Phenotypic characterization of cryptic species in the fungal pathogenHistoplasma

Phenotypic characterization of cryptic species in the fungal pathogen Histoplasma | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Phenotypic characterization of cryptic species in the fungal pathogen Histoplasma View ORCID Profile Victoria E. Sepúlveda , View ORCID Profile Jonathan A. Rader , Jingbaoyi (Janet) Li , William E. Goldman , View ORCID Profile Daniel R. Matute doi: https://doi.org/10.1101/2024.01.08.574719 Victoria E. Sepúlveda 1 Department of Biology, University of North Carolina at Chapel Hill Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Victoria E. Sepúlveda Jonathan A. Rader 1 Department of Biology, University of North Carolina at Chapel Hill Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jonathan A. Rader Jingbaoyi (Janet) Li 1 Department of Biology, University of North Carolina at Chapel Hill Find this author on Google Scholar Find this author on PubMed Search for this author on this site William E. Goldman 2 Department of Microbiology and Immunology, University of North Carolina at Chapel Hill Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel R. Matute 1 Department of Biology, University of North Carolina at Chapel Hill Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel R. Matute For correspondence: dmatute{at}email.unc.edu Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Histoplasmosis is an endemic mycosis that often presents as a respiratory infection in immunocompromised patients. Hundreds of thousands of new infections are reported annually around the world. The etiological agent of the disease, Histoplasma, is a dimorphic fungus commonly found in the soil where it grows as mycelia. Humans can become infected by Histoplasma through inhalation of its spores (conidia) or mycelial particles. The fungi transitions into the yeast phase in the lungs at 37°C. Once in the lungs, yeast cells reside and proliferate inside alveolar macrophages. We have previously described that Histoplasma is composed of at least five cryptic species that differ genetically, and assigned new names to the lineages. Here we evaluated multiple phenotypic characteristics of 12 strains from five phylogenetic species of Histoplasma to identify phenotypic traits that differentiate between these species: H. capsulatum sensu stricto , H. ohiense , H. mississippiense , H. suramericanum , and an African lineage. We report diagnostic traits for two species. The other three species can be identified by a combination of traits. Our results suggest that 1) there are significant phenotypic differences among the cryptic species of Histoplasma , and 2) that those differences can be used to positively distinguish those species in a clinical setting and for further study of the evolution of this fungal pathogen. IMPORTANCE Identifying species boundaries is a critical component of evolutionary biology. Genome sequencing and the use of molecular markers have advanced our understanding of the evolutionary history of fungal pathogens, including Histoplasma , and have allowed for the identification of new species. This is especially important in organisms where morphological characteristics cannot be used for this purpose. In this study, we revise the taxonomic status of the four named species of the genus Histoplasma : H. capsulatum sensu stricto , H. ohiense , H. mississippiense , and H. suramericanum and propose the use of species-specific phenotypic traits to aid their identification when genome sequencing is not available. These results have implications not only for evolutionary study of Histoplasma , but also for clinicians, as the Histoplasma species could determine the outcome of disease and treatment needed. INTRODUCTION Histoplasma is an ascomycete dimorphic fungus and the causal agent of histoplasmosis. This disease is arguably the most common fungal respiratory infection, with hundreds of thousands of new infections occurring annually worldwide ( Cano and Hajjeh 2001 ; Kauffman 2007 ; Hage et al . 2015 ; Bongomin et al . 2019 ). The disease is particularly common in immunocompromised patients and the majority of cases are reported in patients that have undergone chemotherapy ( Adderson 2004 ; Hess et al . 2017 ), organ transplant ( Freifeld et al . 2005 ), or are suffering from AIDS ( Adenis et al . 2018 ; Myint et al . 2020 ). Histoplasmosis is not of mandatory report, and for that reason, the true disease burden remains largely unknown ( Hage et al . 2015 ; Armstrong et al . 2018 ; Oladele et al . 2018 ; Scully and Baddley 2018 ; Adenis et al . 2018 ). Nonetheless, there are indications that the disease is more important than is currently understood. Histoplasmin skin reactivity tests suggest that by age 20, more than 90% of individuals residing in the Continental United States are skin test-positive for a previous infection, or at least exposure to the pathogen ( Manos et al . 