Abstract

   In this study, Penicillium expansum, a common destructive phytopathogen
   and patulin producer was isolated from naturally infected apple fruits
   and identified by morphological observation and rDNA‐internal
   transcribed spacer analysis. Subsequently, a global view of the
   transcriptome and proteome alteration of P. expansum spores during
   germination was evaluated by RNA‐seq (RNA sequencing) and iTRAQ
   (isobaric tags for relative and absolute quantitation) approaches. A
   total of 3,026 differentially expressed genes (DEGs), 77 differentially
   expressed predicted transcription factors and 489 differentially
   expressed proteins (DEPs) were identified. The next step involved
   screening out 130 overlapped candidates through correlation analysis
   between the RNA‐seq and iTRAQ datasets. Part of them showed a different
   expression trend in the mRNA and protein levels, and most of them were
   involved in metabolism and genetic information processing. These
   results not only highlighted a set of genes and proteins that were
   important in deciphering the molecular processes of P. expansum
   germination but also laid the foundation to develop effective control
   methods and adequate environmental conditions.

   Keywords: germination, iTRAQ, Penicillium expansum, RNA sequencing

1. INTRODUCTION

   Penicillium expansum, one of major causal agents of blue mold rot, is a
   commonly distributed and destructive phytopathogen. Besides its usual
   hosts of deciduous fruits such as apples, pears, and peaches and
   products derived from them, it can infect some less common hosts
   including vegetables, kiwifruits, tomatoes, strawberries, and cherries
   (Morales, Marín, Ramos, & Sanchis, [36]2010; Zhu, Yu, Brecht, Jiang, &
   Zheng, [37]2016) and lead to extensive maceration utilizing a common
   mechanism of tissue acidification (Barad et al., [38]2012). In
   addition, P. expansum is an important producer of the mycotoxin patulin
   (Puel, Galtier, & Oswald, [39]2010). As a nonvolatile secondary
   metabolite, patulin produces acute and chronic toxicity, primarily
   genotoxicity and immunotoxicity, as well as cytotoxicity in humans.
   Ingesting patulin can lead to nausea, vomiting, gastrointestinal
   trauma, kidney damage, the formation of cancerous tumors, genetic
   mutations, and embryonic developmental defects (Barad, Sionov, &
   Prusky, [40]2016; Glaser & Stopper, [41]2012). The assessment of the
   health risks due to patulin consumption by humans resulted in the US
   Food and Drug Administration (FDA) and the Commission for the European
   Community (EC) setting a maximum tolerance limit of 50 μg·L^−1 in
   apple‐derived food products. EC Regulation 1881/2006 set the maximum
   limit of patulin for certain contaminants in food equal to 10 μg·kg^−1
   (or 10 μg·L^−1) in apple juice and solid apple products for infants and
   young children (Sarubbi, Formisano, Auriemma, Arrichiello, & Palomba,
   [42]2016; Snini et al., [43]2016). In addition, P. expansum is a
   psychrophilic mold with a minimum growth temperature near freezing and
   an optimum growth temperature of 25°C. It can be easily disseminated by
   different vectors in orchards and by equipment (Morales et al.,
   [44]2010). Therefore, P. expansum may cause serious economic losses to
   the international horticultural industry every year.

   In addition to proper sanitation and careful handling during storage
   and processing, cold temperature, fungicide, controlled atmosphere,
   natural antimicrobials from plants, generally recognized as safe
   substances, and antagonistic microorganisms are usually utilized to
   inhibit the growth and patulin production of P. expansum (Cheon et al.,
   [45]2016; Daniel, Lennox, & Vries, [46]2015; De Paiva et al., [47]2017;
   Moake, Padilla‐Zakour, & Worobo, [48]2005; Morales et al., [49]2010).
   However, the overuse of synthetic fungicides has resulted in extensive
   concern about its effects on the environment and human health. In
   addition, the overuse of certain control strategies has led to
   increasing resistance of pathogens (Palou, Ali, Fallik, & Romanazzi,
   [50]2016). Therefore, it is imperative to understand the genetic basis
   of the infection, pathogenicity, toxigenicity, and virulence before
   developing new ecologically friendly control approaches and risk
   assessment models.

