth microbial activity. To what extend the ratio can be used to comparatively infer investment in microbial biomass production versus maintenance-related synthesis following substrate induction remains to be studied. We measured the RNA:dsDNA ratios of representative soil types of four different ecozones before and after glucose addition to prove the prediction capacity of physiological status of soil microorganisms. The RNA:dsDNA ratio remained stable after activation of microorganisms in Retisol and Luvisol indicating balanced growth. In contrast, very moderate increase in DNA in sandy Calcisol was accompanied by disproportionally high increase in RNA. As a result, the RNA:dsDNA ratio in Calcisol with low nutrient status increased by 36-fold after glucose amendment, indicating strong non-growth related processes. The RNA recovery decreased exponentially with increasing clay content, indicating the strong association to the textures of the soil types. This suggests, that the underestimation of RNA yields in clayey soils biased the RNA:dsDNA ratio, and subsequently the physiological state of the microbial community is not adequately represented in soils clay contents exceeding 30%. Monitoring the relative changes in dynamics are required to overcome the restricted applicability of RNA:dsDNA ratio in soils with high clay content.

1. Introduction Bulk soil is regarded as an oligotrophic environment, as it is generally poor in labile organic compounds directly available for microorganisms (Van Elsas and van Overbeek, 1993). Active microbes utilize the available substrate and therefore maintain biochemical transformations (Gunina et al., 2014). The total microbial biomass consists of only about 0.1–2% of active microorganisms in absence of easily available substrates (e.g. root exudates), whereas potentially active microorganisms contribute up to 60% of the total microbial biomass (Blagodatskaya and Kuzyakov, 2013). The potentially active microbial population permanently exists in soil between the active and dormant physiological states (De Nobili et al., 2001). However, the mechanisms controlling the percentage of microbes being active are poorly defined.

⁎

The low amount of readily-available organic carbon (C) precludes slow bacterial growth and low activity. Many soil organisms, therefore, have very low metabolic activity and frequently spend most of their lifetime in dormant or resting phases, especially in soils of low C and N contents (Sparling et al., 1981; Van Elsas and van Overbeek, 1993). The input of readily assimilated C substrates in soil hot spots, such as the rhizosphere, detritusphere and drillosphere shifts microbial population from dormancy to activity, thereby strongly accelerates microbial metabolism, and may induce microbial growth. A recent study showed that total microbial biomass in the rhizosphere was 14–31% higher than that in the root free soil, while the growing (active) part of microbial biomass was 45–83% higher (Blagodatskaya et al., 2014). This switch leads to increasing both DNA- and RNA- content. Microbial activity can have various expressions: 1) Growing cells – actively

Corresponding author at: Dept. of Biogeochemistry of Agroecosystems, University of Göttingen, Büsgenweg 2, 37077 Göttingen, Germany. E-mail addresses: [email protected], [email protected] (S. Loeppmann).

https://doi.org/10.1016/j.ecolind.2017.11.020 Received 29 June 2017; Received in revised form 7 November 2017; Accepted 8 November 2017 1470-160X/ © 2017 Elsevier Ltd. All rights reserved.

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et al., 2000) can be used as a measure of total microbial biomass (Joergensen and Emmerling, 2006; Renella et al., 2006). Highly sensitive fluorescent dye PicoGreen (Life Technologies) is applied for dsDNA quantitative analysis (Fornasier et al., 2014; Smets et al., 2016; Terrat et al., 2012). Specifically binding to dsDNA, PicoGreen can be used for quantification of total dsDNA yields even in the presence of co-extracted and co-purified components such as humic acids, cellular debris and organic solvent residues (Bachoon et al., 2001). The positive linear correlation between dsDNA content and total microbial biomass was already confirmed by the number of studies (Blagodatskaya et al., 2003; Anderson and Martens, 2013). Another advantage of DNA-based approach is related to the relatively small contribution of plant dsDNA to total dsDNA in comparison with the contribution of root residues to quantification of microbial biomass by chloroform fumigation-extraction. DNA content is much lower in plants compared to microbial cells, the plant dsDNA never exceeded 2.6% of total dsDNA content for a wide range of soils (Gangneux et al., 2011). Similarly, the extracellular DNA content does not exceed 3% of total DNA (Blagodatskaya et al., 2003; Gangneux et al., 2011). Relatively stable conversion factor from dsDNA units to microbial C in a narrow range of 5.0 (Anderson and Martens, 2013), 5.4 (Blagodatskaya et al., 2003) and 5.6 (Lloyd-Jones and Hunter, 2001) has been frequently revealed. A literature review based the conversion factor from dsDNA into microbial biomass averaged at 6, indicating that approximately 13% of microbial C stems from DNA (Joergensen and Emmerling, 2006). The RNA is more difficult to extract from soil than the DNA, and quantitative extraction of soil RNA comprises several challenges. The RNA pool of a microbial cell is mainly composed of rRNA (82–90%) and in much lesser extent of mRNA and tRNA (Neidhardt, 1987). The RNA recovery from soil remains very low and rarely exceeds 10% (Duarte et al., 1998). The RNA yields extracted from soil range from tens of nanograms to several micrograms per gram of soil (Borneman and Triplett, 1997; Bürgmann et al., 2003; Mettel et al., 2010; Moran et al., 1993; Sessitsch et al., 2002; Wang et al., 2008,2009). Such a wide range of RNA yield may be caused by activity state of soil microorganisms, contamination of RNA sample by humic substances or the loss of RNA during purification (Wang et al., 2012). Furthermore, the strong RNA losses during isolation may be caused by an RNase activity and by adsorption to the clay fraction. Although, both DNA and RNA could be adsorbed by soil particles (Goring and Bartholomew, 1952), the adsorption of single-strand RNA molecules is much stronger in soils with clay and clay-loamy texture (Tournier et al., 2015). Despite low recovery, the relative changes in RNA content within the same soil type can shed light on shifts in physiological state of soil microorganisms (Bakken and Frostegård, 2006; Blagodatskaya and Kuzyakov, 2013). Since not all active microorganisms are growing, but all the growing microorganisms are active (Blazewicz et al., 2013), a differentiation between microbial growth and activity in soils remains a great challenge to identify the underlying mechanisms of microbial functioning. To investigate physiological state of microorganisms in soils with contrasting nutrient status, we sampled soils from four zonal soil types in Russia (Fig. 1). We hypothesized that 1) despite the substrate addition, the growth of microorganisms is strongly dependent on the initial C and N status of the respective soils 2) strong growth of microorganisms subsequently indicates high microbial activity. To test these hypotheses we determined the RNA:dsDNA of the soils varying in soil organic carbon content (Corg), soil nitrogen content (Ntot) and particle size distribution. Virgin and arable Chernozems were characterized by the highest Corg, Ntot, soil microbial biomass (Cmic) and soil C:N ratios. Retisol and Luvisol had almost similar soil properties, and Calcisol was lowest in Corg, Ntot and Cmic. Thus, these four soil types represent the reduced enrichment gradient of C and N from Chernozems over Retisol and Luvisol to Calcisol. Based on these range of properties, we expected that the highest DNA- and RNA-contents would be found in the C- and N-rich Chernozem, and the lowest in Calcisol. We switched physiological state of soil microorganisms by addition of glucose in