1956 ). Over 100 outbreaks were reported in the twentieth-century in the USA ( Benedict and Mody 2016 ). Similar population assessments indicate that a large proportion of the population has been exposed to Histoplasma at some point in their life. Among immunosuppressed patients, the population most at risk, 25% of AIDS patients living in endemic regions of Histoplasma develop histoplasmosis; untreated cases usually lead to patient death and infected individuals often need intense and prolonged antifungal therapy ( Kauffman 2007 ; Hage et al . 2015 ). Producing the sexual stage of Histoplasma in laboratory conditions is exceedingly difficult (but see ( Kwon-Chung 1972a ; b, 1973; Muniz et al . 2014 )), which has made the study of potential species boundaries challenging for decades. Initial assessments of diversity proposed three different subspecies for Histoplasma capsulatum. Histoplasma capsulatum var. capsulatum was thought to mainly be found in human patients and caused the classical pulmonary form of histoplasmosis, H. capsulatum var. duboisii allegedly caused a milder version of the disease with granulomatous lesions in skin and osseous tissues, and H. capsulatum var . farciminosum was thought to be a pathogen of mules and horses ( Ajello 1968 ). The application of phylogenetics using molecular markers revealed that these lineages were artifactual and did not follow the evolutionary history of the pathogen ( Kasuga et al . 1999 , 2003 ). Multilocus sequence typing revealed at least six lineages within Histoplasma ( Kasuga et al . 2003 ). Another classification of Histoplasma is based on the presence/absence of the polysaccharide α-(1, 3)-glucan in the cell wall ( AGS1 locus), produced only during the yeast phase. Strains that possess α-(1, 3)-glucan have a rough colony morphology and are classified as chemotype 2 strains, which represent the majority of the strains found worldwide. Strains that lack α-(1, 3)-glucan have a smooth colony morphology, are classified as chemotype 1 strains, and are restricted to a North American lineage ( Domer 1971 ; Reiss 1977 ; Reiss et al . 1977 ; Rappleye et al . 2004; Edwards et al . 2011 ). The virulence requirements for α-(1, 3)-glucan have been shown to differ among Histoplasma lineages ( Edwards et al . 2011 ). Additional, as-yet unidentified lineages are likely to exist within Histoplasma ( Kasuga et al . 2003 ; Teixeira et al . 2016 ) The implementation of genome sequencing confirmed the existence of differentiated genetic lineages and revealed that these clades were sufficiently diverged to be considered phylogenetic species ( Sepúlveda et al . 2017 ; Almeida-Silva et al . 2021 ; Jofre et al . 2022 ). Five species satisfied the first assessment of genome concordance and differentiation: H. ohiense , H. mississippiense , H. capsulatum sensu stricto , H. suramericanum , and a Histoplasma lineage from continental Africa. Additional genome sequencing revealed the existence of two additional phylogenetic species, one endemic to the Indian subcontinent ( Jofre et al . 2022 ), and one endemic to Southern Brazil ( Almeida-Silva et al . 2021 ). These seven species in the Histoplasma genome diverged over 1.5 million years ago and have accrued extensive genetic differences that make them advanced along the speciation continuum ( Sepúlveda et al . 2017 ). The taxonomic rearrangement of the Histoplasma genus set the basis for further studies and propelled important developments in understanding the biology of Histoplasma . Genome assembly of strains from each of these species suggested genome content differences and rearrangements which, in turn, have suggested a rapid turnover of genome structure in the genus ( Voorhies et al . 2022 ). Surveys of gene exchange have also revealed low levels of admixture among lineages which indicates that hybridization might be of importance in the evolution of Histoplasma ( Maxwell et al . 2018 ; Jofre et al . 2022 ). From a more applied perspective, Sepúlveda et al. (2017) reported extensive genetic differences along the genome and the possibility of using molecular markers for molecular detection which could be harnessed by clinical researchers and inform the epidemiological patterns of each of these lineages. Despite all the genomic progress, no systematic assessment has been performed to determine whether these phylogenetic species differ phenotypically. Clearly there is extensive genetic differentiation in the genus, but taxonomic revisions should be accompanied by descriptions that can serve clinical and evolutionary researchers alike ( Matute and Sepúlveda 2019 ; Chethana et al . 