   Fungal asexual spores that involve reproduction without the fusing of
   gametes are the main form of contamination or inoculation, since they
   are easily dispersed through the air or water. These spores are
   considered resistant to a broad spectrum of unfavorable environmental
   conditions due to both their cell wall properties and the accumulation
   of protective compatible solutes in the cytoplasm (Latgé & Beauvais,
   [51]2014). Mature conidia possess a condensed cell wall and a dense
   cytoplasm that contains a high content of energy‐storage materials such
   as the major carbon storage compounds trehalose and mannitol and an
   exogenously imposed dormancy (Voegele et al., [52]2005). Germination of
   the fungal conidia refers to the emergence of cells from resting
   asexual spores and that form sporeling hyphae or thalli under optimal
   growth conditions (Van Leeuwen et al., [53]2013). During this process,
   the hydrophobicity of the conidia results a progressive reduction that
   leads to the swelling of the conidia knows as isotropic growth and
   subsequent polarized growth characterized by germ tube formation
   (Dague, Alsteen, Latgé, & Dufrêne, [54]2008). It greatly affects fungal
   aggressiveness and colonization and can be considered to be one of the
   main steps of the disease cycle. Various studies have been conducted on
   the physiological and biochemical changes of P. expansum spores under
   different environment conditions including pH, nutrients, antimicrobial
   substances, temperature, and atmosphere composition (Kalai, Anzala,
   Bensoussan, & Dantigny, [55]2017; Long et al., [56]2017; Nanguy,
   Perrier‐Cornet, Bensoussan, & Dantigny, [57]2010). Ballester et al.
   ([58]2015) and Li et al. ([59]2015) have completed genomic sequencing
   of P. expansum and some other Penicillia strains that will aid further
   molecular genetic analysis. However, no information is available
   regarding the intrinsic flexibility and variation in P. expansum at the
   transcriptional or translational level at different developmental
   stages such as germination.

   Therefore, this study aimed to assess the transcriptome and proteome
   alternation of P. expansum during its germination stage using high
   throughput sequencing techniques. The results should provide useful
   information to better understand the molecular basis of development and
   pathogenicity of P. expansum and help to develop efficient control
   strategies.

2. MATERIALS AND METHODS

2.1. Fungal isolation and identification

   All of the fruits at commercial maturity were bought from a local
   market in the Xiasha District, Hang Zhou, China. The fungal pathogen
   P. expansum was initially isolated from a naturally infected apple
   (Malus domestica Borkh cv. Red Fuji) fruit with typical blue mold
   symptoms and maintained on potato dextrose agar (PDA) plates at 25°C.
   The pathogen was re‐inoculated on apple, orange (Citrus reticulata
   Blanco.), jujube (Ziziphus jujube Mill. cv Dongzao), tomato
   (Lycopersicon esculentum Mill.), and pear (Pyrus bretschneideri Rehd.)
   fruits to observe its morphological characteristics. Total genomic DNA
   of the pathogen was extracted using a DNeasy Plant Mini Kit (Qiagen,
   Germany) following the manufacturer's instructions. The rDNA‐ITS was
   amplified using the universal primers ITS4 (TCCTCCGCTTATTGATATGC) and
   ITS5 (GGAAGGTAAAAGTCGTAACAAG). The amplified products were purified by
   the Roche kit and sequenced by Shanghai Sunny Biotechnology Co., Ltd.
   The sequencing results were analyzed in
   [60]http://blast.ncbi.nlm.nih.gov/blast.cgi. The distant tree of the
   blast results was produced by BLAST pairwise alignments, and the
   parameters were as follows: Tree method: Neighbor Joining; Max Seq
   Difference: 0.75; Sequence Label: Taxonomic Name.

2.2. Fungal physiological measurements

   Conidial suspensions were obtained by flooding the sporulating cultures
   of P. expansum with sterile distilled water containing 0.05% (v/v)
   Tween‐20. A suitable aliquot of fresh spore suspension was added to a
   250‐ml conical flask containing 100 ml potato dextrose broth (PDB) to
   obtain a final concentration of 1.0 × 10^6 spores·ml^−1 with the aid of
   a hemocytometer. The germination rate and germ tube length of the
   spores were microscopically determined during an incubation of period
   of 3–16 hr at 25°C on a rotary shaker at 200 rpm. 4′,
   6′‐diamidino‐2‐phenylindole (DAPI) was utilized at a concentration of
   50 mg/L to facilitate observation of the morphology of fungal spores as
   described by Lai et al. ([61]2015). The dry weight of the hyphae was
   measured after centrifugation, and then they were repeatedly washed
   with distilled water and dried at 60°C in an oven to a constant weight
   at the indicated time. The mycelial growth in vitro and in vivo was
   measured daily on PDA plates and apple fruits separately as described
   by Lai et al. ([62]2017).

2.3. Determination of patulin production

   Fungal spores with a final concentration of 1.0 × 10^7 spores/ml were
   cultured in PDB medium under static conditions at 25°C for 2, 3 and
   4 days. After centrifugation, the supernatant was filtered through a
   0.22 μm syringe filter (Albet, Spain), and then 10 μl of the filtrates
   was injected into a reversed‐phase HPLC (high performance liquid
   chromatography) system with a Waters XTerra RP18 column (Waters, USA)
   for the quantitative determination of the patulin concentration. The
   mobile phase consisted of 95% water and 5% acetonitrile. The
   operational parameters were as follows: a flow rate of 0.8 ml/min, a
   column oven temperature of 18°C, and a detector wave length of 276 nm.

