Supplementary info to
Non-target screening reveals the mechanisms responsible for the
antagonistic inhibiting effect of biocides DBNPA and glutaraldehyde
on benzoic acid biodegradation
Thomas V. Wagnerᵃᵇ, Rick Helmusa, Silvana Quiton Tapiaᵇ, John R.
Parsonsᵃ, Huub H.M. Rijnaartsᵇ, Pim de Voogtᵃᶜ, Alette A.M.
Langenhoffᵇ
ᵃ Institute for Biodiversity and Ecosystem Dynamics (IBED),
University of Amsterdam, P.O. Box 94248, 1092 GE Amsterdam, The
Netherlands
ᵇ Department of Environmental Technology, Wageningen University,
P.O. Box 17, 6700 EV Wageningen, The Netherlands
ᶜ KWR Water Research Institute, Chemical Water Quality and
Health, P.O. Box 1072, 3430 BB Nieuwegein, The Netherlands
Content:
· Text S1Chemicals
· Text S2Benzoic acid analysis
· Text S3Glutaraldehyde analysis
· Table S1Overview of samples used for non-target analysis
· Text S4R script for non-target data processing of LC-MS/MS
data analysis
· Text S5Explanation of workflow steps in patRoon
· Figure S1Biotic and abiotic degradation of benzoic acid in
absence of biocides
· Figure S2Biotic degradation of benzoic acid in presence of
glutaraldehyde
· Figure S3Biotic and abiotic degradation of glutaraldehyde
· Figure S4Biotic degradation of benzoic acid in presence of
DBNPA
· Text S6Detailed info about DBNPA
· Text S7Detailed info about TP1 : MBNPA
· Text S8Detailed info about TP2 : 2,2-dibromopropanediamide
· Figure S5Component clustering of DBNPA
· Figure S6Component clustering of MBNPA
· Text S9Detailed info about TP3-mz183
· Text S10Detailed info about TP6-mz263
· Text S11Detailed info about TP7-mz280, TP8-mz280 and
TP9-mz280
· Figure S7MS/MS fragmentation of fragment 161.9429
· References
Text S1: Chemicals
Benzoic acid, glutaraldehyde (25% w/w in H2O), DBNPA (96%)
(Table SI1), dibromoacetonitrile, bromoacetonitrile,
dibromomalonamide and sodium azide were obtained from Sigma-Aldrich
(Zwijndrecht, the Netherlands). Monobromonitrilopropionamide
(MBNPA) was obtained from Santa Cruz Biotechnology (Dallas, USA).
Stock solutions of benzoic acid, DBNPA, benzoic acid and sodium
azide were prepared in ultrapure Milli-Q water (Millipore,
Burlington, U.S.). Stock solutions of dibromoacetonitrile,
bromoacetonitrile, dibromomalonamide and MBNPA were prepared in
acetonitrile and further diluted in Milli-Q water. All chemicals
used for the preparation of mineral medium (paragraph 2.2) and
analytical analysis (paragraph 2.3) were of the highest available
grade.
Text S2 : Benzoic acid analysis
Benzoic acid was determined by HPLC-DAD. The sample injection
volume was 30 μl. Chromatographic separation of the sample was
achieved with a Kinetex XB-C18 column (100 x 3.00 mm with a mean
particle size of 2.6μm) (Phenomenex, Utrecht, the Netherlands)
installed in a Prominence HPLC-DAD (Shimadzu, Den Bosch, the
Netherlands) consisting of two LC-20AD XR pumps, a SIL-20AC XR
auto-sampler and a CTO-20AC column oven. The mobile phase consisted
of solution A: H2O with 0.1% acetic acid and solution B: methanol.
An isocratic elution method with washing step was used that started
at 40% B, which was maintained for 4 minutes, after which it
increased to 100% B in 1 min. This was maintained for 4 minutes,
after which it was decreased back to 40% B in 1 min. The system was
then equilibrated for 7 minutes prior to injection of the next
sample. The flowrate was 0.4 ml/min and the column temperature was
45°C. A deuterium lamp was used and quantification was performed at
a wavelength of 246 nm by an 11-point external calibration line
reaching from 0.1 – 60 mg/L in LCSolutions software (Shimadzu, Den
Bosch, the Netherlands).