dividing and consequently increasing DNA and RNA contents, and 2) producing proteins and enzymes for metabolism. In the latter case, the DNA remains nearly constant but RNA increases (Blazewicz et al., 2013). DNA and RNA molecules are responsible for the storage of genetic information and for translation of this genetic information for protein synthesis, correspondingly. The DNA extracted from soil in relatively large amounts represents organisms at any physiological state – dead, dormant and active (Levy-Booth et al., 2007; Blagodatskaya and Kuzyakov, 2013). In contrast to DNA, the RNA content in dormant cells is extremely low, showing often reduced concentrations of lipids, fatty acids and proteins, while it increases dramatically after microbial activation (Lennon and Jones, 2011). Since the RNA amount per cell is proportional to metabolic activity of microorganisms (Mills et al., 2004; Molin and Givskov, 1999), the RNA-based approaches provide information on the metabolically active part of microbial community. More than a hundred studies are available using rRNA to indicate active microorganisms in batch studies but also in the marine and terrestrial environment (Blazewicz et al., 2013; Hunt et al., 2013; Jones and Lennon, 2010; Wu et al., 2011). The response of microbial growth and activity on substrate addition is mainly focused on pure cultures (Scott and Hwa, 2011; Taymaz-Nikerel et al., 2013), e.g. for model microorganisms such as Escherichia coli or Bacillus sp. (Ferenci, 1999). Substrate addition to pure cultures studies caused multiple regulative responses, such as shifts in metabolism, storage products and osmoregulation (Van Bodegom, 2007). However, knowledge from these studies cannot be directly linked to the response of diverse microbial communities in soils. For 17 soils and sediments under steady-state, the RNA:DNA ratio ranged between 0.01 and 0.35 with a mean ratio of 0.13 (Hurt et al., 2001). A high ratio reflects enhanced microbial activity, subsequently resulting in increased soil organic matter (SOM) decomposition rates. Comparison of eco-physiological indices defined by RNA:dsDNA ratio (i.e. where the ratio reflects microbial C mineralization) may help us to understand how microbial physiological responses relate to soil properties like nutrient content, texture, or land use history (Anderson and Domsch, 1993; Bending et al., 2004). The microbial metabolic quotient (qCO2) was determined, as an integrative indicator of substrate availability microbial activity, maintenance requirements and carbon use efficiency (Insam and Haselwandter, 1989). Disturbances or nutrient limitations in soil (e.g. land-use change) have negative impact on microbial properties, and subsequently increases the metabolic quotient (Papst et al., 2016). Microbially-mediated processes have been incorporated into new generation ecosystem models as a parameter for SOM decomposition (Wang et al., 2014). However, continued development is still required to assess microbial processes into global climate models (Wang et al., 2015) because these models neglect physiological state changes of microorganisms. This could result in incorrect estimates of microbial biomass, which subsequently infers deficiencies in model parameterization and predictions of SOM decomposition. Physiological status indicators such as the RNA:dsDNA ratio under substrate-induced conditions might be useful in modelling organic matter dynamics with land-use changes that shift soil textures and organic input quality. Great progress in molecular technologies ensured quantitative extraction of DNA and RNA even from soil samples making the RNA:DNA ratio a promising indicator of the physiological status of bacterial (Dell’Anno et al., 1998; Kerkhof and Ward, 1993; Muttray and Mohn, 1998) and of microbial communities as a whole in soils (Hahn et al., 1990; Tsai and Olson, 1991). The sensitivity and applicability of this ratio in various soil types, differing in texture and nutrient status, require experimental prove. Quantitative determination of DNA content in soil is well established (Marstorp and Witter, 1999; Blagodatskaya et al., 2003) and is possible by application of commercially available kits (Fornasier et al., 2014). Quantitative extraction of microbial DNA from soil (Marstorp 604

