Deep learning approaches for natural product discovery from plant endophytic microbiomes

Role of Endophytic Fungi in the Biosynthesis of Metal Nanoparticles and Their Potential as Nanomedicines

Metal nanoparticles (MNPs) produced through biosynthesis approaches have shown favourable physical, chemical, and antimicrobial characteristics. The significance of biological agents in the synthesis of MNPs has been acknowledged as a promising alternative to conventional approaches such as physical and chemical methods, which are confronted with certain challenges. To meet these challenges, the use of endophytic fungi as nano-factories for the synthesis of MNPs has become increasingly popular worldwide in recent times. This review provides an overview of the synthesis of MNPs using endophytic fungi, the mechanisms involved, and their important biomedical applications. A special focus on different biomedical applications of MNPs mediated endophytic fungi involved their antibacterial, antifungal, antiviral, and anticancer applications and their potential as drug delivery agents. Furthermore, this review highlights the significance of the use of endophytic fungi for the green synthesis of MNPs and discusses the benefits, challenges, and prospects in this field.

Defining the limits of plant chemical space: challenges and estimations

The plant kingdom, encompassing nearly 400,000 known species, produces an immense diversity of metabolites, including primary compounds essential for survival and secondary metabolites specialized for ecological interactions. These metabolites constitute a vast and complex phytochemical space with significant potential applications in medicine, agriculture, and biotechnology. However, much of this chemical diversity remains unexplored, as only a fraction of plant species has been studied comprehensively. In this work, we estimate the size of the plant chemical space by leveraging large-scale metabolomics and literature datasets. We begin by examining the known chemical space, which, while containing at most several hundred thousand unique compounds, remains sparsely covered. Using data from over 1,000 plant species, we apply various mass spectrometry-based approaches-a formula prediction model, a de novo prediction model, a combination of library search and de novo prediction, and MS2 clustering-to estimate the number of unique structures. Our methods suggest that the number of unique compounds in the metabolomics dataset alone may already surpass existing estimates of plant chemical diversity. Finally, we project these findings across the entire plant kingdom, estimating that the total plant chemical space likely spans millions, if not more, with most still unexplored.

Endophytic fungi: A future prospect for breast cancer therapeutics and drug development

Globally, breast cancer is a primary contributor to cancer-related fatalities and illnesses among women. Consequently, there is a pressing need for safe and effective treatments for breast cancer. Bioactive compounds from endophytic fungi that live in symbiosis with medicinal plants have garnered significant interest in pharmaceutical research due to their extensive chemical composition and prospective medicinal attributes. This review underscores the potentiality of fungal endophytes as a promising resource for the development of innovative anticancer agents specifically tailored for breast cancer therapy. The diversity of endophytic fungi residing in medicinal plants, success stories of key endophytic bioactive metabolites tested against breast cancer and the current progress with regards to in vivo studies and clinical trials on endophytic fungal metabolites in breast cancer research forms the underlying theme of this article. A thorough compilation of putative anticancer compounds sourced from endophytic fungi that have demonstrated therapeutic potential against breast cancer, spanning the period from 1990 to 2022, has been presented. This review article also outlines the latest trends in endophyte-based drug discovery, including the use of artificial intelligence, machine learning, multi-omics approaches, and high-throughput strategies. The challenges and future prospects associated with fungal endophytes as substitutive sources for developing anticancer drugs targeting breast cancer are also being highlighted.

Microbial allies: exploring fungal endophytes for biosynthesis of terpenoid indole alkaloids

Terpenoid indole alkaloids (TIAs) are natural compounds found in medicinal plants that exhibit various therapeutic activities, such as antimicrobial, anti-inflammatory, antioxidant, anti-diabetic, anti-helminthic, and anti-tumor properties. However, the production of these alkaloids in plants is limited, and there is a high demand for them due to the increasing incidence of cancer cases. To address this research gap, researchers have focused on optimizing culture media, eliciting metabolic pathways, overexpressing genes, and searching for potential sources of TIAs in organisms other than plants. The insufficient number of essential genes and enzymes in the biosynthesis pathway is the reason behind the limited production of TIAs. As the field of natural product discovery from biological species continues to grow, endophytes are being investigated more and more as potential sources of bioactive metabolites with a variety of chemical structures. Endophytes are microorganisms (fungi, bacteria, archaea, and actinomycetes), that exert a significant influence on the metabolic pathways of both the host plants and the endophytic cells. Bio-prospection of fungal endophytes has shown the discovery of novel, high-value bioactive compounds of commercial significance. The discovery of therapeutically significant secondary metabolites has been made easier by endophytic entities' abundant but understudied diversity. It has been observed that fungal endophytes have better intermediate processing ability due to cellular compartmentation. This paper focuses on fungal endophytes and their metabolic ability to produce complex TIAs, recent advancements in this area, and addressing the limitations and future perspectives related to TIA production.

