transformation product analysis: ready to go beyond ... · comprehensive enumeration of tps,...
TRANSCRIPT
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Transformation product analysis: Ready to go beyond suspect screening?
Kathrin Fenner, Rebekka Gulde, Juliane Hollender, Jennifer Schollée, Heinz Singer
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Thoughts of a chemist parent
Similar looks
Behavior
At the playground
ON
N
N
HNN
N
In the lab
Reaction
Similar looks
Behavior
Reaction
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TP search with high-resolution MS Overview of strategies
Rel
atio
nshi
p
Context
Reaction
Known parent Unknown parent Experiment Field samples Field samples
Suspect screening & mass defect filtering
Multiple observations
Single sample
Multiple observations
MS1
MS2/ MSn
Trend
Structural similarity
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Prioritization
Confirmation
m/z extracted ion chromatogram
Generation of suspect list
Suspect screening Looking for expected transformation products
Parents of interest
Structural interpretation
Environmental relevance, chemical classes
Batch studies, literature, prediction systems, library of transformations
HR-MS full scan of sample(s)
Isotope fit, plausible retention time, absence in blank, ionization behavior
Expected trend, intensity
Data-dependent MS/MS, interpretation of fragmentation patterns
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Suspect screening Transformation products in natural waters
52 highly used biocides, pesticides and pharmaceuticals
1794 possible transformation products from literature and Eawag-PPS
HR-MS full scan of sample(s)
Isotope fit, plausible retention time, absence in blank, ionization behavior
Expected trend, intensity
Data-dependent MS/MS, interpretation of fragmentation patterns Kern et al., 2008, ES&T
Prioritization
Confirmation
m/z extracted ion chromatogram
Generation of suspect list
Parents of interest
Structural interpretation
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• Artificial intelligence system to predict likely microbial transformation pathways
• About 250 biotransformation rules applied • Batch mode available upon request
Eawag-PPS
BBD Home > PPS Home > 1 [Zoom In][Zoom Out][Print Size][Clear][?]
UM-BBD: Pathway Prediction Results
[About] [Tips] [Not Predicted] [All Rules]
The predicted pathway:
Aerobic Likelihood:
Very likely Likely Neutral
Prediction Options:
Show Biotransformations:
www.eawag-bbd.ethz.ch/predict envipath.org
Prediction of microbial biotransformation
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Suspect screening Transformation products in natural waters
1057 peaks found in at least on of 7 water samples
957 without blank peaks 640 peaks intensity ≥105
438 reasonable retention time 415 peaks with correct isotope pattern 371 peaks with correct ionization mode
Data-dependent MS/MS, interpretation of fragmentation patterns
52 highly used biocides, pesticides and pharmaceuticals
1794 possible transformation products from literature and Eawag-PPS
Kern et al., 2008, ES&T
Prioritization
Confirmation
m/z extracted ion chromatogram
Generation of suspect list
Parents of interest
Structural interpretation
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Retention time filter
Reversed phase X-Bridge, MeOH-water gradient
Crude, realistic, but still efficient
Kern et al., 2008, ES&T
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Suspect screening Transformation products in natural waters
1057 peaks found in at least on of 7 water samples
957 without blank peaks 640 peaks intensity ≥105
438 reasonable retention time 415 peaks with correct isotope pattern 371 peaks with correct ionization mode
19 proposed structures
52 highly used biocides, pesticides and pharmaceuticals
1794 possible transformation products from literature and Eawag-PPS
O
N N
O
N
O
OH
OH3C
NH
NN
O CH3
Metamitron-desamino
Azoxystrobin acid
N
O
O
H2N
OH3C
HN
CH3OH
N-desmethyl venlafaxin
Carbamazepine-10,11-epoxide ...
OH3C
OCH3
NH3C
CH3HN
CH3
O
OH
O
OH
NHH3C
CH3
Atenolol acid
D617 (Verapamil TP)
...