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Fig. 1. Map of Russia – Area of interest on a scale of 1:4,400,000 including climate data, sample sites and terrestrial biomes. Created with QGIS 2.8.1-Wien (WGS 84, EPSG-Code: 4326) Icon “Thermometer” made by Yannick [http://yanlu.de] from http://www.flaticon.com; Icon “Rain” made by Yihsuan Lu [https://thenounproject.com/Yihsuanlu/] from http:// thenounproject.com.

samples were stirred with a common handheld kitchen blender for one minute (Anderson and Domsch, 1978).

order to reveal shifts of the RNA:dsDNA ratio during microbial growth. These changes of the RNA:dsDNA ratio are linked to the shift in physiological state of microorganisms, and we aimed to prove whether this ratio provide reliable prediction on microbial activity and growth in these contrasting soil types.

2.2. Soil dsDNA extraction procedure For quantitative soil and DNA and RNA extraction, MP Bio kits (MP Biomedicals, Germany) were chosen. These kits provided the highest DNA and RNA yields in our pre-experimental testing, and were previously chosen for effective extraction of DNA (Fornasier et al., 2014). DNA extraction was performed according to the manufacturer’s protocol with 0.5 g of fresh moist soil treated by the FastDNA® SPIN kit for Soil (MP Biomedicals, Germany). Bead beating and a silica matrix were used to isolate DNA from soil. Before extraction, soils were placed into a freezer (−20 °C) overnight to ensure higher DNA yields. Soils were added to lysing matrix tubes containing silica and glass spheres of different diameters, were treated with sodium phosphate buffer (Na2HPO4; pH 8.0, 0.12 M) and MT buffer (1% sodium dodecyl sulfate – SDS, 0.5% Teepol, and PVP40 with EDTA and Tris) were subjected to bead beating in the FastPrep® instrument for 40 s at a speed setting of 6.0 and processed by protein precipitation solution (150 μl of 3 M CH3COOK and 4% glacial acetic acid). DNA was bound to a DNA binding matrix (1 ml of glassmilk diluted 1:5 with 6 M guanidine isothiocyanate), washed by a salt ethanol wash solution (SEWS – ultrapure 100% ethanol and 0.1 M sodium acetate) and finally, eluted in DNase-free water (DES). After extraction, purified DNA samples were immediately measured according to the dsDNA quantification procedure (see below).

2. Material and methods 2.1. Soils, sampling sites and experimental setup The dynamics of RNA:dsDNA ratio were tested in top 10 cm-layers of five soils located in European part of Russia: Gleyic Retisol, Luvisol, virgin and arable Chernozem and Haplic Calcisol (IUSS Working Group, 2015). A pit has been dug to sample soil from each of the 3 walls (within same horizon). Terrestrial biomes, precipitation, and temperature for these soils are displayed as map (Fig. 1). Retisol was sampled at the bottom (accumulative) part of the slope in Tver region (56°46′N, 36° 3′O). The Luvisol was taken at the top (autonomous) part of the slope at the right bank of the Oka River near the town Pushchino in Moscow region (54°49′N, 37°35′O). Chernozem was sampled in Russian Federal Nature Preserve “Kamennaya Step” located in Talovsky District in Voronezh region, at the watershed of rivers Bitug and Khoper (51°02′N, 40°72′ O). Calcisol was sampled in the Astrakhan region, located in the south-east of the East European Plain within the Caspian lowland (47°93′N, 46°11′ O). Ten gram soil samples (dry soil weight) were filled into incubation glass vessels (100 ml), adjusted to 60% of water-holding capacity and closed with a polyethylene film. To determine shifts of the RNA:dsDNA ratio during microbial growth, a glucose solution (4 mg C g−1 soil) was amended and incubated for 72 h at 22 °C. Glucose was used, because it is one of the abundant components of root exudates (Whipps and Lynch, 1983; Derrien et al., 2004). The fraction of glucose-utilizing microorganisms amounts to 50–70% of a whole soil microbial assemblage (Blagodatskaya and Kuzyakov, 2013). Substrate input was sufficient to assure unlimited exponential growth of microorganisms. The amount of mineral salts was selected so that the added substrate did not change the pH of soil (< 0.1) (Blagodatskaya et al., 2007, 2009). Mineral salts amounted 1.9 mg g−1 (NH4)SO4, 2.25 mg g−1 K2HPO4 and 3.8 mg g−1 MgSO4-7H2O. After addition of the substrate nutrient mixture the soil

2.3. Soil RNA extraction procedure RNA extraction was performed according to the manufacturer’s protocol with 0.5 g of fresh moist soil by the FastRNA® Pro Soil Direct kit (MP Biomedicals, Germany). Soil samples were placed to lysing matrix tubes containing silica and glass spheres of different diameters, treated with RNApro™ Soil Lysis Solution provided RNase inhibition, subjected to bead beating in the FastPrep® instrument. Phenol:Chloroform (1:1) solution was added; the upper aqueous phase were taken and processed by Inhibitor Removal Solution and cold 100% isopropanol. After mixing, the solution was incubated for 30 min at 605

