|
 
 |
| REVIEW ARTICLE |
|
| Year : 2022 | Volume
: 24
| Issue : 3 | Page : 15-24 |
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Role of gene sequencing for the diagnosis, tracking and prevention of fungal infections
Rajendra Gudisa, Shivaprakash M Rudramurthy
Department of Medical Microbiology, Division of Mycology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| Date of Submission | 17-Sep-2022 |
| Date of Acceptance | 29-Sep-2022 |
| Date of Web Publication | 11-Nov-2022 |
Correspondence Address:
Shivaprakash M Rudramurthy
Department of Medical Microbiology, Division of Mycology, Postgraduate Institute of Medical Education and Research, Chandigarh - 160 012
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jacm.jacm_16_22
The precise diagnosis of fungi is utmost important owing to the morbidity and mortality caused especially in various susceptible hosts. Among the diagnostic methods though the phenotypic methods are being routinely used among laboratories but they have inherent hindrances of being tedious, time-consuming and entail experience. These roadblocks acting as a major obstacle in precise identification of fungi, underlined the requisite for implementation of genotypic methods for routine diagnosis. Since sequencing forms the cornerstone of molecular identification of fungi, many sophisticated modalities and platforms have been developed. The role of fungal sequencing isn't limited merely to the identification of known fungal species in routine laboratory, but is of utmost significance in deciphering the emerging pathogenic and saprophytic fungal species that have the potential to infect humans. It was with the use of these sequencing techniques that the complex fungal nomenclature based on presence or absence of sexual form of the fungus, could be simplified and unified effort led to adoption of 'one-fungus--one-name' rule. Panfungal PCR targeting 28S rRNA when used in conjunction with sequencing for detection of etiological agents in patients with invasive fungal disease (IFD) from deep tissue samples has shown encouraging results. Though many sequencing modalities are available, an ideal diagnostic platform is yet awaited to meet the diversity of fungal infections in initial stages itself. The early diagnosis enables the clinician to administer appropriate therapy as and when required. The same helps in delimiting the undesired affects of antifungals as well as indirectly help in antimicrobial stewardship as well.
Keywords: Diagnostics, gene sequencing, mycology
How to cite this article:
Gudisa R, Rudramurthy SM. Role of gene sequencing for the diagnosis, tracking and prevention of fungal infections. J Acad Clin Microbiol 2022;24, Suppl S1:15-24 |
How to cite this URL:
Gudisa R, Rudramurthy SM. Role of gene sequencing for the diagnosis, tracking and prevention of fungal infections. J Acad Clin Microbiol [serial online] 2022 [cited 2023 Jun 3];24, Suppl S1:15-24. Available from: https://www.jacmjournal.org/text.asp?2022/24/3/15/360978 |
| Introduction | |  |
The kingdom fungi comprises of ~ 1.5–5 million species and embodies the second largest eukaryotic group. Fungi have their ecological niche in the environment as saprophytes and are constantly evolving and adapting to enhance their virulence and pathogenic potential. The members of the kingdom fungi are not only implicated in human diseases, but a plethora of plant and animal infections have also been attributed to fungi.[1],[2] Fungi are also one of the emerging causes of morbidity and mortality, especially amongst various susceptible hosts,[3] thereby, making precise identification of fungi imperative for the management. The diagnosis of fungi can either be based on phenotypic or genotypic methods. The phenotypic methods usually rely on microscopic and morphological features, whereas genotypic methods involve unraveling the fungal genome. Of these, phenotypic methods are most commonly used in routine laboratories but also have inherent hindrances of being tedious, time-consuming and entail experience. Moreover, morphological features are not well defined for all the fungal species and can lead to misdiagnosis at times. Another major drawback of the phenotypic methods is in the identification of rare, cryptic or novel species, for which well-characterised morphological features are not known. These roadblocks acting as a major hindrance in the precise identification of fungi, underlined the requisite for the adoption of genotypic methods in routine diagnostics. Multiple modalities such as polymerase chain reaction (PCR), multilocus sequence typing (MLST) and restriction fragment length polymorphisms have been exploited for the same, however, no single technique could provide reliable results. PCR is considered the simplest and inexpensive modalities and is usually one of the preliminary steps involved in new-sophisticated molecular tests as well. The results of a conventional PCR are readout in the form of the presence or absence of bands, with the size of the band discriminating varied species, but owing to the lack of sensitivity and specificity, this method is still under evaluation. To increase the sensitivity and specificity of this PCR, restriction enzymes can be added or real-time PCR can be performed using Taqman probes, molecular beacons or SYBR green, instead of carcinogenic ethidium bromide.[4],[5] Although a number of non-fungal non-culture-based molecular panels are Food and Drug Administration (FDA)-approved, the number of panels approved for fungi remains low. Concurrently, tremendous escalation in the number of pathogenic fungi being increasingly isolated from immunocompromised as well as immunocompetent individuals has coerced the eminent mycologists to search beyond the routine phenotypic methods and incorporate molecular modalities to facilitate the timely identification of fungi.
