Molecular Detection and Identification of Candida

Abstract

Human opportunistic yeast infections have become more common in recent years. Many infections are difficult to treat and diagnose due to the large number and diversity of organisms that can cause sickness. In addition, infectious strains eventually develop resistance to one or more antifungal medicines, severely limiting treatment choices and emphasizing the need of early detection of the infective agent and its drug sensitivity profile. Current techniques for detecting species and resistances are insensitive and specific, and they frequently need pre-cultivation of the causal agent, which delays diagnosis. New high-throughput technologies, such as next-generation sequencing or proteomics, make it possible to identify yeast infections more sensitively, accurately, and quickly. Opportunistic yeast pathogens, cause a wide spectrum of superficial and systemic infections, many of which are lethal. In this work, we give an overview of current and newly created approaches. It may be used to determine the presence of yeast infections as well as their medication resistance. Throughout the book, we highlight the following points: Explaining the benefits and drawbacks of each strategy, as well as the most promising advancements on their route to success.

1. Introduction

Various infections, ranging from superficial to systemic, are caused by opportunistic yeast pathogens, which are often deadly [1]. These viruses have become more common in recent years, making them a leading source of life-threatening illnesses. This is due in part to medical advancements, which have increased the survival rate of patients who are particularly vulnerable, such as premature babies, the elderly, and those with compromised immune systems. Furthermore, the widespread use of catheters, antibiotics, and abdominal surgery promotes opportunistic yeast expansion outside of their natural symbiont habitats [2]. Despite recent advances, death rates from invasive candidiasis remain high, at over 40%, and treatment is complicated by antifungal resistance and the advent of novel infections [3, 4]. Non-candida species such as Candida dubliniensis, Candida glabrata, Pichia kudriavzevii, Candida parapsilosis, and Candida tropicalis are becoming increasingly widespread. Candida auris has been around for a long time [5, 6].

Candida spp. have been identified as the cause of candidiasis [7, 8, 9]. Crossing pathogenic and non-pathogenic strains can result in the emergence of new virulent variants [10]. Candida spp. does not belong to a single genus in the phylogenetic sense, as different Candida species may be found across the Saccharomycotina tree [11, 12].

Many therapeutically significant Candida species may be renamed as a result of current work on yeast genes and taxonomy, and physicians should be aware of this potential. Because virulence and antifungal resistance differ between species [13] and even between strains of the same species [14, 15], making treatment decisions at the species level (or even higher) is critical. As a result, it’s vital to identify the infection’s causal agent precisely, accurately, and rapidly so that proper antifungal medication may be started right once, especially in those with life-threatening candidiasis.

Candidasis is diagnosed using microscopy, selective culture, and/or biochemical methods [16, 17]. All of these approaches require isolating and cultureing the infectious agent from clinical samples, which takes around 48 hours for most pathogenic yeasts but may take longer for other samples or species. Furthermore, identification procedures need specialized expertise, can provide perplexing findings, and are time-consuming, all of which add to the time it takes to achieve an accurate diagnosis. As a result, alternative techniques based on direct detection of diagnostic compounds are gaining popularity [18].

Proteomics-based methods and targeted DNA sequencing are two examples of molecular diagnostic approaches that might be used directly on clinical samples. The need for infectious agent culture, the possibility to utilize a direct clinical sample, sensitivity and accuracy, cost, time, and knowledge requirements, as well as the spectrum of species that may be identified, all differ between the current and future techniques. Some sophisticated approaches promise quick identification of both types of infectious agents as well as the emergence of treatment resistance. The existence of infected cells does not necessarily correspond to DNA detection, which is a common flaw in DNA-based approaches [19].

As a result, several modern approaches concentrate on identifying RNA from actively transcribed genes, which is a better proxy for active cells and can also provide indications that differentiate invasive from commensal activity [20].

The field of yeast infection diagnosis has substantially advanced in the last decade, and is presently experiencing a revolution, thanks to the advent of sophisticated sequencing and proteomics methods. However, there is still a long way to go between the novel diagnostic method’s effective proof of concept and its acceptability for broad clinical application. Diagnostic tools should be low-cost, quick, sensitive, accurate, and simple to use [20].

