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EDITORIAL article

Editorial: microbial secondary metabolites: recent developments and technological challenges.

• 1 Department of Biotechnology, Aizawl, Mizoram University, Aizawl, India
• 2 School of Computing, Engineering and Physical Sciences, University of west of Scotland, Paisley, United Kingdom
• 3 IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
• 4 Department of Biology, Faculty of Philosophy, Science and Letters of Ribeirão Preto, University of São Paulo, São Paulo, Brazil
• 5 State Key Laboratory of Biocontrol and Guangdong Provincial Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China

Editorial on the Research topic Microbial Secondary Metabolites: Recent Developments and Technological Challenges

Introduction

Microbial secondary metabolites, like antibiotics, pigments, growth hormones, antitumor agents, and others, are not essential for the growth and development of microorganism, but they have shown a great potential for human and animal health ( Ruiz et al., 2010 ). Among the microorganisms producing the above-mentioned compounds, bacteria, including actinobacteria, and fungi produce a diverse array of bioactive small molecules with significant potential to be used in medicine ( O‘Brien and Wright, 2011 ). These bioactive compounds are mainly produced by the activation of cryptic gene clusters which are not active under normal conditions and, thus, the expression of these clusters would be helpful in the exploitation of the chemical diversity of microorganisms ( Pettit, 2011 ; Xu et al., 2019 ).

Although several reports on microbial secondary metabolites have been published in recent years ( Passari et al., 2017 ; Zothanpuia et al., 2018 ; Overy et al., 2019 ), our understanding to enhance the production of bioactive secondary metabolites is still limited. The research topic “Microbial Secondary Metabolites: Recent Developments and Technological Challenges” comprises 25 articles covering important aspects on biodiversity, exploitation and utilization of microbial resources (terrestrial, marine, and endophytic) for the production of secondary metabolites together with their biological functions.

The current knowledge and potential of marine fungi for producing anticancer compounds has been reviewed ( Deshmukh et al .) and the ability of the sea-derived Streptomyces helimycini for the production of actinomycins is presented ( Zhu et al .). In a very interesting study, Wakefield et al . proved that the co-cultivation of fungi and bacteria led to the production of new secondary metabolites. There is a growing interest in looking for unique sources for the exploration of novel microbial populations having prospective to produce bioactive natural products. Thereby, the bacterial and fungal population obtained from Aquilaria malaccensis tree and soil enhanced the production of agarospirol within 3 months of artificial infection ( Chhipa and Kaushik ).

The present research topic includes four important research papers dealing with the production of bioactive secondary metabolites. Thus, a study by Alenezi et al . emphasized that the biological activity of Aneurinibacillus migulans isolates was directly correlated with the production of a new gramicidin. Narsing Rao et al. has focused on the importance of pigments originated from fungi and bacteria and their wide applications in health and industry. The article by Li et al. presented the production of somalimycin, a new antimycin-type depsipeptide, from a mutant of the deep-sea-derived Streptomyces somaliensis . Similarly, Thøgersen et al . demonstrated the production of the potentially antibacterial compounds violacein and indolmycin by a mae A mutant of the sea bacterium Pseudoalteromonas luteoviolacea .

A cluster of three articles gives emphasis to the biosynthetic gene clusters involved in microorganisms for the production of secondary metabolites. Hence, Derntl et al . demonstrated the role of genes, namely sor 1, sor 3, and sor 4 of the orbicillinoid gene cluster and disclosed the function of sor 4 which was not known. Another article by Rojas-Aedo et al . explained the role of the adr gene cluster involved in the biosynthesis of the potent antitumor compoundandrastin A in Penicillium roqueforti . In this article, the authors also have demonstrated that all the 10 genes of adr gene cluster were essential for the production of andrastin A. Lastly, Nah et al . reviewed the potential of the phylum Actinomycetes for natural production (NP) through biosynthetic gene clusters (BGC) heterologous expression systems as well as recent strategies specialized for the large-sized NP BGCs in Streptomyces heterologous hosts.

Other important candidates for the production of secondary metabolites are the endophytic microorganisms which were addressed by Mefteh et al . Thus, they presented that plants under biotic stress offered new and unique endophytes with diverse bioactivities as compared to healthy plants. Sharma et al . reported that the application of dietary components like grape skin and turmeric extracts enhanced the production of cryptic and bioactive metabolites, with anti-oxidant and antibacterial potential, by the endophytic fungus Colletotrichum gloeosporioides . Also, the endophytic fungi Chaetomium globosum isolated from Egyptian medicinal plants, proved to have anti-rheumatoid activity ( Abdel-Azeem et al. ).

In summary, the articles gathered in the research topic “Microbial Secondary Metabolites: recent development and Technological Challenges” explore the role of microorganisms from different sources showing biological activities. This will further enhance the present knowledge on the potential of microbial secondary metabolites in health and industry. One challenge which needs to be answered is the development of methods to understand the detailed mechanisms of cryptic genes and their relation to the production of bioactive compounds. Researchers also need to give more emphasis on the co-cultivation of different microorganisms having positive synergistic effect to produce novel bioactive molecules. We believe that this special issue gives some in-depth information about one of the important matters of the microbial world. Finally, our great thanks to all contributions, in total 165 authors, for the cohesive information in the form of reviews and research articles which have been compiled in this ebook. We strongly believe that the information compiled and presented in this ebook will be useful for the readers and will be the basis for the future investigation on “microbial secondary metabolites.”

Author Contributions

All authors mentioned have made significant contributions in the production of the editorial and have approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank all of the contributing authors and also the Frontiers team, for their constant efforts and support throughout in managing the research topic. BPS is thankful to the Department of Biotechnology, Ministry of Science and Technology for financial support in the form of DBT Unit of Excellence programme for NE (102/IFD/SAN/4290-4291/2016-2017).

O‘Brien, J., and Wright, G. D. (2011) An ecological perspective of microbial secondary metabolism. Curr. Opin. Biotechnol. 22, 552–558. doi: 10.1016/j.copbio.2011.03.010

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Overy, D. P., Rämä, T., Oosterhuis, R., Walker, A. K., and Pang, K. L. (2019). The neglected marine fungi, sensu stricto, and their isolation for natural products' discovery. Mar. Drugs 17:E42. doi: 10.3390/md17010042

Passari, A. K., Mishra, V. K., Singh, G., Singh, P., Kumar, B., Gupta, V. K., et al. (2017). Insights into the functionality of endophytic actinobacteria with a focus on their biosynthetic potential and secondary metabolites production. Sci. Rep. 7:11809. doi: 10.1038/s41598-017-12235-4.

Pettit, R. K. (2011). Small-molecule elicitation of microbial secondary metabolites. Microb. Biotechnol. 4, 471–478. doi: 10.1111/j.1751-7915.2010.00196.x

Ruiz, B., Chávez, A., Forero, A., García-Huante, Y., Romero, A., Sánchez, M., et al. (2010) Production of microbial secondary metabolites: regulation by the carbon source. Crit. Rev. Microbiol. 36, 146–167. doi: 10.3109/10408410903489576.

Xu, F., Wu, Y., Zhang, C., Davis, K. M., Moon, K., Bushin, L. B., et al. (2019) A genetics-free method for high-throughput discovery of cryptic microbial metabolites. Nat. Chem. Biol. 15, 161–168. doi: 10.1038/s41589-018-0193-2.

Zothanpuia, Passari, A K, Leo, V. V., Kumar, B., Chnadra, P., Nayak, C., et al. (2018). Bioprospection of actinobacteria derived from freshwater sediments for their potential to produce antimicrobial compounds. BMC Microb. Cell Fact. 17:68. doi: 10.1186/s12934-018-0912-0

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Keywords: microbial, secondary, metabolite, change, challenges

Citation: Singh BP, Rateb ME, Rodriguez-Couto S, Polizeli MdLTdM and Li W-J (2019) Editorial: Microbial Secondary Metabolites: Recent Developments and Technological Challenges. Front. Microbiol. 10:914. doi: 10.3389/fmicb.2019.00914

Received: 15 February 2019; Accepted: 10 April 2019; Published: 26 April 2019.

Reviewed by:

Copyright © 2019 Singh, Rateb, Rodriguez-Couto, Polizeli and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Bhim Pratap Singh, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Secondary metabolites from endophytic fungi: Production, methods of analysis, and diverse pharmaceutical potential

• Published: 08 June 2023
• Volume 90 , pages 111–125, ( 2023 )

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• Vivek Kumar Singh 1 &
• Awanish Kumar   ORCID: orcid.org/0000-0001-8735-479X 1  

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The synthesis of secondary metabolites is a constantly functioning metabolic pathway in all living systems. Secondary metabolites can be broken down into numerous classes, including alkaloids, coumarins, flavonoids, lignans, saponins, terpenes, quinones, xanthones, and others. However, animals lack the routes of synthesis of these compounds, while plants, fungi, and bacteria all synthesize them. The primary function of bioactive metabolites (BM) synthesized from endophytic fungi (EF) is to make the host plants resistant to pathogens. EF is a group of fungal communities that colonize host tissues' intracellular or intercellular spaces. EF serves as a storehouse of the above-mentioned bioactive metabolites, providing beneficial effects to their hosts. BM of EF could be promising candidates for anti-cancer, anti-malarial, anti-tuberculosis, antiviral, anti-inflammatory, etc. because EF is regarded as an unexploited and untapped source of novel BM for effective drug candidates. Due to the emergence of drug resistance, there is an urgent need to search for new bioactive compounds that combat resistance. This article summarizes the production of BM from EF, high throughput methods for analysis, and their pharmaceutical application. The emphasis is on the diversity of metabolic products from EF, yield, method of purification/characterization, and various functions/activities of EF. Discussed information led to the development of new drugs and food additives that were more effective in the treatment of disease. This review shed light on the pharmacological potential of the fungal bioactive metabolites and emphasizes to exploit them in the future for therapeutic purposes.

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1 Introduction

2020 ). Around 33 million populations are infected with HIV and reported to be two million deaths last decade (Laga et al. 2015 ). Mycobacterium tuberculosis is recorded to infect around one-third of the world's population. In tropical and subtropical areas, underprivileged populations are infected with malaria, accounting for about one million deaths (Cárdenas et al. 2021 ). Similarly, COVID-19 (Pokhrel and Chhetri 2021 ) and other pandemics as monkeypox (Bhattacharya et al. 2022 ), immunosuppressive disorders, and the appearance of highly virulent viruses open a new discussion for future treatment and remedies to address those problems (Chatelain 2015 ). An international health agency prioritizes the conservation of natural resources to create novel therapeutics. Plants are major natural resources and have therapeutic potential that has been tracked down thousand years ago and utilized for the treatment of many diseases (Abel 2013 ). Today around 40% of modern medicines are plant-derived because of fewer side effects. Plants generate various secondary metabolites, which may be broken down into the chemical categories of phenolics, terpenes, and alkaloids (Sharma and Singh 2021 ). Secondary metabolites secreted by the plant during different stress and developmental stages make them competitive in their inhabitant (Pang et al. 2021 ). These small molecules exert a significant impact on plants themselves and also on humans and other organisms (Teoh 2016 ).

Plants are the natural inhabitant of microorganisms as they form a symbiotic association with them to accomplish one and all requirements. Endophytes (endosymbiotic microbes colonize in plants) and microbes and their bioactive metabolites are important natural sources for promising therapeutic agents. The therapeutic potential of endophytes and their metabolites as biotherapeutic agents has garnered a lot of interest (Xia et al. 2022 ). Endophytes are associated with the healthy tissue of the plant (Sadrati et al. 2013 ) and reside in the interior sections of the plant, such as the root, the stem, the petiole, and other components that are referred to as endophytes (Ma et al. 2013 ; Deepthi et al. 2018 ). In 1898, Vogl was the first person who reported that endophytic mycelium was present in the grass seeds of Latium teinutentuin (Waghunde et al. 2021 ). Around 300,000 plant species, each individual having one or more endophytes and having one to hundred strains of endophytes varying according to the host system (Gao et al. 2018 ). They got much attention when it became apparent that entophytes can produce bioactive secondary metabolites with varied molecular structures, which are barely impersonated by synthetic chemistry (Mengistu 2020 ). Endophytes play a crucial role in plant development, and survival, and regulate some defense mechanisms. They are formed to be a composite equilibrium to achieve the host boundary and build a mutualistic association with the host (Alam et al. 2021 ; Nanda et al. 2019 ). During stressed conditions in the plant, endophytes secreted a range of secondary metabolites in plant cells and incorporated them into the stressed pathways (Nanda et al. 2019 ). Secretion of stress-controlled molecules like gibberellins (GB), cytokinin (CIS), salicylic acid (SA), and indole-3-acetic acid (IAA) enhance plant growth and development and is responsible for many physiological changes in the plant (Shekhawat et al. 2021 ; Duc et al. 2018 ; Shaffique et al 2022 ).

The biochemical and pharmaceutical industries rely on endophytic fungi as a source of new therapeutic biomolecules that could be immune-suppressant compounds, anti-cancer drugs, plant growth promoters, anti-microbial volatiles, insecticides, anti-oxidants, and antibiotics, offer significant potential for application in medicine. Furthermore, endophytic microorganisms can lessen a plant's capacity to endure nutritional deficiency, high temperatures, salt, trace metals, and water scarcity (Eid et al. 2021 ). Amylase, cellulase, lyase, and laccase are important enzymes that have significant industrial applications and endophytes play a role in their synthesis (Sharma et al. 2021 ). Research suggested that the fungal endophytes found to be heterotrophic organisms with various life cycles in natural ecosystems form a symbiotic relationship with plants. The fossil record also reported that endophytic fungi and host plants were associated for 400 million years and intimately involved in the ecology, proliferation, fitness, and steer the evolution of their life (Krings et al. 2007 ). Plant systems change their habits from water to terrestrial atmosphere with many challenges like nutrient-deficient soil, high carbon dioxide varying temperature conditions, and water availability. Fungal endophytes provided tolerance to the plant during adverse conditions and fixed them in the soil. In the evolutionary period, fungal endophytes evolved themselves in the plant environment through an alteration of genetic behavior, uptake of the DNA, and started producing secondary metabolites (Arora and Ramawat 2017 ). At least one plant harbour one or more fungal endophytes, especially woody plants containing hundreds of species of fungal endophytes. The fungal endophytes are found in different geographical and climate regions, fin-ray ubiquitously distributed, and rich in species diversity. The abundance of fungal endophytes with great extent, ubiquitous nature, diversity, and wide range of ecological functions are shown to be greatly adapted for plants under worldwide distribution and selective pressure (Rodriguez et al 2009 ).

All these findings indicate that fungal endophytic population colonization inside the host tissue confers tolerance under specific environmental stresses condition and is responsible for the survival of plants. Extensive research on fungal endophytes (FE) found their crucial role in abiotic and biotic stress tolerance, nutrient supply, growth, and plant development. When searching for natural products mediated by endophytes, researchers may explore EF. It is well established that many bioactive metabolites (BM) with potential pharmacological effects are produced by EF (Tiwari and Bae 2022 ). The bioactive compounds produced by EF albeit receiving much less attention. The development and production of biomass need a thorough understanding of endophyte ecology, bioactive components, and the biotransformation of substrates. Considering the information available on EF, the article is aimed to review the sampling, optimization, production, and extraction of the secondary metabolites from EF and the pharmacological relevance of the different bioactive metabolites produced by endophytic fungi associated with plants (Fig. 1 ).

Entry and Colonization of the fungal endophytes in response to biotic (herbivores and pathogens) and abiotic (drought, salinity, heavy metals, and others) stresses and production of secondary metabolites to modulate plant defense, plant growth, and functional role in the pharmacology

2 Sampling and optimization of media for the production of BM from EF

The first steps in sampling and isolation of bioactive molecules from EF are collecting plant material having EF from different geographical areas and pre-processing the plant material, which may involve surface cleaning, slicing, and selecting media. Surface sterilizing agents like mercuric chloride (HgCl 2 ), ethanol, etc., are used to wash and surface sterilize the plant components (leaves, stems, seeds, etc.) (Bisht et al. 2016 ). They are subsequently diced and cultured in LB media on a PDA (potato dextrose agar) plate. Hyphal tips are harvested from the fungus and then transferred to PDA slants, where they are tested for bioactive secondary metabolites after an extended period of incubation (Sharma et al 2016 ). It is vital to build a suitable cultivation system for commercial use because EF can produce many biologically active metabolites. Endophytes can be grown through liquid-submerged or solid-state fermentation (SSF). EF fermentation is fruitful, continuous, and environmentally beneficial. Submerged culture produces mycelial biomass and bioactive metabolites faster. It needs less time and has fewer contamination risks. Extensive research has been done on bioactive compounds produced by endophytic fungus in submerged fermentation. In liquid fermentation, temperature, pH, aeration, and agitation affect secondary metabolite production. In addition, each of these different characteristics has been optimized, which has led to an increase in the overall production of BM (Debbab et al. 2013 ; Brader et al 2014 ). Various researchers used liquid-submerged fermentation to manufacture myriads of BM. These include antibiotics, anti-oxidants, pigments, enzymes, etc. (Mrudula and Murugammal 2011 ; Patil et al. 2016 ). Similarly, cellulase enzyme production from  Pestalotiopsis  sp (Chen et al. 2011 ) and glucoamylase from  Aspergillus flavus (Karim et al 2017 ) has been performed previously. Vimal and Kumar 2022 reported optimized production of medically important L-asparaginase enzyme under solid-state and submerged fermentation from agricultural wastes. Similarly, microorganisms are cultured on Wheat bran, Cajanuscajan (red gram), Phaseolus mungo (mung bean), and Glycine max (soybean) bran in solid-state fermentation (SSF). Chitosan production by A. terreus was reported by submerged fermentation in the optimized condition (Abo Elsoud et al. 2023 ). SSF from fungal cultures provides various advantages over submerged fermentation to manufacture bioactive chemicals in the food, agricultural, and pharmaceutical industries. Furthermore, this includes comparatively improved productivity, greater product concentrations, and simple equipment requirements for the fermentation process of BM (Patil et al 2016 ). Lovastatin, a potent medication for decreasing blood cholesterol, was extracted from the healthy tissues of Taxusbaccata by an endophytic fungus, A. niger PN2, using SSF with wheat bran as the substrate (Raghunath et al. 2012 ). Further, we discuss the production and extraction of the SM from EF.

