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Article

Comparison of Culturing and Metabarcoding Methods to Describe the Fungal Endophytic Assemblage of Brachypodium rupestre Growing in a Range of Anthropized Disturbance Regimes

by
María Durán
*,
Leticia San Emeterio
† and
Rosa Maria Canals
†
Department of Agricultural Engineering, Biotechnology and Food, Institute on Innovation and Sustainable Development in Food Chain (IS-FOOD), Public University of Navarre (UPNA), Arrosadia Campus, 31006 Pamplona, Spain
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biology 2021, 10(12), 1246; https://doi.org/10.3390/biology10121246
Submission received: 18 October 2021 / Revised: 20 November 2021 / Accepted: 25 November 2021 / Published: 29 November 2021
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Abstract

:

Simple Summary

The richness (number of species) of the fungi kingdom is estimated at 1.5 million species, but the vast majority remains unknown. Many of them inhabit plants—the so-called fungal endophytes—and may establish different types of interactions with their host plant. Fungal endophytes have been traditionally studied by letting them grow in appropriate culturing media in petri dishes, but novel massive DNA sequencing techniques which do not require a cultivation step (metabarcoding) are gaining ground. Both techniques were applied and compared to characterize the mycobiome of plants of a tall grass (Brachypodium rupestre) growing in high-mountain grasslands with different plant diversity (low and high). The two methods showed similar trends comparing endophyte richness between plant tissue types (root > rhizome > shoot) and between grasslands (low-diversity > high-diversity). However, the metabarcoding identified almost six times more endophyte species than the culturing although the most isolated fungal species via culturing, Omnidemptus graminis, was not recognized via metabarcoding. We conclude that the complementation of both techniques is still the best option to obtain a complete characterization of the fungal endophytic assemblage of the plant species.

Abstract

Fungal endophytes develop inside plants without visible external signs, and they may confer adaptive advantages to their hosts. Culturing methods have been traditionally used to recognize the fungal endophytic assemblage, but novel metabarcoding techniques are being increasingly applied. This study aims to characterize the fungal endophytic assemblage in shoots, rhizomes and roots of the tall grass Brachypodium rupestre growing in a large area of natural grasslands with a continuum of anthropized disturbance regimes. Seven out of 88 taxa identified via metabarcoding accounted for 81.2% of the reads (Helotiaceae, Lachnum sp. A, Albotricha sp. A, Helotiales A, Agaricales A, Mycena sp. and Mollisiaceae C), revealing a small group of abundant endophytes and a large group of rare species. Although both methods detected the same trends in richness and fungal diversity among the tissues (root > rhizome > shoot) and grasslands (low-diversity > high-diversity grasslands), the metabarcoding tool identified 5.8 times more taxa than the traditional culturing method (15 taxa) but, surprisingly, failed to sequence the most isolated endophyte on plates, Omnidemptus graminis. Since both methods are still subject to important constraints, both are required to obtain a complete characterization of the fungal endophytic assemblage of the plant species.

1. Introduction

The study of microorganisms in their natural environment is a recent branch of research compared to microbial investigations undertaken in disciplines such as medicine and agronomy, with high impact on human health and development [1,2]. Nowadays, microbial ecology, i.e., their diversity in nature, their response to prevailing and future environmental conditions, the associations they establish with plants and the complex network of interactions and functions they are involved in, are gaining ground in ecological research [3,4,5].
One example involves examining the associations that endophytic fungi establish with plants. These associations were first studied in agronomic grasses [6,7,8] and the research has extended to natural plant communities in recent decades [9,10,11]. Scientific literature has shown that these hidden associations are ubiquitous in nature and that all plants harbor an endophyte assemblage that delivers different functions and constitutes a collective and complex holobiont [12].
Nowadays, two techniques, culturing and metabarcoding, are used for the determination of fungal endophyte assemblages [13]. The protocols of culturable techniques have a longer record and have been implemented in many laboratories [14]. In this method, important constraints include the possibility that some fungal species are unculturable on artificial medium and the accumulation of inaccuracies and errors due to different sterilization times, diverse species growth rates and the presence of surface contaminants [15]. Metabarcoding techniques (culture-independent) [16], despite appearing very promising, still remain costly and lack a complete repository of sequences with taxonomic identification, a task which is under way [17,18]. In the latter, the potential for providing quantitative data based on the proportion of read sequences makes it a very powerful ecological tool [19,20].
The genus Brachypodium encompasses several perennial tall grasses, native to European calcareous grasslands, which have been expanding aggressively in the last decades due to the global change conditions (B. pinnatum, B. genuense and B. rupestre) [21,22,23,24,25]. This tall grass expansion causes a decline of the biodiversity of the natural grasslands and also has an impact on the ecosystem service of provisioning [26]. The competitive strategies of this group of species that explain the expansive process is a matter of interest [27,28,29,30,31,32,33], as it is the study of the mycobiome that may help to understand these advantages. To date, the research in the Brachypodium genus has focused on the systemic fungi of the Clavicipitaceae family hosted by B. sylvaticum [34,35], B. phoenicoides [36,37] and B. pinnatum [38]. Only a previous study of our research team has characterized the systemic and non-systemic mycobiome of Brachypodium rupestre under a gradient of grazing and fire disturbances using culturable techniques [39].
The aim of this research is to provide a characterization of the endophytic mycobiome of the tall grass species Brachypodium rupestre and to compare culture and metabarcoding techniques applied to conditions with restricted sampling effort due to the high cost of the novel technique. The comparison includes the aboveground (shoot) and the underground (rhizome and root) component of a set of B. rupestre individuals growing in the same region but subjected to different levels of anthropic disturbance (grasslands with different regimes of grazing and prescribed burning and, consequently, encompassing a different plant community composition). Through this range of regional variation, and considering different tissues and different environmental drivers, we are interested in determining the capacity of the two methods to identify and characterize the fungal endophyte assemblage of B. rupestre.

2. Materials and Methods

2.1. The Study Area

The Aezkoa valley (Navarra county, Spain) is the westernmost valley of the southern Pyrenees (42.53–43.3′ N, 1.8–1.17′ W) (Figure 1d). The climate is snowy and cold in winter, and mild and foggy in summer. The annual temperature averages 9.3 °C and the accumulated precipitation reaches 1856 mm per year (Irabia climatic station, http://meteo.navarra.es accessed on 17 October 2021). The landscape is a mosaic of forests (e.g., Fagus sylvatica, Abies alba), shrubland communities (e.g., Erica spp., Ulex gallii) and grasslands. The area of study is part of the Special Area of Conservation (SAC) Roncesvalles-Selva de Irati (code ES0000126; Figure 1f) and is located in the north of the valley. High-altitude grasslands (800–1400 m asl) comprise diverse communities of perennial grasses (Festuca gr. rubra, Agrostis capillaris, Brachypodium rupestre, Danthonia decumbens), forbs (Achillea millefolium, Potentilla erecta, Gallium saxatile) and legumes (Trifolium repens, Lotus corniculatus). Sandstones and calcareous clays dominate the substrate, upon which develop acidic, deep and organic soils, with clay-loamy and loamy textures.
Depending on the grazing pressure of the livestock during the summer months, farmers schedule different types of burnings to control the build-up of litter and resprouting of woody species. As a result, traditional (bush-to-bush) burnings applied every 6–7 years coexist with more intense fire regimes, applied across the whole surface every 1–2 years in the less grazed areas. The regional plant community composition reflects the dominant grazing/burning regime, which leads to a mosaic of high-diversity grasslands (more grazed, less burned) and low-diversity grasslands highly dominated by B. rupestre (less grazed, more burned). Based on previous floristic surveys undertaken in the area [26], we selected two representative locations according to the percentage of B. rupestre cover. A low-diversity grassland (LD) in Arpea, with a dominant cover of B. rupestre up to 80%, and a high-diversity grassland (HD) located in Urkulu, with a B. rupestre cover lower than 25% (Table 1).