Currently, there are several molecular diagnostic approaches for yeasts on the market. They do, however, concentrate on the most prevalent pathogenic yeast species, leaving the rare and emerging pathogenic yeast species to be found later. Massive outbreaks of drug-resistant Corynebacterium auris isolates in hospital settings have underlined this fact, which were first misread by existing commercial approaches [3, 4]. In this article, we give a comprehensive review of the existing approaches for characterizing yeast infection and treatment resistance profiles. During the review, we underline the advantages and disadvantages of each approach, as well as the prospective new advances brought about by modern technology. The primary accessible techniques and strategies are shown in Figure 1.

Figure 1.

An overview of fungal infection detection methods. This graph depicts the many ways for identifying fungal species. It’s possible to utilize mass spectrometry (blue backdrop), nucleic acid (red background), or antibody-based approaches (orange background). Techniques that combine more than one of these characteristics are represented in the section borders.

2. Molecular identification of targeted DNA regions

It was found that polymerase chain reaction (PCR) allows for the selective amplification of a specific segment of DNA, yielding millions of copies of the sequence (amplicon) in a matter of hours. This method has a lot of diagnostic potential since it allows for the detection of small quantities of target DNA using specific oligonucleotides. To make the diagnosis, the existence of the amplicon (if unique to the target species), its size, or its exact sequence, which may be determined by sequencing or hybridization to a particular probe, can be employed. The combination of particular PCR designs with post-analysis has resulted in several alternative PCR-based approaches that are increasingly being employed in the diagnosis of yeast infections (Figure 2).

Figure 2.

PCR-based techniques for fungal diagnoses are depicted in this diagram. In diagnostics, there are two types of PCR-based procedures: (A) traditional PCR-based methods and (B) real-time PCR-based methods.

Furthermore, specific patterns in the DNA of infectious microorganisms can be detected without the use of selective PCR amplification, for example, by direct hybridization with specific probes or by recognizing patterns in the length of fragments resulting from enzymatic digestion of DNA by exonucleases. These strategies will also be discussed in this section [21].

2.1 End-point PCR-based amplification

A typical approach for detecting and identifying infectious agents in cultures or clinical samples is the endpoint [22]. Primers that preferentially amplify the target locus have historically been used to detect and identify infections. The locus might be species-specific, producing an amplicon only if the target species is present, or it could have a broader range, producing an amplicon from several species. In the latter situation, differences in length, melting temperature, or sequence between the amplicons may allow for a more precise identification. A target location for a conserved rDNA gene found in multiple copies has been frequently utilized [23].

The existence of numerous copies, which allows amplification of even a small number of cells, and the intrinsically high degree of variation found in some locations, which allows the construction of species-specific tests, are two aspects that make this site excellent for diagnosis. The internal transcription spacer (ITS) of the rDNA locus has been acknowledged as the worldwide gold standard for fungal species identification [24, 25], and various global primers amplify this region. Other parts of the rDNA locus, such as Trichosporum’s Intergenic Spacer region 1 (IGS1) may be more useful for identifying certain clusters or species [26].

Additional markers, such as beta-tubulin or translation elongation factor genes, can also be utilized in other fungal species [27]. It is now simpler, quicker, and more specific to find particular or diagnostic areas because to advances in bioinformatics and the availability of whole-genome sequencing data [28, 29].

In normal laboratories, PCR primers for common fungal species belonging to the major human pathogenic genera, such as Candida, Aspergillus, Cryptococcus and Pneumocystis, are used more frequently than broad-spectrum primers. The lack of species-specific commercial testing for less common species within those genera, as well as for other new fungal genera that often cause severe and rapidly progressing infections [30], is a drawback. Fungal or broad-spectrum PCR primers have the advantage of being able to recognize both common and unusual fungi. However, due to the sensitivity of the test, even a non-pathogenic fungus, symbiont fungi, or mycorrhizae may provide a positive result, the results should be evaluated by experts [31].

The YEAST panel is a newly constructed multiplexer panel that can identify 21 clinically significant yeast species from the genera Candida, Trichosporon, Rhodotorula, Cryptococcus, and Geotrichum, which account for 95% of yeast infections [32]. In many circumstances, amplicon sequencing is necessary to make a particular diagnosis. PCR’s potential goes beyond species identification to the detection of more subtle genetic variations, such as those that contribute to a particular resistance profile.

Due to sensitivity limits and a lack of specialized techniques and commercial assays for many rare and developing fungal diseases, endpoint PCR is frequently not included in normal investigations to detect fungal pathogens on clinical samples [33]. However, in order to employ this excellent methodology for direct diagnosis utilizing patient samples, additional strategies for increasing sensitivity are being developed. Given the small number of infectious cells present in the test samples, the high sensitivity and specificity that PCR may theoretically give is an attractive prospect. There are various additional restrictions that may render PCR inefficient when DNA templates are acquired from clinical samples [34].