Antimicrobial Action Mechanisms of Natural Compounds Isolated from Endophytic Microorganisms

Infectious diseases are a significant challenge to global healthcare, especially in the face of increasing antibiotic resistance. This urgent issue requires the continuous exploration and development of new antimicrobial drugs. In this regard, the secondary metabolites derived from endophytic microorganisms stand out as promising sources for finding antimicrobials. Endophytic microorganisms, residing within the internal tissues of plants, have demonstrated the capacity to produce diverse bioactive compounds with substantial pharmacological potential. Therefore, numerous new antimicrobial compounds have been isolated from endophytes, particularly from endophytic fungi and actinomycetes. However, only a limited number of these compounds have been subjected to comprehensive studies regarding their mechanisms of action against bacterial cells. Furthermore, the investigation of their effects on antibiotic-resistant bacteria and the identification of biosynthetic gene clusters responsible for synthesizing these secondary metabolites have been conducted for only a subset of these promising compounds. Through a comprehensive analysis of current research findings, this review describes the mechanisms of action of antimicrobial drugs and secondary metabolites isolated from endophytes, antibacterial activities of the natural compounds derived from endophytes against antibiotic-resistant bacteria, and biosynthetic gene clusters of endophytic fungi responsible for the synthesis of bioactive secondary metabolites.

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Fungal secondary metabolites (SMs) contribute to the diversity of fungal ecological communities, niches, and lifestyles. Many fungal SMs have one or more medically and industrially important activities (e.g., antifungal, antibacterial, and antitumor). The genes necessary for fungal SM biosynthesis are typically located right next to each other in the genome and are known as biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted machine learning models that predicted SM bioactivity from bacterial BGC data with accuracies as high as 80% to fungal BGC data. We trained our models to predict the antibacterial, antifungal, and cytotoxic/antitumor bioactivity of fungal SMs on two data sets: (i) fungal BGCs (data set comprised of 314 BGCs) and (ii) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs). We found that models trained on fungal BGCs had balanced accuracies between 51% and 68%, whereas training on bacterial and fungal BGCs had balanced accuracies between 56% and 68%. The low prediction accuracy of fungal SM bioactivities likely stems from the small size of the data set; this lack of data, coupled with our finding that including bacterial BGC data in the training data did not substantially change accuracies currently limits the application of machine learning approaches to fungal SM studies. With >15,000 characterized fungal SMs, millions of putative BGCs in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.IMPORTANCEFungi are key sources of natural products and iconic drugs, including penicillin and statins. DNA sequencing has revealed that there are likely millions of biosynthetic pathways in fungal genomes, but the chemical structures and bioactivities of >99% of natural products produced by these pathways remain unknown. We used artificial intelligence to predict the bioactivities of diverse fungal biosynthetic pathways. We found that the accuracies of our predictions were generally low, between 51% and 68%, likely because the natural products and bioactivities of only very few fungal pathways are known. With >15,000 characterized fungal natural products, millions of putative biosynthetic pathways present in fungal genomes, and increased demand for novel drugs, our study suggests that there is an urgent need for efforts that systematically identify fungal biosynthetic pathways, their natural products, and their bioactivities.

