Nontargeted Profiling of Coenzyme A thioesters in biological samples

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Non-targeted profiling of coenzyme A thioesters in biological samples by tandem mass spectrometry Michael Zimmermann, Verena Thormann, Uwe Sauer, and Nicola Zamboni Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/ac401555n • Publication Date (Web): 29 Jul 2013 Downloaded from http://pubs.acs.org on August 3, 2013

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Analytical Chemistry

Non-targeted profiling of coenzyme A thioesters in biological samples by tandem mass spectrometry Michael Zimmermann, Verena Thormann, Uwe Sauer and Nicola Zamboni* Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland

KEYWORDS coenzyme A (CoA), ultra-high pressure liquid chromatography coupled tandem mass spectrometry (UHPLC-MS/MS), parent ion scanning, reversed-phased ionpairing chromatography (RP-IPC), metabolic profiling, mycobacteria, cholesterol degradation

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Coenzyme A (CoA) thioesters are ubiquitously present in metabolic networks and play a pivotal role in enzymatic formation and cleavage of carbon-carbon bonds. We present a method allowing non-targeted profiling of CoA-thioesters in biological samples. The reported UHPLC-MS/MS approach employes ion-pairing chromatography to separate CoA-metabolites carrying different chemical functionalities such as hydroxyl or multiple carboxyl groups and to distinguish between isomers. Selective detection of CoAthioesters is accomplished by precursor ion scanning on a triple quadrupole mass spectrometer and takes advantage of the abundant fragment with m/z = -408 that originates from the CoA-moiety. We used a mixture of 19 commercially available CoAderivatives to develop and optimize our method. As a proof of concept we demonstrated detection of the major CoA-intermediates of branched chain amino acid degradation in biological samples. We then applied our method to investigate degradation of lipids in the microorganism Mycobacterium smegmatis. Profiling of CoA-thioesters led to the discovery of a novel intermediate of cholesterol degradation. This demonstrates the power of our method for untargeted profiling of CoA-thioesters and adds a missing link in mycobacterial cholesterol catabolism.

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INTRODUCTION

Thioesters of carboxylic acids with coenzyme A (CoA) are key biological components of metabolism. They act as intermediates of important biochemical pathways such as lipid synthesis and degradation, amino acid catabolism and central carbon metabolism. Thioesterification of organic acids with CoA leads to their chemical activation and is a widely exploited process to form or break carbon-carbon bonds in cells. Queries in public metabolite repositories reveal the existence of >250 different annotated and presumably naturally occuring CoA-thioesters1. They are structurally characterized by a shared carboxyl group required for thioesterification and an acyl-moiety that can contain diverse functional groups such as hydroxyl and carbonyl groups, tertiary carbon atoms, and ethylene groups to eventually generate a large and heterogeneous class. Historically, CoA-thioesters were analyzed either by monitoring the enzymatic conversion of the CoA-moiety or by UV-detection coupled to chromatographic separation1-3. Sensitivity could be increased by resorting to gas chromatography - mass spectrometry or fluorimetric analysis upon derivatization4-6. To exploit the selectivity of mass spectrometry on discriminating CoA-compounds but circumventing the laborious and potentially error-prone derivatization step required for GC-MS, methods of liquid chromatography-mass spectrometry (LC-MS) were developed7,8. A burst in sensitivity was obtained by adopting single and multiple reaction monitoring in tandem mass spectrometry9-12. Reaction monitoring was established on the basis of a priori single compound optimization done on mass spectrometers with pure analytical standards of

1

http://www.genome.jp/dbget-bin/www_bfind_sub?dbkey=compound&keywords=CoA 3 ACS Paragon Plus Environment


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commercially available CoA-thioesters. The targeted strategy adopted to increase selectivity and sensitivity, however, limited the number of CoA-thioesters monitored. The sheer number and heterogeneity of CoA-thioesters potentially co-occurring in cells motivated us to develop a method that allows non-targeted profiling of CoA-thioesters in biological samples. We set out to screen for ions characteristic for collisionally induced fragmentation of CoA. A panel of 19 different CoA-thioesters was analyzed to design a general profiling method by precursor ion scanning on a triple quadrupole instrument. These CoA-thioesters were selected based on their commercial availability. Furthermore, we aimed at covering versatile chemical properties with these standard compounds, such as alkyl chain length and hydroxyl, carboxyl and alkene functionalities. Discrimination of CoA-thioesters with similar molecular weight was enhanced by ion pairing – reverse phase ultra-high pressure liquid chromatography13. We demonstrate the method with the mix of pure standards containing 19 CoA-thioesters and validated it with metabolic extracts of bacterial cultures. We then applied the method to follow mycobacteria’s mechanisms of beta-oxidation of fatty acids. The non-targeted method allowed us to discover novel CoA-derivatives related to the catabolism of cholesterol.


