Using deep RNA sequencing for the structural annotation of the Laccaria bicolor mycorrhizal transcriptome

Rapid genome functional annotation pipeline anchored to the house sparrow (Passer domesticus, Linnaeus 1758) genome reannotation

The house sparrow (Passer domesticus) is a valuable avian model for studying evolutionary genetics, development, neurobiology, physiology, behavior, and ecology, both in laboratory and field-based settings. The current annotation of the P. domesticus genome available at the Ensembl Rapid Release site is primarily focused on gene set building and lacks functional information. In this study, we present the first comprehensive functional reannotation of the P. domesticus genome using intestinal Illumina RNA sequencing (RNA-Seq) libraries. Our revised annotation provides an expanded view of the genome, encompassing 38592 transcripts compared to the current 23574 transcripts in Ensembl. We also predicted 14717 protein-coding genes, achieving 96.4% completeness for Passeriformes lineage BUSCOs. A substantial improvement in this reannotation is the accurate delineation of untranslated region (UTR) sequences. We identified 82.7% and 93.8% of the transcripts containing 5'- and 3'-UTRs, respectively. These UTR annotations are crucial for understanding post-transcriptional regulatory processes. Our findings underscore the advantages of incorporating additional specific RNA-Seq data into genome annotation, particularly when leveraging fast and efficient data processing capabilities. This functional reannotation enhances our understanding of the P. domesticus genome, providing valuable resources for future investigations in various research fields.

Genome Improvement and Core Gene Set Refinement of Fugacium kawagutii

Cataloging an accurate functional gene set for the Symbiodiniaceae species is crucial for addressing biological questions of dinoflagellate symbiosis with corals and other invertebrates. To improve the gene models of Fugacium kawagutii, we conducted high-throughput chromosome conformation capture (Hi-C) for the genome and Illumina combined with PacBio sequencing for the transcriptome to achieve a new genome assembly and gene prediction. A 0.937-Gbp assembly of F. kawagutii were obtained, with a N50 > 13 Mbp and the longest scaffold of 121 Mbp capped with telomere motif at both ends. Gene annotation produced 45,192 protein-coding genes, among which, 11,984 are new compared to previous versions of the genome. The newly identified genes are mainly enriched in 38 KEGG pathways including N-Glycan biosynthesis, mRNA surveillance pathway, cell cycle, autophagy, mitophagy, and fatty acid synthesis, which are important for symbiosis, nutrition, and reproduction. The newly identified genes also included those encoding O-methyltransferase (O-MT), 3-dehydroquinate synthase, homologous-pairing protein 2-like (HOP2) and meiosis protein 2 (MEI2), which function in mycosporine-like amino acids (MAAs) biosynthesis and sexual reproduction, respectively. The improved version of the gene set (Fugka_Geneset _V3) raised transcriptomic read mapping rate from 33% to 54% and BUSCO match from 29% to 55%. Further differential gene expression analysis yielded a set of stably expressed genes under variable trace metal conditions, of which 115 with annotated functions have recently been found to be stably expressed under three other conditions, thus further developing the "core gene set" of F. kawagutii. This improved genome will prove useful for future Symbiodiniaceae transcriptomic, gene structure, and gene expression studies, and the refined "core gene set" will be a valuable resource from which to develop reference genes for gene expression studies.

Multi-Omics Approach Identifies Molecular Mechanisms of Plant-Fungus Mycorrhizal Interaction

In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root-mycorrhizal fungi sensor systems, we analyzed in vitro experiments of Populus tremuloides (aspen tree) and Laccaria bicolor (mycorrhizal fungi) interaction and leveraged over 200 previously published transcriptomic experimental data sets, 159 experimentally validated plant transcription factor binding motifs, and more than 120-thousand experimentally validated protein-protein interactions to generate models of pre-mycorrhizal sensor systems in aspen root. These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with 15 transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and jasmonic acid. This multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation.

