Discovery of the molecular mechanisms of the novel chalcone-based Magnaporthe oryzae inhibitor C1 using transcriptomic profiling and co-expression network analysis
© The Author(s) 2016
Received: 24 May 2016
Accepted: 26 September 2016
Published: 22 October 2016
In our previous studies, we discovered a series of chalcone-based phytopathogenic fungus inhibitors. However, knowledge of their effects, detailed targets and molecular mechanisms in Magnaporthe oryzae (M. oryzae) remained limited.
To explore the expression and function of differentially expressed genes in M. oryzae after treatment with compound C1, we analyzed the expression profile of mRNAs using a microarray analysis and GO, KEGG and WGCNA analysis, followed by qRT-PCR and Western blots to validate our findings.
A total of 1013 up-regulated and 995 down-regulated mRNAs were differentially expressed after M. oryzae was treated with C1 compared to those of the control samples. Among these, cytochrome P450, glycylpeptide N-myristoyltransferase (NMT) and peroxisomal membrane protein 4 were identified as the most significant DEGs and were validated by experiments.
In conclusion, our study suggests that the combination of transcriptomic microarray, bioinformatics analysis and weighted gene co-expression networks can be used to predict potential therapeutic targets and to map the pathways regulated by small molecular natural product-like drugs.
Fungal infections are one of the most important phytopathogens that affects agricultural output (Lu et al. 2014; Moghaddam et al. 2015; Moreira et al. 2015). Some fungicides have been used to control these diseases and protect livestock, but their use has led to toxic chemical accumulation in the environment, causing servere environmental and public health problems (Kim et al. 2014; Wang et al. 2015; Wu et al. 2014; Lopez et al. 2006, 2011; Svetaz et al. 2007). Of the phytopathogenic fungi, Magnaporthe oryzae is one of the most common rice blast pathogens; M. oryzae is resilient to environmental stresses such as changes in nutrients, illumination and temperature (Duan et al. 2014; Hao et al. 2012a, b). M. oryzae has gradually become resistant to existing fungicides; consequently, it is essential to discover novel, environmentally friendly compounds with high antifungal activity and clear molecular mechanisms of action (Xu et al. 2015; Dong et al. 2015; Wang et al. 2014; Chen et al. 2013).
In vitro antifungal assay
The antifungal activity of compound C1 against B. maydis, S. scleotiorum, M. orzae, G. zeae and B. cintrea was investigated by measuring the inhibitory effects of radial growth in a petri dish with agar medium. Briefly, compound C1 was dissolved in DMSO, diluted with a 1 % Tween solution to the appropriate concentration and added to a glucose/potato/water agar medium to reach the final concentration. The medium was then poured into 9.0 cm diameter sterile Petri dishes, and a mycelial disk (0.5-cm diameter) cut from the growing culture was placed in the center of the agar plate. The inhibitory effects of C1 on radial growth was measured after several days. Inhibitory growth ratios were calculated as the percentage of the inhibition of radial growth relative to the control group.
RNA isolation and labeling
Total mRNA was extracted from M. orzae samples using TRIzol, according to the manufacturer’s instructions. A NanoDrop ND-2000 was used to assess the concentration and quality of RNA before the extracted RNA was denatured for agarose gel electrophoresis. The purified RNA was amplified and transcribed into fluorescent cRNA after the removal of rRNA according to Agilent’s Quick Amp Labeling protocol.
Microarray analysis was performed by Boao Bio-tech, Beijing, China. Briefly, the labeled cRNAs were hybridized onto the 4 × 44 K Agilent Microarray (Arraystar, Rockville, MD) at 65 °C for 17 h. An Agilent Microarray Scanner G2565BA was used to collect the hybridization images. The transcriptomic data extraction and analysis were performed using the Agilent Feature Extraction package and GeneSpring GX v11.5.1 software, respectively.
The compound C1 was submitted to various web-based inverse docking servers: TarFisDock (Gao et al. 2008; Li et al. 2006), DRAR-CPI (Luo et al. 2011), and PharmMapper (Wang et al. 2016; Liu et al. 2010). These web servers selected the known target proteins within their collections to profile the scaffolds that had potential binding affinity.
