Foliar fungal endophyte communities remain unaltered under urban air-pollution but differentially express stress-related genes

doi:10.21203/rs.3.rs-3761022/v1

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Foliar fungal endophyte communities remain unaltered under urban air-pollution but differentially express stress-related genes

https://doi.org/10.21203/rs.3.rs-3761022/v1

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Air pollution caused by tropospheric ozone contributes to the decline of forest ecosystems; for instance, coniferous forests dominated by sacred fir, Abies religiosa (Kunth) Schltdl. & Cham., in the peri-urban forests of Mexico City. Individual trees within these forests exhibit variation in their response to ozone exposure, such as the presence or absence of visible symptoms in needles. Using RNA-Seq metatranscriptomic data and ITS2 metabarcoding, we investigated whether such symptom variation correlates with the taxonomic and functional composition of fungal endophytes from needles collected in a highly polluted area in the surroundings of Mexico City. Our findings indicate that ozone-related symptoms do not significantly correlate with changes in the taxonomic composition of fungal endophytes. However, 21 putative proteins were differentially expressed in fungal endophytes from asymptomatic needles, including eight genes previously associated with resistance to oxidative stress. These results suggest that fungal endophyte communities likely play a role in mitigating the oxidative burst caused by tropospheric ozone to sacred fir. Our study illustrates the feasibility of using RNA-Seq data, accessible from global sequence repositories of plants, for the characterization of fungal endophyte communities and gene expression.

metatranscriptomics

metabarcoding

oxidative stress

tropospheric ozone

foliar endophytes

Abies religiosa

Tropospheric ozone (O₃) is a serious threat to human health, biodiversity and ecosystem function [1]. Although the effects of O₃ on plants have been widely studied [2], the impacts of O₃ on fungal endophytes and interactions with stressed host plants are poorly understood. Fungal endophytes are plant-inhabiting fungi that do not produce symptoms of colonization in their host for most of their life cycle [3]. Fungal endophytes exhibit a broad spectrum of life-history strategies, from parasitic to mutualistic [4]. When acting as mutualists, fungal endophytes can enhance their host fitness when they face biotic and abiotic stresses [5], including oxidative stress [6].

The taxonomic and functional composition of fungal endophytes can be affected by several biotic and abiotic stressors. For example, fungal pathogens and bacteria can cause the regreening of senescent leaf tissues in Acer while reducing the overall fungal richness and altering leaf fungal communities [7], although such changes differ among Acer species. In contrast, fungal endophyte communities are not correlated with pathogenic symptoms in Vanilla or Eucalyptus plants affected by pathogenic fungi and a wasp, respectively [8, 9]. However, in the case of Eucalyptus plants, differentially expressed genes associated with secondary metabolism and fungal biomass were observed in resistant and susceptible plants, respectively [8]. Regarding O₃ stress, Liu et al. [10] found a general decrease in overall fungal richness and an increase in pathogenic fungi abundance in plants experimentally exposed to high levels of O₃. Other studies have suggested that fungal endophytes may minimize the extent of damage caused by O₃ to their plant hosts by directly secreting antioxidant agents or by inducing the activation of plant antioxidants [11]. However, no study so far has investigated the effect of O₃ on the gene expression of fungal endophytes.

Tropospheric ozone is particularly harmful in heavily populated cities, like Mexico City. The geographic location and topography of Mexico City, which is situated above 2,200 m asl and encircled by mountains in three directions, concentrate high amounts of O₃ for extended periods of time [12], exacerbating its detrimental effects on urban and peripheral forests [13, 14]. Air pollutants drain across the surrounding mountains, particularly toward the southwest, where forests of the conifer sacred fir (Abies religiosa (Kunth) Schltdl. & Cham.) dominate the native vegetation (Fig. 1a-b). In regions such as the Desierto de los Leones National Park, situated ca. 30 km southwest of Mexico City, O₃ concentrations can be as high as 117 ppb between March and June (the dry-warm season) [12]. These values exceed by five times the recommended level for safeguarding forest trees [15]. In these forests, observed symptoms related to O₃ stress include leaf chlorosis, needle casting, branch loss, and eventually death [16].

Sacred fir is a native coniferous tree to central and southern Mexico and western Guatemala [19], where it forms monodominant forests (Fig. 1a-b). These forests represent one of the few remaining forested areas in the vicinity of Mexico City that provide ecosystem services, such as erosion control, carbon sequestration and water retention [20]. Most of the remaining forests are conserved within Desierto de los Leones National Park, Santa Rosa Xochiac communal land and other local communities territories [21]. Visible signs of O₃ damage initially appear as faint whitish spots on the upper surface of needles, gradually coalescing and developing into larger lesions with brownish-red coloration [22] (Fig. 1c-b). Despite the elevated O₃ levels at Desierto de los Leones National Park, visible needle symptoms can vary among individuals, with some displaying symptoms in several, but not all, branches while others remain completely asymptomatic [22]. The presence of visible symptoms was later associated with decreased terpenoid accumulation and distinct gene expression patterns associated with the response to various abiotic and biotic stresses [23].

