Candida albicans phylogenetics: historical context and recent advances

Candida albicans phylogenetics: historical context and recent advances | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 22 October 2025 V1 Latest version Share on Candida albicans phylogenetics: historical context and recent advances Authors : Abdul-Rahman Adamu Bukari and Aleeza C. Gerstein 0000-0002-0781-9356 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176109489.98319484/v1 356 views 141 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Candida albicans is a prevalent opportunistic fungal pathogen that typically resides as a commensal in multiple niches in the human body. C. albicans exhibits substantial phenotypic and genotypic diversity, driven by standard mutational events and genomic mechanisms such as loss of heterozygosity, aneuploidy, and a parasexual cycle. For the past 60 years, efforts have been made to characterize intrapopulation diversity to identify C. albicans relatedness groups. The methods used for strain delineation have transitioned from low-resolution phenotypic typing methods to more robust sequence-based approaches, including multilocus sequence typing (MLST) and, more recently, whole-genome sequencing (WGS). MLST provided the first widely adopted framework for phylogenetic classification, distinguishing genetically distinct clusters among C. albicans isolates. However, in recent years, WGS has offered improved resolution, revealing evidence of gene flow and recombination. These methodological advances have also enhanced our understanding of population structure and associated traits, including antifungal resistance and virulence. This review traces the development of methods used to characterize genetic phenotypic and diversity in C. albicans , outlines current common practices in describing its population structure, and highlights opportunities for greater consistency in how phylogenetic clusters are named and defined. Introduction The genus Candida , initially delineated by Berkhout in 1923, was used to encompass species of ascomycete yeasts characterized by asexual reproduction through budding and an absence of ascospore formation, thus distinguishing them within the order Saccharomycetales (Berkhout, 1923). Historically, Candida encompassed a heterogeneous assemblage of yeast species isolated from a diverse spectrum of ecological niches, including soil, plant matter, and the mucosal surfaces of animals. Throughout the twentieth century, the use of predominantly morphological criteria in the taxonomic delineation of Candida created a polyphyletic genus of over 200 species. The development of phylogenetic methods based on molecular signatures in the second half of the 20 th century catalyzed a new understanding of the relationships between Candida species (Daniel et al., 2014; Guzmán et al., 2013; Kurtzman and Robnett, 1994). Genomic analyses revealed that numerous species belonged to discrete evolutionary lineages (Kurtzman and Robnett, 2003), necessitating their reclassification into more phylogenetically coherent genera, including Clavispora (Rodrigues de Miranda, 1979), Meyerozyma (Kurtzman and Suzuki, 2010), and Debaryomyces (Kurtzman, 2011). A total of 21 species previously classified under the genus Candida were renamed to other genera as part of a major taxonomic revision proposed between 2018 and 2019 (Borman and Johnson, 2023). Among the species that retained the Candida genus name are those that belong to the CTG clade (where the CTG codon is predominantly translated as serine rather than the standard leucine). This includes some of the most common yeasts, such as C. albicans , Candida auris , Candida dublinensis , and Candida tropicalis (Santos et al., 2011). The contemporary application of the genus name Candida thus reflects a historically pragmatic, yet increasingly phylogenetically untenable, classification system. The recent taxonomic realignment has generated tension within the clinical community, where stability in naming is often prioritized for diagnostic consistency and communication, thus leaving the names in clinical use (i.e., “ Candida ” and “candidiasis” to lump all of these species together) sometimes in conflict with phylogenetic accuracy (Denning, 2024; Kidd et al., 2023). C. albicans was recognized by the late nineteenth century as an etiological agent of both mucosal and invasive human infections (Knoke and Bernhardt, 2006; Roberts, 1988). Globally, it is responsible for the majority of superficial and systemic