2.2. Plant Sampling

In summer 2018, a total of 10 turfs of B. rupestre were collected (turfs included shoots, rhizomes and roots surrounded by soil) from the two locations (Figure 1f). The distance between turf samples was ca 150 m to avoid collecting clonal individuals. Turfs were transported to the UPNA laboratory and processed in the following days.
One B. rupestre plant with high biomass was selected from each turf. Tissues were separated (shoots, rhizomes and roots) and cut into fragments of ca 2 cm, surface-disinfected via immersion in a solution of 20% commercial bleach (1% active chlorine) containing 0.02% Tween 80 (v:v) for 10 min and finally rinsed with sterile water. The rhizome and root fragments were also treated with an aqueous solution of 70% ethanol for 30 s. Thirty fragments (10 shoots, 10 rhizomes and 10 roots) assigned to the metabarcoding method were ground using a pestle with liquid nitrogen and preserved at −20 °C until shipment.

2.3. Isolation and Identification of Fungi Using the Culturing Method

We plated 300 tissue fragments of B. rupestre onto 30 culture media plates (10 fragments/tissue/plate, 90 mm diameter), containing PDA medium (potato dextrose agar) with chloramphenicol (200 mg/L). Dishes with tissue fragments were kept at room temperature and ambient light and checked daily for 4 weeks. Any emerging mycelium was transferred and individually isolated in a new mini petri dish (60 mm diameter). Isolates with the same morphological characteristics (colony color, exudates, growth type and general appearance) were grouped into morphotypes, and at least one of them was genotyped for taxonomic analysis.
A small amount of mycelium was collected and its DNA extracted using a Phire Plant Direct PCR Kit (Thermo Fisher Scientific). The complete ITS region (ITS1-5.8S-ITS2) was amplified using ITS4 and ITS5 primers [40]. The amplification cycles followed were: 98 °C for 5 min, 95 °C for 5 s (35 repeated cycles), 54 °C for 5 s, 72 °C for 20 s and a final phase of 72 °C for 1 min. PCR amplicons were purified (Favor PrepTM Plant Genomic DNA Extraction Mini Kit, Favorgen) and sequenced using the Sanger method, copying single-stranded DNA, at STABVIDA enterprise. The returned DNA sequences were grouped using the CD_HIT program at 97% identity threshold [41,42], considering the clustered sequences to represent the same taxon. A representative sequence of each cluster was selected and contrasted to the closest match of the ITS region from fungal types at the National Centre for Biotechnology Information (NCBI) using the BLAST algorithm [43]. The database UNITE was also interrogated for sequences.

2.4. Metabarcoding Analysis and Taxonomic Assignment

A total of 30 samples was sent to AllGenetics services for metabarcoding analysis. The DNA of samples was isolated using a Dneasy PowerSoil DNA isolation kit (Qiagen, Hilden, Germany), and the complete ITS2 region was amplified using the primers ITS86F and ITS4 [40,44], to which the Illumina sequencing primer sequences were attached to their 5’ ends. The PCR cycle consisted of an initial denaturation at 95 °C for 5 min, followed by 35 cycles of 95 °C for 30 s, 49 °C for 30 s, 72 °C for 30 s and a final extension step at 72 °C for 10 min. The index sequences required for multiplexing libraries were attached in a second PCR with the same conditions but only 5 cycles and 60 °C as the annealing temperature. Libraries were purified using Mag-Bind RXNPure Plus magnetic beads (Omega Biotek, Norcross, GA, USA), pooled in equimolar amounts and sequenced in a MiSeq PE300 run (Illumina, San Diego, CA, USA).
The Illumina raw files R1 (forward) and R2 (reverse) reads were trimmed and checked using the software FastQC (www.bioinformatics.babraham.ac.uk accessed on 17 October 2021). FLASH2 was used to merge reads and CUTADAPT software 1.3 to remove sequences that did not contain the PCR primers and those shorter than 100 nucleotides [45,46]. The sequences were filtered by quality using Qiime v1.9.1. and the FASTA file was processed using VSEARCH [47]. Sequences were dereplicated, sorted and clustered at a similarity threshold of 100%. Artefacts were detected and filtered using the UCHIME algorithm implemented in VSEARCH [48]. Sequences were then assigned to OTUs, and those occurring at a frequency below 0.005% in the whole dataset were removed. In the same way as the sequences obtained from the culture method, sequences were grouped using the CD_HIT program at 97% identity threshold [41,42]; we considered that the clustered OTUs were the same taxon. A representative OTU of each cluster was selected and compared with the NCBI and UNITE data using the BLAST algorithm [43].

2.5. Data Analysis

For the metabarcoding data, we estimated accumulation curves with and without singletons (OTUs and taxa that were only present in one sample) to evaluate the sampling effort and to compare the importance of rare taxa/OTUs between grasslands and tissue types. We calculated the OTU richness and Shannon and Simpson diversity indexes and we analyzed the effects of tissue and grassland type on fungal endophyte richness and diversity using two-way ANOVAs [49]. We calculated the relative abundance at the taxonomic level of phyla, orders, families and OTUs grouped into taxa using read sequences, within each tissue (shoot, rhizome and root) and each grassland type (LD and HD). We evaluated the effects of tissue and grassland type on fungal endophyte assemblages of B. rupestre using nonmetric multidimensional scaling (NMDS) with a Bray-Curtis dissimilarity index matrix, and we identified the distinctive fungal endophytes of a specific tissue and grassland type using indicator species tests [50], measuring the fidelity of the taxa to a particular situation [51].