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EXPERIMENTAL SECTION

Chemicals. All chemicals were purchased from Sigma-Aldrich (Schnelldorf, Switzerland). CoA-thioesters were dissolved in nananopure water from a NANOpure purification unit (Barnstead, Dubuque, United States) at a concentration of 10 mM14. To account for impurities and unknown formula weights of the commercially available CoAcompounds the exact concentration of each solution was determined using UV-absorption at 260 nm14. A solution containing 100 μM of each CoA-thioester was prepared in a biological matrix of fully

13

C-labeled metabolic extract of baker’s yeast as described

previously15. Two-fold dilutions were prepared in the same

13

C-labeled yeast extract to

assay the method’s detection limits and the linear range of measurements.

Liquid chromatography. Separation of CoA-thioesters was achieved by an ion pairing-reverse phase chromatography at ultra-high pressure using a Waters Acquity UPLC (Waters Corporation, Milford, MA, United States) with a Waters Acquity HSS T3 column with dimensions 150 mm × 2.1 mm × 1.8 μm (Waters Corporation, Milford, MA, United States) at 40°C15. A gradient of mobile phases A (10 mM tributylamine, 15 mM acetic acid at pH 5.0, 5% (v/v) methanol) and B (2-propanol) was applied and the flow rate was adjusted in order to keep the system pressure below 14’500 psi throughout the run. This yielded in the following settings: initial conditions: 0% A, 0.4 mL/min; 0.5 min: 0% A, 0.4 mL/min; 1.5 min: 12% B, 0.4 mL/min; 10 min: 27.5% B; 20 min: 90% B, 0.15 mL/min; 25 min: 90 % B, 0.15 mL/min; 28 min: 0 % B; 0.15 mL/min; 35 min: 0% B; 0.4



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mL/min. 10 μL were injected with full loop injection. The column was equilibrated with 5.3 column volumes before each injection.

Mass spectrometry. A Thermo TSQ Quantum Ultra triple quadrupole instrument (Thermo Fisher Scientific, Waltham, MA, United States) with a heated electrospray ionization source (Thermo Fisher Scientific, Waltham, MA, United States) was used for all experiments and operated in negative mode. Electrospray ionization parameters were optimized as described previously15: spray voltage 2500 V, sheath gas pressure 80 arbitrary units, auxiliary gas pressure 50 arbitrary units, ion sweep gas pressure 5 arbitrary units, capillary temperature 380 °C, spray temperature 400 °C. Ion optics were set to 0.1 amu Q1 resolution operating in a full scan mode (from m/z: 750 to 1250), 3.0 amu Q3 resolution, 0.01 amu scan width, and 10 ms dwell time. In the optimized method for precursor ion scanning of CoA-metabolites, the collision gas pressure was set to 1.5 mTor and the collision energy to 37 V. Single compound optimization was done with direct infusion of pure standards at a concentration of 1 mM in 50% methanol (LC-MS grade, Sigma-Aldrich, Schnelldorf, Switzerland). The tube lens settings were optimized for maximally 8 different fragments per precursor ion whereas the collision energy was ramped up for each fragment individually.

Bacterial experiments. Mycobacterium smegmatis mc2 155 was purchased from the American Type Culture Collection (ATCC). Precultures were inoculated directly from glycerol stocks and cultured overnight in Middlebrook 7H9 based medium supplemented


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with 2.5 g/l glycerol, 1g/l bovine serum albumin and 0.05% tyloxapol (Table S1) at 37°C and 300 rpm. To assay metabolites in specific catabolic pathways, bacterial cultures in mid-exponential growth phase (OD600 between 0.5 to 1.5) were sampled (t = 0), subject to a medium change, and sampled again 1 h after switching to a different culture medium. To change medium, bacteria were harvested by centrifugation at 1’600 x g for 3 min at room temperature and washed twice in Middlebrook 7H9 broth base without any carbon source. After centrifugation, the cells were resuspended in Middlebrook 7H9 base salts supplemented with 1 g/L bovine serum albumin, 0.025% tyloxapol, and specific carbon sources at a concentration of 2 g/L or 4 g/L for lipids or amino acids, respectively (Table S1). Metabolite samples were collected following the principle of fast filtration described for pathogenic Mycobacterium tuberculosis16. Briefly, a bacterial culture volume corresponding to a biomass of 8 mL at an OD600 = 1.0 was filtered through 0.45 µm filter membranes (HVLP, Millipore, Billerica, MA, USA). Bacteria on the filter were washed with 1 mL freshly prepared ammonium carbonate buffer (75 mM, pH 6.6). For metabolite extraction, filters were rapidly transferred into extraction solution (chloroform : methanol, 2 : 1) at -80°C or any of the other solvent system and respective temperatures described in the results part. The samples were kept at -80°C for one hour, dried under constant

airflow

and

subsequently

resuspended

in

3

times

700

µL

acetonitrile/methanol/water (2:1:1) buffer. Samples were dried under vacuum at ambient temperature and solubilized in 50 μL nanopure water. Samples derived from extraction procedures using either acetonitrile/methanol/water (2:1:1) or ethanol (70%) were



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directly dried under vacuum. After centrifugation at 4’500 x g for 10 min at 4°C supernatants were stored at -80°C.

