The stringent response regulator (p) ppGpp mediates virulence gene expression and survival in Erwinia amylovora

Ho-wen Yang; Menghao Yu; Jae Hoon Lee; Tiyakhon Chatnaparat; Youfu Zhao

doi:10.1186/s12864-020-6699-5

. 2020 Mar 30;21:261. doi: 10.1186/s12864-020-6699-5

The stringent response regulator (p) ppGpp mediates virulence gene expression and survival in Erwinia amylovora

Ho-wen Yang ¹, Menghao Yu ¹, Jae Hoon Lee ¹, Tiyakhon Chatnaparat ¹, Youfu Zhao ^1,^✉

PMCID: PMC7106674 PMID: 32228459

Abstract

Background

The nucleotide second messengers, i.e., guanosine tetraphosphate and pentaphosphate [collectively referred to as (p) ppGpp], trigger the stringent response under nutrient starvation conditions and play an essential role in virulence in the fire blight pathogen Erwinia amylovora. Here, we present transcriptomic analyses to uncover the overall effect of (p) ppGpp-mediated stringent response in E. amylovora in the hrp-inducing minimal medium (HMM).

Results

In this study, we investigated the transcriptomic changes of the (p) ppGpp⁰ mutant under the type III secretion system (T3SS)-inducing condition using RNA-seq. A total of 1314 differentially expressed genes (DEGs) was uncovered, representing more than one third (36.8%) of all genes in the E. amylovora genome. Compared to the wild-type, the (p) ppGpp⁰ mutant showed down-regulation of genes involved in peptide ATP-binding cassette (ABC) transporters and virulence-related processes, including type III secretion system (T3SS), biofilm, and motility. Interestingly, in contrast to previous reports, the (p) ppGpp⁰ mutant showed up-regulation of amino acid biosynthesis genes, suggesting that it might be due to that these amino acid biosynthesis genes are indirectly regulated by (p) ppGpp in E. amylovora or represent specific culturing condition used. Furthermore, the (p) ppGpp⁰ mutant exhibited up-regulation of genes involved in translation, SOS response, DNA replication, chromosome segregation, as well as biosynthesis of nucleotide, fatty acid and lipid.

Conclusion

These findings suggested that in HMM environment, E. amylovora might use (p) ppGpp as a signal to activate virulence gene expression, and simultaneously mediate the balance between virulence and survival by negatively regulating DNA replication, translation, cell division, as well as biosynthesis of nucleotide, amino acid, fatty acid, and lipid. Therefore, (p) ppGpp could be a promising target for developing novel control measures to fight against this devastating disease of apples and pears.

Keywords: Erwinia amylovora, RNA-seq, (p) ppGpp, Virulence factors, T3SS

Background

During the early stage of infection, plant pathogenic bacteria are exposed to environmental stresses, including nutrient starvation and oxidative stress. To overcome these adverse conditions, bacteria produce linear nucleotide second messengers, i. e. guanosine tetraphosphate and pentaphosphate [collectively referred to as (p) ppGpp], to regulate gene expression from replication and growth to colonization and survival [1]. This phenomenon is so-called the stringent response, one of the global regulatory systems in bacteria [1]. Biosynthesis of (p) ppGpp is mainly attributed to the RelA/SpoT homologue proteins (RSH). RelA is a ribosomal associated protein which synthesizes (p) ppGpp in response to amino acid starvation. On the other hand, SpoT is a dual function protein which synthesizes (p) ppGpp in response to fatty acid, carbon, phosphorous, and iron limitations, and also degrades (p) ppGpp to prevent replication halt due to high concentration of (p) ppGpp [1–4]. It has been reported that the relA/spoT double mutant resulted in multiple defects, including autotrophies for several amino acids [5].

Several models have been proposed for the molecular mechanisms of the stringent response [1, 2, 6]. It has been reported that (p) ppGpp, along with a small RNA polymerase (RNAP) binding protein DksA, directly binds to RNAP and then destabilizes its open complex [6, 7]. On the other hand, (p) ppGpp regulates gene expression indirectly by sigma factor competition [2]. High concentration of (p) ppGpp inhibits sigma factor σ⁷⁰, which allows more free RNAPs interact with alternative sigma factors, including σ⁵⁴, to activate genes in response to stresses [3, 8]. Moreover, (p) ppGpp also influences gene expression other than through RNAP [9] by directly down-regulating stable RNA (rRNA and tRNA) and genes related to transcription and translation, while directly up-regulating amino acid biosynthesis genes [1, 3, 8, 10, 11]. It has been reported that over 30% genes in Escherichia coli genome were differentially expressed by (p) ppGpp, including up-regulation of genes related to stress response and down-regulation of genes related to macromolecular structures in isoleucine starvation condition [12]. About 500 genes were found to be differentially expressed in E. coli strain MG1655 under serine hydroxamate (SHX) treatment, which mimics serine starvation [13].

Previous studies showed that (p) ppGpp is required for virulence gene expression in Salmonella enterica [14], E. coli [15], Pseudomonas syringae [16], and Erwinia amylovora [17]. E. amylovora is the causal agent of the fire blight disease, a devastating disease that causes severe economic losses in apples and pears [18]. One of the major pathogenicity factors in E. amylovora is the hypersensitive response and pathogenicity (hrp)-type III secretion system (T3SS) [19]. The alternative sigma factor HrpL is the master regulator of T3SS, which in turn is activated by another alternative sigma factor 54 (RpoN), along with several other proteins, including HrpS, IHF, and YhbH [20–23]. Previous study has demonstrated that (p) ppGpp activates the RpoN and HrpL sigma factor cascade to trigger the T3SS gene expression. Furthermore, a recent study showed that (p) ppGpp activates expression of a two-component system HrpXY, which in turn regulates the expression of the hrpS gene [23]. In this study, we investigated transcriptomic profiles of the wild-type strain (WT) and the (p) ppGpp⁰ mutant at 3 h post incubation (hpi), and we also compared global gene expression between WT grown at 3 and 6 hpi in HMM.

Results and discussion

Overview of the global effect of (p) ppGpp in gene expression in Erwinia amylovora

The linear nucleotide second messengers (p) ppGpp have been studied for more than four decades [13]. Based on previous reports, (p) ppGpp swiftly and robustly mediates target gene expression, such as genes related to transcription [24] and translation [25, 26]. Consequently, bacteria growth [3], surface organelle production (fimbriae and flagella) [27], cell size, and virulence [28] are affected. In this study, the global effect of (p) ppGpp in E. amylovora on gene expression was examined using RNA-seq. In summary, 13,167,843 to 15,637,863 reads for each biological sample were generated for E. amylovora WT and its (p) ppGpp⁰ mutant at 3 h, and the percentage of reads mapped to E. amylovora genome ranged from 97.1 to 97.8%; whereas 15,618,174 to 17,669,201 reads for each biological sample were obtained for E. amylovora WT at 6 h, and the percentage of reads mapped to E. amylovora genome were from 97.2 to 97.6%.

The gene expression dynamics was first characterized by principal component analysis (PCA) for substantially expressed genes (log₂CPM ≥ 2 in at least 3 samples, CPM: counts per million reads) (Fig. 1). PC1 and PC2 explained 70.7 and 16.1% of the total variability, respectively. PC1 mainly explained the variability between WT and the (p) ppGpp⁰ mutant (P < 0.01), indicating that gene expression patterns were changed dramatically in the (p) ppGpp⁰ mutant. On the other hand, PC2 mainly explained the variability of gene expression at different time point for WT at 3 h and 6 h (P < 0.01). The PCA plot also showed obvious separation of the WT at 3 and 6 h as well as the (p) ppGpp⁰ mutant strain at 3 h. Nevertheless, three biological samples for each treatment were mostly clustered together, indicating excellent sample repetition (Fig. 1).