3. Results

3.1. Comparison of B. rupestre Mycobiome Obtained by Culturing and Metabarcoding Methods

For the culture method, we obtained 28 isolates which were classified into a total of 19 morphotypes. Their corresponding sequences were matched in databases, a total of 15 taxa were obtained and classified to species (2), genus (9), family (3) and order (1) rank (Table 2). Ten taxa were isolated in plants collected in the LD grassland (66.6%), while eight were from the HD grassland (53.3%). We identified 2, 5 and 11 taxa from shoots, rhizomes and roots, respectively (Table 3).
The thirty samples of B. rupestre analyzed using the metabarcoding method produced 1,622,980 reads from 1822 OTUs before filtering and 513,671 reads from 352 OTUs after the filtering process. We obtained 316 OTUs from the LD grassland (61.1%) and 246 OTUs from the HD grassland (38.9%). There were 19,197 and 340 OTUs from shoots, rhizomes and roots, respectively. The OTU clustering process returned a total of 88 taxa: 38 assigned to genus, 16 to family, 19 to order, 9 to class and the remaining 6 to phylum or still unidentified (Appendix A). According to grassland type, 75 taxa were identified in the LD grassland (85.2%) and 52 in the HD grassland (59.1%). According to tissue type, 15, 37 and 82 taxa were identified in shoots, rhizomes and roots, respectively (Table 3).
The culturing method isolated 13 taxa out of 88 sequenced via metabarcoding. Since we used a conservative approach in the process of identification, it is likely that we arrived at different taxonomic levels of identification depending on the methodology, for example, Codinaea sp. (culturing) vs. Chaetosphaeriaceae (metabarcoding), Didymosphaeriaceae (culturing) vs. Paracamarosporium sp. (metabarcoding) and Mollisia sp. and Phialocephala sp. (culturing) vs. Mollisiaceae (metabarcoding). The rest of the isolated taxa did match at the taxonomic level assigned (Albotricha sp., Drechslera sp., Falciphora sp., Helotiaceae, Lachnum sp., Microdochium phragmitis and Neoascochyta sp.). Table 4 shows the complete information obtained from both methods for each sample, as well as the samples where the same taxon was isolated via the culturing method and also sequenced via the metabarcoding analysis. The two taxa isolated via culturing but not sequenced via metabarcoding were Ilyonectria sp. and Omnidemptus graminis. The latter was the most isolated fungal endophyte from shoots of B. rupestre using the culturing method.
Despite the remarkable differences in the number of sequences obtained using the two methods (28 isolates vs. 513,671 reads), the pattern of fungal endophyte richness and diversity among grassland and tissue types followed a similar trend, with the highest values in the root tissue and plants collected from the LD grassland.

3.2. The Mycobiome of B. rupestre According to the Metabarcoding Method

3.2.1. Fungal Endophytic Richness and Diversity

The quantitative data from metabarcoding, based on read sequences, allowed an exhaustive characterization of the endophytic diversity of B. rupestre. Both the OTUs (352) and the clustering of OTUs into taxa (88) produced non-asymptotic species accumulation curves (Figure 2). However, 23 out of 88 taxa and 71 out of 352 OTUs were sequenced in only one sample (designated as singletons). Additional curves were constructed without singletons, suggesting that an increase in sampling effort would increase the number of rare taxa/OTUs but not the more common ones (Figure 2a,d). Accumulation curves comparing tissues and grassland types did not approach horizontal asymptotes (Figure 2b,c,e,f), therefore, greater sampling effort is required for reliable richness estimates.
The two factor ANOVA showed a significant effect of plant tissue (F = 19.9, p < 0.001) but not of grassland type (F = 2.5, p = 0.126) on OTU richness, whereas Shannon and Simpson indexes showed significant differences between grassland types (F = 5.1, p = 0.033 and F = 4.4, p = 0.046, respectively) and tissues (F = 32.7, p < 0.001 and F = 9.5, p < 0.001, respectively) (Figure 3).

3.2.2. Taxonomic Assemblages for Grassland Types and Tissues

The relative abundance of phyla, orders and families was estimated from the read sequences. Most taxa were included in the phyla Ascomycota (71.21%) and Basidiomycota (21.21%). Figure 4 shows the relative abundance of orders and families according to tissue and grassland type.
Pleosporales dominated in shoots of plants from both grassland types (52.59% LD and 65.39% HD), followed by Phyllachorales (20.49%) and Pucciniales (16.05%) in the LD grassland and Hypocreales (17.45%) and Capnodiales (12.73%) in the HD grassland. The other orders did not exceed 4%, except Xylariales in the LD grassland (5.58%). Helotiales dominated in the belowground tissues of both grassland types ranging from 85.81% (Rhizome-HD) to 77.47% (Rhizome-LD), followed by Agaricales ranging from 20.93% (Rhizome-LD) to 7.17% (Root-LD). The other orders did not exceed 2.5% except Pleosporales in roots from the HD grassland (6.74%) (Figure 4, left).
Phaeosphaeriaceae dominated in shoots from both grassland types (63.48% HD and 34.08% LD), followed by unidentified family A (22.28%), Didymellaceae (16.94%) and Pucciniaceae (16.05%) in the LD grassland and unidentified family A (17.45%) and Mycosphaerellaceae (12.73%) in the HD grassland. The rest of the families did not exceed 5.5%. Hyaloscyphaceae dominated in belowground tissues ranging from 54.47% (Rhizome-HD) to 32.72% (Root-HD), followed by Helotiaceae ranging from 35.05% (Root-HD) to 13.16% (Rhizome-HD). Other families with relatively high abundance were Tricholomataceae and unidentified family A, ranging from 20.93% (Rhizome-LD) to 5.18% (Root-LD) and from 24.21% (Root-LD) to 8.18% (Root-HD), respectively. The other families did not exceed 4% (Figure 4, right).
The relative abundance of endophytic taxa after the OTU clustering process according to their high genetic similarity (97% threshold) was estimated from the read sequences. The most abundant read sequences were located in the root tissue and were reached by Helotiaceae (22.60%), Lachnum sp. A (21.94%), Helotiales A (8.29%) and Albotricha sp. A (7.00%). All of these were more abundant in plants collected in the LD grassland, with the exception of Albotricha sp. A.
In the roots, taxa with abundances higher than 5% were Lachnum sp. A (35.08%), Helotiaceae (24.51%) and Helotiales A (12.43%) in LD grassland plants and Helotiaceae (31.09%), Albotricha sp A (18.83%), Lachnum sp. A (12.50%), Agaricales A (9.65%) and Helotiales A (6.03%) in HD grassland plants (Table 5).
The dominant taxon in the shoots was Phaeosphaeriaceae (34.08% LD and 58.80% HD). In LD grasslands, it was accompanied by Phyllachorales (20.49%), Puccinia sp. (16.05%), Neoascochyta sp. A (14.94%) and Microdochium sp. (5.58%) and in HD grasslands by Sordariomycete A (17.45%), Mycosphaerellaceae (12.73%) and Ophiosphaerella sp. (4.68%). The remaining taxa did not exceed 4% (Table 6).
The dominant taxa in the rhizomes of the LD grassland were Lachnum sp A. (29.13%), Helotiaceae (25.95%), Mycena sp. A (20.88%), Helotiales A (10.98%) and Albotricha sp. A (5.23%) and in the rhizomes of the HD grassland Albotricha sp. A (50.58%), Helotiaceae (16.49%), Lachnum sp. A (9.01%), Parasola sp. (5.81%) and Tricholomataceae (4.21%) (Table 5).

3.2.3. Indicator Species of the Fungal Assemblages

NMDS analysis showed that the fungal endophyte assemblage from above and belowground tissues of B. rupestre was clearly different (Figure 5a). In addition, fungal assemblages from root tissues (Figure 5d), unlike shoots (Figure 5b) and rhizomes (Figure 5c), displayed significant differences between grassland types.
The indicator species for shoot tissues were Phaeosphaeriaceae (p = 0.002), Neoascohyta sp. A (p = 0.005) and Phyllachorales (p = 0.029) and for root tissues were Helotiales A (p = 0.001), Lachnum sp. A (p = 0.001), Mollisiaceae A (p = 0.001), Pleosporales A (p = 0.001), Drechslera sp. (p = 0.001), Lachnum sp. B (p = 0.002), Agaricales A (p = 0.001), Cladophialophora sp. (p = 0.003), Leohumicola sp. (p = 0.003), Pseudolachnella sp. A (p = 0.007), Mollisiaceae C (p = 0.011), Pseudolachnella sp. B (p = 0.023), Unidentified B (p = 0.041), Phragmocephala sp. A (p = 0.019) and Paracamarosporium sp. (p = 0.036). No species were indicative of rhizome tissues.
The indicator species for grassland type were Phyllachorales (p = 0.044) and Neoascochyta sp. A (p = 0.035) in shoots collected in the LD grassland (Figure 5b). Drechslera sp. (p = 0.012) and Pleosporales A (p = 0.036) were indicators in roots from the HD grassland, while Lachnum sp. A (p = 0.007), Phragmocephala sp. A (p = 0. 042), Paracamarosporium sp. (p = 0.037) and Pseudolachnella sp. A (p = 0.047) were indicators in roots from the LD grassland (Figure 5d). No species in the rhizome tissues were indicative of plant community (Figure 5d).