2021 ). The initial species description suggested that previous assessments of phenotypic differentiation in Histoplasma might follow species boundaries ( Sepúlveda et al . 2017 ). Nonetheless, no survey has measured potential intraspecific and interspecific variation in common conditions. Here we bridge that gap. We explored whether the genetic differentiation within Histoplasma might explain some variability in the group and whether phenotypic variation follows species boundaries. In this report, we quantified four phenotypic traits and found yeast-culture-based diagnostic characters for two of the Histoplasma species, H. ohiense , and H. mississippiense . The other three species, H. capsulatum, H. suramericanum , and the African lineage, can be identified by a combination of multiple traits. Using this information, we revise the taxonomic status of the four named species of the genus Histoplasma . MATERIALS AND METHODS Fungal strains and culture conditions Histoplasma isolates used in this study were donated to W.G. during a span of 15 years. Information pertinent to each isolate is listed in Table 1 . All isolates were kept in 15% glycerol at -80°C until they were ready to be subcultured. An aliquot of the frozen culture was streaked into Histoplasma Macrophage Medium (HMM) plates. Strains were then grown in HMM (solid or liquid) at 37°C with 5% CO2 as previously described ( Worsham and Goldman 1988 ) . Solid medium contained 0.6% agarose (SeaKem ME grade) and 25 mM FeSO4. All liquid cultures were incubated at 37°C with 5% CO 2 on an orbital shaker (Infors HT Multitron) at 150 rpm. All reference strains were deposited in the Westerdijk Fungal Biodiversity Institute CBS collection ( Table 1 ). View this table: View inline View popup Download powerpoint TABLE 1. Histoplasma Isolates used in this study. Yeast colony morphology We scored the yeast colony morphology of 12 Histoplasma isolates (at least two isolates from each species). For each isolate, we added 10 μ l of a late exponential phase culture on a HMM plate. We grew 36 aliquots per Petri dish. We incubated plates at 37°C in 5% CO 2 for at least 10 days before we imaged each colony. Colonies were classified as rough or smooth. To ensure reproducibility, we scored at least 12 colonies per species but no species showed intralineage variation in colony morphology. To compare the proportions of rough vs. smooth colonies among species, we used a 2-sample test for equality of proportions with continuity correction (function prop.test , library stats , R Core Team 2018). Evaluation of extracellular proteolytic activity The second trait we evaluated was proteolytic activity. Several studies have reported the existence of extracellularly secreted serine proteases in Histoplasma . In particular, isolates from the RFLP1 group (later named H. mississippiense ) were the only ones that manifested this phenotype ( Zarnowski et al . 2007 cf . Muotoe-Okafor et al . 1996 for reports of proteolytic activity in African strains). To evaluate extracellular proteolytic activity in different species of Histoplasma , we grew the 12 Histoplasma isolates ( Table 1 ) in HMM plates supplemented with 1.5% skim milk. Strains with a proteolytic activity show a clear halo around their yeast colonies. 15 g of instant nonfat dry milk (Hoosier Hill Farm brand,Middleton, WI ) were reconstituted in 500 ml of distilled water. Once the skim milk was fully dissolved, 6 g of agarose (SeaKem ME grade) were added and autoclaved to make HMM plates as previously described (Worsham PL and Goldman WE, 1988). 10 μ l of a late exponential phase culture were spotted onto HMM plates supplemented with skim milk. We spotted 4 strains per plate to allow for any transparent clearance area around fungal spots to appear, indicative of proteolytic activity. We incubated the experiment using the same conditions as described immediately above to study yeast colony morphology. We scored 12 colonies per isolate for presence / absence of a halo, and when present, measured halo size. The size of the halo was measured as the distance from the edge of the colony to the outer edge of the cleared ring. To compare halo sizes, we used a Welch’s Two Sample t -test (function t.test , library stats , (R Core Team 2018)). Optical density and growth curves of Histoplasma yeast cultures We also measured the growth rate of different Histoplasma genotypes in liquid media. For the growth curves, we inoculated 30 ml of HMM broth with 1 × 10 6 yeast/ml and grew the culture for 11 days. We repeated this procedure for each of the 12 strains. We removed 600 μ l from each culture and mixed them with 300 μ l of 3M NaOH in a plastic cuvette, which was covered with Parafilm and vortexed for 10 seconds to separate yeast clumps and measure optical density (OD) in a GENESYS 10vis