A fast method for quantitation of marine biotoxins

WEBINAR
Target quantitation and metabolomic
studies of marine biotoxins in algae and
shellfish using QTOF technology
Philipp Hess1
Thomas Glauner2, Bernhard Wüst2,
Manoëlla Sibat1, Florence Mondeguer1, Fabienne Hervé1, Marie Geiger1,
Zita Zendong1
1
Laboratoire Phycotoxines IFREMER – 44300 Nantes - France
2
Agilent Technologies R&D - 76337 Waldbronn - Germany
Content
 Context
 Development of a quantitative, rapid method for marine biotoxins
using full scan Q-ToF mass spectrometry


Full scan techniques
Chromatographic developments
 Structural confirmation
 Development of a database and library for marine biotoxins
 Databases (collection of molecular formulae)
 Libraries (database plus structures, spectra and metadata)
 “Home-made” versus commercially available tools
 Metabolomic techniques in chemotaxonomy and the elucidation of
unknowns (dereplication)
 “Metabolome of a sample”
 Metabolomes of micro-algae (individual, comparative, chemotaxonomy)
 Standardisation of work-flow
 Linking dereplication work-flow to miniaturised bioassays
 Conclusion
2
Biodiversity = Chemodiversity
Unknown toxins France 2003 – 2008
(as detected per mouse bioassay for lipophilic toxins)
 27 % of unexplained mouse bioassays
European or global perspective:
 Europe is a single market, hence all toxins
encountered in Europe must be monitored
 Europe imports shellfish from a wide variety of third
countries, hence an even larger, virtually global
range of toxins must be taken into account for
imports (AU, NZ, JP, KO, Thailand, Vietnam,
Jamaica, CA, Chile, Peru, Uruguay, Greenland,
Morocco, Tunisia and Turkey)
 Legislative change from mouse bioassay for
lipophilic toxins to targeted LC-MS/MS methods as
reference requires increased vigilance
5
1- Full scan analysis:
Example of full scan power MS Mass Hunter software to
evaluate molecular and adduct ion clusters.
Okadaic acid
6
A fast method for quantitation of marine biotoxins
Fast LC-MS/MS method tested on:
Kinetex C18 100*2.1mm 2.6µm (Phenomenex)
Poroshell 120 EC-C18 100*2.1mm 2.7µm (Agilent)
Zorbax Extend-C18 50*2.1mm 1.8 µm (Agilent)
Zorbax SB C8 50*2.1mm 1.8µm (Agilent)
7
8
Calibration curves
Quantitative rapid method (full scan)
Certified standards
Six calibration levels
R2 > 0,98
OA
AZA 1
SPX 1
PnTX-G
PlTX
LOD (ng/mL)
Q-ToF API 4000
4.5
0.5
0.4
0.1
0.3
0.05
0.3
0.05
68
10
9
Standard curves pre- and post- matrix injections
Matrix effects (PnTX-G)
Rdt moyen (%)
78
V. decussata WF
M. galloprovincialis WF
71
M. galloprovincialis HP
53
CV (%)
4%
4%
10%
2000000
1800000
1600000 y = 17413x + 8757.4
R² = 0.999
1400000
1200000
y = 16583x + 15438
R² = 0.9966
1000000
800000
600000
400000
200000
0
0
50
100
150
Signal (% of expected)
V decussata WF
Mussels WF
Mussels HP
C theor = 4.9 ng/ml
C theor = 24.5 ng/ml
C theor = 50.0 ng/ml
 Matrix effects are comparable to those observed in
triple-stage quadrupole mass spectrometry !
