Natural Product Discovery with LC-MS/MS Diagnostic Fragmentation Filtering: Application for Microcystin Analysis

Podsumowanie

Diagnostic fragmentation filtering, implemented into MZmine, is an elegant, post-acquisition approach to screen LC-MS/MS datasets for entire classes of both known and unknown natural products. This tool searches MS/MS spectra for product ions and/or neutral losses that the analyst has defined as being diagnostic for the entire class of compounds.

Streszczenie

Natural products are often biosynthesized as mixtures of structurally similar compounds, rather than a single compound. Due to their common structural features, many compounds within the same class undergo similar MS/MS fragmentation and have several identical product ions and/or neutral losses. The purpose of diagnostic fragmentation filtering (DFF) is to efficiently detect all compounds of a given class in a complex extract by screening non-targeted LC-MS/MS datasets for MS/MS spectra that contain class specific product ions and/or neutral losses. This method is based on a DFF module implemented within the open-source MZmine platform that requires sample extracts be analyzed by data-dependent acquisition on a high-resolution mass spectrometer such as quadrupole Orbitrap or quadrupole time-of-flight mass analyzers. The main limitation of this approach is the analyst must first define which product ions and/or neutral losses are specific for the targeted class of natural products. DFF allows for the subsequent discovery of all related natural products within a complex sample, including new compounds. In this work, we demonstrate the effectiveness of DFF by screening extracts of Microcystis aeruginosa, a prominent harmful algal bloom causing cyanobacteria, for the production of microcystins.

Wprowadzenie

Tandem mass spectrometry (MS/MS) is a widely used mass spectrometry method that involves isolating a precursor ion and inducing fragmentation via application of activation energy such as collision induced dissociation (CID)¹. The manner in which an ion fragments is intimately linked to its molecular structure. Natural products are often biosynthesized as mixtures of structurally similar compounds rather than as a single unique chemical². As such, structurally related compounds that are part of the same biosynthetic class often share key MS/MS fragmentation characteristics, including shared product ions and/or neutral losses. The ability to screen complex samples for compounds that possess class-specific product ions and/or neutral losses is a powerful strategy to detect entire classes of compounds, potentially leading to the discovery of new natural products^3,4,5,6. For decades, mass spectrometry methods such as neutral loss scanning and precursor ion scanning performed on low resolution instruments have allowed ions with the same neutral loss or product ions to be detected. However, the specific ions or transitions needed to be defined prior to performing the experiments. As high-resolution mass spectrometers have become more popular in research laboratories, complex samples are now commonly screened using non-targeted, data-dependent acquisition (DDA) methods. In contrast to traditional neutral loss and precursor ion scanning, structurally related compounds can be identified by post-acquisition analysis⁷. In this work, we demonstrate a strategy we have developed termed diagnostic fragmentation filtering (DFF)^5,6, a straight-forward and user-friendly approach to detect entire classes of compounds within complex matrices. This DFF module has been implemented into the open-source, MZmine 2 platform and available by downloading MZmine 2.38 or newer releases. DFF allows users to efficiently screen DDA datasets for MS/MS spectra which contain product ion(s) and/or neutral loss(es) that are diagnostic for entire classes of compounds. A limitation of DFF is characteristic product ions and/or neutral losses for a class of compounds must be defined by the analyst.

For example, each of the more than 60 different fumonisin mycotoxins identified^8,9 possess a tricarballylic side chain, that generates a m/z 157.0142 (C₆H₅O₅^-) product ion upon fragmentation of the [M-H]^- ion⁴. Therefore, all putative fumonisins in a sample can be detected using DFF by screening all MS/MS spectra within a DDA dataset that contain the prominent m/z 157.0142 product ion. Similarly, sulfated compounds can be detected by screening DDA datasets for MS/MS spectra that contain a diagnostic neutral loss of 79.9574 Da (SO₃)³. This approach has also been successfully applied for detecting new cyclic peptides⁵ and natural products that contain tryptophan or phenylalanine residues⁶.

To demonstrate the effectiveness of DFF and its ease of use within the MZmine platform¹⁰, we have applied this approach to the analysis of microcystins (MCs); a class of over 240 structurally related toxins produced by freshwater cyanobacteria^11,12,13.

The most commonly reported cyanotoxins are MCs, with the MC-LR (leucine [L]/arginine [R]) congener most frequently studied (Figure 1). MCs are monocyclic non-ribosomal heptapeptides, biosynthesized by multiple cyanobacteria genera including Microcystis, Anabaena, Nostoc, and Planktothrix^12,13. MCs are composed of five common residues and two variable positions occupied by L-amino acids. Nearly all MCs possess a characteristic β-amino acid 3-amino-9-methoxy-2,6,8-trimethyl-10-phenyldeca-4,6-dienoic acid (Adda) residue at position 5¹¹.  The MS/MS fragmentation pathways of MCs are well described^14,15; the Adda residue is responsible for the prominent MS/MS product ion, m/z 135.0803⁺ (C₉H₁₁O⁺) as well as other product ions including m/z 163.1114⁺ (C₁₁H₁₅O⁺) (Figure 2). Non-targeted DDA datasets of Microcystis aeruginosa cellular extracts can be screened for all microcystins present using these diagnostic ions, granted that the microcystins have an Adda residue.