The Target Fishing Dock (TarFisDock) identified the potential target proteins of submitted molecules in the Potential Drug Target Database using the DOCK 4.0 molecular docking program (Kang et al. 2004; Ewing et al. 2001); only the top 2 % of potential targets were considered for further study. The PharmMapper server is a structure-based pharmacophore approach that accelerates the screening of putative binding targets for small molecular drugs (Wang et al. 2016). The DRAR-CPI server predicts adverse drug reactions and therapeutic indications for small molecular drugs based on the interaction profile of molecules towards their targets (Iyer et al. 2015; Chen 2014).
GO & KEGG enrichment analysis
Gene Ontology (GO) analysis is an annotation set including gene descriptions and gene product attributes for many organisms (http://www.geneontology.org) (Chicco and Masseroli 2016; Anney et al. 2011; Harris et al. 2004; Ashburner et al. 2000). Gene ontology has three components: cellular components, biological processes and molecular functions. The overlaps between the lists of DEGs were detected by Fisher’s exact test. P-value denotes the significance of a GO term enrichment in DEGs clusters and/or pathway correlations (P-value < 0.05 was considered significant). In addition, the pathway enrichment was used to map DEGs into KEGG pathways (Ogata et al. 1999; Ogata et al. 1998).
Weighted gene co-expression network analysis (WGCNA)
WGCNA is a statistical tool to cluster genes that have a similar expression pattern across a group of samples (Malki et al. 2013; DiLeo et al. 2011; Langfelder and Horvath 2008). The input data for the WGCNA were the normalized gene expression values for each sample. First, all available samples from each groups were collected to identify modules that had different expression patterns. Next, a soft threshold was assigned to create networks with a scale free topology, using the method developed by Horvath et al. After the networks were built, many gene modules with similar expression patterns were created, and the eigengenes of these modules were calculated. Finally, correlations between these eigengenes and the factor of interest were calculated.
Quantitative real-time PCR (qRT-PCR)
Total mRNA was extracted from M. orzae using TRIzol (Invitrogen), according to the manufacturer’s protocols. mRNAs were then converted into cDNA using a Fermentas RT kit. qRT-PCR was performed in a total reaction volume of 25 μL (including 12.5 μL of SYBR Premix Ex Taq (2×), 2 μL of cDNA, 1 μL of forward primer (10 μM), 1 μL of reverse primer (10 μM), 0.5 μL of ROX Reference Dye II (50×), and 8 μL of double-distilled water). The amplification conditions were as follows: 10 min at 95 °C to initiate denaturation; 40 cycles of 5 s at 95 °C, 30 s at 63 °C, and 30 s at 72 °C; and a final extension for 5 min at 72 °C. The amplification efficiency was evaluated using standard curve fitting. All samples were normalized to actin, and the experiment was performed with three duplicates.
Western blot analysis
Briefly, the total protein of C1-treated M. orzae was extracted with RIPA buffer (SolarBio, Beijing, China), which contained 1 % (v/v) PMSF (SolarBio), 0.3 % (v/v) protease inhibitor (Sigma, St. Louis, MO, USA) and 0.1 % (v/v) phosphorylated proteinase inhibitor (Sigma). Then, the supernatant was collected after centrifugation at 12,000 rpm for 10 min with refrigeration. The concentration of total protein was quantified using a BCA protein assay kit (Pierce, Waltham, MA, USA). The total protein was separated via standard SDS-PAGE gel electrophoresis and then transferred to PVDF membranes. The membranes were further treated with skimmed milk or BSA to block non-specific binding. The primary antibodies were added to PVDF membranes for two hours at room temperature or overnight at 4 °C. Finally, the primary antibodies bound to the membranes were incubated with HRP-conjugated secondary antibodies (Abmart, Shanghai, China). The target proteins were detected using an ECL kit (enhanced chemiluminescence kit, Millipore, Billerica, MA, USA) according to the manufacturer’s instructions.
