Quantitative analysis of proteome data and quality validation of LC-MS/MS data
To expand our understanding of the response of maize during infection with the pathogenic fungus S. turcica, the maize leaves which inoculated with S. turcica after 24 and 72 hours (hpi) were collected as treatments and 0 hpi as control (CK). Firstly, the expression level of two pathogenesis-related genes (Zmprp4, Zm00001d018738 and Zmprp5, Zm00001d031158) were analyzed to explore whether the artificial inoculation was successful. The semi-RT-PCR result shown that the expression level of Zmprp4 and Zmprp5 increased with the infection time (Table S1, Fig 1a), this indicated that the maize had initiated its defense response. Further, total protein was extracted from each sample, and an integrated method involving TMT labelling and liquid chromatography–tandem MS (LC-MS/MS) was used for the spectrophotometric quantification of the protein abundance of different samples. Pearson correlations were computed for all nine samples (three replicates × three stages) to assess the reproducibility and accuracy of the biological replicates. Averages of 0.76 for the three proteome replicates were obtained (Fig. 1b). MS data was used to detect the peptides after quality validation. The mass delta values of all identified peptides are shown in Fig. 1c. Almost 92.5% of all obtain peptides had lengths of 7–19 amino acid residues (Fig. 1d). The similarity between these results and the properties of known tryptic peptides suggests that the prepared samples met the required standards.
By using the 24,114 unique peptides identified in our study, A total of 5,646 proteins (protein groups, detected at least two replicates), encoded by 5,587 gene were identified. Amongst of the 5,646 proteins, 4,740 were quantified which encoded by 4,711 genes (Table S2). We compared the protein abundances of a number of selected genes with those of well-studied genes to further evaluate the quality of our proteome data. We found that the pathogenesis-related protein (ZmPRP5, Zm00001d031158), β-1,3-glucanases (ZmGEB1, Zm00001d042143) and lipoxygenase 4 (ZmLOX4, Zm00001d033624), which are defense marker genes with functions in maize defense response [23-25], were up-regulated at the 24 and 72 hpi (Fig. S1). The increment in the protein abundances of the selected marker genes after infection with S. turcica indicated that maize had initiated its defense response process in protein level and the method used for quantification of protein abundance processed well.
Integration of maize protein activity and cellular function during S. turcica infection
In order to review the detail cellular function of the detected proteins, the k-means clustering algorithm was used to group them into 10 clusters, and each cluster contained proteins with similar expression patterns. Further, the MapMan term was carried out to assign proteins to functional categories (Table S3).
The modules can be divided into three categories on the basis of cluster analysis results (Fig. 2): induced-expression, depressed-expression and uncertain-expression pattern modules. The induced-expression modules (represented by modules C1, C2, C3 and C7), comprised the proteins that may positively regulate the defense response. Indeed, we found there were 27 well studied proteins which related to biotic stress in these module, including ZmPRP3, ZmPRP5 and the NB-ARC domain-containing disease resistance protein (Zm00001d018734). Moreover, these modules were typified by the overrepresentation of protein synthesis elongation, protein folding, tricarboxylic acid cycle, glycolysis, cell vesicle transport, signalling calcium and signalling G-proteins (Table S3). These characteristics indicated that the defense process of maize requires high energy consumption and frequent signalling conduction. The depressed-expression modules, which were represented by C4, C8 and C10. The proteins related to these modules may negatively regulate the defense response or be suppressed by the S. turcica. Further analysis shown that these modules enrichment by the reduced expression of the PS lightreaction photosystem, serine protease, jasmonate degradation, redox thioredoxin and cell cycle peptidylprolyl isomerase. These results suggested that the photosynthetic rate and cell proliferation of maize leaves decreased during S. turcica infection. Modules C5, C6 and C9 included proteins which exhibited irregular expression patterns and were related to protein synthesis and degradation, hormone metabolism and mitochondrial electron transport. Taken together, these data show that the defense responses of maize during infection partially originated from the highly coordinated dynamics amongst the abundances of different proteins.
Detection of differentially expressed proteins (DEPs) at different infection stages
Among of the 4,711 quantified proteins, 710, 1,096 and 465 were identified as DEPs (minimum fold-change of ±1.3 or greater and P < 0.05) for 24 hpi vs CK, 72 hpi vs CK and 72 vs 24 hpi, respectively (Table S4). The number of up-regulated DEPs was higher than that of down-regulated DEPs (Fig. 3a). Moreover, the distribution of the DEPs was illustrated in a Venn diagram (Fig. 3b). This diagram shows that the three compare shared 131 DEPs in common. Amongst the 131 common DEPs, 105 were up-regulated, and almost 80% (81) DEPs were continuously induced. For instance, the wound-induced protein1 (ZmWIP1, Zm00001d008548), which is associated with hypersensitivity reaction (HR) defense response in maize , was up-regulated by 3.51-fold at 24 hpi and 12.55-fold at 72 hpi, indicating the important role of ZmWIP1 for maize HR defense response to S. turcica. These results suggest maize has initiated HR defense response at 24 hpi and gradually increased as the infection progresses and eventually leads to cell death lesions. Furthermore, the functions of DEPs were annotated by using GO terms (Fig. S2). Overall, the distribution proportion of the DEPs function was similar among the three comparison (24 hpi vs CK, 72 hpi vs CK and 24 hpi vs 72 hpi). Notably, the distribution proportion of the term ‘response to stimulus’ at 72 vs 24 hpi was significantly higher (Fisher’s exact test P < 0.05) than that at CK vs 24 hpi. These results suggests that, although, the DEPs of different infection stages were different, the similar defense reaction may be involved in maize response to infection and the response strength is increasing during the infection process.
