Fungal metabolite analysis in genomics and phenomics
Michael C Jewett, Gerald Hofmann and Jens Nielsen
Metabolomics consists of strategies to quantitatively identify
cellular metabolites and to understand how trafficking of these
biochemical messengers through the metabolic network
influences phenotype. The application of metabolomics to
fungi has been strongly pursued because these organisms are
widely used for the production of chemicals, are well known for
their diverse metabolic landscape and serve as excellent
eukaryotic model organisms for studying metabolism and
systems biology. Within the context of fungal systems, recent
progress has been made in the development of analytical tools
and mathematical strategies used in metabolite analysis that
have enhanced our ability to crack the code underpinning the
cellular inventory, regulatory schemes and communication
mechanisms that dictate cellular function. Metabolomics has
played a key role in functional genomics and strain
classification.
Addresses
Center for Microbial Biotechnology, BioCentrum-DTU, Technical
University of Denmark, 2800 Kgs. Lyngby, Denmark
Corresponding author: Nielsen, Jens (jn@biocentrum.dtu.dk)
Current Opinion in Biotechnology 2006, 17:191­197
This review comes from a themed issue on
Food biotechnology
Edited by Michiel Kleerebezem
Available online 20th February 2006
0958-1669/$ ­ see front matter
# 2005 Elsevier Ltd. All rights reserved.
DOI 10.1016/j.copbio.2006.02.001
Introduction
Being major players in food and pharmaceutical biotechnology,
intense interest surrounds the study of fungal
kingdom members, such as filamentous fungi and yeasts.
The enormous biodiversity within the Mycota has
resulted in their application as model organisms for the
production of fuels, chemicals, food ingredients, pharmaceuticals
and enzymes. Equally important is the central
role that fungi occupy as model systems for basic research.
For example, Saccharomyces cerevisiae, one of the most well
established eukaryotic model organisms [1], has played
and continues to play a crucial role in the development of
numerous techniques that simultaneously detect multiple
signals at the molecular level (e.g. DNA microarrays)
for elucidating general rules and descriptions about living
cells. Finally, the relevance of fungi in food and feed
spoilage (primarily through mycotoxin production) and
their pathogenicity has also elevated the importance of
fungal research.
To understand, characterize and exploit fungi more completely,
research efforts have sought to build a more
detailed understanding of fungal metabolism. Metabolism
captures the most salient traits of fungi and, in
particular, the exquisite chemical diversity of their metabolites
and the utilization of eukaryotic cellular features
to ensure operation of dedicated pathways in different
compartments. By offering a window to core attributes
responsible for diverse phenotypes, metabolome analysis
capitalizes on the information content obtained from
primary and secondary metabolism to elucidate fundamental
principles that describe the relationship between
genotype and phenotype (Figure 1).
Genome sequencing and annotation identifies the inventory
of parts that make up the cell. A parts list, however,
void of contextual, integrative and quantitative information
describing active elements, lacks the power to completely
decipher the mechanisms of cellular function [2].
As the intermediates of biochemical reactions, metabolites
link together a web of complex interactions, cellular
pathways, molecular participants (e.g. DNA, mRNA,
proteins) and environmental stimuli. They act as a spoken
language, broadcasting signals from the genetic architecture
and the environment through the metabolic network
to help achieve the functional objectives of the cell. The
integrated nature of metabolic networks, predominantly
in primary metabolism, has been underscored by genomescale
metabolic models [3­7]. First, we observe that more
than 70% of all metabolites participate in more than just
two reactions (Figure 2a) [4]. Second, cellular biochemical
reactions usually involve more than one substrate and
one product (Figure 2b) [3]. Third, the average path
length to get from any metabolite to any other metabolite
is approximately three [5,8
]. Clearly, the maze-like nature
of metabolism reveals that even small perturbations in
metabolite concentrations are likely to impact the overall
functional operation of the network [8
]. This coordinated
structural organization highlights the significance of
metabolite quantification in achieving a systems-level
understanding.
In addition to finding utility in systems biology applications,
metabolite analyses have also proven effective for
identification of bioactive molecules with potential for
therapeutic application and fungal taxonomy. Here,
black-box approaches, in which there is no need for gene
sequence information, can be taken to explore the enormous
metabolic chemodiversity present in fungi.
www.sciencedirect.com Current Opinion in Biotechnology 2006, 17:191­197
Although this can generally be applied to all parts of
metabolism, secondary metabolic products are often more
interesting for such purposes because of their specificity,
uniqueness and high diversity.
