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Article

Transcriptome Analysis of mfs2-Defective Penicillium digitatum Mutant to Reveal Importance of Pdmfs2 in Developing Fungal Prochloraz Resistance

by
Rongrong Cuan
1,
Shaoting Liu
2,
Chuanyou Zhou
1,
Shengqiang Wang
1,
Yongliang Zheng
3 and
Yongze Yuan
1,*
1
Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan 430079, China
2
School of Political and Law, Huanggang Normal University, Huanggang 438000, China
3
Hubei Key Laboratory of Economic Forest Germplasm Improvement and Resources Comprehensive Utilization & Hubei Collaborative Innovation Center for the Characteristic Resources Exploitation of Dabie Mountains, Huanggang Normal University, Huanggang 438000, China
*
Author to whom correspondence should be addressed.
Microorganisms 2024, 12(5), 888; https://doi.org/10.3390/microorganisms12050888
Submission received: 20 March 2024 / Revised: 18 April 2024 / Accepted: 24 April 2024 / Published: 28 April 2024
(This article belongs to the Special Issue Fungicide Resistance in Plant Pathogen)
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Abstract

:
Demethylation inhibitors (DMIs), including prochloraz, are popular fungicides to control citrus postharvest pathogens such as Penicillium digitatum (green mold). However, many P. digitatum strains have developed prochloraz resistance, which decreases drug efficacy. Specific major facilitator superfamily (MFS) transporter gene mfs2, encoding drug-efflux pump protein MFS2, has been identified in P. digitatum strain F6 (PdF6) to confer fungal strain prochloraz resistance. However, except for the drug-efflux pump function of MFS2, other mechanisms relating to the Pdmfs2 are not fully clear. The present study reported a transcriptome investigation on the mfs2-defective P. digitatum strain. Comparing to the wild-type strain, the mfs2-defective strain showed 717 differentially expressed genes (DEGs) without prochloraz induction, and 1221 DEGs with prochloraz induction. The obtained DEGs included multiple isoforms of MFS transporter-encoding genes, ATP-binding cassette (ABC) transporter-encoding genes, and multidrug and toxic compound extrusion (MATE) family protein-encoding genes. Many of these putative drug-efflux pump protein-encoding genes had significantly lower transcript abundances in the mfs2-defective P. digitatum strain at prochloraz induction, as compared to the wild-type strain, including twenty-two MFS transporter-encoding genes (MFS1 to MFS22), two ABC transporter-encoding genes (ABC1 and ABC2), and three MATE protein-encoding genes (MATE1 to MATE3). The prochloraz induction on special drug-efflux pump protein genes in the wild-type strain was not observed in the mfs2-defective strain, including MFS21, MFS22, ABC2, MATE1, MATE2, and MATE3. On the other hand, the up-regulation of other drug-efflux pump protein genes in the mfs2-defective strain cannot recover the fungal prochloraz resistance, including MFS23, MFS26, MFS27, MFS31, MFS33, and ABC3 to ABC8. The functional enrichment of DEGs based on Kyoto Encyclopedia of Genes and Genomes (KEGG), Clusters of Orthologous Groups (COG), and euKaryotic Orthologous Groups (KOG) database resources suggested some essential contributors to the mfs2-relating prochloraz resistance, including ribosome biosynthesis-related genes, oxidative phosphorylation genes, steroid biosynthesis-related genes, fatty acid and lipid metabolism-related genes, and carbon- and nitrogen-metabolism-related genes. The results indicated that the MFS2 transporter might be involved in the regulation of multiple drug-efflux pump protein gene expressions and multiple metabolism-related gene expressions, thus playing an important role in developing P. digitatum prochloraz resistance.

1. Introduction

Postharvest citrus fruits are usually infected by Penicillium digitatum pathogens during storing and transporting processes, and, as such, green mold disease significantly reduces citrus fruit production in markets [1,2]. A large number of chemical drugs (fungicides) have been applied to control green mold, including demethylation inhibitors (DMI) class fungicides. Currently, among the common classes of antifungal drugs with specific targets, the DMI class of fungicides is considered more suitable to inhibit phytopathogenic Penicillium ssp., especially P. digitatum [3,4,5]. DMI fungicides including triadimefon, imazalil and prochloraz, all of which target the key step in the biosynthesis of fungal ergosterol (i.e., the lanosterol 14α-demethylation). However, the long time required by such DMI-fungicide treatments led to increasing the efforts to develop drug-resistant fungal strains in the field. Regarding green mold control, the high frequency needed to develop triadimefon- and imazalil-resistant P. digitatum isolates in storing and transporting conditions has undesirably lowered the control efficacy of these two DMI-fungicides [6,7]. Now prochloraz, a chemical compound of azole DMI, is still widely used in the green mold control in China’s citrus industry chains, as result of its economic cost-effectiveness [5]. Nevertheless, P. digitatum strains with high resistance to prochloraz have emerged, which brought about more attention to the underlying mechanisms. One high prochloraz-resistant strain of P. digitatum has been isolated and characterized in our laboratory, and through gene-knockout and complementation experiments, a drug efflux-pump protein-encoding gene, Pdmfs2, has been identified to be an important contributor to the fungal prochloraz resistance [8]. Based on these fungal materials, more mechanisms underlying the Pdmfs2-relating prochloraz resistance can be further studied.
Fungal resistance to DMI fungicides including prochloraz is usually developed from several major mechanisms. The first is the over-expression of fungicide-targeting proteins or enzymes such as sterol 14α-demethylase, which is encoded by the gene erg11 (i.e., cyp51) [9]. The other mechanism regarding the gene cyp51 has been known as gene mutations in its coding sequence or promoter region, including a 199-bp insertion [7,10,11], specific ‘CC’ insertion [12], and point mutations [13]. Besides the gene cyp51-targeted mechanisms, the over-expression of genes encoding drug efflux-pump proteins can be an essential strategy for pathogenic fungi to develop resistance against various fungicides including prochloraz. Such drug efflux-pump proteins can be classified into three superfamilies: (1) the major facilitator superfamily (MFS), (2) ATP-binding cassette (ABC) superfamily, and (3) multidrug and toxic compound extrusion (MATE) transporter superfamily. In fungal cells, these drug efflux-pump proteins are responsible for exporting fungicide(s) out of membrane to decrease intracellular drug concentration to induce fungicide resistance [14,15]. Some of the drug efflux-pump proteins, especially MFS and ABC superfamily members, also serve as comprehensive metabolism-relating transporters with multiple physiological functions. MFS transporters have been verified as secondary active transporters to produce ion gradients that are directly associated with cellular energy metabolisms such as oxidative phosphorylation [16,17]. Such MFS subfamily members also function as drug H+ antiporters to develop fungal multidrug resistance [18,19,20]. MFS transporters are also extensively involved in fungal virulence to their hosts, especially at no fungicide conditions [21,22,23]. On the other hand, at fungicide treatments, over-expression of specific MFS transporters led to antifungal resistance [24]. On the contrary, the knockout of MFS-encoding gene(s) decreased fungal resistance to fungicide(s) including prochloraz [8,23,25]. Besides MFS, ABC transporter superfamily genes also contribute to fungal resistance at fungicide treatments. ABC genes including ABC1, ABC2, and ABC3 are extensively involved in fungal resistance to prochloraz, both in P. digitatum and in P. italicum [9,26]. The simultaneous over-expression of MFS and ABC genes has been found in the highly prochloraz-resistant fungus including Candida spp. isolates, P. digitatum strain HS-F6 and P. italicum strain YN1 [9,26,27,28]. Unlike MFS and ABC, MATE superfamily transporters are usually associated with bacterial antibiotic resistance [29], and in contrast, contribute to fungal drug resistance in a few cases [30,31].
Genomics and RNA-seq studies revealed multiple iso-genes encoding MFS and ABC transporters as contributors to developing fungicide resistance. P. digitatum genomes (i.e., Pd1 and PdW03 genomes) have been identified to contain more than 80 locus encoding MFS-type transporter proteins [32,33]. Some of these MFS-encoding genes were responsible for the fungal resistance to chemical fungicides, including PdMfs1 in the azole- or DMI-resistant P. digitatum strain PdW03 [25], Pdmfs2 in the prochloraz-resistant P. digitatum strain PdHS-F6 [8], and PdMFS1 in the multidrug-resistant P. digitatum strain Pd1 [20,34]. Genomic studies also showed that multiple copies and chromosomal locations of ABC transporter proteins are highly associated with fungicide resistance in the P. digitatum strains, including Pd01-ZJU [35], Pd1 [32], and PdW03 [33]. RNA-seq evidence has suggested simultaneous up- or down-regulation of specific MFS and ABC transporter genes in P. digitatum strains to develop fungicide resistance [9,26,36]. Such transcriptomic responses were also reported in many other pathogenic fungi with fungicide resistance phenotypes [28,37,38,39]. Considering those genes encoding drug-pump proteins, usually as MFS-, ABC-, and MATE-type transporters, all of which exhibited multiple isoforms in the P. digitatum genomes, the underlying mechanisms to develop fungicide resistance need further investigation.
On the other hand, fungi can make adaptive responses to fungicide stress conditions by changing metabolism-relating gene expression patterns. These metabolisms and cellular stimuli processes in response to specific fungicide(s) have been studied, including ergosterol biosynthesis pathways, lipid and fatty acid oxidation pathways, cell wall maintenance, oxidative-stress-responsive processes, carbohydrate and amino acid metabolisms, cellular energy metabolisms, post-translational modification processes, and signal transduction pathways. All these pathways are highly dependent on many stress-responsive genes, including various ERG-encoding genes (such as erg1, erg3, erg11, erg24, erg25 and so on) [36,40,41,42], acetyl-CoA carboxylase (ACCase)-encoding genes [43,44,45,46,47], reactive oxygen species (ROS)-metabolizing enzyme-encoding genes [48,49,50,51], mitochondrial respiratory chain protein-encoding genes [52,53,54,55], ubiquitin-encoding genes [56,57,58,59], and a series of protein kinase-encoding genes involved in mitogen-activated signal transductions [55,60,61]. As reported, MFS and ABC transporters played multiple roles in the transport of a diverse range of metabolic substrates and intermediates [62,63]. P. digitatum MFS transporters can display different roles during pathogen–fruit interaction [20]. It would be necessary to explore the association of MFS transporter(s) with the sophisticated metabolic responses in developing fungicide resistance.
The present study investigated transcriptomic changes between two P. digitatum strains, i.e., PdF6 and PdF6Δmfs2 at prochloraz induction or no prochloraz induction, using RNA-sequencing and the following differentially expressed gene identification and enrichments based on COG, KOG, and KEGG databases to show the importance of gene Pdmfs2 in P. digitatum prochloraz resistance as well as the relating metabolism backgrounds.

2. Materials and Methods

2.1. Strains and Media

The P. digitatum strain used in this study was previously isolated by our research group [64], which was highly resistant to DMI-fungicide prochloraz with an EC50 value of 7.90 mg·L−1. Meanwhile, the mfs2-deleted P. digitatum strain (PdF6Δmfs2) was less resistant to prochloraz with an EC50 value of 6.80 mg·L−1 [8]. In the present study, the prochloraz resistance of these two P. digitatum strains was verified using methods described before [8,64]. P. digitatum strains were cultivated on potato dextrose agar (PDA) medium (extract of 200 g potato boiled water, 20 g dextrose, and 15 g agar per liter) at 28 °C for 5 days to prepare respective conidial suspension (107 spores mL−1) as previously described [26]. Then, 20 μL of conidial suspension (107 spores mL−1) of each strain was cultured in potato dextrose broth (PDB) medium at 28 °C for about 2 days. Prochloraz induction experiment was carried out for sample preparation. Prochloraz at the concentration of EC50 (7.90 mg·L−1 for wild-type strain and 6.80 mg·L−1 for mfs2-deleted strain) was added to 100 mL PDB medium with 180 rpm shaking for an extra 6 h of growth at 28 °C. The mycelia were filtered and washed several times using double distilled water. In the present study, 4 samples in total were used for the following RNA manipulations: prochloraz-induced and not induced wild-type P. digitatum strain (designated as Pd-wt-I and Pd-wt-NI, respectively); and prochloraz-induced and not induced mfs2-deleted P. digitatum strain (designated as Pd-d-I and Pd-d-NI, respectively).

2.2. RNA Extraction, RNA-seq Library Construction and Illumina Sequencing

Total RNA was extracted using RNAiso Plus (TaKaRa Biotech. Co., Dalian, China) according to the manufacturer’s protocol. All RNA samples were treated with DNase I (TaKaRa Biotech. Co., Dalian, China). RNA degradation and contamination were monitored on 1% agarose gels. RNA purity was checked using the Nano-Photometer® spectrophotometer (IMPLEN, Westlake Village, CA, USA). RNA concentration was measured using Qubit® RNA Assay Kit in Qubit® 2.0 Flurometer (Life Technologies, San Francisco, CA, USA). RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). From each sample, 3 μg of total RNA was taken to construct strand-specific cDNA libraries using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, Ipswich, MA, USA) following the manufacturer’s instructions. The required fragments were enriched by PCR amplification, and the products were purified using AMPure XP system (Beckman Coulter, Brea, CA, USA). Library quality was assessed on the Agilent Bioanalyzer 2100 system. After cluster generation, the library preparations were sequenced on an Illumina Hiseq 2000 platform (Illumina, San Diego, CA, USA) and the resulting paired-end reads (raw reads) in ~150 bp length were deposited for further analysis.

2.3. Assembly of Reads and Unigenes and Analysis of SNP Sites

Prior to sequence analysis, high-quality clean reads were obtained by removing reads containing adapter and low-quality reads. Transcriptome data and reference genome sequence alignment was accomplished by the HISAT platform, a highly efficient system for aligning reads from RNA sequencing [65]. All pair-end clean reads were aligned to reference genome Penicillium digitatum Pd1 (GenBank accession number: GCA_000315645.2). Then, the aligned reads of each sample were assembled by StringTie methods [66] to obtain transcripts and unigenes. The alignment efficiency was estimated by the percentage of mapped reads, uni-mapped reads, and multiple-mapped reads to the total clean reads. Prior to differentially expressed gene analysis, the read counts were adjusted according to one scaling normalized factor for each library using edgeR program packages [67]. Based on the alignment results of each sample against the reference genome, single-base mismatches between the sequenced samples and reference genome were identified to recognize potential single-nucleotide polymorphisms (SNP) sites using the GATK method [68]. The two types of SNP sites (i.e., transition sites and transversion sites), according to different base substitution styles, were both assessed in the present study.

