Ecological generalism drives hyperdiversity of secondary metabolite gene clusters in xylarialean endophytes

Jolanta Miadlikowska

Summary Although secondary metabolites are typically associated with competitive or pathogenic interactions, the high bioactivity of endophytic fungi in the Xylariales, coupled with their abundance and broad host ranges spanning all lineages of land plants and lichens, suggests that enhanced secondary metabolism might facilitate symbioses with phylogenetically diverse hosts. Here, we examined secondary metabolite gene clusters (SMGCs) across 96 Xylariales genomes in two clades (Xylariaceae s.l. and Hypoxylaceae), including 88 newly sequenced genomes of endophytes and closely related saprotrophs and pathogens. We paired genomic data with extensive metadata on endophyte hosts and substrates, enabling us to examine genomic factors related to the breadth of symbiotic interactions and ecological roles. All genomes contain hyperabundant SMGCs; however, Xylariaceae have increased numbers of gene duplications, horizontal gene transfers (HGTs) and SMGCs. Enhanced metabolic diversity of endop...

Downloaded from orbit.dtu.dk on: Nov 07, 2023 Ecological generalism drives hyperdiversity of secondary metabolite gene clusters in xylarialean endophytes Franco, Mario E. E.; Wisecaver, Jennifer H.; Arnold, A. Elizabeth; Ju, Yu-Ming; Slot, Jason C.; Ahrendt, Steven; Moore, Lillian P.; Eastman, Katharine E.; Scott, Kelsey; Konkel, Zachary Total number of authors: 36 Published in: New Phytologist Link to article, DOI: 10.1111/nph.17873 Publication date: 2022 Document Version Peer reviewed version Link back to DTU Orbit Citation (APA): Franco, M. E. E., Wisecaver, J. H., Arnold, A. E., Ju, Y-M., Slot, J. C., Ahrendt, S., Moore, L. P., Eastman, K. E., Scott, K., Konkel, Z., Mondo, S. J., Kuo, A., Hayes, R. D., Haridas, S., Andreopoulos, B., Riley, R., LaButti, K., Pangilinan, J., Lipzen, A., ... U'Ren, J. M. (2022). Ecological generalism drives hyperdiversity of secondary metabolite gene clusters in xylarialean endophytes. New Phytologist, 233(3), 1317-1330. https://doi.org/10.1111/nph.17873 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Accepted Article DR MARIO E.E. FRANCO (Orcid ID : 0000-0002-4959-1257) DR RICHARD HAYES (Orcid ID : 0000-0002-5236-7918) PROF. FRANÇOIS LUTZONI (Orcid ID : 0000-0003-4849-7143) DR JANA M U'REN (Orcid ID : 0000-0001-7608-5029) Article type : Regular Manuscript Ecological generalism drives hyperdiversity of secondary metabolite gene clusters in xylarialean endophytes Mario E.E. Franco1, Jennifer H. Wisecaver2, A. Elizabeth Arnold3, Yu-Ming Ju4, Jason C. Slot5, Steven Ahrendt6, Lillian P. Moore1, Katharine E. Eastman2, Kelsey Scott5, Zachary Konkel5, Stephen J. Mondo6, Alan Kuo6, Richard D. Hayes6, Sajeet Haridas6, Bill Andreopoulos6, Robert Riley6, Kurt LaButti6, Jasmyn Pangilinan6, Anna Lipzen6, Mojgan Amirebrahimi6, Juying Yan6, Catherine Adam6, Keykhosrow Keymanesh6, Vivian Ng6, Katherine Louie6, Trent Northen6, Elodie Drula7,8, Bernard Henrissat9,10, Huei-Mei Hsieh4, Ken Youens-Clark1, François Lutzoni11, Jolanta Miadlikowska11, Daniel C. Eastwood12, Richard C. Hamelin13, Igor V. Grigoriev6,14, Jana M. U’Ren1* 1BIO5 Institute and Department of Biosystems Engineering, The University of Arizona, Tucson, Arizona, 85721, United States of America; 2Department of Biochemistry, Purdue University, West Lafayette, Indiana, 47907, United States of America; 3School of Plant Sciences and Department of Ecology and Evolutionary Biology, The University of Arizona, Tucson, Arizona, 85721, United States of America; 4Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 11529, Taiwan; 5Department of Plant Pathology, The Ohio State University, Columbus, Ohio, 43210, United States of America; 6Department of Energy, The Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, United States of America; 7Architecture et Fonction des This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/NPH.17873 This article is protected by copyright. All rights reserved Accepted Article Macromolécules Biologiques, CNRS, Aix-Marseille Université, Marseille, 13288, France; 8INRAE, Marseille, 13288, France; 9Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, DK-2800, Denmark;10Department of Biological Sciences, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; 11Department of Biology, Duke University, Durham, North Carolina, 27708, United States of America; 12Department of Biosciences, Swansea University, Swansea, Wales, SA2 8PP, United Kingdom; 13Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada; 14Department of Plant and Microbial Biology, University of California, Berkeley, California, United States of America. * Corresponding author Email: juren@email.arizona.edu, Phone: (520) 626-0426 Received: 16 August 2021 Accepted: 7 November 2021 Author's ORCID Mario E.E. Franco (0000-0002-4959-1257) Jennifer H. Wisecaver (0000-0001-6843-5906) A. Elizabeth Arnold (0000-0002-7013-4026) Yu-Ming Ju (0000-0002-8202-6145) Jason C. Slot (0000-0001-6731-3405) Steven Ahrendt (0000-0001-8492-4830) Katharine E. Eastman (0000-0002-4438-3854) Kelsey Scott (0000-0003-1378-5348) Sajeet Haridas (0000-0002-0229-0975) Kurt LaButti (0000-0002-5838-1972) Anna Lipzen (0000-0003-2293-9329) This article is protected by copyright. All rights reserved Accepted Article Vivian Ng (0000-0001-8941-6931) Elodie Drula (0000-0002-9168-5214) Bernard Henrissat (0000-0002-3434-8588) Huei-Mei Hsieh (0000-0002-7142-3209) Ken Youens-Clark (0000-0001-9961-144X) François Lutzoni (0000-0003-4849-7143) Jolanta Miadlikowska (0000-0002-5545-2130) Daniel C. Eastwood (0000-0002-7015-0739) Jana M. U’Ren (0000-0001-7608-5029) This article is protected by copyright. All rights reserved Accepted Article SUMMARY ● Although secondary metabolites are typically associated with competitive or pathogenic interactions, the high bioactivity of endophytic fungi in the Xylariales, coupled with their abundance and broad host ranges spanning all lineages of land plants and lichens, suggests that enhanced secondary metabolism might facilitate symbioses with phylogenetically diverse hosts. ● Here, we examined secondary metabolite gene clusters (SMGCs) across 96 Xylariales genomes in two clades (Xylariaceae s.l. and Hypoxylaceae), including 88 newly sequenced genomes of endophytes and closely related saprotrophs and pathogens. We paired genomic data with extensive metadata on endophyte hosts and substrates, enabling us to examine genomic factors related to the breadth of symbiotic interactions and ecological roles. ● All genomes contain hyperabundant SMGCs; however, Xylariaceae have increased numbers of gene duplications, horizontal gene transfers (HGTs), and SMGCs. Enhanced metabolic diversity of endophytes is associated with a greater diversity of hosts and increased capacity for lignocellulose decomposition. ● Our results suggest that as host and substrate generalists, Xylariaceae endophytes experience greater selection to diversify SMGCs compared to more ecologically specialized Hypoxylaceae species. Overall, our results provide new evidence that SMGCs may facilitate symbiosis with phylogenetically diverse hosts, highlighting the importance of microbial symbioses to drive fungal metabolic diversity. Keywords: Ascomycota, endophyte, plant-fungal interactions, saprotroph, specialized metabolism, trophic mode, symbiosis, Xylariales This article is protected by copyright. All rights reserved Accepted Article INTRODUCTION Fungal endophytes inhabit asymptomatic, living photosynthetic tissues of all major lineages of plants and lichens to form one of earth’s most prevalent groups of symbionts (Arnold et al., 2009; Peay et al., 2016). Known from a wide range of biomes and agroecosystems (e.g., U’Ren et al., 2012, 2019), endophytes impact plant health, productivity, and evolution (Rodriguez et al., 2009). Although classified together due to ecologically similar patterns of colonization, transmission, and in planta biodiversity (Rodriguez et al., 2009), foliar fungal endophytes represent a diversity of evolutionary histories, life history strategies, and functional traits (Porras-Alfaro & Bayman, 2011). Despite the recent surge of interest in plant microbiome research (Trivedi et al., 2020) the genomic and molecular mechanisms foliar fungal endophytes employ to establish symbiotic host associations remain largely unknown. Global, large-scale surveys of phylogenetically diverse plant and lichen hosts have revealed that many foliar endophyte species preferentially associate with particular host species and lineages, resulting in host structured endophyte communities at local to global scales (e.g., U’Ren et al., 2019). In