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−20 °C, centrifuged at 14.000 × g for 15 min, and the pellet was washed by cold 70% ethanol (with DEPC-H2O). RNAMATRIX Binding Solution and RNAMATRIX Slurry were used to bind RNA molecules. The binding matrix with caught RNA was washed by RNAMATRIX Wash Solution and pure RNA extract was eluted by DEPC-H2O. Purified RNA samples were immediately measured according to the RNA quantification procedure.

the RNA extraction was high and the data variability was low (5–10%). We assume, therefore, that even low amount of RNA recovered is representative for the relative changes within the same soil type. 2.6. Estimation of microbial biomass and basic characteristics of soils Microbial biomass C was analyzed by chloroform fumigation-extraction (CFE) (Brookes et al., 1985; Jenkinson and Powlson, 1976; Vance et al., 1987). We extracted the unfumigated soil samples (5 g) with 20 ml of 0.05 M K2SO4 and agitated the samples for 1 h with an overhead shaker (40 rev min−1). The same amount of soils was fumigated with ethanol-free chloroform and then extracted in the same way. The fumigation was done in desiccators at 20 °C for 24 h (Friedel and Scheller, 2002; Joergensen and Mueller, 1996). After 5 min centrifugation of the soil suspension at 2500g, the supernatant was filtered through Rotilabo-rondfilters (type 15A, Carl Roth GmbH & Co.KG). The centrifugation of soil suspension was applied to shorten the filtration time (Rousk and Jones, 2010). The organic C-content of the K2SO4 extracts was measured using a multi N/C analyzer (multi N/C analyzer 2100S, Analytik Jena, Germany). Microbial biomass C was calculated by dividing the microbial C flush (EC), i.e. the difference between extracted C from fumigated and non-fumigated soil samples, with a kEC factor of 0.45 (Joergensen and Mueller, 1996; Wu et al., 1990). The soil moisture content was determined gravimetrically by drying the soil samples for 24 h at 105 °C (Black, 1965). Corg was analyzed in sieved (1 mm) and dried samples which had been pounded with a mortar to a fine powder prior to removing inorganic carbon by HCltreatment (Nelson and Sommers, 1982). Corg and Ntot contents were determined using a multi N/C analyzer (multi N/C analyzer 2100S, Analytik Jena, Germany).

2.4. Soil dsDNA and RNA quantification The quantity of dsDNA obtained in the extract was determined by diluting the extract 150-fold TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 7.5). Aliquots of 0.1 ml were transferred to 96-well microplates (Brand pureGrade, black). For staining the dsDNA a 200-fold dilution of the dsDNA fluorescence dye PicoGreen® (Molecular Probes, Life Technologies, Germany) was prepared in plastic containers. The dye (0.1 ml) was added to the wells with diluted DNA extract (final 300-fold dilution of the extracts) and left to react at 23 °C protected from light for 2 min. Fluorescence intensity was measured with an automated fluorometric plate-reader (Wallac 1420, Perkin Elmer, Turku, Finland) of excitation 485 nm, emission 525 nm and measurement time 1.0 s. The dsDNA of bacteriophage lambda provided in the Quant-iT™ PicoGreen® dsDNA Assay Kit was used as a standard; samples for the standard curve were prepared in TE-buffer in the same way as the experimental samples (Blagodatskaya et al., 2014). The quantity of RNA obtained in the extract was determined by making a 5-fold dilution of the extract in RNase-free TE buffer (with DEPC-treated water). Aliquots of 0.1 ml were then transferred to 96well microplates. For staining the RNA a 200-fold dilution of the fluorescence dye RiboGreen® (Molecular Probes, Life Technologies, Germany) in RNase-free TE buffer was prepared in plastic containers. The dye (0.1 ml) was added to the wells with diluted RNA extract (final 10-fold dilution of the extracts) and left to react at 23 °C protected from light for 2 min. Fluorescence intensity was measured with an automated fluorometric plate-reader of excitation 485 nm, emission 525 nm and measurement time 1.0 s. The ribosomal RNA (16S and 23S rRNA from E. coli) from Ribogreen assay kit provided in the Quant-iT™ RiboGreen® RNA Assay Kit was used as a standard; samples for the standard curve were prepared in RNase-free TE-buffer in the same way as the experimental samples.

2.7. Particle size distribution analysis Particle size distribution analysis was performed with a LaserParticle-Sizer «Analysette 22 comfort» (FRITSCH, Germany), equipped with a low-power (2 mW) Helium-Neon laser with a wavelength of 632.8 nm as the light source. The device has active beam length of 2.4 mm, and it operates in the range 0.01–1250 μm, combining out of two measurements with focal lengths of 9 and 474 mm in the same suspension. The suspension is pumped through a sample cell placed in the convergent laser beam and the forward scattered light falls on the 31 photosensitive sensor rings. The sample obscuration was adjusted to an optimal value of 45%. The reference refractive index for standard deionized water was 1.33. Before determination, the samples were introduced into the ultrasonic bath. Particle size distribution was obtained by fitting full Mie scattering functions for spheres (Kerker, 1969). The Mie theory approach was selected instead of the Fraunhofer one, because it provides a better estimation of particle size in the clay fraction (deBoer et al., 1987).

