Journal of Medical Microbiology Papers in Press. Published November 23, 2014 as doi:10.1099/jmm.0.082297-0 1 1
The future of novel diagnostics in medical mycology
2 3
Running title: Rapid diagnostic tests for medical mycology
4 5
Contents category: review paper
6 7
Fernando Teles1,2, Jorge Seixas2,3
8 9 10
1
Group of Mycology / Unit of Medical Microbiology, Institute of Hygiene and Tropical Medicine
(IHMT), Universidade Nova de Lisboa (UNL). Rua da Junqueira, 100, 1349-008 Lisboa, Portugal.
11 12
2
13
(IHMT), Universidade Nova de Lisboa (UNL). Rua da Junqueira, 100, 1349-008 Lisboa, Portugal.
14
3
15
Lisboa (UNL). Rua da Junqueira, 100, 1349-008 Lisboa, Portugal.
Centre for Malaria and Other Tropical Diseases (CMDT), Institute of Hygiene and Tropical Medicine
Tropical Clinic Unit, Institute of Hygiene and Tropical Medicine (IHMT), Universidade Nova de
16 17
Corresponding author: Fernando Teles. Instituto de Higiene e Medicina Tropical. Rua da Junqueira,
18
100,
19
[email protected] 1349-008
Lisboa,
Portugal.
Telephone:
(351)
213652600
(ext.
318).
E-mail:
20 21
Keywords: fungal diagnosis; lab-on-a-chip; microfluidics; nanotechnology; point-of-care; rapid tests
22 23 24 25 26 27 28 29 30 31 32 33 34
→
Abbreviations: BG, (1 3)-β-D-glucan; CNT, carbon nanotube; FISH, fluorescence in situ hybridization; FDA, Food and Drug Administration; GM, galactomannan; GXM, glucuronoxylomannan; HAART, highly-active antiretroviral therapy; HIV, human immunodeficiency virus; ITS, internal transcribed spacer; IA, invasive aspergillosis; MALDI-TOF, matrix-assisted laser desorption ionization-time of flight; MB, molecular beacon; NP, nanoparticle; NASBA, nucleic acid sequence-based amplification; PNA, peptide nucleic-acid; POC, point-of-care; QD, quantum dot; SDI, Sexually Transmitted Diseases Diagnostics Initiative; WHO, World Health Organization
35
Abstract
36
Several fungal diseases have become serious threats to human health and life, especially upon the
37
advent of human immunodeficiency virus (HIV)/AIDS epidemics and of other typical
38
immunosuppressive conditions of modern life. Accordingly, the burden posed by these diseases and,
39
concurrently, by intensive therapeutic regimens against these diseases has increased worldwide. When
40
existing and available, rapid tests for point-of-care (POC) diagnosis of important fungal diseases
41
enable surpassing the limitations of current laboratory methods for detection and identification of
42
medically-important fungi, both in low-income countries and for first-line diagnosis (screening) in
43
richer countries. As with conventional diagnostic methods and devices, former immunodiagnostics
44
have been challenged by molecular biology-based platforms, as a way to enhance the sensitivity and
45
shorten the assay time, thus enabling early and more accurate diagnosis. Most of these tests have been
46
developed in-house, without adequate validation and standardization. Another challenge has been the
47
DNA extraction step, which is especially critical when dealing with fungi. In this paper, we have
48
identified three major research trends in this field: the application of newer biorrecognition
49
techniques, often applied in analytical chemistry; the development of new materials with improved
50
physicochemical properties; and novel bioanalytical platforms, allowing fully automated testing.
51
Keeping up-to-date with the fast technological advances registered in this field, primarily at the proof-
52
of-concept level, is essential for wise assessment of those that, more likely, will be more cost-
53
effective and, as already observed for bacterial and viral pathogens, may leverage the current tepid
54
developmental status of novel and improved diagnostics for medical mycology.
55 56
Introduction
57
The development of novel methods and devices for detection, identification and quantification of
58
human pathogens is witnessing unprecedented advances, mainly as a result of the combination of
59
established molecular biology principles with the recent technological developments on new materials
60
and platforms for biosensing. This has resulted, in particular, from the consolidation of the
61
microfluidics and lab-on-a-chip concepts as suitable platforms for bioassays and of nanoengineered
62
structures with enhanced physicochemical properties for ultrasensitive and specific diagnostics
63
(Fortina et al., 2007; Lee et al., 2010). Biorrecognition of bacteria and viruses causative of important
64
human diseases has greatly benefited from such advances, but this has not been so for pathologies of
65
fungal aetiology. Several reasons for this can be pointed out, either due to the inherent biological
66
characteristics of fungi or to the peculiar clinical and epidemiological features of mycotic diseases.
67
However, the immunodeficiency virus (HIV)/AIDS epidemics and the increased incidence of other
68
immunosuppressive factors, such as chemotherapy, organ transplantation and new immunological
69
treatments for autoimmune diseases, have increased the prevalence and severity of opportunistic
70
mycoses in the past few decades. This is an obvious burden for management by the health systems of
71
low-resource countries. In parallel, developed countries have faced the emergence of antifungal drug
2
72
resistance arising from intensive chemotherapeutic and immunological treatments and regimens
73
(World Health Organization, 2014). Under this scenario, there is increased awareness for the
74
limitations of current diagnostic procedures and, accordingly, for the need of novel methods, able to
75
precociously and accurately detect and identify the major human fungal pathogen species in a fast,
76
timely and less invasive manner. This paper briefly overviews the current status and limitations of
77
diagnosis in medical mycology, whilst pointing out some expected trends in this field.