3 Production and extraction of the secondary metabolites (SM) from endophytic fungi (EF)

Biomolecules like polysaccharides, polypeptides, unsaturated fatty acids, and glycoproteins are commonly known as elicitors. EF produces the elicitors or signaling molecules stimulated by the bioactive phytochemical accumulation in plants. Some of them provide defense to plants against disease-causing organisms. They also stimulate the production of several phytochemicals, including alkaloids, flavonoids, terpenoids, saponins, and phenols (Chandran et al. 2020 ). The oligosaccharide components of  Colletotrichum gloeosporioides'  crude endophytic mycelium have been demonstrated to stimulate artemisinin synthesis in hairy root cultures of  Artemannua  (Hussain et al. 2015 ). The bacterial culture was incubated at 32 °C in broth for 36 h, whereas fungal cultures were incubated at 28 °C for two weeks with 150 rpm shaking. Several solvents were employed alone or in combination to extract metabolites. Ethyl acetate, methanol, dichloromethane, hexane, and ethanol are routinely used to extract metabolites from the culture broth. The solubility of the desired component determines the extraction solvent. Equal amounts of solvents were added to the filtrate and agitated for 10 min until two transparent immiscible layers appeared. The extracted compounds were separated from the solvent using a funnel. The solvent was evaporated and the compound was dried in a rotator vacuum evaporator to produce the crude metabolite (Bhardwaj et al. 2015 ). The crude extract was diluted with dimethyl sulphoxide and kept at 4 °C. Phytochemical screening was conducted to look for alkaloids, saponins, tannins, flavonoids, steroids, sugars, and cardiac glycosides (Mathew et al. 2012 ). The isolation and characterization methods of secondary metabolites isolated from fungal endophytes are discussed in Table 1 and in this section; we discuss some high throughput methods precisely used for analysis of SM from EF. Chromatographic methods such as TLC and HPLC were employed to get the secondary metabolite extract as pure as feasible. The recovered fractions are usually analyzed by the use of gas chromatography-mass spectrometry (GC–MS), Fourier transform infrared (FTIR), and nuclear magnetic resonance (NMR). NMR and MS are the principal techniques exploited in the structural characterization of BM. According to Madhusudhan et al. ( 2015 ), X-ray diffraction (XRD) is also promising for crystalline biomolecules.

TLC analysis was used to extract the bioactive components of Pestalotiopsis neglecta BAB-5510, a fungal endophyte that was isolated from the leaves of Cupressus torulosa D. Don. Two distinct fractions were observed on the silica gel TLC plates after being developed in dichloromethane and methanol at a ratio of 90:10, with the second fraction having an R f value of 0.79. TLC was employed to separate the extracted secondary metabolites synthesized by fungal endophytes, which have been isolated from Mentha piperita . The R f values of the metabolites that were isolated from bacterial endophytes were found to be quite comparable to those that were obtained by the TLC chromatogram.

According to the findings of gas chromatography performed on Pestalotiopsis neglecta BAB-5510, the most important active compounds of Pestalotiopsis sp. BAB-5510 are nonadecane (19.74%), 1,2,3-propanetriol, 1-acetate (17.21%), bis (2-Ethylhexyl) phthalate (14.41%), and 4 Hpyran-4-one, 2,3-dihydro-3,5-d (Bunaciu et al. 2015 ). The GC–MS spectra revealed that these metabolites were terpenes, more notably cinnamaldehyde, cinnamyl alcohol, and eugenol (Kumar et al. 2017 ) and GC–MS is particularly suitable to identify volatile organic compounds.

3.3 HPLC & LC–MS

Silica gel column chromatography and high-performance liquid chromatography were used to purify BMs. The Taxus cuspidate culture media was treated with di-chloromethane to extract taxol, which was then purified and quantified by employing HPLC. Finally, the structure of taxol was confirmed by utilizing LC–MS and H-NMR spectroscopy (Sharma et al. 2016 ). Ethyl acetate was used to extract vinblastine and vincristine from the fungus endophyte Fusarium oxysporum .

To evaluate the molecular masses of the purified compounds, electrospray ionization mass spectrometry (ESI–MS) and tandem mass spectrometry (MS–MS) followed by nuclear magnetic resonance (NMR) analysis utilized by Kumar et al. 2013 . They performed isolation, purification, and characterization of Vinblastine and Vincristine from EF Fusarium oxysporum isolated from Catharanthus roseus .

4 Pharmaceutical application of the endophytic fungal secondary metabolites

EFs are eminent for their competence in synthesizing a distinct variety of pharmacologically significant chemicals with immense therapeutic promise, including antiviral, antifungal, antibacterial, antitumor, and anti-cancer activity. Various EFs are potential sources of plant growth factors and hormones. Some endophytes have been demonstrated to release a broad spectrum of extracellular enzymes, such as phosphatase enzyme, which converts insoluble phosphates to soluble forms for easier assimilation by plants. Secondary metabolites of EFs have been demonstrated to strengthen the host's immune system, reducing the severity of infections and the damage caused by pathogenic microorganisms (Sharma et al. 2021 ). The biocontrol systems of plants are responsible for the production of various kinds of BMs, which shield plants against potentially lethal diseases and stimulate their development (Santos et al. 2018 ; Hardoim et al. 2015 ). Their pharmaceutical role was experienced against various diseases (Table 1 ) and they are discussed below.

4.1 Antibiotics

Antibiotic synthesis through metabolic pathways is widely regarded as an effective strategy for protecting plants against illness. Phytopathogens can be inhibited by various bioactive substances and out of them few have been researched (Suryanarayanan 2013 ; Daguerre et al. 2017 ). Endophytes produced diverse metabolites, most of which have anti-microbial properties. These metabolites include alkaloids, flavonoids, peptides, phenols, polyketides, quinones, steroids, and terpenoids (Lugtenberg et al. 2016 ; Fadiji and Babalola 2020 ). DAPG, also known as 2, 4-diacetyl phloroglucinol, is a phenolic antibiotic with a broad spectrum of activity and has shown Pseudomonas spp. It contributes to the biological control of plant diseases, particularly soil-borne (Bonaterra et al. 2022 ). The novel alkaloid altersetin was isolated from the endophyte Alternaria spp. showing substantial antibacterial activity against various pathogenic gram-positive bacteria. Fungal endophytes isolated from Artemisia annua have been shown to inhibit the development of most phytopathogenic organisms in vitro by secreting n-butanol and ethylacetate (Fadiji and Babalola 2020 ). In addition, pseudomonads spp. generate cyclic lipopeptides (CLPs) amphiphilic molecules with chains of 7–25 amino acids that act as biosurfactants and are significant in biological control because of their favourable competitive capability with numerous groups of microorganisms (Flury et al. 2017 ; Bonaterra et al. 2022 ).

4.2 Siderophore

Micronutrient metals including nickel, copper, zinc, and iron are essential for soil plants and microorganisms, however, their bio-availability is often low due to environmental factors (Satapute et al. 2019 ). Reduced bioavailability of Fe(III) directly results from forming insoluble oxyhydroxide phases in response to harsh environmental conditions. Due to a lack of iron, plants develop chlorosis and have lessened metabolic activity and biomass. In response to various stresses, plants, and microbes have evolved a chelation strategy to increase metal availability (Chowdappa et al. 2020 ). Endophytes secreted small molecules called siderophores capable of chelating iron and increasing the bioavailability of the iron molecule to the plant (Yadav 2018 ). The iron in the soil can be dissolved with the assistance of secreted siderophores, which have a strong affinity for the substrate and the potential to assimilate it. The bacterial iron-siderophore complex makes iron accessible for plant development while simultaneously reducing the acquisition of iron by phytopathogens, which restricts phytopathogen proliferation (Santos et al 2018 ). Despite this, the chemical composition of different microorganisms' siderophores can be somewhat distinct. For instance, bacterial hydroxamates are composed of hydroxylated and acylated alkylamines, whereas fungal hydroxamates are composed of hydroxylated and acylated ornithine groups. Chelating compounds can be obtained from endophytic fungi derived from Cymbidium aloifolium . This medicinal orchid can secrete exogenous siderophores and form stable complexes with the metal ion Fe 3+ . These metabolites have the great potential to act as antibacterial siderophores. In a similar vein, a hydroxamate-type siderophore obtained from  P. crysogenum  was found to possess potent antibacterial activities against some of the most virulent phytopathogens, hence safeguarding peanuts and rice plants (Chowdappa et al. 2020 ). To evaluate the inhibitory efficacy of exogenous deferoxamine-B and siderophores-exochelin MS (a pentapeptide derivative) against methicillin-resistant Staphylococcus aureus, metallo-lactamase producers Acinetobacter baumannii , and Pseudomonas aeruginosa , disc diffusion, micro broth dilution, and turbidimetric growth tests were utilized (Gokarn and Pal 2018 ). The combination of siderophores and antibiotics was effective against the drug-resistant isolates. They can treat antibiotic-resistant bacteria and acute iron intoxications such as hemochromatosis by producing sideromycins. Evidence suggests they help to treat malaria (Chowdappa et al. 2020 ), bioremediating mercury (Pietro-Souza et al. 2020 ), and other plant diseases. According to some research, microorganisms' sensitivity to oxidative stress is lowered by siderophores. They can be used in cosmetics, cancer therapy, combat fish infections, and against bacterial/fungal phytopathogens (Chowdappa et al. 2020 ; Peralta et al. 2016 ).

4.3 Hydrolyzing enzyme

Endophytes make lytic enzymes like amylases, lipases, cellulases, pectinases, proteases, phosphatases, hemicellulases, chitinases, and 1, 3-glucanases (Mishra et al. 2019 ), which help them form symbiotic relationships with host plants and control of plant pathogens. Cellulase produced by endophytic fungi such as Epicoccum nigrum , Trichoderma asperellum , and Alternaria longipes has been shown to suppress the development of Epicoccum sorghinum , Alternaria alternata , Fusarium thapsinum , and Curvularia lunata in vitro by hydrolyzing the cell wall (Fadiji and Babalola 2020 ). The biocontrol efficacy against tall fescue leafspot and sugar beet damping-off was reduced after mutations in the 1,3-glucanase genes of Lysobacteren zymogenes . The lytic enzymes produced by Streptomyces are also effective against cocoa witch broom (Gao et al. 2018 ). Lytic enzymes produced by endophytes are often more robust and can function on a broader pH, temperature, and pressure range as compared to enzymes produced using traditional chemical catalysts (Tiwari 2015 ). These features also bode well for the commercial use of these enzymes in the food, detergent, paper, pharmaceutical, energy, and biofuel industry (Rana et al. 2019 ) because endophytic amylase hydrolyzes starch by accelerating the creation of 1,4 glycosidic bonds (Tiwari 2015 ). Bacterial pectinases, which depolymerize pectin linkages, are also widely utilized and have many applications in juice and food industries as well as in paper/pulp production, composting, recycling, etc. (Haile and Ayele 2022 ).

4.4 Growth-promoting hormones

Plant growth stimulation is bolstered, and phytohormones produced by fungal endophytes alter the plant's shape and form. This quality of endophytes has helped them progress in sustainable agriculture (Fadiji and Babalola 2020 ). Phytohormones or plant growth regulators are chemical substances that control, impede, or accelerate growth promotion and development of plants at low concentrations (Damam et al. 2016 ). Many phytohormones, including auxins, gibberellins, abscisic acid, cytokinins, ethylene, strigolactones, brassinosteroids, and jasmonates are produced by endophytic microorganisms (Santoyo et al 2016 ; Shahzad et al. 2016 ). The primary function of indole-3-acetic acid (IAA) in plants is to stimulate cell growth and division. The IAA generated by the bacteria in symbiosis facilitates nutrient availability, increases root exudation, and encourages the growth of adventitious and lateral roots. The bacteria of the genera Azospirillum , Herbaspirillum , Azotobacter , Alcaligenes , Pseudomonas , Enterobacter , Klebsiella , Burkholderia , Pantoea , Rhizobium , Bacillus , Rhodococcus , Acetobacter, etc., are familiar endophytic IAA producers (Eid et al. 2021 ). IAA synthesis also increases the size of bacterial cell walls, speeds up the release of exudates, and makes more of the nutrients that help other beneficial bacteria thrive in the rhizosphere more accessible. As a result, IAA is the primary effector molecule of endophytic bacteria involved in phytostimulation, pathogenicity, and plant–microbe interaction (Etesami et al. 2015 ). A specific form of bacterial endophyte contains 1-aminocyclopropane-1-carboxylate deaminase, which allows it to convert the ethylene precursor ACC into ammonia and beta-ketobutyrate. (Eid et al. 2021 ). Due to the release of ACC deaminases, these bacterial endophytes can stimulate plant development in nitrogen-limiting environments. Additional benefits include a more robust immune system and increased resistance to abiotic stress. In the case of sorghum plants, Pseudomonas brassicacearum (SVB6R1) increases the expression of ACC deaminase, increasing the plant's resistance to salt stress (Gamalero et al. 2020 ).

4.5 Nutrients

The expansion of agricultural production is facilitated by biofortification, which simplifies the uptake of nutrients by plants. Fungal endophytes can colonize plant structures such as roots, stems, and leaves and they are less likely to be outcompeted than microbes that live in the soil, therefore, they can be utilized as a replacement to boost the plant's ability to fix nitrogen (Santos et al. 2018 ). The presence of many microorganisms in the soil allows for the breakdown of insoluble phosphate and thus phosphorus is made available to the plants (Alori et al. 2017 ). In addition to their involvement in the release of organic acids into the soil, endophytes are also involved in the solubilization of phosphate complexes and the conversion of these compounds into the ortho-phosphates that plants absorb more readily. Ca3(PO 4 ) 2 solubility was observed for poplar endophyte strains WP5 and WP42, and subsequent solubilization tests validated this observation (Kandel et al. 2017 ; Khan et al. 2015 ; Varga et al. 2020 ). Selenobacteria is a plant endophyte that can draw selenium out of the soil and transmit it to the host plant, where it can be used to promote plant development. Selenium biofortification in Glycine max may be enhanced by the endophyte Paraburkholderia megapolitana (MGT9) during drought (Trivedi et al. 2020 ). Zn solubilizing endophytes (such as B. subtilis DS-178 and Arthrobacter sp. DS-179) improve the translocation and enrichment of Zn to grain in specific Wheat genotypes (Singh et al. 2018 ).

4.6 Phytoremediation

As the global economy has become more industrialized over the past century, various anthropogenic chemicals have been released into the environment. These include polycyclic aromatic hydrocarbons (PAHs), petroleum hydrocarbons (PHC), halogenated hydrocarbons, salt, solvents, pesticides, and heavy metals (Bisht et al. 2015 ). Microbe-assisted phytoremediation is a method that deals with these issues. Due to endophytes' closer relationship with their host plants, phytoremediation may be enhanced (Li et al. 2012 ). It has been discovered that many endophytes can withstand high concentrations of heavy metals and degrade organic pollutants. Because of their prolonged exposure to the high metal concentrations stored in the hyperaccumulators of these plants, the endophytes associated with these plants may have become more resistant to the negative consequences of metal exposure (Aishwarya et al. 2014 ). Furthermore, it has been demonstrated that releasing low-molecular-mass organic acids by specific endophytes might boost heavy-metal mobilization. For example, the organic acids produced by endophytes caused the pH of a solution to fall when the water-soluble Pb concentration increased. When the concentration of water-soluble Pb rose, the pH of the solution decreased because of the organic acids generated by endophytes (Yongpisanphop et al. 2020 ).