Text S3 : Glutaraldehyde analysis
Glutaraldehyde was quantified by colorimetry according to the
instructions of a Hach Lange test kit for glutaraldehyde (VWR,
Amsterdam, the Netherlands). First, 1 mL of sample was added to 4
mL of 3-methyl-2-benzothiazolone hydrazine (MBTH) in a 20 mL glass
vial and gently mixed. Subsequently, the vial was placed in a
boiling water bath for 2.5 minutes. Afterwards, the sample was
cooled to room temperature for at least 30 min. Ferric chloride
reagent was then added and allowed to react with the solution for
at least 5 min, after which the solution turned blue. The
absorbance was measured on a Cecile 1010 photospectrometer
(Cambridge, UK) at 600 nm. Quantification was performed according
to a 7-point calibration line reaching from 0.1 – 10 mg/L with
standards made in the mineral medium of the biodegradation
experiments.
Table S1.
Table S1. Overview of samples used for non-target analysis
Sample composition
Sample time and code
Ultra-pure (UP) water
1 h (UP-1)
48 h (UP-48)
96 h (UP-96)
DBNPA in UP water
1 h (D-UP-1)
48 h (D-UP-48)
96 h (D-UP-96)
Glutaraldehyde in UP water
1 h (G-UP-1)
48 h (G-UP-48)
96 h (G-UP-96)
DBNPA + glutaraldehyde in UP water
1 h (DG-UP-1)
48 h (DG-UP-48)
96 h (DG-UP-96)
UP water + inoculum
1 h (UP-CW-1)
48 h (UP-CW-48)
96 h (UP-CW-96)
DBNPA in UP water + inoculum
1 h (D-CW-1)
48 h (D-CW-48)
96 h (D-CW-96)
Glutaraldehyde in UP water + inoculum
1 h (G-CW-1)
48 h (G-CW-48)
96 h (G-CW-96)
DBNPA + glutaraldehyde in UP water + inoculum
1 h (DG-CW-1)
48 h (DG-CW-48)
96 h (DG-CW-96)
Text S4. R script that uses ‘patRoon’ for non-target data
processing of LC-MS/MS data analysis
library(patRoon)
workPath <-
"D:/maXis/Projects/Thomas/process/20180919-DBNPA_gluta_neg"
setwd(workPath)
options(patRoon.path.metFragCL =
"D:/maXis/Projects/Thomas/process/MetFrag2.4.3-CL.jar")
# Load analysis table
anaInfo <- read.csv("analyses.csv", stringsAsFactors = FALSE,
colClasses = "character")
# Set to FALSE to skip data pretreatment (e.g. calibration,
export, ...)
doDataPretreatment <- TRUE
if (doDataPretreatment)
{
setDAMethod(anaInfo,
"D:/maXis/Projects/Thomas/process/20180919-neg.m")
recalibrarateDAFiles(anaInfo)
exportDAFiles(anaInfo, format = "mzML",
overWrite = TRUE)
}
# Find all features.
# NOTE: see manual for many more options
fList <- findFeatures(anaInfo, "openms")
# Group and align features between analysis
fGroups <- groupFeatures(fList, "openms")
# Basic rule based filtering
fGroups <- filter(fGroups, intensityThreshold = 10000,
intraRGroupAbundance = 1,
minBlankThreshold = 5, repetitions = 2, retentionRange = c(60,
-1))
# Retrieve MS peak lists
# NOTE: please check all arguments, especially
precursorMzWindow!
