Next Article in Journal
Purine Auxotrophic Starvation Evokes Phenotype Similar to Stationary Phase Cells in Budding Yeast
Next Article in Special Issue
Absolute Configuration Determination of Two Diastereomeric Neovasifuranones A and B from Fusarium oxysporum R1 by a Combination of Mosher’s Method and Chiroptical Approach
Previous Article in Journal
The Resonance and Adaptation of Neurospora crassa Circadian and Conidiation Rhyth ms to Short Light-Dark Cycles
Previous Article in Special Issue
A Comprehensive Insight into Fungal Enzymes: Structure, Classification, and Their Role in Mankind’s Challenges
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JoF
Volume 8
Issue 1
10.3390/jof8010028
jof-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Recent Developments in Metabolomics Studies of Endophytic Fungi

by
Kashvintha Nagarajan
1,
Baharudin Ibrahim
2,3,
Abdulkader Ahmad Bawadikji
2,
Jun-Wei Lim
4,
Woei-Yenn Tong
5,
Chean-Ring Leong
5,
Kooi Yeong Khaw
6 and
Wen-Nee Tan
1,*
1
Chemistry Section, School of Distance Education, Universiti Sains Malaysia, Penang 11800, Malaysia
2
School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
3
Department of Clinical Pharmacy & Pharmacy Practice, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur 50603, Malaysia
4
Department of Fundamental and Applied Sciences, HICoE-Centre for Biofuel and Biochemical Research, Institute of Self-Sustainable Building, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, Malaysia
5
Drug Discovery and Delivery Research Laboratory, Malaysian Institute of Chemical and Bioengineering Technology, Universiti Kuala Lumpur, Alor Gajah, Melaka 78000, Malaysia
6
School of Pharmacy, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway 47500, Malaysia
*
Author to whom correspondence should be addressed.
J. Fungi 2022, 8(1), 28; https://doi.org/10.3390/jof8010028
Submission received: 30 November 2021 / Revised: 24 December 2021 / Accepted: 24 December 2021 / Published: 29 December 2021
(This article belongs to the Special Issue Fungi and Fungal Metabolites for the Improvement of Human and Animal Life, Nutrition and Health 2.0)
Browse Figures
Versions Notes

Abstract

:
Endophytic fungi are microorganisms that colonize living plants’ tissues without causing any harm. They are known as a natural source of bioactive metabolites with diverse pharmacological functions. Many structurally different chemical metabolites were isolated from endophytic fungi. Recently, the increasing trends in human health problems and diseases have escalated the search for bioactive metabolites from endophytic fungi. The conventional bioassay-guided study is known as laborious due to chemical complexity. Thus, metabolomics studies have attracted extensive research interest owing to their potential in dealing with a vast number of metabolites. Metabolomics coupled with advanced analytical tools provides a comprehensive insight into systems biology. Despite its wide scientific attention, endophytic fungi metabolomics are relatively unexploited. This review highlights the recent developments in metabolomics studies of endophytic fungi in obtaining the global metabolites picture.

1. Introduction

Endophytic fungi are highly diverse microorganisms that inhabit intracellular and intercellular plant tissues. According to Rashmi et al. (2019), there are approximately one million endophytic fungi [1]. Their wide appearance and distribution in the natural ecosystem indicate the importance of ecological and evolutionary fungi [1,2,3]. Endophytic fungi offer significant advantages to the host plants by producing various metabolites to counter the attack by pathogens, insects, and herbivores. They are a promising group of microorganisms that produce plant-associated bioactive metabolites with diverse chemical entities and structural functions [4,5,6]. Metabolites isolated from endophytic fungi exhibited various pharmacological properties, such as antimicrobial, anticancer, antioxidant, anti-inflammatory, and antidiabetic activities. For instance, Taxol is one of the clinical anticancer drugs produced by endophytic fungi used in the treatment of human cancer diseases [7,8]. Nevertheless, there are challenges in the isolation of bioactive metabolites from endophytic fungi due to the high complexity of the crude extracts. The traditional extraction techniques are laborious and slow. Occasionally, some bioactive metabolites are detected in minor and trace quantities. Thus, metabolomics has emerged as an indispensable approach in the comprehensive analysis of complex crude samples [9,10].
Metabolomics is an emerging research field that utilizes technological advances in analytical chemistry to measure and compare the metabolites present [11,12]. Nonetheless, the vast chemical diversity of metabolites has posed challenges in data analysis and interpretation. The enormous metabolomics data obtained from analytical tools require multivariate analysis in classifying different sample groups and metabolites distribution when subjected to different experimental parameters. Therefore, advances in analytical tools coupled with multivariate analysis allow the overview of the metabolites presented aided with visual representation. Principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and orthogonal partial least square discriminant analysis (OPLS-DA) are among the primary analyses used for this function [9,13]. Generally, metabolomics studies start with study design, followed by sample preparation, data acquisition using analytical tools, and then proceed with data processing and analysis. Lastly, metabolites are identified using the established databases (Figure 1) [11,14]. To date, the most commonly used analytical tools in metabolomics studies are mass spectrometry (MS) coupled with liquid chromatography (LC) and nuclear magnetic resonance (NMR). In metabolomics studies, analytical reproducibility is considered one of the most important criteria. Multiple replicates are recommended to account for the effects and/or variations during the data acquisition [12,15].
Endophytic fungi have been regarded as a less exploited niche despite their substantial roles in plants’ protection and drug discovery [16,17]. The present review focused on the recent developments in the metabolomics studies of endophytic fungi isolated from host plants. In the following sections, contemporary metabolites extraction from endophytic fungi is described and discussed. Advanced analytical tools, in particular, liquid chromatography–high-resolution mass spectrometry (LC-HRMS) and NMR are reviewed in relation to metabolomics of endophytic fungi (Table 1). In addition, various experimental parameters, such as fungal culture media, time-based incubation, and analysis, as well as bioactivity of fungal extracts that affect the metabolomics data, are also discussed. Perspectives pertaining to the prospects and challenges of metabolomics studies in endophytic fungi are then concluded.

2. Multivariate Analysis

Complications in data analysis in metabolomics studies have posed challenges to researchers. Multivariate analysis plays an important part in mining the key metabolites information from the vast raw dataset [25]. PCA is the most widely used multivariate analysis in metabolomics studies. It is employed to observe the distribution pattern of the sample in a general view along with/without the outliers. Meanwhile, partial least squares (PLS) are used to detect the correlations between variables. The quality and effectiveness of the statistical model are then validated using PLS-DA by obtaining the predictable variables and degree. OPLS-DA is then applied to maximize the variations between different groups in the model. In addition to that, statistical significance using analysis of coefficient is performed to summarize the metabolites information [26,27]. In metabolomics studies, the selection of appropriate multivariate analysis is crucial in achieving the experimental goals. The multivariate analysis provides an essential platform to understand the complex metabolites information in a metabolic system [28].

