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Using NMR-based metabolomics to monitor the biochemical composition of agricultural soils: A pilot study
European Journal of Soil Biology 83 (2017) 98–105
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European Journal of Soil Biology journal homepage: www.elsevier.com/locate/ejsobi
Using NMR-based metabolomics to monitor the biochemical composition of agricultural soils: A pilot study
MARK
Christian W. Johnsa, Alex B. Leeb, Tzvia I. Springerc,1, Erin N. Rosskopfd, Jason C. Hongd, William Turechekd, Nancy Kokalis-Burelled, Natosha L. Finleya,c,∗ a
Cell, Molecular, and Structural Biology Program, Miami University, 700 E. High Street, Oxford, OH, 45056, USA Department of Computer Science and Software Engineering, Miami University, 700 E. High Street, Oxford, OH, 45056, USA c Department of Microbiology, Miami University, 700 E. High Street, Oxford, OH, 45056, USA d USDA-ARS, U.S. Horticultural Research Laboratory, 2001 S. Rock Road, Fort Pierce, FL, 34945, USA b
A R T I C L E I N F O
A B S T R A C T
Handling Editor: Y. Kuzyakov
NMR-based metabolomics plays a major role in the study of complex living systems. While there is a known connection between soil microbial metabolism and productivity in agricultural systems, very few researchers describe the application of NMR to the evaluation of agricultural soil metabolomes. Here, we introduce a simple protocol for the NMR metabolic analysis of biochemical compounds from agricultural soils where microbial communities are influenced by the application of anaerobic soil disinfestation (ASD). Following ASD treatment, aqueous metabolites were extracted from soil samples and the compound identities of the resulting mixtures were determined using 1D and 2D NMR in combination with metabolome database searches. It was found that the ASD treatments altered the metabolite composition of soil as evidenced by the detection of natural biochemical products such as organic acids. Principle component analysis revealed distinct biochemical differences between non-treated and ASD-treated soils. Our findings support that this protocol is efficacious in the rapid and reliable determination of metabolite profiles even within complex mixtures obtained from ASD-treated agricultural soils. Thus, NMR spectroscopy has the potential to impact soil science by investigating the adaptable biochemical fingerprint of soil metabolomes in agricultural fields, knowledge of which may be used to improve crop production.
Keywords: Nuclear magnetic resonance 2D NMR Metabolomics Organic acid Soil
1. Introduction Soil is composed of both organic and inorganic chemical constituents as well as a complex microbial community, the combination of which is better described as the soil metabolome. Microbes contribute to nutrient cycling and the general biological complexity found within soil [1]. The interplay between the soil metabolome and crop production is an area of intensive study. The composition and diversity of soil microbes influence agricultural yield [2], but the many mechanisms contributing to this biological phenomenon remain to be determined. Ongoing efforts are directed toward characterizing the soil microbiome using genetic approaches, which provide details about the make-up of the microbial community [3], but very little is known about how the soil metabolome as a whole impacts disease resistance in plants and
food production. Nuclear magnetic resonance (NMR) is a rapid and effective tool proven to be useful in the identification and quantification of compounds in biological samples. Despite being relatively insensitive, NMR spectroscopy yields highly reproducible data and is unparalleled in its reliably in determining compound structures in metabolomics studies. Furthermore, NMR spectroscopy does not require separation of compounds from biological mixtures and is noninvasive. While NMR-based metabolomics has proven useful in the study of normal and diseased states in biological systems [4], there is considerably less information available for the study of agricultural soil metabolomes. Although NMR has been used to examine cross-linked humic substances [5] and water soluble organic matter [6], which are important features in the composition of organic matter in soil, efforts have also been directed toward
Abbreviations: ASD, anaerobic soil disinfestation; ALG, Algae; BSM, black strap molasses; CBL, composted broiler litter; cm, centimeter; DSS, 4,4-dimethyl-4-silapentane-1-sulfonic acid; HPLC, High performance liquid chromatography; NMR, nuclear magnetic resonance; NaN3, sodium azide ∗ Corresponding author. Cell, Molecular, and Structural Biology Program, Department of Microbiology, Miami University, 700 East High Street, 32 Pearson Hall, Oxford, OH, 45056, USA. E-mail address: fi[email protected] (N.L. Finley). 1 Present address: Department of Biophysics, Medical College of Wisconsin, 8701 W. Watertown Plank Road, Milwaukee, Wisconsin, 2042, USA. https://doi.org/10.1016/j.ejsobi.2017.10.008 Received 1 July 2017; Received in revised form 27 October 2017; Accepted 28 October 2017 1164-5563/ © 2017 Elsevier Masson SAS. All rights reserved.
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and 13.9 m3 ha−1 molasses (ALG/BSM). Composted broiler litter and algae were incorporated as a nitrogen source for crop production and rates were adjusted based on nitrogen mineralization rates to provide a similar quantity of plant-available N for each treatment. Application procedures followed those previously described [9,10]. Briefly, amendments were applied and incorporated into the top 20 cm of the bed top by rotovation. Two drip irrigation lines (30.5-cm emitter spacing, 0.91 L ha−1 emitter rate) were installed at the same time that black/white 0.03-mm polyethylene VaporSafe® totally impermeable film (TIF) was applied to the beds (black side up). Irrigation was applied to soil saturation, approximately 5 cm, to each of the ASD plots. The NT received irrigation, was covered with TIF, but received no amendment. Two oxidation-reduction probes (Pt combination electrodes, Ag/AgCl reference, Sensorex, Garden Grove, CA) were installed to a depth of 15 cm to measure redox potential. Electrodes were monitored using an automatic data logging system (CR-1000 with AM 16/32 multiplexers, Campbell Scientific, Logan, UT) and the collected data was used to calculate the cumulative number of hours under anaerobic conditions. Hourly average soil redox potential values below the calculated critical redox potential (CEh) were considered to be indicative of anaerobic conditions. The CEh values were determined using the formula: CEh – [595(mV) – 60 (mV0] x soil pH [10]. Five soil cores were sampled from each plot 21 days post-treatment using a soil probe (1.75 cm inner diameter, 15 cm length). Soil cores collected from each plot were homogenized manually. In order to minimize the time required for 2D NMR analysis in this pilot study, four replications were sampled for each ASD treatment, while three replications of NT were sampled. Soil samples were flash frozen and stored at −80 °C prior to extraction.
understanding the adaptable, metabolic profiles of soil communities. Two notable reports have focused on NMR-based metabolomic studies of soil samples and demonstrate promise for the development of comprehensive techniques to characterize the biochemical features of industrial and agricultural soils [7,8]. As summarized by these research groups, an improved understanding of the correlation between soil chemistry, microbial metabolism, and industrial/agricultural practices is critical to soil science in general with potential benefits including the optimization of ecologically-sustainable farming practices and bioremediation of polluted soils. It is known that manipulation of the soil metabolome impacts the chemical and biological environment of agricultural soils [3,9–11]. The composition of the soil microbial community can be altered by controlling nutrient availability and oxygen concentration in the system. When soils are modified by anaerobic soil disinfestation (ASD) implemented prior to planting crops, this technique provides a potential alternative to chemical soil fumigation [12,13]. ASD involves the incorporation of a labile carbon source into the soil, tarping the beds with an oxygen impermeable plastic film mulch, then irrigating to soil saturation to limit oxygen availability and promote growth of beneficial facultative anaerobes [10]. It is speculated that low oxygen availability and/or the accumulation of organic acids during ASD treatment results in soils that suppress soil-borne pests of crops [9,14]. Currently, there is a critical gap in our understanding of how ASD-induced metabolites impact agricultural productivity. When evaluating complex mixtures extracted from agricultural soils, spectral overlap in one dimensional (1D) nuclear magnetic resonance (NMR) experiments may complicate compound characterization. To circumvent this problem, the usage of two dimensional (2D) NMR experiments provides increased resolution, which facilitates the validation of compound identification. The purpose of this study was to develop an effective, flexible method for the extraction of small molecules from soil samples and to utilize 1D and 2D NMR experiments in combination with multivariate statistical analyses to evaluate the influence of ASD on metabolite composition. In particular, we were interested in developing a method to monitor the organic compound composition of complex mixtures extracted from samples subjected to ASD treatment. We showed that many metabolites were detectable in the NMR spectra of complex mixtures, but that discrete compounds are readily identifiable, even without performing chromatographic separation, which makes this approach amendable to high-throughput applications. Our analyses found that organic acids, including lactic, acetic, butyric, succinic, and malic acids were the compounds that most impacted the soil metabolome during ASD treatment, which is similar to previous reports of microbial fermentation processes in the soil [14]. We found that our method readily detects that the relative organic acid composition varies depending on the type of labile carbon source applied to the sample. Taken together, these data support that this protocol allows for fast, economical, and consistent characterization of the metabolic profiles of soil microbial communities modulated by ASD treatments. We shed light on the utility of NMR-based metabolomics in the assessment of agricultural practices aimed at managing soil-borne pathogens and parasites.