To further understanding the defense response of maize, the GO enrichment analysis was performed. For the enriched GO terms, some were exists in 24 hpi vs CK (Fig. 3c) and 72 hpi vs CK (Fig. 3d), such as ‘response to biotic stimulus’, ‘defense response’, ‘COPI vesicle coat’, ‘oxidoreductase activity’, ‘hydrogen peroxide metabolic process’ and ‘eukaryotic translation elongation factor 1 complex’, suggesting the proteins involved in the above terms may function with regulatory roles in basal responses. Some were specifically enriched in one stages. To illustrate, ‘regulation of protein metabolic process’, ‘protein insertion into membrane’ and ‘lipid transport’ were enriched in 24 hpi, while ‘hydrolase activity’, ‘disaccharide and oligosaccharide biosynthetic process’ and ‘trehalose biosynthetic process’ were enriched in 72 hpi. In addition, when compared 72 hpi to 24 hpi (Fig. 3e), the GO term ‘hydrolase activity’, ‘glucosyltransferase activity’, ‘amino acid transport’, ‘anchored component of membrane’ and ‘photosystem I reaction center’ were enriched. Taken together, these results indicated some induced proteins function in basal responses, whereas some were specifically involved in invasion.
DEPs related to the glutathione S-transferase (GST) family
To further understanding the defense response of maize, the DEPs function were annotated by mapman. Compared with the DEPs in 24 hpi vs CK, more DEPs of 72 hpi vs CK were took part in the defense response. It’s worth noting that, the number of DEPs belong to the GST family had grown from one to ten in 72 hpi (Fig. 4a). Among of them, the ZmGST23 (Zm00001d020780) which had proved to be associated with modest levels of resistance to NCLB in maize , was up-regulated at 72 hpi (Fig. 4A). To have an insight into the function of GSTs, we analyzed the expression pattern of the above ten DEPs in transcriptional level by using the published RNA-Seq, which contain two inbred lines with opposite resistance (B73: Susceptible; B73Htn1: contain the NCLB resistance gene Htn1) with four infected stage . Almost all of above GSTs were highest expression at the latest infection stage (240 hpi) of the studies in B73 (Fig. 4b), while, in the B73Htn1, they were highly expression in the earlier period of infection (9 and 72 hpi) (Fig. 4c). These results indicated the glutathione-dependent detoxification participate in defense response and the earlier time of induction of which may contribute to the resistance to S. turcica.
DEPs related to the Secondary metabolites pathway
Many secondary metabolites found in plants have a role in defence against pathogens. It was well known that the phenylpropanoids and benzoxazinoids (BXs) are involved in plant defense in plant [29, 30]. So, the DEPs related to phenylpropanoid and BXs biosynthesis were analyzed further (Fig. 5). In total, 19 DEPs were annotated as phenylpropanoid lignin biosynthesis pathway enzyme, and they belong to the other 8 enzymes, except F5H (ferulate 5-hydroxylase) and C3H (p-coumarate-3-hydroxylase) (Fig. 5a). We found two DEPs (ZmPAL1, Zm00001d017274; ZmPAL2, Zm00001d003016) which belong to the phenylalanine ammonia-lyase the rate-limiting enzyme of phenylpropanoid biosynthesis pathway were down-regulated in 24 and 72 hpi. Moreover, the DEPs of the key enzymes in lignin biosynthesis which produced through the activity of the phenylpropanoid pathway were analyzed. The Cinnamate 4-hydroxylase (ZmC4H, Zm00001d009858) and hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl transferase (ZmHCT, Zm00001d050455 and Zm00001d020530) were significantly increased in infected plants at 24 and 72 hpi. While, Cinnamyl alcohol dehydrogenase (ZmCAD, Zm00001d015618), Cinnamyl alcohol dehydrogenase (ZmCCR, Zm00001d032152) and Caffeic acid 3-O-methyltransferase (ZmCOMT, Zm00001d049541) were down-regulated significantly in 24 and 72 hpi. These results shown that there were a complex response pattern of phenylpropanoid lignin biosynthesis pathway enzymes to S. turcica infection. In addition, four DEPs which belong to BXs biosynthesis pathway were identified (Fig. 5b). Except the benzoxazinone synthesis 10 (BX10) which catalyze the conversion of 2,4-dihydroxy-7-methoxy-2H-1,4-benzoxazin-3(4H)-one-Glc (DIMBOA-Glc) to 2-hydroxy-4,7-dimethoxy-1,4-benzoxazin-3-one-Glc (HDMBOA-Glc), the BX4, BX6 and BX7 which are responsible for the synthesis of DIMBOA-Glc were down-regulated. Furthermore, we found the β-glucosidase (ZmGLU1, Zm00001d023994) which was responsible for hydrolyzing DIMBOA-Glc to DIMBOA and glucose , was up-regulated 2.2 and 7.46 fold in 24 hpi and 72 hpi, respectively. These results suggested the enzymes responsible for synthesis of DIMBOA-Glc were reduced and the decomposition were increased in maize leaves when response to S. turcica.