3. Metabolites Extraction

Metabolites extraction from endophytic fungi plays a fundamental role in obtaining a comprehensive metabolites profile [29]. Before metabolites extraction, the isolation of the endophytic fungus from the host takes place. It involves the combination of the sterilized tissue from endophytic fungus with the plant tissue and streaking onto nutrient agar. Occasionally, small sterilized tissues could be plated on nutrient agar [30,31]. Fungal fermentation is followed in either solid-state fermentation (SSF) or submerged fermentation (SMF). Generally, SSF is conducted on a solid substrate. It requires low energy but produces a high concentration of metabolites. SSF has a low water content due to water absorption by the solid substrate. Thus, it enhances the oxygen transfer for the growth of microorganisms. Rice straw, rice hull, and sugarcane bagasse are among the common substrates used in SSF [32,33]. Meanwhile, SMF is a simple fermentation process occurring in excess of water content. Fungi are grown on soluble and/or insoluble substrates, which are submerged in liquid nutrient media. It is widely used due to its better control of fermentation parameters. The pH, temperature, types of culture media, dissolved oxygen, etc., in SMF, could be altered to obtain a wide variety of metabolites [34,35].
Subsequently, metabolites are extracted using appropriate organic solvents such as chloroform, dichloromethane, or ethyl acetate. A suitable and optimized extraction technique is vital to obtain a full profile of metabolites [36]. Liquid–liquid extraction (LLE) and ultrasonic-assisted extraction (UAE) are among the popular techniques used in the preparation of endophytic fungal extract for metabolomics studies. LLE, or known as solvent extraction and partitioning, involves the transferring of metabolites from aqueous to organic solvents by phase separation. This technique separates the metabolites based on solubility in two different immiscible liquids. Separation funnel extraction is the conventional way of performing LLE [37,38,39]. On the contrary, UAE employs high frequency to produce cavitation, providing better penetration between sample and solvent during the extraction process. The ultrasound waves increase the extraction efficiency by disrupting cells for effective mass transfer. UAE is capable of shortening the extraction duration to achieve the ideal extraction efficiency [37,40].

4. Advanced Analytical Tools for Metabolomics Studies

Analytical tools that are primarily used in metabolomics studies are LC-HRMS and NMR spectroscopy. They possess their own strengths and limitations in metabolomics studies. It is essential to select the right analytical tool to detect metabolites of interest. The choice of the analytical tool is fundamentally relying on the nature of the metabolomics samples as well as the essence of the study [15,41,42]. Developments and challenges of LC-HRMS and NMR in the metabolomics studies of endophytic fungi are highlighted in the following sections.