2.2. Sample preparation Soil samples were thawed on ice and 0.5 g was subjected to extraction using 2 mL of Millipore-purified, sterile water followed by sonication on ice in 30 s bursts for a total of 2 min for each replicated sample. No effort was made to limit our study to metabolites originating from the microbial community alone. Our technique included any water soluble biochemical compound present in the NT and ASDtreated soils during this characterization. In this way, the metabolites isolated under our experimental conditions reflected the chemical composition of the entire soil matrix, which included compounds derived from plants, soil, or microbes that were present at the time of the study. The aqueous extract of each sample was separated from the residual soil matrix by centrifugation for 20 min at 15,000 rpm at 4 °C. The solutions were filter-sterilized using a 0.2 μM filter, frozen using liquid N2, lyophilized, and stored at −80 °C until analyzed. The resulting dry powder samples were dissolved in 640 μL of 50 mM phosphate buffer (pH 7.5) containing 100% deuterium oxide supplemented with 500 μM 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS) and 500 μM sodium azide (NaN3).
2.3. NMR data collection, processing, and analysis 2. Methods NMR experiments were performed on Bruker Avance (Bruker Biospin) spectrometers operating at 600 MHz and 850 MHz 1H frequencies and equipped with 5 mm triple resonance probes at 298 K. For each sample, one-dimensional (1D) 1H NMR presaturation experiments were performed. For added resolution and to verify compound identity, two-dimensional (2D) correlation spectroscopy (COSY), (2D) 1H-1H total correlation spectroscopy (TOCSY) spectra, and 2D 1H-13C heteronuclear single-quantum coherence spectroscopy (HSQC) experiments were collected. Data were processed, baseline corrected, referenced relative to DSS, and analyzed using NMRDraw, NMRPipe, and Sparky [15].
2.1. Soil treatments Soils for NMR analysis were collected from a strawberry production system experimental trial in which the ASD treatments were replicated four times and the non-treated control (NT) was replicated three times in a randomized complete block design. Treatments included: NT to which no organic amendments were added, ASD utilizing soil amendments of 13 Mg ha−1 of composted broiler litter and 13.9 m3 ha−1 molasses (CBL/BSM), ASD with 13 Mg ha−1 CBL and 27.8 m3 ha−1 molasses (CBL/BSM2), and ASD with 26.9 Mg ha−1 composted algae 99
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2.4. Compound identification Carbon and proton chemical shifts were recorded for each sample group using HSQC spectra. The resulting peak lists were submitted to the COLMAR (http://spin.ccic.ohio-state.edu/index.php/colmar) [16] and BMRB databases (http://www.bmrb.wisc.edu/metabolomics/) [17]. COLMAR results were manually verified by analyzing Matching ratio and Uniqueness parameters and cross referenced with data available on the BMRB. The chemical shift assignment for each peak was recorded and compared to hits from the known databases for compound identification. Chemical identities were confirmed using 1 H-13C chemical shift assignments from HSCQ and 1H correlations obtained from TOSCY and COSY experiments. Compound quantification was determined by relative comparison of compound peak integrals to a known concentration of the internal DSS standard [18]. 2.5. Statistical analyses To test the differences between the metabolite composition in NT and ASD-treated samples, principal component analysis (PCA), a multivariate statistical analysis commonly used to evaluate NMR-based metabolomics data, was performed. NMR resonances were recorded for each treatment sample, including all replicates, and distinct resonances that did not overlap with others were chosen as the representative compounds. Peak intensity tables were created for each sample then peaks were normalized to total peak intensity using a resonance from the internal DSS standard. Input data were subjected to log transformation and mean centered scaling prior to statistical analysis using MetaboAnalyst 3.0 (http://www.metaboanalyst.ca) and JMP Pro 13.0 (SAS Institute, Cary, NC). A fold change threshold of 2 was used to detect important biochemicals and multivariate analysis was used to examine samples for groupings in an unsupervised approach with principal component analysis (PCA). PCA is appropriate for comparing metabolomics sets in which each treatment has at least three replications [19]. Samples that were determined to be outliers were removed from statistical analysis. Inspection of the data was carried out using 2D scores plots and their corresponding loading plots to identify features important in discriminating between of NT to ASD-treated samples and to look for metabolic differences between ASD-treated samples. The metabolic pathways of compounds found in ASD treated soils were mapped using the pathway analysis module in MetaboAnalyst 3.0. 3. Results Fig. 1. Representative 1H NMR spectra of extracted metabolites from ASD-treated soil samples. Spectra collected at 600 MHz 298 K. Soil amendments are specified for each sample: NT control (A), CBL/BSM2 (B), ALG/BSM (C), and CBL/BSM (D).
3.1. Sample preparation, NMR analysis, and compound identification The soil redox potential was measured and the ASD treated soils were determined to have significantly higher anaerobic conditions in comparison to NT controls as previously reported [9–11,20]. The CPL/ BMS2 and CPL/BSM treatments resulted in statistically similar cumulative hours under anaerobic conditions with 30,971 and 21,410 mVhr respectively. The cumulative mVhr under anaerobic conditions in the ALG/BSM treatment (14,857 mVhr) was not statistically differentiable from the NT (9558 mVhr), but was also similar to the treatment receiving the lower rate of molasses (CBL/BSM). Complex metabolite mixtures were obtained by extraction of the water-soluble compounds from NT and ASD-treated soil samples as evidenced by comparison of representative 1D 1H NMR (Fig. 1). The predominant number of peaks were observed in regions of the spectra where sugars and organic acids are found [7]. The COLMAR query analysis revealed the presence of organic acids, alcohol, and sugar alcohols. Compounds that exhibited Matching ratios of 1 and the highest Uniqueness parameters (at least 3/4, which is considered to be an acceptable match [16]), were selected for further analysis and are summarized in Table 1. The chemical identities of these compounds were further validated by visual analyses of 2D NMR 1H-13C HSQC, 1H-1H
TOCSY, and 1H-1H COSY spectra (representative 1H-1H TOCSY spectra of NT and ALG/BSM samples are shown in Figs. S1 and S2 respectively of the Supporting Information). Chemical shift assignments of compounds identified by database analysis and confirmed by 2D NMR experimentation are summarized in Table S1. In NT, the presence of acetic acid and ethanol was detected, but the relative abundance was low (Fig. 2). Upon adding labile carbon amendments to the soil, lactic acid, in addition to acetic acid, was formed in the ALG/BSM- and CBL/BSM-treated samples. These results suggest the fermentative metabolic pathways were modified in the presence of a labile carbon source. Interestingly, CBL/BSM2-treated samples did not have similar amounts of acetic acid as compared to ALG/BSM- and CBL/BSM-treated samples, but lactic acid was present in substantial concentration as evidenced by peaks observed in the aliphatic region of the 2D HSQC (Fig. 2B). Furthermore, succinic acid, malic acid, butyric acid, and mannitol (Table 1) were present in CBL/ BSM2-treated soils whereas CBL/BSM-treated soil exhibited elevated succinic acid, butyric acid, and mannitol production, but these 100
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products were key components in the differences between these soils, we performed prokaryotic specific analyses on the compounds identified as being most affected by ASD treatment using MetaboAnalyst (Table 1). As summarized in Fig. 4, NMR-based metabolomics identified several metabolic pathways which could be impacted by ASD treatment. Comparison of the biochemical composition of ASD-treated samples revealed elevation of lactic acid and acetic acid in ASD samples, which is indicative of modulations in pyruvate metabolism and glycolysis, as compared to NT controls. Moreover, exposure to ASD treatment induced increases in butyric acid and succinic acid production suggesting that butanoate metabolism is important in these anaerobic soil environments.