Fig. 1 — Principal component analysis (PCA) for characterization of gene expression dynamics in WT at 3 h and 6 h, as well as the (p) ppGpp⁰ mutant (*relA/spoT*) at 3 h in the *hrp*-inducing minimal medium

For analyzing genes that might be (p) ppGpp-dependent, DEGs were identified by comparing the (p) ppGpp⁰ mutant with WT at 3 h. A total of 1314 DEGs were identified, representing more than one third of genes (36.8%) in the E. amylovora genome. Among them, 612 DEGs (46.6%) were up-regulated and 702 DEGs (53.4%) were down-regulated in the (p) ppGpp⁰ mutant (Fig. 2a and Fig. 3a, Additional file 1: Table S1). Most DEGs were functionally categorized according to the clusters of orthologous groups (COG) (Fig. 4a). Most of the DEGs categorized as amino acid metabolism, coenzyme metabolism, translation, posttranslational regulation, replication/ recombination/DNA repair, as well as nucleotide metabolism, were negatively regulated by (p) ppGpp. Conversely, most of the DEGs categorized as T3SS, cell motility, and energy production/conversion were positively regulated by (p) ppGpp (Fig. 4a). These results supported the dogma that (p) ppGpp globally regulates gene expression.

Fig. 2 — Identification of differentially expressed genes (DEGs) between the (p) ppGpp⁰ mutant and WT by quasi-likelihood (QL) F-test in edgeR. a Expression level and fold change of each gene by comparing the (p) ppGpp⁰ mutant versus WT at 3 h. The X and Y axes correspond to mean of normalized log2-based count per million values (log₂CPM) and log₂((p) ppGpp⁰/WT at 3 h) ratio, respectively. b Expression level and fold change of each gene by comparing the WT at 6 h versus WT at 3 h. The X and Y axes correspond to mean of normalized log2-based count per million values (log₂CPM) and log₂(WT at 6 h /WT at 3 h) ratio, respectively

Fig. 3 — Heatmapt showing expression pattern of differentially expressed genes in three biological samples each for (a) the (p) ppGpp⁰ mutant and WT at 3 h (b) WT at 6 h and WT at 3 h. White represents mean of expression level (log₂CPM), dark blue represents minimal gene expression, and bright red represents maximal gene expression

Fig. 4 — Classification and verification of differentially expressed genes (DEGs). Functional categories of DEGs according to the clusters of orthologous groups (COG) database. a the (p) ppGpp⁰ mutant and WT at 3 h (b) WT at 6 h and WT at 3 h. Red: up-regulated; blue: down-regulated

On the other hand, to investigate the hierarchical natural of response over time in HMM, we identified the DEGs between WT at 6 h and WT at 3 h. A total of 122 DEGs were identified, where 97 DEGs (87.4%) were up-regulated, and 14 (12.6%) DEGs were down-regulated in WT at 6 h (Fig. 2b and Fig. 3b). The majority of up-regulated genes are T3SS (n = 34) and amino acid metabolism (n = 17) (Fig. 4b, Additional file 2: Table S2), suggesting that after activation by (p) ppGpp, the expression of the T3SS genes was higher at 6 h as reported previously [23]. To verify the result of RNA-seq, qRT-PCR was conducted for several randomly selected DEGs, and the results of qRT-PCR were mostly in the similar trend as the RNA-seq data (Fig. 5a and b). In addition, expression of T3SS genes was previously verified [17].

Fig. 5 — Validation of RNA-seq data by qRT-PCR. a the (p) ppGpp⁰ mutant and WT at 3 h (b) WT at 6 h and WT at 3 h. Numbers on the bar indicated fold changes obtained for the gene in RNA-seq

Positive regulation of virulence-related genes by (p) ppGpp

During the early stage of infection when bacteria are subjected to stress response, such as nutrient limitation and oxidative stress, (p) ppGpp is produced [17]. Previous research revealed that (p) ppGpp activates T3SS to trigger virulence [17]. Consistent with this result, our RNA-seq data showed that (p) ppGpp positively regulates virulence gene expression in E. amylovora, including most of the T3SS, amylovoran biosynthesis and levan production genes (Fig. 6a and Table 1). Among the T3SS genes, the hrpL, hrpA, hrpN and hrpW gene expression exhibited a very high negative fold change (log₂FC = − 6.02 to − 6.49). The deficiency of T3SS gene expression in the (p) ppGpp⁰ mutant indicated that (p) ppGpp is required for T3SS expression in E. amylovora [17]. Down-regulation of the T3SS genes, accompanied by attenuated virulence and reduced growth, was also reported in the P. syringae (p) ppGpp⁰ mutants [16]. Similar results were also reported in Bordetella pertussis in response to glutamine limitation [29] and in E. coli in response to nutrient starvation [15]. Previous studies have demonstrated that the T3SS gene expression in E. amylovora reached the highest level at 6 hpi in HMM [23]. We found that 34 out of 97 up-regulated DEGs in comparison of WT at 6 h and WT at 3 h belongs to T3SS. Both hrpA and hrpN exhibited up-regulation more than two folds in WT at 6 h (log₂FC = 2.54 & 2.11, respectively) (Table 2), indicating that T3SS might be continuously expressed after activation by (p) ppGpp at 3 h.

Fig. 6 — Differentially expressed genes positively regulated by (p) ppGpp. a T3SS, amylovoran, and levan. b motility-related genes. White represents mean of expression level (log₂CPM), dark blue represents minimal gene expression, and bright red represents maximal gene expression. In the side bar (right), dark green represents higher negative fold change (log₂FC), and bright red represents the higher positive log₂FC

Table 1.

List of differentially expressed genes^a associated with virulence for the (p) ppGpp⁰ mutant versus WT at 3 h

Accession	Gene Description	Log₂FC^b	Mean^c
T3SS
EAMY_0519	hrpK, pathogenicity locus protein	−2.08	4.91
EAMY_0520	hsvA, Hrp-associated systemic virulence protein	−1.75	3.35
EAMY_0525	hrcU, type III secretion protein	−2.21	4.88
EAMY_0526	hrcT, type III secretion apparatus protein	−2.84	3.67
EAMY_0527	hrcS, type III secretion protein	−3.87	2.55
EAMY_0529	hrcQ, type III secretion system apparatus protein	−2.53	5.50
EAMY_0530	hrpP, type III secretion protein	−4.06	4.70
EAMY_0531	hrpO, type III secretion protein	−5.86	4.53
EAMY_0532	hrcN, type III secretion system ATPase	−5.01	5.61
EAMY_0533	hrpQ, type III secretion system protein	−5.65	5.37
EAMY_0534	hrcV, type III secretion inner-membrane protein	−5.3	6.15
EAMY_0535	hrpJ, type III secretion system protein	−5.00	5.88
EAMY_0536	hrpL, RNA polymerase sigma factor	−5.89	6.20
EAMY_0539	hrpS, sigma-54-dependent enhancer-binding protein	−1.45	6.69
EAMY_0542	hrpA, Hrp pili protein	−6.49	9.12
EAMY_0543	hrpB, type III secretion system protein	−5.86	7.46
EAMY_0544	hrcJ, type III secretion inner-membrane protein	−5.42	6.44
EAMY_0545	hrpD, type III secretion protein	−5.19	4.78
EAMY_0546	hrpE, type III secretion apparatus protein	−3.82	4.29
EAMY_0547	hrpF, type III secretion protein	−5.75	5.74
EAMY_0548	hrpG, type III secretion protein	−5.75	6.26
EAMY_0549	hrcC, type III secretion system outer membrane pore	−4.70	7.37
EAMY_0550	hrpT, type III secretion lipoprotein	−4.11	4.43
EAMY_0551	hrpV, type III secretion protein	−4.07	4.35
EAMY_0552	hrpN, harpin protein	−6.32	7.96
EAMY_0553	orfA, Tir chaperone family protein	−4.38	6.87
EAMY_0554	orfB, avirulence protein	−3.33	5.12
EAMY_0555	orfC, HrpW-specific chaperone	−4.98	6.33
EAMY_0556	hrpW, harpin protein	−6.07	7.36
EAMY_0557	dspE, Hrp secreted pathogenicity-like protein	−4.4	9.23
EAMY_0558	dspF, Hrp secreted pathogenicity-like protein	−2.05	6.23
EAMY_0653	eop2, type III effector	−3.34	6.98
EAMY_3175	avrRpt2, cysteine protease avirulence protein	−1.07	6.20
Amylovora
EAMY_2242	amsL, amylovoran biosynthesis protein	−1.04	5.64
EAMY_2243	amsK, glycosyltransferase	−1.26	5.46
EAMY_2244	amsJ, exopolysaccharide biosynthesis protein	−1.10	5.47
EAMY_2245	amsF, exopolysaccharide biosynthesis protein	−1.48	6.10
EAMY_2247	amsD, Amylovoran biosynthesis glycosyltransferase	−1.03	3.53
EAMY_2248	amsC, exopolysaccharide biosynthesis protein	−1.18	4.53
EAMY_2249	amsB, glycosyltransferase	−1.62	5.59
Levan
EAMY_3695	lscC, levansucrase	−3.60	8.19
EAMY_0559	rlsA, transcriptional regulator	−2.62	4.51

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^aDifferentially expressed genes (DEGs) between the (p) ppGpp⁰ mutant and WT at 3 h with |log₂FC| value ≥ 1 and an adjusted p value < 0.05. WT: wild type. FC: fold change. FC values below 0 mean that the gene has lower expression in the (p) ppGpp⁰ mutant than in WT.

