Metabolite-based detection of aspergillosis
Aspergillosis is a group of diseases caused by the inhalation of ubiquitous Aspergillus
spores, generally Aspergillus fumigatus [1], that evade the host immune system [2].
Its most aggressive form, invasive aspergillosis (IA), carries a particularly grim
prognosis in immunocompromised patients [2]. High mortality and the associated socioeconomic
burden [3] necessitate early, accurate, and sensitive detection of Aspergillus infection.
Many clinical diagnostic tests are available [4]; however, the identification of improved
methods for the detection of aspergillosis is still an active field of research.
Pathogens possess a rich metabolism and a vast array of secondary metabolites, many
unique to their species, that constitute a pathogen “fingerprint” [5]. Pathogens can
leave their imprint on the host in other ways; for instance, host–pathogen interactions
can alter the host’s own metabolome, leaving a “signature” of the disease caused by
the pathogen [6]. Detecting metabolic evidence of the pathogen’s presence underlies
several emerging methods of disease diagnosis. Due to the absence of an ideal diagnostic
test, we have seen new applications of metabolite detection for Aspergillus in recent
years. Methods include detection of gliotoxin [7,8] and siderophores [9], siderophore
uptake [10,11], volatile organic compounds (VOCs) [12–14], and changes to the host’s
metabolome in serum [15]. Although still at the research stage, all of these techniques
offer distinct advantages for aspergillosis detection (Table 1).
10.1371/journal.ppat.1006486.t001
Table 1
Emerging metabolite-based methods of aspergillosis detection: Advantages and disadvantages
of metabolite-based methods of aspergillosis detection at the research stage of development.
Methods of aspergillosis detection
Advantages*
Disadvantages*
Gliotoxin
7
and methylated gliotoxin detection
8
• Noninvasive• Rapid• Sensitive• Quantifiable• Low sample cost• Has the potential
for standardization• Potential detection of actively dividing Aspergillus since gliotoxin
release is associated with hyphal growth• Amenable to automation
• Not specific to Aspergillus
• Does not determine infection location• HPLC-MS/MS equipment is costly• Requires
sample preparation and metabolite extraction, which increases labor and may introduce
contamination• Calibration and addition of internal standard needed to test HPLC-MS/MS
performance• The precise relation between fungal metabolite level with time course
and severity of infection and IA status according to EORTC/MSG still needs to be determined
Siderophore detection
9
• Noninvasive• Rapid• Sensitive• Quantifiable• Low sample cost• Has the potential
for standardization• Potential detection of actively dividing Aspergillus since TAFC
release is associated with nutrient procurement for growth• Potential use for detection
in early stages of infection• Amenable to automation
• More specific than gliotoxin but still not specific solely to Aspergillus
• Does not determine infection location• UPLC-MS/MS equipment is costly• Requires
sample preparation and metabolite extraction, which increases labor and may introduce
contamination• Calibration and addition of internal standard needed to test instrument
performance• The precise relation between fungal metabolite level with time course
and severity of infection and IA status still needs to be determined
68
Ga-labeled siderophores uptake
10,11
• Noninvasive• Targeted, specifically identifies Aspergillus since uptake by other
fungi and bacteria was found to be limited• Imaging-based technique, potential to
locate the site of infection• Potential detection of actively dividing Aspergillus
since TAFC release is associated with nutrient procurement for growth• Can differentiate
from invading Aspergillus versus inert spores
• Sensitivity uncertain• Cross-reactivity still possible with other fungal genera•
Requires very specialized radio facilities to produce positron emitter 68Ga• Requires
very specialized and expensive imaging equipment• Exposes patient to low level of
ionizing radiation• The toxicity of TAFC administration to the patient needs to be
assessed
eNose detection of VOCs
12,13
• Noninvasive• Rapid• Low sample cost• Relatively low equipment cost• Point-of-care
testing possible• “Breathprint” profiles or biomarkers have the potential for species
identification• Specifically detects disease state, not just the presence of inert
Aspergillus spores• Has potential to be specific to Aspergillus, but sensitivity and
specificity are still under investigation
• Does not determine whether infection has spread past the lungs• Contamination from
exogenous substances from the air/environment is possible• Confounding parameters
still uncertain• Calibration needed to test instrument performance• Initially requires
construction of prediction models
GC-MS detection of VOCs
14
• Noninvasive• Rapid• Low sample cost• “Breathprint” profiles or biomarkers could
enable species identification• Has potential to be specific to Aspergillus, but sensitivity
and specificity are still under investigation• Amenable to automation
• Does not determine whether infection has spread past the lungs• GC-MS/MS equipment
is costly• Contamination from exogenous substances from the air/environment is possible•
May require preconcentration of breath samples• Confounding parameters still uncertain•
Calibration and addition of internal standard needed to test instrument performance•
Initially requires determination of VOCs unique to infecting pathogen
NMR metabolomics
15
• Noninvasive• Semi-quantifiable• Low sample cost• Does not require identification
of new or unique metabolites specific to Aspergillus
• Does not require sample manipulation• Amenable to automation
• Low sensitivity• Does not determine site of infection• NMR equipment is costly and
requires expensive routine maintenance• Confounding parameters still uncertain• Will
initially require construction of prediction models
*Since these methods are still in the research stages of development, some of the
stated advantages are only potential advantages, and some of the stated disadvantages
may yet be resolved.