| DNA Sequencing | | |
Sequencing techniques have lately been adopted for diagnosis as these help in the precise identification of known as well as new/novel and rare fungal species. Sequencing techniques usually target ribosomal RNA genes encoded in the nucleus and exist in multiple copy numbers, thereby increasing the diagnostic sensitivity of the test. Moreover, these loci have multiple conserved ribosomal subunit genes in contiguity, facilitating the primers to target multiple sites simultaneously. Furthermore, the internal transcribed spacer (ITS) region is organised in such a way that variable regions are separated from the ribosomal units and consist of ITS1 and ITS2 that are spliced out soon after the transcription step and do not form the part of the ribosome. Another D1/D2 region is also located within 28S subunit, stipulating the required variability in sequencing[4],[6] [Figure 1]. | Figure 1: Common gene targets used for the identification of the fungi by sequencing
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| Gene Targets | | |
The sequencing and other molecular modalities were not quite successful initially owing to the absence of a 'universal target'. It was in 2007 at the pioneering workshop conducted in Virginia (US), where various gene candidates for fungal barcoding were extensively conferred. The vibrant discussion during this workshop paved the path for the multinational consortium of mycologists to hold a meeting pertaining the fungal barcoding in 2011 in Amsterdam.[7] The selected candidates for fungal barcoding included Small subunit ribosomal RNA (SSU), large subunit ribosomal RNA(LSU), internal transcriber spacer (ITS), RNA polymerase (RPB), RNA polymerase II (RPB2) and mini chromosome maintenance (MCM7). Many extensive studies were conducted on varied members of Ascomycetes and Basidiomycetes. These studies deciphered ITS as the most suitable candidate for fungal barcoding with the highest probability of precise identification of a larger group of fungi. Subsequently, in 2012 International Fungal Barcoding Consortium conferred ITS as the official 'universal fungal barcode' for fungi.[8],[9],[10],[11] The unique characteristics that assisted ITS to stand out among other candidates were the highest variation and that ITS has evolved the fastest amongst others, thereby aiding in the high discriminatory power for distinguishing closely related species. Moreover, it has the ease of amplification with an apparently larger barcode gap, the difference between inter and intraspecies. These findings have been confirmed by various studies.[4],[12],[13],[14],[15] As soon as ITS was selected as the universal barcode, a surge in submitted ITS sequences of clinical as well as environmental origin was noted in the GenBank. However, as the time elapsed, it was noted that merely ITS sequencing does not perform so impeccably for ~30% of fungi, including fungi that are pathogenic to humans as well as like Aspergillus, Cladosporium, Fusarium, Penicillium and Trichoderma.[8],[16],[17],[18],[19],[20] Furthermore, no single cutoff for the discriminatory power has been well-established and an arbitrary cutoff of ≤3%–5% sequence divergence is used to connote the conspecific fungi.[21],[22] Average-weighted infraspecific variability in ITS has been noted to be 1.96% and 3.33%, with a standard deviation of 3.73% and 5.62% for Ascomycota and Basidiomycota, respectively.[23],[24]
Owing to the hurdles faced while using ITS as the sole fungal barcode, other non-rDNA loci like SSU and LSU have been reviewed in detail. SSU evolved at a much slower rate, thereby, used for the discrimination of species at a higher taxonomic level using the primers NS1 and NS4. LSU with two hypervariable domains D1 and D2, on the other hand, is used for intermediate-level discrimination. In the absence of any other suitable ribosomal genes, protein-encoding genes were extensively delineated for their role as the barcode. Protein-encoding genes are comparatively difficult to amplify and are present merely as a single copy in the genome, unlike tandem repeats of ribosomal genes. Besides, its resolution is better in discriminating higher taxonomic levels due to the presence of introns that evolve at a much faster rate than ITS, are easier to align and show less variability in length. The potential candidates include subunits of beta-tubulin, calmodulin (CamM), RNA polymerase (RPB1 and RPB2), translation elongation factor (tef1) and MCM7 protein.