10
Confirmation of the identity of novel analogues
Using LC-HRMS
for definitive
confirmation of
identity:
Example of
isobaric PnTX-G
and SPX-B & 13dm SPX-D
458 ion is specific to
PnTX-G and does
not exist for SPXs
12
Agilent suite of software
PCDL Manager - module
Database in construction (currently 275 compounds)
All major phycotoxins in Europe
Structures created in ChemDraw and then inserted into the PCDL library as .mol-files
13
Database development – literature search…
Number
Toxin family
Toxin group
Abbreviation
ASP
Domoic acid
DSP
Cyanobacteria
Cyclic imines
(FAT)
PSP
NSP
Compound
Molecular structure
DA
9
9
Okadaic acid &
dinophysistoxins
OA+DTXs
65
10
Azaspiracids
AZAs
30
/
Pectenotoxins
PTXs
16
4
Yessotoxins
YTXs
31
/
Oscillatoxins
n/a
9
/
Gymnodimines
GYMs
4
4
Spirolides
SPXs
11
11
Pinnatoxins and Pteriatoxins
PnTXs+PterTX
s
12
12
Saxitoxins
STXs
18
18
Tetrodotoxins
TTXs
18
/
Palytoxins
PLTXs
8
1
Brevetoxins
PbTXs (BTX)
16
/
Pacific Ciguatoxins
P-CTXs
27
/
Caribbean Ciguatoxins
C-CTXs
2
/
14
Database development – entering spectra…
 LC-MS/MS spectra are reputed to be nonreproducible between instruments of different
manufacturers
 This can be overcome to some extent by acquiring
spectra at low, medium and high collision energies
 We are working with Agilent to establish whether a
simple mass indicator can be used to set increasing
CEs as a standard approach
15
Metabolomics Approach:
NL:
9.69E2
C 4 H 10 O 2 N 3:
C 4 H 10 O 2 N 3
p (gss, s /p:40) Chrg 1
R: 20.0 PPM @FWHM
NL:
1.11E6
281008023#93 RT: 2.46
AV: 1 SB: 1782
3.11-49.81 , 0.01-2.31 T:
+ c ESI Full ms
[50.00-800.00]
110
133.08020
100
90
80
Relative Abundance
70
MSn
60
50
40
30
133.07378
20
133.09665
10
133.08236
0
133.07
133.08
133.09
133.10
m/z
Structure elucidation
1000’s ions
Biomarkers ?
Statistical
analyses
Data retreatment
Fingerprints(LC/
HRMS)
Sample Prep
intensités
Also:
rt
m/z

Fingerprints

Footprints

Crosstalk
16
Agilent work-flow for non-targeted analysis
17
Dealing with data complexity – Metabolome of A. spinosum
TIC
BPC
ECC
6800 compounds > 1500 counts
AZA accumulation,
detoxification and
biotransformation in M. edulis
experimentally fed A.
spinosum
A. spinosum & Isochrysis affinis galbana
3-week experiment :
=> 1-week continuous feeding
8
1
6
3
4
5
1 : Algal tank
2 : Electrovalve
3 : Air lift system
4 : Cylindroconical tank
(160L, 4Kg of mussel)
5 : Bin (overflow)
6 : Fluorometer
7 : Cryostat (12.5°C)
8 : Sofware (lab-view)
5 000 cells.mL-1 of A. spinosum
5
4
6
10 000 cells.mL-1 of A. spinosum
7
5 000/5 000 cells.mL-1 of A. spinosum and
T-iso
10 000 cell.mL-1 of T-Iso
=> 2-weeks of detoxification
3
8
2
with T-Iso (10 000 cells.mL-1)
1
20
AZAs accumulation and depuration in M. edulis
(µg.kg-1 wet flesh)
700
Depuration
Contamination
600
AZA (µg.kg-1)
500
400
300
200
100
0
0
2
4
6
8
10
12
14
16
18
20
Time (days)
Diet 1
Diet 2
Diet 3
Control
After 6h of contamination : above the EU limit on AZA content
Maximum AZA content obtained ±600 µg kg-1 of wet flesh
Jauffrais T., Marcaillou C., Herrenknecht C., Truquet P., Séchet V., Nicolau E., Tillmann U., Hess P. (2012) Azaspiracid accumulation
and detoxification in blue mussels (Mytilus edulis), experimentally fed Azadinium spinosum. Toxicon 60 582-595.
