Inonotus hispidus mushroom is a traditional medicinal fungus with anti-cancer, antioxidation, and immunomodulatory activities, and it is used in folk medicine as a treatment for indigestion, cancer, diabetes, and gastric illnesses. Although I. hispidus is recognized as a rare edible medicinal macrofungi, its genomic sequence and biosynthesis potential of secondary metabolites have not been investigated. In this study, using Illumina NovaSeq combined with the PacBio platform, we sequenced and de novo assembled the whole genome of NPCB_001, a wild I. hispidus isolate from the Aksu area of Xinjiang Province, China. Comparative genomic and phylogenomic analyses reveal interspecific differences and evolutionary traits in the genus Inonotus. Bioinformatics analysis identified candidate genes associated with mating type, polysaccharide synthesis, carbohydrate-active enzymes, and secondary metabolite biosynthesis. Additionally, molecular networks of metabolites exhibit differences in chemical composition and content between fruiting bodies and mycelium, as well as association clusters of related compounds. The deciphering of the genome of I. hispidus will deepen the understanding of the biosynthesis of bioactive components, open the path for future biosynthesis research, and promote the application of Inonotus in the fields of drug research and functional food manufacturing.

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Genomic and Metabolomic Analyses of the Medicinal Fungus Inonotus hispidus for Its Metabolite’s Biosynthesis and Medicinal Application

COVID-19 pandemic has disrupted the normal functioning of the world at both the physical as well as biological level. Various biotechnological approaches form the core for finding the drug for COVID. But at the backend, it is highly supported by intensive computational algorithms for drug designing approaches. Artificial intelligence is one such area that can be used to mimic the living system by training it with multi-dimensional datasets. The rapid advancements in the field of artificial intelligence and machine learning have facilitated to build high-accurate generative models. Deep learning is a subset of artificial intelligence that is being used in the present study for ligand designing models. The results obtained by a deep learning-based model called variational autoencoder—general adversarial networks (VAE-GAN) show promising results in terms of ligand design and can also be utilized for drug repurposing. In this paper, we have attempted to build a VAE-GAN model which was trained on isomeric simplified molecular-input line-entry system (Iso-SMILES) data and molecular structure images of the COVID-19 drug dataset. The Iso-SMILES and molecular structure data analysis are useful, but the system needs more improvement to cater to the data loss in terms of spatial structures and orientation of the chemical compounds taken for the analysis. However, the system can be optimized with the help of higher hardware support and increased training datasets, which can help in more precise analysis for generating ligand molecules of interest. 

A Variational Autoencoder—General Adversarial Networks (VAE-GAN) Based Model for Ligand Designing

Reaction-based de novo design is the computational generation of novel molecular structures by linking building blocks using reaction vectors derived from chemistry knowledge. In this work, we first adopted a recurrent neural network (RNN) model to generate three groups of building blocks with different functional groups and then constructed an in silico target-focused combinatorial library based on chemical reaction rules. Mer tyrosine kinase (MERTK) was used as a study case. Combined with a scaffold enrichment analysis, 15 novel MERTK inhibitors covering four scaffolds were achieved. Among them, compound 5a obtained an IC50 value of 53.4 nM against MERTK without any further optimization. The efficiency of hit identification could be significantly improved by shrinking the compound library with the fragment iterative optimization strategy and enriching the dominant scaffold in the hinge region. We hope that this strategy can provide new insights for accelerating the drug discovery process.

Effective Reaction-Based De Novo Strategy for Kinase Targets: A Case Study on MERTK Inhibitors

Oudemansiella raphanipes, considered as a well-known culinary edible mushroom with a high content of natural bioactive substances, is widely cultivated in China with the commercial name Changgengu. However, due to the lack of genomic data, molecular and genetic study on O. raphanipes is rare. To obtain a comprehensive overview of genetic characteristics and enhance the value of O. raphanipes, two mating-compatible monokaryons isolated from the dikaryon were applied for de novo genome sequencing and assembly using Nanopore and /or Illumina sequencing platforms. One of the monokaryons, O. raphanipes CGG-A-s1, was annotated with 21,308 protein-coding genes, of which 56 were predicted to be involved in the biosynthesis of secondary metabolites such as terpene, type I PKS, NRPS, and siderophore. Phylogenetic and comparative analysis of multiple fungi genomes revealed a close evolutionary relationship between O. raphanipes and Mucidula mucid based on single-copy orthologous protein genes. Significant collinearity was detected between O. raphanipes and Flammulina velutipes on the synteny of inter-species genomes. 664 CAZyme genes in CGG-A-s1 were identified with GHs and AAs families significantly elevated when compared with the other 25 sequenced fungi, indicating a strong wood degradation ability. Furthermore, the mating type locus analysis revealed that CGG-A-s1 and CGG-A-s2 were conserved in the gene organization of the mating A locus but various in that of the mating B locus. The genome resource of O. raphanipes will provide new insights into its development of genetic studies and commercial production of high-quality varieties.