78 79
The importance of diagnosis
80
Fungal species of Candida, Aspergillus and Cryptococcus are major etiologic agents of invasive
81
infections in immunocompromised patients. The global mortality rate by invasive aspergillosis (IA)
82
increased more than 350% in the past 25 years. This disease became a leading cause of death in 60-
83
90% of immunodepressed individuals (McNeil et al., 2001). Around one million new cases of
84
cryptococcal meningitis occur each year, killing more than half a million HIV-infected persons in sub-
85
Saharan Africa and in Southeast Asia every year, which corresponds to up to 90% of all yearly deaths
86
in the world by cryptococcal meningitis (Park et al., 2009). Concerning Candida, systemic and
87
mucosal infections caused by these species have remained high since the 1990s. Importantly, the
88
current status of research in medical mycology allows considering all fungi as potentially harmful in
89
severely immunocompromised hosts (Alexander & Pfaller, 2006). The burden of these diseases is
90
milder in richer countries, with wider access to the highly active antiretroviral therapy (HAART), but
91
worrisome in poorer countries (Teles, 2013). In richer countries, the intensive and increased use of
92
chemotherapeutic regimens and of new immunological treatments for malignancy, transplantation and
93
autoimmune disease management has led to aggressive and often fatal fungal diseases. As such, early
94
and accurate disease diagnosis is crucial for prompt beginning of therapy, thus avoiding the long-term
95
complications and high death rates currently observed, but also for rapid shifting of therapeutics once
96
drug resistant strains are detected, especially in cases of chronic infections (Teles & Martins, 2011).
97
Ironically, as the variety and efficacy of antifungal drugs increase, the need for accurate and rapid
98
diagnosis increases, in order to preclude inadequate presumptive antifungal therapy. This does not
99
only renders less toxic effects, but also better prognosis, due to decreased drug resistance. In parallel,
100
the continuous description of new fungal species poses additional challenges to existing detection and
101
identification methods, especially when such species exhibit different virulence or antifungal
102
susceptibility patterns.
103 104
Limitations of conventional laboratorial diagnosis
105
Understanding the current situation of the development of point-of-care (POC) diagnostic tests in
106
medical mycology requires prior comprehension about the limitations of conventional diagnosis and
107
hence of challenges in the field. Culture and histopathology of infected tissues have been the most
108
traditional diagnostic methods for most mycoses, but obtaining biopsies from sterile body sites is a
3
109
highly invasive approach, a serious handicap, especially in severely ill patients. Plus, a positive
110
culture from a sterile site may indicate transient colonization and not true infection, especially for
111
opportunistic fungi. Culture followed by microscopic examination requires skilled manpower and
112
long assay times for cell culturing, when in vitro growing is feasible. New culture media, lysis
113
centrifugation and automated blood culture systems have decreased the assay time (Chandrasekar,
114
2009), particularly for detection of Candida spp., but without definitely improving the sensitivity of
115
the detection (Archibald et al., 2000; Chandrasekar, 2010). Direct examination, whenever possible, is
116
simpler, faster and cheaper than culture, but a negative test does not definitely exclude a fungal
117
infection. When infected tissue is available, histopathological diagnosis is a common approach, but it
118
lacks sensitivity and selectivity, as several filamentous fungi may exhibit undistinguishable
119
morphologies. This is particularly troublesome when distinguishing Aspergillus from related fungi
120
requiring different therapeutics (Chandrasekar, 2009). Serological techniques may be targeted to
121
detect fungal antigens or antibodies in human serum through immunological or biochemical reactions.
122
For antigen detection, tests have used to target two cell wall polysaccharides: (1
123
and galactomannan (GM). The first has been used to diagnose invasive infections caused by several
124
opportunistic fungi (Odabasi et al., 2004). It is especially useful for presumptive diagnosis
125
(screening), envisaging early beginning of antifungal therapy, and not for fungal identification
126
(Alexander & Pfaller, 2006). Given the ubiquitous presence of glucan in the environment, this test is
127
prone to false-positive results (Chandrasekar, 2009). Moreover, it is not useful for detection of
128
Cryptococcus and Mucorales spp., because the cell walls of their etiologic fungi have too low
129
amounts of this biomarker (Miyazaki et al., 1995). The second test has been applied for the diagnosis
130
of IA (Mennink-Kersten et al., 2004). Yet, its sensitivity and accuracy may decrease under antifungal
131
drug usage, which causes less GM to be released from fungal cell walls (Maertens et al., 2001). More
132
specific serological tests, such as those for cryptococcal meningitis (Vilchez et al., 2002) and for
133
disseminated histoplasmosis (Wheat et al., 2002), have also been employed. Antibody detection tests
134
usually lack sensitivity in cases of immunocompromised patients due to the low levels of circulating
135
antibodies (Teles & Martins, 2011). Plus, diagnosis of an acute infection requires paired sera
136
(Alexander & Pfaller, 2006). A general drawback of immunoassays is potential cross-reactivity
137
between different circulating antibodies and antigens and hence low selectivity. The reliability of
138
antibody assays for diagnosis of invasive candidiasis is particularly limited; the sensitivity is usually
139
low for immunocompromised patients (which are at higher risk for infection), the same happening
140
with the specificity, as Candida spp. are ubiquitous in the normal human flora (Arvanitis et al., 2014).