4.7 Anti-cancer activity

Cancer is the second largest cause of mortality worldwide due to its high incidence rate. Malignant cells kill 15 million people annually, and the number is rising. Cancer can be treated with safe, biocompatible, less toxic, and more resistant natural chemicals from endophytic organisms. These natural chemicals are cancer treatment alternatives to chemotherapeutic medicines. These natural chemicals are anti-cancer and can control many malignancies. Due to their abundance, they can be employed to treat cancer. Endophytic fungi including Taxomyces andreanae ,  Seimatoantlerium nepalense ,  Alternaria alternate , and  Chaetomellaraphigera have been linked to the production of the anti-cancer drug paclitaxel (Kousar et al. 2022 ). Paclitaxel treats Kaposi's sarcoma, prostate, lung, and ovarian cancer (Weaver 2014 ). It binds to tubulin and inhibits depolymerization during cell division (Leung and Cassimeris 2019 ). Taxol isolated from sick Chilli plant fruits has shown cytotoxic activity against human cell lines MCF-7, HLK-210, and HL-251. Endophytes  Sinopodophyllum hexandrum  and  Dysosma veitchii  generate podophyllotoxin which is used to treat leukemia, testicular, prostate, lung, and ovarian cancer (Leung and Cassimeris 2019 ). Camptothecin is an effective cytotoxic compound for the treatment of solid tumors of the liver, bladder, lungs, and ovaries. A study found that camptothecin (extracted from  A. niger ) caused apoptosis when given to colon cancer cell lines, and cell death occurs at dosages as low as 7.8 mg/L and as high as 1000 mg/L with the highest and lowest cell viability occurring at concentrations of 11.85 and 65.13%, respectively (Aswani and Soundhari 2018 ).  Fusarium oxysporum  generates the anti-cancer compound vinblastine in  Cathranthus roseus , which is helpful against lymphoblastic leukemia and cancer cell lines HepG-2 at 7.48 g/mL (Kousar et al. 2022 ).

4.8 Immunosuppressive activity

An immediate quench strategy to overcome graft rejection and autoimmune diseases; researchers have been looking for a substance that might dampen the immune system (Rajamanikyam et al. 2017 ). Fungal endophytes can synthesize some substances with immunosuppressive effects (Egbuta et al. 2017 ). Synthetic chemical immunosuppressive medications have serious side effects because of the time they took to treat diseases. Infection risks like hyperlipidemia, nephrotoxicity, hypertension, and neurotoxicity have side effects from long-term usage of chemical immunosuppressive medications (Hošková et al. 2017 ). A recent study has revealed that immunosuppressive medicines produced from fungal endophytes are highly effective. Sydoxanthones A and B, 13-O-acetylsydowinin B, colutellin A, methyl peniphenone, dibenzofurane, xanthone derivatives, subglutinols A and B, peniphenone, lipopeptide, benzophenone derivatives, (-) mycousnine, etc. are used in the treatments. There was speculation that cyclosporin A, an immunosuppressant medication, originated in the fungus Tolypocladiumin flatum (El-Gowelli and El-Mas 2015 ). cyclosporin A, an extract from the endophytic soil fungus Trichoderma polysporum , was discovered as a critical immunosuppressive agent. Endophytic fungus Fusarium subglutinan was discovered in Tripterygium wilfordii , where it produces noncytotoxic diterpene pyrenes and immunosuppressive Subglutinol A and B (Vasundhara et al. 2016 ; Adeleke et al. 2021 ).

4.9 Anti-diabetic activity

Recent research has suggested that endophytic fungus might be a source of substances with anti-diabetic properties. The endophytic fungus Nigrosporaoryzae in Combretum dolichopetalum leaves has been proven to lower fasting blood sugar in diabetic mice when its purified components are administered (Uzor et al. 2017 ). These chemicals include abscisic acid, 70-hydroxy abscisic acid, and 4-des-hydroxyl altersolanol A. Indrianingsih and Tachibana ( 2017 ) showed that 8-hydroxy-6,7-dimethoxy-3-methyl isocoumarins exhibit strong glucosidase inhibitory effect and are produced by the endophytic fungus Xylariaceae spp. in the stem of Quercusgilva Blume. Oral administration of glucose and alloxan to Wistar albino rats accompanied with Salvadoraoleoides extracts of Phoma spp. and Aspergillus spp. induced anti-diabetic and hypolipidemic effects (Ezekwesili and Ogbunugafor 2015 ). Endophytic fungi isolated from medicinal plants such as Rauwolfia densiflora and Leucas ciliata have been tested as a potential therapeutics for diabetes through bioprospecting. There is evidence that compounds generated from Fusarium spp. and Alternaria spp. exhibit anti-diabetic action, suggesting that these fungal endophytes may serve as a source of multifunctional therapies (Adeleke et al. 2021 ).

4.9.1 Anti-malarial activity

Due to the rapid spread of anti-drug resistance malaria parasites in recent years, there is an urgent need for novel malaria therapy drugs. It was shown that the anti-malarial activity of two endophytic fungi, munumbicins E-4 and E-5, was twice as potent as that of chloroquine (Fadiji and Babalola 2020 ). The endophyte Diaporthemiriciae is responsible for producing the secondary metabolite epoxy cytochalasin H. This compound exhibits robust anti-malarial suppression against a strain of Plasmodium falciparum resistant to chloroquine (Ferreira et al. 2017 ). Ateba et al. ( 2018 ) found that the endophyte species Paecilomyces lilcinus and Penicillium janthinellum are great resources for new compounds that are active against Plasmodium falciparum and show potential in the treatment of malaria. It has been proven that various endophytic fungi, in addition to Aspergillus niger , Fusarium spp., and Nigrospora spp. can produce bioactive compounds with an antiplasmodial effect against Plasmodium falciparum (Kaushik et al. 2014 ).

4.9.2 Antituberculosis

Tuberculosis (TB), an infection of the lungs caused by Mycobacterium tuberculosis , is a worldwide health concern. It has lasted for centuries and is one of the world's most devastating illnesses. The World Health Organization (WHO) estimates that 10 million individuals are currently living with TB. Ending the TB pandemic by 2030 is one of the health aims of the sustainable development goals of the United Nations. M. tuberculosis has been proven to acquire resistance to numerous synthetic medications. To this end, it is crucial to keep looking for new, natural antimycobacterial medicines that may kill mycobacteria without causing resistance. Bioprospecting for fungal endophytes as a cure for TB is an exciting field of study since many fungal metabolites are naturally antimycobacterial. Azadirachta indica and Parthenium hysterophorus fungal endophytes have been found to exhibit antibacterial effects against TB (Mane et al. 2017 ). Phomopsis spp., an endophytic fungus isolated from Garcinia spp. is responsible for the production of Phomoxanthone A and B, which have been shown to suppress the development of M. tuberculosis (Kumar et al. 2017 ). Alternaria alternate and Phomopsis spp., two fungal endophytes isolated from Thai medicinal plants, have been shown to generate 3-nitro propionic acid and tenuazonic acid, exhibit indecisive action against M. tuberculosis (H37Ra) (Kumar et al. 2017 ; Deshmukh et al. 2015 ). Endophytic fungus Phomopsis spp. isolated from Garcinia adulcis generates bioactive metabolites with anti-tuberculosis potential (Kumar et al. 2017 ). These metabolites include phomoenamide and phomonitroester. Benzopyran, diaportheone A and B are bioactive chemicals produced by Diaporthe spp. They are associated with the leaves of Pandanus amaryllifolius and suppress aggressive strains of M. tuberculosis (Chepkirui and Stadler 2017 ).

4.9.3 Antiviral

Evidence suggests that endophytic fungi can create antiviral drugs that are effective against a wide range of viruses, including HIV (Farooq et al. 2016 ), human CMV (Raekiansyah et al. 2017 ), Dengue virus (Liu et al. 2019 ), and influenza A (HINI) virus (Ambele et al. 2020 ). Antiviral activity has been found in two novel substances, cytonic acid A and cytonic acid B, which are isolated from Cytonaema spp. With the use of mass spectrometry and nuclear magnetic resonance, the structures of ptrideside isomers were conferred, leading to the identification of novel inhibitors of the protease activity of human cytomegalovirus. Fungal endophytes in the phyllosphere (leaves) of an oak tree (Quercuscoccifera) generate the antiviral chemical Hinnuliquinone, which has been linked to the inhibition of HIV-1 protease activity (Adeleke et al. 2021 ). Alternaria tenuissima QUE1Se is an endophytic fungus that generates altertoxins, a substance with potent anti-HIV-1 action. In addition to emerimidine (A, B), dehydroaustin, austinol, aspernidine (A, B), austin, emeriphenolicins (A, D), and acetoxydehydroaustin, many other compounds isolated from Emericella spp. (HKZJ) have been found to have antiviral activity against the influenza A virus (H1N1) (Raekiansyah et al. 2017 ). The antiviral activity of most medicinal plant mixtures is relatively high, even in their crudest forms. Antiviral activity in certain actinomycetes has been demonstrated (Fadiji and Babalola 2020 ). The antiviral chemical 2-(furan-2-yl)-6-(2S, 3S, 4-trihydroxybutyl) pyrazine was initially isolated from the plant species Jishengella endophytica 161,111. This chemical is adequate to combat the spread of the influenza A (H1N1) virus (Fadiji and Babalola 2020 ; Raekiansyah et al. 2017 ).

4.9.4 Other pharmacological potentials of the endophytic fungal secondary metabolites

In addition to their potential uses in food, agriculture, medicine, and cosmetics, the bioactive metabolites found in endophytes are excellent pharmaceutical sources for treating various disease conditions (Shukla et al. 2014 ). The metabolites generated by fungal endophytes contain a wide variety of functional groups including alkaloids, flavonoids, terpenoids, phenolic acids, quinones, steroids, benzopyranones, tannins, tetralones, and chinones (Gouda et al. 2016 ). Half of all deaths worldwide are attributable to infectious and parasitic disorders. It has been established that endophytes are the origin of a wide variety of commercially accessible bioactive chemicals and secondary metabolites. New compounds produced by endophytic microbes have shown promise as antibiotics, anti-inflammatory agents, cancer treatments, immunosuppressants, anti-diabetic activity, anti-malarial activity, and even insecticides (Fadiji and Babalola 2020 ).

5 Conclusion

The purpose of this article is to gather updated knowledge on secondary metabolites of endophytic fungi, their production, methods of analysis, pharmaceutical potential, and application. Microorganisms that are endophytic to a plant provide benefits to the host plant and stimulate plant growth via several direct and indirect mechanisms of action. Fungal endophytes represent an inexhaustible reservoir of pharmacologically essential compounds. Endophytic fungi are an essential component for the production of novel biomolecules for the biochemical and pharmaceutical industries. The fungal endophytes are in a pivotal position in producing certain enzymes, such as amylase, cellulase, laccase, lyase, etc., that have significant commercial and pharmaceutical applications. Several promising pharmaceutical lead molecules have been reported and derived from endophytic fungi. Because they produce physiologically active metabolites that are immune suppressants, anti-cancer agents, promote plant growth, anti-microbial volatiles, anti-oxidants, and antibiotics. Future insights are necessary to understand more about dynamic fungal endophytes, host interactions, and molecular players of fungal endophytes involved in producing biopharmaceuticals of human interest. Finally, the use of metagenomics in combination with next-generation sequencing technologies is anticipated to unlock Endophytes have the potential to offer at least partial, if not total, answers to critical concerns such as rising strain on the global food supply, climate change, environmental degradation, and therapeutics. As a result, the biology of endophytes has to be investigated more if we are going to reap the advantages of these organisms in the fields of agriculture, industry, and medicine. Information on bioactive natural compounds found in endophytic fungi has not been explored effectively. The reservoir of pharmacologically important chemicals that may be found in fungal endophytes is almost endless. To get a better understanding of the dynamic fungal endophytes, the host relationships, and the molecular actors of fungal endophytes that are engaged in the production of biopharmaceuticals of human interest, more research is required. In conclusion, the use of metagenomics in conjunction with next-generation sequencing technologies is projected to open a wide variety of hitherto undiscovered pools of antimicrobials that are released by endophytic microorganisms that have not yet been farmed. Endophytes have just come to be seen as an important contributor to the overall pool of biological variety and numerous bioactive molecules secreted by as-yet-uncultivated endophytic microbes are not explored. The resources that endophytes give may be used for a variety of purposes, including formulations and bioprospecting. Because of this, further research into the biology of endophytes is required if we want to benefit from the presence of these organisms in the sectors of agriculture, industry, and medicine. The discovery of bioactive natural chemicals discovered in endophytic fungus has had an indelible impact on the treatment of a variety of diseases, including cancer, diabetes, and neurological conditions. With the use of cutting-edge biotechnology like genetic engineering and the microbial fermentation process, these microbial resources may be better utilized for human benefit.

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Singh, V.K., Kumar, A. Secondary metabolites from endophytic fungi: Production, methods of analysis, and diverse pharmaceutical potential. Symbiosis 90 , 111–125 (2023). https://doi.org/10.1007/s13199-023-00925-9

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Epigenetic regulation of fungal secondary metabolism.

1. Introduction

2. secondary metabolism in fungi, 3. epigenetic regulation, 3.1. dna methylation, 3.2. histone methylation, 3.3. histone acetylation, 3.4. other epigenetic regulation, 4. cross-regulation of secondary metabolism by epigenetic and global regulation, 4.3. sire/hst4, sirb/hst2, 5. conclusions and outlook, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Zhang, Y.; Yu, W.; Lu, Y.; Wu, Y.; Ouyang, Z.; Tu, Y.; He, B. Epigenetic Regulation of Fungal Secondary Metabolism. J. Fungi 2024 , 10 , 648. https://doi.org/10.3390/jof10090648

Zhang Y, Yu W, Lu Y, Wu Y, Ouyang Z, Tu Y, He B. Epigenetic Regulation of Fungal Secondary Metabolism. Journal of Fungi . 2024; 10(9):648. https://doi.org/10.3390/jof10090648

Zhang, Yufei, Wenbin Yu, Yi Lu, Yichuan Wu, Zhiwei Ouyang, Yayi Tu, and Bin He. 2024. "Epigenetic Regulation of Fungal Secondary Metabolism" Journal of Fungi 10, no. 9: 648. https://doi.org/10.3390/jof10090648

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• Published: 27 November 2020

Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences

• Michael A. Skinnider   ORCID: orcid.org/0000-0002-2168-1621 1 , 2 , 3 , 4 ,
• Chad W. Johnston 1 , 2 , 3 , 5 ,
• Mathusan Gunabalasingam 1 , 2 ,
• Nishanth J. Merwin 1 , 2 ,
• Agata M. Kieliszek 3 ,
• Robyn J. MacLellan 3 ,
• Haoxin Li 3 ,
• Michael R. M. Ranieri 1 , 2 ,
• Andrew L. H. Webster 1 , 2 ,
• My P. T. Cao 1 , 2 ,
• Annabelle Pfeifle   ORCID: orcid.org/0000-0001-8150-8129 3 ,
• Norman Spencer   ORCID: orcid.org/0000-0003-3405-1079 3 ,
• Q. Huy To 1 , 2 ,
• Dan Peter Wallace 3 ,
• Chris A. Dejong   ORCID: orcid.org/0000-0001-5761-0086 3 &
• Nathan A. Magarvey 1 , 2  

Nature Communications volume  11 , Article number:  6058 ( 2020 ) Cite this article

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• Biosynthesis
• Cheminformatics
• Genome informatics
• Natural products

Novel antibiotics are urgently needed to address the looming global crisis of antibiotic resistance. Historically, the primary source of clinically used antibiotics has been microbial secondary metabolism. Microbial genome sequencing has revealed a plethora of uncharacterized natural antibiotics that remain to be discovered. However, the isolation of these molecules is hindered by the challenge of linking sequence information to the chemical structures of the encoded molecules. Here, we present PRISM 4, a comprehensive platform for prediction of the chemical structures of genomically encoded antibiotics, including all classes of bacterial antibiotics currently in clinical use. The accuracy of chemical structure prediction enables the development of machine-learning methods to predict the likely biological activity of encoded molecules. We apply PRISM 4 to chart secondary metabolite biosynthesis in a collection of over 10,000 bacterial genomes from both cultured isolates and metagenomic datasets, revealing thousands of encoded antibiotics. PRISM 4 is freely available as an interactive web application at http://prism.adapsyn.com .

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Introduction.

The overwhelming majority of antibiotics currently in clinical use are derived from naturally occurring small molecules produced by microbes 1 . The biosynthetic pathways responsible for the production of these molecules have been honed over long evolutionary time scales in order to provide microbes with competitive advantages in their natural environments 2 . These pathways are encoded within the genomes of the producing organisms, and comparative genomics studies have suggested a wealth of novel antibiotics encoded in the genomes of both culturable and unculturable organisms that remain to be discovered 3 , 4 , 5 . Directed discovery of these unknown antibiotics, guided by genome sequencing data, could provide a means to address the growing clinical need for new antibiotics to combat drug-resistant pathogens 6 .