4.1. LC-HRMS-Based Metabolomics

LC-HRMS is a versatile analytical tool that employs the separation technique of LC with high-resolution mass spectrometry (HRMS) in measuring the mass-to-charge (m/z) ratio of metabolites. In order to produce the metabolites’ peak signals for identification, ionization occurs within the metabolomics sample when injected into the instrument. Electrospray ionization (ESI) is among the common ionization methods used in LC-HRMS-based metabolomics. Meanwhile, the high resolution of mass enables the separation or differentiation of metabolites with identical nominal mass but distinct elemental compositions [15,41,43].
Over the past decades, LC-HRMS-based metabolomics studies in endophytic fungi have increased rapidly, accounting for ~80% of the published literature. The rise has indicated the unique strengths of LC-HRMS-based metabolomics. Generally, LC-HRMS is highly sensitive and selective. It can detect samples with concentrations up to nanomolar (nM) with extensive coverage of metabolites. It is a superior tool for targeted and non-targeted metabolomics studies. Another merit in employing LC-HRMS-based metabolomics is the large access to the spectral databases, which is the key to metabolites identification [44,45,46]. For instance, the database MS-Finder has provided more than 290,000 and 35,000 spectral libraries for positive and negative mode MS, respectively [47,48]. METLIN is among the major electronic databases used in LC-HRMS-based metabolomics. It consists of MS/MS data from positive and negative modes collected at three collision energies (10, 20, and 40 V). Comprehensive high-resolution MS/MS spectral data have allowed the researchers to access the spectral resources for metabolomics studies freely. Furthermore, databases such as AntiBase, ChEBI, Dictionary of Natural Products (DNP), Drugbank, FooDB, KNApSaCK, MarineLit, NANPDB, and National Institute of Science and Technology (NIST) have developed rapidly over the past decades [49,50].
Metabolomics studies of endophytic fungi using different culture media and incubation periods are gaining attention in the mining of bioactive metabolites [51,52]. Endophytic fungi Lasiodiplodia theobromae grown in liquid and rice media for 7-, 15-, and 30-day incubation were studied in correlation to anti-trypanosomal activity. Based on the results, 7- and 15-day incubation extracts provided a similar chemical profile while a 30-day incubation extract yielded lesser metabolites. However, a bioactivity assay revealed a 30-day rice medium incubated extract exhibited the lowest minimum inhibitory concentration (MIC). Bioactive metabolites 6,8-dihydroxy-3-methylisocoumarin (1), 6-oxo-de-O-methyllasiodiplodin (2), preussomerins-C and H (3-4), palmarumycin CP17 (5), cladospirone B (6), phomopsin B (7), and desmethyl-lasiodiplodin (8) (Figure 2) were identified in the study using OPLS-DA model [18]. Tawfike and co-workers performed metabolomics profiling on Curvularia sp. extracts from liquid and rice media on 7-, 15- and 30-day incubation periods in correspondence to cytotoxicity. The findings revealed that 7- and 30-day incubation extracts produced more metabolites than a 15-day incubation extract. A 15-day fungal incubation was regarded as an intermediate phase where metabolites were consumed for survival to counter environmental stress. Different chemical profiles were noticed over the incubation period as different metabolites were synthesized and depleted for different mechanisms. Mass spectral dereplication showed that N-acetyl-leucine (9), afalanine (10), herbarin A (11), picroroccellin (12), dihydroxyisoechinulin A (13), cyclopiamine B (14), sengosterone (15), and (E)-11-hydroxyoctadeca-12-enoic acid (16) were detected as unique bioactive metabolites during the growth phase [19]. In a study conducted by Attia et al. (2020), Aspergillus ochraceus MSEF6 isolated from Medicago sativa was grown in different media, namely, potato dextrose broth (PDB), Sabouraud broth (SAB), malt extract broth (MEB), and rice extract broth (REB) to discover its most potent antimicrobial activity. Fungal extract cultured in PDB was found as the most active with a minimum inhibitory concentration (MIC) of 15–30 mg/mL. Metabolomics-based chemical profiling revealed anisole (17), 3-hydroxytoluquinone (18), versicolin (19), phenoxyacetic acid (20), terreic acid (21), terremurin (22), terredionol (23), fumigatin (24), aspyrone (25), isoaspinonene (26), 4-hydroxymellein (27), and nidulol (28) were present and may contribute to the observed activity [20]. In another study, endophytic fungi Aspergillus terreus isolated from soybean was cultured in PDB, acidified potato dextrose broth (MPDB), SAB, MEB, and REB. The obtained ethyl acetate fungal extracts were then subjected to LC-HRMS to produce 2319 and 1230 peaks in positive and negative modes, respectively. Eighteen metabolites were identified after dereplication using the Dictionary of Natural Products database. The metabolites comprised mainly quinones, isocoumarins, and polyketides. Multivariate data analysis reported that MEB, PDB, and MPDB extracts consisted of characteristic chemical fingerprints compared to other cultured media extracts [21]. Endophytic fungi are stimulated by different growth mediums and culture conditions to produce different metabolites. Optimization of culture parameters, co-culture fermentation, as well as the addition of elicitors may be used to increase the production of bioactive metabolites. Metabolomics plays a crucial role in the search for bioactive metabolites from a specific endophyte at specific conditions and parameters [16,53,54,55].
Chemical fingerprints of fungal endophytes may affect by metabolites extraction methods [5,56,57,58]. George et al. (2019) investigated the metabolites produced by Penicillium setosum by employing UAE and LLE techniques. Based on the results, endophytic fungal extracts from both extraction techniques gave an almost similar chromatogram when subjected to liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-Q-ToF-MS). Fourteen metabolites were identified from LLE, while eleven metabolites were detected from UAE. They comprised of chemical classes flavonol, flavone, dihydroflavonol, anthraquinone, coumarin, Penicillium metabolites, and other organic compounds [22]. The utilization of suitable extraction methods is vital in discriminating the metabolites profiles of endophytic fungi when subjecting to different extraction methods [5,59,60].
Statistical analyses are indispensable in any research study. Multivariate analysis associated with metabolomics serves as a significant approach in dealing with a large number of datasets produced by multiple experiments [25,61,62,63]. Three endophytic fungi (AFL, AFSt, and AFR) isolated from Artemisia annua and Medicago sativa were studied for their metabolites profiles using LC-HRMS. It revealed the presence of 682 metabolites in the ethyl acetate extracts of AFL, AFSt, and AFR. Phenolic derivatives paeonol (29), p-hydroxy benzoic acid (30), (Figure 2) p-coumaric acid (31), dihydrosinapic acid (32), osmundacetone (33), shikimic acid (34), parvulenone (35), nidulol (36), tyrosol (37), asperpanoid A (38), maltoryzine (39), isopestacin (40), and globoscinic acid (41) were identified as the major metabolites. Additionally, coumarins, alkaloids, and polyketides were detected, among others. Multivariate analysis employing PCA has discriminated the endophytic fungal extracts into three different clusters, indicating their distinctive chemical fingerprints. The PLS-DA-derived heat map displayed the abundance of phenolics in AFL and coumarins in AFSt. Meanwhile, polyketides and alkaloids were predominant in the extract of AFR. The findings are in accordance with the strongest antioxidant activity shown by AFL, followed by AFSt and AFR [1]. Triastuti and colleagues recently worked on a co-culture endophytic fungi Cophinforma mamane and Fusarium solani in a time-series metabolomics study. By using ultra-high-performance liquid chromatography–high-resolution mass spectrometry (UHPLC-HRMS), 120 and 108 metabolites were identified from positive and negative ionization modes, respectively. Differences in metabolites profile were observed over time (3, 5, and 10 days) in the endophytic fungal extracts of monoculture as well as co-culture. Multivariate analysis using PLS-DA has revealed 25 metabolites that contributed to the group discrimination. Among them, botryosulfuranols B and C (42-43), cyclosporins A and E, (R)-(-)-mellein (44), cyclo-(L-Pro-L-Val) (45), and cyclo-(L-Leu-L-Leu-D-Leu-L-Leu-L-Val) were identified. In detail, the Venn diagram was used to analyze the metabolites variation in the monoculture and co-culture of fungal extracts. Generally, the number of metabolites increased over time in both monoculture and co-culture of fungal extracts. It is worth noting that co-culture endophytic fungal extract has induced five de novo metabolites, of which three were identified as altenuene (46), N-palmitoyl proline (47), and pestalotin (48). Hence, the findings indicated that fungal co-culture in the time-series analysis is worth exploring by researchers in an attempt to discover interesting metabolomes as well as the biosynthetic pathways [23]. Ibrahim and co-workers investigated Xylaria ellisii, a new endophytic fungus from Vaccinium angustifolium. Fungal filtrates and mycelium from V. angustifolium grown in wild and highbush were studied. The ethyl acetate fungal filtrate extract and methanol/acetone (1:1) mycelium extract were profiled using LC-MS-based metabolomics. Nineteen metabolites were identified, consisting mainly of nonribosomal peptides and polyketides. Ellisiiamides D–H (49–53) were detected as outliers in the extracted filtrates and mycelium employing the OPLS-DA model [13]. In a recent study on endophytic fungi isolated from Artemisia annua, antimalarial metabolites were identified using LC-HRMS-based metabolomics. Eleven endophytic fungal isolates from the family of Trichocomaceae, Nectriaceae, and Pleosporaceae were fermented and extracted with ethyl acetate via the UAE technique. Among the 2363 peaks detected in LC-HRMS, eight metabolites were identified. Physcion (54), emodine (55), katenarin (56), norjavanicin (57), dechlorogriseofulvin (58), benzyl benzoate (59), 4-hydroxybenzyl benzoate (60), and benzyl anisate (61) were found to be positively correlated with the anti-plasmodial activity using multivariate analysis. In further investigation, neural network and deep learning-based software were employed to identify metabolites with the most possible active hits [9]. By applying multivariate analysis in metabolomics, metabolites of interest could be systematically detected and identified from a complex mixture of chemical compounds [64,65,66].

4.2. NMR-Based Metabolomics

NMR spectroscopy is a non-destructive, strongly quantitative, and reproducible analytical tool. Owing to its robustness, NMR is automatable, has high throughput, and requires simple sample preparation [67,68,69]. Among the various NMR experiments, one-dimensional proton-NMR (1D 1H-NMR) is broadly employed in metabolomics due to its high intense NMR signals and short collection time. The 1H-NMR spectrum could be obtained within a minute, and it is well suited for large-scale sample analysis. Furthermore, different nucleic experiments (13C, 15N, and 31P) could be used in NMR to study different chemical classes of metabolites [44,45,70]. Despite its numerous advantages, there are challenges in performing NMR-based metabolomics. NMR is 10–100 times less sensitive compared to MS-based metabolomics. More often, overlapping peaks in 1H-NMR spectra have become a great challenge in the characterization of metabolites. Limited NMR databases and software have restricted NMR in metabolomics applications [44,45,71]. Thus, the literature studies employing solely NMR-based metabolomics in endophytic fungi are rather scarce.
Metabolomics investigations using 1H-NMR on endophytic fungal isolates (Colletotrichum sp., Diaporthe sp. and Periconia sp.) from Crescentia alata Kunth discriminated the classes of compounds present in each extract. The multivariate PLS-DA revealed the strongest anti-inflammatory activity was exhibited by Colletotrichum extract, followed by Diaporthe and Periconia extracts. Chemical shifts δH 0.76, 1.2, 1.44, 1.72, and 1.76 ppm were noticed in Colletotrichum extract, suggesting the presence of terpene-type metabolites. These metabolites were believed to contribute to the observed activity in the Colletotrichum extract. Meanwhile, olefinic and aromatic protons were detected in the Periconia and Diaporthe extracts, respectively. The presence of characteristic NMR signals among the extracts has discriminated the chemical profiles of fungal endophytes isolated from the same host [24]. NMR chemical shift databases could potentially target a list of metabolites hits to the peaks generated from metabolomics experiments. Nonetheless, selecting the right metabolite could be challenging when dealing with metabolites of low concentration. Moreover, the peak assignment becomes complicated when some of the NMR peaks are partly and/or entirely overlapped by highly concentrated metabolites signals [72,73,74,75].