→3)-β-D-glucan (BG)
141 142
The advent of molecular biology-based diagnosis
143
The growing technical developments in nucleic acid-based molecular diagnostic methods, especially
144
PCR-based techniques for genome amplification and genome sequencing, as well as their increasing
145
cost-effectiveness, have improved the reliability and performance of these techniques in mycological
4
146
diagnosis, as recently reviewed in the literature (Teles, 2013), with particular descriptions for the
147
major human fungal pathogens (Arvanitis et al., 2014; Gómez, 2014). They usually perform better (in
148
terms of sensitivity and selectivity) and quicker than more conventional methods, allowing earlier
149
diagnosis. PCR has already been reported for the diagnosis of candidemia and of IA (Yeo & Wong,
150
2002). Fungal genome amplification by PCR with conserved (panfungal) oligonucleotide primers
151
followed by further amplification with species-specific primers (allowing multiplexed PCR, i.e.,
152
simultaneous detection and identification of multiple fungal species in a single assay) or by another
153
molecular identification technique (e.g., sequencing or restriction endonuclease analysis) has been
154
particularly promising for mycological diagnosis (Hsiao et al., 2005; Iwen et al., 2002). Compared to
155
more conventional methods, nucleic-acid based bioassays usually provide faster and more sensitive
156
results. The exquisite ultrahigh sensitivity of most of these methods coupled to accurate thresholds
157
determination enables tailoring quantitative assays, thus differentiating true fungal infections from
158
simple colonization, as well as monitoring and predicting clinical outcomes from therapeutic
159
interventions (Zhang, 2013). Among them is real-time PCR which, coupled to fluorescent detection,
160
allows gains in sensitivity and minimization of contamination, compared to conventional PCR; yet, it
161
remains a too complex and expensive technique for rapid and inexpensive testing. Another promising
162
PCR-based technique is nucleic-acid sequence-based amplification (NASBA), which allows
163
isothermal direct amplification of RNA from organisms with RNA genomes, and has already proven
164
usefulness for diagnosing IA (Loeffler et al., 2001). PCR amplification of fungal genetic markers has
165
been in the core of DNA barcoding-based fungal identification and classification. However,
166
continuous changes in fungal taxonomy and the inexistence of a widely conserved region in fungal
167
genomes at the species and genus levels have delayed wider developments on molecular diagnostics
168
for fungi. Ribosomal genes have been particularly used as barcodes for fungi, for containing
169
sequences common to all fungi, able to be used for fungal infection screening, but also variable and
170
highly-variable domains, such as the internal transcribed spacer (ITS) region, which is suitable for
171
species identification (Yeo & Wong, 2002). ITS regions provide high taxonomic resolution and are
172
widely characterized in popular sequence databases, such as GenBank (Balajee et al., 2009). Recently,
173
a study from an international research consortium proposed ITS as the primary fungal barcode marker
174
(Schoch et al., 2012). Higher performance in species discrimination can be achieved by using
175
additional sets of primers targeting other fungal genome markers. Overall, this constitutes a prior step
176
towards future development of standardized molecular methods for fungal diagnosis. In this regard, it
177
is also expected that the development of more reliable databases and of sequencing techniques may
178
consolidate molecular methods (for which there is currently no formal approval of regulatory entities
179
for fungal diagnosis) as valuable alternatives to conventional methods for fungal identification
180
(Alexander & Pfaller, 2006). Three alternative options, of development of a consensus protocols by an
181
ad-hoc experts working group, of centralization of molecular testing within a certified reference
182
laboratory or of commercialization via production of quality-controlled diagnostic kits and regents,
5
183
have been proposed for standardization of molecular methods for fungal diagnosis (Zhang, 2013). A
184
fundamental obstacle of molecular diagnostics for fungi is that fungal cells are usually delimited by
185
thick walls that constitute challenging physical barriers for isolation of trace amounts of nucleic-acids.
186
In the future, full packaging and automation of the nucleic-acid extraction step will likely contribute
187
to overcome this problem, as well as to minimize contamination of the biological sample. Such
188
automated methods and devices would avoid the need for bulky clean rooms and achieve higher
189
performances than manual extraction methods (Kessler et al., 2001).
190 191
Rapid tests for point-of-care diagnosis
192
For successful POC and field applications, especially in remote and resource-depleted regions where
193
appropriate diagnostic facilities, equipment and trained manpower often lack, diagnostic devices
194
should accomplish the ASSURED criteria, as defined by the World Health Organization (WHO)
195
Sexually Transmitted Diseases Diagnostics Initiative (SDI): they must be affordable, sensitive,
196
specific, user-friendly, rapid, robust, equipment-free and deliverable to end-users (Peeling et al.,
197
2006). Rapidity, in particular, means that fewer samples will reach laboratories for analysis, allowing
198
improved decision-making. Indeed, the importance of the concept of rapidity and its inherent
199
relationship with the other parameters has been responsible for the term of ‘rapid (diagnostic) tests’
200
usually assigned to diagnostics meeting these criteria. Commercial diagnostic kits for detection and
201
identification of several clinically-important fungi have been available for several years, such as the
202
AccuProbe tests (from former Gen-Probe, San Diego/CA, USA). Their development and
203
commercialization have greatly benefited from the advances in molecular methods for fungal
204
detection and identification, particularly in the case of dimorphic fungi (Alexander & Pfaller, 2006).
