Research Article
Print
Research Article
Characterisation and comparative analysis of mitochondrial genomes of false, yellow, black and blushing morels provide insights on their structure and evolution
expand article infoGang Tao‡§, Steven Ahrendt|, Shingo Miyauchi, XiaoJie Zhu‡, Hao Peng‡, Kurt Labutti|, Alicia Clum|, Richard Hayes|, Patrick S. G. Chain#, Igor V. Grigoriev|¤, Gregory Bonito«, Francis M. Martin§
‡ Guizhou Minzu University, Guiyang, China
§ Université de Lorraine, Champenoux, France
| U.S. Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, United States of America
¶ Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa, Japan
# Los Alamos National Laboratory, Los Alamos, United States of America
¤ University of California Berkeley, Berkeley, United States of America
« Michigan State University, East Lansing, United States of America

Corresponding author: Gang Tao ( tg0729@163.com )

Corresponding author: Francis M. Martin ( francis.martin@inrae.fr )

Academic editor: Michael Bradshaw
© 2025 Gang Tao, Steven Ahrendt, Shingo Miyauchi, XiaoJie Zhu, Hao Peng, Kurt Labutti, Alicia Clum, Richard Hayes, Patrick S. G. Chain, Igor V. Grigoriev, Gregory Bonito, Francis M. Martin.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Tao G, Ahrendt S, Miyauchi S, Zhu XJ, Peng H, Labutti K, Clum A, Hayes R, Chain PSG, Grigoriev IV, Bonito G, Martin FM (2025) Characterisation and comparative analysis of mitochondrial genomes of false, yellow, black and blushing morels provide insights on their structure and evolution. IMA Fungus 16: e138363. https://doi.org/10.3897/imafungus.16.138363
Open Access

Abstract

Morchella species have considerable significance in terrestrial ecosystems, exhibiting a range of ecological lifestyles along the saprotrophism-to-symbiosis continuum. However, the mitochondrial genomes of these ascomycetous fungi have not been thoroughly studied, thereby impeding a comprehensive understanding of their genetic makeup and ecological role. In this study, we analysed the mitogenomes of 30 Morchellaceae species, including yellow, black, blushing and false morels. These mitogenomes are either circular or linear DNA molecules with lengths ranging from 217 to 565 kbp and GC content ranging from 38% to 48%. Fifteen core protein-coding genes, 28–37 tRNA genes and 3–8 rRNA genes were identified in these Morchellaceae mitogenomes. The gene order demonstrated a high level of conservation, with the cox1 gene consistently positioned adjacent to the rnS gene and cob gene flanked by apt genes. Some exceptions were observed, such as the rearrangement of atp6 and rps3 in Morchella importuna and the reversed order of atp6 and atp8 in certain morel mitogenomes. However, the arrangement of the tRNA genes remains conserved. We additionally investigated the distribution and phylogeny of homing endonuclease genes (HEGs) of the LAGLIDADG (LAGs) and GIY-YIG (GIYs) families. A total of 925 LAG and GIY sequences were detected, with individual species containing 19–48HEGs. These HEGs were primarily located in the cox1, cob, cox2 and nad5 introns and their presence and distribution displayed significant diversity amongst morel species. These elements significantly contribute to shaping their mitogenome diversity. Overall, this study provides novel insights into the phylogeny and evolution of the Morchellaceae.

Key words:

Genomic synteny, homing endonuclease gene, mitochondrial genome, Morchella, protein-coding gene

Introduction

Mitochondria are semi-autonomous organelles that play a pivotal role in fungal respiratory metabolism and energy production (Burger et al. 2003; Wai and Langer 2016). They also play a role in regulating ion balance and intermediary metabolic processes and have been linked to cell death and virulence of pathogenic fungi (Verma et al. 2018; Black et al. 2021). Each mitochondrion has a distinct mitogenome. In fungi, mtDNA exists in either a circular or linear configuration and multiple copies may be present in a single cell. Although smaller than that of the nuclear genome, mtDNA exhibits significant variability (Burger et al. 2003; Aguileta et al. 2014).

The evolutionary history of fungal mitogenomes warrants further investigation, particularly considering their high variability in size and gene arrangement. Previous studies have reported diverse genome sizes, ranging from 11.2 kb (Hanseniaspora pseudoguilliermondii) to 332.2 kb (Golovinomyces cichoracearum), which have primarily been attributed to differences in intergenic regions, intron number and intronic ORFs (Bullerwell et al. 2003; Sethuraman et al. 2009; Joardar et al. 2012; Deng et al. 2018). Variations in gene arrangements make them valuable tools for examining fungal evolution (Bullerwell and Lang 2005; Megarioti and Kouvelis 2020; Christinaki et al. 2022). Despite these variations, most fungal mitogenomes contain a conserved set of 15 core protein-coding genes (PCGs) dedicated to energy metabolism (Li et al. 2021), a single rps3 gene responsible for transcriptional regulation (Korovesi et al. 2018), 22-36 tRNA genes and two rRNA genes (Barroso et al. 1995; Formighieri et al. 2008).

The presence of repeated DNA sequences, such as introns that display self-splicing and insertion endonuclease activity, can significantly impact the structural dynamics of fungal mitochondrial genomes. This can result in variations in gene order, dispersion of repetitive elements and introduction of new genes through horizontal gene transfer (HGT) (Ferandon et al. 1995; Ferandon et al. 2010). Additionally, the distribution of transfer RNAs (tRNAs) can influence gene arrangement, with tRNAs being capable of editing, excising and integrating into different locations within the genome, allowing for their participation in HGT events, as reported by Tuller et al. (2011). Analysis of tRNA placement in fungal mitochondrial genomes is a valuable tool for investigating fungal evolution and extracting phylogenetic information, as highlighted by Cedergren and Lang (1985).

Homing endonuclease genes (HEGs) are commonly located within the introns of fungal mitogenomes and are distributed amongst the cox, cob, nad and rRNA genes (Novikova and Belfort 2017; Gomes et al. 2018; Li et al. 2018; Li et al. 2020). These genes play a crucial role in the high diversity observed and can modify the organisation and size of the mitogenome (Ferandon 2010; Hafez and Hausner 2012; Ferandon and Barroso 2013). In fungi, HEGs of three families are found, but only the LAGLIDADG and GIY-YIG genes are unique to the fungal mitogenomes (Hafez and Hausner 2012; Li et al. 2019a; Wang et al. 2020). These genes exhibit a wide range of sizes and subtypes, resulting in variable intergenic regions and inclusion of introns within their sequences (Belfort et al. 2007; Megarioti et al. 2020). HEGs possess independent open reading frames (ORFs) and employ unique self-splicing mechanisms that play a crucial role in the co-evolution of introns and HEGs (Belfort and Roberts 1997; Chevalier and Stoddard 2001; Megarioti et al. 2020). Additionally, some HEGs may serve supplementary biological functions, such as maturase activity and transcriptional repression (Stoddard 2014). The presence of HEGs significantly contributes to the diversification and evolution of fungal mitogenomes.

Morels belong to the Morchella genus (Ascomycota Morchellaceae). Although only 59 are formally recognised as valid Latin binomials (Machuca et al. 2021; Clowez et al. 2022; Loizides et al. 2022), over 358 species have been documented in the Index Fungorum database (http://www.indexfungorum.org/Names/Names.asp). The fruiting bodies of several morel species are known for their high nutritional value and diverse medicinal activities, including anti-inflammatory, anti-oxidative, anti-viral and anti-tumour effects (Liu et al. 2016; He et al. 2017; Liu et al. 2018). Additionally, these species exhibit a wide range of ecological lifestyles along the saprotrophism-to-symbiosis continuum (O’Donnell et al. 2011). Although most Morchella species, including those cultivated for human consumption, are generally regarded as soil or litter decomposers (Hobbie et al. 2016; Benucci et al. 2019), some species are thought to establish associations with plant roots (Baynes et al. 2012) or exhibit a propensity for bacterial interactions (Pion et al. 2013). The structure and activity of the mitogenome can differ, based on the ecology of morels. Regrettably, only a few morel mitogenomes have been extensively examined (Liu et al. 2020a, 2020b). This scarcity impedes the development of a comprehensive understanding of genetic traits and various ecological functions.

In this study, we sequenced, annotated and characterised the mitogenomes of 30 Morchellaceae species that encompass a range of phylogenetic lineages (false morels, blushingmorels, black morels and yellow morels) and ecological types. Our objectives were: to elucidate the genetic characteristics of these mitogenomes; compare the phylogeny based on the core PCGs with the nuclear genome phylogeny; analyse the composition, distribution and synteny of PCGs and HEGs; and gain insight into the phylogenetic features and potential impact of lifestyle. This study constitutes the first comprehensive investigation of the mitogenomes of Morchellaceae and provides valuable information on the phylogeny, genomics and ecological roles of this significant fungal group.

Materials and methods

Samples collection and mitogenome sequencing

Thirty fungal strains, including 28 Morchella species and two false morels (Disciotis venosa NRRL24433 and Verpa conica TJ0815), were collected from specific locations (Table 1). The Morchella species were primarily categorised into three groups: black, yellow and blushing morels, which were supported by molecular phylogenetic data and described as the Esculenta, Elata and Rufobrunnea clades (O’Donnell et al. 2011) and the Morchellaceae tree on the basis of their nuclear genomes constructed by the Joint Genome Institute (JGI) MycoCosm (Tree-Morchellaceae (https://mycocosm.jgi.doe.gov/mycocosm/species-tree/tree;Kglla0?organism=morchellaceae). The specimens were subsequently deposited as vouchers at the collection centre.

