Dating divergence of Polystigma and other Sordariomycetes

Document Type: Original Article

Authors

1 Department of Biodiversity, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran

2 Department of Plant Protection, Faculty of Agriculture, University of Shiraz, Shiraz, Iran

Abstract

Studies on the evolutionary history of ascomycetes in terms of time scale will help to understand historical patterns that shape their biodiversity. Until now most of dating studies of ascomycetes have focused on major events in fungal evolution but not on divergence events within smaller groups of fungi e.g. within Sordariomycetes. We used molecular dating to estimate the time of separation of Polystigma from other groups of Sordariomycetes with a Bayesian approach using a relaxed clock model and secondary calibration. Sequences from ITS region and SSU gene of rDNA were used for this purpose. We inferred evolutionary dates in Sordariomycetes particularly for Xylariomycetidae. Dating analyses showed that Polystigma diverged from Xylariales approximately 90 Million years ago in the late Cretaceous in which most other diversification events occurred. Our results also suggest that Polystigma amygdalinum and P. rubrum diverged in early Eocene concurrently with the divergence of their hosts, also providing a base for speculation on the location of evolution of these pathogens.

Keywords

Main Subjects


INTRODUCTION

Based on morphological characteristics Canoon (1996) classified Polystigma DC. in the order Phyllachorales, in the Sordariomycetidae, while Habibi et al. (2015) by using ITS, LSU and SSU regions showed that they are close to the Xylariales in the Xylariomycetidae. Polystigma species are characterized principally by brightly colored stromata occurring on living leaves of Prunus spp. in the Euro-Asiatic regions. Leaves are infected in spring with ascospores, which are discharged from overwintering leaves just before flowering till fruit set. The first symptoms of infection are accompanied with the formation of stromata within the host tissue in which fruiting bodies and filiform spores develop. The disease symptoms continue to develop during the summer, followed by the development of perithecia during the autumn and winter in the fallen leaves on the ground. Polystigma amygdalinum P.F. Cannon and Polystigma rubrum (Pers.) DC. are the causal agents of almond red leaf blotch and plum red leaf spot diseases respectively, and often cause premature defoliation of their hosts (Banihashemi 1990; Saad and Masannat 1997).

The evolutionary history of Polystigma genus and the related lineages in Sordariomycetes is still unknown and no study has tried to infer a timing of their origin in the fungal tree of life. Until now, most dating studies and molecular clock analyses of dating evolutionary divergences in the fungal tree of life have focused on major events (Beimforde et al. 2014; Berbee and Taylor 1993, 2010; Lücking et al. 2009; Padovan et al. 2005; Prieto and Wedin 2013) but not on divergences at lower levels e.g. within the Sordariomycetes. The divergence of Basidiomycota and Ascomycota has been estimated between 390 Mya (million years ago) to 1.5 Bya (billion years ago) based on different calibration points used for analyses (Taylor and Berbee 2006). Prieto and Wedin (2013) estimated diversification dates for major clades in the Pezizomycotina. They showed that the Pezizomycotina started to segregate in the Cambrian, and radiations in the Jurassic and Cretaceous caused the diversity of the main modern groups. In addition, these authors provided estimates for diversification dates of major classes, orders and some families of lichenized and non-lichenized groups of Pezizomycotina. Beimforde et al. (2014) extended the initiation of diversification of Pezizomycotina to have started in the Ordovician and continued throughout the Phanerozoic. They discussed the evolutionary history of main lineages in ascomycetes but not within the smaller groups of ascomycetes, i.e. orders and families of Sordariomycetes.

Systematics seeks to construct an accurate “time tree” of life showing both the organismic relationships and their dates of origin (Benton & Ayala 2003; Pyron 2011). Understanding the major processes that had shaped fungal biodiversity requires connecting biological evolution with climate changes, geological evolution and other historical patterns (Parham et al. 2011). In a recent study, we have determined the phylogenetic placement of Polystigma spp. in the fungal tree of life (Habibi et al. 2015).