Prioritization
Confirmation
m/z extracted ion chromatogram
Generation of suspect list
Parents of interest
Structural interpretation
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Suspect screening & case-control Transformation products along river
54 after blank subtraction, intensity filter, and isotope pattern check 19 with increasing trends (Attx < 0) 8 after retention time filter (ΔlogKow vs ΔRT) and ionization plausibility check
8 proposed structures
32 organic micropollutants (out of 2560) with decreasing trends along 4 river stretches
1315 possible transformation products from Eawag-PPS (3 generations)
Li et al., 2016, manuscript in preparation
Prioritization
Confirmation
m/z extracted ion chromatogram
Generation of suspect list
Parents of interest
Structural interpretation
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TP search with high-resolution MS Overview of strategies
Rel
atio
nshi
p
Context
Reaction
Known parent Unknown parent Experiment Field samples Field samples
Suspect screening & mass defect filtering
Multiple observations
Single sample
Multiple observations
Trend
Structural similarity
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A few words on mass defect filtering “Extended suspect screening”, example nefazodone
Zhang et al., 2009, J. Mass. Spectrom.
: First generation suspects B, C and D
40 mD
a
50 Da
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TP search with high-resolution MS Overview of strategies
Rel
atio
nshi
p
Context
Reaction
Known parent Unknown parent Experiment Field samples Field samples
Suspect screening & mass defect filtering
Multiple observations
Single sample
Multiple observations
Trend
Structural similarity
Trend or case-control filtering
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Trend screening in spike experiments Comprehensive enumeration of TPs, pathway elucidation
Helbling et al., 2010, ES&T Gulde et al., 2016, ES&T
Extracted ion chromatogram (XIC)
HR-MS full scan of sample(s), automated peak detection, blank subtraction
Further prioritization Componentization; reasonable molecular formula , m/z and RT relative to parent compound
Structural interpretation
Trend filter Increasing or increasing/decreasing trend, absence in matrix controls
t=0 1h 2h 4h 8h 1d 2d
Sample time series t=0 1h 2h 4h 8h 1d 2d
A3 A2 A1 A6 A5 A4 A7
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Pargyline
ortho-chlorophenyl
piperazine
Pheniramine Pyrilamine
Primaquine
Venlafaxine
Mexiletine
N-demethyl venlafaxine
Chlorcyclizine
Deprenyl
N-demethyl pheniramine
Lidocaine Spiroxamine N,N-dimethyl
-p-chloroanilin
Fenfluramine
1[(4-chlorophenyl) phenylmethyl]
piperazine
Norfluoxetine
Fluoxetine
Clenisopenterol
Trend screening in spike experiments
Gulde et al., 2016, ES&T
Comprehensive enumeration of amine transformation pathways
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Sample time series
Trend screening in spike experiments Comprehensive enumeration of TPs, pathway elucidation
Helbling et al., 2010, ES&T Gulde et al., 2016, ES&T
Extracted ion chromatogram (XIC)
Trend filter Prioritized suspect list
Structural interpretation
Triplicates of 19 bioreactors spiked with separate compounds, 9 time points per compound/reactor
144 peaks (or components) with expected trend and plausible molecular formula
Further prioritization
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Trend screening in spike experiments Exemplary results for pyrilamine
Gulde et al., 2016, ES&T
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Pyrilamine (parent):
Prediction of sum formula based on likely atoms; consistent isotopic pattern à Molecular formula: C16H21N3O Level 4
Difference to parent compound: à -CH2
Demethylation at ether or tertiary amine group Level 3
MS spectrum
Data for TP: Confidence levels:
Structure elucidation Communicating confidence
Level 1: Confirmed structure by reference standard
Level 2: Probable structure a) by library spectrum match b) by diagnos6c evidence
Level 3: Tenta9ve candidate(s) structure, subs6tuent, class
Level 4: Unequivocal molecular formula
Level 5: Exact mass of interest
Schymanski et al., 2014, ES&T
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19
Marked fragments provide diagnostic evidence for demethylation at the ether group
à Structure identifiable with high confidence Level 2b
m/z
Inte
nsity
[cou
nts]
(10^
3)
Pyrilamine (parent):
Confidence levels:
Structure elucidation Communicating confidence
Level 1: Confirmed structure by reference standard
Level 2: Probable structure a) by library spectrum match b) by diagnos6c evidence
Level 3: Tenta9ve candidate(s) structure, subs6tuent, class
Level 4: Unequivocal molecular formula
Level 5: Exact mass of interest
Schymanski et al., 2014, ES&T
MS2 spectrum
Data for TP:
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Trend screening in spike experiments Exemplary results for pyrilamine
Gulde et al., 2016, ES&T
(…) I applaud the authors for making all of their hard and
meticulous work available to the ES&T readers. The transparency
and clarity in this submission is refreshing, and something that most
contributors to ES&T should aspire to.