contrast, endophytic fungi in the Xylariales (Sordariomycetes, Pezizomycotina, Ascomycota) appear unique in that they typically have broad host ranges that span multiple lineages of land plants (e.g., angiosperms, conifers, lycophytes, ferns, and mosses) as well as green algae and cyanobacteria within lichen thalli (e.g., Arnold et al., 2009; U’Ren et al., 2016). In contrast, the majority of described Xylariales species are typically associated with angiosperms as wood- or litter-degrading saprotrophs or woody pathogens (Hsieh et al., 2005, 2010). Although the genetic factors that determine foliar endophyte host range are unknown, research on fungal pathogens has shown that host specificity is often determined by the presence of avirulence proteins (i.e., effectors), proteinaceous host-specific toxins, and secondary metabolites (SMs) (Li et al., 2020). Horizontal gene transfer (HGT) of these host-determining genes frequently alters and/or expands pathogen host range (Li et al., 2020). Xylariales genomes sequenced to date have revealed a rich repertoire of secondary metabolite gene clusters (SMGCs) (Wibberg et al., 2021), often exceeding the numbers reported for saprotrophic fungi well-known for their SM production (Aspergillus, Penicillium) (Nielsen et al., 2017; Drott et al., 2021). Previously, it was postulated that intense competition with diverse communities of soil This article is protected by copyright. All rights reserved Accepted Article organisms increases selection to maintain and diversify SMGCs (Slot, 2017). However, the high bioactivity of Xylariales fungi (>500 SMs reported to date; (Becker & Stadler, 2021)), their broad host ranges as endophytes and ability to persist in leaf litter as saprotrophs that decompose lignocellulose (U’Ren et al., 2016; U’Ren & Arnold, 2016), led us to hypothesize that enhanced secondary metabolism might play a role in facilitating ecological generalism in both substrate use and the phylogenetic breadth of their symbiotic associations with plants and lichens. To test this hypothesis, we examined the genomic factors associated with endophyte host range and ecological roles (i.e., endophytic, pathogenic, and saprotrophic) across 96 genomes of Xylariales, including 88 newly sequenced genomes of endophytes, saprotrophs, and plant pathogens within two major clades of Xylariales (Hypoxylaceae and Xylariaceae s.l.). We paired genomic data with extensive metadata on endophyte host associations, geographic distributions, and substrate usage gleaned from a collection of >6,000 xylarialean endophytes isolated from phylogenetically diverse plants and lichens across North America (U’Ren et al., 2016), enabling us to examine for the first time the genomic factors related to the breadth of symbiotic interactions and ecological roles in this dynamic and ecologically important fungal clade. MATERIALS AND METHODS Fungal strain selection. We sequenced genomes of 44 endophytic taxa (U’Ren et al., 2012; U’Ren & Arnold, 2016) and 44 named taxa of Xylariaceae s.l. and Hypoxylaceae representing ca. 24 genera and 80 species, as well as an additional two undescribed species of endophytic Xylariales (Pestalotiopsis sp. NC0098 and Xylariales sp. AK1849) included in the outgroup (Table S1). Isolates were selected based on their phylogenetic position and ecological mode from (U’Ren et al., 2016) Although classifying fungal ecological modes broadly as ‘‘endophytic” or ‘‘saprotrophic” based on the condition of the tissue from which they are cultured is often insufficient to adequately define their ecological roles, for the purposes of this study, isolates cultured from living host tissues (either plant or lichen) are referred to as endophytes even if other isolates in the same fungal operational taxonomic unit (OTU) were found in non-living tissues as well. Isolates were defined as saprotrophs only if all isolates in the OTU were cultured from non-living plant tissues such as senescent leaves or leaf litter (U’Ren et al., 2016). To minimize the effect of phylogeny when assessing the impact of This article is protected by copyright. All rights reserved Accepted Article ecological mode on genome evolution, we also selected 15 pairs of closely related sister taxa with contrasting ecological modes (i.e., endophyte vs. non-endophyte) (U’Ren et al., 2016). For reference species that lacked host and substrate metadata, ecological modes were estimated based on information for that species in the literature as described in U’Ren et al. (2016). DNA and RNA purification. We used two different mycelial growth and cultivation techniques to obtain DNA for either Illumina or PacBio Single-Molecule Real-Time (SMRT) sequencing. DNA isolations were performed using modified phenol:chloroform extractions (Supporting Information Methods S1). RNA was extracted for each isolate with the Ambion Purelink RNA Kit (Thermo Fisher Scientific, Waltham, MA). DNA and RNA were quantified with a Qubit fluorometer (Invitrogen) and sample purity was assessed with a NanoDrop (BioNordika). RNA was treated with DNase (Thermo Fisher Scientific) following the manufacturer’s instructions and RNA integrity was assessed on a BioAnalyzer at the University of Arizona Genomics Core Facility. Genome and transcriptome sequencing and assembly. Genomes were generated at the Department of Energy Joint Genome Institute using Illumina and PacBio technologies (Table S1). For 66 isolates, Illumina standard shotgun libraries (insert sizes of 300bp or 600bp) were constructed and sequenced using the NovaSeq platform. Raw reads were filtered using the JGI QC pipeline. An assembly of the target genome was generated using the resulting non-organelle reads with SPAdes (Bankevich et al., 2012). PacBio SMRT sequencing was performed for 22 isolates of Xylariaceae and Hypoxylaceae, as well as Xylariales spp. NC0098, and AK1849 on a PacBio Sequel. Library preparation was performed either using the PacBio Low Input 10kb or PacBio >10kb with AMPure Bead Size Selection. Filtered sub-read data were processed with the JGI QC pipeline and de novo assembled using Falcon (SEQUEL) or Flye (SEQUEL II). Stranded RNASeq libraries were created and quantified by qPCR and transcriptome sequencing was performed on an Illumina NovaSeq S4. For both Hypoxylaceae and Xylariaceae ~25% of genomes were sequenced with PacBio, although a higher proportion of endophyte genomes were sequenced with PacBio than Illumina (43% vs. 28% overall). Genome completeness was assessed by Benchmarking Universal Single-Copy Orthologs (BUSCO) v2.0 using the "eukaryota_odb9" (2016-11-02) dataset (https://doi.org/10.1093/bioinformatics/btv351). This article is protected by copyright. All rights reserved Accepted Article Genome annotation. Gene prediction and annotation was performed using the JGI pipeline (Kuo et al., 2014; Grigoriev et al., 2014) (Supporting Information Methods S1). Predicted genes were annotated using functional information from InterPro (Mitchell et al., 2019), PFAM (El-Gebali et al., 2019), Gene Ontology (GO) (The Gene Ontology Consortium, 2019), Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa et al., 2006), Eukaryotic Orthologous Groups of Proteins (KOG) (Tatusov et al., 2003), the Carbohydrate-Active EnZymes database (CAZy) (Lombard et al., 2014), MEROPS database (Rawlings et al., 2016), the Transporter Classification Database (TCDB) (Saier et al., 2016), SignalP v3.0a (Nielsen, 2017), and EffectorP 2.0 (Sperschneider et al., 2018). CAZymes involved in the degradation of the plant cell wall were classified by substrate (Kameshwar et al., 2019). We examined repetitive elements using RepeatScout (Price et al., 2005), which identifies novel repeats in the genomes, and RepeatMasker (http://repeatmasker.org), which identifies known repeats based on the Repbase library (Bao et al., 2015). Candidate effectors were predicted using EffectorP v2.0 (Sperschneider et al., 2018). Orthogroup prediction, functional annotation, and ancestral state reconstruction. For comparative analyses, data from an additional eight taxa in Xylariaceae sensu lato (Wu et al., 2017) and 23 additional genomes of Sordariomycetes were obtained from MycoCosm (Grigoriev et al., 2014) (Table S1). Orthologous gene families (i.e., orthogroups) for all 121 genomes (ingroup and outgroup) were inferred by OrthoFinder v2.3.3 (Emms & Kelly, 2019), which was executed using DIAMOND v0.9.22 (Buchfink et al., 2015) for the all-versus-all sequence similarity search and MAFFT v7.427 (Katoh & Standley, 2013) for sequence alignment. Orthogroups were assigned functional annotations with KinFin v1.0 (Laetsch & Blaxter, 2017), which performs a representative functional annotation of the orthogroups based on both