Responses to O₃ stress have been extensively documented in plants [24]. However, a comprehensive understanding of the physiological effects of O₃ on plant-associated fungal endophyte communities, as well as its impact on the interactions between plants and their associated mycobiota, have not been fully elucidated. Several studies on the effects of O₃ on fungi have focused on its use as an antifungal agent for seed sterilization and for controlling contamination caused by Aspergillus, Penicillium, and Rhizopus [25, 26]. The antifungal effect of O₃ is attributed to its ability to increase fungal cell membrane permeability, induce damage to fungal cell walls, and thereby alter lipid, carbohydrate, and protein metabolism [26]. These alterations ultimately lead to the excessive accumulation of reactive oxygen species (ROS) and death [25].

RNA-seq data is an increasingly important tool for plant studies on development, stress response, and biotic interactions, among others [27, 28]. RNA extracted from plant tissues most likely contains RNA from the associated microbiome (fungi and bacteria), allowing the study of the gene expression of plant microbiomes [29]. For example, Chialva et al. [30] mined previously generated RNA-Seq data from tomato plants to infer the taxonomy and function of the root microbiota. In this study, we characterized fungal communities and fungal gene expression from O₃-related symptomatic and asymptomatic A. religiosa needles using ITS2 metabarcoding and plant RNA-Seq data generated by Reyes-Galindo [23]. We hypothesized that 1) the presence of visible O₃-associated symptoms is correlated with changes in the taxonomic composition of fungal communities within A. religiosa needles, leading to a reduction in foliar endophyte species richness and relative abundance, along with a simultaneous increase in pathogenic taxa richness and relative abundance; and 2) fungal endophytes present in asymptomatic needles differentially express genes associated with antioxidant mechanisms. Finally, we evaluated whether RNA-Seq data targeting host plants could provide significant taxonomic and functional information about the endophytic fungal communities of these plants.

Sampling and data generation

Samples used in this study were previously collected and processed by Reyes-Galindo [23]. Briefly, we collected samples from the Santa Rosa Xochiac community (19.285 N, 99.301 W), in the buffer zone of the Desierto de los Leones National Park, southwestern Mexico City, in May 2017.

We selected this area due to the elevated mortality rate of A. religiosa individuals, which can be attributed to a combination of factors including air pollution, drought, nutritional deficiencies, insect pathogens, and a lack of forest management after recurrent anthropogenic forest fires [31]. We sampled individuals 10–15 years old within an area of approximately 1 ha. We selected five individuals with reddish needles (i.e., symptomatic) and five other individuals without visible symptoms. We collected symptomatic needles (Fig. 1c) from symptomatic trees and asymptomatic needles (Fig. 1d) from asymptomatic trees. In both cases we collected needles situated on branches from the previous two growth seasons (2015 and 2016). Needles were preserved in RNAlater (Sigma–Aldrich) and stored at -70°C until processing.

We extracted total RNA from 40–45 mg of plant tissue using the Spectrum RNA Plant Kit (SIGMA) and evaluated its integrity via 1% agarose gel electrophoresis. The quality and purity of the RNA were determined using a NanoDrop spectrophotometer based on the 260/280 and 260/230 ratios. Quantification of the RNA concentration was performed using the Qubit RNA assay (Invitrogen). Library construction was performed through mRNA enrichment by polyadenylated RNA capture (polyA-capture), retrotranscription, and subsequent sequencing using a HiSeq 4000 150×2 paired-end protocol (Illumina). The library preparation and sequencing procedures were carried out at the University of Berkeley, USA. Demultiplexing was performed by the sequencing service.

DNA was extracted from 80 mg of tissue ground with liquid nitrogen using a DNeasy Plant Mini Kit (QIAGEN), following the manufacturer's protocol. We amplified the ITS2 region of nuclear rDNA using the fungal-specific primers gITS7ngs and ITS4ngsUNI [32] with the Platinum PCR SuperMix High Fidelity Kit (Invitrogen). PCR conditions were as follows: initial denaturation at 94°C for 1 min, followed by 35 cycles at 94°C for 30 s, 52°C for 30 s, 68°C for 30 s, and a final extension step at 68°C for 7 min. We constructed Amplicon libraries with Nextera adapters (Illumina), which were normalized at equimolar concentrations, purified with Agencourt AMPure XP beads (Beckman) and sequenced using a MiSeq 300×2 paired-end protocol (Illumina) at the University of Arizona Genetics Core, USA.