Amplification of DNA-extracted blood samples is hampered by the presence of hemoglobin and anticoagulants [35, 36]. Some DNA extraction businesses address this issue by incorporating treatment stages to eliminate potential inhibitors, which can be a problem with other methods. Modified PCR methods are being developed to overcome concerns such as low specificity. Using two overlapping primer pairs in nested PCR, for example, can enhance both specificity and sensitivity [37].

2.2 Analysis of fragment length polymorphisms

The fact that sequence differences can be identified after digestion with a sequence-specific restriction endonuclease is exploited by restriction fragment length polymorphism (RFLP). After amplification of the appropriate DNA fragments, this method is frequently used in combination with polymerase chain reaction. Candida palmiolate, fermented Candida, Candida albicans, Candida duplexensis, Candida refractory, and C. albicans were effectively identified using PCR-RFLP [38, 39, 40]. RFLP analysis requires large data sets, which limits its application in the clinic. Amplification fragment length polymorphism (AFLP), a related technique, reverses the order of polymerase chain reaction (PCR) and restriction cleavage [41].

This technique was utilized to analyze interspecific variability and identify various fungi in clinical isolates, such as Cryptococcus neoformans/gattii complex species and Candida species [42]. Despite the fact that AFLP takes longer and costs more than RFLP, it has been proven to be reliable, fast, and highly repeatable under controlled settings [43].

2.3 Real-time PCR

Quantitative PCR (qPCR), originally known as real-time PCR, quantifies the quantity of PCR product using fluorescent probes or interfacial dyes [44]. Dyes (for example, SYBR Green) are less costly than probes, but they have the drawback of attaching to dsDNA in non-specific ways, such as primer dimers and non-targeting DNA [45].

Primer-probe hairpins (e.g., Scorpion probes), hybridization probes (e.g., Molecular Beacons), hydrolysis probes (e.g., TaqMan), unnatural bases (PlexorTM primer), and synthetic-based probes are all now available. Peptide nucleic acids (PNAs) and locked nucleic acids (LNAs) (Faltin, Zengerle, and von Stet hydrolysis and hybridization probes) are being employed frequently in clinical diagnostics [46].

There are numerous categories for identifying main candida species. With the support of criteria such as the minimal information required to publish qPCR experiments, these approaches have been standardized [47]. The key benefit of qPCR over traditional PCR is that it can identify the payload of infectious diseases, although at a higher cost. Although in the clinic, simple positive or negative testing for the presence of the pathogen is frequently required, knowledge of pregnancy can be useful in monitoring the effect of treatment or identifying infection in a non-sterile human environment where overgrowth rather than simple presence is required [48].

Another clinical use of qPCR is to track the level of azole resistance in Candida species. Because significant levels of transcription are required when the predominant route of resistance is up-regulation of the gene encoding drug target or drug efflux pumps [48, 49, 50], these genes are linked to azole resistance.

2.4 MCA

MCA uses the temperature-dependent dissociation kinetics of dsDNA to discriminate PCR amplicons. The temperature at which half of a dsDNA molecule splits into single DNA is known as the melting point (Tm). Because the G-C base pairs produce three hydrogen bonds vs. two in the A-T base pairs, the Tm is sequence dependent, needing more energy to solve the first. As a result, a higher Tm level corresponds with a higher G/C concentration. Using split fluorescent dyes that glow only when bound to dsDNA, the dissociation process may be monitored as a reduction in fluorescence during progressive heating [51].

HRMA (High Resolution Melt Analysis) is a modernized version of classic MCA [52]. HRMA employs more advanced algorithms and fluorescence sensors, as well as brighter pigments in higher concentrations.

HRMA can detect and monitor minor fluorescence variations induced by changes in Tm below 0.5°C, allowing one base pair precision detection of sequence discrepancies. A Tm change of 41/length of sequence C occurs when a single G-C is substituted with an A-T [53].

As a result, amplicon length is an important consideration when organizing HRMA studies. Short fragments (50–300 bp) give a single, well-defined fusion region and simple profiles, but bigger fragments may represent several peaks and reduce discriminatory power [54].

Furthermore, selecting a suitable fluorescent dye is critical. Unsaturated colors (such as SYBR Green) hinder polymerization at maximum brightness dosages. Saturated dyes from the most recent generation (such as SYTO9 and ResoLight) do not have this inhibitory effect and can thus be utilized when saturated. Unsaturated dyes, on the other hand, can re-link to free sites during dissolution, resulting in more fuzzy forms [55].

Decath et al. (2013) effectively differentiated cultivated strains of 16 Candida species, including pathogenic primary Candida species, in 6 hours using MCA in the ITS2 region [56]. MCA is also utilized in the commercial multiplexed qPCR kit kiAsperGenius R. [56].

This group not only finds and distinguishes Aspergillus fumigatus, Aspergillus terreus, and Aspergillus spp., but also gives information on Ammophilus fumigatus resistance by detecting resistance-related mutations in the cyp51a gene [57].

Different approaches such as differential media culture (Candida ID, CHROMagar), MALDI-TOF mass spectrometry, and DNA sequencing have been compared to HRMA [58]. Because MCA and HRMA employ G/C content to differentiate two unique DNA fragments, they are limited in their ability to detect all amplicon sequence changes. The species pairings of Candida orthopsilosis and Candida metapsilosis [59], and Candida fabianii and Meyerozyma guilliermondii are indistinguishable due to similar G/C structure and Tm overlap [60].

The HRMA approach is inexpensive, employs generic tools, takes a short amount of time to perform, is straightforward, and uses a closed tube format, which eliminates the danger of PCR contamination [61]. As a consequence, HRMA offers a quick and low-cost method for measuring and identifying the most common clinical forms of Candida, as well as detecting co-infections with these species, straight from clinical samples [62].

2.5 Detection of SNPs

Detecting alterations at the single nucleotide level can be extremely important in the clinic, especially if the mutation is linked to medication resistance. Polymorphisms can be detected with a high degree of specificity using PCR-based methods. In these strategies, the following tactics are typically used: MCA is collected using real-time PCR with hydrolysis probes, hybridization probes, or fluorescent dye coupled to dsDNA; (ii) PCR (ASP) selectively amplifies target alleles using Allelespecific Taq DNA polymerase and 3-end allele-specific primers [63].

ASP can identify single core alterations, as well as modest insertions and deletions. The amplification thermal mutagenesis system (ARMS) and PCR amplification are two techniques that are comparable [64]. The combination of ASP with quantitative PCR (AS-qRT-PCR) and droplet PCR (AS-droplet-PCR) may improve genotyping and quantification of chimerism in recipients as compared to a standard short tandem polymerase chain reaction [65]. Hybridization using SNP-specific probes is another possibility. DNA array devices that combine parallel hybridization with many probes may provide a quick and simple testing platform. All of these methods, however, have the limitation of requiring extensive knowledge of the most critical SNPs [66].

All of these methods, including group systems, were utilized to find resistance mutations in a variety of fungal infections. To differentiate C. albicans isolates with and without hotspot mutations in ERG11, which provide azole resistance, MCA was utilized. PCR-based technologies are used in a variety of ways [67].

It was tweaked to detect SNPs in clinical samples. For Resistance mutations in the FKS1 and FKS2 genes in C. glabrata, and in FKS1 in C. albicans [66] have been developed PCR tests to detect echinocandin. Mutations in FKS1 and FKS2 in C. glabrata were also studied utilizing MCA and Luminex technology [65]. Finally, there are a variety of SNP detection technologies that may be utilized to uncover variants that cause resistance [67].

3. Conclusions

To summarize, molecular approaches for quantifying resistance in clinical samples take a significant amount of effort. Only a few commercially accessible diagnostic procedures include clinical testing. However, with resistance rates on the rise, clinical specimen resistance screening is becoming more important. Furthermore, molecular approaches may only confirm the existence of known resistance mutations; they cannot rule out resistance based on unreported mutations or other biological processes like biofilm formation. As a result, traditional susceptibility testing will continue to be an important method for detecting resistance variations.

A trustworthy, speedy, and user-friendly application approach for correctly identifying Candida species, particularly in clinical specimens, is real-time polymerase chain reaction. It has a high sensitivity and specificity and can identify fungal DNA in blood, different bodily fluids, and biopsy samples within six hours. Because antifungal susceptibility patterns vary between different species, accurate identification of Candida morphologies is crucial. Correct identification facilitates the choice of antifungal medications for both prevention and therapy. More clinical studies are required to determine the full potential of these novel treatments for various patient populations. Future research must assess the potential advantages of early therapy for individuals at risk for invasive Candida infection based on real-time polymerase chain reaction.

Conflict of interest

There is no conflict of interest.