^blog₂FC valus was calculated by the log based 2 value of (p) ppGpp⁰ / WT at 3 h

^cThe average of log₂CPM was calculated. CPM: count per million reads

Table 2.

List of differentially expressed genes^a associated with virulence for WT at 6 h versus WT at 3 h

Accession	Gene description	Log₂FC^b	Mean^c
T3SS
EAMY_0519	hrpK, pathogenicity locus protein	1.38	6.50
EAMY_0520	hsvA, Hrp-associated systemic virulence protein	1.6	4.98
EAMY_0521	hsvB, Hrp-associated systemic virulence protein	1.4	4.58
EAMY_0522	hsvC, Hrp-associated systemic virulence protein	1.02	5.64
EAMY_0525	hrcU, type III secretion protein	1.68	6.64
EAMY_0526	hrcT, type III secretion apparatus protein	1.71	5.51
EAMY_0527	hrcS, type III secretion protein	1.9	4.54
EAMY_0528	hrcR, type III secretion apparatus protein	1.75	6.51
EAMY_0529	hrcQ, type III secretion system apparatus protein	1.75	7.37
EAMY_0530	hrpP, type III secretion protein	1.83	6.74
EAMY_0531	hrpO, type III secretion protein	1.78	6.58
EAMY_0532	hrcN, type III secretion system ATPase	1.72	7.64
EAMY_0533	hrpQ, type III secretion system protein	1.67	7.37
EAMY_0534	hrcV, type III secretion inner-membrane protein	1.6	8.13
EAMY_0535	hrpJ, type III secretion system protein	1.63	7.86
EAMY_0536	hrpL, RNA polymerase sigma factor	1.04	7.89
EAMY_0542	hrpA, Hrp pili protein	2.54	11.62
EAMY_0543	hrpB, type III secretion system protein	1.67	9.49
EAMY_0544	hrcJ, type III secretion inner-membrane protein	1.7	8.47
EAMY_0545	hrpD, type III secretion protein	1.69	6.78
EAMY_0546	hrpE, type III secretion apparatus protein	1.65	6.19
EAMY_0547	hrpF, type III secretion protein	1.71	7.77
EAMY_0548	hrpG, type III secretion protein	1.71	8.30
EAMY_0549	hrcC, type III secretion system outer membrane porein	1.68	9.38
EAMY_0550	hrpT, type III secretion lipoprotein	1.71	6.39
EAMY_0551	hrpV, type III secretion protein	1.82	6.37
EAMY_0552	hrpN, harpin protein	2.11	10.21
EAMY_0553	orfA, Tir chaperone family protein	1.61	8.27
EAMY_0554	orfB, avirulence protein	1.64	8.76
EAMY_0555	orfC, HrpW-specific chaperone	1.75	7.15
EAMY_0556	hrpW, harpin protein	1.68	9.40
EAMY_0557	dspE, Hrp secreted pathogenicity-like protein	1.86	11.32
EAMY_0558	dspF, Hrp secreted pathogenicity-like protein	2.11	8.21
EAMY_0653	eop2, type III effector	1.36	8.74

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^aDifferentially expressed genes (DEGs) between the WT at 6 h and at 3 h with |log₂FC| value ≥ 1 and an adjusted p value < 0.05. FC: fold change. WT: wild type. FC values over 0 mean that the gene has higher expression in WT at 6 h than at 3 h

^blog₂FC valus was calculated by the log based 2 value of (p) ppGpp⁰ / WT at 3 h

^cThe average of log₂CPM was calculated. CPM: count per million reads

In addition, levan and amylovoran are also virulence factors and contribute to biofilm formation in E. amylovora [30]. Diminished biofilm formation and attenuated virulence has been reported in the (p) ppGpp-deficient mutant of E. coli [31] and Enterococcus faecalis [32]. We found that both amylovoran biosynthesis (amsBCDFJKL) and levan production (lscC and rlsA) genes exhibited negative fold change (Fig. 6a and Table 1), indicating that (p) ppGpp positively regulates virulence by affecting most of the levan and amylovoran production genes.

Flagella-mediated motility is another important virulence determinant [33]. There are three classes of flagellar genes in hierarchical order: class I (flhDC, master regulator of flagellar formation), class II (hook and basal body formation), and class III (filament and motor assembly [34]. A defect in motility due to the loss of flagella in the (p) ppGpp⁰ mutant has been reported in S. enterica [35], E. coli [27], P. syringae [16], and E. amylovora (Additional file 3: Fig. S1). We found that 32 DEGs related to motility (Fig. 6b, Table 3) were differentially expressed between the (p) ppGpp⁰ mutant and the WT. Almost all DEGs belongs to class II, including flgEFDC and fliFIHGLJ, which exhibited the highest negative fold changes (− 6.1 ≤ |log₂FC| ≤ − 5.3, − 5.1 ≤ |log₂FC| ≤ − 6.1, respectively).

Table 3.

List of differentially expressed genes^a associated with motility for the (p) ppGpp⁰ mutant versus WT at 3 h

Accession	Gene Description	Log₂FC^b	Mean^c
EAMY_1508	fliI, flagellum-specific ATP synthase	−6.25	6.73
EAMY_1509	fliH, flagellar assembly protein	−5.98	6.74
EAMY_1456	flgE, flagellar hook protein	−5.97	9.06
EAMY_1511	fliF, flagellar M-ring protein	−5.73	7.65
EAMY_1457	flgF, flagellar basal body rod protein	−5.71	7.79
EAMY_1510	fliG, flagellar motor switch protein	−5.66	6.86
EAMY_1453	flgB, flagellar basal body protein	−5.4	7.22
EAMY_1459	flhH, flagellar L-ring protein	−5.33	6.57
EAMY_1455	flgD, flagellar hook capping protein	−5.26	7.96
EAMY_1454	flgC, flagellar basal body rod protein	−5.23	7.01
EAMY_1507	fliJ, flagellar biosynthesis chaperone	−5.22	6.24
EAMY_1505	fliL, flagellar basal body-associated protein	−5.03	6.53
EAMY_1506	fliK, flagellar hook-length control protein	−4.88	7.96
EAMY_1503	fliN, flagellar motor switch protein	−4.77	6.23
EAMY_1458	flgG, flagellar basal body rod protein	−4.71	7.75
EAMY_1504	fliM, flagellar motor switch protein	−4.66	7.17
EAMY_1460	flgI, flagellar P-ring protein	−4.33	6.58
EAMY_1461	flgJ, flagellar rod assembly protein	−4.19	6.49
EAMY_1502	fliO, flagellar biogenesis protein	−4.13	5.93
EAMY_1512	fliE, flagellar hook-basal body protein	−3.67	5.80
EAMY_1501	fliP, flagellar biosynthetic protein	−3.54	6.15
EAMY_1452	flgA, flagellar basal body P-ring biosynthesis protein	−3.3	6.32
EAMY_1500	fliQ, flagellar biosynthetic protein	−2.81	4.63
EAMY_2139	fliA, RNA polymerase sigma factor	−2.41	6.33
EAMY_2138	fliZ, flagellar regulatory protein	−2.15	5.81
EAMY_2143	fliS, flagellin-specific chaperone	−2.08	6.03
EAMY_2144	fliT, flagellar export chaperone	−1.89	5.71
EAMY_2142	fliD, flagellar capping protein	−1.75	7.72
EAMY_2682	fliR, flagellar biosynthetic protein	−1.54	2.85
EAMY_1499	fliR, flagellar biosynthetic protein	−1.5	5.59
EAMY_1463	flgL, flagellar hook-associated protein	−1.28	6.56
EAMY_1462	flgK, flagellar hook-associated protein	−1.25	7.56
EAMY_2660	flgJ, flagellar rod assembly protein	−1.00	2.41
EAMY_2662	flgH3, Flagellar L-ring protein	−1.07	1.70
EAMY_2689	fliK3, flagellar hook-length control protein	−1.06	3.62