5. Conclusions and Future Perspectives

Endophytic fungi are promising producers of biologically interesting metabolites. The bioactive metabolites play a vital role in the treatment and enhancement of human health. Occasionally, potent bioactive metabolites are present at low yields. Thus, metabolites isolation could be challenging at the preliminary stage of works. As such, metabolomics has appeared as a robust approach to measuring the global metabolites from a minimal amount of samples. Innovations in analytical tools have advanced the progress of metabolomics, particularly in endophytic fungi. In-depth research on endophytic fungi by integrating both MS and NMR is warranted in order to perform high-throughput metabolomics studies with better detection and metabolites identification. Continuous metabolomics research on endophytic fungi must remain to ensure contribution to the chemical database of fungal metabolites is increasing for biomarker discovery.
Though metabolomics of endophytic fungi is still in its developing stage, it is highly prospective that the next 10 years will be impactful by integrating both LC-HRMS and NMR in the study design for direct comparison and correlation of the metabolites data. In addition, statistical tools and software, as well as machine learning and neural networks, may significantly aid the data analysis and visual representation of the metabolomics experiments. Studies involving the mechanisms between host plant–endophyte interaction, genetic manipulations of endophytic fungi, and the modified biosynthetic pathways for natural bioactive metabolites from fungal endophytes could be focused on.

Author Contributions

Conceptualization, W.-N.T.; data collection; methodology, K.N.; software, W.-N.T. and J.-W.L.; writing—original draft preparation, K.N., A.A.B. and W.-N.T.; writing—review and editing, B.I., W.-Y.T., C.-R.L., K.Y.K. and W.-N.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme with Project Code: FRGS/1/2018/STG01/USM/02/3.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to acknowledge Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme with Project Code: FRGS/1/2018/STG01/USM/02/3 and Universiti Sains Malaysia for facilities and support.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ChEBIChemical Entities of Biological Interest
ESIElectrospray ionization
LCLiquid chromatography
LC-HRMSLiquid chromatography–high-resolution mass spectrometry
LC-Q-ToF-MSLiquid chromatography–quadrupole time-of-flight-mass spectrometry
LLELiquid–liquid extraction
MEBMalt extract broth
MPDBAcidified potato dextrose broth
MSMass spectrometry
m/zMass-to-charge
MICMinimum inhibitory concentration
NISTNational Institute of Science and Technology
NANPDBNorthern African Natural Products Database
NMRNuclear magnetic resonance
OPLS-DAOrthogonal partial least square discriminant analysis
PLS-DAPartial least square discriminant analysis
PCAPrincipal component analysis
PDBPotato dextrose broth
REBRice extract broth
SABSabouraud broth
SSFSolid-state fermentation
SMFSubmerged fermentation
T3DBThe Toxin and Toxin Target Database
UHPLC-HRMSUltra high-performance liquid chromatography–high-resolution mass spectrometry
UAEUltrasonic-assisted extraction
1D 1H-NMROne-dimensional proton-NMR