Data analysis. Qualitative data analyzes was performed using the Xcalibur software (Thermo Fisher Scientific, Waltham, MA, United States). Peak detection in centroidic data was performed using the open source software MZmine 2.817. The noise level was set to 104 counts, minimal peak width 0.1 min, mass tolerance 0.4 m/z and minimal peak height 105 counts. Data were imported into Matlab (Mathworks, Natick, MA United States) for quantitative and statistical analysis.


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RESULTS AND DISCUSSION

Mass spectrometer parameter estimation. To identify mass spectrometer settings that allow detection of CoA-containing compounds with best overall sensitivity, we performed single compound optimization on 19 commercially available CoA-containing metabolites (Table 1). For all compounds we obtained a list of the fragment ions detectable upon collisional fragmentation. For the most abundant fragment ions of each compound, the tube lens voltage and the collision energy were ramped to identify the ranges that maximized detection. Among the product ion scans of all compounds, two fragments with m/z -408 and -426 could always be detected as previously reported10. Reproducibly higher signals were obtained for the 408 fragment, which was therefore preferred. Consequently, mass spectrometer settings for precursor ion scanning were based on the optimized values collected for the 19 CoA standards for the 408 ion. This resulted in a collision gas pressure of 1.5 mTor and a collision energy of 37 eV.

Optimization of chromatography. Chromatographic separation upstream of mass spectrometric detection aims at reducing matrix effects, distributing analytes of interest over chromatographic time to assist identification and detection, and to separate compounds which are not distinguishable by the mass spectrometer. The latter point is particularly challenging for CoA-compounds, as their physico-chemical variability is based on the thioesterified organic acid only, while they share the CoA-unit being the major structural portion. For method development, we dedicated particular attention at the temporal separation of the four pairs of CoA-thioesters with indistinguishable



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molecular weight. The importance such separation of mass isomers is emphasized by the fact that the 268 naturally occurring CoA-thioesters currently listed in the KEGG database only lead to a total of 186 unique masses and hence roughly one third has overlapping mass. All attempts initially made with regular reverse phase liquid chromatography led to insufficient separation of isomers. Satisfactory results could be obtained by supplementing the mobile phase with the ion pairing agent tributylamine. Such advantageous effect of ion pairing agents for the chromatographic separation of CoA-thioesters has been reported previously

13,18

. In combination with a reversed phase

gradient at ultra-high pressure we could achieve good separation of most CoA-standards, including 3 of 4 pairs with similar molecular weight (Figure 1, Table 1).

Linearity in cellular extracts. The mixture of CoA-standards was used to test the method’s detection limits and the range of linearity in a complex biological matrix. Thus, the compounds were diluted in a concentration range between 0.4 and 100 µM in fully 13

C-labeld metabolic extract of baker’s yeast 15. 10 µL of each dilution were analyzed by

ion pairing-reverse phase-ultra high pressure liquid chromatography-precursor ion scanning-mass spectrometry (IP-RP-UHPLC-PIS-MS). Chromatographic peak areas of the scanned precursor ion intensities were integrated and the minimal concentration and linear range of the measurement were determined. The detection limit of most compounds was in the low pmol range per injection (Table 1). Only long chain acylCoAs (lauroyl-, palmitoyl- and oleoyl-CoA) showed a higher lower limit of detection.


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Furthermore, 12 of the 19 tested CoA-thioesters responded linearly over a range of 5 log(2) units or more (Table 1).

Compatibility with metabolite extraction procedures. Most protocols for the analysis of CoA-thioesters contain an enrichment step such as solvent partitioning, solidphase extraction, or the use of adenosine binding resins. To ensure minimally biased profiling of CoA derivatives, we aimed at applying a direct protocol with analysis of complete metabolome extracts obtained from the cells by fast filtration and liquid extraction. Since various extraction solvents are commonly used in metabolomic studies, we tested the compatibility of our chromatographic separation method with three different extraction protocols. These used either (i) 60% ethanol at 78°C, (ii) a mixture of acetonitrile, methanol and water at -40°C, or (iii) a mixture of methanol and chloroform at -80°C

15,16,19

. Metabolome samples were extracted from the microorganism

Mycobacterium smegmatis cultivated in a culture medium supplemented with a chemically defined lipid mixture and samples were collected and extracted using the different solvent systems. The retention times of the recorded CoA-specific profiles were comparable between the three extraction methods (Figure S1). However, as one could expect from the extraction solutions’ properties, ethanol extraction yielded more polar CoA-thioesters, while more apolar CoA-thioesters were detected in the chloroform and methanol extracts. We conclude from these results that the IP-RP-UHPLC-PIS-MS method is compatible with different organic solvents and that the extraction protocol should be selected according to the specific question addressed. For purposes of absolute



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quantification, appropriate internal standards (e.g. isotopically labeled) are obviously necessary to compensate for these variations in extraction efficiency 15,20,21.