the proportion of proteins in the group carrying a specific annotation as well as the proportion of taxa in the cluster with such annotation. KinFin also was used to perform network analysis of orthogroups, classify orthogroups and SMGCs into isolate-specific, clade-specific (Hypoxylaceae and Xylariaceae), and universal (i.e., orthogroups present in all taxa) categories, and to identify orthogroups that were significantly enriched or depleted in the Xylariaceae or Hypoxylaceae using the Mann-Whitney U test. We used Count v10.04 (Csurös, 2010) with the This article is protected by copyright. All rights reserved Accepted Article unweighted Wagner parsimony method (gain and loss penalties both set to 1) to assess changes in the size of orthologous gene families over evolutionary time. Orthogroup annotations were also used to reconstruct the ancestral gene content for subsets of orthologous gene families corresponding to different functional categories. Phylogenomic analysis. Protein sequences of 1,526 single-copy orthogroups defined by OrthoFinder were aligned using MAFFT v7.427 (Katoh & Standley, 2013), concatenated, and analyzed using maximum-likelihood in IQ-TREE multicore v1.6.11 (Nguyen et al., 2015) with the Le Gascuel (LG) substitution model. Node support was calculated with 1,000 ultrafast bootstrap replicates. Additional phylogenomic analyses with different models of evolution, gene sets, and outgroup taxa resulted in nearly identical topologies (Supporting Information Methods S1). Metabolic gene cluster prediction. SMGCs were predicted using antiSMASH version 5.1.0 (Blin et al., 2019) setting the strictness to 'relaxed' and enabling 'KnownClusterBlast', 'ClusterBlast', 'SubClusterBlast', 'ActiveSiteFinder', 'Cluster Pfam analysis' and 'Pfam-based GO term annotation'. Clinker and clustermap.js were used to visualize and compare SMGCs (Gilchrist & Chooi, 2021). Sequence similarity network analysis of the SMGCs was performed using BiG-SCAPE v1.0.1 (Navarro-Muñoz et al., 2020). BiG-SCAPE was executed under the hybrid mode, enabling the inclusion of singletons and the SMGCs from the MIBiG repository version 1.4 (Medema et al., 2015). The output from BiG-SCAPE was incorporated into KinFin (Laetsch & Blaxter, 2017) to visualize gene content similarity as network graphs and examine SMGC distribution across clades. We used a custom pipeline (https://github.com/egluckthaler/cluster_retrieve) to examine fungal metabolic gene clusters involved in the degradation of a broad array of plant phenylpropanoids (Gluck-Thaler et al., 2018) (hereafter, catabolic gene clusters: CGCs). Cluster_retrieve searches for multiple "cluster models'' containing one of 13 anchor genes (Gluck-Thaler et al., 2018). Homologous genes in each locus were defined by a minimum BLASTp (v2.2.25+) bitscore of 50, 30% amino acid identity, and target sequence alignment 50-150% of the query sequence length. Homologs of query genes were considered clustered if separated by < 7 intervening genes. However, CGCs often share many gene families among classes, resulting in overlapping and adjacent clusters detected by different This article is protected by copyright. All rights reserved Accepted Article cluster profile searches. As the majority of CGCs have not been functionally characterized, rather than splitting loci by functional annotation alone, we empirically assessed the spatial distribution of genes in 25 contigs that contained multiple consolidated cluster predictions. Based on these results, we selected a gap size of 30kb to define discrete clusters (i.e., clusters on the same contig were consolidated if separated by less than 30kb). Homologous cluster families across genomes were inferred using a modified version of BiG-SCAPE (Navarro-Muñoz et al., 2020) (i.e., adding catabolic anchor genes to “anchor_domains.txt” and manually tuning the “Others” cluster type model parameters until known related clusters, such as quinate dehydrogenase clusters, merged into families). Tuning resulted in the values 0.35 for the Jaccard dissimilarity of cluster Pfams, 0.63 for Pfam sequence similarity, 0.02 adjacency index, and 2.0 anchor boost. Detection of HGT events. We used the Alien Index (AI) pipeline (https://github.itap.purdue.edu/jwisecav/wise) (Wisecaver et al., 2016; Verster et al., 2019) to identify HGT candidate genes. Each predicted protein sequence was queried against a custom protein database using Diamond v0.9.22.123 (Buchfink et al., 2015). The custom database consisted of protein sequences from NCBI RefSeq (release 98) (O’Leary et al., 2016), the Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP) (Keeling et al., 2014), and the 1000 Plants transcriptome sequencing project (OneKP) (Matasci et al., 2014). Diamond results were sorted based on the normalized bitscore (nbs), where nbs was calculated as the bitscore of the single best high scoring segment pair (HSP) in the hit sequence divided by the best bitscore possible for the query sequence (i.e., the bitscore of the query aligned to itself). To identify HGT candidates, an ancestral lineage is first specified, and the AI score calculated using the formula: AI=nbsO-nbsA, where nbsO is the normalized bit score of the best hit to a species outside of the ancestral lineage and nbsA is the normalized bit score of the best hit to a species within the ancestral lineage. AI scores range from -1 to 1, being greater than zero if the predicted protein sequence had a better hit to species outside of the ancestral lineage and can be suggestive of either HGT or contamination (Wisecaver et al., 2016). To identify HGTs present in multiple species, a recipient sub-lineage within the larger ancestral lineage may also be specified to identify their shared HGT candidates (Fig. S1). All hits to the recipient lineage are skipped so as not to be included in the This article is protected by copyright. All rights reserved Accepted Article nbsA calculation. To identify candidate HGTs acquired from distant gene donors (e.g. viruses, bacteria, or plants) we performed a first AI screen using Ascomycota (NCBI:txid4890) and Xylariomycetidae (NCBI:txid 222545) as the ancestral and recipient lineages, respectively (Fig. S1). To identify candidate horizontal transfers of genes predicted by antiSMASH to be in a SMGC from more closely related donors (e.g., other filamentous fungi), we ran the AI pipeline a second time using Xylariales (NCBI:txid 37989) as the ancestral lineage and manually curated subclades (see Table S1) as recipient lineages (see Fig. S1). Genes from both the first (i.e., all genes, distant donors) and second (i.e., SMGC genes, closely related donors) Genes were considered putative HGT candidates if they passed the following filters: (i) AI score of > 0, (ii) significant hits to at least 25 sequences in the custom database, and (iii) at least 50% of top hits to sequences outside of the ancestral lineage. Candidates from the first AI screen were further validated using phylogenetic analyses (described below) and designated as either high or low confidence HGT. Full-length proteins corresponding to the top < 200 hits (E-value < 1 × 10-3) to each AI screen 1 candidate were extracted from the custom database using esl-sfetch (Eddy, 2009). As our initial query-based trees often lacked sufficient taxon sampling to assess HGT, we combined all orthogroup sequences with all extracted top hits to each AI candidate. Sequences were aligned using MAFFT v7.407 using --auto (Katoh & Standley, 2013) and the number of well aligned columns was determined with trimAL v.1.4. rev15 using its gappyout strategy (Capella-Gutiérrez et al., 2009). Only alignments with ≥50 retained columns after trimAL were retained for phylogenetic analysis. Phylogenetic trees were constructed with IQ-TREE v1.6.10 (Nguyen et al., 2015) in a single run with ModelFinder (Kalyaanamoorthy et al., 2017) and SH-aLRT combined with ultrafast bootstrapping analyses (1,000 replicates each). Phylogenies were visualized using iTOL v4 (Letunic & Bork, 2019). Each phylogenetic tree was manually curated to verify HGT with either high or low confidence. High confidence HGT events had to meet the following criteria: (i) the association between donor and recipient clades was supported by ultrafast bootstrap >= 95 and (ii) recipient clade consisted of sequences from two or more species. If the candidate met one of the two criteria, HGT was considered lower confidence. Statistical analyses. To assess whether genes within different functional categories are associated with endophytic ecological mode we performed phylogenetically independent contrasts (PICs) This article is protected by copyright. All rights reserved Accepted Article (Felsenstein, 1985) with the function 'brunch' of the package 'caper' version 1.0.1 (Orme et al., 2012) in R version 3.6.1. All other statistical analyses were done in R version 3.6.1 or JMP version 15.1 (SAS Institute Inc., Cary, NC). RESULTS AND DISCUSSION Genomes of 96 Xylariales taxa correspond to the previously recognized family Xylariaceae (Ju & Rogers, 1996) that was recently split into multiple families (Hypoxylaceae, Xylariaceae, Graphostromataceae, Barrmaeliaceae (Voglmayr et al., 2018; Wendt et al., 2018) (Fig. 1a; Fig. S2). Here, we use the term xylarialean to refer to this monophyletic clade within the Xylariales. In addition, because our analyses revealed seven undescribed endophytic isolates in five distinct clades (i.e., clades E2, E4, E5, E6, and E6; Fig. S2) nested between the Graphostromataceae and Xylariaceae sensu stricto, we refer to the sister clade to Hypoxylaceae as Xylariaceae sensu lato (hereafter, Xylariaceae) following Voglmayr et al. (2018) (Fig 1). Genome sequencing yielded eukaryotic BUSCO values ≥95% (Table S1). Xylarialean genomes ranged in size from 33.7-60.3 Mbp (average 43.5 Mbp; Fig. S3; Table S1) and contained ca. 8,000-15,000 predicted genes (mean 11,871; Fig. S3), congruent with average genome and proteome sizes of other Pezizomycotina (Shen et al., 2020). The percentage of repetitive elements per genome ranged from <1- 24% (average 1.6%; Table S2), but unlike mycorrhizal fungi (Miyauchi et al., 2020), repeat content was not corrected with ecological mode (Fig. S3). Xylariaceae and Hypoxylaceae genomes contain hyperdiverse metabolic gene clusters. To investigate the diversity and composition of metabolic gene clusters in xylarialean genomes, we used antiSMASH (Blin et al., 2019) to mine genomes for SMGCs, as well as a custom pipeline to examine catabolic gene clusters (CGCs) involved in fungal degradation of a broad array of plant phenylpropanoids (Gluck-Thaler et al., 2018). Across 96 xylarialean genomes we predicted a total of 6,879 putative SMGCs (belonging to 3,313 cluster families) and 973 putative CGCs (belonging to 190 cluster families) (Tables S3 and S4). In comparison, recent large-scale analyses predicted 3,399 SMGCs (in 719 cluster families) across 101 Dothideomycetes genomes (Gluck-Thaler et al., 2020) and 1,110 CGCs across 341 fungal genomes (Gluck-Thaler & Slot, 2018). Only 25% of predicted This article is protected by copyright. All rights reserved Accepted Article SMGCs (n = 1,711 belonging to 816 cluster families) had BLAST hits to 168 unique MIBiG (Medema et al., 2015) accession numbers (Table S3). Total SMGCs diversity in the Xylariaceae and Hypoxylaceae is reflected in a high number of SMGCs per genome: the average number of SMGCs per genome was 71.2 (median 68), which is significantly higher than the average for other fungi in the Pezizomycotina (mean 42.8; Fig. 1b). At least eight xylarialean genomes contained more than 100 predicted SMGCs, with a maximum of 119 in Anthostoma avocetta NRRL 3190 (Fig. 1b; Table S3). In comparison, a recent study of 24 species of Penicillium found an average of 54.9 SMGCs per genome, with a maximum number of 78 SMGCs observed in P. polonicum (Nielsen et al., 2017). Genomes of Xylariaceae and Hypoxylaceae contained on average 3.3X more CGCs per genome (average 10.1; Table S4) compared to other genomes of Pezizomycotina (average 3.0 (Gluck-Thaler et al., 2018)). Every xylarialean genome contained SMGCs for the production of polyketides (PK; 2,871 total), nonribosomal peptides (NRP; 2,482 total), and terpenes (1,322 total; Fig. 1b; Table S3). SMGCs for ribosomally synthesized and post-translationally modified peptides (RiPPs) and hybrid NRP-PK compounds occurred less frequently (Fig. 1b). The most widely distributed and abundant CGCs were pterocarpan hydroxylases (n = 93), putatively involved in isoflavonoid metabolism (Fig. 1d,e; Table S5). CGCs involved in the breakdown of plant salicylic acid (Ambrose et al., 2015) (n = 251 salicylate hydroxylases) and plant flavonoids (n = 170 naringenin 3−dioxygenases) also were abundant (Fig. 1d,e). CGCs classified into nine other categories (e.g., phenol 2-monooxygenase, quinate dehydrogenase) (Gluck-Thaler et al., 2018) occurred more rarely (Table S4). Vanillyl alcohol oxidases, which were previously shown to be enriched in genomes of soil saprotrophs (Gluck-Thaler et al., 2018), were absent in xylarialean genomes. Consistent with the hyperdiversity of SMGCs in the Hypoxylaceae and Xylariaceae, we observed that only ca. 10% of SMGCs were shared among genomes from both Xylariaceae and Hypoxylaceae (Fig. 1c), and no SMGCs were universally present in both clades (Table S3). On average, 21.4% and 28.2% of SMGCs per genome were unique to either a taxon in the Hypoxylaceae or the Xylariaceae, respectively (range 0-82%; Fig. 1c; Table S4), but no SMGCs were universally present within either clade. For most isolates, the majority of SMGCs were unique (i.e., 'isolate specific'; Fig. 1c). Isolate specific SMGCs represented an average of 36.6% (SD ± 21.1) of the This article is protected by copyright. All rights reserved Accepted Article clusters per genome (range 0-85.7%; Fig. 1c). Even when multiple isolates of the same species were compared (e.g., Nemania serpens clade) 30-41% of the SMGCs appeared specific to a single isolate (Fig 1b; Table S3), similar to intraspecific SMGC variation in Aspergillus flavus (Drott et al., 2021). Impact of HGT on xylarialean genome evolution. To assess the role of HGT in shaping the genome evolution of Xylariaceae and Hypoxylaceae we performed two Alien Index (AI) analyses (Alexander et al., 2016; Wisecaver et al., 2016; Gonçalves et al., 2018). The first AI screen—designed to detect candidate HGTs from more distantly related donor lineages (e.g., bacteria, plants)—flagged 4,262 genes representing 647 orthogroups (Table S5). Using a custom phylogenetic pipeline (see Methods) we manually validated 168 of these genes as likely HGT events to Xylariaceae and Hypoxylaceae. Based on branch support and the presence of multiple xylarialean taxa in the recipient clade, we deemed 92 of these genes as high-confidence HGTs and the remaining 76 as lower confidence HGTs (Fig. 2; Table S5). Similar to previous studies (Marcet-Houben & Gabaldón, 2010; Lawrence et al., 2011), the majority of high-confidence HGTs are predicted to have been acquired from bacteria (n = 86) (Fig. 2). Overall, 66% of genes identified as HGT from bacterial donors do not contain introns (compared to 6% of genes across 121 genomes). Other donor lineages include viruses (n = 3), Basidiomycota (n = 2), and plants (n = 1) (Fig. 2; Table S5). On average, xylarialean genomes had 16.2 high-confidence HGT events per genome (range: 7-30; Table S5). The highest number of highconfidence HGT events per genome occurred in the genome of Xylaria flabelliformis CBS 123580 (n = 30). HGT candidate genes were typically distributed across taxa in numerous diverse clades (n = 85 of 92 genes) rather than in monophyletic clades (Fig. 2). For example, an Enoyl-acyl carrier protein reductase protein (EC 1.3.1.9)—a key enzyme of the type II fatty acid synthesis (FAS) system (Massengo-Tiassé & Cronan, 2009)—occurred in bacteria (putative donor) and four distantly related recipient taxa: Xylariales sp. PMI 506, Hypoxylon rubiginosum ER1909; H. cercidicola CBS 119009; H. fuscum CBS 119018 (HGT0001; Table S5). Multiple evolutionary scenarios could result in patchy taxonomic distributions. For example, multiple fungi could have independently acquired the same gene from closely related bacterial donors (Marcet-Houben & Gabaldón, 2010). Alternatively, an initial HGT from bacteria to fungi may have been followed by fungal-fungal HGTs. In total, 38 HGT This article is protected by copyright. All rights reserved Accepted Article candidate genes occurred in genomes of both Sordariomycetes outgroup and Xylariales genomes, 28 were found in only Xylariales genomes, and 26 were only observed in genomes of Xylariaceae and Hypoxylaceae (Fig. 2; Table S5). Functional annotation revealed the majority of candidate HGT genes were associated with at least one type of annotation (i.e., 95% of the highly confident and 82% of the ambiguous events; Table S5). Six high-confidence HGT candidate genes were annotated as CAZymes, including three predicted plant cell wall degrading enzymes (PCWDEs) transferred from bacteria to diverse Xylariales (Fig. 2). No genes predicted in CGCs were identified as candidate HGTs, consistent with convergent evolution to result in similar clustering of fungal phenolic metabolism genes (GluckThaler et al., 2018). However, 43% of candidate HGT genes were predicted to be part of a SMGC (i.e., 40 of 92) (Fig. 2; Tables S3 and S5). These include 13 genes predicted to have a biosynthetic function, such as a putative FsC-acetyl coenzyme A-N2-transacetylase (HGT076; Table S5), which is part of the siderophore biosynthetic pathway in Aspergillus implicated in fungal virulence (Blatzer et al., 2011). Due to the high prevalence of HGT among genes predicted to be part of SMGCs, we performed a second AI screen to detect intra-fungal HGT events of genes within the boundaries of SMGCs (n = 93,066 genes) (see Methods; Fig. S1). AI identified 1,148 genes in 660 SMGCs (belonging to 594 cluster families) that were putatively transferred from other fungi to members of the Xylariales (Table S5). Candidate HGT genes were primarily for polyketide and nonribosomal peptide production (518 PKSs, 270 NRPSs, and 180 PKS-NRPS hybrid clusters). In addition, >75% of hits to MIBiG contain genes identified by AI analyses as putative HGTs (see Fig. S4, bottom). SMGCs with HGT candidate genes include those with 100% similarity to MIBiG accessions from Aspergillus, Fusarium, and Parastagonospora involved in mycotoxin (e.g., cyclopiazonic acid, alternariol, fusarin) and antimicrobial compound (asperlactone, koraiol) production, and clusters from Alternaria that produce host-selective toxins (e.g., ACT-Toxin II) (Tables S3 and S5). Although the second AI analysis did not flag every gene in these clusters as potential HGTs (e.g., only 4 of the 19 genes in the alternariol cluster from Hypoxylon cercidicola CBS 119009 were HGT candidates based on AI; Table S5) and we were not able to further validate candidates based on the same criteria used for high- This article is protected by copyright. All rights reserved Accepted Article confidence HGT, the phylogenetic distribution of many of these SMGCs across Xylariales is consistent with the acquisition of SMGCs via HGT (Fig. S4). In addition to the AI screen for HGT candidates, we identified additional putative HGTs of SMGCs to Xylariaceae and Hypoxylaceae based on their (i) high similarity to fungal MIBiG accessions from distantly related fungi, and (ii) discontinuous phylogenetic distributions (Fig. S4). Putative HGT of SMGCs included xylarialean SMGCs with >70% similarity to clusters for ergoline alkaloids and their precursors (e.g., loline, ergovaline, and lysergic acid production) produced by Clavicipitaceae endophytes, as well as the phytotoxin cichorine cluster from Aspergillus (Fig. S4; Table S3). The griseofulvin cluster from Penicillium aethiopicum, which produces a potent antifungal compound (Chooi et al., 2010), also appears horizontally transferred to the clade containing X. castorea and X. flabelliformis isolates (Figs. S4-S5). Although the discontinuous phylogenetic distributions of SMGCs observed here may represent unequal gene loss across taxa (Slot, 2017; Rokas et al., 2018), the presence of entire clusters known from Eurotiomycetes and Sordariomycetes in multiple endophytic and non-endophytic taxa provides additional support for HGTs. Overall, our first AI analysis provides the highest support for HGTs primarily from distantly related hosts such as bacteria (Fig. 2) (see also (Marcet-Houben & Gabaldón, 2010), yet our second AI screen and comparisons of SMGCs to MiBIG within our phylogenomic framework also support fungal-fungal HGT as an important mechanism of metabolic innovation in the Xylariales, similar to pathogenic fungi (Qiu et al., 2016). Expansion of Xylariaceae genomes due to increased gene duplication and HGTs. Despite the close evolutionary relationship and similar ecological niches of taxa in the Xylariaceae and Hypoxylaceae, genomes of Xylariaceae were on average ca. 7.2 Mbp larger than genomes of Hypoxylaceae (Fig. 3a; Table S6). Larger genome size was associated with higher repeat content: Xylariaceae contained an average of 2-fold more repetitive elements (Fig. 3b; Table S6) and had a higher density of repetitive elements surrounding genes (including effectors and genes identified as HGT candidates) compared to Hypoxylaceae genomes (Fig. S6). In addition to greater repeat content, Xylariaceae genomes also contained on average 750 more protein-coding genes compared to Hypoxylaceae (P<0.0001; Table S6). Ancestral state This article is protected by copyright. All rights reserved Accepted Article reconstructions reveal that Xylariaceae genomes have experienced significantly more gene gains (n = 472), gene duplication events (n = 136), orthogroup gains (n = 313), and orthogroup expansion events (n = 90) compared to Hypoxylaceae clade since the radiation from their last common ancestor (Fig. 3c-d), although both clades underwent similar numbers of gene losses (t95 = 0.51, P=0.61; Table S6). Xylariaceae genomes also experienced on average ca. 2-fold more HGTs events compared to Hypoxylaceae genomes (Fig. 3e). Increased genome sizes resulting from HGTs were positively associated with increased numbers of SMGCs across both clades (Fig. 3f), reflecting the fact that clustered metabolite genes in fungi are more likely to undergo HGT compared to unclustered genes (Wisecaver et al., 2014). Genomes of Xylariaceae contained on average ca. 20 more SMGCs than Hypoxylaceae genomes (Table S6) and ca. 2-fold greater cumulative richness of SMGCs compared to Hypoxylaceae clade (2,336 vs. 1,075 total; 587 vs. 282 non-singleton). Rarefaction analysis reveals the richness of SMGCs increases at a greater rate in the Xylariaceae clade (Fig. S7). Genomes of Xylariaceae also contained a greater fraction of isolate specific SMGCs compared to Hypoxylaceae, regardless of SMGC type (Xylariaceae: 31.2 ± 16.1; Hypoxylaceae: 19.8 ± 15.3; P = 0.0007; Fig. 1c; Fig. S8). Yet despite the high variation of SMGCs among taxa, network analysis illustrates that the composition of SMGCs is more similar among isolates from the same clade, regardless of ecological mode (Fig. S9). In contrast to the pattern observed for SMGCs, genomes of Hypoxylaceae contained a greater number of CGCs than Xylariaceae genomes (Xylariaceae: 9.5 ± 0.4; Hypoxylaceae: 11.0 ± 0.4; P = 0.0068; Table S4) and different classes of CGC dominated the two clades (Fig. 1d,e). For example, salicylate hydroxylases were the most abundant CGCs among Hypoxylaceae, but were absent from 25% Xylariaceae genomes (Fig. 1d). Four types of CGCs were universally present across Hypoxylaceae: salicylate hydroxylase, pterocarpan hydroxylase, naringenin 3-dioxygenase, phenol 2monooxygenase (Fig. 1d). CGCs classified as pterocarpan hydroxylases were the most abundant CGC type in genomes of Xylariaceae (Fig. 1d), but were not found in all Xylariacaee genomes. Only CGCs classified as naringenin 3-dioxygenases were found across all Xylariaceae genomes. In addition to distinct metabolic gene cluster content and prevalence of HGT, comparison of gene ontology (GO) terms for shared orthogroups significantly enriched in either Xylariaceae or Hypoxylaceae (i.e., 74 and 26, respectively) revealed that the Hypoxylaceae had a significant increase This article is protected by copyright. All rights reserved Accepted Article in the number of GO terms associated with membrane transport, whereas Xylariaceae had a significant increase in the number of GO terms for catalytic activities and binding (Fig. S10). Xylariaceae genomes also contained greater numbers of genes with signaling peptides, as well as genes annotated as effectors, membrane transport proteins, transcription factors, peptidases, and CAZymes compared to Hypoxylaceae, even after accounting for differences in genome size (Table S6). On average genomes of Xylariaceae contained ca. 50 more CAZymes than Hypoxylaceae (Xylariaceae 579.9 ± 7.7; Hypoxylaceae 529.6 ± 9.1, P <0.0001), including a significant increase in PCWDEs involved in the degradation of cellulose, hemicellulose, lignin, pectin, and starch (Table S6). As genomes of fungi with saprotrophic lifestyles typically contain more CAZymes and PCWDEs compared to plant pathogens and mycorrhizal symbionts (Knapp et al., 2018; Haridas et al., 2020; Miyauchi et al., 2020), our genomic results are consistent with the potential for Xylariaceae fungi (including endophytes) to have greater saprotrophic abilities compared to Hypoxylaceae fungi (Osono, 2006). To test this prediction, we compared the abilities of 20 isolates to degrade leaves of Pinus and Quercus. Regardless of trophic mode, isolates of Xylariaceae with expanded CAZymes and PCWDEs repertoires caused greater mass loss compared to taxa with fewer genes predicted to degrade lignocellulose (i.e., Hypoxylaceae and Xylariaceae from animal-dung clade; Fig. S11). In addition to increased capacity for lignocellulose degradation, Xylariaceae endophyte species associate