Quantitative Isotope-Dilution High-Resolution-Mass-Spectrometry

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Quantitative isotope dilution high-resolution mass spectrometry analysis of multiple intracellular metabolites in Clostridium autoethanogenum using uniformly C-labelled standards derived from Spirulina 13

Sarah Schatschneider, Salah Abdelrazig, Laudina Safo, Anne Meint Henstra, Thomas Millat, Dong-Hyun Kim, Klaus Winzer, Nigel Peter Minton, and David A Barrett Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b04758 • Publication Date (Web): 13 Mar 2018 Downloaded from http://pubs.acs.org on March 14, 2018

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Analytical Chemistry

1

Quantitative isotope dilution high-resolution mass spectrometry analysis of

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multiple intracellular metabolites in Clostridium autoethanogenum using

3

uniformly 13C-labelled standards derived from Spirulina

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Sarah Schatschneider*†§, Salah Abdelrazig*†, Laudina Safo†, Anne M. Henstra‡, Thomas

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Millat‡, Dong-Hyun Kim†, Klaus Winzer‡, Nigel P. Minton‡, David A. Barrett†

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*Sarah Schatschneider and Salah Abdelrazig are joint first authors.

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Affiliations:

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†

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Nottingham, NG7 2RD, UK

Centre for Analytical Bioscience, School of Pharmacy, University of Nottingham,

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‡

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Centre, School of Life Sciences, University of Nottingham, Nottingham, NG7 2RD, UK.

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§

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Germany.

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Corresponding author:

15 16 17 18 19 20 21

David A. Barrett, Centre for Analytical Bioscience, School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD, UK Email: [email protected] Tel: +44 (0) 115 951 5062 Fax: +44 (0) 115 951 5102 ORCID iD: 0000-0001-6900-9474

Clostridia Research Group, SBRC-Nottingham, a BBSRC/EPSRC Synthetic Biology Research

Present address: Evonik Nutrition & Care GmbH, Kantstraße 2, 33790 Halle/Westphalia,

ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Abstract

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We have investigated the applicability of commercially available lyophilised Spirulina

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(Arthrospira platensis), a microorganism uniformly labelled with 13C, as a readily accessible

25

source of multiple

26

determination of intracellular bacterial metabolites. Metabolites of interest were analysed

27

by hydrophilic interaction liquid chromatography coupled with high resolution mass

28

spectrometry. Multiple internal standards obtained from uniformly (U)-13C-labelled extract

29

from Spirulina were used to enable isotope dilution mass spectrometry (IDMS) for the

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identification and quantification of the intracellular metabolites. Extraction of intracellular

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metabolites of C. autoethanogenum using 2:1:1 chloroform:methanol:water was found

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optimum in comparison to freeze-thaw, homogenisation, and sonication methods. Limits of

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quantification were ≤ 1 µM with excellent linearity of all calibration curves (r2 ≥ 0.99) for 74

34

metabolites. Precision and accuracy were found to be within a relative standard deviation

35

(RSD) of 15% for 49 of the metabolites and within an RSD 20% for all metabolites. The

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method was applied to study the effect over 35 days of feeding different levels of carbon

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monoxide (as carbon source) on the central metabolism and the Wood-Ljungdahl pathway

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of C. autoethanogenum grown in continuous culture. LC-IDMS using U-13C Spirulina

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successfully quantified 52 metabolites in the samples including amino acids, carboxylic

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acids, sugar phosphates, purines, and pyrimidines. The method provided absolute

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quantitative data on intracellular metabolites suitable for computational modelling to

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understand and optimise the metabolic pathways of C. autoethanogenum active in gas

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fermentation.

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Keywords: Isotope dilution mass spectrometry, Spirulina, Arthrospira, Clostridia, biofuels,

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gas fermentation, metabolites.

13

C-labelled metabolites suitable as internal standards for quantitative


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Quantitative biology aims to explain the function of an entire biological system and

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intracellular metabolite analysis is an important aspect of this approach since it reveals

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functional information about the biochemical and physiological state of cells1,2.

