Genome-wide mapping using new AFLP markers to explore intraspecific variation among pathogenic Sporothrix species

Abstract

Sporotrichosis is a chronic subcutaneous mycosis caused by Sporothrix species, of which the main aetiological agents are S. brasiliensis, S. schenckii, and S. globosa. Infection occurs after a traumatic inoculation of Sporothrix propagules in mammals’ skin and can follow either a classic route through traumatic inoculation by plant debris (e.g., S. schenckii and S. globosa) or an alternative route through zoonotic transmission from animals (e.g., S. brasiliensis). Epizootics followed by a zoonotic route occur in Brazil, with Rio de Janeiro as the epicenter of a recent cat-transmitted epidemic. DNA-based markers are needed to explore the epidemiology of these Sporothrix expansions using molecular methods. This paper reports the use of amplified-fragment-length polymorphisms (AFLP) to assess the degree of intraspecific variability among Sporothrix species. We used whole-genome sequences from Sporothrix species to generate 2,304 virtual AFLP fingerprints. In silico screening highlighted 6 primer pair combinations to be tested in vitro. The protocol was used to genotype 27 medically relevant Sporothrix. Based on the overall scored AFLP markers (97–137 fragments), the values of polymorphism information content (PIC = 0.2552–0.3113), marker index (MI = 0.002–0.0039), effective multiplex ratio (E = 17.8519–35.2222), resolving power (Rp = 33.6296–63.1852), discriminating power (D = 0.9291–0.9662), expected heterozygosity (H = 0.3003–0.3857), and mean heterozygosity (H_avp = 0.0001) demonstrated the utility of these primer combinations for discriminating Sporothrix. AFLP markers revealed cryptic diversity in species previously thought to be the most prevalent clonal type, such as S. brasiliensis, responsible for cat-transmitted sporotrichosis, and S. globosa responsible for large sapronosis outbreaks in Asia. Three combinations (#3 EcoRI-FAM-GA/MseI-TT, #5 EcoRI-FAM-GA/MseI-AG, and #6 EcoRI-FAM-TA/MseI-AA) provide the best diversity indices and lowest error rates. These methods make it easier to track routes of disease transmission during epizooties and zoonosis, and our DNA fingerprint assay can be further transferred between laboratories to give insights into the ecology and evolution of pathogenic Sporothrix species and to inform management and mitigation strategies to tackle the advance of sporotrichosis.

Author summary

Sporotrichosis is a subacute or chronic infection characterized by nodular lesions of the (sub)cutaneous tissues and adjacent lymphatics. Sporothrix brasiliensis, S. schenckii, and S. globosa are the main agents of sporotrichosis in humans and other mammals. Sporothrix propagules gain entrance by traumatic implantation in the skin following two main routes of infection, which include animal transmission (e.g., cat-cat and cat-human) and plant origin. In recent years there has been a significant increase in the number of atypical and more severe cases of sporotrichosis, along with the expansion of the area of occurrence of Sporothrix species, such as the highly virulent S. brasiliensis. We investigated the usefulness of the AFLP technology, a DNA fingerprinting technique, which is based on the selective amplification of genomic restriction fragments by PCR to explore genetic diversity and population structure in Sporothrix during ongoing outbreaks. We report six highly effective sets of AFLP markers to discriminate Sporothrix at species and strain level, thus allowing tracking the spread of sporotrichosis. Adding molecular data in an outbreak response context can reveal better ways to improve public policies to contain the advance of sporotrichosis, by early detection, response, intervention, and follow-up.

Introduction

Sporothrix (Ascomycota: Ophiostomatales) comprises 53 species reported in the literature [1]. Within a genus showing an essentially environmental core associated with plant debris, decaying wood, insects, and soil, only a few members have emerged in recent years with the ability to infect warm-blooded hosts [1]. Sporothrix brasiliensis, S. schenckii, and S. globosa are the main etiological agents of sporotrichosis in humans and other mammals. To a lesser extent, the disease is also caused by members of the S. pallida complex, such as S. chilensis, S. mexicana, and S. pallida s. str. [2, 3], or members of the S. stenoceras complex [4] which are usually non-virulent to mammals.

Sporotrichosis is a subacute or chronic fungal infection characterized by nodular lesions of the cutaneous or subcutaneous tissues and adjacent lymphatics, which suppurate, ulcerate and drain [5, 6]. Sporothrix propagules gain entrance by traumatic implantation in the skin following two main routes of infection, which include animal transmission (e.g., cat-cat and cat-human) and plant origin (i.e., classic sapronosis). In humans, cutaneous lesions develop at the site of inoculation, and dissemination can occur through the lymphatics during the first 2–3 weeks of infection [7]. Cats are highly susceptible to Sporothrix, and the most common clinical manifestations include multiple skin nodules and ulcers, often associated with nasal mucosa lesions and respiratory signs [8–11], which can lead to the development of severe forms that are difficult to treat and may lead to the death of animals [12, 13]

Infections transmitted via either animal or plant vectors usually escalate to outbreaks or epidemics [14]. Transmission routes of Sporothrix vary among species and host populations. Understanding how transmission spreads following host shifts is of major importance when considering the emergence of Sporothrix in humans and animals. For example, S. schenckii and S. globosa are cosmopolitan pathogens that appear to be widespread environmentally following traumatic inoculation of contaminated plant material [14]. This route affects specific occupational populations, including agricultural workers, florists and gardeners, and was termed “Gardner’s disease” [15] or reed toxin [16]. However, in the alternative route the highly virulent offshoot S. brasiliensis has spread successfully via animal horizontal transmission or zoonotic transmission in South America, so it seems that the sapronotic route plays a minor role during outbreaks caused by S. brasiliensis. Shifts from plant to animal transmission have led to the emergence of sporotrichosis in Brazil, and host jumping is an important feature among the Ophiostomatales, distinguishing cat-transmitted sporotrichosis as an occupation-independent disease [14].

The disease has been reported around the world, mainly in areas with high moisture and temperature [17]. The global burden of sporotrichosis is >40,000 cases [18], and the regions where the disease is endemic include Latin America (especially Brazil, Colombia, Mexico, Peru, and Venezuela) [19], Asia (especially China, India, and Japan) [20] and Africa [21]. In Brazil, recent epidemics are peculiar, especially in the South and Southeast regions, with the potential for zoonotic transmission of S. brasiliensis, nearly always related to cats as the main source of fungal infection for humans, dogs and other cats [22].