Comparative Analysis of Rhizosphere and Endosphere Fungal Communities in Healthy and Diseased Faba Bean Plants

This study used the ITS approach based on Illumina MiSeq sequencing to assess the endosphere and rhizosphere fungal communities in healthy and diseased faba bean plants. The findings indicate that the most predominant phyla in all samples were Ascomycota (49.89-99.56%) and Basidiomycota (0.33-25.78%). In healthy endosphere samples, Glomeromycota (0.08-1.17%) was the only predominant phylum. In diseased endosphere samples, Olpidiomycota (0.04-1.75%) was the only predominant phylum. At the genus level, Penicillium (0.47-35.21%) was more abundant in rhizosphere soil, while Paraphoma (3.48-91.16%) was predominant in the endosphere roots of faba bean plants. Significant differences were observed in the alpha diversity of rhizosphere samples from different germplasm resources (p < 0.05). The fungal community structures were clearly distinguished between rhizosphere and endosphere samples and between healthy and diseased endosphere samples (p < 0.05). Saccharomyces was significantly enriched in diseased endosphere samples, whereas Apiotrichum was enriched in healthy endosphere samples. Vishniacozyma and Phialophora were enriched in diseased rhizosphere samples, while Pseudogymnoascus was enriched in healthy rhizosphere samples. Diseased samples displayed more strongly correlated genera than healthy samples. Saprotrophs accounted for a larger proportion of the fungal microbes in rhizosphere soil than in endosphere roots. This study provides a better understanding of the composition and diversity of fungal communities in the rhizosphere and endosphere of faba bean plants as well as a theoretical guidance for future research on the prevention or control of faba bean root rot disease.

A Multi-Label Learning Framework for Predicting Chemical Classes and Biological Activities of Natural Products from Biosynthetic Gene Clusters

Natural products (NP) or secondary metabolites, as a class of small chemical molecules that are naturally synthesized by chromosomally clustered biosynthesis genes (also called biosynthetic gene clusters, BGCs) encoded enzymes or enzyme complexes, mediates the bioecological interactions between host and microbiota and provides a natural reservoir for screening drug-like therapeutic pharmaceuticals. In this work, we propose a multi-label learning framework to functionally annotate natural products or secondary metabolites solely from their catalytical biosynthetic gene clusters without experimentally conducting NP structural resolutions. All chemical classes and bioactivities constitute the label space, and the sequence domains of biosynthetic gene clusters that catalyse the biosynthesis of natural products constitute the feature space. In this multi-label learning framework, a joint representation of features (BGCs domains) and labels (natural products annotations) is efficiently learnt in an integral and low-dimensional space to accurately define the inter-class boundaries and scale to the learning problem of many imbalanced labels. Computational results on experimental data show that the proposed framework achieves satisfactory multi-label learning performance, and the learnt patterns of BGCs domains are transferrable across bacteria, or even across kingdom, for instance, from bacteria to Arabidopsis thaliana. Lastly, take Arabidopsis thaliana and its rhizosphere microbiome for example, we propose a pipeline combining existing BGCs identification tools and this proposed framework to find and functionally annotate novel natural products for downstream bioecological studies in terms of plant-microbiota-soil interactions and plant environmental adaption.

Bio Prospecting of Endophytes and PGPRs in Artemisinin Production for the Socio-economic Advancement

The growing demand for Artemisia annua plants in healthcare, food, and pharmaceutical industries has led to increased cultivation efforts to extract a vital compound, Artemisinin. The efficacy of Artemisinin as a potent drug against malaria disease is well established but its limited natural abundance. However, the common practice of using chemical fertilizers for maximum yield has adverse effects on plant growth, development, and the quality of phytochemicals. To address these issues, the review discusses the alternative approach of harnessing beneficial rhizosphere microbiota, particularly plant growth-promoting rhizobacteria (PGPR). Microbes hold substantial biotechnological potential for augmenting medicinal plant production, offering an environmentally friendly and cost-effective means to enhance medicinal plant production. This review article aims to identify a suitable endophytic population capable of enabling Artemisia sp. to thrive amidst abiotic stress while simultaneously enhancing Artemisinin production, thereby broadening its availability to a larger population. Furthermore, by subjecting endophytes to diverse combinations of harsh conditions, this review sheds light on the modulation of essential artemisinin biosynthesis pathway genes, both up regulated and down regulated. The collective findings suggest that through the in vitro engineering of endophytic communities and their in vivo application to Artemisia plants cultivated in tribal population fields, artemisinin production can be significantly augmented. The overall aim of this review to explore the potential of harnessing microbial communities, their functions, and services to enhance the cultivation of medicinal plants. It outlines a promising path toward bolstering artemisinin production, which holds immense promise in the fight against malaria.

