Article pubs.acs.org/est
Tracking the Fate of Microbially Sequestered Carbon Dioxide in Soil Organic Matter Kris M. Hart,† Anna N. Kulakova,‡ Christopher C. R. Allen,*,‡ Andre J. Simpson,§ Seth F. Oppenheimer,∥ Hussain Masoom,§ Denis Courtier-Murias,§,△ Ronald Soong,§ Leonid A. Kulakov,‡ Paul V. Flanagan,‡ Brian T. Murphy,† and Brian P. Kelleher*,†,⊥ †
School of Chemical Sciences, Dublin City University, Glasnevin, Dublin 9, Ireland The School of Biological Sciences, Queen’s University Belfast, Medical Biology Centre, Lisburn Road, Belfast, BT9 5AG, Northern Ireland § Department of Chemistry, Division of Physical and Environmental Science, University of Toronto at Scarborough, 1265 Military Trail, Toronto, Ontario M1C 1A4, Canada ∥ Department of Mathematics and Statistics, Shackouls Honors College, Mississippi State University, Mississippi State, Mississippi 39762, United States ⊥ The Irish Separation Science Cluster, Ireland ‡
S Supporting Information *
ABSTRACT: The microbial contribution to soil organic matter (SOM) has recently been shown to be much larger than previously thought and thus its role in carbon sequestration may also be underestimated. In this study we employ 13C (13CO2) to assess the potential CO2 sequestration capacity of soil chemoautotrophic bacteria and combine nuclear magnetic resonance (NMR) with stable isotope probing (SIP), techniques that independently make use of the isotopic enrichment of soil microbial biomass. In this way molecular information generated from NMR is linked with identification of microbes responsible for carbon capture. A mathematical model is developed to determine real-time CO2 flux so that net sequestration can be calculated. Twenty-eight groups of bacteria showing close homologies with existing species were identified. Surprisingly, Ralstonia eutropha was the dominant group. Through NMR we observed the formation of lipids, carbohydrates, and proteins produced directly from CO2 utilized by microbial biomass. The component of SOM directly associated with CO2 capture was calculated at 2.86 mg C (89.21 mg kg−1) after 48 h. This approach can differentiate between SOM derived through microbial uptake of CO2 and other SOM constituents and represents a first step in tracking the fate and dynamics of microbial biomass in soil.
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INTRODUCTION The sequestration of CO2 in soil represents a potential solution to rising atmospheric carbon concentrations.1,2 Our knowledge of the long-term storage of carbon in soils is limited and there is disagreement as to what constitutes stable and unstable (labile) carbon.3,4 Soil complexity makes the tracking of carbon from different sources in varying soil systems a challenge. Overcoming this challenge would allow us to track the fate of carbon in soil over time and thus develop and verify management procedures that encourage carbon stabilization in soil. Furthermore, there is now a realization that the contribution of microbes to soil organic matter (SOM) has been seriously underestimated.5−9 Any attempt to store carbon in soil for long periods will have to be coupled with an understanding of the uptake of carbon by soil microorganisms and the fate of this carbon in soil. Chemoautotrophic bacteria are ubiquitous in most soil types10 and uniquely they derive energy from inorganic © XXXX American Chemical Society
substrates using both aerobic and anaerobic respiration pathways.11 They are significant as they can continue to fix CO2 while in the absence of light and organic matter, and therefore, large scale CO2 consumption could be taking place in varied and diverse locations (e.g., deep subsurface, aquifers, cave systems, lake beds) provided that a constant stream of appropriate chemical electron donors is cycled through the system. The study of microbial ecology has been greatly advanced by the development of DNA stable isotope probing (SIP).12−16 The advantage of SIP is that substrates containing unnaturally high concentrations of rare isotopes can be used to identify the activity of specific taxa within a mixed or unknown culture. SIP Received: December 14, 2012 Revised: April 18, 2013 Accepted: April 24, 2013
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dx.doi.org/10.1021/es3050696 | Environ. Sci. Technol. XXXX, XXX, XXX−XXX
Environmental Science & Technology
