Fungi act as major decomposers that promote plant growth. Humans since long have used fungi in food, fermentation, antibiotics, and pesticides. Owing to the immense importance of fungi in bioeconomy, their use has been implicated in sustainable global development. Research into fungi has increased over time in order to produce biological solutions to important global problems¹.

The first fungal genome (Saccharomyces cerevisiae strain S288C) was made public in 1996². Since then, the number of available fungal genomic sequences has increased exponentially. The Fungal Genome Initiative of the BROAD institute of MIT and Harvard University has sequenced over 100 fungi. Recently, the Department of Energy's Joint Genome Institute conducted a project to sequence 1,000 fungal genomes, focusing on divergent fungal species (http://1000.- fungalgenomes.org)^3,4. Fungal genomic studies provide the genome sequences of fungi, and they also lay the groundwork for solutions to major challenges in health, agriculture, enzyme biotechnology, bioenergy, and ecological diversity⁵. Majority of the fungal genomic studies have focused on sequencing fungi that cause plant diseases or fungi used in enzyme biotechnology and bioenergy. Advances in sequencing techniques have enabled the production of genome-scale functional data, such as fungal transcriptomes and proteomes. Researchers need to acquire the knowledge of bioinformatics to process the huge amount of biological data.

In the field of dermatology, new classifications of dermatophytes and next-generation sequencing (NGS) have been utilized for investigation into the impacts of fungi. Most dermatologists find the new sequencing technologies challenging because they are novel and unfamiliar. In this review, we provide a brief introduction to bioinformatic approaches to fungal genomics in the field of dermatology.

Until recently, the analyses of fungal cultures and preserved specimens have been the dominant approach to study fungal diversity. However, over the past several decades, genetic barcoding of fungi has become increasingly important. Initially, Walker and Doolittle⁶ used 5S rRNA gene sequences to analyze the associations among diverse groups of fungi. White et al.⁷ proposed a set of fungal DNA primers including internal transcribed spacer (ITS) primers ITS1 and ITS4 with advances in PCR and DNA sequencing technologies. Barcording involves rapid biodiversity assessment by combining DNA-based species identification and high-throughput DNA sequencing. Fungal barcoding uses universal PCR primers to mass amplify DNA barcodes from large collections of organisms or from environmental DNA.

Sequencing methods have evolved from Sanger's enzymatic di-deoxy sequencing method to Maxam and Gilbert's chemical degradation technique and then to NGS platforms^8,9. NGS platforms commonly used in fungi are Illumina, SOLiD, Ion Torrent, and Pacific Biosciences. With these platforms, NGS can be used to analyze exomes, large gene panels, complete RNA transcriptomes, and chromatin immunoprecipitation.

DNA sequence-based analysis can be used to identity of an isolate based upon its similarity to known sequences. Advances in NGS techniques allow the sequencing of thousands or millions of barcodes or even the entire genomes, in parallel. These barcode (amplicon) and metagenome sequencing methods are useful for describing the composition of entire microbial communities. Massively parallel DNA sequencing, such as that used in NGS and amplicon sequencing, generates large datasets. These datasets require large storage capacities, and the extraction of information is often challenging. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been popular for the identification of microorganisms¹⁰. In comparison with DNA sequencing-based methods, MALDI-TOF MS uses whole cells or crude cell extracts. The simple preparation of samples and reference spectra, short measurement times, and low costs per sample are advantages of MALDI-TOF MS (Table 1).

DNA barcoding employs standardized 500~800-bp sequences to identify species from all eukaryotic kingdoms, using primers that are applicable to the broadest possible taxonomic groups. A region of the mitochondrial gene encoding the cytochrome c oxidase subunit 1 (CO1) is the barcode for animals, and it is the default marker adopted for all groups of organisms by the Consortium for the Barcode of Life¹¹.

The nuclear rRNA cistron has been used for fungal diagnostics and phylogenetics¹². The eukaryotic rRNA cistron consists of the 18S, 5.8S, and 28S rRNA genes, which are transcribed as a unit by RNA polymerase I. Post-transcriptional processes split the cistron, removing two ITSs. These two spacers, including the 5.8S gene, are usually referred to as the ITS region (Figure 1). The barcode ITS region is widely used for the identification of fungi. The 28S nuclear ribosomal large subunit rRNA gene (LSU) is sometimes used alone to discriminate species, or can be combined with ITS. For yeasts, the D1/D2 region of LSU was adopted for characterizing species long before the concept of DNA barcoding was promoted.