With the amount of microbial genome sequence information deposited in public databases continuing to increase at an exponential rate (Supplementary Fig.  1 ), methods to leverage this data towards antibiotic discovery are urgently needed. However, whereas a plethora of methods are available to identify the genomic loci responsible for natural antibiotic biosynthesis 7 , 8 , 9 , few tools exist to link these loci to the specific chemical structures of their encoded products. The challenges inherent to the latter task far exceed those involved in genome annotation: nature employs a dizzying array of enzymatic catalysts to construct structurally complex molecules from simple building blocks. Moreover, these catalysts are arranged in multi-gene clusters that can be categorized into dozens of distinct families. Existing tools can generate predictions of genomically encoded natural antibiotic structures from small regions of this vast biosynthetic space 7 , 10 , but a comprehensive platform is lacking.

We previously described PRISM, a genome analysis toolkit and web application to predict the complete chemical structures of genomically encoded nonribosomal peptides and polyketides 11 . However, despite subsequent extension to families such as ribosomally encoded and posttranslationally modified peptides (RiPPs) 12 , PRISM’s coverage of industrially important chemical space remained incomplete. Here, we present PRISM 4, which enables genome-guided chemical structure prediction for every class of bacterial natural antibiotics currently in clinical use, including aminoglycosides, nucleosides, β-lactams, alkaloids, and lincosamides among other classes of metabolites. Moreover, PRISM 4 achieves a dramatic increase in coverage of enzymatic tailoring reactions encoded within canonical thiotemplated pathways (Fig.  1 and “Methods”). PRISM achieves accurate structure prediction by connecting biosynthetic genes to the enzymatic reactions they catalyze, permitting the in silico reconstruction of complete biosynthetic pathways (Supplementary Figs.  2 and 3 ) as well as their final products (Fig.  1a, b ). In total, PRISM 4 includes 1772 hidden Markov models (HMMs) and implements 618 in silico tailoring reactions in order to predict the chemical structures of 16 different classes of secondary metabolites, making it a comprehensive resource to link microbial genome sequence information to the natural antibiotics encoded within (Fig.  1c , Supplementary Table  1 , and Supplementary Data  1 ).

a Schematic overview of PRISM 4. Microbial genome sequences are annotated using a library of 1,772 HMMs, and secondary metabolite BGCs are identified using a rule-based approach. Combinatorial, graph-based chemical structure prediction is effected using a library of 618 virtual tailoring reactions. b Total number of HMMs, virtual tailoring reactions, substrates, and sugars incorporated in PRISM 4. c Examples of predicted chemical structures generated by PRISM 4 for newly added families of secondary metabolites. Source data are provided as a Source Data file.

PRISM 4 generates accurate structure predictions for known BGCs

To evaluate the accuracy of PRISM 4, we assembled a comprehensive set of 1281 biosynthetic gene clusters (BGCs) with known products from public databases and extensive literature curation, subject to multiple rounds of manual review by a team of natural products chemists to correct errors in chemical structures or the boundaries of deposited nucleotide sequences (Methods). PRISM 4 detected 1230 of these reference BGCs (96%), representing an increase of 40% over the original PRISM release, as well as a slight increase in sensitivity over antiSMASH 5, which detected 1212 (Fig.  2a ). Moreover, PRISM 4 generated at least one predicted chemical structure for 1157 of the 1230 detected BGCs (94%), an increase of at least 54% over antiSMASH 5 or NP.searcher, which predicted structures for 753 and 398 BGCs, respectively (Fig.  2b ). To quantify the similarity of predicted structures to the true cluster products, we calculated the Tanimoto coefficient 13 (Tc) between real and predicted structures from each cluster, a measure of chemical similarity that reflects the fraction of substructures shared between the two molecules, and compared these to predicted and true structures from random BGCs pairs (Methods). Using this metric, we found PRISM 4 achieved statistically significant predictive accuracy across a wide range of secondary metabolite classes (Fig.  2e ). For the subset of 385 BGCs with structure predictions generated by all four programs, we compared the Tc between true products and predicted structures from PRISM 4, antiSMASH 5, and NP.searcher, finding that PRISM 4 was significantly more accurate in both comparisons (both p  < 10 –15 , paired Brunner–Munzel test; Fig.  2c and Supplementary Data  2 ); pairwise comparisons were likewise highly significant ( n  = 398 and 753, respectively, both p  < 10 –15 ; Supplementary Fig.  4 ). We additionally quantified the accuracy of structure predictions based on the functional groups they contained 14 . Using the Jensen-Shannon divergence to compare the distributions of functional groups found in true and predicted structures, we observed that the functional group content of PRISM 4 predicted structures was significantly more similar to that of true products than that of structures predicted by antiSMASH 5 or NP.searcher (bootstrap p  < 0.001; Fig.  2d ).

a Number of BGCs within a manually curated gold standard set ( n  = 1,281; dotted line) identified by PRISM 4, antiSMASH 5, and NP.searcher. b Number of BGCs within the gold standard set with at least one structure predicted by each program. c Median Tanimoto coefficient between true and predicted structures for the subset of gold standard BGCs with at least one predicted structure generated by all four programs ( n  = 385). d Jensen–Shannon divergence between functional group content of true and predicted structures for each program. Errors bars show standard deviation of bootstrap resampling. e Median and maximum Tanimoto coefficients between true and predicted structures generated by PRISM 4 for the gold standard set, by biosynthetic family, and compared to the median Tanimoto coefficient between predicted structures and non-matched BGCs (“random pairs”). Top, statistical significance of the comparison between median and random Tanimoto coefficients (*** p  < 0.001; ** p  < 0.01; * p  < 0.05, two-sided t -test). Bottom, number of BGCs from each family in the gold standard set (n). Box plots show median (horizontal line), interquartile range (hinges), and the smallest and largest values no more than 1.5 times the interquartile range (whiskers) throughout. Source data are provided as a Source Data file.

In some cases, the precise substrate of the reaction catalyzed by a given enzyme is not unambiguously predictable from protein sequence alone: for instance, a halogenase may catalyze chlorination at a number of different sites within a molecule. For this reason, PRISM considers all possible sites of each tailoring reaction, and combinations thereof, when generating predicted structures. To validate this strategy, we compared the median and maximum Tc between predicted and true structures for each cluster, finding the maximum Tc to be significantly greater ( p  < 10 –15 ; Fig.  2e and Supplementary Fig.  5 ). We also quantified the size of the combinatorial search space for each family of metabolites (Supplementary Fig.  6 ), finding that the majority of classes could usually be predicted within a dozen or fewer combinatorial plans, but a subset of families were associated with a greater degree of structural uncertainty (most notably aminoglycosides, in which the configurations of the glycosidic bonds cannot be predicted from primary sequence).

PRISM 4 predicts natural product-like products for cryptic BGCs

To gain a broader perspective on PRISM 4’s ability to predict encoded metabolite structures from genome sequence, we used PRISM 4 to analyze secondary metabolism in a collection of 3,759 dereplicated complete bacterial genomes 15 . For this comparison, we focused on PRISM and antiSMASH, as platforms designed to analyze BGCs from a wide range of biosynthetic families. Among 22,446 identified clusters, PRISM 4 generated at least one predicted structure for 7404, a significantly greater proportion than antiSMASH 5 ( p  < 10 –15 , χ 2 test), with 3184 clusters having structures predicted only by PRISM 4, compared to 500 only by antiSMASH 5 (Fig.  3 and Supplementary Data  3a ). Notably, PRISM 4 predicted hundreds of complete chemical structures for families of metabolites such as β-lactams, alkaloids, phosphonates, cyclodipeptides, bisindoles, and aminoglycosides, for which antiSMASH 5 predicted only a handful of structures, or none at all (Fig.  3a ). PRISM 4 also generated the majority of structure predictions for several bacterial phyla whose biosynthetic capacity has historically not been widely appreciated, such as Desulfobacterota, Spirochaetota, or Campylobacterota (Fig.  3b ). Given that phylogenetically distinct organisms are more likely to produce novel products 4 , 16 , this finding suggests PRISM 4 may have particular utility for genome mining of molecules with scaffolds or activities that diverge from those present in well-studied organisms.

a , b Number of BGCs with at least one chemical structure predicted by PRISM 4, antiSMASH 5, or both methods in a collection of 3,759 dereplicated complete bacterial genomes, by biosynthetic family ( a ) and phylum of producing organisms ( b ), as classified in the Genome Taxonomy Database (GTDB) 15 . c – g Structural features of n  = 4220 pairs of predicted secondary metabolites from BGCs with products predicted by both PRISM 4 and antiSMASH 5. c Percent of predicted structures in Lipinski rule of five space 20 . Error bars show the standard error of the sample proportion. d Molecular weight of predicted structures. e Bertz topological complexity index 21 of predicted structures. f Internal diversity of predicted structures, as quantified by median Tanimoto coefficient to all other predicted structures in the set. g Similarity of predicted structures to known natural products, as quantified by the median Tanimoto coefficient to the set of known natural products in the Natural Products Atlas. Box plots show median (horizontal line), interquartile range (hinges), and the smallest and largest values no more than 1.5 times the interquartile range (whiskers) throughout. Source data are provided as a Source Data file.

Because the true structures of the metabolites encoded by these loci are not known, we were unable to directly assess the accuracy of structure prediction. Instead, we asked whether predicted structures had structural features characteristic of known natural products 17 . Previous studies have found that a relatively small proportion of natural products are within the chemical space defined by Lipinski’s “rule of five,” a set of guidelines developed to facilitate the design of orally bioavailable drugs 18 , 19 . Relative to structures predicted by antiSMASH 5, a lower proportion of PRISM 4 predictions were within Lipinski’s rule of five space 20 ( χ 2 test, p  < 10 –15 ; Fig.  3c ), having greater molecular weights (paired Brunner–Munzel test, p  < 10 –15 ; Fig.  3d ), more hydrogen bond donors and acceptors ( p  < 10 –15 ; Supplementary Fig.  7a, b ), and greater octanol-water partition coefficients ( p  < 10 –15 ; Supplementary Fig.  7c ). PRISM 4 predictions were also more structurally complex, as quantified using the Bertz topological complexity index 21 ( p  < 10 –15 ; Fig.  3e ), a measure of molecular complexity that incorporates both the complexity of the bonding and the distribution of heteroatoms. Moreover, PRISM 4 predictions were also more structurally diverse, as quantified by the median intra-set Tc ( p  < 10 –15 ; Fig.  3f ). Finally, PRISM 4 predictions displayed a greater degree of structural similarity to known natural products, as quantified either by their median Tc to the set of known natural products in Natural Products Atlas ( p  < 10 –15 ; Fig.  3g ), or by their ‘natural product-likeness’ score 22 ( p  < 10 –15 ; Supplementary Fig.  7d ). Taken together, these results indicate PRISM generates complex, diverse, and natural product-like chemical structure predictions from large genomic datasets.

To evaluate the BGC detection functionality of PRISM and antiSMASH, we carried out a blinded review of 200 randomly sampled clusters detected only by one of the two methods. Manual annotation suggested up to 55% of antiSMASH-only BGCs represented false positives (FPs), compared to up to 37% of PRISM-only BGCs ( p  = 0.016, χ 2 test; Supplementary Fig.  8 ). Among antiSMASH-only BGCs, recurrent categories of FPs included minimal fatty acid synthases, DUF692-associated bacteriocins, putative phosphonate BGCs associated with cell wall biosynthesis machinery, and isolated prenyltransferases classified as terpene BGCs. It should be noted that a trade-off between specificity and sensitivity is inherent to any prediction task, and the higher rate of FPs for antiSMASH also expectantly affords it a greater ability to detect—though not to predict structures for—novel or divergent BGC types.

PRISM 4 enables chemical structure prediction from metagenomic data

Rapid progress in metagenomic sequencing technologies, accompanied by rapid advances in computational approaches for genome assembly from metagenomic data 23 , has revealed a wealth of undiscovered antibiotics within uncultured organisms 3 . We used PRISM 4 to analyze secondary metabolism in a collection of 6,362 dereplicated metagenome-assembled genomes (MAGs) 15 , 23 . PRISM 4 generated predicted structures for 2630 of 10,814 clusters, representing the vast majority of structure predictions for this collection of genomes (~96%), and significantly more than antiSMASH 5 ( p  < 10 –15 , χ 2 test; Supplementary Fig.  9a, b and Supplementary Data  3b ). In addition to well-studied classes of metabolites, notably those originating from thiotemplated assembly lines (nonribosomal peptides and polyketides) as well as ribosomally synthesized and posttranslationally modified peptides (RiPPs), we found biosynthesis of phosphonate-containing natural products to be surprisingly common among uncultured organisms (Supplementary Fig.  9a ). PRISM 4 metagenomic structure predictions also possessed structural features characteristic of known natural products, including a lower proportion in rule-of-five space, larger molecular weights, greater topological complexity, increased internal diversity, and greater similarity to known natural product structures than a matched set of structure predictions from antiSMASH 5 (Supplementary Fig.  9c–g ). Collectively, these results reinforce the notion that a wealth of biologically active metabolites are encoded within the genomes of uncultured organisms, and highlight the value of PRISM 4 for interrogation of antibiotic biosynthesis in large metagenomic datasets.

Quantitative structure-activity relationships of cryptic molecules

Taken together, these analyses indicate PRISM 4 generates realistic structure predictions from the genomes of diverse cultured and uncultured organisms, with a high degree of chemical similarity to true products in the case of known BGCs, and structural features characteristic of known secondary metabolites in the case of cryptic BGCs discovered by genome mining. We therefore asked whether these high-quality predicted structures could be leveraged to address another key challenge in genome-guided discovery of natural antibiotics: namely, prioritizing particular BGCs or producing organisms with the greatest likelihood of producing biologically active metabolites for targeted discovery. We undertook an extensive literature review to systematically curate bioactivity data for the 1281 BGCs in the gold standard set, and trained support vector machines (SVMs) to predict the probability that a given BGC produces a compound with antibacterial, antifungal, antiviral, antitumor, or immunomodulatory activity, using tenfold cross-validation to evaluate model accuracy. To evaluate the performance of these models, we calculated the area under the receiver operating characteristic curve (AUC), and compared the observed AUCs to those expected from random predictors 24 . In all cases, these models yielded significantly more accurate predictions of biological activity than random expectation (all p  < 10 –15 , Wilcoxon rank-sum test; Fig.  4a ). Furthermore, classifiers trained on the chemical fingerprints of PRISM predicted structures were significantly more accurate than classifiers trained on Pfam domains, with a mean increase of 7.5% in the AUC ( p  < 10 –15 , Fisher integration of DeLong tests; Fig.  4a ). This increase in performance supports the notion that chemical structure prediction is essential to high-accuracy prediction of the biological activity of genetically encoded metabolites. We refer to this approach as quantitative predicted structure-activity relationship modeling, or QPSAR.

a Receiver operating characteristic (ROC) curves for support vector machine (SVM) models trained on Pfam domains found within biosynthetic gene clusters or chemical fingerprints of PRISM predicted structures. b Distribution of BGCs predicted to produce secondary metabolites with antibacterial, antitumor, immunomodulatory, antifungal, antiviral, multiple, or no biological activities in a collection of 10,121 complete or metagenome-assembled prokaryotic genomes, by biosynthetic family (left) or producing organism phylum (right), as classified in the Genome Taxonomy Database (GTDB) 15 . c , d Visualization of predicted structure chemical space by uniform manifold approximation and projection (UMAP) 25 , colored by biological activity ( c ) or genome origin ( d ). e Enrichment or depletion of secondary metabolites by predicted biological activity in metagenome-assembled genomes (MAGs), relative to complete bacterial genomes. Source data are provided as a Source Data file.

We next used the trained QPSAR models to systematically discover biosynthetic loci responsible for the production of bioactive metabolites within the complete collection of over 10,000 complete or metagenome-assembled bacterial genomes. At a false discovery rate of 10%, PRISM 4 identified 1589 BGCs producing antibacterial compounds, 331 antiviral BGCs, 289 immunomodulatory BGCs, 272 antifungal BGCs, and 248 antitumor BGCs, in addition to a further 1055 BGCs with more than predicted biological activity (Fig.  4b ). To obtain a global overview of the chemical diversity within this dataset, we applied the non-linear dimensionality reduction technique UMAP (uniform manifold approximation and projection) 25 to the chemical fingerprints of PRISM predicted structures. Unlike some other non-linear dimensionality reduction methods, UMAP approximately preserves global structure, meaning points that are close in the low-dimensional space are also close in the high-dimensional space, and vice-versa. This visualization of the complete predicted chemical space revealed substantial chemical diversity within each bioactivity class (Fig.  4c ). Notably, predicted structures from complete and MAGs were evenly distributed across the manifold (Fig.  4d ), suggesting that potential differences in the quality or completeness of MAGs 26 do not necessarily preclude realistic structure prediction. We also asked whether the MAGs, recovered predominantly from environmental and non-human gastrointestinal samples 23 , were enriched or depleted for the production of metabolites with specific biological activities, relative to the set of complete genomes. Intriguingly, this comparison revealed a marked enrichment for biosynthesis of immunomodulatory agents within the latter set ( χ 2 test, p  = 3.7 × 10 –8 ; Fig.  4e ), suggesting a particularly underappreciated diversity of this class of metabolites beyond well-studied microbes. Collectively, these results highlight the importance of chemical structure prediction in deriving accurate models of biological activity for cryptic biosynthetic loci, and provide a roadmap for targeted discovery of thousands of antibiotic, antitumor, and immunomodulatory compounds encoded within sequenced bacterial genomes.