Results and discussion
C1 efficiently inhibited a panel of phytopathogens
The in vitro antifungal results of compound C1 are shown in Fig. 1b, c. The results demonstrate that C1 showed variable degrees of antifungal activity against the tested phytopathogen fungi. The inhibitory ratio at 20 μg/mL clearly demonstrates that C1 exhibited 100 % inhibition against R. solani and an inhibition of 60–80 % against B. maydis, S. scleotiorum, M. orzae, G. zeae and B. cintrea. Moreover, the EC50 index of compound C1 for the tested fungi was in the range of 1.20–37.84 at 20 μg/mL, much lower than that of the positive control compound, carbendazim, on R. solani and M. orzae (Fig. 1c).
Inverse docking results
Differential expression of genes after C1 treatment determined by transcriptome microarray
Numbers of mRNAs differentially expressed after C1 treatment 24 h
Number of DEGs
The top GO biological process terms of DEGs
GO:0055114 (oxidation–reduction process)
GO:0044271 (cellular nitrogen compound biosynthetic process)
GO:0005375 (copper ion transmembrane transporter activity)
GO:0009103 (lipopolysaccharide biosynthetic process)
GO:0043581 (mycelium development)
GO:0000105 (histidine biosynthetic process)
The enriched KEGG pathways in the DEGs
The first level
Xenobiotics biodegradation and metabolism
Metabolism of terpenoids and polyketides
Amino acid metabolism
Transport and catabolism
Biosynthesis of other secondary metabolites
Metabolism of cofactors and vitamins
Amino acid metabolism
Weighted gene co-expression analysis
qRT-PCR and Western-blot confirmation
As a novel chalcone-based phytopathogenic fungi inhibitor, C1 has good potency in protecting against the infections of various pathogens. In the current study, we found that the compound C1 could efficiently inhibit the mycelium development of M. oryzae, one of the most important pathogenic fungi of rice. After performing reverse-docking using three different methods, we predicted the potential target proteins of compound C1. The results indicated the possibility that cytochrome p450, N-myristoyltransferase (NMT), β(1,3)-glucansynthase and chitin synthase interact with compound C1. Furthermore, we evaluated the differential expression of mRNAs between C1-treated and control samples of M. oryzae using microarray analysis. Among the 13,448 embedded genes, 2008 were significantly differentially expressed. Furthermore, cytochrome p450, N-myristoyltransferase (NMT) and peroxisomal membrane protein 4 (PXMP4) were significantly differentially expressed. Collectively, these findings showed that compound C1 influenced the expression of P450, NMT and PXMP4 in M. oryzae and that inhibited mycelium development and exerted oxidative stress via regulation of relative downstream gene expression. In line with these results, GO analysis revealed that the DEGs were mainly enriched for GO terms associated with the response to mycelium development and oxidation–reduction processes. The KEGG pathway analysis also indicated that metabolism-associated pathways, such as xenobiotics biodegradation metabolism pathways, were the most enriched pathways. The pathway enrichment results are consistent with those of the GO term analysis, supporting the notion that mycelium development is blocked at an early stage of metabolism inhibition. Based on our data from the GO and pathway analysis, we constructed a weighted gene co-expression network to further analyze the correlations of DEGs. It has been suggested that P450, NMT and PXMP4 may be hub genes and correlate with cell death and the regulation of metabolism induced by compound C1 treatment. In general, Cytochrome P450 genes (CYPs) were key heme-proteins in primary and secondary metabolism pathways and are responsible for most oxidative/reductive reactions in the xenobiotics metabolism (Hernandez-Martinez et al. 2016; Aung et al. 