Impact of Different Methods of Soil Sampling and DNA Extraction on the Identification of Soil Bacterial Abundance under Elevated CO2

DOI: 10.4236/oalib.1103527   PDF   HTML   XML   319 Downloads   734 Views  

Abstract

Elevated atmospheric CO2 (eCO2) is anticipated to have marked effects on soil microbial populations. While there is experimental evidence to support this view there are also studies where changes in microbial populations, such as the abundance of bacteria, are suggested but cannot be statistically established. We conducted this study to identify whether the sampling and sample treatment methods used could influence the results obtained using bacterial abundance as the variable of interest. We tested three different sampling methods and two different DNA extraction kits. The first because microbes are distributed heterogeneously in soil so the sampling procedure might be expected to influence the accuracy and precision of the population estimate and the second because the quantity and quality of DNA extracted influences the microbial analyses that can be performed and can introduce bias. Samples were taken from a long-running FACE experiment on grassland from under plants ofAgrostis capillaris. We found that bacterial abundance was consistently lower under eCO2 but we were only able to establish a statistical difference where a more intense sampling regime was used and bulking of the soil sample was avoided. A reduction in bacterial abundance is a consistent outcome in eCO2 field experiments but the only other occasion where this reduction has been found to be significant was also where individual soil cores were analysed rather than the samples being bulked. We conclude that while there is extra work and cost attached to more detailed sampling this approach is highly desirable if we are to make robust conclusions about the impacts of eCO2 on soil microbes.

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Li, F. , Bowatte, S. , Newton, P. and Luo, D. (2017) Impact of Different Methods of Soil Sampling and DNA Extraction on the Identification of Soil Bacterial Abundance under Elevated CO2. Open Access Library Journal, 4, 1-12. doi: 10.4236/oalib.1103527.

1. Introduction

There is strong experimental evidence that soil properties are altered by elevated concentrations of atmospheric CO2 (eCO2) [1] [2] [3] . These changes have important consequences for agricultural production through issues such as fertiliser use [4] [5] and the nutritional composition of crops [6] , for the function of natural ecosystems including biodiversity and for future concentrations of atmospheric CO2 as soils may act a source or sink for carbon (C) [7] [8] . Changes in soil properties are primarily a consequence of the activities of soil microbes [9] [10] (Schimel and Bennett 2004; Van Der Heijden et al. 2008) which are themselves responding to changes in their environment and resources resulting from plant responses to eCO2 [11] [12] [13] [14] [15] . A full understanding of the likely direction of change in soil properties and the mechanism causing these changes therefore requires an understanding of microbial population size and activity under eCO2 [16] [14] .

Measuring microbial populations is difficult. Two factors that are particularly challenging are a) ensuring that the soil sampling methodology provides an accurate estimation of the population given that microbes are frequently distributed heterogeneously in soil [17] [18] [19] ; and b) ensuring that the method chosen for DNA extraction is appropriate as the choice of method potentially influences the precision and accuracy of the data and thus the statistical inferences that can be drawn about the microbial populations [20] [21] [18] [19] . Table 1 shows examples where ‘differences’ have been observed in bacterial abundance under eCO2 but these effects have not been statistically significant. Additionally there are costs in both time and materials that may differ between sampling methods and have a bearing on the choice of protocol.

In this paper we compare methods for quantifying bacterial populations in a pasture exposed to eCO2 in a long-running Free Air Carbon Dioxide Enrichment (FACE) experiment on grassland [22] . We took soil samples from under a specific grass species―Agrostis capillaris L.; soil samples from this species have previously been shown to differ in their nitrification potential depending on the atmospheric CO2 concentration so we expected associated changes in bacterial population abundance [11] . We tested three sampling approaches and two methods of DNA extraction commonly used in eCO2 experiments [23] [24] [25] [26] [27] .

2. Materials and Methods

2.1. New Zealand Free Air Carbon Dioxide Enrichment Experiment (NZFACE)

The NZFACE is situated on a mixed plant species sheep-grazed pasture near

Table 1. Soil sampling protocol and DNA extraction kit used in field experiments with elevated CO2 and the impact of eCO2 on bacterial abundance measured as copy number of the 16S rRNA gene and showing whether the result was significant (sig.) or non-significant (NS).