plists <- generateMSPeakLists(fGroups, "mzr", avgMzWindow =
0.005, avgTopPeaks = 50,
avgMinIntensity = 500, precursorMzWindow = 8)
# uncomment and configure for extra filtering of MS peak
lists
# plists <- filter(plists, absMSIntThr = NULL, absMSMSIntThr
= NULL, relMSIntThr = NULL,
#
relMSMSIntThr = NULL, topMSPeaks = NULL, topMSMSPeaks = NULL,
#
deIsotopeMS = FALSE, deIsotopeMSMS = FALSE)
# Calculate all formulas
forms <- generateFormulas(fGroups, "genform", plists,
maxMzDev = 5,
adduct = "M-H", elements = "CHNOBr")
formulas <- consensus(forms, fGroups = fGroups,
formAnaThreshold = 0.75,
maxFormulas = 10, maxFragFormulas = 10)
# Perform automatic compound identification
compounds <- generateCompounds(fGroups, plists, "metfrag",
method = "CL", dbRelMzDev = 5, fragRelMzDev = 5, fragAbsMzDev =
0.002, isPositive = FALSE,adduct = -1, database =
"ExtendedPubChem",maxCandidatesToStop = 2500,scoreTypes =
c("FragmenterScore",
"OfflineMetFusionScore","PubChemNumberPatents",
PubChemNumberPubMedReferences"), preProcessingFilters =
UnconnectedCompoundFilter","IsotopeFilter",
"ElementExclusionFilter"), extraOpts = list(FilterExcludedElements
= c("Cl", "I", "F", "S", "P")))
compounds <- addFormulaScoring(compounds, formulas, TRUE)
compoundsF <- filter(compounds, topMost = 25) # keep only 25
with highest score
compoundsF_form <- filter(compoundsF, minFormulaScore =
1)
# Perform automatic generation of components
components <- generateComponents(fGroups, "ramclustr",
ionization = "negative")
# Report & export results
reportCSV(fGroups, path = "report", reportFeatures = FALSE,
formConsensus = formulas,compounds = compoundsF,
compoundNormalizeScores = TRUE, components = components)
reportPDF(fGroups, path = "report", reportFGroups = TRUE,
formConsensus = formulas, reportFormulaSpectra = TRUE,compounds =
compoundsF, compoundNormalizeScores = TRUE,
components = components, MSPeakLists = plists)
reportMD(fGroups, path = "report", reportFGroups = TRUE,
reportChord = TRUE, formConsensus = formulas, reportFormulaSpectra
= TRUE, compounds = compoundsF, compoundNormalizeScores =
TRUE,components = components, MSPeakLists = plists, selfContained =
TRUE)
Text S5. Explanation of workflow steps of ‘patRoon’
The workflow of ‘patRoon’ (Fig. 1) starts with the import of
UHPLC-q-ToF-MS/MS measurements. The analyses were converted to open
MS data format mzML by DataAnalysis. Subsequently, the replicates
were grouped according to the sample names in Table 2 and the
blanks for blank subtraction were determined. Afterwards, features
were extracted from the total ion chromatograms for each
measurements with OpenMS using the FeatureFinderMetabo algorithm
(Röst et al., 2016), aligned according to retention time using the
MapAlignerPoseClustering algorithm and grouped using the
FeatureLinkerUnlabeled algorithm. To clean up the data and reduce
the number of features, an intensity threshold of 10000 was used,
and the features intensity had to be five times as high as the
blanks. In addition, the feature had to be present in all the
replicates of a sample. In the next step, MS and MS/MS data were
extracted using mzR (Chambers et al., 2012). An average m/z window
of 0.005 Da was used combined with a required minimal peak
intensity of 500. The 50 highest MS and MS/MS peaks were kept. An
default m/z window of 8 Da was used to find MS/MS spectra of a
precursor ion. With GenForm (Meringer et al., 2011), the MS and
MS/MS data was used to predict molecular formulas. The elements
used for molecular formula calculation were limited to C, H, N, O
and Br to limit the calculation of improbable formulas and save
calculation time. Any formula for a certain feature group was
supposed to be detected in >75% of the measurements and the
number of postulated formulas was limited to the 10 with the
highest ranking. Subsequently, MetFrag (Ruttkies et al., 2015) was
used for compound identification. A relative mass deviation of 5
ppm and an absolute mass deviation of 0.002 Da were used as
thresholds. Potential candidates with the elements Cl, I, F, S and
P were excluded. The potential candidates were retrieved from
PubChem and scored according to their similarity in fragmentation
to in-silico generated MS/MS spectra and MassBank of North America
(MoNA, http://mona.fiehnlab.ucdavis.edu/) derived mass spectra. The
25 candidates with the highest score were reported and considered
for further interpretation. Next step was the generation of
components. Isotopologues, multiple ionization adducts and
in-source fragmentation can lead to different features with
different m/z belonging to the same compounds. RAMclustR
(Broeckling et al., 2014) was used to cluster potential features in
components that are likely to be the same compound. The last step
of the ‘patRoon’ script consisted of the generation of a report of
the results in CSV, PDF and HTML format. These reports were
manually checked for relevant features that could be compounds
formed as result of the interaction between glutaraldehyde and
DBNPA. First prerequisite was that the compound was only present in
DG sample groups (Table 2), in at least 1 time-point (Table S2).
Subsequently, an identification confidence level was assigned
according to Schymanski et al., 2014. This was done by examining
MS/MS spectra, peak shape and intensity of extracted ion
chromatograms in Data Analysis (Bruker Daltonics, Wormer, the
Netherlands), excluding isotopologues, excluding adducts and, where
possible, confirmation with standards.
Figure S1.