Candida infections (Pfaller et al., 2019; Pfaller and Diekema, 2007; Thompson et al., 2010). The genome of C. albicans is approximately 14 Mb and spans eight chromosomes (Muzzey et al., 2013). C. albicans is typically diploid, though isolates of altered ploidy (haploid or polyploid), and karyotype (whole or partial chromosomal aneuploidy) have also been recovered from clinical sources (Forche et al., 2009; Ford et al., 2015; Hickman et al., 2013; Legrand et al., 2004; Ropars et al., 2018; Suzuki et al., 1986). The C. albicans genome is heterozygous, with approximately one heterozygous SNP every 204 kb (Ropars et al., 2018), and is thought to be hybrid in origin (Mixão and Gabaldón, 2020). C. albicans has been isolated clinically from across the world from different body sites and infection contexts that vary widely in terms of abiotic and biotic pressures, as well as occasionally from environmental sources (Bensasson et al., 2019; Opulente et al., 2019). There have been many attempts to determine whether biological relatedness among C. albicans isolates matches epidemiological patterns, virulence, drug resistance, or other phenotypes of interest (Chen et al., 2004; Jung et al., 2016; MacCallum et al., 2009; Takakura et al., 2008; Wang et al., 2021). To do so properly requires a robust method to assess relatedness among isolates. In this review, we provide a historical overview of the different approaches that have been used to cluster C. albicans isolates over nearly six decades of research. In doing so, we also highlight the challenges that remain in determining intraspecific phylogenetic relationships even in the era of whole-genome sequencing (WGS). Methods for Candida albicans strain typing Strain typing of C. albicans has historically progressed through three main approaches from phenotypic characterization, electrophoretic techniques, and finally, to DNA sequencing-based methods. An effective typing scheme is typically evaluated based on three key criteria: typability (the proportion of isolates in a population that can be successfully typed); reproducibility (the consistency of results both within and between laboratories); and discriminatory power (the ability to distinguish between unrelated strains). Additional considerations include cost, the time required to perform the test, and ease of use, including the availability of necessary laboratory equipment. Phenotypic typing methods As with other medically important fungi, such as Cryptococcus neoformans , early attempts in the 1960s to characterize within-species variation in C. albicans relied on phenotypic characterization (particularly serotyping, reviewed extensively in Hunter, 1991; Soll, 2000). By the late 20th century, three serotyping techniques had been developed, utilizing antisera HSN1 and HSN2 (Hasenclever and Mitchell, 1961a, 1961b), the Iatron Candida Check factor 6 (“IF6”) (Poulain et al., 1985), and agglutination with monoclonal antibody H9 (Brawner and Cutler, 1989). Although serotyping was extensively used into the 1990s (Brawner and Cutler, 1989; Mercure et al., 1996; Odds, Kibbler, et al., 1989; Stiller et al., 1982; Whelan et al., 1990), these methods had low discriminatory power and reproducibility. They grouped C. albicans into only a few groups (low discriminatory power), lacked consistent correlation with one another, and yielded low epidemiological resolution (Brawner, 1991). Antigen expression was also found to vary with the growth phase, and serotype B cells could produce serotype A antigens (Poulain et al., 1985), further undermining the use of serotyping as a robust classification method. Other phenotypic typing methods to delineate isolates were developed and proposed including morphotyping (Hunter et al., 1989; Phongpaichit et al., 1987; Quindós et al., 1992), resistotyping (Hunter and Fraser, 1987; McCreight et al., 1985; McCreight and Warnock, 1982; Quindós et al., 1996; Warnock et al., 1979), killer yeast typing (Buzzini et al., 2007; Caprilli et al., 1985; Polonelli et al., 1983, 1985), enzyme typing (Román and Linares Sicilia, 1983; Williamson et al., 1986, 1987), sugar assimilation typing (Buesching et al., 1979; Fricker-Hidalgo et al., 1996) and a complex typing system that was a combination of resistotyping and carbon source assimilation reactions based on assessment of growth patterns of yeasts on 10-14 test media plates (Odds, Auger, et al., 1989; Odds and Abbott, 1980, 1983). These biotyping methods typically suffered from similar pitfalls as