Diversity and structure of the root-associated bacterial microbiomes of four mangrove tree species, revealed by high-throughput sequencing

Background

Root-associated microbes of the mangrove trees play important roles in protecting and maintaining mangrove ecosystems. At present, most of our understanding of mangrove root-related microbial diversity is obtained from specific mangrove species in selected geographic regions. Relatively little is known about the composition of the bacterial microbiota existing in disparate mangrove species microenvironments, particularly the relationship among different mangrove species in tropical environments.

Methods

We collected the root, rhizosphere soil, and non-rhizosphere soil of four mangrove trees (Acanthus ilicifolius, Bruguiera gymnorrhiza, Clerodendrum inerme, and Lumnitzera racemosa) and detected the 16S rRNA gene by a conventional PCR. We performed high throughput sequencing using Illumina Novaseq 6000 platform (2 × 250 paired ends) to investigate the bacterial communities related with the different mangrove species.

Results

We analyzed the bacterial diversity and composition related to the diverse ecological niches of mangrove species. Our data confirmed distinct distribution patterns of bacterial communities in the three rhizocompartments of the four mangrove species. Microbiome composition varied with compartments and host mangrove species. The bacterial communities between the endosphere and the other two compartments were distinctly diverse independent of mangrove species. The large degree of overlap in critical community members of the same rhizocompartment across distinct mangrove species was found at the phylum level. Furthermore, this is the first report of Acidothermus found in mangrove environments. In conclusion, understanding the complicated host-microbe associations in different mangrove species could lay the foundation for the exploitation of the microbial resource and the production of secondary metabolites.

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning

Fungal secondary metabolites (SMs) play a significant role in the diversity of ecological communities, niches, and lifestyles in the fungal kingdom. Many fungal SMs have medically and industrially important properties including antifungal, antibacterial, and antitumor activity, and a single metabolite can display multiple types of bioactivities. The genes necessary for fungal SM biosynthesis are typically found in a single genomic region forming biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted previously used machine learning models for predicting SM bioactivity from bacterial BGC data to fungal BGC data. We trained our models to predict antibacterial, antifungal, and cytotoxic/antitumor bioactivity on two datasets: 1) fungal BGCs (dataset comprised of 314 BGCs), and 2) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs); the second dataset was our control since a previous study using just the bacterial BGC data yielded prediction accuracies as high as 80%. We found that the models trained only on fungal BGCs had balanced accuracies between 51-68%, whereas training on bacterial and fungal BGCs yielded balanced accuracies between 61-74%. The lower accuracy of the predictions from fungal data likely stems from the small number of BGCs and SMs with known bioactivity; this lack of data currently limits the application of machine learning approaches in studying fungal secondary metabolism. However, our data also suggest that machine learning approaches trained on bacterial and fungal data can predict SM bioactivity with good accuracy. With more than 15,000 characterized fungal SMs, millions of putative BGCs present in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.

Global analyses of biosynthetic gene clusters in phytobiomes reveal strong phylogenetic conservation of terpenes and aryl polyenes

There are gaps in our understandings on how did the evolutionary relationships among members of the phytobiomes shape their ability to produce tremendously complex specialized metabolites under the influence of plant host. To determine these relationships, we investigated the phylogenetic conservation of biosynthetic gene clusters (BGCs) on a global collection of 4,519 high-quality and nonredundant (out of 12,181) bacterial isolates and metagenome-assembled genomes from 47 different plant hosts and soil, by adopting three independent phylogenomic approaches (D-test, Pagel's λ, and consenTRAIT). We report that the BGCs are phylogenetically conserved to varying strengths and depths in their different classes. We show that the ability to produce specialized metabolites qualifies as a complex trait, and the depth of conservation is equivalent to ecologically relevant complex microbial traits. Interestingly, terpene and aryl polyene BGCs had the strongest phylogenetic conservation in the phytobiomes, but not in the soil microbiomes. Furthermore, we showed that terpenes are largely uncharacterized in phytobiomes and pinpointed specific clades that harbor potentially novel terpenes. Taken together, this study sheds light on the evolution of specialized metabolites' biosynthesis potential in phytobiomes under the influence of plant hosts and presents strategies to rationally guide the discovery of potentially novel classes of metabolites. IMPORTANCE This study expands our understandings of the biosynthetic potential of phytobiomes by using such worldwide and extensive collection of microbiomes from plants and soil. Apart from providing such vital resource for the plant microbiome researchers, this study provides fundamental insights into the evolution of biosynthetic gene clusters (BGCs) in phytobiomes under the influence of plant host. Specifically, we report that the strength of phylogenetic conservation in microbiomes varies for different classes of BGCs and is influenced as a result of plant host association. Furthermore, our results indicate that biosynthetic potential of specialized metabolites is deeply conserved equivalent to other complex and ecologically relevant microbial traits. Finally, for the most conserved class of specialized metabolites (terpenes), we identified clades harboring potentially novel class of molecules. Future studies could focus on plant-microbe coevolution and interactions through specialized metabolites building upon these findings.