Table 1 Selected list of matched compounds identified in 1H-13C HSQC spectra of extracts from ASD-treated soils using COLMAR Query. Treatment
Compound
1
NT
Acetic acid Ethanol Lactic acid Malic acid Succinic acid Mannitol 3-Hydroxypropionic acid Lactic acid Acetic acid Butyric acid Succinic acid 3-Hydroxypropionic acid Lactic acid Acetic acid Succinic acid Mannitol Butyric acid 3-Hydroxypropionic acid
0.0005 0.0015 0.0021 0.0046 0.0027 0.0126 0.0016 0.0031 0.008 0.003 0.0003 0.0056 0.0102 0.0093 0.004 0.0165 0.0036 0.0135
CBL/BSM2
ALG/BSM
CBL/BSM
Ha
13
C
0.001 0.007 0.0225 0.0187 0.037 0.0568 0.0475 0.0245 0.002 0.026 0.01 0.063 0.1065 0.109 0.245 0.0688 0.1995 0.052
Mb
Uc
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1/1 2/2 3/3 3/3 1/1 3/4 2/2 2/2 1/1 3/3 1/1 2/2 2/2 1/1 1/1 4/4 3/3 2/2
4. Discussion Previous studies have implicated the accumulation of organic acids and other compounds which inhibit the growth of certain soil-borne pathogens and parasites in crop production [9,10,14,21–24]. In this study, NMR-based metabolomics was utilized as a tool by which to identify the chemical composition of normal and ASD-treated soils. ASD treatment induced distinctive metabolic profiles that can potentially account for the pathogen-suppressive qualities found in disinfested soils. The goal of this study was to develop a rapid and accurate method by which to monitor the biochemical composition of soils using NMRbased metabolomics. The utility of ASD as a potential alternative to chemical fumigants is known [9–11,24,25]. To date, the influence of volatile organic acids and other organic acids excreted by the soil microbiome have been characterized by gas chromatography-mass spectrometry (GC-MS) and high performance liquid chromatography (HPLC), but more work needs to be done to understand how the presence of microbial metabolites impacts growth and disease susceptibility in plants [24,26]. Furthermore, these studies have shown that the chemical identities of many microbial metabolites could not be determined using GC-MS and HPLC, which emphasizes the need for techniques whereby high-resolution structural information can be obtained. In this regard, NMR-based studies of the soil microbiome and its biochemical composition in relation to metabolism are ideal. NMR is a powerful tool for the detection and identification of metabolite compounds, but more research is needed in order to establish its utility for usage in the study of the soil metabolome. Here, we showed that the combination of NMR metabolomics and multivariate statistical analysis is a valuable, flexible, and accurate strategy for measuring and interpreting biochemical differences between NT and ASD-treated soils. Our findings provide evidence that NMR-based metabolomics can identify fermentation products in agricultural soils and discriminate between different fermentation pathways impacted by the application of ASD treatments. Our findings suggest that under low oxygen conditions and in the presence of exogenous supplementary carbon, microbial fermentation likely occurs in soils. In particular, the metabolic profile of ASD-treated soil samples confirmed that distinct organic acid compositions are present and that these biochemicals are likely to contribute to production of suppressive soils when compared to NT controls [9–11]. An increased number of peak resonances were observed for CBL/BSM2, ALG/BSM, and CBL/ BSM-treated soils as compared to NT soils, which suggests that increased levels of biochemical compounds are present in ASD-treated soils. Based on this observation, we propose that spectral differences observed between these two groups may serve to identify metabolites important in ASD-induced modifications of agricultural soils. Under the conditions of this study, all amendments tested resulted in sustained anaerobic soil conditions as evidenced by mean cumulative soil redox potential; CBL/BSM2 and CBL/BSM treatment combinations were particularly effective in promoting accumulation of biochemical products that are likely derived from microbial fermentation. Among the biochemical compounds characterized, the most readily identifiable in ASD-treated soils were organic acids and short chain fatty acids,
a The average 1H and13C chemical shift differences between the matched experimental value and the public database chemical shifts are recorded in units of ppm. b M = matching ratio where a value of 1 indicates a perfect match. c U = uniqueness value of 1 which describes when the number of peaks in the experimental HSQC is matched only to the identified compound; however, U = 3/4, which is the threshold defined by Bingol et al. as being acceptable for compound identification.
biochemicals were absent in NT controls. Similarly, only ASD-treated samples exhibited detectable levels of 3-hydroxypropionic acid (3-HPA) which suggested that a transition toward secondary metabolite production occurred in response to ASD treatment. These results were consistent with the accumulation of ASD-induced fermentation and likely secondary metabolite products in treated soil samples. The relative concentrations of these compounds are listed in Table S2. 3.2. Statistical analyses Treatment replicates that did not exhibit a fold change of greater than 2 were omitted from further statistical analyses. PCA revealed that the biochemical composition of the ASD-treated samples was significantly different than NT controls. Fig. 3A shows that NT samples (Treatment 0) clustered together while ASD samples (Treatments1-3) are located in discrete regions away from the control group, suggesting that the metabolite profiles between these samples were distinctly different. The greatest variation was observed in PC1 (59.9%) which provides evidence that ASD-treated and NT controls have distinct metabolic signatures. Variation attributable to PC2 (24.1%), which describes the differences observed within the treated and control groups, revealed that less significant alterations in metabolism exist between different ASD treatments. Separation between NT and ASD-treated samples was based on the contribution of six metabolites as shown in the PCA loading plot of NT vs. ASD-treated samples (Fig. 3B). Microbial fermentation products, such as acetic acid, lactic acid, and butyric acid, contributed to the group separation observed in the plots. To further examine the distinction between ASD-treated samples, PCA analysis was performed in the absence of NT groups (Fig. 3C). Treatment groups were found to be segregated according to different metabolite compositions. The differentiation between treatment groups was based largely on PC1 (63.7%), while less influence was observed from PC2 (24.9%). The contribution of key metabolites was observable in the PCA loading plot (Fig. 3D). Based on the PCA analysis, mannitol, butyric, acetic, succinic, and lactic acids played an important role in driving the variation between treatment groups which further supported that fermentative metabolic responses in the soil microbiome are induced by ASD-treatments. Given that our data strongly implied that microbial fermentation 101
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Fig. 2. Select region of the aliphatic 1H-13C HSQC spectra from NT Control and ASD-treated soil samples collected at 600 MHz 298 K. Peaks corresponding to DSS, acetic acid, and lactic acid are labeled for spectra of NT control (purple-A), CBL/BSM2 (red-B), ALG/BSM (blue-C), and CBL/BSM (green-D). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
the predominance of fermentation based metabolism in this environmental system. In the presence of excess carbohydrate and under limited oxygen availability, facultative anaerobes utilize glycolysis, while minimizing metabolic flux through the TCA cycle, in order to produce ATP [27]. To effectively regenerate reducing equivalents during robust fermentation, it is possible for facultative anaerobes to oxidize pyruvate to lactic acid, which permits the continuation of glycolysis at the
which suggest the involvement of key pathways from primary metabolism that are characteristic of fermentative soil microbes. Interestingly, we found that among the pathways most impacted under anaerobic conditions were glycolysis, pyruvate metabolism, the tricarboxylic acid (TCA) cycle, and butanoate metabolism. The substantial increase in lactic acid and acetic acid was important in the separation between NT and ASD-treated groups, which suggests
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Fig. 3. Multivariate analysis of extracted metabolites from NT and ASD-treated soils. Principle component analysis (PCA) score plot (A) demonstrating the separation between NT and ASD-treated soils: NT (Treatment 0 - red), CBL/BSM2 (Treatment 1 - green), ALG/BSM (Treatment 2 - blue), CBL/BSM (Treatment 3 - orange). The loading plot (B) highlighting the differentiation between NT vs ASD-treated soils by the characterized metabolites. PCA score plot (C) indicating differences in the extracted metabolite composition between the ASDtreatment groups. The loading plot (D) reveals which metabolites influence the separation between different ASD-treatments. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
with repression of the TCA cycle, may serve as a potential regulatory mechanism in disinfestation techniques. ASD treatments also impacted butanoate metabolism as evidenced by the detection of butyric acid and succinic acid. These data suggested the anaerobic microbes, such as those in the genus Clostridium which are known to manufacture butyric acid, may be key players in this microbial community [3]. While we found that fructose metabolism is only marginally impacted in this study (Fig. 4, pathway impact 0.05), Saha et al. (2006) reported that certain lactic acid bacteria and fungi convert fructose in molasses to mannitol, a compound that is present in both CBL/BSM2 and CBL/BSM treated soils. These observations are in agreement with the premise that fermentative soil microbes consume carbon sources, such as the fructose and glucose present in the molasses amendments, during ASD treatments. Two possible mechanisms for ASD-induced biochemical change exist. The first being that the existing
expense of ATP production. In contrast, facultative anaerobic microbes generating acetic acid, another oxidized metabolic intermediate made when the TCA cycle is down regulated, are capable of producing cellular energy in the form of ATP. Our findings point to the soil metabolome oxidizing labile carbon from the ASD amendments during increased glycolysis, while minimizing influx into the TCA cycle reactions in the presence of low oxygen saturation, which results in the production of the partially oxidized metabolic intermediates detectable by NMR. Moreover, we found that malic acid, which is associated with both pyruvate metabolism and the TCA cycle, is present in CBL/BSM2 treated soils. Malic acid can also be converted lactic acid during fermentative reactions when the TCA cycle is blunted, which would further promote the regeneration of reducing equivalents for glycolysis in the absence of aerobic growth. Cross-talk between these metabolic pathways, which promotes increased rates of glycolysis concomitant 103
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to expand these studies to include a more complete characterization of ASD-induced metabolites. In this current study, sample numbers were limited due to the extensive utilization of 2D NMR experiments to thoroughly characterize these agricultural soil samples. In future studies, we aim to minimize the number of 2D NMR experiments used to identify biochemicals while increasing the number of sample replicates evaluated in order to expand the scope of this study. We will explore the development of 1H-13C NMR techniques to measure quantifiable differences between NT and ASD-treated soils using isotope-labeled compounds to further elucidate metabolic networks, which has been shown to enhance the sensitivity of metabolic studies [29]. This would permit the expansion of the study's scope to explore the influences of time and soil microbiome composition on biochemical trends. We conclude that NMR-based investigations into the metabolism of the agricultural soil metabolome offer much potential to support the development of sustainable agricultural practices and the potential identification of bioactive natural products. Author contributions CWJ, TIS, and NLF prepared samples, carried out NMR experiments, and performed data analyses. ENR, JH, WT, and NKB developed and conducted ASD treatment of soil samples. ABL and NLF performed bioinformatics and statistical analyses. NLF conceived of and directed the implementation of the NMR experimentation. CWJ, ENR, and NLF wrote the manuscript.