with a greater phylogenetic diversity of plant and lichen hosts compared to species of Hypoxylaceae endophytes (t42 = 2.25; P = 0.0294; Fig. 3g). Host breadth of Xylariaceae endophytes also is positively associated with the number of total HGT events (r = 0.43, P = 0.0193), as well as the number of peptidases (r = 0.37, P = 0.0444) and nonribosomal peptide (NRP) SMGCs (Fig. 3h). Genomic differences between endophytic and non-endophytic fungi. Both culture-based and culturefree studies of healthy photosynthetic tissues of plants and lichens demonstrate the abundance and novel diversity represented by xylarialean endophytes (U’Ren et al., 2016). However, some endophytes can occur in both living host tissues as well as decomposing plant materials (Okane et al., 2008; U’Ren et al., 2016; U’Ren & Arnold, 2016) and often are closely related to described species of saprotrophs and pathogens (U’Ren et al., 2016). This suggests that for some species, endophytism This article is protected by copyright. All rights reserved Accepted Article may represent only part of a complex life cycle that blurs the lines between distinct ecological modes (U’Ren et al., 2016; Chen et al., 2018) and few genomic signatures may be associated with the evolution of endophytism in the Xylariaceae and Hypoxylaceae. Overall, when we analyzed all ingroup genomes we observed no clear distinctions in genome size or content due to different ecological modes, even after taking phylogeny into account (Table S6). One exception was the reduced genomes and CAZyme content of termite-associated Xylaria spp. (i.e., X. nigripes YMJ 653, X. sp. CBS 124048, and X. intraflava YMJ725; Figs. S3 and S12) that reflects a single evolutionary transition to specialization on termite nest substrates decomposed by a basidiomycete fungus (Hsieh et al., 2010). However, as evolutionary distance among taxa can impede detection of finer-scale genomic differences due to ecological mode (Harrington et al., 2019), we restricted our analyses to comparisons of 15 pairs of sister taxa across both clades with contrasting ecological modes. These pairwise comparisons revealed that endophytic Hypoxylaceae genomes contain significantly fewer genes with signaling peptides, protein coding genes, transporters, peptidases, PCWDEs (especially those involved in decomposition of cellulose and lignin), SMGCs, and CGCs compared to non-endophytes (Fig. 4). Yet, similar to the lack of reduced genome repertoires in some root endophytes (Xu et al., 2015; Lahrmann et al., 2015), no significant differences in genomic content were observed between paired endophytes and non-endophytes in the Xylariaceae clade (Fig. 4; Table S6). These results suggest that compared to endophytes and saprotrophs in the Hypoxylaceae, Xylariaceae taxa may have less distinct ecological modes, and their increased metabolic versatility may be the result of selection maintaining diverse genes for both endophytism and saprotrophy. As saprotrophs, fungi experience strong selection to maintain highly diverse SMGCs that increase competitive abilities in diverse microbial communities (Richards & Talbot, 2013; Rokas et al., 2018; Naranjo‐Ortiz & Gabaldón, 2020), as well as large gene repertoires to degrade lignocellulosic compounds (Haridas et al., 2020). Accordingly, we observed that in genomes of non-endophytic Xylariaceae and Hypoxylaceae SMGC abundance is positively correlated with the number of genes important for saprotrophy (e.g., CAZymes, transporters) and putative pathogenicity (e.g., signaling peptides, effectors, peptidases), even after accounting for differences among clades and genome sizes (Fig. 5; Table S6). In contrast, we found that endophyte SMGC abundance was decoupled from the This article is protected by copyright. All rights reserved Accepted Article majority of genomic factors involved in plant-fungal interactions (Fig. 5), due in part to fewer numbers of CAZymes, transports, and peptidases annotated in SMGCs (Table S6). These results are consistent with different selection pressures and ecological roles of SMGCs in endophytic and nonendophytic fungi and highlight the importance of phylogenetically informed comparisons to detect genomic differences associated with endophytism, as well as complexity of linking genotype to phenotype for complex traits, especially in dynamic genomes undergoing frequent HGT. CONCLUSIONS Our analysis of 96 phylogenetically and ecologically diverse Xylariaceae and Hypoxylaceae genomes reveals that gene duplication, gene family expansion, and HGT of SMGCs, effectors, and peptidases from putative bacterial and fungal donors drives metabolic versatility in the Xylariaceae. Expanded metabolic diversity and secondary metabolism of Xylariaceae taxa is associated with greater ecological generalism in both substrate usage and the phylogenetic breadth of symbiotic associations compared to Hypoxylaceae taxa. Correlations between endophyte host breadth, HGT, and abundance of NRPs also indicate that SMGCs may play a key role in facilitating xylarialean endophyte colonization of diverse hosts. For example, although NRPs are known for their role as virulence factors of phytopathogenic fungi (e.g., host-selective toxins or siderophores) (Oide & Turgeon, 2020), previous research has shown that an NRPS is essential for the endophyte Neotyphodium/Epichloë to establish symbiosis with its host (Johnson et al., 2007). Overall, our results highlight the importance of plant-fungal symbioses to drive not only fungal speciation and ecological diversification (Joy, 2013), but vast chemical biodiversity that can be leveraged for novel pharmaceuticals and agrochemicals (Becker & Stadler, 2021; Robey et al., 2021). ACKNOWLEDGEMENTS Funding for the project was provided by the DOE JGI Large-scale Community Science Project (Grant number 503506 to JMU, JHW, AEA). MEEF was funded by the Office for Research, Innovation and Impact at the University of Arizona and the University of Arizona BIO5 Postdoctoral Fellowship Program. FL and JM received financial support from NSF DEB-1541548 and DEB-1046065, and AEA received support from NSF DEB-1541496 and DEB-1045766. These awards and NSF DEB- This article is protected by copyright. All rights reserved Accepted Article 0640996 to AEA and DEB-1010675 to AEA and JMU supported the initial collections of endophytes. We thank F. Martin, P. Gladieux, J. Spatafora, R. Vilgalys, and K. O`Donnell for permission to use unpublished JGI F1000 genomes; D. Bellomo, Y. Sanchez-Rosario, and S. Valdez for laboratory assistance; and the Genomics Analysis and Sequencing Core (GATC), the Arizona Genomics Institute (AGI), and the High-Performance Computer (HPC) at the University of Arizona for technical support. The authors declare no competing interests. AUTHOR CONTRIBUTIONS Designed research: JMU, JHW, AEA, MEEF; Performed field or laboratory research: JMU, LPM, YMJ, HMH, AEA, FL, JM; Contributed fungal isolates: YMJ, HMH, AEA, DCE, RCH; Genome and transcriptome sequencing, assembly, annotation: SA, SJM, AK, RH, SH, BA, RR, K. LaButti, JP, AL, MA, JY, CA, KK, VN, IVG; Metabolomics: K. Louie, TN. Contributed analytic tools: JHW, JCS, KYC; Analyzed data: MEEF, JMU, JHW, SA, KEE, KS, ZK, ED, BH; Wrote the paper: MEEF, JMU, JHW, with contributions from AEA, JCS, FL, JM, IVG, SA. DATA AVAILABILITY Raw sequence data, assembled sequences, and genome annotations are available through the corresponding MycoCosm portal (https://mycocosm.jgi.doe.gov/). NCBI accession numbers are listed in Table S1. All other data, including Supporting Data Note S1, can be found in FigShare Repository (DOI 10.6084/m9.figshare.c.5314025). This article is protected by copyright. All rights reserved Accepted Article REFERENCES Alexander WG, Wisecaver JH, Rokas A, Hittinger CT. 2016. Horizontally acquired genes in early-diverging pathogenic fungi enable the use of host nucleosides and nucleotides. Proceedings of the National Academy of Sciences of the United States of America 113: 4116–4121. Ambrose KV, Tian Z, Wang Y, Smith J, Zylstra G, Huang B, Belanger FC. 2015. Functional characterization of salicylate hydroxylase from the fungal endophyte Epichloë festucae. Scientific Reports 5: 10939. 