In Brazil, cases of sporotrichosis in humans and animals have increased significantly in recent years. The emergence of S. brasiliensis is associated with the appearance of atypical and more severe clinical manifestations in humans [23, 24]. This phenomenon is recent, since for decades feline sporotrichosis in Brazil occurred only as sporadic, self-limiting clusters [25, 26]. However, the current outbreak of feline sporotrichosis due to S. brasiliensis in South and Southeast Brazil has risen to epidemic status, creating a public health emergency of international concern because of the potential of zoonotic transmission [14, 22, 27]. Remarkably, S. brasiliensis has been showing spatial expansion, with a wave of expansion tracking northwards through Northeast Brazil over the last five years [1].

Tracking the movement of Sporothrix during outbreaks to understand the epidemiological processes driving population expansion is needed to understand epidemic sporotrichosis. However, researchers lack powerful genome-wide markers with which to address the epidemiology of Sporothrix. The use of amplified fragment length polymorphisms (AFLP) is one of the most informative and cost-effective DNA fingerprinting technologies applicable for any organism, without the need for prior sequence knowledge [28]. This technique provides an effective means of genotyping using a highly discriminatory panel of genome-wide markers, which is helpful in many areas of population genetics [29]. In fungi, AFLP markers have been successfully applied in studies of Cryptococcus spp., Candida spp., Histoplasma capsulatum, Aspergillus fumigatus, Fusarium oxysporum, Phytophthora pinifolia, Monilinia fructicola, Fonsecaea spp. [30–38] and Sporothrix spp., being considered appropriate for investigating genetic variation [20, 39, 40]. However, in some cases, failure to recover the correct tree topology displaying paraphyletic groups [20] associated with the description of no correlation between AFLP genotypes and the geographical origins of isolates [39] or clinical pictures [39, 40] led us to improve the design of these genome-wide markers, aiming finer-scale epidemiological patterns.

AFLP recognizes genetic differences between any two fungal genomes using a combination of restriction enzyme digestion of genomic DNA and PCR amplification. A pair of AFLP primers is sufficient to generate complex DNA fingerprints, and different sets of primers will yield unique profiles. Selectivity is achieved by designing primers that anneal specifically to the adaptor and the recognition site and carry one or more arbitrary chosen nucleotides at the 3′ end, which reduces the number of genomic fragments being amplified and analyzed. By using AFLP, polymorphisms can be detected by (i) a mutation in the restriction site for enzymes, (ii) a mutation in the sequence corresponding to the selective bases (primer extension), and (iii) a deletion/insertion within the amplified fragment. To this end, we took advantage of Sporothrix genomes available in the GenBank to optimize, through extensive in silico analysis, the AFLP technique by targeting selective bases that can best answer questions related to epidemiology, genetic diversity and population structure in Sporothrix species. Once developed, the use of AFLP patterns of medically relevant Sporothrix as well as environmental species was compared to determine their genetic relationship and to explore levels of intraspecific diversity, with a focus amongst species involved in outbreaks such as S. brasiliensis. To achieve higher resolution, different primer combinations were tested in vitro to determine the best combination to be used for a focal Sporothrix species. Here, we report new AFLP primer combinations that will be used to test epidemiological and evolutionary hypotheses in Sporothrix and as high-throughput technology for assessing the degree of intraspecific variability in Sporothrix species during ongoing outbreaks of this disease.

Methods

Ethics approval

All Sporothrix strains used in this study belong to the culture collection of the Federal University of São Paulo (UNIFESP), Paulista School of Medicine (EPM), and were described earlier [41–44]. The protocol was approved by the Ethics in Research Committee of the Federal University of São Paulo under protocol number 2443270218.

Fungal strains

This study included 27 Sporothrix isolates (S. brasiliensis, n = 9; S. schenckii, n = 8; S. globosa, n = 6; S. mexicana, n = 2; S. chilensis, n = 1, and S. pallida n = 1), obtained from clinical lesions of patients with varying degrees of disease severity (n  =  20), from animals (n  =  3) or from environmental sources (n  =  4). The isolates were identified down to species level by comparison with the descriptions presented by Rodrigues et al. [41–44], and were stored at room temperature in slant cultures on Sabouraud dextrose agar (Difco Laboratories, Detroit, MI, USA) [45].

DNA extraction

Total DNA was obtained and purified directly from 14-day-old monosporic colonies on Sabouraud slants by following the Fast DNA kit protocol (MP Biomedicals, Irvine, CA, USA), as previously described [44]. All isolates were characterized down to species level using a Sporothrix species-specific PCR targeting the gene encoding calmodulin, as described before [46]. Reference strains representing the main phylogenetic groups in Sporothrix were included in all experiments (Table 1).

Table 1. Strains, species, source, origin, haplotypes, and GenBank accession numbers of Sporothrix spp. isolates used in this study.

Isolate code	Other Code	Species	Source	Origin	CAL hapa	GenBank	Reference
Ss09	-	S. brasiliensis	Human	Brazil	H1	KC693833	[16]
Ss43	-	S. brasiliensis	Human	Brazil	H2	JX077112	[13]
Ss53	CBS 132989	S. brasiliensis	Feline	Brazil	H1	KC693846	[16]
Ss55	-	S. brasiliensis	Human	Brazil	H1	KC693847	[16]
Ss94	-	S. brasiliensis	Human	Brazil	H1	KF943664	[15]
IPEC 16919	-	S. brasiliensis	Human	Brazil	H1	AM116898	[1–3]
IPEC 16490T	CBS 120339	S. brasiliensis	Human	Brazil	H1	AM116899	[1–3]
Ss256	CBS 133015	S. brasiliensis	Feline	Brazil	H1	KC693889	[16]
Ss261	-	S. brasiliensis	Human	Brazil	H1	KC693894	[16]
Ss01	CBS 132961	S. schenckii	Feline	Brazil	H6	KC693828	[16]
Ss03	CBS 132963	S. schenckii	Human	Brazil	H6	JX077117	[13]
Ss04	-	S. schenckii	Human	Brazil	H6	JX077118	[13]
Ss36	-	S. schenckii	Human	Brazil	H7	KC693843	[16]
Ss58	-	S. schenckii	Human	Brazil	H8	KF943646	[15]
Ss61	-	S. schenckii	Soil	Brazil	H9	KF561244	[24]
Ss137	-	S. schenckii	Human	Brazil	H7	KF574462	[24]
Ss143	-	S. schenckii	Human	Brazil	H10	JQ041906	[13]
Ss06	CBS 132922	S. globosa	Human	Brazil	H3	JF811336	[13]
Ss41	CBS 132923	S. globosa	Human	Brazil	H4	JF811337	[13]
Ss49	CBS 132924	S. globosa	Human	Brazil	H4	JF811338	[13]
FMR 8600T	CBS 120340	S. globosa	Human	Spain	H5	AM116908	[47, 48]
FMR 8595	CBS 130104	S. globosa	Human	Spain	H4	AM116905	[47, 48]
Ss236	CBS 132925	S. globosa	Human	Brazil	H4	KC693877	[47, 48]
FMR 9107	CBS 120342	S. mexicana	Vegetal	Mexico	H11	AM398392	[47, 48]
FMR 9108T	CBS 120341	S. mexicana	Soil	Mexico	H11	AM398393	[47, 48]
FMR 8803	-	S. pallida	Insect	China	H12	AM398998	[47, 48]
Ss469	CBS 139891	S. chilensis	Human	Chile	H13	KP711815	[14]

^aCalmodulin haplotype; IPEC, Instituto de Pesquisa Clínica Evandro Chagas, Fiocruz, Brasil; FMR, Facultat de Medicina I Cièncias de La Salut, Reus, Spain; CBS, culture collection of the Westerdijk Fungal Biodiversity Institute, Utrecht, The Netherlands; All “Ss” strains belong to the culture collection of Federal University of São Paulo (UNIFESP), Paulista School of Medicine (EPM).