2.5. Soil dsDNA and RNA extraction efficiency To verify DNA and RNA recovery we performed a pre-experiment adding defined amount of DNA and RNA standards to the lysing matrix tube from the kit with and without soils. The DNA recovery ranged from 94% to almost 100% for all samples tested. An efficiency of RNA extraction was verified by adding 3 μg of rRNA standard: 1) directly to the soil sample, 2) to the control probe without soil, 3) to the untreated soil already as suspension with the RNApro™ Soil Lysis Solution (inhibitor of RNase) from the kit and, finally, 4) to the three-times autoclaved soil suspension with attached inhibitor solution. In contrast to DNA extraction, we encountered a problem with RNA stability which was reflected in low percentage of recovery. RNA standard added directly to the soil sample was completely decomposed, e.g. no change in RNA content was detected. This effect was expected when considering the activity of air and soil RNases. Transferred RNA standard to the control probe without any soil recovered in average 5.5%. The recovery of the RNA standards added to the untreated soil suspensions with RNase inhibitor varied in a small range between 4 and 6% and averaged at 5.5%. The highest yields (10% of added RNA standard) were revealed for those probes where RNA was added to the suspension of autoclaved soil with RNase inhibitor. Since our experiment was carried out with non-autoclaved samples, we used RNA recovery index of 5.5% for recalculation of total soil RNA yield. Although low recovery is common for most of RNA studies, the reproducibility of

2.8. Basal and substrate-induced respiration Microbial respiration was determined by substrate-induced respiration (SIR) based on CO2 efflux after adding glucose and mineral salts (Anderson and Domsch, 1985). The SIR method was conducted in a climate chamber (22 °C). Thereby, 23 g (dry weight) of each soil sample was incubated in flasks for 4 h after addition of the substrate. The amended substrate mixture was similar to that described above (Blagodatsky et al., 2000). Gas samples (15 ml) were taken hourly and the CO2 concentrations were analyzed by gas chromatography (GC 6000 VEGA series 2, Carlo Erba instruments, UK). The basal respiration (BR) was measured in the same way as the SIR without any substrate amendment and a sampling time interval of 2 h. The slopes of hourly measured CO2 concentrations were corrected by the specific gas flux and multiplied with the headspace volume (1098 cm3), to obtain CO2 flux rates. Afterwards we related the CO2 606

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fluxes to the soil carbon content and incubation duration. The metabolic quotient indirectly reflects the microbial maintenance expenses, availability and efficiency of microbial substrate utilization and was determined by the ratio of BR to Cmic (Anderson and Domsch, 1990).

Chernozem, Retisol, Luvisol and Calcisol, respectively during 72 h, indicating strong increase in active microbial cells. In contrast to dsDNA, the lowest RNA contents were determined in C-rich Chernozems for both non-activated and activated soils, despite dsDNA-derived Cmic and Corg were the highest. The lowest RNA:dsDNA ratios (P < 0.05) were revealed in virgin and arable Chernozem soils compared to all other soil types and increased by 131 and 28%, respectively during 72 h after glucose amendment (Fig. 3). In Retisol, the RNA:dsDNA ratio increased by 132% during the first 24 h and then decreased by 52% to the end of incubation. The RNA:dsDNA ratio of Calcisol increased by 53-fold during the first 24 h and decreased by 31% to the end of incubation. In summary, we distinguished three distinct patterns of the RNA:dsDNA responses. 1) in Calcisol, the increase in RNA was much faster than dsDNA (0–72 h and 0–24 h). 2) in Luvisol, the RNA and dsDNA increased simultaneously (0–72 h and 24–72 h), and it was reflected by a comparatively constant RNA:dsDNA ratio, ranging from −19 to 10% during incubation. Finally, 3) in Retisol, the initial increase of RNA decelerated after 24 h, while the dsDNA content progressively increased during incubation.

2.9. Statistics The means of three replicates with standard errors are presented in tables and figures. A Shapiro-Wilk test was applied to test for Gaussian distribution. We used the Pearson correlation coefficients to interpret the degree of linear relationships. Significant differences in time between the soil types were assessed by repeated measurements ANOVA including a Holmes-Sidak post-hoc correction. A multiple t-test was performed to test for significant (P < 0.05) differences of basic parameter using GraphPad Version 6 software (Prism, USA). 3. Results 3.1. Soil properties, dsDNA, RNA contents and RNA:dsDNA ratio Corg, Ntot, and Cmic contents decreased from virgin and arable Chernozems over Retisol and Luvisol to the Calcisol (Fig. 2). Corg, Ntot, Cmic and clay content varied significantly (P < 0.05) between the soil types (except for Corg, Ntot in Retisol and Luvisol). For non-activated soil samples, the total dsDNA content ranged from 26 to 107 μg g−1 soil (Fig. 3). They were characterized by strong positive linear correlation with Corg (R2 = 0.97, P < 0.0001), Cmic (R2 = 0.85, P < 0.0001) and Ntot (R2 = 0.84, P < 0.0001) (not shown). The conversion factor from dsDNA to Cmic was 4.75 (R2 = 0.96) (Supplementary Fig. 1). During the first 24 h after glucose addition, the dsDNA content increased (P < 0.05) by 21, 67 and 51% for Luvisol, Retisol and Calcisol, respectively (Fig. 3), and thereafter microbial growth lowered slightly. In case of Chernozems, the DNA contents increased by 56–83% during the first 24 h, and it leveled off, thereafter. In total, the dsDNA content increased from 64% in virgin Chernozems up to 154% in Retisol, respectively during 72 h after C supply, reflecting the growth of microbial cells. For non-activated soils, the total RNA content ranged from 0.3 to 4.2 μg g−1 soil (Fig. 3). The low dsDNA and RNA contents in non-activated Calcisol are in line to the high metabolic quotient determined in Calcisol (Fig. 5). By the addition of glucose, the RNA contents increased by the factor of 2.8, 1.5, 1.8, 1.2 and 90 for virgin and arable

