Metabolic dynamics analysis by massive data integration

A new metabolic dynamics analysis approach has been developed in which massive data sets from time-series of 1H and 13C NMR spectra are integrated in ...
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Metabolic dynamics analysis by massive data integration: application to tsunami-affected field soils in Japan Tatsuki Ogura,†,‡ Yasuhiro Date,†,‡ Yuuri Tsuboi,† and Jun Kikuchi*,†,‡,§ †

RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan § Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya 464-0810, Japan ‡

S Supporting Information *

ABSTRACT: A new metabolic dynamics analysis approach has been developed in which massive data sets from time-series of 1H and 13C NMR spectra are integrated in combination with microbial variability to characterize the biomass degradation process using field soil microbial communities. On the basis of correlation analyses that revealed relationships between various metabolites and bacteria, we efficiently monitored the metabolic dynamics of saccharides, amino acids, and organic acids, by assessing time-course changes in the microbial and metabolic profiles during biomass degradation. Specific bacteria were found to support specific steps of metabolic pathways in the degradation process of biomass to short chain fatty acids. We evaluated samples from agricultural and abandoned fields contaminated by the tsunami that followed the Great East earthquake in Japan. Metabolic dynamics and activities in the biomass degradation process differed considerably between soil from agricultural and abandoned fields. In particular, production levels of short chain fatty acids, such as acetate and propionate, which were considered to be produced by soil bacteria such as Sedimentibacter sp. and Coprococcus sp., were higher in the soil from agricultural fields than from abandoned fields. Our approach could characterize soil activity based on the metabolic dynamics of microbial communities in the biomass degradation process and should therefore be useful in future investigations of the environmental effects of natural disasters on soils.

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conditions.11,12 In addition, SCFA-producing bacteria such as Lactobacillus spp. stimulate plant germination and the growth of shoots and roots in soil ecosystems.13 Thus, SCFAs and the bacteria that produce them are key factors that affect the composition of soil environments. Plant-derived saccharides are degraded by soil microbial communities and eventually return to the soil.9 Recycling in an ecosystem occurs through symbiosis among termite gut microbial communities,14 specific protists and fungi,15,16 and soil microbial communities. However, little information is available on the biomass degradation process with respect to complex soil microbial communities, especially the dynamics of microbial communities and SCFA metabolism. Because information on the complex chemical composition of plantderived biomass and the dynamics of the metabolic processes is necessary to characterize this degradation process, we decided to develop a new strategy using comprehensive and multiple measurement data such as metabolomic and meta-sequencing analyses in a previous study to analyze plant-derived biomass in order to obtain this data. Using the developed method, we

rimordial microbial communities that perform anaerobic digestion are considered the most ancient metabolic communities. These communities became segregated from aerobic environments by the emergence of photosynthetic organisms that initially increased oxygen levels on Earth. Many species in the microbial communities of anaerobic environments, such as soil and aquatic bottom sediments, can degrade the biomass produced by photosynthetic organisms. These species are currently used in material recycling, such as in the degradation of animal or plant remains1,2 and as nitrogen-fixing agents for agricultural use.3 Thus, anaerobic digestion remains essential to the biogeochemical cycling of organic and inorganic matter. Among these, anaerobic microbial communities that are responsible for the digestion of plant saccharides into short chain fatty acids (SCFAs) have been investigated in several systems, including the animal gut,4,5 waste management,6−8 and soil from paddy fields.9 Because animals and plants have evolved to utilize the metabolic activity of microbial symbionts,10 major symbiont-derived substances, such as SCFAs, may be essential for the survival of the symbiont’s host such as animals and plants. Moreover, SCFAs act as electron donors and play a role in reducing heavy metals such as Fe(III) and Mn(IV) under different environmental © XXXX American Chemical Society

Received: July 31, 2014 Accepted: May 22, 2015

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spectral analyses for the characterization of complex chemical mixtures, especially focusing on saccharide components. This study revealed a new aspect of this dynamic and complex ecosystem by demonstrating the relationships between the metabolic pathways of biomass degradation and the rapidly evolving microbial communities. This network will serve as a platform to determine the impact of soil contamination on agricultural fields and to guide future efforts in the restoration of crop production.