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^blog₂FC valus was calculated by the log based 2 value of (p) ppGpp⁰ / WT at 3 h

^cThe average of log₂CPM was calculated. CPM: count per million reads

Differential regulation of amino acid and peptide biosynthesis genes by (p) ppGpp

Negative regulation of amino acid biosynthesis by (p) ppGpp

It has been demonstrated that (p) ppGpp and DksA directly activate amino acid biosynthesis under nutrient limited conditions [3, 36]. In contrast, we found that among the 127 DEGs related to amino acid metabolism, 98 (77.2%) were up-regulated in the (p) ppGpp⁰ mutant (Additional file 3: Fig. S2a; Table 4), indicating (p) ppGpp negatively regulates genes involved in amino acid biosynthesis. First, the metREFBKALJ operon genes for methionine biosynthesis exhibited very high expression in the (p) ppGpp⁰ mutant. Among them, metR, encoding a transcriptional regulator, and metAFE, which are regulated by metR in E. coli [37], all showed high positive fold change (log₂FC = from 3.4 to 5.47). When compared WT at 6 and 3 hpi, methionine biosynthesis genes metABEFKN were also up-regulated in WT at 6 h (Table 5), suggesting that methionine might be synthesized in WT at 6 h, whereas (p) ppGpp acts rapidly and robustly to suppress methionine biosynthesis in WT at 3 h. Since methionine plays an important role in translation initiation, it is reasonable for bacteria to synthesize methionine under stress conditions.

Table 4.

List of differentially expressed genes^a associated with amino acid biosynthesis and degradation for the (p) ppGpp⁰ mutant versus WT at 3 h

Accession	Gene Description	Log₂FC^b	Mean^c
Histidine
EAMY_1255	hutH, histidine ammonia-lyase	6.97	8.62
EAMY_1254	hutU, urocanate hydratase	5.8	8.85
EAMY_1259	hutI, imidazolonepropionase	4.93	4.93
EAMY_1258	hutF, formiminoglutamate deiminase	4.75	6.60
EAMY_1260	hutG, N-formylglutamate amidohydrolase	3.2	6.31
EAMY_1256	hutC, transcriptional regulator	1.09	6.71
Methionine
EAMY_0207	metR, transcriptional regulator	5.47	9.21
EAMY_0208	metE, methionine synthase II	4.86	13.30
EAMY_0141	metF, 5,10-methylenetetrahydrofolate reductase	4.71	10.82
EAMY_0138	metB, cystathionine gamma-synthase	4.56	10.43
EAMY_0603	metK, S-adenosylmethionine synthetase	4.32	11.75
EAMY_3342	metA, homoserine transsuccinylase	3.4	9.95
EAMY_0139	metL, bifunctional aspartokinase	3.31	10.48
EAMY_0137	metJ, transcriptional regulator of met regulon	2.41	8.53
Arginine
EAMY_1553	argC, acetylglutamate semialdehyde dehydrogenase	2.41	11.13
EAMY_0144	argB, acetylglutamate kinase	2.17	10.74
EAMY_0297	argR, arginine repressor	1.93	8.35
EAMY_0146	argH, argininosuccinate lyase	1.88	10.84
EAMY_0145	argG, argininosuccinate synthase	1.83	11.69
EAMY_3415	argD, 4-aminobutyrate aminotransferase	1.83	10.43
EAMY_2082	argS, arginyl-tRNA synthetase	1.37	8.58
EAMY_1631	astB, succinylarginine dihydrolase	−3.05	6.66
EAMY_1630	astD, NAD-dependent aldehyde dehydrogenase	−1.98	6.45
EAMY_1628	astC, succinylornithine transaminase	−1.88	7.19
EAMY_1629	astA, arginine N-succinyltransferase	−1.60	6.15
Tryptophan
EAMY_1915	trpE, anthranilate synthase component I	3.67	11.22
EAMY_1916	trpG, anthranilate synthase component II	2.73	8.92
EAMY_1917	trpD, anthranilate phosphoribosyltransferase	2.4	9.21
EAMY_1918	trpC, indole-3-glycerol phosphate synthase	1.82	9.45
EAMY_1919	trpB, tryptophan synthase beta chain	1.49	9.28
EAMY_3425	trpS, tryptophanyl-tRNA synthetase	1.31	8.67
Isoleucine
EAMY_0158	ilvG, acetolactate synthase isozyme III large subunit	3.60	10.64
EAMY_0159	ilvM, acetolactate synthase isozyme II small subunit	2.69	8.25
EAMY_0160	ilvE, branched-chain amino acidaminotransferase	2.17	9.33
EAMY_0161	ilvD, dihydroxy-acid dehydratase	1.21	9.57
EAMY_0163	ilvY, transcriptional regulator	1.12	5.24

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^blog₂FC valus was calculated by the log based 2 value of (p) ppGpp⁰ / WT at 3 h

^cThe average of log₂CPM was calculated. CPM: count per million reads

Table 5.

List of differentially expressed genes^a associated with amino acid biosynthesis and degradation for WT at 6 h versus WT at 3 h

Accession	Gene description	Log₂FC^b	Mean^c
Methionine
EAMY_0208	metE, methionine synthase II	2.5	10.93
EAMY_0141	metF, 5,10-methylenetetrahydrofolate reductase	1.92	8.30
EAMY_0603	metK, S-adenosylmethionine synthetase	1.52	9.40
EAMY_3342	metA, homoserine transsuccinylase	1.36	8.37
EAMY_2728	metN, methionine ABC transporter ATP-binding protein	1.15	7.49
EAMY_0138	metB, cystathionine gamma-synthase	1.04	7.60
Arginine
EAMY_1630	astD, NAD-dependent aldehyde dehydrogenase	−1.32	6.62
EAMY_1631	astB, succinylarginine dihydrolase	−1.27	7.00
EAMY_1628	astC, succinylornithine transaminase	−1.22	7.40
EAMY_1629	astA, arginine N-succinyltransferase	−1.18	6.31

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^aDifferentially expressed genes (DEGs) between the WT at 6 h and at 3 h with |log₂FC| value ≥ 1 and an adjusted p value < 0.05. WT: wild type. FC: fold change. FC values over 0 mean that the gene has higher expression in WT at 6 h than at 3 h. and vice versa

^blog₂FC valus was calculated by the log based 2 value of (p) ppGpp⁰ / WT at 3 h

^cThe average of log₂CPM was calculated. CPM: count per million reads

Second, the argBCDGHR operon genes in the arginine biosynthesis pathway [38] were up-regulated in the (p) ppGpp⁰ mutant (1.83 ≤ log₂FC ≤ 2.41). Consistent with this result, the astABCD operon genes, which have been reported for degrading arginine to glutamine [39], were down-regulated (− 1.6 ≤ |log₂FC| ≤ − 3.05) in the (p) ppGpp⁰ mutant and in WT at 6 h (− 1.18 ≤ |log₂FC| ≤ − 1.32) as well (Tables 4 and 5). Furthermore, trpBCDEGS involved in tryptophan biosynthesis, livGMEDY for isoleucine biosynthesis, and hutCFGHIU in histidine utility pathway were all up-regulated in the (p) ppGpp⁰ mutant. Among them, the trpEG genes, encoding anthranilate synthase [40], livGM encoding acetolactate synthase isozymes at the beginning of isoleucine biosynthesis operon, and hutHUI genes all exhibited relatively high fold change (Table 4). The hutHUI genes have been reported to participate in the degradation of histidine to glutamate which serves as an important donor of amino acid and for nucleotide biosynthesis [41]. Thus, our results indicated that genes involved in the biosynthesis of methionine, arginine, tryptophan, and isoleucine were all negatively regulated by (p) ppGpp, suggesting that up-regulation of these amino acid biosynthesis-related genes in the (p) ppGpp⁰ mutant might be indirect by (p) ppGpp in E. amylovora. This is consistent with previous reports that regulation of amino acid biosynthesis genes might be indirect by (p) ppGpp [6, 42]. Sanchez-Vazquez and colleagues found that the promoter of amino acid biosynthesis genes cannot be activated, which was in contrast with other reports [36], and concluded that it might be due to different culture conditions [6]. Consistent with this observation, Traxler and colleagues reported that amino acid biosynthesis genes couldn’t be induced en masse in WT under amino acid starvation condition [12]. It is reasonable to speculate that (p) ppGpp negatively regulated amino acid biosynthesis might also be due to the specific growth condition (HMM) used.