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Metabolomics can provide ‘global’ information on metabolites, but frequently this is not

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quantitative in nature (for example not providing precise concentrations) and is thus

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difficult to apply for accurate modelling of intracellular processes required in systems

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biology and metabolic engineering. LC-MS methods have previously been reported for the

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simultaneous quantification of

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microorganisms using mainly hydrophilic interaction liquid chromatography (HILIC)3,4 and

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ion-pair reversed-phase chromatography5,6. Although such LC-MS methods produce

56

comprehensive data, there are significant limitations in achieving absolute quantification of

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multiple metabolites, with matrix effects causing ion enhancement or ion suppression in LC-

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MS analysis, metabolites degrading during sample preparation and unexpected variation in

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instrument response. These factors all result in a potentially biased quantification of

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measured metabolite concentrations.

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True quantitative analysis can be achieved using synthesised isotopic internal standards (IS)

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for a small number of metabolites simultaneously7. Isotope dilution mass spectrometry

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(IDMS) is the most reliable technique for the generation of accurate MS-derived

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quantitative metabolite data since this approach can correct for most aspects of analytical

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bias8,9. Historically, the application of isotope dilution utilised radioactive tracers for the

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quantification of a single or a small set of analytes, now replaced by the use of non-

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radioactive stable isotopes8. However, as the number of metabolites monitored has

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increased in recent years, the availability of matching isotopic standards for analytes has

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become increasingly difficult to achieve. Most isotopically labelled compounds are difficult

70

to synthesize and are not available commercially. Biosynthesis of multiple uniformly-13C-

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labelled (U-13C) IS for LC-IDMS is an alternative approach which attempts to bypass the

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synthetic bottleneck by cultivation of an organism on

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carbon source. For example, E. coli or yeast grown on 13C glucose or 15N labelled substrate

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sources, have been used to generate labelled IS of metabolites for LC-IDMS for targeted

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metabolomics, metabolic flux analysis10-13 or MS method improvements14,15. However,

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biosynthesis of multiple U-13C IS is time consuming, requires an expensive U-13C carbon

different classes

of

13

intracellular metabolites

in

C-labelled substrates as the sole



77

source and access to a microbiological laboratory and associated skills. Commercially

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available U-13C Spirulina (Arthrospira platensis) has the potential to be used as a cheap and

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readily accessible source of multiple U-13C IS suitable for quantitative LC-IDMS of bacterial

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metabolites.

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C. autoethanogenum is a Gram-positive bacterium of biotechnological interest as it can fix

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CO/CO2/H2 to produce industrially useful chemicals such as ethanol and 2,3 butanediol16-18.

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The yield of these chemicals is low in the wild-type organism and metabolic engineering as

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well as optimisation of bioreactor conditions are required for efficient production.

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Specifically, our work required the generation of accurate and quantitative measurements

86

of key intracellular metabolites of C. autoethanogenum to deliver important information for

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computational metabolic modelling.

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It is difficult to biosynthesize U-13C-labelled internal standards in C. autoethanogenum

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because of the challenges with growth on CO2 and CO as carbon sources. Therefore, in this

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study, we evaluated a new method for LC-IDMS analysis using purchasable U-13C Spirulina

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material as a novel source of multiple IS for quantitative metabolomics. In previous studies,

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labelled 15N or 13C Spirulina has mainly been used as a feedstock for stable isotope labelling

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of mammals19-21 and has not yet been explored for analytical applications. Spirulina is

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supplied as a ready-to-use powder, thus there is no need for cultivation of microorganisms

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to generate fully labelled IS. The developed method was applied to the simultaneous

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quantification of 74 key intracellular metabolites in continuous culture of gas fermentation

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extracts of C. autoethanogenum.


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Experimental section

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Chemicals

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HPLC grade ammonium carbonate and all analytical standards were purchased from Sigma-

101

Aldrich (Gillingham, UK) unless otherwise noted. Uniformly-13C-labelled Spirulina (U-13C,

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97%) and

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UK). MS grade acetonitrile and methanol were purchased from VWR (Langenfeld, Germany).

104

A set of 74 metabolites representing the key intracellular metabolic pathways of

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C. autoethanogenum was selected to monitor the intracellular metabolic changes. Individual

106

stock solutions of 100 mM

107

prepare 3 different 1 mM standard mixtures in 50% (v/v) methanol/water.