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Whole Genome Sequence of an Edible Mushroom Oudemansiella raphanipes (Changgengu)

Cancer is responsible for lifelong disability and decreased quality of life. Cancer-associated changes in metabolism, in particular carbohydrate, lipid, and protein, offer a new paradigm of metabolic hits. Hence, targeting the latter, as well as related cross-linked signalling pathways, can reverse the malignant phenotype of transformed cells. The systemic toxicity and pharmacokinetic limitations of existing drugs prompt the discovery of multi-targeted and safe compounds from natural products. Mushrooms possess biological activities relevant to disease-fighting and to the prevention of cancer. They have a long-standing tradition of use in ethnomedicine and have been included as an adjunct therapy during and after oncological care. Mushroom-derived compounds have also been reported to target the key signature of cancer cells in in vitro and in vivo studies. The identification of metabolic pathways whose inhibition selectively affects cancer cells appears as an interesting approach to halting cell proliferation. For instance, panepoxydone exerted protective mechanisms against breast cancer initiation and progression by suppressing lactate dehydrogenase A expression levels and reinducing lactate dehydrogenase B expression levels. This further led to the accumulation of pyruvate, the activation of the electron transport chain, and increased levels of reactive oxygen species, which eventually triggered mitochondrial apoptosis in the breast cancer cells. Furthermore, the inhibition of hexokinase 2 by neoalbaconol induced selective cytotoxicity against nasopharyngeal carcinoma cell lines, and these effects were also observed in mouse models. Finally, GL22 inhibited hepatic tumour growth by downregulating the mRNA levels of fatty acid-binding proteins and blocking fatty acid transport and impairing cardiolipin biosynthesis. The present review, therefore, will highlight how the metabolites isolated from mushrooms can target potential biomarkers in metabolic reprogramming.

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Mushroom-Derived Compounds as Metabolic Modulators in Cancer

L. sulphureus is a parasitic basidiomycete fungus that causes brown rot. The fruiting bodies of L. sulphureus are used as ancient medicines in China and Europe to cure cancer, analgesia, cough, and rheumatism and are considered a functional food that regulates the body and improves health. L. sulphureus was inferred to be a tetrapolar system based on a high-quality genome, which will aid molecular breeding and artificial farming. ABSTRACT Laetiporus sulphureus mushroom is a complementary and alternative medicine that has anticancer, antioxidation, and analgesic effects and immunomodulatory activity; it is used as a treatment for cough and rheumatism and is a functional food that can improve physical fitness. Even though L. sulphureus has garnered considerable biotechnological and pharmacological interest due to its excellent cellulose-degrading ability and diverse biological activities, its biosynthetic potential regarding polysaccharides and secondary metabolites has not been thoroughly examined. In this study, we sequenced and assembled the whole genome of a wild L. sulphureus isolate, NWAFU-1, from the Qinling Mountains in China. Comparative genomes analysis revealed genomic differences between subspecies, and phylogenomic analysis revealed evolutionary divergence as well as genome expansion and contraction of individual Polyporaceae family species. Bioinformatics investigation identified candidate genes associated with mating type, polysaccharide synthesis, carbohydrate-active enzymes, and secondary-metabolite biosynthesis, which included multiple terpenoids, nonribosomal peptides, and polyketides. The locations of biosynthetic core genes were mapped and displayed on chromosomes and contigs. Totals of 143 proteins from 126 coding genes were identified and divided into 14 cytochrome P450 families. Furthermore, the biosynthetic network of tetracyclic triterpenoid active components was postulated by genome mining of related genes combined with the molecular network of metabolites. The genome analysis of L. sulphureus in this study improves the understanding of the biosynthesis of active compounds, which will lay a theoretical foundation for subsequent research on active-compound biosynthesis and promote the application of Laetiporus in the field of drug research and functional-food creation. IMPORTANCE L. sulphureus is a parasitic basidiomycete fungus that causes brown rot. The fruiting bodies of L. sulphureus are used as ancient medicines in China and Europe to cure cancer, analgesia, cough, and rheumatism and are considered a functional food that regulates the body and improves health. L. sulphureus was inferred to be a tetrapolar system based on a high-quality genome, which will aid molecular breeding and artificial farming. Screening polysaccharide synthesis candidate genes and comparing carbohydrate-associated genes in brown-rot basidiomycetes help understand their growth. Identifying core genes for secondary-metabolite biosynthesis, gene cluster family analysis, and comparative cluster analysis will guide heterologous-biosynthesis investigations of these genes and help elucidate the biosynthetic pathways for L. sulphureus bioactive natural components. The biosynthesis network of tetracyclic triterpenes was mapped using metabolite profiling and genome scanning. This work explores the biosynthetic capacity of L. sulphureus-derived natural products and lays the foundation for biosynthetic studies of them.