It has been reported that amino acid metabolism might be important for virulence [43–45]. In E. amylovora, mutants deficient in arginine, isoleucine/valine, and tryptophan metabolism exhibited reduced virulence [44], and the argD mutant of E. amylovora not only led to arginine auxotrophy, but also exhibited attenuated or no virulence in apples and pears [45]. A methionine metabolism regulator MetR has been identified as a new virulence regulator [46]. Tryptophan biosynthesis gene trpD has been reported for its role in inducing quorum-sensing and T3SS in Pseudomonas aeruginosa [47]. Durand and Björk reported that a combination of methionine and arginine restore the virulence of the tgt mutant, which lacks tRNA and exhibited reduced virulence gene expression in Shigella flexneri [43]. A relatively higher expression of methionine and arginine biosynthesis-related genes and down-regulation of arginine degradation genes in the (p) ppGpp⁰ mutant suggest that increased biosynthesis of arginine and/or methionine may help E. amylovora survive.

Inverse regulation of amino acid and peptide transporter genes by (p) ppGpp

Similar to amino acid biosynthesis genes, 12 out of the17 DEGs related to amino acid ABC (ATP-binding cassette) transport systems were up-regulated in the (p) ppGpp⁰ mutant. Genes (metNI, EAMY_0862, and artPI) encoded in methionine and arginine import systems were up-regulated in the (p) ppGpp⁰ mutant (1.31 ≤ log₂FC ≤ 3.24, 1.33 ≤ log₂FC ≤ 2.24, respectively; Additional file 3: Fig. S2b and Table 6). In addition, seven genes related to polar amino acid uptake transporter (PAAT) were also up-regulated in the (p) ppGpp⁰ mutant (1.12 ≤ log₂FC ≤ 5.28), though their specific substrates remain unknown. In contrary to amino acid ABC transport systems, 16 DEGs related to peptide ABC transport systems, including genes in the dpp and opp operons (dppABCDEF, oppABCDF) and three genes (yliD, yliC, yejA) belonging to peptide/opine/nickel uptake transporter (PepT) family, were down-regulated in the (p) ppGpp⁰ mutants (Additional file 3: Fig. S2b and Table 6).

Table 6.

List of differentially expressed genes^a associated with amino acid and peptide transport systems for the (p) ppGpp⁰ mutant versus WT at 3 h

Accession	Gene Description	Log₂FC^b	Mean^c
amino acid transport system
Methionine
EAMY_2728	metN, methionine ABC transporter ATP-binding protein	3.24	9.03
EAMY_2729	metI, methionine ABC transport system	1.31	7.53
Arginine (PAAT)
EAMY_0862	ABC-type arginine/histidine transport system, permease component	2.24	3.66
EAMY_1315	artP, arginine ABC transport system	1.67	9.20
EAMY_1314	artI, arginine ABC transport system	1.33	9.86
Other polar amino acid uptake transport system (PAAT)
EAMY_0860	ABC transporter substrate-binding protein	5.28	4.96
EAMY_0266	yhdW, ABC transporter substrate-binding protein	3.47	6.70
EAMY_0861	polar amino acid ABC transporter permease	3.36	4.07
EAMY_0863	ABC transporter ATP-binding protein	2.52	4.44
EAMY_0263	yhdZ, ABC-type polar amino acid transport system	1.42	4.52
EAMY_0264	yhdY, ABC-type amino acid transport system	1.27	4.15
EAMY_0265	yhdX, ABC-type amino acid transport system	1.12	4.63
peptide transport system
*Opp*
EAMY_1936	oppB, ABC transporter permease componenet	−2.65	5.91
EAMY_1935	oppC, ABC transporter permease componenet	−2.43	5.27
EAMY_1937	oppA, ABC transporter periplasmic component	−2.29	9.25
EAMY_1934	oppD, ABC transporter ATPase component	−2.22	5.84
EAMY_1933	oppF, ABC-type oligopeptide transport system	−1.63	6.52
*Dpp*
EAMY_3609	dppF, ABC transporter ATP-binding protein	−2.43	5.77
EAMY_3611	dppC, transporter	−2.23	5.73
EAMY_3610	dppD, ABC transporter ATP-binding protein	−2.17	6.15
EAMY_3613	dppA, ABC transporter periplasmic component	−2.04	9.75
EAMY_3612	dppB, ABC transporter	−1.94	6.78
Peptide/Opine/Nickel Uptake Transporter (PepT) Family
EAMY_1292	yliD, ABC-type dipeptide/oligopeptide/nickel transport system	−1.55	6.42
EAMY_1291	yliC, ABC-type dipeptide/oligopeptide/nickel transport system	−1.41	6.66
EAMY_2311	yejA, ABC-type oligopeptide transport system	−1.20	6.22

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^blog₂FC valus was calculated by the log based 2 value of (p) ppGpp⁰ / WT at 3 h

^cThe average of log₂CPM was calculated. CPM: count per million reads

Small peptides can be used as carbon and nitrogen sources in bacteria, like E. coli and Salmonella sp. [48, 49]. Both Opp and Dpp have been reported for importing dipeptides and tripeptides, as well as uptake of essential amino acids in Streptococcus pyogenes [50, 51]. The Opp system also recycles cell-wall peptide and senses environment [49, 52]. Kim and colleagues suggested that peptide transporters provide peptides containing essential amino acids for both survival and infection in Salmonella [53]. Previous study showed that both Opp and Dpp are hijacked for importing antibiotics, but are dispensable for virulence in E. amylovora [54]. Taken together, these results suggested that (p) ppGpp positively regulates peptide uptake systems in WT, but negatively regulates genes involved in amino acid uptake systems and amino acid biosynthesis in the HMM environment.

Negative regulation of genes contributing to survival by (p) ppGpp

Translation

Inhibition of (p) ppGpp in translation by repressing the synthesis of tRNA, rRNA and ribosome has been well documented [1, 26, 55]. The slow growth of the (p) ppGpp⁰ mutant [17] might be related to negative regulation of (p) ppGpp in ribosomal proteins as reported previously [56]. Consistently, 98 of 106 genes (92.5%) related to translation were up-regulated in the (p) ppGpp⁰ mutant. Among them, 33 genes (rps, rpm, and rpl) associated with ribosomal subunits were up-regulated in the (p) ppGpp mutant (1.15 ≤ log₂FC ≤ 2.86; Additional file 3: Fig. S2c and Table 7), indicating that (p) ppGpp negatively mediates ribosomal protein biosynthesis. Lemke and colleagues found that r-protein promoter activities decreased in WT after SHX treatment, suggesting a direct negative regulation by ppGpp and DksA [55]. Besides ribosomal protein genes, infA and tufA, encoding a translation initiation factor and a translation elongation factor, respectively, were also up-regulated in the (p) ppGpp⁰ mutant (log₂FC = 1.52 & 2.24), indicating that (p) ppGpp negatively regulates translation through down-regulating initiation and elongation factors. Srivatsan and Wang reported that (p) ppGpp inhibits and interferes the functions of the initiation factor IF2 and the elongation factors EF-Tu and EF-G in E coli. In addition, (p) ppGpp binds to IF2 and EF-G to inhibit translation when competing with GDP and GTP [26]. Overall, (p) ppGpp might control translation capacity in the cell to prevent the depletion of cell resources under stress conditions.

Table 7.