3.2. dsDNA and RNA content as affected by particle size distributions All soil types showed significantly (P < 0.05) different particle size distributions. Highest clay content was in Chernozems, intermediate for Retisol and Luvisol and lowest for Calcisol (Fig. 2). The dsDNA content after glucose addition increased (P < 0.05) linearly with the clay content (24 h; Fig. 4a ). Extractable RNA and RNA:dsDNA ratio decreased exponentially with increasing clay contents (Fig. 4b, c).

3.3. Metabolic quotient and microbial biomass The highest metabolic quotient was determined for Calcisol, indicating microbial stress-responses (Fig. 5). We determined the ratio between SIR-Cmic and CFE-Cmic to compare the two methods of microbial biomass quantification. The ratio between SIR-Cmic and CFECmic was < 1 solely for Calcisol (Supplementary Fig. 2), whereas all other soil types reflected values close to 1.

Fig. 2. Basic parameter, such as total organic C (Corg), total nitrogen (Ntot) and microbial biomass content (Cmic) as well as the soil clay content for the four different soil types. Significant (P < 0.05) differences between the soil types were given by lowercase letters.

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Fig. 3. Dynamic of total soil dsDNA and RNA contents as well as RNA:dsDNA ratio in non-activated soils (0 h) and after glucose amendment (24 and 72 h). Significant differences in time between the soil types were assessed by repeated measurements ANOVA including a Holmes-Sidak post-hoc correction. Capital letters indicate significant (P < 0.05) differences in time of the same soil type. Significant (P < 0.05) differences between the soil types were given by lower-case letters.

4. Discussion

Luvisol, the silt fraction was the dominant, and clay content was much higher than in Retisol. Therefore, glucose solution passed more slowly into the pore spaces of Luvisol. At the same time, soil microorganisms strongly adsorbed by clay could not move rapidly towards the zones of substrate availability. This led to lower microbial response in Luvisol compared to Retisol. The RNA:dsDNA ratios in soils were 10-times lower than the ratios obtained for pure cultures of various bacteria (Kerkhor and Ward, 1993). As cellular DNA concentration does not vary strongly due to environmental changes (Muttray et al., 2001), and because of its considerably strong correlation to microbial biomass in the wide range of soil types (Anderson and Martens, 2013; Blagodatskaya et al., 2003; Joergensen and Emmerling, 2006), we considered the quantitative evaluation of dsDNA as a stable indicator for microbial biomass. The dsDNA increased in all soils after glucose addition, indicating microbial growth in all soil types. Calcisol, with the lowest C- and N –content (Fig. 2), demonstrated a completely different pattern of metabolic activity and microbial growth compared to Retisol and Luvisol with intermediate C- and N –contents (Fig. 6). Calcisol revealed a strong increase in RNA content after glucose addition, reflecting higher microbial activity, whereas Retisol and Luvisol showed a slight increase in RNA. However, the microbial biomass doubled in the latter two soil types, indicating strong microbial growth. Remarkably, in contrast to fast and very strong increase in RNA within 24 h, the increase in DNA was low in nutrient-poor sandy Calcisol and was observed after essential delay in 72 h. This is a clear illustration of the events occurring during lag-time, e.g. switch of microbial metabolism from dormancy to growth. The RNA:dsDNA ratio increased during incubation for most of the soil types, especially for Calcisol (36-fold). Possibly, the microbial

4.1. RNA:dsDNA ratio Overall, we found three pattern of RNA:dsDNA ratio: 1) fast increase of RNA outgoing (anticipating) DNA increase indicating strong investments in protein synthesis preceding growth (Calcisol), 2) simultaneous increase of RNA and dsDNA indicating balanced growth (Luvisol), and 3) decelerated growth of RNA while DNA progressively increases (Retisol). This is consistent with batch culture studies, which reported a decrease of RNA:dsDNA ratio of activated microorganisms with time (Muttray et al., 2001). For Synechococcus and Prochlorococcus strains analyzed in pure culture, a three-phase relationship between growth and rRNA concentration was suggested: (1) at low growth rates, rRNA concentration remains constant, (2) at intermediate growth rates, rRNA content increases linearly with growth rate and (3) at higher growth rates, rRNA content decreases as growth rate increases (Blazewicz et al., 2013; Worden and Binder, 2003). The rRNA concentration was identified as a non-robust proxy for microbial growth rates and of current activity since changes in growth-associated activity must impact total activity (Worden and Binder, 2003). In the heterogeneous soil environment, this three-phase model may not be such straight forward processes as they are affected not solely by biotic but also by abiotic factors, e.g. by clay content. Considering strong differences in soil texture, we assume faster growth rates in Retisol than in Luvisol. Retisol and Luvisol had similar Corg, and Ntot values, but these two soils strongly differed in particle size distribution. Retisol was characterized by high content of coarse particles (> 63 μm) and by extremely low clay content (only 4%), which resulted in a lower absorption of glucose solution by soil particles and in the higher microbial response. In 608