This review highlights recent advances in fungal metabolomics.
Because the genome provides the code of
cellular parts, which helps to define the theoretical
boundary of the metabolic network, we begin with a brief
update on fungal genomics. This is followed by a general
discussion on analytical approaches for metabolite detection.
Next, we concentrate on new methods for quantitative
metabolite measurement used in functional
genomics and strain classification. Finally, we underscore
the need for improvements in standardization of how we
generate and report our data. Despite our focus on fungal
systems, many of the technological advances have been
mirrored by or originated from similar reports in plants.
Genomics
To map changes in metabolites back to gene sequence, an
accurate genomic blueprint is required. The era of fungal
genomics started in 1996 with the publication of the
complete genome sequence of S. cerevisiae [9]. Since then,
the number of available fungal genome sequences has
increased dramatically. Although only 12 complete fungal
genomic sequences have been published, more than 51
fungal genomes can be accessed using BLAST searches at
the National Center for Biotechnology Information
(NCBI) internet website (http://www.ncbi.nlm.nih.gov/).
Because no comprehensive repository for fungal genomes
is available, Table 1 provides a list of URL links that can
be consulted for up-to-date information on the status of
fungal genome sequencing and for access to various
genome data. Although genome sequences might ultimately
play an essential role in increasing the information
content from metabolomics studies, the intrinsically complex
relationship between genes and metabolites has
made interpretations of these data difficult.
Metabolomics
We define a metabolite as any chemical compound of the
cell that is not genetically encoded and is a substrate,
intermediate or product of metabolism. Metabolome
analysis [10] seeks to identify and quantify the entire
collection of intracellular and extracellular metabolites.
Conceptually, there are two basic approaches used in
metabolomics (Figure 3) [11
]. Mainly exploited for
classification, metabolite profiling strategies investigate
192 Food biotechnology
Figure 1
Directions in fungal metabolism research. In this schematic, specific
research areas in fungal metabolomics are distributed according to the
influence of primary and secondary metabolism.
Figure 2
The integrated nature of metabolic networks. (a) Metabolite participation
in the reaction network. Less than 30% of metabolites, M, are involved in
two reactions or less, whereas approximately 4% of all metabolites
participate in more than 20 reactions [3]. (b) The high degree of
connectivity in the metabolic network. Greater than 50% of biochemical
reactions involve more than one substrate and one product [4].
Metabolites are indicated: A, B, C, D.
Table 1
Internet resources for fungal genomics.
Broad Institute http://www.broad.mit.edu/annotation/fgi/
Genomes OnLine Database http://www.genomesonline.org/
Joint Genome Institute http://genome.jgi-psf.org/euk_home.html
National Center for Biotechnology Information http://www.ncbi.nlm.nih.gov/genomes/FUNGI/funtab.html
Sanger Center http://www.sanger.ac.uk/Projects/Fungi/
The Institute for Genomic Research http://www.tigr.org/tdb/fungal/index.shtml
Current Opinion in Biotechnology 2006, 17:191­197 www.sciencedirect.com
qualitative scanning of all detectable metabolites
observed by a selected analytical technique. Here, the
pattern of known and unknown metabolites (or spectra
from chromatography or mass spectrometry) is used to
find discriminatory elements via high-throughput detection
followed by data deconvolution methods [12,13].
Metabolite profiling comprises metabolic fingerprinting,
which covers the endometabolome (intracellular metabolites),
and metabolic footprinting, which covers the exometabolome
(metabolites secreted into the growth media
or extracellular fluid). The other general method used in
metabolomics is target analysis. Here, absolute, or at least
semi-, quantification and unambiguous detection of predefined
metabolites are achieved. Although target analysis
has been historically reserved for interrogating
relatively small numbers of metabolites (e.g. <20), new
developments enable quantitative analysis of moreexpanded
metabolome coverage [14
,15,16].