4. Discussion

4.1. The Mycobiome of B. rupestre According to the Metabarcoding Data

The results of the metabarcoding showed that 88 taxa constituted the mycobiome of B. rupestre and that only seven taxa sequenced from the belowground tissues accounted for 81.2% of the total reads (Helotiaceae, Lachnum sp. A, Albotricha sp. A, Helotiales A, Agaricales A, Mycena sp. A and Mollisiaceae C), while the other 81 taxa were responsible for the remaining 18.8%, and 25 of them were only sequenced in a single sample. Therefore, a restricted sampling effort using the metabarcoding method was able to identify a small group of abundant fungal endophytes and a large group of rare species. The accumulation curves also supported the idea that extension of the sampling effort would enrich the group of rare species but not the most common species. This pattern of fungal endophyte distribution seems common to grasses [52] and indicates that a limited sampling effort is enough to provide good characterization of the dominant fungal species in plants, which is important considering the high cost of metabarcoding. However, when addressing studies on fungal richness and diversity, more extended sampling appears necessary to avoid an underestimation of the values.
The results of the study also highlight the importance of sampling the different tissues of plants to obtain a reliable characterization of its mycobiome [53,54]. Aboveground fungal assemblages were much poorer in species, less diverse and taxonomically different from those of rhizomes and roots, and this pattern was consistent between the grassland types, as observed by other authors in different plant species and different habitats [55,56,57]. The soil rhizosphere is the main route of fungal transmission to plants [58,59], and the high biomass of rhizome and roots developed by B. rupestre offers a large surface in contact with the soil microbiome. The majority of taxa identified were specific to a tissue, or exhibited a strong preference for it, and only five taxa appeared in all tissues (Helotiaceae, Lachnum sp. A, Ophiosphaerella sp., Microdochium sp. and Epicoccum sp.). As expected, the relative abundances of taxonomic orders and families also varied between tissues, with Pleosporales and Phaeosphaeriaceae more abundant in shoots and Helotiales and Hyaloscyphaceae more abundant in rhizomes and roots.
When comparing these results with previous characterizations of fungal endophyte assemblages in perennial temperate grasses based on culture techniques and extensive surveys, we realize the power of the metabarcoding tool, which is capable of identifying a large set of taxa with much less sampling effort. In Dactylis glomerata, 22 and 48 taxa were identified using culturing methods from the leaves and the roots of 120 samples [60], and in Holcus lanatus, 77 and 79 were identified in the same tissues of 77 samples [61]. The results of our survey of the leaves and roots of B. rupestre (2 and 11 taxa identified using the culturing method and 12 and 82 taxa identified using metabarcoding) obtained from a small number of samples in a regional sampling suggest that the real diversity and richness of the endophytic fungal assemblages of the previously studied grass species have probably been underestimated and would increase greatly if the novel metabarcoding techniques were used.

4.2. Culturing vs. Metabarcoding Methods

Modern massive sequencing techniques are gaining ground over traditional culturing methods due to the quantitative power of data that they are able to generate. With equal sampling effort, metabarcoding identified 13, 32 and 71 more taxa than culturing methods in shoots, rhizomes and roots, respectively, which means around ×5.8 times more species identified by the novel technique consistently in the three tissues. In similar studies comparing both methods, the metabarcoding identified ×5.2 and ×4.3 times more OTUs in roots of Elymus repens and Deschampsia flexuosa respectively than the culturing technique [62,63]. A parallel study using 240 plants of B. rupestre recognized 45 fungal endophytic taxa using the culturing method [39], in contrast to the 88 taxa sequenced using metabarcoding from 10 plants in the current survey. In this parallel study, the singletons isolated accounted for 48.9% of the taxa identified via culturing methods and 28.4% of the taxa identified via metabarcoding (with OTUs clustered with a 97% of similarity threshold).
Regarding belowground tissues, four fungal species with high incidence in root tissues were identified via both methodologies: Albotricha sp., Helotiaceae, Lachnum sp. and Mollisiaceae. In shoots, surprisingly, the most frequent shoot endophyte identified via the culturing method, Omnidemptus graminis, was not identified using the metabarcoding technique. O. graminis is a recently described taxon, included in a family associated with ongoing taxonomic changes due to molecular advances [64,65]. Its fast mycelial growth observed on culture plates may suggest the encrypting of other endophytes, but how O. graminis escaped the sequencing process of the metabarcoding is a matter that needs further study.
At this point, some issues need to be discussed when comparing the technical procedures of sequencing in both techniques. The ITS region is a universal and commonly used DNA barcode marker for fungi [66], and in the metabarcoding study undertaken by an external company, only the ITS2 region was amplified to identify the fungal sequences [67,68]. In the culturing method undertaken in the UPNA’s lab, the fungal mycelium was collected and the complete ITS region was amplified (ITS1-5.8S-ITS2), generating longer DNA sequences. We suggest that, since the ITS2 region is more restrictive, taxonomic inconsistencies may occur when short sequences are compared in the databases, thus affecting taxon identification [18]. The percentages of taxa identified for the metabarcoding were in the range 78.1–100%, and 97.6–100% for the culture sequencing, evidencing this restriction and indicating the value of sequencing the complete ITS region to achieve better fungal taxa identification. As a particular example, the taxon proposed as Codinaea sp. reached a match of 99.74% with the complete ITS region sequenced, while this percentage decreased to 97.52% when considering only the ITS2 region. As a consequence, the species was identified as Chaetosphaeriaceae in the metabarcoding, following a more conservative approach, although it was probably the same taxon. Similar situations may occur in other closely related taxa, when there is no reference specimen in the database [43,69]. Taxa identified as Mollisiaceae in our study probably belong to the genera Mollisia and/or Phialocephala [70,71] and the family Helotiaceae to the genera Glarea and/or Hymenoscyphus [72]. Both families were abundant in our samples. Other highly inclusive taxa, such as Pleosporales, raised similar doubts in the identification due to the still high uncertainty in the genetic characterization of the type specimens.
Despite the remarkable differences between the quantitative data generated using the two methods, the characteristics of the fungal assemblages in the different plant communities and tissues types are consistent between methods. Root tissues display the most diverse and rich fungal assemblages, and the endophytic community in plants collected in more disrupted, LD grasslands had the highest diversity and richness. Similar patterns have been reported in previous research in the area, conducted with a much greater sampling effort and using the culturing method [39], that analyzed the fungal assemblages in terms of the ecological mechanisms favored by the different disturbance regimes.