serotyping, including inconsistent results among labs and methods (e.g., Odds, Auger, et al., 1989). Fingerprinting typing methods Phenotypic typing methods gradually became less common as fingerprinting methods emerged in the 1980s that used electrophoretic techniques to identify variable elements. Polymorphisms were identified from whole-cell protein profiles (Bruneau and Guinet, 1987; Ibrahim-Granet et al., 1986) and among isoenzyme patterns using multilocus enzyme electrophoresis (Lehmann et al., 1989). The DNA fingerprinting methods that were commonly used (reviewed extensively in Bai, 2014; Magee et al., 1992; Odds, 2010; Soll, 2000) included restriction fragment length polymorphisms (“RFLPs”, Bart-Delabesse et al., 1993; Magee et al., 1992; Voss et al., 1995), rDNA (Magee et al., 1987; Mercure et al., 1993; Stein et al., 1991), mDNA probes (Olivo et al., 1987), repetitive and complex DNA probes (Sadhu et al., 1991; Scherer and Stevens, 1988), and random amplified polymorphic DNA (“RAPD”, Holmberg and Feroze, 1996). Southern blot hybridization with the repetitive and complex DNA probe Ca3 was particularly widely used. It was first introduced in 1993 (Anderson et al. 1993) and was found to correlate well while offering higher resolution than both RAPD and MLEE (Pujol et al., 1997). Using dendrograms based on similarity coefficients calculated from band positions, the three methods similarly clustered 26 independent isolates into three groups (Clusters I-III), with later studies that added additional isolates identifying a predominantly South African cluster (Cluster SA; Blignaut et al., 2002) and a European-biased cluster (Cluster E; Pujol et al., 2002). Fingerprinting was later replaced with DNA sequencing approaches, including multilocus microsatellite typing (Schönian et al., 1993) and multilocus sequence typing (“MLST”; reviewed in McManus and Coleman, 2014). Early MLST efforts for C. albicans were hampered by different research groups characterizing single-nucleotide polymorphisms (SNPs) within different gene sets (Bougnoux et al., 2002; Tavanti et al., 2003). To improve consistency across studies, two groups collaborated to establish a unified and discriminatory seven-gene set ( AAT1a , ACC1 , ADP1 , MPIb , SYA1 , VPS13 , and ZWF1b; Bougnoux et al., 2003). This seven-gene MLST scheme includes genes on six of the eight chromosomes (chromosomes 3 and 5 are not represented). In MLST analysis, each unique locus (gene) sequence is assigned a genotype number, and each unique combination of seven genotype numbers is consequently assigned a sequence type (ST) number. Because C. albicans is diploid, the sequence types generated by MLST are referred to as diploid sequence types (DSTs). A more extensive MLST approach was later proposed, which involved sequencing 24 genes, strategically selecting one gene located at the center and one at each arm of all eight chromosomes (Odds, 2010). However, with the rise of relatively cost-effective WGS at approximately the same time, this expanded scheme was never widely adopted by the community. In 2007, Odds et al. published a formative study using MLST analysis to group 1391 C. albicans isolates into clusters (Odds et al., 2007). The proportion of nucleotide differences between two sequences across the concatenated MLST loci (pairwise “P” distance) was calculated. A threshold of 0.04 was chosen for clade designations; isolates that differed by less than 4% were grouped (corresponding to fewer than 115 bases out of the 2874 total base pairs sequenced). The threshold value was justified on the basis that it separated clusters containing isolates known to belong to clusters II and IV (what had previously been referred to as clade SA), as identified by Ca3 fingerprinting, as above. A UPGMA (unweighted pair group method with arithmetic mean) phylogenetic analysis of the isolates identified 17 clades (referred to as clades 1-17; of note, ‘cluster’, ‘clade’ and ‘group’ seem to be used somewhat interchangeably throughout the C. albicans phylogenetic literature(Odds et al., 2007). Forty-four isolates formed clusters of fewer than 10 isolates (including those that did not cluster with others) and were labelled as singletons. The majority of isolates were concentrated in five dominant clades (clades 1-4 and clade 11). Several clades showed evidence of geographic enrichment, though none were geographically exclusive: clade 2 was primarily composed of isolates from the UK, clades 1 and 3 were enriched for North American isolates, and