2014). CYPs could detoxified and transformed various xenobiotic compounds, e.g. CYPs could converted some aromatic hydrophobic xenobiotic chemicals into non-toxic and water-soluble less metabolites via the diverse xenobiotics degradation and metabolism processes. In addition, it has been reported that M. oryzae and other rice blast pathogenic fungus species could tolerated high concentrations nonpolar xenobiotic chemicals. Herein, we identified that cytochrome P450 genes that might be involved in the biodegradation of xenobiotic compounds of the host plants and in sterol biosynthesis and resistance to environmental stress. It had been well known that CYPs family was one of the most abundant and diverse in M. oryzae. Numbers of reports had suggested that members of CYPs family to lipopolysaccharide metabolism, drug resistance, and xenobiotics metabolism by the oxidation/reduction pathways (Hernandez-Martinez et al. 2016; Huang et al. 2014; Chen et al. 2014). The CYPs family had a number of isoforms that exhibit diverse conversion capacities towards long chain alkanes, fatty acids or related molecules with different structures. In particular, it was reported that CYP proteins such as CYP1A1 could maintained signals by its trans-membrane domains and then made them localized ER proteins (Cotman et al. 2004; Szczesna-Skorupa and Kemper 2001; Szczesna-Skorupa et al. 2003). On the contrary, when such signals is lacking, proteins are transported to other regions within the cell, as was the case for the human CYP1A1. Therefore, the peroxisomal CYPs might played important roles in the metabolism of xenobiotic compounds, biosynthesis of cholesterol and hydroxylation of lipopolysaccharide, etc. In previous studies, some studies also suggested mitochondrial P450 cytochromes could be stimulated by ER P450s (Hernandez-Martinez et al. 2016; Jung and Di Giulio 2010); moreover, further studies were demanded to better understand the role of the diverse biological functions of P450s in M. oryzae. To confirm the above analyses, the expression of mRNAs and proteins were detected using qRT-PCR and Western blotting, respectively. These experimental results were highly consistent with the microarray and bioinformatics analyses; taken together, our findings indicate that P450 and NMT are the direct target proteins of compound C1 and that PXMP4 plays an important role in the signaling transduction networks induced by C1.
In conclusion, our study suggests that the combination of transcriptomic microarray, bioinformatics analysis and weighted gene co-expression networks can be used to predict the potential targets and regulated pathways of small molecular natural product-like drugs. Moreover, we have shown that these high-throughput and computational results could be validated using various experimental methods. We have indicated an approach for profiling potential target and molecular mechanism in species with limited genomic and/or signaling pathway knowledge. We believe that this target profiling workflow can be helpful for identifying novel targets for therapeutics and for overcoming drug resistance to rare pathogens.
RL and TH conceived and designed the experiments; HC and XW performed the experiments; HC and HJ analyzed the data; HC, RL and TH wrote the paper. All authors read and approved the final manuscript.
This work is financially supported by the National Natural Science Foundation of China (81402245).
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Anney RJ, Kenny EM, O’Dushlaine C, Yaspan BL, Parkhomenka E, Buxbaum JD, Sutcliffe J, Gill M, Gallagher L, Autism Genome P et al (2011) Gene-ontology enrichment analysis in two independent family-based samples highlights biologically plausible processes for autism spectrum disorders. Eur J Hum Genet 19:1082–1089View ArticlePubMedPubMed CentralGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT et al (2000) Gene Ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet 25:25–29View ArticlePubMedPubMed CentralGoogle Scholar