The effect of elevated carbon dioxide on the interaction between Eucalyptus grandis and diverse isolates of Pisolithus sp. is associated with a complex shift in the root transcriptome

Using the newly available genome for Eucalyptus grandis, we sought to determine the genome-wide traits that enable this host to form mutualistic interactions with ectomycorrhizal (ECM) Pisolithus sp. and to determine how future predicted concentrations of atmospheric carbon dioxide (CO2 ) will affect this relationship. We analyzed the physiological and transcriptomic responses of E. grandis during colonization by different Pisolithus sp. isolates under conditions of ambient (400 ppm) and elevated (650 ppm) CO2 to tease out the gene expression profiles associated with colonization status. We demonstrate that E. grandis varies in its susceptibility to colonization by different Pisolithus isolates in a manner that is not predictable by geographic origin or the internal transcribed spacer (ITS)-based phylogeny of the fungal partner. Elevated concentrations of CO2 alter the receptivity of E. grandis to Pisolithus, a change that is correlated to a dramatic shift in the transcriptomic profile of the root. These data provide a starting point for understanding how future environmental change may alter the signaling between plants and their ECM partners and is a step towards determining the mechanism behind previously observed shifts in Eucalypt-associated fungal communities exposed to elevated concentrations of atmospheric CO2 .

MicroRNA-like small RNAs prediction in the development of Antrodia cinnamomea

Antrodia cinnamomea, a precious, host-specific brown-rot fungus that has been used as a folk medicine in Taiwan for centuries is known to have diverse bioactive compounds with potent pharmaceutical activity. In this study, different fermentation states of A. cinnamomea (wild-type fruiting bodies and liquid cultured mycelium) were sequenced using the next-generation sequencing (NGS) technique. A 45.58 Mb genome encoding 6,522 predicted genes was obtained. High quality reads were assembled into a total of 13,109 unigenes. Using a previously constructed pipeline to search for microRNAs (miRNAs), we then identified 4 predicted conserved miRNA and 63 novel predicted miRNA-like small RNA (milRNA) candidates. Target prediction revealed several interesting proteins involved in tri-terpenoid synthesis, mating type recognition, chemical or physical sensory protein and transporters predicted to be regulated by the miRNAs and milRNAs.

The Mutualist Laccaria bicolor Expresses a Core Gene Regulon During the Colonization of Diverse Host Plants and a Variable Regulon to Counteract Host-Specific Defenses

The coordinated transcriptomic responses of both mutualistic ectomycorrhizal (ECM) fungi and their hosts during the establishment of symbiosis are not well-understood. This study characterizes the transcriptomic alterations of the ECM fungus Laccaria bicolor during different colonization stages on two hosts (Populus trichocarpa and Pseudotsuga menziesii) and compares this to the transcriptomic variations of P. trichocarpa across the same time-points. A large number of L. bicolor genes (≥ 8,000) were significantly regulated at the transcriptional level in at least one stage of colonization. From our data, we identify 1,249 genes that we hypothesize is the 'core' gene regulon necessary for the mutualistic interaction between L. bicolor and its host plants. We further identify a group of 1,210 genes that are regulated in a host-specific manner. This variable regulon encodes a number of genes coding for proteases and xenobiotic efflux transporters that we hypothesize act to counter chemical-based defenses simultaneously activated at the transcriptomic level in P. trichocarpa. The transcriptional response of the host plant P. trichocarpa consisted of differential waves of gene regulation related to signaling perception and transduction, defense response, and the induction of nutrient transfer in P. trichocarpa tissues. This study, therefore, gives fresh insight into the shifting transcriptomic landscape in both the colonizing fungus and its host and the different strategies employed by both partners in orchestrating a mutualistic interaction.

Expanding genomics of mycorrhizal symbiosis

The mycorrhizal symbiosis between soil fungi and plant roots is a ubiquitous mutualism that plays key roles in plant nutrition, soil health, and carbon cycling. The symbiosis evolved repeatedly and independently as multiple morphotypes [e.g., arbuscular mycorrhizae (AM), ectomycorrhizal (ECM)] in multiple fungal clades (e.g., phyla Glomeromycota, Ascomycota, Basidiomycota). The accessibility and cultivability of many mycorrhizal partners make them ideal models for symbiosis studies. Alongside molecular, physiological, and ecological investigations, sequencing led to the first three mycorrhizal fungal genomes, representing two morphotypes and three phyla. The genome of the ECM basidiomycete Laccaria bicolor showed that the mycorrhizal lifestyle can evolve through loss of plant cell wall-degrading enzymes (PCWDEs) and expansion of lineage-specific gene families such as short secreted protein (SSP) effectors. The genome of the ECM ascomycete Tuber melanosporum showed that the ECM type can evolve without expansion of families as in Laccaria, and thus a different set of symbiosis genes. The genome of the AM glomeromycete Rhizophagus irregularis showed that despite enormous phylogenetic distance and morphological difference from the other two fungi, symbiosis can involve similar solutions as symbiosis-induced SSPs and loss of PCWDEs. The three genomes provide a solid base for addressing fundamental questions about the nature and role of a vital mutualism.

