1. Introduction
The potential of fungal endophytes to produce bioactive substances that can be utilized in a variety of applications, such as agriculture, medicine, and biocontrol methods, has attracted a significant amount of attention. Fungal endophytes are organisms that live within the interior tissues of plants. The neem plant, Azadirachta indica, is a model that is particularly useful for the investigation of these endophytes. Several studies have documented the presence of several fungal taxa throughout diverse plant parts, including leaves, stems, and bark [1,2]. Based on these findings, research has demonstrated that the diversity of fungal endophytes associated with neem is extensive.
Verma et al. [2] carried out a comprehensive investigation that resulted in the identification of 233 different endophytic fungi from neem, with most of these fungi being of the Hyphomycetes species. According to Sharma et al. [3], this diversity is extremely important since different endophytes have the potential to produce different secondary metabolites, each of which can have a unique set of biological activities. According to Umurhurhu et al. [4], the capacity of endophytes to produce a diverse array of compounds is determined by a number of factors, including the species of the plant that serves as their host, the conditions of the surrounding environment, and the particular interactions that occur between the endophytes and their host.
According to the findings of the research conducted on fungal endophytes derived from neem, these endophytes have the potential to be used in biological control, notably against phytopathogens. Nthuku et al. [1] demonstrated that fungal endophytes isolated from neem had high biocontrol potential against Fusarium oxysporum f.sp. cubense, which is a pathogen responsible for producing Fusarium wilt in bananas. The findings of this study demonstrate the significance of endophytes in improving the health of plants and developing their resistance to illnesses. It is also important to highlight the wide variety of bioactive substances that endophytic fungi create in terms of their chemical composition. For instance, Fulzele’s research on Preussia isabellae, an endophyte that was isolated from neem, found that it possesses the ability to create lovastatin, a chemical that possesses strong antibacterial characteristics [5]. In a similar way, Chukwuemerie et al. highlighted the antimalarial potentials of secondary metabolites obtained from Curvularia lunata, which is another endophyte that originates from neem. This highlights the medicinal potential of these fungi [6]. Based on this evidence, it appears that the endophytic community that is associated with neem not only makes an impact on the overall health of the plant, but it also acts as a reservoir for the discovery of new bioactive chemicals.
Endophytic fungi that are produced from neem have a wide range of potential applications, particularly in the field of sustainable agriculture. The application of these fungi as biocontrol agents has the potential to reduce the reliance on synthetic pesticides, hence supporting practices that are more environmentally friendly. Studies have shown that endophytic fungi have the ability to successfully restrict the growth of agricultural pests and pathogens, which in turn leads to an increase in crop yield and quality [1,4]. Furthermore, the investigation of endophytes for their antimicrobial properties has significance for the development of new medications. This is because many endophytes produce compounds that have antibacterial, antifungal, and antiviral activity [7].
Keeping in view the rich diversity of endophytes associated with neem, coupled with their ability to produce a wide array of bioactive compounds, positions them as valuable resources for biocontrol and therapeutic applications; we tried isolating various fungi associated with neem and resulted in the isolation of an interesting new species of Alanomyces. The genus Alanomyces belongs to the Aplosporellaceae Slippers, Boissin, and Crous family [8]. This family, Aplosporellaceae, was erected in 2013 under the order Botryosphaeriales. Aplosporella was the genus included in this family at the time of its establishment. The type genus was Aplosporella Speg. and the type species was Aplosporella chlorostroma Speg. [9]. Later, in 2017, another genus, Alanomyces, was established and included in this family. The type species was Alanomyces indica. This is the only species reported to date in this genus; it was isolated from soil attached to the fruiting body of an unidentified macrofungus from India [10]. Many species of Aplosporella have been associated with thin, dead twigs and rarely occur on leaves or thicker branches [11]. Species of Aplosporella have been reported from Acacia cochlearis, Acacia erioloba, Acacia mellifera, Acacia tortillas, Acer buergerianum, Artocarpus heterophyllus, Celtis africana, Cerasus yedoensis, Cercis chinensis f. chinensis, Chaenomeles sinensis, Eucalyptus gomphocephala, Gleditsia sinensis, Juglans regia, Juniperus chinensis, Mimetes cucullate, Prunus persica var. nucipersica, Searsia lancea, Sophora japonica, Ziziphus jujuba, etc. [12]. In this study, we describe a new species belonging to the genus Alanomyces isolated as an endophyte from the bark of Azadirachta indica from Mulshi, Maharashtra. Also, we present here the genomic and metabolic profiling of this novel species, which can help improve its applicability for the benefit of humanity.
2. Materials and Methods
2.1. Collection, Isolation, and Morphological Characterization
The bark of Azadirachta indica was collected from Mulshi, Maharashtra, India, on 11 February 2024 and was placed in sterile polythene bags and transported carefully to the laboratory. The surface adherents were removed after thoroughly washing them under tap water. Then, larger pieces were chopped into smaller ones and subjected to surface sterilization following a modified method by Dobranic et al. [13]. Concisely, the bark of Azadirachta indica was first dipped in ethanol (70%) for 5 s, followed by sodium hypochlorite (4%) for 90 s, and later rinsed with sterile water for 10 s (four times). These surface sterilized pieces were cut into small pieces using a sterilized sharp blade No. 10 (Sigma-Aldrich Chemicals Private Ltd., Bangalore, India) and inoculated on potato dextrose agar (PDA) plates. These plates were kept at 25 °C until any vegetative growth appeared from the inoculated tissues. Individual colonies from inoculated tissues were transferred to fresh PDA plates by hyphal tipping and allowed to grow into pure cultures [14]. For further studies, two similar-looking colonies were maintained on PDA. Colony characteristics of this isolate were studied on PDA, malt extract agar (MEA), V8 juice agar, corn meal agar (CMA), rose bengal agar (RBA), czapek dox agar (CDA), potato carrot agar (PCA), and sabouraud dextrose agar (SDA). Methuen’s Handbook of Color was referred to for recording the colors of the colonies on different agar media [15]. Microscopic structures of the isolates were recorded from pure culture using a staining cum-mounting medium, lactophenol cotton blue, under a Carl Zeiss Image Analyzer 2 (Oberkochen, Germany) microscope. Measurements and photomicrographs of the fungal structures were recorded using Axiovision Rel 4.8 software and Digi-Cam attached with the Carl Zeiss Image Analyzer 2 microscope. The holotype specimen was deposited and accessioned in the Ajrekar Mycological Herbarium (AMH 10702) and ex-type pure culture was deposited and accessioned in the National Fungal Culture Collection of India (NFCCI 5738).
2.2. DNA Extraction, PCR Amplification, and DNA Sequencing
Genomic DNA was extracted from pure colonies raised from single spore isolation on PDA Petri plates. After approximately one week of incubation, DNA extraction was performed using a simple, easy, and rapid DNA extraction protocol using the FastPrep®24 tissue homogenizer (MP Biomedicals GmbH, Eschwege, Germany) [16]. The amplification and sequencing of ITS and LSU gene regions were carried out. The primers involved in amplification and sequencing were ITS-5 (5′ GGAAGTAAAAGTCG-TAACAAGG 3′) and ITS-4 (5′ TCCTCCGCTTATTGATATGC 3′) for ITS [17] and LR-0R (5′ ACCCGCTGAACTTAAGC 3′) [18] and LR-7 (5′ TACTACCACCAAGATCT 3′ [19] for the 28S large subunit of the nrDNA (LSU). A 25 μL reaction was used to perform the PCR using 12.5 μL 2× Invitrogen Platinum SuperFi PCR Mastermix, 2 μL template DNA (10–20 ng), 1.5 μL 10 pmol primer, 5 μL 5× GC enhancer, and H2O (Sterile Ultra-Pure Water, Sigma-Aldrich, St. Louis, MI, USA), with the total volume made to equal 25 μL. The conditions of the thermocycling involved those as follows: for the ITS gene region, an initial denaturation at 94 °C for 5 min, 35 cycles of 1 min at 94 °C, 30 s at 52 °C, 1 min at 72 °C, and lastly an extension at 72 °C for 8 min; for LSU, 5 min denaturation at 95 °C, 35 cycles of 60 s at 95 °C, 50 s at 52 °C, and 1.2 min at 72 °C, with a final 8 min extension at 72 °C. Unfortunately, after repeated trials to amplify the LSU gene region, we failed; therefore, the sequence of the LSU gene region was collected from the whole-genome sequencing data (details of sequencing, assembly, and annotation are mentioned subsequently in this paper). Per the manufacturer’s instructions, the PCR amplicons were purified with a FavorPrep™ PCR Purification Kit (Favorgen Biotech Corp., Ping Tung, Taiwan). Purified PCR products of both marker genes were checked on 1.2% agarose gel electrophoresis stained with 0.5 μg/mL ethidium bromide. They were further subjected to a sequencing PCR using a BigDye®Terminator v3.1 Cycle Sequencing Kit per the manufacturer’s instructions. In brief, the sequencing PCR of 20 μL included 4 μL 5× sequencing buffer, 2 μL BigDye™ Terminator premix, 4 μL primer (5 pmol), and 4 μL purified amplicon and H2O (Sterile Ultra-Pure Water, Sigma), with the volume equaling 20 μL. Thermal cycling conditions consisted of denaturation at 96 °C for 3 min, 30 cycles of 94 °C for 10 s, 50 °C for 40 s, and 60 °C for 4 min were performed. The BigDye® terminators and salts were removed using the BigDye Xterminator® Purification Kit (Thermo Fisher Scientific, Waltham, MA, USA) per the manufacturer’s instructions. After performing cycle sequencing with the BigDye™ terminator, 80 μL SAM™ solution and 20 μL XTerminator™ solution were added to each tube. The mixture was vortexed for 30 min and then centrifuged at 10,000 rpm for 30 s. After transferring the supernatant to a 96-well microplate, the module was selected and the run was set up. The sequence was elucidated using the Applied Biosystems SeqStudio Genetic Analyzer (Applied Biosystems, Foster City, CA, USA). Sequences obtained were submitted to NCBI GenBank.
2.3. Phylogenetic Analysis
To determine the phylogenetic status of this novel isolate, Alanomyces manoharacharyi, ITS and LSU gene regions were used to compare the present isolate with already known authentic strains. The sequences of the related authentic strains were retrieved from NCBI. Sixty-eight isolates belonging to Aplosporellaceae and associated families were used in the phylogenetic analysis and aligned with the sequences of A. manoharacharyi. Fusicladium oleagineum CBS 113427 and F. convolvularum CBS 112706 were selected as the outgroup taxa. The strains used in making phylogenetic trees, their accession numbers, and other related details are presented in Table 1. Each gene region was aligned with the MAFFT v. 7 web server (
2.4. Identification of the Isolate by MALDI-TOF Mass Spectrometry
The present isolate was inoculated in 3 mL sabouraud dextrose broth in a 15 mL Eppendorf conical tube for 24 h at 28 °C in a rotator to shake overhead at 80 rpm. The tubes were removed and placed on a bench for 10 min. Then, 1.5 mL from the sedimented liquid culture was used to prepare the sample for MALDI-TOF MS measurements. The mycelium was pelleted by centrifugation at 13,000 rpm for 2 min, after which the supernatant was removed. The pellet was again dissolved in 1 mL HPLC water and vortexed. The mycelium was pelleted by centrifugation at 13,000 rpm for 2 min, after which the supernatant was removed. Once again, 1 mL of HPLC water was used to dissolve the pellet. The mycelium was vortexed with HPLC water, centrifuged for two minutes at 13,000 rpm, and the supernatant was discarded. Later, 300 μL HPLC water and 900 μL ethanol were added and vortexed. Later, the supernatant was discarded, and the pellet was completely dried after centrifugation at 13,000 rpm for 2 min. Then, 40 μL formic acid and 40 μL acetonitrile were added and mixed carefully. Later, it was centrifuged at full speed for 2 min. Afterward, 1 µL of the supernatant was pipetted onto the MALDI target, overlaid with 1 µL of HCCA matrix, and analyzed with MALDI-TOF. This preparation was placed in three sample positions on a MALDI Biotarget plate. The resulting spectra were assessed using the Bruker Filamentous Fungi Library 3.0.
2.5. High Molecular Weight DNA Extraction for Whole-Genome Sequencing
DNA extraction of Alanomyces manoharacharyi NFCCI 5738 was carried out using the MasterPure™ Complete DNA and RNA Purification Kit (Cat #MC85200) (LGC Biosearch Technologies, Hoddesdon, UK). Fungal biomass was added to a PowerBead Pro tube (Qiagen, Hilden, Germany). Later, 150 μL tissue and cell lysis solution was added, secured in a TissueLyser (Qiagen, Hilden, Germany), and processed at maximum speed for 0.5–1 min. Subsequently, 1 μL Proteinase K and 150 μL tissue and cell lysate solution were introduced, thoroughly mixed, and incubated at 65 °C for 15 min. After cooling the sample to 37 °C, 1 μL of 5 μg/μL RNase A was added. After the mixture was well mixed, the sample was incubated at 37 °C for 30 min. The sample was placed on ice for 3–5 min; later, 175 μL MPC protein precipitation reagent was added to the lysed sample and mixed well. Centrifugation for 10 min was used to pellet the debris at 4 °C and ≥10,000× g. After that, the supernatant was moved to a fresh microcentrifuge tube, to which 500 μL isopropanol was added and thoroughly mixed. Centrifugation for ten minutes was used to pellet the DNA at 4 °C. After discarding the supernatant, the pellet was twice washed with 70% ethanol. DNA was resuspended in TE Buffer. The integrity was evaluated by 1% agarose gel electrophoresis and purity was accessed by a NanoDrop™ 1000 Spectrophotometer (Thermo Fisher Scientific).