2.4. Analysis of Differentially Expressed Genes (DEGs)

In order to reflect transcript abundance in the present RNA-sequencing, the number of mapped reads in the samples and the length of transcripts both required normalization, i.e., gene expression quantification. The Fragments Per Kilobase of transcript per Million fragments mapped (FPKM) was used to measure transcript or gene expression levels according to the statistics methods described before [69]. Differentially expressed genes (DEGs) analysis was performed using the DEGSeq R package (1.20.0) to calculate fold-changes in the expression level for each gene, defined as the ratio of the FPKM values. The p-values were statistically corrected by using the Benjamini–Hochberg method to assess the significance for the differences in transcript abundance [70]. The cut-off value to define differentially expressed genes (DEGs) was the adjusted p-value ≤ 0.05 and at least 2-fold change (i.e., the absolute value of log2 Fold Change (log2FC) ≥ 1.0) in transcript abundance between two comparison samples. The identified DEGs were hierarchically clustered by Cluster 3.0 [71], and then subjected to heat-map analysis by Plotly 4.0 (Montreal, Quebec) software. The distribution of up- and down-regulated DEGs versus unchanged unigenes was visualized using Volcano plots [72]. All the DEGs were annotated, and then functionally classified and enriched according to the three common databases, including the Clusters of Orthologous Groups (COG) database (http://www.ncbi.nlm.nih.gov/COG/; 25 November 2020) [73], clusters of euKaryotic Orthologous Groups (KOG) database (http://www.ncbi.nlm.nih.gov/KOG/; 25 November 2020) [73], and Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/; 1 January 2022) [74]. The KOBAS software (version 3.0) was applied to perform the COG-, KOG-, and KEGG-based classification and enrichment of the present DEGs according to the method of Mao et al. [75].

2.5. Validation of DEGs with Quantitative Real-Time PCR (qRT-PCR)

Quantitative real-time PCR (qRT-PCR) was carried out to validate the expression patterns of DEGs, including genes encoding MFS, ABC and MATE drug-pump proteins and the other fungicide resistance genes involved in multiple cellular metabolic processes. RNA samples were collected independently from RNA-seq experiments, and the first-strand cDNA was generated using PrimeScriptTMRT reagent Kit with gDNA Eraser (TaKaRa, Dalian, China) according to the manufacturer’s instructions. qRT-PCR reactions were conducted in a BIO-RAD CFX96 qPCR system using SYBR Premix Ex Taq™ II kits (Takara, Dalian, China). The primers in the present study were designed using the software Primer Premier 5.0, as listed in Table S1, and their quality in the qRT-PCR amplification was evaluated by melting curve analysis. All qRT-PCR reactions were performed with three technical replicates and the thermal conditions were as follows: 30 s at 95 °C, followed by 35 cycles of 20 s at 95 °C, 30 s at 58 °C and 30 s at 72 °C. The relative quantification of each gene expression level was normalized according to the β-actin gene expression and calculated from the threshold cycle according to the 2−ΔΔCt method.

3. Results

3.1. Transcriptome Sequencing and Reads Assembly

In the present study, wild-type and mfs2-deleted P. digitatum strains were treated with or without DMI-fungicide prochloraz to prepare four RNA-seq samples, including Pd-d-I (i.e., mfs2-deleted P. digitatum strain with prochloraz induction), Pd-d-NI (i.e., mfs2-deleted P. digitatum strain with no prochloraz induction), Pd-wt-I (i.e., wild-type P. digitatum strain with prochloraz induction), and Pd-wt-NI (i.e., wild-type P. digitatum strain with no prochloraz induction). The Illumina sequencing data are summarized in Table S2. The four transcriptomic libraries contained 6,536,999,320, 7,141,474,368, 6,966,924,166, and 6,621,791,146 clean bases, respectively. By removing adaptor sequences and undesirable reads including ambiguous, low-quality, and duplicated sequence reads, 21,847,213, 23,870,541, 23,294,756 and 22,149,643 clean reads were generated from the four libraries with Q30 > 90%, and the GC content of the four libraries was around 50%. These results suggested high quality for the present RNA-sequencing.
The percentages of reads mapping to the reference genome in different samples are shown in Table S3. Total reads in the sample Pd-d-I, Pd-d-NI, Pd-wt-I and Pd-wt-NI were 43,694,426, 47,741,082, 46,589,512 and 44,299,286, and 95.90%, 94.03%, 87.74% and 94.04% of the total reads were mapped to the reference genome Pd1 for the above four samples, respectively. In addition, the uniquely mapped reads occupied 93.79%, 92.38%, 86.36% and 91.98% of the total mapped reads for the four samples, respectively. In contrast, multiple mapped reads accounted for only a small fraction, that is, 2.12%, 1.64%, 1.38%, and 2.06% for the four samples, respectively. Table S3 also showed high equivalence between the reads mapped to plus and minus strand of genome sequence. These results indicated high quality of mapping analysis based on the Pd1 reference genome.
The SNP analysis for the four samples was summarized in Table S4. The SNP numbers were 2458, 2527, 2311, and 2580 for the four samples, respectively. In detail, the number of Genic SNP in the genetic region was 1592, 1609, 1511, and 1675, respectively, and the number of Intergenic SNP in the intergenic region was 866, 918, 800, and 905, respectively. In addition, the percentage of the number of transitional SNP in the total number of SNP was 61.92%, 61.65%, 62.48%, and 61.94%, respectively. In addition, the percentages of transversion and heterozygosity in the total number of SNP were similar, i.e., both between 30% and 40%. These results indicated little change in P. digitatum genome structures with the gene mfs2 deletion.

3.2. Analysis of Differentially Expressed Genes (DEGs)

According to the criteria p-value ≤ 0.05 and the absolute value of fold change ≥ 1.0, the present study totally identified 460 DEGs in wild-type P. digitatum strain with prochloraz induction compared to that with no prochloraz induction (i.e., Pd-wt-(I/NI)) (Table S5). As shown in Table S5, there were 147 DEGs in the mfs2-deleted P. digitatum strain with prochloraz induction compared to that with no prochloraz induction (i.e., Pd-d-(I/NI)); 1221 DEGs in the mfs2-deleted P. digitatum strain with prochloraz induction compared to wild-type P. digitatum strain with prochloraz induction (i.e., I-(Pd-d/Pd-wt)); and 717 DEGs in the mfs2-deleted P. digitatum strain with no prochloraz induction compared to wild-type P. digitatum strain with no prochloraz induction (i.e., NI-(Pd-d/Pd-wt)).
DEGs were annotated by alignment analysis using the common public databases. In total, 427, 128, 1169 and 676 DEGs in Pd-wt-(I/NI), Pd-d-(I/NI), I-(Pd-d/Pd-wt) and NI-(Pd-d/Pd-wt) were functionally annotated in the eight databases (Table S6). For the above four comparative groups, there were 147, 32, 409 and 258 DEGs annotated in the COG database (http://www.ncbi.nlm.nih.gov/COG/; 25 November 2020), respectively; there were 170, 35, 486 and 288 DEGs annotated in the KOG database (https://www.ncbi.nlm.nih.gov/research/cog-project/; 25 November 2020), respectively; there were 243, 68, 689 and 408 DEGs annotated in the GO database (http://www.geneontology.org/; 1 July 2019), respectively; there were 73, 18, 299 and 162 DEGs annotated in the KEGG database (http://www.genome.jp/kegg/; 1 January 2022), respectively; there were 258, 65, 750 and 434 DEGs annotated in the Pfam database (http://pfam.xfam.org/; 1 January 2021), respectively; there were 196, 51, 586 and 346 DEGs annotated in the Swiss-Prot database (http://www.uniprot.org/; 1 January 2020), respectively; there were 364, 104, 1044 and 577 DEGs annotated in the eggNOG database (http://eggnogdb.embl.de/download/emapperdb-5.0.0/; 19 March 2019), respectively; and there were 426, 128, 1167 and 676 DEGs annotated in the NR database (https://ftp.ncbi.nlm.nih.gov/blast/db/; 30 July 2021), respectively.

3.3. DEG Analysis between the Wild-Type and mfs2-Deleted P. digitatum Strains at No Prochloraz Induction

The volcano plot analysis identified 717 DEGs in the mfs2-deleted strain as compared to the control, including 366 up-regulated and 351 down-regulated (Figure 1A). All unigene expression levels were determined by the FPKM values, and based on these values, a hierarchical cluster (i.e., heat map) analysis was performed to visualize DEG profiles between the wild-type and mfs2-deleted P. digitatum strains (Figure 1B). Further, KEGG enrichments classified the 366 down-regulated DEGs into 42 pathways with two significant enrichments, i.e., ‘pentose and glucuronate interconversions’ (ko00040) and ‘starch and sucrose metabolism’ (ko00500) (Figure 1C). In contrast, the 351 up-regulated DEGs were classified by KEGG enrichment into one significantly enriched pathway in the total 44 pathways, i.e., ‘nitrogen metabolism’ (ko00910) (Figure 1D).
According to the q-values, the top 3 significantly KEGG pathways, as well as the DEGs involved, were listed in Table 1, including ‘pentose and glucuronate interconversions’ (ko00040), ‘starch and sucrose metabolism’ (ko00500), and ‘peroxisome’ (ko04146). The DEGs in the three KEGG pathways included dihydrodipicolinate synthetase-encoding gene, exopolygalacturonase-encoding gene, exo-β-1,3-glucanase-encoding gene, α-L-rhamnosidase-encoding gene, fatty acyl-CoA oxidase-encoding gene, and carnitine acetyl transferase-encoding gene. In addition, energy-metabolism-related genes including ATP synthase-coding gene, NADH dehydrogenase-encoding gene, and cytochrome b-encoding gene were enriched into the KEGG pathway ‘Oxidative phosphorylation’ (ko00190). All the DEGs in the four KEGG pathways were down-regulated in the comparison NI-(Pd-d/Pd-wt). On the other hand, some DEGs were up-regulated in the comparison NI-(Pd-d/Pd-wt), which is also summarized in Table 1. These up-regulated genes responsive to the Pdmfs2 knockout at no prochloraz treatment were mainly enriched in another four KEGG pathways associated with nitrogen and amino acid metabolisms, including nitrogen metabolism (ko00910), ‘tyrosine metabolism’ (ko00350), ‘phenylalanine metabolism’ (ko00360), and ‘tryptophan metabolism’ (ko00380).
Function classification of down-regulated DEGs between the wild-type and mfs2-deleted P. digitatum strains at no prochloraz induction was performed based on the COG and KOG databases. Both COG and KOG enrichments showed that these DEGs were mainly involved in ‘general function prediction only’ (R), ‘amino acid transport and metabolism’ (E) and ‘carbohydrate transport and metabolism’ (G) (Figure 2). As listed in Table 2, some of the DEGs were enriched in more than one COG or KOG class, including dihydrodipicolinate synthetase-encoding gene enriched in both ‘amino acid transport and metabolism’ (E) and ‘cell wall/membrane/envelope biogenesis’ (M) COG classes, α-L-Rhamnosidase-encoding gene enriched in ‘carbohydrate transport and metabolism’ (G) class in both COG and KOG databases, peroxin-encoding gene enriched in ‘general function prediction only’ (R) COG class and ‘intracellular trafficking, secretion, and vesicular transport’ (U) KOG class.

3.4. DEG Analysis in the Wild-Type and mfs2-Deleted P. digitatum Strains with Prochloraz Induction

The comparison of the wild-type and mfs2-deleted P. digitatum strains with prochloraz induction identified 460 and 147 DEGs, respectively, including 240 and 50 up-regulated genes and 220 and 97 down-regulated genes (Figure 3). Heatmap analysis was performed between strains with prochloraz induction and with no prochloraz induction (Figure 4).
In the comparison Pd-wt-(I/NI), up-regulated DEGs were significantly enriched in ‘oxidative phosphorylation’ (ko00190) and ‘steroid biosynthesis’ (ko00100) pathways (Figure 5A). These DEGs are listed in Table 3, including cytochrome c oxidase-encoding gene, NADH dehydrogenase-encoding gene, cytochrome b-encoding gene, ATP synthase-encoding gene and Erg-encoding genes. In addition, DEGs enriched in other KEGG pathways are also listed in Table 3. In the comparison Pd-d-(I/NI), up-regulated DEGs were significantly enriched in seven KEGG pathways, including three lipid metabolic pathways (i.e., ‘ether lipid metabolism’ (ko00565), ‘inositol phosphate metabolism’ (ko00562) and ‘glycerophospholipid metabolism’ (ko00564)), two carbohydrate metabolic pathways (i.e., ‘galactose metabolism’ (ko00052) and ‘starch and sucrose metabolism’ (ko00500)), ‘biosynthesis of antibiotics’ (ko01130), and the most significant pathway, ‘steroid biosynthesis’ (ko00100) (Figure 5B). The DEGs involved in these KEGG pathways are listed in Table 4. Among these DEGs, Erg25-encoding gene was up-regulated in Pd-wt-(I/NI) and Pd-d-(I/NI). In contrast, cytochrome c oxidase-encoding gene and NADH dehydrogenase-encoding gene were down-regulated in both comparisons.
In the comparison Pd-wt-(I/NI), the most up-regulated DEGs were enriched in the following KOG classes: 1) ‘general function prediction only’ (R), including 20 DEGs such as NADH-quinone oxidoreductase-encoding gene and aldehyde reductase-encoding gene; and 2) ‘energy production and conversion’ (C), including 18 DEGs such as NADH dehydrogenase-encoding gene, glyoxylate reductase-encoding gene, and cytochrome c oxidase-encoding gene. In the comparison Pd-d-(I/NI), the most up-regulated DEGs were enriched in the following KOG classes: (1) ‘general function prediction only’ (R) with only 4 hypothetical protein-encoding genes; and (2) ‘lipid transport and metabolism’ (I) and ‘secondary metabolites biosynthesis, transport and catabolism’ (Q) (Figure 6 and Table 5).