References

  1. Rashmi, M.; Kushveer, J.S.; Sarma, V.V. A worldwide list of endophytic fungi with notes on ecology and diversity. Mycosphere 2019, 10, 798–1079. [Google Scholar] [CrossRef]
  2. Sayed, A.M.; Sherif, N.H.; El-Gendy, A.O.; Shamikh, Y.I.; Ali, A.T.; Attia, E.Z.; El-Katatny, M.H.; Khalifa, B.A.; Hassan, H.M.; Abdelmohsen, U.R. Metabolomic profiling and antioxidant potential of three fungal endophytes derived from Artemisia annua and Medicago sativa. Nat. Prod. Res. 2020. In Press. [Google Scholar] [CrossRef]
  3. Hartley, S.E.; Eschen, R.; Horwood, J.M.; Gange, A.C.; Hill, E.M. Infection by a foliar endophyte elicits novel arabidopside-based plant defence reactions in its host, Cirsium arvense. New Phytol. 2015, 205, 816–827. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Nagarajan, K.; Tong, W.-Y.; Leong, C.-R.; Tan, W.-N. Potential of endophytic Diaporthe sp. as a new source of bioactive compounds. J. Microbiol. Biotechnol. 2021, 31, 493–500. [Google Scholar] [CrossRef] [PubMed]
  5. Liu, J.; Liu, G. Analysis of secondary metabolites from plant endophytic fungi. Methods Mol. Biol. 2018, 1848, 25–38. [Google Scholar] [CrossRef]
  6. Vinale, F.; Nicoletti, R.; Lacatena, F.; Marra, R.; Sacco, A.; Lombardi, N.; d’Errico, G.; Digilio, M.C.; Lorito, M.; Woo, S.L. Secondary metabolites from the endophytic fungus Talaromyces pinophilus. Nat. Prod. Res. 2017, 31, 1778–1785. [Google Scholar] [CrossRef] [Green Version]
  7. Uzma, F.; Mohan, C.D.; Hashem, A.; Konappa, N.M.; Rangappa, S.; Kamath, P.V.; Singh, B.P.; Mudili, V.; Gupta, V.K.; Siddaiah, C.N.; et al. Endophytic fungi—Alternative sources of cytotoxic compounds: A review. Front. Pharmacol. 2018, 9, 309. [Google Scholar] [CrossRef] [PubMed]
  8. Gupta, J.; Sharma, S. Endophytic fungi: A new hope for drug discovery. In New and Future Developments in Microbial Biotechnology and Bioengineering; Singh, J., Gehlot, P., Eds.; Elsevier: Amsterdam, The Netherlands, 2020; pp. 39–49. [Google Scholar]
  9. Alhadrami, H.A.; Sayed, A.M.; El-Gendy, A.O.; Shamikh, Y.I.; Gaber, Y.; Bakeer, W.; Sheirf, N.H.; Attia, E.Z.; Shaban, G.M.; Khalifa, B.A.; et al. A metabolomic approach to target antimalarial metabolites in the Artemisia annua fungal endophytes. Sci. Rep. 2021, 11, 2770. [Google Scholar] [CrossRef]
  10. Naik, B.S. Developments in taxol production through endophytic fungal biotechnology: A review. Orient. Pharm. Exp. Med. 2019, 19, 1–13. [Google Scholar] [CrossRef]
  11. Dayalan, S.; Xia, J.; Spicer, R.A.; Salek, R.; Roessner, U. Metabolome Analysis. In Encyclopedia of Bioinformatics and Computational Biology; Ranganathan, S., Gribskov, M., Nakai, K., Schönbach, C., Eds.; Academic Press: Cambridge, MA, USA, 2019; pp. 396–409. [Google Scholar]
  12. Manchester, M.; Anand, A. Metabolomics: Strategies to define the role of metabolism in virus infection and pathogenesis. In Advances in Virus Research; Kielian, M., Mettenleiter, T.C., Roossinck, M.J., Eds.; Academic Press: Cambridge, MA, USA, 2017; Volume 98, pp. 57–81. [Google Scholar]
  13. Ibrahim, A.; Tanney, J.B.; Fei, F.; Seifert, K.A.; Cutler, G.C.; Capretta, A.; Miller, J.D.; Sumarah, M.W. Metabolomic-guided discovery of cyclic nonribosomal peptides from Xylaria ellisii sp. nov., a leaf and stem endophyte of Vaccinium angustifolium. Sci. Rep. 2020, 10, 4599. [Google Scholar] [CrossRef] [Green Version]
  14. Tawfik, N.F.; Tawfike, A.F.; Abdou, R.; Abbott, G.; Abdelmohsen, U.R.; Edrada-Ebelm, R.; Haggag, E.G. Metabolomics and bioactivity guided isolation of secondary metabolites from the endophytic fungus Chaetomium sp. J. Adv. Pharm. Res. 2017, 1, 66–74. [Google Scholar] [CrossRef]
  15. Alonso, A.; Marsal, S.; Julia, A. Analytical methods in untargeted metabolomics: State of the art in 2015. Front. Bioeng. Biotechnol. 2015, 3, 23. [Google Scholar] [CrossRef] [Green Version]
  16. Fadiji, A.E.; Babalola, O.O. Elucidating mechanisms of endophytes used in plant protection and other bioactivities with multifunctional prospects. Front. Bioeng. Biotechnol. 2020, 8, 467. [Google Scholar] [CrossRef]
  17. Manganyi, M.C.; Ateba, C.N. Untapped potentials of endophytic fungi: A review of novel bioactive compounds with biological applications. Microorganisms 2020, 8, 1934. [Google Scholar] [CrossRef]
  18. Kamal, N.; Viegelmann, C.V.; Clements, C.J.; Edrada-Ebel, R. Metabolomics-guided isolation of anti-trypanosomal metabolites from the endophytic fungus Lasiodiplodia theobromae. Planta Med. 2017, 83, 565–573. [Google Scholar] [CrossRef] [Green Version]
  19. Tawfike, A.F.; Abbott, G.; Young, L.; Edrada-Ebel, R. Metabolomic-guided isolation of bioactive natural products from Curvularia sp., an endophytic fungus of Terminalia laxiflora. Planta Med. 2018, 84, 182–190. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Attia, E.Z.; Farouk, H.M.; Abdelmohsen, U.R.; El-Katatny, M.H. Antimicrobial and extracellular oxidative enzyme activities of endophytic fungi isolated from alfalfa (Medicago sativa) assisted by metabolic profiling. S. Afr. J. Bot. 2020, 134, 156–162. [Google Scholar] [CrossRef]
  21. El-Hawary, S.S.; Mohammed, R.; Bahr, H.S.; Attia, E.Z.; El-Katatny, M.H.; Abelyan, N.; Al-Sanea, M.M.; Moawad, A.S.; Abdelmohsen, U.R. Soybean-associated endophytic fungi as potential source for anti-COVID-19 metabolites supported by docking analysis. J. Appl. Microbiol. 2021, 131, 1193–1211. [Google Scholar] [CrossRef]
  22. George, T.K.; Devadasan, D.; Jisha, M.S. Chemotaxonomic profiling of Penicillium setosum using high-resolution mass spectrometry (LC-Q-ToF-MS). Heliyon 2019, 5, e02484. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Triastuti, A.; Haddad, M.; Barakat, F.; Mejia, K.; Rabouille, G.; Fabre, N.; Amasifuen, C.; Jargeat, P.; Vansteelandt, M. Dynamics of chemical diversity during co-cultures: An integrative time-scale metabolomics study of fungal endophytes Cophinforma mamane and Fusarium solani. Chem. Biodivers. 2021, 18, e2000672. [Google Scholar] [CrossRef] [PubMed]