characterized the structure and composition of rice straw biomass (input data) and analyzed the metabolic dynamics in the biomass degradation process (output data).9 Metabolic profiling is a powerful tool for the analysis of complex reactions in microbial ecosystems. This technique can be used to characterize total metabolic activity and identify biomarkers. Nuclear magnetic resonance (NMR) is one of the most common approaches to identify molecules and is used in a wide variety of systems.17−19 Although 1H NMR is easy to perform and versatile, its limited spectral resolution is insufficient to tackle the overlapping signals of saccharides. The introduction of 13C-nuclei to NMR analysis was a useful technological advance for the resolution of overlapping signals. For instance, 13C-labeling and multidimensional NMR was successfully used to define the metabotype of the Caenorhabditis elegans sir-2.1 mutant as the decoupling between catabolic pathways and ATP generation.20 Bingol et al. presented general strategies for the quantification of uniformly 13 C-labeled metabolites.21 Advantages of the 13C-labeling method also include the characterization of compounds in complex components, the determination of compound structures, and the tracking of microbial metabolic pathways using NMR or other analytical techniques.20,21 We have previously successfully performed 13C-labeling in plants22,23 and microbial systems,24 as well as metabolic profiling using two- and three-dimensional NMR techniques.25−29 NMR-based metabolic profiling is helpful but not enough for evaluation of microbial community variations in microbial ecosystem. In terms of microbial community analysis, nextgeneration sequencing is useful for the analysis of environmental samples because of its capacity for high-throughput measurements. Moreover, the relationships between metabolite production and microbial dynamics have also been analyzed using correlation analysis.6,8 However, environmental evaluation using a combined analysis of metabolomics and metasequencing data has not been performed until very recently.30 We have recently tackled challenges associated with characterizing complex environment systems by making use of such integrated analysis and published several reports in which metabolic, elemental, and microbial massive data approaches were integrated for environmental evaluation.19,31−36 The integrated approaches were useful tools to evaluate the environmental complexities generated by interactions between various organic and inorganic molecules and microbial species in various ecosystems. In March 2011, the Great East Japan earthquake was followed by a tsunami that caused serious damage to many coastal areas, particularly the Tohoku area (Supporting Information Figure S1).37 Agricultural fields were contaminated by seawater,38,39 and salt removal and sludge disposal are still underway in an attempt to restore the disaster area. A recent study showed that the tsunami profoundly modified the soil microbial communities in the Tohoku area: sulfur-oxidizing bacteria were more abundant, whereas nitrite-oxidizing bacteria were scarcer after the tsunami.40 The careful analysis of the impact of the tsunami on the soil microbial communities and their chemical compositions is therefore crucial for designing an effective plan to revive agriculture in this region. The present study evaluated the metabolic dynamics of biomass degradation by soil microbial communities from Tohoku using plants with distinct chemical compositions and elucidated the soil activities in agricultural and abandoned fields in Tohoku. To this end, we developed an integrated approach using 1H and 13C NMR

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RESULTS AND DISCUSSION Integrated Approach for Evaluation of Biomass Degradation in Tohoku Soil. In the integrated approach, the chemical composition of each of the three kinds of plant samples was characterized using 1H−13C heteronuclear single quantum coherence (HSQC) and HSQC-total correlation spectroscopy (HSQC-TOCSY; Supporting Information Figure S2 and Supporting Information Table S1). Biomass degradation activity of a Tohoku soil on the plant samples was evaluated using integrated analysis of 1H and 13C NMR and microbial analysis (Supporting Information Figures S3−S6). Although 1H NMR was a powerful tool for the metabolic analysis, saccharide metabolism was difficult to evaluate because of signal overlap. Thus, 13C NMR spectra, which have the ability to resolve and annotate saccharides, were also measured, and both kinds of NMR data were integrated into the analysis (Supporting Information Figure S3). Microbial profiles and relationships between metabolites and microbial communities were evaluated using principal component analysis (PCA) and correlation analysis (Supporting Information Figures S4 and S5). Analysis of microbial population dynamics indicated that each profile exhibited similar dynamics and that the predominant microbial species based on population changed from Klebsiella sp. to Lactobacillus sp. over time (Supporting Information Figure S4M). The predicted relationships between metabolites and microbial communities also indicated that Lactobacillus sp. was responsible for the degradation of some monosaccharides, such as D-fructose and D-galactose, and the production of SCFAs, such as acetate and lactate. Bacteria such as Yersinia sp. and Pseudomonas sp. produced amino acids, such as L-asparagine and L-aspartate in soybean and komatsuna samples (Supporting Information Figure S5A and S5C). In contrast, Lactobacillus sp. produced acetate and ethanol and Pediococcus sp. produced Lasparagine in tomato samples (Supporting Information Figure S5B). Furthermore, the metabolic pathways of the microbial communities supporting the plant degradation process were elucidated using correlation analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (http:// www.genome.jp/kegg/pathway.html;41,42 Supporting Information Figure S6). The pathways indicated that each saccharide compound was consumed by and converted to various SCFAs or amino acids by distinct bacteria (detailed results and discussion are shown in the Supporting Information text). Thus, we were able to visualize the metabolic pathways that regulate biomass degradation processes in microbial ecosystems. This network identifies the metabolic step(s) targeted by the bacterial species that dominate different stages of the biomass degradation process. Soil Microbial and Elemental Profiling in the Tohoku Area. To profile the soil microbial community in the Tohoku area destroyed by the tsunami in 2011, denaturing gradient gel electrophoresis (DGGE) was performed on the soils collected from this area (Figure 1A). Microbial community patterns were B