2.6. Library Preparation and Sequencing
DNA fragmentation and library construction were conducted using the NEBNext® Ultra™ II FS DNA Library Prep Kit (New England Biolabs, Ipswich, MA, USA) for Illumina protocol (Cat #E7805) per the manufacturer’s instructions. DNA fragmentation, end repair, and dA-tailing were carried out in a 0.2 mL-thin-wall PCR tube (Sigma-Aldrich, St. Louis, MI, USA) with 250 ng genomic DNA, 7 µL of NEBNext Ultra II FS Reaction Buffer, and 2 µL of NEBNext Ultra II FS Enzyme Mix. The total reaction volume was made to be 36 µL using nuclease-free water. The incubation was carried out in a thermocycler; conditions involved incubation at 37 °C for 15 min followed by incubation at 65 °C for 30 min. For the ligation of the adaptors to this FS Reaction Mixture, 30 µL of NEBNext Ultra II Ligation Master Mix, 1 µL of NEBNext Ligation Enhancer (New England Biolabs, Ipswich, MA, USA), and 2.5 µL of NEBNext Adaptor for Illumina were added. The thermal cycler was used to incubate this mixture at 20 °C for 15 min. Later, 3 μL USER® enzyme was added to the ligation mixture and incubated at 37 °C for 15 min. NEB-Next sample purification beads were added to clean the adaptor-ligated DNA, which was eluted using 17 μL 0.1× TE. For PCR enrichment of the adaptor-ligated DNA, 15 μL adaptor-ligated DNA fragments, 25 μL NEBNext Ultra II Q5 Master Mix, and 10 μL index/universal primers were mixed and subjected to PCR amplification. The conditions included initial denaturation at 98 °C for 30 s, followed by five cycles of denaturation at 98 °C for 10 s, annealing/extension at 65 °C for 75 s, and a final extension at 65 °C for 5 min. The PCR was cleaned up using 0.9× NEBNext sample purification beads. The DNA was eluted from the beads by adding 33 μL of 0.1× TE. Furthermore, the quality of the library was assessed on tapestation and quantification of the sequencing library was carried out by a Qubit fluorometer (Thermo Fisher Scientific, MA, USA). The libraries were sequenced using Illumina NovaSeq 6000 sequencer v1.5-chemistry (Illumina, San Diego, CA, USA) for 150 bp paired-end sequencing, according to the manufacturer’s procedure.
2.7. Genome Assembly
The raw data quality was checked using FastQC v0.12.1 [
2.8. Genome Prediction and Annotation
Gene prediction was performed using Augustus 3.4.0 as well as GeMoMa [33,34,35,36]. Based on the gene function and metabolic pathway of the existing databases, the function annotation was performed using BLAST searches against these databases: NRs (NCBI non-redundant protein sequences), NTs (non-redundant nucleotide sequences), Swiss-Prot, the COG (Cluster of Orthologous Groups) of proteins, the KEGG (Kyoto Encyclopedia of Genes and Genomes), GO (Gene Ontology), the PHI-base (Pathogen–Host Interactions Database), CAZy (the Carbohydrate-Active Enzymes Database).
2.9. Analysis of Secondary Metabolite Biosynthetic Gene Clusters
Secondary metabolites biosynthetic gene cluster analysis of Alanomyces manoharacharyi NFCCI 5738 was carried out by AntiSMASH fungal 7.1.0 [37]. To further study the obtained gene clusters, the NCBI Genome Portal Software Platform (
2.10. Comparative Genomics and Phylogenetic Analysis
Orthologous proteins were identified using OrthoFinder version 2.5.5 [38] and the results were used to build a species tree in MEGA 11 using ML. For visualization and editing of the tree, iTOL: Interactive Tree of Life version 6.9 was used [39]. The taxa used for studying the orthologous proteins and construction of the phylogenetic tree are presented in Table 2. The average nucleotide identity (ANI) analysis was performed using the Pyani script and ANIb as an algorithm for the alignment [40]. The taxa used in the analysis of the ANI are presented in Table 2. Another ML phylogenetic tree was built using raxmlHPC v7.2.8 on the core genome SNPs identified in a pan-genome analysis performed using Panseq v3.2.1, with the run mode set to pan, the fragment size at 500 nucleotides, the percentage of identity cut off at 90%, and core genome threshold set at 2 to find out the sequences in common among all the taxa [41].
2.11. Characterization of Transcripts
The putative secreted proteins involved in pathogenesis were identified using SignalP version 5.0b with the cutoff set ≥ 0.5 [42]. TargetP version 2.0 was used to identify the signal peptide (SP), mitochondrial transit peptide (mTP), and potential cleavage sites (CSs) [43]. The prediction of transmembrane proteins was performed using TMHMM version 2.0 [44]; for the prediction of non-coding RNA, tRNAscan, RNAmmer, and rfam_scan were used.
2.12. Fermentation and Extraction of Metabolites of the Isolate for Metabolomic Study
Alanomyces manoharacharyi was initially grown on PDA for four days. Then, a single colony from PDA was inoculated in 1.2 L (400 mL × 3) potato dextrose broth in a 2 L Erlenmeyer flask (3 No.) at 180 rpm at 26 °C for a week. Later, the biomass was separated from the filtrate using Whatman No. 4 filter paper. The filtrate was extracted twice with an equal volume of ethyl acetate. The ethyl acetate extract was dried in a rotatory evaporator. The dried extract was dissolved in methanol. The biomass obtained was rinsed twice with distilled water and filtered. The biomass was dried at 45 °C and extracted overnight using methanol. Later, this biomass and methanolic mixture was subjected to sonication and filtered through Whatman No. 4 filter paper. This filtered methanolic extract was dried in a rotatory evaporator. The dried extract was dissolved in methanol. The extracts were diluted to a concentration of 1 mg/mL for further analysis.
2.13. Sample Preparation for Untargeted Metabolomics
In total, 100 μL of the sample was taken and 10 μL internal standard (ISTD) was added, followed by the addition of 400 μL Extraction Agent 1 (4 times the sample). The mixture was vortexed and mixed thoroughly. The incubation was carried out on ice for 20 min. After incubation, the sample was vortexed again and centrifuged at 14,000× g for 15 min at 4 °C. Without disturbing the pellet, 200 μL supernatant was carefully transferred to a fresh tube, followed by the addition of 800 μL Extraction Agent 1 and 200 μL Extraction Agent 2. The mixture was vortexed and mixed thoroughly. The supernatant was dried under a nitrogen beam and reconstituted in 200 μL RS Buffer. A 0.2-micron filter was wetted with 100 μL Extraction Agent 2 and the concentrated sample was filtered (Metabolomics Kit Catalog no. 912308). The filtered samples were collected into the autosampler vial. This method was targeted for both samples, i.e., filtrate and biomass extract.
2.14. UHPLC and MS Parameters
The metabolomic analysis was conducted using an Ultra-High-Performance Liquid Chromatography (UHPLC) system (Elute UHPLC, Bruker, Billerica, MA, USA). This system was equipped with a quaternary pump coupled with an Ion Trap mass spectrometer (Amazon Speed, Bruker) that utilized an Electrospray Ionization (ESI) interface. Chromatographic separation was achieved using an Acquity BEH C18 reversed-phase column (50 × 2.1 mm, 1.7 μm particle size) (Waters, Milford, MA, USA). The column was maintained at a temperature of 30 °C. The mobile phases consisted of an aqueous solution of 5 mM ammonium acetate and formic acid (FA), used in both positive and negative modes. The flow rate was kept constant at 0.3 mL/min. The gradient elution was optimized to start with 95% of Mobile Phase A, followed by a linear decrease to 25% of Mobile Phase B over 18 min. This was then reduced to 2% B over the next 5 min, held for 7 min, and then switched to 95% B. The column was equilibrated to the initial conditions over the next 5 min, resulting in a total analysis time of 35 min. The injection volume for the samples was set at 5 μL. The ESI-MS/MS analysis was performed in AUTO MSn mode, operating in positive and negative polarities. Two MS/MS transitions were acquired per analyte, with a dwell time between 0.017 and 0.130 s. The maximum accuracy time in the trap control section was 50 ms and the scan range was 100 to 2000 m/z. The nebulizer temperature was set at 29.0 psi, the dry gas flow rate was 10 L/min, and the dry temperature was 126.9 °C. The capillary voltage and end plate offset were set at 4500V and 500V, respectively. The ICC target in negative mode was 70,000 and in positive mode was 200,000.
2.15. Data Analysis Pipeline for Metabolomics
The pipeline began with the normalization of raw data on features, ensuring the preservation of data quality. Low-quality peaks were disqualified and the data were then cross-referenced against multiple databases, such as PubChem CID, CHEBI ID, HMDB ID, KEGG ID, ChemSpider ID, METLIN ID, BMRB ID, MetaCYC ID, Plant Metabolite Hub (Pmhub), YMDB ID, DRUGBANK, and LIPID MAPS. The entire mass of data generated was filtered to find biological features. The best-matched data generated were filtered to find biologically relevant features. The best-matched data were then tabulated for abundance.
3. Results
3.1. Phylogenetic Analysis
The sequence alignments of ITS and LSU were used to confirm the identity of this isolate. The concatenated file had sequence data of 70 taxa (Table 1). The alignment contained 1644 columns, 480 parsimony-informative sites, 760 distinct patterns, 150 singleton sites, and 1014 constant sites. TIM2e + I + G4 was considered the best model and was selected based on the Bayesian Information Criterion (BIC). The phylogenetic tree was generated using the ML method based on the above-mentioned model. The log-likelihood of the consensus tree was −13128.93. Rate parameters were A–C: 1.27194, A–G: 2.38395, A–T: 1.27194, C–G: 1, C–T: 4.41005, and G–T: 1; base frequencies were A: 0.25, C: 0.25, G: 0.25, and T: 0.25; and the proportion of invariable sites was 0.489 and the gamma shape alpha parameter was 0.65 (Figure 1).
Combined phylogenetic analysis using ITS and LSU nested the Alanomyces manoharacharyi isolate in a distinct and unique clade in the family Aplosporellaceae. The clade was well supported with robust SH-like aLRT and ultrafast bootstrap (UFBoot) (Figure 1).
3.2. Taxonomy
Alanomyces manoharacharyi S. Rana and S.K. Singh, sp. nov. Figure 2 and Figure 3.
MycoBank Number: MB 854034.
Holotype: AMH 10702.
Etymology: Named in honor of Prof. Chakravarthula Manoharachary, an eminent mycologist from India.
Host/distribution: Endophyte bark of Azadirachta indica collected from Mulshi, Pune, Maharashtra, India.
Original description: Hyphae: branched, septate, pigmented, constricted near septa, wall thickened and darkened, rough walled, lateral hyphae narrowing towards the apex, dark olivaceous brown, hyaline towards apex, 1.2–33.5 μm ( = 13.2 μm, n = 30). Conidiomata: pycnidial, abundantly produced, globose to subglobose to irregular, dark brown to blackish brown, outer layer composed of dark brown textra angularis, nonsetose, 40.5–246.4 × 35.4–218.4 μm ( = 111.4 × 98.7 μm, n = 30). Conidiophores: short, stumpy, reduced in size, and hyaline. Conidiogenous cells: terminal, integrated, phialidic, ampuliform, smooth-walled, aseptate, hyaline, 11.6–20 μm ( = 16.3 μm, n = 30). Conidia: cylindrical, apex broadly fusoid, base sub-rounded to rounded, smooth-walled, aseptate, hyaline, 6.0–14.6 × 3.5–6 μm ( = 11.1 × 4.8 μm, n = 30).
Culture characteristics: Colonies on PDA reaching 80 mm diam. after 10 days, at 25 °C: irregular, cottony, slightly raised, margins undulate; front olive brown (4D8), reverse smoke brown (4F2) to olive brown (4D8). Colonies on SDA reaching 70 mm diam. after 10 days, at 25 °C: velvety, sulcate, flat, margins irregular to undulate; front brownish grey (11F2) to (11D2), reverse sepia brown (4F3) to butter yellow (4A5). Colonies on PCA reaching 85 mm diam. after 10 days, at 25 °C: circular, flat, margins filiform to undulate; front sepia (brown) (4F3) to khaki (4D5), reverse goose turd (3F4) to olive (3D5). Colonies on CDA reaching 85 mm diam. after 10 days, at 25 °C: flat, irregular, velvety, margins filiform to undulate; front bluish grey (20D2), reverse purplish grey (13F3). Colonies on RBA reaching 65 mm diam. after 10 days, at 25 °C: flat, irregular, velvety, margins filiform to undulate; front olive brown (4E4), reverse smoke brown (4F2) to olive brown (4F8). Colonies on CMA reaching 78 mm diam. after 10 days, at 25 °C: flat, irregular, cottony, margins irregular to undulate; front sepia brown (4F3) to olive brown (4E6), reverse smoke brown (4F2). Colonies on V8 juice agar reaching 85 mm diam. after 10 days, at 25 °C: flat, irregular, cottony, margins undulate; front Café au lait (6D2) to negro (6F3), reverse grey (6F1) to teak brown (6F5). Colonies on MEA reaching 80 mm diam. after 10 days, at 25 °C: flat, irregular, cottony, margins undulate; front sepia (brown) (4F3) to yellow (3A6), reverse goose turd (3F3) to pastel yellow (3A4) (Figure 2).
