Intracellular metabolite profiling and the evaluation of metabolite extraction solvents for Clostridium carboxidivorans fermenting carbon monoxide

Journal Pre-proof Intracellular metabolite profiling and the evaluation of metabolite extraction solvents for Clostridium carboxidivorans fermenting carbon monoxide Jungyeon Kim, Joongsuk Kim, Youngsoon Um, Kyoung Heon Kim

PII:

S1359-5113(19)30883-9

DOI:

https://doi.org/10.1016/j.procbio.2019.10.012

Reference:

PRBI 11800

To appear in:

Process Biochemistry

Received Date:

10 June 2019

Revised Date:

22 September 2019

Accepted Date:

13 October 2019

Please cite this article as: Kim J, Kim J, Um Y, Kim KH, Intracellular metabolite profiling and the evaluation of metabolite extraction solvents for Clostridium carboxidivorans fermenting carbon monoxide, Process Biochemistry (2019), doi: https://doi.org/10.1016/j.procbio.2019.10.012

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Revised for Process Biochemistry

Intracellular metabolite profiling and the evaluation of metabolite extraction solvents for Clostridium carboxidivorans fermenting carbon monoxide

Jungyeon Kim1, Joongsuk Kim1,2, Youngsoon Um2,3,* and Kyoung Heon Kim1,*

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Department of Biotechnology, Graduate School, Korea University, Seoul 02841,

South Korea 2

Clean Energy Research Center, Korea Institute of Science and Technology, Seoul

02792, South Korea 3

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Clean Energy and Chemical Engineering, Korea University of Science and

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Technology, Daejeon 34113, South Korea

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Correspondence and requests for materials should be addressed to K.H.K. (E-mail:

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[email protected]) or Y.U. (E-mail: [email protected])

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Graphical abstract

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Highlights 

Unique intracellular metabolome of C. carboxidivorans fermenting CO was

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identified.

Substantial amounts of fatty acids accumulate in the cells as they ferment CO.

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WiPM is the best solvent for extraction of metabolites in C. carboxidivorans.

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

ABSTRACT

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Clostridium carboxidivorans ferments CO, CO2, and H2 via the Wood-Ljungdahl pathway. CO, CO2, and H2 are unique substrates, unlike other carbon sources like

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glucose, so it is necessary to analyze intracellular metabolite profiles for gas fermentation by C. carboxidivorans for metabolic engineering. Moreover, it is

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necessary to optimize the metabolite extraction solvent specifically for C. carboxidivorans fermenting syngas. In comparison with glucose media, the gas media allowed significant abundance changes of 38 and 34 metabolites in the exponential and stationary phases, respectively. Especially, C. carboxidivorans cultivated in the 2

gas media showed changes of fatty acid metabolism and higher levels of intracellular fatty acid synthesis possibly due to cofactor imbalance and slow metabolism. Meanwhile, the evaluation of extraction solvents revealed the mixture of waterisopropanol-methanol (2:2:5, v/v/v) to be the best extraction solvent, which showed a higher extraction capability and reproducibility than pure methanol, the conventional extraction solvent. This is the first metabolomic study to demonstrate the unique

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intracellular metabolite profiles of the gas fermentation compared to glucose fermentation, and to evaluate water-isopropanol-methanol as the optimal metabolite extraction solvent for C. carboxidivorans on gas fermentation.

Metabolomics;

extraction

optimization;

Clostridium

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carboxidivorans; carbon monoxide

solvent;

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Keywords:

1. Introduction

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Clostridia are receiving increasing attention, as these organisms can produce biofuels such as ethanol and butanol from syngas that contains CO, CO2, and H2 [1,2].

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Clostridium carboxidivorans, a gram-positive and strictly anaerobic bacterium, is an acetogenic Clostridium species that has recently been isolated from sediments of an

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agricultural settling lagoon [3]. C. carboxidivorans converts CO and CO2 from syngas into other metabolites including acetic acid, butyric acid, ethanol, and butanol via the

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Wood-Ljungdahl (W-L) pathway [4-6]. Although the utilization of syngas by C. carboxidivorans has received much attention, its industrial applications are restricted, due to several limitations. For instance, the growth and utilization of CO by C. carboxidivorans are very slow and negatively affects the production of bio-based 3

products from syngas [4]. To overcome these limitations, metabolic engineering [7] and optimization of environmental factors, such as composition of medium [8], pH [9], and temperature [10], are necessary. One of the most important purposes of microorganisms in metabolic engineering is to discover and resolve the slow steps that lead to a metabolic bottleneck [11]. Therefore, it is necessary to elucidate the intracellular metabolism of the organism in

