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Seasonal and spatial variability in total and active bacterial communities from desert soil
Pedobiologia - Journal of Soil Ecology 74 (2019) 7–14
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Seasonal and spatial variability in total and active bacterial communities from desert soil C. Baubina,1, A.M. Farrellb,1, A. Šťovíčeka, L. Ghazaryana, I. Giladib,1, O. Gillora,
T
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a
Zuckerberg Institute for Water Research, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Israel The Mitrani Department of Desert Ecology, Swiss Institute for Dryland Environmental and Energy Research, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Israel
b
A R T I C LE I N FO
A B S T R A C T
Keywords: Ribosome Next generation sequencing Soil Arid Illumine Ecosystem engineer Fertility island
Studies describing the diversity of microorganisms in drylands are based mainly on the total (DNA), and seldom on the metabolically active (RNA) portion of the bacterial communities. We predicted that in desert environments, the majority of bacteria would display low activity during the hot and dry season, resulting in comparable diversity of the total and active communities. But during the wet periods, when rain activates certain bacterial groups, the total and active communities would differ. To test our predictions, the rDNA and rRNA extracted from desert soil, were analysed in samples collected during the dry and wet seasons from three patches: under the canopy of the dominant shrub, near ant nests and in open patches. The results disproved our predictions because the RNA- and DNA-based communities significantly differed in the dry season but matched very well in the wet season samples. Further comparative analysis of the rRNA/rDNA ratio revealed the composition and structure of metabolically active members within the patches’ communities. Our results suggest that in desert environments, the activity of soil bacterial communities is not restricted by water availability or patch types and could be fully described only, by concomitant analysis of the total and active members.
1. Introduction Hot desert environments are characterised by infrequent and unpredictable precipitation events, high evapotranspiration, irradiation, and large fluctuations in diurnal and annual temperatures. Desert landscape are dominated by areas of low productivity intercepted by patches of higher productivity, often associated with the presence of perennial plants and/or insect nests (Wilby et al., 2001; Wright et al., 2006). Shrubs and ant nest patches have been shown to support diverse communities of annuals (Wright et al., 2006), invertebrates (Wright et al., 2006) and microorganisms, including protozoa (Robinson et al., 2002), fungi (Camargo-Ricalde and Dhillion, 2003) and bacteria (Ginzburg et al., 2008; Bachar et al., 2012). The majority of studies describing desert soil microbial communities were based on the sequencing of the rRNA encoding genes. Consequently, the described total community includes, in addition to active cells, dormant and sporulating cells, (Makhalanyane et al., 2015) both representing major survival strategies in the harsh condition of arid climate (Bar et al., 2002; Chanal et al., 2006). In addition, extracellular DNA that is preserved in the soil by either adsorption to soil
particles or as component of the extra-polymeric substances that engulf soil biofilms (Costa et al., 2018) may also appear as part of the gene pool. In contrast, the analysis of ribosomes may describes the metabolically active parts of the community (all the active cells), even though studies have shown that dormant cells and spores contain ribosomes (Dworkin and Shah, 2010; Steven et al., 2017). Additionally, the number of ribosomes varies among different microorganisms, ranging from tens to thousands of copies (Dennis and Bremer, 2008), which could result in a differential biased estimation of the relative abundance of various operational taxonomic units (OTUs). Some studies, that co-analysed both the total and active soil microbial communities, revealed fundamentally different patterns (Blazewicz et al., 2013; Blagodatskaya and Kuzyakov, 2013). The discrepancies were justified by suggesting that the RNA-based community was a subset of the DNA-based community, indicating the presence of non-active taxa (Baldrian et al., 2012; Angel et al., 2013; Carini et al., 2016; Gill et al., 2017). It was suggested that in order to survive dry periods, most cells need to maintain very low metabolic activities to support growth independent functions, such as regulation of osmotic pressure, motility and more. Therefore, these cells may remain below
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Corresponding author at: Zuckerberg Institute for Water Research, Blaustein Institutes for Desert Research, Ben-Gurion University, 84990, Israel. E-mail address: [email protected] (O. Gillor). 1 These authors contributed equally to the study. https://doi.org/10.1016/j.pedobi.2019.02.001 Received 13 September 2018; Received in revised form 12 January 2019; Accepted 18 February 2019 0031-4056/ © 2019 Published by Elsevier GmbH.
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and physico-chemical analysis (at least 250 g).
the detection limit in the RNA-based community even though they are alive and active. However, sometimes, taxa were detected in the RNAbut not in the DNA-based community (Klein et al., 2016); a result that is counter intuitive. It has been suggested that such reversed pattern is due to the inclusion of rare members that are metabolically active (expressing thousands of ribosomes), but were below the detection limit in the DNA-based community due to their low abundance (Aanderud et al., 2015). Yet, how would the RNA- and the DNA-based communities differ in extreme environments where cells’ survival may depend on low metabolic activity? One promoter of change in a given bacterial community could result from fluctuations in environmental conditions that could lead to induction of metabolic activity without altering the total community. In this study, we compared the structure and composition of the RNA- and DNA-based bacterial communities in desert soil patches at the end of the wet (November to March) and dry (April to October) seasons. Soil samples were retrieved from three distinct habitats: barren soil, under the canopy of the predominant shrub; and in proximity to ant nests, both latter patches being considered ecosystem engineers or fertility islands in desert environments (Bachar et al., 2012; Gilad et al., 2004). We hypothesized that the presence of the ecosystem engineers, that alter the physical and chemical conditions of the soil (Pariente, 2002), would influence the soil microbial community (Ginzburg et al., 2008; Bachar et al., 2012). We also predicted that the modified resource distribution in the different patches would alter the metabolic activity of the soil bacterial communities, hence resulting in differences between rRNA- and rDNA- derived communities. Yet, we expected that metabolic activity of desert bacteria during the hot and dry season would be low and thus the diversity and composition of the total and active communities would follow similar patterns. In contrast, during the wet periods, when rain activates some soil bacterial taxa, we predicted that the total and active communities would differ.