3.5. DEG Analysis between the Wild-Type and mfs2-Deleted P. digitatum Strains at Prochloraz Induction

The volcano plot showed the distribution of DEGs between the wild-type and mfs2-deleted P. digitatum strains at prochloraz induction (Figure 7A), including 608 up-regulated DEGs and 613 down-regulated DEGs. All the unigene expression levels were also determined by FPKM values, and based on these values, the heat map analysis was performed to visualize DEG profiles between the wild-type and mfs2-deleted P. digitatum strains at prochloraz induction (Figure 7B). Further, KEGG enrichment was performed to classify the DEGs in the mfs2-deleted P. digitatum strains at prochloraz induction. Among the down-regulated DEGs (Figure 7C), there were 20 DEGs significantly enriched in the KEGG pathway ‘ribosome’ (ko03010), including ribosomal protein-encoding gene and acidic ribosomal phosphoprotein-encoding gene. There were 17 DEGs enriched in another KEGG pathway ‘oxidative phosphorylation’ (ko00190), including cytochrome c oxidase-encoding gene, ATP synthase-encoding gene, NADH dehydrogenase-encoding gene, and cytochrome b-encoding gene. Generally, as shown in Figure 7D, the up-regulated DEGs were enriched in the KEGG pathways involved in fungal growth, lipid and fatty acid biosynthesis, and nitrogen-containing nutrient metabolisms, including ‘fatty acid biosynthesis’ (ko00061) and ‘nitrogen metabolism’ (ko00910). The DEGs enriched in the above KEGG pathways are listed in Table 6.
KOG-based annotation and functional classification confirmed the results of KEGG enrichments in the comparison I-(Pd-d/Pd-wt). The deletion of mfs2 led to the down-regulation of many genes in the P. digitatum strain. These down-regulated DEGs were mainly classified into seven KOG classes, including ‘general function prediction only’ (R), ‘energy production and conversion’ (C), ‘translation, ribosomal structure and biogenesis’ (J), ‘amino acid transport and metabolism’ (E), ‘carbohydrate transport and metabolism’ (G), ‘post-translational modification, protein turnover, chaperons’ (O), and ‘secondary metabolites biosynthesis, transport and catabolism’ (Q) (Figure 8A). Meanwhile, the up-regulated DEGs in the comparison I-(Pd-d/Pd-wt) were classified into the similar KOG classes, as compared to the down-regulated DEGs (Figure 8B). However, the gene number of the up-regulated DEGs was lower than that of down-regulated DEGs in the four KOG classes (i.e., KOG class C, E, G, and J). In contrast, the gene number of the up-regulated DEGs was higher than that of down-regulated DEGs in the three KOG classes (i.e., KOG class K, O, and T). Such a difference in DEG distribution in the KOG classes might reflect different mechanisms to develop prochloraz resistance. The DEGs involved in the above KOG functional classifications are listed in Table 7. Actually, the down-regulated DEGs in the comparison I-(Pd-d/Pd-wt) did function in the ‘translation, ribosomal structure and biogenesis’ and ‘energy production and conversion’, including ribosomal protein-encoding genes, cytochrome c oxidase-encoding gene, ATP synthase-encoding gene, NADH dehydrogenase-encoding gene, and cytochrome b-encoding gene. The simultaneous down-regulation of these genes with gene Pdmfs2 knockout indicated their potential correlation with Pdmfs2, i.e., their important roles in developing prochloraz resistance. On the other hand, the up-regulated DEGs in the comparison I-(Pd-d/Pd-wt) did function in the ‘transcription’, ‘post-translational modification, protein turnover, chaperons’, and ‘signal transduction mechanisms’, including specific transcription factor-encoding genes, ubiquitin carboxyl-terminal hydrolase-encoding gene, and MAPKKK-encoding gene. The up-regulation of these genes with the gene Pdmfs2 knockout indicated some compensatory effects in the potential to sustain prochloraz resistance of the mfs2-defective P. digitatum strain.

3.6. Drug Pump Protein-Encoding Gene Expression Profiles

In the present study, a series of homologous genes encoding the three main types of drug pump proteins, including MFS-, ABC- and MATE-encoding isogenes, were selected to investigate their fold changes in the four comparative groups. The MFS-, ABC- and MATE proteins encoded by these isogenes were subjected to multiple amino acid-sequence alignments, respectively, using Clustal_X2 software and online tool ENDscript server (version 3.0). As shown in Figure S1, there were significantly high homologs in the primary structure and the classical motifs between all the 33 MFS isoforms (i.e., MFS1~MFS33). The similar results were obtained in the multiple sequence alignments of ABC (i.e., ABC1~ABC8) and MATE (MATE1~MATE3) proteins, respectively (Figure S1).
As summarized in Table 8, drug-pump gene MFS2 was up-regulated in the wild-type P. digitatum strain with prochloraz induction, but such an up-regulation was not observed in the mfs2-defective strain due to the knockout of the MFS2-encoding gene. In the comparison Pd-wt-(I/NI), besides the MFS2-encoding gene, the other two MFS isogenes were up-regulated after prochloraz treatment, i.e., MFS21 (PDIP_55680) and MFS22 (PDIP_19590). These two MFS-encoding genes were not up-regulated in the mfs2-defective P. digitatum strain with prochloraz treatment. In the absence of prochloraz, the knockout of Pdmfs2 alone also led to the down-regulation of multiple MFSisogenes, including MFS1 (PDIP_66230), MFS3 (PDIP_34090), MFS4 (PDIP_53210), MFS5 (PDIP_21030), MFS8 (PDIP_77890), MFS9 (PDIP_77880), MFS12 (PDIP_57820), MFS15 (PDIP_18570), and MFS18 (PDIP_11120). These MFS-encoding genes were also down-regulated in the comparison I-(Pd-d/Pd-wt) with similar fold changes to those of the comparison NI-(Pd-d/Pd-wt). Such lower transcript abundances of the above nine MFS-encoding genes might cause lower prochloraz resistance for the mfs2-defective P. digitatum strain. However, the nine MFS-encoding genes down-regulated in the two comparisons (i.e., I-(Pd-d/Pd-wt) and NI-(Pd-d/Pd-wt)) cannot be induced in the mfs2-defective P. digitatum strain after prochloraz treatment. Such transcriptional evidence also indicated these nine MFS-encoding genes might play roles in sustain the essential baselines of prochloraz resistance for both wild-type and mfs2-defective P. digitatum strains.
On the other hand, in the absence of prochloraz, the knockout of Pdmfs2 alone also led to the up-regulation of multiple MFS isogenes, including MFS23 (PDIP_36610), MFS26 (PDIP_64100), MFS27 (PDIP_55370), MFS31 (PDIP_19850), MFS33 (PDIP_55020) (Table 8). All these five MFS-encoding genes did not show up-regulation in the comparison Pd-d-(I/NI); however, three of them (i.e., MFS23, MFS26 and MFS27) showed up-regulation in the comparison I-(Pd-d/Pd-wt). These three MFS-encoding genes might exert some compensatory effects to sustain the prochloraz resistance of the mfs2-defective P. digitatum strain. Interestingly, in the comparison I-(Pd-d/Pd-wt), the amount of down-regulated MFS isogenes was obviously higher than that of up-regulated MFS isogenes. Such a profile might indicate that the compensatory effects by several MFS isogenes in potential could not compensate in full for the loss of Pdmfs2 gene that induced a simultaneous down-regulation of most of the MFS-encoding genes.
In addition to the MFS-encoding genes, another class of drug pump protein-encoding genes (i.e., ABC-encoding genes) exhibited similar changing profiles (Table 8), as compared to MFS isogenes. After prochloraz treatment, the only one ABC-encoding gene, i.e., ABC2 (PDIP_58890), was up-regulated in the wild-type P. digitatum strain after prochloraz treatment, but not up-regulated in the mfs2-defective P. digitatum strain. Considering there was not any other ABC-encoding gene up-regulated in the wild-type P. digitatum strain after prochloraz treatment, the ABC2-encoding gene might be the essential contributor to developing prochloraz resistance. On the other hand, the knockout of Pdmfs2 led to the up-regulation of multiple ABC isogenes in the absence of prochloraz, including ABC3 (PDIP_13640), ABC4 (PDIP_19230), ABC5 (PDIP_78490), ABC6 (PDIP_37050), ABC7 (PDIP_37060), and ABC8 (PDIP_57360). Among them, the genes encoding ABC3, ABC4 and ABC5 were simultaneously up-regulated in the comparison I-(Pd-d/Pd-wt). These three ABC-encoding genes might exert some compensatory effects to sustain prochloraz resistance of the mfs2-defective P. digitatum strain. Regarding the third class of drug pump protein-encoding genes (i.e., MATE-encoding genes), all the three MATE isogenes were up-regulated in the wild-type P. digitatum strain after prochloraz treatment, including MATE1 (PDIP_56750), MATE2 (PDIP_40930), and MATE3 (PDIP_05620) (Table 8). However, such up-regulation was not observed in the comparison Pd-d-(I/NI). That is to say, the three MATE-encoding genes were not up-regulated in the mfs2-defective P. digitatum strain after prochloraz treatment. Not similar to the changing profiles of MFS- and ABC-encoding genes, none of the present MATE isogenes were induced by the Pdmfs2 gene knockout at no prochloraz treatment, and accordingly, the three MATE-encoding genes were simultaneously down-regulated in the comparison I-(Pd-d/Pd-wt). These lines of evidence indicated that the decreased prochloraz resistance of the mfs2-defective P. digitatum strain might be in part due to the loss of Pdmfs2 gene that led to the simultaneous down-regulation of all three MATE isogenes.

3.7. qPCR Validation of DEGs

The results of qPCR validation for the selected DEGs are summarized in Table 9, including MFS-encoding genes, ABC-encoding genes, MATE-encoding genes, and multiple metabolism-relating and stress-responsive protein-encoding genes. In general, the transcript abundance change profiles of all DEGs in the present four comparative groups, obtained using qPCR, were well correlated with those obtained by RNA-seq analysis. At prochloraz induction, with comparison to no prochloraz induction, some of the up-regulated MFS-, ABC-, and MATE-encoding genes in the wild-type P. digitatum strain did exhibit no up-regulation or lower folds of up-regulation in the mfs2-defective P. digitatum strain, including MFS6, MFS7, MFS10, MFS16, MFS21, MFS22, ABC2, ABC8, MATE1, MATE2, and MATE3. Similar changing profiles were also found in those of multiple metabolism-relating and stress-responsive protein-encoding genes.

4. Discussion

Pdmfs2 has been reported as an essential gene to develop high prochloraz resistance of the P. digitatum strain, as the knockout of this drug-pump protein-encoding gene led to a significantly lower prochloraz resistance [8]. The underlying mechanisms need further studies regarding how the Pdmfs2 regulates fungal prochloraz resistance.
According to studies in the past years, more and more evidence has emerged to support comprehensive metabolism backgrounds underlying fungicide resistance. Such backgrounds include various ERG-encoding genes in fungal ergosterol biosynthesis pathways [36,41,42], acetyl-CoA carboxylase (ACCase)-encoding genes in the lipid and fatty acid oxidation pathways [45,46,47], reactive oxygen species (ROS)-metabolizing enzyme-encoding genes in the cell wall maintenance and oxidative-stress-responsive processes [50,51], mitochondrial respiratory chain protein-encoding genes in the cellular energy metabolisms [53,54,55], ubiquitin-encoding genes in the post-translational modification processes [57,58,59], and protein kinase-encoding genes involved in mitogen-activated signal transductions [55,60,61]. In the present study, RNA-seq analysis revealed that Pdmfs2 knockout led to the down-regulation of genes involved in peroxisome (ko04146) and oxidative phosphorylation (ko00190) at no prochloraz treatment (Table 1 and Table 2, and Figure 1 and Figure 2). Specially, the down-regulation of lipid metabolism-relating genes in the comparison NI-(Pd-d/Pd-wt), including fatty acyl-CoA oxidase-encoding gene and carnitine acetyl transferase-encoding gene (Table 2), was also reported in Pisolithusmicrocarpus and Beauveria bassiana [76,77]. And the down-regulation of energy metabolism-relating genes in the comparison NI-(Pd-d/Pd-wt), including cytochrome b-encoding gene and ATP synthase-encoding gene (Table 2), was also reported in Botrytis cinerea and Corynespora cassiicola [55,78]. Thus, even in the absence of fungicide, there has been an association of Pdmfs2 with multiple metabolisms required in the fungi adaptation to their growth environments, and such an adaptation might be one aspect of the physiological basis to develop fungicide resistance.
In the present RNA-seq analysis, the genes involved in oxidative phosphorylation, steroid biosynthesis, biosynthesis of fatty acids including unsaturated fatty acids, ubiquinone biosynthesis, and ribosome processes were all up-regulated in the wild-type P. digitatum strain after prochloraz treatment (Table 3 and Figure 3). Such a simultaneous up-regulation in the prochloraz induction suggested that the prochloraz resistance did require these multiple metabolism backgrounds. Similar requirements were observed in the prochloraz-treated mfs2-defective P. digitatum strain (Table 4 and Figure 3). However, after prochloraz treatment, the up-regulated KEGG classes in the mfs2-defective P. digitatum strain were obviously less than those in the wild-type P. digitatum strain (Table 3 and Table 4). Actually, many up-regulated DEGs in the comparison Pd-wt-(I/NI), relating to ergosterol biosynthesis, lipid and fatty acid oxidation, oxidative-stress-responsive processes, and cellular energy metabolisms, were not identified as DEGs in the comparison Pd-d-(I/NI) (Table 5 and Figure 4, Figure 5 and Figure 6). For example, neither NADH dehydrogenase-encoding genes, nor cytochrome b-encoding gene, cytochrome c oxidase-encoding gene, ATP synthase-encoding gene, Erg-encoding genes (i.e., Erg1, Erg3, and Erg25), or ribosomal protein-encoding genes were up-regulated in the mfs2-defective P. digitatum strain (Table 5). The present results indicated the importance of Pdmfs2 in developing prochloraz resistance. That is to say, the Pdmfs2 might be functionally associated with multiple metabolism-relating genes, and they are cooperatively expressed to confer P. digitatum prochloraz resistance through specific mechanisms that need further study.
On the other hand, the down-regulated DEGs of the comparison I-(Pd-d/Pd-wt) were mainly enriched into ‘ribosome’ and ‘oxidative phosphorylatio’ KEGG pathways (Table 6 and Figure 7), including ribosomal protein-encoding genes, cytochrome c oxidase-encoding gene, and ATP synthase-encoding gene. Similar results were obtained in the KOG classification (Table 7 and Figure 8). At the prochloraz treatment, the transcriptional abundances of these genes in the mfs2-defective P. digitatum strain were all lower than those of the wild-type P. digitatum strain. The results suggested (1) the important functions of these genes to sustain fungal prochloraz resistance, and (2) the potential association of these genes with Pdmfs2. In contrast, some genes were up-regulated in the comparison I-(Pd-d/Pd-wt) (Table 6 and Table 7). These genes and the relating metabolic pathways might in part compensate for the Pdmfs2 gene-deletion, sustaining the prochloraz resistance baseline of the mfs2-defective P. digitatum strain.
Multiple iso-genes encoding MFS, ABC, and MATE transporters have been identified in the fungal genomics and RNA-seq studies [32,33]. Simultaneous over-expression of MFS and ABC genes has been found in the highly prochloraz-resistant fungus [9,26,27,28]. Similar results were obtained in the present study, as multiple MFS-, ABC-, and MATE-encoding genes were up-regulated in the wild-type P. digitatum strain, whereas most of the putative drug-pump genes were not up-regulated in the mfs2-defective P. digitatum strain (Table 8 and Table 9). This correlation of Pdmfs2 with other drug-pump genes suggested the importance of Pdmfs2 in the drug-pump-induced prochloraz resistance. On the other hand, the over-expression of specific MFS- and ABC-encoding genes cannot compensate for the loss of Pdmfs2, which further verifies the important role of Pdmfs2 in developing the prochloraz resistance.