List of differentially expressed genes^a associated with translation for the (p) ppGpp⁰ mutant versus WT at 3 h

Accession	Gene Description	Log₂FC^b	Mean^c
Translation initiation
EAMY_1327	infA, translation initiation factor IF-1	1.52	9.39
*Translation elongation*
EAMY_0232	tufA, translation elongation factors	2.24	12.20
ribosomal protein synthesis
*rpl*
EAMY_0303	rplM, ribosomal protein L13	2.70	12.08
EAMY_2320	rplY, ribosomal protein L25	2.65	11.32
EAMY_3376	rplN, ribosomal protein L14	1.93	11.83
EAMY_3375	rplX, ribosomal protein L24	1.93	11.86
EAMY_3374	rplE, ribosomal protein L5	1.90	12.34
EAMY_0236	rplA, ribosomal protein L1	1.81	10.97
EAMY_3371	rplF, ribosomal protein L9	1.75	11.37
EAMY_0235	rplK, ribosomal protein L11	1.68	10.54
EAMY_3370	rplR, ribosomal protein L18	1.60	10.61
EAMY_3367	rplO, ribosomal protein L15	1.54	11.59
EAMY_3142	rplI, ribosomal protein L9	1.40	10.49
EAMY_0332	rplU, ribosomal protein L21	1.36	10.76
EAMY_3360	rplQ, ribosomal protein L17	1.27	10.39
EAMY_3386	rplC, ribosomal protein L3	1.17	11.07
EAMY_3385	rplD, ribosomal protein L4	1.15	10.38
*rpm*
EAMY_0136	rpmE, ribosomal protein L31	2.64	9.97
EAMY_0078	rpmG, ribosomal protein L33	2.43	10.07
EAMY_3368	rpmD, ribosomal protein L30	1.8	10.52
EAMY_0077	rpmB, ribosomal protein L28	1.76	10.47
EAMY_0333	rpmA, ribosomal protein L27	1.64	11.06
*rps*
EAMY_0304	rpsI, ribosomal protein S9	2.86	11.55
EAMY_0352	rpsO, ribosomal protein S15	2.51	10.25
EAMY_0417	rpsU, ribosomal protein S21	2.12	10.15
EAMY_2940	rpsT, ribosomal protein S20	1.84	10.18
EAMY_2760	rpsB, ribosomal protein S2	1.82	11.96
EAMY_3373	rpsN, ribosomal protein S14	1.76	11.7
EAMY_3372	rpsH, ribosomal protein S8	1.72	11.23
EAMY_3369	rpsE, ribosomal protein S5	1.57	11.12
EAMY_3143	rpsR, ribosomal protein S18	1.30	10.04
EAMY_3390	rpsG, ribosomal protein S7	1.26	10.84
EAMY_0816	rpsP, ribosomal protein S16	1.22	9.43
EAMY_3145	rpsF, ribosomal protein S6	1.21	10.8
EAMY_3387	rpsJ, ribosomal protein S10	1.24	10.35

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^aDifferentially expressed genes (DEGs) between the (p) ppGpp⁰ mutant and WT at 3 h with |log₂FC| value ≥ 1 and an adjusted p value < 0.05. WT: wild type. FC: fold change. FC values over 0 mean that the gene has higher expression in the (p) ppGpp⁰ mutant than in WT.

^blog₂FC valus was calculated by the log based 2 value of (p) ppGpp⁰ / WT at 3 h

^cThe average of log₂CPM was calculated. CPM: count per million reads

Biosynthesis of purine and pyrimidine

Thirty eight out of 47 DEGs (80.9%) related to nucleotide metabolism were up-regulated in the (p) ppGpp⁰ mutant. Among them, 14 and 12 DEGs are related to purine and pyrimidine biosynthesis, respectively (Additional file 3: Fig. S2d and Table 8). The purCDHIMNTU operon genes (1.68 ≤ log₂FC ≤ 1.86) are involved in synthesizing inosine monophosphate (IMP), a nucleotide monophosphate for generating AMP and GMP from 5-phosphoribosyl diphosphate (PRPP) in E. coli [57]. The deoD and gpt genes (log₂FC = 2.41 & 1.97, respectively) were involved in purine salvage pathway for synthesizing IMP from hypoxanthine [57]. Moreover, two GMP synthesis genes, guaA and guaB, were also up-regulated in the (p) ppGpp⁰ mutant (log₂FC = 1.74 and 3.02, respectively), which supported a previous report of an uncontrollable increase of GTP level (~ 10 mM or higher) in the (p) ppGpp⁰ mutant [58]. In consistent with our results, (p) ppGpp has been reported to inhibit enzymes that initiate ATP and GTP biosynthesis [1, 59]. Furthermore, several genes in both UMP de novo biosynthesis pathway (carAB and pyrBFI), UMP salvage pathway (udp and udk), and CMP biosynthesis-related genes (pyrG and cmK) were up-regulated in the (p) ppGpp⁰ mutant (Additional file 3: Fig. S2d and Table 8). UMP is the precursor of CTP biosynthesis, and PyrG/CTP synthase is an importance enzyme for the conversion of UMP to CMP [60]. Overall, these results indicate that (p) ppGpp negative controls purine and pyrimidine biosynthesis pathways [27, 58].

Table 8.

List of differentially expressed genes^a associated with nucleitide metabolism for the (p) ppGpp⁰ mutant versus WT at 3 h

Accession	Gene Description	Log₂FC	Mean^b
Purine
GMP
EAMY_2568	guaB, inosine-5′-monophosphate dehydrogenase	2.94	10.85
EAMY_2567	guaA, GMP synthase	1.66	9.06
EAMY_2859	guaC, GMP reductase	1.55	8.44
IMP
EAMY_2052	purT, phosphoribosylglycinamide formyltransferase II	1.79	9.79
EAMY_2542	purM, phosphoribosylformylglycinamidine cyclo-ligase	1.74	10.49
EAMY_2610	purl, FGAM synthase	1.5	10.89
EAMY_2529	purC, SAICAR synthase	1.4	9.8
EAMY_0262	purH, bifunctional purine biosynthesis protein	1.28	9.07
EAMY_1965	purU, formyltetrahydrofolate deformylase	1.16	8.47
EAMY_2543	purN, phosphoribosylglycinamide formyltransferase	1.02	8.57
EAMY_0261	purD, phosphoribosylamine-glycine ligase	1	9.04
EAMY_2978	deoD, uridine phosphorylase	2.34	9.11
EAMY_0884	gpt, xanthine phosphoribosyltransferase	1.89	7.3
EAMY_0864	pucG, serine-pyruvate aminotransferase	1.59	5.14
Pyrimidine
UMP
EAMY_1900	pyrF, orotidine-5′-phosphate decarboxylase	2.16	7.42
EAMY_2932	carA, carbamoyl-phosphate synthase small subunit	1.93	11.18
EAMY_2283	cdd, cytidine deaminase	1.88	6.95
EAMY_0366	pyrB, aspartate carbamoyltransferase	1.83	8.42
EAMY_2931	carB, carbamoyl-phosphate synthase large chain	1.25	11.53
EAMY_0074	dut, deoxyuridine 5′-triphosphate nucleotidohydrolase	1.53	7.54
EAMY_0210	udp, uridine phosphorylase	1.47	7.34
EAMY_0365	pyrI, aspartate carbamoyltransferase	1.27	7.49
EAMY_2257	udk, uridine kinase	1.04	7.62
CMP
EAMY_0737	pyrG, CTP synthase	2.12	10.37
EAMY_1346	cmK, cytidylate kinase	1.06	7.98
TMP
EAMY_2980	deoA, thymidine phosphorylase	1.63	7.04

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^cThe average of log₂CPM was calculated. CPM: count per million reads

DNA replication/recombination/repair

Thirty out of 37 DEGs (81.1%) related to replication/recombination/repair were up-regulated in the (p) ppGpp⁰ mutant (Additional file 2: Table S2). Among them, 11 genes were involved in DNA-inducible SOS function (Additional file 3: Fig. S2e and Table 9). Two SOS response-associated genes (recAN) and an inhibitor of SOS response gene lexA were all highly expressed in the (p) ppGpp⁰ mutant (log₂FC = 1.97, 2.96 and 6.95; respectively). The recA gene activates the recN gene, and helps co-ordinate the recombination of DNA double strand breaks [61]. Whereas LexA could self-cleavage in the present of RecA [62]. Under severe DNA damage, expression of the recA-lexA genes could result in an apoptosis-like death as an extreme SOS response in E coli [63]. In addition, several SOS response-associated genes, including dinP, ruvA and ruvB, which have been reported being repressed by (p) ppGpp under amino acid starvation [64], were also up-regulated in the (p) ppGpp⁰ mutant (log₂FC = 2.15, 1.66, 1.18, respectively). Kim and colleagues found that overexpression of dinB/dinP resulted in enhancing mutagenesis in E. coli [65]. Therefore, expression of large number of DNA repair and SOS inducible genes indicates that DNA damage or mismatch may commonly occur in the (p) ppGpp⁰ mutant, which eventually leads to cell death as reported previously [16].

Table 9.