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environmental samples, such as cell physiology (Licht et al., 1999), cell life history and life strategy (Lepp and Schmidt, 1998), rRNA synthesis and degradation rates (Gausing, 1977). Under certain stress conditions, e.g. the co-limitation of nutrients, microorganisms can dramatically increase the portion of metabolism geared toward non-growth maintenance activities (Schimel et al., 2007; Meyer et al., 2017). 4.2. Microbial respiratory responses High alkalinity and the presence of carbonates in arid and semi-arid soils such as the Calcisol lead to strong underestimation of microbial respiration and subsequently the microbial biomass C by the SIR approach, because of CO2 retention in soil solution due to high pH and CO32− exchange with carbonates. This clearly reflected the restriction of SIR method in alkaline soils, whereas dsDNA-derived microbial biomass C determinations are favored under such conditions (not shown). In line with Malik et al. (2015) we revealed sufficient correspondence between dsDNA, SIR-, and CFE-derived microbial biomass C for all soil types, except the Calcisol via SIR method. This indicated that cell lysis soluble extracts (via CFE) are strongly linked to microbial metabolism. Cellular material is intensively recycled by microorganisms. That microbial access to soluble C is a significant constraint to overall decomposition (Bailey et al., 2017). The metabolic quotient is known as a sensitive stress indicator to soil microorganisms (Killham, 1985; Pabst et al., 2016). One explanation for the 2-times higher metabolic quotient determined in Calcisol is the watering of the soil which may increase the stress-level of microorganisms in arid and semi-arid soils. 4.3. Effect of clay particles distribution on dsDNA and RNA recovery The RNA yield in nutrient-poor sandy Calcisol exceeded for hundredfold the RNA yields in clay-rich Chernozems after glucose amendment. The number of both bacterial and archaeal active cells determined by RNA-FISH method in Chernozem were 10-times higher than in Calcisol (Semenov et al., 2016). From methodological perspective, RNA-FISH provides direct intracellular detection of RNA and, therefore, avoiding its contact with RNases or soil environment. Indeed, external RNases may have declined total RNA recovery for all samples during RNA extraction procedure. Nevertheless, the RNA contents, with about 4 μg g−1 soil (non-activated) extracted from Retisol and Luvisol, corresponded well to the yields extracted from soil by Tournier et al. (2015). By contrast, the RNA contents extracted from the Chernozems were up to 30-times lower independently of soil activation, reflecting possible adhesion of RNA to clay particles, known to reduce RNA recovery in soils (Wang et al., 2012). Since the RNA content in the range of soils varied within the two orders of magnitude, this high difference in RNA yields could be the result of RNA underestimation in some of the soils due to various interfering factors (Ehlers et al., 2010). The correlation between dsDNA and clay content showed a positive linear relationship, indicating larger microbial biomass in clayey versus sandy soil. The RNA content was exponentially inversely related to the clay content, reflecting the interference of adsorption processes. The correlation between RNA:dsDNA ratios and clay contents corresponded well to the study of Tournier et al. (2015), and was negatively affected by high-clay soils (Novinscak and Filion, 2011). The RNA:dsDNA ratio, as an indicator of microbial activity is not only affected by biotic factors, such as the substrate quality (Loeppmann et al., 2016b), but also it strongly decrease with higher clay-contents, caused by immobilization of nutrients and organo-mineral associations (Vogel et al., 2014). Our study revealed unsatisfactory RNA recovery from soils with clay content above 30%. Another consequence of such small quantities of isolated RNA is that we are still unable to properly apply molecular biological RNA-based approaches (transcripts sequencing or quantification by RT-PCR, etc.) on clayey soils. Nonetheless, high reproducibility (by replicates) and low availability of RNA content enables

Fig. 4. a) dsDNA content vs. clay content; b) RNA content vs. clay content; and c) the RNA:dsDNA ratios vs. clay content after 24 and 72 h incubation with glucose were considered ( ± SE; n = 3).

Fig. 5. The metabolic quotient in non-activated soils determined as a ratio between basal respiration and CFE-derived microbial biomass C ( ± SE; n = 3). Significant (P < 0.05) differences between the soil types were given by lower-case letters.

investments in protein synthesis and secretion of enzymes are favored in Calcisol compared to other soil types, which is reflected by a large fraction of active biomass (Loeppmann et al., 2016a). Different factors affect the relationship between microbial activity and RNA in 609

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Fig. 6. RNA:dsdNA contents (initial and after glucose amendment) in relation to soil type, nutrient status (N), and clay content. The distribution of RNA of most of the soil types is fully consistent with the RNA:DNA patterns.

high RNA yield in clay-rich soils. In soils with low and intermediate clay contents the RNA:dsDNA ratio satisfactorily reflected the metabolic state of microbes. Therefore, the RNA:dsDNA ratio reflects an activity indicator, which can be incorporated into the microbial dormancy concept of microbial enzyme-mediated decomposition (MEND) model to improve the confident prediction capacity of SOM decomposition affected by climate warming. Physiological status indicators are valuable in modelling organic matter dynamics with land-use changes. Nevertheless, further research is required to capture kinetics in the context of temperature and moisture changes.

