Integration of Metabolomes and Transcriptomes Provides Insights into Morphogenesis and Maturation in Morchella sextelata.

True morels (Morchella, Pezizales) are a popular edible and medicinal fungus with great nutritional and economic value. The dynamics and regulatory mechanisms during the morphogenesis and maturation of morels are poorly understood. In this study, the metabolomes and transcriptomes of the mycelium (MY), primordium differentiation (PR), young fruiting body (YFB), and mature fruiting body (MFB) were comprehensively analyzed to reveal the mechanism of the morphogenesis and maturation of Morchella sextelata. A total of 748 differentially expressed metabolites (DEMs) and 5342 differentially expressed genes (DEGs) were detected, mainly enriched in the carbohydrate, amino acid, and lipid metabolism pathways, with the transition from the mycelium to the primordium being the most drastic stage at both the metabolic and transcriptional levels. The integrated metabolomics and transcriptomics highlighted significant correlations between the DEMs and DEGs, and specific amino acid and nucleotide metabolic pathways were significantly co-enriched, which may play key roles in morphological development and ascocarp maturation. A conceptual model of transcriptional and metabolic regulation was proposed during morphogenesis and maturation in M. sextelata for the first time, in which environmental factors activate the regulation of transcription factors, which then promote metabolic and transcriptional regulation from vegetative to reproductive growth. These results provide insights into the metabolic dynamics and transcriptional regulation during the morphogenesis and maturation of morels and valuable resources for future breeding enhancement and sustainable artificial cultivation.

Dynamics of soil microbiome throughout the cultivation life cycle of morel (Morchella sextelata).

Although Morchella sextelata (morel) is a well-known, edible, and medicinal fungus widely cultivated in China, the dynamics and roles of its soil microbiome during cultivation are unclear. Using rhizosphere soil samples collected throughout the M. sextelata cultivation life cycle, we conducted a high-throughput metagenomic sequencing analysis, with an emphasis on variations in soil microbial composition, characteristic biomarkers, and ecological functions. We found that microbial relative abundance, alpha diversity, and structure varied significantly among fungal growth stages. A total of 47 stage-associated biomarkers were identified through a linear discriminant analysis of effect size. In addition, horizontal comparison of soil microbiomes exhibiting successful and failed primordium formation further confirmed primordium-associated microbes with possible key roles in primordium formation. A microbial function analysis revealed that nutrient metabolism-related pathways were enriched during mycelium and fruiting body stages, whereas the signal transduction pathway was enriched during the primordium stage. This result indicates that diverse microbes are required at different growth stages of M. sextelata. Our research has revealed the dynamic scenario of the soil microbiome throughout the cultivation life cycle of M. sextelata. The high-resolution microbial profiles uncovered in the present study provide novel insights that should contribute to the improvement of morel cultivation using microbial inoculants.

Fast Utility Mining on Sequence Data.

High-utility sequential pattern (HUSP) mining is an emerging topic in the field of knowledge discovery in databases. It consists of discovering subsequences that have a high utility (importance) in sequences, which can be referred to as HUSPs. HUSPs can be applied to many real-life applications, such as market basket analysis, e-commerce recommendations, click-stream analysis, and route planning. Several algorithms have been proposed to efficiently mine utility-based useful sequential patterns. However, due to the combinatorial explosion of the search space for low utility threshold and large-scale data, the performances of these algorithms are unsatisfactory in terms of runtime and memory usage. Hence, this article proposes an efficient algorithm for the task of HUSP mining, called HUSP mining with UL-list (HUSP-ULL). It utilizes a lexicographic q -sequence (LQS)-tree and a utility-linked (UL)-list structure to quickly discover HUSPs. Furthermore, two pruning strategies are introduced in HUSP-ULL to obtain tight upper bounds on the utility of the candidate sequences and reduce the search space by pruning unpromising candidates early. Substantial experiments on both real-life and synthetic datasets showed that HUSP-ULL can effectively and efficiently discover the complete set of HUSPs and that it outperforms the state-of-the-art algorithms.