- Aung AK, Haas DW, Hulgan T, Phillips EJ (2014) Pharmacogenomics of antimicrobial agents. Pharmacogenomics 15:1903–1930View ArticlePubMedPubMed CentralGoogle Scholar
- Boeck P, Leal PC, Yunes RA, Filho VC, Lopez S, Sortino M, Escalante A, Furlan RL, Zacchino S (2005) Antifungal activity and studies on mode of action of novel xanthoxyline-derived chalcones. Arch Pharm 338:87–95View ArticleGoogle Scholar
- Chen SJ (2014) A potential target of Tanshinone IIA for acute promyelocytic leukemia revealed by inverse docking and drug repurposing. Asian Pac J Cancer Prev 15:4301–4305View ArticlePubMedGoogle Scholar
- Chen C, Lian B, Hu J, Zhai H, Wang X, Venu RC, Liu E, Wang Z, Chen M, Wang B et al (2013) Genome comparison of two Magnaporthe oryzae field isolates reveals genome variations and potential virulence effectors. BMC Genom 14:887View ArticleGoogle Scholar
- Chen W, Lee MK, Jefcoate C, Kim SC, Chen F, Yu JH (2014) Fungal cytochrome p450 monooxygenases: their distribution, structure, functions, family expansion, and evolutionary origin. Genome Biol Evol 6:1620–1634View ArticlePubMedPubMed CentralGoogle Scholar
- Chicco D, Masseroli M (2016) Ontology-based prediction and prioritization of gene functional annotations. IEEE/ACM Trans Comput Biol Bioinform 13:248–260View ArticlePubMedGoogle Scholar
- Cotman M, Jezek D, Fon Tacer K, Frangez R, Rozman D (2004) A functional cytochrome P450 lanosterol 14 alpha-demethylase CYP51 enzyme in the acrosome: transport through the Golgi and synthesis of meiosis-activating sterols. Endocrinology 145:1419–1426View ArticlePubMedGoogle Scholar
- DiLeo MV, Strahan GD, den Bakker M, Hoekenga OA (2011) Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome. PLoS One 6:e26683ADSView ArticlePubMedPubMed CentralGoogle Scholar
- Dong Y, Li Y, Zhao M, Jing M, Liu X, Liu M, Guo X, Zhang X, Chen Y, Liu Y et al (2015) Global genome and transcriptome analyses of Magnaporthe oryzae epidemic isolate 98-06 uncover novel effectors and pathogenicity-related genes, revealing gene gain and lose dynamics in genome evolution. PLoS Pathog 11:e1004801View ArticlePubMedPubMed CentralGoogle Scholar
- Duan L, Liu H, Li X, Xiao J, Wang S (2014) Multiple phytohormones and phytoalexins are involved in disease resistance to Magnaporthe oryzae invaded from roots in rice. Physiol Plant 152:486–500View ArticlePubMedGoogle Scholar
- Ewing TJ, Makino S, Skillman AG, Kuntz ID (2001) DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 15:411–428ADSView ArticlePubMedGoogle Scholar
- Gao Z, Li H, Zhang H, Liu X, Kang L, Luo X, Zhu W, Chen K, Wang X, Jiang H (2008) PDTD: a web-accessible protein database for drug target identification. BMC Bioinform 9:104View ArticleGoogle Scholar
- Hao Z, Wang L, Huang F, Tao R (2012a) Expression of defense genes and antioxidant defense responses in rice resistance to neck blast at the preliminary heading stage and full heading stage. Plant Physiol Biochem 57:222–230View ArticlePubMedGoogle Scholar
- Hao Z, Wang L, Huang F, Tao R (2012b) Expression patterns of defense genes in resistance of the panicles exserted from the caulis and from the tillers to neck blast in rice. Plant Physiol Biochem 60:150–156View ArticlePubMedGoogle Scholar
- Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C et al (2004) The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 32:D258–D261View ArticlePubMedGoogle Scholar
- Hernandez-Martinez F, Briones-Roblero CI, Nelson DR, Rivera-Orduna FN, Zuniga G (2016) Cytochrome P450 complement (CYPome) of Candida oregonensis, a gut-associated yeast of bark beetle, Dendroctonus rhizophagus. Fungal Biol 120:1077–1089View ArticlePubMedGoogle Scholar