Investigation of branched chain amino acids catabolism in Mycobacteria. With the CoA-profiling method in place, we were eager to apply it on various physiological conditions under which CoA-thioesterification is crucial for the functioning of metabolism. We set out to identify CoA-intermediates that are involved in the catabolism of branched chain amino acids (BCAAs), fatty acids, and cholesterol in the microorganism M. smegmatis. We therefore performed media shift experiments, in which the intracellular CoA-thioesters in bacteria were profiled during growth in a standard 7H9 medium with glycerol and glucose as carbon sources or the aforementioned alternative carbon sources after washing and re-inoculation of the bacteria. Metabolic samples were collected right before and one hour after the shift using the methanol/chloroform extraction at -80°C. The measured CoA-profiles were differentially analyzed to identify CoA-compounds emerging upon medium switch. We first investigated bacterial degradation of BCAAs by switching M. smegmatis to a culture medium containing either leucine, isoleucine, or valine. We sought systematically for peaks in all chromatograms obtained by extracting specific traces for all nominal masses of the precursor ion acquired during scanning (Table 2). CoA-metabolites with a role in central carbon metabolism such as acetyl-CoA and succinyl-CoA were detected before and after the medium shift. Seven CoA-compounds could be detected uniquely in BCAA-supplemented culture media. The chemical identity of three of these compounds (3-methylcrotonoyl-CoA,

3-methylbutanoyl-CoA

and

isobutyryl-CoA)

could

be


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confirmed using pure standards based on their mass and retention time. The remaining four CoA-metabolites were annotated based on their mass, knowledge of degradation pathways and their retention time relative to the 19 CoA standards used. Overall, our method allowed to detect 7 of the 12 CoA-compounds known to be involved in the degradation of BCAAs.

Monitoring degradation of fatty acids. Catabolic degradation of fatty acids in cellular metabolism relies on -oxidation, in which CoA-thioesterification promotes oxidation of the -carbon, which eventually gets thioesterified itself upon cleavage of acetyl-CoA (Figure 2A). Mycobacteria have the ability to utilize a broad range of different lipids as carbon and energy sources, which is particularly relevance for the pathogen Mycobacterium tuberculosis22,23. To assay these pathways in M. smegmatis, we profiled as above the CoA-metabolite content in microbes transferred to media containing either butanoic or octanoic acid. In both cases, novel CoA-derivatives could be detected one hour after the media switch (Figure 2B). The measured molecular weights were in line with step-wise loss of ethylene units with 28 Da in each -oxidation cycle. In addition to the acyl-CoAs, we also detected the -hydroxyl-CoA intermediates. However, the according keto forms could not be detected. We hypothesize that their abundance might be below detection limit or that the compounds are too reactive and lost during sample preparation. In conclusion, our method proves to be a sensitive analytical tool to measure intermediate metabolites of fatty acid degradation.



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Degradation of cholesterol. Next we applied our method for characterizing cholesterol degradation in M. smegmatis. In mycobacteria, cholesterol is catabolized in two distinct pathways

24-28

(Figure 3A). The aliphatic side chain is degraded through -oxidation

upon oxidative activation by CYP450 enzymes. The sterol scaffold is split into 2hydroxy-hexa-2,4-dienoic acid (HDD) and 9,17-dioxo-1,2,3,4,10,19-hexanorandrostan-5oic acid (DOHNAA) through cleavage of the B ring

28

. While HDD is known to be

degraded to pyruvate and propionyl-CoA, DOHNAA gets enzymatically activated by CoA-thioesterification29. However, the further fate of DOHNAA-CoA remains unclear.

Upon shift to a cholesterol culture medium, we detected six CoA-thioesters with m/z 960, 986, 988, 1042 and 1044. The peak with m/z 986 matches the theoretical m/z of singly charged DOHNAA-CoA. To confirm its identity, we performed an experiment switching to a medium containing DOHNAA instead of cholesterol. The same ion with m/z 986 and similar retention time could be detected in the extracts confirming annotation (Figure 3B). In addition to DOHNAA-CoA, in the extracts prepared from DOHNAA medium we also detected the ions with m/z 960 and 988. We speculated that the 988 ion might correspond to DOHNAA-CoA with one of its two keto-groups reduced30,31. This hypothesis was corroborated by the detection of the ion with m/z 960, which resulted from one cycle of -oxidation of putatively reduced DOHNAA-CoA. Such removal of C2H4 from the propionic acid side chain of DOHNAA was suggested in the study of fermentative processes of hexahydroindanpropionic acid derivatives31. The two molecular ions with m/z 1042 and 1044 could not be annotated and were not detected