Early microbial genome sequencing projects revealed dozens of cryptic biosynthetic loci within the genomes of well-studied, industrially important microorganisms, spurring predictions that genome mining would usher in a second ‘golden age’ of antibiotic discovery. Yet, despite notable successes, the impact of genomics on natural antibiotic discovery has been considerably more modest than originally anticipated. Although it is now straightforward to identify clusters of genes responsible for secondary metabolite biosynthesis, translating between genome sequence and the complete chemical structures of the natural antibiotics encoded therein represents a key challenge, and one that has taken on an increasing importance in an era of growing global antibiotic resistance. PRISM 4 represents the most comprehensive effort to address this challenge to date. Our analyses of the natural molecules encoded within thousands of sequenced genomes uncover a vast undiscovered landscape of evolved chemistry. We show that these predicted chemical structures can further be leveraged to develop accurate models of biological activity, which we use to identify thousands of antibiotic, antitumor, and immunomodulatory agents. We make this resource freely available to spur discovery at http://prism.adapsyn.com .

Some limitations should be noted. In developing PRISM 4, we set out to codify an enormous corpus of knowledge, accumulated over decades of research in biosynthesis and enzymology, into an algorithmically tractable form. An inevitable consequence of this approach is that PRISM relies on homology between newly detected proteins and known enzymatic machinery in order to reveal BGCs and predict the structures of their genetically encoded products. For this reason, PRISM can neither identify BGCs from undescribed families, nor predict novel enzymatic activities. More generally, current models of secondary metabolite biosynthesis are incomplete, which places an inherent limit on the accuracy of structure prediction; we have sought to address this by revising the systems used for BGC detection and structure prediction as additional information has become available. Recently, we and others have shown that deep learning-based methods can enable more flexible and accurate detection or characterization of BGCs or individual biosynthetic components 27 , 28 . However, at present these approaches still rely on interfacing with rule-based systems such as that employed by PRISM 4 to permit structure prediction 27 , or else are not capable of generating predicted structures 28 . In the future, more sophisticated machine-learning approaches might enable the end-to-end prediction of encoded small molecules directly from primary sequence. Finally, PRISM 4 was designed primarily for prokaryotic genome analysis and thus cannot identify BGCs families thought to be specific to eukaryotes, and—like all tools for genome annotation—may produce incongruous results when applied to fragmented or low-quality genome assemblies.

Overview of PRISM 4

PRISM 4 is a cloud-based, interactive web application, with a back-end written in the Java programming language. The web application itself consists of a VueJS front-end, paired with a Python API that distributes submissions to background workers, and is available at http://prism.adapsyn.com . A number of steps have been taken to ensure the high performance of the web application, including horizontal distribution of individual PRISM runs over the cloud, as well as optimization of key bottlenecks to reduce the runtime by approximately an order of magnitude over PRISM 3 (ref. 29 ). Here, we provide a brief overview of the PRISM workflow and the essential changes that distinguish PRISM 4 from previous versions. In Supplementary Note 1, we provide a comprehensive description of the web server, including the user interface and output, a complete description of the methodology underlying BGC detection and chemical structure prediction, and the approaches taken to extend structure prediction to additional BGC families or expand existing ones within PRISM 4. In brief, PRISM 4 takes as input a DNA sequence in FASTA or GenBank format, then queries open reading frames (ORFs) identified therein against a library of 1772 HMMs, complemented by collections of BLAST databases, conserved protein motifs, and machine-learning classifiers, to identify enzymatic domains involved in secondary metabolite biosynthesis and, in some cases, assign them to subtypes, infer their substrates, or otherwise predict their activity. BGCs are identified using a rule-based approach, generally requiring two or more biosynthetic domains to be found in close genomic proximity to reduce the rate of FPs 12 .

The biosynthetic information identified from DNA sequence in this manner is subsequently used to predict complete chemical structures for the encoded product(s) of each BGC. A key challenge in this process is to address cases where the precise substrate of a reaction catalyzed by a given enzyme is not unambiguously predictable from protein sequence alone. As an illustrative example, reactions catalyzed by phosphotransferases or sulfotransferases can generally occur at any free hydroxyl within a molecule. PRISM 4 takes a combinatorial, graph-based approach to structure prediction, with the goal of enumerating all possible products of the identified set of biosynthetic domains. Under this paradigm, the complete biosynthetic pathway and its product are modeled as a series of transformations of a chemical graph, which itself comprises a set of chemical subgraphs. These subgraphs are inferred based on the enzymatic content of the BGC. Each subgraph represents an individual residue, such as a nucleotide or proteinogenic amino acid, or combination of residues with a fixed pattern of connectivity, such as ketide units activated by adjacent modules in a polyketide synthase. The chemical graph is then derivatized based on an in silico knowledgebase of 618 virtual tailoring reactions, each of which links a single enzyme to the reaction it catalyzes. A tailoring reaction involves a series of bond order changes (including bond addition or removal) and atom removal, though never atom addition, to the chemical graph of a biosynthetic intermediate. All 618 reactions are implemented as Java classes, rather than as pattern-based transformations such as the SMIRKS notation, affording a great deal of flexibility in reaction modeling. An example of the distinction between chemical subgraphs and tailoring reactions is depicted in Supplementary Fig.  2 , in which the isonitrile geranyltranferase FamD2 activates geranyl pyrophosphate (a chemical subgraph), then catalyzes geranylation of an indole ring (a tailoring reaction). Finally, the complete set of potential biosynthetic pathways, or a large random sample thereof, is inferred when any ambiguity is present in either the chemical graphs or reactions associated with a given BGC. Modeling biosynthesis as a series of tailoring reactions executed on a set of chemical subgraphs allows PRISM 4 to faithfully represent complete biosynthetic pathways, as illustrated in Supplementary Figs.  2 and 3 for two exemplary natural antibiotics. Finally, PRISM generates rich interactive web pages as output, including HTML5-based graphics, to assist the user in exploring the results.

Previous versions of PRISM introduced complete chemical structure prediction for select classes of secondary metabolites, most notably nonribosomal peptides and polyketides and RiPPs 11 , 12 , 29 , 30 . However, coverage of pharmaceutically and industrially relevant secondary metabolite classes remained incomplete. We undertook a comprehensive effort to develop genome-guided chemical structure prediction functionality for all biosynthetic classes of bacterial natural antibiotics that are currently in clinical use. We developed libraries of 145 HMMs and 133 virtual tailoring reactions to predict chemical structures for 21 subtypes of nucleoside natural products; libraries of 40 HMMs and 28 virtual tailoring reactions to predict structures for 11 subtypes of β-lactams, including both β-lactam antibiotics and β-lactamase inhibitors; libraries of 39 HMMs and 39 virtual tailoring reactions to predict lincosamide structures; and libraries of 12 HMMs and 11 virtual tailoring reactions to identify and predict isonitrile alkaloid structures. In addition, we developed a library of 63 HMMs, and revised and extended our previously described algorithm for deoxy sugar prediction 31 , in order to predict aminoglycoside chemical structures. The complete sets of HMMs and virtual tailoring reactions are enumerated in Supplementary Data  1 , and further detail on these additional classes is provided in Supplementary Note 1. Compared to PRISM 3, PRISM 4 includes a total of 1083 newly developed HMMs (an increase of 145%) and 334 new reactions (an increase of 118%) that were not included in previous versions (Supplementary Fig.  10 ).

We also extended structure prediction functionality for existing biosynthetic families within PRISM. RiPP structure prediction was augmented by the addition of three additional families, and refinement of some existing HMMs or reactions on the basis of updates to the current understanding of RiPP biosynthesis. More significantly, we undertook a systematic effort to expand structure prediction for canonical thiotemplated (nonribosomal peptide and polyketide) BGC products. First, we identified specific chemotypes that were poorly predicted within PRISM, such as pyrrolobenzodiazepines, tetrahydroisoquinolines, or lipocyclocarbamates. Second, we identified unusual monomers that were not accounted for in PRISM, such as homotyrosine, tambroline, or aziridine-containing amino acids. Finally, we incorporated limited structure prediction functionality for two additional minor classes of secondary metabolites (phenazines and isopropylstilbenes), and expanded the scope of BGC detection to include bacteriocins and nonribosomal peptide synthetase (NRPS)-independent siderophores. Complete details of the updates to PRISM functionality are provided in Supplementary Note 1. In total, PRISM 4 can predict complete chemical structures for 17 biosynthetic families and identify BGCs for a further 11 (Supplementary Table  1 ).

Validation of PRISM 4 structure predictions

In order to validate PRISM 4 structure predictions, we undertook the curation of a comprehensive ‘gold standard’ database of 1281 prokaryotic BGCs, linked to known secondary metabolites with unambiguously assigned chemical structures. We compiled BGCs from a number of existing databases, including MIBiG 32 , ClusterMine360 33 , DoBISCUIT 34 , and NRPS-PKS 35 . These were complemented by extensive manual searching of the NCBI database to retrieve known BGCs observed to be absent from any of these resources, as well as from our own in-house database. We further created a series of PubMed alerts, using a number of terms related to secondary metabolite biosynthesis, and manually reviewed articles on a weekly basis to identify newly published BGCs.

During the course of investigation, we noticed that many of the chemical structures associated with known BGCs in public databases contained errors. These ranged from minor issues likely introduced by cheminformatics software (e.g., representation of amide bonds by their imidic acid tautomers 36 ) to more major structural issues (e.g., structural errors affecting large moieties of the product, or even entirely incorrect products associated with a BGC). Further, a large number of known BGCs did not have an associated chemical structure. We therefore took a systematic review of all chemical structures in order to ensure the accuracy of our ‘gold standard’ dataset. Each structure associated with a gold standard BGC was subject to two independent rounds of manual review by natural products chemists, with any remaining discrepancies resolved by consensus.

A parallel review of the BGC nucleotide sequences was performed and a number of issues were identified, consisting primarily of cases where contigs larger than the BGC itself were deposited in public databases by the original authors. However, we also identified cases where incomplete BGCs had been deposited, as well as BGCs that spanned more than one contig in originally deposited assemblies. The nucleotide sequences of these BGCs were corrected using publicly available genome assemblies when possible, and omitted from the final dataset otherwise. As a final step, we used a combination of nucleotide BLAST and manual review to assign the taxonomy of the producing organism for each BGC, and removed a small number of remaining BGCs from eukaryotic organisms.

In total, this process led to the curation of a dataset of 1281 BGCs associated with 1,434 molecules. 125 BGCs were associated with more than one molecule. To quantitatively assess the accuracy of PRISM 4 chemical structure predictions with reference to these known products, we calculated the Tanimoto coefficient (Tc) between the chemical fingerprints for each pair of true and predicted structures 13 . The ECFP6 chemical fingerprint 37 , with a length of 1024 bits, was employed on the basis of its excellent performance in comparisons of simulated natural products 38 and chemical similarity search more generally 39 , 40 . We note that notwithstanding the excellent performance of the ECFP6 fingerprint in these benchmarks, this algorithm tends to produce ‘sparse’ fingerprints (that is, bit vectors in which most bits are not set), and consequently will generally yield low Tcs for any comparison of two structures that are not perfectly identical 13 . To contrast the observed Tcs with random expectation, we therefore additionally calculated the Tc between PRISM 4 predicted structures and true secondary metabolite structures from all of the remaining, non-matching BGCs. For PRISM and NP.searcher, which can generate more than one predicted structure for a given BGC, the median Tc was compared; when a BGC was associated with more than one product, the median over all pairwise comparisons was calculated. A maximum of 100 predicted structures were considered for each BGC from PRISM and NP.searcher. We also assessed the distribution of functional groups in true and predicted structures, using the algorithm proposed by Ertl 14 and implemented in the RDKit to identify functional groups in an unbiased manner, without relying on a prespecified list of manually curated functional groups. For this analysis, one predicted or true structure was randomly selected for each BGC. NP.searcher 10 source code was obtained from the web server at http://dna.sherman.lsi.umich.edu/ and run with the default mass window parameters (1–5,000 Da), and all of cyclization, glycosylation, and dimerization enabled. antiSMASH 5.1.2 (ref. 7 ) was obtained from Bioconda 41 , and run with default settings. Sites of variability or uncertainty, denoted in SMILES output by antiSMASH as [Rn], where n is an integer, and in SMILES output by NP.searcher as [X], were replaced with the wild card symbol [*] in order to parse predicted structures with the RDKit. Statistical significance was assessed using the Brunner–Munzel paired rank test 42 , a nonparametric test of the difference in medians robust to differences in the shape of the distributions being compared 43 , except in Fig.  2e , where the t -test was used instead because the Brunner–Munzel test produces inflated p -values in comparisons involving fewer than 10 observations 44 .

Analysis of secondary metabolism in 10,121 prokaryotic genomes

We used PRISM 4 and antiSMASH 5 to predict the chemical structures of secondary metabolites encoded within 3759 complete bacterial genomes and 6362 metagenome-assembled genomes (MAGs). All bacterial genomes with an assembly level of ‘Complete’ were downloaded from NCBI Genome, and a set of dereplicated genomes as determined by the Genome Taxonomy Database 15 were retained to mitigate the impact of highly similar genomes on our analysis. Similarly, a set of 7902 MAGs 23 was obtained from NCBI BioProject (accession PRJNA348753) and the subset of dereplicated genomes was retained. Detected BGCs were matched between PRISM and antiSMASH if their nucleotide sequence overlapped over any range. A small number of PRISM BGC types were mapped to more than one antiSMASH BGC type, including aminoglycosides (‘amglyccycl’ and ‘oligosaccharide’), type I polyketides (‘t1pks’ and ‘transatpks’), and RiPPs (‘bottromycin’, ‘cyanobactin’, ‘glycocin’, ‘head_to_tail’, ‘LAP’, ‘lantipeptide’, ‘lassopeptide’, ‘linaridin’, ‘microviridin’, ‘proteusin’, ‘sactipeptide’, and ‘thiopeptide’). The “hybrid” category encompassed all BGCs assigned any combination of two or more cluster types, i.e., it was not limited to hybrid NRPS-PKS BGCs. The “other” category encompassed aryl polyenes, bacteriocins, butyrolactones, ectoines, furans, homoserine lactones, ladderanes, melanins, N-acyl amino acids, NRPS-independent siderophores, phenazines, phosphoglycolipids, resorcinols, stilbenes, terpenes, and type III polyketides. Producing organism taxonomy was based on genome phylogeny and retrieved from the Genome Taxonomy Database 15 .

Cheminformatic metrics, including molecular weight, number of hydrogen bond donors and acceptors, octanol-water partition coefficients, and Bertz topological complexity, were calculated in RDKit. Both platforms occasionally generated very small, non-specific structure predictions (for example, a single unspecified amino acid or a single malonyl unit) that did not provide actionable information about the chemical structure of the encoded product; to remove these from consideration, we applied a molecular weight filter to remove structures under 100 Da output by either platform. To evaluate the internal structural diversity of each set of predicted structures, we computed the distribution of pairwise Tcs for each set 45 , taking the median pairwise Tc instead of the mean as a summary statistic to ensure robustness against outliers. Structural similarity to known natural products was assessed using the RDKit implementation of the ‘natural product-likeness’ score 22 , and by the median Tc between predicted structures and the known secondary metabolite structures deposited in the NP Atlas database 46 .

Quantitative predicted structure-activity relationship modeling of encoded secondary metabolites

To evaluate the possibility of computationally inferring the likely activity of an encoded secondary metabolite based on its predicted chemical structure in PRISM 4, we undertook an extensive literature review to assign antibacterial, antitumor, immunomodulatory, antifungal, and/or antiviral activities to the metabolites within our ‘gold standard’ set of BGCs. In total, 833 of 1281 BGC products were assigned to at least one of the five activity classes. SVMs were trained in Python using the ‘scikit-learn’ package, using the hyperparameters recommended by Olson et al. 47 . Classifiers trained on 1024-bit hashed ECFP6 chemical fingerprints of PRISM predicted structures were compared to classifiers trained on counts of Pfam domains 48 , identified using Pfam version 31.0 and HMMER version 3.1b2. When more than one structure was predicted for a given BGC, the value of each bit was averaged over all predicted structures (thus, for instance, if a given bit had a value of 1 in two of ten predicted structures for a given cluster, the value of that feature was set to 0.2). Accuracy was assessed using tenfold cross-validation. The statistical significance of classifier performance, as quantified by the area under the ROC curve (AUC), was first evaluated relative to random expectation using the Wilcoxon rank–sum test 24 . The AUC of classifiers trained on PRISM predicted structures was subsequently compared to those trained on Pfam domains using one-sided DeLong tests 49 , which were combined by meta-analysis using Fisher’s method.

