iTRAQ-Based Comparative Proteomic Analysis of Acinetobacter baylyi ADP1 Under DNA Damage in Relation to Different Carbon Sources

Abstract

DNA damage response allows microorganisms to repair or bypass DNA damage and maintain the genome integrity. It has attracted increasing attention but the underlying influential factors affecting DNA damage response are still unclear. In this work, isobaric tags for relative and absolute quantification (iTRAQ)-based proteomic analysis was used to investigate the influence of carbon sources on the translational response of Acinetobacter baylyi ADP1 to DNA damage. After cultivating in a nutrient-rich medium (LB) and defined media supplemented with four different carbon sources (acetate, citrate, pyruvate, and succinate), a total of 2807 proteins were identified. Among them, 84 proteins involved in stress response were significantly altered, indicating the strong influence of carbon source on the response of A. baylyi ADP1 to DNA damage and other stresses. As the first study on the comparative global proteomic changes in A. baylyi ADP1 under DNA damage across nutritional environments, our findings revealed that DNA damage response in A. baylyi ADP1 at the translational level is significantly altered by carbon source, providing an insight into the complex protein interactions across carbon sources and offering theoretical clues for further study to elucidate their general regulatory mechanism to adapt to different nutrient environments.

Biological significance

Acinetobacter baylyi ADP1 is a well-established soil model microorganism for gene manipulation for its natural transformation ability. Its DNA damage response mechanism is not well-understood and remains unclear. A gel-free quantitative proteomic analysis (iTRAQ combined with LC/MS/MS) in this study explored the proteomic profiling of A. baylyi ADP1 with mitomycin C-induced DNA damage and cultivated with different carbon sources. This work illustrates a comprehensive picture of the dynamic changes in proteomics of A. baylyi ADP1 response to DNA damage stress, providing a deeper and broader understanding of the carbon source-dependent DNA damage response. Proteomics has great potential as an advanced technique for analyzing stress responses of microbes at the translational level.

Introduction

A variety of sources can induce stress to bacterial cells, e.g., radiation (Sankaranarayanan, 1996), heat (Roncarati and Scarlato, 2017), salt (Egamberdieva et al., 2017), chemical mutagens (Abraham et al., 2006), carbon starvation (Handtke et al., 2018), and metabolites (Vainio et al., 1981; Kulling et al., 2002). To survive and thrive in these extreme conditions, microbes have a repertoire of genes that can be activated or silenced responding to stress (van der Veen and Abee, 2011). Although numerous works have attempted to unravel the changes in gene expression and mRNA transcription under stress response (Zhou et al., 2016; Kotrade et al., 2019; Qiao et al., 2019; Szabo et al., 2019), evidence has shown poor correlations between transcriptional and protein levels (Pascal et al., 2008). Accordingly, recent studies have employed proteomic analysis to investigate microbial global response to environmental stresses, e.g., antibiotic stress (Mathieu et al., 2016; Xiong et al., 2017), cold and light stress (Liu et al., 2018), oxidative stress (van Herwijnen et al., 2003), drying stress (Schott et al., 2017), thermal stress (Delgado-Baquerizo et al., 2018), and nitrate and phosphate depletion (Lin et al., 2017).