  24. Flores-Vallejo, R.C.; Folch-Mallol, J.L.; Sharma, A.; Cardoso-Taketa, A.; Alvarez-Berber, L.; Villarreal, M.L. ITS2 ribotyping, in vitro anti-inflammatory screening, and metabolic profiling of fungal endophytes from the Mexican species Crescentia alata Kunth. S. Afr. J. Bot. 2020, 134, 213–224. [Google Scholar] [CrossRef]
  25. Percival, B.; Gibson, M.; Leenders, J.; Wilson, P.B.; Grootveld, M. Univariate and multivariate statistical approaches to the analysis and interpretation of NMR-based metabolomics datasets of increasing complexity. In Computational Techniques for Analytical Chemistry and Bioanalysis; Wilson, P.B., Grootveld, M., Eds.; Royal Society of Chemistry: London, UK, 2020. [Google Scholar]
  26. Worley, B.; Powers, R. Multivariate Analysis in Metabolomics. Curr. Metab. 2013, 1, 92–107. [Google Scholar] [CrossRef]
  27. Wu, J.F.; Wang, Y. Multivariate analysis of metabolomics data. In Plant Metabolomics; Qi, X., Chen, X., Wang, Y., Eds.; Springer: Dordrecht, Germany, 2015. [Google Scholar]
  28. Ramana, P.; Adams, E.; Augustijns, P.; Van Schepdael, A. Metabonomics and drug development. In Metabonomics. Methods in Molecular Biology; Bjerrum, J., Ed.; Humana Press: New York, NY, USA, 2015; Volume 1277. [Google Scholar]
  29. Synytsya, A.; Monkai, J.; Bleha, R.; Macurkova, A.; Ruml, T.; Ahn, J.; Chukeatirote, E. Antimicrobial activity of crude extracts prepared from fungal mycelia. Asian Pac. J. Trop. Biomed. 2017, 7, 257–261. [Google Scholar] [CrossRef]
  30. Ezeobiora, C.E.; Igbokwe, N.H.; Amin, D.H.; Mendie, U.E. Endophytic microbes from Nigerian ethnomedicinal plants: A potential source for bioactive secondary metabolites—A review. Bull. Natl. Res. Cent. 2021, 45, 103. [Google Scholar] [CrossRef]
  31. de Carvalho, C.C.C.R. Fungi in Fermentation and Biotransformation Systems. In Biology of Microfungi. Fungal Biology; Li, D.W., Ed.; Springer: Cham, Switzerland, 2016; pp. 525–541. [Google Scholar]
  32. Srivastava, N.; Srivastava, M.; Ramteke, P.W.; Mishra, P.K. Solid-state fermentation strategy for microbial metabolites production: An overview. In New and Future Developments in Microbial Biotechnology and Bioengineering; Gupta, V.J., Pandey, A., Eds.; Elsevier: Amsterdam, The Netherlands, 2019; pp. 345–354. [Google Scholar]
  33. Costa, J.A.V.; Treichel, H.; Kumar, V.; Pandey, A. Advances in solid-state fermentation. In Current Developments in Biotechnology and Bioengineering; Pandey, A., Larroche, C., Soccol, C.R., Eds.; Elsevier: Amsterdam, The Netherlands, 2018; pp. 1–17. [Google Scholar]
  34. Martínez-Medina, G.A.; Barragán, A.P.; Ruiz, H.A.; Ilyina, A.; Hernández, J.L.M.; Rodríguez-Jasso, R.M.; Hoyos-Concha, J.L.; Aguilar-González, C.N. Fungal proteases and production of bioactive peptides for the food industry. In Enzymes in Food Biotechnology; Kuddus, M., Ed.; Academic Press: Cambridge, MA, USA, 2019; pp. 221–246. [Google Scholar]
  35. Kapoor, M.; Panwar, D.; Kaira, G.S. Bioprocesses for enzyme production using agro-industrial wastes: Technical challenges and commercialization potential. In Agro-Industrial Wastes as Feedstock for Enzyme Production; Dhillon, G.S., Kaur, S., Eds.; Academic Press: Cambridge, MA, USA, 2016; pp. 61–93. [Google Scholar]
  36. Song, R.; Wang, J.; Sun, L.; Zhang, Y.; Ren, Z.; Zhao, B.; Lu, H. The study of metabolites from fermentation culture of Alternaria oxytropis. BMC Microbiol. 2019, 19, 35. [Google Scholar] [CrossRef] [PubMed]
  37. Fierascu, R.C.; Fierascu, I.; Ortan, A.; Georgiev, M.I.; Sieniawska, E. Innovative approaches for recovery of phytoconstituents from medicinal/aromatic plants and biotechnological production. Molecules 2020, 25, 309. [Google Scholar] [CrossRef] [Green Version]
  38. Urkude, R.; Dhurvey, V.; Kochhar, S. Pesticide residues in beverages. In Quality Control in the Beverage Industry; Grumezescu, A.M., Holban, A.M., Eds.; Academic Press: Cambridge, MA, USA, 2019; pp. 529–560. [Google Scholar]
  39. Kyle, P.B. Toxicology: GCMS. In Mass Spectrometry for the Clinical Laboratory; Nair, H., Clarke, W., Eds.; Academic Press: Cambridge, MA, USA, 2017; pp. 131–163. [Google Scholar]
  40. Zahari, N.A.A.R.; Chong, G.H.; Abdullah, L.C.; Chua, B.L. Ultrasonic-assisted extraction (UAE) process on thymol concentration from Plectranthus Amboinicus leaves: Kinetic modeling and optimization. Processes 2020, 8, 322. [Google Scholar] [CrossRef] [Green Version]
  41. David, A.; Rostkowski, P. Environmental Metabolomics; Elsevier Inc.: Amsterdam, The Netherlands, 2020; pp. 35–64. [Google Scholar]
  42. Zhang, T.; Chen, C.; Xie, K.; Wang, J.; Pan, Z. Current state of metabolomics research in meat quality analysis and authentication. Foods 2021, 10, 2388. [Google Scholar] [CrossRef] [PubMed]
  43. Lei, Z.; Huhman, D.V.; Sumner, L.W. Mass spectrometry strategies in metabolomics. J. Biol. Chem. 2011, 286, 25435–25442. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Emwas, A.-H.; Roy, R.; McKay, R.T.; Tenori, L.; Saccenti, E.; Gowda, G.A.N.; Raftery, D.; Alahmari, F.; Jaremko, L.; Jaremko, M.; et al. NMR spectroscopy for metabolomics research. Metabolites 2019, 9, 123. [Google Scholar] [CrossRef] [Green Version]
  45. Wishart, D.S. NMR metabolomics: A look ahead. J. Magn. Reson. 2019, 306, 155–161. [Google Scholar] [CrossRef] [PubMed]
  46. Sands, C.J.; Gómez-Romero, M.; Correia, G.; Chekmeneva, E.; Camuzeaux, S.; Izzi-Engbeaya, C.; Dhillo, W.S.; Takats, Z.; Lewis, M.R. Representing the metabolome with high fidelity: Range and response as quality control factors in LC-MS-based global profiling. Anal. Chem. 2021, 93, 1924–1933. [Google Scholar] [CrossRef]
  47. Tsugawa, H.; Kind, T.; Nakabayashi, R.; Yukihira, D.; Tanaka, W.; Cajka, T.; Saito, K.; Fiehn, O.; Arita, M. Hydrogen rearrangement rules: Computational MS/MS fragmentation and structure elucidation using MS-FINDER software. Anal. Chem. 2016, 88, 7946–7958. [Google Scholar] [CrossRef]
  48. Lai, Z.; Tsugawa, H.; Wohlgemuth, G.; Mehta, S.; Mueller, M.; Zheng, Y.; Ogiwara, A.; Meissen, J.; Showalter, M.; Takeuchi, K.; et al. Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics. Nat. Methods 2017, 15, 53–56. [Google Scholar] [CrossRef]