Furthermore, protein-coding genes are used in phylogenetic analyses for the identification of fungal species. For example, the Ascomycota, including the genus Aspergillus, are more easily identified using protein-coding genes than by using rRNA genes¹³. There are reports of the use of specific markers for fungal identification, such as translation elongation factor 1-α for Fusarium¹⁴. Among the protein-coding genes, the largest subunit of RNA polymerase II (RPB1) may have potential as a fungal barcode; it is ubiquitous and exists as a single copy, and it has a slow rate of sequence divergence¹⁵. RPB1 primers were developed for the Assembling the Fungal Tree of Life project¹⁶.

In 2012, the International Fungal Barcoding Consortium formally recommended that the ITS regions of the nuclear rRNA gene cluster be used as the primary fungal barcode¹⁷. In fungi, the entire ITS region is approximately 600-bp long and contains two variable spacers, ITS-1 and ITS-2, separated by the highly conserved 5.8S rRNA gene⁷. The ITS region is flanked by the 18S rRNA gene at the 5'-end of the ITS-1 spacer and by the 28S rRNA gene at the 3'-end of the ITS-2 spacer. The highly conserved 18S, 5.8S, and 28S rRNA genes facilitate the design of "universal primers" to amplify the ITS-1 region, ITS-2 region, or the entire ITS region, in the vast majority of fungi. A database containing 3500 ITS sequences representing 572 human and animal fungal species has been established (http://www.isham.org/ and http://its.mycologylab.org/).

Figure 1. Barcode region proposed for fungi, ribosomal RNA (small subunit; SSU, 5.8S, large subunit; LSU, and 5S), and tandem repeated two-spacer regions (intergenetic spacer; IGS and internal transcribed spacer; ITS)

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Massively parallel sequencing is a high-throughput approach to DNA sequencing, known as NGS. Reads are generated as follows. DNA sequencing libraries are generated by clonal amplification of PCR in vitro. The DNA is then sequenced by synthesis, such that the DNA sequence is determined by the addition of nucleotides to the complementary strand rather than through chain-termination chemistry. The spatially segregated, amplified DNA templates are sequenced simultaneously in a massively parallel fashion, without the requirement of a physical separation step. Once the reads have been generated in NGS platforms, alignment and assembly algorithms, base and/or variant calling detectors, and genome viewers are required for sequencing the fungal genomes. The alignment process involves mapping DNA reads by finding overlapping regions between reads. The software for the alignment process includes Maq, SOAP, Bowtie, and BWA. To assemble a genome based on reference genomes, DNA reads are aligned to the reference genome, to reconstruct the original sequence. Assembly algorithms determine whether a given read corresponds to a sequence of bases on the genome. Frequently used assemblers are VELVET, ABySS, ALLPATHS, and CLCbio. After identification, variants must be annotated. ANNOVAR allows functional annotations of genetic variants.

At present, nearly 600 fungal species have been found to be associated with human infections, and the list is growing steadily. For the treatment of fungal infections, the rapid and accurate identification of pathogens is essential for early diagnosis and the application of targeted antifungal therapy. However, traditional methods based on morphological and biochemical characters are time-consuming and often require expertise. Over the past several decades, sequence-based identification has been applied in many studies to understand the diversity of human fungal pathogens.

Fungi that cause diseases have been sequenced, including Candida albicans, Coccidiodes immitis, Coccidiodes posadassi, Aspergillus fumigatus, and Cryptococcus neoformans^18-22. In 2012, Martinez et al.²³ reported the genomic sequence of fungi that cause dermatological diseases, such as Malassezia globose, Malassezia restricta, Trichophyton rubrum, Trichophyton tonsurance, Microsporum canis, and Microsporum gypseum. Martinez's study was performed as part of the Fungal Genomics Group at the BROAD Institute, which is supported by the National Human Genome Research Institute. The sequencing database is freely available at the website of the BROAD Institute (https://www.broadinstitute.org/fungal-genome-initiative).