Table 1.
Download as
CSV
XLSX

General statistics and taxonomic information for the 30 species. mtDNA size, mitochondrial genome size; No. scf, the number of scaffold; 1. No. t RNA: number of tRNA genes; 2. No. LAGs, the number of LAGLIDADG homing endonuclease genes; 3. No. GIYs, the number of GIY-YIG homing endonuclease genes; 4. No. rnL, rRNA large subunit genes; 5. No. rnS, rRNA small subunit genes; 6. No. HGs, hypothetical protein genes.

Portal ID Species name mtDNA size (bp) No. scf G+C% Topology No. tRNA1 No. LAGs 2 /GIYs3 No. rnL 4 No. rnS 5 No. HGs 6
Morcon1 Morchella conifericola Mel32 263666 1 40.72 circular 31 31 2 2 23
Morsep1 Morchella septentrionalis NRRL54509 264912 1 40.65 circular 31 33 2 1 17
Morarb1 Morchella arbutiphila PhC291 261722 1 40.68 circular 31 31 2 2 33
Morbru1 Morchella brunnea NRRL20869 264219 1 40.88 circular 31 29 2 1 19
Morhis1 Morchella hispaniolensis Mel18 272680 1 40.54 circular 31 34 2 2 18
MorM21481-1 Morchella Mel-23 254381 1 40.63 linear 28 29 2 2 25
Mordel1 Morchella deliciosa PhC191 304819 1 40.67 circular 31 38 5 3 14
Morkaki1 Morchella kakiicolor PhC280 339294 1 42.2 linear 33 31 1 2 26
Morsem1 Morchella sp. SEM 339433 1 42.2 circular 33 34 1 2 33
Morgal1 Morchella sp. GAL 340119 1 42.33 circular 32 33 1 2 3
Mordis1 Morchella sp. DIS 339404 1 42.2 circular 33 34 1 2 32
Mordun1 Morchella dunalii PhC240 339982 1 42.41 circular 31 32 3 2 45
Morimp1 Morchella importuna SCYDJ1-A1 274206 2 38.22 linear 30 24 / (2) 2 2 1
Morexi1 Morchella eximia NRRL26621 305324 1 41.46 circular 32 40 / (1) 1 3 3
Morexim1 Morchella eximia DOB1602 282169 1 41.85 circular 32 28 / (1) 1 2 33
Morpop1 Morchella populiphila NRRL22315 370799 1 40.18 linear 32 48 1 3 5
Morpun1 Morchella punctipes GB769 351597 1 40.57 linear 30 43/ (1) 1 2 13
Mortrid1 Morchella tridentina NRRL54570 303561 1 40.06 circular 31 41 3 2 26
MorM1934m1-1 Morchella fluvialis 558743 1 46.81 linear 34 20 1 2 47
MorvulMes17-1 Morchella vulgaris Mes-17 561093 2 46.91 linear 32 21 2 2 108
Morpra1 Morchella prava Mes7 565090 1 46.79 circular 32 22 2 2 56
Morper1 Morchella peruviana NRRL66754 554401 1 46.1 circular 32 29 1 2 68
Morulm1 Morchella ulmaria NRRL36825 558995 1 46.92 linear 33 20 / (1) 3 2 45
Morame1 Morchella americana PhC192 555871 1 48.33 linear 32 19 2 2 36
Mordim1 Morchella diminutiva Mes2 471506 1 46.16 circular 33 22 2 1 30
Morpal1 Morchella steppicola 351320 1 41.23 circular 31 34 2 1 18
Morana1 Morchella anatolica PhC233 475320 1 42.26 circular 32 34 3 63
Morruf1 Morchella rufobrunnea NRRL28464 446548 1 40.92 circular 32 30 2 3 51
Disven1 Disciotis venosa NRRL24433 265955 1 37.45 circular 32 30 3 2 36
Vercon1 Verpa conica TJ0815 217659 1 38.48 circular 37 25 2 2 47

Mitogenome sequencing, assembly and annotation

The draft genomes of the Morchellaceae species were generated at the DOE Joint Genome Institute (JGI) using PacBio technology. A PacBio Multiplexed >10 kb w/ Blue Pippin Size Selection library was constructed and sequenced using SEQUELIIe, which generated >500,000 reads, totalling > 6 Gb. CCS data were filtered with the JGI QC pipeline to remove artefacts. Mitochondrial genomes were assembled separately from CCS reads as follows: CCS reads likely to belong to organelles were separated from nuclear genome reads using coverage and GC filtering. A maximum coverage cutoff of (1.5 * kmer coverage peak) and a maximum GC fraction of 0.40 was used to exclude nuclear content using BBTools (B. Bushnell: BBTools software package, http://sourceforge.net/projects/bbmap) version 38.79 [kmer count exact.sh default; bbnorm.sh pigz passes = 1 bits = 16 target = 9999999 min = 162; bbduk.sh maxgc = 0.4]. Initial mitochondrial assemblies were produced using Flye version 2.9-b1768 [-g 100 —asm-coverage 100—pacbio-hifi] (Kolmogorov et al. 2019). Genes were predicted in the assembly using Prodigal software version 2.6.3 [-p meta] (Hyatt et al. 2010) and were searched against an in-house curated database of mitochondrial HMMs using HMMER hmmsearch version 3.1b2 [-domtblout] (Mistry et al. 2013). Contigs with putative mitochondrial genes were predicted using ribosomal loci masked with BB tools [bbduk.sh k=25 mm=f kmask=N] and an in-house curated database of common eukaryotic nuclear ribosomal sequences. The masked contigs were used to recruit additional CCS reads using BB tools [k=25 mm=f mkf=0.03 ordered ow]. The resulting reads were assembled with flye [-g 100k—asm-coverage 100—pacbio–hifi]. Additional iterations of read recruitment and assembly were performed. Contigs<1 kb were excluded to produce the final assembly, which was then used to filter the CCS reads to produce non-organelle CCS and polished with two rounds of RACON version 1.4.13 [-u-t 36] (Vaser et al. 2017). The mitochondria-filtered CCS reads were then assembled using Flye version 2.9-b1768 [-t 32—pacbio-hifi] and subsequently polished with two rounds of RACON version 1.4.13 racon [-u-t 36]. Mitogenome assemblies were annotated using a workflow developed at JGI (Haridas et al. 2018).

Analyses of protein-coding genes(PCGs)

We analysed the GC content and AT/GC skew for PCGs and the entire genomes of 30 Morchellaceae species. Mitogenome strand asymmetry was assessed using the following formula: AT skew = [A − T]/[A + T] and GC skew = [G − C]/[G + C] (Li et al. 2021). This process was carried out using the R packages rtracklayer and seqinr and custom R scripts (Charif and Lobry 2007; Lawrence et al. 2009).

Phylogenetic analysis

A phylogenetic tree of the Morchellaceae species was constructed using concatenated 15 PCG sequences using the OrthoFinder algorithm (Emms and Kely 2019). Aligned orthologous protein sequences were obtained using MAFFT (Katoh et al. 2013) and concatenated using trimAl (Capella-Gutiérrez et al. 2009). Subsequently, a Maximum Likelihood (ML) tree was inferred using the Blosum 62+F+R3 model with 1000 bootstrap replicates using the IQtree algorithm (Minh et al. 2020). Phylogenetic trees of LAGLIDADG genes were constructed using the ML method, based on the amino acid sequences of LAGLIDAG homing endonucleases from individual morels.Trees of the atp6 and atp8 genes were constructed using nucleotide sequences with FastTree (Price et al. 2010).

Mitogenome synteny and genomic feature association analysis

We analysed the genome statistics and utilised the Synteny-Governed Overview pipeline (SynGO; Hage et al. 2021) to identify the syntenic regions amongst the 30 fungal species. Genomic information from MycoCosm was combined and visualised using the Visually Integrated Numerous Genres of Omics pipeline (VINGO; Looney et al. (2022)). We examined statistically significant variables in genomic features using Permutational Multivariate Analysis of Variance (PERMANOVA). The percentage of variance (R2) contributing to the genomic data was estimated for variables including ecological groups, the size of genomes with genes and phylogenetic distances. The detailed procedures have been previously described (Miyauchi et al. 2020). We tested the differences amongst various groups using the Kruskal-Wallis test with the post hoc Dunn test and the R package DescTools (Signorell et al. 2020). We evaluated the associations between the genomic features. A phylogenetic tree was constructed using the R package ape (Pradis and Schliep 2019). The tree and genomic data were combined using the R package phylobase (Hackathon et al. 2024). Principal components, considering phylogenetic distances, were calculated using the R package adephylo (Jombart and Dray 2010). The generated output files were combined and visualised usinga Proteomic Information Navigated Genomic Outlook (PRINGO; Miyauchi et al. (2020)). Pearson correlation coefficients of genes and genomes were calculated from the size of the genomes with genes using the R basic function cor. The results were visualised with custom R scripts using ggplot2 (Wickham 2016).

Homing endonuclease gene (HEG) Distribution

The LAGLIDADG and GIY-YIG sequences were located within the PCGs using Artemis Software (version 18.2.0, http://sanger-pathogens.github.io/Artemis/Artemis/), as presented in Table 2. Based on the annotated morel and false-morel mitochondrial genomes, each HEG in the mitochondrial genome was identified by reading the GB format files using the Artemis software. Subsequently, the amino acid sequences of these genes were accessed using the View toolbar in the software and downloaded as FASTA files. The amino acid sequences of the HEGs were then aligned (http://www.ebi.ac.uk/Tools/msa/clustalo/) and classified, based on their integration into core protein-coding genes (e.g., cob, cox and nad) and other regions or non-coding areas within the mitochondrial genome. Comparative analyses were conducted to detect highly conserved amino acid sequences in the mitochondrial genomes of the different strains, as shown in Fig. 6 and Suppl. material 6.