  49. Sorokina, M.; Steinbeck, C. Review on natural products databases: Where to find data in 2020. J. Cheminf. 2020, 12, 1–51. [Google Scholar] [CrossRef] [Green Version]
  50. Vinaixa, M.; Schymanski, E.L.; Neumann, S.; Navarro, M.; Salek, R.M.; Yanes, O. Mass spectral databases for LC/MS- and GC/MS-based metabolomics: State of the field and future prospects. Trends Anal. Chem. 2016, 78, 23–35. [Google Scholar] [CrossRef] [Green Version]
  51. Hyde, K.D.; Xu, J.; Rapior, S.; Jeewon, R.; Lumyong, S.; Niego, A.G.T.; Abeywickrama, P.D.; Aluthmuhandiram, J.V.S.; Brahamanage, R.S.; Brooks, S.; et al. The amazing potential of fungi: 50 ways we can exploit fungi industrially. Fungal Divers. 2019, 97, 1–136. [Google Scholar] [CrossRef] [Green Version]
  52. Zimowska, B.; Bielecka, M.; Abramczyk, B.; Nicoletti, R. Bioactive products from endophytic fungi of Sages (Salvia spp.). Agriculture 2020, 10, 543. [Google Scholar] [CrossRef]
  53. Gakuubi, M.M.; Munusamy, M.; Liang, Z.X.; Ng, S.B. Fungal endophytes: A promising frontier for discovery of novel bioactive compounds. J. Fungi 2021, 7, 786. [Google Scholar] [CrossRef]
  54. Vinale, F.; Nicoletti, R.; Borrelli, F.; Mangoni, A.; Parisi, O.A.; Marra, R.; Lombardi, N.; Lacatena, F.; Grauso, L.; Finizio, S.; et al. Co-Culture of Plant Beneficial Microbes as Source of Bioactive Metabolites. Sci. Rep. 2017, 7, 14330. [Google Scholar] [CrossRef] [Green Version]
  55. Rai, N.; Keshri, P.K.; Verma, A.; Kamble, S.C.; Mishra, P.; Barik, S.; Singh, S.K.; Gautam, V. Plant associated fungal endophytes as a source of natural bioactive compounds. Mycology 2021, 12, 139–159. [Google Scholar] [CrossRef] [PubMed]
  56. Ramdani, D.; Chaudhry, A.S.; Seal, C.J. Chemical composition, plant secondary metabolites, and minerals of green and black teas and the effect of different tea-to-water ratios during their extraction on the composition of their spent leaves as potential additives for ruminants. J. Agric. Food Chem. 2013, 61, 4961. [Google Scholar] [CrossRef]
  57. Ser, Z.; Liu, X.; Tang, N.N.; Locasale, J.W. Extraction parameters for metabolomics from cultured cells. Anal Biochem. 2015, 475, 22–28. [Google Scholar] [CrossRef] [Green Version]
  58. Sitnikov, D.; Monnin, C.; Vuckovic, D. Systematic assessment of seven solvent and solid-phase extraction methods for metabolomics analysis of human plasma by LC-MS. Sci. Rep. 2016, 6, 38885. [Google Scholar] [CrossRef] [PubMed]
  59. Mahmud, I.; Sternberg, S.; Williams, M.; Garrett, T.J. Comparison of global metabolite extraction strategies for soybeans using UHPLC-HRMS. Anal. Bioanal. Chem. 2017, 409, 6173–6180. [Google Scholar] [CrossRef] [PubMed]
  60. Tokuoka, M.; Sawamura, N.; Kobayashi, K.; Mizuno, A. Simple metabolite extraction method for metabolic profiling of the solid-state fermentation of Aspergillus oryzae. J. Biosci. Bioeng. 2010, 110, 665–669. [Google Scholar] [CrossRef]
  61. Vinaixa, M.; Samino, S.; Saez, I.; Duran, J.; Guinovart, J.J.; Yanes, O. A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data. Metabolites 2012, 2, 775–795. [Google Scholar] [CrossRef]
  62. Berg, M.; Vanaerschot, M.; Jankevics, A.; Cuypers, B.; Breitling, R.; Dujardin, J.C. LC-MS metabolomics from study design to data-analysis–Using a versatile pathogen as a test case. Comput. Struct. Biotechnol. J. 2013, 4, e201301002. [Google Scholar] [CrossRef]
  63. De Souza, L.P.; Alseekh, S.; Brotman, Y.; Fernie, A.R. Network-based strategies in metabolomics data analysis and interpretation: From molecular networking to biological interpretation. Expert Rev. Proteom. 2020, 17, 243–255. [Google Scholar] [CrossRef]
  64. Beniddir, M.A.; Bin Kang, K.; Genta-Jouve, G.; Huber, F.; Rogers, S.; van der Hooft, J.J.J. Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches. Nat. Prod. Rep. 2021, 38, 1967–1993. [Google Scholar] [CrossRef] [PubMed]
  65. Nalbantoglu, S. Metabolomics: Basic principles and strategies. In Molecular Medicine; Nalbantoglu, S., Amri, H., Eds.; IntechOpen: London, UK, 2019. [Google Scholar]
  66. Kellogg, J.J.; Graf, T.N.; Paine, M.F.; McCune, J.S.; Kvalheim, O.M.; Oberlies, N.H.; Cech, N.B. Comparison of metabolomics approaches for evaluating the variability of complex botanical preparations: Green tea (Camellia sinensis) as a case study. J. Nat. Prod. 2017, 80, 1457–1466. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Crowley, T.E. Nuclear magnetic resonance spectroscopy. In Purification and Characterization of Secondary Metabolites; Crowley, T.E., Ed.; Academic Press: Cambridge, MA, USA, 2020; pp. 67–78. [Google Scholar]
  68. Tampieri, A.; Szabó, M.; Medina, F.; Gulyás, H. A brief introduction to the basics of NMR spectroscopy and selected examples of its applications to materials characterization. Phys. Sci. Rev. 2020, 6, 1–41. [Google Scholar] [CrossRef]
  69. Decker, S.R.; Harman-Ware, A.E.; Happs, R.M.; Wolfrum, E.J.; Tuskan, G.A.; Kainer, D.; Oguntimein, G.B.; Rodriguez, M.; Weighill, D.; Jones, P.; et al. High throughput screening technologies in biomass characterization. Front. Energy Res. 2018, 6, 120. [Google Scholar] [CrossRef] [Green Version]
  70. Vögele, J.; Ferner, J.-P.; Altincekic, N.; Bains, J.K.; Ceylan, B.; Fürtig, B.; Grün, J.T.; Hengesbach, M.; Hohmann, K.F.; Hymon, D.; et al. 1H, 13C, 15N and 31P chemical shift assignment for stem-loop 4 from the 5’-UTR of SARS-CoV-2. Biomol. NMR Assign. 2021, 15, 335–340. [Google Scholar] [CrossRef] [PubMed]
  71. Liu, R.; Bao, Z.-X.; Zhao, P.-J.; Li, G.-H. Advances in the study of metabolomics and metabolites in some species interactions. Molecules 2021, 26, 3311. [Google Scholar] [CrossRef]
  72. Gowda, G.A.N.; Raftery, D. Can NMR solve some significant challenges in metabolomics? J. Magn. Reson. 2015, 260, 144–160. [Google Scholar] [CrossRef] [Green Version]
  73. Bingol, K. Recent advances in targeted and untargeted metabolomics by NMR and MS/NMR Methods. High-Throughput 2018, 7, 9. [Google Scholar] [CrossRef] [Green Version]