205
However, they are essentially lab-based techniques rather than targeted for POC; in general, they do
206
not fully comply with the ASSURED criteria, because they require labor-intensive sample
207
pretreatment and long assay times, as fungal growth in pure cultures is usually required (Teles &
208
Martins, 2011). For Candida species identification, for instance, such tests are relatively expensive
209
and laborious, requiring incubations longer than two days (Marot-Leblond et al., 2004). Indeed,
210
sample pretreatment has been traditionally disregarded in the development of diagnostic tests,
211
especially in the case of mycelia-forming fungi. Hence, their usefulness as true POC tests remains
212
limited. The advent of lateral-flow tests, usually in the form of strips for qualitative detection of
213
antigens of antibodies (immunochromatography), constitutes a successful paradigm in the conception
214
of rapid tests. They have been widely developed, commercialized and used for specific diagnosis of
215
many human pathogens. The decentralization paradigm of disease diagnosis (related with the concepts
216
of POC and in situ testing), allied to high-throughput and multiplexing abilities of novel tests, is
217
opening an appetizing commercial market even for the traditionally unattractive branch of medical
218
mycology. Yet, the old paradigm of bulky, complex and expensive analytical apparatus is still far
219
from perishing. Rapid tests, rather than true competitors, are still, in essence, complementary in their
6
220
application ranges and contexts. They have found particular market niches in POC applications, for
221
decentralized and in situ diagnosis (by non-medical healthcare staff or, ultimately, at the bedside
222
level). In particular, disease screening has been perhaps the main specific context for their wide
223
application worldwide, as first-line diagnostic tools useful to screen for positive results that will be
224
further confirmed with conventional techniques in central laboratories. Some successful examples of
225
rapid diagnostic tests for the major human fungal pathogens using lateral-flow supports have been
226
applied to clinical samples and reported in recent years, as described in Table 1. The test developed to
227
differentiate between Candida albicans and C. dubliniensis (Marot-Leblond et al., 2004) employed a
228
sandwich system with two immobilized mAbs, one of them binding epitopes specifically expressed by
229
the two Candida species, and the other binding only C. albicans. This test precludes the usual need
230
for complex molecular biology techniques, although important differences in performance were
231
observed depending on the used isolation medium. For Cryptococcus neoformans, two tests were
232
reported, able to detect the four main cryptococcal serotypes. The kit commercialized by Immuno-
233
Mycologics (IMMY, OK/USA), which employs mAbs against capsular polysaccharide antigens of the
234
C. neoformans/C. gattii species complex, allows the use not only of blood, but also of urine, an ideal,
235
non-invasive and readily available specimen to use in POC situations, although the limited sensitivity
236
observed in this fluid is probably a result of low fungal burden, due to excretion (Lindsley et al.,
237
2011). The other, more recent test also employs mAbs, against the cryptococcal capsular
238
polysaccharide glucuronoxylomannan (GXM); it is also tailored for using blood and urine, but its
239
evaluation was performed with a relatively small sample size, thus requiring further validation in
240
larger studies. Moreover, its selectivity was not assessed (Narvis et al., 2011). For detection of
241
Aspergillus spp., the first reported lateral-flow device for human serum employed the mAb JF5, which
242
specifically targets an extracellular glycoprotein of Aspergillus spp. (Thornton, 2008). The use of this
243
mAb avoids the proneness to the false-positive and false-negative results characteristic of common
244
immunoassays targeting GM with the mAb EB-A2. Of note, it also enables to discriminate active
245
invasive growth from quiescent spore production, a major issue in cases of IA. However, the
246
sensitivity of this test decreased almost 30 times when passing from protein-depleted from protein-
247
rich serum. Comparison of the performance of this test with that of conventional immunoassays has
248
been troublesome since each assay uses different species of immobilized antibodies and targets
249
different Aspergillus antigens. The second, more recent test makes use of the IgM mAb476, able to
250
detect GM-like antigens from Aspergillus and other molds in urine (Dufresne et al., 2012). Patients
251
with IA were successfully tested, but the cohort was too small to assess meaningful performance
252
characteristics. The study also reported low amount of these antigens in urine and the inhibitory effect
253
of this fluid, thus decreasing the analytical sensitivity of the test. The hypothesis of sample
254
concentration and further desalting/dialysis was raised, highlighting the need for processing complex
255
biological media prior to pathogen detection. All the above-mentioned tests employed immunological
7
256
reactions as the basis for the biorrecognition events, thus suffering from the general and well-known
257
limitations of immunodiagnosis.
258 259
New technologies for diagnostics
260
Most current methods available for medical diagnosis of human mycosis still suffer from a high
261
degree of invasiveness, relatively long assay times and variable performances (in terms of sensitivity
262
and specificity). Understandably, this constitutes a general driving force for the development of
263
improved diagnostic techniques. Integration, automation, miniaturization and packaging of the entire
264
biorrecognition process are the essential underlying concepts driving the development of advanced
265
diagnostic devices. Accordingly, techniques and signal transducers originally applied in conventional
266
analytical chemistry have been reported for detection and identification of fungi; some of these, with
267
examples from the literature, include spectrophotometry for Paracoccidioides brasiliensis (Martins et
268
al., 2012), fluorescence in situ hybridization (FISH) for Aspergillus and Candida (Rickerts et al.,
269
2011), matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) for Candida (Lacroix
270
et al., 2014), Aspergillus, Fusarium, Mucorales (Carolis et al., 2012) and Cryptococcus spp.
271
(McTaggart et al., 2011), microgravimetry for Aspergillus niger and Saccharomyces cerevisiae (as
272
nonpathogenic models of, respectively, A. fumigatus and C. albicans) (Nugaeva et al., 2005), and
273
electrochemical techniques for C. albicans (Mulero et al., 2009; Villamizar et al., 2009). Among
274
them, MALDI-TOF is undoubtedly a step ahead, given its wide use worldwide for diagnosis of fungal
275
and other microbial infections. This has been so especially after multicenter clinical trials (Spanu et
276
al., 2012) and regulatory approval of the first of these systems by the US Food and Drug
277
Administration (FDA) for clinical use (Arvanitis et al., 2014). Another major research trend in this
278
regard has been the development of cheaper materials for the construction of analytical platforms,
279
thus making disposability (crucial when dealing with infectious agents) economically feasible. In this
280
context, many innovations have arisen from three emergent technological fields. The first one is
281
nanotechnology, through the generation of nanostructures enabling improved sensitivity and
282
specificity, as a result of the unusual or enhanced physicochemical properties of nanosized materials,
283
mostly derived from the very high surface-to-volume ratio of these structures. In bioassays,
284
nanoparticles (NPs) may act as immobilization supports for biomolecules, labels for signal
285
amplification or even as bioprobes for specific recognition of biological targets (Fortina et al., 2007).