2.4. RNA‐Seq analysis

   Fungal spores were cultured in PDB medium under shaking conditions for
   6 and 12 hr as described previously. The harvested spores and mycelia
   were washed twice by sterile distilled water and quickly frozen with
   liquid nitrogen for subsequent RNA‐Seq and iTRAQ analysis. Total RNA
   preparation and sequencing were performed using a service from Beijing
   Genomics Institute (BGI) Co., Ltd. The experiment was repeated once.
   Briefly, high‐quality total RNA with a 28S:18S ratio >1.5 and an
   absorbance 260/280 ratio between 1.7 and 2.0 was extracted using TRIzol
   reagent (Invitrogen, USA) following the manufacturer's instruction. The
   cDNA library for each sample was constructed utilizing TruSeq RNA
   Sample Preparation Kits v2 (Illumina, USA) according to the
   manufacturer's instructions. After quantification and qualification
   during the quality control step, the sample library was sequenced on
   the BGISEQ‐500 platform.

   The raw data were filtered to remove adaptors and reads in which
   unknown bases were more than 10% or the percentage of low quality bases
   was over 50%. The remaining clean reads were stored as FASTQ format and
   mapped to reference
   ([63]https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=27334)
   utilizing Bowtie2
   ([64]http://computing.bio.cam.ac.uk/local/doc/Bowtie2.html#) and HISAT
   ([65]http://ccb.jhu.edu/software/hisat2/manual.shtml). The gene
   expression level was quantified utilizing the software package RSEM (Li
   & Dewey, [66]2011). Then, samples were first grouped so that the
   comparison between every two groups as a pairwise of different
   developmental stages was performed later. Differentially expressed gene
   (DEG) screening utilized the NOISeq method (Tarazona, Carcía‐Alcalde,
   Dopazo, Ferrer, & Conesa, [67]2011) according to the following
   criteria: fold change ≥ 2 and diverge probability ≥ .8. The detailed
   data are provided in Table [68]S1. Gene ontology annotation and
   enrichment analysis of the DEGs were performed using WEGO software
   based on the Gene Ontology Consortium
   ([69]http://www.geneontology.org/). The Kyoto Encyclopedia of Genes and
   Genomes (KEGG) database ([70]http://www.kegg.jp/kegg/pathway.html) was
   used to conduct pathway enrichment analysis of the DEGs. The normalized
   data table is provided in the appendices in Table [71]S1. The hmmsearch
   were used to search the characteristics of the domains in the PlantTFDB
   database ([72]http://plntfdb.bio.uni-potsdam.de/v3.0/) and then to
   conversely predict if a gene codes for a transcription factor.
   Transcription factors related to DEGs are listed in Table [73]S2. The
   clean reads could be acquired in Sequence Read Archive (SRA)
   ([74]https://trace.ncbi.nlm.nih.gov/Traces/sra/). The accession IDs for
   Pe‐6h‐Replicate1, Pe‐6h‐Replicate2, Pe‐12h‐Replicate1 and
   Pe‐12h‐Replicate2 are SRR5830890, SRR5839793, SRR5819793 and
   SRR5816892, respectively.

2.5. iTRAQ‐based quantitative proteomics analysis

   Isobaric tags for relative and absolute quantitation (iTRAQ) analysis
   was performed utilizing a service from LC‐Bio of LC Science (USA) as
   described by Lai et al. ([75]2016). Protein preparation utilized the
   standard extraction procedure of LC‐Bio of LC Science. One hundred
   micrograms of the total protein was digested with Trypsin Gold
   (Promega, USA) at a ratio of 30:1 (protein: trypsin) at 37°C for 16 hr.
   The peptides were reconstituted in 0.5 mol·L^−1 TEAB and processed
   according to the manufacturer's instructions for the 8‐plex iTRAQ
   reagent (Applied Biosystems, USA). The labeled peptide mixtures were
   loaded onto an LC‐20AB HPLC Pump system (Shimadzu, Japan) with a
   4.6 × 250 mm Ultremex SCX column and a Strata X C18 column
   (Phenomenex). After SCX chromatography, LC‐ESI‐MS/MS analysis was
   performed utilizing an LC‐20AD NanoHPLC (Shimadzu, Japan), a TripleTOF
   5600 System fitted with a NanoSpray III source (AB SCIEX, USA) and a
   pulled quartz tip as the emitter (New Objective, USA).