ITS2 metabarcoding

We ‘denoised’ the sequences and clustered them into operational taxonomic units (OTUs) at 97% similarity using the AMPtk 1.3.0 pipeline [33] following Bermúdez-Contreras et al. [34]. OTU taxonomy was assigned in AMPtk by aligning reference sequences with the UNITE database [35]. We used negative and positive controls according to Nguyen et al. [36]. We removed OTU occurrences that accounted for < 0.5% of sequence counts per sample to eliminate potential sequencing artifacts. To account for the variation in the number of ITS2 reads per sample, we transformed read counts into relative abundances by averaging the number of reads per sample, multiplying them by 1000 and transforming them to the next integer, which was used as counts [37].

RNA-Seq metatranscriptomics

We used Abies religiosa RNA-Seq data generated from Reyes-Galindo [23]. We removed low quality reads and adapters using Trimmomatic v0.39 [38] and verified read quality using FastQC v0.11.8 [39] and MultiQC v1.0.dev0 [40]. We removed the host reads by mapping the reads to the Abies balsamea transcriptome (GenBank Accession: GGJG00000000, Bioproject: PRJNA437248) using the BWA-MEM software [41]. We then assembled both paired and unpaired reads that did not map to the reference transcriptome into contigs using SPAdes v3.13.0 [42], and estimated assembly statistics with QUAST v5.0.2 [43].

Additionally, to maximize the number of assembled genes for functional analyses, we produced five de novo assemblies using host-filtered RNA-Seq reads from all samples: one with rnaSPAdes v3.13.0 [42] with default parameters, and four with Trinity v2.8.5 [44], using the parameters specified in Table S1. We evaluated assembly quality, by measuring the number of genes and isoforms with RSEM v1.3.3 [45], and assembly length with metaquast v3.2 [46]; we then selected the assembly providing the highest number of assembled genes, isoforms, and total length.

To assign taxonomy to RNA-Seq reads, we implemented two widely used algorithms in shotgun metagenomic studies for fungal taxonomic classification: Kraken2 v.2.1.2-Bracken [47, 48] and Kaiju v1.8.0 [49]. We used the RefSeq database, limiting the search parameters to complete fungal genomes/proteins, and performed both analyses using high-quality filtered reads and contigs separately. We evaluated the performance of both algorithms and data types by counting the number of identified taxa at each taxonomic rank and comparing them to the number of identified taxa using ITS2 metabarcoding. The database-algorithm combination with the smallest ratio of unique taxa was selected for further analyses.

Taxonomic profiling

We characterized and visualized fungal communities in R v4.0.2 [50] using the phyloseq [51], ggvenn v0.1.9 [52], microbiome v1.13.12. [53], vegan v2.5-7 [54], eulerr v6.1.1 [55], ggplot2 v3.4.2 [56], DESeq2 v.40.1 [57], indicspecies v1.7.13 [58] and Ampvis2 v2.8.3 [59] packages. Putative fungal endophytes and plant pathogens were assigned to OTUs based on FungalTraits [60] using the "Foliar endophyte" (Endophytic interaction capability) and "Plant pathogenic capacity” annotations. We tested for differences in community observed richness between datasets (ITS2 metabarcoding or RNA-Seq metatranscriptomics), needle conditions (symptomatic or asymptomatic), and guilds (foliar endophyte or plant pathogen). These analyses were carried out using generalized linear models with a Poisson distribution. To test for differences in guild relative abundance between needle condition and dataset, we used a generalized linear model with a binomial distribution. Fungal community composition was visualized using nonmetric multidimensional scaling (NMDS) with Raup–Crick distances, based on OTU presence/absence, after eliminating OTUs that were present in only one sample. Differences in community composition between datasets and conditions were tested with a PERMANOVA with 9999 permutations.

To evaluate if there was an association between specific OTU relative abundances and host needle condition, we calculated the indicator value index (IndVal) [61] with the multipatt function in indicspecies. To assess differences in relative abundance among fungal classes between conditions, we calculated the log2FoldChange using the symptomatic condition as reference and conducted a Wald test with adjusted p-values for multiple comparisons, as implemented in DESeq2.

Functional profiling

We predicted open reading frames (ORFs) with Transdecoder v.5.5.0 [62] annotated them with eggNOG-mapper v2 [63] against the eggNOG 5.0 database [64]. We estimated transcription levels by mapping host-filtered reads to predicted ORFs using Salmon v1.8.0 [65] and imported them into R v4.0.2 using the tximport package [66]. We visualized the functional composition of each sample by evaluating the relative abundance of all COG categories assigned to each ORF using ggplot2 and performed differential expression analyses with DESeq2 [57], using samples of symptomatic needles as reference. We evaluated statistically significant differences in log₂FoldChange using the Wald test as implemented in DESeq2.