Protokół

1. Preparation of non-targeted liquid chromatography (LC)-MS/MS datasets

NOTE: DFF can be performed using any high-resolution mass spectrometer and analytical method optimized for a target class of analytes. MC optimized LC-MS/MS conditions on Orbitrap mass spectrometer are listed in the Table of Materials.

1. Downloading MZmine 2 (http://mzmine.github.io/)
   NOTE: Example data CPCC300.raw can be found at https://drive.google.com/open?id=1HHbLdvxCMycSasyNXPRqIe5pkaSqQoS0.
   1. Under the Raw data methods drop down menu, select the Raw data import option.
   2. Choose the data file(s) to be analyzed. Single or multiple files may be imported.
2. (Optional) If vendor data format is not supported by MZmine, use Proteowizard¹⁶ to generate centroided .mzml data files.
   1. Choose the Peak Picking filter to apply vendor-supplied centroiding algorithm.

2. Diagnostic fragmentation filtering of imported DDA files

1. Using the cursor, select and highlight the data file(s) in the Raw data files column of the main MZmine screen.
2. Under the Visualization drop down menu, select the Diagnostic fragmentation filtering option.
3. In the DFF dialogue box that appears (Figure 3), input the following options:
   1. Retention time – use Auto range or define the range of retention times in minutes when the targeted class of analytes will elute.
   2. Precursor m/z - use Auto range or define the m/z range of the targeted class of analytes, including the possibility for multiple charged compounds when appropriate.
   3. m/z tolerance – Input the achievable MS/MS mass accuracy of the MSinstrument; 0.01 m/z or 3.0 ppm is appropriate for an Orbitrap platform. If only diagnostic product ions will be investigated, input 0.0 into the Diagnostic neutral loss value (Da) option. Conversely, if only diagnostic neutral losses will be investigated, input 0.0 into the Diagnostic product ions (m/z) option.
   4. Diagnostic product ions (m/z) – Input the class specific product ion(s) m/z. Separate multiple product ions with a comma.
      NOTE: Inputting multiple product ions will visualize spectra that contain all listed product ions.
   5. Diagnostic neutral loss value (Da) – Input the class specific neutral loss(es). Separate multiple neutral losses with a comma.
      NOTE: Inputting multiple neutral losses will visualize spectra that contain all listed neutral losses. Inputting both diagnostic product ions and neutrals losses will visualize spectra that satisfy all the criteria.
   6. Minimum diagnostic ion intensity (% base peak) – As a % of the base peak of the MS/MS spectra, define the minimum intensity for diagnostic product ions and/or neutral losses to be considered.
   7. Peaklist output file – Select a path and filename to output the results.
   8. Click the OK button to start the DFF analysis. A DFF plot will appear upon successfully completing the above steps
      NOTE: Two .csv data files will be generated. {Peaklist output file}.csv contains the precursor m/z, scan numbers, and retention times of the scans. This can be used in existing MZmine modules including Raw data methods > Peak detection > Targeted peak detection to generate extracted ion chromatograms of precursors that met the defined DFF criteria. {Peaklist output file}_data.csv contains the precursor m/z, product ion m/z and retention times to allow generation of DFF plots outside of MZmine.

3. Example use of DFF for microcystin analysis

1. Sample preparation
   1. Sterilize 250 mL Erlenmeyer flasks containing 30 mL of sterile MA media¹⁷ or other cyanobacteria growth media (BG-11) fitted with a foam stopper.
   2. Inoculate sterilized growth media with a cyanobacteria culture to approximately 5 × 10⁵ cells mL^-1 under aseptic conditions. Monitor cell density with a hemocytometer. In this example, grow M. aeruginosa strain CPCC300 photoautotrophically at 27 °C, illuminated with cool white fluorescent light (30 µE m^-2 s^-1) using a 12 h light: 12 h dark regime. Swirl the cells once per day.
   3. Separate the cells from the culture medium after 26 days by vacuum filtration using 47 mm diameter GF/C glass microfiber filter papers.
   4. Add 3 mL of 80% methanol _(aq) to harvested cells in 14 mL test tube(s).
   5. Vortex and subsequently sonicate the test tube(s) containing cyanobacteria cells for 30 s each. Store the test tube(s) at -20 °C for 1 h. Return the test tube to room temperature and allow the sample(s) to thaw for 15 min.
   6. Repeat step 3.1.5 two additional times to effectively lyse the cells.
   7. Filter the resulting cyanobacteria cell extract(s) through a 0.22 μm PTFE syringe filter(s).
   8. Dry extract(s) with an evaporator at a temperature of 30 °C using a gentle stream of nitrogen gas. Store the extract dry at -20 °C until LC-MS/MS analysis.
   9. Reconstitute the dried residue with 500 µL of 90% methanol _(aq) and vortex for 30 s in an amber HPLC vial prior to analysis.
2. Analyze the cyanobacteria extract using a DDA acquisition method on a high-resolution mass spectrometer.
   NOTE: The optimized LC-MS conditions for MC analysis used here are listed in Table of Materials.
3. Prepare the DDA datafile(s) and import into MZmine following steps 1.1 and 1.2.
4. Select the datafiles and start the DFF modules following steps 2.1-2.2.
5. For MC analysis, use the following settings within the DFF module (Figure 3).
   1. Retention time – Input the range of 2.00 to 6.00 min.
   2. Precursor m/z – Input m/z range of 430.00 to 1200.00.
   3. m/z tolerance – Apply m/z tolerance of 0.01 m/z or 3.0 ppm.
   4. Diagnostic product ions (m/z) – Input m/z of 135.0803, 163.1114 as the diagnostic product ions
   5. Diagnostic neutral loss value (Da) – Input 0.0 to define that no diagnostic neutral losses are being used.
   6. Minimum diagnostic ion intensity (% base peak) – Use 15.00 as the minimum intensity threshold
   7. Peaklist output file – Define the output file as putative_MCs.csv.
6. Click the OK button to start the DFF analysis. A DFF plot (Figure 4) will appear upon successfully completing the above steps