   Raw data files acquired from the Orbitrap were converted into MGF files
   utilizing Proteome Discoverer 1.2 (Thermo, USA) and the MGF files were
   searched. Protein identification was carried out utilizing the Mascot
   search engine (Matric Science, UK). A mass tolerance of 0.05 Da for
   intact peptide masses, 0.1 Da for fragmented ions, and one missed
   cleavage in the trypsin digests were permitted. The parameters of the
   mass values [monoisotopic], variable medications [Gln‐ > pyro‐Glu
   (N‐term Q), Oxidation (M), iTRAQ8plex (Y)], instrument type [Default],
   and fixed medications [carbamidomethyl (C), iTRAQ8plex (N‐term),
   iTRAQ8plex (K)] were also set. The quantitative protein ratios were
   weighted and normalized utilizing the median ratio in Mascot. The
   proteins whose ratio with p‐value < .05 and fold change > 1.2 in two
   biological repeats were considered to be the differentially expressed
   proteins (DEPs). Functional annotations of the proteins were conducted
   using the Blast2GO program against the non‐redundant protein database
   (NR; NCBI). The Kyoto Encyclopedia of Genes and Genomes (KEGG) database
   ([76]http://www.kegg.jp/kegg/pathway.html) and the COG database
   ([77]http://www.ncbi.nlm.mih.gov/COG) were used to classify and group
   these identified proteins. The statistics of GO and pathway enrichment
   of DEPs are shown by ggplot2 ([78]http://ggplot2.org/) in Figure [79]4.
   The detailed data are shown in Table [80]S3.

Figure 4.

   Figure 4
   [81]Open in a new tab

   Scatter plots of the top 20 Gene Ontology (a) and KEGG (b) enrichment
   of DEPs involved in spore germination of P. expansum. A deeper color in
   the color code represents the higher confidence for the biological
   process. Rich factor: the number of DEPs in one GO or KEGG/the number
   of total identified proteins in the same GO or KEGG. A higher value
   indicated higher enrichment level. The detailed information of these
   DEPs is listed in Table [82]S3

   Comparison of the RNA‐seq and iTRAQ datasets was based on the semantic
   similarity of their GO terms. Detail information of the common DEGs
   acquired from the two approaches is presented in Figure [83]6.
   HemI1.0.1 was utilized to prepare heat maps (Deng, Wang, Liu, Cheng, &
   Xue, [84]2014).

Figure 6.

   Figure 6
   [85]Open in a new tab

   Global overview of the changes in phenotype, transcriptome and proteome
   of P. expansum spores during germination. (a) Phenotypic changes of
   P. expansum spores during development. The red arrow corresponds to the
   focused stage in this study; (b) Scatter plots of all expressed genes
   acquired from the RNA‐seq method during the spore germination stage.
   The X‐axis and Y‐axis show the log[2] value of gene expression. Blue,
   orange, and brown dots correspond to down‐regulated, up‐regulated and
   nonregulated genes, respectively; (c) Venn diagram of the overlap of
   DEGs from RNA‐seq and iTRAQ datasets. (d) Protein ratio distribution
   acquired from the iTRAQ method during spore germination. The X‐axis
   presents the −log[2] value of the protein ratio (6 hr/12 hr). Deep
   pink, azure, and gray dots correspond to up‐regulated, down‐regulated
   and nonregulated proteins, respectively. (e) Pathway classification of
   overlapped DEGs acquired from RNA‐seq and iTRAQ methods

2.6. Statistical analysis

   Except for the experiments where specified, the data were pooled across
   three independent repeat experiments, and statistical analysis were
   performed with SPSS software (SPSS Inc., Chicago, IL, USA). Analysis of
   variance (ANOVA) was used to compare more than two means, and Duncan's
   multiple range test was used for mean separations. Differences at
   p < .05 were considered to be significant.

3. RESULTS

3.1. Identification of a strain of P expansum from decaying market fruit in
the Chinese province of Zhejiang

   The fungus, isolated from decaying market fruit, proved to be a major
   causative agent by artificial infection tests, and it was highly
   pathogenic. Therefore, we decided to identify and characterize this
   pathogen by morphological observation and rDNA‐internal transcribed
   spacer analysis. After an artificial re‐inoculation in vivo and in
   vitro, the cultural and morphological characteristics of the purified
   pathogen were consistent with those of blue mold. In apple fruits, the
   color of the lesions was light brown. The infected tissue with an
   earthy‐musty odor and blue pustules became soft and mushy, and had a
   watery consistency. On the older lesions, the pathogen formed typically
   brush‐like sporulating structures that produced single celled spores
   (Figure [86]1a). The spore masses initially appeared as white mycelia
   that then turned bluish‐green in color from the edge to the center of
   the colonies on PDA plates (Figure [87]1b). The conidia were spherical
   or elliptical and contained one or multiple nuclei (Figure [88]1c and
   d).

Figure 1.

   Figure 1
   [89]Open in a new tab

   Genetic and morphological features of blue mold isolated from apple
   fruits. Symptoms of blue mold on apple (a), orange (e), jujube (f),
   tomato (g) and pear (h) fruits are shown. Colonial morphology of
   Penicillium expansum on PDA medium (b), morphology of spores stained by
   DAPI after 6 hr of culturing (c), and spore formation after 12 hr of
   culturing in PDB medium (d) are illustrated. The distant tree of blast
   results produced by BLAST pairwise alignments demonstrated the fungal
   genetic background (i)

   The pathogen was also highly pathogenic to orange, jujube, tomato, and
   pear fruits (Figure [90]1e–h). After amplification using ITS universal
   primers, an approximately 800‐bp fragment was identified (Figure
   [91]S1). Through sequencing and MegaBLAST analysis, the submitted
   sequence was found to match perfectly with the ITS of P. expansum
   strains (Figure [92]1i). Therefore, the isolated pathogen was confirmed
   as P. expansum.