Sexual morph: Not observed.
Known distribution: Mulshi, Pune, Maharashtra, India.
Material examined: INDIA, Maharashtra, Pune, Mulshi, from the bark of Azadirachta indica, S.K. Singh, 11 February 2024, AMH 10702 (holotype), deposited in Ajrekar Mycological Herbarium (AMH) of India, ex-type culture is deposited in the National Fungal Culture Collection of India (NFCCI 5738).
GenBank numbers: PP669818 (ITS) and PP669820 (LSU).
Other specimens examined: India, Maharashtra, Pune, Mulshi, Azadirachta indica, S.K. Singh, 11 February 2024, NFCCI 5739; GenBank numbers: PP669819 (ITS), PP669821 (LSU).
Notes: The family Aplosporellaceae currently possesses two genera; one is Aplosporella Speg. [9] and the second one is Alanomyces Sharma [10]. The literature review indicates that the genus Alanomyces was recently established by Sharma et al. [10] with the type species Alanomyces indica Sharma. The comparison of the morphotaxonomic features of our new collection, Alanomyces manoharacharyi, reveals that it is morphologically different from the type species A. indica. The pycnidia are significantly smaller without setae in the present collection 40.5–246.4 × 35.4–218.4 μm ( = 111.4 × 98.7 μm, n = 30) while larger and setose in the type species A. indica 100–200 × 10–12 μm. The conidia/pycniospores were prominently found in the ruptured pycnidia/conidiomata. The conidiogenous cells in A. manoharacharyi were prominently found in the juvenile conidia/pycniospores produced in Figure 3I.
In addition to the morphological characteristics, Alanomyces manoharacharyi and A. indica differ in their habitat too. The present collection was isolated as an endophyte from the bark of Azadirachta indica while Alanomyces indica was isolated from soil attached to the fruiting body of an unidentified macrofungus.
Based on the MegaBLAST algorithm search on NCBI for Alanomyces manoharacharyi NFCCI 5738, the closest hit using the ITS gene sequence was found to be Bagnisiella examinans CBS 551.66 showing 93.15% (884 out of 949 bp) identity and having twenty-one gaps (2.21%), with Aplosporella prunicola CBS 121167 showing 92.44% (856 out of 926 bp) identity and having twenty-seven gaps (2.92%), and with Alanomyces indica CBS 134264 showing 94.73% (557 out of 588 bp) identity and having nine gaps (1.53%).
Based on the distinguished morphological features, and phylogenetic analysis, the present collection, Alanomyces manoharacharyi, is treated here as a novel species of Alanomyces (second in the genus).
3.3. Identification of the Isolate by MALDI-TOF Mass Spectrometry
MALDI-TOF MS spectra of the protein profile (2–20 KD) of Alanomyces manoharacharyi NFCCI 5738 were studied using MALDI-TOF Mass Spectrometry (Figure 4 and Table 3). Interestingly, and as expected, the results displayed “No organism identification possible” with a score value of 1.17 as this isolate is novel and did not match with the existing species listed available in the database. MALDI-TOF MS spectra also indicate the novelty of the isolate.
3.4. Genome Sequencing and Assembly of Alanomyces manoharacharyi NFCCI 5738
The sample passed the QC threshold (Q30 > 85%). The total number of raw reads generated was 150169230, GC% was 46, and %Q30 was 94.3. The number of reads that passed the quality check was 148,991,456. Based on the assembly statistics from the assemblers MaSurCa 4.0.5, Megahit v1.2.9, and Spades v3.15.4, it was determined that the Spades assembler yielded the best results (Table 4). Spades assembly was used further for downstream analysis.
The complete number of BUSCOs (C) was 1677 (98.3%), complete and single-copy BUSCOs (S) was 1674 (98.1%), complete and duplicated BUSCOs (D) was 9 (0.2%), fragmented BUSCOs was (F) 7 (0.4%), missing BUSCOs (M) was 22 (1.3%), and total BUSCO groups searched was 1706.
The genome sequence of Alanomyces manoharacharyi NFCCI 5738 was assembled and deposited in the NCBI GenBank database (BioProject PRJNA1114393; BioSample SAMN41484321). To represent the genome of A. manoharacharyi NFCCI 5738, the contigs were sorted from largest to smallest and the top 90 contigs were represented using CIRCOS as a genome diagram. The genome diagram of A. manoharacharyi NFCCI 5738 shows that there are nine circles in the circle diagram (Figure 5), which are as follows from inside to outside: the first circle in orange and green (I) shows the GC skew, the second circle in red and blue (H) represents GC variation, the third circle in black (G) represents rRNA genes, the fourth circle in purple (F) represents repeat regions, the fifth circle in red (E) represents the signal peptide and cleavage site (Signal LIP), the sixth circle in blue and green (D) represents the reference map with Aplosporella prunicola CBS 121167, the seventh circle in green (C) indicates that CDS is in a positive chain, and the eighth circle in green (B) indicates that CDS is in a negative chain. The outer rim shows the contigs.
The genome length was 35,009,973 bp. The total number of contigs generated was 264. The total contigs length was 35,550,828 bp. Contig maximum length was 1,303,379 bp. GC content was found to be 50.01%. The N50 value was found to be 408,258 bp. The Augustus prediction method was used to predict the encoding genes; in total, 7870 protein-coding genes were predicted. The length was 3,902,274 bp. The gene’s average length was 495.8416773 bp. The gene length/genome was 10.976605. GC content in the gene region was 54.06%. In addition to this, the gene prediction was also conducted using GeMoMa in which the reference genome used for the prediction of genes was Aplosporella prunicola CBS 121,167. As mentioned earlier, the number of genes predicted using Augustus was 7870; similarly, the number of genes predicted using GeMoMa was 8101, the number of genes only predicted by Augustus was 6201, the number of genes predicted by GeMoMa was 6432, and the number of genes which were found to be common and predicted by both Augustus and GeMoMa was 1669 (Supplementary Materials sheet attached for further details).
3.5. Genome Sequence Annotation of Alanomyces manoharacharyi NFCCI 5738
Simultaneously, the three prediction methods, Proteinmask, and RepeatMasker were used to predict repeated sequences. Proteinmask predicted that the number of repeating sequences was 1832, occupying 4.65% of the whole genome, and RepeatMasker predicted that the number of repeating sequences was 13437, occupying 3.25% of the entire genome. For non-coding RNA, we predicted three-hundred and fourty-four secondary structures of RNA and tRNA by tRNAscan and three rRNA were predicted by RNAmmer. At the same time, 104 snRNA were predicted with the Rfam database by rfam_scan.
To predict the protein sequences, 7870 non-redundant genes of A. manoharacharyi NFCCI 5738 were subjected to a similarity search based on various public databases. Many genes were mapped using the Clusters of Orthologous Groups (COGs) database (3504 genes/44.52%), the Kyoto Encyclopedia of Genes and Genomes (KEGG) (2836 genes/36.04%), Gene Ontology (GO) (4064 genes/51.64%), Swiss-Prot (4094 genes/52.02%), NCBI non-redundant nucleotide sequences (NTs) (1983 genes/25.20%), and NCBI non-redundant protein sequences (NRs) (6461 genes/82.10%) and, overall, (6885 genes/87.48%) were annotated.
As per the COG database, “Carbohydrate transport and metabolism” was related to many genes (353), followed by “Translation, ribosomal structure and biogenesis (333)”, “General function prediction only (297)”, “Amino acid transport and metabolism (296)”, “Lipid transport and metabolism (281)”, and “Signal transduction mechanisms (258)” (Figure 6) [45]. These results depict that A. manoharacharyi NFCCI 5738 possesses a varied and enriched array of functions for carbohydrates, amino acid metabolism, and lipid transport and metabolism that may favor better energy conversion efficiency.
The findings from the KEGG functional classification suggest that the predicted proteins fell under various categories, such as digestive system (26) [carbohydrate degradation (26)]; drug development (3) [antifungal biosynthesis (3)]; genetic information processing (80) [protein modification (67), tRNA modification (11), bacterial outer membrane biogenesis (1), cell wall biogenesis (1)]; and metabolism (635) [secondary metabolite biosynthesis (131), amino-acid biosynthesis (68), mycotoxin biosynthesis (68), cofactor biosynthesis (29), glycan metabolism (27), carbohydrate metabolism (23), lipid metabolism (22), purine metabolism (19), amino-acid degradation (17), metabolic intermediate biosynthesis (14), glycolipid biosynthesis (11), phospholipid metabolism (11), siderophore biosynthesis (11), steroid metabolism (11), polyol metabolism (10), carbohydrate biosynthesis (9), pigment biosynthesis (9), sulfur metabolism (9), carbohydrate acid metabolism (8), nucleotide-sugar biosynthesis (8), pyrimidine metabolism (8), polyketide biosynthesis (7), xenobiotic degradation (7), alkaloid biosynthesis (6), amine and polyamine biosynthesis (6), aromatic compound metabolism (6), nitrogen metabolism (6), one-carbon metabolism (6), porphyrin-containing compound metabolism (6), energy metabolism (4), glycan degradation (4), isoprenoid biosynthesis (4), protein biosynthesis (4), sesquiterpene biosynthesis (4), steroid biosynthesis (4), glycan biosynthesis (3), glycerolipid metabolism (3), hormone biosynthesis (3), organic acid metabolism (3), alcohol metabolism (2), amino-acid metabolism (2), antibiotic biosynthesis (2), carotenoid biosynthesis (2), cofactor degradation (2), cofactor metabolism (2), organosulfur degradation (2), phytoalexin biosynthesis (2), alkaloid degradation (1), amine and polyamine degradation (1), amino-sugar metabolism (1), flavonoid metabolism (1), ketone metabolism (1), membrane lipid metabolism (1), phytotoxin biosynthesis (1), plant hormone metabolism (1), secondary metabolite metabolism (1), sphingolipid metabolism (1)] (Figure 7) [46]. The results indicate that a varied and enriched array of metabolic functions is present that will probably provide higher secondary metabolism efficacy.
GO annotation depicts varied genes possessed by A. manoharacharyi NFCCI 5738, which may be involved in biological processes, molecular functions, and cellular components (Figure 8) [47]. In total, 1003 (12.74%) genes were involved in biological processes, which included genes involved in the carbohydrate metabolic process (50), cell cycle (42), cell division (89), cell wall organization (57), DNA repair (66), intracellular protein transport (59), meiotic cell cycle (48), mRNA splicing via spliceosome (46), phosphorylation (124), protein transport (95), proteolysis (92), regulation of transcription (77), rRNA processing (59), translation (54), and transmembrane transport (45). A total of 4397 (55.87%) genes were involved in cellular component function, which included genes involved in the cell division site (75), chromatin (97), cytoplasm (780), cytosol (694), endoplasmic reticulum (170), endoplasmic reticulum membrane (157), extracellular region (194), Golgi apparatus (103), membrane (267), mitochondrial inner membrane (89), mitochondrion (285), nucleolus (152), nucleoplasm (81), nucleus (976), and plasma membrane (277). A total of 2403 (30.53%) genes were involved in molecular function, which included genes involved in ATP binding (537), ATP hydrolysis activity (226), DNA binding (156), GTP binding (90), GTPase activity (63), heme binding (73), metal ion binding (440), mRNA binding (67), oxidoreductase activity (119), protein serine kinase activity (82), protein serine/threonine kinase activity (66), RNA binding (171), structural constituent of ribosome (82), transmembrane transporter activity (85), and zinc ion binding (146).
3.6. Genome Sequence Annotation of Alanomyces manoharacharyi NFCCI 5738 for Carbohydrate Genes
Carbohydrate-active enzymes (CAZymes) represent a diverse group of enzymes responsible for both breaking down and building up glycoconjugates, as well as various forms of glycans, such as oligosaccharides and polysaccharides [48]. These enzymes are pivotal in fungal metabolism, facilitating carbohydrates’ degradation, modification, and biosynthesis [49]. The CAZy database is a specialized resource for carbohydrate enzymes, encompassing their ability to alter, create, and dismantle glycosidic bonds [49]. The analysis showed 145 genes encoded for CAZymes dispersed in the A. manoharacharyi NFCCI 5738 genome. These include two auxiliary activities (AAs), three carbohydrate-binding modules (CBMs), one group of carbohydrate esterases (CEs), eighty-seven glycoside hydrolases (GHs), fourty-eight glycosyltransferases (GTs), and four polysaccharide lyases (PLs; Figure 9A). Details of the families are shown in Figure 9B. These findings show that A. manoharacharyi NFCCI 5738 can show a remarkable ability to make and break complex carbohydrates and is possibly a candidate for industrial applicability.