strains from the Far East dominated clades 14-17. Interestingly, only 14.3% of isolates in clade 4 (i.e., clade ‘South Africa’) were from Africa, while 37.4% of clade 4 isolates were from the UK. The isolate panel was heavily skewed toward Europe and North America, however. Of the 1,391 isolates, nearly 620 came from the UK and France alone, compared to just 72 from Africa, 25 from Australasia and 15 from the Middle East. The isolate set included a morphologically distinct group of predominantly genital isolates that had previously been proposed as a separate species ( Candida africana ), based on morphological characteristics (Tietz et al., 2001) and rDNA sequence analyses (Forche et al., 1999). These isolates indeed formed a distinct cluster (clade 13). Yet, compared with the well-established sister taxon Candida dubliniensis , their genetic distance was not deemed consistent with cryptic species status. The MLST clade annotations from Bougnoux et al. (2003) continue to be employed (e.g., Alkhars et al., 2024; Gong et al., 2018; Rhimi et al., 2025). DST profiles from the seven-gene scheme are currently curated in the PubMLST database (Jolley et al., 2018), which, as of October 2025, included MLST information for 3674 DSTs derived from 5841 C. albicans isolates collected over seven decades from five continents (Figure 1). Figure 1: Temporal and geographic distribution of Candida albicans isolates in the PubMLST database (as of October 2025). (A) Bar plot showing the number of isolate records by year. The observed peak in Candida albicans data entries on PubMLST during 2014–2015 likely reflects the migration of MLST data from the original mlst.net server to the unified PubMLST platform (Jolley et al., 2018). This transition, completed around 2016, centralized most MLST schemes on a single platform, improving accessibility and consistency. The temporary surge in entries during this period is therefore likely an artifact of data consolidation, rather than an actual increase in new submissions. (B) Choropleth map showing the number of isolates reported per country (225 isolates lacked country metadata). The map highlights regions with denser sampling and illustrates uneven representation across countries (drawn in Datawrapper: https://www.datawrapper.de/maps/choropleth-map). Short-read whole-genome sequencing The most recent technology to be widely employed for isolate clustering is short-read whole-genome sequencing (“WGS”). Ropars et al. called genomic variants from short-read WGS data from 182 isolates. They then constructed a maximum-likelihood phylogenetic analysis based on 264999 SNPs across the 182 isolates (Ropars et al., 2018). The isolate set was again predominantly from Europe (113), with additional representation from Africa (32), Asia (23), South America (5), and North America (1). Isolates were sampled from a variety of anatomical sites, including vaginal (61), urogenital (29), oral (26), bloodstream (26), gastrointestinal (8), skin (3), and environmental sources (21; this includes 19 isolates from food spoilage and two isolates from starlings). Clades were assigned with NGSAdmix and named by matching isolates in each identified clade with their previously identified MLST clade designations; this recovered 12 of the previously defined 17 MLST clades (Clades 1-4, 8-13, 16 by Odds et al. 2007) and clade 18 (defined as “new clade” by Shin et al., 2011) (Figure 2). Alphabetical labels were assigned to five clusters that contained fewer than 10 but more than two isolates (clades A-E; note this is in contrast to the initial MLST clusters). Figure 2: WGS phylogenetic tree showing the population structure of C. albicans . An approximate maximum-likelihood phylogeny was constructed from whole-genome sequencing data for 182 C. albicans isolates from Ropars et al. (2018). Variants were called using the HaplotypeCaller tool from GATK, and the phylogeny was inferred with FastTree. The colored rings indicate the clade assignments of the isolates as initially reported by Ropars et al. (2018). Note that two isolates (C90 and C91) were both clustered with clade 1 here, yet were previously grouped with clade 3 or as a singleton. The black bubbles indicate branches with bootstrap support values of 0.9 or higher. The thin grey lines represent branch length intervals increasing by 0.001 substitutions per site. The lines are shown for visualization purposes to illustrate the difficulty of picking a hard cut-off value to demarcate clades. There are some differences