3.2. Growth dynamic analysis of P. expansum in vitro and in vivo

   As one of the most prevalent postharvest diseases, the growth dynamics
   of P. expansum that partially represent fungal competitiveness and
   dominance are important to determine. In this study, several growth
   indicators were determined at different incubation times. After 6 hr of
   cultivation in PDB, the average volume of the spores was visually
   enlarged. After 12 hr, the germination rate had reached more than 90%
   (Figure [93]2a). After 16 hr, the average length of the germ tube was
   approximately 60 μm (Figure [94]2b). After 48 hr, the mycelial dry
   weight derived from 1.0 × 10^8 spores had reached about 1.2 g
   (Figure [95]2c). After 6 days of cultivation on PDA, colonies nearly
   extended to the entire petri dish (Figure [96]2d). In addition,
   P. expansum was strongly pathogenic to apple fruits and was highly
   destructive (Figure [97]2e). After 6 days of infection, the lesion
   diameter on apple fruits was almost 4 cm. Patulin is an important
   indicator of pathogenicity of P. expansum. Patulin production
   significantly increased with the time of culture. The concentration of
   patulin was greater than 130 μg·ml^−1 after 4 days of incubation
   (Figure [98]2f).

Figure 2.

   Figure 2
   [99]Open in a new tab

   Growth dynamic analysis of P. expansum in vitro and in vivo. Spore
   germination (a), germ tube length (b), mycelial dry weight (c), colony
   diameter on PDA (d), and lesion diameter on apple fruits (e) of
   P. expansum were determined at the time points indicated. Patulin
   production of P. expansum under static conditions was detected by a
   reverse‐phase HPLC system as well (f). Bars represent the standard
   deviation of the means of three independent experiments. Lower case
   letters indicate significant differences at p < .05 at each time point

3.3. Identification and functional classification of DEGs during spore
germination of P. expansum

   RNA‐seq‐based transcriptome analysis has several advantages such as the
   production of large‐scale data, high reproducibility, enhanced
   sensitivity, and relatively low cost. Thus, it serves an essential role
   to identify genetic networks underlying cellular, physiological,
   biochemical and biological systems. To explore and determine the
   underlying molecular and cellular processes during spore germination of
   P. expansum, two key time points of 6 hr and 12 hr were selected for
   transcriptome profiling based on light microscopy studies. After 6 hr,
   significant multiple developmental stages were observed, and the
   dormant spores had been fully activated and were initiating vegetative
   growth with cell wall expansion. After 12 hr of incubation, the spores
   had almost finished germination and generated germ tubes that would
   grow and develop into hyphae. Our goal was to focus on identifying
   genes with stage‐specific expression patterns or altered expression
   levels corresponding to substantial morphological and physiological
   changes between the two important stages. Finally, about 23.96 million
   raw sequencing reads and about 23.95 clean reads of each sample were
   generated after filtering out the low‐quality ones. The average mapping
   ratio with the reference gene is 76.13%, and the average genome mapping
   ratio is 97.51%. A total of 3,026 genes were found to have changed
   abundantly during spore germination of P. expansum. Compared with those
   after 6 hr of incubation, the expression of 1,814 DEGs was
   down‐regulated, while 1,212 DEGs were up‐regulated. Detailed
   information on all of the DEGs is summarized in Table [100]S1. The
   biological functions and intracellular distribution of these DEGs are
   shown in Figure [101]3a. The majority of these DEGs possessed binding
   capability or catalytic activity and were involved in cellular,
   metabolic and single‐organism processes. The pathway enrichment
   statistics and KEGG classification of the DEGs are shown in
   Figure [102]3b and c. The pathway classification indicates that most of
   the DEGs participate in metabolism and genetic information processing.
   In addition, to investigate whether master regulators control the
   transcriptional change during spore germination, a total of 77
   transcription factors among DEGs belonging to the Alfin‐like, bHLH BSD,
   bZIP, C2C2‐GATA, C2H2, C3H, FHA, HSF, MADS, MYB‐related, TIG, and
   Zn‐clus families are predicted (Table [103]S2). However, the functions
   of most of them are unknown.

Figure 3.