Bulls (40˚14'S, 173˚16'E) in the North Island of New Zealand. The average annual temperature at the site is 12.9˚C and average total annual rainfall 870 mm. The experiment consists of six rings of 12 m in diameter with three of these left at ambient CO2 (aCO2) and three enriched with CO2 during the photoperiod, with enrichment continuous throughout the year. From October 1997 until December 2011 the enrichment was to 475 ppm. The CO2 enrichment was suspended in December 2011 until July 2013 to conduct technical maintenance and then resumed with a target concentration of 500 ppm. From 1997 to 2011 the rings were grazed periodically by adult sheep under a protocol described in Newton et al (2014) [28] ; thereafter (from 2013 onwards) the rings were cut with a mower and the clippings removed from the site. In October 2012 an irrigation system was installed and all the rings were irrigated to maintain pasture growth during periods of water deficit (usually December-February).

The pasture is a permanent grassland―uncultivated for at least 50 years― containing a mixture of C3 and C4 grasses, forbs and legumes [29] [28] . C3 grasses are the most abundant group with Agrostis capillaris (browntop), a grass typically found in low-fertility situations [30] , one of the dominant species under both ambient CO2 (aCO2) and eCO2 [31] .

The soil at the site is a black loamy fine-sand (a Mollic Psammaquent). During the course of the experiment fertiliser was added―superphosphate, potassium and magnesium sulphate―to maintain soil concentrations at recommended levels [32] (Cornforth and Sinclair 1984). No nitrogen (N) fertiliser was added, the N inputs coming solely from biological nitrogen fixation by the legume species. Details of soil properties and changes over time can be found in [2] .

2.2. Soil Sampling

In January 2014 soil samples were collected for this experiment. Patches of browntop were identified and soil was sampled from these sites. Soil cores were taken to a depth of 75 mm using a 25 mm diameter steel soil corer. There were five browntop patches in each ring and a core was taken from each patch. Three experimental protocols were then used: first (Figure 1(A)) DNA was extracted

Figure 1. Schematic to show the different experimental protocols compared in this paper.

from each of the five cores sampled per ring and qPCR completed on all the samples; second (Figure 1(B)), the five soil cores from each ring were bulked and DNA extracted from this single sample per ring; third (Figure 1(C)) as for the first protocol (A) but the DNA was bulked after extraction giving a single sample per ring for qPCR. Note that in each case n = 3 for the CO2 treatment but there were five qPCR samples per ring for method A) and one qPCR sample for (B) and (C). Importantly, the three approaches used the same soil cores thus allowing a direct comparison of the protocols and extraction kits. The soil samples were stored at −20˚C until DNA extraction. A sub-sample of soil was used to measure soil moisture by oven-drying at 105˚C for 24 h.

2.3. DNA Extraction from the Soil and DNA Quality and Quantity Assessment

Two DNA extraction kits were evaluated in this study, the FastDNA Spin kit for soil (MP Biomedicals, Solon, OH, USA) (hereafter described as the Fast kit) and the MoBio PowerSoil DNA isolation kit (MO BIO, Carlsbad, CA, USA) (hereafter described as the MoBio kit). All operations were conducted according to the manufactures instructions. DNA was extracted from 0.5 g fresh soil (approximately 0.3 g dry soil) for the Fast kit and 0.25 g fresh soil (approximately 0.15 g dry soil) for the MoBio kit. The quantity and quality of extracted DNA was assessed on the basis of absorbance at 260 and 280 nm using the NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA). An A260/A280 ratio of 1.8 - 2.0 is indicative of high purity [33] [34] . All extracted DNA was stored at −20˚C until required for further analysis.

