Skip to content

Advertisement

  • Research
  • Open Access

Discovery of the molecular mechanisms of the novel chalcone-based Magnaporthe oryzae inhibitor C1 using transcriptomic profiling and co-expression network analysis

Contributed equally
SpringerPlus20165:1851

https://doi.org/10.1186/s40064-016-3385-9

  • Received: 24 May 2016
  • Accepted: 26 September 2016
  • Published:

Abstract

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Keywords

  • Magnaporthe oryzae
  • Transcriptome
  • Gene co-expression network
  • Molecular docking

Background

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).

Past research has detailed the antifungal activities, the structure–activity relationships (SARs) and the inhibitory capacity of the fungal cell wall synthesis pathway for a series of chalcone derivatives (Lopez et al. 2001, 2006, 2011; Svetaz et al. 2004, 2007; Boeck et al. 2005). In our previous study of the anti-phytopathogenic fungus capacities of chalcone derivatives, we screened a large number of compounds and provided information on the SARs of these compounds (Ren et al. 2015; Zhang et al. 2011; Teng et al. 2010; Yu et al. 2009; Liu et al. 2009; Jin et al. 2009). Of these chalcone derivatives, 1-(2′,4′-dichlorophenyl)-3-(2-furyl)-2-propen-1- one (compound C1, Fig. 1a) is one of the most potent compounds with a broad antifungal spectrum of phytopathogens. Zacchino et al. reported C1 could inhibit β(1,3)-glucan synthase and chitin synthase in Saccharomyces cerevisiae (Svetaz et al. 2004; Lopez et al. 2001); however, its antifungal capacity toward M. oryzae, as well as its molecular mechanisms, remain unknown so far. In the current study, we integrate results from a transcriptomic microarray, a bioinformatics analysis, a gene co-expression network analysis and experimental validations to profile the potential targets and related pathways of chalcone-based C1 treatment in M. oryzae (Scheme 1). Our results revealed that the mRNA expression profile was dramatically different between C1-treated and control groups. Cytochrome p450 and N-myristoyltransferase (NMT) might bind to C1 and be downregulated following C1 treatment. Next, we performed a weighted gene co-expression network analysis of DEGs and found that cytochrome p450, NMT and PXMP4 were involved with the hub genes, indicating that the mechanism of compound C1 might involve multiple targets and multiple pathways. Finally, we performed qRT-PCR and western blot experiments to verify the results of the microarray and bioinformatics analysis.
Fig. 1
Fig. 1

Chemical structure and inhibitory effects on the phytopathogens of chalcone-based compound C1: a the chemical structure of C1; b the growth inhibitory ratio of C1 on a panel of phytopathogens at 40 μg/mL; c the EC50 values of C1 on several phytopathogens

Scheme 1
Scheme 1

Flowchart of the methods. The scheme includes three steps. Firstly, differentially expressed mRNAs in drug-treated M. oryzae compared with the control group were identified via microarray analysis. Then, bioinformatics analysis, including GO and KEGG pathway enrichment, inverse docking and WGCNA network, were performed for speculating the correlation between potential targets and DEGs. Subsequently, we utilized qRT-PCR and Western blot to validate the microarray results

Methods

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

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.

Inverse docking

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).

In addition to the growth inhibition activity of C1 of M. oryzae, the stability of M. oryzae mycelium was also affected by treatment with compound C1. As shown in Fig. 2, the scan electronic microscopic (SEM) images show that the mycelium of M. oryzae was shrunken and deformed after C1 treatment for 48 h.
Fig. 2
Fig. 2

Inhibitory effects of C1 on M. oryzae mycelium observed under SEM: a the control group (×1000); b the C1 treated group (×1000); c the control group (×3000); d the C1 treated group (×3000)

Inverse docking results

A total of 73 common potential targets for compound C1 were identified using the three web server-based approaches (Fig. 3a). Following assessment for drug-ability using the Potential Drug Target Database (PDTD) (Liu et al. 2010), 185 proteins were predicted to be potential therapeutic targets by Tarfisdock. There were 190 and 206 potential targets predicted by DARA-CPI and PharmMapper, respectively. Among the 73 common targets, four proteins were reported to be involved in fungi’s biological processes. All of the four targets, cytochrome p450, N-myristoyltransferase (NMT), β(1,3)-glucansynthase and chitin synthase were used for further studies.
Fig. 3
Fig. 3

a The inverse docked potential targets determined by Tarfisdock (ID), DARA-CPI(DC) and Pharmapper (PM) methods; b the boxplot of gene expression in different groups; c the scatter plot of gene expression in different groups