  74. Dona, A.C.; Kyriakides, M.; Scott, F.; Shephard, E.A.; Varshavi, D.; Veselkov, K.; Everett, J.R. A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. Comput. Struct. Biotechnol. J. 2016, 14, 135–153. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Garcia-Perez, I.; Posma, J.M.; Serrano-Contreras, J.I.; Boulangé, C.L.; Chan, Q.; Frost, G.; Stamler, J.; Elliott, P.; Lindon, J.; Holmes, E.; et al. Identifying unknown metabolites using NMR-based metabolic profiling techniques. Nat. Protoc. 2020, 15, 2538–2567. [Google Scholar] [CrossRef]
Jof 08 00028 g001 550
Figure 1. A workflow in metabolomics studies of fungal endophytes.
Figure 1. A workflow in metabolomics studies of fungal endophytes.
Jof 08 00028 g001
Jof 08 00028 g002a 550Jof 08 00028 g002b 550
Figure 2. Chemical structures of metabolites (1)–(61).
Figure 2. Chemical structures of metabolites (1)–(61).
Jof 08 00028 g002aJof 08 00028 g002b
Table
Table 1. Metabolites identification in endophytic fungi via LC-HRMS- and NMR-based metabolomics.
Table 1. Metabolites identification in endophytic fungi via LC-HRMS- and NMR-based metabolomics.
Endophytic FungiHost PlantMetabolite ExtractionSolvent UsedAnalytical ToolDatabaseMetabolitesRef.
Aspergillus terreus (AFL, AFSt, AFR)Artemisia annua, Medicago sativaUAEEthyl acetateLC-HRMSMarinLit, Dictionary of Natural ProductsPaeonol, p-hydroxy benzoic acid, p-coumaric acid, dihydrosinapic acid, osmundacetone, shikimic acid, parvulenone, nidulol, tyrosol, asperpanoid A, maltoryzine, isopestacin, globoscinic acid, 5,7-dihydroxy-4-methylcoumarin, β-methylumbelliferone, hymecromone, 7-hydroxycoumarin, scopoletin, citropten, similanpyrone A, flavipin, gliotoxin, isotryptoquivaline, neoxaline, ochratoxin B, indole-3-acetic acid, phenethylamine, gregatin A, aflatoxin B1, aflatoxin B1 exo-8,9-epoxide, penicillic acid, terrein, physcion[2]
Aspergillus terreus,
A. favus, A. oryzae, Penicillium commune, P. chrysogenum, P. chrysogenum, Talaromyces piophilus, T. piophilus, Fusarium oxysporum, F. nematophilum, Pleosporaceae sp.		Artemisia annuaUAEEthyl acetateLC-HRMSDictionary of Natural ProductsPhyscion, emodin, katenarin, norjavanicin, dechlorogriseofulvin, benzyl benzoate, 4-hydroxy benzyl benzoate, benzyl anisate[9]
Xylaria ellisii sp. nov.Vaccinium angustifoliumLLEEthyl acetate; Methanol/acetone (1:1)LC-HRMS-Griseofulvin, dechlorogriseofulvin, cytochalasin D, zygosporin E, epoxycytochalasin D, hirsutatin A, piliformic acid, 2,3-dihydro,2,4- dimethylbenzofuran-7-carboxylic acid, cyclic pentapeptide 1 and 2, xylarotide A, ellisiiamides A-H[13]
Lasiodiplodia theobromaeVitex pinnataLLEEthyl acetateLC-HRMSAntiBase, Dictionary of Natural Products6,8-Dihydroxy-3-methylisocoumarin, 6-oxo-de-O-methyllasiodiplodin, preussomerins-C and H, palmarumycin CP17, cladospirone B, phomopsin B, desmethyl-lasiodiplodin[18]
Curvularia sp.Terminalia laxifloraLLEEthyl acetateLC-HRMSNatural Product DatabaseN-Acetyl-leucine, afalanine, herbarin A, picroroccellin, dihydroxyisoechinulin A, cyclopiamine B, sengosterone, (E)-11-hydroxyoctadeca-12-enoic acid[19]
Aspergillus ochraceus MSEF6Medicago sativaLLEEthyl acetateLC-HRMSDictionary of Natural Products, METLINAnisole, 3-hydroxytoluquinone, versicolin, phenoxyacetic acid, terreic acid, terremurin, terredionol, fumigatin, aspyrone, isoaspinonene, 4-hydroxymellein, nidulol, aspyrone[20]
Aspergillus terreus GMEF1Glycine max L.UAEEthyl acetateLC-HRMSDictionary of Natural ProductsTerreic acid, terremutin, (-)-terredionol, terremutin hydrate, 3-methylorsellinic acid, flavipin, astepyrone, reticulol, (3S,6S)-terramide A, emodin, terrelactone A, aspergiketal, 4-hydroxykigelin, 8-hydroxyquadrone, dihydrocitrinone, aspergillide B1, sulochrin, 3α-hydroxy-3,5-dihydromonacolin L[21]
Penicillium setosumWithania somniferaUAE/LLEDichloromethane: ethyl acetate: methanol (3:2:1)/Ethyl acetateLC-Q-TOF-MSMETLINKaempferol, quercetin, quercetin acetate, luteolin, dihydroqueretin, dihydromyricetin, quinalizarin, isofraxidin, andrastin D, citromycetin, patulin, 6-deoxyerythronolide B, vanillic acid, 2-dehydro-3-deoxy-darabino-heptonate 7-phosphate (DAHP)[22]
Coohinforma mamane, Fusarium solaniC. mamane (from Bixa orellana L.), F. solani (from Plantago lanceolata)UAEDichloromethane, methanol and waterUHPLC-HRMSDictionary
of Natural Products, SciFinder, MS Finder, Natural Product Database, KNApSAcK, Chemical Entities of Biological Interest (ChEBI), STOFF, The Toxin and Toxin Target Database (T3DB), Northern African Natural Products Database (NANPDB), Drugbank, FooDB, PlantCyc		Cyclosporins A and E, botryosulfuranol C and B, cyclo-(L-Pro-L-Val), (R)-(-)-mellein, cyclo-(L-Leu-L-Leu-D-Leu-L-Leu-L-Val)[23]
Colletotrichum sp., Diaporthe sp., Periconia sp.Crescentia alata
Kunth		UAE/LLEMethanol/Ethyl acetate1H-NMR-Terpenes, phenolics, alkaloids, pigments, steroids, polyketides, glycosides[24]
Note: UAE = Ultrasonic-assisted extraction; LLE = Liquid–liquid extraction.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Nagarajan, K.; Ibrahim, B.; Ahmad Bawadikji, A.; Lim, J.-W.; Tong, W.-Y.; Leong, C.-R.; Khaw, K.Y.; Tan, W.-N. Recent Developments in Metabolomics Studies of Endophytic Fungi. J. Fungi 2022, 8, 28. https://doi.org/10.3390/jof8010028

AMA Style

Nagarajan K, Ibrahim B, Ahmad Bawadikji A, Lim J-W, Tong W-Y, Leong C-R, Khaw KY, Tan W-N. Recent Developments in Metabolomics Studies of Endophytic Fungi. Journal of Fungi. 2022; 8(1):28. https://doi.org/10.3390/jof8010028

Chicago/Turabian Style

Nagarajan, Kashvintha, Baharudin Ibrahim, Abdulkader Ahmad Bawadikji, Jun-Wei Lim, Woei-Yenn Tong, Chean-Ring Leong, Kooi Yeong Khaw, and Wen-Nee Tan. 2022. "Recent Developments in Metabolomics Studies of Endophytic Fungi" Journal of Fungi 8, no. 1: 28. https://doi.org/10.3390/jof8010028

APA Style

Nagarajan, K., Ibrahim, B., Ahmad Bawadikji, A., Lim, J. -W., Tong, W. -Y., Leong, C. -R., Khaw, K. Y., & Tan, W. -N. (2022). Recent Developments in Metabolomics Studies of Endophytic Fungi. Journal of Fungi, 8(1), 28. https://doi.org/10.3390/jof8010028

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop