Methods for examining immune and skin microbiome associations with dermatomycoses in reptiles: a review

1 Introduction

The past two and a half decades have seen a wide range of diseases emerging in wildlife (Tompkins et al., 2015). Emerging fungal diseases (EFDs) represent a significant proportion of novel wildlife diseases and have increased in global prevalence due to climate change, increasing environmental disturbance such as agriculture, and long-distance dispersal via global trade (Fisher et al., 2020). Examples include chytridiomycosis in amphibians and white nose disease in bats, which have both decimated populations globally (Cheng et al., 2011, 2021; Scheele et al., 2019). Compared to endotherms, ectotherms may provide thermal environments more ideal for fungal pathogen infection, or mycoses (Robert and Casadevall, 2009). Emerging fungal diseases, especially those caused by onygenalean fungi, have become especially prominent in both captive and wild reptiles (Schmidt, 2015; Schilliger et al., 2023).

Fungi in the order Onygenales, specifically those in the genera Ophidiomyces, Nannizziopsis, and Paranannizziopsis, are increasingly linked to dermatomycoses in reptiles (Latney and Wellehan, 2020). In reptiles, examples of onygenalean dermatophytes, or fungi causing skin infections, include Ophidiomyces ophidiicola causing ophidiomycosis (formerly known as snake fungal disease) in snakes and Nannizziopsis spp causing skin infections in captive coastal bearded dragons (Pogona barbata; Johnson et al., 2011), green iguanas (Iguana iguana; Abarca et al., 2010), and recently multiple wild Australian lizard species (Peterson et al., 2020). Many onygenalean dermatophytes have evolved to break down and utilize keratin (Latney and Wellehan, 2020). Thus, infections in reptiles often result in scale damage, such as skin lesions and thickened or discolored scales (Schmidt, 2015; Paré and Sigler, 2016; Paré et al., 2021). Lesions can also hinder breathing, sight, and associated behaviors such as foraging, ingestion of food, and locomotion when located near the mouth and eyes (Baker et al., 2019; Haynes and Allender, 2021). Additionally, infections can become systemic, resulting in lethargy, difficulty breathing, and even mortality (Lorch et al., 2016; Paré and Sigler, 2016; Paré et al., 2021).

Since dermatomycoses affect cutaneous barriers and local immune processes, understanding immunity and the microbiome together is essential for estimating the impact of skin fungal diseases on reptile populations. While both immunology and skin microbiome research has been conducted independently in the context of reptile dermatomycoses, little research exists integrating both together, though it has been examined further in amphibians (reviewed in Woodhams et al., 2023). More investigation has examined interactions between gut microbiota and immune function in reptiles (reviewed in Siddiqui et al., 2022).

Current research into reptile immunology has discovered that reptiles rely heavily on components of their innate immune system (e.g., phagocytes, the complement protein cascade, natural antibodies, antimicrobial peptides) to combat pathogens and injuries (Zimmerman et al., 2010). Specific immune responses to fungal pathogens in reptiles have been minimally studied, though mechanisms of defense against fungal pathogens have been extensively examined in mammals (reviewed in Romani, 2011). Fortunately, reptilian ecoimmunology is becoming prominent and emphasizes the study of reptile immunology in response to ecological stressors such as pathogens through hematology, functional immune assays, molecular assays, and immune-endocrine indicators (Field et al., 2022).

Skin microbiome-associated immune defense likely impacts progression of dermatomycoses in reptiles, though little investigation has been conducted in reptiles. The skin microbiome is often the first line of defense against pathogens and is considered a component of the immune response (Harris et al., 2006; King et al., 2016). The skin microbiome both primes a host’s immune system in preparation for pathogen invasion and can respond to pathogen invasion to further mediate immune responses (Stevens et al., 2021; Graham and Xavier, 2023). The immune system is conditioned as it responds to microbiome signals and adapts responses for future pathogen invasion (Graham and Xavier, 2023), though microbiome stimulations of the immune system can also lead to maladaptive immune suppression and cytotoxicity, especially in cases of chronic and severe stimuli (Graham and Xavier, 2023). Furthermore, commensal microbiomes can suppress dermatophyte establishment and growth through interactions such as interference competition, exploitation competition, and predation, or facilitate establishment through mutualistic interactions (Kern et al., 2021).

The skin microbiome can become dysbiotic due to environmental disturbance and invasion of pathogenic microbe species, leading to dampened defense against dermatophytes (Ross et al., 2019; Li et al., 2023). Though impacts of environmental disturbances on the skin microbiome have been minimally researched in reptiles, multiple studies have examined changes in the amphibian skin microbiome in response to changing climate (Muletz-Wolz et al., 2019; Sun et al., 2025) and habitat disturbance (Jiménez et al., 2020; Walke et al., 2021; Bates et al., 2022). Pathobionts, or components of the microbiota that have the potential to be pathogenic, may take advantage of lowered diversity in response to stress, production of metabolites by other microbes, the transmission of microbe components by other microbes, specific microbiome cues necessary to initiate infection, as well as immune suppression due to microbes causing increased activity of immune cells (reviewed in Stevens et al., 2021). In contrast, after recovery, the microbiome may be primed to respond to the pathogen in a more defensive manner (the adaptive microbiome hypothesis; reviewed in Woodhams et al., 2023).

While it is clear that skin microbiome and immune system interactions are important in the context of reptile dermatomycoses, very little research has been done at the intersection of these three topics. In this paper, we examine current research on both skin microbiota and immune response associations with emerging dermatomycoses in reptiles. We introduce significant EFDs impacting reptiles (Box 1) and review current literature on skin microbiome and immune responses associated with identified dermatomycoses, focusing on techniques used to detect and diagnose dermatomycoses as well as characterize microbiome and immune associations. By combining immune and microbiome techniques in future investigations of reptile dermatomycoses, we can further conservation and management of at-risk reptile populations.

Pathogen Overviews

**Ophidiomycosis (previously known as snake fungal disease):**

Pathogen and Hosts – The fungal species Ophidiomyces ophidiicola (Oo) has emerged as the primary causative agent of ophidiomycosis in a wide range of snake species.