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Figure 1. Characterization of soil microbial communities (A, PCA), carbon and nitrogen contents (B), and elemental profiles (C−E) in the tsunami disaster area of the Tohoku region using DGGE, a CHNS analyzer, and ICP-OES, respectively. PCA (C) and OPLS-DA (D) based on elemental profiles and their relative abundance in an abandoned field compared with an agricultural field (E) are shown. 1, salt-destroyed agricultural field in Hisanohama; 2, bottom of Hisanohama port; 3, riverside of the Oohisa River; 4, bottom of the Oohisa River; 5, agricultural field near the Abukuma River; 6, agricultural field in Torinoumi; 7, agricultural field near the Natori River; 8, abandoned field near the Natori River; 9, agricultural field in Shichigahama; 10, abandoned field in Arahama. Sampling dates were Jan. 22, 2012 (circle), Oct. 13, 2013 (triangle), and Nov. 2, 2013 (square). Sampling sites shown are the Abukuma River group in Miyagi (red-colored symbols), the Natori River group in Miyagi (blue-colored symbols), Fukushima (yellow-colored symbols), salt-removed fields (closed symbols), abandoned fields (open symbols), and the hydrosphere of the saltdamaged area (gray-colored symbols).

observed to be clustered into three groups (the Hisanohama and Oohisa River group in Fukushima, the Abukuma River group in Miyagi, and the Natori River group in Miyagi), although some overlap was observed (i.e., in the samples from the agricultural field of Natori River). Microbial community profiles thus were not affected by soil conditions but rather depended on geographical location. In addition, organic and inorganic elemental analyses were performed to characterize the distribution patterns of the elements from each sampling site of the Tohoku area (Figure 1B−E). It is likely that elemental profiles depended on not only the geographical location but also the soil conditions of abandoned and nonabandoned agricultural fields (Figures 1C and D). The abundance of barium (Ba) and manganese (Mn) was significantly higher in agricultural fields compared with abandoned fields (Figure 1E). We conclude that soil analyses conducted using a conventional approach are only able to capture a few differences, such as Ba and Mn content, with respect to agricultural field soils. Consequently, a novel approach for the characterization of soil conditions is essential to meaningfully evaluate “soil activities” and to capture the “health” of soils. To address this need, we applied our advanced

method to the soils collected from the Tohoku area to characterize differences between soil conditions. Metabolic and Microbial Variations in the Soils in the Tohoku Disaster Area. Differences in the biomass degradation profiles (i.e., variations in the metabolites and microbial communities) between agricultural and abandoned fields in the Tohoku disaster area were compared using the soil evaluation method we advanced in this study (Figure 2 and Supporting Information Figures S7−S10). Shortly after the incubation of the soil microbial communities with the plant biomass started, the metabolic profiles of the soil samplesin terms of concentrations of acetate, propionate, and butyratediffered widely in terms of the PCA score plot; however, the profiles eventually converged in a PC1 negative and PC2 positive direction (Figure 2A). Production levels were highest in the soil sample derived from the agricultural field in Shichigahama (Figure 2B and Supporting Information Figure S9). In particular, the production of acetate and propionate was 3 times higher in the Shichigahama sample than in the sample from the abandoned field near the Natori River. Additionally, microbial community profiles were completely different between Shichigahama and the other regions (Figure 2C). C

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Figure 2. Comparison of metabolites (A and B) and microbial communities (C and D) between abandoned field and agricultural field soils using 1 H−13C NMR spectra and MiSeq Sequencer, respectively. PCA score plots (A and C) and abundance of metabolites (B) and microbes (D) in each term: early (pale orange), mid (orange), and late (burnt orange). Symbols are shown for an agricultural field in Shichigahama (triangle), an abandoned field near the Natori River (square), and an abandoned field in Arahama (circle). The terms were defined as “Early” from day 1 to day 6, “Mid” from day 7 to day 12, and “Late” from day 13 to day 22.

