Download Metabolic-pathway– directed targeted metabolomics

Application Note # ET-38
Compass PathwayScreenerTM – Metabolic-pathway–
directed targeted metabolomics
Introduction
Metabolomics has grown in importance over the past
decade and has taken its place in the list of established
OMICS topics in biology. However, the growing number of
detectable and identifiable substances increases the need
for efficient data analysis and interpretation. Metabolic
pathways enable biologists to structure and link biochemical
processes, and therefore they are especially beneficial for
the interpretation of targeted metabolomics experiments.
Given a start compound, the Compass PathwayScreener
interrogates a pathway database to automatically generate
a list of biochemically relevant compounds that can be
searched for in complex MS data sets.
In addition to identifying known target compounds,
metabolomics researchers are often also interested in
detecting significant changes in the abundance of previously
unidentified metabolites. Using Bruker’s compactTM,
impactTM and maXisTM QTOF instrument lines and data
processing software, both these approaches can be
addressed using the same data set.
An earlier profiling study (Bruker Application Note
#LCMS-79) enabled differentiation of coffee types based
on their assigned flavour intensity, and identified trigonelline
(N-methyl-nicotinic acid) as a compound characteristic for
weak coffee.
Authors
Dr. Heiko Neuweger, Dr. Verena Tellström,
Dr. Aiko Barsch; Bruker Daltonik GmbH, Bremen,
Germany
Keywords
Instrumentation and
Software
Compass PathwayScreener
compact
compact, impact, maXis
Q-TOF
targeted Metabolomics
Compass PathwayScreener
Pathway driven analysis
ProfileAnalysis
Non-targeted data evaluation: Trigonelline is a compound characteristic for weak coffee
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Figure 1: Non-targeted data evaluation of a LC-QTOF
MS-based coffee metabolomics study. A: PCA scores
In-silico frag.
plot reveals separation of samples on PC1 according to
coffee intensity assigned by the coffee manufacturer,
from 3 (weak) to 10 (strong). B: The corresponding PCA
Trigonelline
loadings plot points to compounds mainly
contributing
to this differentiation. C: Identification of trigonelline
as a compound characteristic for weak coffee —
SmartFormula3D provided a unique molecular formula
based on accurate mass and isotopic pattern information
in MS and MS/MS spectra. In-silico fragmentation using
the FragmentEditor following database
queries pointed to
MS/MS
trigonelline as the likely structure for the compound.
spectrum
Here we demonstrate a novel workflow using the Compass
PathwayScreener software, which enables a pathwaydriven targeted metabolomics approach. This hypothesisdirected data mining workflow is based on the same
high-resolution data files that were acquired on a compact
QTOF instrument and used for an initial non-targeted
profiling. The Compass PathwayScreener enabled quick
determination of other biochemically related compounds
whose abundance differed significantly in weak and strong
coffee.
Experimental
The experimental setup for the LC-MS measurements
was the same as described in Bruker Application Note
#LCMS-79. Coffee extracts were diluted with water
before three 5 μL replicates were analyzed by UHPLC-MS.
Chromatographic separation was carried out using an RSLC
system (Thermo Fisher Scientific) with a 50 x 2.1 mm BEH
C18, 1.7 μm column (Waters) at a flow rate of 0.45 mL/min
(Solvent A: Water + 0.1% HCOOH and Solvent B: methanol
+ 0.1% HCOOH). An LC gradient with a linear increase
from 2% to 98% B (over 5 min) and constant 98%B (for
1 min) was used. MS detection was performed using a
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0.35 min;
138.055 m/z
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C
In-silico frag.
Trigonelline
MS/MS
spectrum
SmartFormula3D
result
SmartFormula3D
result
compact QTOF mass spectrometer (Bruker Daltonics). The
instrument was operated in ESI positive mode acquiring MS
full scan data at an acquisition speed of 3 Hz.
The Compass PathwayScreener software tool was used
for metabolic-pathway–driven generation of a target
screening metabolite list and automated screening for these
compounds within the acquired batch of high-resolution
LC-MS data files. Tailored views enabled quick data
evaluation and manual reintegration of peak areas. Results
of the complete batch were exported to ProfileAnalysis 2.1
for further statistical data evaluation.
Results and Discussion
Non-targeted profiling provides a starting point for pathwaydriven analysis
In an initial study, 13 different coffee extracts were analyzed
using a non-targeted LC-QTOF based metabolomics
approach (Bruker Application Note #LCMS-79, [1]). The
manufacturer assigned different intensities to each coffee
type, with values ranging from 3 (weak) to 10 (strong).