Table 2.
Download as
CSV
XLSX

The LAGLIDADG and GIY-YIG genes/introns occurring within the core genes among the 30 mitogenomes.

Core gene cox1 nad4 nad3 cob nad1 nad2 atp9 rps3 rnl nad5 nad4L cox2 cox3 rns Total gene numbers of LAG or GIY **
Portal ID
Morcon1 12/8 /1 8/4 /2 /1 /1 3/7 2/2 /4 31/30
Morsep1 12/8 /1 1/2 /2 /1 4/7 1 3/3 /4 33/28
Morarb1 11/8 /1 8/4 /2 /1 /1 3/7 1 2/2 /4 31/30
Morbru1 11/8 /1 7/4 /2 /1 3/7 2/2 /4 29/29
Morhis1 9/7 /1 6/5 1/3 /1 6/8 1 3/3 1/4 34/32
MorM21481-1 11/8 /1 8/4 /2 /1 3/7 1 2/2 /4 29/29
Mordel1 10/7 /1 6/4 /2 /1 /1 5/7 4/4 /4 38/31
Morkaki1 11/8 /1 1 7/5 1/3 /1 1/7 1/2 /4 31/31
Morsem1 12/8 /1 1 7/5 1/3 /1 1/7 1/2 /4 34/31
Morgal1 12/8 /1 1 7/4 1/3 /1 1/7 1/2 /4 33/30
Mordis1 12/8 /1 1 7/5 1/3 /1 1/7 1/2 /4 34/31
Mordun1 12/8 /1 1 7/5 1/3 /1 1/7 2/3 /4 32/32
Morimp1 10 (1 GIY) /9 /1 5 (*1 rps3) /3 /2 /3 1 /5 3/3 /4 26 (2 GIYs) /30
Morexi1 12/9 1/1 1 8/4 1/3 /1 /6 1 1/2 /4 1 41 (1 GIY) /30
Morexim1 10/9 1 8/4 /2 /1 /6 1 1/2 /4 29 (1 GIY) /27
Morpop1 21/8 1/1 5/4 /2 /1 5/6 2/2 /4 1 48/28
Morpun1 17/8 1/1 5/4 /1 4/7 2/3 1/4 44 (1 GIY) /28
Mortrid1 10/9 /1 8/4 1/3 /1 /1 3/5 1 3/2 /4 1 41/30
MorM1934m1-1 7/8 /1 2/4 /2 /2 /1 4/9 1 1/3 /4 20/34
MorvulMes17-1 7/8 /1 2/4 /2 /2 3/9 1/3 /4 21 /33
Morpra1 7/7 /1 2/4 /2 /2 /2 3/9 1/4 /4 22/35
Morper1 8/8 /1 3/4 /2 /2 /2 3/9 2/4 /4 29/36
Morulm1 8/8 /1 3/4 /2 /2 /1 2/8 1/3 /4 21 (1 GIY) /33
Morame1 6/7 /1 1/4 /2 /2 /2 3/9 1/3 /4 19/34
Mordim1 6/8 /1 3/3 /2 /2 1 3/9 1 1/3 1/4 22/32
Morpal1 9/9 /1 11/4 /2 /1 1/4 1 1/3 /4 1 34/28
Morana1 12/9 /1 1/2 /3 /2 /1 6/9 2/5 1/3 34/35
Morruf1 9/11 /1 1/2 1/3 /2 /2 7/9 1/4 1/3 1 30/37
Disven1 7/8 7/3 /1 3/5 1 /2 /3 30/22
Vercon1 5/4 1/2 /2 /2 1/3 2/4 25/17

* means the overlap between cob and rps3; ** means LAGLIDADG or GIY-YIG.

Abbreviations

Mitogenome Mitochondrial genome

HEG Homing endonuclease gene

HGT Horizontal gene transfer

ORF Open reading frame

ML Maximum Likelihood

PCG Protein-coding gene

atp ATP synthase

cox cytochrome c oxidase

cob cytochrome b-coding gene

NADH Nicotinamide adenine dinucleotide

rps ribosomal protein

Results

Mitogenome features and Morchellaceae phylogeny

Mitochondrial genomes of 30 Morchellaceae species were sequenced and annotated. The selected species included false morels, blushing and black and yellow morels, representing distinct ecological lifestyles including soil/litter decomposers, endophytes and Y-mycorrhizal species (Buscot 1992; Dahlstrom et al. 2000).The mitogenomes of Morchellaceae were predominantly circular, although some species exhibited a linear arrangement (Table 1). The assemblies comprised a single scaffold with the exception of M. importuna and Morchella vulgaris Mes-17 which have two scaffolds (Table 1 and Fig. 1). The mitogenome size ranged from 254,381 bp in Morchella Mel-23to 565,090 bp in M. prava Mes7, with an average size of 377,541 bp (Table 1; Fig. 1; Suppl. material 7: figs S1–S3). The GC content ranged from 38.22% in M. importuna SCYDJ1-A1 to 48.33% in M. americanaPhC192, with the highest GC content (48.33%) observed in M. americana PhC192 (Table 1). Each of the 28 morel mitogenomes contained two types of rRNA genes, one small subunit ribosomal RNA gene (rnS) and one large subunit ribosomal RNA gene (rnL), with the exception of M. anatolica PhC233, which had three rnS genes and a missed rnL gene (Table 1, Fig. 2). Furthermore, the mitochondrial genomes encode 28–37 tRNA genes for 20 standard amino acids (Table 1, Fig. 3).

Figure 1. 

Overview of 30 mitogenomes. General genomic features includes 30 Morchellaceae species in the evolutionary order according to the maximum likelihood tree (see Methods). The trophic type indicates N/A: no data. B/E: Biotrophic/endophytic. S/B/E: Saprotrophic/Biotrophic/Endophytic. The order of 15 protein-coding genes (PCGs), and the size of genomes with the number of scaffolds. Phenotypic groups are colour-coded. Grey: Black morels. Yellow: Yellow morels. Red: Blushing morels. Green: False morels. Green gradient bars show the size of the scaffolds and total size of the genomes. The bubbles beside the graph indicate the number of scaffolds in the genome assembly. Note that the gene positions were adjusted in a circular way to facilitate visible relative comparisons. See the linear version of detailed syntenic comparisons (Suppl. material 7: figs S1, S2, S3). See phylogenetic trees of atp6 and atp8 genes (Suppl. material 7: fig. S4).

Figure 2. 

The length of mitogenomic core genes. The core genes are abbreviated. atp: ATP synthase. cob: apocytochrome b; cox: cytochrome c oxidase. nad: NADH dehydrogenase. rnl: Large subunit rRNA. rns: small subunit rRNA. rps: ribosomal protein. The species are in the evolutionary order based on a multi-gene maximum likelihood tree constructed (see Methods). Phenotypic groups are colour-coded. Grey: Black morels. Yellow: Yellow morels. Red: Blushing morels. Green: False morels.

Figure 3. 

The gene order of tRNA and rRNA in the mitogenomes. The species are in the evolutionary order based on a multi-gene maximum likelihood tree constructed (see Methods). Phenotypic groups are colour-coded. Grey: Black morels. Yellow: Yellow morels. Red: Blushing morels. Green: False morels. See details (Suppl. material 1).

Maximum Likelihood phylogenetic analysis was performed using concatenated amino acid sequences from the 15 PCGs (Fig. 1). The resulting tree displayed a high degree of congruence with previous nuclear genome-based phylogenetic topologies (O’Donnell et al. 2011) and the Morchellaceae phylogeny, based on nuclear genomes constructed by MycoCosm using single-copy conserved nuclear protein genes (https://mycocosm.jgi.doe.gov/mycocosm/species-tree/tree;vtOK-f?organism=morch-ellaceae). The species tree confirmed that true morels were divided into three main clades corresponding to known phenotypic groups, with the exception of M. parazonii, which formed an outgroup to the black morel group (Fig. 1). Notably, blushing and yellow morels were grouped within the same cluster.

The base composition of protein-coding genes (PCGs)

PCGs, tRNA genes and rRNA genes were annotated for the 30 Morchellaceae mitogenomes. Fifteen core PCGs were detected: atp6, atp8, atp9, cob, cox1, cox2, cox3, nad1, nad2, nad3, nad4, nad4L, nad5, nad6 and rps3. Additionally, a single rps3 gene, involved in transcriptional regulation, was identified (Fig. 2). The mitogenome sizes of the yellow morels weresignificantly larger than those of the black and false morels (Fig. 1; Suppl. material 7: fig. S5; FDR adj. p < 0.05). Trends in mitogenome size were explained by the phylogenomic distances of the species (Suppl. material 7: fig. S6; R squared of PC1-3 = 0.84; p < 0.05), suggesting that closely-related species have similar-sized genomes.