The dominant bacteria in the Shichigahama sample were Denitrobacter sp. and unclassified Lachnospiraceae bacteria, which increased in abundance during the incubation experiment (Figure 2D and Supporting Information Figure S10). The time-course variations of these bacteria differed; the Denitrobacter sp. population increased rapidly from day 5 until day 8 and then gradually decreased for the remainder of the experiment, whereas the population of unclassified Lachnospiraceae increased rapidly from day 9 until day 12, followed by a slight decrease. Unclassified Enterobacteriaceae bacteria were dominant in all soil samples prior to the incubations but decreased in all samples as the degradation process continued. In particular, a decrease of relative abundance in unclassified Enterobacteriaceae in the Shichigahama sample was observed with an increase in other bacteria (i.e., Denitrobacter sp. and bacteria belonged to unclassified Lachnospiraceae) and metabolites (i.e., acetate, butyrate, and propionate) in “Mid” and “Late” terms. Correlation Analysis of Metabolic and Microbial Dynamics. Correlation analyses of the time-course changes of metabolites and microbial communities were performed for

all samples from each incubation experiment (Figure 3). In agricultural field soil, species of the Clostridiales group, such as Sedimentibacter sp. and Coprococcus sp., were positively correlated with SCFAs and negatively correlated with saccharide components (SCs). Sedimentibacter saalensis and other clostridial species are known to produce SCFAs,43 which suggests that the Clostridiales group contributed to the production of SCFAs through SCs. Denitrobacter sp. were also negatively correlated with SCs such as galactose, glucose, and mannose, which suggests that these species contributed to the degradation of these components. Because bacteria categorized as the Bacillales group were positively correlated with SCFAs in the incubation experiments on abandoned field soil, these bacteria were believed to be related to SCFA production in the experimental system. Lactococcus sp. was negatively and weekly correlated with maltose and galactose, and Denitrobacter sp. was positively correlated with arginine and butyrate and negatively correlated with maltose and galactose. The profiles of these bacteria and their correlations with the metabolites differed from those of the bacteria in the incubation experiments on the agricultural field soil, although it remains D

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Figure 3. Correlation analysis of time-course variations between metabolites and soil microbial communities obtained from NMR and metasequencing analysis, respectively. Two-dimensional correlation maps for an agricultural field in Shichigahama (A), an abandoned field near the Natori River (B), and an abandoned field in Arahama (C) are shown. Red and blue denote positive and negative correlations, respectively. The positive correlation implies that a metabolite and a bacterium exhibit similar behavior, or the concentration of metabolite is similarly correlated with its source organism over the time course. This correlation map indicates linear relationships between bacteria and metabolites in time-varying data. 1 ∗, saccharides; 2 ∗, SCFAs; 3 ∗, amino acids; 6 ∗, other compounds.

elucidate the degradation process of saccharides and organic and amino acids in a complex ecosystem. Correlation analyses between the dynamic changes in metabolites and the composition of the microbial community during biomass degradation revealed multiple and significant relationships. Using this information, this highly integrated network allowed us to associate key bacteria with specific steps in the metabolic pathways of saccharides and revealed dynamic interplay between the microbial community and biomass degradation. Application of this approach for the evaluation of soils from the Tohoku disaster area showed that the metabolic dynamics and activities in the biomass degradation process differed considerably in soil from agricultural versus abandoned fields. Levels of SCFA production (such as acetate and propionate) in particular were higher in the soil from agricultural fields than soil from abandoned fields. This approach was thus able to characterize soil activity based on the metabolic dynamics of microbial communities in biomass degradation processes and should be useful for the evaluation of environmental effects of natural disasters on soils. It may serve as a platform to determine the impact of soil contamination on agricultural fields and to guide future efforts in the restoration of crop production.

unclear whether the bacteria were introduced by the disaster or is derived from just the differences between fallow and cultivated soils. Relationship of Biomass Degradation and Soil Microbial Communities in Tohoku Field Soils. Biomass degradation profiles and microbial activities in tsunami-affected, abandoned field soils from the Tohoku region were determined through integrated analysis of metabolic and microbial dynamics (Figure 4 and Supporting Information Figure S11). Biomass degradation profiles and especially microbial activities depended on soil conditions. In addition, this study showed that microbial diversity was higher in agricultural field soil than in abandoned field soils and was accompanied by higher levels of SCFA production, such as those of acetate and propionate. The time-course variations in the microbial activities and diversities with higher levels of SCFA production may serve as biomarkers for the evaluation of soil activities in environments such as agricultural fields and contaminated sites, although further studies are needed to refine the applications. The advanced approach described here, including the integrated analysis of 1H and 13C NMR spectra and the correlation analysis of microbial variations and metabolic dynamics using a soil incubation method, should be useful for characterizing the metabolic dynamics in environmental ecosystems and for understanding the “health” of soils. Conclusion. The present study offers a new metabolomic approach that integrates 1H and 13C NMR spectral analysis and correlation analysis of metabolic profiles and variations in microbial communities, combined with a time-course incubation study using targeted soils to profile the biomass degradation process. The integrated analysis allowed us to

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METHODS

Soil Samples from the Tohoku Disaster Area. Soil samples were collected on January 22, 2012, October 13, 2013, and November 2, 2013, from a salt-destroyed agricultural field in Hisanohama (37°8′51.48″ N, 140°59′39.74″ E), the bottom of Hisanohama port (37°15′2.18″ N, 141°0′2.39″ E), the riverside and bottom of the Oohisa River (37°8′50.59″ N, 140°59′48.64″ E and 37°8′50.06″ N, 140°59′49.35″E, respectively), agricultural fields near the Abukuma E

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inverse (i.e., with proton coils positioned nearest to the sample) 5 mm 1 H/13C/15N cryoprobe. The peak of sodium 2,2-dimethyl-2silapentane-5-sulfonate (DSS) was used as the internal reference (δC 80, δH 0 ppm). NMR spectra were acquired from 11.704 to −2.296 ppm in F2 (1H) using 2048 data points for an AQ of 104 ms, D1 of 2 s, and 88−48 ppm in F1 (13C) using 142 increments (F1 acquisition time, 6.8 ms) of 48 scans. All 1D Watergate spectra and 13C-adiabatic distortionless enhancement by polarization transfer (DEPT) spectra were acquired for the culture supernatants incubated from day 1 to 22 using the same NMR machine to analyze the degradation metabolism. Watergate spectra were measured from 14 to −3 ppm at 25 °C using 32 k data points. 13C-adiabatic DEPT spectra were measured from 220 to −15 ppm using 65 k data points and 4 k scans. Elemental Analysis of the Soil Samples Collected from the Tohoku Disaster Area. Total soil carbon and nitrogen analyses were performed using a CHNS/O Elemental Analyzer (vario MICRO cube; Elementar Analysensysteme GmbH, Hanau, Germany) following previous studies.19,31 Inorganic elements were analyzed using an inductively coupled plasma-optical emission spectrometry (ICP-OES; SPS5510; SII Nanotechnology Inc., Tokyo, Japan) instrument following previous studies.19,31,44 PCR-DGGE and Metasequencing. Bacterial DNA extraction was performed using the PowerSoil DNA Isolation Kit (MO BIO Laboratories Inc., Carlsbad, CA, USA), according to manufacturer’s instructions. For the PCR-DGGE analysis, standard polyacrylamide gel electrophoresis conditions were used as described previously.24 DGGE images were analyzed using Quantity One software (Bio-Rad). Signal intensities and band positions in each lane were divided into a spectrum of 100 variables, and the data were preprocessed for statistical analysis using methods reported previously.44 The PCR protocol used for metasequencing has been described previously.45,46 Sequencing was performed on a 454 GS JUNIOR sequencer (Roche Applied Science, Penzberg, Germany) and MiSeq sequencer (Illumina, San Diego, CA, USA), according to the manufacturer’s instructions. The data were analyzed using QIIME software (http://qiime.org/).47 Each sample was separated by a 454 barcode-attached 27 mod-338R primer and chimera check using Usearch software (http://drive5.com/usearch/manual/uchime_algo. html).48 Resultant operational taxonomic unit data were assigned sequences using the Ribosomal Database Project (RDP; http://rdp. cme.msu.edu/seqmatch/seqmatch_intro.jsp) classifier.49 Integrated Analysis Using 1H and 13C NMR and Metasequencing. All 1D-NMR data were processed using Topspin 3.1 software (Bruker Biospin), and raw data were exported as .txt files. Exported data were processed over a range of 9 to 0 ppm with approximately 5 k data points for 1H NMR and a range of 150 to 0 ppm with approximately 5 k data points for 13C NMR. Each region was integrated and normalized using the sum of the DSS integral regions. The integrated data were analyzed by PCA using R software, as has been previously described.50 A correlation analysis was performed between the metabolites identified from the 1H and 13C NMR spectra data and the bacteria identified from the metasequencing data. Pearson’s product-moment correlation coefficients were calculated using R software in the same way as in a previous study.6

Figure 4. Relationship of metabolites and microbes in the plant biomass degradation process. Symbol colors indicate bacteria that increased in number (purple), metabolites produced in high quantities (dark green), metabolites produced in low quantities (light green), and degraded saccharides (brown), respectively. Each relationship was inferred by correlation analysis. Mal, maltose; Mel, melibiose; Glu, glucose; Gal, galactose; Fru, fructose; Man, mannose; Xyl, xylose; BuA, butyrate; PrA, propionate; AcA, acetate. River (38°5′79.59″ N, 140°91′38.56″ E), in Torinoumi (38°3′93.21″ N, 140°91′12.42″ E), the Natori River (38°18′49.53″ N, 140°95′3.16″ E), in Shichigahama (38°31′7.84″ N, 141°4′70.6″ E), and abandoned agricultural fields near the Natori River (38°17′64.02″ N, 140°95′37.49″ E) and Arahama (38°4′36.72″ N, 140°90′52.46″ E). The height of the tsunami in Hisanohama, Natori, Arahama, and Shichigahama was 6.8, 9.1, 7.7, and 12.1 m, respectively. These data were helpfully provided by Tohoku Gakuin University (http://www. tohoku-gakuin.ac.jp/about/sinsai/record/chap_7/chap07_07.html). The soil type of Natori was gray lowland soils, while that of Arahama and Shichigahama was gleysols. This information was helpfully provided by National Institute for Agro-Environmental Sciences (http://www.niaes.affrc.go.jp/). Soil Evaluation Method Using Biomass Degradation Processes. The soil microbial communities were prepared by mixing 100 g of agricultural field soil with 400 mL of distilled water. Aliquots of 80 mL were poured into four 100 mL vials. Then, 5 g of milled biomass sample was added to each vial and incubated at 30 °C with shaking (160 rpm) for 22 days. Each day, samples were centrifuged to separate the supernatant from the pellet. The metabolic profile of the soil microbial community was determined from the supernatant using 1 H and 13 C NMR and from the pellet using DGGE and pyrosequencing analysis. For the terms of incubation, “Early” was defined as from day 1 to day 6, “Mid” from day 7 to day 12, and “Late” from day 13 to day 22. One- and Two-Dimensional NMR Analyses. The culture supernatants from the degradation experiments were analyzed using 1 H−13C HSQC and HSQC-TOCSY to identify the components. NMR spectra were acquired at 25 °C using a 700 MHz (AV700) Bruker Biospin (Rheinstetten, Germany) instrument equipped with an

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ASSOCIATED CONTENT

S Supporting Information *

Supporting Information provides supplementary methods and results of integrated approach for evaluation of biomass degradation with the relative Figures and Tables. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/cb500609p.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +81455039490. Fax: +81455039489. E-mail: jun. [email protected]. F

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The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors wish to thank Matsui M., Kato T., and Moriya S. (RIKEN) for their useful advice and assistance with the sequencing analysis. This research was supported in part by Grants-in-Aid for Scientific Research (Grant No. 25513012, to J.K.) and the Advanced Low Carbon Technology Research and Developmental Program (Grant No. 200210023, ALCA to J.K.) from the Ministry of Education, Culture and Sports, and also partially supported by the Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), “Technologies for creating nextgeneration agriculture, forestry and fisheries” funded from Biooriented Technology Research Advancement Institution (NARO).

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DOI: 10.1021/cb500609p ACS Chem. Biol. XXXX, XXX, XXX−XXX