However, the evolutionary history of Polystigma spp. in relation to the related lineages in the Xylariomycetidae of Sordariomycetes is unclear. This study was aimed at estimating the divergence time of Polystigma from other Sordariomycetes, discussing the evolutionary history of the genus and possible morphological and biogeographic factors underlying the diversification events in Xylariomycetidae. This study used a relaxed clock model and a secondary calibration to perform a molecular clock analysis to species level in Polystigma in relation to related lineages.

MATERIALS AND METHODS

Taxon sampling

The small ribosomal subunits (nuSSU) and ribosomal internal transcribed spacers (ITS) were used in this study. Sequences were obtained from various fresh and dried specimens representing P. amygdalinum and P. rubrum which were collected from infected almond and plum orchards in various parts of Iran (Habibi and Banihashemi 2015) and from Genbank (NCBI) which were extracted from reliable studies (Table 1).

DNA Amplification

Freeze-dried infected plant material, was homogenized using sea sand (Fluka, Darmstadt, Germany) and plastic disposable pestles. Cells were lysed using CTAB solution and DNA was extracted using DNGTM-plus DNA extraction solution (Cinaclon, Iran) (Mostowfizadeh-Ghalamfarsa and Mirsolaeimani 2012). DNA concentrations were estimated by a NanoDrop spectrophotometer (NanoDrop Technologies, USA). DNA extractions were each diluted to 20 ng.mL-1 in sterile distilled water for use as template DNA in PCR. Primers PyITS1 (Green et al. 2004) and ITS4 (White et al. 1990) were used to amplify ITS regions, and NS1 and NS4 primers (White et al. 1990) were used to amplify small subunit regions. Twenty-five μL PCR reactions contained 1 × reaction buffer, 0.4 mM of each primer, 200 mM dNTPs, 2.5 mM MgCl2, 20 ng of DNA and 1 unit of Taq polymerase. PCR was carried out in a CG1-96 thermo cycler (Corbett Research) and cycling conditions consisted of 94°C for 3 min followed by 30 cycles of 94°C for 30 s, 60°C for 30 s, and 72°C for 1 min followed by 5 min at 72°C. Sequencing was performed by Elim Biopharmaceuticals, Inc. (USA). ITS and SSU sequences were deposited in GenBank and the accession numbers were obtained (Table 1).

Initial phylogenetic analysis

Datasets for each genomic region (SSU and ITS) were aligned separately using Geneious version 7 (Biomatters, USA). Models of sequence evolution were evaluated for each dataset and model parameter estimates obtained with JModeltest 2.1.1 (Darriba et al. 2012) and models were chosen according to the Bayesian information criterion (BIC, Schwarz 1978). The Bayesian information criterion supported the TIM+G model with equal base frequencies, six substitution rate parameters (11.0000, 2.1366, 1.0000, 1.0000, 4.4032, 1.000) and gamma distributed rates (shape parameter 0.7130) for SSU and the TIM2ef+I+G model with equal base frequencies, six substitution rate parameters (1.3892, 1.4482, 1.3892, 1.0000, 2.4398, 1.000) and gamma distrib­uted rates (shape parameter 0.6960) for ITS. Topological congruence of the datasets was assessed by visual comparison of phylogenetic trees obtained from maximum likelihood-based analysis heuristic searches in PAUP v. 4.0a133 (Swofford 2002). Bayesian analyses were carried out using Markov Chain Monte Carlo (MCMC) approach in the software package MrBayes v3.2.2 (Ronquist et al. 2012) to generate a reasonable starting tree for subsequent analyses of divergence date estimates in BEAST. For analyses, a general time-reversible model of evolution was used. Rate heterogeneity across sites was modelled with a gamma distribution. Four chains starting with a random tree were run for 10,000,000 generations, retaining each 1000th tree and the first 25% of each analysis were discarded as burn-in.

Molecular clock analysis

For calibration we used the divergence time estimation of 247 Mya for the Sordariomycetes and Leotiomycetes split, inferred in recent molecular dating analysis by Prieto and Wedin (2013) who used six fossils reliable in age and identification including Paleopyrenomycites devonicus Taylor, Hass, Kerp, M. Krings & Hanlin as the oldest calibration point.