Differential expression of genes after C1 treatment determined by transcriptome microarray

We performed a microarray analysis of M. oryzae after treatment with C1 for 24 h to profile the differentially expressed genes. A total number of 13,448 coding transcripts (mRNA) were detected in C1 treated M. oryzae; 2008 of which (approximately 9.81 %), were differentially expressed (>2.0-fold, P < 0.05) (Fig. 3b, c; Table 1). Among the 2008 deregulated mRNAs, 1013 mRNAs were upregulated, and 995 mRNAs were downregulated (P < 0.05, Additional files 1, 2). Interestingly, the proportion of upregulated and downregulated mRNAs was approximately 50/50 (Table 1). A scatter plot was used to visualize the mRNAs based on their expression levels (Fig. 3c).
Table 1

Numbers of mRNAs differentially expressed after C1 treatment 24 h

Category

Detected genes

Number of DEGs

Total genes

13,448

2008

Up-regulated genes

6013

1013

Down-regulated genes

7435

995

DEGs differentially expressed genes

Bioinformatics analysis

It is widely known that the GO enrichment analysis of DEGs (differentially expressed genes) may help provide novel insight into the numerous DEGs with diverse functions. In general, the GO analysis contains three components: biological processes, cellular components and molecular functions. The primarily enriched GO terms of biological processes targeted by DEGs included the oxidation–reduction process, the cellular nitrogen compound biosynthetic process, copper ion transmembrane transporter activity, the lipopolysaccharide biosynthetic process, mycelium development and the histidine biosynthetic process, as well as a number of up-regulated and down-regulated DEGs (Fig. 4a; Table 2). The corresponding p-values of each GO term are shown in Fig. 4b. In addition, the GO terms oxidation–reduction process and mycelium development were associated with cytochrome p450 and N-myristoyltransferase (NMT), which had been identified by inverse docking methods as potential targets.
Fig. 4
Fig. 4

GO enrichment and KEGG pathway analysis of differentially expressed genes in M. oryzae according to biological processes. a The up-regulated and down-regulated genes in top GO terms enriched among differentially expression genes; b the enrichment score (−Log10 P-value) of top GO terms enriched among differentially expression genes; c the up-regulated and down-regulated genes in the top enriched pathways among differentially expression genes; d the enrichment score of the top enriched pathways among differentially expression genes

Table 2

The top GO biological process terms of DEGs

GO terms

Genes_In_Term

DEGs

Up

Down

P_value

GO:0055114 (oxidation–reduction process)

777

113

60

53

2.24658E−10

GO:0044271 (cellular nitrogen compound biosynthetic process)

38

13

3

10

0.000142802

GO:0005375 (copper ion transmembrane transporter activity)

8

6

1

5

0.000153889

GO:0009103 (lipopolysaccharide biosynthetic process)

10

6

5

1

0.000705866

GO:0043581 (mycelium development)

468

60

17

43

0.000785477

GO:0000105 (histidine biosynthetic process)

8

5

0

5

0.001888677

Total

1301

198

86

112

 
The KEGG pathway enrichment analysis of DEGs also provided insight into the cellular pathways associated with these DEGs. Our results indicated that there were seven pathways corresponding to the differentially expressed mRNAs that we identified (Fig. 4c, d; Table 3). Xenobiotics biodegradation and metabolism was the top pathway enriched both in number of DEGs and P values. This result suggested that this pathway may contribute to the pathogenesis of M. oryzae. Moreover, the PXMP4 gene was involved in both the GO term mycelium development and the xenobiotics biodegradation and metabolism pathway.
Table 3