Phylogenetic and haplotype analysis

Genetic relationships for the calmodulin encoding gene sequences (exons 3–5) were investigated by phylogenetic analysis using the neighbor-joining (NJ), maximum likelihood (ML), and maximum parsimony (MP) methods. Phylogenetic trees were constructed with MEGA7 [49]. Evolutionary distances were computed using the Kimura 2-parameter distance [50] (for NJ and ML analysis), and the robustness of branches was assessed by bootstrap analysis of 1,000 replicates [51].

The nucleotide (π), as well as the haplotype (Hd) diversities [52], were estimated using DnaSP version 6 [53]. Haplotype network analysis was carried out using the median-joining method [54], implemented in NETWORK v4.6.1.0 (Fluxus Technology, Suffolk, UK), and was used to visualize differences and diversity among Sporothrix species sequence data. Sites containing gaps and missing data were not considered in the analysis.

In silico AFLP analyses

Whole-genome sequences of nine Sporothrix isolates (Table 2) were analyzed in silico to predict AFLP markers of corresponding lengths (50–500 bp) that might be generated in vitro during the standard AFLP procedure. In silico AFLP analyses were performed using AFLPinSilico [55] and ISIF (In Silico Fingerprinting) [56]. Briefly, Sporothrix genomes were retrieved from the GenBank and in silico digested with EcoRI and MseI restriction enzymes. Afterward, a total of 256 combinations of two selective bases (EcoRI+2 and MseI+2) were used to mine a subset of selective fragments. Finally, to properly simulate the AFLP procedure, we determined the length of all the peaks of the in silico AFLP profile, with the addition of the adaptor and primer lengths. The number and size of the fragments were used to construct a matrix of fragments and data were visualized using heatmaps. Hierarchical cluster analysis of in silico AFLP profiles was performed using Heatmapper [57], based on average linkage and Euclidean distance applied to each row-cluster.

Table 2. Genomes of Sporothrix species retrieved from NCBI Genome database (https://www.ncbi.nlm.nih.gov/genome) for in silico analysis.

Strain	Species	Source	Origin	INSDC1 (WGS)	Total length	BioProjects	Reference
5110	S. brasiliensis	Feline	Brazil	AWTV01	33.2 Mb	PRJNA218075	[58]
ATCC 58251	S. schenckii	Human	USA	AWEQ01	32.5 Mb	PRJNA217088	[59]
1099–18	S. schenckii	Human	USA	AXCR01	32.5 Mb	PRJNA218070	[58]
SsMS1	S. schenckii	Human	Colombia	PGUU01	32.6 Mb	PRJNA401003	[60]
SsEM7	S. schenckii	Human	Colombia	NTMI01	32.8 Mb	PRJNA401003	[60]
CBS 120340	S. globosa	Human	Spain	LVYW01	33.4 Mb	PRJNA315855	[61]
SS01	S. globosa	Human	China	LVYX01	33.4 Mb	PRJNA315862	[61]
SPA8	S. pallida	Soil	Spain	JNEX02	37.8 Mb	PRJNA248334	[62]
RCEF 264	S. insectorum	Insect	China	AZHD01	34.7 Mb	PRJNA72727	[63]

¹International Nucleotide Sequence Database Collaboration (INSDC; http://www.insdc.org/)

Genotyping by AFLP fingerprinting

Digestion of DNA, adapter ligation, non-selective, and selective amplifications were carried out in vitro as described previously by Vos et al. [64] with some modifications. Briefly, 200 ng of Sporothrix genomic DNA was digested by EcoRI and MseI (New England Biolabs, Ipswich, MA) and ligated to EcoRI and MseI adapters simultaneously (Integrated DNA Technologies, USA) [64]. A preselective amplification was performed with EcoRI+0 and MseI+0 primers [64]. Fluorescent AFLP was done with 6-carboxyfluorescein (FAM; blue) fluorescent dye-labeled EcoRI primer, with two selective bases (5′-GAC TGC GTA CCA ATT CNN-3′), and unlabeled MseI primer with two selective bases (5′-GAT GAG TCC TGA GTA ANN-3′). Six different combinations were chosen to evaluate the potential for genetic characterization of clinical Sporothrix isolates (combination #1 EcoRI-GA/MseI-AA; #2 EcoRI-AA/MseI-AA; #3 EcoRI-GA/MseI-TT; #4 EcoRI-AA/MseI-TT; #5 EcoRI-GA/MseI-AG; #6 EcoRI-TA/MseI-AA). All oligonucleotides were supplied by Integrated DNA Technologies (IDT, San Diego, CA, USA). PCR-amplified AFLP fragments were resolved by capillary electrophoresis with an ABI3100 Genetic Analyzer alongside a LIZ500 internal size standard (Applied Biosystems. Foster City, CA, USA) at the Human Genome and Stem Cell Research Center Core Facility (University of São Paulo, São Paulo, Brazil) under previously described conditions. At least two independent electropherograms for each combination of selective primers were imported in BioNumerics v7.6 (Applied Maths, St. Martens-Latem, Belgium) and analyzed to verify the ability to accurately reproduce results.

The selection of the amplified restriction products was automated, and only strong and high-quality fragments were considered. To minimize scoring errors, each electropherogram was carefully inspected to exclude doubtful peaks, setting a minimum threshold at 100 relative fluorescent units, and considering only peaks with sizes between 50 and 500 base pairs. The size of the AFLP fragments was determined by BioNumerics v7.6. Peak patterns were converted to the dominant presence (1) or absence (0) at probable fragment positions.

Relationships among Sporothrix specimens and taxa were evaluated employing distance-based methods, as recommended for dominant anonymous markers. Therefore, pairwise genetic distances were calculated using the band-based Jaccard’s similarity coefficient combined with a "fuzzy logic" option in BioNumerics v7.6. Dendrograms were created according to the unweighted pair group mean arithmetic method (UPGMA). Branch resampling support was conducted using the cophenetic correlation coefficient and the standard deviation was used to express the consistency of a given cluster, which determines the correlation between the dendrogram-derived similarities and the matrix similarities.