Role of Arbuscular Mycorrhizal Fungi in Regulating Growth, Enhancing Productivity, and Potentially Influencing Ecosystems under Abiotic and Biotic Stresses

Arbuscular mycorrhizal fungi (AMF) form symbiotic relationships with the roots of nearly all land-dwelling plants, increasing growth and productivity, especially during abiotic stress. AMF improves plant development by improving nutrient acquisition, such as phosphorus, water, and mineral uptake. AMF improves plant tolerance and resilience to abiotic stressors such as drought, salt, and heavy metal toxicity. These benefits come from the arbuscular mycorrhizal interface, which lets fungal and plant partners exchange nutrients, signalling molecules, and protective chemical compounds. Plants' antioxidant defence systems, osmotic adjustment, and hormone regulation are also affected by AMF infestation. These responses promote plant performance, photosynthetic efficiency, and biomass production in abiotic stress conditions. As a result of its positive effects on soil structure, nutrient cycling, and carbon sequestration, AMF contributes to the maintenance of resilient ecosystems. The effects of AMFs on plant growth and ecological stability are species- and environment-specific. AMF's growth-regulating, productivity-enhancing role in abiotic stress alleviation under abiotic stress is reviewed. More research is needed to understand the molecular mechanisms that drive AMF-plant interactions and their responses to abiotic stresses. AMF triggers plants' morphological, physiological, and molecular responses to abiotic stress. Water and nutrient acquisition, plant development, and abiotic stress tolerance are improved by arbuscular mycorrhizal symbiosis. In plants, AMF colonization modulates antioxidant defense mechanisms, osmotic adjustment, and hormonal regulation. These responses promote plant performance, photosynthetic efficiency, and biomass production in abiotic stress circumstances. AMF-mediated effects are also enhanced by essential oils (EOs), superoxide dismutase (SOD), peroxidase (POD), ascorbate peroxidase (APX), hydrogen peroxide (H2O2), malondialdehyde (MDA), and phosphorus (P). Understanding how AMF increases plant adaptation and reduces abiotic stress will help sustain agriculture, ecosystem management, and climate change mitigation. Arbuscular mycorrhizal fungi (AMF) have gained prominence in agriculture due to their multifaceted roles in promoting plant health and productivity. This review delves into how AMF influences plant growth and nutrient absorption, especially under challenging environmental conditions. We further explore the extent to which AMF bolsters plant resilience and growth during stress.

Endophytic fungi mediates production of bioactive secondary metabolites via modulation of genes involved in key metabolic pathways and their contribution in different biotechnological sector

Endophytic fungi stimulate the production of an enormous number of bioactive metabolites in medicinal plants and affect the different steps of biosynthetic pathways of these secondary metabolites. Endophytic fungi possess a number of biosynthetic gene clusters that possess genes for various enzymes, transcription factors, etc., in their genome responsible for the production of secondary metabolites. Additionally, endophytic fungi also modulate the expression of various genes responsible for the synthesis of key enzymes involved in metabolic pathways of such as HMGR, DXR, etc. involved in the production of a large number of phenolic compounds as well as regulate the expression of genes involved in the production of alkaloids and terpenoids in different plants. This review aims to provide a comprehensive overview of gene expression related to endophytes and their impact on metabolic pathways. Additionally, this review will emphasize the studies done to isolate these secondary metabolites from endophytic fungi in large quantities and assess their bioactivity. Due to ease in synthesis of secondary metabolites and their huge application in the medical industry, these bioactive metabolites are now being extracted from strains of these endophytic fungi commercially. Apart from their application in the pharmaceutical industry, most of these metabolites extracted from endophytic fungi also possess plant growth-promoting ability, bioremediation potential, novel bio control agents, sources of anti-oxidants, etc. The review will comprehensively shed a light on the biotechnological application of these fungal metabolites at the industrial level.