2.3. Soil physico-chemical analysis Soil samples were dried at 65 °C and then analysed according to standard methods. The following soil characteristics were measured in Gilat Hasade Services Laboratory (Moshav Gilat, Israel): organic matter content by dichromate oxidation; pH and salinity (electrical conductivity – EC) in saturated soil extract (Angel et al., 2010). Soil water content was determined by gravimetric method (Bachar et al., 2012). 2.4. Microbial community Total nucleic acids were extracted from 0.5 g of soil as previously described (Angel et al., 2012) and the extract was divided equally for DNA and RNA purification. The DNA subsample was purified with the Exgene™ Soil SV kit (GeneAll, Seoul, Korea) according to the manufacturer’s instructions. The RNA subsample was purified with the MasterPure™ RNA purification kit (Epicenter, Madison, WI, USA) following the manufacturer’s protocol. The purified RNA was reverse transcribed with Im-PromII™ Reverse Transcription System (Promega, Madison, WI, USA) for 60 min at 42 °C, according to the manufacturer’s instructions. Both DNA and RNA subsamples were cleaned using the AccuPrep® PCR purification kit (BioNeer, Smith’s, Bermuda) according to the manufacturer’s protocol. All DNA samples were stored at −20 °C and RNA samples at −80 °C. 2.5. Amplification and sequencing The V3-V4 region of the 16S rRNA genes was amplified in triplicate using 341 forward (5′ CCTACGGGAGGCAGCAG 3′) and 806 reverse primer (5′ GGTCTGGACTACHVGGGTWTCTAAT 3′) (Klindworth et al., 2012). The PCR mixture consisted of 1 mM bovine serum albumin (Takara, Kusatsu, Japan), 2.5 μl 10× standard buffer, 0.2 μM 341 F and 10 μM 806R primers, 0.8 mM dNTPs, 0.4 μl DreamTaq DNA polymerase (Takara), 4 μl template (purified DNA/cDNA), and 12.6 μl RNAase-free water. PCR conditions were as follows: 95 °C for 30 s; 28 cycles of 95 °C for 15 s, 50 °C for 30 s, 68 °C for 30 s; 68 °C for 5 min. DNA concentration and purity were determined using 1% agarose gel electrophoresis and Nanodrop spectrophotometer ND-1000 (Thermo Fisher Scientific, Waltham, MA, USA). Sequencing libraries were constructed using TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, CA, USA) following the manufacturer's recommendations and index sequences (‘barcodes’) were added to the amplicons of each sample. The amplicon libraries were sequenced (250 base pairs, pair-end) on the Illumina MiSeq platform at the Research Resources Centre at the University of Illinois.
2. Material and methods 2.1. Study site The study was carried out in the Central Negev Desert, Israel (Zin Plateau, 34°80′E, 30°86′N) at a long-term ecological research (LTER) site. The site is located in an arid environment with mean annual precipitation of 90 mm and an annual average temperature of 30 °C (www. data.lter-europe.net). The vegetation is mainly composed of sparsely distributed perennial shrubs (Hammada scoparia and Atriplex halimus) and a short burst of annuals (mainly Stipa capensis, Calendula arvensis and Malva spp) at the end of the wet season (Olsvig-Whittaker et al., 1983). 2.2. Sampling scheme
2.6. Sequence analysis We have sampled three distinct patch types: (i) bulk soil (Open); (ii) under the canopy of the dominant perennial shrub H. scoparia after the removal of the litter (Shrub); and (iii) 20–30 cm from Messor ebeninus nests (Nest), a granivorous ant prevalent in the Negev Desert (Warburg and Steinberger, 1997). Soil samples were collected during 2015, in March, at the end of the wet season following an accumulative rainfall of 128.2 mm (˜40% above average rainfall; IMS, 2018); and in October, at the end of the dry season after six-months drought. The sampling was conducted in 14 experimental blocks randomly positioned in the study site, each containing the three patch types. Samples were collected from the top five centimetres of the soil, following the removal of crust and debris. In order to obtain a sufficient amount of soil with minimal disturbance to each block, samples for each patch types were compiled from two adjacent blocks (N = 28 soil samples from 7 blocks). The soil was processed within 24 h of collection. Each sample was pooled, homogenized through 2 mm sieve. Subsamples were stored in −80 °C for molecular analysis (at least 5 g)
The sequences were analysed using a UPARSE (Edgar, 2013) pipeline after paired-end reads were merged using CASPER (Kwon et al., 2014). The quality of the reads was controlled by eliminating any read with most probably number of errors per read equal to 0. Moreover, all singleton reads were discarded before the OTU clustering process. Representative sequences of the resulting OTUs were taxonomically assigned using the SINA aligner (Pruesse et al., 2012) against a SILVA 128 database (Quast et al., 2013). The total number of sequences and OTUs for the DNA- and RNA-based communities can be found in Supplementary Tables 1 and 2, respectively. All sequences retrieved in this study were uploaded to BioPrject (https://www.ncbi.nlm.nih.gov/ bioproject) submission number PRJNA484096. 2.7. Statistical analysis The sequences were adjusted to the sequencing depth (7404 8
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sequences for DNA and 6460 sequences for RNA). All statistical analyses were conducted in R (R Core Team, 2016). The data was analysed for the major part with the phyloseq (McMurdie et al., 2017) and the DESeq2 (Love et al., 2018) packages. The richness (Observed, Chao1, ACE), evenness (Camargo, Simpson, Pielou, Evar and Bulla) and diversity (Shannon, Simpson and Fisher) indices were determined. The bacterial community was represented with a non-metric multidimensional scaling (NMDS) plot based on Bray-Curtis dissimilarities for season and patch type and the group significance was tested with ANOSIM (Oksanen et al., 2014). In addition, an analysis of the effect of season and nucleic acid type was performed using DESeq2 (Love et al., 2018) package to compare the differential abundance of OTUs between the active and total communities.
Table 1 Significance values (p-values) calculated when testing the difference between seasons for all the soil physico-chemical characteristics. DF = Degree of freedom. Values in bold represent the significant values. Soil parameter
T(13)
DF
p-value
Water content Conductivity pH Ammonium Nitrate Phosphorus Organic matter
−7.18 1.051 −2.238 12.008 1.317 0.371 1.302
12 12 12 12 12 12 12
7.15E-06 0.312 0.0434 2.07E-08 0.211 0.717 0.215
were significantly different from the control in the wet season (DNA: R = 0.3839, p = 0.003; RNA: R = 0.434, p = 0.001) (Fig. 1 and Supplementary Table 3). Fig. 1 shows similar patterns both for the rDNAand rRNA-based communities. Indeed, both communities shared 90% of their orders.
3. Results 3.1. Diversity of DNA- and RNA-based communities The diversity and composition of the total and active soil bacterial communities were tested using ANOSIM showing no differences between patch types (RNA: R = 0.4343, p > 0.05, DNA: R = 0.4713, p > 0.05) in samples collected during the wet season (Fig. 1). Moreover, the wet season samples from all patch types did not differ (DNA: R = 0.09297, p = 0.003; RNA: R = 0.01306, p = 0.366) from the control samples collected during the dry season (Fig. 1). In contrast, the OTUs detected in the DNA and RNA extracted from samples collected under the canopy of H. scoparia and near ant nests during the dry season
3.2. Composition of DNA- and RNA-based communities 3.2.1. Seasonal effect The seasonal effect was tested using ANOVA univariate test with random block design and was found significant for electric conductivity (F(1,6) = 5.36 p = 0.060), water content (F(1,6) = 191.92 p < 0.001) and organic matter (F(1,6) = 27.20 p = 0.002), while the soil pH did not differ between seasons. Organic matter, and salinity (except in the
Fig. 1. NMDS (Nonmetric Multidimensional Scaling) for DNA (A) and RNA (B) based soil bacterial communities in different patch types in the dry and wet seasons. The NMDS shows the similarity between the open (circles), ant nest (triangles) and shrub (squares) patches. The wet and dry seasons are marked in red and blue colours, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). 9
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Fig. 2. Relative abundance of the soil bacterial phyla in DNA (A) and RNA (B) based communities in different patch types in the dry season. For the DNA and RNA detected phyla, only abundance of more than 5% and 1% respectively, were considered. The patch type is indicated on the x-axis and the different colours represent the different phyla present in the soil bacterial communities.