286
Among NPs, some of the most important and already used in bioassays for bacterial or viral detection
287
include Au-NPs, quantum dots (QDs), molecular beacons (MBs), DNA dendrimers, carbon nanotubes
288
(CNTs), liposomes, nanowires and peptide nucleic-acids (PNAs) (Algar et al., 2009; Pumera et al.,
289
2007); some of these have also been tested and applied for detection of pathogenic fungi, but only
290
rarely in clinical samples (Table 2). The second is micro/nanofabrication, commonly inspired in the
291
methods used in silicon planar circuitry industry, potentially rendering mass-production of
292
inexpensive and very reproducible diagnostic microdevices (biochips), but already through advanced
8
293
techniques of nanofabrication (Fortina et al., 2007). The third field is microfluidics, consisting in
294
small sized platforms for fluid propelling through microchannels, in the course of the bioassay;
295
ultimately, the goal is the production of lab-on-a-chip analytical platforms, incorporating, in packaged
296
and self-powered miniaturized devices, all bioanalytical steps (from sample pretreatment to final
297
detection) traditionally performed with bulky equipment in large laboratory settings (Choi et al.,
298
2011; Lee et al., 2010). These devices can also be designed for inter- or even intra-specific
299
simultaneous analysis of many different pathogens, by taking advantage of the increasing ability to
300
miniaturize and build dense arrays of different immobilized probes (multiplexing) onto low-cost and
301
portable platforms (such as disposable cartridges). Another remarkable advantage of these devices is
302
the possibility of handling and testing sample volumes several orders of magnitude smaller than those
303
used in conventional diagnostic methods. Very few applications of these technologies for fungal
304
detection have been reported so far and, even among these, usually for C. albicans only. One of them
305
refers to a generic detection of unknown rare blood pathogens (including C. albicans) with a
306
microfluidic device, magnetic microbeads coated with an opsonin, able to recognize and capture most
307
blood pathogens (Cooper et al., 2014). After this pre-concentration step, fluorescence scanning was
308
employed for final detection, with excellent sensitivity (less than 1 yeast cell per ml of human blood).
309
The magnetic capture/concentration step did not affect pathogen viability, thus allowing, upon
310
detection and subsequent antifungal susceptibility testing, rapid diagnosis of sepsis. In another work, a
311
real-time PCR-based microfluidic platform, from Advanced Liquid Logic, Inc. (Morrisville/NC,
312
USA), was employed for detection of C. albicans DNA in human blood (Schell et al., 2012). Unlike
313
the first microfluidic devices, with microchannels for fluid flow, this more versatile second-generation
314
layout employs the principle of electrowetting, in which fluid flow is directed by computer-controlled
315
electrodes (digital microfluidics). This device exhibited slightly less sensitivity than conventional
316
microfluidics and excellent specificity (100%) towards non-candidemic blood samples. So far, most
317
of these schemes and devices have been employed only at the prototype level, i.e., without industrial
318
production and commercialization. Some of these fungal bioassays have shown not only to be
319
potentially cheaper than traditional diagnostic techniques (Dhiman et al., 2011), but also more
320
advantageous towards antifungal therapy costs’ savings (Forrest et al., 2006). Although most of these
321
technical and scientific developments have occurred for biorrecognition of bacteria and viruses,
322
innovations in fungal biomolecular detection may proceed faster compared to those microorganisms,
323
by taking advantage of the valuable examples and accumulated knowledge, from the past and present,
324
with such biological agents. Coupled to well-established signal transduction schemes (e.g., of optical
325
or electrochemical nature), new biomaterials with improved properties and suitable platforms for
326
cheaper, simpler and faster biorrecognition of infectious agents promise to significantly move
327
forward, and even to create a breakthrough in the current paradigm of diagnosis of human infectious
328
agents and of related diseases.
329 9
330
Final remarks
331
There is nowadays a vastness of available techniques, analytical platforms and biomolecular probes
332
and tags already tested, at least at the proof-of-concept level, with a whole range and combination of
333
characteristics and performances for detection/identification of many human pathogens and diagnosis
334
of related pathologies. Hopefully, in the future, the key-point in decision-making at the level of
335
infectious disease diagnosis will not be whether available diagnostic tools suitable for common human
336
infectious diseases will be able to reach required performance thresholds (of sensitivity, selectivity,
337
etc.) or conditions (e.g., type of biological sample, time period after the infection); instead, taking into
338
account that analytical performance usually runs in parallel with complexity and costs, the essential
339
issue will probably be what minimum pathogen discriminatory ability and detection limit will be
340
necessary to guarantee quick and effective disease management, especially envisaging the selection of
341
the most appropriate therapeutic regimen for a given pathological condition. Given the relatively low
342
number of fungi able to cause severe diseases in humans (compared to other types of
343
microorganisms), there has been reluctance and difficulty of national health systems for funding and
344
promoting more widely the research, development and implementation of novel and improved
345
diagnostic tests for mycological diagnosis. This is especially true for low-income countries where
346
most human pathogenic fungi and related diseases are endemic. In developed and richer countries,
347
important systemic and opportunistic mycoses have posed significant public health burdens,
348
especially due to the emergence of antifungal drug resistance in immunocompromised patients. Even
349
so, and taking into account that many cases of mycotic infections do not develop severe clinical
350
manifestations, the research and development of new diagnostics for fungal diseases has not been
351
considered a health priority, especially in times of severe budget haircuts (as nowadays), particularly
352
in the private sector. On the other hand, especially for infections caused by filamentous fungi, early
353
and accurate diagnosis remains a challenge. Remarkably, profitable market niches may be envisaged
354
in the private sector. This will likely constitute a promising driving motor to pursue new
355
developments in improved and economically competitive diagnostics for human fungal diseases in the
356
future. Fungi have some challenging structural features for successful biomolecular diagnosis, but it is
357
time to learn with the accumulated experiences obtained from other microorganisms, especially
358
bacteria and viruses, in order to definitely leverage and improve the current stagnant status of
359
diagnosis in medical mycology, in a context of a growing burden posed by very complex disease
360
conditions and critically ill patients.
361 362
References
363
Alexander, B.D. & Pfaller, M.A. (2006). Contemporary tools for the diagnosis and management of
364
invasive mycosis. Clin Infect Dis 43, S15-S27.