Advances in phylogenetic analysis have enabled more accurate delineation of taxonomic boundaries. Therefore, there have been revisions to the classification and naming of existing pathogens. Changes to the phylogeny of fungi of medical importance were published by Warnock^24,25. The taxonomic changes result from the adoption of the Amsterdam Declaration and were established by the International Commission on the Taxonomy of Fungi (http://www.fungaltaxonomy.org /subcommissions) and the Nomenclature Committee for Fungi. Medical mycologists, including dermatologists, should be aware of these changes (Table 2)²⁶.

Dermatophytoses have long been classified into three genera―Trichophyton, Microsporum, and Epidermophyton―in the family Arthrodermataceae of the order Onygenales. Five important species, Microsporum audouinii, Epidermophyton floccosum, Trichophyton schoenleinii, Trichophyton tonsurans and Trichophyton mentagrophytes, were described between 1840 and 1875²⁷. Trichophyton rubrum was identified as an emerging dermatophyte in the 1980s²⁸. Biological analyses by Dawson²⁹ and Stockdale³⁰ revealed dermatophyte teleomorphs―the sexual reproductive stage. Several geophilic and zoophilic dermatophytes were found to produce sexual states, and the genera Arthroderma and Nannizzia were described. Recent multilocus phylogenetic analyses have demonstrated that Trichophyton is polyphyletic, and this finding has resulted in the recognition of seven genera of dermatophytes and their relative taxa. Under this classification, almost all anthropophilic dermatophytes are retained in the genera Trichophyton and Epidermophyton, together with several zoophilic species that regularly infect humans. The genus Microsporum is now restricted to Microsporum canis and some closely related species. Most geophilic species and those zoophilic species that are rare causes of human disease are now divided among Arthroderma, Lophophyton, Paraphyton, and Nannizzia (Table 3).

Skin is the outermost organ of the human body, and it is the first line of defense against external agents. The skin is home to millions of bacterial, fungi, and viruses, which make up the skin microbiota. Dysbiosis of skin microbiota is thought to cause or aggravate skin diseases. Advances in sequencing technology have provided tremendous insights into the human skin microbiome, and the pathogenesis of several skin diseases. It is relatively easy to collect samples of the skin microbiota in a noninvasive way.

In atopic dermatitis, high-throughput whole-metagenome sequencing has been used to evaluate the skin microbiome³¹. These metagenomics studies provide insights into how the skin microbial community, skin surface microenvironment, and immune system cross-modulate one another. Malassezia species have been associated with atopic dermatitis; in particular, Malassezia dermatis and Malassezia sympodialis have been found to be abundant in atopic dermatitis³¹. In a Korean study, Malassezia sloofiae and Malassezia dermatis were associated with atopic dermatitis³². However, in atopic dermatitis, the role of bacteria is considered more important than the role of fungi, and the most skin microbiome studies in atopic dermatitis have focused on bacterial biodiversity.

Furthermore, fungal sequencing studies into cutaneous infections in Korea have evolved with advances in technology over the past two decades. Suh et al.³³ reported a phylogenetic analysis of the CHS1 gene, which provided a database for the classification of dermatophyte species and enabled better understanding of the dermatophytes. Lee et al.³⁴ identified Malassezia species using 26S rDNA PCR-RFLP analysis, and Song et al.³⁵ introduced a pyrosequencing method for identification of Malassezia species. Since then, clinical studies of cutaneous fungal diseases have identified the causal fungi through DNA sequencing of the ITS region^36-40 and MALDI- TOF MS^41,42.

With advancements in sequencing techniques, more and more fungal genomes are becoming available. Although large genomic datasets are difficult to analyze, integration of different types of information will facilitate functional genomics studies in fungi. Until now, there have been few studies on fungal metagenomics in dermatologic fungal diseases. Cutaneous fungal diseases, such as tinea capitis, tinea pedis, and onychomycosis, have a high prevalence and are associated with a variety of medical conditions. Recently, changes in the mycobiome in patients with interdigital tinea pedis have been reported⁴³. Since there are many fungal dermatological diseases that are yet to be identified, dermatologists need to play a central role in elucidating the role of fungal genomics in dermatological disease.