21
Screening of Azadinium and mussels exposed to Azadinium using the database
File
A. spin - 1
A. spin - 2
A. spin - 3
Mussels-1
Mussels-2
Mussels-3
A. spin - 1
A. spin - 2
A. spin - 3
Mussels-1
Mussels-2
Mussels-3
Mussels-1
Mussels-2
Mussels-3
Mussels-1
Mussels-2
Mussels-3
Mussels-1
Mussels-2
Mussels-3
Mussels-1
Mussels-2
Mussels-3
Mussels-1
Mussels-1
Mussels-2
Mussels-2
Mussels-3
Mussels-3
Mussels-1
Mussels-2
Mussels-3
ID
AZA-1
AZA-1
AZA-1
AZA-1
AZA-1
AZA-1
AZA-2
AZA-2
AZA-2
AZA-2
AZA-2
AZA-2
AZA-17
AZA-17
AZA-17
AZA-19
AZA-19
AZA-19
AZA-10
AZA-10
AZA-10
AZA-3
AZA-3
AZA-3
AZA-4
AZA-4
AZA-4
AZA-4
AZA-4
AZA-4
AZA-6
AZA-6
AZA-6
C
C47
C47
C47
C47
C47
C47
C48
C48
C48
C48
C48
C48
C47
C47
C47
C48
C48
C48
C47
C47
C47
C46
C46
C46
C46
C46
C46
C46
C46
C46
C47
C47
C47
H
H71
H71
H71
H71
H71
H71
H73
H73
H73
H73
H73
H73
H69
H69
H69
H71
H71
H71
H71
H71
H71
H69
H69
H69
H69
H69
H69
H69
H69
H69
H71
H71
H71
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
O
RT
O12
O12
O12
O12
O12
O12
O12
O12
O12
O12
O12
O12
O14
O14
O14
O14
O14
O14
O13
O13
O13
O12
O12
O12
O13
O13
O13
O13
O13
O13
O12
O12
O12
m/z
11.021
11.13
11.079
10.868
10.873
10.873
11.637
12.861
12.76
11.45
11.423
11.481
10.393
10.407
10.374
10.709
10.657
10.59
10.027
10.024
10.008
10.66
10.707
10.682
9.453
9.794
9.45
9.808
9.45
9.791
10.709
10.715
10.707
Mass
Mass
Score
842.5067
841.499
841.4976
842.5067
841.499
841.4976
864.4857
841.499
841.4976
842.5054
841.4977
841.4976
842.5053
841.4977
841.4976
842.5049
841.4975
841.4976
878.5015
855.5144
855.5133
856.5203
855.5129
855.5133
856.5208
855.5132
855.5133
856.5203
855.5128
855.5133
856.5203
855.513
855.5133
856.5198
855.5143
855.5133
894.4598
871.4721
871.4718
872.4797
871.4719
871.4718
872.4786
871.4712
871.4718
886.4941
885.4866
885.4875
886.494
885.4867
885.4875
886.4953
885.488
885.4875
858.499
857.4917
857.4925
858.4988
857.4917
857.4925
858.4983
857.4918
857.4925
828.4885
827.481
827.482
828.4884
827.4809
827.482
828.4883
827.4811
827.482
844.4834
843.4763
843.4769
844.483
843.4753
843.4769
844.4827
843.4752
843.4769
844.4828
843.4755
843.4769
844.4829
843.4758
843.4769
844.483
843.4747
843.4769
842.5037
841.4979
841.4976
842.5038
841.4963
841.4976
842.5043
841.5026
841.4976
Diff (DB,
ppm)
Ions
96.7
1.33
96.71
1.38
97.1
1.36
99.26
0.1
99.23
0.08
99.37
-0.15
97.35
1.15
98.2
-0.35
98.83
-0.07
99.01
-0.46
99.12
-0.27
95.62
1.02
98.38
0.32
98.55
0.1
98.16
-0.64
98.51
-0.83
98.98
-0.78
97.45
0.53
98.26
-0.87
98.21
-0.85
89.95
-0.73
98.4
-1
98.37
-1.05
98.62
-0.85
92.08
-0.54
95.88
-1.61
97.13
-1.64
96.81
-1.36
97.93
-1.1
80.38
-2.23
94.21
0.26
95.23
-1.3
74.52
4.93
Area
12
12
12
12
12
9
10
10
9
10
11
10
11
11
12
12
8
7
8
8
8
11
8
8
6
9
8
10
8
8
10
9
9
39342550
86901383
104250164
2451823
2562262
1310148
11821349
1494584
2447278
1190684
1228746
559856
2593854
2182779
540624
615806
502690
173586
235107
229583
251621
502099
439507
339649
21690
135150
90541
123139
225203
159807
121235
113820
114723
Automated screening of knowns in full scan mode
Comparative metabolomics in musels
357 compounds only present in Azadinium-exposed mussels compared to Azadinium
AZA-metabolites
51 compounds only present in Azadinium-exposed mussels compared to control mussels
AZA-metabolites
AZA2
AZA1
 Semi-automatic metabolite identification becomes practical !
24
Examples of algal metabolomes:
Alexandrium ostenfeldii
7,232 min 13,19 Didesmethyl-SPX-C (M+H)+ =
692,4520
Examples of algal metabolomes:
Karenia selliformis
6,48 min Gymnodimine A (M+H)+ = 508,3433
Examples of algal metabolomes:
Prorocentrum lima
11,38 min OA (M+H)+ = 805,4733
12,71 min DTX1 (M+H)+ = 819,4901
Examples of algal metabolomes:
Pseudo-nitzschia
5,49 min DA (M+H)+
312,1445
Comparative metabolomes:
Azadinium obesum
AZA1 (M+H)+ 842.5068
AZA2 (M+H)+ 856.5224
AZA? (M+H)+ 716.4748
Azadinium spinosum
AZA1-methyl ester (M+H)+ 856.5201
 77 features only in A. obesum, 59 features only in A. spinosum, and 95 common features !