[25] These protein-encoding genes showed encouraging results when used in conjunction with other ribosomal proteins such as ITS and LSU, and thereby, have been referred to as 'secondary barcode markers'. The precise barcoding for Aspergillus is undertaken using CaM more reliably than tub2 and RPB2, for Penicillium, RPB2/CaM are preferred over tub2 and tef1 is preferred for barcoding of Trichoderma.[25]
Studies have been performed to determine the performance of 18S rDNA PCR in comparison to ITS PCR and culture. A study by Wagner et al. has shown 91% concordance with culture and 94% concordance with ITS sequencing. The most important finding was the detection of fungal DNA in 13 culture-negative and 12 ITS-negative samples from clinically suspected fungal infections. Two samples positive by ITS PCR and negative by 18S reverse transcription (RT)-PCR were attributed to a lower yield of DNA in samples with ct >36, which could have been easily degraded by repeated freeze-thaw.[26] Moreover, 18S rDNA RT-PCR has less turnaround time, more sensitivity compared to ITS sequencing, and can thereby, be incorporated in medical laboratories for the identification of fungal pathogens from the clinical samples.
| Platforms for Sequencing | | |
Since sequencing forms the cornerstone of molecular identification of fungi, many sophisticated modalities and platforms have been developed. The gold standard of sequencing that has maintained its stance as the reference method for a long is the first-generation 'Sanger Sequencing'. It was Sanger sequencing that helped to decode the human genome and the same has been exploited in deciphering fungal pathogens as well. Sanger's sequencing is the first-generation technique, which involved the synthesis of newer DNA strands from the same site and amplifying it. It is based on the main principle of the replacement of dideoxy bases with fluorescent-labelled dideoxy bases. The identification of the nucleotide is based on the incorporation of differently labelled dideoxy nucleotides that terminates the chain, facilitating the identification. The products are further fractionated using electrophoresis on the basis of their size and the resulting peaks are analysed for decoding the query sequence. However, the major bottleneck of the modality is that only one fragment can be sequenced per Sanger reaction. Moreover, Sanger sequencing is used only for the short sequences of 300–1000 bp length and the obtained sequences are not of good quality for the initial 15–40 bps and after 700–900 bps. An error rate of 0.3% has been noted with Sanger sequencing. Another modality of the first-generation sequencing is Max-Gilbert sequencing, which utilises hydrazine, a neurotoxin in the reaction mixture. Gilbert sequencing is not suitable for sequences >500 bps and is technically more complex and time-consuming. Furthermore, there is the reduction in the read length due to incomplete cleavage reactions.[27],[28],[29]
Owing to the roadblocks of these first-generation sequencing techniques in the way of deciphering precise genome, second-generation sequencing technologies (SGSTs) came into play. Second-generation modalities have higher sensitivity to detect low-frequency variants, with lesser turnaround time, can amplify the massive amounts of DNA, have higher throughput with sample multiplexing, lower limit of detection, can sequence hundreds-to-thousands of genes simultaneously and do not require electrophoresis. The scheme of workflow is the isolation and purification of target DNA, followed by sample preparation, library validation, amplification, sequencing, imaging and data analysis [Figure 2].