List of differentially expressed genes^a associated with DNA repair/replication for the (p) ppGpp⁰ mutant versus WT at 3 h

Accession	Gene Description	Log₂FC	Mean^b
DNA-repair: SOS response
EAMY_2641	recN, DNA repair protein	2.96	9.84
EAMY_3327	lexA, SOS-response transcriptional repressors	2.95	10.27
EAMY_3296	ssb, single-stranded DNA-binding protein	2.22	9.95
EAMY_0882	dinP, DNA polymerase IV	2.15	6.96
EAMY_0805	recA, recombinase A	1.97	11.18
EAMY_2064	ruvA, Holliday junction ATP-dependent DNA helicase	1.66	6.84
EAMY_1211	uvrB, excinuclease UvrABC subunit B	1.56	8.45
EAMY_1251	dinG, ATP-dependent helicase	1.23	7.05
EAMY_2063	ruvB, Holliday junction ATP-dependent DNA helicase	1.18	7.44
EAMY_0194	uvrD, DNA helicase II	1.13	8.55
EAMY_3297	uvrA, excinuclease ATPase subunit	1.01	9.29
DNA replication
EAMY_3296	ssb, single-stranded DNA-binding protein	2.22	9.95
EAMY_2345	gyrA, DNA gyrase A subunit	1.64	10.46
EAMY_0725	exo, 5′-3′ exonuclease	1.47	6.31
EAMY_0844	dnaQ, DNA polymerase III epsilon subunit	1.36	6.88
EAMY_3144	priB, primosomal replication protein N	1.24	10.24
EAMY_1122	holA, DNA polymerase III delta subunit	−1.21	6.86

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^blog₂FC valus was calculated by the log based 2 value of (p) ppGpp⁰ / WT at 3 h

^cThe average of log₂CPM was calculated. CPM: count per million reads

It has been reported that DNA replication was inhibited by (p) ppGpp [66]. Consistently, five genes related to DNA replication were up-regulated in the (p) ppGpp⁰ mutant (Additional file 3: Fig. S2e and Table 9). The ssb gene, encoding a single strand DNA-binding protein, is essential for DNA replication, recombination and repair [67], and is also involved in SOS system [68]. Another gene encoding a DNA polymerase III subunit epsilon processes a proofreading function of polymerase III holoenzyme [69]. It has been reported that replication forks arrested under amino acid starvation conditions, especially at the time of replication initiation [70]. It is possible that rapid and reversible replication arrest might help bacteria stabilize genome DNA during starvation.

Fatty acid/lipid metabolism and cell cycle

Sixteen out of 23 DEGs involved in lipid metabolism were up-regulated in the (p) ppGpp⁰ mutant (Additional file 3: Fig. S2f and Table 10), including the fabBZ genes, which are involved in unsaturated fatty acid biosynthesis. A fatty acid degradation gene fadA, on the other hand, was down-regulated in the (p) ppGpp⁰ mutant (|log₂FC| = − 1.22), indicating that (p) ppGpp negatively regulates fatty acid biosynthesis genes in E. amylovora. It has been reported that both fabB and fadA are under control of a dual transcriptional regulator fadR. During fatty acid starvation, fadR represses fadA operon to prevent fatty acid degradation [71] and activates fabB to enhance fatty acid synthesis [72]. In E. coli, (p) ppGpp and DksA inhibited fadH expression directly or indirectly through fadR to down-regulate fatty acid biosynthesis [73].

Table 10.

List of differentially expressed genes^a associated with lipid metabolism/cell cycle for the (p) ppGpp⁰ mutant versus WT at 3 h

Accession	Gene Description	Log₂FC^b	Mean^c
Lipid metabolism
Fatty acid biosynthesis
EAMY_2423	fabB, 3-oxoacyl-(acyl-carrier-protein) synthase	2.38	11.6
EAMY_2408	accD, acetyl-CoA carboxylase beta subunit	1.86	9.97
EAMY_2748	fabZ, 3-hydroxymyristoyl/3-hydroxydecanoyl-(acyl carrier protein) dehydratases	1.46	8.39
EAMY_0948	yajB, acyl carrier protein phosphodiesterase	1.2	5.16
EAMY_1242	cfa, cyclopropane fatty acid synthase	−1.7	6.96
Fatty acid degradation
EAMY_2827	vraB, 3-ketoacyl-CoA thiolase	−1.72	4.77
EAMY_0222	fadA, acetyl-CoA acetyltransferase	−1.22	10.1
Cell division
chromosom partition
EAMY_1925	intracellular septation protein A	1.8	7.37
EAMY_1357	mukF, chromosome partition protein	1.75	8.20
EAMY_2278	mrp, ATPases involved in chromosome partitioning	1.36	8.77
EAMY_1358	mukE, chromosome partition protein	1.27	7.22
cell division
EAMY_1387	sulA, cell division inhibitor	2.19	8.22
EAMY_0129	zapB, cell division protein	1.2	8.58
EAMY_2482	zipA, cell division protein	1.12	9.29

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^aDifferentially expressed genes (DEGs) between the (p) ppGpp⁰ mutant and WT at 3 h with |log₂FC| value ≥ 1 and an adjusted p value < 0.05. FC: fold change. WT: wild type. WT: wild type. FC: fold change. FC values below 0 mean that the gene has lower expression in the (p) ppGpp⁰ mutant than in WT, and vice versa

^blog₂FC valus was calculated by the log based 2 value of (p) ppGpp⁰ / WT at 3 h

^cThe average of log₂CPM was calculated. CPM: count per million reads

In addition, eight out of nine DEGs involved in cell cycle were up-regulated in the (p) ppGpp⁰ mutant (Additional file 3: Fig. S2f and Table 10). The mukEF genes are involved in chromosome condensation and segregation [74]. Ferullo and Lovett showed that chromosome segregation was arrested by (p) ppGpp in E. coli after SHX treatment [75]. Moreover, genes related to cell division (sulA, zapB, zipA) were also up-regulated in the (p) ppGpp⁰ mutant (Additional file 3: Fig. S2f and Table 10). The sulA gene, encoding a cell division inhibitor, and the zapB and zipA genes are all essential for cell division [76, 77]. Accumulation of SulA protein causes rapid arrest of cell division, resulting in long and non-separate filament [76]. Indeed, the (p) ppGpp⁰ mutant exhibited longer cells in both E amylovora [17] and P. syringae [16]. Traxler and colleagues showed that the (p) ppGpp⁰ mutant produced an average of around 50% more biomass than that of the WT under isoleucine limited condition [12]. Taken together, DNA replication, biosynthesis of nucleotide metabolism, cell wall, fatty acid, as well as cell division all contribute to biomass [12]. The lack of (p) ppGpp caused abnormal up-regulation of DNA replication, biosynthesis of nucleotides, cell wall, fatty acid, as well as cell division genes, which may further deplete cell resources, eventually leading to cell death.

Conclusions

Based on our current as well as previous reported results [17], a simple working model was proposed (Fig. 7). When E. amylovora tries to colonize plant and starts its infection process, perturbations, such as limited nutrients, acidity, or oxidative stress, activate the RelA/SpoT system and promote (p) ppGpp production. In HMM medium, the (p) ppGpp triggers the expression of T3SS, motility and peptide ABC transporter genes. Simultaneously, genes for biosynthesis of amino acid, and nucleotide, fatty acid, lipid, SOS system, DNA replication, chromosome segregation, as well as translation are suppressed by (p) ppGpp. In this environment, (p) ppGpp redistributes cell resources to virulence gene expression, and at the same time maintains the balance between survival by its quick reversal of the stringent response.

Methods

Bacterial strains and growth conditions

The E. amylovora WT strain Ea1189 and the relA/spoT double mutant strain, i. e. the (p) ppGpp⁰ mutant [17], were routinely grown in Luria-Bertani (LB) broth. The hrp-inducing minimal medium (HMM) (1 g (NH₄)₂SO₄, 0.246 g MgCl₂ • 6H₂O, 0.1 g NaCl, 8.708 g K₂HPO₄, 6.804 g KH₂PO₄]/Liter) supplemented with 10 mM galactose as carbon source, was used for T3SS gene expression and RNA-seq [17, 20]. Antibiotics were used at the following concentrations when appropriate: 50 μg/mL kanamycin (Km) and 25 μg/mL chloramphenicol (Cm). Primers used in this study were listed in Table S3 (Additional file 4).

