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Morchellaspecies 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 30Morchellaceaespecies, 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, 28–37tRNAgenes and 3–8rRNAgenes were identified in theseMorchellaceaemitogenomes. The gene order demonstrated a high level of conservation, with thecox1gene consistently positioned adjacent to thernSgene andcobgene flanked byaptgenes. Some exceptions were observed, such as the rearrangement ofatp6andrps3inMorchella importunaand the reversed order ofatp6andatp8in certain morel mitogenomes. However, the arrangement of thetRNAgenes 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 thecox1,cob,cox2andnad5introns 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 theMorchellaceae. 

Summary The ectomycorrhizal (ECM) symbiosis has independently evolved from diverse types of saprotrophic ancestors. In this study, we seek to identify genomic signatures of the transition to the ECM habit within the hyperdiverse Russulaceae.We present comparative analyses of the genomic architecture and the total and secreted gene repertoires of 18 species across the order Russulales, of which 13 are newly sequenced, including a representative of a saprotrophic member of Russulaceae,Gloeopeniophorella convolvens.The genomes of ECM Russulaceae are characterized by a loss of genes for plant cell wall‐degrading enzymes (PCWDEs), an expansion of genome size through increased transposable element (TE) content, a reduction in secondary metabolism clusters, and an association of small secreted proteins (SSPs) with TE ‘nests’, or dense aggregations of TEs. Some PCWDEs have been retained or even expanded, mostly in a species‐specific manner. The genome ofG. convolvenspossesses some characteristics of ECM genomes (e.g. loss of some PCWDEs, TE expansion, reduction in secondary metabolism clusters).Functional specialization in ECM decomposition may drive diversification. Accelerated gene evolution predates the evolution of the ECM habit, indicating that changes in genome architecture and gene content may be necessary to prime the evolutionary switch. 

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyze 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical, and gene neighborhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.