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in DOHNAA medium. They potentially hint at non-sequential degradation of cholesterol’s side chain and the sterol scaffold as suggested earlier32. The results of our non-targeted profiling of cellular CoA-compounds confirm the recently reported activation of cholesterol derived DOHNAA by thioesterification in actinobacteria29 and propose partial reduction of one of DOHNAA’s two keto groups. Furthermore, our findings evidence subsequent degradation by at least one cycle of betaoxidation of putatively reduced DOHNAA-CoA to release acetyl-CoA. We could not detect any additional CoA-thioesters that would suggest subsequent steps of the degradation of sterol rings C and D. A possible explanation could be CoA-independent disassembly of this cholesterol remnant. Because our metabolic profiling is specific for CoA-thioesters, such compounds would not have been measured. Overall, our CoAspecific profiling was able to discover novel CoA-thioesters, which provide insights into previously unknown mechanisms of mycobacteria’s cholesterol catabolism. This will serve as a basis for future investigation aiming at a better understanding of mycobacteria’s CoA-dependent and -independent cholesterol degradation.



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CONCLUDING REMARKS We presented a method for the specific profiling of intracellular CoA-metabolites exploiting ion-pairing, ultra-high pressure liquid chromatography and precursor ion scanning on a triple quadrupole mass spectrometer. The method was validated for the degradation of branched chain amino acids and fatty acids. Expected key CoAintermediates of corresponding catabolic pathways could be detected and quantified without enrichment of CoA-metabolites during sample preparation. Investigation of mycobacteria’s cholesterol degradation demonstrated the strength of our unbiased profiling approach for the discovery of novel CoA-compounds. Discoveries of previously undescribed metabolites can provide valuable hints to decipher poorly understood metabolic pathways. Given the importance of CoA-thioesterification in a broad variety of metabolic processes, we believe that the described method will be a valuable tool for further studies of cellular metabolism. Its application will be in both profiling expected CoA-compounds of described metabolic pathway under various conditions and discovering novel CoA-thioesters opening the door to previously unexplored metabolic processes.


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Table 1. CoA-standards used in this study No Compound

PubChem ID

Formula

Monoisotopic mass [amu]

Q1 mass [amu]

Tube Lens [V]

Q3 massesa

CE [au]

RT [min]

Detection Limit [pmol]b

Linear range [log2]

1

CoA

87642

C21H36N7O16P3S

767.11

766.11

128

408; 419; 339

32; 32; 45

6.3

4

5

2

Acetyl-CoA

444493

C23H38N7O17P3S

809.13

808.13

104

408; 461; 426;