5. Conclusions

The present study provided some transcriptome evidence regarding the important role of drug-efflux pump protein gene mfs2 in P. digitatum prochloraz resistance. The knockout of mfs2 led to the simultaneous down-regulation of other drug-efflux pump protein gene expression, and led to the simultaneous down-regulation of cellular metabolism-related gene expression, including ribosome biosynthesis-related genes, oxidative phosphorylation genes, steroid biosynthesis-related genes, fatty acid biosynthesis-related genes, and carbon- and nitrogen-metabolism-related genes. These results indicated a more comprehensive background, regarding a crosslink between various drug-efflux pump proteins and between multiple cellular metabolisms, which might be associated with mfs2-introduced prochloraz resistance in the P. digitatum strain (PdF6).

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms12050888/s1, Figure S1: Protein homology alignments for multiple isoforms of MFS (MFS1-33), ABC (ABC1-8), and MATE (MATE1-3) in the present study; Table S1: Primers used in the present qRT-PCR.; Table S2: Summary of the reads’ quality in the present study; Table S3: Summary of percentages of reads mapping to the reference genome in the present four samples; Table S4: SNP analysis in the present four samples; Table S5: DEG numbers in the present four comparative groups; Table S6: Number summary of the DEGs functionally annotated in the eight public databases.

Author Contributions

Y.Y. designed this study and drafted the manuscript. Y.Y. and S.L. acquired the National Natural Science Foundation of China grant (No. 32072361). S.W. and Y.Z. designed this study and acquired the special fund from the Hubei key laboratory and Hubei collaborative innovation center (No. 201931903). Y.Y. supervised all the research activities. R.C., S.L. and C.Z. prepared and verified all the fungal samples used in RNA-sequencing. Y.Y., R.C., S.L., C.Z. and Y.Z. performed bioinformatics analysis and figure production. R.C., S.L. and C.Z. designed primers and conducted qRT-PCR experiments. Y.Y. and R.C. substantially revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (grant No. 32072361), and by the Special Fund from Hubei Key Laboratory of Economic Forest Germplasm Improvement and Resources Comprehensive Utilization and Hubei Collaborative Innovation Center for the Characteristic Resources Exploitation of Dabie Mountains (No. 201931903). The funding bodies did not play any role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Acknowledgments