To assess the existence of topological congruence between any two AFLP dendrograms or between AFLP dendrograms and DNA-sequencing phylogeny, and the associated confidence level, Newick trees were used to calculate the congruence index (Icong), as described by de Vienne and colleagues [65], based on maximum agreement subtrees (MAST).

The minimum spanning tree (MST) model was used to investigate the evolutionary relationships among all the observed genotypes of medically relevant Sporothrix species. AFLP-derived MSTs were executed in BioNumerics v7.6 with AFLP data after conversion into a band matching table. When a set of distances is given between n entries, a minimum spanning tree is the tree that connects all entries in such a way that the summed distance of all branches of the tree is the shortest possible [66]. All figures were exported and treated using Corel Draw X8 (Corel, Ottawa, Canada).

Reproducibility of AFLP markers

Reproducibility of AFLP fragment profiles was assessed by repeated digestion of DNA, adapter ligation, non-selective and selective amplification of all individuals genotyped per species [67]. A single error rate was calculated for all the samples analyzed. Error rates were determined as the percentage of loci that were mismatched between the replicate pairs [68].

Genetic diversity and statistical analysis

To evaluate which of the six AFLP primer combinations were the most informative, the following polymorphism indices for dominant markers were calculated: polymorphic information content (PIC) [69], expected heterozygosity (H) [70], effective multiplex ratio (E) [71], arithmetic mean heterozygosity (H_avp) [71], marker index (MI) [71, 72], discriminating power (D) [73], and resolving power (Rp) [74].

Dimensioning analysis

Principal component analysis (PCA) and multi-dimensional scaling (MDS) were used as alternative grouping methods, producing three-dimensional plots in which the entries were spread according to their relatedness. Dimensioning techniques were executed with AFLP data after conversion into a band matching table. Automated band matching was performed on all fingerprint entries within the comparison, considering minimum profiling of 5%, with the optimization and position tolerances for selecting bands set to 0.10%. Default settings were applied for PCA and MDS in BioNumerics v7.6, subtracting the average for characters [38]. All Figs were exported and treated using Corel Draw X8.

Results

We selected 27 well-characterized Sporothrix isolates and used calmodulin to explore genetic diversity and intraspecific variability in our dataset. The aligned CAL sequences were 623 bp long, including 437 invariable characters, 167 variable parsimony-informative sites (26.8%), and 19 singletons. The clade of pathogenic Sporothrix species was well supported with high bootstrap values (100), including S. brasiliensis (clade I), S. schenckii (clade II), and S. globosa (clade III) (Fig 1A). Comparison of CAL sequences revealed significant polymorphisms among S. schenckii isolates (π  =  0.00908), whereas sequences from S. brasiliensis (π  =  0.00000) and S. globosa (π  =  0.00112) were much less diverse. The haplotype diversity was assessed using the DnaSP software [53], which allowed identifying 13 distinct haplotypes. The overall values of haplotype (Hd  = 0.8889) and nucleotide diversities (π  = 0.08680) were high for Sporothrix. However, when considering only S. brasiliensis or S. globosa just two (Hd = 0.2222) and three (Hd = 0.6000) haplotypes were found, respectively, suggesting that CAL works as a suitable barcoding marker for diagnosis but may cover cryptic diversity in these species (Fig 1B). The Simpson index of diversity [75] was calculated as 0.2279.

Fig 1.

Phylogenetic tree (A), inferred using the maximum likelihood method and Kimura 2-parameter model of the calmodulin sequences of 27 strains of Sporothrix. Numbers close to the branches represent ML/NJ/MP respectively. Bootstraps higher than 95 based on 1000 replications are represented in bold branches. Haplotype network of Sporothrix (B) was done using the median-joining method. The circumference size is proportional to the frequency of haplotype. The median vectors are displayed by black dots and represent hypothetical unsampled or extinct haplotypes in the population.

To test the hypothesis of cryptic diversity in S. brasiliensis and S. globosa, we developed AFLP markers to explore genetic variation in these emerging agents. The first step in our strategy involved the in silico characterization of Sporothrix genomes deposited with NCBI, comprising medically relevant members as well as environmental species. AFLPinSilico and ISIF were used to scan restriction sites for EcoRI and MseI. Afterward, a subset of modified genomic fragments was created by adding EcoRI and MseI adaptors, and an enriched population of modified genetic fragments was chosen based on two selective bases for EcoRI+2 and MseI+2 primers. Therefore, 256 possible combinations were investigated for nine genomes, producing a matrix of 2,304 virtual AFLP profiles, which are presented as a heatmap in Fig 2. A remarkable diversity of fragments was generated, which ranged from 0–56 and 0–62 in 256 combinations tested in AFLPinSilico and ISIF, respectively (Fig 3, S1 Table). Sporothrix pallida presented the largest genome core (~37.8 Mb) and it was represented in our in silico scan by the largest number of AFLP markers in 62.5–70.7% combinations (Pearson correlation = 0.861, r² = 0.7421, P = 0.00283) (Fig 3).

Fig 2.

Heatmap of fragments generated by in silico analyses with 256 combinations of selective primer pairs in two programs–AFLPinSilico (A) and ISIF (B)–for nine genome sequences of Sporothrix retrieved from the GenBank. The highest numbers of fragments are represented by red shading and the lower by blue shading.

Fig 3.

A total of 256 combinations of selective EcoRI+2 and MseI+2 primer pairs were employed to generate 2,304 virtual AFLP profiles AFLPinSilico (A) and ISIF (B). The dots located on the left X-axis represent the number of fragments generated for each combination. The bold bar represents the average of fragments generated for all combinations. The white dots located on the right X-axis represent the genome size, estimated by whole genome sequencing.

Among all Sporothrix species evaluated, S. brasiliensis and S. globosa had little genetic diversity (Fig 1B), so the diversity of fragments generated for these emerging species were fundamental for the selection of putative combinations (Fig 3A). Therefore, we highlighted six combinations (#1–6) to be tested in vitro, which showed the highest number of polymorphic markers (i.e., number and size) with the potential to be used to speciate Sporothrix and explore intraspecific variation (S2 Table).

A total of 685 loci were amplified using the selective primers EcoRI+2 and MseI+2, among them 135, 137, 106, 111, 99 and 96 polymorphic fragments, for combinations #1 to #6, respectively. The averages of fragments varied per isolate for the six combinations between 19.5–32.8 for S. brasiliensis, 19.3–40.6 for S. schenckii, 15.6–31.5 for S. globosa, 14.5–41.5 for S. mexicana, 23–44 for S. pallida and 16–45 for S. chilensis. The details of marker attributes for different AFLP primer combinations are given in Table 3.

