Strain and Growth Conditions
Leptographium qinlingensis (NCBI Taxonomy ID: 717526) was deposited at the College of Forestry, Northwest A&F University (Yangling, China).
Leptographium qinlingensis was grown on an MEA medium containing 1% Oxoid Malt Extract Agar and 1.5% Agar Technical (Oxoid Ltd., Basingstoke, Hampshire, UK) and topped with cellophane, and the pH was adjusted to 5 ~ 6.
Fungal growth under different nutrition
We characterized the effect of different nutrients on the growth rate of L. qinlingensis. The fungal strain was acclimatized at room temperature for 1 week on 25 mL MEA media following long-term storage at 4°C. According to the treatment for the mountain pine beetle-fungal symbiont Grosmannia clavigera (DiGuistini et al. 2011), mycelial plugs were transferred to a new Petri dish containing 25 mL of six different media [wood (W): 10 g/plate Chinese white pine sawdust; 1.5% granulated agar; starch (S); organic nitrogen (ON); inorganic nitrogen (IN); olive oil (OO); Chinese white pine methanol extract (CWPE): complete medium (0.17% YNB, 1.5% granulated agar, 1% maltose, 0.1% PHP, 0.3% asparagine) with 200 µl of the crude Chinese white pine methanol extract (Dai et al., 2015).
All plates were incubated at 28°C in the dark, and growth (in cm) was measured every 4 days in four directions and averaged until the strain brought the fungus to the edge of the plate. For the six different nutrition media, the growth rates were obtained by calculating the area of the colony. To assess whether different parameters affect the growth rate, we performed curve fitting with a logistic equation [Y = A/(1 + B·e− kt), where Y is the size of the colony (cm2) and t is the culture time] using SPSS software (IBM SPSS Statistics, Chicago, IL, USA).
Inhibition of Terpenoids
Monoterpenes (+)-limonene (95%), (+)-3-carene (90%), (±)-α-pinene (98%), (-)-β-pinene (99%), and turpentine were selected as fungistats for MIC screening and mixed at a ratio of 5:3:1:1. A 1% malt extract microdilution susceptibility assay was performed according to the Clinical and Laboratory Standards Institute M38-A2 protocol to evaluate the initial MIC. The final terpenoid concentration ranged from 10%~0.0465% (v/v) for all terpenoids. An equal volume of 1 × 105 spores was mixed with the 1% malt extract microdilution susceptibility assay. The MIC of terpenoids was defined as the lowest concentration of the drug that produced no visible growth following 72 h of incubation at 27°C. The MIC determination was repeated five times.
To determine the magnitude of the synergy, the MICs for the monoterpene mixture can be compared with the MICs for (+)-limonene, (+)-3-carene, (+)-α-pinene and (-)-β-pinene alone. The synergy index (SI) was determined using the equation SI = QA/Qa + QB/Qb according to the method for antibacterial or fungicide mixtures (Zwart Voorspuij and Nass 1957; Kull et al. 1961).
Identification of Leptographium qinlingensis P450s
Total RNA was isolated from mycelia grown on MEA medium for 7 days according to the protocol supplied with the E.Z.N.A.™ Fungal RNA Kit (Omega Bio-Tek, Norcross, GA, USA), and its integrity was assessed on 1% agarose gels and quantified by spectrophotometry with a NanoDrop 2000 (Thermo Scientific, Pittsburgh, PA, USA). The purity was estimated by the A260/A280 equation (µg/mL = A260 × dilution factor × 40).
Samples were shipped on dry ice to Annoroad Gene Technology Co., Ltd. (Beijing, China) for paired-end sequencing. During the QC steps, an Agilent 2100 Bioanalyser and ABI StepOnePlus Real-Time PCR System were used for quantification and qualification of the sample library. Finally, the library was sequenced using an Illumina HiSeq™ 2000 system. Raw data were processed with Perl scripts to ensure the quality of the data used in further analyses. For paired-end sequencing data, both reads were filtered out if any reads of the paired-end reads were adaptor-polluted.
The reads were assembled using Trinity (Grabherr et al. 2011), and unigene sequences were identified as candidate coding regions with TransDecoder to find an open reading frame (ORF).
Trinotate was used to perform the functional annotation of unigenes and ORFs. The functional annotation included homology searches of known sequence data (BLAST), protein domain identification (PFAM), protein signal peptide and transmembrane domain prediction (SignalP), and comparison to current annotation databases, namely, the UniProt (Universal Protein), eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) and GO (Gene Ontology) pathway databases. Protein function information could be predicted from the annotation of the most similar proteins in those databases.
To identify all of the unique P450 transcripts in the hybrid assembly, we assessed these unigenes and translated ORFs against the BLASTx, BLASTp, PFAM, and eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) databases (e-value < 0.00001) to identify potential P450 sequences. The remaining unigenes were identified as potential P450 genes in L. qinlingensis (Table S1).