Figure S1. Model fit of biodegradation of benzoic acid in
absence of biocides (red line), experimental results (green
triangles) and abiotic controls (blue diamonds)
Figure S2.
Figure S2. Model fit and experimental data-points of the
biodegradation of benzoic acid with different concentrations of
glutaraldehyde
Figure S3.
Figure S3. Biotic and abiotic degradation of 5 mg/L (blue) and 1
mg/L (orange) of glutaraldehyde
Figure S4.
Figure S4. Model fit and experimental data-points of the
biodegradation of benzoic acid with different concentrations of
DBNPA.
Text S6 – Detailed info about DBNPA
Name:2,2-dibromo-3-nitrilopropionamide (DBNPA)
Origin:Parent compound
Formula:C3H2Br2N2O
m/z:240.8609
Smiles:C(#N)C(C(=O)N)(Br)Br
Confidence level:1
Molecular structure:
Reasoning:Confirmation with reference standard
MS/MS:
Text S7 – Detailed info about MBNPA
Name:2-bromo-2-cyanoacetamide (MBNPA)
Origin:Direct transformation product of DBNPA
Formula:C3H3BrN2O
m/z162.9473 (+); 160.9354 (-)
Smiles:C(#N)C(C(=O)N)Br
Confidence level:1
Molecular structure:
Reasoning:Confirmation with reference standard
MS/MS:
Text S8 – Detailed info about 2,2-dibromopropanediamide
Name:2,2-dibromopropanediamide
Origin:Direct transformation product of DBNPA
Formula:C3H4Br2N2O2
m/z258.8714
Smiles:C(=O)(C(C(=O)N)(Br)Br)N
Confidence level:1
Molecular structure:
Reasoning:Confirmation with reference standard
MS/MS:
Figure S5.
Figure S5. Component clustering for DBNPA (M241), its
isotopologues (M243 + M245), in-source fragment dibromoacetonitrile
(M198) and adducts (+NH4 : M260; +Na : M265; +CH3OH+H : M275) and
their isotopoloques, and unidentified mass M400, which was triply
brominated according to its mass defect.
Figure S6.
Figure S6. Component clustering for TP1-MBNPA (M163) and
bromoacetonitrile (M120) and both their isotopologues
Text S9 – Detailed info about TP3-mz183
Name:-
Origin:Reaction product between DBNPA and glutaraldehyde
Formula:C8H10N2O3
m/z: 183.0760
Smiles:N#CCC(=O)/N=C(O)/CCCC=O
Confidence level:4
Proposed molecular structure:
Reasoning:Molecular formula; knowledge on parent compounds
formula and structure; knowledge on parent compound’s chemical
behaviour
MS/MS:
Text S10 – Detailed info about TP4-263
Name:-
Origin:Reaction product between DBNPA and glutaraldehyde
Formula:C11H12N4O4
m/z: 263.0783
Smiles:N#CCC(=O)/N=C(O)/CCC/C(O)=N/C(=O)CC#N
Confidence level:4
Proposed molecular structure:
Reasoning:Molecular formula; knowledge on parent compounds
formula and structure; knowledge on parent compound’s chemical
behaviour
MS/MS:
Text S11 – Detailed info about TP5-mz280, TP6-mz280 and
TP7-mz280
Name:-
Origin:Reaction product between DBNPA and glutaraldehyde
Formula:C10H14N2O5
m/z:280.0292 (+)
Smiles:N=C(O)CCC/C(O)=N/C(=O)C(Br)CN
Confidence level:3
Proposed molecular structure:
Reasoning:Molecular formula; knowledge on parent compounds
formula and structure; knowledge on parent compound’s chemical
behaviour; MS/MS fragmentation
MS/MS:
Figure S7
Figure S7. MS/MS fragmentation of fragment 161.9429 of
TP5-mz280. Peaks in blue were assigned to corresponding molecular
formula by MetFrag and Genform. Peaks in grey were assigned to
corresponding molecular formula by MetFrag. Peaks without
corresponding formula could not be assigned.
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5 mg/L
biotic0.04.285.884.461.10.4680.04.285.884.461.10.4680.02.024.2545.573.594.5100.059.940.128.78.1400000000000010.4685
mg/L
abiotic0.04.681.821.441.660.4770.04.681.821.441.660.4770.02.024.2545.573.594.5100.050.933.521.315.912.21
mg/L biotic0.02.020.2522.2524.25100.045.725.816.10.8951 mg/L
abiotic0.02.020.2522.2524.25100.045.529.819.813.6
Time (h)
Glutaraldehyde (%)