DEPs related to the jasmonic acid biosynthesis pathway
The responses mediated by jasmonic acid (JA) and salicylic acid (SA) play a key role in plant defenses against pathogens . Form the mapman results, we found the DEPs related to JA were almost up-regualted, while, the DEPs related to SA was not detected in our study, indicating the JA may play the most importance roles in the defense response to S. turcica infected. Furthermore, the JA biosynthesis pathway were analyzed next. In this study, 18 proteins which are related to JA biosynthesis were quantified and 11 of which were DEPs (Fig. 6). Among of the 11 DEPs, 5 DEPs were different expressed both in 24 and 72 hpi when compared with CK, including three lipoxygenases (ZmLOX1; ZmLOX4 and ZmLOX5) which catalyzes the oxygenation of polyunsaturated fatty acids and two 12-Oxo-PDA-reductase (ZmOPR1 and ZmOPR4) which catalyzes the reduction of cyclopentenone rings. In addition to those of ZmLOX1 and ZmLOX5, the expression levels of ZmLOX4, ZmOPR1 and ZmOPR4 were up-regulated at 24 hpi relative to those at 72 hpi. In addition, ZmAOS2 was up-regulated only at 24 hpi, whereas ZmLOX2, ZmLOX6, ZmLOX11, ZmAOS1 and ZmOPR8 were only differentially expressed at 72 hpi. Combined with the above results, the DEPs that related to jasmonic acid biosynthesis, excepted for ZmLOX6 and ZmLOX11, were up-regulated at 24 or 72 hpi. In addition, we noticed that the Zmlox6 gene expression was down-regulated in maize during infection with Cochliobolus heterostrophus . This result suggested that Zmlox6 may play a negative role in responses to fungal infection. Moreover，the proteins which were the downstream of JA signal transduction pathway were also up-regulated, for instance, two 60 kDa jasmonate-induced protein, Zm00001d004573 was up-regulated in 24 hpi and 72 hpi, Zm00001d004591 was specifically up-regulated in 72 hpi; The MYC2 (Zm00001d030028), which is a major regulator in the JA signaling pathway  , was also up-regulated in 24 hpi (Table S4). The above results suggested that jasmonic acid biosynthesis was induced during S. turcica infection, and the downstream proteins of JA had been activated.
Protein-protein interaction (PPI) networks of the DEPs
In order to further interpret the DEPs in the biological context, the PPI networks of DEPs were constructed PPI which could be used to predict the relationship between DEPs. In total, 499 DEPs of 0 vs 24 hpi and 841 DEPs of 0 vs 72 hpi were identified as network nodes by using the medium confidence cut-off (confidence score > 0.4). The PPI networks showed significantly more interactions than a random set of proteins of similar sizes (P < 1.0e-16) (Table S5), indicating the DEPs were at least partially biologically connected. PPI nodes involved in biological processes were strongly associated with response to biotic stimulus (GO:0009607, P = 9.7e-12 and 3.4e-20 at 24 and 72 hpi, respectively) and defense response to ‘fungus, incompatible interaction’ (GO:0009817, P = 0.0017 and 7.1e-08 at 24 and 72 hpi, respectively). Further, the PPI nodes which were related to the term ‘defense response to fungus, incompatible interaction’ were analyzed further. We found that 5 and 12 nodes at 24 hpi and 72 hpi were belong to this GO term and interacted with 26 and 80 nodes, respectively (Fig. 7). Furthermore, the nodes of interaction network at 72 hpi almost overlapped with those identified at 24 hpi (Fig. S3), suggesting the maize had initiated additional reactions to response the infection at 72 hpi. In addition, some of the interacted nodes were also related to defense response. These proteins included the well-studied proteins ZmWIP1, ZmPRP5 and chitinase (ZmCHN1, Zm00001d043988) [25, 26, 34].
Parallel Reaction Monitoring (PRM) validation
In order to confirm the LC-MS/MS quantification results, PRM method were used to validate the abundance of 14 functional DEPs (Table S7). For the validation DEPs, they included the pathogenesis-related proteins, GST family protein, proteins related to phenylpropanoid and jasmonic acid biosynthesis and other proteins with important functions, which were mention in the above results. High consistency (correlation coefficients were all greater than 0.90, P < 0.001) between PRM and LC-MS/MS quantification were found (Fig. 8). For instance, in the defense response process, the ZmPRP6, ZmWIP1 and ZmGEB1 were significantly induced, while the ZmPAL and ZmCCR was significant down-regulation. These results further lent confidence to our LC-MS/MS data.