spectrophotometer (Thermo Spectronic) starting at day 0, and at every 24 hours after that until day 11. To quantify the rate of growth, we used a four-parameter logistic model with the form: where a is the OD at the beginning of the experiment (presumably close to zero), b is the rate of increase in OD at point c , the inflection point of the curve, and d is the maximum OD in the curve, the asymptote. This model allows for an initial growth where cells are dividing but do not increase the OD value and includes an asymptote, calculated from the data, at which cells do not replicate anymore. Since nonlinear logistic regression has difficulties optimizing the values for each of the four constants in the equations, we tried 10 starting values per constant and found the model with the lowest Akaike Information Criterion (AIC, ( Akaike 1973 )) with the function ‘ AIC’ , (library ‘ stats’ , (R Core Team 2018)). To fit the regressions, we pooled isolates within phylogenetic species. To determine whether the four fitted parameters differed among species, we generated 1,000 bootstrapped regressions using the R function nls.boot (library nlstools , (Baty et al . 2013, 2015 )). We then compared the values of b , c and d across species using non-parametric tests (Wilcoxon rank sum test with continuity correction, function wilcox.test , library stats , (R Core Team 2018)). Yeast Area Finally, we studied the area of individual yeast cells in different Histoplasma isolates. We grew one isolate per Histoplasma species to evaluate phenotypic variability between species. Table 1 lists the isolates included in this study. 10 μ l from a yeast culture that had large yeast clumps removed were mixed with 10 μ l of Lactophenol Cotton Blue on a glass slide. Differential interference contrast (DIC) images were obtained using 100X/1.4 Oil UPlan S Apo PSF quality objective on an Olympus BX-61 microscope and collected using a QImaging RETIGA 4000R color camera and Volocity 6.3 acquisition software. Exposure was adjusted to ensure pixel intensities were not saturated (Pixel size: 0.0608 μ m / pixel). Yeast cells were measured by drawing an ellipse around each imaged cell in imageJ ( Schneider et al . 2012 ). Ellipse area (in μ m 2 ) was taken as a measure of cell size. To compare the yeast cell size across different species, we used a linear model in which cell area was the response and the species identity was the grouping factor. We used the R function aov (library stats , (R Core Team 2018)). Finally, we compared among lineages (all pairwise comparisons) using Tukey contrasts with the R function TukeyHSD (library stats, (R Core Team 2018)). RESULTS Histoplasma ohiense differs in their yeast colony morphology Multiple previous studies have reported variation in yeast colony morphology across isolates of Histoplasma ( Domer 1971 ; Reiss 1977 ; Reiss et al . 1977 ). Some isolates show smooth colonies while some others show rough ones. We studied whether this phenotypic variation was species-specific or whether there was intraspecific variation within five phylogenetic species of Histoplasma . Figure 1 shows the yeast colony morphology for 12 different Histoplasma isolates after growing at 37°C for 10 days in HMM media. All isolates from four Histoplasma species, H. capsulatum , H. mississippiense , H. suramericanum , and the Africa clade, had rough yeast colonies ( n =12 per species). On the other hand, all the replicates across isolates of H. ohiense ( n =24) had smooth yeast colonies. Not surprisingly, these proportions are significantly different (2-sample test for equality of proportions with continuity correction: χ 2 = 44.083, df = 1, p = 3.147×10 -11 ). This morphological difference within Histoplasma has been attributed to the lack of α-(1,3) glucan in their cell wall ( Klimpel and Goldman 1987 , 1988 ; Rappleye et al . 2004; Sepúlveda et al . 2014 ). These comparisons indicate that yeast colony morphology is a diagnostic trait of H. ohiense and is sufficient to differentiate the species from the other four lineages. Download figure Open in new tab FIGURE 1. Colony morphology in Histoplasma species. Fungal strains were grown on agarose-solidified HMM plates. Smooth morphology occurs in the absence of α-(1,3)-glucan in the cell walls. Histoplasma ohiense is the only species that shows a smooth colony morphology due to the lack of α-(1,3)-glucan. Production of extracellular proteolytic activity is restricted to H. mississippiense Production of extracellular proteolytic activity using HHM media supplemented with skim milk had been previously described in some isolates of Histoplasma ( Zarnowski et al . 