Abbreviations: 68Ga, gallium-68; EORTC-MSG, European Organization for Research and
Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National
Institute of Allergy and Infectious Diseases Mycoses Study Group; GC-MS, gas-chromatography
mass spectrometry; HPLC-MS/MS; high-performance liquid chromatography tandem mass
spectrometry; IA, invasive aspergillosis; NMR, nuclear magnetic resonance; TAFC, triacetylfusarinine
C; UPLC-MS/MS, ultra-performance liquid chromatography tandem mass spectrometry; VOCs,
volatile organic compounds
Aspergillosis diagnosis based on metabolite detection or uptake
Gliotoxin is a secondary metabolite secreted during hyphal growth by a number of fungi,
including A. fumigatus [7], and it has been postulated that its detection might coincide
with the early stages of infection. Gliotoxin is a potent immune suppressor and may
contribute to the failure of the host immune system to prevent opportunistic fungal
infections [8]. Gliotoxin has been investigated as a possible biomarker for aspergillosis
[16,17], and a comprehensive study employing high-performance liquid chromatography
tandem mass spectrometry (HPLC-MS/MS) demonstrated high accuracy and sensitivity for
gliotoxin quantification in analytical standards and human serum [7]. Using this accurate
method, the authors compared the technique to the galactomannan (GM) assay, a clinical
ELISA aspergillosis detection method. Serum samples negative for GM were generally
lacking gliotoxin (85%); however, half the GM positive samples were also devoid of
gliotoxin [7].
Several scenarios could explain the lack of correlation between positive GM and gliotoxin
level: (1) false GM positives, (2) decline in gliotoxin at late stages of infection
when hyphal growth might slow as fungal burden increases, or (3) chemical instability
of the gliotoxin disulfide in vivo [8]. The study did not classify cases of IA according
to European Organization for Research and Treatment of Cancer/Invasive Fungal Infections
Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses
Study Group (EORTC/MSG) definitions, so it is not possible to ascertain the degree
to which false positive GM assays may have influenced the results of this study. Accurate
IA status and duration of illness are also required to determine whether gliotoxin
levels correlate with early infection versus late infection, and this was similarly
not reported. Therefore, while the accuracy and sensitivity of the test is promising,
an in-depth analysis with IA status and time frame will be required to evaluate the
precise relation of gliotoxin to IA diagnosis.
To address the possibility of in vivo chemical instability of gliotoxin, levels of
an inactive methylated metabolite of gliotoxin, bis(methylthio)gliotoxin (bmGT), were
assayed in patient sera by high-performance thin-layer chromatography (HPTLC). These
studies indicated that bmGT may be a better metabolic biomarker than gliotoxin, possibly
due to its greater stability [8]. In this study, the IA status of patients was assessed
according to EORTC/MSG criteria by clinicians who were unaware of the results of the
gliotoxin and bmGT assays. Then, prediction of IA status by the GM assay was compared
to prediction by bmGT quantification. Employing bmGT quantification had greater sensitivity
and positive predictive value for IA than GM, and, combined, the 2 diagnostic tests
identified all positive IA cases and almost totally avoided false negatives [8]. This
study incorporated EORTC/MSG definitions of IA, strengthening the clinical applicability
of these findings compared to the findings of the earlier gliotoxin study [7].
Siderophores are secondary metabolites utilized by microorganisms to scavenge the
micronutrient iron, necessary for growth, from the environment or host [9]. Therefore,
their presence implies an actively dividing pathogen. Triacetylfusarinine C (TAFC)
is produced by a few fungal genera, Aspergillus amongst them, with no known human
function, so it is neither anticipated in healthy hosts [9] nor expected to be taken
up by host cells [10]. TAFC in serum from patients at risk of IA can be quantified
by ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) [9].
Fifty-eight suspected, probable, or proven IA cases classified according to EORTC
criteria had serum assayed for GM (≥0.5 index threshold) and TAFC. A positive correlation
was observed between GM score and TAFC (Pearson r value = 0.77), but, interestingly,
TAFC levels above the threshold of detection were observed more frequently in suspected
IA than in probable/proven IA, possibly because the latter received antifungals [9].