The authors acknowledge the technical aids provided by Biomarker Technologies Company Limited (Beijing, China) to conduct RNA sequencing.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Palou, L.; Smilanick, J.L.; Usall, J.; Viñas, I. Control of postharvest blue and green molds of oranges by hot water, sodium carbonate, and sodium bicarbonate. Plant Dis. 2001, 85, 371–376. [Google Scholar] [CrossRef]
  2. Cheng, Y.L.; Lin, Y.L.; Cao, H.H.; Li, Z.G. Citrus postharvest green mold: Recent advances in fungal pathogenicity and fruit resistance. Microorganisms 2020, 8, 449. [Google Scholar] [CrossRef]
  3. Ma, B.; Tredway, L.P. Induced overexpression of cytochrome P450 sterol 14α-demethylase gene (CYP51) correlates with sensitivity to demethylation inhibitors (DMIs) in Sclerotinia homoeocarpa. Pest Manag. Sci. 2013, 69, 1369–1378. [Google Scholar] [CrossRef] [PubMed]
  4. Zhang, J.; Zhang, B.; Zhu, F.X.; Fu, Y.P. Baseline sensitivity and fungicidal action of propiconazole against Penicillium digitatum. Pestic. Biochem. Physiol. 2021, 172, 104752. [Google Scholar] [CrossRef]
  5. Zhang, Y.C.; Zhang, B.; Luo, C.X.; Fu, Y.P.; Zhu, F.X. Fungicidal actions and resistance mechanisms of prochloraz to Penicillium digitatum. Plant Dis. 2021, 105, 408–415. [Google Scholar] [CrossRef] [PubMed]
  6. Sánchez-Torres, P.; Tuset, J.J. Molecular insights into fungicide resistance in sensitive and resistant Penicillium digitatum strains infecting citrus. Postharvest Biol. Technol. 2011, 59, 159–165. [Google Scholar] [CrossRef]
  7. de Ramón-Carbonell, M.; Sánchez-Torres, P. Significance of 195 bp-enhancer of PdCYP51B in the acquisition of Penicillium digitatum DMI resistance and increase of fungal virulence. Pestic. Biochem. Physiol. 2020, 165, 104522. [Google Scholar] [CrossRef]
  8. Wu, Z.; Wang, S.Q.; Yuan, Y.Z.; Zhang, T.F.; Liu, J.; Liu, D.L. A novel major facilitator superfamily transporter in Penicillium digitatum (PdMFS2) is required for prochloraz resistance, conidiation and full virulence. Biotechnol. Lett. 2016, 38, 1349–1357. [Google Scholar] [CrossRef] [PubMed]
  9. Zhang, T.F.; Cao, Q.W.; Li, N.; Liu, D.L.; Yuan, Y.Z. Transcriptome analysis of fungicide-responsive gene expression profiles in two Penicillium italicum strains with different response to the sterol demethylation inhibitor (DMI) fungicide prochloraz. BMC Genom. 2020, 21, 156. [Google Scholar] [CrossRef]
  10. Hamamoto, H.; Hasegawa, K.; Nakaune, R.; Lee, Y.J.; Makizumi, Y.; Akutsu, K.; Hibi, T. Tandem repeat of a transcriptional enhancer upstream of the sterol 14α-demethylase gene (CYP51) in Penicillium digitatum. Appl. Environ. Microbiol. 2000, 66, 3421–3426. [Google Scholar] [CrossRef]
  11. Sun, X.P.; Wang, J.Y.; Feng, D.; Ma, Z.H.; Li, H.Y. PdCYP51B, a new putative sterol 14α-demethylase gene of Penicillium digitatum involved in resistance to imazalil and other fungicides inhibiting ergosterol synthesis. Appl. Microbiol. Biotechnol. 2011, 91, 1107–1119. [Google Scholar] [CrossRef] [PubMed]
  12. Pan, X.Y.; Cao, H.J.; Yu, J.J.; Yu, M.N.; Qi, Z.Q.; Song, T.Q.; Du, Y.; Yong, M.L.; Zhang, R.S.; Yin, X.L.; et al. Monitoring and analysis of rice pathogen Ustilaginoidea virens isolates with resistance to sterol demethylation inhibitors in China. Phytopathol. Res. 2020, 2, 24. [Google Scholar] [CrossRef]
  13. Lucio, J.; Gonzalez-Jimenez, I.; Rivero-Menendez, O.; Alastruey-Izquierdo, A.; Pelaez, T.; Alcazar-Fuoli, L.; Mellado, E. Point mutations in the 14-α sterol demethylase Cyp51A or Cyp51C could contribute to azole resistance in Aspergillus flavus. Genes 2020, 11, 1217. [Google Scholar] [CrossRef] [PubMed]
  14. Morschhäuser, J. Regulation of multidrug resistance in pathogenic fungi. Fungal Genet. Biol. 2010, 47, 94–106. [Google Scholar] [CrossRef] [PubMed]
  15. Sui, X.Y.; Cheng, X.Y.; Li, Z.D.; Wang, Y.H.; Zhang, Z.P.; Yan, R.Y.; Chang, L.; Li, Y.C.; Xu, P.; Duan, C.Z. Quantitative proteomics revealed the transition of ergosterol biosynthesis and drug transporters processes during the development of fungal fluconazole resistance. Biochim. Biophys. Acta Gene Regul. Mech. 2023, 1866, 194953. [Google Scholar] [CrossRef] [PubMed]
  16. Yan, N. Structural advances for the major facilitator superfamily (MFS) transporters. Trend Biochem. Sci. 2013, 38, 151–159. [Google Scholar] [CrossRef]
  17. Stephen, J.; Salam, F.; Lekshmi, M.; Kumar, S.H.; Varela, M.F. The major facilitator superfamily and antimicrobial resistance efflux pumps of the ESKAPEE pathogen Staphylococcus aureus. Antibiotics 2023, 12, 343. [Google Scholar] [CrossRef]
  18. Dias, P.J.; Seret, M.L.; Goffeau, A.; Correia, I.S.; Baret, P.V. Evolution of the 12-spanner drug:H+ antiporter DHA1 family in hemiascomycetous yeasts. Omics 2010, 14, 701–710. [Google Scholar] [CrossRef] [PubMed]
  19. Hassan, K.A.; Brzoska, A.J.; Wilson, N.L.; Eijkelkamp, B.A.; Brown, M.H.; Paulsen, I.T. Roles of DHA2 family transporters in drug resistance and iron homeostasis in Acinetobacter spp. J. Mol. Microbiol. Biotechnol. 2011, 20, 116–124. [Google Scholar]
  20. de Ramón-Carbonell, M.; Sánchez-Torres, P. Penicillium digitatum MFS transporters can display different roles during pathogen-fruit interaction. Int. J. Food Microbiol. 2021, 337, 108918. [Google Scholar] [CrossRef]
  21. Kretschmer, M.; Leroch, M.; Mosbach, A.; Walker, A.S.; Fillinger, S.; Mernke, D.; Schoonbeek, H.J.; Pradier, J.M.; Leroux, P.; De Waard, M.A.; et al. Fungicide-driven evolution and molecular basis of multidrug resistance in field populations of the grey mouldfungus Botrytis cinerea. PLoS Pathog. 2009, 512, e1000696. [Google Scholar]
  22. Roohparvar, R.; De Waard, M.A.; Kema, G.H.; Zwiers, L.H. MgMfs1, a major facilitator superfamily transporter from the fungal wheat pathogen Mycosphaerellagraminicola, is a strong protectant against natural toxic compounds and fungicides. Fungal Genet. Biol. 2007, 44, 378–388. [Google Scholar] [CrossRef]
  23. Qadri, H.; Shah, A.H.; Mir, M.A.; Qureshi, M.F.; Prasad, R. Quinidine drug resistance transporter knockout Candida cells modulate glucose transporter expression and accumulate metabolites leading to enhanced azole drug resistance. Fungal Genet. Biol. 2022, 161, 103713. [Google Scholar] [CrossRef]
  24. Ntasiou, P.; Samaras, A.; Papadakis, E.N.; Menkissoglu-Spiroudi, U.; Karaoglanidis, G.S. Aggressiveness and patulin production in Penicillium expansum multidrug resistant strains with different expression levels of MFS and ABC transporters, in the presence or absence of fludioxonil. Plants 2023, 12, 1398. [Google Scholar] [CrossRef]
  25. Wang, J.Y.; Sun, X.P.; Lin, L.Y.; Zhang, T.Y.; Ma, Z.H.; Li, H.Y. PdMFS1, a major facilitator superfamily transporter from Penicilliundigitatum, is partially involved in the imazalil-resistance and pathogenicity. Afr. J. Microbiol. Res. 2012, 6, 95–105. [Google Scholar]
  26. Liu, J.; Wang, S.Q.; Qin, T.T.; Li, N.; Niu, Y.H.; Li, D.D.; Yuan, Y.Z.; Geng, H.; Xiong, L.; Liu, D.L. Whole transcriptome analysis of Penicillium digitatum strains treatmented with prochloraz reveals their drug-resistant mechanisms. BMC Genom. 2015, 16, 855. [Google Scholar] [CrossRef]
  27. Morschhäuser, J.; Barker, K.S.; Liu, T.T.; BlaB-Warmuth, J.; Homayouni, R.; Rogers, P.D. The transcription factor Mrr1p controls expression of the MDR1 efflux pump and mediates multidrug resistance in Candida albicans. PLoS Pathog. 2007, 3, e164. [Google Scholar] [CrossRef]
  28. Tan, X.L.; Long, C.Y.; Meng, K.X.; Shen, X.M.; Wang, Z.T.; Li, L.; Tao, N.G. Transcriptome sequencing reveals an inhibitory mechanism of Penicillium digitatum by sodium dehydroacetate on citrus fruit. Postharvest Biol. Technol. 2022, 188, 111898. [Google Scholar] [CrossRef]
  29. Oliveira-Tintino, C.D.D.; Muniz, D.F.; Barbosa, C.R.D.D.D.; Pereira, R.L.S.; Begnini, I.M.; Rebelo, R.A.; da Silva, L.E.; Mireski, S.L.; Nasato, M.C.; Krautler, M.I.L.; et al. NorA, Tet(K), MepA, and MsrAefflux pumps in Staphylococcus aureus, their inhibitors and 1,8-naphthyridine sulfonamides. Curr. Pharm. Des. 2023, 29, 323–355. [Google Scholar]
  30. Ma, Q.B.; Yi, R.; Li, L.; Liang, Z.Y.; Zeng, T.T.; Zhang, Y.; Huang, H.; Zhang, X.; Yin, X.L.; Cai, Z.D.; et al. GsMATE encoding a multidrug and toxic compound extrusion transporter enhances aluminum tolerance in Arabidopsis thaliana. BMC Plant Biol. 2018, 18, 212. [Google Scholar] [CrossRef] [PubMed]
  31. Kaur, R.; Kumar, P.; Kumar, A.; Praba, U.P.; Birdi, R.; Singh, R.; Kaur, G.; Lore, J.S.; Neelam, K.; Vikal, Y. OsMATE6 gene putatively involved in host defense response toward susceptibility against Rhizoctonia solani in rice. J. Plant Interact. 2022, 17, 744–755. [Google Scholar] [CrossRef]
  32. Marcet-Houben, M.; Ballester, A.R.; de la Fuente, B.; Harries, E.; Marcos, J.F.; González-Candelas, L.; Gabaldón, T. Genome sequence of the necrotrophic fungus Penicillium digitatum, the main postharvest pathogen of citrus. BMC Genom. 2012, 13, 646. [Google Scholar] [CrossRef]
  33. Wang, M.S.; Ruan, R.X.; Li, H.Y. The completed genome sequence of the pathogenic ascomycete fungus Penicillium digitatum. Genomics 2021, 113, 439–446. [Google Scholar] [CrossRef]
  34. de Ramón-Carbonell, M.; López-Pérez, M.; González-Candelas, L.; Sánchez-Torres, P. PdMFS1 transporter contributes to Penicilliundigitatum fungicide resistance and fungal virulence during citrus fruit infection. J. Fungi 2019, 5, 100. [Google Scholar] [CrossRef]
  35. Sun, X.P.; Ruan, R.X.; Lin, L.Y.; Zhu, C.Y.; Zhang, T.Y.; Wang, M.S.; Li, H.Y.; Yu, D.L. Genomewide investigation into DNA elements and ABC transporters involved in imazalil resistance in Penicillium digitatum. FEMS Microbiol. Lett. 2013, 348, 11–18. [Google Scholar] [CrossRef]
  36. Ruan, R.X.; Wang, M.S.; Liu, X.; Sun, X.P.; Chung, K.R.; Li, H.Y. Functional analysis of two sterol regulatory element binding proteins in Penicillium digitatum. PLoS ONE 2017, 12, e0176485. [Google Scholar] [CrossRef]
  37. Prasad, R.; Nair, R.; Banerjee, A. Multidrug transporters of Candida species in clinical azole resistance. Fungal Genet. Biol. 2019, 132, 103252. [Google Scholar] [CrossRef]
  38. Pérez-Cantero, A.; López-Fernández, L.; Guarro, J.; Capilla, J. Azole resistance mechanisms in Aspergillus: Update and recent advances. Int. J. Antimicrob. Agents 2020, 55, 105807. [Google Scholar] [CrossRef]
  39. Zhao, J.; Zhang, D.Y.; Wang, Z.; Tian, Z.H.; Yang, F.; Lu, X.J.; Long, C.A. Genome sequencing and transcriptome analysis of Geotrichumcitri-aurantii on citrus reveal the potential pathogenic- and guazatine-resistance related genes. Genomics 2020, 112, 4063–4071. [Google Scholar] [CrossRef] [PubMed]
  40. Lees, N.D.; Bard, M.; Kirsch, D.R. Biochemistry and molecular biology of sterol synthesis in Saccharomyces cerevisiae. Crit. Rev. Biochem. Mol. Biol. 1999, 34, 33–47. [Google Scholar] [PubMed]
  41. Fernandes, T.; Silva, S.; Henriques, M. Effect of voriconazole on Candida tropicalis biofilms: Relation with ERG genes expression. Mycopathologia 2016, 181, 643–651. [Google Scholar] [CrossRef]
  42. Park, M.Y.; Jeon, B.J.; Kang, J.E.; Kim, B.S. Synergistic interactions of schizostatinidentified from Schizophyllum commune with demethylation inhibitor fungicides. Plant Pathol. J. 2020, 36, 579–590. [Google Scholar] [CrossRef]
  43. Tong, L.; Harwood, H.J., Jr. Acetyl-coenzyme A carboxylases: Versatile targets for drug discovery. J. Cell. Biochem. 2006, 99, 1476–1488. [Google Scholar] [CrossRef]
  44. Galdieri, L.; Vancura, A. Acetyl-CoA carboxylase regulates global histone acetylation. J. Biol. Chem. 2012, 287, 23865–23876. [Google Scholar] [CrossRef] [PubMed]
  45. Bozaquel-Morais, B.L.; Madeira, J.B.; Venâncio, T.M.; Pacheco-Rosa, T.; Masuda, C.A.; Montero-Lomeli, M.A. Chemogenomic screen reveals novel Snf1p/AMPK independent regulators of acetyl-CoA carboxylase. PLoS ONE 2017, 12, e0169682. [Google Scholar] [CrossRef]
  46. Wang, Y.A.; Wang, M.K.; Li, M.; Zhao, T.; Zhou, L. Cinnamaldehyde inhibits the growth of Phytophthora capsica through disturbing metabolic homoeostasis. PeerJ 2021, 9, e11339. [Google Scholar] [CrossRef] [PubMed]
  47. Duke, S.O.; Pan, Z.; Bajsa-Hirschel, J.; Tamang, P.; Hammerschmidt, R.; Lorsbach, B.A.; Sparks, T.C. Molecular targets of herbicides and fungicides—Are there useful overlaps for fungicide discovery? J. Agric. Food Chem. 2023, 71, 20532–20548. [Google Scholar] [CrossRef] [PubMed]
  48. Bink, A.; Vandenbosch, D.; Coenye, T.; Nelis, H.; Cammue, B.P.A.; Thevissen, K. Superoxide dismutases are involved in Candida albicans biofilm persistence against miconazole. Antimicrob. Agents Chemother. 2011, 55, 4033–4037. [Google Scholar] [CrossRef]
  49. Orta-Zavalza, E.; Guerrero-Serrano, G.; Gutiérrez-Escobedo, G.; Cañas-Villamar, I.; Juárez-Cepeda, J.; Castaño, I.; De Las Peñas, A. Local silencing controls the oxidative stress response and the multidrug resistance in Candida glabrata. Mol. Microbiol. 2013, 88, 1135–1148. [Google Scholar] [CrossRef]
  50. Zhao, W.B.; Zhao, Z.M.; Ma, Y.; Li, A.P.; Zhang, Z.J.; Hu, Y.M.; Zhou, Y.; Wang, R.; Luo, X.F.; Zhang, B.Q.; et al. Antifungal activity and preliminary mechanism of pristimerin against Sclerotinia sclerotiorum. Ind. Crops Prod. 2022, 185, 115124. [Google Scholar] [CrossRef]
  51. Liu, F.; Gao, R.Q.; Zhang, F.; Ren, Y.; Li, W.; He, B. Postharvest biocontrol of green mold (Penicillium digitatum) in citrus by Bacillus velezensis strain S161 and its mode of action. Biol. Control. 2023, 187, 105392. [Google Scholar] [CrossRef]
  52. Neubauer, M.; Zhu, Z.J.; Penka, M.; Helmschrott, C.; Wagener, N.; Wagener, J. Mitochondrial dynamics in the pathogenic mold Aspergillus fumigatus: Therapeutic and evolutionary implications. Mol. Microbiol. 2015, 98, 930–945. [Google Scholar] [CrossRef] [PubMed]
  53. Bouillaud, F. Inhibition of succinate dehydrogenase by pesticides (SDHIs) and energy metabolism. Int. J. Mol. Sci. 2023, 24, 4045. [Google Scholar] [CrossRef] [PubMed]
  54. Zhang, J.R.; Fernando, S.D. Identification of fungicide combinations targeting Plasmoparaviticola and Botrytis cinerea fungicide resistance using machine learning. Microorganisms 2023, 11, 1341. [Google Scholar] [CrossRef]
  55. Zhang, J.R.; Gelain, J.; Schnabel, G.; Mallawarachchi, S.; Wang, H.Q.; Mulgaonkar, N.; Karthikeyan, R.; Fernando, S. Identification of fungicide combinations for overcoming Plasmoparaviticola and Botrytis cinerea fungicide resistance. Microorganisms 2023, 11, 2966. [Google Scholar] [CrossRef] [PubMed]
  56. Leach, M.D.; Stead, D.A.; Argo, E.; Maccallum, D.M.; Brown, A.J.P. Molecular and proteomic analyses highlight the importance of ubiquitination for the stress resistance, metabolic adaptation, morphogenetic regulation and virulence of Candida albicans. Mol. Microbiol. 2011, 79, 1574–1593. [Google Scholar] [CrossRef] [PubMed]
  57. Retanal, C.; Ball, B.; Geddes-McAlister, J. Post-Translational Modifications drive success and failure of fungal-host interactions. J. Fungi 2021, 7, 124. [Google Scholar] [CrossRef] [PubMed]
  58. Toyotome, T.; Onishi, K.; Sato, M.; Kusuya, Y.; Hagiwara, D.; Watanabe, A.; Takahashi, H. Identification of novel mutations contributing to azole tolerance of Aspergillus fumigatus through in vitro exposure to tebuconazole. Antimicrob. Agents Chemother. 2021, 65, e0265720. [Google Scholar] [CrossRef]
  59. Poudel, B.; Mullins, J.; Fiedler, J.; Zhong, S.B. Genome-wide association study of fungicide sensitivity in a Fusarium graminearum population collected from North Dakota. Phytopathology 2024. [CrossRef]
  60. Sellers-Moya, Á.; Nuévalos, M.; Molina, M.; Martín, H. Clotrimazole-induced oxidative stress triggers novel yeast Pkc1-independent cell wall integrity MAPK pathway circuitry. J. Fungi 2021, 7, 647. [Google Scholar] [CrossRef]
  61. Wang, J.; Wen, Z.; Chen, Y.; Ma, Z.H. The sucrose non-fermenting-1 kinase Snf1 is involved in fludioxonil resistance via interacting with the high osmolarity glycerol MAPK kinase Hog1 in Fusarium. Phytopathol Res. 2023, 5, 52. [Google Scholar] [CrossRef]
  62. Liu, Y.Q.; Shang, L.G.; Zhan, Y.H.; Lin, M.; Liu, Z.; Yan, Y.L. Genome-wide analysis of sugar transporters identifies the gtsA gene for glucose transportation in Pseudomonas stutzeri A1501. Microorganisms 2020, 8, 592. [Google Scholar] [CrossRef] [PubMed]
  63. Liu, N.; Wang, Q.N.; He, C.Z.; An, B. CgMFS1, a major facilitator superfamily transporter, is required for sugar transport, oxidative stress resistance, and pathogenicity of Colletotrichum gloeosporioides from Hevea brasiliensis. Curr. Issues. Mol. Biol. 2021, 43, 1548–1557. [Google Scholar] [CrossRef] [PubMed]
  64. Wang, J.L.; Yu, J.H.; Liu, J.; Yuan, Y.Z.; Li, N.; He, M.Q.; Qi, T.; Hui, G.; Xiong, L.; Liu, D.L. Novel mutations in CYP51B from Penicillium digitatum involved in prochloraz resistance. J. Microbiol. 2014, 52, 762–770. [Google Scholar] [CrossRef] [PubMed]