Fig. 4. Major metabolic pathways impacted as evidenced by the organic acids identified in this study. The x-axis correspond to the metabolic impact factor ad the yaxis is the statistical significance resulting in each treated versus non-treated group.
Funding microbial community alters its metabolism or, alternatively, the composition of the entire microbial community shifts in response to ASD treatment. Mowlick et al. (2013) report a global shift in the composition of the microbial community during ASD-induced anaerobiosis, which is largely influenced by the type of soil amendment utilized during treatments. While genome sequencing is beyond the scope of this study, our findings clearly indicate an ASD-induced shift in the chemical fingerprint of the soil metabolome, but the origin remains to be determined. The detection of these biochemical compounds in the soil using NMR-based metabolomics further supports that microbial fermentation products are likely the fundamental metabolic response induced during ASD. While this methodology proved useful characterizing the accumulation of fermentation products, our study also demonstrated NMR's utility in the detection of potential secondary metabolites such as 3-HPA, which is known to be an antimicrobial compound of importance in agricultural systems [28]. Based on our observations in the current and previous studies [9,10,20], we propose there is a correlation between the robustness of the metabolic response of the soil metabolome and the ability to confer disease resistance in crops, but additional studies are needed to more thoroughly understand this process.
NLF was supported in part by National Institutes of Health grant R15GM117478; NLF and CWJ were supported in part by United States Department of Agriculture Project number 6034-22000-041-24. Acknowledgements We thank Dr. Theresa Ramelot for maintaining the NMR spectrometers and optimizing pulse sequences. The authors greatly appreciate the efforts of Dr. Ann E. Hagerman in the review of this manuscript. Appendix A. Supplementary data Supplementary data related to this chapter can be found at http:// dx.doi.org/10.1016/j.ejsobi.2017.10.008. References [1] C. Gougoulias, J.M. Clark, L.J. Shaw, The role of soil microbes in the global carbon cycle: tracking the below-ground microbial processing of plant-derived carbon for manipulating carbon dynamics in agricultural systems, J. Sci. Food Agric. 94 (2014) 2362–2371, http://dx.doi.org/10.1002/jsfa.6577. [2] P.R. Hirsch, T.H. Mauchline, The importance of the microbial N cycle in soil for crop plant nutrition, Adv. Appl. Microbiol. 93 (2015) 45–71, http://dx.doi.org/10. 1016/bs.aambs.2015.09.001. [3] S. Mowlick, T. Takehara, N. Kaku, K. Ueki, A. Ueki, Proliferation of diversified clostridial species during biological soil disinfestation incorporated with plant biomass under various conditions, Appl. Microbiol. Biotechnol. 97 (2013) 8365–8379, http://dx.doi.org/10.1007/s00253-012-4532-z. [4] T.W.M. Fan, A.N. Lane, Applications of NMR spectroscopy to systems biochemistry, Prog. Nucl. Magn. Reson. Spectrosc 92–93 (2016) 18–53, http://dx.doi.org/10. 1016/j.pnmrs.2016.01.005. [5] T.W.-M. Fan, A.N. Lane, E. Chekmenev, R.J. Wittebort, R.M. Higashi, Synthesis and physico-chemical properties of peptides in soil humic substances, J. Pept. Res. 63 (2004) 253–264, http://dx.doi.org/10.1111/j.1399-3011.2004.00142.x. [6] F. Grosso, F. Temussi, F. De Nicola, Water-extractable organic matter and enzyme activity in three forest soils of the Mediterranean area, Eur. J. Soil Biol. 64 (2014) 15–22. [7] O.A.H. Jones, S. Sdepanian, S. Lofts, C. Svendsen, D.J. Spurgeon, M.L. Maguire, J.L. Griffin, Metabolomic analysis of soil communities can be used for pollution assessment, Environ. Toxicol. Chem. 33 (2014) 61–64, http://dx.doi.org/10.1002/ etc.2418. [8] S. Rochfort, V. Ezernieks, P. Mele, M. Kitching, NMR metabolomics for soil analysis
5. Conclusions In summary, we believe that NMR-based metabolomics is a powerful tool that should be utilized in the evaluation of metabolite production in the soil metabolome. In particular, the methodology reported here offers the efficacious and straightforward means by which to identify individual compounds in complex mixtures extracted from the soil metabolome. Information derived from such studies would be essential in the development of a database that catalogues the biochemical profiles of soils and their ecological or agricultural qualities. It would be of particular interest to determine the biochemical features of soils that are suppressive under specific environmental conditions. Specifically, a more accurate profiling of the microbial communities in the soil metabolome and its relationship to variable metabolite production is necessary to expand our knowledge in the agroecological sciences. We submit that these results provide a foundation from which 104
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[9]
[10]
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Chim. Acta 980 (2017) 1–24, http://dx.doi.org/10.1016/j.aca.2017.05.011. [19] J. Xia, D.S. Wishart, Comprehensive Metabolomics Data Analysis, (2016), pp. 1–91, http://dx.doi.org/10.1002/cpbi.11. [20] D.M. Butler, N. Kokalis-Burelle, J.P. Albano, T.G. McCollum, J. Muramoto, C. Shennan, E.N. Rosskopf, Anaerobic soil disinfestation (ASD) combined with soil solarization as a methyl bromide alternative: vegetable crop performance and soil nutrient dynamics, Plant Soil 378 (2014) 365–381, http://dx.doi.org/10.1007/ s11104-014-2030-z. [21] G. Bonanomi, V. Antignani, C. Pane, F. Scala, Suppession of soilborne fungal diseases with organic amendments, J. Plant Pathol. 89 (2007) 311–324, http://dx.doi. org/10.2307/41998410. [22] N. Momma, K. Yamamoto, P. Simandi, M. Shishido, Role of organic acids in the mechanisms of biological soil disinfestation (BSD), J. Gen. Plant Pathol. 72 (2006) (2006) 247–252. [23] Y. Oka, Mechanisms of nematode suppression by organic soil amendments-A review, Appl. Soil Ecol. 44 (2010) 101–115, http://dx.doi.org/10.1016/j.apsoil.2009. 11.003. [24] M. van Agtmaal, G.J. van Os, W.H. Gera Hol, M.P.J. Hundscheid, W.T. Runia, C.A. Hordijk, W. de Boer, Legacy effects of anaerobic soil disinfestation on soil bacterial community composition and production of pathogen-suppressing volatiles, Front. Microbiol. 6 (2015) 1–12, http://dx.doi.org/10.3389/fmicb.2015. 00701. [25] N. Momma, Y. Kobara, M. Momma, Fe2+ and Mn2+, potential agents to induce suppression of Fusarium oxysporum for biological soil disinfestation, J. Gen. Plant Pathol. 77 (2011) 331–335, http://dx.doi.org/10.1007/s10327-011-0336-8. [26] O. Tyc, C. Song, J.S. Dickschat, M. Vos, P. Garbeva, The ecological role of volatile and soluble secondary metabolites produced by soil bacteria, Trends Microbiol. 25 (2017) 280–292, http://dx.doi.org/10.1016/j.tim.2016.12.002. [27] A.J. Wolfe, The acetate switch, Microbiol. Mol. Biol. Rev. 69 (2005) 12–50, http:// dx.doi.org/10.1128/MMBR.69.1.12-50.2005. [28] F.L.S. Sebastianes, N. Cabedo, N. El, N. El Aouad, A.M.M.P. Valente, P.T. Lacava, J.L. Azevedo, A.A. Pizzirani-Kleiner, D. Cortes, 3-hydroxypropionic acid as an antibacterial agent from endophytic fungi Diaporthe phaseolorum, Curr. Microbiol. 65 (2012) 622–632, http://dx.doi.org/10.1007/s00284-012-0206-4. [29] T.W.-M. Fan, P.K. Lorkiewicz, K. Sellers, H.N.B. Moseley, R.M. Higashi, A.N. Lane, Stable isotope-resolved metabolomics and applications for drug development, Pharmacol. Ther. 133 (2012) 366–391, http://dx.doi.org/10.1016/j.pharmthera. 2011.12.007.