3.7. Genome Sequence Annotation of Alanomyces manoharacharyi NFCCI 5738 for Pathogen–Host Interactions
The Pathogen–Host Interactions Database (PHI-base) is an extensively curated database constructed by experts, relying on experimental data. It contains genes associated with virulence, effector molecules, and pathogenicity factors derived from various pathogens, including fungi, bacteria, and oomycetes. These pathogens infect multiple hosts, including plants, animals, insects, and fungi [50]. The amino acid sequences of A. manoharacharyi NFCCI 5738 were compared with the PHI-base. As shown in Figure 10, A. manoharacharyi NFCCI 5738 possesses abundant PHI-base genes, including reduced virulence (560), unaffected pathogenicity (338), loss of pathogenicity (78), lethal (69), increased virulence (hypervirulence; 19), sensitivity to chemicals (1), effector (plant avirulence determinant; 1), and resistance to chemicals (1) [51]. Reduced virulence, unaffected pathogenicity, and loss of pathogenicity were the significant annotation genes indicating that Alanomyces manoharacharyi NFCCI 5738 is not a pathogenic strain as expected as it was isolated as an endophyte and can be targeted without hesitation for industrial application.
3.8. AntiSMASH Analysis of Alanomyces manoharacharyi NFCCI 5738
AntiSMASH analysis indicated that A. manoharacharyi isolate NFCCI 5738 contains twenty-six secondary metabolite biosynthetic gene clusters (BGCs), including three terpenes, six Type I PKSs (polyketide synthases; T1PKS), one beta-lactone-containing protease inhibitor (beta lactone), seven nonribosomal peptide synthetases (NRPSs), six NRPS-like fragments (NRPS-like), one fungal RiPP-like, one hybrid NRPS + indole, and one hybrid NRPS + T1PKS (Table 5 and Figure 11). AntiSMASH results revealed the potential of this isolate to produce exciting compounds, such as 1,3,6,8-tetrahydroxynaphthalene, aspterric acid, metachelin C/metachelin A/metachelin A-CE/metachelin B/dimerumic acid 11-mannoside/dimerumic acid, chaetocin, viridicatumtoxin/previridicatumtoxin/5-hydroxyanthrotainin/8-O-desmethylanthrotainin, phomasetin, (-)-Mellein, cryptosporioptide B/cryptosporioptide A/cryptosporioptide C, 11-mannoside/dimerumic acid, biotin, and patulin.
Only eleven BCGs showed homologies with known clusters, of which three BCGs showed 100% similarity with known clusters, i.e., 1,3,6,8-tetrahydroxynaphthalene, aspterric acid, and (-)-Mellein. Almost 58% of BGCs did not match with any known gene clusters, indicating that there are many unknown products to be explored and that A. manoharacharyi NFCCI 5738 has the potential to biosynthesize more novel compounds. Genes within the region 6.1 (BGC 6.1) (7560 nucleotides) displayed 100% similarity with the 1,3,6,8-tetrahydroxynaphthalene biosynthetic gene cluster from the Glarea lozoyensis (Figure 11) BGC (MIBiG: BGC0001258; NCBI GenBank: AF549411.1) [52]. Genes within the region 14.1 (BGC 14.1) (21,193 nucleotides) displayed 100% similarity with the aspterric acid biosynthetic gene cluster from the Aspergillus terreus NIH2624 (Figure 11) BGC (MIBiG: BGC0001475; NCBI GenBank: NT_165929.1) [53]. Genes within the region 65.1 (BGC 65.1) (45,412 nucleotides) displayed 100% similarity with the aspterric acid biosynthetic gene cluster from the (-)-Mellein biosynthetic gene cluster from the Parastagonospora nodorum (Figure 11) BGC (MIBiG: BGC0001244; NCBI GenBank: KM365454.1) [54].
Many of these metabolites have been reported earlier to be capable of possessing various bioactivities. Additionally, (-)-Mellein has been proven to be produced from termites and reported to display an inhibitory effect on the growth of entomopathogenic fungi (Metarhizium anisopliae and Beauveria bassiana) [55]. Additionally, 1,3,6,8-tetrahydroxynaphthalene has been reported to be a melanin precursor with many uses; it can protect against ionizing radiation, including ultraviolet, X-ray, gamma-ray, and particulate radiation. These melanins can be used to improve human health, environments, and industries [56]. In addition to this, aspterric acid and 6-hydroxymellein have been reported to inhibit pollen development in Arabidopsis thaliana [57]. Biotin (vitamin H or B7) has become the new trend for consumers wishing to have longer, healthier hair and nails [58]. Numerous studies have demonstrated a wide range of antitumor activities of chaetocin in vitro and in vivo. It has also been isolated and reported in Chaetomium species [59]. Cryptosporioptide is an antibiotic that has been reported to exhibit both lipoxygenase inhibitory and anti-Bacillus megaterium activities; it has been reported to be produced from the endophytic fungus Cryptosporiopsis sp. [60]. In the 1960s, patulin was used for treating common colds and nose infections because of its antiviral, antiprotozoal, and antibacterial properties; it has been mainly reported to be produced by fungal genera, like Penicillium, Aspergillus, and Byssochlamys [61]. Phomasetin is known to inhibit HIV-1 integrase and has been reported to be produced from Phoma sp.
3.9. Comparative Phylogenetics and Genomics
3.9.1. Average Nucleotide Identity
Average nucleotide identity (ANI) analysis performed on the A. manoharacharyi NFCCI 5738 genome provided an overall idea of the sequence identity between the allied genera in comparison with A. manoharacharyi strain NFCCI 5738, as depicted in the heatmap in Figure 12. Alanomyces manoharacharyi NFCCI 5738 was grouped with Aplosporella prunicola CBS 121167, thus confirming that it falls under the family Aplosporellaceae, order Botryosphaeriales. ANIb analysis included 55 genomes of various species from allied genera of Alanomyces.
3.9.2. Phylogeny Based on Orthologous Proteins
Alanomyces manoharacharyi NFCCI 5738 was grouped with Aplosporella prunicola CBS 121167 in the phylogenetic tree, which was constructed based on the orthologous proteins of allied taxa of Alanomyces and figured out using OrthoFinder v2.5.5. This confirms the strain NFCCI 5738 clustering in the Aplosporellaceae family (Figure 13).
3.9.3. Phylogeny Based on Single Nucleotide Polymorphisms (SNPs)
To further disentangle the phylogenetic relationship among the A. manoharacharyi NFCCI 5738 and allied taxa strains, a MLtree was built on 130874 SNPs found in the core genome alignment derived from Panseq. As illustrated in Figure 14, the clustering of A. manoharacharyi NFCCI 5738 with Aplosporella prunicola CBS 121167 was confirmed, thus confirming that A. manoharacharyi falls under the Aplosporellaceae family.
3.10. Characterization of Transcripts
One-thousand and sixty-six analogous genes were identified from the PHI-base. Among these, 449 genes were detected as putative secreted proteins using SignalP. TargetP helped to predict the presence of N-terminal presequences. It was found that there were 149 mitochondrial transit peptides (mTPs), 563 signal peptides (SPs), and 7158 no-targeting peptides (Other) (Figure 15).
3.11. Metabolites from Alanomyces manoharacharyi
Metabolite profiling of the methanolic extract of the dried biomass and the ethyl acetate extract of the filtrate revealed a variety of compounds (Figure 16 and Figure 17; Table 6). Various interesting compounds, like Aspartate semialdehyde, N-Acetyl-D-leucine, PG(20:0/25:0), Dihydroaltersolanol and Dihydroaltersolanol C, Propericiazine (oxide), 2-trans,6-trans-farnesyl diphosphate, Mooreamide A, 5-Dihydroergosterol, Mycocerosic acid (C28), Aspergiolide B, Alpha-Carotene, PE-NMe2(10:0/12:0), DG(15:0/20:0/0:0), Flavocristamide A, N-(24-Hydroxytetracosanoyl)phytosphingosine, 5-Sulfosalicylic acid, Penicoffrazin B/C, Feruloylcholine, 7,10-dihydroxydeacetyldihydrobotrydial-1(10)-ene, Gregatin G1 and Gregatin G2, coscinolactam D, Photopiperazine A, 17-hydroxy-glaciapyrrole B, clarhamnoside or LLG-1, GlcNAcbeta1-3Galbeta1-4(Fucalpha1-3)GlcNAcbeta1…, Pregnane-3,6,20-triol, (3alpha,5beta,6alpha,20S), Chaetochiversin A, aplyviolene, Thiomuracin E, Cer(d22:0/39:0), GlcNAc (N-acetylglucosamine), CL(1′-[16:0/18:0],3′-[18:2(9Z,12Z)/20:4(5Z,8Z,1…, Anvilone B, Oscillatoxin b1/b2, DG(16:1(9Z)/18:0/0:0), TG(10:0/10:0/14:1(9Z)), DG(20:0/20:1(13Z)/0:0), Maltulose, Nagelamide R/Z, and Sarcohydroquinone sulfate B, were detected to be produced by A. manoharacharyi. Analysis of the crude methanolic extract of the dried biomass and the ethyl acetate extract of the filtrate from A. manoharacharyi showed moderate production of secondary metabolites despite the relatively high abundance of biosynthetic gene clusters. This recommends that many of these clusters are completely silent or expressed at low levels under the provided culture conditions. To overcome this, epigenetic manipulation experiments can be set up.
Most of the metabolites detected from the methanolic and ethyl acetate extracts have been reported earlier to hold great importance. Here are some earlier reports on the metabolites identified from the methanolic and ethyl acetate extract of Alanomyces manoharacharyi. Many microorganisms have been reported to require aspartate semialdehyde to produce essential amino acids and metabolites [62]. Since 1957, N-acetyl-leucine has been available over the counter to treat vertigo [63]. Mice treated with N-acetyl-L-leucine after traumatic brain injury were capable of showing improved functional recovery, reduced neurodegeneration and neuroinflammation, and partially restored autophagy flux [64]. It has been discovered that dihydroaltersolanol C, isolated from Stemphylium globuliferum, found inside the plant Juncus acutus, can moderately inhibit S. aureus growth [65]. The medication propericiazine, sometimes known as pericyazine, is used to treat prevailing hostility, impulsivity, and aggression. In the treatment of schizophrenia, it is a standard antipsychotic medication [66]. According to Tan and Phyo [67], Mooreamide A is known to be a cannabimimetics/CNS modulatory agent. Aspergiolide isolated from cultures of the marine-derived fungus Aspergillus glaucus was found to selectively inhibit the proliferation of A549, HL-60, BEL-7402, and P388 cancer cell lines [68] and animal tests with mice indicated that aspergiolide inhibited tumor growth in vivo [69]. Antioxidant and potentially anti-carcinogenic α-carotene may also improve immunological function. Some epidemiological studies, but not all, found that a higher intake of α-carotene was associated with a lower occurrence of cancer and cardiovascular disease [70]. Because of its increased barrier permeability, N-(24-Hydroxytetracosanoyl) phytosphingosine is a phytoceramide essential for preserving skin health. Dryness and wrinkles result from a lower ceramide concentration in the skin. Ceramides in food may compensate for the ceramide concentration in the skin [71]. Trefely et al. [72] state that 5-Sulfosalicylic acid is an antibacterial agent. A class of related secondary metabolites known as gregatins inhibits certain facets of gram-negative bacteria’s quorum sensing [73]. According to Marino et al. [74], coscinolactam D exhibited considerable anti-inflammatory effects due to its capacity to decrease the production of PGE2 and NO. According to Kim et al. [75], photopiperazines are extremely cytotoxic metabolites that exhibit specific toxicity towards the ovarian cancer cell lines SKOV3 and U87 glioma. Thiomuracin E is a thiopeptide and potent antibiotic [76]. Osteoarthritis (OA) is commonly treated with N-acetylglucosamine (GlcNAc) [77]. Anvilone B from Phorbas sp. (sponge) has been isolated and reported. According to Nagai et al. [78], oscillatoxin isolated from Moorea producens has been shown to have diatom growth-inhibition activity against the marine diatom Nitzschia amabilis and cytotoxicity against the L1210 murine lymphoma cell line. Hydroquinone (HQ) is widely used in the dye industry for skin whitening, cosmetics, antioxidants, polymers, pharmaceuticals, and anticancer agents [79]. Penicoffrazin B/C has been reported to be produced from the fungus Penicillium coffeae isolated from Laguncularia racemose (Leaves, Combretaceae) [80].
The metabolite profiling of both the methanolic extract of the dried biomass and the ethyl acetate extract of the filtrate unveiled a diverse array of compounds, many of which are documented in the literature for their significant bioactivities, highlighting the potential therapeutic value and biological relevance of the compounds in these extracts. These findings not only underscore the richness of the chemical profile but also provide a promising foundation for further investigation into the specific mechanisms underlying their bioactivity. Future studies could elucidate the potential applications of these compounds in drug development and other therapeutic areas.
4. Conclusions
Fungi are highly appealing organisms for discovering new metabolites and biocatalysts for industrial applications. To date, around 156,000 existing species have been described from an estimated 2 to 11 million total species, with only a few thousand genomes fully sequenced and available [81]. There is an urgent need to discover new species before they become extinct. In this study, a new species of Alanomyces was found and its genome was sequenced and annotated. The genomic data revealed a diverse array of functional genes and pathways, suggesting a broad spectrum of biological capabilities and potential applications.
Complementing the genomic findings, LC–MS metabolite profiling of both the methanolic extract of the dried biomass and the ethyl acetate extract of the filtrate highlighted a rich and varied chemical composition. This study found that this new species of Alanomyces is a prolific producer of various metabolites of great industrial importance. Many of the detected metabolites are associated with known bioactivities, further emphasizing the significance of this new fungal species. The integration of genomic and metabolomic data underscores the potential of Alanomyces manoharacharyi as a source of novel bioactive compounds and biotechnological applications.