For QPSAR modeling of predicted structures encoded in the complete collection of 10,121 bacterial genomes, a probability threshold corresponding to a 10% FDR was calculated from the ROC curves of each activity classifier based on tenfold cross-validation. Classifiers were subsequently trained on the entire ‘gold standard’ set and used to predict biological activity for genomically encoded BGC products, averaging variable bits across predicted structures as above. Visualization of the complete chemical space of predicted structures was accomplished using UMAP 25 , using the implementation in the ‘uwot’ R package, and the first three principal components of the chemical fingerprint matrix as input. The 10 nearest neighbors were used for manifold approximation, with all other parameters set to their default values. A single predicted chemical fingerprint was sampled at random from each BGC in cases where more than one existed, and duplicate fingerprints were removed. To mitigate overplotting, 50% of points in the UMAP manifold were sampled at random for plotting.

Statistical analyses were performed in R, using the ‘nparcomp’, ‘AUC’, ‘pROC’ and packages. Other aspects of data analysis were performed with the ‘jsonlite’, ‘magrittr’, ‘tidyverse’, and ‘uwot’ packages. Plotting was performed with the ‘ggplot2’ and ‘patchwork’ packages. See the Life Sciences Reporting Summary for further details.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

The genomes analyzed in this study are publicly available from the NCBI Genome database and the Sequence Read Archive (accession PRJNA348753). Predicted and true chemical structures from the ‘gold standard’ set of 1,281 BGCs are provided in Supplementary Data  2 . Predicted chemical structures from the collection of 10,121 complete or metagenome-assembled prokaryotic genomes analyzed in this study are provided in Supplementary Data  3 . FASTA files for the ‘gold standard’ BGCs are available via Zenodo ( https://doi.org/10.5281/zenodo.3985982 ). PRISM output files, in JSON format, for all of the genomes analyzed in this study are available via Zenodo ( https://doi.org/10.5281/zenodo.3985978 ).  Source data are provided with this paper.  Source data are provided with this paper.

Code availability

Source code used to conduct the analyses described in the manuscript is available from GitHub ( https://github.com/Adapsyn/prism-4-paper ).

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Acknowledgements

We thank D. Capstick, M. Chasse, K. Dial, W. Mousa, and L. Zettle for assistance with curation of newly published BGCs, and B. Lake, D. Levin, Y. Lin, V. Marando, and J. Pierscianowski for assistance with chemical structure curation. This work was supported by a Canada Research Chair (CIHR) New Investigator Award (to N.A.M.); an Ontario Early Investigator Award (to N.A.M.); the Canada Research Chairs Program (to N.A.M.); a CIHR Operating Grant (to N.A.M.); and a CIHR–Joint Programming Initiative on Antimicrobial Resistance Research Grant (to N.A.M.).

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Department of Biochemistry & Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada

Michael A. Skinnider, Chad W. Johnston, Mathusan Gunabalasingam, Nishanth J. Merwin, Michael R. M. Ranieri, Andrew L. H. Webster, My P. T. Cao, Q. Huy To & Nathan A. Magarvey

Department of Chemistry & Chemical Biology, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada

Adapsyn Bioscience, Hamilton, ON, Canada

Michael A. Skinnider, Chad W. Johnston, Agata M. Kieliszek, Robyn J. MacLellan, Haoxin Li, Annabelle Pfeifle, Norman Spencer, Dan Peter Wallace & Chris A. Dejong

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Michael A. Skinnider

Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

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Contributions

M.A.S. developed PRISM 4, performed analysis, and wrote the manuscript. C.W.J. contributed to prediction of most secondary metabolite classes. M.G. contributed to expanded thiotemplated prediction. C.A.D. and N.J.M. contributed to the analysis. R.J.M. organized curation of the ‘gold standard’ BGC set. A.M.K., M.P.T.C., A.P., N.S., and Q.H.T., performed curation of ‘gold standard’ BGCs and their products. M.R.M.R. and H.L. contributed to aminoglycoside prediction. A.L.H.W. contributed to β-lactam prediction. D.P.W. contributed to data analysis and web server development. N.A.M. supervised the project.

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Correspondence to Michael A. Skinnider or Chris A. Dejong .

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N.A.M. is a founder of Adapsyn Bioscience. M.A.S. and C.W.J. are or were at one time consultants to Adapsyn Bioscience. M.G., N.J.M., A.M.K., R.J.M., H.L., A.P., N.S., D.P.W., and C.A.D. are or were at one time employed by Adapsyn Bioscience. The remaining authors declare no competing interests.

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Skinnider, M.A., Johnston, C.W., Gunabalasingam, M. et al. Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences. Nat Commun 11 , 6058 (2020). https://doi.org/10.1038/s41467-020-19986-1

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Biologically Active Secondary Metabolites from the Fungi

Affiliations.

• 1 Texas Therapeutics Institute, The Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77054.
• 2 Department of Chemistry, University of Iowa, Iowa City, IA 52245.
• PMID: 27809954
• DOI: 10.1128/microbiolspec.FUNK-0009-2016

Many Fungi have a well-developed secondary metabolism. The diversity of fungal species and the diversification of biosynthetic gene clusters underscores a nearly limitless potential for metabolic variation and an untapped resource for drug discovery and synthetic biology. Much of the ecological success of the filamentous fungi in colonizing the planet is owed to their ability to deploy their secondary metabolites in concert with their penetrative and absorptive mode of life. Fungal secondary metabolites exhibit biological activities that have been developed into life-saving medicines and agrochemicals. Toxic metabolites, known as mycotoxins, contaminate human and livestock food and indoor environments. Secondary metabolites are determinants of fungal diseases of humans, animals, and plants. Secondary metabolites exhibit a staggering variation in chemical structures and biological activities, yet their biosynthetic pathways share a number of key characteristics. The genes encoding cooperative steps of a biosynthetic pathway tend to be located contiguously on the chromosome in coregulated gene clusters. Advances in genome sequencing, computational tools, and analytical chemistry are enabling the rapid connection of gene clusters with their metabolic products. At least three fungal drug precursors, penicillin K and V, mycophenolic acid, and pleuromutilin, have been produced by synthetic reconstruction and expression of respective gene clusters in heterologous hosts. This review summarizes general aspects of fungal secondary metabolism and recent developments in our understanding of how and why fungi make secondary metabolites, how these molecules are produced, and how their biosynthetic genes are distributed across the Fungi. The breadth of fungal secondary metabolite diversity is highlighted by recent information on the biosynthesis of important fungus-derived metabolites that have contributed to human health and agriculture and that have negatively impacted crops, food distribution, and human environments.

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Home > Books > Latest Scientific Findings in Ruminant Nutrition - Research for Practical Implementation [Working Title]

The Role of Secondary Metabolites on Methane Reduction in Small Ruminants

Submitted: 30 March 2024 Reviewed: 05 April 2024 Published: 04 September 2024

DOI: 10.5772/intechopen.1005461

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Methane emission from livestock is a huge global concern because it is a powerful greenhouse gas and also causes a 6–10% waste of energy in the feed that can be used for productive purposes. Plant secondary metabolites strategies for methane mitigation have been regarded as secure, cost-efficient, and successful. Tannins, saponins, flavonoids, and essential oils have all been studied for their potential to reduce methane production in small ruminants. Tannins inhibit methane synthesis in the rumen by suppressing methanogens or the protozoal population. Saponins may provide nutritional benefits by increasing microbial protein synthesis due to protozoa suppression resulting in methane reduction. Flavonoids negatively impact methanogenesis by blocking H 2 -releasing processes or removing H 2 during carbohydrate fermentation. Essential oils can either directly restrict the growth and activity of methanogenic microorganisms or indirectly reduce the number of protozoa associated with methanogens. Plant secondary metabolites have proven to have the potential to reduce methane in small ruminants without adversely affecting the overall performance, health, or productivity. Proper understanding of this information is important for the battle against climate change and its contribution toward global warming.

• essential oils
• greenhouse gas

Author Information

Diego maredi matabane.

• Department of Agricultural Economics and Animal Production, University of Limpopo, Sovenga, South Africa

Jones Wilfred Ng’ambi

• Department of Agriculture and Animal Health, College of Agriculture and Environmental Sciences, University of South Africa, Florida, South Africa

Monnye Mabelebele

Busisiwe gunya, tlou grace manyelo *.

*Address all correspondence to: [email protected]

1. Introduction

Small ruminants’ production is vital for socio-economic development on each continent. In addition to producing 25.6 million tons of milk and 1.5 million tons of meat, according to the FAO’s 2016 report, this industry also supplies products to niche markets and helps preserve ecosystems, landscapes, and biodiversity [ 1 ]. Climate change, which is brought on by rising levels of greenhouse gases such as methane (CH 4 ) and other gases in the atmosphere, will have an impact on agricultural production systems in the future years (causing drought, floods, etc.). CH 4 emissions to the atmosphere are significantly increased by ruminant production systems [ 2 ]. In particular, the “ruminal microbiota,” a microbial complex of bacteria, archaea, protozoa, and fungi, produces over 115 million tons of CH 4 each year in ruminants [ 3 ], especially in large-scale agricultural systems, ruminant production accounts for over 80% of anthropogenic CH 4 emissions [ 4 ]. Since sheep and goats make up roughly 56% of all ruminants worldwide, the small ruminant production sector is under scrutiny for reducing methane emissions [ 5 ].

Methane emissions from livestock, especially when forage-based diets are supplied, account for an energy loss from livestock on the order of 6–10% of the gross energy intake of small ruminants, apart from their contribution to global warming [ 6 ]. The potential for this powerful greenhouse gas to cause global warming is 28 times greater than that of carbon dioxide (CO 2 ), and significant scientific efforts are being made to try and reduce it [ 7 ]. The defensive function of plant secondary metabolites against plant predators has long been recognized as being important. The synthesis of these metabolites is controlled by external, seasonal, or environmental factors. Secondary metabolites have long been regarded as poisonous to animals and as anti-nutritional elements [ 8 ]. However, due to their advantageous effect on the reduction of methane synthesis, those metabolites have recently received much interest in animal nutrition. Tannins [ 9 , 10 ], saponins [ 11 , 12 ] essential oils [ 13 , 14 ], and flavonoids [ 11 , 15 ] have all had been reported to have potential to reduce enteric CH 4 in small ruminants evaluated. Their impacts on the rumen microbial population are frequently indirect as opposed to direct. Plants may produce a wide range of secondary metabolites in high or low concentrations, which may affect how they interact with rumen microbes. This present review has summarized some of the available studies based on the role of plant secondary metabolites on methane mitigation in ruminants.

2. Methodology

A comprehensive search was conducted to identify eligible studies using a five-stage process. In the first stage, a search to obtain all relevant studies that were published before May 2023 was performed using databases such as the Web of Science, Science Direct, Google Scholar, and the Wiley Online Database. The search strategy involved a combination of the following keywords: tannins, flavonoids, saponins, essential oils, and methane. The search was not restricted by language, date, or study type. During the second stage, the search was narrowed down by adding the words “goats and sheep.” Furthermore, the search was narrowed down to the time scale of 2010 to 2023 (the period was chosen to capture as wide a range of articles as possible). In the third stage, the exclusion criteria included articles where the abstract could not be found and written in a language that could not be understood by the authors (i.e., German, Dutch, Spanish, or Italian). A final total of 69 remaining full-text studies on plant secondary metabolites were consequently assessed for eligibility. The fourth stage involved the reading of article titles and abstracts through screening of the retrieved articles. Thereafter, the full-length individual manuscripts were screened and papers not satisfying the inclusion criteria were excluded. In the fifth stage, the remaining additional literature was included by examining the reference lists in the literature extracted, academic resources (master’s and doctoral dissertations), PLoS ONE, and the Directory of Open Access Journals.

3. Methane production

It is challenging to comprehend all the mechanisms underlying the functioning, complexity, and interactions of the rumen microbiome because it has not been thoroughly studied [ 16 ]. Protozoa, bacterial, and fungal communities in the rumen ferment proteins, carbohydrates, and starches through enzymatic mechanisms. Volatile fatty acids, CO 2 , and metabolic H 2 are formed during the fermentation process and utilized by methanogenic archaea for the synthesis of CH 4 [ 17 ]. Starch, cellulose, hemicellulose, pectin, and soluble sugars are utilized by protozoa to produce volatile fatty acids and metabolic H 2 , which is then utilized by the attached archaea to produce CH 4 [ 18 ]; as a result, there is a relationship between archaea and protozoa in the rumen [ 19 , 20 ]. In a series of biochemical reactions connected to adenosine triphosphate synthesis, rumen methanogens use the H 2 produced by the fermentation of carbohydrates to reduce CO 2 to CH 4 where CO 2 is used as a carbon source and H 2 is the primary electron donor. 4 moles of H 2 are utilized in this process to create 1 mole of CH 4 [ 21 ]. The chemical reaction for methane synthesis is CO 2  + 4H 2  → CH 4  + 2H 2 O.

4. Plant secondary metabolites

Plant secondary metabolites are a diverse group of compounds that are produced by secondary metabolic pathways in plants. A large group of structurally diverse compounds found in plant secondary metabolites are derived from either primary metabolites or intermediates in the metabolic pathways of these primary metabolites [ 22 ]. Plant secondary metabolites are classified into several large molecular families based on their biosynthesis processes, including phenolics, terpenes, steroids, alkaloids, and flavonoids [ 23 ].

Secondary metabolites in plants provide a number of roles, including plant growth and development, innate immunity [ 22 ], defensive response signaling [ 24 ], and reaction to environmental threats [ 25 ]. Plant secondary metabolites also provide essential functions such as repelling pests and pathogens, functioning as signals for plant-microbe symbiosis, and altering microbial populations associated with hosts [ 26 ]. Many plant secondary metabolites have highly valued effects on human health [ 27 , 28 ] and agriculture production, contributing significantly to the economy.

5. Importance of plant secondary metabolites in small ruminants

Even though there have been a lot of studies on reducing methane emissions, few CH 4 reduction strategies are now accessible for producers to use, with the exception of sustainable intensification of livestock production [ 29 ]. The level of adoption of methane reduction techniques varies due to concerns about their efficacy, a lack of information about animal production, and increased implementation costs.

Therefore, affordable CH 4 reduction techniques that also guarantee energy efficiency are required. Such initiatives would not only lower the financial burden on farmers and consumers, but they would also enable widespread implementation to reduce CH 4 emissions linked to the production of small ruminants. Due to their natural occurrence in a variety of plants and the fact that ruminant producers may easily access them, the utilization of secondary metabolites from plants may present such a possibility. The use of plants rich in secondary metabolites in the diet of small ruminants to reduce methane emission has been reported by several authors [ 9 , 21 , 30 , 31 ].

5.1 Tannins

Tannins are the most abundant polyphenolic secondary metabolites, accounting for 5 to 10% of dry vascular plant materials [ 32 ] primarily found in the bark, stems, seeds, roots, buds, and leaves [ 32 , 33 , 34 ]. Tannin-rich terrestrial plants are abundant in ruminant grazing areas. Tannins are widely found in numerous leguminous and non-leguminous leaves of trees or shrubs (e.g., Acacia angustissima , Argania spinosa , and Ceratonia siliqua ) that are fed to small ruminants in tropical regions. Some of tannin tannin-rich plants are presented in Table 1 .

Nutritive content of tannin-rich plants.

Tannins can either directly or indirectly inhibit methane synthesis in the rumen by suppressing methanogens or the protozoal population. There are various possibilities that could explain how tannins reduce enteric CH 4 levels [ 10 ]. According to Bhatta et al. [ 9 ], tannins can directly reduce methanogenesis by impacting rumen archaea rather than by defaunation (removal of protozoa). Protozoa can offer H 2 as a source of electrons to methanogens in a synergistic manner, hence tannins with antiprotozoal effects would be expected to reduce CH 4 production by methanogens linked to protozoa. Another concept suggests that condensed tannins themselves operate as hydrogen sinks, reducing their availability for carbon dioxide reduction to methane, meaning that 1.2 mol methane is reduced per mol of catechin (i.e., 6 H 2 atoms per molecule of catechin) [ 10 ]. Several studies have reported the effect of tannins in the diets of small ruminants on methane reduction ( Table 2 ).

Effect of tannins on methane reduction in small ruminants.

Bhatta et al. [ 9 ] showed that tannins from Mimosa spp. at low concentrations (2.8 g/kg DM) can decrease CH 4 emissions in goats without influencing the digestibility of dietary components. However, at greater tannin concentrations (5.6 g/kg DM), CH 4 reduction was also attributed to decreased organic matter fermentation. These findings suggested that tannins have the potential to reduce CH 4 emissions from small ruminants and to develop viable methods for utilizing tree leaves containing a significant amount of tannins. Delgado et al. [ 63 ] reported that the inclusion of 27% of Leucaena leucocephala in a Pennisetum purpureum basal diet reduced methane production by 15.6% without affecting the apparent digestibility of nutrients in sheep. It has been proposed that high molecular weight condensed tannins fractions of Leucaena leucocephala have higher protein-binding affinities than low molecular weight fractions, and thus the effect may be associated with the ability to bind to cell membranes, preventing nutrient transport into the cell and inhibiting microbial growth [ 64 ]. Rita et al. [ 60 ] investigated methane reduction in an in vitro and in vivo experiments using tannin-rich plants ( Gliricidia sepium, Leucaena leucocephala , and Manihot esculenta ). In an in vitro experiment, tannin-rich plants given at 39, 75, and 92 g (CT/kg DM, respectively) lowered CH 4 production due to the high amount of condensed tannins, which inhibited archaea growth [ 60 ]. However, the tannin-rich extracts had no influence on the methanogen population in an in vivo experiment [ 60 ].