2007 ). We studied whether the five different species differed in their proteolytic ability. We grew five of the previously identified lineages in skim milk media to determine whether they showed proteolytic activity. Figure 2 shows an example of each of the five species growing as yeast in HMM media supplemented with skim milk at 37°C. Of the five species, H. mississippiense was the only lineage to show a clearance halo which is a proxy of the ability of the colony to break down proteins. Within H. mississippiense , there was variation in the size of the halo. WU24 had a halo distance of 0.395 cm (SD=0.028), while CI-19 had a halo distance of 0.195cm (SD=0.050) which differed significantly from each other (Welch’s Two Sample t -test, t = -11.047, df = 14.302, p = 2.164×10 -8 ) suggesting the existence of intraspecific variation in the genetic mechanisms involved in this trait within H. mississippiense , though a halo is always present. The observation is consistent with previous studies that suggested that isolates from this lineage (originally termed RFLP1) are the only ones with a extracellular protease ability ( Zarnowski et al . 2007 ), and suggest that proteolytic activity is a diagnostic trait of H. mississippiense . Download figure Open in new tab FIGURE 2. Demonstration of extracellular proteolytic activity in Histoplasma isolates. Fungal strains were grown on agarose-solidified HMM supplemented with 1.5% skim milk. Secreted proteolytic activity is visible as transparent clearance halos around fungal colonies was assessed after 10 days of growth at 37°C in 5% CO 2 . The presence of extracellular proteases was observed only in H. mississippiense strains ( A ). The size of the halo varied within H. mississippiense ( B ). The other four species of Histoplasma included in this report showed no proteolytic activity (i.e., no halo; C and D ). Growth curves and optical density We evaluated whether different genotypes of Histoplasma had differences in their growth rate and if such differences corresponded with species boundaries. We used optical density as a proxy for the number of cells in a liquid culture, and fitted logistic models that modeled the rate of increase of the different species. Figure 3 shows the results of the best fit for each species. The growth curves of the five species show a better-fit to a logistic dose-response function than to a linear function ( Table S1 ). Non-linear regressions are highly dependent on the seed values used for the optimizations, so we maximized the fit using AIC values ( Table S2 ). Download figure Open in new tab FIGURE 3. Four-parameter logistic models for the rate of growth of five Histoplasma species. All experiments were done in HMM broth media at 37°C with 5% CO 2 . Growth was measured by recording the optical density (OD600) of liquid cultures at different time points (0, 24, 48, 72, 120, 144, 168, and 192). A. Histoplasma capsulatum . B. African lineage C. Histoplasma mississippiense D. Histoplasma ohiense E. H. suramericanum . Semitransparent lines show 1,000 bootstrapped model fits. We focused on two of the four calculated parameters, the intercept ( a ) and the asymptote ( d ). We found that even though there are significant differences among bootstrapped distributions of the intercept ( a , Table 2 ), all intercepts are also centered around zero ( Figure 4A ). Since the intercepts were similar, comparisons among asymptote (d) values are informative and indicate whether the species have differences in the growth saturation point. Indeed, the values of all the inferred asymptotes differed among the five species, but there were two clearly differentiated groups ( Table 2 ). The growth curves at 196 hours for two of the species ( H. capsulatum ss, and H. ohiense ) had OD asymptotes higher than 2. On the other hand, the other three species ( H. mississippiense, the African lineage, and H. suramericanum ) had OD asymptotes lower than 2 ( Figure 4B , Table 2 ). This difference can be used to discriminate between these two clusters of species, and suggest that OD-based growth curves can be a taxonomic trait that can aid species identification in Histoplasma , but one that does not serve as a diagnostic trait in isolation. Download figure Open in new tab FIGURE 4. Intercept and asymptote distributions of bootstrapped regressions. A. Intercept ( a in Equation 1 ). B. Asymptote ( d in Equation 1 ). Each boxplot shows 1,000 values of bootstrapped non-linear regressions (shown as semitransparent lines in Figure 3 ). capsu : H. capsulatum ss, H. missi : H. mississippiense , H. ohien : H. ohiense , H. suram: H. suramericanum . View this table: View inline View popup Download powerpoint TABLE 2. Pairwise comparisons between two of the parameters of the logistic regression, a and d . a corresponds to the intercept, d corresponds to the asymptote. The lower triangular matrix shows the W from the Wilcoxon test. Upper triangular matrix shows