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External CO2 concentration (the laboratory atmosphere) varies over time according to environmental conditions. The laboratory atmospheric flux of CO2 (in the vicinity of the ECIC) was measured in three sequential experiments using a pSense portable CO2 detector (AQ Controls Ltd., Stomnevagen, Sweden), each lasting for 7 days to determine the mean concentration (data not shown). We treat the external CO2 concentration as constant using the average external CO2 flux (data not shown), fixing it at a mean value C̅ = 401.6 ppm. Final conversion factor for ppm to g is given as ((xvd)/10 × 105), where chamber volume (v) is 40.059 L, density (d) is 1.97 g L−1, and x is CO2 (ppm). Soil respiration was incorporated into the correction factor when determining atmospheric removal rates of CO2 at a particular partial pressure. Respiration rates are variable for each soil and dependent on current conditions, and therefore control soil incubations are carried out with each different soil to determine respiration rate. For a more detailed account of the algorithm, please see Hart et al.25 Determination of the 13C mass for all experimental samples was carried out according to the following equation, where A is the mass of sample (g), B is the total carbon content (%), C is the total 13C content (%), and Z is the unknown mass of 13C of the sample (g):
can be used to provide a snapshot of the key players in soil autotrophy after incubation of samples in conjunction with isotopically labeled gases (e.g., 13CO2), followed by molecular analysis of the 13C-labeled biomaterial, especially nucleic acids.17 These techniques can be used to analyze the incorporation of autotrophically fixed CO2 into the soil. NMR spectroscopy is a powerful approach for describing the molecular characteristics of microbial biomass in soil and SOM.18,19 Increasing the relative abundance of (13C) through isotopic enrichment greatly enhances NMR sensitivity20 and also intensifies through-bond and through-space couplings that occur between atoms that form the basis of structural identification.21,22 The introduction of 13C into soil organic components can increase the sensitivity of 13C−13C by ∼10 000. Therefore, 13C NMR makes a powerful structural tool to both quantify and identify carbon assimilated from a 13C enriched atmosphere. Although autotrophy is a well-known process, studies that employ stable isotopes to identify autotrophic microorganisms are uncommon in the literature.23,24 Here we employ 13Clabeling (13CO2) of temperate soil dwelling chemoautotrophic bacteria with the emphasis on combining, for the first time, NMR and DNA-SIP to assess the fate of the inorganic 13Clabeled substrate in soil. NMR and SIP can independently make use of the isotopic enrichment of soil biomass and effectively allow us to link the molecular incorporation of atmospheric carbon into bacteria (NMR) while identifying the taxa responsible (SIP). We quantitatively measured the consumption of CO2 (i.e., the substrate) directly into biomaterial following the chemoautotrophic oxidation of S2O32−. This was carried out in a soil/slurry microcosm, under elevated CO2 concentrations, using both NMR and a computational algorithm as separate determinations of uptake. The work presented here demonstrates how multidisciplinary techniques can be combined to track and quantify the fate of inorganic atmospheric carbon within complex matrices such as soil.
Z = (ABC)/10 000
To determine the percent consumption (and hence, respiration) of CO2 the following equation was used, where D is the % of carbon in CO2, E is the total amount of substrate consumed (g), F is the mass of 13C retained in the OM fraction at T48 (g), and X is the percent of 13C retained in the OM fraction at T48: X = (F × 100%)/((ED)/100%)
(3)
Statistical analysis (unmatched t test) was carried out using Dont Panic III.26 Culture Conditions for 13C Microbial Biomass. Preparation of a heterotrophic soil biome, grown exclusively on 13C −glucose (C6H12O6) as the additional source of carbon, was performed to determine the incorporation of 13C into biomass. This allowed us to draw comparisons with the dynamic soil biome created to propagate chemoautotrophy. The soil culture was prepared from serial dilutions of a 0.1-g soil aliquots (n = 3) and continuously grown on 1 mM 12 C 6H 12O 6 amended MSM (shaken at 120 rpm) and subcultured every 48 h into fresh (sterile) media (n = 4). Final subculturing required two sets of triplicate samples subdivided between 99% 13C and 12C isotopically labeled 1 mM C6H12O6 substrates. Two separate sets of triplicate controls were prepared as above, with one prepared without addition of soil culture and another devoid of glucose substrate. After 48 h, 100 mL of each culture was centrifuged to collect biomass, washed twice with a phosphate buffer to remove salts, and freeze-dried. Supernatant of a 5.0-mL aliquot (0.2-μm filtered) was collected to determine substrate concentration using a glucose assay HK kit (Sigma-Aldrich). UV−vis absorbance analysis was carried out using a Varian Cary UV−vis 50 Spectrometer (Agilent Technologies, UK) at 340 nm. CHN analysis was carried out on 50 mg of biomass, and 100 mg of biomass was subjected to solid state NMR according to the protocol laid out below with the exception that 10% hydrofluoric acid (HF) treatment was not carried out. Nuclear Magnetic Resonance. All soil samples were freeze-dried and subjected to 10% HF treatment to remove