365
10
366
Algar, W.R., Massey, M. & Krull, U.J. (2009). The application of quantum dots, gold nanoparticles
367
and molecular switches to optical nucleic-acid diagnostics. Trends Anal Chem 28, 292-306.
368 369
Archibald, L.K., McDonald, L.C., Addison, R.M., McKnight, C., Byrne, T., Dobbie, H.,
370
Nwanyanwu, O., Kazembe, P., Reller, L.B. & Jarvis, W.R. (2000). Comparison of BACTEC
371
MYCO/F LYTIC and WAMPOLE ISOLATOR 10 (lysis-centrifugation) systems for detection of
372
bacteremia, mycobacteremia, and fungemia in a developing country. J Clin Microbiol 38, 2994-2997.
373 374
Arvanitis, M., Anagnostou, T., Fuchs, B.B., Caliendo, A.M. & Mylonakis, E. (2014). Molecular
375
and nonmolecular diagnostic methods for invasive fungal infections. Clin Microbiol Rev 27, 490-526.
376 377
Balajee, S.A., Borman, A.M., Brandt, M.E., Cano, J., Cuenca-Estrella, M., Dannaoui, E.,
378
Guarro, J., Haase, G., Kibbler, C.C. & other authors (2009). Sequence-based identification of
379
Aspergillus, Fusarium, and Mucorales species in the clinical mycology laboratory: where are we and
380
where should we go from here? J Clin Microbiol 47, 877-884.
381 382
Carolis, E. de, Posteraro, B., Lass-Flörl, C., Vella, A., Florio, A.R., Torelli, R., Girmenia, C.,
383
Colozza, C., Tortorano, A.M. & other authors (2012). Species identification of Aspergillus,
384
Fusarium and Mucorales with direct surface analysis by matrix-assisted laser desorption ionization
385
time-of-flight mass spectrometry. Clin Microbiol Infect 18, 475-484.
386 387
Chandrasekar, P. (2009). Diagnostic challenges and recent advances in the early management of
388
invasive fungal infections. Eur J Haematol 84, 281-290.
389 390
Choi, S., Goryll, M., Sin, L.Y.M., Wong, P.K. & Chae, J. (2011). Microfluidic-based biosensors
391
toward point-of-care detection of nucleic acids and proteins. Microfluid Nanofluid 10, 231-247.
392 393
Cooper, R.M., Leslie, D.C., Domansky, K., Jain, A., Yung, C., Cho, M., Workman, S., Super, M.
394
& Ingber, D.E. (2014). A microdevice for rapid optical detection of magnetically captured rare blood
395
pathogens. Lab Chip 14, 182-188.
396 397
Dhiman, N., Hall, L., Wohlfiel, S.L., Buckwalter, S.P. & Wengenack, N.L. (2011). Performance
398
and cost analysis of matrix-assisted laser desorption ionization-time of flight mass spectrometry for
399
routine identification of yeast. J Clin Microbiol 49, 1614-1616.
400
11
401
Dufresne, S.F., Datta, K., Li, X., Dadachova, E., Staab, J.F., Patterson, T.F., Feldmesser, M. &
402
Marr, K.A. (2012). Detection of urinary excreted fungal galactomannan-like antigens for diagnosis
403
of invasive aspergillosis. PloS ONE 7, e42736.
404 405
Forrest, G.N., Mankes, K., Jabra-Rizk, M.A., Weekes, E., Johnson, J.K., Lincalis, D.P. &
406
Venezia, R.A. (2006). Peptide nucleic acid fluorescence in situ hybridization-based identification of
407
Candida albicans and its impact on mortality and antifungal therapy costs. J Clin Microbiol 44, 3381-
408
3383.
409 410
Fortina, P., Wang, J., Surrey, S., Park, J.Y. & Kricka, L.J. (2007). Beyond microtechnology –
411
nanotechnology in molecular diagnosis. In Integrated biochips for DNA analysis, pp. 187-197. Edited
412
by R.H. Liu & A.P. Lee. New York, USA: Landes Bioscience and Springer Science+Business Media.
413 414
Gómez, B.L. (2014). Molecular diagnosis of endemic and invasise mycoses: advances and
415
challenges. Rev Iberoam Mícol 31, 35-41.
416 417
Heer, K. de, Schee, M.P. van der, Zwinderman, K., Berk, I.A.H. van den, Visser, C.E., Oers, R.
418
van & Sterk, P.J. (2013). Electronic nose technology for detection of invasive pulmonary
419
aspergillosis in prolonged chemotherapy-induced neutropenia: a proof-of-principle study. J Clin
420
Microbiol 51, 1490-1495.
421 422
Hsiao, C.R., Huang, L., Bouchara, J.P., Barton, R., Li, H.C. & Chang, T.C. (2005). Identification
423
of medically important molds by an oligonucleotide assay. J Clin Microbiol 43, 3760-3768.
424 425
Iwen, P.C., Hinrichs, S.H. & Rupp, M.E. (2002). Utilization of the internal transcribed spacer
426
regions as molecular targets to detect and identify human fungal pathogens. Med Mycol 40, 87-109.
427 428
Kessler, H.H., Mühlbauer, G., Stelzl, E., Daghofer, E., Santner, B.I. & Marth, E. (2001). Fully
429
automated nucleic acid extraction: MagNA pure LC. Clin Chem 47, 1124-1126.
430 431
Lacroix, C., Gicquel, A., Sendid, B., Meyer, J., Accoceberry, I., François, N., Morio, F.,
432
Desobeaux, G., Chandenier, J. & other authors (2014). Evaluation of two matrix-assisted laser
433
desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) systems for the
434
identification of Candida species. Clin Microbiol Infect 20, 153-158.
435
12
436
Lee, W.G., Kim, Y.-G., Chung, B.G., Demirci, U. & Khademhosseini, A. (2010).
437
Nano/microfluidics for diagnosis of infectious diseases in developing countries. Adv Drug Deliv Rev
438
62, 449-457.