comparative monitoring of relative changes in dynamics. 4.4. Perspectives of linking RNA:DNA ratio with other soil attributes The RNA:DNA ratio provides potential as an ecological indicator of the physiological state of microorganisms. Therefore, it would be reasonable to compare the dynamics of the RNA:DNA ratio with other soil attributes determined by e.g. DNA- and RNA-based microbial community composition, isotope enrichment of DNA and RNA or the ratio of ribosomal RNA yield: total RNA yield derived from Bioanalyzer or Experion automated electrophoresis. Based on these indicators the prediction capacity of e.g. SOM decomposition models can be improved. The increase in absolute PLFA content in activated soil reflects the switch of dormant microorganisms to active state and not necessarily microbial growth (Dungait et al., 2011). The PLFA increase indicates an increase in cell-membrane content and is also related to an increase in mitochondrion number in active cells. Thus, the conversion factors for absolute PLFA content in active microbial biomass, still require experimental proof. Moreover, the sensitivity of PLFA as an indicator of the active microbial biomass is biased by the PLFAs originating from non-active cells or humic-acid-derived fatty acids (Nielsen and Petersen, 2000). Malik et al. (2015) concluded that the PLFA biomarkers may be less appropriate for monitoring fast carbon pulses that are often applied to study carbon fluxes in the rhizosphere.

Acknowledgments We would like to thank Prof. Eugeniy Milanovskiy and Anna Yudina for supplying with particle size distribution analysis and interpretation of obtained data. This research was supported by DAAD program «Forschnungsstipendien für Doktoranden und Nachwuchswissenschaftler» 2013–2014 and Russian Science Foundation, Project No 14-14-00625. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecolind.2017.11.020. References Anderson, J.P.E., Domsch, K.H., 1978. Physiological method for quantitative measurement of microbial biomass in soils. Soil Biol. Biochem. 10, 215–221. Anderson, T.H., Domsch, K.H., 1985. Determination of ecophysiological maintenance carbon requirements of soil microorganisms in a dormant state. Biol. Fertil. Soils 1 (2), 81–89. Anderson, T.H., Domsch, K.H., 1993. The metabolic quotient for CO2 (qCO2) as a specific activity parameter to assess the effects of environmental conditions, such as pH, on the microbial biomass of forest soils. Soil Biol. Biochem. 25, 393–395. Anderson, T.-H., Martens, R., 2013. DNA determinations during growth of soil microbial biomasses. Soil Biol. Biochem. 57, 487–495. Bürgmann, H., Widmer, F., Sigler, W.V., Zeyer, J., 2003. mRNA extraction and reverse transcription-PCR protocol for detection of nifH gene expression by Azotobacter vinelandii in soil. Appl. Environ. Microbiol. 69, 1928–1935. Bachoon, D.S., Otero, E., Hodson, R.E., 2001. Effects of humic substances on fluorometric DNA quantification and DNA hybridization. J. Microbiol. Methods 47, 73–82. Bailey, V.L., Smith, A.P., Tfaily, M., Fansler, S.J., Bond-Lamberty, B., 2017. Differences in soluble organic carbon chemistry in pore waters sampled from different pore size domains. Soil Biol. Biochem. 107, 133–143. Bakken, L.R., Frostegård, A., 2006. Nucleic acid extraction from soil. In: Nannipieri, P., Smalla, K. (Eds.), Soil Biology. Springer-Verlag, Berlin Heidelberg, pp. 49–73. Bending, G.D., Turner, M.K., Rayns, F., Marx, M.C., Wood, M., 2004. Microbial and biochemical soil quality indicators and their potential for differentiating areas under contrasting agricultural management regimes. Soil Biol. Biochem. 36 (11), 1785–1792. Black, C.A., 1965. Methods of Soil Analysis: Part 1 Physical and Mineralogical Properties. American Society of Agronomy Madison, WI, USA. Blagodatskaya, E., Kuzyakov, Y., 2013. Active microorganisms in soil: critical review of estimation criteria and approaches. Soil Biol. Biochem. 67, 192–211. Blagodatsky, Sergey, A., Heinemeyer, Otto, Richter, Jörg, 2000. Estimating the active and total soil microbial biomass by kinetic respiration analysis. Biol. Fertil. Soils 32.1, 73–81.

5. Conclusions The quantification of total soil dsDNA and RNA yields and determination of RNA:dsDNA ratios were successfully applied to reveal the physiological state of microorganisms in soil types of contrasting nutrient availability. The RNA:dsDNA ratio was measured to infer metabolic reconfiguration (i.e. investment in protein synthesis) versus investment in microbial growth. In general, soil RNA yields increased stronger than soil DNA yields after microbial stimulation by glucose addition. Consequently, the RNA:dsDNA ratio was mostly governed by the dynamics and the behavior of RNA. In contrast to Chernozems with highest N contents, the RNA:dsDNA ratio remained comparatively constant after soil activation in Retisol and Luvisol with intermediate N contents, concluding a more balanced microbial growth in the latter soil types. The RNA:dsDNA ratio extracted from sandy Calcisol increased strongly by 36-fold after glucose amendment, indicating rather intensive investment in non-growth processes. The RNA yields were strongly affected by the clay content of the soils, indicated by the lower RNA recovery in virgin and arable Chernozem compared to soil types with lower clay contents. This suggests, that the underestimation of RNA yields in clayey soils biased the RNA:dsDNA ratio, and subsequently the physiological state of the microbial community is not adequately represented in soils with clay contents exceeding 30%. Thus, development of RNA extraction methods is required to provide 610

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