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Applied Microbiology and Biotechnology 101: 2603–2618. Xu X-H, Su Z-Z, Wang C, Kubicek CP, Feng X-X, Mao L-J, Wang J-Y, Chen C, Lin F-C, Zhang C-L. 2015. The rice endophyte Harpophora oryzae genome reveals evolution from a pathogen to a mutualistic endophyte. Scientific Reports 4: 5783. SUPPORTING INFORMATION Methods S1. Additional information on strain selection, fungal growth and nucleic acid extraction, genome and transcriptome sequencing, gene prediction and genome assembly, phylogenetic analyses, comparative genomic analyses, litter decomposition assays, and metabolomics. Note S1. List and description of appendices S1-S10 available on FigShare Repository, DOI 10.6084/m9.figshare.c.5314025. Fig. S1. Overview of Alien Index (AI) calculations to identify HGT. Fig. S2. Results of phylogenomic and network analysis of 1,526 universal single-copy orthologous protein sequences. Fig. S3. Phylogenomic reconstruction of Xylariaceae s.l. and Hypoxylaceae and genome statistics. Fig S4. Dynamic distribution of 168 Xylariaceae and Hypoxylaceae SMGCs with hits to known metabolites in the MIBiG repository. Fig. S5. Similarity of the griseofulvin SMGC in Penicillium and Xylaria supports HGT. This article is protected by copyright. All rights reserved Accepted Article Fig. S6. The density of repetitive elements surrounding genes was higher for Xylariaceae s.l. than for Hypoxylaceae genomes. Fig. S7. Rarefaction analysis illustrates higher SMGC diversity in Xylariaceae compared to Hypoxylaceae. Fig. S8. The majority of SMGCs are specific to Hypoxylaceae or Xylariaceae s.l. clades or individual isolates regardless of SMGC type. Fig. S9. Network analysis illustrates the importance of clade rather than ecological mode for SMGC content. Fig. S10. Orthogroup enrichment suggests functional differences for Xylariaceae s.l. and Hypoxylaceae. Fig. S11. Xylariaceae s.l. taxa demonstrate increased decomposition abilities (estimated via mass loss) on leaf litter compared to fungi with reduced genomes (i.e., Hypoxylaceae and animal dung Xylariaceae s.l. in the Poronia clade). Fig. S12. Relative abundance of functional gene categories across Xylariaceae s.l. and Hypoxylaceae. Table S1. Sequencing and assembly statistics for the 121 genomes included in this study. Table S2. RepeatMasker, RepeatScout, and RepBase Update classification of repetitive elements for 96 genomes of Xylariaceae s.l. and Hypoxylaceae. Table S3. Secondary metabolite gene clusters (SMGCs) and cluster families for the 96 Hypoxylaceae and Xylariaceae s.l. genomes included in this study. This article is protected by copyright. All rights reserved Accepted Article Table S4. Catabolic gene clusters (CGCs) and cluster families for the 96 Hypoxylaceae and Xylariaceae s.l. genomes included in this study. Table S5. Taxonomic, phylogenetic, and functional annotation information for HGT candidate genes identified by Alien Index (AI) analyses. Table S6. Counts and statistical comparisons of genome content as a function of major clade (Xylariaceae s.l. vs. Hypoxylaceae) and ecological mode (endophyte vs. non-endophyte). FIGURE LEGENDS Fig 1. Xylariaceae s.l. and Hypoxylaceae genomes are characterized by hyperdiverse and dynamic metabolic gene clusters. (a) Maximum likelihood phylogenetic analyses of 1,526 universal, single-copy orthogroups support the sister relationship of the Xylariaceae s.l. (containing Xylariaceae sensu stricto, Graphostromataceae, and Barrmaeliaceae) and the Hypoxylaceae, as well as previously denoted relationships among genera (see Fig. S2). Phylogenetic analyses included genomes of 25 outgroup taxa representing five other families of Xylariales and eight orders of Sordariomycetes (total 121 genomes; Fig. S2). Taxon names are colored by ecological mode and branches colored by major clade (red: Xylariaceae s.l.; blue: Hypoxylaceae). Taxa with asterisks (*) represent 15 pairs of endophyte/non-endophyte sister taxa used to assess differences in genomic content due to ecological mode (see Fig. 4). Within this phylogenetic framework, we compared the: (b) abundance of different secondary metabolite gene cluster (SMGC) families per genome. Dotted lines indicate the averages for Pezizomycotina (black), Xylariaceae s.l. (red), and Hypoxylaceae (blue); (c) relative abundance of family-specific, clade-specific, and isolate-specific SMGCs; (d) relative abundance and (e) presence/absence of catabolic gene clusters (CGCs), colored by anchor gene identity (Gluck-Thaler & Slot, 2018). Hierarchical clustering of CGCs (see bottom) was performed with the unweighted pair group method with arithmetic mean (UPGMA). This article is protected by copyright. All rights reserved Accepted Article Fig 2. Phylogenetic distribution and functional annotation of high confidence horizontal gene transfers (HGTs) to genomes of Xylariaceae s.l. and Hypoxylaceae. Phylogeny matches Fig. 1a. Blue boxes represent genes predicted to be high-confidence HGT events (detected with the first round of Alien Index analyses; Table S5). HGT events are ordered from left to right based on their abundance. Transfers with more than one gene copy per genome are indicated with >1. Colored boxes indicate putative functional annotations of HGTs: secondary metabolite gene clusters (SMGCs), effectors, signaling peptides, transporters, peptidases, and carbohydrate active enzymes (CAZymes). SMGCs predicted as 'biosynthetic-core' and 'biosynthetic-additional' are shown with darker purple, whereas other genes in SMGCs are shown with light purple. For CAZyme predictions, dark brown color indicates plant cell wall-degrading enzymes (PCWDEs). The bottom panel (Transfer Direction) indicates the taxonomic identity of putative donor and recipient lineage(s) inferred from phylogenetic analyses. Fig 3. Larger genomes in the Xylariaceae s.l. clade reflect increased repetitive regions, gene gains and duplications, and horizontal gene transfers (HGTs). Median (a) genome size, (b) repetitive element content, (c) gene gains, (d) gene duplications, and (e) number of putative HGT events (high confidence only) for genomes of Xylariaceae s.l. (red) and Hypoxylaceae (blue). Box plot boundaries reflect the interquartile range. Summary statistics (mean, standard deviation, and sample size) are reported in Table S6. Gene gains/losses were inferred with Wagner Parsimony under a gain penalty=loss penalty=1; (f) Relationship between the number of HGT events and secondary metabolite gene clusters (SMGCs) as a function of clade. The shaded region indicates 95% confidence interval of linear fit. Statistics represent Pearson's correlation coefficient (r) and p-value; (g) A quantile box plot showing the interquartile range and median of endophyte host breadth (measured as total number of plant families and lichen orders with which a fungal operational taxonomic unit (OTU) was cultured (U’Ren et al., 2016)) as a function of major clade (color). A similar pattern was observed when only the number of plant families are compared (Wilcoxon: 𝛘2 = 4.14, P=0.0413), but not lichen orders (Wilcoxon: 𝛘2 = 1.77, P=0.1834). (h) Relationship of Xylariaceae endophyte host breadth and the number of SMGCs classified as nonribosomal peptides This article is protected by copyright. All rights reserved Accepted Article (NRPs) per genome. The shaded region indicates 95% confidence interval of linear fit. Statistics represent Pearson's correlation coefficient (r) and p-value. Fig 4. Pairwise comparisons of sister taxa illustrate ecological modes are more distinct in the Hypoxylaceae. Box plots of the median and interquartile difference in gene counts of plant cell walldegrading enzymes (PCWDEs), peptidases, secondary metabolite gene clusters (SMGCs) (y-axis on left), and transporters (y-axis on right) between 15 pairs of sister taxa with contrasting ecological modes for Xylariaceae s.l. and Hypoxylaceae (sister taxa are indicated with asterisks in Fig. 1a). Values greater than zero indicate higher gene counts in non-endophytic taxa, whereas differences less than zero indicate higher gene counts in endophytes. Statistical differences were assessed with least squares means contrast under the null hypothesis: non-endophyte value - endophyte value = 0 (see Table S6 for summary statistics). P-values <0.05 are indicated with an asterisk (*). Fig. 5. Correlation of secondary metabolite gene cluster (SMGC) content and genes involved in saprotrophy and/or pathogenicity. Relationship between SMGC abundance and number of genes annotated as (a) carbohydrate active enzymes (CAZymes), (b) effectors, (c) peptidases, and (d) transporters for endophytes (top row) and non-endophytes (bottom row). Values for each genome represent the residuals after accounting for genome size. Points, linear regression line, and shaded 95% confidence intervals of fit are color-coded by clade (red, Xylariaceae; blue Hypoxylaceae). Statistical values represent Pearson correlation coefficient. See Table S6 for additional details. This article is protected by copyright. All rights reserved Xylariaceae s.l. Hypoxylaceae (a) (c) (b) (d) (e) Eutypa lata UCREL1 Microdochium bolleyi J235TASD1 Microdochium trichocladiopsis