5. Conclusions

The endophytic mycobiome of B. rupestre is composed of a few abundant and many rare species, the identification of which depends on the sampling effort. Despite the restricted sampling effort, the two methodologies produced consistent results and detected the same trends in endophytic richness and diversity among tissues (roots > rhizomes > shoot) and grassland types (low-diversity > high-diversity). Comparatively, the metabarcoding method allowed the identification of a much larger number of taxa than the culturing method and revealed differences in richness and diversity that were not apparent with the culturing method (even when a larger number of samples was collected [39]).
Despite the promising results of the metabarcoding technique, the data indicate that a combination of the two methodologies is the best current option to obtain an adequate characterization of the plant fungal assemblage. In this study, metabarcoding did not identify Omnidemptus graminis, the most abundant fungal endophyte isolated in shoots via culturing; this recently described species is included in a family where there have been repeated taxonomic restructurings as a result of molecular advances [65].

Author Contributions

Conceptualization, M.D., L.S.E. and R.M.C.; methodology, M.D. and L.S.E.; writing, M.D., R.M.C. and L.S.E.; visualization, M.D.; supervision, R.M.C.; All authors have read and agreed to the published version of the manuscript.

Funding

M.D. got funding through UPNA’s doctorate scholarship. This research was supported by “la Caixa” Foundation and CAN foundation (LCF/PR/PR13/51080004), Ecoembes-SEO/Birdlife (Libera 2017), Government of Navarra (STEM research project 2018) and Interreg Sudoe Programme, European Regional Development Fund, European-Union, Open2preserve Project (SOE2/P5/E0804).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The sequencing data have been deposited in GenBank at NCBI with their accession number.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Table with the 88 identified taxa from the metabarcoding method.
Table A1. Table with the 88 identified taxa from the metabarcoding method.
Match Taxon (NCBI)Match Taxon (UNITE)
Accession NumberGreatest Percentage Identity (%) Accession NumberGreatest Percentage Identity (%)Taxon ProposedAccession Number
1Gerronema sp.NR_16627882.37Delicatula integrellaUDB03420399.77Agaricales AOK430888
2Gerronema sp.NR_16627878.9Mycena sp.KT22493489Agaricales BOK430889
3Ramariopsis flavescensNR_11991385.06AgaricalesJX45691695.2Agaricales COK430890
4Gerronema indigoticumNR_16627878.06MycenaceaeKT22493494.29Agaricales DOK430891
5Laccaria aurantiaNR_15411378.57Mycena floridulaMH85666099.35Agaricales EOK430892
6Radulotubus resupinatusNR_15345884.66AgaricomycetesLR86483799.32AgaricomycetesOK430893
7Lachnellula hyalinaNR_16520293.02Albotricha sp.JN995639100Albotricha sp. AOK430894
8Lachnellula hyalinaNR_16520291.59Albotricha sp.JN99563998.71Albotricha sp. BOK430895
9Lachnellula hyalinaNR_16520291.59Albotricha sp.HM136666100Albotricha sp. COK430896
10Funiliomyces biseptatusNR_15986296.39Acremonium sp.MT911439100Ascomycota AOK430897
11Tricladium terrestreNR_16014493.67Ascomycota sp.KR26658493.67Ascomycota BOK430898
12Auricularia scissaNR_12580780.48Oliveonia sp.MT23565297.16AuricularialesOK430899
13Hydnum albidumNR_16402578.7Sistotrema sp.KC96569293.87CantharellalesOK430900
14Codinaeae sp.NR_16879997.52Codinaea sp.MT62658798.35ChaetosphaeriaceaeOK430901
15Chalara hyalocuspicaNR_13756891.25Chalara sp.MK96577898.33Chalara sp.OK430902
16Cladophialophora tengchongensisNR_17239990.07Cladophialophora sp.KP889848100Cladophialophora sp.OK430903
17Coccomyces pinicolaNR_15829583.54Coccomyces dentatusKU98678293.82Coccomyces sp.OK430904
18Conlarium duplumascosporaNR_13838294.9Conlarium sp.MK16465496.85Conlarium sp.OK430905
19Laburnicola centaurearNR_15413193.6Laburnicola sp.MK01855397.95DidymosphaeriaceaeOK430906
20Pseudoseptoria collarianaNR_15656097.63Pseudoseptoria donacisMH85914199.6DothidealesOK430907
21Roussoella thailandicaNR_15571780.56DothideomycetesKJ82795295Dothideomycetes AOK430908
22Pirozynskiella laurisilvicaNR_15348891CapnodialesKX40368891Dothideomycetes BOK430909
23Drechslera sp.NR_16446692.89Drechslera sp.MT81643399.6Drechslera sp.OK430910
24Entoloma luteofuscumNR_15290095.24Entoloma conferendumMT741744100Entoloma sp.OK430911
25Epicoccum phragmosporaNR_16592099.19Epicoccum sp.MW054426100Epicoccum sp.OK430912
26Falciphora oryzaeNR_15397298.86Falciphora oryzaeMH20189899.23Falciphora sp.OK430913
27Glarea lozoyensisNR_13713898.48Glarea sp.KT268823100Glarea sp.OK430914
28Glarea lozoyensisNR_13713895.96Glarea sp.KF617491100HelotiaceaeOK430915
29Loramyces macrosporusNR_13837989.8Loramyces sp.KF61806099.58Helotiales AOK430916
30Loramyces macrosporusNR_13837989.07Mollisia sp.UDB077889099.59Helotiales BOK430917
31Triposporium cycadicolaNR_15658789.71Hymenoscyphus sp.HQ62546199.58Helotiales COK430918
32Bisporella shangrilanaNR_15362897.02HelotialesLR86304399.58Helotiales DOK430919
33Hyaloscypha finlandicaNR_12127992.27Hyaloscypha vraolstadiaeKC87624896.23HyaloscyphaceaeOK430920
34Lachnellula hyalinaNR_16520291.12Lachnum sp.MT91362696.61Lachnum sp. AOK430921
35Lachnum fusiformeNR_15412289.91Lachnum sp.MK80896897.45Lachnum sp. BOK430922
36Proliferodiscus sp.NR_16430486.67Lachnum sp.MH62822899.57Lachnum sp. COK430923
37Leohumicola minimaNR_121307100Leohumicola sp.FM999596100Leohumicola sp.OK430924
38Variabilispora flavaNR_16590686.83HelotialesAY96999495.65LeotiomycetesOK430925
39Menispora ciliataNR_17174099.5Menispora ciliataMH86001799.12Menispora sp.OK430926
40Microdochium phragmitisNR_132916100Microdochium phragmitisMN077456100Microdochium sp.OK430927
41Phialocephala sp.NR_11948290.38Phialocephala sp.MG06646097.88Mollisiaceae AOK430928
42Mollisia scopiformisNR_11946093.22Phialocephala sp.MK80824498.72Mollisiaceae BOK430929
43Mollisia monilioidesNR_17126196.22Phialocephala sp.MT911435100Mollisiaceae COK430930
44Mollisia prismaticaNR_17125891.9Phialocephala sp.MK96578999.57Mollisiaceae DOK430931
45Mollisia asteliaeNR_17303795.15Mollisia sp.MH633925100Mollisiaceae EOK430932
46Mollisia diesbachianaNR_17125996.77Mollisia sp.MT179560100Mollisiaceae FOK430933
47Mortierella gemmiferaNR_11155994.81MortierellaceaeLR86303399.43Mortierella sp.OK430934
48Podila horticolaNR_11157299.09Mortierella sp.DQ38881899.7MortierellaceaeOK430935
49Mycena fulgorisNR_16330093.29Mycena sp.JF51918698.4Mycena sp. AOK430936
50Mycena fulgorisNR_16330093.29Mycena sp.MK96119799.67Mycena sp. BOK430937
51Mycena fulgorisNR_16330093.31Mycena arcangelianaJF90840299.35Mycena sp. COK430938
52Mycena fulgorisNR_16330087.99Mycena sp.UDB020406100Mycena sp. DOK430939
53Mycena fulgorisNR_16330089.64Mycena sp.HQ62548199.32Mycena sp. EOK430940
54Cercospora coniogrammesNR_14726097.89Cercospora sp.MN97052897.89MycosphaerellaceaeOK430941
55Myrmecridium spartiiNR_15537696.25Myrmecridium sp.MW13387698.32Myrmecridium sp.OK430942
56Pseudomassariella vexataNR_16421787.78Fusidium sp.HG936132100NectriaceaeOK430943
57Neoascochyta europaeaNR_13613197.03Neoascochyta europaeaMK19067497.17Neoascochyta sp. AOK430944
58Neoascochyta soliNR_158269100Neoascochyta paspaliMT373264100Neoascochyta sp. BOK430945
59Ophiosphaerella aquaticaNR_15435289.96Ophiosphaerella sp.MH06379998.38Ophiosphaerella sp.OK430946
60Paracamarosporium fagiNR_15431899.18Paracamarosporium fagiMN24422199.18Paracamarosporium sp.OK430947
61Parasola parvulaNR_16050994.43Parasola schroeteriUDB02463999.67Parasola sp.OK430948
62Periconia epilithographicolaNR_15747794.55Periconia sp.MG543950100Periconia sp.OK430949
63Pezicula rhizophilaNR_155659100Pezicula sp.MN385513100Pezicula sp.OK430950
64Parastagonospora poagenaNR_16814797.94Parastagonospora nodorumMN31334999.17PhaeosphaeriaceaeOK430951
65Phragmocephala garethjonessiNR_14763692.21Phragmocephala garethjonessiMN66075292.21Phragmocephala sp. AOK430952
66Phragmocephala garethjonessiNR_14763690.2Phragmocephala atraMN66075290.61Phragmocephala sp. BOK430953
67Phyllachora sp.NR_15661185Phyllachora graminisAF25711196.68PhyllachoralesOK430954
68Pleotrichocladium opacumNR_15569694.21PleosporalesKY22853199.58Pleosporales AOK430955
69Camposporium multiseptatumNR_171863100Camposporium sp.MN758889100Pleosporales BOK430956
70Anteaglonium rubescensNR_16448989.92Lophiostoma sp.EU97728793.17Pleosporales COK430957
71Pseudolachnella fusiformisNR_15428094.24Pseudolachnella fusiformisAB93408094.24Pseudolachnella sp. AOK430958
72Pseudolachnella fusiformisNR_15428093.78Pseudolachnella fusiformisAB93408093.77Pseudolachnella sp. BOK430959
73Puccinia aizaziiNR_15892999.2Puccinia brachypodiiGQ457303100Puccinia sp.OK430960
74Plectosphaerella niemeijerarumNR_15667788.24PlectosphaerellaceaeMK76221588.23Sordariomycetes AOK430961
75Phaeoacrenonium cinereumNR_ 13206680.62SordaryomycetesKP05060480.62Sordariomycetes BOK430962
76Cordana pauciseptataNR_15477188.98SordarialesUDB06704196.69Sordariomycetes COK430963
77Neomyrmecridium guizhouenseNR_17002482.45SordariomycetesLR865231100Sordariomycetes DOK430964
78Atractospora verruculosaNR_15354289.53SordarialesEU754966100Sordariomycetes EOK430965
79Subulicistidium oberwinkleriNR_15906086.42TrechisporalesJF519283100Trechisporales AOK430966
80Subulicystidium oberwinkleriNR_15906080.53TrechisporalesUDB02043683.77Trechisporales BOK430967
81Trichoderma hispanicumNR_13845199.25Trichoderma koningiiMT78195899.24Trichoderma sp.OK430968
82Corinarius hadrocroceusNR_13185479.62TricholomataceaeKX115676100Tricholomataceae AOK430969
83Mycena seminauNR_15417088.82TricholomataceaeMH01664299.67Tricholomataceae BOK430970
84Phialocephala humicolaNR_10357087.7ChaetosphaerialesHM136627100Unidentified AOK430971
85Rhodosporidiobolus fluvialisNR_07708993.65AgaricomycetesUDB0327559100Unidentified BOK430972
86Mycosymbioces mycenaphilaNR_13780785.06HelotialesUDB0779249100Unidentified COK430973
87Mollisia monilioidesNR_17126190.34HelotialesKT20303796.61Unidentified DOK430974
88Linteromyces quintiniaeNR_17198986.25XylarialesMN21878299.62XylarialesOK430975