  65. Kim, D.; Langmead, B.; Salzberg, S.L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 2015, 12, 357–360. [Google Scholar] [CrossRef] [PubMed]
  66. Prasad, R.; Banerjee, A.; Khandelwal, N.K.; Dhamgaye, S. The ABCs of Candida albicans multidrug transporter Cdr1. Eukaryot. Cell. 2015, 14, 1154–1164. [Google Scholar] [CrossRef] [PubMed]
  67. Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26, 139–140. [Google Scholar] [CrossRef] [PubMed]
  68. McKenna, A.; Hanna, M.; Banks, E.; Sivachenko, A.; Cibulskis, K.; Kernytsky, A.; Garimella, K.; Altshuler, D.; Gabriel, S.; Daly, M.; et al. The genome analysis toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010, 20, 1297–1303. [Google Scholar] [CrossRef] [PubMed]
  69. Florea, L.; Song, L.; Salzberg, S.L. Thousands of exon skipping events differentiate among splicing patterns in sixteen human tissues. F1000Res 2013, 2, 188. [Google Scholar] [CrossRef]
  70. Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple hypothesis testing. J. R. Stat. Soc. B 1995, 57, 289–300. [Google Scholar] [CrossRef]
  71. de Hoon, M.J.; Imoto, S.; Nolan, J.; Miyano, S. Open source clustering software. Bioinformatics 2004, 20, 1453–1454. [Google Scholar] [CrossRef] [PubMed]
  72. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef]
  73. Tatusov, R.L.; Galperin, M.Y.; Natale, D.A.; Koonin, E.V. The COG database: A tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000, 28, 33–36. [Google Scholar] [CrossRef] [PubMed]
  74. Kanehisa, M.; Araki, M.; Goto, S.; Hattori, M.; Hirakawa, M.; Itoh, M.; Katayama, T.; Kawashima, S.; Okuda, S.; Tokimatsu, T.; et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008, 36, D480–D484. [Google Scholar] [CrossRef] [PubMed]
  75. Mao, X.Z.; Cai, T.; Olyarchuk, J.G.; Wei, L.P. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics 2005, 21, 3787–3793. [Google Scholar] [CrossRef]
  76. Plett, K.L.; Buckley, S.; Plett, J.M.; Anderson, I.C.; Lundberg-Felten, J.; Jämtgård, S. Novel microdialysis technique reveals a dramatic shift in metabolite secretion during the early stages of the interaction between the ectomycorrhizal fungus Pisolithusmicrocarpus and its host Eucalyptus grandis. Microorganisms 2021, 9, 1817. [Google Scholar] [CrossRef] [PubMed]
  77. Cai, Q.; Wang, J.J.; Xie, J.T.; Jiang, D.H.; Keyhani, N.M. The Spt10 GNAT superfamily protein modulates development, cell cycle progression, and virulence in the fungal insect pathogen, Beauveria bassiana. J. Fungi 2021, 7, 905. [Google Scholar] [CrossRef]
  78. Sun, B.X.; Zhou, R.J.; Zhu, G.X.; Xie, X.W.; Chai, A.L.; Li, L.; Fan, T.F.; Zhang, S.P.; Li, B.J.; Shi, Y.X. The mechanisms of target and non-target resistance to QoIs in Corynespora cassiicola. Pestic. Biochem. Physiol. 2024, 198, 105760. [Google Scholar] [CrossRef]
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Figure 1. DEG analysis between the wild-type and mfs2-deleted P. digitatum strains at no prochloraz induction. (A) Volcano analysis of all DEGs; (B) heatmap analysis of all DEGs; (C) KEGG enrichment of down-regulated DEGs; (D) KEGG enrichment of up-regulated DEGs. Pd-wt-NI indicated the wild-type P. digitatum strain at no prochloraz induction. Pd-d-NI indicated the mfs2-deleted P. digitatum strain at no prochloraz induction. In the panel (A), the two dashed lines at vertical axis indicated the cut-off values to define DEGs in terms of log2(FC), i.e., “−1” for the left line and “1” for the right; the one dashed line at horizontal axis indicated the cut-off value (i.e., “2”) to define DEGs in terms of −log10(FDR).
Figure 1. DEG analysis between the wild-type and mfs2-deleted P. digitatum strains at no prochloraz induction. (A) Volcano analysis of all DEGs; (B) heatmap analysis of all DEGs; (C) KEGG enrichment of down-regulated DEGs; (D) KEGG enrichment of up-regulated DEGs. Pd-wt-NI indicated the wild-type P. digitatum strain at no prochloraz induction. Pd-d-NI indicated the mfs2-deleted P. digitatum strain at no prochloraz induction. In the panel (A), the two dashed lines at vertical axis indicated the cut-off values to define DEGs in terms of log2(FC), i.e., “−1” for the left line and “1” for the right; the one dashed line at horizontal axis indicated the cut-off value (i.e., “2”) to define DEGs in terms of −log10(FDR).
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Microorganisms 12 00888 g002
Figure 2. COG and KOG function classification of down-regulated DEGs between the wild-type and mfs2-deleted P. digitatum strains at no prochloraz induction. (A) COG classification; (B) KOG classification.
Figure 2. COG and KOG function classification of down-regulated DEGs between the wild-type and mfs2-deleted P. digitatum strains at no prochloraz induction. (A) COG classification; (B) KOG classification.
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Microorganisms 12 00888 g003
Figure 3. Volcano analysis of DEGs in the wild-type and mfs2-deleted P. digitatum strains with prochloraz induction. (A) Volcano analysis of DEGs in the wild-type P. digitatum strain with prochloraz induction (Pd-wt-I) in comparison to that with no prochloraz induction (Pd-wt-NI); (B) volcano analysis of DEGs in the mfs2-deleted P. digitatum strain with prochloraz induction (Pd-d-I) in comparison to that with no prochloraz induction (Pd-d-NI). The two dashed lines at vertical axis indicated the cut-off values to define DEGs in terms of log2(FC), i.e., “−1” for the left line and “1” for the right; the one dashed line at horizontal axis indicated the cut-off value (i.e., “2”) to define DEGs in terms of −log10(FDR).
Figure 3. Volcano analysis of DEGs in the wild-type and mfs2-deleted P. digitatum strains with prochloraz induction. (A) Volcano analysis of DEGs in the wild-type P. digitatum strain with prochloraz induction (Pd-wt-I) in comparison to that with no prochloraz induction (Pd-wt-NI); (B) volcano analysis of DEGs in the mfs2-deleted P. digitatum strain with prochloraz induction (Pd-d-I) in comparison to that with no prochloraz induction (Pd-d-NI). The two dashed lines at vertical axis indicated the cut-off values to define DEGs in terms of log2(FC), i.e., “−1” for the left line and “1” for the right; the one dashed line at horizontal axis indicated the cut-off value (i.e., “2”) to define DEGs in terms of −log10(FDR).
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Figure 4. Heatmap clustering of DEGs in the wild-type and mfs2-deleted P. digitatum strains with prochloraz induction. (A) Heatmap clustering of DEGs in the wild-type P. digitatum strain with prochloraz induction (Pd-wt-I) in comparison to that with no prochloraz induction (Pd-wt-NI); (B) heatmap clustering of DEGs in the mfs2-deleted P. digitatum strain with prochloraz induction (Pd-d-I) in comparison to that with no prochloraz induction (Pd-d-NI).
Figure 4. Heatmap clustering of DEGs in the wild-type and mfs2-deleted P. digitatum strains with prochloraz induction. (A) Heatmap clustering of DEGs in the wild-type P. digitatum strain with prochloraz induction (Pd-wt-I) in comparison to that with no prochloraz induction (Pd-wt-NI); (B) heatmap clustering of DEGs in the mfs2-deleted P. digitatum strain with prochloraz induction (Pd-d-I) in comparison to that with no prochloraz induction (Pd-d-NI).
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Microorganisms 12 00888 g005
Figure 5. KEGG enrichment of up-regulated DEGs in the wild-type and mfs2-deleted P. digitatum strains with prochloraz induction. (A) KEGG enrichment of up-regulated DEGs in the wild-type P. digitatum strain with prochloraz induction (Pd-wt-I) in comparison to that with no prochloraz induction (Pd-wt-NI); (B) KEGG enrichment of up-regulated DEGs in the mfs2-deleted P. digitatum strain with prochloraz induction (Pd-d-I) in comparison to that with no prochloraz induction (Pd-d-NI).
Figure 5. KEGG enrichment of up-regulated DEGs in the wild-type and mfs2-deleted P. digitatum strains with prochloraz induction. (A) KEGG enrichment of up-regulated DEGs in the wild-type P. digitatum strain with prochloraz induction (Pd-wt-I) in comparison to that with no prochloraz induction (Pd-wt-NI); (B) KEGG enrichment of up-regulated DEGs in the mfs2-deleted P. digitatum strain with prochloraz induction (Pd-d-I) in comparison to that with no prochloraz induction (Pd-d-NI).
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Microorganisms 12 00888 g006
Figure 6. KOG function classification of up-regulated DEGs in the wild-type and mfs2-deleted P. digitatum strains with prochloraz induction. (A) KOG function classification of up-regulated DEGs in the wild-type P. digitatum strain with prochloraz induction (Pd-wt-I) in comparison to that with no prochloraz induction (Pd-wt-NI); (B) KOG function classification of up-regulated DEGs in the mfs2-deleted P. digitatum strain with prochloraz induction (Pd-d-I) in comparison to that with no prochloraz induction (Pd-d-NI).
Figure 6. KOG function classification of up-regulated DEGs in the wild-type and mfs2-deleted P. digitatum strains with prochloraz induction. (A) KOG function classification of up-regulated DEGs in the wild-type P. digitatum strain with prochloraz induction (Pd-wt-I) in comparison to that with no prochloraz induction (Pd-wt-NI); (B) KOG function classification of up-regulated DEGs in the mfs2-deleted P. digitatum strain with prochloraz induction (Pd-d-I) in comparison to that with no prochloraz induction (Pd-d-NI).
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Microorganisms 12 00888 g007
Figure 7. DEG analysis between the wild-type and mfs2-deleted P. digitatum strains at prochloraz induction. (A) Volcano analysis of all DEGs; (B) heatmap analysis of all DEGs; (C) KEGG enrichment of down-regulated DEGs; (D) KEGG enrichment of up-regulated DEGs. Pd-wt-I indicated the wild-type P. digitatum strain at prochloraz induction. Pd-d-I indicated the mfs2-deleted P. digitatum strain at prochloraz induction. In the panel A, the two dashed lines at vertical axis indicated the cut-off values to define DEGs in terms of log2(FC), i.e., “−1” for the left line and “1” for the right; the one dashed line at horizontal axis indicated the cut-off value (i.e., “2”) to define DEGs in terms of −log10(FDR).
Figure 7. DEG analysis between the wild-type and mfs2-deleted P. digitatum strains at prochloraz induction. (A) Volcano analysis of all DEGs; (B) heatmap analysis of all DEGs; (C) KEGG enrichment of down-regulated DEGs; (D) KEGG enrichment of up-regulated DEGs. Pd-wt-I indicated the wild-type P. digitatum strain at prochloraz induction. Pd-d-I indicated the mfs2-deleted P. digitatum strain at prochloraz induction. In the panel A, the two dashed lines at vertical axis indicated the cut-off values to define DEGs in terms of log2(FC), i.e., “−1” for the left line and “1” for the right; the one dashed line at horizontal axis indicated the cut-off value (i.e., “2”) to define DEGs in terms of −log10(FDR).
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Microorganisms 12 00888 g008
Figure 8. KOG function classification of DEGs between the wild-type and mfs2-deleted P. digitatum strains at prochloraz induction. (A) KOG function classification of down-regulated DEGs in the mfs2-deleted P. digitatum strain (Pd-d-I) in comparison to wild-type P. digitatum strain (Pd-wt-I) at prochloraz induction; (B) KOG function classification of up-regulated DEGs in the mfs2-deleted P. digitatum strain (Pd-d-I) in comparison to wild-type P. digitatum strain (Pd-wt-I) at prochloraz induction.
Figure 8. KOG function classification of DEGs between the wild-type and mfs2-deleted P. digitatum strains at prochloraz induction. (A) KOG function classification of down-regulated DEGs in the mfs2-deleted P. digitatum strain (Pd-d-I) in comparison to wild-type P. digitatum strain (Pd-wt-I) at prochloraz induction; (B) KOG function classification of up-regulated DEGs in the mfs2-deleted P. digitatum strain (Pd-d-I) in comparison to wild-type P. digitatum strain (Pd-wt-I) at prochloraz induction.
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Table 1. KEGG-enriched DEGs between the wild-type and mfs2-deleted P. digitatum strains at no prochloraz induction.
Table 1. KEGG-enriched DEGs between the wild-type and mfs2-deleted P. digitatum strains at no prochloraz induction.
KEGG (ID)Annotated Function of DEGRegulatedLog2FCFDR Value
Pentose and glucuronate interconversions (ko00040)Dihydrodipicolinate synthetaseDown−1.541.66 × 10−10
ExopolygalacturonaseDown−1.072.31 × 10−4
Starch and sucrose metabolism (ko00500)Exo-β-1,3-glucanaseDown−1.253.74 × 10−5
α-L-RhamnosidaseDown−1.468.31 × 10−8
β-GlucosidaseDown−1.122.35 × 10−5
ExopolygalacturonaseDown−1.072.31 × 10−4
Peroxisome (ko04146)PeroxinDown−1.239.75 × 10−7
Fatty acyl-CoA oxidaseDown−1.141.40 × 10−6
Carnitine acetyl transferaseDown−1.051.07 × 10−5
Oxidative phosphorylation (ko00190)NADH dehydrogenaseDown−1.933.17 × 10-3
Cytochrome bDown−2.271.52 × 10−7
ATP synthaseDown−1.455.26 × 10−3
Nitrogen metabolism (ko00910)NAD+-dependent glutamate dehydrogenaseUp1.041.10 × 10−5
Nitrite reductaseUp1.321.55 × 10−8
Nitrate reductaseUp1.191.27 × 10−6
NitrilaseUp1.721.19 × 10−6
Tyrosine metabolism (ko00350)Maleylacetoacetate isomeraseUp1.472.30 × 10−6
4-Hydroxyphenylpyruvate dioxygenaseUp1.622.28 × 10−12
Amine oxidaseUp1.663.46 × 10−13
Aldehyde dehydrogenaseUp1.021.69 × 10−5
Phenylalanine metabolism (ko00360)4-Hydroxyphenylpyruvate dioxygenaseUp1.622.28 × 10−12
Amine oxidaseUp1.663.46 × 10−13
Aldehyde dehydrogenaseUp1.021.69 × 10−5
Tryptophan metabolism (ko00380)NitrilaseUp1.171.19 × 10−6
Indoleamine/pyrrole 2,3-dioxygenaseUp1.078.21 × 10−6
CatalaseUp1.131.65 × 10−5
Table 2. DEGs in the COG and KOG function classification of down-regulated DEGs between the wild-type and mfs2-deleted P. digitatum strains at no prochloraz induction.
Table 2. DEGs in the COG and KOG function classification of down-regulated DEGs between the wild-type and mfs2-deleted P. digitatum strains at no prochloraz induction.
Annotated Function of DEGDatabaseClass NameID
Dihydrodipicolinate COGAmino acid transport and metabolismE
COGCell wall/membrane/envelope biogenesisM
Exo-β-1,3-glucanaseCOGCarbohydrate transport and metabolismG
α-L-RhamnosidaseCOGCarbohydrate transport and metabolismG
KOGCarbohydrate transport and metabolismG
β-GlucosidaseCOGCarbohydrate transport and metabolismG
PeroxinCOGGeneral function prediction onlyR
KOGIntracellular trafficking, secretion, and vesicular transportU
Fatty acyl-CoA oxidaseCOGLipid transport and metabolismI
KOGLipid transport and metabolismI
Carnitine acetyl transferaseKOGLipid transport and metabolismI
NADH dehydrogenaseCOGEnergy production and conversionC
KOGEnergy production and conversionC
Cytochrome bCOGEnergy production and conversionC
KOGEnergy production and conversionC
ATP synthaseCOGEnergy production and conversionC
KOGEnergy production and conversionC
Table 3. Up-regulated DEGs involved in KEGG enrichment and classification in the wild-type P. digitatum strain with prochloraz induction (i.e., Pd-wt-(I/NI)).
Table 3. Up-regulated DEGs involved in KEGG enrichment and classification in the wild-type P. digitatum strain with prochloraz induction (i.e., Pd-wt-(I/NI)).
KEGG (ID)Annotated Function of DEGLog2FCFDR
Oxidative phosphorylation (ko00190)Cytochrome c oxidase2.541.54 × 10−22
NADH dehydrogenase3.658.77 × 10−34
Cytochrome b2.396.76 × 10−18
ATP synthase3.534.74 × 10−34
Steroid biosynthesis (ko00100)Erg241.305.65 × 10−8
Erg11.351.17 × 10−8
Erg251.439.29 × 10−10
Glutathione metabolism (ko00480)Glutathione S-transferase1.131.03 × 10−3
Biosynthesis of unsaturated fatty acids (ko01040)1,3,6,8-Tetrahydroxynaphthalene reductase1.089.74 × 10−5
Biotin metabolism (ko00780)1,3,6,8-Tetrahydroxynaphthalene reductase1.089.74 × 10−5
Fatty acid biosynthesis (ko00061)1,3,6,8-Tetrahydroxynaphthalene reductase1.089.74 × 10−5
Ubiquinone and other terpenoid-quinone biosynthesis (ko00130)NADH-quinone oxidoreductase1.438.80 × 10−10
Fatty acid metabolism (ko01212)1,3,6,8-Tetrahydroxynaphthalene reductase1.089.74 × 10−5
Glycerolipid metabolism (ko00561)Glycerol kinase1.621.24 × 10−4
Pentose and glucuronate interconversions (ko00040)Pectate lyase1.511.02 × 10−8
Ribosome (ko03010)40S Ribosomal protein1.013.49 × 10−5
Ribosomal protein1.013.00 × 10−5
Glycerophospholipid metabolism (ko00564)Phosphatidylserine decarboxylase1.142.23 × 10−6
Tyrosine metabolism (ko00350)Maleylacetoacetate isomerase1.101.42 × 10−3
Peroxisome (ko04146)Superoxide dismutase1.251.90 × 10−7
Biosynthesis of antibiotics (ko01130)Erg251.439.29 × 10−10