MPI-CAGE-CH0230 Whalleya microplaca YMJ1829* Hypoxylaceae sp. FL0662B* Durotheca rogersii YMJ 92031201 Hypoxylon fuscum CBS 119018 Hypoxylon sp. FL1284 Hypoxylon sp. FL1150* Hypoxylon rubiginosum ER1909 Hypoxylon cercidicola CBS 119009* Hypoxylon rubiginosum CBS 119005 Hypoxylon fragiforme CBS 206.31 Hypoxylon crocopeplum CBS 119004 Hypoxylon sp. NC1633 Hypoxylon monticulosum FL0542 Hypoxylon submonticulosum NC0708 Daldinia eschscholtzii EC12 Daldinia eschscholtzii FL0578 Daldinia bambusicola CBS 122872 Daldinia caldariorum CBS 122874 Daldinia sp. FL1419 Daldinia vernicosa CBS 139.73 Daldinia grandis CBS 114736 Daldinia decipiens CBS 113046 Daldinia loculata AZ0526* Daldinia loculata CBS 113971* Hypoxylon trugodes CBS 135444* Hypoxylon sp. CI-4A* Hypoxylon sp. FL1857 Hypoxylon sp. FL0890 Hypoxylon sp. FL0543 Hypoxylon sp. NC0597 Hypoxylon sp. EC38 Hypoxylon sp. CO27-5 Annulohypoxylon minutellum CBS 135445 Annulohypoxylon bovei var. microspora CBS124037 Annulohypoxylon moriforme CBS 123579 Annulohypoxylon maeteangense CBS 123835 Annulohypoxylon truncatum CBS 140777 Rostrohypoxylon terebratum CBS 119137 Annulohypoxylon stygium FL0470* Annulohypoxylon nitens CBS 120705* Xylariaceae sp. FL2044 Biscogniauxia marginata CBS 124505 Biscogniauxia nummularia BnCUCC2015 Camillea tinctor CBS 203.56 Biscogniauxia sp. FL1348 Biscogniauxia mediterranea AZ0048* Biscogniauxia mediterranea CBS 129072* Xylariaceae sp. FL0641 Barrmaeliaceae sp. FL0016 Barrmaeliaceae sp. FL0804 Xylariaceae sp. FL0255 Xylariaceae sp. FL1272 Xylariaceae sp. FL1019 Xylariaceae sp. FL1651 Anthostoma avocetta NRRL 3190* Xylariaceae sp. AK1471* Xylariaceae sp. FL0594* Poronia punctata CBS 180.79* Xylaria nigripes YMJ 653 Xylaria sp. CBS 124048 Xylaria intraflava YMJ 725 Xylaria hypoxylon OSC100004 Xylaria digitata CBS 161.22 Xylaria grammica CBS 120713 Xylaria sp. FL1777 Kretzschmaria deusta IL1129* Kretzschmaria deusta CBS 826.72* Xylaria arbuscula FL1030* Xylaria bambusicola CBS 139988* Xylaria arbuscula CBS 124340 Xylaria venustula FL0490 Xylaria sp. FL1042 Xylaria sp. FL0064 Xylaria sp. FL0043 Xylaria sp. FL0933 Entoleuca mammata CFL468 Nemania sp. CBS 527.63* Nemania diffusa NC0034* Nemania abortiva FL1152 Nemania sp. FL0031 Nemania sp. FL0916 Nemania sp. NC0429 Nemania serpens AK0226 Nemania serpens AZ0576* Nemania serpens CBS 679.86* Xylaria palmicola CBS 124036 Astrocystis sublimbata CBS 130006 Xylaria telfairii CBS 121673 Xylaria cf. heliscus FL0509 Xylaria acuta CBS 122032 Xylaria longipes CBS 148.73 Xylaria cf. castorea CBS 124033 Xylaria flabelliformis CBS 116.85* Xylaria flabelliformis NC1011* Xylaria flabelliformis CBS 123580 Xylaria flabelliformis CBS 114988 Newly sequenced genome PacBio Illumina Ecological Mode Endophytic (living leaves, lichens) Saprotrophic (dead wood, litter, fruit) Saprotrophic (insect/animal dung) Pathogenic 0 20 40 60 80 100 120 0 No. of Predicted SMGCs SMGC Classification PKS other PKS Other NRPS Terpene RiPP PKS-NRP Hybrid 20 40 60 80 100 Relative Abundance (%) SMGC Distribution Specific to: Isolate Xylariaceae/Hypoxylaceae Hypoxylaceae Xylariaceae s.l. Other 0 25 50 75 100 Relative Abundance (%) Catabolic Gene Cluster (CGC) Classification Salicylate Hydroxylase Pterocarpan Hydroxylase Naringenin 3-dioxygenase Phenol 2-monooxygenase Quinate Dehydrogenase Benzoate 4-monooxygenase Ferulic Acid Esterase Epicatechin Laccase Stilbene Dioxygenase Catechol Dioxygenase Vanillyl Alcohol Oxidase Ferulic Acid Decarboxylase Hybrid (families) Aromatic Ring-opening Dioxygenase HGT125 HGT123 HGT122 HGT121 HGT117 HGT116 HGT115 HGT108 HGT105 HGT087 HGT083 HGT081 HGT059 HGT052 HGT042 HGT041 HGT029 HGT025 HGT024 HGT011 HGT092 HGT017 HGT002 HGT110 HGT113 HGT112 HGT107 HGT104 HGT103 HGT100 HGT098 HGT095 HGT088 HGT084 HGT075 HGT074 HGT032 HGT023 HGT114 HGT049 HGT048 HGT007 HGT106 HGT094 HGT001 HGT093 HGT091 HGT089 HGT082 HGT079 HGT062 HGT009 HGT096 HGT073 HGT072 HGT069 HGT054 HGT012 HGT010 HGT003 HGT080 HGT078 HGT070 HGT071 HGT067 HGT129 HGT066 HGT063 HGT013 HGT064 HGT061 HGT058 HGT057 HGT008 HGT053 HGT056 HGT055 HGT050 HGT051 HGT043 HGT047 HGT046 HGT045 HGT044 HGT040 HGT038 HGT039 HGT128 HGT036 HGT030 HGT031 HGT014 HGT037 HGT035 HGT026 HGT015 HGT033 HGT027 HGT019 HGT018 HGT016 HGT005 >1 Eutypa lata UCREL1 M. bolleyi J235TASD1 M. trichocladiopsis MPI-CAGE-CH0230 Whalleya microplaca YMJ1829 Hypoxylaceae sp. FL0662B Durotheca rogersii YMJ 92031201 Hypoxylon fuscum CBS 119018 Hypoxylon sp. FL1284 Hypoxylon sp. FL1150 Hypoxylon rubiginosum ER1909 Hypoxylon cercidicola CBS 119009 Hypoxylon rubiginosum CBS 119005 Hypoxylon fragiforme CBS 206.31 Hypoxylon crocopeplum CBS 119004 Hypoxylon sp. NC1633 Hypoxylon monticulosum FL0542 Hypoxylon submonticulosum NC0708 Daldinia eschscholtzii EC12 Daldinia eschscholtzii FL0578 Daldinia bambusicola CBS 122872 Daldinia caldariorum CBS 122874 Daldinia sp. FL1419 Daldinia vernicosa CBS 139.73 Daldinia grandis CBS 114736 Daldinia decipiens CBS 113046 Daldinia loculata AZ0526 Daldinia loculata CBS 113971 Hypoxylon trugodes CBS 135444 Hypoxylon sp. CI-4A Hypoxylon sp. FL1857 Hypoxylon sp. FL0890 Hypoxylon sp. FL0543 Hypoxylon sp. NC0597 Hypoxylon sp. EC38 Hypoxylon sp. CO27-5 A. minutellum CBS 135445 A. bovei var. microspora CBS 124037 A. moriforme CBS 123579 A. maeteangense CBS 123835 A. truncatum CBS 140777 Rostrohypoxylon terebratum CBS 119137 Annulohypoxylon stygium FL0470 Annulohypoxylon nitens CBS 120705 Xylariaceae sp. FL2044 Biscogniauxia marginata CBS 124505 Biscogniauxia nummularia BnCUCC2015 Camillea tinctor CBS 203.56 Biscogniauxia sp. 304 FL1348 Biscogniauxia mediterranea AZ0048 Biscogniauxia mediterranea CBS 129072 Xylariaceae sp. FL0641 Barrmaeliaceae sp. FL0016 Barrmaeliaceae sp. FL0804 Xylariaceae sp. FL0255 Xylariaceae sp. FL1272 Xylariaceae sp. FL1019 Xylariaceae sp. FL1651 Anthostoma avocetta NRRL 3190 Xylariaceae sp. AK1471 Xylariaceae sp. FL0594 Poronia punctata CBS 180.79 Xylaria nigripes YMJ 653 Xylaria sp. CBS 124048 Xylaria intraflava YMJ 725 Xylaria hypoxylon OSC100004 Xylaria digitata CBS 161.22 Xylaria grammica CBS 120713 Xylaria sp. FL1777 Kretzschmaria deusta IL1129 Kretzschmaria deusta CBS 826.72 Xylaria arbuscula FL1030 Xylaria bambusicola CBS 139988 Xylaria arbuscula CBS 124340 Xylaria venustula FL0490 Xylaria sp. FL1042 Xylaria sp. FL0064 Xylaria sp. FL0043 Xylaria sp. FL0933 Entoleuca mammata CFL468 Nemania sp. CBS 527.63 Nemania diffusa NC0034 Nemania abortiva FL1152 Nemania sp. FL0031 Nemania sp. FL0916 Nemania sp. NC0429 Nemania serpens AK0226 Nemania serpens AZ0576 Nemania serpens CBS 679.86 Xylaria palmicola CBS 124036 Astrocystis sublimbata CBS 130006 Xylaria telfairii CBS 121673 Xylaria cf. heliscus FL0509 Xylaria acuta CBS 122032 Xylaria longipes CBS 148.73 Xylaria cf. castorea CBS 124033 Xylaria flabelliformis CBS 116.85 Xylaria flabelliformis NC1011 Xylaria flabelliformis CBS 123580 Xylaria flabelliformis CBS 114988 SMGCs Functional Effectors Signal Peptides annotations Transporters Peptidases Donor CAZymes Virus Actinobacteria Proteobacteria Bacteria Plants Fungi Basidiomycota Sordariomycetes Xylariales Xylariaceae/Hypoxylaceae Transfer direction Recipient >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 >1 (a) (b) (c) (d) (e) 3000 2000 Putative HGT Events 4e+07 6000 Gene Duplications 5e+07 30 1500 4000 9000 Gene Gains Repeative Elements Genome Size (bp) 6e+07 1000 500 3000 1000 t95 = -7.35, P < 0.0001 0 (g) (f) 55 r = 0.72, P < 0.0001 r = 0.72, P < 0.0001 45 40 35 30 25 20 15 Xylariaceae s.l. Hypoxylaceae 20 40 60 8 0 t95 = 2.11, P = 0.038 t95 = -9.67, P < 0.0001 (h) r = 0.43, P = 0.0195 Endophyte Host Breadth Putataive HGT Events 50 10 t95 = 3.40, P = 0.001 t95 = -4.66, P < 0.0001 20 80 SMGCs 100 120 6 4 2 0 -2 -4 -6 t42 = 2.25, P = 0.0294 10 15 20 25 30 Nonribosomal peptide (NRP) SMGCs PCWDEs 40 * Xylariaceae s.l. Hypoxylaceae Higher value non-endophyte 50 Transporters SMGCs Peptidases * 75 50 * 30 * 20 20 10 0 -10 -20 0 Higher value endophyte Difference Non-Endophyte - Endophyte 60 P=0.7739 P=0.0366 P=0.1468 P=0.0269 P=0.2545 P=0.0387 P=0.4862 P=0.0106 -25 (a) SMGCs (residuals) ENDOPHYTE 40 (b) (c) (d) r = 0.45, P = 0.0683 r = 0.35, P = 0.0727 r = 0.19, P = 0.4679 r = 0.33, P = 0.0938 r = 0.31, P = 0.2241 r = 0.32, P = 0.1092 r = 0.13, P = 0.6125 r = -0.05, P = 0.8169 r = 0.84, P < 0.0001 r = 0.75, P < 0.0001 r = 0.52, P = 0.0106 r = 0.77, P < 0.0001 r = 0.82, P < 0.0001 r = 0.80, P < 0.0001 r = 0.77, P < 0.0001 r = 0.54, P = 0.0027 20 0 -20 -40 SMGCs (residuals) NON-ENDOPHYTE 40 20 0 -20 Xylariaceae s.l. Hypoxylaceae -40 -100 -50 0 50 CAZymes (residuals) 100 -50 0 50 Effectors (residuals) 100 -50 0 50 Peptidases (residuals) 100 -200 -100 0 100 Transporters (residuals)

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