2.4. Real-Time PCR Analysis

The abundance of the 16S rRNA gene of bacteria was quantified in triplicate by real-time PCR using a LightCyclerTM 480П (Roche, Vienna, Austria). The real time PCR reaction mixture contained 10 µl 10 × SYBR Green iCycler iQ mixture (Roche Applied Science, Germany), 0.2 µl each of the primers Bacteria 968F; AACGCGAAGAACCTTAC and Bacteria 1378R; CGGTGTGTACAAGGCCCG GAACG [35] [36] , 5µl diluted soil DNA (1 ng/µl) and 4.6 µl ultra-pure water in a total of 20 µl reaction volume. Standard curves were obtained using serial dilutions of a known amount of plasmid DNA containing a fragment of the respective genes. The thermal cycle conditions were 95˚C 10 s, 58˚C 10 s, 72˚C 10 s, 45 cycles. Copy numbers of all gene targets were normalized based on grams of dry soil.

2.5. Statistical Analysis

The effects of atmospheric CO2, sampling protocol and DNA extraction kits were tested by a mixed effects model with heterogeneous variance structure using the packages “nlm” and “predictmeans” in R version 3.1 [37] . The effect of DNA extraction kit and the CO2 on the DNA yield (ng DNA/g dry soil) and quality (A260/A280 ratio) were tested using the data from individual soil cores. The effect of elevated atmospheric CO2, and sampling protocol on bacterial 16S rRNA gene abundance were conducted separately for the two DNA extraction kits. The mixed effects model used had the CO2 ring nested within block as a random effect while atmospheric CO2 concentration, sampling protocol and their interaction were considered as fixed effects. Mean separation was performed using Fisher’s LSD test.

3. Results

3.1. The Effect of Different Sampling Extraction Kits on DNA Yield under Elevated CO2

The DNA yield from the MoBio kit was significantly lower than that from the Fast kit (P < 0.001) (Figure 2(A)). The DNA yield from both kits was significantly lower in the eCO2 soil and the percentage reduction was similar (22% for MoBio and 25% for Fast kit) (Figure 2(A)). The DNA extracted from aCO2 and eCO2 soils was of the same quality (CO2 P = 0.357) but the MoBio kit extracted significantly (P < 0.001) higher quality DNA than the Fast kit irrespective of the CO2 treatment (Figure 2(B)).

3.2. The Effect of Different Extraction Kits on Bacterial Abundance under Elevated CO2

Comparing the values between Figure 3(A) and Figure 3(B) (note the different scales) shows bacterial abundance measured as 16S rRNA copy number was much lower measured from the DNA extracted from the MoBio kit compared to the DNA from the Fast kit. There was no significant effect (P = 0.722) of the experimental protocol on bacterial abundance measured from the DNA of the MoBio kit (Figure 3(A)); by contrast, a significant difference between the sam-

Figure 2. The (A) yield and (B) quality of DNA extracted from soil samples taken under Agrostis capillaris plants growing under ambient (filled bars) or elevated (open bars) atmospheric CO2 using two DNA extraction kits and soil sampling method (A) from Figure 1. * denotes significant difference between CO2 treatments using Fisher’s LSD test at the P ≤ 0.05 level.

Figure 3. The abundance of bacteria in soil sampled under plants growing at ambient (closed bar) or elevated (open bars) atmospheric CO2 measured as copy numbers of the 16S rRNA gene and identified using three experimental protocols and two DNA extraction kits: (A) MoBio kit, (B) Fast kit. ** denotes significant difference between CO2 treatments using Fisher’s LSD test at the P ≤ 0.01 level.

pling method was evident when using the Fast kit (P < 0.001) (Figure 3(B)). The DNA from both kits showed soil bacterial abundance at eCO2 was marginally lower (P = 0.100 MoBio and 0.084 Fast) compared to aCO2. However, only the individual soil core method was able to detect this difference at a significant level as calculated using Fisher’s LSD at the P ≤ 0.01 level (Figure 3).