37; 38; 33

6.7

4

4

3

Propionyl-CoA

92753

C24H40N7O17P3S

823.14

822.14

120

408; 426; 395

32; 31; 43

7.8

4

5

4

Crotonyl-CoA

5280381

C25H40N7O17P3S

835.14

834.14

106

408; 487; 426

38; 37; 35

8.5

4

6

5

Butyryl-CoA

439173

C25H42N7O17P3S

837.16

836.16

78

408; 489; 426

38; 34; 31

9.4

4

6

6

Isobutyryl-CoA

3036931

C25H42N7O17P3S

837.16

836.16

150

408; 489; 426

37; 38; 34

9.3

4

6

7

3-Methylcrotonyl-CoA

9549326

C26H42N7O17P3S

849.16

848.16

151

408; 501; 426

37; 36; 34

10.0

4

7

8

Aceto-acetyl-CoA

16061175

C25H40N7O18P3S

851.14

850.14

79

766; 408; 419

29; 41; 38

6.2

4

5

9

3-Methylbutanoyl-CoA

439855

C26H44N7O17P3S

851.17

850.17

90

408; 503; 426

37; 36; 34

10.9

4

5

10

3-Hydroxybutanoyl-CoA

9543037

C25H42N7O18P3S

853.15

852.15

92

408; 339; 428

37; 50; 34

6.3

4

4

11

Malonyl-CoA

16061176

C24H38N7O19P3S

853.12

852.12

79

808; 408; 426

26; 35; 33

6.8

4

4

12

Succinyl-CoA

439161

C25H40N7O19P3S

867.13

866.13

140

408, 339; 426

39; 52; 39

6.8

8

5

13

Methyl-malonyl-CoA

123909

C25H40N7O19P3S

867.13

866.13

122

822; 408; 475

26; 38; 39

7.0

4

5

14

Glutaryl-CoA

3081383

C26H42N7O19P3S

881.15

880.15

162

408; 339; 426

36; 51; 32

7.0

4

5

15

Octanoyl-CoA

445344

C29H50N7O17P3S

893.22

892.22

125

408; 545; 428

38; 33; 34

14.2

8

4

16

3-Hydroxy-3-methyl-glutarylCoA

445127

C27H44N7O20P3S

911.16

910.16

150

408; 339; 426

42; 54; 37

7

4

5

17

Lauroyl-CoA

165436

C33H58N7O17P3S

949.28

948.28

146

408; 426; 601

41; 39; 45

16.3

63

4



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18

Palmitoyl-CoA

52922017

C37H66N7O17P3S

1005.34

1004.34

153

408; 657; 428

40; 40; 38

18

63

4

19

Oleoyl-CoA

5497111

C39H68N7O17P3S

1031.36

1030.36

145

408; 426; 272

44; 43; 56

18.1

63

4

a

The three ion fragments with the highest intensities upon collision induced fragmentation are indicated

b

Amount injected on column.


Page 19 of 27

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


Table 2. CoA-compounds measured upon catabolism of branched chain amino acids (BCAAs).

glycerolb

leucine b

isoleucine b

valine b

[pmol]

[pmol]

[pmol]

[pmol]

Std

96 ± 8.5

80 ± 15

72 ± 32

65 ± 11

6.7

Std

25 ± 2.2

36 ± 2.8

25 ± 3.7

22 ± 6.3

822.14

7.8

Std

0.58 ± 0.28

0.59 ± 0.10

0.25 ± 0.15

0.44 ± 0.12

3036931

836.16

9.3

Std

ND

3.7 ± 1.1

1.8 ± 0.36

3.4 ± 0.85

2-Methylcrotonoyl-CoAc

25244790

848.16

9.9

Ile degradation

ND

ND

1.9 ± 0.39

ND

3-Methylcrotonoyl-CoA

9549326

848.16

10.0

Std

ND

6.6 ± 2.8

ND

ND

2-Methylbutanoyl-CoAd

23724628

850.17

10.7

Ile degradation

ND

ND

2.4 ± 0.07

ND

3-Methylbutanoyl-CoA

439855

850.17

10.9

Std

ND

13 ± 2.8

ND

ND

3-Hydroxy-isobutanoylCoAe

6857371

852.14

7.1

Val degradation

ND

ND

ND

0.02 ± 0.006

3-Hydroxy-2methylbutanoyl-CoAe

440326

866.16

7.3

Ile degradation

ND

ND

0.07 ± 0.02

ND

Succinyl-CoA

439161

866.13

6.8

Std

4.9 ± 1.4

8.0 ± 1.7

9.7 ± 0.05

5.9 ± 0.76

Methylmalonyl-CoA

123909

866.13

7.0

Std

5.7 ± 0.92

7.6 ± 1.4

8.9 ± 2.6

5.2 ± 1.1

Compound

PubChem

Q1-mass

RT

ID

[amu]

[min]

CoA

87642

766.11

6.3

Acetyl-CoA

444493

808.13

Propionyl-CoA

92753

Isobutyryl-CoA

annotationa

a

The compounds were annotated based either on commercially available standards (Std) or their physiological context (Ile, Val). b

Amounts were estimated based on the dilution of standard compounds (Table 1) and are normalized to a biomass corresponding to 1 mL of culture at an OD600 of 1.0. Chemically similar compounds were used to estimate the quantities of compounds that are not commercially available (see notes). c

Amounts estimated based on dilution series of 3-methylcrotonyl-CoA.

d

Amounts estimated based on dilution series of 3-methylbutanoyl-CoA

e

Amounts estimated based on dilution series of 3-hydroxybutanoyl-CoA


19


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Page 20 of 27

FIGURE CAPTIONS

Figure 1. Separation of 19 different commercially available CoA-compounds by ion-pairing chromatography. Four pairs of isomers are highlighted. Numbers correspond to Table 1.

Figure 2. Monitoring beta-oxidation of fatty acids. (A) CoA-profiles of M. smegmatis cultured in glycerol media and after the shift to either a butyric or octanoic acid containing culture media. (B) Schematic representation of beta-oxidation’s generic steps.

Figure 3. Investigating cholesterol degradation in mycobacteria. (A) Schematic representation of side chain degradation and cleavage of the sterol scaffold (adapted from Ouellet et al.