Custom scripts for all analyses performed are available at https://github.com/valeriafloral/Abies_fungal_endophytes

Taxonomic profiling

ITS2 metabarcoding produced 59,698–156,065 reads per sample that clustered into 259 OTUs. In turn, between 89.2% and 95.8% of the quality-filtered RNA-Seq reads matched to A. religiosa which, after removal, yielded 12,500,000–21,200,000 nonhost reads per sample; these were assembled into 4,988–20,572 contigs per sample. The Kraken2-Bracken algorithm yielded 24,108–105,092 nonhost reads and 1,485–5,545 contigs that were taxonomically classified into 86 OTUs. The Kaiju algorithm allowed the identification of more taxa, for a total of 482 OTUs detected from contigs and 484 from reads (Fig. S1) but also produced a higher ratio of uniquely identified taxa than Kraken2-Bracken (Fig. S2). For subsequent analyses we therefore selected the dataset from Kraken2-Bracken (reads).

Diversity analyses

A greater number of OTUs was detected when using ITS2 metabarcoding (n = 259) than when using RNA-Seq metatranscriptomics (n = 86). Both methods further retrieved different numbers of taxa at each taxonomic level; indeed, the two datasets barely overlapped, sharing no species, one genus, five families, five orders, five classes and two phyla (Fig. 2a). Based on NMDS ordination and PERMANOVA, we found that the dataset used (RNA-Seq metatranscriptomics or DNA metabarcoding) was significantly correlated with differences in fungal community composition (R² = 1, p-value = 0.001), whereas needle condition remained non-significant (Fig. 2b).

We found that each dataset retrieved a different number of fungal classes (15 for ITS2 metabarcoding and nine for RNA-Seq metatranscriptomics). Nevertheless, both datasets shared five classes that presented the highest relative abundances, including Dothideomycetes, Eurotiomycetes, and Sordariomycetes (Fig. 3). Although no significant differences in fungal class relative abundances were found between needle conditions in any dataset, class Leotiomycetes exhibited a marginally significant increase in asymptomatic needles (p value = 0.06). Using the ITS2 metabarcoding dataset, we observed a significantly greater relative abundance of two OTUs in asymptomatic needles, identified as Lecanorales sp. (IndVal = 0.89, p value = 0.048) and Phaeomoniella sp. (IndVal = 0.89, p value = 0.047), both within Ascomycota. On the other hand, we found no significant differences in the relative abundance of any OTU between needle conditions when using the RNA-Seq metatranscriptomic dataset.

Regarding guilds, we identified 20 OTUs corresponding to foliar endophytes and 78 OTUs corresponding to plant pathogens in the ITS2 metabarcoding dataset. Likewise, using the RNA-Seq metatranscriptomic dataset, we identified 17 OTUs as foliar endophytes and 85 OTUs as plant pathogens. We found no differences in the observed richness between samples from symptomatic and asymptomatic needles for the entire fungal community or for putative fungal endophytes or plant pathogens (Fig. 4a and c). However, the observed richness was significantly different between datasets for all fungi (β = -0.19, p-value = 0.003), plant foliar endophytes (β = 0.78, p-value < 0.001) and plant pathogens (β = 0.31, p-value = 0.036). We also found that regardless of the needle condition and dataset, plant pathogen richness was significantly greater than foliar endophyte richness (β = 0.8, p-value < 0.001).

We did not find differences in the relative abundance of putative foliar endophytes or plant pathogens between samples from symptomatic and asymptomatic needles. However, we did find significant differences between datasets (β = 0.33, p value < 0.001); the interaction between dataset and guild was also significant (β = -0.21, p value < 0.001) (Fig. 4b and d).

Functional profiling

Based on the number of assembled transcripts and genes and assembly length, the best assembly was version 1 from Trinity (Table S1). After filtering by expression level, we were able to annotate as “fungi”, with at least one COG assignment, 89,195 (38.5%) of the 231,173 ORFs predicted. Among these ORFs, 57,345 ORFs were observed for needles of both symptomatic and asymptomatic needles, the former presented 16,277 uniquely predicted ORFs, and the latter presented 13,173 ORFs (Fig. 5a). The relative abundance of COG categories was identical between samples from symptomatic and asymptomatic needles (Fig. S3), although 21 of the annotated ORFs exhibited significant expression differences in expression between needle condition (adjusted p value < 0.05); twenty of such ORFs were upregulated in the asymptomatic needles, while one was downregulated (Fig. 5b). Six of these ORFs (ORF 4, ORF 11, ORF 16, ORF 5, ORF 7 and ORF 19) have been previously reported in upregulated fungal proteins in response to oxidative stress or redox systems (Table 1).