A Bayesian Markov Chain Monte Carlo algorithm was applied for estimating divergence times using data from the ribosomal DNA internal transcribed spacer region (ITS) and the 18S small-subunit of ribosomal DNA (SSU rDNA). The analyses were carried out using BEAST v1.8.0 software package (Drummond and Rambaut 2007). The tree topology and divergence time were estimated. Using BEAUTI (BEAST package) priors were set for the analyses and the necessary XML input for BEAST was produced. An HKY model of amino acid substitution and a gamma site heterogeneity model with four rate categories as priors were used. An uncorrelated relaxed clock model used to allow rates of molecular evolution to be uncorrelated across the tree Yull speciation process which specifies a constant rate of species divergence was applied. For the Leotiomycetes and Sordariomycetes divergence, a prior normal distribution with a mean of 247 Mya and a standard diviation of 10 Mya was assigned to the node age. Beast analyses were run 10 milion generations, sampling parameters and trees every 1000 generations. The BEAST output was analyed with Tracer v1.6 (Rambaut and Drummond 2009). One hundred trees were removed from each run as burn-in and the rest of the trees were used to generate a maximum clade credibility tree which is the tree with the highest product of the posterior probability of all its nodes using TreeAnnotator v1.8.0 (BEAST package).

RESULTS and DISCUSSION

The maximum clade credibility tree obtained from BEAST analysis (Fig. 1) was topologically identical to the best tree obtained with our maximum likelihood and bayesian analyses. By few exeptions the topologies resulted from BEAST analysis were congruent with the results of other phylogenies of Sordariomycetes reported by Zhang et al. (2006) and Eriksson (2006). The phylogenetic placement of Polystigma within the Xylariomycetidae was consistent with previous study (Habibi et al. 2015) and Polystigma built a sister group to the Xylariales clade in our analyses.

Divergence time estimates of Sordariomycete groups using 247 Mya calibration point for the Sordariomycetes and Leotiomycetes split, are shown in Fig. 1. Age estimates with means and 95% confidence intervals for some of the major Sordariomycete group splits are summarized in Table 2. According to our results, the first divergence within Sordariomycetes ([Xylariomycetidae + Sordariomycetidae] and Hypocreomycetidae) took placed in the early Jurassic (node 2; 201 Mya; 149-240 Mya credibility interval) (Fig. 1). The Xylariomycetidae and Sordariomycetidae split took placed in the Cretaceous (node 5; 172 Mya; 115-222 Mya credibility interval). Our analyses show that successive radiations in the Jurassic and mostly Cretaceous have generated the diversity in the main Sordariomycetes groups. 

 

Table 1. Accession numbers of fungal species included in the study.

Species

Order

GenBank Accession No.

ITS rDNA

18S rDNA

 

Camarops ustulinoides

Boliniales

AY908991

DQ470989

 

Chaetosphaeriacurvispora

Chaetosphaeriales

 

AY502933

 

Diaporthe phaseolorum

Diaporthales

KC343180

AY779278

 

Valsella melostoma

Diaporthales

AF191184

 

 

Valsella salicis

Diaporthales

 

DQ862057

 

Aspergillus niger

Eurotiales

FJ878652

KF225022

 

Erysiphefriesii

Erysiphales

AB000939

AB033478

 

Colletotrichum gloeosporioides

Glomerellales

DQ084498

JN940370

 

Glomerella miyabeana

Glomerellales

 

 

 

Glomerella cingulata

Glomerellales

GQ373209

AY083798

 

Hypomyces chrysospermus

Hypocreales

HQ604858

AB027339

 

Kohlmeyeriella tubulata

Lulworthiales

 

AY878998

 

Lindra thalassiae

Lulworthiales

 

DQ470994

 

Lulworthia fucicola

Lulworthiales

 

AY879007

 

Lulworthia lignoarenaria

Lulworthiales

 

 

 

Haloguignardia irritans

Lulworthiales

AY581943

AY566252

 

Magnaporthe salvinii

Magnaporthales

JF414838

DQ341477

 

Meliola centellae

Meliolales

KC252606

 

 