Appendix B

Table
Table A2. Complete table with all identified taxa in underground tissues of the B. rupestre via metabarcoding method. The relative abundance is based on number of reads, number of OTUs and infected plants (out of five). Shaded taxa were sequenced in both underground tissues.
Table A2. Complete table with all identified taxa in underground tissues of the B. rupestre via metabarcoding method. The relative abundance is based on number of reads, number of OTUs and infected plants (out of five). Shaded taxa were sequenced in both underground tissues.
ROOTRHIZOME
Endophyte TaxonRelative Abundance (%)ReadsOTUsInfected PlantsRelative Abundance (%)ReadsOTUsInfected Plants
LDHDLDHDLDHDLDHDLDHDLDHDLDHDLDHD
Helotiaceae24.5131.0964,13251,9681141162425.9516.4912,2674897941824
Lachnum sp. A35.0812.591,79020,88936205529.139.0113,77126765744
Albotricha sp. A1.7118.83446531,47376335.2350.58247315,0184633
Helotiales A12.436.0332,53410,07240285510.982.77518882325123
Agaricales A3.559.65928116,12433240.0502402010
Mycena sp. A2.030.1753232891024120.880987001010
Mollisiaceae C4.170.4510,91374521410.420.04198132111
Pleosporales A0.564.0414766751243500.040120101
Glarea sp.0.413.9410606589212100.290860101
Mollisiaceae B0.431.891118316113232.351.4411114291322
Mollisiaceae D0.891.072330178212210.783.5636910561222
Chaetosphaeriaceae1.7604608040100.0703301010
Mycena sp. B02.0803479030103.3409930101
Tricholomataceae B01.4802474010204.21012510101
Lachnum sp. B0.381.2710072119117440.870.14411431111
Cantharellales1.30339702010
Parasola sp.00.901503010105.81017250301
Unidentified A1.210.013174192111
Ophiosphaerella sp.0.960.32251353521210.110.0650171111
Mollisiaceae A0.880.4230966643450.030.2813831111
Drechslera sp.0.031.438723882225
Paracamarosporium sp.0.9202419010400.0602801020
Agaricales C0.580.0715141141121
Auriculariales0.50130802010
Tricholomataceae A0.480126601010
Unidentified B0.130.4334071811320.190.2990861111
Pseudolachnella sp. B0.420.0410977211320.1105101010
Trichoderma sp.0.410.011076151121
Didymosphaeriaceae00.5809630201
Conlarium sp.0.350925010300.0703501010
Helotiales C0.34090001010
Phragmocephala sp. A0.28073402040
Agaricales B0.260.02675322111
Menispora sp.0.27070201020
Cladophialophora sp.0.230.046047411330.0401801010
Pleosporales B0.110.22993411212
Pseudolachnella sp. A0.230.01606112141
Mortierellaceae0.110283020100.43020103010
Mollisiaceae F 1.01047601010
Chalara sp.0.060.031685722320.40.09187261111
Sordariomycetes D0.17043501010
Helotiales B0.03087010100.210.73992181111
Agaricomycetes0.12032501010
Ascomycota B0.10266010100.0904101010
Mollisiaceae E00.130216010100.310910101
Microdochium sp.0.070187010300.25011901010
Mortierella sp.0.12030502010
Sordariomycetes B0.11029701010
Pezicula sp.00.1602720102
Coccomyces sp.0.090.02227311121
Albotricha sp. B00.130217020100.060190101
Leohumicola sp.0.060.031665711420.0301301010
Lachnum sp. C0.09022801010
Pleosporales C00.1302130103
Nectriaceae0.08021201010
Phragmocephala sp. B0.060.03147451112
Sordariomycetes C00.1101880102
Ascomycota A0.06016801030
Sordariomycetes E0.06016701010
Trechisporales B00.09121511111
Trechisporales A0.030760102000.280820101
Agaricales D0.06015301010
Myrmecridium sp.0.06014901020
Dothideomycetes A0.040117010100.0603001010
Entoloma sp.0.06014401010
Falciphora sp.0.05014101010
Leotiomycetes0.05013901010
Mycena sp. D0.050.01120141111
Mycena sp. E00.0801330101
Unidentified D0.05013201010
Agaricales E0.05012801010
Dothideomycetes B00.0801270101
Unidentified C0.05012401010
Albotricha sp. C0.05012301010
Helotiales D0.05012101010
Neoascochyta sp. B0.01017010100.22010201030
Mycena sp. C0.030860101000.070220101
Hyaloscyphaceae0.030.0273271121
Periconia sp.0.0205101020
Epicoccum sp.00120101000.090260101
Phaeosphaeriaceae00.020280101
Xylariales sp.0.040.01105121121
100100261673167154 1001004726829692