Table 1

Annotation of significant differentially expressed ORFs
ID	COG category	Preferred name	PFAMs	Description	Stress	Target species
1	S	-	-	-	-	-
2	S	-	Grg1	Pfam:DUF2823	Carbon starvation [96]	Podospora anserina
3	G	-	MFS_1	Major facilitator superfamily	Response to toxic compounds [97]	Colletotrichum higginsianum
4	GO	-	DUF1996, WSC	Yeast cell wall integrity and stress response	Osmotic, oxidative and cell wall perturbation [83]	Beauveria bassiana
5	G	GPD	Gp_dh_C, Gp_dh_N	Glyceraldehyde-3-phosphate dehydrogenase	Salinity [84], Oxidative [85, 86]	Aspergillus sydowii, Saccharomyces cerevisiae
6	S	-	But2	Gpi anchored cell wall	-	-
7	S	-	Ferritin_2	Ferritin-like domain	Oxidative [111]	-
8	S	-	CFEM	CFEM domain	Oxidative [91]	Magnaporthe oryzae
9	S	-	-	-	-	-
10	E	-	GMC_oxred_C, GMC_oxred_N	Alcohol oxidase	Oxidative [92]	Aspergillus nidulans
11	S	-	Peroxidase_2	Peroxidase, family 2	Oxidative [112]	-
12	S	-	GFA	Glutathione-dependent Formaldehyde-activating enzyme	-	-
13	G	-	GFA,SLT	Transglycosylase SLT domain	-	-
14	P	qutD	Sugar_tr	-	-	-
15	Q	-	ADH_N, ADH_N_assoc, ADH_zinc_N	Dehydrogenase	-	-
16	S	-	Peroxidase_2	Peroxidase, family 2	Oxidative [112]	-
17	C	ATP1	ATP-synt_ab, ATP-synt_ab_C, ATP-synt_ab_N	ATP synthase	-	-
18	E	-	DHQ_synthase	3-dehydroquinate synthase	-	-
19	S	-	Ferritin_2	Ferritin-like domain	Oxidative [111]	-
20	G	gel4	Glyco_hydro_72, X8	Beta-glucan production	-	-
21	S	-	Arrestin_C,Arrestin_N	Arrestin (or S-antigen), C-terminal domain	-	-

Fungal endophytes from symptomatic and asymptomatic individuals are taxonomically similar

Contrary to our first hypothesis, we did not observe differences in the taxonomic composition of fungal communities of needles from symptomatic and asymptomatic A. religiosa needles when using either ITS2 metabarcoding or RNA-seq metatranscriptomics; these included pathogen and endophyte richness and abundance. Classes Dothideomycetes, Eurotiomycetes and Sordariomycetes were the most abundant in our analyses, regardless of the tree condition or dataset used (Fig. 3). These classes are commonly detected in studies characterizing leaf-inhabiting fungal communities of both angiosperms and gymnosperms, e.g., in Solanum [67], Quercus [68], Vitis [69] and Abies species such as A. koreana [70]. In our study, class Leotiomycetes exhibited a marginally significant increase in observed richness from asymptomatic needles (Fig. 3). This class includes many foliar fungal endophytes that are particularly common in coniferous trees, like Pinus and Abies [71].

We found only two significantly more abundant OTUs in asymptomatic needles, one identified to belong to genus Phaeomoniella and the other belonging to order Lecanorales. Phaeomoniella species include the pathogenic P. chlamydospora, which has been associated with Petri and esca diseases in grapevines [72], and the epiphytic species P. zymoides and P. pinifoliorum, which are acidic tolerants [73]. Additional studies are needed to determine the specific role of this genus in Abies. In turn, the presence of an OTU belonging to order Lecanorales, a mostly lichenized lineage [74], could represent a propagule of a lichen. Nevertheless, other studies using metabarcoding recorded the presence of some unidentified taxa belonging to this order in leaves of Poaceae and Asteraceae species [75].

Liu et al. [10] reported a shift in the fungal endophyte community of the tree Euonymus japonicus, toward a phyllosphere enriched in pathogenic fungi, in plants exposed experimentally to elevated ozone levels (4,500 ~ 11,000 ppb). Although O₃ concentration in the surroundings of Mexico City can reach as high as 117 ppb locally [12], the sampled trees may not have been as exposed as a plant within a greenhouse experiment, where there is no wind that can dissipate air pollutants. Furthermore, other confounding environmental factors, such as drought, may also explain the absence of differences in observed richness or relative abundance of plant pathogens and foliar endophytes between symptomatic and asymptomatic needles.