Biosynthesis of terpenoid natural products in fungi

Tens of thousands of terpenoid natural products have been isolated from plants and microbial sources. Higher fungi (Ascomycota and Basidiomycota) are known to produce an array of well-known terpenoid natural products, including mycotoxins, antibiotics, antitumor compounds, and phytohormones. Except for a few well-studied fungal biosynthetic pathways, the majority of genes and biosynthetic pathways responsible for the biosynthesis of a small number of these secondary metabolites have only been discovered and characterized in the past 5-10 years. This chapter provides a comprehensive overview of the current knowledge on fungal terpenoid biosynthesis from biochemical, genetic, and genomic viewpoints. Enzymes involved in synthesizing, transferring, and cyclizing the prenyl chains that form the hydrocarbon scaffolds of fungal terpenoid natural products are systematically discussed. Genomic information and functional evidence suggest differences between the terpenome of the two major fungal phyla--the Ascomycota and Basidiomycota--which will be illustrated for each group of terpenoid natural products.

Transcriptome and proteome exploration to provide a resource for the study of Agrocybe aegerita

BACKGROUND

Agrocybe aegerita, the black poplar mushroom, has been highly valued as a functional food for its medicinal and nutritional benefits. Several bioactive extracts from A. aegerita have been found to exhibit antitumor and antioxidant activities. However, limited genetic resources for A. aegerita have hindered exploration of this species.

METHODOLOGY/PRINCIPAL FINDINGS

To facilitate the research on A. aegerita, we established a deep survey of the transcriptome and proteome of this mushroom. We applied high-throughput sequencing technology (Illumina) to sequence A. aegerita transcriptomes from mycelium and fruiting body. The raw clean reads were de novo assembled into a total of 36,134 expressed sequences tags (ESTs) with an average length of 663 bp. These ESTs were annotated and classified according to Gene Ontology (GO), Clusters of Orthologous Groups (COG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways. Gene expression profile analysis showed that 18,474 ESTs were differentially expressed, with 10,131 up-regulated in mycelium and 8,343 up-regulated in fruiting body. Putative genes involved in polysaccharide and steroid biosynthesis were identified from A. aegerita transcriptome, and these genes were differentially expressed at the two stages of A. aegerita. Based on one-dimensional gel electrophoresis (1-DGE) coupled with electrospray ionization liquid chromatography tandem MS (LC-ESI-MS/MS), we identified a total of 309 non-redundant proteins. And many metabolic enzymes involved in glycolysis were identified in the protein database.

CONCLUSIONS/SIGNIFICANCE

This is the first study on transcriptome and proteome analyses of A. aegerita. The data in this study serve as a resource of A. aegerita transcripts and proteins, and offer clues to the applications of this mushroom in nutrition, pharmacy and industry.

Genome-wide transcriptome and proteome analysis on different developmental stages of Cordyceps militaris

BACKGROUND

Cordyceps militaris, an ascomycete caterpillar fungus, has been used as a traditional Chinese medicine for many years owing to its anticancer and immunomodulatory activities. Currently, artificial culturing of this beneficial fungus has been widely used and can meet the market, but systematic molecular studies on the developmental stages of cultured C. militaris at transcriptional and translational levels have not been determined.