Modern Trends in Natural Antibiotic Discovery

Natural scaffolds remain an important basis for drug development. Therefore, approaches to natural bioactive compound discovery attract significant attention. In this account, we summarize modern and emerging trends in the screening and identification of natural antibiotics. The methods are divided into three large groups: approaches based on microbiology, chemistry, and molecular biology. The scientific potential of the methods is illustrated with the most prominent and recent results.

Metapangenomics of wild and cultivated banana microbiome reveals a plethora of host-associated protective functions

BACKGROUND

Microbiomes are critical to plants, promoting growth, elevating stress tolerance, and expanding the plant's metabolic repertoire with novel defense pathways. However, generally microbiomes within plant tissues, which intimately interact with their hosts, remain poorly characterized. These endospheres have become a focus in banana (Musa spp.)-an important plant for study of microbiome-based disease protection. Banana is important to global food security, while also being critically threatened by pandemic diseases. Domestication and clonal propagation are thought to have depleted protective microbiomes, whereas wild relatives may hold promise for new microbiome-based biological controls. The goal was to compare metapangenomes enriched from 7 Musa genotypes, including wild and cultivated varieties grown in sympatry, to assess the host associations with root and leaf endosphere functional profiles.

RESULTS

Density gradients successfully generated culture-free microbial enrichment, dominated by bacteria, with all together 24,325 species or strains distinguished, and 1.7 million metagenomic scaffolds harboring 559,108 predicted gene clusters. About 20% of sequence reads did not match any taxon databases and ~ 62% of gene clusters could not be annotated to function. Most taxa and gene clusters were unshared between Musa genotypes. Root and corm tissues had significantly richer endosphere communities that were significantly different from leaf communities. Agrobacterium and Rhizobium were the most abundant in all samples while Chitinophagia and Actinomycetia were more abundant in roots and Flavobacteria in leaves. At the bacterial strain level, there were > 2000 taxa unique to each of M. acuminata (AAA genotype) and M. balbisiana (B-genotype), with the latter 'wild' relatives having richer taxa and functions. Gene ontology functional enrichment showed core beneficial functions aligned with those of other plants but also many specialized prospective beneficial functions not reported previously. Some gene clusters with plant-protective functions showed signatures of phylosymbiosis, suggesting long-standing associations or heritable microbiomes in Musa.

CONCLUSIONS

Metapangenomics revealed key taxa and protective functions that appeared to be driven by genotype, perhaps contributing to host resistance differences. The recovery of rich novel taxa and gene clusters provides a baseline dataset for future experiments in planta or in vivo bacterization or engineering of wild host endophytes.

Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review

This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.

Anti-Tumor Secondary Metabolites Originating from Fungi in the South China Sea's Mangrove Ecosystem

A mangrove is a unique ecosystem with abundant resources, in which fungi are an indispensable microbial part. Numerous mangrove fungi-derived secondary metabolites are considerable sources of novel bioactive substances, such as polyketides, terpenoids, alkaloids, peptides, etc., which arouse people's interest in the search for potential natural anti-tumor drugs. This review includes a total of 44 research publications that described 110 secondary metabolites that were all shown to be anti-tumor from 39 mangrove fungal strains belonging to 18 genera that were acquired from the South China Sea between 2016 and 2022. To identify more potential medications for clinical tumor therapy, their sources, unique structures, and cytotoxicity qualities were compiled. This review could serve as a crucial resource for the research status of mangrove fungal-derived natural products deserving of further development.