28

). (B)

Identification of DOHNAA-CoA (986 m/z) and putatively reduced DOHNAA-CoA (988 m/z) upon cholesterol degradation. Reduced DOHNAA-CoA (888 m/z) is further degraded by betaoxidation yielding a CoA-compound at 960 m/z. Control stands for cultures before the shift to cholesterol or DOHNAA supplemented culture medium. AD: 4-andro-stenedione ; 3,4-DHSA: 3,4-dihydroxy-9,10-seconandrost-1,3,5(10)-triene-9,17-dion; HHD: 2-hydroxy-hexa- 2,4-dienoic acid, DOHNAA: 9,17-dioxo-1,2,3,4,10,19-hexanorandrostan-5-oic acid


20

Page 21 of 27

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ASSOCIATED CONTENT Supporting Information. Table S1: Composition of the culture media. Figure S1: Comparison of CoA-profiles derived from M. smegmatis by different extraction solvents. This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION Corresponding Author. * Nicola Zamboni, ETH Zürich, Institute of Molecular Systems Biology, Wolfgang-Pauli Strasse 16, 8093 Zurich

Author Contributions. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

ACKNOWLEDGMENT This research was supported by funding of the EU FP7 project SysteMTb.

ABBREVIATIONS CoA, coenzyme A; UHPLC-MS/MS, ultra-high pressure liquid chromatography coupled tandem mass spectrometry; RP-IPC, reversed-phased ion-pairing chromatography; AD, 4-androstenedione; 3,4-DHSA: 3,4-dihydroxy-9,10-seconandrost-1,3,5(10)-triene-9,17-dion; HHD, 2hydroxy-hexa- 2,4-dienoic acid, DOHNAA, 9,17-dioxo-1,2,3,4,10,19-hexanorandrostan-5-oic acid


21


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Page 22 of 27

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Corkey, B. E. In Methods in enzymology; Methods in Enzymology; Elsevier, 1988; Vol. 166, pp. 55–70. Bieber, L. L. Anal Biochem 1992, 204, 228–230. Knudsen, J.; Faergeman, N. J.; Skott, H.; Hummel, R.; Borsting, C.; Rose, T. M.; Andersen, J. S.; Hojrup, P.; Roepstorff, P.; Kristiansen, K. Biochemical Journal 1994, 302, 479. Kopka, J.; Ohlrogge, J. B.; Jaworski, J. G. Anal Biochem 1995, 224, 51–60. Shimazu, M.; Vetcher, L.; Galazzo, J. L.; Licari, P.; Santi, D. V. Anal Biochem 2004, 328, 51–59. Larson, T. R.; Graham, I. A. Plant J. 2001, 25, 115–125. Kasuya, F.; Oti, Y.; Tatsuki, T.; Igarashi, K. Anal Biochem 2004, 325, 196–205. Dalluge, J. J.; Gort, S.; Hobson, R.; Selifonova, O.; Amore, F.; Gokarn, R. Anal Bioanal Chem 2002, 374, 835–840. Haynes, C. A. Biochim Biophys Acta 2011, 1811, 663–668. Haynes, C. A.; Allegood, J. C.; Sims, K.; Wang, E. W.; Sullards, M. C.; Merrill, A. H. The Journal of Lipid Research 2008, 49, 1113–1125. Blachnio-Zabielska, A. U.; Koutsari, C.; Jensen, M. D. Rapid Commun. Mass Spectrom. 2011, 25, 2223–2230. Park, J. W.; Jung, W. S.; Park, S. R.; Park, B. C.; Yoon, Y. J. Journal of mass spectrometry : JMS 2007, 42, 1136–1147. Baker, F. C.; Schooley, D. A. In Methods in enzymology; Methods in Enzymology; Elsevier, 1981; Vol. 72, pp. 41–52. Teufel, R.; Gantert, C.; Voss, M.; Eisenreich, W.; Haehnel, W.; Fuchs, G. J Biol Chem 2011, 286, 11021–11034. Buescher, J. M.; Moco, S.; Sauer, U.; Zamboni, N. Anal Chem 2010, 82, 4403–4412. Watanabe, S.; Zimmermann, M.; Goodwin, M. B.; Sauer, U.; Barry, C. E.; Boshoff, H. I. PLoS Pathog 2011, 7, e1002287. Pluskal, T.; Castillo, S.; Villar-Briones, A.; Oresic, M. BMC Bioinformatics 2010, 11, 395. Coulier, L.; Bas, R.; Jespersen, S.; Verheij, E.; van der Werf, M. J.; Hankemeier, T. Anal Chem 2006, 78, 6573–6582. Brauer, M. J.; Yuan, J.; Bennett, B. D.; Lu, W.; Kimball, E.; Botstein, D.; Rabinowitz, J. D. Proc Natl Acad Sci USA 2006, 103, 19302–19307. Mashego, M. R.; Wu, L.; Van Dam, J. C.; Ras, C.; Vinke, J. L.; Van Winden, W. A.; Van Gulik, W. M.; Heijnen, J. J. Biotechnol Bioeng 2004, 85, 620–628. Ewald, J. C.; Heux, S.; Zamboni, N. Anal Chem 2009, 81, 3623–3629. Russell, D. G.; VanderVen, B. C.; Lee, W.; Abramovitch, R. B.; Kim, M.-J.; Homolka, S.; Niemann, S.; Rohde, K. H. Cell Host Microbe 2010, 8, 68–76. Muñoz-Elías, E. J.; McKinney, J. D. Cell Microbiol 2006, 8, 10–22. Van der Geize, R.; Yam, K.; Heuser, T.; Wilbrink, M. H.; Hara, H.; Anderton, M. C.; Sim, E.; Dijkhuizen, L.; Davies, J. E.; Mohn, W. W.; Eltis, L. D. Proc Natl Acad Sci USA 2007, 104, 1947–1952. García, J. L.; Uhía, I.; Galán, B. Microbial Biotechnology 2012. Nesbitt, N. M.; Yang, X.; Fontán, P.; Kolesnikova, I.; Smith, I.; Sampson, N. S.;