At the gene level, several PCGs in yellow and blushing morels were larger than their orthologs in black morels, accounting for the mitogenome enlargement observed in these groups (Fig. 2; Suppl. material 7: fig. S5). Notably, these genes included those encoding cytochrome c oxidase (e.g., cox1, cox2 and cox3) and NADH dehydrogenase subunits (e.g., nad1, nad2, nad4 and nad5), which are highly correlated with genome size (Suppl. material 7: fig. S7; Pearson coefficient > 0.7, p < 0.05). In contrast, the cytochrome b-coding gene (cob) of black morels was significantly larger than that of yellow morels (Suppl. material 7: fig. S5; FDR adj. p < 0.05). In addition, the size of some genes (e.g., cob, cox and nad) was significantly associated with species relatedness (Suppl. material 7: fig. S6; R squared of PC1 and PC2 > 0.6; P < 0.05).

The GC content ranged from 37.45% to 48.33%. Morchella americana (Morame1) had the highest GC content. Yellow morels displayed a higher GC content than black morels and false morels had the lowest GC content. Upon closer examination, the average GC content of each PCG in the 30 fungi samples varied from 25.17% for atp8 to 44.42% for cox3. Notably, nad1, nad2, nad4 and cob showed higher GC contents in yellow morels than in black morels (Fig. 4a; Suppl. material 2). The variation in GC content of PCGs was partially explained by the groups (Suppl. material 7: fig. S6). There was a tendency for yellow morels to show significantly higher GC content than black morels (Suppl. material 7: fig. S5). The GC content of cox1, cox2, nad1, nad2, nad4, nad5 and cob was highly positively correlated with the genome size (p < 0.05; Suppl. material 2).

Figure 4. 

The GC content in the mitogenomes a GC content of each of 15 PCGs among 30 fungal mitogenomes b AT skew c GC skew. Phenotypic groups are colour-coded. Grey: Black morels. Yellow/Orange: Yellow morels. Red: Blushing morels. Green: False morels. See details (Suppl. material 2).

A clear pattern of positive AT skew (0.016–0.15) was observed for cox1, cox2, nad2, cob, atp6, atp8, nad3, nad4, nad6 and nad4L, indicating a higher frequency of A than that of T in the forward strand. Conversely, atp6, atp8, nad3, nad6 and rps3 showed a negative AT skew, ranging from −0.065 to −0.25. Additionally, all PCGs, except nad1, atp9, cox3 and nad5, exhibited positive or negative AT skew for each PCG amongst the different models. Furthermore, all PCGs, except for atp8, atp6, nad3, nad6 and rps3, displayed a positive GC skew ranging from 0.022 to 0.122, indicating a higher frequency of G than C in the forward strand. M. importuna rps3 exhibited a positive GC skew with a value of 0.385 (Fig. 4).

PCG microsynteny

We assessed mitogenome gene synteny with a specific focus on the spatial distribution of PCGs across black, yellow and blushing morels (Fig. 1; Suppl. material 7: figs S2, S3). The relative positions and orders of the 15 PCGs were highly conserved within the Morchellaceae family. Most PCGs, including cox1, rnS, cob and apt, displayed remarkable conservation across all morel clades, signifying robust evolutionary stability. Nevertheless, distinctive differences in gene order were observed, such as the rearrangement of atp6 and rps3 in M. importuna (Morimp1) and the reversed order of atp6 and atp8 in M. populiphila (Morpop1), M. punctipes (Morpun1), M. steppicola (Morpal1), M. anatolica (Morana1) and M. rufobrunnea (Morruf1) (Fig. 1). Furthermore, the genomic structure of Morchella vulgaris Mes-17, a yellow morel, was truncated because of its division into two scaffolds (Fig. 1). Consequently, only the larger scaffold was considered in synteny analysis (Suppl. material 7: fig. S3a). Notably, the genomic arrangement of M. anatolica and M. rufobrunnea significantly deviated amongst blushing morels, with the exception of specific PCGs, such as nad4 (Suppl. material 7: fig. S3b), which is likely attributable to disparities in the sequencing starting points and directions.

Comparisons of atp6 and atp8 amongst the 30 morels revealed distinct patterns (Suppl. material 7: fig. S4). The atp6 gene showed that M. populiphila (Morpop1) and M. punctipes (Morpun1) were outgroups of the black morel group. Intriguingly, M. importuna (Morimp1) was distant from black morels and grouped instead with false morels. Meanwhile, the atp8 gene exhibited mostly identical sequences amongst the fungi, except for some species including D. venosa, V. conica (Disven1; Vercon1; false morels), M. populiphila, M. punctipes (Morpop1; Morpun1; black morels) and M. ulmaria (Morulm1; yellow morel).

tRNA and rRNA gene distribution

The positions of tRNA genes exhibited a remarkable degree of consistency across various species (Fig. 3; Suppl. material 1). However, certain black morels of Morchella importuna (Morimp1), M. populiphila (Morpop1), M. punctipes (Morpun1) and M. tridentina (Mortrid1), yellow morels of Morchella fluvialis (MorM1934m1), M. steppicola (Morpal1) and M. diminutiva (Mordim1) and blushing morel species of M. anatolica (Morana1) and M. rufobrunnea (Morruf1), along with false morels (Morchellaceae), display additional tRNA genes and rearrangements of the tRNA within their mitogenomes. These observed variations contributed to alterations in the relative order of the genes within the respective mitogenomes (Fig. 3; Suppl. material 1).

All the Morchellaceae species examined in this study contained tRNAs corresponding to all 20 natural amino acids within their mitogenomes. Notably, trnR (tRNA-Arg (ACG) and tRNA-Arg (TCT)) were observed with 4-7 copies, whereas trnM (tRNA-Met(CAT)), trnS (tRNA-Ser(TGA) and tRNA-Ser(GCT)), trnL (tRNA-Leu (TAA) and tRNA-Leu (TAG)) and trnI (tRNA-Ile (GAT) and tRNA-Ile (TAT)) exhibited 2-4 copies. The anticodons associated with trnR, trnS, trnL and trnI were CGU and AGA, UCA and AGC, UUA and CUA and AUC and AUA, respectively (Fig. 3; Suppl. material 1). Nevertheless, certain morel species, such as M. brunnea (Morbrun1), exhibited five copies of trnC (tRNA-Cys(GCA)), whereas three other morel species, namely M. populiphila (Morpop1), M. diminutiva (Mordim1) and M. punctipes (Morpun1), were characterised by only one copy of trnI (tRNA-Ile(GAT)) (Fig. 3; Suppl. material 1).

The positions of the rRNA genes within the Morchellaceae species in this study displayed a notable level of consistency, with rnS consistently appearing in close proximity to the cox1 gene. However, certain morel species exhibit additional rRNA genes and rRNA rearrangements within their mitogenomes. For instance, M. deliciosa (Mordel1) has five rnL and three rnS genes, whereas M. tridentina (Mortrid1) and M. ulmaria (Morulm1) possess three rnL and two rnS genes. Furthermore, some species featured only one copy of the rnL or rnS gene or lacked it entirely, leading to distinct gene order variations (Fig. 3; Suppl. material 1).

The distribution and phylogeny of HEGs within mitogenomes

A total of 925 LAGLIDADG (LAGs) and GIY-YIG (GIYs) genes and 913 introns have been identified within the mitogenomes of Morchellaceae. Individual species exhibited a range of 19–48 LAGs or GIYs. The presence and distribution of LAGs and GIYs were predominantly observed in PCGs, such as cox1, cob, cox2 and nad5 (Table 2; Suppl. material 7: figs S5, S6; Suppl. materials 1, 6). The number of LAGs with GIYs in the black morels was significantly higher than that in the yellow morels (Suppl. material 7: fig. S5; FDR adj. p < 0.05). Notably, GIY-YIG genes were limited to a small subset, identified in only five strains out of the total 30 fungi (Table 2).

The LAGs within each morel species displayed a significant diversity in size and content (Fig. 5). Phylogenetic trees segregated LAGs and GIYs of each representative morel into distinct clades with various motifs (Fig. 5). Examples include the black morel M. populiphila NRRL22315 (Morpop1) with a maximum of 48 LAGs, yellow morel M. peruviana NRRL66754 (Morper1) with 29 LAGs and blushing morel M. anatolica PhC233 (Morana1) with 34 LAGs (Table 2). The total number of LAGs with GIYs and those present in the cox1 gene were highly associated with species relatedness (Suppl. material 7: fig. S6; R squared of PC1 and PC2 > 0.4; P < 0.05). Noteworthy instances include introns that house multiple LAGs. The total number of introns in the yellow and blushing morels was significantly higher than that in the black morels (Suppl. material 7: fig. S5; FDR adj. p < 0.05). A remarkable diversity of LAGs was evident in each morel mitogenome, with certain LAGs being identical or homologous across species (Fig. 6). Importantly, these LAGs were consistently inserted into core genes such as cox1, cob, cox2 and nad5. For instance, within cox1, a singular subtype of LAG, Disven1-LAG13-cox1-6 (Fig. 6a), was shared between 25 morels and one false morel. This subtype exhibited a high degree of similarity, forming distinct clusters within the same or closely-related clades.

Figure 5. 

Phylogenetic trees of LAGLIDADG coding genes. Maximum-likelihood phylogenetic trees were constructed with three representative morel species based on the amino acid sequences of LAGLIDADG genes in their mitogenomes a Morchella populiphila NRRL22315 (Morpop1), a black morel b Morchella peruviana NRRL66754 (Morper1), a yellow morel c Morchella anatolica PhC233 (Morana1), a blushing morel. Support values are shown at the nodes. Clades represent the amino acid sequences of all LAGLIDADG genes, which are either inserted within core genes or located externally in the mitogenomes. The genes are labelled with detailed information. For example, ”Morpop1-LAG25-cox1-21” describes the 25th LAGLIDADG gene inserted into the core gene cox1 at position 21 in M. populiphila NRRL22315, while, “Morper1-LAG15” denotes the 15th LAGLIDADG gene located outside the core genes in M. peruviana NRRL66754.