108

LC-IDMS analysis

109

LC-IDMS was performed using Accela and/or Dionex UHPLC systems (Thermo Fisher

110

Scientific, USA) coupled to high-resolution orbital trap mass spectrometers (Exactive/Q-

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Exactive, Thermo Fisher Scientific, USA). A ZIC-pHILIC column (4.6 x 150 mm, 5 µm particle

112

size, Merck Sequant, Watford, UK), maintained at 45°C with a flow rate of 300 µL/min, was

113

used for chromatographic separation. Mobile phase A was composed of 20 mM ammonium

114

carbonate in water (pH 9.1) and mobile phase B of acetonitrile. The gradient started with

115

20% A and increased to 95% A over 8 min, followed by equilibration to give a 15 min run

116

time. The injection volume was 5 µL and samples were maintained at 4°C during the

117

analysis. MS was performed in simultaneous ESI+ and ESI- full scan modes with spray

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voltage (kV) of 4.5 (ESI+), 3.5 (ESI-), and capillary voltage (V) of 40 (ESI+), -30 (ESI-).

119

Sheath:auxiliary:sweep gas flow rates (arbitrary unit) of 40:5:1 with capillary and heater

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temperature (°C) of 275 and 150, respectively, were used in both modes. Data were

121

acquired with automatic gain control of 1×106 and a resolution of 140,000 from m/z 70-

122

1050.

123

Extraction and characterisation of U-13C-labelled Internal Standards (IS) from Spirulina

124

U-13C-labelled intracellular metabolites were extracted from Spirulina (0.5% w/v) using

125

methanol (-20 °C), vortex-mixed for 30 s, stored overnight (-20 °C) and centrifuged (10,000

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g, 5 min) to remove cell debris. The supernatant was filtered through a 3 kDa cut-off filter

12

C Spirulina lyophilised cell powders were obtained from CK Isotopes (Ibstock,

12

C authentic standards of these metabolites were used to



127

(Amicon Ultra, Merck, Watford, UK) and spiked with 20 µM 2-fluoro-2-deoxyuridine

128

(fluorinated-12C IS). The extract of Spirulina was stored at -80 °C to be used as U-13C-labelled

129

multiple IS for LC-IDMS. Untargeted analysis was performed to check the coverage of

130

metabolites in both organisms. Extracts of Spirulina and C. autoethanogenum and blanks

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were prepared in biological triplicates and analysed with QC sample and 5 mixtures of 250

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authentic standards in a single analytical run. XCMS22, mzMatch23 and IDEOM software24

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were then used for untargeted data analysis as detailed by Kim et al.25. Metabolite

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identification was carried out by matching the accurate masses and RT of the

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chromatographic peaks with the authentic standards (Level 1 identification) according to

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metabolomics standards initiative26, whereas Level 2 identification was carried out for the

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rest of the metabolites when standards were not available and therefore, accurate mass

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and predicted RT were used (see Supporting Information Table S-1).

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Validation and quality control of LC-IDMS for the quantification of C. autoethanogenum

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metabolites

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Calibration curves, validation and metabolite quantification were generated using

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TraceFinder 3.1 software (Thermo Fisher Scientific, USA). 13 calibration standards in the

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range of 1 nM – 200 µM were prepared in water from each standard mixture and spiked

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with 1:1 (v/v) 0.5% Spirulina extract (U-13C IS) and analysed with LC-IDMS (n=5). The validity

145

of the analytical method was assessed by measuring linearity, limit of detection (LOD), limit

146

of quantification (LOQ), precision, accuracy, and repeatability for each standard according to

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FDA guidelines for bioanalysis27. Signal-to-noise ratios of 3:1 and 10:1 were used to estimate

148

the LODs and LOQs respectively, whereas the correlation coefficient (R2) was used as a

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measure of linearity. Precision was reported as the relative standard deviation (RSD%) of the

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standard response while accuracy was determined as the percentage of the measured

151

concentration of the analyte compared with the true value. For metabolite quantification,

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all calibration standards, blank and extracted bioreactor batch samples were analysed in a

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single analytical run with pooled QC samples interspaced every 10 samples. Concentrations

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of the metabolites of C. autoethanogenum in the samples were determined using standard

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calibration curves and the obtained concentrations were normalised to the corresponding

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sample OD of the bioreactor runs.