provide complementary, orthogonal data to MIR and traditional soil chemistry approaches-A land use study, Magn. Reson. Chem. 53 (2015) 719–725, http://dx. doi.org/10.1002/mrc.4187. D.M. Butler, E.N. Rosskopf, N. Kokalis-Burelle, J.P. Albano, J. Muramoto, C. Shennan, Exploring warm-season cover crops as carbon sources for anaerobic soil disinfestation (ASD), Plant Soil 355 (2012) 149–165, http://dx.doi.org/10.1007/ s11104-011-1088-0. D.M. Butler, N. Kokalis-Burelle, J. Muramoto, C. Shennan, T.G. McCollum, E.N. Rosskopf, Impact of anaerobic soil disinfestation combined with soil solarization on plant-parasitic nematodes and introduced inoculum of soilborne plant pathogens in raised-bed vegetable production, Crop Prot. 39 (2012) 33–40, http:// dx.doi.org/10.1016/j.cropro.2012.03.019. F. Di Gioia, M. Ozores-Hampton, J. Hong, N. Kokalis-Burelle, J. Albano, X. Zhao, Z. Black, Z. Gao, K. Moore, M. Swisher, H. Guo, E.N. Rosskopf, C. Wilson, J. Thomas, The effects of anaerobic soil disinfestation on weed and nematode control, fruit yield, and quality of Florida fresh-market tomato, HortScience 51 (2016) 703–711. J.G. Lamers, W.T. Runia, L.P.G. Molendijk, P.O. Bleeker, Perspectives of anaerobic soil disinfestation, Acta Hortic. (2010) 277–283, http://dx.doi.org/10.17660/ ActaHortic.2010.883.34. J. Muramoto, C. Shennan, A. Fitzgerald, S. Koike, M. Bolda, O. Daugovish, E. Rosskopf, N. Kokalis-Burelle, D. Butler, Effect of anaerobic soil disinfestation on weed seed germination, Proc. Annu. Int. Res. Conf. Methyl Bromide Altern. Emiss. Reductions (2008) 11–14. N. Momma, Biological soil disinfestation (BSD) of soilborne pathogens and its possible mechanisms, Jpn. Agric. Res. Q. 42 (2008) 7–12, http://dx.doi.org/10. 6090/jarq.42.7. T.D. Goddard, D.G. Kneller, Sparky—NMR Assignment and Integration Software, Univ. California, San Fr, 2006. K. Bingol, L. Bruschweiler-li, O.A. Cabrera, T. Megraw, F. Zhang, Unified and Isomer-speci Fi C NMR Metabolomics Database for the Accurate Analysis of 13 C − 1 H HSQC Spectra, (2015). J.L. Markley, M.E. Anderson, Q. Cui, H.R. Eghbalnia, I.A. Lewis, A.D. Hegeman, J. Li, C.F. Schulte, M.R. Sussman, W.M. Westler, E.L. Ulrich, Z. Zolnai, New bioinformatics resources for metabolomics, Pac. Symp. Biocomput 168 (2007) 157–168, http://dx.doi.org/10.1142/9789812772435. S. Kostidis, R.D. Addie, H. Morreau, O.A. Mayboroda, M. Giera, Quantitative NMR analysis of intra- and extracellular metabolism of mammalian cells: a tutorial, Anal.
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European Journal of Soil Biology journal homepage: www.elsevier.com/locate/ejsobi
Using NMR-based metabolomics to monitor the biochemical composition of agricultural soils: A pilot study
MARK
Christian W. Johnsa, Alex B. Leeb, Tzvia I. Springerc,1, Erin N. Rosskopfd, Jason C. Hongd, William Turechekd, Nancy Kokalis-Burelled, Natosha L. Finleya,c,∗ a
Cell, Molecular, and Structural Biology Program, Miami University, 700 E. High Street, Oxford, OH, 45056, USA Department of Computer Science and Software Engineering, Miami University, 700 E. High Street, Oxford, OH, 45056, USA c Department of Microbiology, Miami University, 700 E. High Street, Oxford, OH, 45056, USA d USDA-ARS, U.S. Horticultural Research Laboratory, 2001 S. Rock Road, Fort Pierce, FL, 34945, USA b
A R T I C L E I N F O
A B S T R A C T
Handling Editor: Y. Kuzyakov
NMR-based metabolomics plays a major role in the study of complex living systems. While there is a known connection between soil microbial metabolism and productivity in agricultural systems, very few researchers describe the application of NMR to the evaluation of agricultural soil metabolomes. Here, we introduce a simple protocol for the NMR metabolic analysis of biochemical compounds from agricultural soils where microbial communities are influenced by the application of anaerobic soil disinfestation (ASD). Following ASD treatment, aqueous metabolites were extracted from soil samples and the compound identities of the resulting mixtures were determined using 1D and 2D NMR in combination with metabolome database searches. It was found that the ASD treatments altered the metabolite composition of soil as evidenced by the detection of natural biochemical products such as organic acids. Principle component analysis revealed distinct biochemical differences between non-treated and ASD-treated soils. Our findings support that this protocol is efficacious in the rapid and reliable determination of metabolite profiles even within complex mixtures obtained from ASD-treated agricultural soils. Thus, NMR spectroscopy has the potential to impact soil science by investigating the adaptable biochemical fingerprint of soil metabolomes in agricultural fields, knowledge of which may be used to improve crop production.
Keywords: Nuclear magnetic resonance 2D NMR Metabolomics Organic acid Soil
1. Introduction Soil is composed of both organic and inorganic chemical constituents as well as a complex microbial community, the combination of which is better described as the soil metabolome. Microbes contribute to nutrient cycling and the general biological complexity found within soil [1]. The interplay between the soil metabolome and crop production is an area of intensive study. The composition and diversity of soil microbes influence agricultural yield [2], but the many mechanisms contributing to this biological phenomenon remain to be determined. Ongoing efforts are directed toward characterizing the soil microbiome using genetic approaches, which provide details about the make-up of the microbial community [3], but very little is known about how the soil metabolome as a whole impacts disease resistance in plants and
food production. Nuclear magnetic resonance (NMR) is a rapid and effective tool proven to be useful in the identification and quantification of compounds in biological samples. Despite being relatively insensitive, NMR spectroscopy yields highly reproducible data and is unparalleled in its reliably in determining compound structures in metabolomics studies. Furthermore, NMR spectroscopy does not require separation of compounds from biological mixtures and is noninvasive. While NMR-based metabolomics has proven useful in the study of normal and diseased states in biological systems [4], there is considerably less information available for the study of agricultural soil metabolomes. Although NMR has been used to examine cross-linked humic substances [5] and water soluble organic matter [6], which are important features in the composition of organic matter in soil, efforts have also been directed toward
Abbreviations: ASD, anaerobic soil disinfestation; ALG, Algae; BSM, black strap molasses; CBL, composted broiler litter; cm, centimeter; DSS, 4,4-dimethyl-4-silapentane-1-sulfonic acid; HPLC, High performance liquid chromatography; NMR, nuclear magnetic resonance; NaN3, sodium azide ∗ Corresponding author. Cell, Molecular, and Structural Biology Program, Department of Microbiology, Miami University, 700 East High Street, 32 Pearson Hall, Oxford, OH, 45056, USA. E-mail address: fi[email protected] (N.L. Finley). 1 Present address: Department of Biophysics, Medical College of Wisconsin, 8701 W. Watertown Plank Road, Milwaukee, Wisconsin, 2042, USA. https://doi.org/10.1016/j.ejsobi.2017.10.008 Received 1 July 2017; Received in revised form 27 October 2017; Accepted 28 October 2017 1164-5563/ © 2017 Elsevier Masson SAS. All rights reserved.