5.2 Saponins

Saponins are a type of plant secondary metabolite that has a high level of complexity in both structure and biological activity [ 30 ]. Saponins are found in many tropical trees and bushes, and small ruminants enthusiastically ingest their leaf or pods while browsing. Some of saponin saponin-rich plants are presented in Table 3 . It is widely assumed that their primary biological effect is on cell membranes. Their anti-protozoal activity is exerted by interactions with cholesterol in the cell membrane, which causes disruption, disintegration, lysis, and, eventually, cell death. According to Ramos-Morales et al. [ 81 ], the effect of saponins on protozoa is only temporary since bacteria can break down saponins into sapogenins, which cannot affect protozoa.

Nutritive content of saponin-rich plants.

Torres et al. [ 12 ] reported that adding saponins and nitrates to diets reduced methane production, but more research is needed to validate these findings and better understand the mechanisms that interact with sheep responses to saponin and nitrate supplementation. Shilwant et al. [ 82 ] suggested that a composite plant extract from Dolichos biflorus (horse gram), root of Asparagus racemosus (shatavari), bark of Amoora rohituka (rohitaka), and peel of Punica granatum (pomegranate) rich in both phenolics and saponins can increase ruminal fermentation, milk production, and nutritional utilization in lactating goats with improved health while reducing methane emissions. In a series of in vitro investigations, the addition of papaya leaf (a saponin-rich source), methanolic extract of papaya leaf, and other solvent extracts of papaya leaf reduced CH 4 synthesis by 37, 34, and 30%, respectively, when compared to the control group [ 83 , 84 , 85 ].

The intraruminal administration of polymeric media-coated gynosaponin (8 g/kg) reduced methane production in Xinjiang goats, according to Li et al. [ 86 ]. Li et al. [ 86 ] also stated that polymeric media-coated gynosaponin (8 g/kg) may predominantly inhibit methanogens and bacteria, resulting in lower acetate concentrations and the acetate to propionate ratio, which may result from hydrogen accumulation. According to Guo et al. [ 87 ], using tea saponin to reduce methanogenesis resulted in decreased activity of the mcrA gene (an indication of the methanogenic activity of the methanogen population) without affecting overall methanogen numbers. Tea saponins at 3 g/day in sheep diets, on the other contrary, had no effect on methanogen populations [ 31 , 59 ]. Furthermore, in addition to inhibiting CH 4 synthesis, saponins may provide nutritional benefits by increasing microbial protein synthesis due to protozoa suppression and by increasing the fiber-degrading bacteria and fungus in the rumen, which is useful for utilization in low-quality-based diets [ 60 ].

5.3 Flavonoids

Flavonoids are naturally occurring polyphenolic phytochemicals present in plants that are responsible for a wide range of biological functions [ 88 ]. They are linked to a group of secondary metabolites in plants that have a polyphenolic structure [ 89 ]. Flavonoids are categorized into eight main flavonoid categories based on their molecular structure: flavanol, flavandiol, flavanone, dihydroflavonol, flavone, flavonol, isoflavone, and anthocyanidin [ 90 ].

Flavonoids have been proposed for inclusion in ruminant feeds to enhance productivity by increasing propionate production relative to acetate [ 11 ]. Some of the nutritive content of plants rich in flavonoids is presented in Table 4 .

Nutritive content of flavanoid-rich plants.

In vitro , the flavonoids naringin and quercetin inhibited methane synthesis, ciliate protozoa, and hydrogenotrophic methanogens, according to Oskoueian et al. [ 104 ].

According to Santas et al. [ 105 ], quercetin and kaempferol have the ability to suppress Gram-positive bacteria such as Bacillus cereus , Staphylococcus aureus , Microcroccus luteus , and Listeria monocytogenes. An in vitro review investigated at the potential of eight flavonoids to reduce CH 4 emissions (epicatechin, luteolin-7-glucoside, quercetin, isoquercetin, catechin, gallocatechin, epigallocatechin, and epigallocatechin gallate) and found that uteolin-7-glucoside (50 mg/g DM) has promising potential to reduce CH 4 and ammonia formation during ruminal fermentation [ 106 ].

Mulberry leaf biomass effectively decreased daily CH 4 emission in ewes in a study by Ma et al. [ 31 ] by reducing the population of protozoa and methanogens. This increase in cellulolytic bacteria population was linked with a decrease in protozoal population. Mulberry leaf biomass (150 mg/kg diet) improved in vitro dry matter digestibility, and increased total gas production, and volatile fatty acids, while reducing CH 4 production in sheep ruminal fluid [ 107 ] ( Table 5 ). In a study by Ban et al. [ 15 ], feeding mangosteen peel powder (rich in condensed tannins, flavonoids, and cinnamic acid) to meat goats reduced methane emissions. The fermentation of enzymatically structural carbohydrates, starch, and proteins in the rumen for the production of metabolic H 2 , CO 2 , and volatile fatty acids is a complex process, and the fermentation end-products are used by rumen methanogens for CH 4 synthesis during methanogenesis [ 108 ]. More volatile fatty acids produced during fermentation, in particular, yield more CH 4 . Mangosteen peel powder, on the other hand, had a negative impact on methanogenesis by blocking H 2 -releasing processes or removing H 2 during carbohydrate fermentation [ 15 ]. To sum up, flavonoids have a significant potential to mitigate CH 4 emissions, according to available data, but more research on small ruminants and lot of work have been done on dairy cows.

The inhibitory effect of flavonoids on methane synthesis in small ruminants.

5.4 Essential oils

Essential oils are aromatic chemicals that are mostly volatile and can be found in food, medicinal, and herbal plants. Some of the plants rich in essential oils are presented in Table 6 . They are created in distinctive cells in various sections of plants, including roots, seeds, fruit, leaves, flowers, bark, petals, and stems [ 128 ]. The essential oil composition is distinct and unique to the plant species and is responsible for the aroma [ 13 ]. Essential oils can either directly restrict the growth and activity of methanogenic microorganisms or indirectly reduce the number of protozoa associated with methanogens [ 13 ].

Nutritive content of plants rich in essential oils.

Due to its richness in phenolic components, Salvia officinalis essential oil is distinguished by its antioxidant and antibacterial properties. In an in vitro experiment utilizing goat rumen fluid, Saber et al. [ 129 ] found that the addition of Salvia officinalis essential oil to oat hay reduced methane (CH 4 ) production in a dose-dependent manner starting at the dose of 20 g/ml. By reducing rumen protozoa in goats, Abubakr et al. [ 130 ] observed that adding decanter cake and palm kernel cake at up to 80% inclusion reduces methanogenesis in an experiment involving Boer X Catcang crossbred goats. Essential oil-cobalt complexes that directly inhibited methanogenic archaea decreased the amount of methane produced [ 14 ].

In sheep feedstuffs, Naseri et al. [ 131 ] showed that the addition of essential oils from Pistacia atlantica gum can replace antibiotics and reduce the relative population of methanogens in the rumen. In a study by Soltan et al. [ 132 ], sheep fed a basal diet containing 200 and 400 mg/kg of a microencapsulated blend of essential oils showed a decrease in methane emissions. One key strategy for reducing methane emissions is to minimize the number of H 2 producers such as protozoa in the rumen [ 133 ]. Angelica ( Heracleum persicum Desf. ex-Fischer ) and Eucalyptus ( Eucalyptus globulus Labill ) essential oils decreased the protozoa population in sheep, which led to a decline in methane production [ 134 ].

6. Conclusion

Tannins have the potential to reduce CH 4 emissions from small ruminants by inhibiting methane synthesis in the rumen (suppressing methanogens or the protozoal population).

Saponins may provide nutritional benefits by increasing microbial protein synthesis due to protozoa suppression and by increasing the fiber-degrading bacteria and fungus in the rumen, which is useful for utilization in low-quality-based diets.

Flavonoids have a negative impact on methanogenesis by blocking H 2 -releasing processes or removing H 2 during carbohydrate fermentation, therefore resulting in decreased methane production in small ruminants.

Essential oils can either directly restrict the growth and activity of methanogenic microorganisms or indirectly reduce the number of protozoa associated with methanogens in small ruminants.

Based on the literature discussed in this chapter, it can be concluded that plant secondary metabolites have proven to have the potential to reduce methane emissions in small ruminants without adversely affecting their performance, health, or productivity. However, more research is required in order to acquire more information about the relationship between plant secondary metabolites, rumen microorganisms, and methanogenesis.

Author contributions

Matabane D.M: Writing – Original Draft, Gunya B, Mabelebele M, Ng’ambi JW and Manyelo TG: Visualization, Validation, Manyelo T.G: Gunya B, and Ng’ambi JW: Supervision, Writing – Review & Editing.

Declaration of Generative AI and AI-assisted technologies in the writing process

None of any artificial intelligence-assisted technologies have been used in the writing process.

Declaration of interest

Financial support statement.

This research received financial support from Water Research Commission (WRC) grant number: (C2023-2024-01433).

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Zalom and Chiu Labs: Targeting the Spotted-Wing Drosophila

Christine tabuloc of chiu lab is lead author of newly published paper.

• by Kathy Keatley Garvey
• September 13, 2024

Quick Summary

• UC Davis First to Publish Paper Characterizing Molecular Mechanisms of Insecticide Resistance to Spotted-Wing Drosophila

Back in 2010, the UC Davis entomology labs of integrated pest management specialist Frank Zalom and molecular geneticist and physiologist Joanna Chiu   joined forces to target the spotted-wing drosophila (SWD), a serious threat to berry production in California.

Drosophila suzukii,   native to   southeast Asia and first discovered in California in 2008, lays its eggs in such soft-skinned, ripening fruits as strawberries, raspberries, cherries, blueberries, peaches, nectarines, apricot and grape.

It packs a powerful economic impact. The first year of its discovery in California, the economic loss amounted to $500 million. Latest statistics from 2015 indicate a $700 million national economic loss.

The Zalom lab discovered the first SWD field populations with insecticide resistance in 2017. As the pest continues to spread throughout much of the country, anxious growers are worried about its increased resistance to pesticides.

The team’s newly published research in Scientific Reports  is the first to characterize the molecular mechanisms of insecticide resistance in  D. suzukii and provide insights into how current management practices can be optimized.

Lead author of the paper, “ Transcriptome Analysis of  Drosophila suzukii  Reveals Molecular Mechanisms Conferring Pyrethroid and Spinosad Resistance ,” is Christine Tabuloc , then a doctoral candidate and now a postdoctoral researcher working under the mentorship of Professors Chiu and Zalom.

"In this work, we leveraged high throughput sequencing to identify biomarkers of insecticide resistance in  D. suzukii,”  Tabuloc explained. “We found that different genes are responsible for resistance to different chemicals. Specifically, we found that genes involved in metabolism are highly expressed in flies resistant to pyrethroid insecticides. We also observed evidence of two different mechanisms of resistance in 2 lines generated from a single spinosad-resistant population. We found an increased expression of metabolic genes in one line and increased expression of cuticular genes in the other.”

“Therefore, we developed a diagnostic panel using these biomarker genes to differentiate between pyrethroid resistance and spinosad resistance,” Tabuloc related. “Not only can our assay now inform whether there is resistance, it can tell us which chemical the population is resistant to and how severe the resistance is. Additionally, this method is faster, enables for the testing of more populations, and is more comprehensive as compared to bioassays, the conventional way of testing for resistance.”

Tabuloc added that “our work has enabled for the detection of resistance in California populations, and we are currently doing a nationwide screening to determine whether resistance is now present in other states. Currently, we are working with the Zalom lab to use the results of our assays to try and combat resistance. There are experiments in progress trying to increase the efficacy of insecticides by blocking some of the genes involved in resistance, such that the enzymes encoded by those genes have decreased function."

Zalom, a UC Davis distinguished professor emeritus who directed the UC Statewide Integrated Pest Management Program for 16 years, said he has been “working on spotted-wing drosophila with Dr. Chiu and her lab members since she joined the UC Davis faculty in 2010, and it has been an absolute pleasure.”

Zalom, a past president of the 7000-member Entomological Society of America (ESA) and an elected Honorary Member, ESA’s highest honor, praised Chiu, a 2019-2024 Chancellor’s Fellow professor and now chair of the Department of Entomology and Nematology, as “one of the most collaborative researchers who I have ever worked with. When our lab found the first SWD field populations with insecticide resistance in 2017, it seemed obvious to ask Dr. Chiu about identifying the mechanism of resistance to different chemical classes and if it would be possible to develop a molecular diagnostic to confirm presence of  insecticide resistance in field populations without conducting the time consuming and labor-intensive bioassays that we were using.”

“Dr. Chiu and her PhD student Christine Tabuloc took on this challenge and their work culminated in the description of genes associated with the resistance and the diagnostic assay presented in this paper,” Zalom said.

The Zalom lab “selected the SWD isolines from California field populations displaying resistance to pyrethroids and spinosyns after conducting bioassays on many hundreds of SWD adults, then helped Dr. Tabuloc validate  results of the molecular assay,” he said.

“This work not only represents good science; it has very practical implications," Zalom said. "Dr. Tabuloc and I presented results of the work from both of our labs at a special berry grower seminar on insecticide resistance organized by UC Agriculture and Natural Resources (UC ANR) Farm Advisor Mark Bolda in Watsonville. The presentations were extremely well-received. The original program was targeted for about 1.5 hours, but the meeting extended to over three hours due to the extent of questions and great discussion that followed. Growers and their consultants are hungry for new information that they find interesting and potentially useful, and this work was clearly of interest to them.”

Bolda, strawberry and caneberry farm advisor in Santa Cruz, Monterey and San Benito counties, "was the first person who found the insect and asked me to come down to look at it and the problem," Zalom remembered. " That was 2008, and we weren’t able to get an actual species identification until 2009!"

Bolda noted that the recent berry grower meeting targeted SWD resistance on the Central Coast, with the UC Davis entomologists presenting. “The research was top shelf and the need, of course, is very great,” Bolda said. “Some of the information that Frank and Christine presented has been put into immediate use in the industry.”

“What made it really special was that since we were moving only a month or so later, and this was the last Extension meeting to be held at my UC Cooperative Extension Office,” Bolda said. “After 40 some years, that’s saying a lot and it was totally apropros that Frank, given his many, many years of service, was the very last to run a meeting there….and Christine presented also…she was really great and presented fabulous information.”

Among the “iconic individuals of Central Coast strawberries” who attended, Bolda said, was retired entomologist Ed Show (Driscoll Strawberry Association, Inc.) “who has been a part of strawberries since the 1970s.”

"It was nostalgic for me since it was the last meeting that was held there because they were moving to a new office," Zalom said. "I must have done 80 presentations at that auditorium over the years, including some of the very first ones that I did when returning to California when I was doing research on brussels sprouts and apples in that area."

Fruitville Collaboration. Professor Chiu said the publication “culminated years of fruitful collaboration and hard work by our lab and Professor Zalom’s lab, primarily driven by Christine…This research could not have been accomplished without Christine who is uniquely qualified to successfully lead the project; she combines her knowledge in insect genomics and bioinformatics with Drosophila molecular genetics.”

Chiu pointed out that “When the Zalom lab and my lab first started our collaboration in 2010, we were already worried about the potential development of insecticide resistance in D. suzukii given the primary method for management is insecticide application and the generation time of these flies are short. Unfortunately, this became a reality in 2017.”

“We hope that our research in developing more efficient molecular diagnostics to identify resistant populations will help prevent the spread of resistance to other U.S. states by allowing them to be proactive; perhaps to adjust their management program as soon as low level of resistance is detected,” Chiu said. “We are continuing to perform research to determine if resistance, once found, can be ‘weakened’ and what are mechanisms that could drive it. We think this will be very beneficial for growers in California, who currently have to tackle D. suzukii with insecticide resistance.”

Tabuloc, who joined the Chiu lab as an undergraduate research assistant in 2012, received her bachelor of science degree in biochemistry and molecular biology from UC Davis in 2015, and her doctorate from UC Davis in 2023.

In her doctoral exit seminar , in February 2023, Tabuloc detailed how she investigated the impact of insecticide applications on the fruit fly. "Specifically, I performed RNA sequencing analysis on D. suzukii flies that are either susceptible or resistant to common insecticides to determine genetic mechanisms underlying insecticide resistance in this agricultural pest. My results revealed that enhanced metabolic detoxification confers pyrethroid resistance while spinosad resistance is the result of both metabolic and penetration resistance. Finally, we identified alternative splicing as an additional mechanism of resistance. These results will facilitate the development of efficient molecular diagnostics to identify insecticide resistance in the field and enable growers to adjust D. suzukii spray programs to control this devastating pest more effectively."