the p -value. Each parameter estimate is estimated from the non-linear regression; the standard error (SE) was calculated from the distribution of the 1,000 bootstrap samplings shown in Figure 3 . capsu : H. capsulatum ss; Africa: African lineage; miss : H. missisippiense ; ohien : H. ohiense ; suram : H. suramericanum . Yeast cell size differs among Histoplasma species We fit a linear model to compare yeast cell size across species. We found cell size variation among species ( F 4,126 = 10.31, p = 2.96 ×10 -7 ). Table 3 shows all the pairwise comparisons among species. Histoplasma ohiense had a smaller size than the other four species ( Table 3 ). The significance of the H. ohiense - H. suramericanum pairwise comparison was borderline. Histoplasma suramericanum had a slightly larger cell size than H. missisippiense , but the distributions of the two species were largely overlapping ( Table 3 ). These results indicate that yeast cell size might serve as a diagnostic trait for H. ohiense , but not for the remaining Histoplasma species. View this table: View inline View popup Download powerpoint TABLE 3. Tukey HSD pairwise comparisons show that H. ohiense has a smaller yeast cell size than other species of Histoplasma . sd: Standard deviation. capsu : H. capsulatum ss; Africa: African lineage; miss : H. mississippiense ; ohien : H. ohiense ; suram : H. suramericanum . Taxonomy Table 4 summarizes the results of our phenotypic surveys. The combination of these traits is sufficient to differentiate between the five cryptic species of Histoplasma . Using these phenotyping surveys, we re-describe the three named species of Histoplasma and provide a dichotomous key to differentiate between the five phylogenetic species in this report. View this table: View inline View popup Download powerpoint TABLE 4. Phenotypic differences among five different species of Histoplasma . Species diagnostic traits are underlined. Histoplasma mississippiense V.E. Sepúlveda, R. Márquez, Turissini, W.E. Goldman & Matute, sp. nov. MB XXXXXX For a detailed description see Sepúlveda et al., mBio 8 (6): e01339-17, 12 (2017). Holotype: ATCC 38904, CBS145497 preserved in a metabolically inactive state. previously published as Histoplasma mississippiense V.E. Sepúlveda, R. Márquez, Turissini, W.E. Goldman & Matute, mBio 8 (6): e01339-17, 12 (2017), nom. inval., Art. 40.7 (Shenzhen), MB 823360] Histoplasma ohiense V.E. Sepúlveda, R. Márquez, Turissini, W.E. Goldman & Matute, sp. nov. MB XXXXX For a detailed description see Sepúlveda et al., mBio 8 (6): e01339-17, 13 (2017). Holotype: ATCC 26032, CBS145495 preserved in a metabolically inactive state. previously published as Histoplasma ohiense V.E. Sepúlveda, R. Márquez, Turissini, W.E. Goldman & Matute, mBio 8 (6): e01339-17, 12 (2017), nom. inval., Art. 40.7 (Shenzhen), MB 823360] Histoplasma suramericanum V.E. Sepúlveda, R. Márquez, Turissini, W.E. Goldman & Matute, sp. nov. MB XXXXX For a detailed description see Sepúlveda et al., mBio 8 (6): e01339-17, 13 (2017). Holotype: 3_11G, CBS145499 preserved in a metabolically inactive state. previously published as Histoplasma suramericanum V.E. Sepúlveda, R. Márquez, Turissini, W.E. Goldman & Matute, mBio 8 (6): e01339-17, 12 (2017), nom. inval., Art. 40.7 (Shenzhen), MB 823360] Histoplasma dichotomous key 1A. OD600 after 196 in HMM is higher than 2 … 2 1B. OD600after 196 in HMM is lower than 2 … 3 2A. Yeast colony morphology at 37°C is smooth. Yeast size area is 5.106μ 2 ± 1.130 … H. ohiense 2B . Yeast colony morphology at 37°C is rough. Yeast size area is larger than 6.0μ 2 … H. capsulatum 3A. Yeast colonies show proteolytic activity in HMM media at 37°C… H. mississippiense 3B. Yeast colonies show no proteolytic activity in HMM media at 37°C… 4 4A. Isolate collected in the Americas. H-antigen precursor amplified with the forward primer 5′-CGCAGTCACCTCCATACTATC 3′ and reverse primer 5′-GCGCCGACATTAACCC-3′ (28) harbors three diagnostic SNPs (positions 591, 622, and 716, ( Kasuga et al . 2003 )). … H. suramericanum 4B. Isolate collected in Africa … African lineage DISCUSSION Identifying species boundaries has been a challenge in microbial eukaryotes because producing sexual stages and making direct measurements of reproductive isolation, the signature of speciation, is usually impractical and often unfeasible (reviewed in ( Taylor et al . 2000 ; Schön et al . 2009 ; Cai et al . 2011 ; Chethana et al . 2021 )). Measuring the extent of genetic divergence, and identifying reductions in gene flow, has been a powerful substitute to uncover cryptic speciation in fungal pathogens ( Birky 2013 ; Galtier 2019 ). The incorporation of genomics has opened the door to describe the evolutionary processes that govern speciation and trait diversification in fungal pathogens ( Taylor et al . 