The enriched KEGG pathways in the DEGs

The first level

Function

Pathway

Genes_In_Term

DEGs

Up

Down

P_value

Metabolism

Xenobiotics biodegradation and metabolism

ko00627

153

29

18

11

1.58E−06

Metabolism

Metabolism of terpenoids and polyketides

ko00903

110

18

15

3

0.001865393

Metabolism

Amino acid metabolism

ko00350

117

18

6

12

0.003563739

Cellular processes

Transport and catabolism

ko04146

108

16

14

2

0.008760919

Metabolism

Biosynthesis of other secondary metabolites

ko00960

36

8

3

5

0.015219215

Metabolism

Metabolism of cofactors and vitamins

ko00740

30

7

2

5

0.019672753

Metabolism

Amino acid metabolism

ko00330

63

10

2

8

0.042220544

Weighted gene co-expression analysis

To explore the regulation of M. oryzae development and metabolism and to discover hub genes involved in related biological processes, we constructed weighted gene co-expression networks based on the whole transcriptome (WGCNA method). First, seven gene co-regulation modules related with M. oryzae development and metabolism (correlation coefficient >0.7, P < 0.05) were extracted from the whole transcriptomic microarray results (Fig. 5a). Three modules (“brown”, “cyan” and “blue”) were subjected to further analysis.
Fig. 5
Fig. 5

Weighted gene co-expression network analysis (WGCNA) of differentially expressed genes in M. oryzae. a The gene co-expression modules in M. oryzae were identified by hierarchical average linkage clustering; b for the dendrogram dataset, a hierarchical clustering of the topological overlap matrix was generated

We selected three genes whose differential expressions were significant in the C1 treated group to construct a gene co-expression network. These coding genes are involved in multiple biological processes, including mycelium development, oxidation and metabolism. The gene co-expression network showed that P450 was negatively correlated with PXMP4 (Fig. 6), which correlated with the oxidation–reduction process. The relationships in the modularity of the co-expression network is demonstrated via the topological overlap matrices (Fig. 5b). Interestingly, cytochrome p450, N-myristoyltransferase (NMT) and PXMP4 were involved in the hub gene modules (Fig. 6); P450, NMT and PXMP4 were differentially expressed, and the mRNA levels of β(1,3)-glucansynthase and chitin synthase were not changed and not involved in the gene co-expression network. Accordingly, we focused on P450, NMT and PXMP4 for further functional studies to elucidate its changes using experimental validation.
Fig. 6
Fig. 6

The gene co-expression networks of the three significantly DEGs. The network represents co-expression correlations between the P450, NMT, PXMP4 and their interacted DEGs. Nodes represent genes, red indicates up-regulated genes and green indicates down-regulated gens

qRT-PCR and Western-blot confirmation

The mRNA and protein expression levels of three significantly DEGs, P450, NMT and PXMP4 were detected by qRT-PCR and Western blot analysis, respectively. Agreed with the microarray results, qRT-PCR analysis revealed that the expression of P450 and NMT was upregulated, whereas PXMP4 expression was downregulated after C1 treatment (Fig. 7a). In addition, after the compound C1 treated, we found that C1 could remarkably decrease the expression of P450 and NMT but increased PXMP4 expression with a time-dependent manner, suggesting that C1 negatively regulated xenobiotic degradation and metabolism, and potentially affected the oxidation–reduction processes of M. oryzae by regulation of peroxisome function (Fig. 7b). Therefore, the experimental validation confirmed the accuracy of the microarray results at the mRNA and protein expression levels.
Fig. 7
Fig. 7

Validation of the microarray results by qRT-PCR and Western blot. a Values indicate the relative fold-change between the groups (drug treated vs. control group) detected by microarray (black) or qRT-PCR (blue); b the Western blot analysis of P450, NMT and PXMP4 protein expression levels after drug treatment

Discussion

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.

Conclusions

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.

Notes

Declarations

Authors’ contributions

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.

Acknowledgements

This work is financially supported by the National Natural Science Foundation of China (81402245).

Competing interests

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.

Authors’ Affiliations

(1)
Key Laboratory of Bio-Resource and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, 610064, China
(2)
State Key Laboratory of Oral Disease, West China School of Stomatology, Sichuan University, Chengdu, 610041, China

References

  1. 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
  2. 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
  3. Aung AK, Haas DW, Hulgan T, Phillips EJ (2014) Pharmacogenomics of antimicrobial agents. Pharmacogenomics 15:1903–1930View ArticlePubMedPubMed CentralGoogle Scholar
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9:559View ArticleGoogle Scholar
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. Moreira ASN, Braz R, Mussi-Dias V, Vieira IJC (2015) Chemistry and biological activity of Ramalina lichenized fungi. Molecules 20:8952–8987View ArticlePubMedGoogle Scholar
  37. Ogata H, Goto S, Fujibuchi W, Kanehisa M (1998) Computation with the KEGG pathway database. Biosystems 47:119–128View ArticlePubMedGoogle Scholar
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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

Copyright

Advertisement