4. Discussion

4.1. Effect of Different Methods of Soil Sampling and DNA Extraction on the Identification of Quantity and Quality under Elevated CO2

The DNA extraction rates convert to about 7 µg DNA g dry soil-1 for the Fast kit and 2 µg DNA g dry soil-1 for the MoBio kit. These values are higher than those reported byJossi et al. (2006) [16] in another grassland under elevated CO2 where the average values for two perennial grasses were 6.0 and 8.1 µg DNA g fresh soil-1. The amount of DNA extracted was significantly greater using the Fast kit and this kit extracted a significantly greater amount from the aCO2 compared to the eCO2 soil. The greater amount of DNA extracted by the Fast kit is consistent with much higher 16s rRNA gene copy numbers using this kit. Similar to our studyVishnivetskaya et al (2014) [38] extracted more DNA using the Fast kit than the MoBio kit from permafrost soils in the Arctic; although in their study both the Fast and MoBio kits extracted high quality DNA (see Table 1 of Vishnivetskaya et al (2014) for comparison of the kits) [38] . The DNA extracted by the MoBio kit in our study was of a higher quality than that extracted using the Fast kit. The quantity and quality of the soil DNA extracted are important for downstream techniques to analyse microbial community structure [39] . Feinstein et al (2009) found that incomplete DNA extraction from soil resulted in biased estimates of ribosomal copy number and bacterial community composition [40] . The quality of DNA characterised by A260/280 ratio is important as it indicates co-extraction of impurities such as humic substances with DNA that can inhibit the activity of polymerase chain reaction [41] .

4.2. Effect of Different Methods of Soil Sampling and DNA Extraction on the Bacterial Abundance and Cost of the 16S rRNA Gene under Elevated CO2

We found that bacterial abundance measured as copy number of the 16S rRNA gene and showing whether the result was significant or non-significant was more impacted by the soil sampling approach than the DNA extraction kit (Table 1). The soil sampling approach most widely used in CO2 studies is the bulked soil method providing a single soil sample per FACE ring or OTC; examples where bacterial abundance has been measured are given in Table 1 and there are further examples where the object was to study bacterial community structure and diversity [42] [16] . The results for bacterial abundance in eCO2 field experiments shows a consistent reduction in abundance with eCO2; the only exception in the experiments listed being a slight and non-significant response in one set of measurements in this paper (Figure 3(B)). However, despite the consistent response across experiments there were only three examples where the bacterial abundance was significantly different between aCO2 and eCO2 and this was when individual soil cores were studies rather than a bulked sample (Table 1).

Using the individual core method meant that in our case 30 samples were required to be analysed compared to 6 for the bulk sampled method. The approximate total cost for single gene abundance (DNA extraction and qPCR) for 30 samples was 5 fold higher than for 6 bulked samples but similar for the two kits. There was also 3 fold increase in technical time required for the individual soil core approach compared to the bulked soil sample approach. Although the cost of analysis of individual soil core approach can be reduced by 1.5 fold by extracting DNA from individual cores and then bulking the DNA for qPCR (method C) in Figure 1) the statistical analysis indicated that this method resulted in high variability and ultimately a non-significant difference between treatments although the same trend was observed as with individual soil core method.

5. Conclusions

Three different sampling methods and two different DNA extraction kits tested. The first because microbes are distributed heterogeneously in soil so the sampling procedure might be expected to influence the accuracy and precision of the population estimate and the second because the quantity and quality of DNA extracted influences the microbial analyses that can be performed and can introduce bias. We found that bacterial abundance was consistently lower under eCO2 but we were only able to establish a statistical difference where a more intense sampling regime was used and bulking of the soil sample was avoided. A reduction in bacterial abundance is a consistent outcome in eCO2 field experiments but the only other occasion where this reduction has been found to be significant was also where individual soil cores were analysed rather than the samples being bulked. We conclude that while there is extra work and cost attached to more detailed sampling this approach is highly desirable if we are to make robust conclusions about the impacts of eCO2 on soil microbes.

Our study demonstrated that increasing number of replicates per CO2 treatment improves the precision of microbial population estimates and that the extra effort and cost is likely to be a justifiable investment. This aspect of experimentation has not received attention in CO2 experiments in the past and our results highlights that the choice of soil sampling protocol and DNA extraction method needs to be considered carefully if microbial responses to eCO2 are to be characterised accurately.

Acknowledgements

Research costs were provided by AgResearch. The China Scholarship Council provided a Research Fellowship to Fengxia Li. The authors are grateful to Phil Theobald and Shona Brock for technical assistance.

Conflicts of Interest

The authors declare no conflicts of interest.

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