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and 13.9 m3 ha−1 molasses (ALG/BSM). Composted broiler litter and algae were incorporated as a nitrogen source for crop production and rates were adjusted based on nitrogen mineralization rates to provide a similar quantity of plant-available N for each treatment. Application procedures followed those previously described [9,10]. Briefly, amendments were applied and incorporated into the top 20 cm of the bed top by rotovation. Two drip irrigation lines (30.5-cm emitter spacing, 0.91 L ha−1 emitter rate) were installed at the same time that black/white 0.03-mm polyethylene VaporSafe® totally impermeable film (TIF) was applied to the beds (black side up). Irrigation was applied to soil saturation, approximately 5 cm, to each of the ASD plots. The NT received irrigation, was covered with TIF, but received no amendment. Two oxidation-reduction probes (Pt combination electrodes, Ag/AgCl reference, Sensorex, Garden Grove, CA) were installed to a depth of 15 cm to measure redox potential. Electrodes were monitored using an automatic data logging system (CR-1000 with AM 16/32 multiplexers, Campbell Scientific, Logan, UT) and the collected data was used to calculate the cumulative number of hours under anaerobic conditions. Hourly average soil redox potential values below the calculated critical redox potential (CEh) were considered to be indicative of anaerobic conditions. The CEh values were determined using the formula: CEh – [595(mV) – 60 (mV0] x soil pH [10]. Five soil cores were sampled from each plot 21 days post-treatment using a soil probe (1.75 cm inner diameter, 15 cm length). Soil cores collected from each plot were homogenized manually. In order to minimize the time required for 2D NMR analysis in this pilot study, four replications were sampled for each ASD treatment, while three replications of NT were sampled. Soil samples were flash frozen and stored at −80 °C prior to extraction.
understanding the adaptable, metabolic profiles of soil communities. Two notable reports have focused on NMR-based metabolomic studies of soil samples and demonstrate promise for the development of comprehensive techniques to characterize the biochemical features of industrial and agricultural soils [7,8]. As summarized by these research groups, an improved understanding of the correlation between soil chemistry, microbial metabolism, and industrial/agricultural practices is critical to soil science in general with potential benefits including the optimization of ecologically-sustainable farming practices and bioremediation of polluted soils. It is known that manipulation of the soil metabolome impacts the chemical and biological environment of agricultural soils [3,9–11]. The composition of the soil microbial community can be altered by controlling nutrient availability and oxygen concentration in the system. When soils are modified by anaerobic soil disinfestation (ASD) implemented prior to planting crops, this technique provides a potential alternative to chemical soil fumigation [12,13]. ASD involves the incorporation of a labile carbon source into the soil, tarping the beds with an oxygen impermeable plastic film mulch, then irrigating to soil saturation to limit oxygen availability and promote growth of beneficial facultative anaerobes [10]. It is speculated that low oxygen availability and/or the accumulation of organic acids during ASD treatment results in soils that suppress soil-borne pests of crops [9,14]. Currently, there is a critical gap in our understanding of how ASD-induced metabolites impact agricultural productivity. When evaluating complex mixtures extracted from agricultural soils, spectral overlap in one dimensional (1D) nuclear magnetic resonance (NMR) experiments may complicate compound characterization. To circumvent this problem, the usage of two dimensional (2D) NMR experiments provides increased resolution, which facilitates the validation of compound identification. The purpose of this study was to develop an effective, flexible method for the extraction of small molecules from soil samples and to utilize 1D and 2D NMR experiments in combination with multivariate statistical analyses to evaluate the influence of ASD on metabolite composition. In particular, we were interested in developing a method to monitor the organic compound composition of complex mixtures extracted from samples subjected to ASD treatment. We showed that many metabolites were detectable in the NMR spectra of complex mixtures, but that discrete compounds are readily identifiable, even without performing chromatographic separation, which makes this approach amendable to high-throughput applications. Our analyses found that organic acids, including lactic, acetic, butyric, succinic, and malic acids were the compounds that most impacted the soil metabolome during ASD treatment, which is similar to previous reports of microbial fermentation processes in the soil [14]. We found that our method readily detects that the relative organic acid composition varies depending on the type of labile carbon source applied to the sample. Taken together, these data support that this protocol allows for fast, economical, and consistent characterization of the metabolic profiles of soil microbial communities modulated by ASD treatments. We shed light on the utility of NMR-based metabolomics in the assessment of agricultural practices aimed at managing soil-borne pathogens and parasites.
2.2. Sample preparation Soil samples were thawed on ice and 0.5 g was subjected to extraction using 2 mL of Millipore-purified, sterile water followed by sonication on ice in 30 s bursts for a total of 2 min for each replicated sample. No effort was made to limit our study to metabolites originating from the microbial community alone. Our technique included any water soluble biochemical compound present in the NT and ASDtreated soils during this characterization. In this way, the metabolites isolated under our experimental conditions reflected the chemical composition of the entire soil matrix, which included compounds derived from plants, soil, or microbes that were present at the time of the study. The aqueous extract of each sample was separated from the residual soil matrix by centrifugation for 20 min at 15,000 rpm at 4 °C. The solutions were filter-sterilized using a 0.2 μM filter, frozen using liquid N2, lyophilized, and stored at −80 °C until analyzed. The resulting dry powder samples were dissolved in 640 μL of 50 mM phosphate buffer (pH 7.5) containing 100% deuterium oxide supplemented with 500 μM 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS) and 500 μM sodium azide (NaN3).
2.3. NMR data collection, processing, and analysis 2. Methods NMR experiments were performed on Bruker Avance (Bruker Biospin) spectrometers operating at 600 MHz and 850 MHz 1H frequencies and equipped with 5 mm triple resonance probes at 298 K. For each sample, one-dimensional (1D) 1H NMR presaturation experiments were performed. For added resolution and to verify compound identity, two-dimensional (2D) correlation spectroscopy (COSY), (2D) 1H-1H total correlation spectroscopy (TOCSY) spectra, and 2D 1H-13C heteronuclear single-quantum coherence spectroscopy (HSQC) experiments were collected. Data were processed, baseline corrected, referenced relative to DSS, and analyzed using NMRDraw, NMRPipe, and Sparky [15].
2.1. Soil treatments Soils for NMR analysis were collected from a strawberry production system experimental trial in which the ASD treatments were replicated four times and the non-treated control (NT) was replicated three times in a randomized complete block design. Treatments included: NT to which no organic amendments were added, ASD utilizing soil amendments of 13 Mg ha−1 of composted broiler litter and 13.9 m3 ha−1 molasses (CBL/BSM), ASD with 13 Mg ha−1 CBL and 27.8 m3 ha−1 molasses (CBL/BSM2), and ASD with 26.9 Mg ha−1 composted algae 99
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2.4. Compound identification Carbon and proton chemical shifts were recorded for each sample group using HSQC spectra. The resulting peak lists were submitted to the COLMAR (http://spin.ccic.ohio-state.edu/index.php/colmar) [16] and BMRB databases (http://www.bmrb.wisc.edu/metabolomics/) [17]. COLMAR results were manually verified by analyzing Matching ratio and Uniqueness parameters and cross referenced with data available on the BMRB. The chemical shift assignment for each peak was recorded and compared to hits from the known databases for compound identification. Chemical identities were confirmed using 1 H-13C chemical shift assignments from HSCQ and 1H correlations obtained from TOSCY and COSY experiments. Compound quantification was determined by relative comparison of compound peak integrals to a known concentration of the internal DSS standard [18]. 2.5. Statistical analyses To test the differences between the metabolite composition in NT and ASD-treated samples, principal component analysis (PCA), a multivariate statistical analysis commonly used to evaluate NMR-based metabolomics data, was performed. NMR resonances were recorded for each treatment sample, including all replicates, and distinct resonances that did not overlap with others were chosen as the representative compounds. Peak intensity tables were created for each sample then peaks were normalized to total peak intensity using a resonance from the internal DSS standard. Input data were subjected to log transformation and mean centered scaling prior to statistical analysis using MetaboAnalyst 3.0 (http://www.metaboanalyst.ca) and JMP Pro 13.0 (SAS Institute, Cary, NC). A fold change threshold of 2 was used to detect important biochemicals and multivariate analysis was used to examine samples for groupings in an unsupervised approach with principal component analysis (PCA). PCA is appropriate for comparing metabolomics sets in which each treatment has at least three replications [19]. Samples that were determined to be outliers were removed from statistical analysis. Inspection of the data was carried out using 2D scores plots and their corresponding loading plots to identify features important in discriminating between of NT to ASD-treated samples and to look for metabolic differences between ASD-treated samples. The metabolic pathways of compounds found in ASD treated soils were mapped using the pathway analysis module in MetaboAnalyst 3.0. 3. Results Fig. 1. Representative 1H NMR spectra of extracted metabolites from ASD-treated soil samples. Spectra collected at 600 MHz 298 K. Soil amendments are specified for each sample: NT control (A), CBL/BSM2 (B), ALG/BSM (C), and CBL/BSM (D).