Next-generation sequencing (NGS) based on the backbone of cell-free systems was introduced to combat all the shortcomings of conventional modalities. These systems have an added advantage over the conventional cloning methods as some of the DNA sequences cannot be cloned in Escherichia coli, are easily decoded by NGS and thereby, this avoids the gaps in sequence coverage, as are noted with Sanger sequencing. NGS involves the construction of 'mate paired' libraries after ligation of DNA segments from both ends or via intermolecular ligation, trailed by template preparation and sequencing with the generation of base cells, which are subsequently analyzed. The final analysis does not involve the use of electrophoresis systems and all the steps in DNA sequencing are monitored at all the steps in situ, thereby, generating a large amount of data in a single run.[27]
The first sequencing modality to be commercially put into use in 2005 was 454 sequencer. Roche 454 sequencer system is pyrosequencing and bead-based PCR that makes use of luciferase enzyme-based chemiluminescence.[27],[30],[31],[32] Subsequently, other NGS platforms such as Illumina, ABI/SOLiD and Ion Torrent came into play. Illumina is based on the bridge amplification system, wherein clonal DNA fragments form a cluster and are consequently, detected using fluorescence-based signal detection. SOLiD system, on the contrary, is bead-based electronic-PCR on the glass slide. The more recent platforms of these are ton torrent proton system, a microchip and bead-based PCR that detects changes in H+, pH and voltage. These NGS techniques have been exploited to a larger extent in studying varied fungi.[27],[30],[31],[32] However, these SGSTs also suffer from some drawbacks. SGSTs are prone to PCR errors, GC bias, have long run times, high cost of installation and also face difficulty in mapping the repeat regions. Apart from these crunches, phases of alternating nucleotide incorporation and signal detection also hinder the performance of these platforms. Thereby, in order to combat these problems, third-generation sequencing techniques surfaced. These fundamentally include the real-time sequencing of nucleic acids, either by synthesis or by ligation. In techniques using sequencing by synthesis like SMRT (single molecule real-time sequencing), incorporation as well as the detection of the signal simultaneously, while sequencing by ligation involves oxford nanopore.[27],[30],[31],[32] However, error rates as high as ~ 15% have been reported with SMRT sequencing, which are not encountered with Nanopore. The nanopore sequencing utilizes Staphylococcus aureus alpha hemolysis toxin or Mycobacterium smegmatis porin A and a potential/voltage is applied across this bilayer using a salt gradient, enabling DNA/RNA molecules electrophoretically drive across this nanopore. Nanopore sequencing requires minimal sample preparation, without the requisite of polymerase/ligase enzyme, has the capability of reading large sequences (>10,000–50,000 nucleotides) and the instrument is handy and less-expensive.[33]
| Analysis Trailing Sequencing | | |
The obtained query sequence is subjected to universal search tools to match with the available sequences in databases. The most commonly used search engine is International Sequence Database i.e., NCBI BLAST.[34] Query coverage of ≥80% and 97%–100% sequence homology is considered equitable for assigning the species name. In spite of the widespread use and humongous advantage of the BLAST, the results should be dealt with caution as ~27% of sequences submitted to NCBI BLAST have incomplete taxonomic information and ~20% are incorrectly annotated.[35],[36] Moreover, the taxonomic nomenclature of submitted sequences has not been updated to-date and type strain is not indicated for many of the sequences, thereby, searching merely in the NCBI BLAST leads to misidentification of isolates, and consequently, a requisite for curated databases was felt. At present, several online databases and websites are available, by the virtue of which accurate identification can be achieved after corresponding the sequence with authenticated data. Some of the substantial databases available include ISHAM database which focuses mainly on fungi pathogenic to humans and animals;[37],[38],[39] UNITE that unravels the sequences on basis of ITS sequencing;[40] Q-bank for phytopathogenic genera[41] and RefSeq RTL (targeted loci database) which is dedicated to ITS Ref sequences.[42] The direct links for ISHAM and UNITE are available on the NCBI platform. Besides these curated databases for a broader range of fungi, dedicated databases are also available for specific genera like FusariumID for Fusarium[43] and TrichoBLAST/TrichoKeys for Trichoderma.[44] CBS-KNAW is the alternative database that incorporates all the major databases and assists the user by allowing him to select varied curated databases simultaneously, thereby, enabling more precise identification across various fungal databases.[45]