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Strain and culture conditions of C. autoethanogenum

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C. autoethanogenum strain (DSM 10061) was purchased from the Deutsche Sammlung von

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Mikroorganismen und Zellkulturen GmbH culture collection (DSMZ, Braunschweig,

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Germany). The organism was revived by growth on YTF agar medium as stated elsewhere28

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in an anaerobic cabinet (Don Whitley) at 37°C. The CO gas shift experiment was conducted

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using 3 L water-jacketed bioreactors in a continuous setup (BIOFLO 115, New Brunswick

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Scientific). In each bioreactor, C. autoethanogenum seed culture was inoculated into 1.5 L

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aqueous culture medium (LanzaTech patent WO 2013/147621). Details of the continuous

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CO-fed bioreactor cultivation of C. autoethanogenum, medium composition and bioreactor

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conditions are given in Supporting Information.

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Sample preparation for C. autoethanogenum intracellular metabolite determination

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The harvest of bioreactor samples of C. autoethanogenum was started when OD

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measurement at 600 nm (OD600) reached 3.95. Three samples (1 mL) of the culture were

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collected every day throughout each experiment (35 days); samples were immediately

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cooled down on ice to arrest metabolism and centrifuged at 10,000 g for 5 min at 4 °C. The

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supernatant was then removed thoroughly, and the cell pellets were snap frozen in liquid

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nitrogen and kept at -80 °C for LC-IDMS analysis.

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Four different methods were evaluated for the extraction of polar and semi-polar

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intracellular metabolites of C. autoethanogenum: 1. Solvent extraction (2:1:1

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chloroform:methanol:water) and 2. Freeze-thaw: The cell pellets were re-suspended in 150

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µL U-13C IS extract, vortexed for 30 s, 150 µL of water and 300 µL chloroform were added

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and kept overnight at -20 °C (solvent extraction) or snap-frozen using liquid nitrogen

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(freeze-thaw). Then samples were centrifuged (10,000 g, 5 min, 4 °C) and the aqueous

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phase was analysed with LC-MS. 3. Homogenisation: 0.5 g of acid washed glass beads

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(Sigma Aldrich, Gillingham, UK) were used to disrupt the cells using Precellys® homogenizer

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(Bertin Instruments, Montigny-le-Bretonneux, France)29 and the Supernatants were

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analysed with LC-MS. 4. Sonication: cell pellet was re-suspended in 0.5 mL deionized water,

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sonicated, centrifuged and analysed.



Page 8 of 22

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Results and Discussion

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Optimisation of LC-HRMS method for simultaneous quantification of C. autoethanogenum

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metabolites

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The optimised ZIC-pHILIC LC-HRMS method provided simultaneous quantitative analysis of a

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standard mixture of 74 metabolites of different classes and structurally related analogues

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within a single 15 min analytical run compared with a similar 45 min method reported by

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others30,31. In this optimised method organic acids were eluted at 4-9 min, amino acids at 8-

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12.5 min and other energy molecules/intermediates between 7-9.5 min. The method

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provided improved separation of organic acids, isobaric compounds

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dihydroxyacetone phosphate (DHAP) and glyceraldehyde-3-phosphate, and phosphorylated

195

sugars (glucose 6-phosphate and fructose 6-phosphate); making the method suitable for

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their quantification without the need for the longer HILIC LC-MS or MS/MS analyses

197

employed by others15,32,33 (see Supporting Information Figure S-1). The main advantages of

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the developed method are: (1) rapid separation and confirmed identification of many key

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intracellular metabolites in 15 min (2) improved annotation of metabolite peaks compared

200

to other LC-MS methods, and (3) the convenience of using commercially available

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Spirulina over the biosynthesised 13C IS.

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Characterisation and extraction of commercially available Spirulina as a potential source

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of IS for LC-IDMS

204

13

such as

C-Labelled lyophilised cell powder, obtained commercially from Spirulina grown on a

13

C

13

C

13

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carbon source, was investigated as a potential source of U- C-labelled metabolites for use

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as multiple U-13C IS for quantitative determination of intracellular C. autoethanogenum

207

metabolites

208

C. autoethanogenum were analysed by LC-HRMS and compared to evaluate if there was

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sufficient overlap in metabolites. Figure 1 shows that a similar range and number of

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metabolites were tentatively identified in both species. The full set of identified metabolites

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in both species is listed in the Supporting Information Table S-1. The observed classes of

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metabolites were mainly related to amino acid metabolism (38% Spirulina, 35% C.