As a global response to DNA damage, the inducible DNA repair network was firstly found in Escherichia coli (Radman, 1975), named SOS response. SOS response network is highly conserved and widely present in bacteria, allowing them to repair or bypass DNA damage and maintain genome integrity (Friedman et al., 2005; Chaudhary et al., 2011). In addition, SOS response network is a key driving force for the evolution of bacterial genomes (Michel et al., 2004; Stavans, 2006), which is also linked to virulence and stress-induced mutagenesis (Sanchez-Alberola et al., 2012). Till now, SOS response network in E. coli is well studied that a LexA/RecA-dependent SOS response system consists of more than 40 enzymes performing diverse functions responding to DNA damage, e.g., homologous recombination, nucleotide excision repair (NER mechanism), and translesion DNA replication (Khan et al., 2001; Friedman et al., 2005; Meng and Zhu, 2011). Although other bacterial strains have similar DNA damage response mechanisms in comparison with that in E. coli (Rauch et al., 1996; Booth et al., 2001; Brezna et al., 2003; DeBruyn et al., 2012), different functional enzymes are found across species. For instance, a soil model microorganism Acinetobacter baylyi ADP1 has UmuDAb protein possessing a post-translational and LexA-like cleavage after DNA damage (Hare et al., 2012). Another recent study found a PafB/PafC-regulated DNA damage response network in Mycobacteria and other Actinobacteria strains (Olivencia et al., 2017). In addition, proteomics analysis has identified diverse proteins under DNA damage stress. For example, the proteomic response of Cryptococcus podzolicus Y3 to citrinin suggested that the up- and down-regulated proteins were associated with structural maintenance of chromosomes (DNA double-strand break repair Rad50 ATPase, etc.), cell apoptosis (cytochrome C), detoxification and energy metabolism (Glyco-syltransferase and malate dehydrogenase), and oxidative stress response (superoxide dismutase [Cu-Zn] and cysteine peroxiredoxin) (Wang et al., 2019).

DNA damage response, at the translational level, is reported to correlate with many environmental variables, e.g., pH (Wu et al., 2018), nutrient fluctuations (Lin et al., 2017), and carbon sources (Seo et al., 2013; Jiang et al., 2015). Among them, carbon source is essential for heterotrophic microorganisms and can influence protein profiles involved in carbohydrate transport and metabolism, energy metabolism, nucleotide metabolism, stress response, and protein biosynthesis (Halimaa et al., 2013; Moreno and Rojo, 2013; Siragusa et al., 2014; Liu et al., 2015; Mahar et al., 2016; Li et al., 2019).

Acinetobacter baylyi ADP1 is a well-established soil microorganism for gene manipulation owing to its natural transformation ability (Barbe et al., 2004) and tolerance to environmental stress (Al-Anizi et al., 2014). Many studies have constructed various bioreporters with A. baylyi ADP1 as hosts for genotoxicity assessment of environmental samples (Song et al., 2009, 2014; Zhang et al., 2013; Jiang et al., 2014, 2017; Jia et al., 2016). A previous study has documented that the DNA damage response in A. baylyi ADP1 was dependent on carbon source at both transcriptional and translational levels (Jiang et al., 2015). Such behavior might affect the performance of A. baylyi ADP1 bioreporters in environmental monitoring, but there is lack of detailed mechanisms of protein profiles change in response to genotoxins cultivated with different carbon sources. Therefore, a deeper insight into the carbon-dependent protein profiles gains our knowledge on the key proteins and processes involved in DNA damage response, benefiting the understanding of DNA damage response network in A. baylyi ADP1 and optimization of A. baylyi hosted bioreporters.

In this work, we studied the influence of carbon sources on proteomic profiles of A. baylyi ADP1 in response to DNA damage induced by mitomycin C. Besides a nutrient-rich medium Luria-Bertani Broth (LB), A. baylyi ADP1 cells were also cultivated in defined media with acetate, citrate, pyruvate, and succinate as sole carbon source, to explore the difference in proteomic profiles via isobaric tags for relative and absolute quantification (iTRAQ) coupled with liquid chromatography coupled with mass spectrometry (LC/MS/MS). Proteins associated with different functions and biological processes were compared, and up- or down-regulated proteins related to energy production and stress response were particularly addressed. A protein–protein network was built to elucidate the protein interactions. This work provides the first comprehensive discussion on DNA damage response of A. baylyi ADP1 under different carbon source conditions, aiming at a better understanding of DNA damage stress response and to improve the performance of A. baylyi ADP1 hosted bioreporters.