| Applications of Sequencing | | |
Applications in taxonomy and genetic diversity
The role of fungal sequencing is not limited merely to the identification of known fungal species in routine laboratory but is of utmost significance in deciphering the emerging pathogenic and saprophytic fungal species that have the potential to infect humans. Recently, humongous genetic diversity was unraveled in the section Flavi of genus Aspergillus, by virtue of the comparative genome sequencing analysis. It was divulged that Aspergillus minisclerotigenes is more closely related to Aspergillus oryzae than Aspergillus flavus.[46] Another study was conducted to decipher the complex phylogenetic relationship amongst Mucor genus. Besides studying the genome of various members, sequencing has also been used to delineate the emergence of new pathogenic species. Four newly sequenced genomes of Mucor environmental isolates, namely Mucor fuscus, Mucor lanceolatus, Mucor racemosus and Mucor endophyticus have recently been identified. These are known to be used in cheese production or are plant saprophytes but have also been reported to be rarely implicated in human and animal infections.[47] Furthermore, multiple omic's techniques have been utilised to decipher the pathogenesis and genomic variability amongst the members of Mucorales. It was noted that Rhizopus microsporus shows wide variation in genome length and mating type locus. On the contrary, Apophysomyces and Lictheimia exhibited a lesser number of transposable elements, with an increase in genes encoding heterokaryon incompatibility and RNA interference pathway.[48] The sequencing techniques have also been exploited to unearth the genomic basis of unconditionally emerging multidrug-resistant Candida auris.[49],[50] Studies on the mitochondrial genome have shown that different clades of C. auris exhibit low genetic variation; however, two sub-branches were noted within the South Asian clade.[49] More studies on the mitochondrial genome of pathogenic fungi are essential to unravel protein targets that are of therapeutic potential.
With the advent of these sequencing techniques, several changes in taxonomy have been made feasible. Recently, Cryptococcus underwent several taxonomic changes after extensive phylogenetic research, Hagen et al. proposed the division of Cryptococcus into 11 genotypes (7 haploid and 4 hybrid). They renewed older names based on known serotypes and RFLP genotypes. Cryptococcus neoformans var. Cryptococcus grubii (Serotype A, VNI, VNII) has been renamed as C. neoformans, Serotype VNIII as C. deneoformans and Serotype D, VNIV as C. neoformans var. neoformans. C. gattii (serotypes B and C) has been further divided into five new species: C. gattii (VGI), Cryptococcus deuterogatii (VGII), Cryptococcus bacillisporus (VGIII), Cryptococcus tetragattii (VGIV) and Cryptococcus decagatii (VGIV/VGIIIc). Besides these, on the basis of evolutionary history and phylogeny, five major lineages within the Tremellomycetes (Cystofilobasidiales, Filobasidiales, Tremellales, Trichosporonales and Holtermannia) have also been reconsidered and few like Papiliotrema laurentii (Cryptococcus laurentii) and Naganishia albidus (Cryptococcus albidus) have been renamed.[51],[52],[53] It was with the use of these sequencing techniques that the complex fungal nomenclature based on the presence or absence of the sexual form of the fungus could be simplified and unified effort led to the adoption of 'one-fungus--one-name' rule.[7]
Sequencing directly from clinical samples
Sequencing techniques are of use not only in research but also contribute substantially to routine diagnostics, especially in cases with unknown aetiology or fastidious organisms, where there is a scant amount of sample and high level of clinical suspicion. For the clinical specimens, two approaches are available, unbiased and targeted mNGS. The unbiased mNGS target the whole nucleic acid, of the patient and pathogen, which further facilitates the simultaneous detection of the plethora of pathogens, namely bacteria, fungi, viruses and parasites. However, background noise and contamination make the computational analysis challenging. On the contrary, targeted mNGS is more sensitive for organism detection can either be amplicon based or target probe based.[54],[55] These techniques help in delineating the causative agents of rare infections as well as in deciphering the outbreaks, so that early preventive and management measures can be taken.