3.1. Sample preparation, NMR analysis, and compound identification The soil redox potential was measured and the ASD treated soils were determined to have significantly higher anaerobic conditions in comparison to NT controls as previously reported [9–11,20]. The CPL/ BMS2 and CPL/BSM treatments resulted in statistically similar cumulative hours under anaerobic conditions with 30,971 and 21,410 mVhr respectively. The cumulative mVhr under anaerobic conditions in the ALG/BSM treatment (14,857 mVhr) was not statistically differentiable from the NT (9558 mVhr), but was also similar to the treatment receiving the lower rate of molasses (CBL/BSM). Complex metabolite mixtures were obtained by extraction of the water-soluble compounds from NT and ASD-treated soil samples as evidenced by comparison of representative 1D 1H NMR (Fig. 1). The predominant number of peaks were observed in regions of the spectra where sugars and organic acids are found [7]. The COLMAR query analysis revealed the presence of organic acids, alcohol, and sugar alcohols. Compounds that exhibited Matching ratios of 1 and the highest Uniqueness parameters (at least 3/4, which is considered to be an acceptable match [16]), were selected for further analysis and are summarized in Table 1. The chemical identities of these compounds were further validated by visual analyses of 2D NMR 1H-13C HSQC, 1H-1H
TOCSY, and 1H-1H COSY spectra (representative 1H-1H TOCSY spectra of NT and ALG/BSM samples are shown in Figs. S1 and S2 respectively of the Supporting Information). Chemical shift assignments of compounds identified by database analysis and confirmed by 2D NMR experimentation are summarized in Table S1. In NT, the presence of acetic acid and ethanol was detected, but the relative abundance was low (Fig. 2). Upon adding labile carbon amendments to the soil, lactic acid, in addition to acetic acid, was formed in the ALG/BSM- and CBL/BSM-treated samples. These results suggest the fermentative metabolic pathways were modified in the presence of a labile carbon source. Interestingly, CBL/BSM2-treated samples did not have similar amounts of acetic acid as compared to ALG/BSM- and CBL/BSM-treated samples, but lactic acid was present in substantial concentration as evidenced by peaks observed in the aliphatic region of the 2D HSQC (Fig. 2B). Furthermore, succinic acid, malic acid, butyric acid, and mannitol (Table 1) were present in CBL/ BSM2-treated soils whereas CBL/BSM-treated soil exhibited elevated succinic acid, butyric acid, and mannitol production, but these 100
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products were key components in the differences between these soils, we performed prokaryotic specific analyses on the compounds identified as being most affected by ASD treatment using MetaboAnalyst (Table 1). As summarized in Fig. 4, NMR-based metabolomics identified several metabolic pathways which could be impacted by ASD treatment. Comparison of the biochemical composition of ASD-treated samples revealed elevation of lactic acid and acetic acid in ASD samples, which is indicative of modulations in pyruvate metabolism and glycolysis, as compared to NT controls. Moreover, exposure to ASD treatment induced increases in butyric acid and succinic acid production suggesting that butanoate metabolism is important in these anaerobic soil environments.
Table 1 Selected list of matched compounds identified in 1H-13C HSQC spectra of extracts from ASD-treated soils using COLMAR Query. Treatment
Compound
1
NT
Acetic acid Ethanol Lactic acid Malic acid Succinic acid Mannitol 3-Hydroxypropionic acid Lactic acid Acetic acid Butyric acid Succinic acid 3-Hydroxypropionic acid Lactic acid Acetic acid Succinic acid Mannitol Butyric acid 3-Hydroxypropionic acid
0.0005 0.0015 0.0021 0.0046 0.0027 0.0126 0.0016 0.0031 0.008 0.003 0.0003 0.0056 0.0102 0.0093 0.004 0.0165 0.0036 0.0135
CBL/BSM2
ALG/BSM
CBL/BSM
Ha
13
C
0.001 0.007 0.0225 0.0187 0.037 0.0568 0.0475 0.0245 0.002 0.026 0.01 0.063 0.1065 0.109 0.245 0.0688 0.1995 0.052
Mb
Uc
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1/1 2/2 3/3 3/3 1/1 3/4 2/2 2/2 1/1 3/3 1/1 2/2 2/2 1/1 1/1 4/4 3/3 2/2
4. Discussion Previous studies have implicated the accumulation of organic acids and other compounds which inhibit the growth of certain soil-borne pathogens and parasites in crop production [9,10,14,21–24]. In this study, NMR-based metabolomics was utilized as a tool by which to identify the chemical composition of normal and ASD-treated soils. ASD treatment induced distinctive metabolic profiles that can potentially account for the pathogen-suppressive qualities found in disinfested soils. The goal of this study was to develop a rapid and accurate method by which to monitor the biochemical composition of soils using NMRbased metabolomics. The utility of ASD as a potential alternative to chemical fumigants is known [9–11,24,25]. To date, the influence of volatile organic acids and other organic acids excreted by the soil microbiome have been characterized by gas chromatography-mass spectrometry (GC-MS) and high performance liquid chromatography (HPLC), but more work needs to be done to understand how the presence of microbial metabolites impacts growth and disease susceptibility in plants [24,26]. Furthermore, these studies have shown that the chemical identities of many microbial metabolites could not be determined using GC-MS and HPLC, which emphasizes the need for techniques whereby high-resolution structural information can be obtained. In this regard, NMR-based studies of the soil microbiome and its biochemical composition in relation to metabolism are ideal. NMR is a powerful tool for the detection and identification of metabolite compounds, but more research is needed in order to establish its utility for usage in the study of the soil metabolome. Here, we showed that the combination of NMR metabolomics and multivariate statistical analysis is a valuable, flexible, and accurate strategy for measuring and interpreting biochemical differences between NT and ASD-treated soils. Our findings provide evidence that NMR-based metabolomics can identify fermentation products in agricultural soils and discriminate between different fermentation pathways impacted by the application of ASD treatments. Our findings suggest that under low oxygen conditions and in the presence of exogenous supplementary carbon, microbial fermentation likely occurs in soils. In particular, the metabolic profile of ASD-treated soil samples confirmed that distinct organic acid compositions are present and that these biochemicals are likely to contribute to production of suppressive soils when compared to NT controls [9–11]. An increased number of peak resonances were observed for CBL/BSM2, ALG/BSM, and CBL/ BSM-treated soils as compared to NT soils, which suggests that increased levels of biochemical compounds are present in ASD-treated soils. Based on this observation, we propose that spectral differences observed between these two groups may serve to identify metabolites important in ASD-induced modifications of agricultural soils. Under the conditions of this study, all amendments tested resulted in sustained anaerobic soil conditions as evidenced by mean cumulative soil redox potential; CBL/BSM2 and CBL/BSM treatment combinations were particularly effective in promoting accumulation of biochemical products that are likely derived from microbial fermentation. Among the biochemical compounds characterized, the most readily identifiable in ASD-treated soils were organic acids and short chain fatty acids,
a The average 1H and13C chemical shift differences between the matched experimental value and the public database chemical shifts are recorded in units of ppm. b M = matching ratio where a value of 1 indicates a perfect match. c U = uniqueness value of 1 which describes when the number of peaks in the experimental HSQC is matched only to the identified compound; however, U = 3/4, which is the threshold defined by Bingol et al. as being acceptable for compound identification.