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autoethanogenum), carbohydrate metabolism (11% Spirulina, 10% C. autoethanogenum)

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and nucleotide metabolism (11% Spirulina, 12% C. autoethanogenum). A total of 211

using

LC-IDMS.

The

intracellular

compositions


of

Spirulina

and

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identified metabolites were cross-detected between the two species; 187 (88.63%) of these

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metabolites were found 13C-labelled in the extract of U-13C Spirulina. Many of these are key

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metabolites in amino acids, nucleotides, carbohydrates, TCA and energy metabolism

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pathways in C. autoethanogenum, confirming that the Spirulina extract is an acceptable

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source of U-13C-labelled IS for the LC-IDMS method described here. Others have generated

220

13

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approaches are more complex and/or expensive than the use of a commercial source of

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13

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Spirulina is similar to those identified for E. coli13,25,37 and more than those reported for

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Saccharomyces cerevisiae10,38, the most common species used to biosynthesise

C IS mixtures by growing organisms such as E. coli or yeast on 13C6-glucose34-36, but these

C-labelled IS. We show that the number of identified metabolites and metabolite classes in

13

C IS for

13

225

bacterial LC-IDMS. Thus, the use of readily available C Spirulina is a potential alternative to

226

these bio-generated approaches. Furthermore, the previous studies used the same

227

organism for producing

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method developed here can be easily transferred to various organisms which cannot be

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grown on 13C-glucose as the sole carbon source.

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The extraction of

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concentrations of Spirulina powder (0.05-0.5%) with methanol and 2:1:1 chloroform:

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methanol:water. Sufficient concentrations of the 13C metabolites were extracted using 0.5%

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(or greater) of Spirulina powder in methanol (Supporting Information Figure S-2). The

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stability and the recovery of

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examined. Extracts of cell pellets of C. autoethanogenum spiked with U-13C Spirulina were

236

kept at 4 °C for 36 h and analysed at different time intervals with the optimised LC-HRMS.

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53 U-13C metabolites were detected and adequately recovered (in the range 80-120%) and

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43 of these metabolites were found stable (≥80%) with a suitable signal to noise ratio ≥ 10

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(i.e. ≥ LOQ) to be used as U-13C IS (Supporting Information Figure S-3). Furthermore, their

240

13

241

minimal 12C present with the majority of 13C metabolites having an isotopic purity of greater

242

than 95% (Supporting Information Figure S-4). This indicates a good coverage of about 60%

243

of C. autoethanogenum metabolites under investigation. The wide spectrum of metabolites

244

identified in Spirulina powder in this study shows the potential of extending the use of

13

13

C IS extracts for absolute quantification10,37,39 but the LC-IDMS

C metabolites from Spirulina was investigated using a range of

13

C-labelled key metabolites from U-13C Spirulina were also

C isotopic purity in the pure extract of U-13C Spirulina (n=13) showed that there was



13

Page 10 of 22

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labelled Spirulina as a readily-available source of

246

C. autoethanogenum metabolites and for potential applications using LC-IDMS.

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Optimisation of the bacterial sample preparation for extraction of intracellular

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metabolites

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A set of eight key intracellular metabolites of C. autoethanogenum was used to optimise the

250

extraction procedure. Extraction of metabolites using homogenisation and freeze-thaw gave

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less than 60% and 80% recovery, respectively, of all metabolites. Extraction using sonication

252

gave 88% recovery of all metabolites except for fumarate, 2-ketoisovalerate, and pyruvate

253

in which lower recovery of 69%, 53% and 16%, respectively, was observed. For solvent

254

extraction, the proportion of solvents used was first evaluated using a different range of

255

solvent combinations. The use of chloroform:methanol:water (2:1:1) extraction gave

256

satisfactory results with 84% recovery or higher of all metabolites (Table 1).

257

The use of cold solvent extraction and sonication as extraction methods gave better

258

recoveries compared to homogenisation and freeze-thaw, consistent with other studies40,41.