Interestingly, we found a greater richness and relative abundance of plant pathogens than of foliar endophytes, regardless of needle condition (symptomatic or asymptomatic) (Fig. 4). These differences can be explained by the great diversity of plant pathogens in plant-associated fungal communities [76]. Nevertheless, we must consider that endophytism occurs along a continuum between mutualism and pathogenicity [4]. Thus, it is essential to acknowledge that recognizing a pathogen or an endophytic behavior is not possible with presence data. This is illustrated within the FungalTraits classification [60], where certain OTUs classified as pathogens also exhibit a secondary lifestyle as endophytic fungi (Table S2). Notably, guild assignments (FungalTraits, [60]) require OTU identification at the genus level, which in our case corresponded to only 25.4% (ITS2 metabarcoding) and 45% (for RNA-Seq metatranscriptomics) of the OTUs. This highlights the limited depth of our understanding of the fungal taxa inhabiting A. religiosa needles, and of tropical/subtropical endophytic fungal diversity in general [77]. Since eDNA identifications rely on reference databases (i.e., UNITE [35]), our understanding of foliar fungal communities is limited if their identities and functional guilds are uncertain.

Despite the relatively small sample size and unsaturated accumulation curve of our dataset (Fig. S4), other studies with varying sample efforts and plant systems (e.g., Eucalyptus, Vitis and Vainilla) found no differences in fungal endophyte communities between asymptomatic and symptomatic hosts [8, 9, 78]. This could imply that the same taxa can exhibit different behaviors (i.e., as harmless endophytes or pathogens), depending on the strength of the environmental stressor, as has been previously reported for many fungal endophytes [4]. On the other hand, we found a high heterogeneity in the relative abundance of fungal classes across samples (Fig. 3), which may reflect the complexity of factors affecting the assembly of fungal endophyte communities. While environmental conditions, such as O₃, may indirectly influence endophytic microbial communities by affecting plant defense mechanisms [79], its effect over extended periods of time is perhaps relatively less significant that that other biotic and abiotic factors, such as host species, plant genotype or spatial distance [80–82]. For instance, fungal endophyte communities of grapevine are less influenced by pathogen stress than by subtle differences in environmental conditions, such as edaphic composition, temperature, humidity, and sunlight exposure [78].

Fungal endophytes from asymptomatic needles differentially express genes associated with antioxidant mechanisms

As expected in our second hypothesis, we found genes expressed differentially in asymptomatic needles when compared to symptomatic needles (Fig. 5). Eight of the 20 differentially expressed ORFs (ORF 4, ORF 5, ORF 7, ORF 8, ORF 10, ORF 11, ORF 16, and ORF 19) contained domains previously reported in upregulated proteins in response to oxidative stress or redox systems in fungi. For instance, ORF 4 harbors WSC (cell wall stress-responsive component) domains found in the Wsc1I protein, known for its role in detecting stress signals in Beauveria bassiana [83], including oxidative and osmotic stresses. ORF 5 contains a GPD domain (glycerol-3-phosphate dehydrogenase) that is upregulated in halophilic fungi under high-salinity conditions [84]. This enzyme catalyzes the reduction of NADH to NADPH and the reoxidation to NADH, a cycle associated with the defense of yeast against reactive oxygen species (ROS) during stressful periods [85, 86].

Other overexpressed ORFs related to oxidative stress in asymptomatic needles (ORF 11 and ORF 16) contain a domain of class II peroxidases that catalyze the oxidation of inorganic and organic substrates using H₂O₂ [87]. These peroxidases reduce H₂O₂ and can oxidize a wide range of substrates. Their most prominent association is with lignin decomposition in several white-rot fungi [88], although they are also found in some necrotrophic or hemibiotrophic ascomycetes [89]. Indeed, two Class II peroxidases have been previously found to be upregulated in oxidative stress conditions in the pathogenic fungus Magnaporthe oryzae [90]. An additional overexpressed ORF (ORF 8) contains a CFEM domain that has been found in the Pth11 protein that regulates ROS homeostasis during appressorium formation in Magnaporthe oryzae [91]. Likewise, ORF 10 contains an alcohol peroxidase domain that is significantly induced in Aspergillus nidulans under long-term exposure to oxidative stress [92].

Further upregulated ORFs (ORF 7 and ORF 19) contained ferritin-like domains. Ferritins are recognized in plants and animals for their role in regulating iron levels within cells, and thus preventing ROS formation [93]. While most fungi lack ferritin enzymes [94], some contain ferritin-like sequences in their genomes [95]. It is likely that ferritin-like proteins in fungi play a similar role as those in plants and animals. However, the links between O₃, iron balance, and ferritin-like proteins in fungi remain largely unexplored.