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(27) (28) (29) (30) (31) (32)

Dubnau, E. Infect Immun 2010, 78, 275–282. Wilbrink, M. H.; Petrusma, M.; Dijkhuizen, L.; van der Geize, R. Appl Environ Microbiol 2011, 77, 4455–4464. Ouellet, H.; Johnston, J. B.; Montellano, P. R. O. de Trends Microbiol 2011, 19, 530– 539. Casabon, I.; Crowe, A. M.; Liu, J.; Eltis, L. D. Mol Microbiol 2013. Miclo, A.; Germain, P. Appl Microbiol Biotechnol 1992, 36. Lee, S. S.; Sih, C. J. Biochemistry 1967, 6, 1395–1403. Capyk, J. K.; Casabon, I.; Gruninger, R.; Strynadka, N. C.; Eltis, L. D. J Biol Chem 2011, 286, 40717–40724.


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Page 24 of 27

6

Signal Intensity [counts]

6

1 2 12 8 11 13 10 14 16

3

4

65

7

9

15

17

18 19

5 4 3 2 1 0 5

8

11

14

17

20

Time [min] 15

x 10

5

butyryl/isobutyryl-CoA 65

10


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

x 10

acetoacetyl/isovaleryl-CoA

6

4

x 10

8

3

9

2

5

1

0

6 5

x 10

8

10

12

malonyl/hydroxybutyryl-CoA

15

6 5

x 10

5

10

12

13 12

10

10

8

methylmalonyl/succinyl-CoA

15

11

10

0

0

5

6

8

10

Time [min]

12

0

6


8

10

Time [min]

12

A

B

7

O

2

R

SCoA

x 10

Acyl-CoA 1.5

FAD

GLYCEROL

1 FADH2

0.5

O

2

OH O SCoA

β-hydroxyacylCoA

NAD+ NADH2

O

SCoA

β-ketoacylCoA

O

Acetyl-CoA

BUTANOIC ACID

0.5 0 2

x 10

1

SCoA

O SCoA

Acyl-CoA (-28 Da)

20

1

1.5

CoA

R

15

7

O

R

1.5

10

-28 Da

β-OH-octanoylCoA

R

2

5 7 x 10

Octanoyl-CoA

H20

0

HexanoylCoA

trans-Δ enoyl-CoA

Butanoyl-CoA

SCoA

β-OH-hexanoyl-CoA

R


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


CoA Acetyl-CoA Succinyl-CoA

Page 25 of 27

OCTANOIC ACID

-28 Da

0.5 0

5

10

15

Time [min] ACS Paragon Plus Environment

20

SIDE CHAIN DEGRADATION

O

A

HO

B

O

SCoA

C D

O

O

Cholesterol

AD HO

STEROL RING DEGRADATION

O

A

O

B

C D

O

O

HHD

OH O

HO HO

O

O

CHOLESTEROL

986 m/z

x 10

OH

x 10

10

2

0

5

2

1

x 10

10

9

11

Time [min]

13

15

SCoA

6

x 10

5 0

5

x 10

6

1 7

x 10

0

5

x 10

O

3

5

x 10

HO

960 m/z

5

0 5

SCoA

DOHNAA-CoA

6

SCoA O

O

1

0 5

O

O

988 m/z

5

2

0

2

O

3,4-DHSA

1

0

Propionyl-CoA

O

AD

5

4

SCoA O

Pyruvate

DOHNAA

2

OH O

HO

O

O

DHOONA

B

Page 26 of 27


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

CONTROL

A


0 5

5

x 10

3 7

9

11

Time [min]

13


15

0 5

7

9

11

Time [min]

13

15

MS2 MS1 Chemistry Page 27 of 27 Analytical H2N N

N HO OH HO P O O

1 2 3

CoA

O

N

N O

OH O P O O O P O OH HO

HN

S

CoA

R

R2

CoA

CoA R1

CoA


HN

O

CoA

R3

CoA

O

Cell Extract

CoA-profile

Nontargeted Profiling of Coenzyme A thioesters in biological samples

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