259

However, lower recovery % of some of the metabolites was observed with sonication. This

260

might reflect the increased oxidation process of these metabolites, which is consistent with

261

observations reported by Ohashi et al.42. For solvent extraction, Lee et al.43 recommended

262

the use of methanol alone for the extraction of intracellular metabolites of

263

C. acetobutylicum for GC-MS, but this solvent was found to be less suitable in our study. This

264

might be because most of the non-polar metabolites were retained in the chloroform layer

265

giving less interference and cleaner chromatography of polar metabolites in the aqueous

266

phase. This is supported by the successful use of chloroform with a varied proportion of

267

chloroform:methanol:water including 3:10:144, 2.5:2.5:142, and 2:2:145. Hence, solvent

268

extraction

269

C. autoethanogenum samples for LC-IDMS.

270

Validation of the LC-IDMS

271

The validation results of the LC-IDMS method are summarised in the Supporting Information

272

Table S-2. All responses of the metabolites were linear over the working range of the

273

calibration curves with R2 ≥ 0.99. Precision and accuracy of the method at low (5 μM),

with

2:1:1

C IS for absolute quantification of

chloroform:methanol:water

was


selected

for

preparing

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medium (10 μM) and high (50 μM) concentrations were found to be within 15% of RSD for

275

49 out of 74 metabolites and within 20% for all metabolites. LODs and LOQs of pure

276

standards were found to be within 1-500 nM and 1-1000 nM, respectively, for the majority

277

of metabolites (66 out of 74) except for 2-hydroxyglutarate, glucose 6-phosphate,

278

glyceraldehyde-3-phosphate, lactate, L-serine, L-tyrosine, L-valine, and NADH in which

279

higher LODs (1-5 µM) and LOQs (5-10 µM) were observed. The performance of the ZIC-

280

pHILIC column was evaluated by monitoring the RTs of the selected metabolites using a QC

281

sample (n=11) interspaced within the analytical run of the extracted samples of

282

C. autoethanogenum (24-36 h). Adequate reproducibility was observed in which the RTs of

283

all metabolites were within the range of RSDs of 0.07-0.94% (see Supporting Information

284

Table S-3). This result demonstrated that the multi-step gradient optimisation with a flow

285

rate ramp from 300 µl/min to 400 µL/min not only improved the method throughput to 15

286

min but also shortened the longer equilibration time usually required with HILIC

287

columns30,31.

288

Precision and accuracy of the quantitative measurement of nucleotides and organic acids

289

were found to be improved compared to other similar

290

sensitivity achieved with the method is notably higher when compared with previous

291

methods reported for nucleotides13,48,49, amino acids13,50 and organic acids13,50 using similar

292

methods. A full list of metabolites comparing the method performance with similar reported

293

studies is available in Supporting Information Table S-3. Our results indicate that the use of

294

orbital trap MS could achieve a comparable or even higher sensitivity to triple quadrupole-

295

based detection for some metabolites without the need to employ MS/MS.

296

Profiling of intracellular metabolites of C. autoethanogenum to optimise CO-feed as a sole

297

carbon source

298

The developed LC-IDMS was then applied to monitor the metabolic changes linked to the

299

production of acetate, ethanol and 2,3-butanediol between the two bioreactor batches fed

300

with CO as the sole carbon source at low and high flow rates (Supporting Information Figure

301

S-5). The LC-IDMS successfully quantified 52 metabolites in C. autoethanogenum in the daily

302

samples analysed over 35 days. A full list of metabolites and their measured concentrations

303

over time are available in the Supporting Information Table S-4. The measured levels of

12

C LC-MS based methods46,47. The



304

metabolites were averaged at each steady state to provide a means of direct comparison

305

between the two bioreactors (Figure 2). In the high CO-fed bioreactor, low levels of

306

metabolites such as glycine, xanthine, ribose-5-phosphate, sedoheptulose-7-phosphate,

307

NADH and AMP were observed during both acetogenic and solventogenic phases compared

308

to the low CO-fed bioreactor. While lactate, alanine, asparagine, tyrosine, acetyl CoA, acetyl

309

phosphate, and 3-phosphoglycerate started to accumulate over time.

310

Mapping of the selected set of metabolites to the main pathways of C. autoethanogenum

311

enhances our understanding about the possible metabolic changes associated with high and

312

low CO-fed conditions (Figure 3). For instance, the increased level of acetyl CoA and acetyl

313

phosphate in the high CO-fed bioreactor may signal an impaired production of ethanol by

314

aldehyde dehydrogenase (Ald), alcohol dehydrogenase (Adh) or aldehyde oxidoreductase

315

(Aor). Recently, Marcellin et al., reported that Adh and Aor were up-regulated during CO

316

fermentation of C. autoethanogenum51. It is believed that Aor and Adh play an important

317

role in the production of ethanol from acetate52,53 and acetyl CoA51 in acetogens. This is

318

consistent with the results from the low CO-fed bioreactor, indicating that a high level of CO

319

intake may down-regulate these enzymes and hence produce a lower level of ethanol.