   Figure 3
   [104]Open in a new tab

   GO function classification, pathway enrichment statistics and KEGG
   classification of DEGs. (a) GO functional classification of DEGs for
   each pairwise. The X axis indicates the number of DEGs (the number is
   presented by its square root value). The Y axis represents GO terms.
   All of the GO terms are grouped into three ontologies: blue is for
   biological process, brown is for cellular component and orange is for
   molecular function; (b) Statistics of the pathway enrichment of DEGs in
   each pairwise. The rich factor is the ratio of DEGs numbers to all gene
   numbers annotated in this pathway term. A greater rich factor indicates
   greater intensity. The Q value is the corrected p value ranging from 0
   to 1, and a lower Q value indicates greater intensity. The top 20 of
   the enriched pathway terms are displayed. (c) KEGG classification of
   DEGs. The X axis indicates the number of DEGs. The Y axis represents
   the second KEGG pathway terms. All of the second pathway terms are
   grouped in the top pathway terms indicated in different colors. The
   detailed information of these DEGs is listed in Table S1

3.4. Identification and functional classification of DEPs during spore
germination of P. expansum

   Although transcriptome profiling provides substantial biological
   information, its ability to provide a comprehensive perspective for
   this biological target is limited. mRNA profiling cannot capture the
   regulatory processes or post‐transcriptional modifications that might
   affect the amount of active proteins. As a complement to genome and
   transcriptome analysis, proteomic analysis has proven to be a powerful
   method to offer a more direct analysis of cellular events and
   facilitate drawing accurate functional inferences from gene expression
   data. Thus, an iTRAQ‐based quantitative proteomic analysis was utilized
   to gain a global view of proteome alterations of P. expansum spores
   during germination. Two independent biological replicates for each
   sample were used for labeling. A total of 3,495 proteins were
   identified in two independent biological replicates according to the
   criteria for identification. A total of 489 proteins were screened as
   differentially expressed proteins. Among them, the expression of 227
   DEPs was down‐regulated and 262 DEPs were up‐regulated. Detailed
   information of the DEPs is summarized in Table [105]S3 with the ratio
   of iTRAQ reporter ion intensities. According to the statistics of gene
   ontology functional enrichment, most of the DEPs were related to the
   nucleus, nonmembrane‐bounded organelle, and cellular component
   organization or biogenesis (Figure [106]4a). According to pathway
   enrichment statistics, these DEPs participated in a variety of
   biological processes and the majority of them are involved in the
   biosynthesis of secondary metabolites, ribosome biogenesis,
   carbohydrate metabolism, and amino acid metabolism (Figure [107]4b).

3.5. Comparative analysis of RNA‐seq and iTRAQ dataset

   Integrative analysis of transcriptomic and proteomic data may present
   complementary information that enables more informative conclusions to
   be drawn. After combination and simplification based on the homologies
   and biological function annotations and deleting the results with
   unknown functions, correlation analysis between iTRAQ and RNA‐seq
   datasets was performed. A total of 130 genes in the overlap were
   identified that were involved in amino acid metabolism (11),
   carbohydrate metabolism (23), energy metabolism (6), lipid metabolism
   (9), metabolism of cofactors and vitamins (9), nucleotide metabolism
   (16), translation (25), folding, sorting and degradation (9),
   transcription (6), transport and catabolism (6), signal transduction
   (6) or other pathways. Generally, the alterations of the expression of
   these genes showed a larger variation range at the level of
   transcription, and the expression trend of about half of the candidates
   was different between the transcriptional and translational levels
   (Figure [108]5 and Figure [109]6c and e). A poor correlation between
   the quantities of these two macromolecules indicated a complex
   regulatory mechanism controlling expression both at the RNA and the
   protein levels during spore germination of P. expansum. In fact,
   similar findings have previously been found in many other biological
   processes such as in humans, yeast, and plants (Hua et al., [110]2016).

Figure 5.

   Figure 5
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   Expression changes of overlapping candidates involved in metabolism
   (a), genetic information processing (b) and others (c) from the iTRAQ
   and RNA‐seq datasets. Each row in the color heat map indicates a single
   protein. The annotation and involved metabolic pathway of each protein
   are listed. Blue to red in the color code indicates down‐ to
   up‐regulated expression of the candidates

4. DISCUSSION

   Penicillium expansum is a destructive phytopathogen, capable of causing
   decay in deciduous fruits and vegetables during postharvest handling
   and storage. It also produces large amounts of secondary metabolites,
   especially patulin (Morales et al., [112]2010; Tannous et al.,
   [113]2014). In this study, P. expansum was purified from naturally
   infected apple fruits and verified by morphological features and
   rDNA‐ITS analysis. The growth characteristics and pathogenicity of this
   pathogen were also determined (Figure [114]1, Figure [115]2 and
   Figure [116]6a). Then, changes in the transcriptome and proteome
   profiles at the germination stage were dissected utilizing RNA‐seq and
   iTRAQ approaches (Figure [117]6b and d). Correlation analysis between
   iTRAQ and RNA‐seq datasets was also performed (Figure [118]6c and e).