Panfungal PCR targeting 28S rRNA when used in conjunction with sequencing for detection of etiological agents in patients with invasive fungal disease (IFD) from deep tissue samples has shown encouraging results. Sensitivity and specificity were noted to be 60.5% and 91.7%, respectively with 86% concordance with culture and 87% with microscopy. The analytical sensitivity was found to be ~100% for deep tissue samples.[56] Another study has shown 95.6% sensitivity and 96.4% specificity of broad-range PCR for IFD cases.[57] It has been noted that broad-range PCR has greater sensitivity and specificity for samples from sterile sites as compared to bronchoalveolar lavage (BAL) (92% vs. 80%).[58] A study by Trubiano et al. showed PCR to be superior modality as compared to culture and histopathology for the detection of fungi.[58] Another study has demonstrated better positivity with PCR in sinus samples as compared to culture (37.1% vs 13.7%) in both fresh and formalin-fixed paraffin-embedded (FFPE) tissue.[59] PCR could detect 14.9% additional samples and positivity was equivalent in both fresh and FFPE (34.5% and 42.3%).[59],[60],[61]
Detection of fungi in blood has always been challenging owing to the lower load of fungi and presence of inhibitors in the blood. Approximately >50% of samples has ≤1CFU/ml, thereby hindering the inclusion of molecular methods in routine diagnosis. Moreover, a standard ct value is difficult to be established and is influenced by clinical conditions, method of DNA extraction and selected gene targets.[62] Recently, light cyclers and targeted PCRs have been developed with the potential to detect ~ 1CFU/ml of Candida.[63],[64] The only FDA-approved commercial system for the detection of Candida in the blood is the T2Candida with the ability to detect five Candida species, which outpaces blood culture for the diagnosis.[65] Multiplex PCR-based panels have also been developed targeting Candida and filamentous fungi in IFD. A recent study by Pereira et al. has shown good sensitivity of 89% and specificity of 100% in these cases, directly from clinical samples.[66] The authors have devised a unique way to interpret the resulting specific peaks using precise fluorescent dyes, thereby eliminating the noise of non-specific amplification.[66]
Other fungi where molecular tests are outperforming the conventional methods in terms of TAT and diagnostic performance include Aspergillus, Mucorales and Pneumocystis jirovecii. Quantitative real-time PCR targeting mtSSU has been reported to be more sensitive by Pneumocystis working group, as compared to other targets such as major surface glycoprotein, mitochondrial large subunit and beta-tubulin (single copy).[67] A recent study has utilised MinION-based NGS for the diagnosis of Penumocystic jirovecii directly from BAL and sputum samples.[68]
Furthermore, owing to the burden and mortality associated with Aspergillosis, the European Aspergillus PCR initiative with the working group of ISHAM collaborated globally to develop, standardise and validate the PCR specific for Aspergillus.[69] Meta-analysis conducted to evaluate the sensitivity and specificity of PCR in BAL samples included 1191 at-risk patients and disclosed 91% sensitivity and 92% specificity of Aspergillus PCR.[70] Outstanding the standardised protocol of Aspergillus PCR, many kits have been validated for the detection of Aspergillus from clinical samples, serum and BAL, with a sensitivity varying from 68% to 95%.[71] Various commercial kits available include MycoReal Aspergillus® (ingenetix GmbH, Austria), RenDX Fungiplex® (Renishaw Diagnostics Ltd., Glasgow, United Kingdom), AsperGenius® (PathoNostics, Maastricht, Netherlands), Aspergillus Real-time PCR Panel® (Viracor Eurofins, Framingham, MA, United States), MycAssay Aspergillus® (Myconostica Ltd., Cambridge, United Kingdom), Aspergillus spp. Alert Kit® (Nanogen, now ELITechGroup, Turin, Italy), Fungiplex Aspergillus® (Bruker Daltonik GmbH, Bremen, Germany) and SeptiFast® (Roche Molecular Diagnostics, Penzberg, Germany).[71]