communities collected from the shrub and nest patches were dominated by the phyla Deinococcus-Thermus, Actinobacteria and Proteobacteria (on average 25, 10 and 39% of the community, respectively) (Fig. 2A). The communities of the control samples were dominated by Actinobacteria and Proteobacteria (on average 32 and 27% of the community, respectively), while the phylum Deinococcus-Thermus was not detected (Fig. 2A). The composition of the ribosome-based communities under the shrub canopy and near ant nests significantly differed from that in the control samples (t = 9.681, p < 0.05, Supplementary Table 3). The ribosome-based shrub and nest communities were dominated by the phyla Actinobacteria, Proteobacteria and Bacteroidetes though at higher abundance (in average 16, 44 and 16%, respectively) compared to the rDNA-based communities (Fig. 2). The control samples were also dominated by Actinobacteria and Proteobacteria (36 and 28% of the community), while Bacteriodetes comprised less than 5% of the community. The richness indices of the shrub and nest communities were higher than those of the control for both the DNA (t= -1.427 and t=-1.922, p < 0.01, respectively) and RNA (t= -3.161 and t=-1.907, p < 0.05, respectively) samples (Supplementary Table 4).
shrub patches) were higher in the dry season, while soil water content was four times higher in the wet season (Table 1). The evenness of both the DNA and RNA based communities was higher in the wet and the dry barren soil samples than in the dry shrub and nest samples (Supplementary Table 5). All bacterial communities were dominated by the Actinobacteria, Proteobacteria and Bacteroidetes phyla (Fig. 2). However, the phylum Deinococcus-Thermus accounted for about 17% of the rDNA-based communities collected in the dry seasons under shrub and in proximity to ant nests. Surprisingly, this phylum was not detected in the rRNAbased community (Fig. 2). Consequently, the relative abundance of Actinobacteria and Proteobacteria in the RNA community slightly exceeded those at the DNA community (Fig. 2). At the order level, the overall abundance was greater during the wet season both for the DNA (50 vs 32 orders) and RNA (53 vs 38 orders) samples (Supplementary Fig. 1). However, the changes in abundance for an order during the dry season were more pronounced on average, compared to the wet season for both the DNA (1.76 vs 1.50, respectively) and the RNA (2.95 and 1.76, respectively) communities. 3.2.2. Patch type effect The composition of the DNA-based communities under shrub canopy and near ant nests significantly differed from those in the control samples (t = 9.723, p < 0.05, Supplementary Table 3). The soil
3.2.3. Order level analysis To further follow the differences between the RNA- and DNA-based communities, abundances at the order level were evaluated and the 10
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Fig. 3. Relative abundance of the soil bacterial phyla in DNA (A) and RNA (B) based communities in different patch types in the wet season. For the DNA and RNA detected phyla, only abundance of more than 5% and 1% respectively, were considered. The patch type is indicated on the x-axis and the different colours represent the different phyla present in the soil bacterial communities.
the dry season. However, we found that taxa differed in their relative abundance (Figs. 2 and 3) between the rRNA and rDNA samples including selective emergence of taxa (Fig. 2). To our surprise these differences were more pronounced in the dry season (Figs. 1 and 2), a time when the communities were expected to be less metabolically active (Schulze-Makuch et al., 2018). The changes in taxa between the RNA and DNA samples could have been the result of dormant or dead members of the bacterial community (Blazewicz et al., 2013) or even ‘relic’ DNA (Carini et al., 2016) because they were not detect them in the wet season. The presence of Deinococcus-Thermus in the total community in the dry season represent this exact case. Indeed, these bacteria are not depicted by the RNA-based analysis and they are absent in the wet season (Fig. 2). These results raise the possibility that the molecular techniques impart bias in the sequencing coverage between the DNA and RNA pools (Tedersoo et al., 2010).
difference between the abundance of each order in the rRNA to rDNA pools calculated. We assessed the increase in the order’s abundance as reflected by the RNA compared to the DNA communities to detect the orders that are metabolically active, presuming that an active order will show larger abundance of ribosomes than the set number of rRNA encoding genes. The orders with the highest rRNA/rDNA ratio were Micrococcales (Actinobacteria) as well as Sphingomonadales and Rhizobiales (Alphaproteobacteria) that were rare in the rDNA community in both seasons (Fig. 3). Only one order, Burkholdeirales (Betaproteobacteria), was abundant in the DNA-pool but its ribosome was rare (Supplementary Fig. 2). The differences detected in the ribosome abundance of the order Streptomycetales (Actinobacteria) were significantly higher (t = 3.068, p < 0.001) near ant nests (40%) than in barren soil (0.5%) or under shrubs (18%) (Supplementary Fig. 3). The ribosome of Actinomicetales (Actinobacteria) and Methylophilalles (Betaproteobacteria) were not detected in the rDNA community, while Thermales (Deinococcus-Thermus), Acidimicrobiales (Actinobacteria) and Rhodobacterales (Alphaproteobacteria) were solely found in the DNA-based community (Supplementary Figs. 2 and 3).
4.1. Deinococcus-Thermus are present in the total but not active communities The structure and composition of the bacterial communities could provide insight into their adaptation to changing environments (Castro et al., 2010), bacterial assembly mechanisms (Nemergut et al., 2013) or key biogeochemical functions (Bardgett et al., 2008). In the wet season, similar members of the soil community were detected in barren soil, under shrubs and near ant nests regardless of the nucleic acid pool used for the comparison (Fig. 2). Yet in the dry season, one phylum,
4. Discussion In this study, we compared rDNA to rRNA based communities in desert soil bacteria across seasonal and spatial scales. The diversity of both communities was found to be higher during the wet compared to 11
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Fig. 4. Relationship between the rRNA to rDNA ratio and the mean abundance of the community (order level) in the dry (A) and wet (B) seasons. The orders are arranged ascendingly by their mean abundance. The change in abundance is presented as the ratio between each order abundance depicted by the RNA- to DNA-analyses [change in abundance calculated according to log2(rRNA)-log2(rDNA)]. The coloured orders are the most abundant and active order in the community.
it could be assumed that the RNA analysis missed this group’s increase upon hydration as samples were taken two weeks after a rain event, while the DNA analysis takes into account both current and past abundances suggesting that ‘relic’ DNA (Dlott et al., 2015) might account for the detected Burkholderiales increase. For Actinobacteria, the main difference between the DNA- and RNApools lays in the amount of Streptomycetales in the nest patch that increased in the RNA but not DNA pool. This taxa was shown to rapidly respond to environmental changes (Debroas et al., 2017) suggesting that in the wet season the Streptomycetales is more active, synthesizing more ribosomes. Therefore, the Streptomycetales might be better detected in the RNA-based community although their total abundance did not differ. When examining the orders abundance and distribution within the soil bacterial community, it seems that the RNA-community is more diverse than the DNA-based community (Fig. 3). The orders Rhizobiales, Sphingomonodales and Microccocales dominate the community both in the dry and the wet season and are more abundant in the RNA pool. The order Sphingomonodales are oligotrophic, aerobic anoxigenic and bacteria (Madigan et al., 2009) that may be active and increase in abundance even during the dry season. Microccocales are strictly aerobic gram-positive cocci that are resistant to a lower water potential and can tolerate drying (Madigan et al., 2009). As for Rhizobiales, these gram-negative alphaproteobacteria, they are often nitrogen fixers and can become symbiotic to a plant (Stacey, 2007) (Fig. 4).