METHODOLOGY/PRINCIPAL FINDINGS

We utilized high-throughput Illumina sequencing to obtain the transcriptomes of C. militaris mycelium and fruiting body. All clean reads were mapped to C. militaris genome and most of the reads showed perfect coverage. Alternative splicing and novel transcripts were predicted to enrich the database. Gene expression analysis revealed that 2,113 genes were up-regulated in mycelium and 599 in fruiting body. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to analyze the genes with expression differences. Moreover, the putative cordycepin metabolism difference between different developmental stages was studied. In addition, the proteome data of mycelium and fruiting body were obtained by one-dimensional gel electrophoresis (1-DGE) coupled with nano-electrospray ionization liquid chromatography tandem mass spectrometry (nESI-LC-MS/MS). 359 and 214 proteins were detected from mycelium and fruiting body respectively. GO, KEGG and Cluster of Orthologous Groups (COG) analysis were further conducted to better understand their difference. We analyzed the amounts of some noteworthy proteins in these two samples including lectin, superoxide dismutase, glycoside hydrolase and proteins involved in cordycepin metabolism, providing important information for further protein studies.

CONCLUSIONS/SIGNIFICANCE

The results reveal the difference in gene expression between the mycelium and fruiting body of artificially cultivated C. militaris by transcriptome and proteome analysis. Our study provides an effective resource for the further developmental and medicinal research of this promising fungus.

Complementation contributes to transcriptome complexity in maize (Zea mays L.) hybrids relative to their inbred parents

Typically, F₁-hybrids are more vigorous than their homozygous, genetically distinct parents, a phenomenon known as heterosis. In the present study, the transcriptomes of the reciprocal maize (Zea mays L.) hybrids B73×Mo17 and Mo17×B73 and their parental inbred lines B73 and Mo17 were surveyed in primary roots, early in the developmental manifestation of heterotic root traits. The application of statistical methods and a suitable experimental design established that 34,233 (i.e., 86%) of all high-confidence maize genes were expressed in at least one genotype. Nearly 70% of all expressed genes were differentially expressed between the two parents and 42%–55% of expressed genes were differentially expressed between one of the parents and one of the hybrids. In both hybrids, ∼10% of expressed genes exhibited nonadditive gene expression. Consistent with the dominance model (i.e., complementation) for heterosis, 1124 genes that were expressed in the hybrids were expressed in only one of the two parents. For 65 genes, it could be shown that this was a consequence of complementation of genomic presence/absence variation. For dozens of other genes, alleles from the inactive inbred were activated in the hybrid, presumably via interactions with regulatory factors from the active inbred. As a consequence of these types of complementation, both hybrids expressed more genes than did either parental inbred. Finally, in hybrids, ∼14% of expressed genes exhibited allele-specific expression (ASE) levels that differed significantly from the parental-inbred expression ratios, providing further evidence for interactions of regulatory factors from one parental genome with target genes from the other parental genome.

Deep insight into the Ganoderma lucidum by comprehensive analysis of its transcriptome

BACKGROUND

Ganoderma lucidum is a basidiomycete white rot fungus and is of medicinal importance in China, Japan and other countries in the Asiatic region. To date, much research has been performed in identifying the medicinal ingredients in Ganoderma lucidum. Despite its important therapeutic effects in disease, little is known about Ganoderma lucidum at the genomic level. In order to gain a molecular understanding of this fungus, we utilized Illumina high-throughput technology to sequence and analyze the transcriptome of Ganoderma lucidum.

METHODOLOGY/PRINCIPAL FINDINGS

We obtained 6,439,690 and 6,416,670 high-quality reads from the mycelium and fruiting body of Ganoderma lucidum, and these were assembled to form 18,892 and 27,408 unigenes, respectively. A similarity search was performed against the NCBI non-redundant nucleotide database and a customized database composed of five fungal genomes. 11,098 and 8, 775 unigenes were matched to the NCBI non-redundant nucleotide database and our customized database, respectively. All unigenes were subjected to annotation by Gene Ontology, Eukaryotic Orthologous Group terms and Kyoto Encyclopedia of Genes and Genomes. Differentially expressed genes from the Ganoderma lucidum mycelium and fruiting body stage were analyzed, resulting in the identification of 13 unigenes which are involved in the terpenoid backbone biosynthesis pathway. Quantitative real-time PCR was used to confirm the expression levels of these unigenes. Ganoderma lucidum was also studied for wood degrading activity and a total of 22 putative FOLymes (fungal oxidative lignin enzymes) and 120 CAZymes (carbohydrate-active enzymes) were predicted from our Ganoderma lucidum transcriptome.

CONCLUSIONS

Our study provides comprehensive gene expression information on Ganoderma lucidum at the transcriptional level, which will form the foundation for functional genomics studies in this fungus. The use of Illumina sequencing technology has made de novo transcriptome assembly and gene expression analysis possible in species that lack full genome information.