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Biology 10 01246 g001 550
Figure 1. The appearance of low (a,b) and high (c) diversity grasslands. Location of the Aezkoa Valley in Spain (d) and within the western Pyrenees (e). The two locations (Arpea and Urkulu) where the samples were collected in the Roncesvalles-Selva de Irati SAC (f).
Figure 1. The appearance of low (a,b) and high (c) diversity grasslands. Location of the Aezkoa Valley in Spain (d) and within the western Pyrenees (e). The two locations (Arpea and Urkulu) where the samples were collected in the Roncesvalles-Selva de Irati SAC (f).
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Figure 2. Taxon and OTU accumulation curves for the endophytic community of B. rupestre from metabarcoding (LD: low-diversity grassland, HD: high-diversity grassland). Black line shows the total number of taxa/OTUs, and vertical colored lines indicate the standard deviation.
Figure 2. Taxon and OTU accumulation curves for the endophytic community of B. rupestre from metabarcoding (LD: low-diversity grassland, HD: high-diversity grassland). Black line shows the total number of taxa/OTUs, and vertical colored lines indicate the standard deviation.
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Figure 3. OTU richness and diversity indexes (Shannon and Simpson) for the endophytic community of B. rupestre from different tissues and grasslands (LD: low-diversity grassland, HD: high-diversity grassland). *** p-value < 0.001; * p-value < 0.05 and ns = no significance. Black points represent outliers.
Figure 3. OTU richness and diversity indexes (Shannon and Simpson) for the endophytic community of B. rupestre from different tissues and grasslands (LD: low-diversity grassland, HD: high-diversity grassland). *** p-value < 0.001; * p-value < 0.05 and ns = no significance. Black points represent outliers.
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Figure 4. Taxonomic structure (orders, left and families, right) of fungal endophytes in B. rupestre tissues (shoot, rhizome and root) in the different grassland types (LD: low-diversity grassland, HD: high-diversity grassland).
Figure 4. Taxonomic structure (orders, left and families, right) of fungal endophytes in B. rupestre tissues (shoot, rhizome and root) in the different grassland types (LD: low-diversity grassland, HD: high-diversity grassland).
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Figure 5. Non-metric multidimensional scaling analysis (NMDS) for the endophytic community of B. rupestre according to the effect of tissue (a) and plant diversity (shoot 1b, rhizome 1c & roots 1d). The ellipse formed by the solid line encompasses the fungal composition of B. rupestre tissues (a). The ellipses formed by broken and solid lines encompass the fungal composition of the low and high-diversity grassland, respectively (b–d). The taxon names in the graphs for shoots (b) and roots (d) are the indicator species for the effect of plant diversity.
Figure 5. Non-metric multidimensional scaling analysis (NMDS) for the endophytic community of B. rupestre according to the effect of tissue (a) and plant diversity (shoot 1b, rhizome 1c & roots 1d). The ellipse formed by the solid line encompasses the fungal composition of B. rupestre tissues (a). The ellipses formed by broken and solid lines encompass the fungal composition of the low and high-diversity grassland, respectively (b–d). The taxon names in the graphs for shoots (b) and roots (d) are the indicator species for the effect of plant diversity.
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Table
Table 1. General description of the study sites.
Table 1. General description of the study sites.
Study SiteARPEAURKULU
Type of GrasslandLD = Low DiversityHD = High Diversity
General descriptionLocation−1°10′57″ W–1°14′38″ W
43°2′12″ N43°2′49″ N
Soil classification (WRB)Cambic UmbrisolDystric Cambisol
Altitude (m.a.s.l.)8931256
Slope (%)4045
ManagementBurning recurrenceHigh 1–2 yearsLow 6–7 years
Type of burningLarge grassland areasBush-to-bush
Grazing levelLow to nonexistentModerate to high
B. rupestre cover (%)>80%<25%
Table
Table 2. Fungal endophytes isolated from B. rupestre via the culturing method, their greatest percentage identity in both databases (NCBI and UNITE), the proposed taxon and the available accession number in GenBank.
Table 2. Fungal endophytes isolated from B. rupestre via the culturing method, their greatest percentage identity in both databases (NCBI and UNITE), the proposed taxon and the available accession number in GenBank.
Match Taxon (NCBI)Match Taxon (UNITE)Taxon ProposedGenBank Accession Number
Accessio NumberGreatest Percentage Identity (%) Accession NumberGreatest Percentage Identity (%)
1Lachnellula hyalinaNR_16520290.11Albotricha sp.HM13666698.22Albotricha sp.MW789554
2Codinaea paniculataNR_16629799.74Codinaea sp.MT11823099.74Codinaea sp.MW789567
3Paracamarosporium sp.NR_15431894.28Paracamarosporium sp.MT88213197.6DidymosphaeriaceaeMW789559
4Drechslera sp.NR_15399294.43Drechslera sp.UDB0174425100Drechslera sp MW789560
5Falciphora oryzaeNR_15397296.69Falciphora sp.UDB016291699.76Falciphora sp.MW789558
6Glarea lozoyensisNR_13713896.18Glarea sp.KF61749199.58HelotiaceaeMW789565
7Ilyonectria leucospermiNR_15288999.36Ilyonectria crassaMT294410100Ilyonectria sp.MW789566
8Lachnellula hualinaNR_16520288.89Lachnum virgineumMT13378398.15Lachnum sp.MW789564
9Microdochium phragmitisNR_132916100Microdochium phragmitisMH861162100Microdochium phragmitisMW789562
10Mollisia asteliaeNR_17303796.44Mollisia sp.KJ18868398.69Mollisia sp.MW789555
11Phialocephala spaheroidesNR_12130295.71Loramyces sp.KF61806099.36MollisiaceaeMW789556
12Neoascochyta dactylidisNR_170041100Neoascochyta sp.MT185527100Neoascochyta sp.MW789561
13Omnidemptus graminisNR_164058100Omnidemptus graminisMK487758100Omnidemptus graminisMW789553
14Phialocephala sphaeroidesNR_12130289Phialocephala sp.JN99564698.87Phialocephala sp.MW789563