A systemic review has delineated the sensitivity and specificity of Aspergillus PCR in blood to be 79.2% and 79.6%, respectively for a single positive result and 59.6% and 95.1%, respectively for two consecutive positive test results.[72] Another advantage of PCR in the blood is early positivity as compared to galactomannan in the diagnosis of invasive aspergillosis (IA). Owing to its specificity and sensitivity, PCR has been added by European Organisation for the Research and Treatment of Cancer (EORTC)/Mycoses Study Group (MSG) in the diagnostic criteria of IA.[73] However, this diagnostic pre-eminence is seen mainly in neutropenic patients and Aspergillus PCR holds low significance in non-neutropenic patients. In non-neutropenic patients, Aspergillus PCR has demonstrated variable sensitivity that can be as low as 11% in intensive care settings and variable in BAL samples.[74],[75],[76] Furthermore, positive PCR results in BAL should always be dealt with caution as patients with structural or functional lung disease might already be colonised with Aspergillus. The substantial hurdle in rolling out of blood Aspergillus PCR in routine diagnostics in non-neutropenic patients is its poor performance in the presence of antifungal prophylaxis, which is a part of standard care in most settings. Thereby, site-specific Aspergillus nested PCR is preferred to blood Aspergillus PCR as sensitivity is noted to be high in site-specific samples with ~ 84% sensitivity in BAL, 100% in CSF and 67% in tissues, in comparison to ~ 8% in blood samples.[77]
Besides these fungi, diagnosis of Mucorales is of paramount connotation and is quite challenging; however, rapid but precise diagnosis is required owing to rapid angioinvasion. Conventional methods have low sensitivity and yield as Mucorales are quite fragile and antifungal therapy initiated beforehand further encumbers the growth. Serological tests do not have many roles in the infection, thereby paving the way for molecular tests. The well-accepted gene targets for Mucorales include ITS, 18S rDNA, ZM1 and ZM3, cytochrome B and 28S rDNA.[78],[79],[80],[81],[82] Recently, studies have shown high sensitivity of rnl, 16-23S rDNA and CotH in the diagnosis of Mucorales.[83],[84],[85] Triple real-time PCR targeting Acoryl, Muc1 and RMuc has also been developed.[86],[87]
Apart from the detection of Mucorales from the tissue samples, studies have been conducted to detect the same in blood and other body fluids. Mucorales DNA can be detected in blood 3–68 days before any conventional test turns positive.[88],[89] Sensitivity of 81%–92% has been reported for the diagnosis of Rhizopus/Mucor, Rhizomucor and Lictheimia with 18S rDNA as target;[88] however, CotH (spore-coating protein) has higher sensitivity and specificity of 90% and 100%, respectively in serum, urine and BAL samples.[85] Mucorales DNA has also been detected in CSF samples in case of cerebral mucormycosis.[87] Recently, MucorGenius (Pathonostics, Maastricht, The Netherlands) kit-based detection has also been made commercially available, although it is presently non-FDA approved.[90],[91] The sensitivity has been noted to be 75%, with 28S rRNA as a gene target for real-time PCR assay. The kit can detect Rhizopus spp., Rhizomucor spp, Mucor spp., Cunninghamella spp. and Lictheimia spp.[90],[91] Despite the accessibility of a wide array of techniques, available methods are still facing the obstacle of the inability to detect DNAemia in patients with low spore burden and are less sensitive in patients on antifungal therapy. Recently, MODIMOUCOR prospective trial has argued the inclusion of molecular detection of Mucorales in EORTC/MSGERC diagnostic criteria. The study has shown 85.2% sensitivity and 89.8% specificity of real-time PCR for the detection of Rhizopus/Mucor, Rhizomucor and Lictheimia. Moreover, the study has exhibited that negativity of Mucorales PCR within seven days of treatment with liposomal amphotericin B is associated with 85% lower 30-day mortality;[92] however, the tests need further standardisation and validation before these can be rolled out in routine diagnostics.
