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This review provides an overview of two complementary approaches to identify biologically active compounds for studies in chemical ecology. The first is activity-guided fractionation and the second is metabolomics, particularly focusing on a new liquid chromatography-mass spectrometry-based method called isotopic ratio outlier analysis. To illustrate examples using these approaches, we review recent experiments using Caenorhabditis elegans and related free-living nematodes. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.
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Metabolomics and Natural-Products Strategies to Study Chemical
Ecology in Nematodes
Arthur S. Edison,
* Chaevien S. Clendinen,* Ramadan Ajredini,* Chris Beecher,*
Francesca V. Ponce* and Gregory S. Stupp
*Department of Biochemistry and Molecular Biology and Southeast Center for Integrated Metabolomics, University of
Florida, Gainesville, FL 32610-0245, USA;
IROA Technologies, Ann Arbor, MI, USA;
The Scripps Research Institute,
Department of Molecular and Experimental Medicine, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
From the symposium ‘‘Chemicals that Organize Ecology: Towards a Greater Integration of Chemoreception,
Neuroscience, Organismal Biology, and Chemical Ecology’’ presented at the annual meeting of the Society for Integrative
and Comparative Biology, January 3–7, 2015 at West Palm Beach, Florida.
Synopsis This review provides an overview of two complementary approaches to identify biologically active compounds
for studies in chemical ecology. The first is activity-guided fractionation and the second is metabolomics, particularly
focusing on a new liquid chromatography–mass spectrometry-based method called isotopic ratio outlier analysis. To
illustrate examples using these approaches, we review recent experiments using Caenorhabditis elegans and related free-
living nematodes.
This review focuses on a variety of analytical tech-
niques and approaches that can be used to solve bi-
ological problems, especially those in chemical
ecology and in behavior. As such, we will summarize
some of the different technologies that are now avail-
able to characterize metabolites and low molecular
weight signaling molecules. Each method has its
own particular strengths and weaknesses, which we
will emphasize by giving specific examples: research
that we, and others, have done over the past decade
on the model organism Caenorhabditis elegans and
related nematodes. Even though it is best known as
a model organism for genetics (Brenner 1974) and
development (Sulston and Horvitz 1977), C. elegans
is also an ideal animal for chemical studies
(Schroeder 2006).
It is worth beginning with a brief discussion
of two areas of chemical science that are particularly
relevant to studies in ecology and behavior. The first
is natural products chemistry, which typically starts
with a complex mixture from an extract of an
organism of interest, partially or fully purifies an ac-
tive compound(s) using activity-guided fractionation
(AGF), and identifies the active compound(s) using
nuclear magnetic resonance (NMR) and/or mass
spectrometry (MS), which often is coupled to
liquid or gas chromatography (LC or GC). The
second is metabolomics, which typically starts
with complex mixtures from two or more groups
that differ by a specific phenotype of interest,
collecting NMR or LC–MS/GC–MS data, and
comparing the datasets statistically to identify bio-
markers that show differences among the groups.
The differences and similarities of natural products
chemistry and metabolomics are summarized in
Fig. 1, which was adapted from a recent review ar-
ticle comparing NMR-based natural products and
metabolomics. The conclusion of that article is that
the two fields are very closely related and, in fact,
are merging in significant ways. Both start with
complex mixtures of small-molecule metabolites,
use the same analytical technologies, and have as a
primary goal the identification of biologically ac-
tive molecules or biomarkers (Robinette et al.
2012). Similar conclusions have been drawn using
LC–MS (Constant and Beecher 1995;Wolfender
et al. 2015).
Integrative and Comparative Biology
Integrative and Comparative Biology, pp. 1–8
doi:10.1093/icb/icv077 Society for Integrative and Comparative Biology
ßThe Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Integrative and Comparative Biology Advance Access published July 2, 2015
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The plan of this review is as follows. The first
section focuses on the use of AGF to target specific
compounds of interest. This is a time-honored ap-
proach that is capable of leading to active com-
pounds, even in low quantities. The drawback is
that AGF is also time-consuming and only focuses
on relatively few compounds. We then show how
metabolomics can be used to obtain chemical infor-
mation on a more global level, particularly focusing
on a new LC–MS technique called isotopic ratio out-
lier analysis (IROA). We summarize several different
approaches that are relevant to chemical ecology
studies. Finally, we close with some ideas regarding
strategies for integrating phenotypic data with natu-
ral products and metabolomics to gain new biologi-
cal insight on a systems level.