We downloaded the P450 protein sequences from Grosmannia clavigera kw1407 (53), Neurospora crassa OR74A (41), Sporothrix schenckii 1099-18 (40) and Ophiostoma piceae UAMH 11346 (43) for the phylogenetic analysis of potential P450 genes (ORFs with at least 200 codons) (37) in L. qinlingensis. To identify the different P450 variants expressed in fungi, a phylogenetic inference analysis of the P450 sequences by the maximum likelihood method was performed with MEGA6 (Tamura et al. 2011). The JTT + F model was supported by the test (-lnL = 998.482), with a gamma parameter value of G = 0.66. To estimate the support of each node, bootstrap values were calculated after 1000 pseudoreplicates.
A pair of primers for 6 annotated P450 sequences was designed to screen the putative P450 genes (Table S2). PCR amplifications were performed in a C1000 thermocycler (Bio-Rad, Hercules, CA, USA). P450 genes were amplified under the indicated conditions in 20 µL reactions containing 1 µl cDNA, 0.25 µM of each primer and 1× EcoTaq PCR SuperMix (TransGen Biotech, Beijing, China). An initial 5 min step at 94°C was followed by 30 cycles of 30 s at 94°C, 30 s at Tm and 30 s at 72°C, with a final extension for 10 min at 72°C.
The PCR products were visualized on 1% agarose gels stained with 1× DuRed and compared with a 2K plus DNA marker (TransGen Biotech, Beijing, China). Amplicons were purified, and the reaction product was cloned using the pMD™ 18-T Vector (TaKaRa, Dalian, China). Cloning reactions were transformed into DH5α chemically competent Escherichia coli cells, and a total of 5 clones with inserts were sequenced directly by GenScript USA Inc. The sequences were manually edited with DNAMAN to obtain the insert sequences. Blastx searches of partial-length sequences were performed against the NCBI database.
Information on the L. qinlingensis CYP65 genes was determined based on corresponding genes from Magnaporthe oryzae, N. crassa, Sordaria macrospora, Penicillium marneffei and Talaromyces stipitatus from the NCBI, and information on the CYP56BJ gene was determined based on corresponding genes from the genus Grosmannia (G. clavigera, G. aureum, G. penicillata) as well as Leptographium longiclavatum and L. terebrantis (Lah et al. 2013), and these data were used in the phylogenetic analyses.
Real-Time Fluorescent Quantitative PCR
We generated and analysed transcript-level data from two sets of growth conditions. For the first set of conditions, mycelia were generated from a suspension of 5 × 105 spores spread on cellophane on the surface of six different nutrition media as above.
In the second set of conditions, mycelia were generated from spores grown on 1% MEA (0.83% malt extract agar and 0.75% technical agar (BD Difco, Sparks, MD, USA)) covered with cellophane for 5 days. The young germinating mycelia were treated with 4 monoterpenes ((+)-limonene, (+)-3-carene, (±)-α-pinene and (-)-β-pinene) and turpentines at the same MIC screening for 24 h. However, terpenoids were added at three concentrations: 5%, 10% and 20% (v/v) in dimethyl sulfoxide (DMSO) solution. We used mycelia grown on 1% MEA with DMSO as a control.
Total RNA isolation of the fungi was performed as described above. cDNA synthesis was performed using the protocol described in the FastQuant RT Kit (with gDNase) (Tiangen Biotech Co., Beijing, China) using 2 µg total RNA in a 20 µl final reaction volume. The cDNA synthesis program was as follows: 42°C for 15 min and 95°C for 3 min. The cDNA was stored at -20°C.
For six P450 genes and the reference gene EF (Dai et al. 2015b), specific primers were designed using Primer Premier 5.0 (Table S2). To estimate the qPCR efficiency and validate the primers for each gene, a linear regression analysis was performed between the mean values of the quantification cycles (Cq) of different dilutions (1.0, 10− 1, 10− 2, 10− 3, and 10− 4) of cDNAs and the initial concentration. These dilutions were made from a cDNA pool, and 2 µl of each dilution was used as a qPCR template. PCR was performed three times for each gene, and its efficiency was estimated with the Eq. (10− 1/slope − 1) × 100, where the E value and R2 are shown in Table S2. Moreover, a melting curve reaction was performed to evaluate their specificity.
The reaction was carried out in a 20 µl volume that included 0.4 µm of each primer, 1 µl cDNA template (100 ng/µl), 8 µl ddH2O, and 10 µl TransStart® Tip Green qPCR SuperMix (TransGen Biotech). All samples were placed in the CFX96™ Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). A 3-step amplification process was performed: 95°C for 30 s, 40 cycles of 95°C for 5 s, Tm (melting temperature of primers) of each pair of primers (Table S2) for 15 s and 72°C for 20 s. Each treatment contained three technical replicates, and each technical replicate contained three biological replicates.
The relative expression values for all of the genes were determined using the Ct (ΔΔCt) method (Livak and Schmittgen 2008) and analysed with Microsoft Excel 2003 (v.11.0.5612). Outlier values identified by a PCR system were excluded from our analysis. To evaluate significant differences in the expression for each gene, 2−ΔΔCt values transformed at log2 were subjected to a one-way ANOVA to determine whether the gene expression differed among the treatments. The 2−ΔΔCt values and standard error (SE) were transformed at log2 to generate graphs. All of the statistical analyses were performed with SPSS 18.0 (IBM SPSS Statistics, Chicago, IL, USA) and plotted with SigmaPlot 12.0 software (Systat Software Inc., San Jose, CA, USA)