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this direction. However, no study has been done on the metabolism of C. carboxidivorans based on intracellular metabolites which directly reflect the intracellular metabolism. To date, only the genomic expression profile of C. carboxidivorans is available, wherein Ukpong et al. [5] identified the genes involved in

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the W-L pathway; however, this information is insufficient to reflect the intracellular

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metabolic profile of the microorganism. Among other syngas-fermenting clostridia, multi-omics analyses were performed for Clostridium ljungdahlii [12]. Unfortunately,

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phenotypes and genes related to syngas fermentation clearly differ between C. ljungdahlii and C. carboxidivorans [5]. The yields of butanol and ethanol production by

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C. carboxidivorans fermenting glucose or CO were improved through metabolic engineering [7]. However, this is still insufficient for industrial application and requires

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further metabolic engineering of the microorganism. Therefore, more extensive studies on the intracellular metabolism of C.

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carboxidivorans for syngas fermentation are necessary for the metabolic engineering of C. carboxidivorans. Metabolomics is a useful tool for the elucidation of intracellular metabolisms [13]. The evaluation of profiles of primary metabolites, which are directly connected with various cellular metabolic pathways and phenotypes, may reveal the 4

intracellular metabolic status and allow the identification of metabolic bottlenecks in microorganisms [13-15]. This information may suggest possibilities for the metabolic engineering of microorganisms, thereby, offering several benefits, such as increased growth and productivity [13-15]. Metabolomics demands optimization of metabolome sampling steps to obtain accurate, reproducible, and reliable metabolome data [16-20]. As types and

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abundances of primary metabolites usually differ, depending on types of organisms and environmental factors, metabolome sampling methods need be optimized for specific organisms and culture conditions [16-18]. To date, no study has been done on the

optimal

metabolome

sampling

methodology

for

syngas-fermenting

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microorganisms. Among clostridia, the optimization of metabolome sampling of

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Clostridium acetobutylicum has been reported [18], in which aerobic processing of metabolome sampling, fast filtration, and use of pure methanol as an optimal

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extraction solvent were described using glucose as the carbon source. Unlike C. acetobutylicum, C. carboxidivorans fixes syngas via the W-L pathway [2]. Given the

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uniqueness of the W-L pathway, the types and abundance of intracellular metabolites of C. carboxidivorans cultivated in syngas medium are likely to be different from those

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observed in general media containing glucose as a carbon source. If extracted metabolite profiles significantly differ between on glucose and syngas fermentations,

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the conventional extraction solvent for C. acetobutylicum (i.e., pure methanol) is not likely to exhibit optimal extraction efficiency in C. carboxidivorans on the gas fermentation.

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In this study, we exploited untargeted but global metabolomics for two objectives, firstly to unveil unique metabolite profiles of C. carboxidivorans fermenting CO, CO2, and H2, and secondly to suggest an optimal solvent for metabolite extraction of C. carboxidivorans on the gas fermentation. In this regard, we compared intracellular metabolite profiles of C. carboxidivorans cultivated in gas medium containing CO, CO2, and H2 (P7) and glucose medium (MP2). In addition, four different commonly-used

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extraction solvents, including pure methanol (PM) [18,19], mixtures of acetonitrilemethanol-water (AMW) [17,18,21], acetonitrile-water (50ACN) [17,18], and waterisopropanol-methanol (WiPM) [16-18] were compared based on their extraction capabilities and reproducibility. For the identification and the relative quantification of

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metabolites, gas chromatography/time-of-flight–mass spectrometry (GC/TOF–MS)

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was used. To our knowledge, this is the first study to analyze the unique intracellular metabolite profiles of CO, CO2, and H2 fermentation compared to glucose fermentation to

evaluate

metabolite

extraction

solvents

for

syngas-fermenting

C.

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and

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carboxidivorans.