between the MLST and WGS trees. Ten isolates that were classified as singletons in the MLST tree were classified into new or existing clades in the WGS tree (two into previously numbered clades and the remainder into new alphabetical ones). A small number of isolates changed clade assignments: single isolates from clade 1 were reassigned to clade 3 and to clade 4; three isolates from clade 3 moved to clade A, and an isolate each from clades 4, 7, 8 and 9 were reclassified as singletons. These changes likely reflect improved phylogenetic resolution afforded by WGS, although they also highlight the dynamic nature of clade assignments when data from more or different isolates are available. The improved WGS resolution also demonstrated ongoing gene flow and recombination across specific clades; a finding that helped to overturn the long-held view of strictly clonal reproduction in this species (Ropars et al., 2018). WGS analysis also continued to show that isolates in clade 13 ( C. africana ) are distinct from other C. albicans isolates. However, the fraction of reads from C. africana isolate sequences that aligned to the SC5314 C. albicans reference genome was, on average, highest for isolates from clade 13 (mean 0.94) compared to other clades (which ranged from 0.83 to 0.89) (Ropars et al., 2018). Relative to other clades, C. africana isolates exhibit reduced gastrointestinal colonization and systemic infection in a mouse model (Kosmala et al., 2024), have lower pathogenicity in the Galleria mellonella infection model (Borman et al., 2013), and reduced growth in nutrient-rich and host-simulative environments at different temperatures (Ropars et al., 2018). There are also physiological differences between C. africana and C. albicans . C. africana is unable to utilize certain carbon sources (Tietz et al., 2001), cannot form biofilms on polyvinyl chloride surfaces, and adheres less effectively to HeLa cells compared to C. albicans (Romeo et al., 2011). C. africana is also unable to produce chlamydospores (Giosa et al., 2017). Together, these findings suggest that C. africana exhibits phenotypic and ecological traits that distinguish it from C. albicans , supporting its potential designation as a separate species. However, given the high degree of genetic similarity and the limited number of isolates studied to date, further work is required to definitively establish the species status of C. africana . The updated clade naming convention introduced by Ropars et al. has since been widely adopted by other WGS-based studies, with 174 citations as of July 2025. For example, Bensasson et al. used it to demonstrate that three C. albicans isolates from oak tree bark were nested within the existing phylogeny; two of the isolates were within existing clades and had closely related clinical isolates, while one was classified as a singleton (Bensasson et al., 2019). Anderson et al (Anderson et al., 2023) also used it to show that isolates from healthy people contain diverse C. albicans strains, with some individuals colonized by multiple genotypes. (Adamu Bukari et al., 2025) used it to show that vaginal and rectal isolate populations are very closely related within individuals with a history of recurrent vulvovaginal candidiasis. Overall, the work by Ropars et al (2018) fundamentally reshaped our understanding of C. albicans by highlighting the dual importance of clonality and genetic exchange in shaping population structure and facilitating adaptation. Recently, Gong et al . added 370 additional isolates from China into the Ropars dataset (Gong et al., 2023)—approximately two-thirds of the isolates clustered among existing clades. However, in addition to the 17 WGS clades (twelve numbered and five lettered clades), they proposed adding 21 new clades, designated as group 1 through group 21, for a total of 38 clades (Gong et al., 2023). These newly proposed groups were composed almost entirely of previously unclassified isolates, except four singleton isolates from Ropars et al., which were reassigned to groups 8, 16, 19, and 21. The specific criteria/cutoff parameters or values used to define these new clades were not clearly stated. In addition to proposing new clades, Gong et al . suggested that a subgroup of isolates within Clade 1 be designated as Clade 1-R, based on shared patterns of LOH at the terminal ends of chromosomes 2 and 3. A majority of isolates (75.0%) in this subclade were resistant to at least one of four tested azoles (fluconazole, voriconazole, itraconazole