320

High flow of CO may affect gluconeogenesis in C. autoethanogenum. This is indicated by the

321

absence of glyceradehyde-3-phosphate, DHAP, and fructose-6-phosphate, while an

322

increased level of 3-phosphoglycerate in all phases and lactate in the solventogenic phase

323

was found. In C. autoethanogenum, pyruvate is converted to either lactate,

324

phosphoenolpyruvate (the precursor in TCA cycle and gluconeogenesis), or acetolacate54.

325

Therefore, these results suggest that phosphoglycerate kinase and glyceraldehyde 3-

326

phosphate dehydrogenase, enzymes in gluconeogenesis, (Figure 3) are downregulated at

327

high CO flow in favour of increased production of lactate, which is consistent with other

328

studies51,55.

329

Conclusion

330

A new LC-IDMS method was successfully developed and validated for the quantification of

331

bacterial metabolites and applied to C. autoethanogenum. The method demonstrated the

332

potential of using readily available labelled microorganisms as an easy route of obtaining

333

multiple U-13C IS. LC-IDMS with the use of these multiple IS providing a powerful approach


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334

for the quantification of a range of key intracellular metabolites. We have successfully

335

analysed more than 1,300 samples with this method. Our approach offers improved

336

quantitative pathway profiling for systematic investigations of metabolic alterations, and is

337

suitable for many applications requiring intra- and extracellular metabolite profiling of

338

microorganisms.

339

Acknowledgements

340

The authors acknowledge support from the UK Biotechnology and Biological Sciences

341

Research Council (BBSRC), as part of the BBSRC Longer and Larger Grant GASCHEM

342

(BB/K00283X/1), and by the industrial partner LanzaTech Inc.

343

Supporting Information

344

Supporting Information for Publication (.pdf file) contains:

345 346 347 348 349 350 351 352 353 354 355 356 357 358

HPLC method. Culture conditions of C. autoethanogenum. Continuous CO-fed bioreactor cultivation of C. autoethanogenum. Figure S-1: Extracted ion chromatograms (XIC) of some intracellular key metabolites of C. autoethanogenum in comparison to 13C Spirulina metabolites analysed with the developed LC-IDMS. • Figure S-2: The extraction of U-13C Spirulina powder using different percentage of methanol or 2:1:1 chloroform:methanol:water. • Figure S-3: Stability of U-13C Spirulina metabolites spiked in the extract of C. autoethanogenum. • Figure S-4: The quality of the selected U-13C-labelled metabolites used as internal standards for the LC-IDMS in the extract of U-13C Spirulina. • Figure S-5: Production of ethanol and 2, 3 butanediol by C. autoethanogenum. Supporting Information for Publication (.xlsx file) contains:

359 360 361 362 363 364 365 366

• • • •

•

• • •

Table S-1: Putatively identified metabolites in Spirulina extract, putatively identified metabolites in C. autoethanogenum and the common metabolites between Spirulina and C. autoethanogenum. Table S- 2: Validation of the developed LC-IDMS showing LOD, LOQ, precision, accuracy, and coverage of IS. Table S-3: LC-IDMS performance with other similar methods Table S-4: the concentrations of metabolites in the low and the high CO-fed bioreactors.



367

Conflict of Interest Disclosure

368

The authors declare no competing financial interest.


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468

Tables and figures: a) Spirulina

b) C. autoethanogenum Amino Acid Metabolism 35%

Amino Acid Metabolism 38%

Carbohydrate Metabolism 10%

Carbohydrate Metabolism 11% Energy Metabolism 3%

Others 34%

Others 34%

Energy Metabolism 3% Metabolism of Cofactors and Vitamins 6%

Metabolism of Cofactors and Vitamins 3%

Nucleotide Metabolism 12%

Nucleotide Metabolism 11%

c) Common metabolites identified between C. autoethanogenum and Spirulina 300

279

C. autoethanogenum Spirulina Common metabolites

267

211

Number of metabolites

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


200

98 101

100

94 90

84

70 33 30

29 30 23 8

7 7

17

22

9 5

0

Identified metabolites

Amino Acid Carbohydrate Energy Metabolism of Nucleotide Metabolism Metabolism Metabolism Cofactors and Metabolism Vitamins