2000 ; Matute and Sepúlveda 2019 ). Nonetheless, genome sequencing alone might be impractical for the identification of pathogens, particularly in clinical settings. In this study, we report phenotypic differences that are sufficient to identify three of five named species of Histoplasma , and revise their taxonomic status. In particular, we report that H. ohiense can be identified by its characteristic smooth colonies and small cell size, and H. mississippiense by its extracellular proteolytic activity. Histoplasma suramericanum , H. mississippiense , and the African lineage all have an OD600 asymptote at 196 hours lower than 2, but only H. mississippiense has extracellular proteolytic activity. This makes H. suramericanum and the African lineage the only species that cannot be differentiated by the morphological traits that we describe in this survey. Nonetheless, H. suramericanum seems to be restricted to the American continent, and the African lineage to Africa. Additionally, the two species can be discriminated with PCR probes ( Kasuga et al . 1999 , 2003 ; Sepúlveda et al . 2017 ). Our results are of importance to evolutionary and clinical mycologists alike because the diagnosis of species boundaries is the first step to understanding evolutionary dynamics, broadly defined, and could shed light into the evolution of different virulence mechanisms. Other studies have reported differences in the morphology of Histoplasma isolates, and among clusters of genotypes. Okeke and Muller ( Okeke and Müller 1991 ) described the presence of extracellular collagenolytic proteinases by Histoplasma capsulatum var. duboisii and Histoplasma capsulatum var. capsulatum . Since these classifications do not follow a phylogenetic framework ( Kasuga et al . 1999 , 2003 ), the results are not immediately comparable. Importantly, our results are consistent with Zarnowski et al . (2007) , where the extracellularly-secreted serine protease activity was restricted to H. mississippiense isolates (formerly known as RFLP class 1 or NAm 1 clade, ( Keath et al . 1992 ; Kasuga et al . 1999 , 2003 )), demonstrating the methodology and media used in both studies is suitable for the selective identification of H. mississippiense , and can be used reliably. To date, the role of the extracellularly-secreted serine protease activity in H. mississippiense virulence remains unexplored. Muotoe-Okafor et al. ( Muotoe-Okafor et al . 1996 ) detected a similar proteolytic activity in a cluster of African samples. Since no other species besides the Africa clade has been isolated from Africa, these results seem to indicate that some African isolates, but not the ones included in this study, might have serine protease activities similar to the ones in H. mississippiense . These two species are not sisters in the phylogenetic tree and thus, these results suggest that this H. mississippiense specific proteolytic activity might be explained by genomic changes that are specific to that lineage, and might have also evolved in the African clade through parallel mutation or introgression. A second, arguably less likely, possibility is that other species of Histoplasma ( H. ohiense , H. capsulatum ss) lost the serine proteinase activity independently. If that is the case, these two species should harbor serine proteinase pseudogenes. Now that species boundaries have been identified in Histoplasma , studies dissecting the processes that lead to serine proteinases in this genus of fungi are within reach. Yeast colony morphology is arguably the most systematically studied phenotypic difference in Histoplasma . The existence of smooth and rough colony morphology in Histoplasma was first reported as early as 1987 ( Klimpel and Goldman 1987 ). Genetic analyses suggested that the smooth phenotype was exclusive to a cluster of genotypes (RFLP2, NAm 2), now dubbed H. ohiense . Detailed studies of the cell wall with transmission electron microscopy demonstrated that reference strains of H. ohiense and H. capsulatum differ in their cell wall thickness, with H. capsulatum yeast cells showing a greater cell wall thickness compared to H. ohiense and that AGS1 expression is dispensable for H. ohiense virulence ( Edwards et al, 2011 ). α-(1, 3)-glucan is required for virulence in H. capsulatum and H. mississippiense (Rappleye et al, 2004 and Sepulveda et al, 2014 respectively); smooth mutants become avirulent once they are unable to produce this polysaccharide. Histoplasma ohiense has smooth colonies and lacks α-(1, 3)-glucan, and yet it remains virulent. The dissection of the genetic basis of differences in virulence between Histoplasma species is a prime example of the importance of understanding species boundaries in eukaryotic pathogens. There is extensive precedent that once fungal species are identified, phenotypic differences between the newfound taxa are subsequently found. In the case of Coccidioides— the first fungal pathogen to undergo a taxonomic revision ( Koufopanou et al . 