BowStrap v1.0: Assigning statistical significance to expressed genes using short-read transcriptome data

BACKGROUND

Background: Deep RNA sequencing, the application of Next Generation sequencing technology to generate a comprehensive profile of the message RNA present in a set of biological samples, provides unprecedented resolution into the molecular foundations of biological processes. By aligning short read RNA sequence data to a set of gene models, expression patterns for all of the genes and gene variants in a biological sample can be calculated. However, accurate determination of gene model expression from deep RNA sequencing is hindered by the presence of ambiguously aligning short read sequences.

FINDINGS

BowStrap, a program for implementing the sequence alignment tool 'Bowtie' in a bootstrap-style approach, accommodates multiply-aligning short read sequences and reports gene model expression as an averaged aligned reads per Kb of gene model sequence per million aligned deep RNA sequence reads with a confidence interval, suitable for calculating statistical significance of presence/absence of detected gene model expression. BowStrap v1.0 was validated against a simulated metatranscriptome. Results were compared with two alternate 'Bowtie'-based calculations of gene model expression. BowStrap is better at accurately identifying expressed gene models in a dataset and provides a more accurate estimate of gene model expression level than methods that do not incorporate a boot-strap style approach.

CONCLUSIONS

BowStrap v1.0 is superior in ability to detect significant gene model expression and calculate accurate determination of gene model expression levels compared to other alignment-based methods of determining patterns of gene expression. BowStrap v1.0 also can utilize multiple processors as has decreased run time compared to the previous version, BowStrap 0.5. We anticipate that BowStrap will be a highly useful addition to the available set of Next Generation RNA sequence analysis tools.

RNA-Seq improves annotation of protein-coding genes in the cucumber genome

BACKGROUND

As more and more genomes are sequenced, genome annotation becomes increasingly important in bridging the gap between sequence and biology. Gene prediction, which is at the center of genome annotation, usually integrates various resources to compute consensus gene structures. However, many newly sequenced genomes have limited resources for gene predictions. In an effort to create high-quality gene models of the cucumber genome (Cucumis sativus var. sativus), based on the EVidenceModeler gene prediction pipeline, we incorporated the massively parallel complementary DNA sequencing (RNA-Seq) reads of 10 cucumber tissues into EVidenceModeler. We applied the new pipeline to the reassembled cucumber genome and included a comparison between our predicted protein-coding gene sets and a published set.

RESULTS

The reassembled cucumber genome, annotated with RNA-Seq reads from 10 tissues, has 23, 248 identified protein-coding genes. Compared with the published prediction in 2009, approximately 8, 700 genes reveal structural modifications and 5, 285 genes only appear in the reassembled cucumber genome. All the related results, including genome sequence and annotations, are available at http://cmb.bnu.edu.cn/Cucumis_sativus_v20/.

CONCLUSIONS

We conclude that RNA-Seq greatly improves the accuracy of prediction of protein-coding genes in the reassembled cucumber genome. The comparison between the two gene sets also suggests that it is feasible to use RNA-Seq reads to annotate newly sequenced or less-studied genomes.

Mutualistic interactions on a knife-edge between saprotrophy and pathogenesis

Saprophytic, ectomycorrhizal (ECM) and pathogenic fungi play a key role in carbon and nutrient cycling in forest ecosystems. Whereas more than 50 genomes of saprotrophic and pathogenic fungi have been published, only two genomes of ECM fungi, Laccaria bicolor and Tuber melanosporum, have been released. Comparative analysis of the genomes of biotrophic species highlighted convergent evolution. Mutualistic and pathogenic biotrophic fungi share expansion of genome size through transposon proliferation and common strategies to avoid plant detection. Differences mainly rely on nutritional strategies. Such analyses also pinpointed how blurred the molecular boundaries are between saprotrophism, symbiosis and pathogenesis. Sequencing of additional ECM species, as well as soil saprotrophic fungi, will facilitate the identification of conserved traits for ECM symbiosis and those leading to the transition from white-rotting and brown-rotting to the ECM lifestyle.