Figure 6. 

Alignments of LAGLIDADG amino acid sequences. Homologous amino acid sequences of LAGLIDADG coding genes were aligned across the Morchella species and false morels. These alignments encompass five representative LAGLIDADG genes present in the core genes; a cox1, b cox2, c cob, d nad5, and e outside the core genes. "Morexi1-LAG19-cox1-3" denotes the 19th LAGLIDADG gene found at the position 3 of the core gene cox1 in Morchella eximia NRRL26621 (Morexi1).

Discussion

In this study, we sequenced the mitogenomes of species belonging to false, blushing, black and yellow morels of the Morchellaceae family. The assembly sizes for these mitogenomes varied from 217.7 kb to 565.1 kb, as shown in Table 1, with some species surpassing the size of the two previously-published morel mitogenomes (Liu et al. 2020a). The mitogenome of the yellow morel M. crassipes is 531.2 kb in size, whereas that of M. importuna, a black morel, is only 272.2 kb. It is important to note that the average mitogenome size within the Morchella genus is currently the largest known amongst fungal genera, with the mitogenome of Morchella prava, a yellow morel, being the largest.

Mitochondrial genes, which are widely used in population genetics, evolution and phylogenetic analyses, present independent evolutionary characteristics that are distinct from those of nuclear genomes. The abundance of molecular markers, particularly mitochondria-specific genes, such as atp6, atp8, atp9, cob, cox1, cox2, cox3, nad1, nad2, nad3, nad4, nad4L, nad5 and nad6, renders them valuable tools (Sanchez et al. 2018; Van de Paer et al. 2018; Wang et al. 2018). Combining datasets of mitochondrial genes, including those mentioned above, has proven to be a reliable molecular marker, demonstrating their potential as single-gene markers for evaluating the phylogenetic relationships of fungi (Korovesi et al. 2018; Li et al. 2019b).

Although cox1 and other PCGs have not been extensively utilised as molecular markers in fungal phylogenetic analyses owing to limited mitochondrial genome availability, our study revealed identical and well-supported evolutionary tree topologies for Morchellaceae fungi, based on mitochondrial gene sets (Fig. 1). Notably, this phylogenetic analysis closely aligns with relationships derived from hundreds of conserved single-copy nuclear sequences, underscoring the robustness of our findings. In the context of fungal phylogenetic analysis, mitochondrial genes may emerge as an effective alternative classification method given their congruence with nuclear genomes. Mitochondria are postulated to have originated from the endosymbiosis of ancestral free-living Alphaproteobacteria, permanently integrated into the host cell (Munoz-Gomez et al. 2017). Currently, mitochondrial DNA (mtDNA) predominantly contains protein-coding genes crucial for oxidative phosphorylation and adenosine triphosphate (ATP) synthesis, serving as the primary energy production mechanism (Verma et al. 2018; Kulik et al. 2021). Additionally, mitochondria contribute to the production of metabolic precursors of macromolecules, such as proteins and lipids and generate metabolic by-products, such as ammonia and reactive oxygen species (Spinelli and Haigis 2018). These organelles are also pivotal for apoptosis, homeostasis and stress responses (Grahl et al. 2012; Verma et al. 2018; Zardoya 2020). Throughout evolution, many mitochondrial genes have migrated to the nuclear genome. In fungi, the majority of the genes associated with mitochondrial function are located in the nuclear genome (Bolender et al. 2008). For instance, in Saccharomyces cerevisiae, the transfer of mtDNA to the nuclear genome occurs under specific nuclear gene mutations, depending on the mitogenome structure and sugar availability for fermentation, contributing to phenotypic variation in anaerobic environments (Shafer et al. 1999; Peter et al. 2018). Previous studies have demonstrated the absence of universally conserved genes in fungal mitogenomes, implying that the content of mitochondrial genes can vary considerably without compromising organelle function (Fonseca et al. 2021). This variability contributes to the adaptation of fungi to different environmental conditions (Malina et al. 2018). In this study, we observed substantial variation in the 15 PCGs amongst the major Morchellaceae clades, particularly in terms of size, GC content and AT and GC skews (Figs 2, 4). Although the order of the 15 PCGs remained highly conserved across most Morchellaceae species, the distribution of rRNA andtRNA genes exhibited additional rearrangements. These variations contributed to the relative order of the genes within the mitogenomes (Figs 1, 3). Our study further highlights the significant variability in core genes within the mitogenomes of different morel species. This variability may play a key role in the adaptation of diverse ecological groups, such as saprotrophic and mycorrhiza-like morels, throughout their evolutionary processes.

In Fungi, the length and genomic composition of mitogenomes may be influenced by the presence of accessory elements such as introns, HEGs and uORFs, introduced through horizontal gene transfer (Himmelstrand et al. 2014; Kanzi et al. 2016). In particular, the distribution of HEGs often reveals the extensive diversity observed in mitogenome size, genomic fragmentation and rearrangements. These endonucleases facilitate site-specific homologous recombination events, leading to the insertion, deletion, mutation or repair of DNA double-strand breaks. This phenomenon has been studied extensively in both yeast and mammalian cells (Rouet et al. 1994; Stoddard 2014).

Previous studies have demonstrated variations in the number and location of HEGs amongst the mitogenomes of six Lactarius species, which significantly contributes to differences in mitogenome organisation and size (Li et al. 2019b). Here, we explored the distribution and phylogeny of LAG and GIY HEG families. The abundance of LAGs and GIYs, ranging from 19 to 48, surpassed that observed for other fungi sequenced to date (Table 2; Suppl. material 7: fig. S5). LAGs displayed significant diversity in size and content within each species, with different Morchellaceae species sharing identical HEGs or homogeneous motifs.

For instance, Disven1-LAG13-cox1-6 was shared by 25 morels and one false-morel within cox1 (Fig. 6a), indicating its ancestral nature. Conversely, some HEGs were shared by fewer morels, such as Disven1-LAG25, which was found in only 14 morels (Fig. 6e), suggesting a more recent evolutionary status. These findings imply strong evolutionary relationships between fungal LAGs and mitogenome size diversity, providing crucial evidence in support of the evolutionary theory proposed by Megarioti et al. (2020), which suggests that the co-evolution of introns and HEGs drives fungal mitogenome diversity (Megarioti et al. 2020).

The findings of this study also underscore the dynamic nature of Morchella mitogenomes and suggest a possible link between HEG abundance and evolutionary adaptability of these fungi (Fonseca et al. 2021). High copy numbers and shared motifs imply that HEGs may play roles beyond genome restructuring, potentially contributing to species-specific mitochondrial functions or adaptations to environmental niches (Belfort and Bonocora 2014; Fonseca et al. 2021).

A particularly intriguing, yet complex question pertains to the trophic status of morels. For the past two decades, it has been hypothesised that certain yellow morel species may form weak associations with trees (Y-mycorrhiza). However, this contention has not yet been substantiated by experimental studies. More broadly, it has been posited that yellow morels (Esculenta group) are associated with root systems during their life cycle, whereas black morels (Elata group) are exclusively saprotrophic. Our observation of a slightly larger genome size in yellow morels raises the question whether this difference is related to their ecology or biology. The observed variation in mitogenome size arose from differences in intron content, mobile genetic elements and repeat sequences (Tables 1, 2). These features may reflect evolutionary and ecological adaptation. For instance, intron proliferation and recombination events can lead to genome expansion or contraction (Tang et al. 2024). However, determining the direct effects of repeat elements on mitochondrial metabolism remains an unresolved question. Identifying correlations between the mitogenome structure (e.g., genome size, HEG copy number and GC%), gene order and ecological or lifestyle traits of morels is a compelling research avenue. To establish links between genetic characteristics and trophic status, extensive sequencing of morel mitogenomes and a comprehensive database of their biological and ecological traits are essential. Currently, knowledge of the biotrophic or saprotrophic nature of morels is limited to a few species, which impedes robust statistical analyses. Our study provides a resource for investigating these relationships; however, without a clear understanding of the trophic status of morel species, it is premature to propose a correlation between mitogenome size and trophic status.

Owing to their exceptional flavour, functional attributes and limited availability, the market value of wild morels has increased to approximately $300 per kilogram (dried) (Liu et al. 2017). Following Ower’s pioneering research in 1982, considerable progress has been made in morel cultivation in China since 2012, particularly for three black species, M. importuna, M. sextelata and M. septimelata, resulting in a cultivated area of 9,000 ha by 2018 (He et al. 2015; Liu et al. 2019). Despite these accomplishments, the morel-cultivation industry continues to grapple with persistent challenges that stem from unresolved issues. Notably, there is a dearth of documented successful cultivation instances for other yellow morels using either the recently proposed field cultivation model or the Ower’s indoor method (Liu et al. 2017). One of several factors leading to a decreased yield of morel-fruiting bodies in industrial cultivation regimes may be related to senescence of the mycelial inoculum and/or mutations in the nuclear and mitochondrial genomes. The expression of mitochondrial genes during morel cultivation needs to be investigated to understand the role of the mitogenome in fruiting body development (if any). Current mitogenomic resources can be used to unravel fundamental biological knowledge and other unidentified factors to improve the cultivation of black morels and the domestication of yellow morels.