Phytotoxic Metabolites Isolated from Aspergillus sp., an Endophytic Fungus of Crassula arborescens

Aspergillus sp., an endophytic fungus isolated from Crassula arborescens, displayed potent inhibitory activity against the seed germination of Arabidopsis thaliana. The bioactivity-guided fractionation of the culture extract of Aspergillus sp. MJ01 led to the isolation of nine compounds, including one previously undescribed furanone, namely aspertamarinoic acid (1), and eight known compounds, (-)-dihydrocanadensolide (2), kojic acid (3), citreoisocoumarin (4), astellolide A (5), astellolide B (6), astellolide G (7), cyclo-N-methylphenylalanyltryptophenyl (8) and (-)-ditryptophenaline (9). In the evaluation of the phytotoxic activities of compounds 1-9, the results suggested that 1 and 5 showed significant inhibitory activity on the seed germination of A. thaliana. This is the first report to disclose the phytotoxic activity of these compounds.

Effective Methods Based on Distinct Learning Principles for the Analysis of Hyperspectral Images to Detect Black Sigatoka Disease

Current chemical methods used to control plant diseases cause a negative impact on the environment and increase production costs. Accurate and early detection is vital for designing effective protection strategies for crops. We evaluate advanced distributed edge intelligence techniques with distinct learning principles for early black sigatoka disease detection using hyperspectral imaging. We discuss the learning features of the techniques used, which will help researchers improve their understanding of the required data conditions and identify a method suitable for their research needs. A set of hyperspectral images of banana leaves inoculated with a conidial suspension of black sigatoka fungus (Pseudocercospora fijiensis) was used to train and validate machine learning models. Support vector machine (SVM), multilayer perceptron (MLP), neural networks, N-way partial least square-discriminant analysis (NPLS-DA), and partial least square-penalized logistic regression (PLS-PLR) were selected due to their high predictive power. The metrics of AUC, precision, sensitivity, prediction, and F1 were used for the models' evaluation. The experimental results show that the PLS-PLR, SVM, and MLP models allow for the successful detection of black sigatoka disease with high accuracy, which positions them as robust and highly reliable HSI classification methods for the early detection of plant disease and can be used to assess chemical and biological control of phytopathogens.

Endophytic Fungi: Key Insights, Emerging Prospects, and Challenges in Natural Product Drug Discovery

Plant-associated endophytes define an important symbiotic association in nature and are established bio-reservoirs of plant-derived natural products. Endophytes colonize the internal tissues of a plant without causing any disease symptoms or apparent changes. Recently, there has been a growing interest in endophytes because of their beneficial effects on the production of novel metabolites of pharmacological significance. Studies have highlighted the socio-economic implications of endophytic fungi in agriculture, medicine, and the environment, with considerable success. Endophytic fungi-mediated biosynthesis of well-known metabolites includes taxol from Taxomyces andreanae, azadirachtin A and B from Eupenicillium parvum, vincristine from Fusarium oxysporum, and quinine from Phomopsis sp. The discovery of the billion-dollar anticancer drug taxol was a landmark in endophyte biology/research and established new paradigms for the metabolic potential of plant-associated endophytes. In addition, endophytic fungi have emerged as potential prolific producers of antimicrobials, antiseptics, and antibiotics of plant origin. Although extensively studied as a “production platform” of novel pharmacological metabolites, the molecular mechanisms of plant–endophyte dynamics remain less understood/explored for their efficient utilization in drug discovery. The emerging trends in endophytic fungi-mediated biosynthesis of novel bioactive metabolites, success stories of key pharmacological metabolites, strategies to overcome the existing challenges in endophyte biology, and future direction in endophytic fungi-based drug discovery forms the underlying theme of this article.

Differentiation and Variability in the Rhizosphere and Endosphere Microbiomes of Healthy and Diseased Cotton (Gossypium sp.)