Materials and Methods

Chemicals and Bacterial Strains

Unless specifically stated, all the reagents used in this study were purchased from Sigma-Aldrich (St. Louis, MO, United States) and of analytical grade. In this work, A. baylyi ADP1 was the model strain to evaluate the carbon-dependent proteome profiles in response to DNA damage. Nutrient-rich medium was LB medium, which consists of 5 g NaCl, 5 g yeast extract, and 10 g peptone in 1.0 L of sterilized water (pH adjusted to 7.0). Nutrient-deficient medium was prepared from minimal medium (MM), which consists of 0.5 g KH₂PO₄, 0.5 g NaCl, 0.1 g CaCl₂, 0.2 g MgSO₄⋅7H₂O, 0.5 g FeSO₄⋅7H₂O, 0.5 g MnSO₄, and 1.5 g (NH₄)₂SO₄ in 1.0 L of sterilized water (Zhang et al., 2015). MM was then supplemented with different carbon sources (Zhang et al., 2011), and the final concentration of potassium acetate, sodium citrate, sodium pyruvate, and sodium succinate was 30 mM, designated as MMA, MMC, MMP, and MMS, respectively.

DNA Damage Treatments

Mitomycin C is a bio-reductive alkylating agent causing DNA cross-linking and genotoxic effects (Abraham et al., 2006), viewed as a classic approach inducing DNA damage and used in this work to investigate the proteomic changes and global SOS response network in response to DNA damage in A. baylyi ADP1 among different carbon sources. In principle, A. baylyi ADP1 was firstly cultivated in nutrient-rich medium (LB) and then exposed to DNA damage induced by mitomycin C in both nutrient-rich medium (LB) and nutrient-deficient media (MMA, MMC, MMP, and MMS), respectively. To be more precise, A. baylyi ADP1 cells were inoculated in LB overnight at 30°C and harvested by centrifugation at 4000 × g for 10 min. After resuspended in the same volume of sterile deionized water, 1 ml of cell suspension was supplemented with 9 ml of fresh medium (LB, MMA, MMC, MMP, and MMS; carbon source final concentration, 27 mM, which falls in the range of 10–40 mM as conventional protocols for cell cultivation) (Selifonova et al., 1993; Zhang et al., 2012, 2013; Jebasingh et al., 2013; Borirak et al., 2015; Jia et al., 2016; Schmidt et al., 2016) and cultivated at 30°C until the early exponential phase when optical density at 600 nm (OD₆₀₀) was 0.10. The suspensions were further added with mitomycin C to the final concentration of 1 μM to induce DNA damage. After 3-h exposure, cells were harvested by centrifugation at 6000 × g for 15 min at 4°C for further analysis. Although some previous studies have reported the application of 3 μM mitomycin C to induce A. baylyi cells, the concentration of mitomycin C used in the present study was set as 1 μM, because our previous work has reported a linear detection range of 0.1 nM to 1 μM for ADPWH_recA postexposure to mitomycin C (Jiang et al., 2017). Accordingly, mitomycin C concentration at 1 μM could induce the highest bioluminescent response of bioreporter cells, whereas higher concentration could significantly inhibit cell growth and might affect proteomic profiles (Song et al., 2009).

Cell Lysis, Protein Extraction, and Protein Digestion

Cell pellets were suspended in a protein extraction buffer (urea [6.0 M], ethylenediamine tetra-acetic acid [EDTA, 0.5 mM], sodium dodecyl sulfate [SDS, 2%, w/v], and protease inhibitors cocktail mixture [Roche] in NH₄HCO₃ [100 mM] lysis buffer) (Borirak et al., 2015), followed by vortex and sonication (Branson Sonifier 450 D, United States). Sonication was set at 300 W in a pulse mode (10 s/10 s) for 20 min at ice bath until the cell suspension became clear. Protein concentration was measured by the Bradford assay (Pierce^TM Coomassie [Bradford] Protein Assay Kit, Thermo Scientific, United Kingdom).

Subsequently, around 100 μg of proteins was subjected to trypsin digestion as previously reported (Pereira et al., 2011). Briefly, 2 μl of Reducing Reagent (supplied in B-PER^TM Complete Bacterial Protein Extraction Reagent, Thermo Scientific, United Kingdom) was added in protein suspension and incubated at 60°C for 1 h. Another 1 μl of Cysteine-Blocking Reagent was added, kept at room temperature for 10 min, and centrifuged for 20 min to discard the supernatant. Subsequently, 100 μl of Dissolution Buffer (supplied by iTRAQ^® Reagent-8PLEX Multiplex Kit, Sigma-Aldrich, United States) was added and transferred to a new collection tube. Trypsin was added, incubated at 37°C overnight, and centrifuged for 20 min twice to collect the digested peptides. The quantity and quality of proteins and trypsin digestion efficiency were analyzed by 10% acrylamide gels.