Conclusions

This study analysed the mitochondrial genomes of 30 Morchellaceae species, encompassing yellow, black, blushing and false morels, to elucidate their genetic architecture and ecological significance. The investigation revealed that these mitogenomes, ranging from 217 to 565 kbp in length, exist as either circular or linear DNA molecules with GC content between 38% and 48%. Although a substantial degree of gene order conservation was observed, several rearrangements were identified, most notably in Morchella importuna. This study also examined the diversity and phylogenetic relationships of HEGs within these mitogenomes, detecting between 19 and 48 HEGs per species. These observations support the contributions of introns and HEGs to mitogenomic diversity in morels. This research provides a novel perspective on the evolutionary dynamics of the Morchellaceae family.

Acknowledgements

We thank Emmanuelle Morin (INRAE) for useful discussions and constructive feedback regarding the construction of the phylogenic tree and Laure Fauchery and Annegret Kohler for providing DNA/RNA material for several species of morels used in this study. We also thank Philippe Clowez and Tim James for providing Morchella and Verpa isolates, respectively and Tom Bruns for permission to use an unpublished genome.

Additional information

Conflict of interest

The authors have declared that no competing interests exist.

Ethical statement

No ethical statement was reported.

Adherence to national and international regulations

All the fungal strains used in this study have been legally obtained, respecting the Convention on Biological Diversity (Rio Convention).

Funding

This work was supported by several grants and funding sources: the JGI Community Science Program proposals (10.46936/10.25585/60001060 awarded to FMM and GB, funded by the U.S. Department of Energy Office of Science, Biological and Environmental Research Division [LANLF59T] and the U.S. National Science Foundation Division of Environmental Biology [DEB-1946445]); 10.46936/10.25585/60001080 to Tom Bruns; and 10.46936/fics.proj.2019.50958/60000125 to PC. The Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy Contract no. DE-AC02-05CH11231. Additional funding was provided by the Laboratory of Excellence ARBRE (ANR-11-LABX-0002-01) to FM and by the China Scholarship Council and the National Natural Science Foundation of China (NSFC 31860520) for GT.

Author contributions

FM and GT designed the research; GT, FM, SA and SM performed the research and analysed the data; XJZ and HP analysed the data; GT and FM wrote the manuscript; KL, AC, RH, GB, IG and PC reviewed and improved the manuscript. All the authors have read and agreed to the published version of the manuscript.

Author ORCIDs

Gang Tao https://orcid.org/0000-0002-0882-3752

Steven Ahrendt https://orcid.org/0000-0001-8492-4830

Shingo Miyauchi https://orcid.org/0000-0002-0620-5547

XiaoJie Zhu https://orcid.org/0009-0004-9333-5893

Hao Peng https://orcid.org/0009-0005-3348-1861

Kurt Labutti https://orcid.org/0000-0002-5838-1972

Alicia Clum https://orcid.org/0000-0002-5004-3362

Richard Hayes https://orcid.org/0000-0002-5236-7918

Patrick S. G. Chain https://orcid.org/0000-0003-3949-3634

Igor V. Grigoriev https://orcid.org/0000-0002-3136-8903

Gregory Bonito https://orcid.org/0000-0002-7262-8978

Francis M. Martin https://orcid.org/0000-0002-4737-3715

Data availability

All of the data that support the findings of this study are available in the main text or Supplementary Information.