Activity-guided fractionation
AGF is conceptually simple and extremely useful as a
method for discovering molecules with specific
functions, but the necessary chromatography can be
quite complex. The most important factor in AGF is
the assay to measure the activity, which should be sen-
sitive, easy, fast, and reproducible. These can be based
on chemistry or biochemistry, such as enzymatic activ-
ity, or involve cells or organisms as detectors. A notable
assay used male moths in cages as detectors in the pu-
rification of the active component of the mating pher-
omone, as reviewed by Hummel (1984). A more recent
and high-tech assay used neuronal recordings of a
moth’s antennal lobe combined with GC to identify
complex odors associated with plant–pollinator rela-
tionships (Riffell et al. 2009). Interestingly, male ele-
phants in a zoo were used to monitor the purification
of males’ mating pheromone. We leave it to the imag-
ination of the readers or to the original reference for a
description of this purification (Rasmussen et al. 1997).
As an evolutionary side-note, moths use the same
mating pheromone as a component of their signaling
system (Rasmussen et al. 1997).
Fig. 1 Schematic comparison of two similar strategies to identify active compounds or biomarkers: natural products using activity-
guided fraction on the left and metabolomics on the right. Both approaches use similar technology but different steps to get to the
same endpoint. Steve Robinette made this figure, which was adapted with permission from Robinette et al. (2012).
2A. S. Edison et al.
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We have used male C. elegans as detectors to
purify the first mating pheromone in nematodes
(Srinivasan et al. 2008). Details are provided in the
primary reference, but the general procedure was to
isolate large quantities of exudates from liquid cul-
tures of C. elegans hermaphrodites and test these for
male-specific responses using simple two-spot assays
on agar plates, in which one spot has a buffer control
and the other spot has a test fraction. We then mon-
itor the time spent by males in each spot (Fig. 2).
There are several important considerations when
doing one of these assays. First, male and hermaph-
roditic worms respond to many things, including
bacterial food. Therefore, bacteria must be separated
from the worms to get a specific and meaningful
response. We developed a ‘‘worm water’’ prepara-
tion, in which a synchronized population of her-
maphrodites at a specific developmental stage was
separated from bacteria using a sucrose gradient
(Srinivasan et al. 2008;Kaplan et al. 2011;Choe
et al. 2012). Then, the worms were allowed to sit
in water or a defined buffer, and the supernatant
was collected for bioassays.
When purifying an unknown compound, it is im-
portant to develop a simple but accurate ‘‘account-
ing’’ system. Since by definition, we do not know
what we are looking for before we find it, we can’t
use molarity. When working with worms, we use our
own units called ‘‘worm equivalents’’, for which 1
worm equivalent is defined as the amount of mate-
rial released by one worm in 1 h. Any convenient and
consistent definition will suffice. Using this simple
metric, it is easy to identify synergy or loss of activity
or to detect other confounding factors.
We discovered after our initial study that male
worms were able to respond to as little as 2.5
worm equivalents, which makes biological sense
(Kaplan et al. 2011). It was also humbling to dis-
cover that our NMR spectrometer required about 4
million worm equivalents to detect the same phero-
mone (Fig. 3). This illustrates possibly the most im-
portant advantage of AGF over the metabolomics
experiments described below: with a very sensitive
bioassay like a male worm or moth, it is possible
to detect the important components at concentra-
tions that would be entirely missed with analytical
Male worms may be extremely sensitive, but this
can add other potential complications to an AGF.
First, many response curves for pheromones are
bell-shaped, with maximal responses tuned to appro-
priate concentrations and falling off at higher or
lower concentrations, presumably stopping mate-
finding behavior when a mate is located (Fig. 4a).
It is important at the start of a new AGF study to
carefully examine the crude extract (e.g., ‘‘worm
water’’ for many of our studies) for activity at a
wide-range of concentrations (e.g., worm equiva-
lents) to avoid putting too much material onto the
assay. However, this can also backfire, because if
there are different compounds that cause the oppo-
site response or block the primary response, the
starting material may not work. A good example of
teasing apart opposite responses in behavioral assays
was an assay developed by Srinivasan et al. (2012) to
characterize simultaneous attractive and repulsive re-
sponses to a mixture of two related pheromones in
C. elegans. Briefly, three concentric scoring regions
were defined, and the time spent in the center was
compared with the time spent in the outer circles.
One of the major complications of AGF is a syn-
ergetic effect of different metabolites responsible for
Fig. 2 AGF results for the purification of the male-specific mating pheromone from cultures of hermaphroditic Caenorhabditis elegans.
The assay is shown in (a), where about five worms were placed on each of the spots marked with ‘‘x’’ and either buffer control or
hermaphrodite-conditioned media were added to the spots in the center. The worms were videotaped and scored by how long they
stayed in each spot, which is plotted in (b). The data in (b) correspond to material collected from controls (C) and hermaphrodites at
different developmental stages (Egg, L1, L2, L3, D ¼dauer, L4, YA ¼young adult, and A ¼adult). Jagan Srinivasan conducted the assays
and made the figure, which is reproduced with permission from Srinivasan et al. (2008).
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the activity of the crude extract. In our study of the
identification of males’ mating pheromone in C. ele-
gans, we lost all activity at a key step in the fraction-
ation (Srinivasan et al. 2008). The loss of activity
could be caused by several factors, including degra-
dation of the molecule(s), loss of the molecule(s) on
the column, or synergy. Combining all the fractions
and testing for activity, which will return if two or
more compounds are acting synergistically, can rule
out degradation or loss. With synergy, AGF gets
more complicated. The evidence of synergy in that
study is shown in Fig. 4b. Often a good start is to
identify a fraction that is necessary but not sufficient
for activity. This will yield at least one component,
which can then be added to other fractions for ad-
ditional bioassays.
In summary, AGF is a powerful but potentially
very time-consuming way to identify biologically sig-
nificant molecules. It can often reveal activity at con-
centrations that would be too low to measure with
analytical instrumentation, and since it is always di-
rected to the activity of interest, positive results will
Fig. 3 1D
H NMR spectrum from our study to isolate and identify a component of the mating pheromone of Caenorhabditis elegans
(Srinivasan et al. 2008). This spectrum was collected using one of the most sensitive NMR probes available (Brey et al. 2006). Because
the pheromone is present in such low concentrations, this spectrum required about 4 million worm equivalents of material. A male
C. elegans can detect as little as 2.5 worm equivalents (Kaplan et al. 2011).
Fig. 4 Dose response curves (a) and synergy (b) of the attraction of male Caenorhabditis elegans to two different mating pheromones.
The assays were as described in Fig. 2. Panel (a) shows that there can be points of maximal attraction and that caution needs to be
used to avoid adding too much material in a bioassay. The blue and red arrows in (a) indicate the concentrations tested in (b), which
shows the results of adding together two pheromones at concentrations that each alone would produce no activity but that together
produce a large response. All of these very interesting biological stories can cause great confusion in an AGF study. Reproduced from
Srinivasan et al. (2008).
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be relevant to the study. Synergy, compounds with
masking or opposing activities, and bell-shaped re-
sponses can all complicate AGF. We have conducted
two large AGF identifications of nematodes’ mating
pheromones, and they both required several years of
work to complete (Srinivasan et al. 2008;Choe et al.
2012); they are definitely not high-throughput!
Metabolomic strategies
The right side of Fig. 1 summarizes a metabolomics
approach to the identification of biomarkers. As
illustrated, the end-point of a metabolomics and
natural-products (AGF) study can be the same, but
the mechanism and steps to get there are quite
different. In metabolomics, the laborious step of
AGF is eliminated. This can be wonderful news to
anyone who has conducted an AGF study. There are
some important differences in terms of overall design
and outcomes of experiments between AGF and
The first is replicates of samples. Historically, AGF
studies have not focused on obtaining large numbers
of unique biological replicates, but in most cases
many different samples need to be generated over
the course of a study for subsequent steps in a pu-
rification scheme. In contrast, metabolomics is com-
pletely dependent upon statistical analysis. Therefore,
a well-controlled design of the study, including ap-
propriate numbers of replicates, is critical. It is
always important in a metabolomics study to have
discussions with an expert statistician.
Another important difference between AGF and
metabolomics is the point at which analytical sensi-
tivity becomes a major factor. Both approaches use
the same types of MS and NMR instruments, but the
reliance on the instruments is significantly different.
In the example described above, male C. elegans were
able to detect a pheromone produced by about 2.5
worm equivalents (Kaplan et al. 2011), while an out-
standing NMR system (Brey et al. 2006) needed
about 4 million worm equivalents of the same ma-
terial. Based on knowledge gained from subsequent
studies, we estimate that a sensitive LC–MS instru-
ment would need about 10,000 worm equivalents for
reliable detection of the same material (Stupp et al.
2013). The critical point here is that if we were re-
lying entirely on NMR and MS instruments, as in the
case in metabolomics, we would not find an active
compound that is below our detection limits.
However, with AGF, we know something of interest
to the male worms is there, even if we need to pro-
duce a lot of material and concentrate it for NMR
and MS. Thus, with metabolomics it is critical to
always remember that the analytical instrumentation
will define the number and concentrations of metab-
olites detected. Important biologically active com-
pounds at concentrations too low to measure will
be completely invisible in a metabolomics study.
One important distinction to make for metabolo-
mics is ‘‘targeted’’ versus ‘‘untargeted’’ or ‘‘global’’
metabolomics. A full discussion of these is beyond
the scope of this review, but excellent review articles
have been published (Fiehn 2002;Dunn et al. 2011).
In many ways, AGF could be considered a type of
targeted metabolomics, for which the goal is to iden-
tify and quantify a specific compound based on ac-
tivity. Targeted assays generally refer to classes such
as ‘‘amino acids’’, ‘‘organic acids’’, but by using a
biological detector like a male worm, a target class
could also be ‘‘the group of compounds that causes
males to be attracted’’. Global metabolomics has sev-
eral compelling advantages over targeted studies if an
unbiased view of a given system (e.g., hypothesis-
generation) is required. There are several different
types of metabolomics experiments and general
approaches to complex mixtures. We have recently
reviewed and contrasted some of the methods used
in NMR (Robinette et al. 2012). Here, we briefly
describe a new metabolomics experiment that we
think has some particular advantages for studies in
chemical ecology.
As suggested above in the comparison of how
many worm equivalents were needed to characterize
a mating pheromone of C. elegans, NMR is less sen-
sitive than MS. NMR typically requires about 100
nanomoles of material, but significant improvements
with specialized technology can lower that to the just
a few nanomoles (Dalisay et al. 2009;Molinski 2010;
Ramaswamy et al. 2013). The major advantage of
NMR is that it provides atom-specific information
that is necessary for the full identification of an un-
known molecule. NMR is also very reproducible, in
part because the sample is in a tube that is not in
contact with the instrument. Finally, NMR can be
quantitative when the spectroscopist allows for full
relaxation between scans. In contrast, MS is ex-
tremely sensitive, requiring much less material than
NMR. For targeted analyses of known molecules, MS
is generally much more efficient than NMR. On the
other hand, LC–MS data can have many artifacts,
and reproducibility is challenging, in part because
the sample is in contact both with the chromato-
graphic column and the detector in the MS instru-
ment. MS data provide the mass/charge of each
detected ion, and some instruments are capable
tandem MS (MS
), which fragments ions that can
be analyzed de novo or matched to databases for
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more definitive identification. Several reviews are
available that expand on these basic introductory
concepts (Katajamaa and Oresic 2007;Moco et al.
2007;Scalbert et al. 2009;Edison and Schroeder
2010;Wolfender et al. 2013).
Isotopic ratio outlier analysis
IROA is a relatively new LC–MS-based experiment
that we have used in experiments on metabolomics
in C. elegans (Stupp et al. 2013). It has been reviewed
(de Jong and Beecher 2012), so here we will focus on
its utility for chemical ecology. In the basic IROA
experiment, two different populations are compared,
one that has been labeled with 5%
C and the other
with 95%
C is a stable isotope that not only is
useful for MS experiments but also is an excellent
NMR nucleus, allowing for some combined analysis.
The basic IROA workflow is shown in Fig. 5. There
are several advantages of IROA for LC–MS metabo-
lomics. First, the patterns of peaks created by the
isotope labeling are easy to detect by a computer,
greatly reducing artifacts. Second, the number of
carbon atoms is known exactly for each MS peak,
allowing for very efficient determination of molecu-
lar formulas. Finally, the relative quantities of the 5%
and 95% channels are easily quantified. The result is
that hundreds to thousands of peaks can be reliably
detected and quantified. It is relatively easy to assign
peaks to a molecular formula, but it is a greater
challenge to figure out the true identity of all the
There are many experiments that one could do
with IROA. We will illustrate these with examples
Fig. 5 IROA method: (a) Experimental and control groups of worms are isotopically labeled at 5% or 95%
C and grown to the stage
of young adults. The experimental group is split into four replicates and is perturbed, while the control group is not split. After
incubation, the control group is split into four replicates, and each replicate is mixed 1:1 with an experimental replicate (b) for uniform
preparation of samples and for LC–MS analysis. (c) Biological compounds are easily distinguished from artifacts by the recognizable
pattern caused by the isotopic enrichment. (d) Using automated software, the fold-changes for all detected biological compounds can
be determined. Reproduced with permission from Stupp et al. (2013).
6A. S. Edison et al.
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from nematodes, but virtually any system that can be
labeled can be studied. For example, wild isolates of
C. elegans, or other free-living species, could be col-
lected and labeled with 5%
C using labeled bacte-
rial food. These could be compared with a common
reference material of the laboratory wild-type (N2)
strain that had been labeled with 95%
C. With a
very large batch of reference material, many different
pairwise experiments can be conducted, and differ-
ences between wild isolates and N2 can be deter-
mined. These metabolic differences can then be
compared with behavioral differences to correlate
molecules or pathways with the phenotype.
Another example would be to label a given strain
with both 5% and 95%
C and then add an unla-
beled pathogen or other perturbation to the 5%
channel. In this scenario, the global metabolic re-
sponses to the perturbation can be measured.
Finally, in an experiment called ‘‘phenotypic
IROA’’ (de Jong and Beecher 2012), the 5%
channel can be replaced by natural abundance
which simply changes the pattern of isotopic distri-
bution on the
C side (blue peaks in Fig. 5). Some
information is lost from the traditional IROA exper-
iment, but with this it is possible to compare differ-
ent species even if some cannot be cultured. For
example, parasitic nematodes are notoriously difficult
to culture, but one could compare these to a refer-
ence 95%
C labeled C. elegans. It is important to
note that in this situation, the experiment is more
similar to a targeted study in which the targeted
compounds are all labeled compounds from the ref-
erence strain, because they are the only ones that can
be detected.
We have attempted to illustrate different approaches
that we have used to study chemical ecology in com-
plex systems. The examples of nematodes follow
from our own work and interests, but many different
types of systems can be studied using similar exper-
iments. There is no ‘‘perfect’’ approach, and in prac-
tice a combination of a natural products-based AGF
and metabolomics is perhaps ideal. To find out the
molecular basis of a specific behavior, a good starting
point would be a simple and reliable bioassay for
that behavior, followed by an AGF of the active com-
pound(s). In parallel or sequentially, a metabolomics
experiment could be developed to look more globally
at the problem, either to avoid bias or to capture
other compounds that may be relevant. A nice ex-
ample of a combined approach is illustrated by
the identification of both a male-specific and a
female-specific mating pheromone in Panagrellus
redivivus (Choe et al. 2012). In that study, the
Edison and Sternberg laboratories conducted AGF
on a mixed culture of worms and continued until
gender-specific components were separated chro-
matographically. Two different ascarosides were dis-
covered, ascr#1 that attracts males and dhas#18 that
attracts females. Simultaneously, the Schroeder labo-
ratory conducted a metabolomics-type experiment
that used LC–MS to detect all ascarosides made by
the species. They discovered ascr#1 and dhas#18,
along with several other ascarosides not found in
the AGF study. However, upon testing of the other
ascarosides, none of them had significant activity in
gender-specific attraction, suggesting other unknown
functions (Choe et al. 2012). This study nicely illus-
trates the power both of natural products AGF and
of metabolomics, which often can be used together
to approach a question from two sides of the same
coin (Robinette et al. 2012).
A.S.E. would like to dedicate this work to the
memory of Dr Peter Teal, a friend and colleague
who taught AGF to members of the Edison labora-
tory. A.S.E. thanks the SCIB organizers for inviting
him to present some of this work in the 2015 meet-
ing in Palm Beach, FL. The authors thank John
Hildebrand and Jim Tumlinson for their feedback.
This work was supported by the Southeast Center for
Integrated Metabolomics (NIH 1U24DK097209).
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8A. S. Edison et al.
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... High-resolution mass spectrometry-based metabolomics has been used in several C. elegans studies (Edison et al., 2015;Hastings et al., 2017;Mor, 2020;Witting et al., 2018). The comprehensive molecular understanding of the nematode model makes it amenable to characterizing systemic biochemistry and perturbations to global metabolism as a result of genetic, environmental, and toxicological perturbations. ...
The proper storage and release of monoamines contributes to a wide range of neuronal activity. Here, we examine the effects of altered vesicular monoamine transport in the nematode C. elegans. The gene cat-1 is responsible for the encoding of the vesicular monoamine transporter (VMAT) in C. elegans and is analogous to the mammalian vesicular monoamine transporter 2 (VMAT2). Our laboratory has previously shown that reduced VMAT2 activity confers vulnerability on catecholamine neurons in mice. The purpose of this paper was to determine whether this function is conserved and to determine the impact of reduced VMAT activity in C. elegans. Here we show that deletion of cat-1/VMAT increases sensitivity to the neurotoxicant 1-methyl-4-phenylpyridinium (MPP+) as measured by enhanced degeneration of dopamine neurons. Reduced cat-1/VMAT also induces changes in dopamine-mediated behaviors. High-resolution mass spectrometry-based metabolomics in the whole organism reveals changes in amino acid metabolism, including tyrosine metabolism in the cat-1/VMAT mutants. Treatment with MPP+ disrupted tryptophan metabolism. Both conditions altered glycerophospholipid metabolism, suggesting a convergent pathway of neuronal dysfunction. Our results demonstrate the evolutionarily conserved nature of monoamine function in C. elegans and further suggest that HRMS-based metabolomics can be used in this model to study environmental and genetic contributors to complex human disease.
... Second, the limits of detection are defined by the assay and not the NMR spectrometer. This is very important, because unknown molecules at concentrations lower than NMR detection limits can be concentrated and identified if sufficient material is available for the bioassay (129). Finally, the pure (or semipure) compound provides a straightforward way to relate NMR and MS data, which is important for a more reliable identification (see Table 1). ...
Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task. Expected final online publication date for the Annual Review of Analytical Chemistry Volume 12 is June 12, 2019. Please see for revised estimates.
... Recently, Isotopic Ratio Outlier Analysis (IROA) has been developed to enable the characterization of carbon information in a given metabolites or a fragment [6][7][8][9][10]. Unlike other stable isotope labeling methods, rather than utilizing substrates with natural abundance (1.1% of 13 C isotopomer seen in carbon atoms in nature) and 98-99% enrichment for the control and experimental populations, respectively [11][12][13][14][15], IROA with prototrophic yeast uses randomized 95% 12 C glucose (5% 13 C), and 95% randomized 13 C glucose (5% 12 C) as carbon sources. ...
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Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA) workflow using enhanced mass spectrometric data acquired with the ultra-high resolution GC-Orbitrap/MS to determine the identity of non-annotated metabolites. The higher resolution of the GC-Orbitrap/MS, together with its wide dynamic range, resulted in more IROA peak pairs detected, and increased reliability of chemical formulae generation (CFG). IROA uses two different 13C-enriched carbon sources (randomized 95% 12C and 95% 13C) to produce mirror image isotopologue pairs, whose mass difference reveals the carbon chain length (n), which aids in the identification of endogenous metabolites. Accurate m/z, n, and derivatization information are obtained from our GC/MS workflow for unknown metabolite identification, and aids in silico methodologies for identifying isomeric and non-annotated metabolites. We were able to mine more mass spectral information using the same Saccharomyces cerevisiae growth protocol (Qiu et al. Anal. Chem 2016) with the ultra-high resolution GC-Orbitrap/MS, using 10% ammonia in methane as the CI reagent gas. We identified 244 IROA peaks pairs, which significantly increased IROA detection capability compared with our previous report (126 IROA peak pairs using a GC-TOF/MS machine). For 55 selected metabolites identified from matched IROA CI and EI spectra, using the GC-Orbitrap/MS vs. GC-TOF/MS, the average mass deviation for GC-Orbitrap/MS was 1.48 ppm, however, the average mass deviation was 32.2 ppm for the GC-TOF/MS machine. In summary, the higher resolution and wider dynamic range of the GC-Orbitrap/MS enabled more accurate CFG, and the coupling of accurate mass GC/MS IROA methodology with in silico fragmentation has great potential in unknown metabolite identification, with applications for characterizing model organism networks.
... Isotopic ratio outlier analysis (IROA) is another isotope labelling technology that is designed to generate specific 13 C isotopic patterns in metabolites for both high resolution LC-MS and GC-MS [17, [40][41][42][43]. Unlike other stable isotope labelling methods, rather than utilising natural abundance and 98-99% enrichment for the control and experimental populations, respectively [44][45][46][47][48], IROA uses an enrichment level of 95% and 5% 13 C. ...
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The metabolome describes the full complement of the tens to hundreds of thousands of low molecular weight metabolites present within a biological system. Identification of the metabolome is critical for discovering the maximum amount of biochemical knowledge from metabolomics datasets. Yet no exhaustive experimental characterisation of any organismal metabolome has been reported to date, dramatically contrasting with the genome sequencing of thousands of plants, animals and microbes. Here, we review the status of metabolome annotation and describe advances in the analytical methodologies being applied. In part through new international coordination, we conclude that we are now entering a new era of metabolome annotation.
... Protocols have been described for studies of the metabolomics of bacteria [39] and plants [40]. Metabolomics, along with activity-guided fractionation followed by structural analysis, constitutes a powerful approach for identifying biologically active compounds for studies in chemical ecology [41 ]. Metabolomics is used regularly in drug discovery programs to uncover the efficacy, specificity, or toxicity of lead compounds [42]. ...
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The two leading analytical approaches to metabolomics are mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Although currently overshadowed by MS in terms of numbers of compounds resolved, NMR spectroscopy offers advantages both on its own and coupled with MS. NMR data are highly reproducible and quantitative over a wide dynamic range and are unmatched for determining structures of unknowns. NMR is adept at tracing metabolic pathways and fluxes using isotope labels. Moreover, NMR is non-destructive and can be utilized in vivo. NMR results have a proven track record of translating in vitro findings to in vivo clinical applications.
... The ultimate goal is to build ecological models with a clear causal chain based on chemistry, but to reach this goal we will need to build computational tools (Vasey et al. 2015) that integrate lower levels of information into community-level models, including selective forces that can successfully predict organismal distribution and diversity based, in part, on chemical interactions between these organisms. Edison et al. (2015) describe how the traditional natural products chemistry approach to identify active biocompounds via bioassay activity-guided fractionation and analysis can be reinforced and informed by a metabolomics approach that seeks to identify unanticipated chemical differences statistically between complex mixtures of biochemicals taken from two or more groups of organisms. ...
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Ecology as a process is shaped by biotic and abiotic influences. In some cases, a single type of chemical can have effects on an ecosystem, effects that are disproportionate to its relative mass, such that the ecosystem would organize differently in the absence of this type of chemical. The hallmark examples are those “molecules of keystone significance” (Ferrer and Zimmer 2012), such as the biosequestered alkaloid tetrodotoxin. Keystone molecules are analogous to keystone species in their capacity to mediate large ecological effects disproportionate to their mass. Some predators/grazers may evolve the ability to sense toxins being released from potential prey, making the toxin a semiochemical, molecules that serve as information-bearing cues among organisms (Ferrer and Zimmer 2012). Not all semiochemicals may be of keystone significance but they do have a greater effect on ecology that would be predicted, based on their abundance relative to all biomolecules, and could be said to “organize ecology.”
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Model organisms are important in many areas of chemical biology. In metabolomics, model organisms can provide excellent samples for methods development as well as the foundation of comparative phylometabolomics, which will become possible as metabolomics applications expand. Comparative studies of conserved and unique metabolic pathways will help in the annotation of metabolites as well as provide important new targets of investigation in biology and biomedicine. However, most chemical biologists are not familiar with genetics, which needs to be considered when choosing a model organism. In this review we summarize the strengths and weaknesses of several genetic systems, including natural isolates, recombinant inbred lines, and genetic mutations. We also discuss methods to detect targets of selection on the metabolome.
Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. This second part of a comprehensive description of the methods of metabolomics focuses on data analysis, emerging methods in metabolomics and the future of this discipline. Copyright
Isotopic Ratio Outlier Analysis (IROA) is a 13C metabolomics profiling method that eliminates sample-to-sample variance, discriminates against noise and artifacts, and improves identification of compounds, previously done with accurate mass LC/MS. This is the first report using IROA technology in combination with accurate mass GC-TOFMS, here used to examine the S. cerevisiae metabolome. S. cerevisiae was grown in YNB media, containing randomized 95% 13C, or 5%13C glucose as the single carbon source, in order that the isotopomer pattern of all metabolites would mirror the labeled glucose. When these IROA experiments are combined, the abundance of the heavy isotopologues in the 5%13C extracts, or light isotopologues in the 95%13C extracts, follows the binomial distribution, showing mirrored peak pairs for the molecular ion. The mass difference between the 12C monoisotopic and the 13C monoisotopic equals the number of carbons in the molecules. The IROA-GC/MS protocol developed, using both Chemical and Electron Ionization, extends the information acquired from the isotopic peak patterns for formulae generation, a process that can be formulated as an algorithm, in which the number of carbons, as well as the number of methoximations and silylations, are used as search constraints. In Electron Impact (EI/IROA) spectra, the artifactual peaks are identified and easily removed, which has the potential to generate "clean" EI libraries. The combination of Chemical Ionization (CI) IROA and EI IROA affords a metabolite identification procedure that enables the identification of co-eluting metabolites, and allowed us to characterize 126 metabolites in the current study.
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Nematodes use an extensive chemical language based on glycosides of the dideoxysugar ascarylose for developmental regulation (dauer formation), male sex attraction, aggregation, and dispersal. However, no examples of a female- or hermaphrodite-specific sex attractant have been identified to date. In this study, we investigated the pheromone system of the gonochoristic sour paste nematode Panagrellus redivivus, which produces sex-specific attractants of the opposite sex. Activity-guided fractionation of the P. redivivus exometabolome revealed that males are strongly attracted to ascr#1 (also known as daumone), an ascaroside previously identified from Caenorhabditis elegans hermaphrodites. Female P. redivivus are repelled by high concentrations of ascr#1 but are specifically attracted to a previously unknown ascaroside that we named dhas#18, a dihydroxy derivative of the known ascr#18 and an ascaroside that features extensive functionalization of the lipid-derived side chain. Targeted profiling of the P. redivivus exometabolome revealed several additional ascarosides that did not induce strong chemotaxis. We show that P. redivivus females, but not males, produce the male-attracting ascr#1, whereas males, but not females, produce the female-attracting dhas#18. These results show that ascaroside biosynthesis in P. redivivus is highly sex-specific. Furthermore, the extensive side chain functionalization in dhas#18, which is reminiscent of polyketide-derived natural products, indicates unanticipated biosynthetic capabilities in nematodes.
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>Metabolomics or biochemical profiling is a fast emerging science; however, there are still many associated bottlenecks to overcome before measurements will be considered robust. Advances in MS resolution and sensitivity, ultra pressure LC-MS, ESI, and isotopic approaches such as flux analysis and stable-isotope dilution, have made it easier to quantitate biochemicals. The digitization of mass spectrometers has simplified informatic aspects. However, issues of analytical variability, ion suppression and metabolite identification still plague metabolomics investigators. These hurdles need to be overcome for accurate metabolite quantitation not only for in vitro systems, but for complex matrices such as biofluids and tissues, before it is possible to routinely identify biomarkers that are associated with the early prediction and diagnosis of diseases. In this report, we describe a novel isotopic-labeling method that uses the creation of distinct biochemical signatures to eliminate current bottlenecks and enable accurate metabolic profiling.
This review provides a summary of cryogenic NMR probes, with a special emphasis on small volume, high-temperature superconducting (HTS) probes. We provide a short historical overview of the development of cryogenic systems and HTS probes and show why these provide improvements in NMR sensitivity. We discuss design considerations in HTS probe technology, with an emphasis on technology available for biomedical applications. Existing microsample cryogenic probes are reviewed, and some experimental considerations for optimal performance of these probes are provided. We end with a brief review of research that has utilized HTS probes to demonstrate the impact that these probes can have on biomedical applications. Keywords: NMR probes; high-temperature superconducting; HTS; YBCO; small volume NMR; high-sensitivity NMR; signal-to-noise; S/N; natural products; structural biology
Arthur S. Edison obtained a B.S. in chemistry from the University of Utah, where he studied monoterpenes isolated from southern Utah sagebrush by NMR. He completed his Ph.D. in biophysics from the University of Wisconsin, Madison, where he developed and applied NMR methods for peptide and protein structural studies under the supervision of John Markley and Frank Weinhold. In 1993, Dr. Edison joined the laboratory of Anthony O. W. Stretton at the University of Wisconsin as a Jane Coffin Childs postdoctoral fellow where he investigated the role of neuropeptides in the nervous system of the parasitic nematode Ascaris suum. He joined the faculty at the University of Florida and the National High Magnetic Field Laboratory in 1996 and is currently the Director of Chemistry & Biology at the NHMFL. Dr. Edison’s current research is in technology development for high-sensitivity NMR and natural product discovery in nematodes and other invertebrates. Dr. Edison is the recipient of the 1997 American Heart Association Robert J. Boucek Award, a CAREER Award from the National Science Foundation in 1999, and, with his postdoctoral scientist Aaron Dossey, the Beal award for the best publication of the year in the Journal of Natural Products in 2007.
Nuclear magnetic resonance spectroscopy (NMR) provides a rich source of structural information which when combined with other spectroscopic data allows structural elucidation of complex compounds as well as a universal detection method for compounds of interest in complex mixtures. NMR is a nondestructive technique that facilitates its coupling with other spectroscopic methods such as mass spectrometry. The key to on-line NMR detection is the design of the flow probe. Suitable probes for continuous flow, stop flow, loop collection, solid-phase extraction, and microflow probes are described emphasizing their general applications and limitations. Sensitivity and probe volume are major limitations of NMR coupling to liquid chromatography. Solvent selection for separations needs to consider the unique characteristics of NMR, for example the use of deuterated solvents for proton detection, and methods available for solvent suppression. Practical applications of LC-NMR are presented to illustrate the use of this technique in different areas of analytical chemistry.
We demonstrate the global metabolic analysis of Caenorhabditis elegans stress responses using a mass spectrometry-based technique called Isotopic Ratio Outlier Analysis (IROA). In an IROA protocol, control and experimental samples are isotopically labeled with 95% and 5% 13C, and the two sample populations are mixed together for uniform extraction, sample preparation, and LC-MS analysis. This labeling strategy provides several advantages over conventional approaches: 1) compounds arising from biosynthesis are easily distinguished from artifacts, 2) errors from sample extraction and preparation are minimized because the control and experiment are combined into a single sample, 3) measurement of both the molecular weight and the exact number of carbon atoms in each molecule provides extremely accurate molecular formulae, and 4) relative concentrations of all metabolites are easily determined. A heat shock perturbation was conducted on C. elegans to demonstrate this approach. We identified many compounds that significantly changed upon heat shock, including many compounds from the purine metabolism pathway, which we use to demonstrate the approach. The metabolomic response information for C. elegans provided by IROA may be interpreted in the context of a wealth of genetic and proteomic information available. Furthermore, the IROA protocol can be applied to any organism that can be isotopically labeled, making it a powerful new tool in a global metabolomics pipeline.
Genuinely new detection methods like the flame ionization detector (FID)3 for gas chromatography (McWilliams and Dewar, 1958; Harley et al., 1958), mass spectrometry for structure determination (Biemann, 1962), and the use of whole insects (Flaschenträger et al., 1957) or isolated insect antennae (Schneider, 1957; Roelofs, 1984, Chapter 5, this volume) as biological detectors significantly increased the number of techniques available to the pioneers in the field 25 years ago (see Hecker and Butenandt, 1984, Chapter 1, this volume). Equally important and numerous are the examples in which a novel combination of already existing techniques opened up additional analytical avenues. Examples are coupled gas-liquid chromatography-mass spectrometry (GLC- MS) (Gohlke, 1959; Ryhage, 1964), tandem GLC-EAD (Moorhouse et al., 1969; Arn et al., 1975; Beevor et al., 1975), tandem GLC single-cell recordings (Wadhams, 1982; Wadhams, 1984, Chapter 7, this volume; Löfsted et al., 1982; Struble and Arn, Chapter 6, this volume), and the tandem GLC-behavior bioassays (t-GLC-BB) (Table 1).
With the advent of Electrospray HPLC/MS instruments and a database like NAPRALERT, a quick and easy method for the dereplication of natural products can now be achieved.