439 440
Lindsley, M.D., Mekha, N., Baggett, H.C., Surinthong, Y., Autthateinchain, R., Sawatwong, P.,
441
Harris, J.R., Park, B.J., Chiller, T. & other authors (2011). Evaluation of a newly developed
442
lateral flow immunoassay for the diagnosis of cryptococcosis. Clin Infect Dis 53, 321-325.
443 444
Loeffler, J., Hebart, H., Cox, P., Flues, N., Schumacher, U. & Einsele, H. (2001). Nucleic acid
445
sequence-based amplification of Aspergillus RNA in blood samples. J Clin Microbiol 39, 1626-1629.
446 447
Maertens, J., Verhaegen, J., Lagrou, K., Van Eldere, J. & Boogaerts, M. (2001). Screening for
448
circulating galactomannan as a non-invasive diagnostic tool for invasive aspergillosis in prolonged
449
neutropenic patients and stem cell transplantation recipients: a prospective validation. Blood 97, 1604-
450
1610.
451 452
Marot-Leblond, A., Grimaud, L., David, S., Sullivan, D.J., Coleman, D.C., Ponton, J. & Robert,
453
R. (2004). Evaluation of a rapid immunochromatographic assay for identification of Candida albicans
454
and Candida dubliniensis. J Clin Microbiol 42, 4956-4960.
455 456
Martins, J.F.S., Castilho, M.L., Cardoso, M.A.G., Carreiro, A.P., Martin, A.A. & Raniero, L.
457
(2012). Identification of Paracoccidioides brasiliensis by gold nanoprobes. Proc SPIE 8219, 82190Z.
458 459
McNeil, M.M., Nash, S.L., Hajjeh, R.A., Phelan, M.A., Conn, L.A., Plikaytis, B.D. & Warnock,
460
D.W. (2001). Trends in mortality due to invasive mycotic diseases in the United States, 1980-1997.
461
Clin Infect Dis 33, 641-647.
462 463
McTaggart, L.R., Lei, E., Richardson, S.E., Hoang, L., Fothergill, A. & Zhang, S.X. (2011).
464
Rapid identification of Cryptococcus neoformans and Cryptococcus gattii by matrix-assisted laser
465
desorption ionization-time of flight mass spectrometry. Clin Microbiol 49, 3050-3053.
466 467
Mennink-Kersten, M.A.S.H., Donnelly, J.P. & Verweij, P.E. (2004). Detection of circulating
468
galactomannan for the diagnosis and management of invasive aspergillosis. Lancet Infect Dis 4, 349-
469
357.
470
13
471
Miyazaki, T., Kohno, S., Mitsutake, K., Maesaki, S., Tanaka, K., Ishikawa, N. & Har, K. (1995).
472
Plasma (1 3)-β-D-glucan and fungal antigenemia in patients with candidemia, aspergillosis, and
473
cryptococcosis. J Clin Microbiol 33, 3115-3118.
→
474 475
Mulero, R., Lee, D.H., Kutzler, M.A., Jacobson, J.M. & Kim, M.J. (2009). Ultra-fast low
476
concentration detection of Candida pathogens utilizing high resolution micropore chips. Sensors 9,
477
1590-1598.
478 479
Naja, G., Hrapovic, S., Male, K., Bouvrette, P. & Luong, J.H. (2008). Rapid detection of
480
microorganisms with nanoparticles and electron microscopy. Microsc Res Tech 71, 742-748.
481 482
Narvis, J.N., Percival, A., Bauman, S., Pelfrey, J., Meintjes, G., Williams, G.N., Longley, N.,
483
Harrison, T.S. & Kozel, T.R. (2011). Evaluation of a novel point-of-care cryptococcal antigen test
484
on serum, plasma, and urine from patients with HIV-associated cryptococcal meningitis. Clin Infect
485
Dis 53, 1019-1023
486 487
Neely, L.A., Audeh, M., Phung, N.A., Min, M., Suchocki, A., Plourde, D., Blanco, M., Demas, V.,
488
Skewis, L.R. & other authors (2013). T2 magnetic resonance enables nanoparticle-mediated rapid
489
detection of candidemia in whole blood. Sci Transl Med 5, 182ra154.
490 491
Nugaeva, N., Gfeller, K.Y., Backmann, N., Lang, H.P., Düggelin, M. & Hegner, M. (2005).
492
Micromechanical cantilever array sensors for selective fungal immobilization and fast growth
493
detection. Biosens Bioelectron 21, 849-856.
494 495
Odabasi, Z., Mattiuzzi, G., Estey, E., Kantarjian, H., Saeki, F., Ridge, R.J., Ketchum, P.A.,
496
Finkelman, M.A., Rex, J.H. & Ostrosky-Zeichner, L. (2004). Beta-D-glucan as a diagnostic
497
adjunct for invasive fungal infections: validation, cutoff development, and performance in patients
498
with acute myelogenous leukemia and myelodysplastic syndrome. Clin Infect Dis 39, 199-205.
499 500
Park, B.J., Wannemuehler, K.A., Marston, B.J., Govender, N., Pappas, P.G. & Chiller, T.M.
501
(2009). Estimation of the current global burden of cryptococcal meningitis among persons living with
502
HIV/AIDS. AIDS 23, 525-530.
503 504
Peeling, R.W., Holmes, K.K., Mabey, D. & Ronald, A. (2006). Rapid tests for sexually transmitted
505
infections (STIs): the way forward. Sex Transm Infect 82(suppl V), v1-v6.
506
14
507
Pumera, M., Sánchez, S., Ichinose, I. & Tang, J. (2007). Electrochemical nanobiosensors. Sens
508
Actuat B 123, 1195-1205.
509 510
Rickerts, V., Khot, P.D., Myerson, D., Ko, D.L., Lambrecht, E. & Fredricks, D.N. (2011).