Geographic Distribution – Ophidiomycosis in snakes has been detected in at least 34 U.S. states and is currently emerging globally (Lorch et al., 2016; Di Nicola et al., 2022).

Individual Impacts –Symptom severity varies greatly between individuals, with some individuals being minimally impacted, while others are highly impacted (Allender et al., 2015a; Grisnik et al., 2018; Di Nicola et al., 2022).

Population Impacts – Ophidiomycosis has led to mortality events and has been implicated in the decline of multiple North American snake populations (Clark et al., 2011; Allender et al., 2015d; Haynes et al., 2022a). Ophidiomycosis has also been indirectly associated with lowered fitness and mating success (Lind et al., 2018). In contrast, even if infections persist, they can be sublethal and unlikely to impact population sizes (Burger et al., 2023; Dillon et al., 2024).

**Paranannizziopsis Infections:**

Pathogen and Hosts – Multiple fungal species in the genus Paranannizziopsis have been associated with dermatomycoses mostly in captive snakes, but infections by Panarannizziopsis australasiensis have also been found in a captive coastal bearded dragon (Pogona barbata) and in tuataras (Sphenodon punctatus; Sigler et al., 2013; Masters et al., 2016; Rainwater et al., 2019; Díaz-Delgado et al., 2020). Both Oo and Paranannizziopsis species have been detected on the same animal (Lorch et al., 2023).

Geographic Distribution – Many Paranannizziopsis infections have been detected in captive reptiles worldwide, but some have also been detected in wild snakes and non-native panther chameleons (Furcifer pardalis) in the U.S. (Claunch et al., 2023; Lorch et al., 2023).

Individual Impacts – Researchers have recently identified Parannanizziopsis species to cause symptoms similar to that of ophidiomycosis in wild snakes (Lorch et al., 2023). Paranannizziopsis crustacea, P. californiensis, and P. longispora infections in tentacled snakes (Erpeton tentaculatum) are often fatal (Bertelsen et al., 2005; Sigler et al., 2013; Mack et al., 2021).

Population Impacts – Population-level impacts have yet to be examined for Paranannizziopsisinfections in reptiles.

**Nannizziopsis Infections (also known as yellow fungus disease):**

Pathogen and Hosts – Multiple fungal species in the genus Nannizziopsis have been associated with dermatomycoses in lizards and crocodiles (Paré and Conley, 2020).

Geographic Distribution – Nannizziopsis crocodili has been associated with outbreaks in farmed saltwater crocodiles (Crocodylus porosus) in Australia (Thomas et al., 2002; Sigler et al., 2013; Hill et al., 2019). Nannizziopsis dermatitidis and a closely related species have been associated with dermatomycosis in captive veiled chameleons (Chamaeleo calyptratus) in Canada, day geckos (Phelsuma sp) in Germany, and leopard geckos (Eublepharis macularius) in a breeding facility in Florida (Sigler et al., 2013; Toplon et al., 2013). While initially documented in captive individuals or collections, Nannizziopsis infections caused by N. barbatae are rapidly spreading in Australian lizard populations (Peterson et al., 2020; Gentry et al., 2021).

Individual Impacts – Nannizziopsis infections in lizards are often highly aggressive and fatal if not treated (Paré and Conley, 2020).

Population Impacts –Though a recent study examined the ability of eastern water dragons (Intellagama lesueurii) to alter their social behavior in response to potential learned fitness costs of associating with infected individuals (Tacey et al., 2023), direct impacts of Nannizziopsis infections on populations have yet to be examined.

Emydomycosis (or turtle shell disease):

Pathogen and Hosts – The fungal species Emydomyces testavorans is morphologically similar, but genetically distinct from other fungi in the Nannizziopsiacean clade (genera Nannizziopsis, Paranannizziopsis, and Ophidiomyces) and is the primary cause of emydomycosis in captive and wild turtles (reviewed in Paré and Conley, 2020). Emydomyces testavorans has been found to infect western pond turtles (Actinemys marmorata) as well as other turtle species, including a spiny softshell turtle (Apalone spinifera), red-eared slider (Trachemys scripta elegans), and an alligator snapping turtle (Macrochelys temminckii; Woodburn et al., 2019).

Geographic Distribution – In the wild, emydomycosis was first observed in western pond turtles in Washington, where they are considered endangered (Groves et al., 2016). Emydomycosis has also been detected in multiple turtle species from zoological or pathological collections in Washington and Illinois, as well as in a subset of turtles that were free-living and initially part of a head-start program in Washington or free-living in Florida (Woodburn et al., 2019, 2021).

Individual Impacts – Emydomyces testavorans has been associated with multiple cases of mortality in captive turtles (Paré and Conley, 2020).

Population Impacts – Emydomycosis is considered a disease of concern for western pond turtle populations, especially given the propensity for outbreaks in head-started populations (Peterson et al., 2020). While emydomycosis has the potential to impact multiple turtle species and populations, its direct impact on populations has not been examined.

Other dermatomycoses:

Other Nannizziopsiacean fungi have been isolated from turtles, including Aphanoascella galapagosensis which has been found to cause keratitis on the carapaces of Galapagos giant tortoises (Chelonoidis nigra; Sutton et al., 2013).

2 Materials and methods

To identify what work has been conducted examining skin microbiome and immune metrics associated with onygenalean dermatomycoses in reptiles, we searched for peer reviewed research papers, unpublished M.S. theses, and Ph.D. dissertations examining the following in reptiles 1) dermatomycosis and skin microbiome, 2) dermatomycosis and immune function, and 3) dermatomycosis, skin microbiome, and immune function. Details on search and filter criteria can be found in Supplementary Material.

3 Results

We found a total of 12 studies (including two M.S. theses) examining the skin microbiome associated with reptile dermatomycosis and a total of 15 studies (including three M.S. theses) examining immune function associated with reptile dermatomycosis (Tables 1, 2 in Supplementary Material). We did not find any published articles integrating skin microbiome and immunology in the context of reptile dermatomycoses (though see the M.S. thesis by Hazemi, 2020). The majority of studies integrating skin microbiome or immunology with reptile dermatomycosis were in the context of ophidiomycosis (previously snake fungal disease) in snakes, while a few studies were conducted on turtles/tortoises, lizards, and alligators.

Table 1. Procedures used in research on reptile skin microbiome or immune responses associated with dermatomycosis, sources conducting each procedure, and study organism (snake, lizard, turtle/tortoise, or alligator) for each source.

Only published manuscripts are included. Theses were excluded.

Table 2. Alpha diversity metrics from previously published literature used to examine reptile skin microbiomes associated with dermatomycoses, brief definitions, and findings for both reptile skin bacteriomes and mycobiomes.

↓ represents a decrease in the metric of interest and ↑ represents an increase in the metric of interest.

Through this review, we provide the following:

• A summary of techniques used to identify and diagnose reptile dermatomycosis, subsequent skin microbiome and immune analysis, and a brief discussion of the rationale for their application.
• An overview of findings associated with identified techniques.
• Suggested techniques and considerations for future research.

3.1 Identifying causative agents

We found two commonly used processes conducted to identify causative dermatophytes in reptiles: 1) Isolating and growing microbes from infection sites through culturing and subsequent morphological identification of microbe colonies and 2) Isolating and growing microbes from infection sites through culturing and subsequent sequencing of isolates using Sanger sequencing.

Culturing and Morphological Identification – Before the development of sequencing technology, studies examining fungal pathogens in reptiles focused on diagnosis through culture, isolation, and morphological identification (reviewed in Jacobson et al., 2000). Culture and isolation involve the inoculation of solid or liquid culture medium with microbes from the infection site (from swabs of lesions or infected tissue) and growing individual microbes into distinct colonies in the laboratory (Hill et al., 2018). Culture and isolation have recently been used as the primary tool to diagnose dermatomycosis in a mud snake (Farancia abacura; Last et al., 2016). Given the difficulties that arise when identifying morphologically similar microbial taxa, morphological identification is now often combined with other molecular techniques (Sigler et al., 2013).

Culturing and Sanger Sequencing – Sanger sequencing provides a method for more accurate identification of microbes at a higher phylogenetic resolution compared to morphological identification (Wong et al., 2025). Through Sanger sequencing, researchers have identified multiple species that were once grouped under the designation “Chrysosporium anamorph of Nannizziopsis vriessi (CANV) complex,” including O. ophidiicola, Nannizziopsis spp, and Paranannizziopsis spp (Paré and Sigler, 2016). Sanger sequencing is often used to identify microbial isolates, cultured from infected sites on a host, which can be incorporated into studies confirming Koch’s Postulates (see Bachmann, 2022; Dubey et al., 2022). Experiments confirming Koch’s postulates have been conducted to identify O. ophidiicola as the causative agent of ophidiomycosis in snakes (Lorch et al., 2015), Nannizziopsis guarroi as the causative agent of yellow fungal disease in central bearded dragons (Pogona vitticeps; Gentry et al., 2021), and Nannizziopsis dermatitidis (CANV before taxonomic reassignment) as the causative-agent of a dermatomycosis in veiled chameleons (Paré et al., 2006; Sigler et al., 2013).

Future Directions – Next generation sequencing (NGS) and long-read sequencing techniques output counts of identified taxa, which can allow researchers to identify the most abundant taxa associated with a dermatomycosis. Often, more than one microbe is associated with infection (Tay et al., 2016) and researchers will culture more than one microbe from infected or non-infected sites. For example, Hill et al. (2018) used culture, isolation, and Sanger sequencing to identify 58 bacterial and five fungal strains on the skin of two snake species (Crotalus horridus, Coluber constrictor) with and without symptoms of ophidiomycosis.

Recent reviews have called for adaptations of Koch’s postulates to incorporate microbial community dynamics (see Byrd and Segre, 2016 and Vonaesch et al., 2018). Because of selection bias of microbes in culturing (due to specific growth media and incubation parameters), unknown causative agents might not be isolated and isolated microbes might not be representative of the microbiome (McCaig et al., 2001; Albright and Louca, 2023). Targeted and non-targeted next-gen and long-read sequencing can be conducted directly on extracted DNA without initial culturing, minimizing biases and identifying a more representative microbial community, given the presence of minimal environmental contamination (culture-independent and dependent methods reviewed in Grogan et al., 2019). For example, Zapanta et al. (2025) conducted Illumina sequencing targeting bacterial and fungal DNA from skin swabs of central bearded dragons and compared abundances of microbes between individuals with or without suspected dermatomycosis to identify a Nannizziopsis species as the causative agent. Using Illumina sequencing output, Zapanta et al. (2025) additionally identified both bacteria and fungi co-occurring and potentially interacting with the causative agent.

3.2 Identifying infected hosts

We identified four procedures conducted to diagnose reptiles with dermatomycosis: 1) Symptom examination, 2) Histological examination of infected tissue, 3) Polymerase chain reaction (PCR) and quantitative polymerase chain reaction (qPCR), and 4) Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS).

Symptom Examination – Dermatomycosis symptoms are often documented in the field or in manipulative studies examining disease progression with the most frequently documented symptoms being skin lesions (Lorch et al., 2016). Approaches to describing lesions vary widely, ranging from simple presence–absence reporting (Jiang et al., 2022; Zhong et al., 2023) to more detailed assessments of lesion severity (Baker et al., 2019; Allain et al., 2024). McCoy et al. (2017) published one of the first severity scales for ophidiomycosis in snakes based on location and number of lesions. Baker et al. (2019) further developed this scale to include lesion type, location, number, and coverage. Additional symptoms of dermatomycosis in reptiles are sometimes documented, including lethargy and behavioral changes (Di Nicola et al., 2022).

Histological Examination – By examining lesioned tissue histologically, researchers can determine whether a dermatophyte has invaded the underlying tissue, indicating an active infection rather than surface growth. To identify ophidiomycosis in snakes, as well as Nannizziopsis and Paranannizziopsis infections in reptiles, researchers look for intralesional hyaline fungal hyphae with parallel walls, transverse septa, and frequent branching, alongside arthroconidia in skin tissue (Paré and Sigler, 2016; Baker et al., 2019; Allain et al., 2024).

PCR and qPCR – In conjunction with histopathology, multiple studies diagnosing dermatomycosis in reptiles use PCR to amplify and detect the target pathogen and classify individuals as “infected” or “uninfected” (Paré and Sigler, 2016). This process involves extracting DNA from skin tissue or sample (swab, shed skin), which can be from a lesioned or non-lesioned location (Mullis and Faloona, 1987; Saiki et al., 1988). PCR on extracted DNA can detect dermatophyte presence, often in conjunction with sequencing (see Sigler et al., 2013; Meason-Smith et al., 2017), while quantitative PCR (qPCR) allows researchers to quantify the dermatophyte present per individual. Methodologies for qPCR have been validated and standardized to detect most well-known onygenalean pathogens infecting reptiles, including O. ophidiicola (Allender et al., 2015b; Bohuski et al., 2015), Paranannizziopsis spp (Lorch et al., 2023), Nannizziopsis spp (Powell et al., 2023; Wong et al., 2025), and E. testavorans (Fredrickson et al., 2024).

MALDI-TOF MS – Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is widely used to identify biomolecules and microorganisms (Rychert, 2019). For microbial identification, isolates from a primary colony are applied to a plate with a specific matrix and ionized with a laser (Lasch et al., 2025). Particles are separated by their mass-to-charge ratio and the amount of time particles takes to reach a detector is recorded, creating a spectrum for identification (Rychert, 2019). While the methodology is more complex than qPCR, MALDI-TOF MS enables identification of dermatophytes without a predefined genetic target and can provide species-level identification when relevant spectral databases are available (Nenoff et al., 2013). However, closely related microbes can display highly similar spectra, making it difficult to parse apart species, and not all microbes are represented in current databases (L’Ollivier and Ranque, 2017; Normand et al., 2017). Despite these limitations, spectra for many onygenalean fungi exist, including Nannizziopsis guarroi (Schneider et al., 2018). MALDI-TOF MS has been successfully applied to identify N. guarroi isolated from lizards (Pogona vitticeps, Iguana iguana, Lacerta viridis) with dermatomycosis (Schneider et al., 2018).

In addition to detecting pathogens such as reptile dermatophytes, MALDI-TOF MS has been used to characterize skin antimicrobial peptides associated with chytrid fungal infection in amphibians (Woodhams et al., 2007) as well as metabolites secreted by microbes that inhibit human dermatophytes (Coccidioides spp; Kollath et al., 2023). Similarly applying MALDI-TOF MS to investigate peptides and molecules associated with reptile skin dermatophytes could provide valuable insights into how these fungi interact with both immune defenses and skin microbes.

Efforts to refine diagnostic criteria highlight the importance of integrating multiple detection methods and differentiating between superficial pathogen presence and true dermatomycosis. Baker et al. (2019) developed a scale to characterize four ophidiomycosis disease states in snakes based on a combination of lesion observations, histopathological observations, and PCR or culturing results. Categories include ophidiomyces present, possible ophidiomycosis, apparent ophidiomycosis, and confirmed ophidiomycosis, with confirmed ophidiomycosis defined as O. ophidiicola detected from skin tissue or sample through PCR, qPCR, or culturing AND histopathology showing characteristic hyphae with arthroconidia (Baker et al., 2019).

3.3 Characterizing the skin microbiome

Given that the skin microbiome strongly influences infection dynamics in affected hosts, it is crucial to investigate how reptile skin microbiomes relate to dermatomycoses. Only a limited number of studies have explored this connection to date. Procedures in reptile skin microbiome studies generally involve obtaining samples (skin swabs or skin sheds), extracting DNA (often with kits or reagents that provide non-biased extraction), and subsequent sequencing and analyses processes (Figure 1). Procedures conducted to characterize reptile skin microbiomes in the context of dermatomycosis have included 1) Targeted, short-read sequencing and 2) Targeted long-read sequencing. Downstream bioinformatic analysis has included 1) Quantification of microbial diversity, 2) Differential analyses to identify microbes associated with dermatomycosis, 3) Co-occurrence analyses to identify microbes co-occurring with dermatophytes, and 4) Investigation of microbe functions. See Figure 2 and Table 1 for an overview of procedures used and a list of associated sources and study organisms.

Short-read Sequencing – Targeted short-read sequencing, specifically Illumina sequencing, is the predominant strategy for acquiring high-throughput microbial community data to support downstream analysis of pathobiomes associated with reptile dermatomycosis. Bacterial community data are most frequently investigated, while a handful of studies have additionally examined mycobiome communities (Hazemi, 2020; Jiang et al., 2022; Zhong et al., 2023). Primers targeting the bacterial 16S rRNA gene and the fungal 18S-ITS1 or ITS2 regions are commonly used to broadly amplify microbial communities present in skin samples (Weisburg et al., 1991; Schoch et al., 2012). Illumina sequencing, which employs sequencing by synthesis, outputs short sequencing reads (150–300 base pairs) that are classified as either operational taxonomic units (OTUs) or amplicon sequence variants (ASVs; Liu et al., 2012; Segura et al., 2024). To date, studies on reptile skin microbiomes associated with dermatomycosis have mostly reported OTUs, clusters of sequences grouped by percent similarity, allowing for comparability between studies (Segura et al., 2024). Only one study focusing on reptile dermatomycosis has estimated ASVs (Green et al., 2025). Compared to OTU estimation, ASV estimation is more sensitive to and likely to identify rare taxa which are common in microbiome data (though may lead to overestimation of unique taxa; Fasolo et al., 2024). Thus, future research including identification and analysis of ASVs may provide higher resolution of reptile skin microbiome data, especially if high abundances of rare taxa are expected.

Long-read Sequencing – Long-read sequencing has been utilized much less to examine skin microbiomes in the context of reptile dermatomycosis but has the capacity to provide higher resolution of taxa and strain identification (Johnson et al., 2019; Tedersoo et al., 2020). Two main options are available for long-read metagenomic sequencing: 1) Nanopore Sequencing (Oxford Nanopore Technologies, ONT) and 2) PacBio continuous long-read (CLR) or HiFi sequencing (Mahmoud et al., 2025). The MinION sequencer (ONT) sequences DNA in real time by ratcheting single strands of DNA through small protein pores and recording changes in ionic current associated with different nucleotides occupying the pore (Jain et al., 2016). MinION sequencing devices are small and portable, allowing them to be used in the field (Jain et al., 2016). Additionally, nanopore sequencing allows for multiplexing of samples, or the sequencing of multiple samples at once, and outputs, in parallel, long-read sequences (averaging 23kb; Wang et al., 2021), which are more suitable for variant calling compared to methods such as Illumina sequencing (Liu et al., 2012). Demonstrating the application of this methodology to reptile dermatomycosis research, Perea Brugal et al. (2024) used nanopore sequencing to compare carapace bacteriomes of Galapagos giant tortoises (Chelonoidis porteri, C. donfaustoi, C. vandenburghi) with and without signs of infection by the dermatophyte, Aphanoascella galapagosensis.

PacBio HiFi sequencing, developed in 2019, provides long-reads at a higher accuracy than short-read and both PacBio CLR and nanopore sequencing (Frank et al., 2016; Mahmoud et al., 2025; Wang et al., 2025). PacBio HiFi sequencing uses circular consensus sequencing (CCS) to provide reads at 10-25kb that can reach an accuracy of 99.9% (Wang et al., 2025). While it has not been incorporated in reptile dermatomycosis research as of yet, the use of PacBio HiFi sequencing will allow researchers to better identify pathogen strains, complex microbiome structures, and microbe functions associated with reptile dermatomycosis.

Microbiome Diversity – Most studies examining the skin microbiome of reptiles associated with dermatomycoses calculated diversity metrics, including alpha and beta diversity. The most common metrics chosen to estimate alpha diversity, species richness, and beta diversity were Shannon’s Diversity Index, total number of taxa, and Bray-Curtis Dissimilarity respectively. Most studies have examined reptile skin microbiome diversity associated with dermatomycosis in situ, though Romer et al. (2022, 2024) examined snake skin microbiome diversity after inoculation with O. ophidiicola. Romer et al. (2024) found bacterial richness to increase as O. ophidiicola load increased until a threshold was reached, at which point richness and Shannon diversity decreased as O. ophidiicola load continued to increase. These results illustrate the benefit of using manipulative experimentation to better understand mechanisms driving changes in the skin microbiome.

The standard estimation of certain metrics allows for consistency and comparability between studies, but inclusion of single metrics may provide limited capacity to accurately characterize microbiome data. See Tables 2–4 for all diversity metrics used in reptile dermatomycosis research, rationale for their use, and findings associated with each metric in the context of reptile dermatomycosis. While Shannon’s Diversity Index, a metric commonly estimated for alpha diversity, encapsulates both number of species and evenness (or distribution of species), it is not sensitive to rare or dominant species, which are both inherent in microbiome data (Willis, 2019). Additionally, while both Bray-Curtis and Jaccard Dissimilarity are commonly estimated beta diversity metrics in reptile dermatomycosis studies, Bray-Curtis is sensitive to dominant taxa, while Jaccard is not (Yang et al., 2021). Additionally, these indices are not sensitive to uneven sample sizes, which is often present in microbiome data, requiring correction procedures such as rarefaction (Willis, 2019). A few reptile dermatomycosis studies have estimated metrics, such as Inverse Simpson or Chao1 alpha diversity metrics, specifically accounting for microbiome data characteristics (Walker et al., 2019; Zhong et al., 2023).

Table 3. Beta diversity metrics from previously published literature used to examine reptile skin microbiomes associated with dermatomycoses, brief definitions, and findings for both reptile skin bacteriomes and mycobiomes.

↓ represents a decrease in the metric of interest and ↑ represents an increase in the metric of interest.

Table 4. Dispersion metrics from previously published literature used to examine reptile skin microbiomes associated with dermatomycoses, brief definitions, and findings for both reptile skin bacteriomes and mycobiomes.

In addition to estimating the usual diversity characteristics, some studies have augmented their analysis with more nuanced metrics. Romer et al. (2022, 2024) calculated Raup-Crick Dissimilarity, a null model approach to estimate whether variation in beta diversity is more or less than expected by random chance given an expected constant community structure taking richness variability into account (Raup and Crick, 1979; Chase et al., 2011). Romer et al. (2024) found that while Bray-Curtis dissimilarity varied between snakes captured in different disease states in situ, Raup-Crick dissimilarity did not, emphasizing that differences in skin bacterial communities between infected and non-infected individuals was highly influenced by changes in richness. Many other options for characterizing skin microbiome diversity exist but have not been examined in association with reptile dermatomycosis (Faith, 2018; Cassol et al., 2025).

Differential Analyses – The majority of studies examining reptile skin microbiomes in the context of dermatomycosis have conducted differential analyses to identify bacterial or fungal taxa that significantly differ in presence or abundance between “diseased” and “non-diseased” phenotypes. A variety of methods have been employed to conduct differential analyses on reptile skin microbiome data which often consists of large tables listing read counts (or derived relative proportions) of each OTU per sample and associated covariate information, such as lesion presence, lesion severity, and qPCR dermatophyte detection. Programs or methods include DESeq2 (Allender et al., 2018; Hazemi, 2020), a package that can identify sequences that are differentially expressed between conditions, while accounting for differences in sequencing depth and overdispersion (Love et al., 2014), analysis of composition of microbes (ANCOM; Romer et al., 2024; Lin and Peddada, 2020), which accounts for the compositional nature of microbiomes, specifically minimizing false discovery rates, and the linear discriminatory analysis effect size (LEfSe) method, which is often employed to find biomarkers in metagenomic data (Jiang et al., 2022; Segata et al., 2011). Microbes identified as important to reptile dermatomycosis disease state vary greatly between studies, though the bacterial genera Paracoccus, Chryseobacterium, and Pseudomonas have been identified in multiple studies (Allender et al., 2018; Jiang et al., 2022; Zhong et al., 2023; Romer et al., 2024).

Co-occurrence Analyses – Microbial abundance data can be input into models to identify co-occurring taxa and microbial taxa that co-occur with dermatophytes. These models can be probabilistic or network-based and can delineate microbes that co-occur significantly more than expected or microbes that are central to microbiome or pathobiome structure (Barberán et al., 2012; Veech, 2013; Kajihara and Hynson, 2024). For example, Zapanta et al. (2025) identified 41 bacterial species that positively co-occurred with infectious Nannizziopsis species from the skin of central bearded dragons, including Salmonella enterica, a species in the order Clostridiales, and a species in the genus Cladosporium. A pitfall to using co-occurrence models to infer microbe associations is their reliance on parametric methods, which can be insufficient to characterize complex microbial patterns (Hirano and Takemoto, 2019). Further analysis of reptile skin microbiomes using non-parametric methods may be especially useful for delineating pathobiomes and suggesting underlying associations. For example, though not a co-occurrence model, Romer et al. (2024) introduced a deep neural network approach capable of identifying microbial taxa that most strongly contribute to distinguishing snake skin samples as positive or negative for O. ophidiicola. Further functional analysis can allow researchers to better understand why microbes may co-occur or be associated with certain disease states.

Functional Analyses – Most investigations of reptile skin microbiomes in the context of dermatomycosis have evaluated microbial function either directly, conducting functional assays, or indirectly, often by manually querying functions for a subset of differentially abundant taxa. For example, using in vitro assays, researchers have examined antifungal or antibiotic activity of microbes associated with reptile dermatomycoses (Hill et al., 2018; Jiang et al., 2022; Romer et al., 2024). To date, only one study has applied a dedicated metagenomic inference tool to characterize microbial functional potential. Jiang et al. (2022) used PICRUSt (Langille et al., 2013) to predict functional profiles of cutaneous bacterial communities based on inferred functional orthologs from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Kanehisa et al., 2016). Their analysis demonstrated significant differences in predicted KEGG functional pathways between ulcerated and non-ulcerated crocodile lizards (Shinisaurus crocodilurus). Additionally, Zhong et al. (2023) annotated microbial functional traits using the online tool BugBase (https://bugbase.cs.umn.edu/) and the FUNGuild database (https://github.com/UMNFuN/FUNGuild). They compared functions between two species of sea snake (Hydrophis cyanocinctus and Hydrophis curtus) and two skin conditions (lesioned or non-lesioned) using one-way ANOVA. However, one limitation of functional inference tools is the variability in taxonomic resolution obtained from short-read sequencing approaches (e.g., Illumina). Short-read sequencing can output sequences identifiable to family or genus given the use of certain classifier pipelines, but often not to the species-level (Earl et al., 2018; Bailén et al., 2020). In contrast, long-read sequencing has the potential to improve taxonomic resolution and enhance subsequent functional analysis. Along with the use of predictive tools, future research can involve direct functional metagenomic approaches, such as short-read functional mapping which uses shot-gun sequencing technology and matches proteins and functions of reads against those in known databases (Franzosa et al., 2018; Rebollar et al., 2018).

Future Directions: Contextualizing Microbiome Analysis – Studies on the skin microbiome of reptiles primarily identify and analyze microbial DNA in vivo. These findings can be further contextualized by 1) augmenting in situ research with in vitro or ex vivo experimentation and 2) examining additional molecular and physiological components. In vitro and ex vivo experiments provide an avenue to examine microbe responses to a defined set of stimuli in a simplified context (National Academies of Sciences, Engineering, and Medicine, 2018). For example, using in vitro methodology, researchers have examined antifungal or antibiotic activity of microbes associated with reptile dermatomycoses (Hill et al., 2018; Jiang et al., 2022; Romer et al., 2024). Further use of in vitro or ex vivo experiments to examine dermatomycosis-skin dysbiosis dynamics may be especially useful and microbial interactions resulting from co-culturing may allow for isolation of previously unculturable microbes (Yan et al., 2023). Additionally, conducting “omics” of hosts such as metabolomics or transcriptomics along with microbe metagenomics can provide insight into mechanisms underlying host-microbiome interactions which can inform management strategies such as bioaugmentation (Rebollar et al., 2016; Chetty and Blekhman, 2024). While little research has been conducted examining metabolomics or transcriptomics associated with skin microbiome disturbance in reptiles, more research has been conducted in amphibians (Becker et al., 2009; Madison et al., 2017; Walke et al., 2021).

3.4 Characterizing immune function

To clarify how fungal pathogens affect reptiles, it is essential to characterize immune responses and mechanisms involved with defense against relevant pathogens. Although studies examining immune responses to reptile dermatomycosis are limited, a few do exist and utilize a broad range of methodological techniques. Procedures conducted to characterize reptile immune function in the context of dermatomycosis can be categorized as 1) Leukocyte quantification (Differential leukocyte counts), 2) Protein quantitation (Plasma protein electrophoresis or acute-phase protein (APP) assays), 3) Functional assays (Microbial killing assays, Hemolysis assays, Enzyme activity assays), and 4) Examination of behavioral responses. Researchers have additionally quantified endocrine metrics (heterophil to lymphocyte ratios, corticosterone concentrations) to place immune function in the context of energy mobilization and stress. See Figure 2 for an overview of procedures used and Table 1 for a list of associated sources and study organisms.

Differential Leukocyte Counts – Researchers have quantified differential leukocyte counts, often as the proportion of each leukocyte type out of a total 100 leukocytes, to examine immune function in relation to reptile dermatomycosis (Fudge, 2000; Davis and Maney, 2018; Campbell and Grant, 2022). Allender et al. (2016) recorded leukocytes in eastern massasauga rattlesnakes (Sistrurus catenatus) in the context of O. ophidiicola infection, finding leukocyte metrics to not vary by dermatophyte detection. In contrast, western pond turtles (Actinemys marmorata) with symptoms of emydomycosis were found to have higher eosinophil and basophil counts compared to those without symptoms (Hazemi, 2020) and Lake Eerie watersnakes (Nerodia sipedon insularum) positive for O. ophidiicola had higher total leukocyte counts and individuals with lesions had higher azurophil proportions and lower lymphocyte proportions compared to those without lesions (Gartlan et al., 2021).

Alternatively, flow cytometry can be used to quantify leukocytes (de Carvalho et al., 2017). In flow cytometry, a cytometer channels cells into a single stream, emits light via a laser toward each cell, and records the light scatter properties (BD FACSAria Handbook). Researchers can plot scatter properties to identify and quantify cell populations in reptilian blood samples with substantially greater throughput compared to traditional microscopy-based methods (Rossi et al., 2009; de Carvalho et al., 2017). Application of flow cytometry within reptile dermatomycosis research remains in its early stages, though emerging work demonstrates its potential utility (Kovach, 2021).

Heterophil to Lymphocyte Ratios – The heterophil to lymphocyte ratio (HL ratio) is often quantified alongside differential leukocyte counts. In response to a stressor, the glucocorticoid hormone, corticosterone (CORT), initiates an increase in circulating heterophils in conjunction with a decrease in circulating lymphocytes as they migrate towards peripheral tissues (Davis et al., 2008). The link between elevated stress, CORT, and HL ratios has been validated throughout a wide range of taxa, though their correlation is extremely variable and arguably non-interchangeable (Davis et al., 2008; Davis and Maney, 2018). With respect to reptile dermatomycosis, Kendall et al. (2023) found temperature to impact changes in heterophil counts and HL ratios after inoculation of prairie rattlesnakes (Crotalus viridis) with O. ophidiicola. They observed an increase in the HL ratio along with symptom severity after inoculation at 20 °C, but not at 26 °C. In contrast Hazemi, 2020 did not find the HL ratio to be associated with shell disease status in western pond turtles, though it was associated with rearing environment.

Plasma Protein Quantification – Protein electrophoresis and acute-phase protein (APP) assays can identify the presence and quantities of proteins produced in response to inflammation, infection, or tissue damage (Jain et al., 2011). Protein electrophoresis separates proteins based on their size and charge, enabling the assessment of relative abundances across major protein fractions (including albumin, α1‐globulins, α2‐globulins, β‐globulins, and γ‐globulins). In contrast, acute-phase protein (APP) assays target individual proteins using methods such as immunosorbent assays (e.g., ELISAs) to provide precise, absolute quantification (Cray et al., 2010). Both of these methods have been used to quantify proteins associated with reptile dermatomycosis. Using both protein electrophoresis and APP assays, Allender et al. (2015c) found a higher proportion of γ‐globulins in ophidiomycosis positive eastern massasauga rattlesnakes as well as lower total protein, albumin, γ‐globulins, albumin:globulin ratios, and higher C-reactive proteins in snakes from a site with assumed ophidiomycosis infections compared to a site without assumed infections. Similarly, Knotkova et al. (2019) found decreased albumin:globulin ratios, and elevated α, β, and γ-globulins in two green iguanas brough into a veterinary clinic with inflammatory skin infections.

Microbial Killing Assays – To quantify responses in complement (an innate immune component) to reptile dermatomycosis, multiple studies have conducted microbial killing assays (MKAs; often called bacterial killing assays, or BKAs) using frozen plasma or serum. MKAs involve the inoculation of blood plasma or serum with specific microbes and subsequent quantification of relative microbial growth and thus killing by serum immune components after an incubation period (Millet et al., 2007; French and Neuman-Lee, 2012). MKAs are a common technique employed in reptile ecoimmunology and are easily adapted to 96-well plates, allowing for the examination of a large quantity of samples at once (French and Neuman-Lee, 2012). Comparison of assays incorporating different serum treatments (fresh, frozen, heat-treated) and using different groups of microbes (gram-positive bacteria, gram-negative bacteria, fungi) can provide information on response mechanisms in different contexts (Terry et al., 2025b). In reptiles, most studies conducting killing assays have used bacterial models (or ran BKAs), with the most often used model being Escherichia coli (French and Neuman-Lee, 2012). To date, the only study on reptile dermatomycosis that has conducted MKAs has used frozen plasma (blood collected in EDTA). McCoy et al. (2017) found microbial killing ability against Escherichia coli in pygmy rattlesnakes (Sistrurus miliarius) to not be associated with ophidiomycosis symptom severity.

Although a limited number of studies have conducted frozen-serum MKAs against E. coli in manipulative settings, specifically in gopher tortoises and pygmy rattlesnakes after inoculation with lipopolysaccharide (LPS; Goessling et al., 2017; Lind et al., 2020), no studies have contextualized immune challenges and MKAs using reptile-specific dermatophytes. Though, Terry et al. (2025a, 2025b) inoculated red-eared slider turtles (Trachemys scripta elegans) with zymosan, a polysaccharide found in yeast cell walls, and conducted MKAs against Candida albicans (Smith et al., 1982). While Candida spp are often commensal, they can also be pathogenic to a wide range of organisms, including reptiles (Jacobson et al., 2000; Wang et al., 2021). Although these assays do not target a reptile-specific dermatophyte, MKAs using C. albicans can still elucidate immune mechanisms triggered by fungal activation of the alternative and lectin complement pathways (Smith et al., 1982; Rawal et al., 2008).

Hemolysis Assays – Hemolysis assays are conducted to assess the functional interaction between the complement system and natural antibodies (NAbs; Matson et al., 2005). In these assays, foreign erythrocytes are introduced to serially diluted blood serum and NAb-complement mediated lysis is quantified as the highest dilution at which hemolysis occurs (Matson et al., 2005). Using a modified sheep red blood cell lysis assay, Haynes et al. (2022b) examined NAb-complement activity in Lake Erie watersnakes with ophidiomycosis symptoms and found activity to increase over time in captivity, but to not differ by disease severity. Hemolysis assays are often conducted in conjunction with agglutination assays (i.e., hemolysis-hemagglutination assays), which quantifies agglutination resulting from only NAbs, however this combined approach has yet to be used to examine immune function related to reptile dermatomycosis.

Enzyme Activity Assays – Enzymatic assays can be used to assess changes in the activity of immune-related enzymes and antibodies associated with pathogen exposure, though there is a paucity of reagents specific for non-model organisms (Garnier et al., 2017). Although few studies have evaluated enzyme activity associated with reptile dermatomycosis, some work has examined chitotriosidase, an enzyme produced by macrophages that degrades chitin, in relation to ophidiomycosis in snakes (Haynes et al., 2022b). Haynes et al. (2022b) found Lake Erie watersnakes exhibiting mild disease to display higher chitotriosidase activity than those with more severe infections (Haynes et al., 2022b). Additionally, Romer et al. (2024) tested coagulase activity (alongside antifungal activity) of bacterial isolates associated with ophidiomycosis in snakes using blood agar plates, finding six out of fifteen strains to display coagulase activity.

Behavioral/Febrile Responses – A few studies have examined behavioral responses to dermatomycosis in reptiles, mostly focusing on temperature regulation and febrile responses. Fever is one of the first non-memory based physiological responses to pathogen exposure, triggering faster immune cell production and mobilization (Zimmerman, 2020). In reptiles, behavioral fever, or an individual moving to warmer substrate or increasing basking behavior, has been observed (Goessling et al., 2017). Godwin et al. (2021) observed eastern ribbon snakes (Thamnophis saurita) inoculated with O. ophidiicola to reach a preferred body temperature more quickly than non-inoculated snakes. Additionally, they found “naïve” snakes, or snakes that had not been exposed to O. ophidiicola before inoculation (not detected through qPCR) sought higher substrate temperatures compared to snakes that were previously exposed (Godwin et al., 2021). Other studies have found evidence that snakes with ophidiomycosis are more likely to bask at the surface, including at the end of their active season while non-infected snakes are more likely to retreat to overwintering refugia (Lorch et al., 2015; Tetzlaff et al., 2017; McKenzie et al., 2021).

Corticosterone Quantification – To investigate tradeoffs between immune components and other physiological processes, assays quantifying corticosterone, or the “energy mobilizing hormone,” can be conducted in conjunction with immune assays (Neuman-Lee and French, 2014, 2017; Terry et al., 2023). Radioimmunoassays (RIAs) are the gold standard for hormone quantification but can be costly, while quantification of CORT using ELISAs is less expensive but often less precise (Abraham, 1975; Sheriff et al., 2011; Kinn Rød et al., 2017). Most studies examining CORT in relation to reptile dermatomycosis have conducted RIAs. Nevarez et al. (2012) found American alligators (Alligator mississippiensis) with dermatitis to have increased CORT responses, but no change in baseline or negative feedback in CORT after treatment. Lind et al. (2018) found CORT and ophidiomycosis symptoms to increase in a wild population of pygmy rattlesnakes in the winter and CORT levels to be higher in severely symptomatic snakes compared to moderately infected and uninfected snakes. In a manipulative study examining CORT and glucose after inoculation with LPS, Lind et al. (2024) found CORT to be elevated in pygmy rattlesnakes with apparent ophidiomycosis and in response to LPS treatment. In contrast, Lind et al. (2023) found pygmy rattlesnakes with apparent ophidiomycosis to not display differences in baseline CORT or CORT responses to capture compared to “healthy” individuals.

Future Directions: Contextualizing Immune Analysis – Incorporation of in vitro models as well as investigation of additional immune components can further contextualize dermatomycosis-immune dynamics. For example, Lamar et al. (2024) examined cellular immune responses in vitro by inoculating peripheral blood mononuclear cells (PBMCs) isolated from tuataras (Sphenodon punctatus) with LPS and Concanavalin A. They were able to compare PBMC metabolic and nitric oxide (immune signaling) responses between individuals at different sampling locations and between sexes (Lamar et al., 2024). Investigation into regulation of genes encoding important immune components (e.g., cytokines and immune receptors) may also be beneficial. Through transcriptomics, immune gene responses to dermatomycoses have been examined in amphibians, especially in the context of chytridiomycosis (Eskew et al., 2018; Chondrelli et al., 2024). Minimal “omics” research has been conducted to examine immune responses to dermatomycosis in reptiles. One notable exception is the study by Mathur et al. (2024), which employed transcriptomic analyses to identify genes potentially involved in the immune response in prairie rattlesnakes inoculated with O. ophidiicola. Given the scarcity of existing studies, integrating immune and skin microbiome analysis with additional “omics” approaches would allow for a more comprehensive and mechanistic understanding of microbiome-immune-dermatomycosis dynamics.

3.5 Final thoughts: combining skin microbiome and immune metrics

By examining both skin microbiome and immune function associated with reptile dermatomycoses, we can better contextualize health of reptiles facing emerging or prevalent fungal diseases. Trevelline et al. (2019) called for the integration of microbiome research into wildlife health research. We further this call for more research to specifically examine skin microbiota and immune system interactions relevant to EFDs in reptiles. Given that the majority of reptile microbiome-dermatomycosis and immune-dermatomycosis studies have focused on snakes, expanding this research to other taxa will broaden our understanding of these dynamics. We suggest coalescing both field-based and manipulative studies with advanced metagenomic sequencing technology (e.g., long-read sequencing, PacBio HiFi Sequencing), bioinformatics analysis (e.g., non-parametric analyses, network analyses), and immune response analysis. Furthermore, microbiome and immune associations with dermatomycoses delineated at multiple scales (see Walker et al., 2019) can be incorporated into models to predict risk across the landscape (see Species Distribution Modeling in Romer et al., 2024). Dermatomycosis risk predictions can then inform conservation and management decisions prioritizing more vulnerable individuals, species, and populations.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. During the preparation of this manuscript, the author (NH) used ChatGPT (V5.2) to improve word choice. The authors reviewed and revised all output and take full responsibility for the final content of the work.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/famrs.2026.1812456/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Faraham Ahmadzadeh, Shahid Beheshti University, Iran

Reviewed by: Sierra N Smith, University of Florida, United States

Víctor Romero, Simón Bolívar University, Venezuela