- Huang FC, Peter A, Schwab W (2014) Expression and characterization of CYP52 genes involved in the biosynthesis of sophorolipid and alkane metabolism from Starmerella bombicola. Appl Environ Microbiol 80:766–776View ArticlePubMedPubMed CentralGoogle Scholar
- Iyer P, Bolla J, Kumar V, Gill MS, Sobhia ME (2015) In silico identification of targets for a novel scaffold, 2-thiazolylimino-5-benzylidin-thiazolidin-4-one. Mol Divers 19:855–870View ArticlePubMedGoogle Scholar
- Jin H, Geng YC, Yu ZY, Tao K, Hou TP (2009) Lead optimization and anti-plant pathogenic fungi activities of daphneolone analogues from Stellera chamaejasme L. Pestic Biochem Phys 93:133–137View ArticleGoogle Scholar
- Jung D, Di Giulio RT (2010) Identification of mitochondrial cytochrome P450 induced in response to polycyclic aromatic hydrocarbons in the mummichog (Fundulus heteroclitus). Comp Biochem Physiol Toxicol Pharmacol 151:107–112View ArticleGoogle Scholar
- Kang X, Shafer RH, Kuntz ID (2004) Calculation of ligand-nucleic acid binding free energies with the generalized-born model in DOCK. Biopolymers 73:192–204View ArticlePubMedGoogle Scholar
- Kim KS, Cui X, Lee DS, Ko W, Sohn JH, Yim JH, An RB, Kim YC, Oh H (2014) Inhibitory effects of benzaldehyde derivatives from the marine fungus Eurotium sp. SF-5989 on inflammatory mediators via the induction of heme oxygenase-1 in lipopolysaccharide-stimulated RAW264.7 macrophages. Int J Mol Sci 15:23749–23765View ArticlePubMedPubMed CentralGoogle Scholar
- Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9:559View ArticleGoogle Scholar
- Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, Luo X, Zhu W, Chen K, Shen J et al (2006) Tarfisdock: a web server for identifying drug targets with docking approach. Nucleic Acids Res 34:W219–W224View ArticlePubMedPubMed CentralGoogle Scholar
- Liu W, Shi HM, Jin H, Zhao HY, Zhou GP, Wen F, Yu ZY, Hou TP (2009) Design, synthesis and antifungal activity of a series of novel analogs based on diphenyl ketones. Chem Biol Drug Des 73:661–667View ArticlePubMedGoogle Scholar
- Liu X, Ouyang S, Yu B, Liu Y, Huang K, Gong J, Zheng S, Li Z, Li H, Jiang H (2010) PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res 38:W609–W614View ArticlePubMedPubMed CentralGoogle Scholar
- Lopez SN, Castelli MV, Zacchino SA, Dominguez JN, Lobo G, Charris-Charris J, Cortes JC, Ribas JC, Devia C, Rodriguez AM et al (2001) In vitro antifungal evaluation and structure-activity relationships of a new series of chalcone derivatives and synthetic analogues, with inhibitory properties against polymers of the fungal cell wall. Bioorg Med Chem 9:1999–2013View ArticlePubMedGoogle Scholar
- Lopez SN, Sierra MG, Gattuso SJ, Furlan RL, Zacchino SA (2006) An unusual homoisoflavanone and a structurally-related dihydrochalcone from Polygonum ferrugineum (Polygonaceae). Phytochemistry 67:2152–2158View ArticlePubMedGoogle Scholar
- Lopez SN, Furlan RL, Zacchino SA (2011) Detection of antifungal compounds in polygonum Ferrugineum wedd. Extracts by bioassay-guided fractionation. Some evidences of their mode of action. J Ethnopharmacol 138:633–636View ArticlePubMedGoogle Scholar
- Lu SQ, Tian J, Sun WB, Meng JJ, Wang XH, Fu XX, Wang AL, Lai DW, Liu Y, Zhou LG (2014) Bis-naphtho-gamma-pyrones from fungi and their bioactivities. Molecules 19:7169–7188View ArticlePubMedGoogle Scholar
- Luo H, Chen J, Shi L, Mikailov M, Zhu H, Wang K, He L, Yang L (2011) DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical–protein interactome. Nucleic Acids Res 39:W492–W498View ArticlePubMedPubMed CentralGoogle Scholar
- Malki K, Tosto MG, Jumabhoy I, Lourdusamy A, Sluyter F, Craig I, Uher R, McGuffin P, Schalkwyk LC (2013) Integrative mouse and human mRNA studies using WGCNA nominates novel candidate genes involved in the pathogenesis of major depressive disorder. Pharmacogenomics 14:1979–1990View ArticlePubMedGoogle Scholar