Following data preprocessing, feature extraction, bucketing,
data filtering and normalization, PCA analysis clearly
PC 1
Compass PathwayScreener workflow
1) Query formula or compound
name in KEGG database
2) Select relevant metabolic
pathway(s)
3) Target compound list automatically
created based on metabolic
pathway information
4) Target compounds automatically
screened by creating hrEICs in
high- resolution QTOF data
5) Tailored views enable straightforward
interpretation of targeted metabolomics
results
Figure 2: : Compass PathwayScreener — Schema for
metabolic-pathway–driven targeted metabolomics
workflow. Wizard-based guidance from initial hypothesis
to biological-pathway–based results.
Tailored views enable straightforward data review
1)Query formula or compound name in KEGG database
Batch of samples
2) Select relevant metabolic
pathway(s)
Area
Chromatogram
Analytes
MS
Analysis
Figure 3: Compass PathwayScreener — Tailored views enable fast insights into biological questions. The selected batch of coffee samples
(uppermost view) was mined for compounds derived from the nicotinic acid biochemical pathway (leftmost view, list of analytes). The color
coded score indicates nicotinate was found in all samples with high mass accuracy, retention time and isotopic pattern accuracy. Plotting the
nicotinic acid peak area for all samples in the Batch Statistics view (lower right) shows a higher intensity for a subset of samples (samples
7–12 = strong coffee).
separated coffee extracts described as strong (9 and 10),
from those that were assigned a weak intensity (3) (see
Figure 1A). The corresponding PCA loadings plot (see
Figure 1B) indicated that a molecular feature eluting at
0.35 min and m/z 138.055 is a compound characteristic
for weak coffee. Accurate mass and isotopic pattern
information in MS and MS/MS spectra for this metabolite
was the basis for an unambiguous molecular formula
generation by SmartFormula3D. Database queries and
in-silico fragmentation indicated that the compound could
be trigonelline (N-methyl-nicotinic acid) (see Figure 1C). This
was confirmed by comparing retention time and MS/MS
information obtained from a reference standard.
Metabolic-pathway–driven targeted metabolomics
Creating a list of metabolites for targeted profiling
Based on the finding that trigonelline is characteristic for
weak coffee, it was postulated that other compounds
contained in metabolic pathways involving trigonelline
might have a significantly higher (or lower) abundance
than in strong coffee and therefore be characteristic for
weak coffee. Biochemically related compounds can be
determined from biochemical pathway maps. The Compass
PathwayScreener submits compound name or molecular
formula queries to the well-known KEGG* database to
retrieve a list of pathways containing a given metabolite,
and the components of each pathway.
Here the Compass PathwayScreener was used to submit
a molecular formula query for trigonelline to the KEGG
database (see Figure 2). The search returned several
metabolic pathways in which trigonelline is involved.
The nicotinate and nicotinamide metabolism metabolic
pathway was selected and the Compass PathwayScreener
automatically created a target compound list representing
all metabolites contained in this pathway map. This initial
screening list was manually extended to include several
compounds known to be characteristic for coffee.
Statistical evaluation of targeted metabolomics data in ProfileAnalysis
strong
weak
Trigonelline
Figure 4: Pathway-driven targeted metabolomics data evaluated in ProfileAnalysis. The PCA statistics reveal a similar picture to that derived
from the non-targeted workflow: Strong and weak coffee samples are separated in the scores plot and N-methyl-nicotinic acid (trigonelline)
is a compound characteristic for weak coffee. Several other metabolites, such as quinic acid can be readily identified as compounds
characteristic for coffee strength.
Automated target compound screening in batches of fullscan high-resolution QTOF data
Accurate mass and isotopic pattern information serves
not only to identify unknown target compounds in nontargeted workflows but also to confirm the presence of
expected analytes in targeted workflows. The Compass
PathwayScreener automatically calculates accurate masses
for all target compounds in a screening list — here the
coffee related metabolite — based on their elemental
compositions. During method generation, adducts or
combinations of adducts that are expected to be formed
for a metabolite (for example, [M+H]+, [M+Na]+) can be
specified. Using these accurate masses, high-resolution
Extracted Ion Chromatograms (hrEIC) were automatically
created for all data files in the batch of coffee samples. The
narrow mass tolerance windows generated for these hrEICs
provide a highly selective method to screen for the presence
of target compounds, even in very complex samples.