Furthermore, a subset of samples from patients with suspected IA were TAFC positive
but GM negative, suggesting TAFC secretion may occur early during infection [9]; however,
the GM results may also have been false negatives, and further work is required to
clarify this.
The analytical methods for metabolite (gliotoxin, bmGT, and TAFC) quantification are
accurate and sensitive, but they require further research to uncover the true correlation
between serum level with IA status. These studies will require evaluating the time
course of metabolite production during the natural history of the disease and in patients
with a range of severity of infection. Use of EORTC/MSG definitions of disease will
be critical to allow comparison of the results of these studies with those evaluating
other diagnostic tests.
TAFC can bind the positron emitter gallium-68 (68Ga) creating 68Ga-TAFC complexes
for positron emission tomography (PET) [10]. Biodistribution studies in healthy mice
demonstrated rapid renal 68Ga-TAFC elimination, suggesting it was not actively taken
up in healthy animals [10]. This was confirmed in Lewis rats in which 68Ga-TAFC uptake
in the lungs was selective in rats with IA and not observed in healthy rats and Aspergillus-challenged
rats that did not develop IA [10]. 68Ga-TAFC uptake was dependent on infection severity,
suggesting deliberate uptake by Aspergillus.
Aspergillus-challenged rats that did not develop IA did not absorb 68Ga-TAFC, implying
that the technique differentiates between actively growing, invading Aspergillus compared
to exogenous, inert spores inhabiting the airways [10]. This observation also suggests
that the method could detect IA early in the course of infection when Aspergillus
is in a phase of rapid growth and prior to the development of a high burden of fungal
disease [10]. In addition, whole body scans can reveal infection spread to organs
other than lungs. 68Ga-TAFC uptake by Aspergillus was greater than by other fungi
and almost nonexistent by other microorganisms, indicating it could be specific for
Aspergillus [11]. The high uptake and retention time of 68Ga-TAFC by Aspergillus enhances
sensitivity [10] but lengthens the duration patients would be exposed to radiation,
a disadvantage of the technique. The application of gallium-68 complexes in nuclear
medicine is well documented [18]; however, the safety of gallium-68 with TAFC in particular
needs to be evaluated, but studies thus far indicate that 68Ga-TAFC uptake may be
a useful diagnostic tool for IA.
Aspergillosis diagnosis based on VOCs
Aspergillus produces several volatile metabolites, which can be present in the breath
of patients with lung infection [19]. Detection of these VOCs is an attractive strategy
for the detection of IA, since breath collection is noninvasive and desirable for
critically ill patients susceptible to IA. Earlier attempts to detect VOCs focused
on the detection of single metabolites such as 2-pentylfuran, which was elevated in
patients with Aspergillus airway colonization [20]. However, 2-pentylfuran is not
unique to Aspergillus, and confounding factors affected diagnosis based solely on
this VOC [21]. Therefore, more recent methods have included the detection of several
VOCs or entire mixtures.
eNoses are affordable, portable options for VOC detection, possibly making point-of-care
medical diagnosis a reality [22]. Numerous commercial eNoses exist that vary in the
sensor material in order to make them suitable for different applications [23,24].
eNoses contain sensor arrays whose physical properties change upon adsorption of volatiles,
e.g., electrical resistance, which is recorded. Volatile metabolites from a sample
bind to the sensor arrays, and the combination of these volatiles will produce a pattern
of response that reflects this mix of metabolites [23,24]. This response pattern is
unique to that particular VOC mixture and forms the basis of detection.
The feasibility of eNose-based detection of aspergillosis was examined in patients
with prolonged chemotherapy-induced neutropenia (PCIN) [12] and cystic fibrosis (CF)
[13]. PCIN patients underwent full diagnostic workup for aspergillosis (classified
by EORTC criteria) whilst CF patients were diagnosed by sputum culture. Each patient’s
breath was analyzed by a Cyranose 320 eNose to produce a “breathprint,” a variation
in the electrical resistance differential across all 32 eNose sensors. All “breathprint”
data from uninfected controls and patients with proven and probable IA were subjected
to principal component analysis, identifying signal components that accounted for
99.9% variance between control and experiment. These components were used to construct
prediction models, which had cross-validation of about 89% or higher [12,13].
The eNose’s ability to detect aspergillosis in the presence of 2 underlying medical
conditions (PCIN and CF) demonstrates its broad applicability [12,13]. Early eNose
detection of VOCs from the host inflammatory response to infection may allow for the
detection of infection prior to the development of significant fungal disease. It
may even be possible to make eNose detection specific for Aspergillus since breathprints
of CF patients with lung coinfections by other microorganisms were distinct from breathprints
from patients with aspergillosis [13], as supported by in vitro tests [25]. The main
drawback of eNose technology is that prior analysis is required to establish the prediction
model, and a reliable calibration system is required to ensure the same operation
between different eNose machines. These challenges will have to be addressed before
eNose technology can become a widely used standardized diagnostic technique.