2. Materials and methods

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2.1 Strains, media, and culture conditions. The strain C. carboxidivorans P7 (DSM 15243) was cultivated in 2× yeast extract, tryptone, and glucose (YTG) medium to

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obtain a seed culture. Cells were harvested when the optical density of culture broth at 600 nm (OD600) reached 2.0–2.5 (considered as the mid-exponential phase). Cells were washed twice with modified P7 medium and inoculated (10%, v/v) with 5 mL of modified P7 medium in 25 mL serum bottles as the main culture. For CO, CO 2, and H2 6

fermentation, a pressure of 1.5 bar was applied in the headspace of the serum bottles using a gas mixture (10% H2, 70% CO, and 20% CO2, v/v/v; Airkorea, Seoul, South Korea), and cells were cultivated at 37°C with shaking at 200 rpm to increase the surface area contacting the substrate and to prevent cells from sinking. For glucose fermentation, 5 mL of modified P2 medium (MP2) containing 5 g/L glucose in a 25 mL serum bottle was used for the main culture [8]. Cells were inoculated in glucose

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medium and incubated at 37°C with shaking at 200 rpm. All media were anaerobically prepared following purging with argon gas (99.9%, w/w). The composition of the 2× YTG, modified P7, and MP2 media is shown in Table S1.

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2.2 Preparation of intracellular metabolite samples and evaluation of extraction

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solvents. To quench C. carboxidivorans for metabolome sampling, the fast filtration method under aerobic conditions was performed with a slight modification to the

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method previously described [18]. Cells cultivated in the gas or glucose media were obtained by filtering 2 mL cell cultures using a nylon membrane filter (0.20 µm pore

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size, Whatman, Piscataway, NJ) under vacuum. Cells on the filter were washed with 10 mL of distilled water at room temperature. Cells and the filter were immediately

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mixed with 10 mL of four different extraction solvents, namely, 50ACN (acetonitrilewater=1:1, v/v), AMW (acetonitrile-methanol-water=2:2:1, v/v/v), PM (pure methanol),

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and WiPM (water-isopropanol-methanol=2:2:5, v/v/v), at −20°C, and each mixture was immersed in liquid nitrogen to completely stop cellular enzyme activities and changes in metabolites. These steps were completed within 30 s. Each mixture was thawed on ice, sonicated for 5 min, vortexed for 3 min, and centrifuged at 16,100 × g for 10 min 7

at 4°C to extract intracellular metabolites. The supernatant from each mixture was collected, vacuum-dried, and stored as an intracellular metabolite sample.

2.3 High-performance liquid and gas chromatographic analyses of extracellular metabolites. Acetic acid and butyric acid as extracellular metabolites were analyzed by

high-performance

liquid

chromatography

(Agilent

1260

Infinity,

Agilent

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Technologies, Santa Clara, CA) equipped with a refractive index detector, UV/Vis detector, and Hi-Plex H column (300 mm length and 7.7 mm inner diameter, Agilent Technologies). As the mobile phase, 5 mM H 2SO4 was used at a flow rate of 0.6 mL/min. Ethanol, and hexanoic acid were analyzed by gas chromatography (GC;

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Agilent 6890N, Agilent Technologies) equipped with a flame ionization detector and

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HP-INNOWAX polyethylene glycol column (30 m length, 0.32 mm inner diameter, and 0.25 μm film thickness; Agilent Technologies). The composition of H 2, CO, and CO2

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conductivity detector.

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was analyzed by GC (Agilent 6890N, Agilent Technologies) equipped with a thermal

2.4 Analysis of intracellular metabolites with GC/TOF–MS. For the identification

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and relative quantification of intracellular metabolites using GC/TOF–MS, the two derivatisation methods, methoximation and silylation, were performed. For

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methoximation, metabolite samples were incubated with 5 µL of 40 mg/mL methoxyamine hydrochloride in pyridine at 30°C for 90 min (Sigma-Aldrich, St. Louis, MO). For silylation, metabolite samples were incubated with 45 μL of N-methyl-Ntrimethylsilyl-trifluoroacetamide at 37°C for 30 min (Fluka, Buchs, Switzerland). As a 8

retention index marker for accurate identification of metabolites, a mixture of fatty acid methyl esters was added to the derivatised metabolite samples. For the identification and the relative quantification of intracellular metabolites, an Agilent 7890B GC (Agilent Technologies) coupled with a Pegasus HT-TOF MS (LECO, St. Joseph, MI) was used. An aliquot of 0.5 µL of derivatised metabolite sample was injected into GC in splitless mode. The injected metabolites were separated on an

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RTX-5Sil MS column (30 m length, 0.25 mm inner diameter, and 0.25 µm film thickness; Restek, Bellefonte, PA) with an additional 10 m guard column. The oven temperature was initially set at 50°C for 1 min, ramped to 330°C at a rate of 20°C/min, and held at 330°C for 5 min. Mass spectra were recorded in a mass range of 85−500 m/z at an

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acquisition rate of 10 spectra/s. Temperatures of the ion source and the transfer line

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of TOF-MS were set at 250°C and 280°C, respectively. The injected metabolite sample

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was ionised by electron impact at 70 eV.

2.5 Data processing for GC/TOF–MS and statistical analyses. For the detection

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and deconvolution of mass spectra, LECO Chroma TOF software (C version; LECO) was used for pre-processing of mass spectra. Pre-processed data were further

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processed using an in-house library, BinBase [22,23]. Metabolites were identified by referring their mass spectra and retention indices of peaks to Fiehn and NIST libraries

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[23,24]. Compared to the peaks of the authentic standards, the peaks with mass spectral similarity above 700 were regarded as identified metabolites [23]. Also, metabolites which were identified in more than 50% of samples were regarded as authentic metabolites. Quantities of identified metabolites were reported as peak 9

heights of their unique ion intensities. To process missing values for the statistical comparisons, average retention times were calculated. For each positively detected spectrum, the lowest background intensity was subtracted from the intensity of the quantified ion in its retention time region of ± 5 s using the MZmine software [23]. Intensities of identified metabolites were normalised to the microbial cell density measured by recording absorbance at 600 nm.

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Normalized data were used in multivariate and univariate statistical analyses, including the partial least squared-discrimination analysis (PLS-DA), permutation tests, hierarchical clustering analysis (HCA), Student’s t-test, analysis of variance (ANOVA) with the post-hoc Tukey’s test, MetaMapp analysis, and metabolite set enrichment

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analysis. PLS-DA and their permutation tests (leaves out 1/7th of the data) were

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performed using the SIMCA-P+ software (version 14.1; Umetrics AB, Umea, Sweden), and HCA using the MultiExperiment Viewer application [25]. Student’s t-test was

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performed with the Statistica software (version 7.1; StatSoft, Tulsa, OK), and ANOVA with the post-hoc Tukey’s test was performed with R software (version 3.3.2; R

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Foundation, Vienna, Austria). MetaMapp was performed using the MetaMapp and Cytoscape software [26]. The MSEA was performed by the MetaboAnalyst 4.0

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software [27].

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2.6 Quality controls. Daily quality control was performed to maintain the high quality of metabolome data. Two method blank samples containing identical reagents and four calibration curve samples comprising 31 pure reference compounds including

10

alanine and pyruvate, were analyzed under the same protocols on the same instruments [22].

2.7 Data availability. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

3.1

Comparison of

growth and

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3. Results fermentation product

profiles of

C.

carboxidivorans P7 cultivated in the gas vs. glucose media. Time courses of growth and fermentation products such as ethanol, acetic acid, butyric acid, and

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hexanoic acid were compared for C. carboxidivorans cultivated in the gas (P7) and

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glucose (MP2) media (Fig. 1). In the gas media containing CO, H 2, and CO2 (Fig. 1C), C. carboxidivorans showed slow growth and fermentation product formation, entering

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the stationary phase 120 h after inoculation (Fig. 1A); butyric acid production started 288 h after inoculation (Fig. 1B). C. carboxidivorans in the gas media mainly produces

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acetic acid. This is because C. carboxidivorans in the gas media hardly produces ATP due to the nature of the W-L pathway to supply ATP as much as possible by converting

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CO to acetic acid [2,12,28]. On the contrary, the microorganism showed much faster growth (Fig. 1D), consumption of substrate (Fig. 1F) and fermentation product

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formation (Fig. 1E) in glucose media. This is because C. carboxidivorans can generate more ATP in glucose media via glycolysis than in the gas media. C. carboxidivorans produced butyric acid or hexanoic acid when acetic acid was produced above a certain concentration, and pH fell below the optimal range (Fig. 1 B, E) [6]. Metabolome 11

sampling was performed in the mid-exponential and stationary phases in each of the gas (Fig. 1A) and glucose (Fig. 1D) media.

3.2 Comparison of intracellular metabolite profiles of C. carboxidivorans cultivated in the gas vs. glucose media. To compare intracellular metabolite profiles of C. carboxidivorans cultivated in the gas and glucose media, intracellular metabolites

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were extracted using pure methanol as the extraction solvent. We detected and identified 82 intracellular metabolites using GC/TOF–MS (Table S2). To compare the intracellular metabolite profiles between the two culture media (gas and glucose) and the two growth phase conditions (mid-exponential and stationary phases), multivariate

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statistical analyses using PLS-DA and HCA models were performed. In the PLS-DA

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analysis, a score plot using the three axes showed the complete separation based on types of substrates (gas and glucose) and growth phases (Fig. 2A). The quality of the

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PLS-DA model using three axes was represented by R2X (cumulative) of 0.754, R2Y (cumulative) of 0.930, and Q2 (cumulative) of 0.893. The high values of R2X and R2Y

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indicate the high goodness-of-fit of the model, and that of Q2 indicates the high predictive capability of the model. The variable importance in the projection (VIP) and

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loading scores of metabolites in the PLS-DA model were listed in Table S3. To validate the PLS-DA model, 100 permutation tests for 3 components were performed (Fig. 2B).

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All points of R2 and Q2 values were lower than the original points (right top), and the regression line of Q2 had a negative intercept (Fig. 2B). These results strongly support the validity of the PLS-DA model. In the PLS-DA model, most fatty acids showed the highest VIP scores and showed higher abundances in the gas media than in glucose 12

media (Table S3). Interestingly, the abundances of many fatty acids such as 1monopalmitin, 1-monostearin, lignoceric acid, myristic acid, and stearic acid were highest in the stationary phase of the gas media (Table S3). For the HCA analysis, the intensity of each metabolite was converted to a z-score. HCA based on Pearson’s correlation showed detailed changes in each metabolite (Fig. 3). Intracellular metabolites from cells in the stationary phase showed higher

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abundance in glucose media than those in the gas media in the top area of HCA. However, intracellular metabolites from cells in the exponential and stationary phases showed higher abundance in the gas media than those in glucose media in the middle and bottom areas of HCA. Only a few metabolites from cells in the exponential phase

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showed higher abundance in glucose media than those in the gas media.

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For a more specific comparison between the gas and glucose media, the Student’s t-test, a univariate statistical test, was performed. Metabolites that meet the two criteria

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(fold change > 2.0 and p < 0.05) were considered as metabolites that were more abundant. First, intracellular metabolites in the exponential phase in the gas and

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glucose media were compared (Table S4). In the gas media, the levels of 21 intracellular metabolites were significantly higher than those observed in glucose

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media and mainly comprised fatty acids. In glucose media, the levels of 17 metabolites were significantly higher than those in the gas media and mainly comprised sugars

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and amines. Second, intracellular metabolites from cells in the stationary phase in the gas and glucose media were compared (Table S5). In the stationary phase, the levels of 27 intracellular metabolites were higher in the gas media than in glucose media, but

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the levels of only 7 intracellular metabolites were higher in glucose media than the gas media. To compare the intracellular metabolite profiles based on biochemical pathways of C. carboxidivorans cultivated in the gas and glucose media, the MetaMapp analysis and metabolite sets enrichment analysis (MSEA) were performed. First, intracellular metabolites from the cells in the exponential phase in the gas and glucose media were

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compared (Fig. 4A, Fig. S1A). From the MetaMapp analysis, most intracellular metabolites related to the metabolism of fatty acids were found to be higher in the gas media. On the contrary, most intracellular metabolites related to amine and sugar metabolism were higher in glucose media (Fig. 4A). From the MSEA, overall, it was

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found that the metabolisms involving fatty acids, such as β-oxidation of fatty acids,

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fatty acid biosynthesis, sphingolipid metabolism, fatty acid metabolism, and glycerolipid metabolism, were significantly different in the gas media compared to

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those in glucose media (Fig. S1A). Second, intracellular metabolites from the cells in the stationary phase in the gas and glucose media were compared (Fig. 4B and Fig.

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S1B). Most intracellular metabolites related to fatty acid and organic acid metabolism were present in higher levels in the gas media (Fig. 4B). From the MSEA, metabolism

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related to fatty acids, such as β-oxidation of fatty acids, glycerolipid metabolism, fatty acid biosynthesis, oxidation of branched-chain fatty acids, and fatty acid metabolism

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were significantly different in the gas compared to those in glucose media (Fig. S1B). Taken together, intracellular metabolite profiles significantly differed depending

on the carbon source in the media, which was mostly due to changes associated with fatty acid metabolism. These results suggest that the analysis of intracellular 14

metabolites needs to be specifically customised and optimized for discrimination between clostridia cultivated in the gas and glucose media.

3.3 Evaluation of metabolite extraction solvents. To determine the optimal extraction solvent for C. carboxidivorans cultivated in the gas media, intracellular metabolites were extracted using 50ACN, AMW, PM, and WiPM. We identified 86

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intracellular metabolites with GC/TOF−MS (Table S6). To compare the intracellular metabolite profiles between the four extraction solvents, multivariate analyses, including PLS-DA and HCA, were performed. In the score plot of the PLS-DA model using three axes, the intracellular metabolite profiles were grouped into four different

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patterns in the exponential (Fig. 5A) and stationary (Fig. 5B) phases. The high R2X

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value (cumulative), 0.651 for the exponential phase and 0.676 for the stationary phase, and the high R2Y value (cumulative), 0.841 for the exponential phase and 0.839 for

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the stationary phase, indicate the high goodness-of -of-fit of the PLS-DA models (Fig. 5). The high values of Q2 (cumulative), 0.675 for the exponential phase and 0.724 for

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the stationary phase, indicate the high predictive ability of the PLS-DA models (Fig. 5). The VIP and loading scores of metabolites in the PLS-DA models were listed in Table

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S7. To validate the PLS-DA models, 100 permutation tests for 3 components were performed (Fig. 5C, D). All points of R2 and Q2 values were lower than the original

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points (right top), and the regression lines of Q2 had negative intercepts (Fig. 5C, D). These results strongly support the validity of the PLS-DA models. In the HCA model based on the Pearson’s correlation, profiles of intracellular metabolites were clearly different with varying extraction solvents (Fig. 6). In addition, the HCA results showed 15

similar profiles between AMW and PM. Among the four extraction solvents, intracellular metabolites extracted using WiPM were the most abundant (Fig. 6). To compare the intracellular metabolite extraction capabilities of the four extraction solvents, sums of identified peak intensities for each chemical class of intracellular metabolites were determined (Fig. 7). In the exponential phase, most of the sums of peak intensities for amines, amino acids, fatty acids, organic acids, and phosphates

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were higher in WiPM than those in other solvents (50ACN, AMW, and PM) (Fig. 7A). A similar result was found for total peak intensities. Based on the comparison of total peak intensities using ANOVA with the post-hoc Tukey’s test at 99% confidence level, WiPM displayed significantly higher abundance as compared with 50ACN, AMW, and

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PM (Table S8). In the stationary phase, most of the sums of peak intensities of amines,

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amino acids, organic acids, sugars, and sugar alcohols were similarly higher in WiPM as compared with those in other extraction solvents (Fig. 7B). Based on the

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comparison of total peak intensities using ANOVA with the post-hoc Tukey’s test at 99% confidence level, WiPM displayed significantly higher abundance than 50ACN and

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AMW (Table S9). In the stationary phase, no significant difference was observed in total peak intensities between WiPM and PM.

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To compare the extraction reproducibility of the four extraction solvents used in this study, the percent coefficient of variation (%CV) of each intracellular metabolite

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was obtained in the presence of different extraction solvents and were organised based on the distribution frequency (Fig. 8). In the exponential phase, the average values of %CV for all intracellular metabolites in the presence of 50ACN, AMW, PM, and WiPM were 28.7, 30.1, 22.5, and 20.9, respectively (Fig. 8A). In the stationary 16

phase, the average values of %CV for all intracellular metabolites in the presence of 50ACN, AMW, PM, and WiPM were 34.6, 34.3, 37.2, and 27.9, respectively (Fig. 8B).

4. Discussion Here we show, for the first time, significant differences in intracellular metabolite profiles of C. carboxidivorans in response to gas (CO, CO2, and H2) and glucose as

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the substrate. Intracellular fatty acids significantly increased, and fatty acid metabolisms were significantly different in C. carboxidivorans on the gas media compared to those on glucose media. Given the differences in metabolite profiles, metabolite extraction solvents were optimized and WiPM was found to be the best

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solvent for the extraction of the intracellular metabolome of C. carboxidivorans

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cultivated in the gas media.

We cultivated C. carboxidivorans in the gas media and found low growth rates (Fig.

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1) and lower abundance of metabolites from sugar and amine metabolism during the exponential phase (Fig. 4A) but higher abundance of metabolites from fatty acid

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metabolism during the exponential (Fig. 4A) and stationary (Fig. 4B) phases as compared with cells cultivated in glucose media. The main difference in the two media

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is the type of carbon source (CO and CO2 versus glucose). In the gas media, C. carboxidivorans produces small amounts of ATP by converting CO to mainly acetic

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acid via the W-L pathway, resulting in imbalance of cofactors including a severe depletion of energy metabolites [12,28,29]. Since CO and CO2 are also be used as carbon sources, the synthesis of high-carbon metabolites is difficult and leads to slow growth (Fig. 1) [29]. Therefore, the accumulation of intracellular fatty acids (Fig. 4) and 17

changed fatty acid metabolism are unexpected (Fig. S1). In contrast, C. carboxidivorans fermenting glucose has a relatively high levels of ATP generated from glycolysis as well as reuse of CO2 which is produced during glycolysis [7,12]. C. carboxidivorans in glucose media mainly produced acetic acid, butyric acid, hexanoic acid, and ethanol (Fig. 1E) and showed much faster cell growth than those in the gas media (Fig. 1A, D). The relatively high levels of intracellular sugars and amines may

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be due to various metabolic intermediates during glycolysis or increased synthesis of the metabolites for rapid growth and cell reproduction (Fig. 4).

The higher abundance of intracellular fatty acids in C. carboxidivorans cultivated in the gas media rather than glucose media may be attributed to the severe imbalance

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in cofactors and slow cellular metabolism. According to a previous study employing

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proteome and targeted metabolite analyses for C. ljungdahlii, because of reducing agents overproduced from the W-L pathway, cellular metabolism was shifted to

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ethanol production to re-oxidise reducing equivalents to achieve redox balance [12]. As the W-L pathway converts CO to acetyl-CoA, the main building block for fatty acid

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synthesis [5,12], C. carboxidivorans may have converted a certain amount of acetylCoA into fatty acids using NAD(P)H solving the redox imbalance to some extent. In

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addition, slow cellular metabolism due to the depletion of energy metabolites may have caused accumulation of intracellular fatty acids in C. carboxidivorans fermenting the

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gas. Most intracellular metabolites are intermediates for synthesis of extracellular products or for cell division [30,31]. Imbalance of cofactors including depletion of energy metabolites can prevent the conversion of intracellular metabolites resulting in accumulation of intermediate metabolites in cells [32]. Similarly, slow cell growth may 18

have induced intracellular fatty acids to accumulate in cells rather than to be used for the synthesis of lipids or the other metabolites. Taken together, the cofactor imbalance derived from CO, CO2, and H2 fermentation may have directed the overall carbon flow toward the synthesis of intracellular fatty acids. However, the above proposed mechanisms are proposed based on the abundance of intracellular metabolites. Therefore, further validation study based on metabolic engineering is necessary.

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The overall carbon flow toward the synthesis of intracellular fatty acids in C. carboxidivorans on the gas media can be used as a basis for metabolic engineering. For example, intracellular free fatty acids and acyl-CoA, an intermediate metabolite of fatty acid synthesis, can be used for production of various fatty alcohols, which are

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widely used in industrial applications such as production of biofuels, lubricants, and

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cosmetics [33]. The increased fatty acids (Fig. 4) and the changes of fatty acid metabolism in C. carboxidivorans fermenting the gas (Fig. S1) may provide helpful

fatty alcohol production [33].

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information for metabolic engineering for redirecting flux from fatty acid synthesis to

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The solvent WiPM was deemed as the best extraction solvent, as it showed the highest extraction capabilities and reproducibility among the four common extraction

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solvents. Most individual metabolites were more abundantly extracted in WiPM than in other solvents such as 50ACN, AMW, and PM in both the exponential (Fig. 6A) and

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stationary (Fig. 6B) phases. Total peak intensities and most of the sums of the peak intensities of chemical classes were higher in WiPM than in other solvents such as 50ACN, AMW, and PM in both the exponential (Fig. 7A) and stationary (Fig. 7B) phases. On the other hand, the average values of %CV of all intracellular metabolites 19

were lower in WiPM as compared to those in other solvents in both the exponential (Fig. 8A) and stationary (Fig. 8B) phases. The earlier study, optimizing metabolome sampling steps of C. acetobutylicum cultivated in glucose media, suggested PM as the best extraction solvent with the highest extraction capability and reproducibility [18]. However, we found that PM showed the second highest extraction capability and reproducibility, suggesting that WiPM is the optimal extraction solvent for the

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metabolite extraction of C. carboxidivorans cultivated in the gas media. This difference may be attributed to the unique intracellular metabolite profiles of C. carboxidivorans fermenting the gas.

To obtain high quality metabolomic data, extraction and optimization of extraction

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solvents are essential. WiPM, the optimal solvent for the extraction of the intracellular

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metabolome from C. carboxidivorans fermenting the gas, may be used generally for the accurate and reproducible metabolome sampling of clostridia fermenting CO-

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containing syngas. To our knowledge, this is the first metabolomic study detailing the unique intracellular metabolite profiles of C. carboxidivorans in CO, CO2, and H2

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fermentation compared to glucose fermentation.

Conflict of Interest

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We declare no interest in this manuscript.

Declarations of interest K.H.K., Y.U., J.K., and J.S.K. have filed patents on this work (Korea Patent Application No. 10-2018-0024259 and Patent Cooperation Treaty No. PCT/KR2019/002146). 20

Acknowledgements We would like to acknowledge the grant support from the C1 Gas Refinery Program through

the

National

Research

Foundation

of

Korea

funded

by

MSIP

(2016M3D3A1A01913268). This work was performed at the Korea University Food Safety Hall for the Institute of Biomedical Science and Food Safety.

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Figure 1. Comparison of the growth and fermentation product profiles of C.

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carboxidivorans P7 cultivated in the gas (A, B, and C), and glucose (D, E, and F) media. Cell density recorded as the optical density at 600 nm (OD 600) (A and D), concentrations of fermentation products, acetic acid, butyric acid, hexanoic acid, and ethanol (B and E) in g/L. Concentrations of substrate recorded as partial pressure for

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gas (C; kPa) and g/L for glucose (F; four independent replicates).

Figure 2. The PLS-DA score plot (A) and PLS-DA permutation tests (B) of 82

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intracellular metabolites of C. carboxidivorans P7 at exponential and stationary phases cultivated in the gas and glucose media. CO_Exponential, cells in the exponential

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phase in the gas medium; CO_Stationary, cells in the stationary phase in the gas media; Glc_Exponential, cells in the exponential phase in glucose medium;

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Glc_Stationary, cells in the stationary phase in glucose media (four independent

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replicates).

Figure 3. Hierarchical clustering analysis of 82 intracellular metabolites of C. carboxidivorans P7 from exponential and stationary phases cultivated in the gas and glucose media. CO_Exponential, cells in the exponential phase in the gas media; 26

CO_Stationary, cells in the stationary phase in the gas media; Glc_Exponential, cells in the exponential phase in glucose media; Glc_Stationary, cells in the stationary phase in glucose media. Clustering of the model was based on Pearson’s correlation and average linkage methods (four independent replicates). Figure 4. MetaMapp analysis of 82 intracellular metabolites of C. carboxidivorans P7 cultivated in the gas and glucose media in the exponential (A) and stationary (B)

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phases. Classes of metabolites are represented by shape. Significant increases and decreases in metabolites in the gas media are represented by color ("Significantly" means p < 0.05); Magnitudes of fold changes are represented by the size of the

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symbol and label (four independent replicates).

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Figure 5. PLS-DA of 86 intracellular metabolites of C. carboxidivorans P7 extracted using four extraction solvents, acetonitrile-methanol-water mixture (AMW; 2:2:1, v/v/v),

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pure methanol (PM), acetonitrile-water (50ACN; 1:1, v/v), and water-isopropanolmethanol mixture (WiPM; 2:2:5, v/v/v) at −20°C from cells harvested under exponential

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(A and C) and stationary (B and D) phases cultivated in the gas media. PLS-DA score

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plots (A, B) and PLS-DA permutation tests (C, D) (four independent replicates).

Figure 6. Hierarchical clustering analysis of 86 intracellular metabolites of C.

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carboxidivorans P7 extracted using four extraction solvents, acetonitrile-methanolwater mixture (AMW; 2:2:1, v/v/v), pure methanol (PM), acetonitrile-water (50ACN; 1:1, v/v), and water/isopropanol/methanol mixture (WiPM; 2:2:5, v/v/v) at −20°C from the exponential (A) and stationary (B) phases cultivated in the gas media. Clustering of 27

the model was based on Pearson’s correlation and average linkage methods. Each column represents an extraction solvent and each row a metabolite (four independent replicates).

Figure 7. Sums of chemical classes of normalised peaks of intracellular metabolites extracted at −20°C using acetonitrile-methanol-water mixture (AMW; 2:2:1, v/v/v), pure

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methanol (PM), acetonitrile-water (50ACN; 1:1, v/v), and water-isopropanol-methanol mixture (WiPM; 2:2:5, v/v/v). (A) Exponential and (B) stationary phases of C. carboxidivorans cultivated in the gas media (four independent replicates).

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Figure 8. Frequency distributions of the percent coefficient of variation (% CV) of 86

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intracellular metabolites extracted at −20°C using the four extraction solvents, acetonitrile/methanol/water mixture (AMW; 2:2:1, v/v/v), pure methanol (PM),

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acetonitrile/water (50ACN; 1:1, v/v), and water/2-propanol/methanol mixture (WiPM; 2:2:5, v/v/v). (A) Exponential and (B) stationary phases of C. carboxidivorans

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cultivated in the gas media (four independent replicates).

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