   Fungal infection of plants is usually initiated by contact of the host
   with spores (conidia) and subsequently by germination. Germination is a
   highly coordinated, genetically programmed, and irreversible phenomenon
   that involves the initiation of biochemical activities with an increase
   in metabolism and an induction of morphological changes (Moir,
   [119]2006; Osherov & May, [120]2000). The first morphological change in
   spore germination is isotropic growth, also designated as swelling.
   Swelling is accompanied by water uptake, cell wall growth, changes in
   cellular composition, and a decrease in cytoplasmic microviscosity (Van
   Leeuwen, Van Doorn, Golovina, Stark, & Dijksterhuis, [121]2010). At the
   same time, swelling is accompanied by numerous metabolic activities
   including respiration and RNA and protein synthesis. Following
   swelling, cell wall deposition of polysaccharides such as chitin
   becomes polarized, and the extension of the fungal cell occurs at a
   restricted area at the tip of the cell. This results in elongation of a
   germ tube and the formation of a branching mycelium (Harris, [122]2006;
   Momany, [123]2002; Taheri‐Talesh et al., [124]2008; Van Leeuwen, Smant,
   De Boer, & Dijksterhuis, [125]2008).

   A substantial amount of research on the germination process of
   bacterial or fungal spores has been published (Xiao, Francke, Abee, &
   Wells‐Bennik, [126]2011; Zhang, Garner, Setlow, & Yu, [127]2011;
   Gougouli & Koutsoumanis, [128]2012; Peleg & Normand, [129]2013;
   Olguín‐Araneda, Banawas, Sarker, & Paredes‐Sabja, [130]2015; Troiano,
   Zhang, Cowan, Yu, & Setlow, [131]2015). In particularly, gene
   expression profiles, either in the form of transcriptomes and/or
   proteomes, provide the means to explore and determine the underlying
   molecular and cellular processes of spore germination at an
   unprecedented depth and coverage. For example, using the Affymetrix
   GeneChip, Seong, Zhao, Xu, Güldener, and Kistler ([132]2008) found that
   185 genes were specifically expressed and 2,566 genes were
   significantly changed at different stages during spore germination of
   Fusarium graminearum. Most of these genes were involved in metabolism,
   carbohydrate utilization, and glycolysis and gluconeogenesis. Through
   DNA‐microarrays, Liu et al. ([133]2007) provided a global view of an
   important system related to Trichophyton rubrum conidial germination
   via hierarchical clustering of 1,561 differentially expressed genes,
   and Bassi et al. ([134]2016) found that 1,646 genes including the
   toxin‐coding genes nheC, cytK and hblC were differentially expressed
   and modulated during Bacillus thuringiensis spore germination and cell
   outgrowth in a vegetable‐based food model. Combining the 2‐DE‐based
   comparative proteomic approach and tandem electrospray ionization mass
   spectrometry (ESI‐MS/MS), Li, Lai, Qin, and Tian ([135]2010) identified
   34 proteins with significant changes in abundance when spore
   germination of P. expansum was inhibited by ambient pH and concluded
   that impairing the synthesis and folding of proteins was one of the
   main reasons. By utilizing a label‐free quantitative proteomics
   approach, Liu et al. ([136]2016) found that 127 proteins were
   influenced during Nosema bombycis spore germination under extremely
   alkaline conditions.

   However, these researchers focused on the molecular processes
   underlying spore germination at the single genome, transcriptional or
   protein level. In fact, molecular differences may be exhibited across
   multiple layers of gene regulation such as genomic variations, gene
   expression, protein translation and post‐translational modifications.
   Analyzing them separately may not be very informative. This
   necessitates the integrative analysis of such multiple layers of
   information to understand the interplay of the individual components.

   When it comes to P. expansum, there are few reports about the changes
   in its gene expression during spore germination, since its genome was
   just completely sequenced in 2015 (Ballester et al., [137]2015; Li
   et al., [138]2015). In addition, integrating the information from
   transcriptomic and proteomic data to gain meaningful insights of fungal
   spore germination has rarely been reported. Therefore, two
   high‐throughput technologies of RNA‐seq and iTRAQ were used to explore
   global gene regulation during spore germination of P. expansum in this
   study. A total of 3,026 genes was found to be differently regulated,
   and 489 proteins showed variation in their abundance. Pathway
   classification was compared between DEGs from RNA‐seq and DEPs from
   iTRAQ analysis. Similar results were observed that the majority of DEGs
   or DEPs were involved in translation, transcription, and metabolism of
   carbohydrates, amino acids, lipids, energy, and nucleotides. They were
   absolutely required for spore metabolism and growth during germination.
   A total of 130 DEPs corresponded to gene sequences that were obtained
   by RNA‐seq, and this enabled a comparison of germination‐related
   differences in specific transcripts or cognate proteins. Therefore, by
   narrowing of the scope and reducing the number, the 130 overlapped DEPs
   could probably yield more precise and valuable information to explore
   the molecular mechanism of P. expansum germination.

   Carbohydrates, technically hydrates of carbon and structurally
   polyhydroxy aldehydes and ketones, are typically stored as long
   polymers of glucose molecules with glycosidic bonds to provide
   structural support or energy storage. Carbohydrate metabolism denotes
   the various biochemical processes responsible for the formation,
   breakdown, and interconversion of carbohydrates. Carbohydrates serve as
   superior short‐term fuel for organisms because they are simpler to
   metabolize than lipids or amino acids. Typically, oligosaccharides
   and/or polysaccharides are cleaved into smaller monosaccharides and
   then are catabolized to produce energy, reducing powers, and components
   of other molecules. During germination with a relatively short time, up
   to 17.7% of the 130 DEPs were involved in carbohydrate metabolism such
   as glycolysis/gluconeogenesis (P3, P84, P193, P299, P330, and P367),
   butanoate metabolism (P68, P118, P412, P454, P464, and P461),
   glyoxylate and dicarboxylate metabolism (P72, P177, P396, and P477),
   fructose and mannose metabolism (P50, P115), starch and sucrose
   metabolism (P377), galactose metabolism (P64, P430), inositol phosphate
   metabolism (P227), and the pentose phosphate pathway (P126, P166). Most
   of these DEPs were up‐regulated in both mRNA and protein levels. This
   means that the metabolism of carbohydrates was necessary for the
   germination of P. expansum spores.

   Proteins are large biomolecules, consisting of one or more long chains
   of amino acid residues. They are the primary participants within the
   cell that implement the changes specified by the information encoded in
   genes. With the exception of certain types of RNA, most other
   biological molecules are relatively inert elements upon which proteins
   act. In this study, 8.5% and 19.2% of DEPs were involved in amino acid
   metabolism and translation, respectively. With the exception of
   aminotransferase and DAHP synthetase, DEPs related to amino acid
   metabolism showed trends that changed differently at the mRNA and
   protein levels. In addition, the expression of all DEPs related to
   translation showed a decreasing trend at the mRNA level, but almost all
   of them showed an increasing trend at the protein level. Apparently,
   the translation system in P. expansum spores had been postponed. It was
   noteworthy that a large number of proteins related to the ribosome such
   as ribosome proteins and the processome, a ribosome assembly
   intermediate (Phipps, Charette, & Baserga, [139]2011), showed
   significant alterations of their expression levels. These changes
   significantly improved the output of translation initiation/elongation
   factors. A component protein of the proteasome whose function was to
   degrade unneeded or damaged proteins by proteolysis was decreased as
   well (Enenkel, [140]2014). Therefore, protein synthesis capacity during
   spore germination had increased and led to the subsequent changes in
   the biophysical or biochemical profiles of P. expansum.

   Transcription is the first step of gene expression, in which a
   particular segment of DNA is copied into RNA, especially mRNA, by the
   enzyme RNA polymerase. In this study, almost all DEPs related to
   nucleotide metabolism and transcription were down‐regulated at the mRNA
   level and up‐regulated at the protein level. Among them, multiple types
   of RNA polymerases were notable, as they are necessary for constructing
   RNA chains using DNA genes as templates and are responsible for the
   synthesis of distinct subsets of RNA. These results indicated that
   genetic information processing had been activated and accelerated from
   dormancy to vegetative growth of P. expansum spores.

   Energy metabolism is a set of metabolic reactions and processes to
   convert biochemical energy from nutrients into adenosine triphosphate
   (ATP) and release waste products. In this study, the expression levels
   of DEPs related to energy metabolism were all down‐regulated at both
   mRNA and protein levels. The possible reasons for this are delineated.
   Before germinating, the dormant spores need to shed their cell walls,
   degrade compatible sugars accompanied by a decrease in microviscosity
   of the cytoplasm, and reorganize the transcriptome including mRNA
   breakdown and the selected up‐regulation of different gene categories
   (Lamarre et al., [141]2008). Therefore, dormant spores need more energy
   than ones that have germinated. In addition, the DEPs related to
   transport, signal transduction, motility, and other molecular types of
   metabolism were recorded. It is reasonable that the conversion of
   dormant cells into vegetative cells requires active metabolism and cell
   division.

   Overall, a large number of genes showed consistency between the
   transcript and protein levels, while some genes that exhibited
   inconsistency between these levels suggested that posttranscriptional
   regulation and modification serve important roles in the regulation of
   fungal germination. Additional research is needed to better understand
   the fact that the turnover and stability of mRNA levels are important
   for the translation of mRNA into proteins. For example, the
   down‐regulation of expression at the transcriptional level combined
   with the up‐regulation of expression at the protein level of some DEGs
   implied that mRNA had already begun to be degraded. Conversely, there
   may be regulation of translational pathways or increased protein
   degradation leading to apparently enhanced transcription. In summary,
   the transcriptomic and proteomic data in this study not only
   highlighted a set of genes and proteins that were important in
   deciphering the molecular processes of the germination of P. expansum
   but also provided the basic knowledge for the development of effective
   control methods and adequate environmental conditions.

CONFLICT OF INTEREST

   None declared.

Supporting information


   [142]Click here for additional data file.^ (441.9KB, pdf)


   [143]Click here for additional data file.^ (369.9KB, xlsx)


   [144]Click here for additional data file.^ (14.4KB, xlsx)


   [145]Click here for additional data file.^ (320.1KB, xlsx)

ACKNOWLEDGMENTS