Overall, the findings from this study not only contribute to the understanding of fungal genomics and metabolomics but also pave the way for future research aimed at exploring the practical applications of this newly discovered species in medicine, agriculture, and industry. These findings open possibilities for targeted genome mining, such as gene knockout and the heterologous expression of genes to biosynthesize newer bioactive secondary metabolites for new drug research and development. Further research will be essential to fully elucidate the functional roles of the identified compounds and their potential therapeutic uses.
S.R.: conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, writing—original draft. S.K.S.: conceptualization, data curation, formal analysis, investigation, methodology, project administration, software, supervision, validation, visualization, writing—review and editing. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The data presented in this study are openly available in the NCBI GenBank database (BioProject PRJNA1114393; BioSample SAMN41484321). Other contributions presented in this study are included in the article/
The authors thank Prashant Dhakephalkar, Director of MACS-Agharkar Research Institute, Pune, for providing the necessary facilities and encouragement to carry out the research work. The authors are thankful to the ICAR-NBAIM Mau for the financial support under the Network Project of Application of Microorganisms in Agriculture and Allied Sectors (AMAAS) project.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. Molecular phylogenetic analysis of the new species Alanomyces manoharacharyi based on the ML method using combined ITS and LSU sequence data. The new species is shown in blue. Statistical support values of 70% or more are displayed next to each node and UFBS values and SH−aLRT are obtained from 1000 replicates using IQ−TREE and the TIM2e + I + G4 model.
Enlarge this image.Figure 2. Colonies on various media after 10 days. (A,B) MEA; (C,D) V8 juice agar; (E,F) CMA; (G,H) RBA; (I,J) CDA; (K,L) PCA; (M,N) SDA; (O,P) PDA; (A,C,E,G,I,K,M,O) front view; (B,D,F,H,J,L,N,P) reverse view.
Enlarge this image.Figure 3. Alanomyces manoharacharyi NFCCI 5738; (A–D) Hyphae; (E) Hyphae showing anastomosis; (F,G) Conidiomata; (H) Ruptured conidiomata; (I) Ruptured conidiomata showing numerous dense conidiophores; the black arrow shows ampulliform conidiogenous cells; the white arrow shows short, stumpy conidiophores; (J) Ruptured conidiomata with numerous conidia; (K–M) Conidia. Bar = 20 µm (A–K), 10 µm (L,M).
Enlarge this image.Figure 4. MALDI-TOF MS spectra of Alanomyces manoharacharyi NFCCI 5738 indicating the protein profile (2–20 KD).
Enlarge this image.Figure 5. Genome diagram of Alanomyces manoharacharyi NFCCI 5738; A: Contig; B: Negative Gene; C: Positive Gene; D: Reference Map with Aplosporella punicola CBS 121167; E: Signal Peptide with cleavage sites (Signal LIP); F: Repeat regions; G: rRNA Genes; H: GC variation and I: GC skew.
Enlarge this image.Figure 6. Functional annotation of Alanomyces manoharacharyi NFCCI 5738 genes encoding for proteins using the Clusters of Orthologous Genes (COGs) database.
Enlarge this image.Figure 7. Functional annotation of Alanomyces manoharacharyi NFCCI 5738 genes encoding for proteins using Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis.
Enlarge this image.Figure 8. Functional annotation of Alanomyces manoharacharyi NFCCI 5738 predicted genes encoding for proteins using Gene Ontology (GO) analysis; Red bars represent biological processes, blue bars represent cellular component and green represent molecular function.
Enlarge this image.Figure 9. Carbohydrate-active enzyme (CAZyme) functional classification and corresponding genes present in the Alanomyces manoharacharyi NFCCI 5738 genome. (A): Carbohydrate-active enzyme functional classes; (B): Carbohydrate-active enzyme functional subclasses.
Enlarge this image.Figure 10. Distribution map of mutation types in the pathogen PHI phenotype of Alanomyces manoharacharyi NFCCI 5738.
Enlarge this image.Figure 11. Comparison of biosynthetic gene cluster components in Alanomyces manoharacharyi NFCCI 5738 with known biosynthetic gene clusters for the biosynthesis of (A) Patulin; (B) Tetrahydroxynaphthalene; (C) Biotin; (D) Aspterric acid; (E) Mellein; (F) Chaetocin; (G) Viridicatumtoxin; (H) Cryptosporioptide; (I) Phomasetin; and (J) Dimerum acid.
Enlarge this image.Figure 12. Heatmap of ANIb percentage identity between the allied genera strains compared with the Alanomyces manoharacharyi NFCCI 5738. ANIb analysis was carried out for all 55 genomes calculated based on genome sequences.
Enlarge this image.Figure 13. Phylogenetic analysis of 55 taxa of Alanomyces manoharacharyi NFCCI 5738 and allied taxa based on the orthologous proteins identified using OrthoFinder. The new species is shown in blue. Only the bootstrap values higher than 70 are shown.
Enlarge this image.Figure 14. The maximum phylogenetic tree is based on the 130874 core genome SNPs identified using Panseq. The number of bootstraps is indicated as well. Only the bootstrap values higher than 70 are shown. The new species is shown in blue.
Enlarge this image.Figure 15. Results of TargetP analysis. Cumulative count of predicted proteins containing a signal peptide (SP), mitochondrial translocation signal (mTP), and no-targeting peptides (other).
Enlarge this image.Figure 16. LC–MS analysis of extracts from Alanomyces manoharacharyi NFCCI 5738 for the identification of constituents. (A) Methanolic extract, Positive ion mode; (B) Ethyl acetate extract, Positive ion mode; (C) Methanolic extract, Negative ion mode; (D) Ethyl acetate extract, Negative ion mode.
Enlarge this image.Figure 17. Metabolites identified from the methanolic extract of biomass and the ethyl acetate extract of the filtrate Alanomyces manoharacharyi NFCCI 5738 using LC–MS in positive and negative ion mode.
List of isolates used for constructing the phylogenetic tree of Alanomyces manoharacharyi.
| Sr. No. | Identity | Strain | ITS | LSU |
|---|---|---|---|---|
| 1 | Alanomyces indica | CBS 134264 | HF563622 | HF563623 |
| 2 | Alanomyces manoharacharyi * | NFCCI 5738 | PP669818 | PP669820 |
| 3 | Alanomyces manoharacharyi * | NFCCI 5739 | PP669819 | PP669821 |
| 4 | Alanphillipsia aloeigena | CPC:21286; CBS:136408 | NR_137121 | NG_069179 |
| 5 | Alanphillipsia aloes | CBS 136410 | MH866093 | MH877631 |
| 6 | Aplosporella artocarpi | MFLU 22-0108 | ON823183 | ON834371.1 |
| 7 | Aplosporella chromolaenae | MFLUCC:17-1517 | MT214340 | MT214434 |
| 8 | Aplosporella hesperidica | CBS 208.37 | MH867398 | |
| 9 | Aplosporella macropycnidia | CGMCC 3.17725 | KT343648 | |
| 10 | Aplosporella papilata | CBS 121780 | EU101328 | NG_070359.1 |
| 11 | Aplosporella prunicola | CBS 121167 | OM238151 | KF766315 |
| 12 | Aplosporella thailandica | MFLU 16-0615 | NR_154722.1 | |
| 13 | Bagnisiella examinans | CBS 551.66 | KF766148 | KF766316 |
| 14 | Barriopsis iraniana | IRAN 1448C | NR_137030 | NG_058662 |
| 15 | Barriopsis stevensiana | CBS:174.26 | MH854882.1 | MH866375.1 |
| 16 | Botryobambusa fusicoccum | MFLUCC 11-0143 | JX646792 | JX646809 |
| 17 | Botryosphaeria dolichospermatii | NP1 | MH491970 | MH562323 |
| 18 | Botryosphaeria qingyuanensis | CERC 2946 | KX278000 | MF410042 |
| 19 | Botryosphaeria ramosa | CMW 26167 | NR_151841.1 | KF766333 |
| 20 | Cophinforma atrovirens | CERC 3490 | KX278009 | MF410051 |
| 21 | Cophinforma mamani | CBS 117444 | KF531822 | DQ377855 |
| 22 | Diplodia allocellula | CMW 36468 | JQ239397 | JQ239410 |
| 23 | Diplodia seriata | CBS 112555 | KF766161 | KF766327 |
| 24 | Dothiorella acacicola | CPC 26349 | KX228269 | KX228320 |
| 25 | Dothiorella brevicollis | CMW 36463 | JQ239403 | JQ239416 |
| 26 | Dothiorella viticola | CBS 117010 | AY905558 | KX464344 |
| 27 | Endomelanconiopsis endophytica | CBS 120397 | KF766164 | EU683629 |
| 28 | Endomelanconiopsis freycinetiae | MFLUCC 17-0547 | NR_158434 | MG646948 |
| 29 | Endomelanconiopsis microspora | CBS 353.97 | KF766165 | KF766330 |
| 30 | Eutiarosporella darliae | CBS 118530 | KX464131 | KX464346 |
| 31 | Fusicladium convolvularum | CBS 112706 | AY251082 | EU035428 |
| 32 | Fusicladium oleagineum | CBS 113427 | KF766166 | KF766331 |
| 33 | Guignardia alliacea | MUCC0014 | AB454263 | |
| 34 | Guignardia bidwellii | CBS 111645 | EU683672 | DQ377876 |
| 35 | Guignardia citricarpa | CBS 828.97 | FJ538318 | KF766334 |
| 36 | Kellermania confuse | CBS 131723 | KF766174 | KF766344 |
| 37 | Kellermania pseudoyuccigena | CBS 136446; CPC:20386 | MH866092 | MH877630 |
| 38 | Kellermania yuccigena | CBS 131727 | KF766186 | KF766356 |
| 39 | Lasiodiplodia parva | CBS 456.78 | KF766192 | KF766362 |
| 40 | Lasiodiplodia theobromae | CBS 164.96 | AY640255 | EU673253 |
| 41 | Macrophomina phaseolina | CBS 162.25 | KF531826 | DQ377905 |
| 42 | Macrophomina tecta | BRIP:70781 | MW591684 | |
| 43 | Melanops fagicola | MFLU 19-2862 | MT185519 | MT183482 |
| 44 | Melanops tulasnei | CBS 116805 | FJ824769 | KF766365 |
| 45 | Mucoharknessia cortaderiae | CPC 19974 | KM108374 | KM108401 |
| 46 | Neodeightonia palmicola | MFLUCC 10-0822 | NR_111550 | NG_042534 |
| 47 | Neofusicoccum grevilleae | CPC 16999 | JF951137 | JF951157 |
| 48 | Neofusicoccum kwambonambiense | CBS:123639 | MH863317 | NG_069915 |
| 49 | Neofusicoccum parvum | CMW 9081 | KF766204 | NG_042409 |
| 50 | Neoscytalidium novaehollandiae | CBS 122071 | KF766207 | MH874720 |
| 51 | Phaeobotryon cupressi | CBS 124700 | MH863400 | KX464538 |
| 52 | Phaeobotryon negundinis | MFLUCC 15-0436 | NR_155669 | NG_069332 |
| 53 | Phyllosticta aspidistricola | MUCC0010 | AB454260 | |
| 54 | Phyllosticta capitalensis | CBS 226.77 | FJ538336 | KF766377 |
| 55 | Phyllosticta citribraziliensis | CBS 100098 | OL957175 | NG_069153 |
| 56 | Phyllosticta philoprina | CBS 174.77 | KF766170 | KF766340 |
| 57 | Phyllosticta podocarpi | CBS 111647 | KF766217 | KF766383 |
| 58 | Pileospora piceae | DAOMC251533 | MH144182 | MH144186 |
| 59 | Pileospora piceae | NB-334-4A | MH144181 | MH144184 |
| 60 | Pseudofusicoccum adansoniae | CBS 122055 | KF766220 | KF766386 |
| 61 | Pseudofusicoccum adansoniae | CMW 26147 | KF766220 | KF766386 |
| 62 | Pseudofusicoccum kimberleyense | CBS 122058 | KF766222 | MH874716 |
| 63 | Pseudofusicoccum stromaticum | CBS 117448 | KF766223 | KF766389 |
| 64 | Saccharata hawaiiensis | CBS 111787 | KX464233 | KX464543 |
| 65 | Saccharata kirstenboschensis | CBS 123537 | KF766225 | FJ372409 |
| 66 | Saccharata leucospermi | CBS:122694 | EU552129 | |
| 67 | Saccharata proteae | CBS 115498 | KX464236 | KX464546 |
| 68 | Septorioides pini-thunbergii | CBS 473.91 | MH862264 | MH873946 |
| 69 | Tiarosporella madreeya | CBS 532.76 | KM108376 | DQ377940 |
| 70 | Tiarosporella tritici | CBS 118719 | KC769961 | DQ377941 |
| 71 | Umthunziomyces hagahagensis | CPC 29917 | KY173472 | KY173561 |
* New species are marked in bold.