Table 3. Summary of polymorphism statistics calculated for different pairs of selective primers (EcoRI+2 and MseI+2) of Sporothrix species.

#1 EcoRI-GA/MseI-AA
Species	Scored bands	H	PIC	E	Havp	MI	D	Rp	Error %
S. brasiliensis	62	0.4991	0.3745	29.6667	0.0009	0.0265	0.7715	21.3333	1.12
S. schenckii	84	0.4995	0.3747	40.6250	0.0007	0.0302	0.7665	38.2500	1.23
S. globosa	43	0.3918	0.3151	31.5000	0.0015	0.0478	0.4641	9.6667	1.05
S. mexicana	45	0.1435	0.1332	41.5000	0.0016	0.0661	0.1503	7.0000	0.00
Overall	135	0.3857	0.3113	35.2222	0.0001	0.0037	0.9320	63.1852	1.04
#2 EcoRI-AA/MseI-AA
Species	Scored bands	H	PIC	E	Havp	MI	D	Rp	Error %
S. brasiliensis	56	0.4997	0.3749	28.6667	0.0010	0.0284	0.7385	16.8889	0.00
S. schenckii	63	0.4854	0.3676	26.1250	0.0010	0.0252	0.8285	26.7500	0.47
S. globosa	34	0.4192	0.3313	23.8333	0.0021	0.0490	0.5097	10.3333	0.64
S. mexicana	37	0.0267	0.0263	36.5000	0.0004	0.0132	0.0270	1.0000	0.00
Overall	137	0.3287	0.2747	28.4074	0.0001	0.0025	0.9570	52.0741	0.28
#3 EcoRI-GA/MseI-TT
Species	Scored bands	H	PIC	E	Havp	MI	D	Rp	Error %
S. brasiliensis	52	0.4974	0.3737	27.8889	0.0011	0.0296	0.7129	19.7778	0.00
S. schenckii	52	0.4833	0.3665	21.2500	0.0012	0.0247	0.8336	17.5000	0.58
S. globosa	28	0.3501	0.2888	21.6667	0.0021	0.0451	0.4023	4.0000	0.00
S. mexicana	21	0.0465	0.0454	20.5000	0.0011	0.0227	0.0476	1.0000	0.00
Overall	106	0.3449	0.2854	23.4815	0.0001	0.0028	0.9510	40.8889	0.16
#4 EcoRI-AA/MseI-TT
Species	Scored bands	H	PIC	E	Havp	MI	D	Rp	Error %
S. brasiliensis	44	0.4999	0.3750	21.7778	0.0013	0.0275	0.7557	16.4444	0.51
S. schenckii	52	0.4967	0.3733	23.8750	0.0012	0.0285	0.7898	19.7500	0.52
S. globosa	38	0.4038	0.3223	27.3333	0.0018	0.0484	0.4835	8.6667	0.00
S. mexicana	28	0.0000	0.0000	28.0000	0.0000	0.0000	0.0000	0.0000	0.00
Overall	111	0.3483	0.2876	24.9259	0.0001	0.0029	0.9496	45.6296	0.32
#5 EcoRI-GA/MseI-AG
Species	Scored bands	H	PIC	E	Havp	MI	D	Rp	Error %
S. brasiliensis	55	0.4993	0.3746	28.5556	0.0010	0.0288	0.7309	18.6667	0.78
S. schenckii	52	0.4958	0.3729	28.3750	0.0012	0.0338	0.7028	14.2500	0.00
S. globosa	29	0.4096	0.3257	20.6667	0.0024	0.0486	0.4933	10.0000	0.00
S. mexicana	28	0.0000	0.0000	28.0000	0.0000	0.0000	0.0000	0.0000	0.00
Overall	99	0.3908	0.3145	26.3704	0.0001	0.0039	0.9291	40.9630	0.29
#6 EcoRI-TA/MseI-AA
Species	Scored bands	H	PIC	E	Havp	MI	D	Rp	Error %
S. brasiliensis	49	0.4787	0.3641	19.4444	0.0011	0.0211	0.8431	21.7778	1.14
S. schenckii	53	0.4639	0.3563	19.3750	0.0011	0.0212	0.8669	22.2500	0.00
S. globosa	36	0.4928	0.3714	15.8333	0.0023	0.0361	0.8077	18.3333	0.00
S. mexicana	15	0.0644	0.0624	14.5000	0.0021	0.0311	0.0667	1.0000	0.00
Overall	97	0.3003	0.2552	17.8519	0.0001	0.0020	0.9662	33.6296	0.44

D = discriminating power; E = effective multiplex ratio; H = expected heterozygosity; Havp = mean heterozygosity; MI = marker index; PIC = polymorphism information content; Rp = resolving power.

The PIC determined for each primer pair was both comparable between species and between markers. Overall, PIC values ranged from 0.2552 to 0.3145, and all markers presented high discrimination power. The highest PIC value was observed for primer combination 5 and the lowest was recorded for primer combination 6, indicating good diversity among the studied Sporothrix. Interestingly, the highest PIC value for S. globosa was obtained in combination 6 (0.3714), demonstrating the potential use of this primer pair to explore diversity in S. globosa. In general, S. brasiliensis and S. schenckii showed slightly higher values than S. globosa (Table 3).

Marker index (MI) as a feature of marker diversity representing the product of the effective multiplex ratio (E) and the arithmetic mean heterozygosity (Havp) was also calculated for all the primer combinations. The MI values ranged from 0.002 to 0.0039. The highest value (0.0039) was obtained with primer pair 5 and the lowest values (0.002) for primer pair 6. A positive correlation was observed between MI and PIC values (Pearson correlation = 0.9950171, r² = 0.9901, P = 0.00001). The resolving power, Rp, is a feature that indicates the discriminatory potential of the marker to distinguish between large numbers of genotypes. Rp ranged from 33.6296 to 63.1852. The highest value (63.1852) was scored with primer combination 1 and the lowest (33.6296) for primer combination 6. The Rp values were not positively correlated with MI (Pearson correlation = 0.4797601, r² = 0.2302, P = 0.335). Nevertheless, combination 6 provided the highest values of Rp (18.3333) and MI (0.0361) for S. globosa.