Deinococcus-Thermus, appeared in high proportion only in the DNA communities near ant nests and under shrubs, while the same samples displayed low abundance after the wet season. This could suggest that this group is not active because the cells died and were present mainly as ‘relic’ DNA (Carini et al., 2016). Alternatively, the cells might be dormant, shutting down the ribosome synthesis mechanism (Blagodatskaya and Kuzyakov, 2013). Species within the DeinococcusThermus taxa were shown to contain approximately four genome copies during stationary or dormant phase (Hansen, 1978; Harsojo et al., 1981) with three to five copies of the 16S rRNA encoding gene per genome (Stoddard et al., 2015). A simple calculation suggest that a single dormant cell may contain at least 12 copies of the rRNA encoding genes, which could explain the high relative abundance detected in the DNA pool. Alternatively, the Deinococcus-Thermus taxa might not be depicted in the RNA community due to technical biases that result from unique properties of their ribosomal RNA (Mundus et al., 2016). The different alternatives are currently investigated.
4.2. Active orders The two dominant phyla, Actinobacteria and Proteobacteria, showed similar relative abundance in the RNA compared to the DNA pool (Supplementary Figs. 2 and 3). Members of both phyla are associated with many biogeochemical processes in soil including deserts (Sims et al., 1996; Kersters et al., 2006), and therefore it is only to be expected that they dominate the soils’ communities. The only noteworthy difference was the abundance of Burkholderiales (Proteobacteria) that was found to be higher in the DNA-based community (Supplementary Fig. 3). The abundance of this taxa was shown to increase following hydration events (Angel and Conrad, 2013). Therefore,
5. Conclusion In conclusion, even though the RNA-based community is more diverse than theDNA-based community, only a DNA-based analysis shows 12
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the strong presence of Deinococcus-Thermus. Therefore, the use of both of a comparative analysis of the DNA and the RNA communities provides the most complete picture of the desert soil community.
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Schulze-Makuch, D., Wagner, D., Kounaves, S.P., Mangelsdorf, K., Devine, K.G., de Vera, J.-P., Schmitt-Kopplin, P., Grossart, H.-P., Parro, V., Kaupenjohann, M., Galy, A., Schneider, B., Airo, A., Frösler, J., Davila, A.F., Arens, F.L., Cáceres, L., Cornejo, F.S., Carrizo, D., Dartnell, L., DiRuggiero, J., Flury, M., Ganzert, L., Gessner, M.O., Grathwohl, P., Guan, L., Heinz, J., Hess, M., Keppler, F., Maus, D., McKay, C.P., Meckenstock, R.U., Montgomery, W., Oberlin, E.A., Probst, A.J., Sáenz, J.S., Sattler, T., Schirmack, J., Sephton, M.A., Schloter, M., Uhl, J., Valenzuela, B., Vestergaard, G., Wörmer, L., Zamorano, P., 2018. Transitory microbial habitat in the hyperarid Atacama desert. Proc. Natl. Acad. Sci. 115, 2670–2675. https://doi.org/10.1073/ pnas.1714341115. Sims, R.A., Baldwin, K.A., Kershaw, H.M., Wang, Y., 1996. Tree species in relation to soil moisture regime in northwestern Ontario, Canada. Environ. Monit. Assess. 39, 471–484. https://doi.org/10.1007/BF00396162. Stacey, G., 2007. The rhizobium-legume nitrogen-fixing symbiosis. Biology of the Nitrogen Cycle. Elsevier, pp. 147–163. https://doi.org/10.1016/B978-044452857-5. 50011-4. Steven, B., Hesse, C., Soghigian, J., Gallegos-Graves, L.V., Dunbar, J., 2017. Simulated rRNA/DNA ratios show potential to misclassify active populations as dormant. Appl. Environ. Microbiol. 83. https://doi.org/10.1128/AEM.00696-17. Stoddard, S.F., Smith, B.J., Hein, R., Roller, B.R.K., Schmidt, T.M., 2015. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res. 43, D593–D598. https://doi. org/10.1093/nar/gku1201. Tedersoo, L., Nilsson, R.H., Abarenkov, K., Jairus, T., Sadam, A., Saar, I., Bahram, M., Bechem, E., Chuyong, G., Kõljalg, U., 2010. 454 Pyrosequencing and Sanger
Acknowledgement This study was supported by the Koshland Foundation to IG and OG. Additional support was provided by the Israel Science Foundation grant number 993/11 to OG. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.pedobi.2019.02.001. References Aanderud, Z.T., Jones, S.E., Fierer, N., Lennon, J.T., 2015. Resuscitation of the rare biosphere contributes to pulses of ecosystem activity. Front. Microbiol. 6. https://doi. org/10.3389/fmicb.2015.00024. Angel, R., Conrad, R., 2013. Elucidating the microbial resuscitation cascade in biological soil crusts following a simulated rain event. Environ. Microbiol. 15, 2799–2815. https://doi.org/10.1111/1462-2920.12140. Angel, R., Soares, M.I.M., Ungar, E.D., Gillor, O., 2010. Biogeography of soil archaea and bacteria along a steep precipitation gradient. ISME J. 4, 553–563. https://doi.org/10. 1038/ismej.2009.136. Angel, R., Claus, P., Conrad, R., 2012. Methanogenic archaea are globally ubiquitous in aerated soils and become active under wet anoxic conditions. ISME J. 6, 847–862. https://doi.org/10.1038/ismej.2011.141. Angel, R., Pasternak, Z., Soares, M.I.M., Conrad, R., Gillor, O., 2013. Active and total prokaryotic communities in dryland soils. FEMS Microbiol. Ecol. 86, 130–138. https://doi.org/10.1111/1574-6941.12155. Bachar, A., Soares, M.I., Gillor, O., 2012. The effect of resource islands on abundance and diversity of bacteria in arid soils. Microb. Ecol. 63, 694–700. https://doi.org/10. 