iTRAQ Labeling, LC/MS/MS Analysis, and Data Processing

The analysis of proteomics was performed using iTRAQ technology by Beijing Proteome Research Centre (Supplementary Figure S1). Briefly, 100 μg of digested peptides was labeled with amine-reactive isobaric tags (supplied in iTRAQ^® Reagent-8PLEX Multiplex Kit, Sigma-Aldrich, United States), incubated at room temperature for 2 h, and stopped by 100 μl of water. Qualification and quantification of peptides were performed by LC/MS/MS. LC fractionation was performed in binary gradient buffers (buffer A and B). The labeled samples were resuspended in buffer A (10 mM NH₄HCO₃, 80% acetonitrile, pH 3.0). The binary gradient began with 5% B (10 mM NH₄HCO₃, 5% acetonitrile, pH 4.0) for 5 min, a linear ramp from 8% to 32% B for 59 min, an extended ramp from 32% to 95% B for 4 min, a further isocratic wash with 95% B for 5 min, and column re-equilibration with 5% B for 2 min. The injection volume was 100 μl and the chromatographic flow rate was constantly at 0.7 ml/min.

Each fragmented peptide tag produced distinct signature ions, which were detected by mass spectrometry (TripleTOF^TM ABI-5600, Applied Biosystems, Wilmington, DE, United States) and distinguished by m/z value (Zilberstein, 2015). Data acquisition was in the positive ion mode with an accumulation time of 1 s. The selected mass detector ranged from 400 to 1250 m/z, and the precursor ion scan was performed within a range of 330–2000 m/z.

The original data were processed by ProteinPilot^TM Software 4.5 (Applied Biosystems, Wilmington, DE, United States), using a database comprising all Acinetobacter protein sequences obtained from National Center for Biotechnology Information (NCBI) database. Acceptance threshold for peptide identification was length ≥ 6, z-score ≥ 5 and p-value ≤ 0.05. A decoy database created by reversing the sequences was used to calculate the false discovery rate (FDR), and proteins identified with at least two peptides and satisfying a 5% FDR were kept for quantitative analysis. The abundance of each peptide in LB treatment was set as the reference, and the relative abundance was calculated as the ratio of the abundance of each peptide in other treatments (MMA, MMC, MMP, and MMS) to that in LB.

Data Analysis

Based on the well-accepted method to classify the up- and down-regulation using p-value in t-test (Lin et al., 2017; Fountain et al., 2018), peptides with relative abundance < 0.5 (p < 0.01), 0.5–0.83 (p < 0.05), 1.2–2.0 (p < 0.05), and >2.0 (p < 0.01) were designated in the present study to be significantly up-regulated or down-regulated. They were further classified according to the Gene Ontology (GO) function and GO biological process, via the Universal Protein Resource Database¹ (O’Donovan and Apweiler, 2011; Lai et al., 2017) and PSORTdb 3.0.² Network analysis of differentially expressed proteins was performed by String Software (V11.0): Protein–Protein Interaction Networks³. Statistical analysis was carried out by SPSS (version 21.0) to evaluate the correlations of the relative abundance of all the identified peptides between different carbon source treatments.

Results

Annotation of Protein Functions and Biological Processes

A total of 2807 proteins with function prediction were identified in all the treatments (Supplementary Figure S2). Analyzed by PSORTdb database and GO, they were categorized into 26 functions, and the most abundant ones included transferase (328 proteins, 11.7%), nucleic acid-binding protein (243 proteins, 8.6%), oxidoreductase (282 proteins, 10.0%), hydrolase (262 proteins, 9.3%), transporter (106 proteins, 3.8%), and lyase (93 proteins, 3.3%) (Supplementary Figure S2A). As for biological processes, most identified proteins were associated with biosynthesis process (257 proteins, 22.5%), metabolite process (219 proteins, 19.2%), transport (149 proteins, 13.1%), amino acid biosynthesis (91 proteins, 7.9%), regulator (73 proteins, 6.4%), and stress response (70 proteins, 6.1%) (Supplementary Figure S2B).