List of taxa used for comparing orthologous proteins with Alanomyces manoharacharyi NFCCI 5738 for phylogenetic analysis and calculation of genome-scale average nucleotide identity.
| Sr. No. | Organism | Strain | GenBank Assembly Accession |
|---|---|---|---|
| 1 | Aplosporella prunicola | CBS 121167 | GCA_010093885.1 |
| 2 | Botryosphaeria agaves | FJII-L1-SW-P2 | GCA_022813555.1 |
| 3 | Botryosphaeria dothidea | sdau11-99 | GCA_011503125.2 |
| 4 | Botryosphaeria dothidea | CK28 | GCA_021650725.1 |
| 5 | Botryosphaeria dothidea | GS.01s2 | GCA_029169245.1 |
| 6 | Botryosphaeria kuwatsukai | KE8637 | GCA_023084525.1 |
| 7 | Botryosphaeria kuwatsukai | PG2 | GCA_004016305.1 |
| 8 | Diplodia corticola | CBS 112549 | GCA_001883845.1 |
| 9 | Diplodia intermedia | M45-28 | GCA_021495925.1 |
| 10 | Diplodia mutila | CBS 112553 | GCA_022560015.1 |
| 11 | Diplodia mutila | KE84208 | GCA_023089405.1 |
| 12 | Diplodia sapinea | KE8364 | GCA_023087385.1 |
| 13 | Diplodia sapinea | KE8391 | GCA_023087305.1 |
| 14 | Diplodia sapinea | KE8634 | GCA_023085605.1 |
| 15 | Diplodia scrobiculata | CMW30223 | GCA_001455585.1 |
| 16 | Diplodia seriata | FDS-637 | GCA_021436955.2 |
| 17 | Diplodia seriata | M28-159 | GCA_021436965.1 |
| 18 | Diplodia seriata | F98.1 | GCA_001975905.1 |
| 19 | Dothiorella sarmentorum | KEdek | GCA_023082095.1 |
| 20 | Dothiorella sarmentorum | KE84560 | GCA_023088305.1 |
| 21 | Lasiodiplodia citricola | KE87127 | GCA_023089105.1 |
| 22 | Lasiodiplodia gonubiensis | CBS 115812 | GCA_009829795.1 |
| 23 | Lasiodiplodia iranensis | CCTCC M2017288 | GCA_030270915.1 |
| 24 | Lasiodiplodia mahajangana | VT137 | GCA_029590625.1 |
| 25 | Lasiodiplodia pseudotheobromae | WHSB4P03s | GCA_029169085.1 |
| 26 | Lasiodiplodia pseudotheobromae | CBS 116459 | GCA_009829805.1 |
| 27 | Lasiodiplodia pseudotheobromae | BaA | GCA_029931825.1 |
| 28 | Lasiodiplodia theobromae | AM2As | GCA_012971845.1 |
| 29 | Lasiodiplodia theobromae | CBS 164.96 | GCA_009829825.1 |
| 30 | Lasiodiplodia theobromae | A20-4 | GCA_018153875.1 |
| 31 | Macrophomina phaseolina | mp053 | GCA_020875535.1 |
| 32 | Macrophomina phaseolina | CBS 205.47 | GCA_022204945.1 |
| 33 | Macrophomina phaseolina | mp102 | GCA_020875235.1 |
| 34 | Macrophomina phaseolina | Al-1 | GCA_008729065.1 |
| 35 | Macrophomina pseudophaseolina | WAC 2767 | GCA_022204955.1 |
| 36 | Macrophomina tecta | BRIP 70781 | GCA_024180945.1 |
| 37 | Neofusicoccum cordaticola | CBS 123638 | GCA_009830905.1 |
| 38 | Neofusicoccum cordaticola | CBS 123634 | GCA_009829355.1 |
| 39 | Neofusicoccum kwambonambiense | CBS 123642 | GCA_009829855.1 |
| 40 | Neofusicoccum kwambonambiense | CBS 123639 | GCA_009829845.1 |
| 41 | Neofusicoccum laricinum | HLJ-QQHE-4 | GCA_029906385.1 |
| 42 | Neofusicoccum laricinum | MAFF 410183 | GCA_022609205.1 |
| 43 | Neofusicoccum parvum | DUCC19944 | GCA_020912385.1 |
| 44 | Neofusicoccum parvum | GX.1 | GCA_029169195.1 |
| 45 | Neofusicoccum parvum | SCCDJF01s | GCA_029169155.1 |
| 46 | Neofusicoccum parvum | AH.3.1.01s | GCA_029169165.1 |
| 47 | Neofusicoccum parvum | NSSI1 | GCA_030270365.1 |
| 48 | Neofusicoccum ribis | M1-105 | GCA_021436925.1 |
| 49 | Neofusicoccum ribis | CBS 121.26 | GCA_009829435.1 |
| 50 | Neofusicoccum ribis | CBS 115475 | GCA_009829445.1 |
| 51 | Neofusicoccum umdonicola | CBS 123644 | GCA_009829365.1 |
| 52 | Neoscytalidium dimidiatum | GCA_900092665.1 | |
| 53 | Scolecobasidium constrictum | UM 578 | GCA_000611715.1 |
| 54 | Verruconis gallopava | CBS 43764 | GCA_000836295.1 |
The peak list of spectra of Alanomyces manoharacharyi NFCCI 5738 indicating the protein profile (2–20 KD).
| Mass-to-Charge Ratio (m/z) | Signal-to-Noise Ratio (S/N) | Relative Intensity | Intensity | Area |
|---|---|---|---|---|
| 6159.890 | 6 | 1017 | 407 | 3152 |
| 6399.786 | 6 | 945 | 343 | 4008 |
| 6736.835 | 4 | 732 | 212 | 2959 |
| 6783.503 | 5 | 631 | 315 | 3820 |
| 6850.893 | 7 | 1115 | 384 | 4403 |
| 6863.095 | 5 | 508 | 278 | 4693 |
| 6885.070 | 4 | 1119 | 225 | 1951 |
| 13,628.300 | 3 | 2080 | 78.4 | 940 |
| 13,647.798 | 4 | 1819 | 93.8 | 1140 |
| 13,666.029 | 6 | 1736 | 137 | 2164 |
| 13,690.444 | 9 | 987 | 211 | 3387 |
| 13,703.697 | 11 | 1893 | 248 | 4071 |
| 13,725.278 | 10 | 1732 | 232 | 3510 |
| 13,744.521 | 7 | 1317 | 154 | 2397 |
| 13,763.051 | 6 | 2049 | 127 | 1642 |
| 13,781.868 | 5 | 1823 | 105 | 1228 |
| 13,799.257 | 5 | 942 | 115 | 2326 |
| 13,816.129 | 5 | 2717 | 111 | 1086 |
| 13,828.615 | 5 | 1979 | 109 | 1272 |
| 13,844.702 | 4 | 1338 | 96.2 | 1390 |
| 13,867.006 | 3 | 1868 | 78.7 | 969 |
| 13,876.685 | 3 | 1644 | 74.6 | 837 |
Summary of assembled genomes.
| Assembly | MaSurCa 4.0.5 | Megahit v1.2.9 | Spades v3.15.4 |
|---|---|---|---|
| Contigs (≥1000 bp) | 1756 | 946 | 264 |
| Contigs (≥5000 bp) | 1118 | 508 | 210 |
| Contigs (≥10,000 bp) | 850 | 383 | 189 |
| Contigs (≥25,000 bp) | 465 | 254 | 157 |
| Contigs (≥50,000 bp) | 189 | 175 | 130 |
| Largest contig | 188,634 | 705,157 | 1,303,379 |
| Total length MB | 35.11 | 35.11 | 35.55 |
| GC (%) | 50.17 | 50.20 | 50.01 |
| N50 | 44,101 | 179,965 | 408,258 |
| N75 | 22,880 | 70,317 | 184,668 |
| L50 | 238 | 62 | 28 |
| L75 | 509 | 140 | 60 |
Putative biosynthetic gene clusters (BGCs) coding for secondary metabolites in the strain Alanomyces manoharacharyi NFCCI 5738.
| Region | Type | From | To | Most Similar Known Cluster | Similarity |
|---|---|---|---|---|---|
| 1.1 | terpene | 524,885 | 549,944 | Unknown | |
| 1.2 | T1PKS | 609,421 | 655,601 | Unknown | |
| 3.1 | T1PKS | 364,877 | 412,826 | Unknown | |
| 3.2 | beta lactone | 788,545 | 819,430 | Unknown | |
| 4.1 | terpene | 542,906 | 564,883 | Unknown | |
| 6.1 | T1PKS | 327,748 | 374,361 | 1,3,6,8-tetrahydroxynaphthalene | 100% |
| 9.1 | NRPS | 1 | 37,540 | Unknown | |
| 11.1 | NRPS | 125,072 | 182,954 | Unknown | |
| 13.1 | NRPS-like | 475,006 | 519,581 | Unknown | |
| 14.1 | terpene | 383,448 | 404,640 | aspterric acid | 100% |
| 17.1 | NRPS-like | 224,629 | 266,991 | Unknown | |
| 21.1 | fungal-RiPP-like | 102,085 | 163,886 | Unknown | |
| 23.1 | NRPS | 7288 | 59,760 | metachelin C/metachelin A/metachelin A-CE/metachelin B/dimerumic acid 11-mannoside/dimerumic acid | 25% |
| 27.1 | NRPS | 267,137 | 313,758 | chaetocin | 26% |
| 31.1 | T1PKS, indole | 334,087 | 379,753 | viridicatumtoxin/previridicatumtoxin/5-hydroxyanthrotainin/8-O-desmethylanthrotainin | 27% |
| 37.1 | NRPS-like | 270,391 | 314,084 | Unknown | |
| 52.1 | T1PKS, NRPS | 119,781 | 174,132 | phomasetin | 28% |
| 59.1 | NRPS-like | 15,287 | 58,424 | Unknown | |
| 65.1 | T1PKS | 6392 | 51,803 | (-)-Mellein | 100% |
| 67.1 | T1PKS | 52,155 | 97,544 | cryptosporioptide B/cryptosporioptide A/cryptosporioptide C | 15% |
| 78.1 | NRPS | 52,473 | 99,306 | metachelin C/metachelin A/metachelin A-CE/metachelin B/dimerumic acid 11-mannoside/dimerumic acid | 25% |
| 79.1 | NRPS-like | 42,248 | 85,160 | Biotin | 66% |
| 92.1 | NRPS | 1 | 66,959 | Unknown | |
| 94.1 | NRPS-like | 58,224 | 93,374 | Unknown | |
| 96.1 | NRPS | 227 | 69,345 | Unknown | |
| 141.1 | T1PKS | 1 | 36,822 | Patulin | 26% |
List of the metabolites along with their classes identified from the methanolic extract of biomass and the ethyl acetate extract of the filtrate Alanomyces manoharacharyi NFCCI 5738 using LC–MS in positive and negative ion mode.
| Compound Name | Class |
|---|---|
| Aspartate semialdehyde | Aldehydes: Amino Acid Metabolite |
| N-Acetyl-D-leucine | Amino Acid Derivative |
| PG(20:0/25:0) | Lipids: |
| Dihydroaltersolanol and Dihydroaltersolanol C | Secondary Metabolites: tetrahydroanthraquinone |
| Propericiazine (oxide) | Secondary metabolites: Phenothiazine |
| 2-trans,6-trans-farnesyl diphosphate | Lipids: Terpene Precursor |
| Mooreamide A | Peptide |
| 5-Dihydroergosterol | Lipids: Sterol |
| Mycocerosic acid (C28) | Lipids: Fatty Acid |
| Aspergiolide B | Secondary metabolites: Anthraquinones |
| Alpha-Carotene | Lipids: Carotenoid |
| PE-NMe2(10:0/12:0) | Lipids: Phospholipid |
| DG(15:0/20:0/0:0) | Lipids: Diacylglycerol |
| Flavocristamide A | Peptide |
| N-(24-Hydroxytetracosanoyl)phytosphingosine | Lipids: Sphingolipid |
| 5-Sulfosalicylic acid | Organic Acid |
| Penicoffrazin B/C | Hydroxybenzoic acid derivatives: Isocoumarins |
| Feruloylcholine | Phenolic Compound |
| 7,10-dihydroxydeacetyldihydrobotrydial-1(10)-ene | Botrydial sesquiterpenoids |
| Gregatin G1 and Gregatin G2 | Secondary metabolites: Polyketide |
| coscinolactam D | Secondary metabolites: Sesterterpenoids |
| Photopiperazine A | Cyclodipeptides: Diketopiperazines |
| 17-hydroxy-glaciapyrrole B | Secondary metabolites: Pyrrolosesquiterpenes |
| clarhamnoside or LLG-1 | Glycoside |
| GlcNAcbeta1-3Galbeta1-4(Fucalpha1-3)GlcNAcbeta1… | Carbohydrates: Oligosaccharide |
| Pregnane-3,6,20-triol, (3alpha,5beta,6alpha,20S) | Steroid |
| Chaetochiversin A | Peptide |
| aplyviolene | Secondary metabolites: Diterpene |
| Thiomuracin E | Thiopeptides: Antibiotic |
| Cer(d22:0/39:0) | Lipids: Ceramide |
| GlcNAc (N-acetylglucosamine) | Carbohydrates: Monosaccharide |
| CL(1′-[16:0/18:0],3′-[18:2(9Z,12Z)/20:4(5Z,8Z,1… | Lipids: Cardiolipin |
| Anvilone B | Secondary metabolites: Sesterterpenoids |
| Oscillatoxin b1/b2 | Toxin |
| DG(16:1(9Z)/18:0/0:0) | Lipids: Diacylglycerol |
| TG(10:0/10:0/14:1(9Z)) | Lipids: Triglyceride |
| DG(20:0/20:1(13Z)/0:0) | Lipids: Diacylglycerol |
| Maltulose | Carbohydrates: Disaccharide |
| Nagelamide R/Z | Peptide |
| Sarcohydroquinone sulfate B | Secondary metabolites: Disulfate heptaprenyl hydroquinone |
Supplementary Materials
The following supporting information can be downloaded at:
References
1. Nthuku, B.M.; Kahariri, E.W.; Kinyua, J.K.; Nyaboga, E.N. Fungal endophytes of Moringa (Moringa oleifera L.), Neem (Azadirachta indica) and Lavender (Lavandula angustifolia) and their biological control of Fusarium wilt of Banana. Microbiol. Res.; 2023; 14, pp. 2113-2132. [DOI: https://dx.doi.org/10.3390/microbiolres14040143]
2. Verma, V.C.; Gond, S.K.; Kumar, A.; Kharwar, R.N.; Strobel, G. The endophytic mycoflora of bark, leaf, and stem tissues of Azadirachta indica A. Juss (neem) from Varanasi (India). Microb. Ecol.; 2007; 54, pp. 119-125. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17394041]
3. Sharma, D.; Pramanik, A.; Agrawal, P.K. Evaluation of bioactive secondary metabolites from endophytic fungus Pestalotiopsis neglecta BAB-5510 isolated from leaves of Cupressus torulosa D. Don. 3 Biotech; 2016; 6, 210. [DOI: https://dx.doi.org/10.1007/s13205-016-0518-3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28330281]
4. Umurhurhu, J.O.; Okezie, U.M.; Ngwoke, K.G. Evaluation of the antimicrobial and antiviral potentials of extracts of endophytic fungi from Azadirachta indica. GSC Biol. Pharm. Sci.; 2023; 23, pp. 061-066. [DOI: https://dx.doi.org/10.30574/gscbps.2023.23.1.0078]
5. Fulzele, V.A.; Qureshi, A.; Fulzele, A.A. Evaluation of antifungal potential of endophytic fungus Preussia isabellae isolated from Azadirachta indica. Int. J. Res. Biosci. Agric.; 2018; 3, pp. 150-153.