A variety of analytical platforms has been utilized for
metabolite detection [11
]. Although most quantitative
strategies couple a separation technique (e.g. capillary
electrophoresis [CE], liquid chromatography [LC] or gas
chromatography [GC]) with MS- or NMR-based detection,
it is not uncommon to make use only of direct
infusion MS for metabolite profiling. From a practical
standpoint, our inability to quantitatively extract and
detect highly diverse families of metabolites in their
original state over a large dynamic range with a single
or even limited set of analytical techniques makes the
analysis of the complete set of all metabolites impossible.
Thus, metabolomics is more appropriately used to
describe an `area of science rather than an analytical
approach' [17].
Metabolomics in functional genomics
A fundamental premise of metabolomics is that acquiring
a snapshot of the metabolic composition will provide
insight into the governing functional and regulatory behavior
that connects gene sequence to gene function. Here,
we highlight several platforms that have emerged to
guide systems-level phenomics by exploring metabolite
levels and flow through the primary metabolic scaffold.
Elucidating a metabolic image of central carbon metabolism
has provided insights for linking normal anabolic and
catabolic trafficking with other branches of metabolism.
For example, the use of LC­MS [18] has been exploited
to map metabolic activity and flexibility through dynamic
analysis of intracellular metabolites during the yeast cellcycle
[19] and to determine the effect of culture age on
metabolite pools [20].
Even though quantification of biomolecules involved in
central metabolism offers many insights into key nodes of
metabolism, other applications have also laid the foundation
for target analysis of metabolic hubs that lie one step
beyond central metabolism. To quantify metabolites
containing an amino or carboxylic acid group, Villas-Bo^as
et al. [14] applied a sensitive GC­MS method coupled to a
statistical data-mining strategy for the integrated analysis
of clearly identified and quantified intracellular and extracellular
metabolites (60) in S. cerevisiae. By isolating
statistically significant differences among metabolite
levels from four biological conditions, they observed
discriminatory metabolic features that hinted at the
potential for future integration with comparative omic
analyses. Highlighting the generality of this method,
Panagiotou et al. [21] have utilized this analytical
approach to determine the influence of aerobic and anaerobic
cultivation conditions on the metabolic state of
Fusarium oxysporum.
Equally important in guiding a systems-level understanding
of the overlapping layers of global regulation and
network flexibility are efforts to measure the flow of
material through central metabolism experimentally.
Characterization of cellular metabolic operation is
achieved by using 13
C-labeled substrates followed by
determination of characteristic metabolite patterns,
which can indicate directional flow [22]. The most general
approach uses proteinogenic amino acid analysis to infer
labeling patterns and flux distributions. However, the
Fungal metabolite analysis Jewett, Hofmann and Nielsen 193
Figure 3
Analytical approaches in metabolome analysis. Metabolome analysis
seeks to identify cellular metabolites through either metabolite profiling
or targeted analysis. In this simplified cartoon, the cellular behavior is
characterized by a flow of information propagating from the genome
through the metabolic network. Proteins: 1,2; identifiable metabolites: A,
B, C; unknown metabolites: ?.
www.sciencedirect.com Current Opinion in Biotechnology 2006, 17:191­197
application of rapid sampling and quenching has recently
been applied to analyze intracellular metabolites directly
from S. cerevisiae, without being impeded by the high
metabolic turnover rates [23]. This approach generates
direct data without inference, but caution must be exercised
because of the rapid dynamics of exchange between
metabolites and amino acids incorporated into cellular
proteins [24].
High-throughput efforts for comparative flux analysis
offer an unprecedented view of the rigidity, flexibility
and performance of metabolic networks. For example,
Blank et al. [25] considered flux data from over 30 mutants
of S. cerevisiae to investigate potentially flexible fitness
reactions during growth on glucose. Combining these
measurements with mathematical modeling revealed that
metabolic network robustness to single gene knockouts
was principally a result of genetic redundancy (duplicate
genes) with alternative pathways (redirection of carbon
flow) having less importance. This approach was taken
further in a larger scale systematic flux analysis of 137 null
mutants of Bacillus subtilis (selected from all major functional
categories) on its preferred substrate [26
]. As in
the previous report, this strategy enabled identification of
fundamental design principles of in vivo network operation.
A key feature is the manifestation of rigid distribution
patterns, which are `largely independent of the rate
and yield of biomass formation'. Specifically, they
observed that network operation in B. subtilis is not solely
operating to optimize growth, but also invests cellular
resources in anticipation of changing environmental
stress. The above cases represent powerful strategies to
uncover the structure and function of the interplay
between genetic regulatory networks and phenotype.
Relying less on connections between genetic sequence
and metabolites, metabolite profiling and target analysis
have also been effectively used to classify the phenotype
of silent and unknown mutations [27­29]. Weckwerth
et al. [28] demonstrated the application of target analysis,
using GC­time-of-flight (TOF)­MS for quantification of
more than 1000 metabolites, to characterize the features
responsible for a silent plant phenotype. Exploiting statistical
tools, metabolic correlations were determined
between identified metabolites (e.g. trehalose-erythritol)
and used to reveal network maps, which suggested
hypotheses for the impact of an exact phenotype on
carbohydrate and amino acid metabolism.
Hierarchical metabolomics, developed in plants, has also
proved to be well suited to guide targeted analysis of
metabolism [30
]. Catchpole et al. used comprehensive
metabolome coverage of conventional and genetically
modified (GM) potato crops to reveal that, apart from
anticipated engineered differences, metabolic compositions
were comparable among several types of cultivars.
First, they applied metabolic fingerprinting of potato
tuber extracts to classify several potato genotypes. Second,
target analysis of defined and specific classes of metabolites,
using LC­MS and GC­TOF­MS, was exploited to
identify specific fructans responsible for the global classifications.
Finally, data analysis tools were applied to
remove the influence of anticipated differences in the
GM crops and show that the GM and conventional crops
were within the variation observed from investigating
several unmodified metabolic phenotypes. Hierarchical
analysis provides a rapid and relatively inexpensive screen
for many functional genomics and screening applications.
Metabolomics in strain classification
Whereas exploiting results obtained with metabolome
analysis in functional genomics is often limited by our
inability to unravel highly interconnected networks of
molecular constituents, the huge metabolic chemodiversity
within the Mycota kingdom has become an indispensable
tool for classification and identification of fungi.
The secondary metabolism of fungi stands second only to
that of plants, and secondary metabolites, which are
generally secreted, represent a highly interesting subset
of the fungal exometabolome, which has been exploited
for fungal taxonomy.
Detection and quantification of mycotoxins are the major
focal points for characterization studies (see [31] for an
overview of mycotoxins). The increase in public awareness
over the safety of food and feed during the past few
years has led to the establishment of many new laws and
guidelines with respect to mycotoxins [32]. For the latest
developments in analysis and detection methods, we refer
the reader to an exceptional review detailing this topic
[33]. A key development is that unconventional biosensor
methods, such as electronic nose or tongue technology,
which typically rely on metabolite profiling, have a strong
potential to mature into key techniques for the detection
of mycotoxins and toxigenic fungi [34].
Use of metabolite profiling in fungal classification and
comparative analysis is more commonly associated with
chromatographic and mass spectrometric techniques
[29,35,36
,37]. Although it is relatively simple to generate
an enormous amount of metabolite data, processing and
extracting useful information is often problematic. To
address this limitation, reference databases and data
deconvolution methods are crucial. For example, Nielsen
and Smedsgaard [38] have collected high-performance
liquid chromatography (HPLC)­UV data of 474 mycotoxins.
By covering the biodiversity of secondary metabolites
from fungi, reference libraries provide an
invaluable tool for the identification of known mycotoxins,
as well as for the discovery of potentially new compounds
within fungal metabolome datasets.
Sophisticated data-mining strategies, such as a new algorithm
named X-hitting that was developed to identify
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new natural products based on HPLC­UV data [39],
greatly enhance the utility of reference databases. In
one example, the X-hitting framework (referred above)
identified two novel spiro-quinazoline metabolites in Penicillium
lapatayae extracts[40].Datadeconvolutionmethods
for high-resolution MS data are also poised to impact
comparativeanalysesfromdatabases[41].Amorethorough
discussion of databases and analysis strategies for metabolome
data was recently described by Goodacre et al. [13].
Challenges in sample preparation and
standardization
Despite several examples establishing the utility of metabolomics
as a tool for functional analysis and classification
in fungi, ensuring unbiased and robust quantification of a
large number of metabolites is still a major challenge. The
main obstacles to this objective, sample preparation and
standardization, have recently received attention.
Relative to the other molecular participants in the cell
(e.g. genes, mRNAs and proteins), metabolites typically
exist on a considerably shorter time-scale (by more than
an order of magnitude). Thus, to obtain an accurate
picture of the metabolic state of the cell, rapid inactivation
of biological activity is needed to prevent enzymatic
exchange and turnover of metabolites [42]. In addition to
short time-scales, the chemical diversity of metabolite
classes and the physical barriers of the cell (e.g. cell
structure and compartments) make metabolome coverage,
particularly for the endometabolome, an issue. As
imaging the metabolic inventory of the cell depends on
access, it is important that the extraction procedure is
consistent and boasts excellent recovery characteristics
with limited degradation and losses. Recently, Villas-Bo^as
and colleagues [43] explored the impact of sample preparation
on targeted metabolite analysis using GC­MS for
the yeast S. cerevisiae. One substantial contribution was
their focus on comparative profiling from six different
extraction protocols. Although the explicit detection tool
was principally directed toward organic and non-organic
amino acids, an important outcome of this work was that
several extraction strategies appear to result in the same
biological story (level of metabolites). Even though their
analysis is organism specific, it provides a benchmark for
future targeted analysis strategies of several metabolite
classes in fungi.
Advances in internal standardization are also paving the
way for more robust metabolite measurements [44
].
Heijnen and co-workers have recently created a platform
for quantitative metabolite analysis, which is independent
of ion suppression effects, metabolite modification/degradation
during extraction and variations in instrument
response. This elegant method holds significant promise
for unifying quantitative metabolome analysis. The foundation
for this strategy, initially proposed by Mashego et al.
[45
], is based on the generation of a stableisotope-labeled
metabolome from 13
C-saturated microbial
cultivations. Labeled metabolite libraries enable correction
for extraction recoveries and provide standardization
strategies for quantitative measurements. To address
several drawbacks from the original methodology, Wu
et al. [46] extended this concept through a more generally
applicable scheme. Although less universal because
limited to nitrogen-containing metabolites, 15
N-saturated
cultivations have also shown a strong potential to impact
metabolome study standardization [47].
Developments to establish guidelines for reporting metabolomic
data and to create public and open-access of mass
spectral identification libraries from MS data are also
important [1,48,49
,50,51]. These coordinated efforts
are expected to make an immediate impact on minimizing
variability between researchers, leading to moreaccurate
biological insight.
Conclusions and future perspectives
Recent progress in the field of metabolite analysis can be
attributed to two major driving forces: the need for identification
and quantification of natural compounds from
complex matrices and the reorientation of biology towards
systems analysis. Together, these factors require not only
the collection of comprehensive datasets from different
molecular levels, but also their integration to ultimately
allow the understanding of the phenotype on the basis of
the genome sequence and the environment. Because of
their integrative nature, metabolome data represent an
important milestone on the road towards this goal. The
combination of metabolite profiling and targeted analysis
through hierarchical metabolomics, as demonstrated by
Catchpole et al. [30
], and strategies to detect and quantify
hundreds of metabolites in one-shot [14
] hold significant
promise. Despite foreseeable improvements in our ability
to quantitatively measure more metabolites using standardized
methods and progress in mathematical approaches to
identify statistically relevant features, the limiting step for
the utilization of metabolome data will be in our ability to
develop appropriate frameworks to integrate and map data
from multiple cellular levels.
Update
Recent work demonstrated the separation and detection
of more than 40 sugars and sugar derivatives from a GCMS
platform [52]. This platform expands the toolbox
available for targeted metabolome analysis.
A comprehensive review by Larsen et al. [53] was published,
describing not only the importance of fungal natural
products and the necessity of intelligent screening for the
discovery of new drugs with the help of chemotaxonomy,
but also the techniques applied for their analysis.
Three fungal genomes have been published [54­56] that
give insight into the genome evolution of the interesting
Fungal metabolite analysis Jewett, Hofmann and Nielsen 195
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genus Aspergillus spp. and show the high diversity of
secondary metabolites in these fungi.
Acknowledgements
MCJ, GH, and JN are most grateful to the National Science Foundation
International Research Fellowship Program, the EU EuroBloodSubstitutes
project (www.eurobloodsubstitutes.com) and the Danish Research Council
for Technology and Production Sciences for supporting their work. The
authors thank K Patil, AP Oliveira and J Hjer-Pedersen for their helpful
comments and suggestions.
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