Meliola niessleana

Meliolales

 

AF021794

 

Microascus cirrosus

Microascales

JQ906771

M89994

 

Coccodiella melastomatum

Phyllachorales

 

U78543

 

Coccodiella toledoi

Phyllachorales

 

CTU78544

 

Phyllachora graminis

Phyllachorales

 

AF064051

 

Sphaerodothis acrocomiae

Phyllachorales

 

SAU76340

 

Ophiodothella vaccinii

-

 

OVU78777

 

Polystigma amygdalinum

-

KC756360a

KM111539a

 

Polystigma rubrum

-

KC966927a

 

 

Cercophora caudate

Sordariales

AY999135

DQ368659

 

Chaetomium elatum

Sordariales

HF548695

M83257

 

Chaetomium globosum

Sordariales

AY429056

JN939003

 

Farrowia longicollea

Sordariales

 

AF207685

 

Gelasinospora tetrasperma

Sordariales

AY681178

DQ471032

 

Lasiosphaeria ovina

Sordariales

GQ922528

AY083799

 

Neurospora crassa

Sordariales

AY681193

X04971

 

Sordaria fimicola

Sordariales

FN392318

AY545724

 

Nigrospora oryzae

Trichosphaeriales

JN198503

FJ176838

 

Xylaria acuta

Xylariales

JQ862676

JQ419764

 

Xylaria hypoxylon

Xylariales

DQ491487

NG_013136

 

asequences generated in this study

Empty spaces mean that sequences were not available.

 

Table 2. Divergence time estimates of Sordariomycetes lineages obtained from Bayesian analysis. For each divergence, the median and range (95% credibility intervals) are provided. Divergence times are provided in millions of years (Mya). The node numbers correspond to numbers used in Fig. 1 to show their placement in the chronogram.

Nodes

 

Geological period

Time (Mya)

Confidence interval (mya)

1

Hypocreomycetidae crown group

Jurassic

172

116-222

2

Hypocreomycetidae-Sordariomycetidae and Xylariomycetidae

Jurassic

201

149-240

3

Sordariomycetidae crown group

Jurassic

148

94-199

4

LeotiomycetesSordariomycetes

Permian

247

225-265

5

Sordariomycetidae -Xylariomycetidae

Jurassic

172

115-222

6

Sordariales crown group

Cretaceous

74

36-121

7

Polystigma spp.- Xylariales

Cretaceous

92

34-169

7

Xylariomycetidae crown group

Cretaceous

92

34-169

 

Fig. 1. Maximum clade credibility chronogram for the Sordariomycetes based on SSU rDNA. The chronogram is the result from BEAST analysis. Each node represents the mean divergence time estimate and bars show their associated 95% credibility interval. Bayesian posterior probabilities (probabilities %) are shown next to the branch points.The scale bar represents the number of changes per sites. Numbers corresponding to dated groups shown in table 2 are written at the nodes in circles. 

 

Most Sordariomycete orders and families originated in the Cretaceous. The first diversification of Sordariomycete groups occurred after the Permian-Triassic (P-T) mass extinction, which had a profound effect on terrestrial and marine ecosystems.

Extreme abundances of fungal remains have been reported in sedimentary organic matter associations from the P-T boundary (Taylor 2004; Visscher et al. 1996). This diversification of fungi may be a response to a large amount of dead plant and organic material. On the other hand, Bell et al. (2005, 2010) estimated that the origin of angiosperms was in Lower Jurassic to Lower Cretaceous which could explain the main diversification events of Sordariomycetes. The high level of diversity and radiation in Sordariomycete groups in the Cretaceous could be influenced by several new environments dominated by the new diversity of angiosperms, which could host these fungi.

The results of this study were similar to those by Prieto and Wedin (2013) and Gueidan et al. (2011). Prieto and Wedin (2013) showed that most families of Ascomycetes have diverged in the Cretaceous- Paleocene. It is not surprising that our results would not significantly depart from the ones from Prieto and Wedin (2013), as we used one of their estimates as secondary calibration. However, Beimforde et al. (2014) suggested an earlier initiation for the diversification of Pezizomycotina starting in the Ordovician and continuing throughout the Phanerozoic. They suggested that the diversification was unaffected by mass extinctions due to ecological diversity within each lineage.