RNA isolation and Illumina sequencing

Bacteria strains cultured overnight in LB broth at 28 °C with appropriate antibiotics were collected by centrifugation at 4000 rpm and washed three times in HMM before being inoculated into 5 mL of HMM at OD₆₀₀ of 0.2 [20]. After 3 and 6 h inoculation at 18 °C with shaking at 250 rpm, 4 ml of RNA protected reagent (Qiagen, Hilden, Germany) was added to 2 ml of bacteria culture, mixed by vortexing, and incubated for 5 min at room temperature to prevent RNA degradation. RNA was extracted by RNeasy® minikit (Qiagen, Hilten, Germany) following the manufacturer’s instructions, and DNase I treatment was performed with a Turbo DNA-free kit (ambion, Austin, TX). The quantity and quality of RNA samples were assessed using NanoDrop ND-100 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) and/or using Agilent RNA 6000 Nano Chip Bioanalyzer (Agilent, Santa Clara, CA, USA). Three biological samples each for WT-3 h, WT-6 h, and (p) ppGpp⁰-3 h were then sent to the Keck Center at the University of Illinois at Urbana-Champaign for library constructions and Illumina HiSeq 2500 (Illumina, San Diego, CA, USA) sequencing. A total of nine stranded libraries were constructed using TruSeq Stranded RNA Sample Prep kit following the manufacturer’s instructions (Illumina, San Diego, CA, USA).

Transcriptomic data profiling and differentially expressed gene detection

The RNA-seq reads were aligned to the reference coding sequences (CDSs) of E. amylovora strain CFBF1430 [78], using the default parameters of the Burrows-Wheeler Aligner (version 0.12.7) [79] (http://bio-bwa.sourceforge.net/). Samtools and bedtools were performed for getting the read counts per CDS. Normalized log2-based count per million values (log₂CPM) were calculated after TMM (trimmed mean of M values) normalization in the edgeR package [80, 81]. To examine gene expression dynamics among all the samples (WT-6 h, WT-3 h, (p) ppGpp⁰-3 h), a principle component analysis (PCA) was conducted by using prcomp in R. Differentially expressed gene (DEGs) between comparisons ((p) ppGpp⁰/WT-3 h and WT-6 h/WT-3 h) were detected in edgeR package [80, 81] and screened by a statistics filter (P < 0.05, |log₂FC| > 1). For functionally categorization of DEGs using COGs, protein sequence of all coding genes were downloaded from NCBI (https://www.ncbi.nlm.nih.gov/). The two FASTA protein files were used as input for protein annotation using eggNOG-mapper (http://eggnogdb.embl.de/#/ app/emapper). COG information for DEGs was extracted from eggNOG output file. The RNA-seq data files have been submitted to Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) with an accession number GSE143324 and GSE128088.

Quantitative reverse transcription real-time polymerase chain reaction (qRT-PCR)

One microgram of total RNA was reversed transcribed to cDNA using Superscript III reverse transcriptase (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instructions. Power SYBR® Green PCR master mix (Applied Biosystems, Foster City, CA, USA) with appropriate primers (Additional file 3: Table S3) was mixed with one microliter of cDNAs, and qRT-PCR was performed using the StepOnePlus Real-Time PCR system (Applied Biosystems) under the following conditions: 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The dissociation curve was measured after the program was completed, and relative gene expression was calculated with the relative quantification (ΔΔCt) method using the rpoD gene as an endogenous control. A P-value was computed using student t-test to measure the significance associated with each relative quantification value. Variations were statistically significant when P < 0.05. The experiment was repeated at least twice.

Supplementary information

12864_2020_6699_MOESM1_ESM.pdf^{(587.8KB, pdf)}

Additional file 1: Table S1. List of differentially expressed genes (DEGs) of (p) ppGpp⁰ versus Ea1189.

12864_2020_6699_MOESM2_ESM.pdf^{(273.1KB, pdf)}

Additional file 2: Table S2. List of DEGs of Ea1189 at 6 h versus at 3 h.

12864_2020_6699_MOESM3_ESM.pdf^{(230.5KB, pdf)}

Additional file 3: Figure S1. Motility of the wild type Ea1189 and the ∆relA/spoT mutant on soft tryptone agar plates (3%) at 28 °C and photographs were taken after 48 h. Fig. S2. Differentially expressed genes negatively regulated by (p) ppGpp. (a) amino acid biosynthesis and degradation. (b) amino acid and peptide transport systems. (c) translation (d) nucleotide metabolism. (e) DNA repair/replication (f) lipid metabolism/cell cycle. White represents mean of expression level (log₂CPM), dark blue represents minimal gene expression, and bright red represents maximal gene expression. In the side bar (right), dark green represents lower negative fold change (log₂FC), and bright red represents the higher positive log₂FC.

12864_2020_6699_MOESM4_ESM.pdf^{(11.3KB, pdf)}

Additional file 4: Table S3. Primers for qRT-PCR used in this study.

Acknowledgements

Not applicable.

Abbreviations

(p) ppGpp: guanosine tetraphosphate and pentaphosphate
T3SS: Type III secretion system
DEGs: Differentially expressed genes
ABC: ATP-binding cassette
RSH: RelA/SpoT homologue
RNAP: RNA polymerase
SHX: Serine hydroxamate
hrp: hypersensitive response and pathogenicity
RpoN: Alternative sigma factor 54
WT: Wild-type
hpi: hour post incubation
PCA: Principal component analysis
CPM: Counts per million reads
COG: Clusters of orthologous groups
HMM: Hrp-inducing medium
FC: Fold change
LB: Luria-Bertani
Km: Kanamycin
Cm: Chloramphenicol
CDS: Coding sequence
TMM: Trimmed mean of M values
GEO: Gene expression omnibus
NCBI: National center for biotechnology information
qRT-PCR: quantitative reverse transcription real-time polymerase chain reaction

Authors’ contributions

Y. Z. designed the research. H. Y., M. Y., T. C., and J. H. L performed the research and analyzed the data. H. Y., M. Y., and Y. Z. wrote the paper. All authors have read and approved the manuscript.

Funding

This project was supported by the Agriculture and Food Research Initiative Competitive Grants Program Grant no. 2016–67013-24812 from the USDA National Institute of Food and Agriculture and USDA-Hatch Project ILLU-802-913 (YFZ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Availability of data and materials

The datasets generated during the current study are available in the Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) with an accession number GSE143324 and GSE128088.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors have declared that no competing interests exist.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s12864-020-6699-5.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12864_2020_6699_MOESM1_ESM.pdf^{(587.8KB, pdf)}

Additional file 1: Table S1. List of differentially expressed genes (DEGs) of (p) ppGpp⁰ versus Ea1189.

12864_2020_6699_MOESM2_ESM.pdf^{(273.1KB, pdf)}

Additional file 2: Table S2. List of DEGs of Ea1189 at 6 h versus at 3 h.

12864_2020_6699_MOESM3_ESM.pdf^{(230.5KB, pdf)}

12864_2020_6699_MOESM4_ESM.pdf^{(11.3KB, pdf)}

Additional file 4: Table S3. Primers for qRT-PCR used in this study.

Data Availability Statement

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PERMALINK

The stringent response regulator (p) ppGpp mediates virulence gene expression and survival in Erwinia amylovora

Ho-wen Yang

Menghao Yu

Jae Hoon Lee

Tiyakhon Chatnaparat

Youfu Zhao

Abstract

Background

Results

Conclusion

Background

Results and discussion

Overview of the global effect of (p) ppGpp in gene expression in Erwinia amylovora

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Positive regulation of virulence-related genes by (p) ppGpp

Fig. 6.

Table 1.

Table 2.

Table 3.

Differential regulation of amino acid and peptide biosynthesis genes by (p) ppGpp

Negative regulation of amino acid biosynthesis by (p) ppGpp

Table 4.

Table 5.

Inverse regulation of amino acid and peptide transporter genes by (p) ppGpp

Table 6.

Negative regulation of genes contributing to survival by (p) ppGpp

Translation

Table 7.

Biosynthesis of purine and pyrimidine

Table 8.

DNA replication/recombination/repair

Table 9.

Fatty acid/lipid metabolism and cell cycle

Table 10.

Conclusions

Fig. 7.

Methods

Bacterial strains and growth conditions

RNA isolation and Illumina sequencing

Transcriptomic data profiling and differentially expressed gene detection

Quantitative reverse transcription real-time polymerase chain reaction (qRT-PCR)

Supplementary information

Acknowledgements

Abbreviations

Authors’ contributions

Funding

Availability of data and materials

Ethics approval and consent to participate

Consent for publication

Competing interests

Footnotes

Supplementary information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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