Gas-chromatography mass spectrometry (GC-MS) is another method that can be used to
identify volatile metabolites based on their mass spectrometry (MS) profile. Combined
with patient breath collection, it is noninvasive and can structurally identify metabolites
in “disease breath.” GC-MS was used to identify secondary metabolite mixtures unique
to A. fumigatus compared to other aspergilli [14]. The combination of monoterpenes
(camphene, α-pinene, β-pinene, limonene) and sesquiterpenes (α-trans-bergamotene,
β-trans-bergamotene) was found to be specific to A. fumigatus in vitro [14]. This
observation was recapitulated in IA patient breath samples, which additionally contained
2 metabolites not detected in vitro, the terpenoid ketone trans-geranylacetone and
a β-vatirenene–like sesquiterpene [14].
The IA status of 64 enrolled patients was evaluated independently by 2 doctors as
“proven,” “probable,” or “possible” according to EORTC/MSG criteria. Breath VOCs were
submitted to GC-MS and analyzed by heat map for the 8 metabolites found to define
A. fumigatus. IA status of 60/64 cases were correctly identified by this GC-MS analysis,
highlighting the potential of this technique for IA diagnosis [14]. GC-MS of patient
breath is noninvasive and can be tailored to detect other invasive pathogens to extend
its utility to the diagnosis of other diseases. That would require preliminary in
vitro work to discover secondary metabolites or combinations unique to a pathogen/strain
followed by validation in patient breath samples. This requires more labor and doesn't
guarantee that findings in vitro translate to patient samples. In addition, it may
not be possible to discover a combination of metabolites unique to the pathogen, which
is the principal drawback of this method.
Aspergillosis diagnosis based on metabolomics
Metabolomics is the untargeted, system-wide detection of metabolites in biological
samples and has great potential for clinical use. It may be employed to either (1)
detect metabolite mixtures unique to infecting pathogens or (2) observe changes to
the host’s metabolome caused by infection [26]. Typically, metabolite detection is
by MS or nuclear magnetic resonance (NMR), followed by statistical analysis to reveal
system-wide differences in all systematically varying metabolites between healthy
versus infected patients [26].
NMR metabolomics is well suited to study biofluids in their native state and was applied
to aspergillosis detection in falcons (gyrfalcons and gyr-x peregrine hybrids) [15].
Blood samples were withdrawn from clinically healthy falcons and falcons with confirmed
aspergillosis and analyzed by 1D 1H-NMR spectroscopy. Multivariate statistical analysis
of all identified host metabolites from the resultant spectra showed a clear metabolic
separation of healthy versus diseased falcons, each with a distinct metabolic profile
[15]. Additionally, because NMR metabolomics can identify metabolites, information
about which contributed to the distinction between healthy versus diseased cohorts
was revealed. In particular, 3-hydroxybutyrate was significantly elevated in the blood
of aspergillosis falcons compared to healthy raptors [15].
NMR metabolomics produces a top-down systematic cataloging of host metabolites that
vary consistently between infected and healthy patients [26]. Unlike other diagnostic
methods that detect Aspergillus or its components, NMR metabolomics produces a host
“disease metabolic profile” of aspergillosis. This indicator of disease differentiates
from the detection of exogenous Aspergillus spores by some methods that could give
false positives [27]. The promising, preliminary study in falcons suggests that 1H-NMR
metabolomics could translate to humans as a detection tool for aspergillosis.
Despite the advantages of the systems-wide approach by NMR metabolomics, it suffers
from low sensitivity and resolution. While the more abundant, primary metabolites
may be readily identified, less abundant and more structurally complex secondary metabolites
are more challenging to identify, although advances in data analysis are constantly
being made.
Aspergillosis detection and beyond: Where else can metabolite detection take us?
Several metabolite-based methods have shown promise for improved Aspergillus detection,
which satisfy the criteria of early detection, low invasiveness, low cost, and point-of-care
[7–15]. The timely detection of aspergillosis will significantly facilitate the decision
to treat infected immunocompromised patients—to improve their prognosis for recovery
whilst also preventing unnecessary prophylactic administration of potentially toxic
antifungals.
Beyond Aspergillus detection, metabolite identification and quantification in addition
to the more recent system-wide metabolomics methods may be extended to other microbial
and viral infections. Metabolomics can address questions pertaining to infection mechanisms
and host–pathogen interactions [28]. These studies may reveal pathogen vulnerabilities
that may be exploited to develop therapeutic strategies. Therefore, in addition to
diagnosis, metabolomics may be applied in drug design, an essential area of research
in this era of mounting microbial resistance [29]. The promise of metabolomics is
only now unfolding, and its utility is being recognized in various research areas
with novel potential future applications.