In addition to Chiu, Zalom and Tabuloc, the 12-member team of researchers and co-authors included Curtis Carlson, Kyle Lewald, Sergio Hidalgo, Cindy Truong and Ching-Hsuan Chen, all from the Chiu lab; Nicole Nicola and Fatemeh Ganjisaffar of the Zalom lab; and Cera Jones and Ashfaq Sial of the Department of Entomology, University of Georgia, Athens. At the time, Truong and Chen were undergraduates, and Ganjisaffar was a postdoctoral fellow, and now a senior environmental scientist with the California Department of Food and Agriculture.

Federal and state grants awarded to Chiu and Zalom funded the project: a USDA National Institute of Food and Agriculture; and a California Department of Food and Agriculture Specialty Crop Block Grant. The team credits Bloomington Drosophila Stock Center for providing D. melanogaster stocks.

Resources: Publication in Scientific Reports   UC IPM: Spotted-Wing Drosophila    Christine Tabuloc Exit Seminar Frank Zalom: Honorary Member of ESA Joanna Chiu: Outstanding Mentor

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Biological Potential and Medical Use of Secondary Metabolites

Ana m. l. seca.

1 cE3c-Centre for Ecology, Evolution and Environmental Changes/Azorean Biodiversity Group, University of Azores, Rua Mãe de Deus, 9501-801 Ponta Delgada, Portugal

2 QOPNA & LAQV-REQUIMTE, University of Aveiro, 3810-193 Aveiro, Portugal

Diana C. G. A. Pinto

This Medicines special issue focuses on the great potential of secondary metabolites for therapeutic applications. The special issue contains 16 articles reporting relevant experimental results and overviews of bioactive secondary metabolites. Their biological effects and new methodologies that improve the lead compounds’ synthesis were also discussed. We would like to thank all 83 authors, from all over the world, for their valuable contributions to this special issue.

This editorial is an introduction to the special issue “Biological Potential and Medical Use of Secondary Metabolites” and contains an overview on the role of secondary metabolites as medicines. In fact, secondary metabolites, used as a single compound or as a mixture, are medicines that can be effective and safe even when synthetic drugs fail. They may even potentiate or synergize the effects of other compounds in the medicine. The research and review articles published in this special issue highlight the secondary metabolites with greater potential for therapeutic application as well as new sources of secondary metabolites well known for their therapeutic properties. The manuscripts published in this special issue are also a showcase of the different methodologies and approaches that researchers use to evaluate, demonstrate, and enhance the properties of secondary metabolites extracted from natural sources including terrestrial plants, marine species, and fungi species such as mushrooms.

Ocimum sanctum L. (according to the “The Plant List” database, this name is a synonym of Ocimum tenuiflorum L.), is an Ayurvedic herb of Southeast Asia with a long history of traditional use to treat cough, respiratory disorders, poisoning, impotence, and arthritis [ 1 ] and with great chemopreventive and therapeutic potential. Flegkas et al. [ 2 ] isolate several secondary metabolites from different classes (four terpenoids, four phenolic derivatives, three flavonoids, two lignans, and one sterol) using chromatographic techniques and elucidate their structures using spectroscopic methods. They also report the interesting proapoptotic and selective activity displayed using (-)-rabdosiin, a tetramer composed of a lignan skeleton connected to two caffeic acids, against MCF-7, SKBR3, and HCT-116 cancer cell lines [ 2 ], suggesting this secondary metabolite to be a leading central structure in the development of anticancer drugs.

Malaria continues to be a disease without much effective treatment because of the appearance of mechanisms of resistance to current drugs, so the development of new antimalarial drugs is an important area of research. Based on previous knowledge about antiplasmodial activity against a chloroquinone-sensitive strain of Plasmodium falciparum of sargahydroquinoic acid, the main metabolite of brown alga Sargassum incisifolium (Turner) C. Aggard, Munedzimwe et al. [ 3 ] converted this meroditerpene into several derivatives using semi-synthesis to look for more active derivatives. Ten sargahydroquinoic acid derivatives were assessed regarding their antiplasmodial activity and to explore some structure–activity relationships. The results show that sarganaphthoquinoic acid and sargaquinoic acid are the most promising selective antiplasmodial derivatives. Additionally, the presence of a quinone and carboxylic acid were important for selective activity against the chloroquine-resistant Gambian FCR-3 strain of P. falciparum [ 3 ].

Several secondary metabolites isolated from the same seaweed, Sargassum incisifolium , and some semisynthetic derivatives were tested to evaluate their potential as modulators of inflammatory bowel diseases, such as Crohn’s disease and ulcerate colitis, using various in vitro assays [ 4 ]. In fact, inflammatory bowel diseases have become a global health challenge since conventional treatments exhibit moderate efficacy and have significant side effects. The natural compound sargahydroquinoic acid was identified as a promising lead compound due to its effects on various therapeutic targets relevant to inflammatory bowel diseases treatment. Conversion of sargahydroquinoic acid to sarganaphthoquinoic acid greatly improved the peroxisome proliferator activated receptor gamma (PPAR-γ) activity, but this structural modification significantly decreased its antioxidant activity and had a minimal effect on cytotoxicity against a HeLa cancer cell line [ 4 ].

Artemisinin is a sesquiterpene lactone compound with a unique chemical structure derived from the sweet wormwood plant, Artemisia annua L. It is a very successful clinical drug used in the treatment of malaria [ 5 ], and now has a second life as an antitumor agent [ 6 ]. Therefore, there is a great demand for new sources of artemisinin, in particular among another Artemisia species. Furthermore, since the biotransformation and accumulation of artemisinin depends on the natural conditions, such as light intensity, Numonov et al. [ 7 ] evaluated the content of the artemisinin in eight Artemisia species collected in Tajikistan, a country with a relatively large number of sunny days per year. The artemisinin content on Artemisia hexane extracts, prepared using ultrasound-assisted extraction, was determinate using HPLC. The highest content found, in this study, was in Artemisia vachanica Krasch. ex Poljakov (0.34% of dried plant), a new source of artemisinin, and the species with the second-highest content after Artemisia annua (0.45 %), while Artemisia leucotricha Krasch. ex Ladygina (according to the “The Plant List” database, this name is a synonym of Seriphidium leucotrichum (Krasch.) Y.R.Ling.) was the only one in which no artemisinin was detected. The same work shows that the treatment of Artemisia annua hexane extract with silica gel as an adsorbent resulted in the enrichment of artemisinin [ 7 ].

Pristimerin and tingenone belong to the class of quinonemethide triterpenoids, known as celastroloids, a relatively small class of compounds that exhibit interesting biological activities, such as cytotoxicity and anti-inflammatory, antimicrobial, and antioxidant properties, and accumulate mainly in the root of Celastraceae species. Taking into account the chemotaxonomic and therapeutic relevance of quinonemethide triterpenoids like pristimerin and tingenone, Taddeo et al. [ 8 ] developed an analytical method for its identification and quantification in the root of species of Maytenus chiapensis Lundell. These authors suggest the use of RP HPLC-PDA for the analysis of n-hexane-Et 2 O extract (1:1), the ideal solvent for extraction of these two bioactive secondary metabolites. The proposed method is useful in the analysis of other species of Celastraceae and in the analysis of commercial samples [ 8 ].

The Boswellia sp. are resiniferous trees and shrubs that produce oleo-gum resin, well known as frankincense [ 9 ], a natural product of high commercial value used in traditional medicine, religious ceremonies, and cosmetic and perfumery products [ 10 ]. Byler and Setzer [ 11 ] identified the biomolecular targets docked by some frankincense secondary metabolites using reverse docking analysis, showing that some diterpenes exhibited selective docking to bacterial protein targets and to acetylcholinesterase, while some triterpenoids targeted specific antineoplastic molecular targets, diabetes-relevant targets, and protein targets involved in inflammatory processes. Several medicinal properties of frankincense were corroborated by the molecular docking properties of their di- and triterpenoids. This study opens the way for further investigations of the biomolecular targets identified in this work regarding the improvement of new inhibitors to be used in the treatment of bacterial infections, and inflammatory, diabetes, and Alzheimer’s diseases.

Quy and Xuan [ 12 ] used a more traditional approach to suggest cordycepin identified in the mushroom Cordyceps militaris (L.) Link ethyl acetate extract as the responsible agent for the extract´s xanthine oxidase inhibitory activity. Using the bio-guided assays approach, they identified the constituents of the most active fractions using GC-MS. They revealed that the fungus Cordyceps militaris , used in traditional medicine, is a potential source of cordycepin, the largest constituent of the fraction exhibiting the highest anti-xanthine oxidase effect. Thus, the Cordyceps militaris fractions and/or its constituent cordycepin could be beneficial for hyperuricemia treatment. However, more in depth studies and in vivo trials on compounds purified from this medicinal fungus are needed.

Polyphenols are a vast and heterogeneous set of secondary metabolites that include flavonoids, stilbenes, lignans, benzoic acid derivatives, and cinnamic acids, among others, which have in common at least one hydroxylated aromatic ring. They are the subject of vast research as they possess biological properties relevant to well-being and improved health [ 13 , 14 , 15 ]. In fact, it is known that the consumption of specific types of food (e.g., fruits) rich in polyphenols exerts a positive effect on health, improving, for example, the antioxidant and anti-inflammatory responses of the organism and helps fight cardiovascular and cancer diseases [ 13 , 16 ]. The antioxidant potential and total polyphenols content in most of the 17 ancient regional varieties of apples from the province of Siena in Tuscany are remarkably higher when compared with two commercial varieties, being in some cases about 8 times higher. In addition, older varieties showed lower glucose contents and higher contents of xylitol and pectins, which are also relevant factors for considering older varieties with the highest potential as nutraceuticals [ 17 ].

The polar extracts of Glycyrrhiza glabra L., Paeonia lactiflora Pall., and Eriobotrya japonica (Thunb.) Lindl., three known species frequently used in traditional Chinese medicine, were analysed using LC-MS and their total phenolic contents, and antioxidant, antimicrobial, and cytotoxic activities, were evaluated [ 18 ]. The terpenoid glycosides was the most abundant class in all three species. Glycyrrhizic acid and (iso)liquiritin apioside isomers were the most abundant secondary metabolites in the Glycyrrhiza glabra , while in the Paeonia lactiflora , the most abundant were paeoniflorin derivatives, and in Eriobotrya japonica , the most abundant were the nerolidol derivatives. The Paeonia lactiflora extract was the most antioxidant one, which was more active than the (-)-epigallocatechin gallate positive control [ 18 ].

The defensins are a family of cysteine-rich peptides with ≈29–42 amino acids, that play a very important role in the defense system of plants, insects, animals, and humans against invasion by microorganisms. Many of these peptides have been proposed as novel natural antibiotics with great potential for application toward human health and agriculture [ 19 , 20 ]. In fact, due to the increase in the phenomena of resistance to conventional antibiotics, the development of new classes of drugs to combat infections by microorganisms has intensified, with defensins being one of those classes that has gained prominence. Ishaq et al. [ 21 ] present the most current overview of the plant defensins applications in the treatment of human infections by viruses, bacteria, and fungi; treatment of hemorrhoids, liver disorders, and cancer; and its use in agriculture as a way to increase agricultural production using natural compounds as phytosanitary agents.

Cannabis species contain more than 545 secondary metabolites of different classes but they are chiefly known to possess a great structural diversity of non-nitrogen compounds capable of interfering with the central nervous system, known as cannabinoids, which also exhibit very interesting pharmaceutical properties [ 22 ]. The increasing interest of patients regarding the medicinal use of Cannabis has been accompanied by a renewed interest of scientists in the potential medical use of various constituents of this plant [ 22 , 23 ]. The review of the literature on cannabinoids identified in Cannabis and their application for therapeutic purposes, on the evaluation of its toxicological effects, and the development and improvement of new methodologies for its detection and quantification presented by Gonçalves et al. [ 24 ] is of great interest. It opens new lines of research in order to increasingly distinguish the recreational use of the medicinal use of both herbal products derived from Cannabis and its secondary metabolites.

Like Cannabis , kratom ( Mitragyna speciosa (Korth.) Havil.) is a species that is also used for medical purposes as an analgesic, and for social and recreational use, being a source of psychoactive agents, mainly alkaloids, and a cheap alternative to opiate-rich substances [ 25 ]. The most recent review of the literature on Mitragyna speciosa [ 26 ] presents the state of the art for its major secondary metabolites, the potential beneficial and toxicological effects derived from its use, and the methodologies for its detection in plant and biological samples. It is concluded that the use of kratom or its metabolites may cause dependence; increase blood pressure; cause liver, renal, and neuronal toxicity; emphysema; excess alveoli inflammation; and even death. On the other hand, kratom has interesting effects, namely antinociceptive, anti-inflammatory, gastrointestinal, antidepressant, antioxidant, and antibacterial properties [ 26 ]. However, further studies are required to support the use of the species or its secondary metabolites for clinical purposes.

Tavares and Seca [ 27 ] demonstrate how Juniperus species are a good bet as a source of secondary metabolites by presenting a review about diterpenes, flavonoids, and one lignan identified in Juniperus as having a high potential for the development of new antitumor, antibacterial, and antiviral drugs. Deoxypodophyllotoxin appears to be the most promising lead compound since it has reported antitumor effects against breast cancer acquired resistant cells (MCF-7/A), with a very interesting IC 50 value in the nanomolar level. The dehydroabietic acid methyl ester derivative, with the substituent (2-(4-(3-( tert -butoxycarbonylamino)phenyl)-1 H -1,2,3-triazol-1-yl)acetamido) at C-14, also seems to be an excellent leader compound since it has shown IC 50 values between 0.7–1.2 µM against PC-3, SK-OV-3, MCF-7, and MDA-MB-231 tumour cell lines, which is an activity higher than the one exhibited by the anticancer agent 5-FU used clinically.

The Scabiosa genus, despite the great controversy regarding the taxonomic classification of its species, is widely considered to be valuable in traditional medicine and the biological potential of its secondary metabolites as effective agents in the treatment of various diseases is well known [ 28 ]. Pinto et al. [ 28 ] present an update on the information about flavonoids, iridoids, and saponins from Scabiosa species that can be highlighted both from the point of view of their biological properties and from the in vivo assays already performed. In fact, these secondary metabolites exhibit interesting effects, such as anti-inflammatory and antitumoral activities, effects that validate and extend some traditional uses of Scabiosa species, as well as inspire the development of new drugs based on extracts or pure secondary metabolites. On the other hand, this review also demonstrates that the phytochemistry of several Scabiosa species has been neglected. These findings should encourage further studies that can reveal the medicinal potential of this species.

An essential oil is a complex mixture of volatile compounds that exhibit the ability to control the infectious/parasitic diseases, which is a great continuing challenge for global health. In fact, essential oils could exhibit a dual role, being able to control vectors, important in the cycle of disease transmission, and they exhibit relevant activity against the pathogens [ 29 ]. However, the solubility and stability of essential oils poses significant problems in the formulation of new products for both vector and parasite control. Echeverría and Albuquerque [ 30 ] review several studies related to the development of nanoemulsions containing essential oils as effective formulations to control diseases in humans and animals, since they have lower cost and ecological toxicity. The authors emphasize these formulations as water-soluble and stable alternatives, able to act as larvicides, insecticides, repellents, and acaricides, as well as having antiparasitic properties, such that they have proved to be very efficient in the treatment and prevention of infectious and parasitic diseases. In addition, the nanoemulsion formulation of essential oils makes this pesticide more environmentally friendly [ 30 ].

The use of bioinformatics and omic workflow is a very recent approach in the effort to discover natural products in various environments, such as soils, aquatic environments, and microbial communities. Chen et al. [ 31 ] present a literature review highlighting several methods, mainly bioinformatics, used to identify biosynthetic gene clusters that encode the biosynthesis of secondary metabolites in the environment, especially in environments where microorganisms are rarely cultivated. There are also several examples of how recent studies have explored the genetic basis for the synthesis of new natural products that have broad medical and industrial applications [ 31 ].

By considering all the information given in this special issue, one can confirm the importance of plants in the development of new medicines. They are an important source of bioactive or inspiring molecules. Skepticism can arise from the use of pure isolated compounds if we consider that plants have a mixture of several bioactive molecules that can synergize the biological effects. However, mixtures can also be developed, and the knowledge of their composition will allow for the optimization of its effect, not only against the disease but also on the patient. The authors of the current editorial hope that this special issue stimulates further research, in particular, research involving clinical trials.

Author Contributions

A.M.L.S. and D.C.G.A.P. conceived, designed, and wrote the editorial.

Funded by FCT—Fundação para a Ciência e a Tecnologia, the European Union, QREN, FEDER, COMPETE, by funding the cE3c Centre (FCT Unit funding (Ref. UID/BIA/00329/2013, 2015–2018) and UID/BIA/00329/2019) and the QOPNA research unit (project FCT UID/QUI/00062/2019).

Conflicts of Interest

The authors declare no conflict of interest.

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