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MATERIALS AND METHODS Soil Incubations. Surface soil (0−10 cm depth) was collected (approximately 5 kg) from Hampstead Park (aka, Albert College Park), Glasnevin, Dublin, Ireland and transported immediately to the laboratory. For details on the site, pretreatment, incubation method, and the environmental carbon dioxide incubation chamber (ECIC) please see Hart et al.25 and Supporting Information (SI) Section S1. CO2 Uptake Calculations and Statistics. High resolution [CO2] data consisting of measurements taken every 30 s were taken throughout each incubation (n = 3). Average CO2 decay rate values during the experimental events were derived by selecting the CO2 data and subtracting the final from the initial recorded value and then dividing by the total time of the CO2 uptake event (hc). We treat the concentration of CO2 in the chamber in ppm as a function of time, where time will be measured in minutes, C(t). We consider only two mechanisms that can cause a change in the CO2 concentration in the chamber: (1) outgassing and (2) some soil/organism action that is either taking up or releasing CO2. In this case, the model looks like dC = hc(C̅ − C(t )) + f (t ) dt
(2)
(1)
where C̅ is the external CO2 concentration, C is the internal CO2 concentration, and f is an unknown source/sink of CO2. B
dx.doi.org/10.1021/es3050696 | Environ. Sci. Technol. XXXX, XXX, XXX−XXX
Environmental Science & Technology
Article
silicates and other magnetic minerals.27 Briefly, ∼30 g of soil was placed into 250-mL polypropylene centrifuge tubes with 100 mL of 10% HF and shaken at 100 reciprocals per minute for 24 h. Samples were spun down at 6400 g for 20 min and the supernatant was discarded. The process was repeated 20×. The samples were neutralized by adding sterile, double-distilled water until the supernatant measured pH 6. Neutralized soils were freeze-dried and stored at −80 °C until analysis. Solid State 13C NMR Analysis. For solid state 13C analysis, samples were packed into 4-mm zirconium oxide rotors with Kel-F rotor caps. 13C cross-polarization with magic angle spinning (CP-MAS) NMR spectra were acquired using a Bruker Avance III 500 MHz spectrometer (Bruker Biospin, Canada) equipped with a Bruker 4-mm H-X MAS probe. Spectra were acquired at 298 K with a spinning rate of 13 kHz, a ramp-CP contact time of 1 ms, 1 s recycle delay, 8192 scans, 1024 time domain points, and 1H decoupling using Spinal64. Spectra were processed using the Bruker Topspin software (version 2.1) with a filling factor of 2 and exponential multiplication resulting in a line broadening of 30 Hz in the final transformed spectrum. Spectral subtractions to produce the difference spectra were performed in the interactive mode of Topspin 2.1. High Resolution−Magic Angle Spinning (HR-MAS) NMR Analysis. Prior to NMR analysis, samples and materials that came into direct contact with the samples (zirconium oxide rotors, Kel-F caps, Kel-F sealing rings, steel spatula, and pipet tips) were dried for one week over phosphorus pentoxide (P2O5) under vacuum at room temperature to reduce traces of molecular water that would interfere with NMR spectra. A 40mg portion of dry sample was then weighed directly in a 4-mm zirconium oxide rotor and 60 μL of DMSO-d6 was added as a swelling solvent. After homogenization using a stainless steel mixing rod, the rotor was doubly sealed using a Kel-F sealing ring and a Kel-F rotor cap. HR-MAS NMR spectra were acquired using a Bruker Avance III 500 MHz spectrometer (Bruker Biospin) equipped with a Bruker 4-mm triple resonance (1H, 13C, 15N) HR-MAS probe with an actively shielded Z gradient and a spinning speed of 6.66 kHz. All HRMAS experiments were acquired at 298 K. Proton (1H) experiments were acquired with 256 scans, 4096 time domain points, and a recycle delay of 2 s. Solvent suppression was achieved by presaturation utilizing relaxation gradients and echoes.28 1H HR-MAS spectra were processed with a zerofilling factor of 2 and exponential multiplication, resulting in a line broadening of 2 Hz in the transformed spectrum. 1H−13C heteronuclear single quantum coherence (HSQC) spectra were collected in phase sensitive mode using Echo/Antiecho-TPPI gradient selection but without sensitivity enhancement. Scans (2048) were collected for each of the 96 increments in the F1 dimension. A relaxation delay of 1 s was employed with 1024 time domain points collected in F2 and a 1J 1H−13C of 145 Hz. The F2 dimension was multiplied by an exponential function corresponding to a 15 Hz line broadening while the F1 dimension was processed using sine-squared functions with phase shifts of π/2. Both dimensions were zero-filled by a factor of 2. Quantification from HSQC was done in the multiintegration mode of AMIX 3.8.7. Regions were defined as follows: protein (phenylalanine resonance) 1H 7−7.3 ppm, 13C 125−130 ppm; lignin (methoxy signal) 1H 3.6−3.8 ppm, 13C 54−58 ppm; carbohydrates (CH2 signal) 1H 3.4−3.6 ppm, 13C 58−63 ppm; lipids (CH2 β to COOH), 1H 1.1−1.33 ppm, 13C 26−32 ppm.