Metabolites detected in the coffee samples by the pathwaydriven targeted data mining were tentatively identified by
comparing accurate mass and isotopic pattern information
of the detected compounds and their theoretically calculated
counterparts. If available, retention time information was
used as a further criterion to confirm the presence of an
analyte.
Data review and optional peak reintegration
Interactive views and result tables in the Compass
PathwayScreener software facilitated a quick evaluation of
all compounds detected within the samples (see Figure 3).
Intuitive colour coding helped to quickly identify compounds
which showed a higher than expected deviation in mass
accuracy, isotopic accuracy (mSigma value), or retention
time. The cause of observed deviations could easily be
checked. If a compound is selected in a result table, the
corresponding mass spectrum and EIC trace are displayed
in the interactive mass spectrum and chromatogram views.
In addition to mass, isotopic pattern, and retention time
accuracy, deviations in peak areas or intensities in particular
samples could be found easily using the Batch Statistics
view (see Figure 3). This view enables users to plot
different data values derived from the raw data against
each other. Plotting analysis number against peak area for a
selected analyte enables direct comparison of the amount
of that analyte in each sample. Selecting a data point in
the Batch Statistics view displays the corresponding EIC
trace in the chromatogram view, enabling quick review and
manual peak integration, if required.
Additional statistical data evaluation in ProfileAnalysis
The pathway-driven targeted metabolomics data evaluation
using the Compass PathwayScreener revealed several
metabolites whose abundance differed significantly in weak
and strong coffee (data not shown). To further evaluate the
results derived from the targeted data mining, results were
exported for statistical evaluation to ProfileAnalysis. One
advantage of the targeted metabolomics data workflow is
that all compounds within the bucket table were tentatively
identified, facilitating the interpretation of the results based
on the biological hypothesis.
Degradation of Chlorogenic acid during coffee roasting
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Figure 5: Schema for degradation
pathway of
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chrorogenic acid during coffee roasting. Bucket statistics
plots for selected compounds visualize the relative
abundance of these compounds across all coffee
samples. The observed intensity differences are in
accordance with a high concentration of chlorogenic
acid in weak coffee. During roasting, chlorogenic acid
is degraded to metabolites such as quinic acid and
catechol, which are believed to contribute to a stronger
“roasted” coffee aroma.
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The PCA scores plot in Figure 4 reveals that the targeted
metabolomics data evaluation shows a similar separation
for weak and strong coffee as initially observed in the
non-targeted profiling (Figure 1A). As expected from the
previous evaluation, trigonelline is the loading with the
largest contribution in separating weak from strong coffee
in this experiment. In addition, the targeted profiling
revealed chlorogenic acid and quinic acid as further
compounds responsible for differentiating weak and strong
coffee respectively. This statistical targeted evaluation
guided the purchase of reference standards for the final
confirmation of the identity of chlorogenic and quinic acid.
The high content of chlorogenic acid in weak coffee has
been previously reported [2]. Chlorogenic acid is degraded
during coffee roasting into quinic acid as well as catechol,
hydroquinone, hydroxyquinone, and pyrogallol (see Figure
5). These metabolites are known to be produced during
coffee roasting and to contribute to a bitter coffee aroma
[3]. The confirmation of the identity of these metabolites
using reference compounds is ongoing.
Conclusion
• Compass PathwayScreener enables mining the same full-scan high-resolution LC-MS data set used for an initial non-targeted metabolomics workflow in a pathway-driven targeted fashion.
• Using a target compound identified by a non-targeted strategy (trigonelline, N-methyl-nicotinic acid) the subsequent hypothesis-driven targeted data evaluation revealed several novel characteristic metabolites for weak and strong coffee.
• A target database can easily be extended to include other compounds of interest to broaden the scope of research.
• Significant changes observed in tentatively identified compounds (accurate mass + isotopic pattern) using the targeted profiling workflow can guide purchase of (sometimes very expensive) reference standards.
• Interpreting data based on biochemical pathway information represents a fast link from mass
spectrometric raw data to biologically relevant conclusions.
[1] Bruker Application Note #LCMS-79
[2] Farah A. et al. J. Agric. Food Chem. 2005, 53, 1505-1513
[3] Müller C. et al. J. Agric. Food Chem. 2006, 54, 10086-10091
*
For research use only. Not for use in diagnostic procedures.
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