Others

469 470 471 472 473 474 475

Figure 1 Comparison of the identified metabolite classes from a) Spirulina and b) C. autoethanogenum. In total, 267 and 279 metabolites were tentatively identified using untargeted LC-MS profiling in the extract of Spirulina and C. autoethanogenum, respectively. 211 metabolites were found as common between the two species, in which the common metabolites in each metabolite class were highlighted (c).

476

Table 1 Selection of a suitable method for extracting the metabolites of C. autoethanogenum. Extraction method (Recovery%) Solvent extraction* Metabolite Fumarate Lactate Malate Citrate Glyoxylate 2-Ketoisovalerate Pyruvate Succinate

477 478

Freeze-thaw 79 ± 9

Homogenisation 57 ± 3

51 ± 5 43 ± 4 31 ± 4 12 ± 4 -

28 ± 2 12 ± 7 35 ± 0.2 8±3 -

Sonication 69 ± 0.39 104 ± 0.02 98 ± 0.32 88 ± 0.51 53 ± 0.11 16 ± 0.26 126 ± 0.22

*Solvent extraction was carried out (2) acetonitrile:methanol:water or (3)methanol:water.

2:1:1(1) 99 ± 5 101 ± 6 94 ± 4 94 ± 4 95 ± 19 107 ± 6 103 ± 6

using


1:2:2(2) 63 ± 8 58 ± 11 70 ± 9 80 ± 16 60 ± 2 59 ± 12 63 ± 3 (1)

1:1(3) 77 ± 6 69 ± 6 81 ± 9 91 ± 5 74 ± 2 80 ± 2 78 ± 3

chloroform:methanol:water,

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

479 480 481 482 483 484 485

Figure 2 The measured level of C. autoethanogenum metabolites in low and high CO-fed bioreactors samples quantified using the developed LC-IDMS. The daily-measured concentration of metabolites (n=3) were averaged at each steady state to provide a means of direct comparison between the two ● bioreactor batches fed with low CO (blue bars) and high CO (orange bars) for 35 days. Indicates insignificant difference in the measured concentration of the metabolite in the low and the high CO bioreactors (p-values ≤ 0.05), while error bars (SD) estimate both analytical and biological variability within each bioreactor steady state. Different Scaling factors of metabolite concentrations has been employed to improve the visual comparison between the steady states of the two bioreactors; Top (clear): scale 1, middle (grey): scale 10, bottom (clear): scale 100.


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Page 21 of 22

CO/CO2+H2 WL pathway

Lactate

2[H]

2,3-Butanediol

Acetoin 2,3-Bdh

Ldh ATP ADP

Acetate

Ack

2[H]

Acetyl-P

Pta

2[H]

Pfor

Acetyl CoA

Pyruvate

2[H], CO2

2[H]

Acetolactate

Isobutanol

2-Ketoisovalerate Leucine

Conc ( M)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47


Valine

Ald, Aad

Aor

Acetaldehyde 3-Phosphoglycerate

Phosphoenolpyruvate 2[H]

Adh, Aad 2[H] step 2

Ethanol

Oxaloacetate

GAPDH

L-Aspartate

ATP Pgk step 1 ADP

Glyceraldehyde-3-P 2[H]

Mdh

ATP

ADP

Malate Glyceraldehyde Fh H2O

DHAP

L-Threonine

Fumarate Fructose-1,6-bisP

2[H] Fmr

Succinate

L-Isoleucine Fructose-6-P

486 487 488 489

Mixed acid fermentation

Amino acid biosynthesis

Carbohydrate biosynthesis

Figure 3 Different levels of intracelular metabolites mapped in the main pathways of C. autoethanogenum involved in CO gas fermentation. The bar graphs represent the level of metabolites during acetate low CO (grey bars), acetate high CO (red bars), ethanol low CO (blue bars), ethanol high CO (green bars) and 2,3 butanediol low CO (orange bars) steady states.



490

For TOC only

491


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Quantitative Isotope-Dilution High-Resolution-Mass-Spectrometry

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