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Amine biotransformation pathways Multiple, but consistent transformations across 19 amines
Gulde et al., 2016, ES&T
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TP search with high-resolution MS Overview of strategies
Rel
atio
nshi
p
Context
Reaction
Known parent Unknown parent Experiment Field samples Field samples
Suspect screening & mass defect filtering
Multiple observations
Single sample
Multiple observations
Trend
Structural similarity
Trend or case-control filtering
Trend or case-control filtering
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Trend screening in process studies Characterization of processes, removal/formation of micropollutants
Getzinger et al., 2016, personal communication
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TP search with high-resolution MS Overview of strategies
Rel
atio
nshi
p
Context
Reaction
Known parent Unknown parent Experiment Field samples Field samples
Suspect screening & mass defect filtering
Multiple observations
Single sample
Multiple observations
Trend
Structural similarity
Trend or case-control filtering
Trend or case-control filtering
Reaction screening
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Principal Component Analysis R ‘prcomp’package Characteris6c peaks extracted
Linkage for parent/TP pairs Difference analysis from known biotransforma6ons
Priori8za8on of links for targeted MS/MS Used peak shape, peak intensity, and significance of intensity difference
Further links priori8za8on ‘OrgMassSpecR’ / ‘RMassBank’ packages Similarity of MS/MS spectra in each link
Schollée et al., 2016, Anal. Chem.
Environmental chemistry: Finding PC-TP pairs in EICs from process samples (ozonation, WWTP)
Reaction screening Process evaluation, finding unknown parent-TP pairs
Morreel et al., 2014, Plant Cell
Systems biology: Structural Characterization of Metabolites in Arabidopsis via Candidate Substrate-Product Pair Networks
Reaction
Trend
Chemical similarity
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MSMS similarity as PC-TP pair filter
Schollée et al., 2016, submitted to Anal. Chem.
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TP search with high-resolution MS Overview of strategies
Rel
atio
nshi
p
Context
Reaction
Known parent Unknown parent Experiment Field samples Field samples
Suspect screening & mass defect filtering
Multiple observations
Single sample
Multiple observations
Trend
Structural similarity
Trend or case-control filtering
Trend or case-control filtering
Reaction screening
Fragment or fragmentation screening (neutral loss, common fragment etc.)
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Parent(s) of interest
XIC from HR-MS
Environmental samples(s)
Fragment screening Comprehensive enumeration of TPs, pathway elucidation
Known or predicted MS/MS spectra
Mistrik et al., 2010, Drug Metabol. Rev.
XIC from (HR)-MSMS Fragment filter
RT search template
Further prioritization
Structural interpretation
Parent(s) of interest
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Fragment screening Comprehensive enumeration of TPs, pathway elucidation
5 pharmaceuticals with high production volume and significant removal in WWTP
Boix et al., 2016, J. Hazard. Mat. Other example: Zonja et al., 2016, ES&T
3-4 known fragment ions of parent and/or known TP
New RT of interest for venlafaxine
New parent ion mass at that RT
XIC from HR-MS
WWTP & SW samples(s)
XIC from (HR)-MSMS Fragment filter Parent(s) of
interest
RT search template
Further prioritization
Structural interpretation
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TP search with high-resolution MS Conclusions
Rel
atio
nshi
p
Context
Reaction
Known parent Unknown parent Experiment Field samples Field samples
Multiple observations
Single sample
Multiple observations
Trend
Structural similarity
Reaction screening
Suspect screening & mass defect filtering
Trend or case-control filtering
Trend or case-control filtering
Fragment or fragmentation screening (neutral loss, common fragment etc.)
Fragment or fragmentation screening (neutral loss, common fragment, MSMS similarity etc.)
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New biotransformation database & prediction tool enviPath (https://envipath.org, former Eawag-BBD)
• Public and private data • Easy data entry and metadata
annotation • Biotransformation rules for
pathway prediction • Relative reasoning models • Machine-learning methods for
training own models
• Central resource for contaminant biotransformation pathways
• Database and pathway prediction
Wicker et al., Nucleic Acids Res., 44, 2016
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The road ahead…
• Data-independent or all ion fragmentation experiments • Data processing workflows to sort together fragment ion
and parent ion peaks
• Efficient data processing software solutions
• Complementary chromatographic methods to capture polar and ionic TPs
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PhD students: Michele Stravs Technicians: Philipp Longrée Postdocs: Damian Helbling Martin Loos Funding:
Thanks…
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