Wyniki

The DFF plot generated following the analysis of M. aeruginosa CPCC300 is shown in Figure 4. The x-axis of this plot is the m/z of the precursor ions that satisfied the defined DFF criteria while the y-axis shows the m/z of all product ions within the MCs MS/MS spectra. For this analysis, the criteria for MC detection included precursor ions within the m/z range of 440-1200, retention times between 2.00...

Dyskusje

DFF is a straight-forward and rapid strategy for detecting entire classes of compounds, especially relevant for natural product compound discovery. The most important aspect of DFF is defining the specific MS/MS fragmentation criteria for the targeted class of compounds. In this representative example, DFF was used to detect all Adda residue containing MCs present in an M. aeruginosa cellular extract. Although the vast majority of MCs contain an Adda residue, other residues at this position have been known, nota...

Materiały

Name	Company	Catalog Number	Comments
Cyanobacteria
Microcystis aeruginosaCPCC300	CANADIAN PHYCOLOGICAL CULTURE CENTRE	CPCC300	https://uwaterloo.ca/canadian-phycological-culture-centre/
Software
Proteowizard (software)		software	http://proteowizard.sourceforge.net/
Mzmine 2		software	http://mzmine.github.io/
LC-MS
Q-Exactive Orbitrap	Thermo	-	Equipped with HESI ionization source
1290 UHPLC	Agilent		Equipped with binary pump, autosampler, column compartment
C18 column	Agilent	959757-902	Eclipse Plus C18 RRHD column (2.1 × 100 mm, 1.8 μm)
Solvents
Optima LC-MS grade Methanol	Fisher	A456-4
OptimaLC-MS grade Acetonitrile	Fisher	A955-4
OptimaLC-MS grade Water	Fisher	W6-4
LC-MS grade Formic Acid	Fisher	A11710X1-AMP
Vortex-Genie 2	Scientific Industries	SI-0236
Centrifuge Sorvall Micro 21	Thermo Scientific	75-772-436
Other
Amber HPLC vials 2 mL/caps	Agilent	5182-0716/5182-0717
0.2-μm PTFE syringe filters	Pall Corp.	4521
Whatman 47mm GF/A glass microfiber filters	Sigma-Aldrich	WHA1820047
Media
MA media (pH 8.6) ( quantity / L)			Watanabe, M. F. & Oishi, S. Effects of environmental factors on toxicity of a cyanobacterium (Microcystis aeruginosa) under culture conditions. Applied and Environmental microbiology. 49 (5), 1342-1344 (1985).
Ca(NO3)·4H2O, 50 mg	Sigma-Aldrich	C2786
KNO3, 100 mg	Sigma-Aldrich	P8291
NaNO3, 50 mg	Sigma-Aldrich	S5022
Na2SO4, 40 mg	Sigma-Aldrich	S5640
MgCl2·6H20, 50 mg	Sigma-Aldrich	M2393
Sodium glycerophosphate, 100 mg	Sigma-Aldrich	G9422
H3BO3, 20 mg	Sigma-Aldrich	B6768
Bicine, 500 mg	Sigma-Aldrich	RES1151B-B7
	P(IV) metal solution, 5 mL
Bring the following to 1 L with ddH2O
NaEDTA·2HO	Sigma-Aldrich	E6635
FeCl3 ·6H2O	Sigma-Aldrich	236489
MnCl2·4H2O	Baker	2540
ZnCl2	Sigma-Aldrich	Z0152
CoCl2·6H2O	Sigma-Aldrich	C8661
Na2MoO4·2H2O	Baker	3764
Cyanobacteria BG-11 50X Freshwater Solution	Sigma-Aldrich	C3061-500mL