Our results suggest that Polystigma diverged from the Xylariales approximately 90 Mya in the late Cretaceous in which most diversification events occurred (Fig 1). It appears that Polystigma and Xylariales may have originated from endophytic ancestors as they both have endophytic behavior in common. There is a hypothesis for the role of endophytic Xylaria species which states that the fungi are simply waiting for their host to senesce (or perhaps to accelerate it), at which times they can begin to decompose cell walls (Davis et al. 2003; Petrini et al. 1995). This is similar to how Polystigma spp. treat their hosts. Polystigma species are known to initially have biotrophic nutrition. They develop on green plant tissue with little evidence of antagonistic relationship and the leaf tissue around the ascomata seems green and healthy (Cannon 1997). Cannon (1991) considered the relationship between this group of fungi and their hosts at least in some instances mutualistic, similar to what has been demonstrated for other endophytic fungi. Endophytes employing of this strategy, would have an advantage over competing saprophytes, having occupied the tissue before decomposition begins. Thus, we suggest that Polystigma species and endophytic Xylariales may have evolved from a common endophytic ancestor.

The analyses show in addition that P. amygdalinum and P. rubrum diverged in the early Eocene (49 Mya; Fig. 2). The fossil evidence for Polystigma spp. is very scanty. However, according to materials from fossil palms in western Canada, these fungi date back to Eocene (50Mya; Cannon, 1997). This divergence time is consistent with the divergence time estimates of the Prunus hosts, inferred by Chin et al. (2014). These authors showed that the genus Prunus appeared ~61 Mya in eastern Asia and diversification of all major lineages may have occurred in early Eocene, triggered by the global warming in early Eocene known as Paleocene-Eocene Thermal Maximum and Early Eocene Climate Optima events. The Cenozoic Era is the period of mammals and Angiosperms. The Era begins with the Paleogene Period containing three successive epochs, Paleocene, Eocene and Oligocene. The Paleocene started with a warm climate, which continued to the early Eocene [ca 50 Mya] (Crane et al. 2000). In addition, Chin et al. (2014) suggested that tectonic collision of the Indian plate with the Eurasian plate (~50 Mya) resulting in the orogenic uplifts of the Tibetan plateau likely contributed to this diversification by exerting vicariance forces. The forces are the processes by which the geographical range of an individual taxon, or a whole biota, is split into discontinuous parts by the formation of a physical or biotic barrier to gene flow.

Because Polystigma species are obligate biotrophs of Prunus spp., their niche is strictly confined to the leaves of the living hosts. Geographic distributions of the early hosts of Polystigma species in the Paleogene period, when the first radiation occurred, may provide a base to speculate on the location of the evolution of these pathogens. The center of diversity for the major Prunus crop species is Eurasia (Watkins 1976). The restriction of P. amygdalinum and P. rubrum distribution to the Euro-Asiatic region may correspond to a coevolution with the hosts in these regions.

 

Fig. 2. Maximum clade credibility chronogram for the Sordariomycetes based on ITS rDNA region. The chronogram is the result from BEAST analysis. Each node represent the mean divergence time estimate and bars show their associated 95% credibility interval. Bayesian posterior probabilities (probabilities %) are shown next to the branch points. The scale bar represents the number of changes per sites.

 

Conclusion

Despite the increasing number of the studies on the origin, diversification and evolutionary history of different fungal groups, we still suffer from the lack of information on the evolutionary history of Ascomycetes, particularly within smaller groups such as orders, families and genera. In this study, using a Bayesian approach and a relaxed clock model, we provided the information which expanded the knowledge of the evolutionary dates in Sordariomycetes particularly within Xylariomycetidae. We also infered that most Sordariomycete orders and families originated in the Cretaceous. Our analyses suggest that the divergence between P. amygdalinum and P. rubrum occurred during the early Eocene concurrently with the divergence of their Prunus hosts. Inferring phylogenies and divergence times provided a base to speculate on the evolution of these pathogens.

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