Outbreak tracking
Molecular modalities have also been exploited to decipher the causative agent in outbreaks. One such instance was the use of real-time nucleic acid sequence-based amplification using molecular beacons to unravel bloodstream infection by Exserohilum rostratum.[93] At present, WGS is being exploited to detect, monitor and prevent the spread of outbreak, simultaneously deciphering the source and propagation.[94] WGS established Exserohilum as the causative agent implicated in meningitis after methylprednisolone injection, where WGS detected single nucleotide polymorphisms and phylogenetic analysis abetted in the diagnosis.[95] WGS has also been utilised for differentiating the indigenous cases of Coccidiodomycosis from cases acquired outside the state, on the basis of clustering in phylogenetic analysis.[96] Furthermore, the ongoing transmission of C. auris is also being tracked down constantly using WGS.[97] Different typing methods such as MLST have also been employed to untangle the phylogeny of the causative agent.[98]
Tracing antifungal resistance
Antifungal resistance is an area of paramount substance and is routinely detected using the minimum inhibitory concentration (MIC) broth microdilution method; however, the same has major roadblocks of longer TAT and poor growth of non-sporulating fungi. Genotypic detection of antifungal resistance should correspond to the findings of MIC and the results should have been specifically documented in the animal models.[67] Most of the studies have been focused on decrypting the mechanisms of resistance to azoles and echinocandins in Candida species and Aspergillus fumigatus.[67]
Syndromic panel-based approach
Owing to the availability of reliable molecular modalities for the detection of fungi, these have further been translated into panels for rapid reliable detection of plethora of microbes implicated in an infectious syndrome. Many FDA-approved panels are available namely meningitis/encephalitis panel, sepsis panel, respiratory panel and gastrointestinal panel. Meningitis panel included the detection of C. neoformans and C. gattii, along with varied bacterial and viral agents. However, the sensitivity has been found lower compared to the antigenic tests available for Cryptococcus.[99],[100] Pneumonia/respiratory infection panels include the detection of Cryptococcus, Pneumocystis and A. fumigatus amongst other agents; however, less data are available from the clinical trials. Another most commonly employed panel is the sepsis panel, which is available as many platforms and mainly target Candida species amongst the fungi. One of the panels from ePlex targets Cryptococcus, Rhodotorula and Fusarium along with Candida species. Apart from the PCR techniques, fluorescent in situ hybridisation has also been employed in the sepsis panel.[67],[101] Although multiple syndromic panels are approved and available, further evaluation in different population sets and for different geographical areas is still warranted before rolling out these for routine diagnostics.
| Conclusion | | |
Although many sequencing modalities are available, an ideal diagnostic platform is yet awaited that could meet the diversity of fungal infections, concurrently with high negative predictive value, less steps requiring sample manipulation and the one that can detect the infection using non-invasive samples, as fast as possible to contain the infection in initial stages itself. The early diagnosis enables the clinician to administer pre-emptive and empirical therapy as and when required. The same helps in delimiting the undesired effects of antifungals as well as indirectly help in antimicrobial stewardship as well.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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