We also detected several upregulated ORFs in needles from asymptomatic needles containing domains associated with other stress responses. For example, ORF 2 (Table 1) has a Grg1 domain whose abundance increases during periods of carbon starvation [96]. Similarly, proteins with the MFS domain, such as ORF 3, play a critical role in transporting both synthetic and naturally occurring toxic compounds [97]. Furthermore, although ORF 20 has not been directly linked to a specific stressor, it contains two domains present in protein gel4, that are relevant for maintaining cell wall integrity in some fungi. Proteins with this domain can also modify cell walls in fungal pathogens, facilitating plant host infection [98].

The only downregulated ORF (ORF 21) found in needles from asymptomatic needles contains two arrestin domains. Arrestins are a family of proteins that play a role in nutrient transport and signaling receptor functions [99]. In fungi, arrestins have been linked to the regulation of several cellular responses. For example, in Cryptococcus neoformans arrestins are involved in controlling processes such as cytokinesis, lipid production for cellular membrane formation, and virulence potential [100]. In Aspergillus nidulans and Arthrobotrys oligospora, arrestins are involved in pH signaling [101, 102]. Furthermore, in A. oligospora, arrestins also influence lifestyle changes (from saprotroph to nematophagous), conidial phenotypes, nuclear distribution within cells and stress resistance [102].

RNA-Seq as a tool for mycobiome studies

Our study illustrates that it is possible to use RNA-Seq data available from global sequence repositories to characterize fungal endophyte communities and gene expression, regardless of the plant phenotype. However, since there is no standardized workflow for analyzing fungal sequences, a careful interpretation of the results and fine-tuned methodologies are needed to address specific questions. The main challenge in using RNA-seq data to characterize endophyte communities lies in the databases available for taxonomic identification using proteins, which often focus on model organisms, neglecting the immense diversity of fungal taxa inhabiting plant tissues. This is crucial because it is difficult to associate potential transcripts with specific taxa.

Other works have used transcripts of the ITS region to identify fungi from RNA-Seq data using ribosomal depletion, which reduces the amount of ribosomal transcripts [103] or by direct amplification of RNA ITS transcripts [104]. However, library construction in our study excluded nonpolyadenylated transcripts, like structural ribosomal RNAs (rRNA) which include the ITS region. Therefore, we did not retrieve sequences from ITS transcripts. The ITS region was selected as the universal fungal barcode [105] and is widely used for the taxonomic identification of fungi using metabarcoding [106]. Therefore, these transcripts would have been very useful for classifying sequences using the available reference databases more accurately (i.e., UNITE [35]). In addition, ITS read numbers would have allowed us to quantify fungal abundances, a task that is currently unreliable using PCR-based methods [107], although care needs to be taken regarding differences in ITS copy numbers among fungal species [108]

Similar to the DNA metabarcoding dataset, we did not find significant differences in the observed richness or relative abundance (Fig. 3) of fungal classes or guilds between symptomatic and asymptomatic needles based on RNA-Seq data. Although the communities recovered using each dataset were different (Fig. 2b), the most representative classes coincided between datasets (Fig. 3). The differences observed could be attributed to the database used to assign taxonomic ranks in each pipeline. For instance, when we examined fungal classes exclusive to the RNA-Seq metatranscriptomics dataset, we found that they comprised predominantly model taxa, such as Saccharomyces or Aspergillus, indicating a potential bias in classification (Table S2). This bias could be reduced by implementing a database that integrates information on environmental taxa, similar to the JGI Mycocosm database [109], which was previously used in metatranscriptomic studies of plant-associated fungi [110]. The two datasets further differed in the greater observed richness and relative abundance of both foliar endophytes and plant pathogens in the RNA-Seq dataset (Fig. 3, which was accompanied by a narrower variance in this dataset.