From metabolomes to chemotaxonomy:
From metabolomes to chemotaxonomy:
Comparative metabolomes of algal fingerprints and algal footprints
Acetone extract of A. spinosum (Fingerprint)
Lysed Azadinium shaken
for 24h in seawater (not
stirred!) and adsorbed onto
passive sampler (HP20)
SPATT A. spinosum (Footprint)
 147 features in SPATT vs. 196 features in A. spinosum
32
Crude extract (algal mix) vs SPE-cleaned extract (HLB)
Crude extract:
1422 compounds
SPE-cleaned extract:
859 compounds
Importance of controls: procedural blanks
(Seawater  Passive sampler  extract)
HLB: 2933 compounds
HP-20: 2654 compounds
 Even though these polymeric materials had been washed with solvent
prior to use they release contamination at elution post experiment !
34
Pilot-scale culture of V. rugosum
Batch
1
2
3
4
5
6
7
8
10
Total
Mass of pellet (g)
26
19
16
13
16
28
28
58 56 29
289g
614 442 303 237 259 411 276 140 246 181
3mg
Masse PnTX G (µg)
Culture conditions
L1
L1
L1* L1
L1
L1*
Duration of culture (d)
41
33
29
25
47 37** 63
39
L1
9
L1 L1 L1
56
43
Metabolome of Vulcanodinium rugosum
- bioguided fractionation
Vulcanodinium
rugosum
Crude extract
Biological Screening
and
Metabolomics: Dereplication1,2
Fractioning
Fractions
Chemical analysis
-Triple quad
-Q-ToF
Evaluation of biological activity
-Cytotoxicity KB cells
-Fly larvae
-bacteria
Data analysis
(comparison to large natural product libraries2)
(1) Kristian F. Nielsen et al. J Nat. Prod. (2011) 74, p2338-2348
(2) http://www.chem.canterbury.ac.nz/marinlit/marinlit.shtml
Fractionation of sample for polar lipids
Scheme of extraction and purification of PnTX-G
CRUDE (A)
(2050 mg)
PnTX G
Crude extr.
DCM-fract.
Aq. MeOH
DCM fraction (B)
(442 mg; 22% of A)
Hexane phase
Aqueous phase
Aq. MeOH fraction (C)
(168 mg; 8% of A)
SiO2-F2
(73 mg; 43 % of C)
SiO2-F3
(39 mg; 23 % of C)
Evaluation of the activity of algal extracts
using cytotoxicity assay
100
80
60
Masse fractions (%)
40
Masse PnTX G (%)
20
0
F1bP3
F2bP3
F3bP3
F4bP3
F5bP3
Dereplication: database screening
results for SiO2 F3
Vulcanodinium
EB DCMMeOHaq SiO2 F3
Pinnatoxin-G
Dereplication: SiO2 F2:
Screen against MarinLit
(database by Blunt & Munro
with > 30 000 cpds
(this process takes around
15 min per 10 min of full
scan data)
Name
Nakijiquinone A
RT
5.68
m/z
402.2278
Score (DB)
99.4
Diff (DB, ppm)
-0.82
Interestingly…
Out of the 144 compounds present in these two
fractions, we were able to identify 45 compounds
applying a filter of 5 ppm, or 36 compounds when
applying a filter of 2 ppm, and 22 compounds at < 1
ppm
About 100 unknowns to follow up on…
Several present that had been initially identified from
sponges: nakijiquinone, petrosaspongiolide, plakinic
acid and sarcotin
We expect to be able to clarify the biological origin
and biogeography of many natural products…
Conclusions
Developed rapid & quantitative method for marine biotoxins
using full scan techniques
Linear over appropriate range
Allows for quantitation of all regulated (EU) lipophilic toxins in
< 9 min
Developed a database and library for marine biotoxins
Ca. 275 compounds entered (ca. 90 structures added)
Demonstrated capability of rapid screening against large-scale
commercial databases
Developed work-flow for assessing finger- and footprints of
microalgae
Mass Profiler allows comparative work (contaminated mussels
versus control mussels)
Made a basis for chemotaxonomy of microalgae
Used dereplication in conjunction with bioscreening
Thanks for your attention !!
Jean-François de Troy « The oyster lunch » 1735 (originally decorating the dining room in Versailles), Musée Condé,
Chantilly, France
43

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