1007/s00248-011-9957-x. Baldrian, P., Kolařík, M., Stursová, M., Kopecký, J., Valášková, V., Větrovský, T., Zifčáková, L., Snajdr, J., Rídl, J., Vlček, C., Voříšková, J., 2012. Active and total microbial communities in forest soil are largely different and highly stratified during decomposition. ISME J. 6, 248–258. https://doi.org/10.1038/ismej.2011.95. Bar, M., Hardenberg, J., Meron, E., Provenzale, A., 2002. Modelling the survival of bacteria in drylands: the advantage of being dormant. Proc. R. Soc. B Biol. Sci. 269, 937–942. https://doi.org/10.1098/rspb.2002.1958. Bardgett, R.D., Freeman, C., Ostle, N.J., 2008. Microbial contributions to climate change through carbon cycle feedbacks. ISME J. 2, 805. Blagodatskaya, E., Kuzyakov, Y., 2013. Active microorganisms in soil: critical review of estimation criteria and approaches. Soil Biol. Biochem. 67, 192–211. https://doi.org/ 10.1016/j.soilbio.2013.08.024. Blazewicz, S.J., Barnard, R.L., Daly, R.A., Firestone, M.K., 2013. Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. ISME J. 7, 2061–2068. https://doi.org/10.1038/ismej.2013.102. Camargo-Ricalde, S.L., Dhillion, S.S., 2003. Endemic Mimosa species can serve as mycorrhizal “resource islands” within semiarid communities of the Tehuacán-Cuicatlán Valley, Mexico. Mycorrhiza 13, 129–136. https://doi.org/10.1007/s00572-0020206-5. Carini, P., Marsden, P.J., Leff, J.W., Morgan, E.E., Strickland, M.S., Fierer, N., 2016. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat. Microbiol. 2, 16242. https://doi.org/10.1038/nmicrobiol.2016.242. Castro, H.F., Classen, A.T., Austin, E.E., Norby, R.J., Schadt, C.W., 2010. Soil microbial community responses to multiple experimental climate change drivers. Appl. Environ. Microbiol. 76, 999–1007. https://doi.org/10.1128/AEM.02874-09. Chanal, A., Chapon, V., Benzerara, K., Barakat, M., Christen, R., Achouak, W., Barras, F., Heulin, T., 2006. The desert of Tataouine: an extreme environment that hosts a wide diversity of microorganisms and radiotolerant bacteria. Environ. Microbiol. 8, 514–525. https://doi.org/10.1111/j.1462-2920.2005.00921.x. Costa, O.Y.A., Raaijmakers, J.M., Kuramae, E.E., 2018. Microbial extracellular polymeric substances: ecological function and impact on soil aggregation. Front. Microbiol. 9. https://doi.org/10.3389/fmicb.2018.01636. Debroas, D., Mone, A., Ter Halle, A., 2017. Plastics in the North Atlantic garbage patch: a boat-microbe for hitchhikers and plastic degraders. Sci. Total Environ. 599–600, 1222–1232. https://doi.org/10.1016/j.scitotenv.2017.05.059. Dennis, P.P., Bremer, H., 2008. Modulation of chemical composition and other parameters of the cell at different exponential growth rates. EcoSal Plus 3. https://doi. org/10.1128/ecosal.5.2.3. Dlott, G., Maul, J.E., Buyer, J., Yarwood, S., 2015. Microbial rRNA:rDNA gene ratios may be unexpectedly low due to extracellular DNA preservation in soils. J. Microbiol. Methods 115, 112–120. https://doi.org/10.1016/j.mimet.2015.05.027. Dworkin, J., Shah, I.M., 2010. Exit from dormancy in microbial organisms. Nat. Rev. Microbiol. 8, 890–896. https://doi.org/10.1038/nrmicro2453. Edgar, R.C., 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998. https://doi.org/10.1038/nmeth.2604.
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sequencing of tropical mycorrhizal fungi provide similar results but reveal substantial methodological biases. New Phytol. 188, 291–301. https://doi.org/10.1111/j.14698137.2010.03373.x. Warburg, I., Steinberger, Y., 1997. On the spatial distribution of nests of the antsMessor arenariusandMessor ebeninus. J. Arid Environ. 36, 671–676. https://doi.org/10. 1006/jare.1996.0245. Wilby, A., Shachak, M., Boeken, B., 2001. Integration of ecosystem engineering and
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Pedobiologia - Journal of Soil Ecology journal homepage: www.elsevier.com/locate/pedobi
Seasonal and spatial variability in total and active bacterial communities from desert soil C. Baubina,1, A.M. Farrellb,1, A. Šťovíčeka, L. Ghazaryana, I. Giladib,1, O. Gillora,
T
⁎,1
a
Zuckerberg Institute for Water Research, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Israel The Mitrani Department of Desert Ecology, Swiss Institute for Dryland Environmental and Energy Research, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Israel
b
A R T I C LE I N FO
A B S T R A C T
Keywords: Ribosome Next generation sequencing Soil Arid Illumine Ecosystem engineer Fertility island
Studies describing the diversity of microorganisms in drylands are based mainly on the total (DNA), and seldom on the metabolically active (RNA) portion of the bacterial communities. We predicted that in desert environments, the majority of bacteria would display low activity during the hot and dry season, resulting in comparable diversity of the total and active communities. But during the wet periods, when rain activates certain bacterial groups, the total and active communities would differ. To test our predictions, the rDNA and rRNA extracted from desert soil, were analysed in samples collected during the dry and wet seasons from three patches: under the canopy of the dominant shrub, near ant nests and in open patches. The results disproved our predictions because the RNA- and DNA-based communities significantly differed in the dry season but matched very well in the wet season samples. Further comparative analysis of the rRNA/rDNA ratio revealed the composition and structure of metabolically active members within the patches’ communities. Our results suggest that in desert environments, the activity of soil bacterial communities is not restricted by water availability or patch types and could be fully described only, by concomitant analysis of the total and active members.