Using next generation transcriptome sequencing to predict an ectomycorrhizal metabolome

BACKGROUND

Mycorrhizae, symbiotic interactions between soil fungi and tree roots, are ubiquitous in terrestrial ecosystems. The fungi contribute phosphorous, nitrogen and mobilized nutrients from organic matter in the soil and in return the fungus receives photosynthetically-derived carbohydrates. This union of plant and fungal metabolisms is the mycorrhizal metabolome. Understanding this symbiotic relationship at a molecular level provides important contributions to the understanding of forest ecosystems and global carbon cycling.

RESULTS

We generated next generation short-read transcriptomic sequencing data from fully-formed ectomycorrhizae between Laccaria bicolor and aspen (Populus tremuloides) roots. The transcriptomic data was used to identify statistically significantly expressed gene models using a bootstrap-style approach, and these expressed genes were mapped to specific metabolic pathways. Integration of expressed genes that code for metabolic enzymes and the set of expressed membrane transporters generates a predictive model of the ectomycorrhizal metabolome. The generated model of mycorrhizal metabolome predicts that the specific compounds glycine, glutamate, and allantoin are synthesized by L. bicolor and that these compounds or their metabolites may be used for the benefit of aspen in exchange for the photosynthetically-derived sugars fructose and glucose.

CONCLUSIONS

The analysis illustrates an approach to generate testable biological hypotheses to investigate the complex molecular interactions that drive ectomycorrhizal symbiosis. These models are consistent with experimental environmental data and provide insight into the molecular exchange processes for organisms in this complex ecosystem. The method used here for predicting metabolomic models of mycorrhizal systems from deep RNA sequencing data can be generalized and is broadly applicable to transcriptomic data derived from complex systems.

Using next generation transcriptome sequencing to predict an ectomycorrhizal metabolome

Background

Mycorrhizae, symbiotic interactions between soil fungi and tree roots, are ubiquitous in terrestrial ecosystems. The fungi contribute phosphorous, nitrogen and mobilized nutrients from organic matter in the soil and in return the fungus receives photosynthetically-derived carbohydrates. This union of plant and fungal metabolisms is the mycorrhizal metabolome. Understanding this symbiotic relationship at a molecular level provides important contributions to the understanding of forest ecosystems and global carbon cycling.

Results

We generated next generation short-read transcriptomic sequencing data from fully-formed ectomycorrhizae between Laccaria bicolor and aspen (Populus tremuloides) roots. The transcriptomic data was used to identify statistically significantly expressed gene models using a bootstrap-style approach, and these expressed genes were mapped to specific metabolic pathways. Integration of expressed genes that code for metabolic enzymes and the set of expressed membrane transporters generates a predictive model of the ectomycorrhizal metabolome. The generated model of mycorrhizal metabolome predicts that the specific compounds glycine, glutamate, and allantoin are synthesized by L. bicolor and that these compounds or their metabolites may be used for the benefit of aspen in exchange for the photosynthetically-derived sugars fructose and glucose.

Conclusions

The analysis illustrates an approach to generate testable biological hypotheses to investigate the complex molecular interactions that drive ectomycorrhizal symbiosis. These models are consistent with experimental environmental data and provide insight into the molecular exchange processes for organisms in this complex ecosystem. The method used here for predicting metabolomic models of mycorrhizal systems from deep RNA sequencing data can be generalized and is broadly applicable to transcriptomic data derived from complex systems.

Deep RNA sequencing improved the structural annotation of the Tuber melanosporum transcriptome

• The functional complexity of the Tuber melanosporum transcriptome has not yet been fully elucidated. Here, we applied high-throughput Illumina RNA-sequencing (RNA-Seq) to the transcriptome of T. melanosporum at different major developmental stages, that is free-living mycelium, fruiting body and ectomycorrhiza. • Sequencing of cDNA libraries generated a total of c. 24 million sequence reads representing > 882 Mb of sequence data. To construct a coverage signal profile across the genome, all reads were then aligned to the reference genome assembly of T. melanosporum Mel28. • We were able to identify a substantial number of novel transcripts, antisense transcripts, new exons, untranslated regions (UTRs), alternative upstream initiation codons and upstream open reading frames. • This RNA-Seq analysis allowed us to improve the genome annotation. It also provided us with a genome-wide view of the transcriptional and post-transcriptional mechanisms generating an increased number of transcript isoforms during major developmental transitions in T. melanosporum.