15Paraphaeosphaeria michotiiNR_15564091.41PleosporalesMN450621100PleosporalesMW789557
Table
Table 3. Total number of reads, OTUs and taxa associated with B. rupestre tissues and the type of grassland where plants were collected (LD: low-diversity grassland, HD: high-diversity grassland).
Table 3. Total number of reads, OTUs and taxa associated with B. rupestre tissues and the type of grassland where plants were collected (LD: low-diversity grassland, HD: high-diversity grassland).
Type of GrasslandTissue
ShootRhizomeRoot
Metabarcoding methodReadsLD313,621468047,268261,673
HD200,050320429,692167,154
Total513,671788476,960428,827
OTUsLD31612165305
HD2461158236
Total35219197340
TaxaLD75102769
HD52102345
Total88153782
Culture methodTaxaLD10236
HD8135
Total152511
Table
Table 4. Culturing and metabarcoding comparison. Total number of reads, OTUs and taxa and match identification for each sample.
Table 4. Culturing and metabarcoding comparison. Total number of reads, OTUs and taxa and match identification for each sample.
SampleCulture MethodMatch MethodsMetabarcoding
Isolated TaxaTaxaOTUsReads
Shoot LD1Neoascochyta sp.✓55233
2Omnidemptus graminis×561639
3Omnidemptus graminis×791619
4Omnidemptus graminis×33207
5 ×44982
Shoot HD6 ×22229
7Omnidemptus graminis×2237
8Omnidemptus graminis×55644
9Omnidemptus graminis×1113
10 ×332281
Rhizome LD1 ×810212,546
2 ×112124,377
3Didymosphaeriaceae×461312
Helotiaceae✓
4 ×57831
5Mollisiaceae✓12418202
Rhizome HD6Helotiaceae✓6225621
7Helotiaceae✓34267
8Phialocephala sp.×111715,380
9 ×452035
10Microdochium phragmitis×11186389
Root LD1Didymosphaeriaceae ×1518049,606
2Falciphora sp.✓295340,482
Codinaea sp.×
3Didymosphaeriaceae×2718470,132
4Mollisia sp.×347352,335
5Pleosporales×319549,118
Didymosphaeriaceae×
Lachnum sp.✓
Root HD6Helotiaceae✓1814162,044
7Mollisiaceae✓2011623,703
8Albotricha sp.✓163212,379
9Albotricha sp.✓205547,814
10Drechslera sp.✓175921,214
Ilyonectria sp.×
Table
Table 5. List of the most abundant taxa in B. rupestre underground tissues. The relative abundance is based on number of reads, number of OTUs and infected plants (out of five). Shaded taxa were sequenced in both underground tissues. The complete table is available in Appendix B.
Table 5. List of the most abundant taxa in B. rupestre underground tissues. The relative abundance is based on number of reads, number of OTUs and infected plants (out of five). Shaded taxa were sequenced in both underground tissues. The complete table is available in Appendix B.
ROOTRHIZOME
Endophyte TaxonRelative Abundance (%)ReadsOTUsInfected PlantsRelative Abundance (%)ReadsOTUsInfected Plants
LDHDLDHDLDHDLDHDLDHDLDHDLDHDLDHD
Helotiaceae24.5131.0964,13251,9681141162425.9516.4912,2674897941824
Lachnum sp. A35.0812.591,79020,88936205529.139.0113,77126765744
Albotricha sp. A1.7118.83446531,47376335.2350.58247315,0184633
Helotiales A12.436.0332,53410,07240285510.982.77518882325123
Agaricales A3.559.65928116,12433240.0502402010
Mycena sp. A2.030.1753232891024120.880987001010
Mollisiaceae C4.170.4510,91374521410.420.04198132111
Pleosporales A0.564.0414766751243500.040120101
Glarea sp.0.413.9410606589212100.290860101
Mollisiaceae B0.431.891118316113232.351.4411114291322
Mollisiaceae D0.891.072330178212210.783.5636910561222
Chaetosphaeriaceae1.7604608040100.0703301010
Mycena sp. B02.0803479030103.3409930101
Tricholomataceae B01.4802474010204.21012510101
Lachnum sp. B0.381.2710072119117440.870.14411431111
Cantharellales1.30339702010
Parasola sp.00.901503010105.81017250301
Unidentified A1.210.013174192111
Ophiosphaerella sp.0.960.32251353521210.110.0650171111
Mollisiaceae A0.880.4230966643450.030.2813831111
Drechslera sp.0.031.438723882225
Table
Table 6. List of taxa in the B. rupestre shoots and their relative abundance based on number of reads, number of OTUs and infected plants (out of five).
Table 6. List of taxa in the B. rupestre shoots and their relative abundance based on number of reads, number of OTUs and infected plants (out of five).
SHOOT
Endophyte TaxonRelative Abundance (%)ReadsOTUsInfected Plants
LDHDLDHDLDHDLDHD
Phaeosphaeriaceae34.0858.80159518842142
Phyllachorales20.49095901040
Puccinia sp.16.05075103010
Neoascochyta sp. A14.940.53699171141
Sordariomycetes A017.4505590101
Mycosphaerellaceae012.7304081001
Microdochium sp.5.581.59261511141
Dothideales3.65017101020
Ophiosphaerella sp.04.6801500101
Epicoccum sp.2.010.7594241111
Helotiaceae1.411.5666501223
Periconia sp.1.5607301010
Lachnum sp. A01.280410101
Phragmocephala sp. B00.630201001
Unidentified C0.2301101010
10010046803204
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Durán, M.; San Emeterio, L.; Canals, R.M. Comparison of Culturing and Metabarcoding Methods to Describe the Fungal Endophytic Assemblage of Brachypodium rupestre Growing in a Range of Anthropized Disturbance Regimes. Biology 2021, 10, 1246. https://doi.org/10.3390/biology10121246

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Durán M, San Emeterio L, Canals RM. Comparison of Culturing and Metabarcoding Methods to Describe the Fungal Endophytic Assemblage of Brachypodium rupestre Growing in a Range of Anthropized Disturbance Regimes. Biology. 2021; 10(12):1246. https://doi.org/10.3390/biology10121246

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Durán, María, Leticia San Emeterio, and Rosa Maria Canals. 2021. "Comparison of Culturing and Metabarcoding Methods to Describe the Fungal Endophytic Assemblage of Brachypodium rupestre Growing in a Range of Anthropized Disturbance Regimes" Biology 10, no. 12: 1246. https://doi.org/10.3390/biology10121246

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Durán, M., San Emeterio, L., & Canals, R. M. (2021). Comparison of Culturing and Metabarcoding Methods to Describe the Fungal Endophytic Assemblage of Brachypodium rupestre Growing in a Range of Anthropized Disturbance Regimes. Biology, 10(12), 1246. https://doi.org/10.3390/biology10121246

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