Erg241.305.65 × 10−8
Erg11.351.17 × 10−8
Spliceosome (ko03040)Pre-mRNA-splicing factor1.123.43 × 10−6
Table 4. Up-regulated DEGs involved in KEGG enrichment and classification in the mfs2-deleted P. digitatum strain with prochloraz induction in comparison to that without prochloraz induction (i.e., Pd-d-(I/NI)).
Table 4. Up-regulated DEGs involved in KEGG enrichment and classification in the mfs2-deleted P. digitatum strain with prochloraz induction in comparison to that without prochloraz induction (i.e., Pd-d-(I/NI)).
KEGG (ID)Annotated Function of DEGLog2FCFDR
Steroid biosynthesis (ko00100)Erg31.111.09 × 10−5
Erg251.129.08 × 10−6
Ether lipid metabolism (ko00565)Phospholipase C1.463.07 × 10−4
Galactose metabolism (ko00052)Extracellular invertase1.884.25 × 10−3
Inositol phosphate metabolism (ko00562)Phospholipase C1.463.07 × 10−4
Biosynthesis of antibiotics (ko01130)Erg31.111.09 × 10−5
Erg251.129.08 × 10−6
Glycerophospholipid metabolism (ko00564)Phospholipase C1.463.07 × 10−4
Starch and sucrose metabolism (ko00500)Extracellular invertase1.884.25 × 10−3
Table 5. KOG function classification of up-regulated DEGs in the Pd-wt-(I/NI) and Pd-d-(I/NI).
Table 5. KOG function classification of up-regulated DEGs in the Pd-wt-(I/NI) and Pd-d-(I/NI).
Function ClassAnnotated Function of DEGPd-wt-(I/NI)Pd-d-(I/NI)
Log2FCFDRLog2FCFDR
AZinc knuckle transcription factor1.082.47 × 10−4//
RNA helicase2.571.19 × 10−4//
CRNA helicase2.571.19 × 10−4//
NADH dehydrogenase subunit 42.901.11 × 10−23//
NADH dehydrogenase subunit 12.028.60 × 10−8//
Hypothetical protein1.151.34 × 10−3//
Glyoxylate reductase1.145.55 × 10−4//
Hypothetical protein1.148.06 × 10−6//
Cytochrome b2.396.76 × 10−18//
Hypothetical protein1.123.43 × 10−6//
Cytochrome c oxidase2.541.54 × 10−22//
ATP synthase subunit 92.479.84 × 10−6//
ATP synthase subunit 63.534.74 × 10−34//
Oxaloacetate hydrolase3.702.35 × 10−27//
Hypothetical protein1.216.71 × 10−4//
FMN dependent dehydrogenase1.166.23 × 10−6//
ENitrilase1.061.30 × 10−5//
Amino acid permease1.174.03 × 10−6//
GGlycerol kinase1.621.24 × 10−4//
MFS1.291.42 × 10−6//
Aquaporin1.251.51 × 10−7//
INRPS-like enzyme1.283.99 × 10−4//
Phosphatidylserine decarboxylase1.142.23 × 10−6//
Erg3//1.111.09 × 10−5
Erg251.439.29 × 10−101.129.08 × 10−6
Epoxide hydrolase1.081.85 × 10−5//
C-14 sterol reductase1.305.65 × 10−8//
JRibosomal protein1.013.00 × 10−5//
40S Ribosomal protein1.013.49 × 10−5//
OProtein-L-isoaspartate O-methyltransferase1.221.22 × 10−6//
Thioredoxin1.004.10 × 10−5//
Glutathione S-transferase1.131.04 × 10−3//
Maleylacetoacetate isomerase1.101.42 × 10−3//
PMetabolite transport protein GIT11.741.93 × 10−3//
Superoxide dismutase1.241.90 × 10−7//
Plasma membrane low affinity zinc ion transporter//1.218.06 × 10−3
QIsopenicillin N synthase1.058.48 × 10−5//
ABC1.523.91 × 10−3//
Phenyloxazoline synthase1.083.42 × 10−3//
Alcohol dehydrogenase1.251.21 × 10−6//
Flavin-binding monooxygenase-like protein//1.101.71 × 10−4
RNADH-quinone oxidoreductase1.438.80 × 10−10//
Aldehyde reductase1.166.37 × 10−3//
Short chain dehydrogenase/reductase1.161.74 × 10−5//
Isopenicillin N synthase1.058.48 × 10−5//
Dienelactone hydrolase1.052.81 × 10−4//
Short chain dehydrogenase/reductase1.042.65 × 10−5//
1,3,6,8-Tetrahydroxynaphthalene reductase1.089.74 × 10−5//
Pre-mRNA-splicing factor1.123.43 × 10−6//
Carbonyl reductase1.577.64 × 10−11//
TErg11.351.17 × 10−8//
C-14 sterol reductase1.305.65 × 10−8//
ZProfilin1.033.34 × 10−4//
Table 6. KEGG-enriched DEGs between the wild-type and mfs2-deleted P. digitatum strains at prochloraz induction.
Table 6. KEGG-enriched DEGs between the wild-type and mfs2-deleted P. digitatum strains at prochloraz induction.
KEGG (ID)Annotated Function of DEGRegulatedLog2FCFDR
Ribosome (ko03010)60S Ribosomal proteinDown−1.281.84 × 10−8
40S Ribosomal proteinDown−1.362.11 × 10−9
Ribosomal proteinDown−1.432.69 × 10−10
60S Acidic ribosomal phosphoproteinDown−1.029.34 × 10−6
Oxidative phosphorylation (ko00190)Cytochrome c oxidaseDown−7.579.35 × 10−141
ATP synthaseDown−8.781.13 × 10−12
NADH dehydrogenaseDown−11.125.82 × 10−44
Cytochrome bDown−6.281.02 × 10−51
ATPase proteolipidDown−1.041.03 × 10−5
NADH-ubiquinone oxidoreductaseDown−1.333.66 × 10−8
Pentose and glucuronate interconversions (ko00040)Mandelate racemase/muconate lactonizing enzymeDown−1.092.49 × 10−6
ExopolygalacturonaseDown−1.604.24 × 10−8
Fatty acid biosynthesis (ko00061)Fatty acid synthase β subunitUp1.381.23 × 10−9
Fatty acid synthase α subunitUp1.253.66 × 10−8
Acetyl-CoA carboxylaseUp1.731.67 × 10−14
Nitrogen metabolism (ko00910)NAD+-dependent glutamate dehydrogenaseUp2.041.46 × 10−19
Nitrite reductaseUp1.292.42 × 10−8
NitrilaseUp1.266.59 × 10−8
Regulation of mitophagy—yeast (ko04139)Transcription factorUp1.073.42 × 10−6
MAP kinase kinasekinaseUp1.345.63 × 10−9
Ubiquitin carboxyl-terminal hydrolaseUp1.141.03 × 10−6
Table 7. KOG function classification of DEGs between the wild-type and mfs2-deleted P. digitatum strains at prochloraz induction.
Table 7. KOG function classification of DEGs between the wild-type and mfs2-deleted P. digitatum strains at prochloraz induction.
Annotated Function of DEGRegulatedClass NameClass ID
60S Ribosomal proteinDownTranslation, ribosomal structure and biogenesisJ
40S Ribosomal proteinDownTranslation, ribosomal structure and biogenesisJ
Ribosomal proteinDownTranslation, ribosomal structure and biogenesisJ
60S Acidic ribosomal phosphoproteinDownTranslation, ribosomal structure and biogenesisJ
Cytochrome c oxidaseDownEnergy production and conversionC
ATP synthaseDownEnergy production and conversionC
NADH dehydrogenaseDownEnergy production and conversionC
Cytochrome bDownEnergy production and conversionC
ATPase proteolipidDownEnergy production and conversionC
NADH-ubiquinone oxidoreductaseDownEnergy production and conversionC
Acetyl-CoA carboxylaseUpLipid transport and metabolismI
NAD+-dependent glutamate dehydrogenaseUpAmino acid transport and metabolismE
Nitrite reductaseUpGeneral function prediction onlyR
NitrilaseUpAmino acid transport and metabolismE
Transcription factorUpChromatin structure and dynamicsB
MAP kinase kinasekinaseUpSignal transduction mechanismsT
Ubiquitin carboxyl-terminal hydrolaseUpPost-translational modification, protein turnover, chaperonesO
Table 8. Changing fold (log2FC) of drug pump protein homologous genes in four comparative groups.
Table 8. Changing fold (log2FC) of drug pump protein homologous genes in four comparative groups.
Gene NameChanging Fold (log2FC) of the Gene Transcription Abundance in the Below Groups in the Present Comparative Analysis
Pd-wt-(I/NI)Pd-d-(I/NI)I-(Pd-d/Pd-wt)NI-(Pd-d/Pd-wt)
MFS1 (PDIP_66230)//−2.28−2.46
MFS2 (PDIP_88410)1.18000
MFS3 (PDIP_34090)//−1.11−1.40
MFS4 (PDIP_53210)//−1.93−1.33
MFS5 (PDIP_21030)//−1.33−1.31
MFS6 (PDIP_86550)//−1.20/
MFS7 (PDIP_02580)//−2.30/
MFS8 (PDIP_77890)//−2.47−2.09
MFS9 (PDIP_77880)//−1.18−1.04
MFS10 (PDIP_68550)//−1.45/
MFS11 (PDIP_83160)//−1.33/
MFS12 (PDIP_57820)//−2.12−1.82
MFS13 (PDIP_05380)/−1.43−1.95/
MFS14 (PDIP_42270)//−1.36/
MFS15 (PDIP_18570)//−1.07−1.14
MFS16 (PDIP_67480)//−1.37/
MFS17 (PDIP_54260)/−1.27−2.20/
MFS18 (PDIP_11120)//−1.71−1.71
MFS19 (PDIP_08540)//−1.40/
MFS20 (PDIP_32140)//−2.34/
MFS21 (PDIP_55680)1.37/−1.08/
MFS22 (PDIP_19590)1.29/−1.04/
MFS23 (PDIP_36610)//2.722.50
MFS24 (PDIP_40610)//1.77/
MFS25 (PDIP_03090)−1.71−1.071.38/
MFS26 (PDIP_64100)//1.531.42
MFS27 (PDIP_55370)//1.221.47
MFS28 (PDIP_70440)//1.26/
MFS29 (PDIP_67290)//1.13/
MFS30 (PDIP_09580)//1.15/
MFS31 (PDIP_19850)/−2.63/2.05
MFS32 (PDIP_28570)−1.23−1.11//
MFS33 (PDIP_55020)/−1.07/1.22
ABC1 (PDIP_64370)//−1.35−1.71
ABC2 (PDIP_58890)//−1.18/
ABC3 (PDIP_13640)−2.75−1.092.841.18
ABC4 (PDIP_19230)−1.05/2.201.43
ABC5 (PDIP_78490)//1.981.79
ABC6 (PDIP_37050)///1.01
ABC7 (PDIP_37060)///1.06
ABC8 (PDIP_57360)///1.37
MATE1 (PDIP_56750)1.57/−1.78/
MATE2 (PDIP_40930)1.12/−1.22/
MATE3 (PDIP_05620)1.15/−1.07/
Table 9. qRT-PCR validation of DEGs.
Table 9. qRT-PCR validation of DEGs.
DEG NamePd-wt-(I/NI)Pd-d-(I/NI)
Relative Fold-Change in the RNA-seqRelative Fold-Change in the qPCRRelative Fold-Change in the RNA-seqRelative Fold-Change in the qPCR
MFS10.750.41 ± 0.030.930.62 ± 0.06
MFS21.591.98 ± 0.21//
MFS30.891.12 ± 0.141.190.91 ± 0.06
MFS41.011.35 ± 0.110.730.95 ± 0.10
MFS51.000.86 ± 0.071.070.91 ± 0.08
MFS61.481.76 ± 0.150.810.57 ± 0.06
MFS71.231.33 ± 0.120.490.75 ± 0.08
MFS80.671.12 ± 0.140.561.20 ± 0.11
MFS90.600.91 ± 0.070.591.05 ± 0.12
MFS101.582.13 ± 0.140.921.34 ± 0.15
MFS110.931.21 ± 0.060.801.30 ± 0.08
MFS120.550.92 ± 0.080.471.05 ± 0.09
MFS130.710.89 ± 0.060.370.55 ± 0.06
MFS141.011.19 ± 0.100.790.95 ± 0.07
MFS150.780.95 ± 0.080.891.06 ± 0.09
MFS161.742.16 ± 0.141.391.05 ± 0.09
MFS170.981.15 ± 0.130.410.76 ± 0.08
MFS180.891.09 ± 0.080.991.12 ± 0.11
MFS190.890.97 ± 0.060.610.79 ± 0.07
MFS200.911.22 ± 0.140.350.56 ± 0.05
MFS212.353.29 ± 0.251.211.77 ± 0.12
MFS222.234.10 ± 0.350.901.95 ± 0.13
MFS230.810.99 ± 0.061.031.15 ± 0.12
MFS240.470.87 ± 0.081.533.51 ± 0.33
MFS250.280.65 ± 0.040.480.79 ± 0.08
MFS260.590.99 ± 0.080.701.10 ± 0.09
MFS270.801.09 ± 0.080.730.95 ± 0.07
MFS280.530.85 ± 0.061.421.99 ± 0.14
MFS290.671.07 ± 0.090.961.15 ± 0.11
MFS300.680.97 ± 0.060.991.24 ± 0.09
MFS310.530.78 ± 0.050.150.62 ± 0.06
MFS320.390.77 ± 0.050.460.89 ± 0.08
MFS330.690.97 ± 0.080.471.07 ± 0.09
ABC10.810.98 ± 0.071.141.34 ± 0.12
ABC22.713.55 ± 0.341.591.27 ± 0.16
ABC30.130.64 ± 0.050.470.88 ± 0.07
ABC40.440.71 ± 0.060.820.85 ± 0.08
ABC50.791.13 ± 0.090.991.25 ± 0.11
ABC61.652.33 ± 0.151.512.71 ± 0.18
ABC71.552.72 ± 0.231.582.57 ± 0.19
ABC81.502.83 ± 0.210.891.38 ± 0.14
MATE12.973.94 ± 0.291.042.72 ± 0.24
MATE22.173.17 ± 0.191.142.13 ± 0.15
MATE32.223.46 ± 0.271.212.28 ± 0.16
1,3,6,8-Tetrahydroxynaphthalene reductase2.111.40 ± 0.080.470.31 ± 0.06
4-Hydroxyphenylpyruvate dioxygenase0.950.81 ± 0.040.770.58 ± 0.04
60S Acidic ribosomal phosphoprotein1.371.72 ± 0.130.820.71 ± 0.06
60S Ribosomal protein1.771.35 ± 0.120.900.76 ± 0.09
40S Ribosomal protein2.011.50 ± 0.060.840.58 ± 0.05
Ribosomal protein2.011.44 ± 0.150.861.21 ± 0.11
Acetyl-CoA carboxylase0.470.30 ± 0.021.491.01 ± 0.07
Alcohol dehydrogenase2.383.37 ± 0.210.881.17 ± 0.08
Aldehyde dehydrogenase1.031.66 ± 0.130.801.07 ± 0.14
Aldehyde reductase2.232.55 ± 0.111.711.37 ± 0.14
Amine oxidase0.581.21 ± 0.130.350.24 ± 0.04
Amino acid permease2.253.11 ± 0.240.891.45 ± 0.14
ATP synthase subunit 611.558.66 ± 0.350.110.57 ± 0.06
Erg12.553.92 ± 0.241.821.38 ± 0.15
Erg31.611.33 ± 0.152.163.97 ± 0.23
Erg242.464.07 ± 0.231.621.22 ± 0.15
Erg252.693.98 ± 0.192.173.33 ± 0.18
Carnitine acetyl transferase0.390.26 ± 0.040.840.67 ± 0.05
Catalase1.781.40 ± 0.060.951.19 ± 0.09
Cytochrome b5.243.66 ± 0.170.320.25 ± 0.04
Cytochrome c oxidase5.828.57 ± 0.220.050.17 ± 0.05
RNA helicase5.947.47 ± 0.251.911.46 ± 0.15
Dienelactone hydrolase2.071.64 ± 0.071.061.34 ± 0.11
Epoxide hydrolase2.112.03 ± 0.161.251.76 ± 0.09
Exopolygalacturonase0.890.57 ± 0.050.670.79 ± 0.05
Exo-β-1,3-glucanase0.790.91 ± 0.060.970.69 ± 0.05
Fatty acid synthase β subunit0.520.52 ± 0.041.231.75 ± 0.11
Fatty acyl-CoA oxidase0.510.33 ± 0.051.261.20 ± 0.14
FMN dependent dehydrogenase2.234.41 ± 0.121.291.69 ± 0.08
Glutathione S-transferase2.193.38 ± 0.240.851.04 ± 0.12
Glycerol kinase3.072.23 ± 0.160.761.05 ± 0.10
Glyoxylate reductase2.204.32 ± 0.221.171.65 ± 0.11
Maleylacetoacetate isomerase2.142.05 ± 0.130.811.17 ± 0.14
MAP kinase kinasekinase0.460.52 ± 0.041.041.35 ± 0.07
NADH dehydrogenase subunit 14.063.77 ± 0.290.430.82 ± 0.08
NADH dehydrogenase subunit 47.468.15 ± 0.330.250.64 ± 0.05
NADH-ubiquinone oxidoreductase1.771.59 ± 0.070.940.77 ± 0.05
Nitrate reductase1.061.35 ± 0.090.980.77 ± 0.06
Nitrite reductase0.770.88 ± 0.060.821.11 ± 0.07
Oxaloacetate hydrolase13.008.96 ± 0.210.220.75 ± 0.06
Pectate lyase2.854.34 ± 0.211.471.62 ± 0.13
Phenyloxazoline synthase2.112.04 ± 0.171.301.83 ± 0.15
Phosphatidylserine decarboxylase2.202.85 ± 0.171.371.62 ± 0.09
Phospholipase C1.231.59 ± 0.132.753.82 ± 0.18
Pre-mRNA-splicing factor2.172.12 ± 0.141.371.44 ± 0.09
Protein-L-isoaspartate O-methyltransferase2.333.63 ± 0.191.181.68 ± 0.15
Superoxide dismutase2.384.37 ± 0.151.051.39 ± 0.12
Thioredoxin2.001.62 ± 0.061.381.03 ± 0.11
Ubiquitin carboxyl-terminal hydrolase0.490.37 ± 0.041.111.52 ± 0.17
α-L-Rhamnosidase0.480.40 ± 0.061.111.47 ± 0.15
DEG NameI-(Pd-d/Pd-wt)NI-(Pd-d/Pd-wt)
Relative Fold-Change in the RNA-seqRelative Fold-Change in the qPCRRelative Fold-Change in the RNA-seqRelative Fold-Change in the qPCR
MFS10.200.65 ± 0.040.160.43 ± 0.05
MFS20.000.000.000.00
MFS30.450.42 ± 0.080.340.51 ± 0.04
MFS40.250.17 ± 0.040.350.25 ± 0.03
MFS50.390.56 ± 0.060.360.55 ± 0.03
MFS60.420.27 ± 0.040.770.81 ± 0.05
MFS70.200.45 ± 0.050.500.77 ± 0.06
MFS80.180.53 ± 0.040.210.51 ± 0.06
MFS90.430.82 ± 0.080.430.72 ± 0.05
MFS100.360.41 ± 0.040.610.69 ± 0.05
MFS110.380.68 ± 0.050.440.65 ± 0.04
MFS120.210.49 ± 0.040.250.44 ± 0.04
MFS130.250.42 ± 0.030.480.67 ± 0.06
MFS140.380.45 ± 0.050.490.58 ± 0.06
MFS150.460.81 ± 0.070.400.71 ± 0.05
MFS160.380.31 ± 0.040.470.62 ± 0.04
MFS170.210.37 ± 0.040.500.59 ± 0.05
MFS180.290.52 ± 0.050.260.49 ± 0.04
MFS190.370.51 ± 0.040.540.65 ± 0.05
MFS200.190.33 ± 0.040.490.69 ± 0.04
MFS210.460.57 ± 0.050.891.10 ± 0.08
MFS220.641.12 ± 0.071.582.25 ± 0.17
MFS236.414.55 ± 0.215.043.97 ± 0.24
MFS243.335.41 ± 0.261.021.33 ± 0.19
MFS252.542.37 ± 0.241.481.95 ± 0.12
MFS262.813.23 ± 0.192.383.11 ± 0.24
MFS272.262.94 ± 0.182.473.58 ± 0.21
MFS282.333.52 ± 0.160.881.53 ± 0.11
MFS292.121.97 ± 0.181.471.96 ± 0.13
MFS302.162.15 ± 0.141.491.87 ± 0.09
MFS311.113.97 ± 0.313.825.16 ± 0.32
MFS320.611.05 ± 0.080.510.99 ± 0.06
MFS331.433.45 ± 0.272.083.27 ± 0.18
ABC10.381.12 ± 0.110.270.77 ± 0.05
ABC20.550.38 ± 0.050.931.16 ± 0.11
ABC37.002.29 ± 0.132.011.73 ± 0.18
ABC44.462.51 ± 0.212.402.06 ± 0.13
ABC53.853.84 ± 0.253.093.53 ± 0.29
ABC61.653.17 ± 0.181.802.78 ± 0.24
ABC71.892.21 ± 0.261.862.51 ± 0.15
ABC81.371.28 ± 0.092.312.93 ± 0.19
MATE10.291.25 ± 0.080.831.84 ± 0.12
MATE20.430.97 ± 0.060.821.54 ± 0.09
MATE30.480.81 ± 0.070.881.25 ± 0.08
1,3,6,8-Tetrahydroxynaphthalene reductase0.080.15 ± 0.030.370.62 ± 0.05
4-Hydroxyphenylpyruvate dioxygenase2.302.48 ± 0.113.073.57 ± 0.15
60S Acidic ribosomal phosphoprotein0.480.27 ± 0.020.810.64 ± 0.02
60S Ribosomal protein0.400.52 ± 0.040.790.95 ± 0.07
40S Ribosomal protein0.390.27 ± 0.040.820.67 ± 0.04
Ribosomal protein0.370.50 ± 0.050.780.61 ± 0.06
Acetyl-CoA carboxylase3.323.46 ± 0.200.931.02 ± 0.05
Alcohol dehydrogenase0.740.40 ± 0.042.071.18 ± 0.09
Aldehyde dehydrogenase0.480.57 ± 0.020.610.89 ± 0.06
Aldehyde reductase1.060.85 ± 0.051.281.58 ± 0.07
Amine oxidase1.710.81 ± 0.033.164.16 ± 0.25
Amino acid permease1.382.61 ± 0.052.455.69 ± 0.34
ATP synthase subunit 60.000.05 ± 0.010.370.81 ± 0.08
Erg10.530.27 ± 0.040.670.85 ± 0.09
Erg31.071.93 ± 0.090.800.61 ± 0.04
Erg240.460.25 ± 0.030.620.86 ± 0.07
Erg250.690.77 ± 0.050.780.82 ± 0.07
Carnitine acetyl transferase1.030.89 ± 0.090.480.33 ± 0.06
Catalase1.051.47 ± 0.082.191.74 ± 0.19
Cytochrome b0.010.03 ± 0.010.210.42 ± 0.05
Cytochrome c oxidase0.010.01 ± 0.010.530.37 ± 0.05
RNA helicase2.071.71 ± 0.026.598.77 ± 0.36
Dienelactone hydrolase0.701.29 ± 0.041.241.61 ± 0.18
Epoxide hydrolase0.711.43 ± 0.021.111.64 ± 0.17
Exopolygalacturonase0.330.43 ± 0.060.480.32 ± 0.03
Exo-β-1,3-glucanase0.470.17 ± 0.030.420.22 ± 0.04
Fatty acid synthase β subunit2.604.92 ± 0.381.031.44 ± 0.12
Fatty acyl-CoA oxidase0.71.19 ± 0.220.450.32 ± 0.05
FMN dependent dehydrogenase0.810.72 ± 0.031.281.78 ± 0.15
Glutathione S-transferase0.440.49 ± 0.021.021.55 ± 0.12
Glycerol kinase0.310.76 ± 0.051.131.67 ± 0.09
Glyoxylate reductase0.710.74 ± 0.031.211.87 ± 0.17
Maleylacetoacetate isomerase1.032.27 ± 0.072.773.96 ± 0.27
MAP kinase kinasekinase2.533.46 ± 0.211.101.35 ± 0.11
NADH dehydrogenase subunit 10.040.03 ± 0.010.270.32 ± 0.02
NADH dehydrogenase subunit 40.020.01 ± 0.010.420.28 ± 0.02
NADH-ubiquinone oxidoreductase0.400.44 ± 0.060.720.91 ± 0.08
Nitrate reductase1.872.12 ± 0.272.283.65 ± 0.16
Nitrite reductase2.455.23 ± 0.292.54.25 ± 0.18
Oxaloacetate hydrolase0.010.02 ± 0.010.370.42 ± 0.03
Pectate lyase0.490.31 ± 0.040.870.66 ± 0.05
Phenyloxazoline synthase0.801.32 ± 0.071.191.46 ± 0.13
Phosphatidylserine decarboxylase0.810.84 ± 0.041.191.56 ± 0.17
Phospholipase C2.203.09 ± 0.050.961.28 ± 0.11
Pre-mRNA-splicing factor0.811.01 ± 0.101.181.39 ± 0.15
Protein-L-isoaspartate O-methyltransferase0.660.62 ± 0.011.191.37 ± 0.12
Superoxide dismutase1.021.24 ± 0.032.353.90 ± 0.25
Thioredoxin0.650.43 ± 0.020.860.67 ± 0.06
Ubiquitin carboxyl-terminal hydrolase2.205.50 ± 0.140.941.38 ± 0.17
α-L-Rhamnosidase0.821.93 ± 0.140.360.54 ± 0.05
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MDPI and ACS Style

Cuan, R.; Liu, S.; Zhou, C.; Wang, S.; Zheng, Y.; Yuan, Y. Transcriptome Analysis of mfs2-Defective Penicillium digitatum Mutant to Reveal Importance of Pdmfs2 in Developing Fungal Prochloraz Resistance. Microorganisms 2024, 12, 888. https://doi.org/10.3390/microorganisms12050888

AMA Style

Cuan R, Liu S, Zhou C, Wang S, Zheng Y, Yuan Y. Transcriptome Analysis of mfs2-Defective Penicillium digitatum Mutant to Reveal Importance of Pdmfs2 in Developing Fungal Prochloraz Resistance. Microorganisms. 2024; 12(5):888. https://doi.org/10.3390/microorganisms12050888

Chicago/Turabian Style

Cuan, Rongrong, Shaoting Liu, Chuanyou Zhou, Shengqiang Wang, Yongliang Zheng, and Yongze Yuan. 2024. "Transcriptome Analysis of mfs2-Defective Penicillium digitatum Mutant to Reveal Importance of Pdmfs2 in Developing Fungal Prochloraz Resistance" Microorganisms 12, no. 5: 888. https://doi.org/10.3390/microorganisms12050888

APA Style

Cuan, R., Liu, S., Zhou, C., Wang, S., Zheng, Y., & Yuan, Y. (2024). Transcriptome Analysis of mfs2-Defective Penicillium digitatum Mutant to Reveal Importance of Pdmfs2 in Developing Fungal Prochloraz Resistance. Microorganisms, 12(5), 888. https://doi.org/10.3390/microorganisms12050888

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