References

  • Aguileta G, de Vienne DM, Ross ON, Hood ME, Giraud T, Petit E, Gabaldon T (2014) High variability in the mitochondrial gene order among fungi. Genome Biology and Evolution 6(2): 451–465. https://doi.org/10.1093/gbe/evu028
  • Barroso G, Blesa S, Labarere J (1995) Wide distribution of mitochondrial genome rearrangements in wild strains of the cultivated Basidiomycete Agrocybe aegerita. Applied and Environmental Microbiology 61(4): 1187–1193. https://doi.org/10.1128/aem.61.4.1187-1193.1995
  • Benucci GMN, Longley R, Zhang P, Zhao Q, Bonito G, Yu F (2019) Microbial communities associated with the black morel Morchella sextelata cultivated in greenhouses. PeerJ 7: e7744. https://doi.org/10.7717/peerj.7744
  • Black B, Lee C, Horianopoulos LC, Jung WH, Kronstad JW (2021) Respiring to infect: emerging links between mitochondria, the electron transport chain, and fungal pathogenesis. PLOS Pathogens 17(7): e1009661. https://doi.org/10.1371/journal.ppat.1009661
  • Bullerwell CE, Forget L, Lang BF (2003) Evolution of monoblepharidalean fungi based on complete mitochondrial genome sequences. Nucleic Acids Research 31(6): 1614–1623. https://doi.org/10.1093/nar/gkg264
  • Buscot F (1992) Mycorrhizal succession and morel biology. Mycorrhizas in Ecosystems 220–224.
  • Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T (2009) trimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25(15): 1972–1973. https://doi.org/10.1093/bioinformatics/btp348
  • Charif D, Lobry JR (2007) SeqinR 1.0-2: a contributed package to the R project for statistical computing devoted to biological sequences retrieval and analysis//Structural approaches to sequence evolution: Molecules, networks, populations. Berlin, Heidelberg: Springer Berlin Heidelberg, 207–232. https://doi.org/10.1007/978-3-540-35306-5_10
  • Chevalier BS, Stoddard BL (2001) Homing endonucleases: structural and functional insight into the catalysts of intron/intein mobility. Nucleic Acids Research 29(18): 3757–3774. https://doi.org/10.1093/nar/29.18.3757
  • Christinaki AC, Kanellopoulos SG, Kortsinoglou AM, Andrikopoulos MA, Theelen B, Boekhout T, Kouvelis VN (2022) Mitogenomics and mitochondrial gene phylogeny decipher the evolution of Saccharomycotina yeasts. Genome Biology and Evolution 14(5): evac073. https://doi.org/10.1093/gbe/evac073
  • Clowez P, Izumi T, Lamiable PB, Shibakusa K, Minculeasa C, Alvarado P (2022) Morchella nipponensis sp. nov. (Ascomycota, Pezizales): a paleoendemic species of section Morchella discovered in Japan. Mycoscience 63(6): 274–283. https://doi.org/10.47371/mycosci.2022.08.005
  • Dahlstrom JL, Smith JE, Weber NS (2000) Mycorrhiza-like interaction by Morchella with species of the Pinaceae in pure culture synthesis. Mycorrhiza 9: 279–285. https://doi.org/10.1007/PL00009992
  • Deng Y, Hsiang T, Li S, Lin L, Wang Q, Chen Q, Xie B, Ming R (2018) Comparison of the mitochondrial genome sequences of six Annulohypoxylon stygium isolates suggests short fragment insertions as a potential factor leading to larger genomic size. Frontiers in Microbiology 9: 2079. https://doi.org/10.3389/fmicb.2018.02079
  • Férandon C, Moukha S, Callac P, Benedetto J-P, Castroviejo M, Barroso G (2010) The Agaricus bisporus cox1 gene: the longest mitochondrial gene and the largest reservoir of mitochondrial group I introns. PLOS ONE 5(11): e14048. https://doi.org/10.1371/journal.pone.0014048
  • Férandon C, Xu J, Barroso G (2013) The 135 kbp mitochondrial genome of Agaricus bisporus is the largest known eukaryotic reservoir of group I introns and plasmid-related sequences. Fungal Genetics and Biology 55: 85–91. https://doi.org/10.1016/j.fgb.2013.01.009
  • Fonseca PL, De-Paula RB, Araújo DS, Tomé LMR, Mendes-Pereira T, Rodrigues WFC, Góes-Neto A (2021) Global characterization of fungal mitogenomes: new insights on genomic diversity and dynamism of coding genes and accessory elements. Frontiers in Microbiology 12: 787283. https://doi.org/10.3389/fmicb.2021.787283
  • Formey D, Molès M, Haouy A, Savelli B, Bouchez O, Bécard G, Roux C (2012) Comparative analysis of mitochondrial genomes of Rhizophagus irregularis – syn. Glomus irregulare – reveals a polymorphism induced by variability generating elements. New Phytologist 196(4): 1217–1227. https://doi.org/10.1111/j.1469-8137.2012.04283.x
  • Formighieri EF, Tiburcio RA, Armas ED, Medrano FJ, Shimo H, Carels N, Góes-Neto A, Cotomacci C, Carazzolle MF, Sardinha-Pinto N, Thomazella DPT, Rincones J, Digiampietri L, Carraro DM, Azeredo-Espin AM, Reis SF, Deckmann AC, Gramacho K, Gonçalves MS, Neto JPM, Pereira GAG (2008) The mitochondrial genome of the phytopathogenic basidiomycete Moniliophthora perniciosa is 109 kb in size and contains a stable integrated plasmid. Mycological Research 112(10): 1136–1152. https://doi.org/10.1016/j.mycres.2008.04.014
  • Gomes FEES, Arantes TD, Fernandes JAL, Ferreira LC, Romero H, Bosco SMG, Theodoro RC (2018) Polymorphism in mitochondrial group I introns among Cryptococcus neoformans and Cryptococcus gattii genotypes and its association with drug susceptibility. Frontiers in Microbiology 9: 86. https://doi.org/10.3389/fmicb.2018.00086
  • Grahl N, Dinamarco TM, Willger SD, Goldman GH, Cramer RA (2012) Aspergillus fumigatus mitochondrial electron transport chain mediates oxidative stress homeostasis, hypoxia responses and fungal pathogenesis. Molecular Microbiology 84(2): 383–399. https://doi.org/10.1111/j.1365-2958.2012.08034.x
  • Hackathon R, Bolker B, Butler M et al. (2024) phylobase: Base package for phylogenetic structures and comparative data. R package version 0.8.12.
  • Hage H, Miyauchi S, Virágh M, Drula E, Min B, Chaduli D, Navarro D, Favel A, Norest M, Lesage-Meessen L, Bálint B, Merényi Z, Eugenio L, Morin E, Martínez AT, Baldrian P, Štursová M, Martínez MJ, Novotny C, Magnuson JK, Spatafora JW, Maurice S, Pangilinan J, Andreopoulos W, La Butti K, Hundley H, Na H, Kuo A, Barry K, Lipzen A, Henrissat B, Riley R, Ahrendt S, Nagy LG, Grigoriev IG, Martin F, Rosso M-N (2021) Gene family expansions and transcriptome signatures uncover fungal adaptations to wood decay. Environmental Microbiology 23(10): 5716–5732. https://doi.org/10.1111/1462-2920.15423
  • He P, Wang K, Cai Y, Liu W (2017) Live cell confocal laser imaging studies on the nuclear behavior during meiosis and ascosporogenesis in Morchella importuna under artificial cultivation. Micron 101: 108–113. https://doi.org/10.1016/j.micron.2017.06.012
  • He PX, Liu W, Cai YL, He XS (2015) Strain identification and phylogenetic analysis of cultivated and wild strains of Morchella belonging to Elata Clade in China. Journal of Zhengzhou University of Light Industry 30: 26–29.
  • Himmelstrand K, Olson Å, Durling MB, Karlsson M, Stenlid J (2014) Intronic and plasmid-derived regions contribute to the large mitochondrial genome sizes of Agaricomycetes. Current Genetics 60: 303–313. https://doi.org/10.1007/s00294-014-0436-z
  • Hobbie EA, Rice SF, Weber NS, Smith JE (2016) Isotopic evidence indicates saprotrophy in post-fire Morchella in Oregon and Alaska. Mycologia 108(4): 638–645. https://doi.org/10.3852/15-281
  • Hyatt D, Chen GL, Lo Cascio PF, Land ML, Larimer FW, Hauser LJ (2010) Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11: 1–11. https://doi.org/10.1186/1471-2105-11-119
  • Joardar V, Abrams NF, Hostetler J, Paukstelis PJ, Pakala S, Pakala SB, Zafar N, Abolude OO, Payne G, Andrianopoulos A, Denning DW, Nierman WC (2012) Sequencing of mitochondrial genomes of nine Aspergillus and Penicillium species identifies mobile introns and accessory genes as main sources of genome size variability. BMC Genomics 13: 1–12. https://doi.org/10.1186/1471-2164-13-698
  • Kanzi AM, Wingfield BD, Steenkamp ET, Naidoo S, van der Merwe NA (2016) Intron derived size polymorphism in the mitochondrial genomes of closely related Chrysoporthe species. PLOS ONE 11(6): e0156104. https://doi.org/10.1371/journal.pone.0156104
  • Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Molecular Biology and Evolution 30(4): 772–780. https://doi.org/10.1093/molbev/mst010
  • Korovesi AG, Ntertilis M, Kouvelis VN (2018) Mt-rps3 is an ancient gene which provides insight into the evolution of fungal mitochondrial genomes. Molecular Phylogenetics and Evolution 127: 74–86. https://doi.org/10.1016/j.ympev.2018.04.037
  • Kouvelis VN, Ghikas DV, Typas MA (2004) The analysis of the complete mitochondrial genome of Lecanicillium muscarium (synonym Verticillium lecanii) suggests a minimum common gene organization in mtDNAs of Sordariomycetes: phylogenetic implications. Fungal Genetics and Biology 41(10): 930–940. https://doi.org/10.1016/j.fgb.2004.07.003
  • Li Q, Chen C, Xiong C, Jin X, Chen Z, Huang W (2018) Comparative mitogenomics reveals large-scale gene rearrangements in the mitochondrial genome of two Pleurotus species. Applied Microbiology and Biotechnology 102: 6143–6153. https://doi.org/10.1007/s00253-018-9082-6
  • Li Q, Ren Y, Shi X, Peng L, Zhao J, Song Y, Zhao G (2019a) Comparative mitochondrial genome analysis of two ectomycorrhizal fungi (Rhizopogon) reveals dynamic changes of intron and phylogenetic relationships of the subphylum Agaricomycotina. International Journal of Molecular Sciences 20(20): 5167. https://doi.org/10.3390/ijms20205167
  • Li Q, Wang Q, Jin X, Chen Z, Xiong C, Li P, Liu Q, Huang W (2019b) Characterization and comparative analysis of six complete mitochondrial genomes from ectomycorrhizal fungi of the Lactarius genus and phylogenetic analysis of the Agaricomycetes. International Journal of Biological Macromolecules 121: 249–260. https://doi.org/10.1016/j.ijbiomac.2018.10.029
  • Li Q, Yang L, Xiang D, Wan Y, Wu Q, Huang W, Zhao G (2020) The complete mitochondrial genomes of two model ectomycorrhizal fungi (Laccaria): features, intron dynamics and phylogenetic implications. International Journal of Biological Macromolecules 145: 974–984. https://doi.org/10.1016/j.ijbiomac.2019.09.188
  • Li Q, Li L, Feng H, Tu W, Bao Z, Xiong C, Wang X, Qing Y, Huang W (2021) Characterization of the complete mitochondrial genome of Basidiomycete yeast Hannaella oryzae: intron evolution, gene rearrangement, and its phylogeny. Frontiers in Microbiology 12: 646567. https://doi.org/10.3389/fmicb.2021.646567
  • Liu C, Sun Y, Mao Q, Guo X, Li P, Liu Y, Xu N (2016) Characteristics and antitumor activity of Morchella esculenta polysaccharide extracted by pulsed electric field. International Journal of Molecular Sciences 17(6): 986. https://doi.org/10.3390/ijms17060986
  • Liu W, Zhang Y, He P (2017) Morel biology and cultivation, Jilin Science and Technology Press, Changchun, China.
  • Liu W, Chen LF, Cai YL, Zhang QQ, Bian YB (2018) Opposite polarity monospore genome de novo sequencing and comparative analysis reveal the possible heterothallic life cycle of Morchella importuna. International Journal of Molecular Sciences 19(9): 2525. https://doi.org/10.3390/ijms19092525
  • Liu W, Cai YL, He PX, Ma XL, Bian YB (2019) Occurrence and control of pests and diseases in field cultivation of Morchella mushrooms. Acta Edulis Fungi 26(2): 128–134.
  • Liu W, Cai Y, Zhang Q, Shu F, Chen L, Ma X, Bian Y (2020a) Subchromosome-scale nuclear and complete mitochondrial genome characteristics of Morchella crassipes. International Journal of Molecular Sciences 21(2): 483. https://doi.org/10.3390/ijms21020483
  • Liu W, Cai Y, Zhang Q, Chen L, Shu F, Ma X, Bian Y (2020b) The mitochondrial genome of Morchella importuna (272.2 kb) is the largest among fungi and contains numerous introns, mitochondrial non-conserved open reading frames and repetitive sequences. International Journal of Biological Macromolecules 143: 373–381. https://doi.org/10.1016/j.ijbiomac.2019.12.056
  • Liu Y, Steenkamp ET, Brinkmann H, Forget L, Philippe H, Lang BF (2009) Phylogenomic analyses predict sister group relationship of nucleariids and fungi and paraphyly of zygomycetes with significant support. BMC Ecology and Evolution 9(1): 1–11. https://doi.org/10.1186/1471-2148-9-272
  • Loizides M, Alvarado P, Moreau PA, Assyov B, Halasů V, Stadler M, Rinaldi A, Marques G, Zervakis GI, Borovička J, Vooren NV, Grebenc T, Richard F, Taşkin H, Gube M, Sammut C, Agnello C, Baroni TJ, Crous P, Fryssouli V, Gonou Z, Guidori U, Gulden G, Hansen K, Kristiansen R, Læssøe T, Mateos J, Miller A, Moreno G, Perić B, Polemis E, Salom JC, Siquier JL, Snabl M, Weholt Ø, Bellanger J-M (2022) Has taxonomic vandalism gone too far? A case study, the rise of the pay-to-publish model and the pitfalls of Morchella systematics. Mycological Progress 21(1): 7–38. https://doi.org/10.1007/s11557-021-01755-z
  • Looney B, Miyauchi S, Morin E, Drula E, Courty PE, Kohler A, Kuo A, La Butti K, Pangilinan J, Lipzen A, Riley R, Andreopoulos W, He G, Johnson J, Nolan M, Tritt A, Barry KW, Grigoriev IV, Nagy LG, Hibbett D, Henrissat B, Matheny PB, Labbé J, Martin FM (2022) Evolutionary transition to the ectomycorrhizal habit in the genomes of a hyperdiverse lineage of mushroom‐forming fungi. New Phytologist 233(5): 2294–2309. https://doi.org/10.1111/nph.17892
  • Machuca A, Gerding M, Chávez D, Oyarzúa PG, Guillén Y, Córdova C (2021) Two new species of Morchella from Nothofagus forests in Northwestern Patagonia (Chile). Mycological Progress 20(6): 781–795. https://doi.org/10.1007/s11557-021-01703-x
  • Malina C, Larsson C, Nielsen J (2018) Yeast mitochondria: an overview of mitochondrial biology and the potential of mitochondrial systems biology. FEMS Yeast Research 18: 40. https://doi.org/10.1093/femsyr/foy040
  • Megarioti AH, Kouvelis VN (2020) The coevolution of fungal mitochondrial introns and their homing endonucleases (GIY-YIG and LAGLIDADG). Genome Biology and Evolution 12(8): 1337–1354. https://doi.org/10.1093/gbe/evaa126
  • Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, Lanfear R (2020) IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Molecular Biology and Evolution 37(5): 1530–1534. https://doi.org/10.1093/molbev/msaa015
  • Mistry J, Finn RD, Eddy SR, Bateman A, Punta M (2013) Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions. Nucleic Acids Research 41(12): e121–e121. https://doi.org/10.1093/nar/gkt263
  • Miyauchi S, Kiss E, Kuo A, Drula E, Kohler A, Sánchez-García M, Morin E, Andreopoulos B, Barry KW, Bonito G, Buée M, Carver A, Chen C, Cichocki N, Clum A, Martin FM (2020) Large-scale genome sequencing of mycorrhizal fungi provides insights into the early evolution of symbiotic traits. Nature Communications 11: 1–17. https://doi.org/10.1038/s41467-020-18795-w
  • Muñoz-Gómez SA, Wideman JG, Roger AJ, Slamovits CH (2017) The origin of mitochondrial cristae from alphaproteobacteria. Molecular Biology and Evolution 34(4): 943–956. https://doi.org/10.1093/molbev/msw298
  • O’Donnell K, Rooney AP, Mills GL, Kuo M, Weber NS, Rehner SA (2011) Phylogeny and historical biogeography of true morels (Morchella) reveals an Early Cretaceous origin and high continental endemism and provincialism in the Holarctic. Fungal Genetics and Biology 48(3): 252–265. https://doi.org/10.1016/j.fgb.2010.09.006
  • Pantou MP, Kouvelis VN, Typas MA (2008) The complete mitochondrial genome of Fusarium oxysporum: insights into fungal mitochondrial evolution. Gene 419(1–2): 7–15. https://doi.org/10.1016/j.gene.2008.04.009
  • Peter J, De Chiara M, Friedrich A, Yue J-X, Pflieger D, Bergström A, Sigwalt A, Barre B, Freel K, Llored A, Cruaud C, Labadie K, Aury J-M, Istace B, Lebrigand K, Barbry P, Engelen S, Lemainque A, Wincker P, Liti G, Schacherer J (2018) Genome evolution across 1,011 Saccharomyces cerevisiae isolates. Nature 556(7701): 339–344. https://doi.org/10.1038/s41586-018-0030-5
  • Pion M, Spangenberg JE, Simon A, Bindschedler S, Flury C, Chatelain A, Bshary R, Job D, Junier P (2013) Bacterial farming by the fungus Morchella crassipes. Proceedings of the Royal Society B: Biological Sciences 280(1773): 20132242. https://doi.org/10.1098/rspb.2013.2242
  • Rouet P, Smih F, Jasin M (1994) Expression of a site-specific endonuclease stimulates homologous recombination in mammalian cells. PNAS 91(13): 6064–6068. https://doi.org/10.1073/pnas.91.13.6064
  • Sanchez G, Setiamarga DHE, Tuanapaya S, Tongtherm K, Winkelmann IE, Schmidbaur H, Umino T, Albertin C, Allcock L, Perales-Raya C, Gleadall I, Nabhitabhata J (2018) Genus-level phylogeny of cephalopods using molecular markers: Current status and problematic areas. PeerJ 6: e4331. https://doi.org/10.7717/peerj.4331
  • Sethuraman J, Majer A, Friedrich NC, Edgell DR, Hausner G (2009) Genes within genes: Multiple LAGLIDADG homing endonucleases target the ribosomal protein S3 gene encoded within the anrnl group I intron of Ophiostoma and related taxa. Molecular Biology and Evolution 26(10): 2299–2315. https://doi.org/10.1093/molbev/msp145
  • Shafer KS, Hanekamp T, White KH, Thorsness PE (1999) Mechanisms of mitochondrial DNA escape to the nucleus in S. cerevisiae. Current Genetics 36: 183–194. https://doi.org/10.1007/s002940050489
  • Tang J, Zhang L, Su J, Ye Q, Li Y, Liu D, Ye Z (2024) Insights into fungal mitochondrial genomes and inheritance based on current findings from yeast-like fungi. Journal of Fungi 10(7): 441. https://doi.org/10.3390/jof10070441
  • Tuller T, Girshovich Y, Sella Y, Kreimer A, Freilich S, Kupiec M, Gophna U, Ruppin E (2011) Association between translation efficiency and horizontal gene transfer within microbial communities. Nucleic Acids Research 39(11): 4743–4755. https://doi.org/10.1093/nar/gkr054
  • Van de Paer C, Bouchez O, Besnard G (2018) Prospects on the evolutionary mitogenomics of plants: A case study on the olive family (Oleaceae). Molecular Ecology Resources 18(3): 407–423. https://doi.org/10.1111/1755-0998.12742
  • Vaser R, Sović I, Nagarajan N, Šikić M (2017) Fast and accurate de novo genome assembly from long uncorrected reads. Genome Research 27(5): 737–746. https://doi.org/10.1101/gr.214270.116
  • Vaughn JC, Mason MT, Sper-Whitis GL, Kuhlman P, Palmer JD (1995) Fungal origin by horizontal transfer of a plant mitochondrial group I intron in the chimeric coxI gene of Peperomia. Journal of Molecular Evolution 41: 563–572. https://doi.org/10.1007/BF00175814
  • Wang X, Song A, Wang F, Chen M, Li X, Li Q, Liu N (2020) The 206 kbp mitochondrial genome of Phanerochaete carnosa reveals dynamics of introns, accumulation of repeat sequences and plasmid-derived genes. International Journal of Biological Macromolecules 162: 209–219. https://doi.org/10.1016/j.ijbiomac.2020.06.142
  • Wang Y, Cao JJ, Li WH (2018) Complete mitochondrial genome of Suwallia teleckojensis (Plecoptera: Chloroperlidae) and implications for the higher phylogeny of stoneflies. International Journal of Molecular Sciences 19(3): 680. https://doi.org/10.3390/ijms19030680
  • Wickham H (2016) ggplot2: Elegant graphics for data analysis. Springer-Verlag, New York. [ISBN 978-3-319-24277-4]