The plant microbiome is a key determinant of health and productivity. However, it is still difficult to understand the structural composition of the bacterial and fungal microbiomes of diseased and healthy plants, especially the spatial dynamics and phylogenies of endophytic and rhizosphere microbial communities. We studied the differentiation and variability in the rhizosphere and endosphere microbiomes of healthy and diseased cotton from north and south of the Tianshan Mountains using the methods of PCR-based high-throughput sequencing and real-time quantitative PCR. The endophytic and rhizosphere bacterial abundances in the diseased plants were greater than those of healthy plants. The numbers of endophytic and rhizosphere fungi associated with diseased plants were greater than those associated healthy plants (p < 0.05). Endophytic and rhizosphere bacteria did not share common OTUs. The dominant rhizosphere bacteria were Proteobacteria (29.70%), Acidobacteria (23.14%), Gemmatimonadetes (15.17%), Actinobacteria (8.31%), Chloroflexi (7.99%), and Bacteroidetes (5.15%). The dominant rhizosphere fungi were Ascomycota (83.52%), Mortierellomycota (7.67%), Basidiomycota (2.13%), Chytridiomycota (0.39%), and Olpidiomycota (0.08%). The distribution of dominant bacteria in different cotton rhizosphere soils and roots differed, with the dominant bacteria Pseudomonas (15.54%) and Pantoea (9.19%), and the dominant fungi Alternaria (16.15%) and Cephalotrichum (9.10%) being present in the greatest numbers. At sampling points in different ecological regions, the total numbers of cotton endophytic and rhizosphere microbiome OTUs from southern to northern Xinjiang showed an increasing trend. There were significant differences in the composition and diversity of rhizosphere microbes and endophytes during the entire cotton growth period and in representative ecological regions (p < 0.01), whereas rhizosphere microbes and endophytes showed no significant differences among the four growth periods and in representative ecological regions. RB41, H16, Nitrospira, and Sphingomonas play important roles in the microbial ecology of cotton rhizosphere soil. Pseudomonas accounted for a large proportion of the microbes in the cotton rhizosphere soil. This study provides an in-depth understanding of the complex microbial composition and diversity associated with cotton north and south of the Tianshan Mountains.

A Lignocellulolytic Colletotrichum sp. OH with Broad-Spectrum Tolerance to Lignocellulosic Pretreatment Compounds and Derivatives and the Efficiency to Produce Hydrogen Peroxide and 5-Hydroxymethylfurfural Tolerant Cellulases

Fungal endophytes are an emerging source of novel traits and biomolecules suitable for lignocellulosic biomass treatment. This work documents the toxicity tolerance of Colletotrichum sp. OH toward various lignocellulosic pretreatment-derived inhibitors. The effects of aldehydes (vanillin, p-hydroxybenzaldehyde, furfural, 5-hydroxymethylfurfural; HMF), acids (gallic, formic, levulinic, and p-hydroxybenzoic acid), phenolics (hydroquinone, p-coumaric acid), and two pretreatment chemicals (hydrogen peroxide and ionic liquid), on the mycelium growth, biomass accumulation, and lignocellulolytic enzyme activities, were tested. The reported Colletotrichum sp. OH was naturally tolerant to high concentrations of single inhibitors like HMF (IC50; 17.5 mM), levulinic acid (IC50; 29.7 mM), hydroquinone (IC50; 10.76 mM), and H2O2 (IC50; 50 mM). The lignocellulolytic enzymes displayed a wide range of single and mixed inhibitor tolerance profiles. The enzymes β-glucosidase and endoglucanase showed H2O2- and HMF-dependent activity enhancements. The enzyme β-glucosidase activity was 34% higher in 75 mM and retained 20% activity in 125 mM H2O2. Further, β-glucosidase activity increased to 24 and 32% in the presence of 17.76 and 8.8 mM HMF. This research suggests that the Colletotrichum sp. OH, or its enzymes, can be used to pretreat plant biomass, hydrolyze it, and remove inhibitory by-products.

Probiotic Endophytes for More Sustainable Banana Production

Climatic factors and pathogenic fungi threaten global banana production. Moreover, bananas are being cultivated using excessive amendments of nitrogen and pesticides, which shift the microbial diversity in plants and soil. Advances in high-throughput sequencing (HTS) technologies and culture-dependent methods have provided valuable information about microbial diversity and functionality of plant-associated endophytic communities. Under stressful (biotic or abiotic) conditions, plants can recruit sets of microorganisms to alleviate specific potentially detrimental effects, a phenomenon known as “cry for help”. This mechanism is likely initiated in banana plants infected by Fusarium wilt pathogen. Recently, reports demonstrated the synergistic and cumulative effects of synthetic microbial communities (SynComs) on naturally occurring plant microbiomes. Indeed, probiotic SynComs have been shown to increase plant resilience against biotic and abiotic stresses and promote growth. This review focuses on endophytic bacterial diversity and keystone taxa of banana plants. We also discuss the prospects of creating SynComs composed of endophytic bacteria that could enhance the production and sustainability of Cavendish bananas (Musa acuminata AAA), the fourth most important crop for maintaining global food security.