and posaconazole). This subgroup was further subdivided into Clade 1-R-α, characterized by an additional LOH event on the terminal end of chromosome R as well as two Erg11p substitutions (A114S and Y257H), and Clade 1-R-β, defined by the presence of a Y132H substitution in the Erg11p protein (Gong et al., 2023). Both the A114S and Y257H mutations in Clade 1-R-α, as well as the Y132H mutation in Clade 1-R-β, have been shown to increase azole minimum inhibitory concentrations (Gong et al., 2023; Kakeya et al., 2000; Xiang et al., 2013). The research community has not widely adopted the naming scheme proposed by Gong et al. (2023); as of September 2025, it has received nine citations (compared to 136 citations in the same timeframe for Ropars et al. 2018; citation assessment was conducted on Web of Science). Regardless, the Gong et al. (2023) study provides important insights, emphasizing that the diversity and origins of the sampled isolates shape within-species phylogenies. They highlight that the (Ropars et al., 2018) phylogeny was unsaturated, as the Asian isolates identified a unique phylogenetic space. The same is likely to be true for isolates from the other underrepresented regions. Taken together, while WGS has significantly enhanced our ability to resolve C. albicans population structure, clade delineation practices remain inconsistent across studies. Any isolate is typable, and the discriminatory power is high. However, the different choices for naming new clades and the lack of clear criteria for clade designation across studies highlight the need for standardized and reproducible approaches to phylogenetic classification. Conclusion and future directions The study of C. albicans strain delineation in the past 60 years has progressed significantly, moving from early phenotypic typing methods to more refined molecular and genomic approaches. Recent work in phylogenetic methodology has shown that even widely used maximum likelihood methods can produce irreproducible results due to factors such as low phylogenetic signal, differences in computational environments, and random variation in heuristic searches, all of which may lead to biologically misleading conclusions (Shen et al., 2020). As datasets grow and analytical tools evolve, integrating robust and transparent phylogenetic frameworks with functional and ecological data will be crucial to fully understanding the diversity and adaptation of C. albicans in both commensal and pathogenic contexts. The advent of ultra-long, high-accuracy long-read sequencing (e.g., PacBio HiFi;(Steiert et al., 2022) presents an opportunity to resolve the full spectrum of structural variations in C. albicans from inversions and translocations to complex segmental duplications and chromosomal fusions. Recent studies have revealed that the C. albicans genome has abundant long repeats (65-6,500 bp) associated with copy-number variation, inversions, and loss of heterozygosity, often involving inverted repeats separated by up to ~1.6 Mb on the same chromosome (Todd et al., 2019; Todd and Selmecki, 2020). Phased diploid assemblies with long-read sequencing have already uncovered inversions over 100 kb and other structural polymorphisms undetectable by short reads (Hamlin et al., 2019). While current WGS phylogenies based on concatenated SNP alignments capture variation at al., 2018), they largely overlook larger-scale rearrangements and copy-number changes that may carry key phylogenetic signals. A complementary pangenome framework (cataloguing core and accessory content, including presence/absence variation and lineage-specific insertions) could also provide insights into gene-gain/loss dynamics and structural hotspots across clades. Given the existence of LOH and mitotic or parasexual recombination in C. albicans , future analyses should incorporate models of recombination or gene flow (e.g., ancestral recombination graphs or network phylogenies). Thus, in the future, SNP-based trees (our current gold standard) could be combined with long-read structural maps, pangenome graphs, and recombination-aware inference to continue the march towards the optimum degree of typeability, reproducibility and discriminatory power in understanding the population structure of C. albicans . Acknowledgements A.C.G. acknowledges the support from the CIFAR Azrieli Global Scholars Program, and funding support from the National Science and Engineering Research Council of Canada. 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