Isopycnic CsCl Gradient Ultracentrifugation. DNA was extracted and purified using FASTDNA Spin Kit for Soil (MP Biomedicals, Illkirch, France) according to the manufacturer’s instructions. DNA quantification was determined using an ND100 Nanodrop Spectrometer (NanoDrop Technologies, Wilmington, DE, USA). CsCl gradient ultracentrifugation was performed on ∼5 μg of DNA. The CsCl gradient was prepared according to Neufeld et al.29 following the ethidium bromide (EtBr) protocol and gradient fractionation. Density of the CsCl stock solution was determined to be 1.791 g mL−1 according to refractive index (RI) measurements (ATAGO-R-5000). Ultracentrifuge conditions were 395 833g, 16 h, and 20 °C (Beckman Coulter Optima TLX-120 Ultracentrifuge). Each CsCl/DNA sample tube was divided into 12 liquid fractions (300 μL) for 12CO2 (n = 2) and 13CO2 (n = 2) incubations. Temperature-corrected buoyant densities were determined according to Buckley et al.30 DNA was precipitated by adding 1.0 μL of glycogen and 2 equal volumes of 30% polyethylene glycol (PEG) (46.8 g NaCl and 150.0 g PEG-6000/500 mL and autoclaved at 120 °C for 15 min). Construction and Analysis of 16S rRNA Library. 16S rRNA genes were amplified with the 63f and 1387r primers from the density gradient 1.592 g mL−1.31 The following temperature profile was used: denaturation at 95 °C for 3 min, followed by 30 cycles of 94 °C for 30 s, 55 °C for 30 s, and 72 °C for 1 min 30 s. DNA amplicon of appropriate size (∼1.3 kb) was purified using High Pure PCR Product Purification kit (Roche Diagnostics GmbH), cloned into pJET1.2/blunt cloning vector (ThermoScientific/Fermentas) and transformed into E. coli α-Select chemically competent cells (BioLine, UK). At least 150 colonies of transformants were screened by PCR using vector primers as suggested by the supplier (to avoid coamplification of E.coli host-cell DNA). Positive transformants (clones carrying an insert of correct size) were identified by 1% agarose gel electrophoresis. Second, nested PCR was performed using primers 63f and 1387r with first PCR products as templates. Aliquots (6.7 μL) of individual PCR products were digested with two restriction enzymes (RsaI + HpaII) for 10− 16 h according to the supplier’s (ThermoScientific/Fermentas) instructions. After thermal inactivation of the enzymes, fragments were sized by electrophoresis on a 1.5% agarose gel. The resulting RFLP patterns were used to classify clones into operational taxonomic units (OTUs). Not less than 10% of clones from each OTU group were sequenced (Dundee Sequencing Service). The sequences of analyzed clones reported in this study were deposited in GenBank database with Accessions JN835225−JN835255. Phylogenetic Analysis of the Microbial Community. Searches for nucleotide sequence similarities were carried out using the BLAST program33 in GenBank; the reference genomic sequences, nucleotide collection, and NCBI Genome databases were used. Three percent of clones identified were E. coli (Group VII) and were assumed to be artifacts. Phylogenetic analysis of alignments and construction of trees were conducted with MEGA 4.32 Trees were generated by neighbor joining using the Maximum Composite Likelihood model.34 To evaluate tree phylogenies, these were also constructed by Maximum Parsimony and UPGMA methods. Phylogenies obtained by these methods were similar, and the trees obtained by neighbor joining are presented. For bootstrap analysis, 1000 data sets were generated. Initial tree(s) for the heuristic search were obtained automatically as follows. When the number of common sites was