Supplementary materials

Supplementary material 1 

Comparisons of gene order in 30 mitochondrial genomes

Gang Tao, Steven Ahrendt, Shingo Miyauchi, XiaoJie Zhu, Hao Peng, Kurt Labutti, Alicia Clum, Richard Hayes, Patrick S. G. Chain, Igor V. Grigoriev, Gregory Bonito, Francis M. Martin

Data type: xlsx

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (20.04 kb)
Supplementary material 2 

AT/GC content and skew

Gang Tao, Steven Ahrendt, Shingo Miyauchi, XiaoJie Zhu, Hao Peng, Kurt Labutti, Alicia Clum, Richard Hayes, Patrick S. G. Chain, Igor V. Grigoriev, Gregory Bonito, Francis M. Martin

Data type: xlsx

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (37.96 kb)
Supplementary material 3 

Genomic coodinates of Morchellaceae mitogenomes

Gang Tao, Steven Ahrendt, Shingo Miyauchi, XiaoJie Zhu, Hao Peng, Kurt Labutti, Alicia Clum, Richard Hayes, Patrick S. G. Chain, Igor V. Grigoriev, Gregory Bonito, Francis M. Martin

Data type: xlsx

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (132.91 kb)
Supplementary material 4 

Genomic statistics used for PERMANOVA

Gang Tao, Steven Ahrendt, Shingo Miyauchi, XiaoJie Zhu, Hao Peng, Kurt Labutti, Alicia Clum, Richard Hayes, Patrick S. G. Chain, Igor V. Grigoriev, Gregory Bonito, Francis M. Martin

Data type: xlsx

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (27.71 kb)
Supplementary material 5 

Statistically significant variables in PERMANOVA

Gang Tao, Steven Ahrendt, Shingo Miyauchi, XiaoJie Zhu, Hao Peng, Kurt Labutti, Alicia Clum, Richard Hayes, Patrick S. G. Chain, Igor V. Grigoriev, Gregory Bonito, Francis M. Martin

Data type: xlsx

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (16.72 kb)
Supplementary material 6 

Amino acid sequences of LAGs and GIYs

Gang Tao, Steven Ahrendt, Shingo Miyauchi, XiaoJie Zhu, Hao Peng, Kurt Labutti, Alicia Clum, Richard Hayes, Patrick S. G. Chain, Igor V. Grigoriev, Gregory Bonito, Francis M. Martin

Data type: xlsx

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (130.07 kb)
Supplementary material 7 

Supplementary figures

Gang Tao, Steven Ahrendt, Shingo Miyauchi, XiaoJie Zhu, Hao Peng, Kurt Labutti, Alicia Clum, Richard Hayes, Patrick S. G. Chain, Igor V. Grigoriev, Gregory Bonito, Francis M. Martin

Data type: pdf

Explanation note: fig. S1.1–1.5: Circular representation of the 30 Morchella mitogenomes; fig. S2: Linear representation of syntenic mitogenomes in black morels; fig. S3: Linear representation of syntenic mitogenomes in yellow and blushing morels; fig. S4: Phylogenetic trees of atp6 and atp8 genes; fig. S5: Trends of genomic features by fungal groups; fig. S6: Significant variables driving trends in 30 mitochondrial genomes; fig. S7: Correlated mitochondrial genes and genomes in size.

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
Download file (14.87 MB)
login to comment