Chromosome-Level Genome Sequences, Comparative Genomic Analyses, and Secondary-Metabolite Biosynthesis Evaluation of the Medicinal Edible Mushroom Laetiporus sulphureus

The application of deep learning in the field of drug discovery brings the development and expansion of molecular generative models along with new challenges in this field. One of challenges in de novo molecular generation is how to produce new reasonable molecules with desired pharmacological, physical, and chemical properties. To improve the similarity between the generated molecule and the starting molecule, we propose a new molecule generation model by embedding Long Short-Term Memory (LSTM) and Attention mechanism in CycleGAN architecture, LA-CycleGAN. The network layer of the generator in CycleGAN is fused head and tail to improve the similarity of the generated structure. The embedded LSTM and Attention mechanism can overcome long-term dependency problems in treating the normally used SMILES input. From our quantitative evaluation, we present that LA-CycleGAN expands the chemical space of the molecules and improves the ability of structure conversion. The generated molecules are highly similar to the starting compound structures while obtaining expected molecular properties during cycle generative adversarial network learning, which comprehensively improves the performance of the generative model.

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Improving de novo Molecule Generation by Embedding LSTM and Attention Mechanism in CycleGAN

Armophorean ciliates constitute an important component of microeukaryotic community in anaerobic or hypoxic environments. Yet, their diversity remains poorly known due to under-sampling or the scarcity of knowledge. In this study, three metopid ciliates, i.e., Metopus paraes sp. n., Metopus spiculatus sp. n., and Metopus parapellitus sp. n., collected from coastal sediments in Qingdao and Rizhao, China, were investigated using live observation, protargol staining, and molecular phylogenetic methods. M. paraes sp. n. can be distinguished by its long caudal cilia. M. spiculatus sp. n. resembles M. vestitus in many ways, but differs mainly in having a beak-like preoral dome end and a conspicuous tail. The most remarkable features of M. parapellitus sp. n. include an ovate body shape, caudal cilia located at the rear end and right posterior body, and an adoral zone that never extends onto the dorsal surface. Sequence divergences supported the species identification of these three species. Phylogenetic analyses confirmed that the Metopus is not monophyletic, and first revealed that all marine species of Metopus form a well-supported clade. The clustering of these marine forms with M. es (type species) is not rejected by the AU test, which infers that the marine clade represents the genus Metopus together with M. es.

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New Contribution to the Diversity of the Anaerobic Genus Metopus (Ciliophora, Armophorea), With Descriptions of Three New Marine Species

Phylogeny of the anaerobic ciliate genus Sonderia (Protista: Ciliophora: Plagiopylea), including the description of three novel species and a brief revision of the genus

Protein engineering uses de novo protein design technology to change the protein gene sequence, and then improve the physical and chemical properties of proteins. These newly generated proteins will meet the needs of research better in properties and functions. The Dense-AutoGAN model is based on GAN, which is combined with an Attention mechanism to generate protein sequences. In this GAN architecture, the Attention mechanism and Encoder-decoder can improve the similarity of generated sequences and obtain variations in a smaller range on the original basis. Meanwhile, a new convolutional neural network is constructed by using the Dense. The dense network transmits in multiple layers over the generator network of the GAN architecture, which expands the training space and improves the effectiveness of sequence generation. Finally, the complex protein sequences are generated on the mapping of protein functions. Through comparisons of other models, the generated sequences of Dense-AutoGAN verify the model performance. The new generated proteins are highly accurate and effective in chemical and physical properties.

Xiaochen Feng

Papers

Chromosome-Level Genome Sequences, Comparative Genomic Analyses, and Secondary-Metabolite Biosynthesis Evaluation of the Medicinal Edible Mushroom Laetiporus sulphureus

Improving de novo Molecule Generation by Embedding LSTM and Attention Mechanism in CycleGAN

New Contribution to the Diversity of the Anaerobic Genus Metopus (Ciliophora, Armophorea), With Descriptions of Three New Marine Species

Phylogeny of the anaerobic ciliate genus Sonderia (Protista: Ciliophora: Plagiopylea), including the description of three novel species and a brief revision of the genus

Generating new protein sequences by using dense network and attention mechanism.