6. Chukwuemerie, O.L.; Ugochukwu, S.B.; Iloh, E.S.; Onwuzuluigbo, C.C.; Onyegbule, F.A.; Okoye, F.B.C. Toxicological analysis and antimalarial potentials of secondary metabolites of Curvularia lunata, an endophyte obtained from the leaves of Azadirachta indica. Afr. J. Pharm. Sci.; 2022; 2, pp. 80-91. [DOI: https://dx.doi.org/10.51483/AFJPS.2.2.2022.80-91]
7. Li, G.H.; Yu, Z.F.; Li, X.; Wang, X.B.; Zheng, L.J.; Zhang, K.Q. Nematicidal metabolites produced by the endophytic fungus Geotrichum sp. AL4. Chem. Biodivers.; 2007; 4, pp. 1520-1524.
8. Slippers, B.; Boissin, E.; Phillips, A.J.; Groenewald, J.Z.; Lombard, L.; Wingfield, M.J.; Postma, A.; Burgess, T.; Crous, P.W. Phylogenetic lineages in the Botryosphaeriales: A systematic and evolutionary framework. Stud. Mycol.; 2013; 76, pp. 31-49. [DOI: https://dx.doi.org/10.3114/sim0020]
9. Agaricus bonaerensis Speg. Anal. Soc. Cient. Argent.; 1880; 9, 278.
10. Sharma, R.; Kulkarni, G.; Sonawane, M.S. Alanomyces, a new genus of Aplosporellaceae based on four loci phylogeny. Phytotaxa; 2017; 297, pp. 168-175. [DOI: https://dx.doi.org/10.11646/phytotaxa.297.2.4]
11. Damm, U.; Fourie, P.H.; Crous, P.W. Aplosporella prunicola, a novel species of anamorphic Botryosphaeriaceae. Fungal Divers.; 2007; 27, pp. 35-43.
12. Dou, Z.P.; Lu, M.; Wu, J.R.; He, W.; Zhang, Y. A new species and interesting records of Aplosporella from China. Sydowia; 2017; 69, pp. 1-7.
13. Dobranic, J.K.; Johnson, J.A.; Alikhan, Q.R. Isolation of endophytic fungi from eastern larch (Larix laricina) leaves from New Brunswick, Canada. Can. J. Microbiol.; 1995; 41, pp. 194-198. [DOI: https://dx.doi.org/10.1139/m95-026]
14. Bills, G.F.; Polishook, J.D. Recovery of endophytic fungi from Chamaecyparis thyoides. Sydowia; 1992; 44, pp. 1-12.
15. Kornerup, A.; Wanscher, J.H. Metheun’s Handbook of Colours; 3rd ed. Metheun and Co. Ltd.: London, UK, 1978.
16. Aamir, S.; Sutar, S.; Singh, S.K.; Baghela, A. A rapid and efficient method of fungal genomic DNA extraction, suitable for PCR based molecular methods. Plant Pathol. Quar. J. Fungal. Biolog.; 2015; 5, pp. 74-81. [DOI: https://dx.doi.org/10.5943/ppq/5/2/6]
17. White, T.J.; Bruns, T.; Lee, S.; Taylor, J.W. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. PCR Protocols: A Guide to Methods and Applications; 1st ed. Innis, M.A.; Gelfand, D.H.; Sninsky, J.J.; White, T.J. Academic Press: Cambridge, MA, USA, 1990; pp. 315-322.
18. Vilgalys, R.; Sun, B.L. Ancient and recent patterns of geographic speciation in the oyster mushroom Pleurotus revealed by phylogenetic analysis of ribosomal DNA-sequences. Proc. Natl. Acad. Sci. USA; 1994; 91, 7832. [DOI: https://dx.doi.org/10.1073/pnas.91.10.4599]
19. Vilgalys, R.; Hester, M. Rapid genetic identification and mapping of enzymatically amplified ribosomal DNA from several Cryptococcus species. J. Bacteriol.; 1990; 172, pp. 4238-4246. [DOI: https://dx.doi.org/10.1128/jb.172.8.4238-4246.1990]
20. Katoh, K.; Rozewicki, J.; Yamada, K.D. MAFFT online service: Multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform.; 2019; 20, pp. 1160-1166. [DOI: https://dx.doi.org/10.1093/bib/bbx108]
21. Larsson, A. AliView: A fast and lightweight alignment viewer and editor for large data sets. Bioinformatics; 2014; 30, pp. 3276-3278. [DOI: https://dx.doi.org/10.1093/bioinformatics/btu531]
22. Darriba, D.; Taboada, G.L.; Doallo, R.; Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Methods; 2012; 9, 772. [DOI: https://dx.doi.org/10.1038/nmeth.2109]
23. Nguyen, L.; Schmidt, H.A.; von Haeseler, A.; Minh, B.Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol.; 2015; 32, pp. 268-274. [DOI: https://dx.doi.org/10.1093/molbev/msu300] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25371430]
24. Ewels, P.; Magnusson, M.; Lundin, S.; Käller, M. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics; 2016; 32, pp. 3047-3048. [DOI: https://dx.doi.org/10.1093/bioinformatics/btw354] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27312411]
25. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics; 2018; 34, pp. i884-i890. [DOI: https://dx.doi.org/10.1093/bioinformatics/bty560]
26. Li, D.; Liu, C.M.; Luo, R.; Sadakane, K.; Lam, T.W. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics; 2015; 31, pp. 1674-1676. [DOI: https://dx.doi.org/10.1093/bioinformatics/btv033]
27. Bankevich, A.; Nurk, S.; Antipov, D.; Gurevich, A.A.; Dvorkin, M.; Kulikov, A.S.; Lesin, V.M.; Nikolenko, S.I.; Pham, S.; Prjibelski, A.D. et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol.; 2012; 19, pp. 455-477. [DOI: https://dx.doi.org/10.1089/cmb.2012.0021]
28. Zimin, A.V.; Marçais, G.; Puiu, D.; Roberts, M.; Salzberg, S.L.; Yorke, J.A. The MaSuRCA genome assembler. Bioinformatics; 2013; 29, pp. 2669-2677. [DOI: https://dx.doi.org/10.1093/bioinformatics/btt476] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23990416]
29. Gurevich, A.; Saveliev, V.; Vyahhi, N.; Tesler, G. QUAST: Quality assessment tool for genome assemblies. Bioinformatics; 2013; 29, pp. 1072-1075. [DOI: https://dx.doi.org/10.1093/bioinformatics/btt086] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23422339]
30. Langmead, B.; Salzberg, S. Fast gapped-read alignment with Bowtie 2. Nat. Methods; 2012; 9, pp. 357-359. [DOI: https://dx.doi.org/10.1038/nmeth.1923] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22388286]
31. Waterhouse, R.M.; Seppey, M.; Simão, F.A.; Manni, M.; Ioannidis, P.; Klioutchnikov, G.; Kriventseva, E.V.; Zdobnov, E.M. BUSCO Applications from Quality Assessments to Gene Prediction and Phylogenomics. Mol. Bio. Evol.; 2018; 35, pp. 543-548. [DOI: https://dx.doi.org/10.1093/molbev/msx319]
32. Krzywinski, M.; Schein, J.; Birol, I.; Connors, J.; Gascoyne, R.; Horsman, D.; Jones, S.J.; Marra, M.A. Circos: An information aesthetic for comparative genomics. Genome Res.; 2009; 19, pp. 1639-1645. [DOI: https://dx.doi.org/10.1101/gr.092759.109]
33. Stanke, M.; Waack, S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics; 2003; 19, pp. 215-225. [DOI: https://dx.doi.org/10.1093/bioinformatics/btg1080] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/14534192]
34. Stanke, M.; Diekhans, M.; Baertsch, R.; Haussler, D. Using native and syntenically mapped cDNA alignments to improve gene finding. Bioinformatics; 2008; 24, pp. 637-644. [DOI: https://dx.doi.org/10.1093/bioinformatics/btn013] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18218656]
35. Keller, O.; Kollmar, M.; Stanke, M.; Waack, S. A novel hybrid gene prediction method employing protein multiple sequence alignments. Bioinformatics; 2011; 27, pp. 757-763. [DOI: https://dx.doi.org/10.1093/bioinformatics/btr010]
36. Keilwagen, J.; Hartung, F.; Grau, J. GeMoMa: Homology-based gene prediction utilizing intron position conservation and RNA-seq data. Methods Mol. Biol.; 2019; 1962, pp. 161-177.