Tropospheric ozone affects plants at several levels, including their associated mycobiome. We characterized the endophytic fungal community in symptomatic and asymptomatic needles of A. religiosa from a peripheral forest next to Mexico City that has been heavily exposed to O₃ contamination for over 40 years [12]. Our results reveal an intriguing pattern: while symptomatic and asymptomatic needles harbor similar fungal communities, they exhibit varying responses depending on the presence of O₃-related symptoms in their hosts. Notably, our observations indicate that genes associated with oxidative stress response are upregulated in fungi from asymptomatic needles, which suggests that endophytic fungi respond to the oxidative stress triggered by O₃. Moreover, we observed the regulation of other metabolic pathways, such as iron homeostasis, cell integrity maintenance, and detoxification of toxic compounds, which indicated that these mechanisms collectively enhanced the performance of fungal communities within asymptomatic needles, benefiting the host plant. However, further studies are needed to demonstrate that fungal endophytes may affect and even enhance the host performance when O₃ concentrations increase, including controlled experiments where the effects of other cooccurring pollutants can be discarded. Future research exploring cotranscription networks between host and fungal communities could help infer such correlations between host plant and fungal endophyte responses to O₃ stress. Finally, we demonstrated that it is possible to use plant RNA-seq metatranscriptomics to gain broad insight into plant-inhabiting fungi and their response to stress; such approaches have limitations that should be addressed in future studies.

Acknowledgments

This paper is part of the requirements of VSFA for obtaining a M.Sc. degree with a specialization in the field of ecology at Posgrado en Ciencias Biológicas, UNAM. Computational analyses were performed using the High Performance Cluster of CONABIO and Centro de Ciencias Genómicas, UNAM. We thank Luis Alfredo Hernández-Hernández for his assistance in preparing the distribution map of Abies religiosa in Mexico, Leonardo Peraza-Reyes for his comments to improve the manuscript and Jeff Stallman for his review of the English language in this manuscript.

Funding

Financing was granted by FORDECYT 2019-5 Project 308488, PRONACES Sistemas Socioecológicos 2021 Project 319083 and a CONAHCYT graduate scholarship to VSFA.

Author information

Authors and affiliations

Posgrado en Ciencias Biológicas, Universidad Nacional Autónoma de México, Av. Ciudad Universitaria 3000, Z.C. 04510, Coyoacán, Ciudad de México, Mexico

Valeria Stephany Flores-Almaraz

ORCID: 0009-0003-8601-8462

Instituto de Biología, Universidad Nacional Autónoma de México, Av. Ciudad Universitaria 3000, 04510, Coyoacán, Ciudad de México, Mexico

Valeria Stephany Flores-Almaraz

Royal Botanic Gardens Victoria, Birdwood Ave, Melbourne, VIC 3004, Australia

Camille Truong

ORCID: 0000-0002-8510-1761

Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Av. Universidad S/N, 62210. Cuernavaca, Morelos, México

Diana Hernández-Oaxaca

ORCID: 0000-0001-6218-8297

Depto. de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Av. Ciudad Universitaria 3000, 04510, Coyoacán, Ciudad de México, Mexico

Juan Pablo Jaramillo Correa

ORCID: 0000-0001-7617-913X

Verónica Reyes-Galindo

ORCID: 0000-0003-0688-2203

Consejo Nacional de Humanidades Ciencias y Tecnología (CONAHCYT), Avenida Insurgentes Sur 1582, Crédito Constructor, Benito Juárez, Ciudad de México. C.P. 03940. México.

Alicia Mastretta-Yanes

ORCID: 0000-0003-2951-6353

Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO). Avenida Liga Periférico-Insurgentes Sur 4903. Colonia Parques del Pedregal, Tlalpan, Ciudad de México. CP 14010. México

Alicia Mastretta-Yanes

Laboratorios de Micología. Depto. de Biología Comparada, Facultad de Ciencias. Universidad Nacional Autónoma de México, Circuito Exterior s/n, Ciudad Universitaria, Coyoacán, 04510. Ciudad de México, México

Rodolfo Salas-Lizana

Contributions

VSFA, CT, AMY, JPJC, and RSL conceived and designed the analysis; CT and VRG collected and acquired the data, VSFA and DHO performed the bioinformatics analyses; and VSFA, CT, and RSL wrote the paper. All the authors contributed to the data interpretation, drafting, and revision of the manuscript.

Corresponding author

Rodolfo Salas-Lizana

Ethics declarations

Consent to participate

All the authors consented to participate in this publication during the drafting phase and contributed to the revision of the manuscript.

Consent for Publication

All the authors consent to the publication of this research.

Conflict of Interest

The authors declare no conflict of interest.

Competing Interests

The authors declare no competing interests.

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You are reading this latest preprint version

Foliar fungal endophyte communities remain unaltered under urban air-pollution but differentially express stress-related genes

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Version 1

Abstract

Figures

Introduction

Methods

Sampling and data generation

ITS2 metabarcoding

RNA-Seq metatranscriptomics

Taxonomic profiling

Functional profiling

Results

Taxonomic profiling

Diversity analyses

Functional profiling

Discussion

Fungal endophytes from symptomatic and asymptomatic individuals are taxonomically similar

Fungal endophytes from asymptomatic needles differentially express genes associated with antioxidant mechanisms

RNA-Seq as a tool for mycobiome studies

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1