- Moghaddam AB, Namvar F, Moniri M, Tahir PM, Azizi S, Mohamad R (2015) Nanoparticles biosynthesized by fungi and yeast: a review of their preparation, properties, and medical applications. Molecules 20:16540–16565View ArticleGoogle Scholar
- Moreira ASN, Braz R, Mussi-Dias V, Vieira IJC (2015) Chemistry and biological activity of Ramalina lichenized fungi. Molecules 20:8952–8987View ArticlePubMedGoogle Scholar
- Ogata H, Goto S, Fujibuchi W, Kanehisa M (1998) Computation with the KEGG pathway database. Biosystems 47:119–128View ArticlePubMedGoogle Scholar
- Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27:29–34View ArticlePubMedPubMed CentralGoogle Scholar
- Ren YH, Jin H, Tao K, Hou TP (2015) Apoptotic effects of 1,5-bis-(5-nitro-2-furanyl)-1,4-pentadien-3-one on Drosophila SL2 cells. Mol Cell Toxicol 11:187–192View ArticleGoogle Scholar
- Svetaz L, Tapia A, Lopez SN, Furlan RL, Petenatti E, Pioli R, Schmeda-Hirschmann G, Zacchino SA (2004) Antifungal chalcones and new caffeic acid esters from Zuccagnia punctata acting against soybean infecting fungi. J Agric Food Chem 52:3297–3300View ArticlePubMedGoogle Scholar
- Svetaz L, Aguero MB, Alvarez S, Luna L, Feresin G, Derita M, Tapia A, Zacchino S (2007) Antifungal activity of Zuccagnia punctata Cav.: evidence for the mechanism of action. Planta Med 73:1074–1080View ArticlePubMedGoogle Scholar
- Szczesna-Skorupa E, Kemper B (2001) The juxtamembrane sequence of cytochrome P-450 2C1 contains an endoplasmic reticulum retention signal. J Biol Chem 276:45009–45014View ArticlePubMedGoogle Scholar
- Szczesna-Skorupa E, Mallah B, Kemper B (2003) Fluorescence resonance energy transfer analysis of cytochromes P450 2C2 and 2E1 molecular interactions in living cells. J Biol Chem 278:31269–31276View ArticlePubMedGoogle Scholar
- Teng Y, Yang Q, Yu ZY, Zhou GP, Sun Q, Jin H, Hou TP (2010) In vitro antimicrobial activity of the leaf essential oil of Spiraea alpina Pall. World J Microb Biot 26:9–14View ArticleGoogle Scholar
- Wang Y, Kwon SJ, Wu J, Choi J, Lee YH, Agrawal GK, Tamogami S, Rakwal R, Park SR, Kim BG et al (2014) Transcriptome analysis of early responsive genes in rice during Magnaporthe oryzae infection. Plant Pathol J 30:343–354View ArticlePubMedPubMed CentralGoogle Scholar
- Wang YP, Wei ZY, Zhang YY, Lin CJ, Zhong XF, Wang YL, Ma JY, Ma J, Xing SC (2015) Chloroplast-expressed MSI-99 in tobacco improves disease resistance and displays inhibitory effect against rice blast fungus. Int J Mol Sci 16:4628–4641View ArticlePubMedPubMed CentralGoogle Scholar
- Wang X, Pan C, Gong J, Liu X, Li H (2016) Enhancing the enrichment of pharmacophore-based target prediction for the polypharmacological profiles of drugs. J Chem Inf Model 6:1175–1183View ArticleGoogle Scholar
- Wu JY, Chen X, Siu KC (2014) Isolation and structure characterization of an antioxidative glycopeptide from mycelial culture broth of a medicinal fungus. Int J Mol Sci 15:17318–17332View ArticlePubMedPubMed CentralGoogle Scholar
- Xu XH, Wang C, Li SX, Su ZZ, Zhou HN, Mao LJ, Feng XX, Liu PP, Chen X, Hugh Snyder J et al (2015) Friend or foe: differential responses of rice to invasion by mutualistic or pathogenic fungi revealed by RNAseq and metabolite profiling. Sci Rep 5:13624ADSView ArticlePubMedPubMed CentralGoogle Scholar
- Yu ZY, Shi GY, Sun Q, Jin H, Teng Y, Tao K, Zhou GP, Liu W, Wen F, Hou TP (2009) Design, synthesis and in vitro antibacterial/antifungal evaluation of novel 1-ethyl-6-fluoro-1,4-dihydro-4-oxo-7(1-piperazinyl)quinoline-3-carboxylic acid derivatives. Eur J Med Chem 44:4726–4733View ArticlePubMedGoogle Scholar
- Zhang H, Jin H, Ji LZ, Tao K, Liu W, Zhao HY, Hou TP (2011) Design, synthesis, and bioactivities screening of a diaryl ketone-inspired pesticide molecular library as derived from natural products. Chem Biol Drug Des 78:94–100View ArticlePubMedGoogle Scholar