1997 , 2001 ; Kirkland and Fierer 2018 ), the two different species, C. posadassi and C. immitis show differences in thermotolerance, which might be of importance for yeast-to-mycelium transformation and in determining their geographic range ( Mead et al . 2020 ). Similarly, different species of Paracoccidioides show differences in antifungal resistance ( Cruz et al . 2013 ) and yeast morphology ( Turissini et al . 2017 ), but also in the host response they elicit in their mammalian hosts ( Teixeira et al . 2014 ; Siqueira et al . 2015 ; De Macedo et al . 2019; Hahn et al . 2019 ). Even though reports of phenotypic variability existed in these fungi (e.g., ( Klimpel and Goldman 1988 )), ascribing these differences to species boundaries was only possible once isolated lineages were described in genera that were considered monotypic for almost 100 years. Our study focuses on five lineages identified through genome sequencing, but there is precedent suggesting that Histoplasma contains additional differentiated lineages. Initial surveys using multilocus-sequence typing reported the existence of over a dozen lineages that might fulfill the criteria for phylogenetic species. Sequencing of samples from other locations has revealed additional clades that fulfill the requirements to be considered monophyletic species (Rio de Janeiro in Brazil: ( Almeida-Silva et al . 2021 ; India: Jofre et al . 2022 ). Genomic studies that quantified the different trajectories along the genome in a worldwide sample are sorely needed. Multiple studies have reported inter-isolate differences in the Histoplasma genus, but a systematic survey that includes not only reference isolates, but also a variety of other strains is needed. For example, the reference isolate of H. ohiense (G217B) is more virulent than its counterpart in H. mississippiense (WU24) in mouse inoculations ( Sepúlveda et al . 2014 ). Similarly, a clinical isolate of H. mississippiense is more resistant to fluconazole than the reference isolate of H. ohiense ( Goughenour et al . 2015 ; Goughenour and Rappleye 2017 ), highlighting the importance of considering which species is responsible for causing disease in a patient when deciding on the course of treatment. Finally, the reference strain of H. capsulatum (G186A) induces a higher infiltration of monocytic cells in the lungs of mice inoculated with a low dose (10 3 yeast) than the representative isolates of H. mississippiense and H. ohiense ( Sepúlveda et al . 2014 ); Jones et al. 2021). All these surveys suffer from the same shortcoming, which is that differences between isolates might not be representative of the differences among species. Nonetheless, they are powerful starting points to propel surveys that quantify the extent of inter- and intraspecies variation. The case of Histoplasma will require a more systematic exploration than that of Coccidioides or Paracoccidioides because the number of lineages in Histoplasma appears to be much higher than in either of those other fungal pathogens. There is already indication that other unnamed Histoplasma lineages show important phenotypic differences. For example, a phylogenetic species restricted to Rio de Janeiro, Brazil seems to have a higher likelihood of causing hemorrhages than other genotypes ( Almeida-Silva et al . 2021 ). It is imperative as we define species boundaries that we also make a systematic effort to find phenotypic traits to aid species identification, as they can become useful tools in the clinical setting and could have an impact on the type of antifungal therapy used to treat infections. Our work demonstrates that morphological differences among Histoplasma species do exist and provides a blueprint for future surveys. SUPPLEMENTARY MATERIAL View this table: View inline View popup Download powerpoint TABLE S1. AIC values for the best fitting dose-response and linear functions for the growth curve of each Histoplasma species. View this table: View inline View popup TABLE S2. Effect of different optimization values for the dose-response functions. ACKNOWLEDGEMENTS We would like to thank our reviewers and members of the Matute lab for helpful comments. This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) under Award R01AI153523 to DRM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. REFERENCES ↵ Adderson E. E ., 2004 Histoplasmosis in a pediatric oncology center . The Journal of pediatrics 144 : 100 – 106 . OpenUrl CrossRef PubMed Web of Science ↵ Adenis A. A. , A. Valdes , C. Cropet , O. Z. McCotter , G. 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