1. Introduction Hot desert environments are characterised by infrequent and unpredictable precipitation events, high evapotranspiration, irradiation, and large fluctuations in diurnal and annual temperatures. Desert landscape are dominated by areas of low productivity intercepted by patches of higher productivity, often associated with the presence of perennial plants and/or insect nests (Wilby et al., 2001; Wright et al., 2006). Shrubs and ant nest patches have been shown to support diverse communities of annuals (Wright et al., 2006), invertebrates (Wright et al., 2006) and microorganisms, including protozoa (Robinson et al., 2002), fungi (Camargo-Ricalde and Dhillion, 2003) and bacteria (Ginzburg et al., 2008; Bachar et al., 2012). The majority of studies describing desert soil microbial communities were based on the sequencing of the rRNA encoding genes. Consequently, the described total community includes, in addition to active cells, dormant and sporulating cells, (Makhalanyane et al., 2015) both representing major survival strategies in the harsh condition of arid climate (Bar et al., 2002; Chanal et al., 2006). In addition, extracellular DNA that is preserved in the soil by either adsorption to soil
particles or as component of the extra-polymeric substances that engulf soil biofilms (Costa et al., 2018) may also appear as part of the gene pool. In contrast, the analysis of ribosomes may describes the metabolically active parts of the community (all the active cells), even though studies have shown that dormant cells and spores contain ribosomes (Dworkin and Shah, 2010; Steven et al., 2017). Additionally, the number of ribosomes varies among different microorganisms, ranging from tens to thousands of copies (Dennis and Bremer, 2008), which could result in a differential biased estimation of the relative abundance of various operational taxonomic units (OTUs). Some studies, that co-analysed both the total and active soil microbial communities, revealed fundamentally different patterns (Blazewicz et al., 2013; Blagodatskaya and Kuzyakov, 2013). The discrepancies were justified by suggesting that the RNA-based community was a subset of the DNA-based community, indicating the presence of non-active taxa (Baldrian et al., 2012; Angel et al., 2013; Carini et al., 2016; Gill et al., 2017). It was suggested that in order to survive dry periods, most cells need to maintain very low metabolic activities to support growth independent functions, such as regulation of osmotic pressure, motility and more. Therefore, these cells may remain below
⁎
Corresponding author at: Zuckerberg Institute for Water Research, Blaustein Institutes for Desert Research, Ben-Gurion University, 84990, Israel. E-mail address: [email protected] (O. Gillor). 1 These authors contributed equally to the study. https://doi.org/10.1016/j.pedobi.2019.02.001 Received 13 September 2018; Received in revised form 12 January 2019; Accepted 18 February 2019 0031-4056/ © 2019 Published by Elsevier GmbH.
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and physico-chemical analysis (at least 250 g).
the detection limit in the RNA-based community even though they are alive and active. However, sometimes, taxa were detected in the RNAbut not in the DNA-based community (Klein et al., 2016); a result that is counter intuitive. It has been suggested that such reversed pattern is due to the inclusion of rare members that are metabolically active (expressing thousands of ribosomes), but were below the detection limit in the DNA-based community due to their low abundance (Aanderud et al., 2015). Yet, how would the RNA- and the DNA-based communities differ in extreme environments where cells’ survival may depend on low metabolic activity? One promoter of change in a given bacterial community could result from fluctuations in environmental conditions that could lead to induction of metabolic activity without altering the total community. In this study, we compared the structure and composition of the RNA- and DNA-based bacterial communities in desert soil patches at the end of the wet (November to March) and dry (April to October) seasons. Soil samples were retrieved from three distinct habitats: barren soil, under the canopy of the predominant shrub; and in proximity to ant nests, both latter patches being considered ecosystem engineers or fertility islands in desert environments (Bachar et al., 2012; Gilad et al., 2004). We hypothesized that the presence of the ecosystem engineers, that alter the physical and chemical conditions of the soil (Pariente, 2002), would influence the soil microbial community (Ginzburg et al., 2008; Bachar et al., 2012). We also predicted that the modified resource distribution in the different patches would alter the metabolic activity of the soil bacterial communities, hence resulting in differences between rRNA- and rDNA- derived communities. Yet, we expected that metabolic activity of desert bacteria during the hot and dry season would be low and thus the diversity and composition of the total and active communities would follow similar patterns. In contrast, during the wet periods, when rain activates some soil bacterial taxa, we predicted that the total and active communities would differ.
2.3. Soil physico-chemical analysis Soil samples were dried at 65 °C and then analysed according to standard methods. The following soil characteristics were measured in Gilat Hasade Services Laboratory (Moshav Gilat, Israel): organic matter content by dichromate oxidation; pH and salinity (electrical conductivity – EC) in saturated soil extract (Angel et al., 2010). Soil water content was determined by gravimetric method (Bachar et al., 2012). 2.4. Microbial community Total nucleic acids were extracted from 0.5 g of soil as previously described (Angel et al., 2012) and the extract was divided equally for DNA and RNA purification. The DNA subsample was purified with the Exgene™ Soil SV kit (GeneAll, Seoul, Korea) according to the manufacturer’s instructions. The RNA subsample was purified with the MasterPure™ RNA purification kit (Epicenter, Madison, WI, USA) following the manufacturer’s protocol. The purified RNA was reverse transcribed with Im-PromII™ Reverse Transcription System (Promega, Madison, WI, USA) for 60 min at 42 °C, according to the manufacturer’s instructions. Both DNA and RNA subsamples were cleaned using the AccuPrep® PCR purification kit (BioNeer, Smith’s, Bermuda) according to the manufacturer’s protocol. All DNA samples were stored at −20 °C and RNA samples at −80 °C. 2.5. Amplification and sequencing The V3-V4 region of the 16S rRNA genes was amplified in triplicate using 341 forward (5′ CCTACGGGAGGCAGCAG 3′) and 806 reverse primer (5′ GGTCTGGACTACHVGGGTWTCTAAT 3′) (Klindworth et al., 2012). The PCR mixture consisted of 1 mM bovine serum albumin (Takara, Kusatsu, Japan), 2.5 μl 10× standard buffer, 0.2 μM 341 F and 10 μM 806R primers, 0.8 mM dNTPs, 0.4 μl DreamTaq DNA polymerase (Takara), 4 μl template (purified DNA/cDNA), and 12.6 μl RNAase-free water. PCR conditions were as follows: 95 °C for 30 s; 28 cycles of 95 °C for 15 s, 50 °C for 30 s, 68 °C for 30 s; 68 °C for 5 min. DNA concentration and purity were determined using 1% agarose gel electrophoresis and Nanodrop spectrophotometer ND-1000 (Thermo Fisher Scientific, Waltham, MA, USA). Sequencing libraries were constructed using TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, CA, USA) following the manufacturer's recommendations and index sequences (‘barcodes’) were added to the amplicons of each sample. The amplicon libraries were sequenced (250 base pairs, pair-end) on the Illumina MiSeq platform at the Research Resources Centre at the University of Illinois.
2. Material and methods 2.1. Study site The study was carried out in the Central Negev Desert, Israel (Zin Plateau, 34°80′E, 30°86′N) at a long-term ecological research (LTER) site. The site is located in an arid environment with mean annual precipitation of 90 mm and an annual average temperature of 30 °C (www. data.lter-europe.net). The vegetation is mainly composed of sparsely distributed perennial shrubs (Hammada scoparia and Atriplex halimus) and a short burst of annuals (mainly Stipa capensis, Calendula arvensis and Malva spp) at the end of the wet season (Olsvig-Whittaker et al., 1983). 2.2. Sampling scheme
2.6. Sequence analysis We have sampled three distinct patch types: (i) bulk soil (Open); (ii) under the canopy of the dominant perennial shrub H. scoparia after the removal of the litter (Shrub); and (iii) 20–30 cm from Messor ebeninus nests (Nest), a granivorous ant prevalent in the Negev Desert (Warburg and Steinberger, 1997). Soil samples were collected during 2015, in March, at the end of the wet season following an accumulative rainfall of 128.2 mm (˜40% above average rainfall; IMS, 2018); and in October, at the end of the dry season after six-months drought. The sampling was conducted in 14 experimental blocks randomly positioned in the study site, each containing the three patch types. Samples were collected from the top five centimetres of the soil, following the removal of crust and debris. In order to obtain a sufficient amount of soil with minimal disturbance to each block, samples for each patch types were compiled from two adjacent blocks (N = 28 soil samples from 7 blocks). The soil was processed within 24 h of collection. Each sample was pooled, homogenized through 2 mm sieve. Subsamples were stored in −80 °C for molecular analysis (at least 5 g)
The sequences were analysed using a UPARSE (Edgar, 2013) pipeline after paired-end reads were merged using CASPER (Kwon et al., 2014). The quality of the reads was controlled by eliminating any read with most probably number of errors per read equal to 0. Moreover, all singleton reads were discarded before the OTU clustering process. Representative sequences of the resulting OTUs were taxonomically assigned using the SINA aligner (Pruesse et al., 2012) against a SILVA 128 database (Quast et al., 2013). The total number of sequences and OTUs for the DNA- and RNA-based communities can be found in Supplementary Tables 1 and 2, respectively. All sequences retrieved in this study were uploaded to BioPrject (https://www.ncbi.nlm.nih.gov/ bioproject) submission number PRJNA484096. 2.7. Statistical analysis The sequences were adjusted to the sequencing depth (7404 8
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sequences for DNA and 6460 sequences for RNA). All statistical analyses were conducted in R (R Core Team, 2016). The data was analysed for the major part with the phyloseq (McMurdie et al., 2017) and the DESeq2 (Love et al., 2018) packages. The richness (Observed, Chao1, ACE), evenness (Camargo, Simpson, Pielou, Evar and Bulla) and diversity (Shannon, Simpson and Fisher) indices were determined. The bacterial community was represented with a non-metric multidimensional scaling (NMDS) plot based on Bray-Curtis dissimilarities for season and patch type and the group significance was tested with ANOSIM (Oksanen et al., 2014). In addition, an analysis of the effect of season and nucleic acid type was performed using DESeq2 (Love et al., 2018) package to compare the differential abundance of OTUs between the active and total communities.
Table 1 Significance values (p-values) calculated when testing the difference between seasons for all the soil physico-chemical characteristics. DF = Degree of freedom. Values in bold represent the significant values. Soil parameter
T(13)
DF
p-value
Water content Conductivity pH Ammonium Nitrate Phosphorus Organic matter
−7.18 1.051 −2.238 12.008 1.317 0.371 1.302
12 12 12 12 12 12 12
7.15E-06 0.312 0.0434 2.07E-08 0.211 0.717 0.215
were significantly different from the control in the wet season (DNA: R = 0.3839, p = 0.003; RNA: R = 0.434, p = 0.001) (Fig. 1 and Supplementary Table 3). Fig. 1 shows similar patterns both for the rDNAand rRNA-based communities. Indeed, both communities shared 90% of their orders.
3. Results 3.1. Diversity of DNA- and RNA-based communities The diversity and composition of the total and active soil bacterial communities were tested using ANOSIM showing no differences between patch types (RNA: R = 0.4343, p > 0.05, DNA: R = 0.4713, p > 0.05) in samples collected during the wet season (Fig. 1). Moreover, the wet season samples from all patch types did not differ (DNA: R = 0.09297, p = 0.003; RNA: R = 0.01306, p = 0.366) from the control samples collected during the dry season (Fig. 1). In contrast, the OTUs detected in the DNA and RNA extracted from samples collected under the canopy of H. scoparia and near ant nests during the dry season
3.2. Composition of DNA- and RNA-based communities 3.2.1. Seasonal effect The seasonal effect was tested using ANOVA univariate test with random block design and was found significant for electric conductivity (F(1,6) = 5.36 p = 0.060), water content (F(1,6) = 191.92 p < 0.001) and organic matter (F(1,6) = 27.20 p = 0.002), while the soil pH did not differ between seasons. Organic matter, and salinity (except in the
Fig. 1. NMDS (Nonmetric Multidimensional Scaling) for DNA (A) and RNA (B) based soil bacterial communities in different patch types in the dry and wet seasons. The NMDS shows the similarity between the open (circles), ant nest (triangles) and shrub (squares) patches. The wet and dry seasons are marked in red and blue colours, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). 9
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Fig. 2. Relative abundance of the soil bacterial phyla in DNA (A) and RNA (B) based communities in different patch types in the dry season. For the DNA and RNA detected phyla, only abundance of more than 5% and 1% respectively, were considered. The patch type is indicated on the x-axis and the different colours represent the different phyla present in the soil bacterial communities.
communities collected from the shrub and nest patches were dominated by the phyla Deinococcus-Thermus, Actinobacteria and Proteobacteria (on average 25, 10 and 39% of the community, respectively) (Fig. 2A). The communities of the control samples were dominated by Actinobacteria and Proteobacteria (on average 32 and 27% of the community, respectively), while the phylum Deinococcus-Thermus was not detected (Fig. 2A). The composition of the ribosome-based communities under the shrub canopy and near ant nests significantly differed from that in the control samples (t = 9.681, p < 0.05, Supplementary Table 3). The ribosome-based shrub and nest communities were dominated by the phyla Actinobacteria, Proteobacteria and Bacteroidetes though at higher abundance (in average 16, 44 and 16%, respectively) compared to the rDNA-based communities (Fig. 2). The control samples were also dominated by Actinobacteria and Proteobacteria (36 and 28% of the community), while Bacteriodetes comprised less than 5% of the community. The richness indices of the shrub and nest communities were higher than those of the control for both the DNA (t= -1.427 and t=-1.922, p < 0.01, respectively) and RNA (t= -3.161 and t=-1.907, p < 0.05, respectively) samples (Supplementary Table 4).
shrub patches) were higher in the dry season, while soil water content was four times higher in the wet season (Table 1). The evenness of both the DNA and RNA based communities was higher in the wet and the dry barren soil samples than in the dry shrub and nest samples (Supplementary Table 5). All bacterial communities were dominated by the Actinobacteria, Proteobacteria and Bacteroidetes phyla (Fig. 2). However, the phylum Deinococcus-Thermus accounted for about 17% of the rDNA-based communities collected in the dry seasons under shrub and in proximity to ant nests. Surprisingly, this phylum was not detected in the rRNAbased community (Fig. 2). Consequently, the relative abundance of Actinobacteria and Proteobacteria in the RNA community slightly exceeded those at the DNA community (Fig. 2). At the order level, the overall abundance was greater during the wet season both for the DNA (50 vs 32 orders) and RNA (53 vs 38 orders) samples (Supplementary Fig. 1). However, the changes in abundance for an order during the dry season were more pronounced on average, compared to the wet season for both the DNA (1.76 vs 1.50, respectively) and the RNA (2.95 and 1.76, respectively) communities. 3.2.2. Patch type effect The composition of the DNA-based communities under shrub canopy and near ant nests significantly differed from those in the control samples (t = 9.723, p < 0.05, Supplementary Table 3). The soil
3.2.3. Order level analysis To further follow the differences between the RNA- and DNA-based communities, abundances at the order level were evaluated and the 10
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Fig. 3. Relative abundance of the soil bacterial phyla in DNA (A) and RNA (B) based communities in different patch types in the wet season. For the DNA and RNA detected phyla, only abundance of more than 5% and 1% respectively, were considered. The patch type is indicated on the x-axis and the different colours represent the different phyla present in the soil bacterial communities.
the dry season. However, we found that taxa differed in their relative abundance (Figs. 2 and 3) between the rRNA and rDNA samples including selective emergence of taxa (Fig. 2). To our surprise these differences were more pronounced in the dry season (Figs. 1 and 2), a time when the communities were expected to be less metabolically active (Schulze-Makuch et al., 2018). The changes in taxa between the RNA and DNA samples could have been the result of dormant or dead members of the bacterial community (Blazewicz et al., 2013) or even ‘relic’ DNA (Carini et al., 2016) because they were not detect them in the wet season. The presence of Deinococcus-Thermus in the total community in the dry season represent this exact case. Indeed, these bacteria are not depicted by the RNA-based analysis and they are absent in the wet season (Fig. 2). These results raise the possibility that the molecular techniques impart bias in the sequencing coverage between the DNA and RNA pools (Tedersoo et al., 2010).
difference between the abundance of each order in the rRNA to rDNA pools calculated. We assessed the increase in the order’s abundance as reflected by the RNA compared to the DNA communities to detect the orders that are metabolically active, presuming that an active order will show larger abundance of ribosomes than the set number of rRNA encoding genes. The orders with the highest rRNA/rDNA ratio were Micrococcales (Actinobacteria) as well as Sphingomonadales and Rhizobiales (Alphaproteobacteria) that were rare in the rDNA community in both seasons (Fig. 3). Only one order, Burkholdeirales (Betaproteobacteria), was abundant in the DNA-pool but its ribosome was rare (Supplementary Fig. 2). The differences detected in the ribosome abundance of the order Streptomycetales (Actinobacteria) were significantly higher (t = 3.068, p < 0.001) near ant nests (40%) than in barren soil (0.5%) or under shrubs (18%) (Supplementary Fig. 3). The ribosome of Actinomicetales (Actinobacteria) and Methylophilalles (Betaproteobacteria) were not detected in the rDNA community, while Thermales (Deinococcus-Thermus), Acidimicrobiales (Actinobacteria) and Rhodobacterales (Alphaproteobacteria) were solely found in the DNA-based community (Supplementary Figs. 2 and 3).
4.1. Deinococcus-Thermus are present in the total but not active communities The structure and composition of the bacterial communities could provide insight into their adaptation to changing environments (Castro et al., 2010), bacterial assembly mechanisms (Nemergut et al., 2013) or key biogeochemical functions (Bardgett et al., 2008). In the wet season, similar members of the soil community were detected in barren soil, under shrubs and near ant nests regardless of the nucleic acid pool used for the comparison (Fig. 2). Yet in the dry season, one phylum,
4. Discussion In this study, we compared rDNA to rRNA based communities in desert soil bacteria across seasonal and spatial scales. The diversity of both communities was found to be higher during the wet compared to 11
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Fig. 4. Relationship between the rRNA to rDNA ratio and the mean abundance of the community (order level) in the dry (A) and wet (B) seasons. The orders are arranged ascendingly by their mean abundance. The change in abundance is presented as the ratio between each order abundance depicted by the RNA- to DNA-analyses [change in abundance calculated according to log2(rRNA)-log2(rDNA)]. The coloured orders are the most abundant and active order in the community.
it could be assumed that the RNA analysis missed this group’s increase upon hydration as samples were taken two weeks after a rain event, while the DNA analysis takes into account both current and past abundances suggesting that ‘relic’ DNA (Dlott et al., 2015) might account for the detected Burkholderiales increase. For Actinobacteria, the main difference between the DNA- and RNApools lays in the amount of Streptomycetales in the nest patch that increased in the RNA but not DNA pool. This taxa was shown to rapidly respond to environmental changes (Debroas et al., 2017) suggesting that in the wet season the Streptomycetales is more active, synthesizing more ribosomes. Therefore, the Streptomycetales might be better detected in the RNA-based community although their total abundance did not differ. When examining the orders abundance and distribution within the soil bacterial community, it seems that the RNA-community is more diverse than the DNA-based community (Fig. 3). The orders Rhizobiales, Sphingomonodales and Microccocales dominate the community both in the dry and the wet season and are more abundant in the RNA pool. The order Sphingomonodales are oligotrophic, aerobic anoxigenic and bacteria (Madigan et al., 2009) that may be active and increase in abundance even during the dry season. Microccocales are strictly aerobic gram-positive cocci that are resistant to a lower water potential and can tolerate drying (Madigan et al., 2009). As for Rhizobiales, these gram-negative alphaproteobacteria, they are often nitrogen fixers and can become symbiotic to a plant (Stacey, 2007) (Fig. 4).
Deinococcus-Thermus, appeared in high proportion only in the DNA communities near ant nests and under shrubs, while the same samples displayed low abundance after the wet season. This could suggest that this group is not active because the cells died and were present mainly as ‘relic’ DNA (Carini et al., 2016). Alternatively, the cells might be dormant, shutting down the ribosome synthesis mechanism (Blagodatskaya and Kuzyakov, 2013). Species within the DeinococcusThermus taxa were shown to contain approximately four genome copies during stationary or dormant phase (Hansen, 1978; Harsojo et al., 1981) with three to five copies of the 16S rRNA encoding gene per genome (Stoddard et al., 2015). A simple calculation suggest that a single dormant cell may contain at least 12 copies of the rRNA encoding genes, which could explain the high relative abundance detected in the DNA pool. Alternatively, the Deinococcus-Thermus taxa might not be depicted in the RNA community due to technical biases that result from unique properties of their ribosomal RNA (Mundus et al., 2016). The different alternatives are currently investigated.
4.2. Active orders The two dominant phyla, Actinobacteria and Proteobacteria, showed similar relative abundance in the RNA compared to the DNA pool (Supplementary Figs. 2 and 3). Members of both phyla are associated with many biogeochemical processes in soil including deserts (Sims et al., 1996; Kersters et al., 2006), and therefore it is only to be expected that they dominate the soils’ communities. The only noteworthy difference was the abundance of Burkholderiales (Proteobacteria) that was found to be higher in the DNA-based community (Supplementary Fig. 3). The abundance of this taxa was shown to increase following hydration events (Angel and Conrad, 2013). Therefore,
5. Conclusion In conclusion, even though the RNA-based community is more diverse than theDNA-based community, only a DNA-based analysis shows 12
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the strong presence of Deinococcus-Thermus. Therefore, the use of both of a comparative analysis of the DNA and the RNA communities provides the most complete picture of the desert soil community.
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