biochemicals were absent in NT controls. Similarly, only ASD-treated samples exhibited detectable levels of 3-hydroxypropionic acid (3-HPA) which suggested that a transition toward secondary metabolite production occurred in response to ASD treatment. These results were consistent with the accumulation of ASD-induced fermentation and likely secondary metabolite products in treated soil samples. The relative concentrations of these compounds are listed in Table S2. 3.2. Statistical analyses Treatment replicates that did not exhibit a fold change of greater than 2 were omitted from further statistical analyses. PCA revealed that the biochemical composition of the ASD-treated samples was significantly different than NT controls. Fig. 3A shows that NT samples (Treatment 0) clustered together while ASD samples (Treatments1-3) are located in discrete regions away from the control group, suggesting that the metabolite profiles between these samples were distinctly different. The greatest variation was observed in PC1 (59.9%) which provides evidence that ASD-treated and NT controls have distinct metabolic signatures. Variation attributable to PC2 (24.1%), which describes the differences observed within the treated and control groups, revealed that less significant alterations in metabolism exist between different ASD treatments. Separation between NT and ASD-treated samples was based on the contribution of six metabolites as shown in the PCA loading plot of NT vs. ASD-treated samples (Fig. 3B). Microbial fermentation products, such as acetic acid, lactic acid, and butyric acid, contributed to the group separation observed in the plots. To further examine the distinction between ASD-treated samples, PCA analysis was performed in the absence of NT groups (Fig. 3C). Treatment groups were found to be segregated according to different metabolite compositions. The differentiation between treatment groups was based largely on PC1 (63.7%), while less influence was observed from PC2 (24.9%). The contribution of key metabolites was observable in the PCA loading plot (Fig. 3D). Based on the PCA analysis, mannitol, butyric, acetic, succinic, and lactic acids played an important role in driving the variation between treatment groups which further supported that fermentative metabolic responses in the soil microbiome are induced by ASD-treatments. Given that our data strongly implied that microbial fermentation 101
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Fig. 2. Select region of the aliphatic 1H-13C HSQC spectra from NT Control and ASD-treated soil samples collected at 600 MHz 298 K. Peaks corresponding to DSS, acetic acid, and lactic acid are labeled for spectra of NT control (purple-A), CBL/BSM2 (red-B), ALG/BSM (blue-C), and CBL/BSM (green-D). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
the predominance of fermentation based metabolism in this environmental system. In the presence of excess carbohydrate and under limited oxygen availability, facultative anaerobes utilize glycolysis, while minimizing metabolic flux through the TCA cycle, in order to produce ATP [27]. To effectively regenerate reducing equivalents during robust fermentation, it is possible for facultative anaerobes to oxidize pyruvate to lactic acid, which permits the continuation of glycolysis at the
which suggest the involvement of key pathways from primary metabolism that are characteristic of fermentative soil microbes. Interestingly, we found that among the pathways most impacted under anaerobic conditions were glycolysis, pyruvate metabolism, the tricarboxylic acid (TCA) cycle, and butanoate metabolism. The substantial increase in lactic acid and acetic acid was important in the separation between NT and ASD-treated groups, which suggests
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Fig. 3. Multivariate analysis of extracted metabolites from NT and ASD-treated soils. Principle component analysis (PCA) score plot (A) demonstrating the separation between NT and ASD-treated soils: NT (Treatment 0 - red), CBL/BSM2 (Treatment 1 - green), ALG/BSM (Treatment 2 - blue), CBL/BSM (Treatment 3 - orange). The loading plot (B) highlighting the differentiation between NT vs ASD-treated soils by the characterized metabolites. PCA score plot (C) indicating differences in the extracted metabolite composition between the ASDtreatment groups. The loading plot (D) reveals which metabolites influence the separation between different ASD-treatments. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
with repression of the TCA cycle, may serve as a potential regulatory mechanism in disinfestation techniques. ASD treatments also impacted butanoate metabolism as evidenced by the detection of butyric acid and succinic acid. These data suggested the anaerobic microbes, such as those in the genus Clostridium which are known to manufacture butyric acid, may be key players in this microbial community [3]. While we found that fructose metabolism is only marginally impacted in this study (Fig. 4, pathway impact 0.05), Saha et al. (2006) reported that certain lactic acid bacteria and fungi convert fructose in molasses to mannitol, a compound that is present in both CBL/BSM2 and CBL/BSM treated soils. These observations are in agreement with the premise that fermentative soil microbes consume carbon sources, such as the fructose and glucose present in the molasses amendments, during ASD treatments. Two possible mechanisms for ASD-induced biochemical change exist. The first being that the existing
expense of ATP production. In contrast, facultative anaerobic microbes generating acetic acid, another oxidized metabolic intermediate made when the TCA cycle is down regulated, are capable of producing cellular energy in the form of ATP. Our findings point to the soil metabolome oxidizing labile carbon from the ASD amendments during increased glycolysis, while minimizing influx into the TCA cycle reactions in the presence of low oxygen saturation, which results in the production of the partially oxidized metabolic intermediates detectable by NMR. Moreover, we found that malic acid, which is associated with both pyruvate metabolism and the TCA cycle, is present in CBL/BSM2 treated soils. Malic acid can also be converted lactic acid during fermentative reactions when the TCA cycle is blunted, which would further promote the regeneration of reducing equivalents for glycolysis in the absence of aerobic growth. Cross-talk between these metabolic pathways, which promotes increased rates of glycolysis concomitant 103
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to expand these studies to include a more complete characterization of ASD-induced metabolites. In this current study, sample numbers were limited due to the extensive utilization of 2D NMR experiments to thoroughly characterize these agricultural soil samples. In future studies, we aim to minimize the number of 2D NMR experiments used to identify biochemicals while increasing the number of sample replicates evaluated in order to expand the scope of this study. We will explore the development of 1H-13C NMR techniques to measure quantifiable differences between NT and ASD-treated soils using isotope-labeled compounds to further elucidate metabolic networks, which has been shown to enhance the sensitivity of metabolic studies [29]. This would permit the expansion of the study's scope to explore the influences of time and soil microbiome composition on biochemical trends. We conclude that NMR-based investigations into the metabolism of the agricultural soil metabolome offer much potential to support the development of sustainable agricultural practices and the potential identification of bioactive natural products. Author contributions CWJ, TIS, and NLF prepared samples, carried out NMR experiments, and performed data analyses. ENR, JH, WT, and NKB developed and conducted ASD treatment of soil samples. ABL and NLF performed bioinformatics and statistical analyses. NLF conceived of and directed the implementation of the NMR experimentation. CWJ, ENR, and NLF wrote the manuscript.
Fig. 4. Major metabolic pathways impacted as evidenced by the organic acids identified in this study. The x-axis correspond to the metabolic impact factor ad the yaxis is the statistical significance resulting in each treated versus non-treated group.
Funding microbial community alters its metabolism or, alternatively, the composition of the entire microbial community shifts in response to ASD treatment. Mowlick et al. (2013) report a global shift in the composition of the microbial community during ASD-induced anaerobiosis, which is largely influenced by the type of soil amendment utilized during treatments. While genome sequencing is beyond the scope of this study, our findings clearly indicate an ASD-induced shift in the chemical fingerprint of the soil metabolome, but the origin remains to be determined. The detection of these biochemical compounds in the soil using NMR-based metabolomics further supports that microbial fermentation products are likely the fundamental metabolic response induced during ASD. While this methodology proved useful characterizing the accumulation of fermentation products, our study also demonstrated NMR's utility in the detection of potential secondary metabolites such as 3-HPA, which is known to be an antimicrobial compound of importance in agricultural systems [28]. Based on our observations in the current and previous studies [9,10,20], we propose there is a correlation between the robustness of the metabolic response of the soil metabolome and the ability to confer disease resistance in crops, but additional studies are needed to more thoroughly understand this process.
NLF was supported in part by National Institutes of Health grant R15GM117478; NLF and CWJ were supported in part by United States Department of Agriculture Project number 6034-22000-041-24. Acknowledgements We thank Dr. Theresa Ramelot for maintaining the NMR spectrometers and optimizing pulse sequences. The authors greatly appreciate the efforts of Dr. Ann E. Hagerman in the review of this manuscript. Appendix A. Supplementary data Supplementary data related to this chapter can be found at http:// dx.doi.org/10.1016/j.ejsobi.2017.10.008. References [1] C. Gougoulias, J.M. Clark, L.J. Shaw, The role of soil microbes in the global carbon cycle: tracking the below-ground microbial processing of plant-derived carbon for manipulating carbon dynamics in agricultural systems, J. Sci. Food Agric. 94 (2014) 2362–2371, http://dx.doi.org/10.1002/jsfa.6577. [2] P.R. Hirsch, T.H. Mauchline, The importance of the microbial N cycle in soil for crop plant nutrition, Adv. Appl. Microbiol. 93 (2015) 45–71, http://dx.doi.org/10. 1016/bs.aambs.2015.09.001. [3] S. Mowlick, T. Takehara, N. Kaku, K. Ueki, A. Ueki, Proliferation of diversified clostridial species during biological soil disinfestation incorporated with plant biomass under various conditions, Appl. Microbiol. Biotechnol. 97 (2013) 8365–8379, http://dx.doi.org/10.1007/s00253-012-4532-z. [4] T.W.M. Fan, A.N. Lane, Applications of NMR spectroscopy to systems biochemistry, Prog. Nucl. Magn. Reson. Spectrosc 92–93 (2016) 18–53, http://dx.doi.org/10. 1016/j.pnmrs.2016.01.005. [5] T.W.-M. Fan, A.N. Lane, E. Chekmenev, R.J. Wittebort, R.M. Higashi, Synthesis and physico-chemical properties of peptides in soil humic substances, J. Pept. Res. 63 (2004) 253–264, http://dx.doi.org/10.1111/j.1399-3011.2004.00142.x. [6] F. Grosso, F. Temussi, F. De Nicola, Water-extractable organic matter and enzyme activity in three forest soils of the Mediterranean area, Eur. J. Soil Biol. 64 (2014) 15–22. [7] O.A.H. Jones, S. Sdepanian, S. Lofts, C. Svendsen, D.J. Spurgeon, M.L. Maguire, J.L. Griffin, Metabolomic analysis of soil communities can be used for pollution assessment, Environ. Toxicol. Chem. 33 (2014) 61–64, http://dx.doi.org/10.1002/ etc.2418. [8] S. Rochfort, V. Ezernieks, P. Mele, M. Kitching, NMR metabolomics for soil analysis
5. Conclusions In summary, we believe that NMR-based metabolomics is a powerful tool that should be utilized in the evaluation of metabolite production in the soil metabolome. In particular, the methodology reported here offers the efficacious and straightforward means by which to identify individual compounds in complex mixtures extracted from the soil metabolome. Information derived from such studies would be essential in the development of a database that catalogues the biochemical profiles of soils and their ecological or agricultural qualities. It would be of particular interest to determine the biochemical features of soils that are suppressive under specific environmental conditions. Specifically, a more accurate profiling of the microbial communities in the soil metabolome and its relationship to variable metabolite production is necessary to expand our knowledge in the agroecological sciences. We submit that these results provide a foundation from which 104
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