511
Comparison of quantitative real time PCR with sequencing and ribosomal RNA-FISH for the
512
identification of fungi in formalin fixed, paraffin-embedded tissue specimens. BMC Infect Dis 11,
513
202.
514 515
Rigby, S., Procop, G.W., Haase, G., Wilson, D., Hall, G., Kurtzman, C., Oliveira, K., Hyldig-
516
Nielsen, J.J. & other authors (2002). Fluorescence in situ hybridization with peptide nucleic acid
517
probes for rapid identification of Candida albicans directly from blood culture bottles. J Clin
518
Microbiol 40, 2182-2186.
519 520
Schell, W.A., Benton, J.L., Smith, P.B., Poore, M., Rouse, J.L., Boles, D.J., Johnson, M.D.,
521
Alexander, B.D., Pamula, V.K. & other authors (2012). Evaluation of a digital microfluidic real-
522
time PCR platform to detect DNA of Candida albicans in blood. Eur J Clin Microbiol Infect Dis 31,
523
2237-2245.
524 525
Schoch, C.L., Seifert, K.A., Huhndorf, S., Reobert, V., Spouge, J.L., Levesque, C.A., Chen, W.
526
& Fungal Barcode Consortium (2012). Nuclear ribosomal internal transcribed spacer (ITS) region
527
as a universal DNA barcode marker for fungi. Proc Natl Acad Sci 109, 6241-6246.
528 529
Spanu, T., Posteraro, B., Fiori, B., D’Inzeo, T., Campoli, S., Ruggeri, A., Tumbarello, M., Canu,
530
G., Trecarichi, E.M. & other authors (2012). Direct MALDI-TOF mass spectrometry assay of
531
blood culture broths for rapid identification of Candida species causing bloodstream infections: an
532
observational study in two large microbiology laboratories. J Clin Microbiol 50, 176-179.
533 534
Teles, F. (2013). Biosensors for medical mycology. In Biosensors and their application in healthcare,
535
pp. 88-111. Edited by D. Ozkan-Ariksoysal. London, UK: Future Science.
536 537
Teles, F.R.R. & Martins, M.L. (2011). Laboratorial diagnosis of paracoccidioidomycosis and new
538
insights for the future of fungal diagnosis. Talanta 85, 2254-64.
539 540
Teles, F.S.R.R., Tavira, L.A.P.T. & Fonseca, L.J.P. (2010). Biosensors as rapid diagnostic tests for
541
tropical diseases. Crit Rev Clin Lab Sci 47, 139-69.
542
15
543
Thornton, C.R. (2008). Development of an immunochromatographic lateral-flow device for rapid
544
serodiagnosis of invasive aspergillosis. Clin Vacc Immunol 15, 1095-1105.
545 546
Vilchez, R.A., Fung, J. & Kusne, S. (2002). Cryptococcosis in organ transplant recipients: an
547
overview. Am J Transplant 2, 575-580.
548 549
Villamizar, R.A., Maroto, A. & Rius, F.X. (2009). Improved detection of Candida albicans with
550
carbon nanotube field-effect transistors. Sens Actuat B 136, 451-457.
551 552
Wheat, L.J., Garringer, T., Brizendine, E. & Connolly, P. (2002). Diagnosis of histoplasmosis by
553
antigen derection based upon experience at the histoplasmosis reference laboratory. Diagn Microbial
554
Infect Dis 43, 29-37.
555 556
World Health Organization. Antimicrobial resistance – global report on surveillance. Geneva,
557
Switzerland: World Health Organization; 2014.
558 559
Yeo, S.F. & Wong, B. (2002). Current status of nonculture methods for diagnosis of invasive fungal
560
infections. Clin Microbiol Rev 15, 465-484.
561 562
Yoo, S.M., Kang, T., Kang, H., Lee, H., Kang, M., Lee, S.Y., Kim, B. (2011). Combining a
563
nanowire SERRS sensor and a target recycling reaction for ultrasensitive and multiplex identification
564
of pathogenic fungi. Small 7, 3371-3376.
565 566
Zhang, S.X. (2013). Enhancing molecular approaches for diagnosis of fungal infections. Future
567
Microbiol 8, 1599-1611.
568 569 570 571 572 573 574 575 576 577 578
16
579
Table 1: Immunochromatographic tests reported in the literature for diagnosis of medically-important
580
fungi. Target fungus
Assay time
Reported LOD or sensitivity
(Ref.)
C. albicans; C. dubliniensis
2,5 h
91.4 - 96.6% (C. albicans)
(Marot-Leblond
98.3% (C. dubliniensis)
et al., 2004)
-1
Aspergillus spp.
15 min
37 ng ml
Aspergillus spp.
----
----
10 min
≥ 5 ng ml
C. neoformans
(Thornton, 2008) (Dufresne et al., 2012)
-1
(Narvis
et
al.,
2011) Cryptococcus spp.
5 - 15 min
100% (serum); 70.7 - 92% (urine)
(Lindsley et al., 2011)
581
LOD, limit of detection
582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604
17
605
Table 2
606
Tests reported in the literature for diagnosis of medically-important fungi based on nanoengineered
607
structures. Target fungus
P. brasiliensis
Assay
Type
time
nanostructure
----
Au-NP
of
Sample
Reported
LOD
(Ref.)
or sensitivity Fungal DNA
≥ 4 mg μl
-1
(Martins
et
al., 2012) C. albicans
1h
CNT
Fungal
-1
50 CFU ml
solution Candida spp.
30 min
Au-NP
Wastewater
et al., 2009) ----
effluent C. albicans
2,5 h
PNA
Blood culture
(Villamizar
(Naja et al., 2008)
100%
(Rigby et al., 2002)
A. C.
Fumigatus; glabrata;
krusei;
C.
Few
Au-nanowire
Fungal DNA
100 fM
hours
(Yoo et al., 2011)
C.
neoformans Candida spp.