37. Blin, K.; Shaw, S.; Augustijn, H.E.; Reitz, Z.L.; Biermann, F.; Alanjary, M.; Fetter, A.; Terlouw, B.R.; Metcalf, W.W.; Helfrich, E.J.N. et al. AntiSMASH 7.0: New and improved predictions for detection, regulation, chemical structures and visualisation. Nucleic Acids Res.; 2023; 51, pp. W46-W50. [DOI: https://dx.doi.org/10.1093/nar/gkad344]
38. Emms, D.M.; Kelly, S. OrthoFinder: Phylogenetic orthology inference for comparative genomics. Genome Biol.; 2019; 20, 238. [DOI: https://dx.doi.org/10.1186/s13059-019-1832-y] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31727128]
39. Letunic, I.; Bork, P. Interactive Tree of Life (iTOL) v6: Recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res.; 2024; 52, gkae268. [DOI: https://dx.doi.org/10.1093/nar/gkae268]
40. Pritchard, L.; Glover, R.H.; Humphris, S.; Elphinstone, J.G.; Toth, I.K. Genomics and taxonomy in diagnostics for food security: Softrotting enterobacterial plant pathogens. Anal. Methods; 2016; 8, pp. 12-24. [DOI: https://dx.doi.org/10.1039/C5AY02550H]
41. Laing, C.; Buchanan, C.; Taboada, E.N.; Zhang, Y.; Kropinski, A.; Villegas, A.; Thomas, J.E.; Gannon, V.P. Pan-genome sequence analysis using Panseq: An online tool for the rapid analysis of core and accessory genomic regions. BMC Bioinform.; 2010; 11, 461. [DOI: https://dx.doi.org/10.1186/1471-2105-11-461]
42. Armenteros, J.J.; Tsirigos, K.D.; Sønderby, C.K.; Petersen, T.N.; Winther, O.; Brunak, S.; von Heijne, G.; Nielsen, H. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol.; 2019; 37, pp. 420-423. [DOI: https://dx.doi.org/10.1038/s41587-019-0036-z]
43. Armenteros, J.J.; Salvatore, M.; Emanuelsson, O.; Winther, O.; von Heijne, G.; Elofsson, A.; Nielsen, H. Detecting sequence signals in targeting peptides using deep learning. Life Sci. Alliance; 2019; 2, e201900429. [DOI: https://dx.doi.org/10.26508/lsa.201900429] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31570514]
44. Krogh, A.; Larsson, B.; Von Heijne, G.; Sonnhammer, E.L. Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes. J. Mol. Biol.; 2001; 305, pp. 567-580. [DOI: https://dx.doi.org/10.1006/jmbi.2000.4315] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11152613]
45. Natale, D.A.; Shankavaram, U.T.; Galperin, M.Y.; Wolf, Y.I.; Aravind, L.; Koonin, E.V. Towards understanding the first genome sequence of a crenarchaeon by genome annotation using clusters of orthologous groups of proteins (COGs). Genome Biol.; 2000; 1, RESEARCH0009. [DOI: https://dx.doi.org/10.1186/gb-2000-1-5-research0009] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11178258]
46. Yamada, Y.; Hirakawa, H.; Hori, K.; Minakuchi, Y.; Toyoda, A.; Shitan, N.; Sato, F. Comparative analysis using the draft genome sequence of California poppy (Eschscholzia californica) for exploring the candidate genes involved in benzylisoquinoline alkaloid biosynthesis. Biosci. Biotechnol. Biochem.; 2021; 85, pp. 851-859. [DOI: https://dx.doi.org/10.1093/bbb/zbaa091]
47. Huntley, R.P.; Harris, M.A.; Alam-Faruque, Y.; Blake, J.A.; Carbon, S.; Dietze, H.; Dimmer, E.C.; Foulger, R.E.; Hill, D.P.; Khodiyar, V.K. et al. A method for increasing expressivity of Gene Ontology annotations using a compositional approach. BMC Bioinform.; 2014; 15, 155. [DOI: https://dx.doi.org/10.1186/1471-2105-15-155]
48. Garron, M.L.; Henrissat, B. The continuing expansion of CAZymes and their families. Curr. Opin. Chem. Biol.; 2019; 53, pp. 82-87. [DOI: https://dx.doi.org/10.1016/j.cbpa.2019.08.004]
49. Liu, Y.; Xu, M.; Tang, Y.; Shao, Y.; Wang, H.; Zhang, H. Genome Features and AntiSMASH Analysis of an Endophytic Strain Fusarium sp. R1. Metabolites; 2022; 12, 521. [DOI: https://dx.doi.org/10.3390/metabo12060521]
50. Urban, M.; Cuzick, A.; Seager, J.; Wood, V.; Rutherford, K.; Venkatesh, S.Y.; Sahu, J.; Iyer, S.V.; Khamari, L.; De Silva, N. et al. PHI-base in 2022: A multi-species phenotype database for pathogen-host interactions. Nucleic Acids Res.; 2022; 50, pp. D837-D847. [DOI: https://dx.doi.org/10.1093/nar/gkab1037]
51. Pernas, L. Cellular metabolism in the defense against microbes. J. Cell Sci.; 2021; 134, jcs252023. [DOI: https://dx.doi.org/10.1242/jcs.252023]
52. Zhang, A.; Lu, P.; Dahl-Roshak, A.M.; Paress, P.S.; Kennedy, S.; Tkacz, J.S.; An, Z. Efficient disruption of a polyketide synthase gene (pks1) required for melanin synthesis through Agrobacterium-mediated transformation of Glarea lozoyensis. Mol. Genet. Genom. MGG; 2003; 268, pp. 645-655. [DOI: https://dx.doi.org/10.1007/s00438-002-0780-4]
53. Yan, Y.; Liu, Q.; Zang, X.; Yuan, S.; Bat-Erdene, U.; Nguyen, C.; Gan, J.; Zhou, J.; Jacobsen, S.E.; Tang, Y. Resistance-gene-directed discovery of a natural-product herbicide with a new mode of action. Nature; 2018; 559, pp. 415-418. [DOI: https://dx.doi.org/10.1038/s41586-018-0319-4]
54. Chooi, Y.H.; Krill, C.; Barrow, R.A.; Chen, S.; Trengove, R.; Oliver, R.P.; Solomon, P.S. An in planta-expressed polyketide synthase produces (R)-mellein in the wheat pathogen Parastagonospora nodorum. Appl. Environ. Microbiol.; 2015; 81, pp. 177-186. [DOI: https://dx.doi.org/10.1128/AEM.02745-14] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25326302]
55. Mitaka, Y.; Mori, N.; Matsuura, K. A termite fungistatic compound, mellein, inhibits entomopathogenic fungi but not egg-mimicking termite ball fungi. Appl. Entomol. Zool.; 2019; 54, pp. 39-46. [DOI: https://dx.doi.org/10.1007/s13355-018-0589-1]
56. Mattoon, E.R.; Cordero, R.J.B.; Casadevall, A. Fungal Melanins and Applications in Healthcare, Bioremediation and Industry. J. Fungi; 2021; 7, 488. [DOI: https://dx.doi.org/10.3390/jof7060488] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34207260]
57. Shimada, A.; Kusano, M.; Takeuchi, S.; Fujioka, S.; Inokuchi, T.; Kimura, Y. Aspterric acid and 6-hydroxymellein, inhibitors of pollen development in Arabidopsis thaliana, produced by Aspergillus terreus. Z. Naturforsch. C. J. Biosci.; 2002; 57, pp. 459-464. [DOI: https://dx.doi.org/10.1515/znc-2002-5-610] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12132685]
58. Patel, D.P.; Swink, S.M.; Castelo-Soccio, L. A Review of the Use of Biotin for Hair Loss. Skin Appendage Disord.; 2017; 3, pp. 166-169. [DOI: https://dx.doi.org/10.1159/000462981]
59. Jiang, H.; Li, Y.; Xiang, X.; Tang, Z.; Liu, K.; Su, Q.; Zhang, X.; Li, L. Chaetocin: A review of its anticancer potentials and mechanisms. Eur. J. Pharmacol.; 2021; 910, 174459. [DOI: https://dx.doi.org/10.1016/j.ejphar.2021.174459]
60. Saleem, M.; Tousif, M.I.; Riaz, N.; Ahmed, I.; Schulz, B.; Ashraf, M.; Nasar, R.; Pescitelli, G.; Hussain, H.; Jabbar, A. et al. Cryptosporioptide: A bioactive polyketide produced by an endophytic fungus Cryptosporiopsis sp. Phytochemistry; 2013; 93, pp. 199-202. [DOI: https://dx.doi.org/10.1016/j.phytochem.2013.03.018]
61. Bacha, S.A.S.; Li, Y.; Nie, J.; Xu, G.; Han, L.; Farooq, S. Comprehensive review on patulin and Alternaria toxins in fruit and derived products. Front. Plant Sci.; 2023; 14, 1139757. [DOI: https://dx.doi.org/10.3389/fpls.2023.1139757]
62. Teakel, S.L.; Fairman, J.W.; Muruthi, M.M.; Abendroth, J.; Dranow, D.M.; Lorimer, D.D.; Myler, P.J.; Edwards, T.E.; Forwood, J.K. Structural characterization of aspartate-semialdehyde dehydrogenase from Pseudomonas aeruginosa and Neisseria gonorrhoeae. Sci. Rep.; 2022; 12, 14010. [DOI: https://dx.doi.org/10.1038/s41598-022-17384-9]
63. Neuzil, E.; Ravaine, S.; Cousse, H. La N-acétyl-DL-leucine, médicament symptomatique de vertigineux. Bull. Soc. Pharm. Bordx.; 2002; 141, pp. 15-38.
64. Sarkar, C.; Lipinski, M.M. N-acetyl-L-leucine: A promising treatment option for traumatic brain injury. Neural Regen Res.; 2022; 17, pp. 1957-1958. [DOI: https://dx.doi.org/10.4103/1673-5374.335146] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35142673]
65. Liu, Y.; Marmann, A.; Abdel-Aziz, M.S.; Wang, C.Y.; Müller, W.E.; Lin, W.H.; Mándi, A.; Kurtán, T.; Daletos, G.; Proksch, P. Tetrahydroanthraquinone derivatives from the endophytic fungus Stemphylium globuliferum. Eur. J. Org. Chem.; 2015; 12, pp. 2646-2653. [DOI: https://dx.doi.org/10.1002/ejoc.201500079]
66. Ikeda, H.; Tsuji, E.; Matsubara, T.; Yukawa, M.; Fujisawa, M.; Yukawa, E.; Aki, H. Incompatibility between propericiazine oral solution and tea-based drink. Chem. Pharm. Bull.; 2012; 60, pp. 1207-1211. [DOI: https://dx.doi.org/10.1248/cpb.c12-00116]
67. Tan, L.T.; Phyo, M.Y. Marine Cyanobacteria: A Source of Lead Compounds and their Clinically-Relevant Molecular Targets. Molecules; 2020; 25, 2197. [DOI: https://dx.doi.org/10.3390/molecules25092197]
68. Du, L.; Zhu, T.; Fang, Y.; Liu, H.; Gu, Q.; Zhu, W. Aspergiolide A, a novel anthraquinone derivative with naphtho[1,2,3-de]chromene-2,7-dione skeleton isolated from a marine-derived fungus Aspergillus glaucus. Tetrahedron; 2007; 63, pp. 1085-1088. [DOI: https://dx.doi.org/10.1016/j.tet.2006.11.074]
69. Sun, X.; Zhou, X.; Cai, M.; Tao, K.; Zhang, Y. Identified biosynthetic pathway of aspergiolide A and a novel strategy to increase its production in a marine-derived fungus Aspergillus glaucus by feeding of biosynthetic precursors and inhibitors simultaneously. Bioresour. Technol.; 2009; 100, pp. 4244-4251. [DOI: https://dx.doi.org/10.1016/j.biortech.2009.03.061]
70. Tanumihardjo, S.A. Carotenoids: Health Effects. Encyclopedia of Human Nutrition; 3rd ed. Caballero, B. Academic Press: Cambridge, MA, USA, 2013; pp. 292-297.
71. Venkataramana, H.S.; Puttaswamy, N.; Kodimule, S. Potential benefits of oral administration of Amorphophallus Konjac glycosylceramides on skin health-a randomized clinical study. BMC Complement. Med. Ther.; 2020; 20, 26. [DOI: https://dx.doi.org/10.1186/s12906-019-2721-3]
72. Trefely, S.; Doan, M.T.; Snyder, N.W. Chapter One-Crosstalk between cellular metabolism and histone acetylation. Methods in Enzymology; 1st ed. Garcia, B.A. Academic Press: Cambridge, MA, USA, 2019; Volume 626, pp. 1-21.
73. Beenker, W.A.G.; Hoeksma, J.; den Hertog, J. Gregatins, a Group of Related Fungal Secondary Metabolites, Inhibit Aspects of Quorum Sensing in Gram-Negative Bacteria. Front. Microbiol.; 2022; 13, 934235. [DOI: https://dx.doi.org/10.3389/fmicb.2022.934235]
74. Marino, S.D.; Festa, C.; D’Auria, M.V.; Bourguet-Kondracki, M.; Petek, S.; Debitus, C.; Andrés, R.M.; Terencio, M.C.; Payá, M.; Zampella, A. Coscinolactams A and B: New nitrogen-containing sesterterpenoids from the marine sponge Coscinoderma mathewsi exerting anti-inflammatory properties. Tetrahedron; 2009; 65, pp. 2905-2909. [DOI: https://dx.doi.org/10.1016/j.tet.2009.02.016]
75. Kim, M.C.; Cullum, R.; Machado, H.; Smith, A.J.; Yang, I.; Rodvold, J.J.; Fenical, W. Photopiperazines A-D, Photosensitive Interconverting Diketopiperazines with Significant and Selective Activity against U87 Glioblastoma Cells, from a Rare, Marine-Derived Actinomycete of the Family Streptomycetaceae. J. Nat. Prod.; 2019; 82, pp. 2262-2267. [DOI: https://dx.doi.org/10.1021/acs.jnatprod.9b00429] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31368305]
76. Hudson, G.A.; Zhang, Z.; Tietz, J.I.; Mitchell, D.A.; Donk, W.A. In Vitro Biosynthesis of the Core Scaffold of the Thiopeptide Thiomuracin. J. Am. Chem. Soc.; 2015; 137, pp. 16012-16015. [DOI: https://dx.doi.org/10.1021/jacs.5b10194]
77. Kubomura, D.; Ueno, T.; Yamada, M.; Tomonaga, A.; Nagaoka, I. Effect of N-acetylglucosamine administration on cartilage metabolism and safety in healthy subjects without symptoms of arthritis: A case report. Exp. Ther. Med.; 2017; 13, pp. 1614-1621. [DOI: https://dx.doi.org/10.3892/etm.2017.4140] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28413518]
78. Nagai, H.; Sato, S.; Iida, K.; Hayashi, K.; Kawaguchi, M.; Uchida, H.; Satake, M. Oscillatoxin I: A New Aplysiatoxin Derivative, from a Marine Cyanobacterium. Toxins; 2019; 11, 366. [DOI: https://dx.doi.org/10.3390/toxins11060366] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31234410]
79. Ghadimi, H.; Ghani, S.A.; Amiri, I.S. Chapter 1–Introduction. Electrochemistry of Dihydroxybenzene Compounds; 1st ed. Ghadimi, H.; Ghani, S.; Amiri, I.S. Elsevier: Amsterdam, The Netherlands, 2017; pp. 1-30.
80. Cao, J.; Li, X.M.; Li, X.; Li, H.L.; Meng, L.H.; Wang, B.G. New lactone and isocoumarin derivatives from the marine mangrove-derived endophytic fungus Penicillium coffeae MA-314. Phytochem. Lett.; 2019; 32, pp. 1-5. [DOI: https://dx.doi.org/10.1016/j.phytol.2019.04.018]
81. Iacovelli, R.; He, T.; Allen, J.L.; Hackl, T.; Haslinger, K. Genome sequencing and molecular networking analysis of the wild fungus Anthostomella pinea reveal its ability to produce a diverse range of secondary metabolites. Fungal Biol. Biotechnol.; 2024; 11, 1. [DOI: https://dx.doi.org/10.1186/s40694-023-00170-1]
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