We also determined expected heterozygosity (H), which is defined as the probability that an individual is heterozygous for the locus in the population. It is equivalent to Nei’s unbiased gene diversity (H_S), as adapted for dominant markers under the assumptions of Hardy-Weinberg equilibrium and the Lynch-Milligan model [76]. The overall average expected heterozygosity for Sporothrix species ranged between 0.3003–0.3908 (Table 4). The high combined expected heterozygosity for S. brasiliensis (H = 0.4787–0.4999) and S. globosa (H = 0.3501–0.4928) is surprising given previous reports [14, 20, 42, 44, 77–79], which showed little or no genetic variation within these pathogens based on DNA sequencing data. Indeed, the indices reported here are comparable to that of S. schenckii (0.4639–0.4995), a species described as more diverse than S. brasiliensis and S. globosa. Based on these criteria, the high number of loci generated for S. brasiliensis, S. schenckii, and S. globosa is sufficient to reveal the true fine-scale population genetic structure, and we recommend the use of combinations #5 (EcoRI-FAM-GA/MseI-AG), #6 (EcoRI-FAM-TA/MseI-AA) and #3 (EcoRI-FAM-GA/MseI-TT) to explore genetic diversity in Sporothrix species.

Table 4. Comparison among the clustering methods used for phylogenetic analysis and AFLP-derived dendrograms for combinations #1 to #6 generated by congruence index values (Icong).

Tree comparison	Leaves	MASTa	Icong	P-value	Congruent
AFLP 1 vs AFLP 2	27	13	1.714	2.19e-05	Yes
AFLP 1 vs AFLP 3	26	11	1.479	0.0009	Yes
AFLP 1 vs AFLP 4	27	12	1.583	0.0001	Yes
AFLP 1 vs AFLP 5	26	13	1.748	1.46e-05	Yes
AFLP 1 vs AFLP 6	26	13	1.748	1.46e-05	Yes
AFLP 2 vs AFLP 3	27	13	1.714	2.19e-05	Yes
AFLP 2 vs AFLP 4	27	14	1.846	2.79e-05	Yes
AFLP 2 vs AFLP 5	27	14	1.846	2.79e-05	Yes
AFLP 2 vs AFLP 6	27	13	1.714	2.19e-05	Yes
AFLP 3 vs AFLP 4	27	16	2.110	4.54e-08	Yes
AFLP 3 vs AFLP 5	26	12	1.613	0.0001	Yes
AFLP 3 vs AFLP 6	26	11	1.479	0.0009	Yes
AFLP 4 vs AFLP 5	27	15	1.978	3.56e-07	Yes
AFLP 4 vs AFLP 6	27	11	1.451	0.0013	Yes
AFLP 5 vs AFLP 6	26	14	1.882	1.82e-06	Yes
AFLP 1 vs Cal	26	11	1.479	0.0009	Yes
AFLP 2 vs Cal	27	12	1.583	0.0001	Yes
AFLP 3 vs Cal	26	10	1.344	0.0075	Yes
AFLP 4 vs Cal	27	23	3.034	2.46e-14	Yes
AFLP 5 vs Cal	26	12	1.613	0.0001	Yes
AFLP 6 vs Cal	26	10	1.344	0.0075	Yes

^a MAST, Maximum Agreement Subtree.

We evaluated the quality of standard fluorescent AFLP genotyping by determining the error rate and reproducibility of our datasets. The suggested and generally acceptable error rate for AFLP data ranges between 2–5% [29, 80]. AFLP electropherograms (50–500 bp) of S. brasiliensis (Fig 4A and 4B) and S. globosa (Fig 4C) show the high reproducibility of the fingerprints obtained using fluorescent capillary electrophoresis, which was crucial to reliably score AFLP markers. A fragment was considered unreliable if it showed any variability within the duplicate tested. For duplicated genotyped Sporothrix isolates, we observed the lowest average error rate across all markers for S. globosa (0.00–1.05%), S. brasiliensis (0.00–1.14%), and S. schenckii (0.00–1.23%). All the fragments scored were reproducible. Error rates never exceeded 1.23%, indicating that our protocol is highly reproducible across Sporothrix species, which are the main agents of human and animal sporotrichosis [80].

Fig 4.

Electropherograms from an ABI3100 depicting the range of fragments between 50 and 500 bp for S. brasiliensis IPEC 16919 (A), S. brasiliensis CBS 120339 (B) and S. globosa CBS 120340 (C), with EcoRI-GA and MseI-TT selective primers labeled with blue (6-FAM). Error rates were never greater than 1.23%, indicating that our protocol is highly reproducible across Sporothrix species.

Typical AFLP dendrograms based on Jaccard’s similarity coefficient are depicted in Fig 5. These show six well-supported clades with a similarity level ranging between 40 and 98%, with high cophenetic values (>90) in the majority of branches (S2 Table). This is in accordance with the generally applied calmodulin-based classification of Marimon et al. [47]. The first clade comprises S. brasiliensis isolates recovered from human and animal cases of sporotrichosis. It was possible to reveal cryptic diversity for S. brasiliensis in all datasets, with similarity levels ranging from 37.53% ± 4.06% to 51.96% ± 2.83% (S2 Table). AFLP markers could separate the S. brasiliensis isolates belonging to Rio Grande do Sul, which remained together in all combinations. The polymorphic fragments ranged from 38–50 fragments in the six combinations for all S. brasiliensis isolates, in a total of 44–62 fragments. The second clade comprised S. schenckii, which showed greater diversity, ranging from 27.44% ± 2.55% to 52.56% ± 1.43% (S2 Table), compared to other isolates of clinical relevance, corroborating the classic findings of DNA sequencing. Polymorphic fragments ranged from 37–77 per combination in a total of 52–84 fragments generated. The third clade comprised S. globosa isolates, which showed a similar pattern for the six isolates tested with cryptic diversity (42.79% ± 1.61% to 74.38% ± 1.82%), but lower than S. brasiliensis and S. schenckii isolates. The total of fragments generated for S. globosa varied from 28–43, with a range of 10 to 31 polymorphic fragments per combination. The remaining isolates represent members of the S. pallida complex, including S. chilensis, S. mexicana, and S. pallida s. str. In clade 4, AFLP markers varied from 14–44 fragments, and S. pallida isolate FMR 8803 grouped apart from the other members of the S. pallida complex (i.e. S. chilensis and S. mexicana) in combinations #1, #2 and #4. The two isolates of S. mexicana presented high genetic similarity (above 90% in 5 out of 6 combinations), generating 15–45 fragments. Polymorphic bands were absent in combinations #3 and #4, whereas in combination #1 we detected seven polymorphic bands. Likewise, S. chilensis produced a range of 15–45 fragments, following the same clustering pattern as combinations #2 to #6.

Fig 5.

Dendrograms of combinations 1 to 6 (A to F, respectively). The dendrograms show the clustering profile of the 27 samples of Sporothrix. The dendrograms were constructed by the Jaccard similarity coefficient and UPGMA clustering in the software BioNumerics v.7.6.

To assess the existence of topological congruence between any two dendrograms or between dendrograms and the calmodulin phylogenetic tree, we used the congruence index (Icong) [65]. Multiple comparisons revealed a similar and consistent clustering pattern, as evidenced by Icong values and their significant associated P-values (Table 4). Therefore, the AFLP dendrogram and calmodulin trees are more congruent than expected by chance, supporting the use of new AFLP markers to speciate Sporothrix with the same confidence of DNA-sequencing methods.

The AFLP markers were used to generate pairwise genetic distance matrices based on Jaccard’s similarity coefficient, which were subjected to a principal component analysis (PCA) with BioNumerics v7.6 to allow 3D graphic visualization of the relationships among each Sporothrix sample with those representing the same taxon. Fig 6 shows the PCA plots for combinations #1–6, and the distribution of 27 Sporothrix isolates among the three co-ordinates agreed with the UPGMA tree. Combinations #3, #5 and #6 revealed the highest cumulative percentage explained, with 58.7–59.2% of the variation explained by the first three components together (coordinates X, Y, and Z). The dimensioning analysis showed clearly both the high degree of intraspecific clustering and the relatively large genetic separation between any individual taxon (interspecific variation). This tight clustering supports cryptic diversity in S. brasiliensis and S. globosa. In all combinations evaluated, S. schenckii continued being a more diverse species than its siblings S. brasiliensis and S. globosa, in line with the higher level of intraspecific variability demonstrated in calmodulin sequencing.

Fig 6.

PCA of combinations 1 to 6 (A to F, respectively). PCAs were constructed in the software BioNumerics v.7.6. The PCAs were used to demonstrate the correlations among the 27 samples of Sporothrix using the six primer pair combinations. The isolates are represented by colored circles.

The AFLP-derived MSTs in Fig 7 essentially confirm the diversity structure of S. brasiliensis, S. schenckii, and S. globosa with the majority of isolates having a unique genotype, in contrast with those found for calmodulin sequencing (Fig 1B). In general, AFLP-derived MSTs were similar in their identification of totally different types of genetic variants. This suggests that despite the enhanced inherent variability of AFLP markers, clusters of isolates remain traceable.

Fig 7.

AFLP-derived minimum spanning trees (MSTs) of Sporothrix isolates using combinations 1 to 6 (A to F, respectively). CAL tree and AFLP MSTs were divergent in their identification of different types of genetic variants, since the finer resolution was obtained using AFLP markers.

Discussion

In this study, we develop a new AFLP technique aimed at studying the genetic epidemiology of medically relevant Sporothrix species. The most promising result is the discovery of cryptic diversity in species previously thought to be prevalent clonal types, such as S. brasiliensis, which is responsible for the cat-transmitted sporotrichosis in Brazil, and S. globosa, responsible for large sapronosis taking place in Asia. Our technique, therefore, will enable finer-scale epidemiological patterns to be described than was previously possible.

The first clues that S. brasiliensis might have cryptic genetic diversity emerged from host-pathogen interaction data, where genetically identical isolates (based on CAL sequences) had distinct virulence profiles when inoculated in BALB/c mice for variables such as animal weight loss, death, and capacity to disseminate to organs [81]. For S. schenckii the higher levels of intraspecific genetic variability are associated with more variable virulence profiles in BALB/c [82] suggesting that genotype is an important factor in determining clinically-relevant phenotypes. Considering all molecular markers previously used to explore genetic diversity in Sporothrix, including chitin synthase [83], elongation factor 1α [2], β-tubulin [2, 83], ITS1/2+5.8s [20, 78, 79] and calmodulin [2, 47, 83], the last perform best in estimating diversity by showing up to 31.69% variable parsimony-informative sites and comprising for the largest number of genotypes. However, our study shows that calmodulin (and other markers) underestimated the genotypic diversity of S. brasiliensis. Therefore, we clearly need new, higher-power, markers to study the recent expansion (2015–2019) of epizooties of feline sporotrichosis due to S. brasiliensis towards Northeastern Brazil [1].

Classic studies in the literature describe the use of AFLP markers as one of the most informative and cost-effective DNA fingerprinting methods for genetic characterization of pathogenic Sporothrix. The technique usually shows a strong geographical population structure and is useful to speciate Sporothrix isolates [20, 39, 40]. Through our markers, we were able to identify Sporothrix down to species level, with results similar to the gold standard DNA sequencing of the calmodulin encoding gene [47], CAL-RFLP [41], or species-specific PCR [46]. Moreover, we were able to differentiate Sporothrix down to strain level, surpassing the resolution provided by the gold-standard method (calmodulin) used to investigate diversity in Sporothrix. Our single protocol was easily transferred between distantly related taxa, including members of the clinical and environmental clades.

The main advantages of using AFLP analysis include the use of a standard protocol in combination with different restriction endonucleases and the choice of adding one or more selective nucleotides in the selective EcoRI and MseI primers to achieve optimal fingerprints without prior knowledge of the organism’s genome sequence. This is useful for non-model organisms such as Sporothrix. Disadvantages include the dominance of alleles, and the possible non-homology of comigrating fragments belonging to different loci, leading to suboptimal reproducibility, particularly across different platforms. However, we took advantage of the growing number of full genome sequences available for Sporothrix [58–63] to generate 2,304 virtual fingerprints of Sporothrix DNA, reducing time and costs related to extensive trials, minimizing possible reproducibility errors.

Therefore, the first step in our standardization included an in silico approach using the programs AFLPinSilico [55] and ISIF [56] to optimize the best combination of selective primers (EcoRI+2 and MseI+2) to produce the largest number of polymorphic AFLP markers and address questions related to speciation, genetic diversity, and population structure. The results revealed that, while the two bioinformatic programs had minor divergences between them, in vitro results were consistent with in silico prediction. The main criterion used was to choose combinations that would reveal cryptic diversity in S. brasiliensis and S. globosa, two species that were previously characterized as having low genetic diversity or clonal population structure using DNA-sequencing methods [14, 20, 42, 44, 77–79].

The size and complexity of the Sporothrix genome can have a tremendous influence on AFLP patterns, due to the amount of DNA that is necessary for the initial restriction/ligation step, as large genomes usually require larger amounts of DNA [84]. Here, 200 ng was enough to produce clear, intense AFLP fragments [85]. Another observation from our Sporothrix genome scan follow-up is that the number of selective bases greatly influenced the number and diversity of fragments. Generating too many fragments is not ideal, since it makes it difficult to unambiguously score AFLP electrophoresis profiles (e.g., owing to comigrating non-homologous fragments). If, however, the primer combination generates a low number of AFLP markers, it will reduce the probability of polymorphism detection. We found that two selective bases (EcoRI+2 and MseI+2) produced the ideal number of AFLP markers (i.e., 96–137 polymorphic fragments) to explore diversity in Sporothrix. This holds true also when dealing with microorganisms having a larger genome size [29]. We found a higher number of AFLP bands in silico in at least 62.5–70.7% of combinations for S. pallida (genome size of ~37.8 Mb) compared to S. brasiliensis, S. schenckii or S. globosa (32.5–33.4 Mb) [85] suggesting that, as expected, AFLP patterns scale in complexity with genome size.

Understanding the spread of Sporothrix species by exploring inter- and intraspecific genetic diversities is fundamental to tackle the increasing number of cases in recent years [86]. Many molecular techniques have been employed for Sporothrix to address these questions [20, 39–41, 46, 85, 87, 88], and have been successful in providing quality data. However, limitations include the low number of polymorphic and reproducible characters evaluated. AFLP has been considered to be highly reproducible and discriminatory, mainly for epidemiological studies [89], and can be employed for any organisms without the need of sequence information [90]. However, only Zhang et al. employed the technique on S. brasiliensis, but they were not able to describe high levels of diversity [20]. Our in vitro AFLP succeeded, demonstrating it is a relevant method for epidemiological studies of Sporothrix, which is in line with previous studies that used the technique [20, 39, 40]. Comparing haplotype and AFLP dendrograms or AFLP-derived MSTs generated here, the DNA fingerprint methods demonstrated meaningful variations in Sporothrix spp.

DNA-sequencing data demonstrated there are at least two different S. brasiliensis populations circulating in Brazil, one belonging to the Rio Grande do Sul cluster and the other related to the long-lasting epidemic taking place in Rio de Janeiro, which is spreading to neighboring states such as São Paulo, Minas Gerais and Espírito Santo [44, 86]. Zhang et al. [20] demonstrated that S. brasiliensis clustered into three different clades in an AFLP study, and one of the clades contained isolates from Southern Brazil, agreeing with the phylogeography proposed by Rodrigues et al. [44]. The same pattern was evidenced in our study, and strains from Rio Grande do Sul (e.g., Ss55, Ss261, and CBS 132989) presented an identical clustering pattern, showing high levels of similarity among the isolates, which differed from those originated from other Brazilian regions. The three clusters of S. brasiliensis found by Zhang et al. [20] were supported by high bootstrap values in phylogenetic analysis (i.e., partial CAL, TEF1, and TEF3), but the AFLP fingerprints showed little variation [20]. Our fingerprints revealed meaningful variations in S. brasiliensis, with a large number of polymorphic bands, showing that the strategy used here based on extensive in silico screening of selective bases was fundamental to guide the choice of the best primer combination.

Among the pathogenic species, S. schenckii is considered to have the highest genetic diversity [20, 47, 83], with evidence of genetic recombination [42]. Diversity in S. schenckii was also found by other researchers, who separated this species into five groups using AFLP [20]. Our AFLP setup was able to detect diversity within S. schenckii.

AFLP markers revealed that S. globosa also presented subtle diversity, but it is important to point out that these isolates were less diverse than S. schenckii and S. brasiliensis, confirming previous findings of Zhang et al., who considered that the DNA fingerprints were identical in S. globosa. Sporothrix globosa is widely distributed in temperate and warm regions [47, 83, 91], and rapid dispersal by unknown vectors could explain the similar genotypes for isolates collected from distant geographical regions [20, 92]. In addition, Zhao et al. [40] also detected cryptic diversity in S. globosa evaluating a higher number of strains, similar to the variation found using our new AFLP markers.

Comparison of the DNA-sequence tree with AFLP dendrogram using the congruence index (Icong) revealed a strong topological congruence between any two AFLP dendrograms or between AFLP dendrograms and a calmodulin tree, supporting the use of all combinations to distinguish Sporothrix species. The congruence observed is in agreement with previous studies, which found that S. brasiliensis, S. schenckii and S. globosa are related in a pathogenic/clinical clade and S. mexicana, S. pallida, and S. chilensis are nested in an environmental clade, supported by high bootstrap values or cophenetic values [2, 42–44, 79]. On one hand, several studies have demonstrated that moderate numbers of AFLP fragments are necessary to recover the correct topology of a DNA-sequence tree with high bootstrap support values (i.e. >70%) [93–95]. On the other hand, a higher number of taxa (i.e., covering all 53 Sporothrix species described so far) may increase the number of possible trees and reduce internode distances for a given tree length, making it less likely to recover the correct tree [95, 96]. However, this size effect is more likely to occur in inferences considering ancient radiations [95, 96], which does not seem to be the case of medically relevant Sporothrix species, especially S. brasiliensis, which likely originated from a recent radiation event (about 3.8–4.9 MYA) based on its geographical distribution and phylogenetic inferences [42, 58, 97].

Using our new AFLP markers, we can now better track routes of disease transmission during epizooties and zoonosis in Brazil, allowing the possibility of linking specific genotypes to antifungal susceptibility profiles as well as addressing links between clinical outcomes in feline and human sporotrichosis. All six primers pairs performed well, but the most precise level of inter-species discrimination and the highest level of intra-species discrimination of the Sporothrix isolates were observed in the AFLP EcoRI-FAM-GA/MseI-TT, EcoRI-FAM-GA/MseI-AG and EcoRI-FAM-TA/MseI-AA sets. These combinations stand out among all combinations for having the best diversity indices and the lowest error rates, and thus may be valuable in human and veterinary diagnostics as well as in epidemiology. Moreover, our DNA fingerprinting assay can be further transferred between laboratories to give insights into the ecology and evolution of pathogenic Sporothrix species and to inform management and mitigation strategies to tackle the advance of sporotrichosis. Although sporotrichosis has a global distribution, pathogenic species are not evenly distributed and individual lineages that vary in pathogenicity still occur in geographically limited ranges, such as S. brasiliensis. Thus, as Sporothrix genotypes continue to expand their range, we need to consider using genome-wide markers to identify, distinguish, and track these emerging pathogens spreading through the mammal hosts.

Supporting information

S1 Table. Diversity of AFLP fragments (50–500 bp) based on in silico characterization of Sporothrix spp. genomes for 256 selective combinations.

(PDF)

S2 Table. Cluster similarities among all the combinations evaluated.

(PDF)

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

AMR was supported by grants from the São Paulo Research Foundation (FAPESP 2017/27265-5) http://www.fapesp.br/, the National Council for Scientific and Technological Development (CNPq 433276/2018-5) http://www.cnpq.br/, and Coordination of Improvement of Higher Education Personnel (CAPES 88887.159096/2017-00) https://www.capes.gov.br/. ZPC was supported by grants from FAPESP (2018/21460-3). MCF is a CIFAR fellow in the ‘Fungal Kingdoms’ programme (https://www.cifar.ca/research/program/fungal-kingdom) and the UK Medical Research Council MR/R015600/1 (https://mrc.ukri.org/). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.