Comparison of Protein Functions and Biological Processes in Different Carbon Sources

To investigate the distinct protein profiles in rich (LB) and defined media (MMA, MMC, MMP, and MMS), the numbers of up- or down-regulated proteins in MMA, MMC, MMP, and MMS treatments were calculated and listed in Table 1. Of all the 2807 identified proteins, the percentage of proteins with significant changes in MMA, MMC, MMP, and MMS was 52.1, 57.9, 51.1, and 48.2%, respectively. It indicated that the relative abundance of approximately half of the proteins was affected by carbon sources. Among the four defined media, the changes in peptide levels in MMC varied most greatly comparing to LB, with 29.5% proteins down-regulated and 28.3% proteins up-regulated, and MMS had 23.4% of proteins down-regulated and 24.7% of proteins up-regulated. However, the up- or down-regulated proteins are different across carbon sources, suggesting that the change of protein profiles is dependent on carbon sources.

TABLE 1.

Number of proteins up- or down- regulated in defined media (MMA, MMC, MMP, and MMS) in comparison with LB.

Treatment	MMA	MMC	MMP	MMS
Significantly down-regulated1	149	210	134	113
Down-regulated to some extent2	595	620	545	545
Unchanged3	1345	1183	1373	1455
Up-regulated to some extent4	540	562	551	541
Significantly up-regulated5	178	232	204	153

¹The ratio of protein expression level in defined medium to that in LB was lower than 0.5 (p < 0.01). ²The ratio of protein expression level in defined medium to that in LB was between 0.5 and 0.83 (p < 0.05). ³The ratio of protein expression level in defined medium to that in LB was between 0.83 and 1.2 (p > 0.05). ⁴The ratio of protein expression level in defined medium to that in LB was between 1.2 and 2.0 (p < 0.05). ⁵The ratio of protein expression level in defined medium to that in LB was higher than 2.0 (p < 0.01).

The correlations of the relative abundance of all the identified peptides between different carbon source treatments are illustrated in Figure 1. The correlation coefficient (R), which is a statistical measure representing the strength of the relationship ranging from −1.0 and 1.0, is used to evaluate the similarity of protein profiles and a higher R value indicates a stronger positive relationship. Among the four treatments, the relative abundance of proteins in citrate treatment differed remarkably from that in acetate (R² = 0.2432, Figure 1A), whereas the proteomic profiles were similar between acetate and pyruvate treatments (R² = 0.5735, Figure 1B), and acetate and succinate treatments (R² = 0.5697, Figure 1C). The proteomic profiles in pyruvate and succinate treatments showed the highest consistency (R² = 0.8278, Figure 1F).

FIGURE 1.

Correlation of relative abundance of proteins among carbon sources. (A) Acetate vs. citrate; (B) acetate vs. pyruvate; (C) acetate vs. succinate; (D) citrate vs. pyruvate; (E) citrate vs. succinate; (F) pyruvate vs. succinate.

According to the classification of protein function, the percentages of proteins with significant fold changes in each carbon source treatment are illustrated in Figure 2. Proteins with each function had their unique profiles among carbon sources. The relative abundance of some proteins showed similar tendency in all treatments, e.g., transferase (24.4–27.7% up-regulated and 22.3–29.9% down-regulated), synthase (19.1–29.8% up-regulated and 21.3–25.5% down-regulated), and transporter (26.4–35.8% up-regulated and 25.5–34.9% down-regulated). The regulation of some other proteins varied significantly across treatments. For instance, 55% of receptor proteins were down-regulated in MMA treatment, whereas only 25, 20, and 30% of them were down-regulated in MMC, MMP, and MMS treatments, respectively. Some proteins even demonstrated entirely opposite patterns. Among them, DNA polymerase was up-regulated in MMC, MMP, and MMS treatments, but down-regulated in MMA treatment. Interestingly, the percentage of up-regulated proteins related to DNA replication was remarkably higher in MMC than that in other treatments, e.g., DNA polymerase (50.0% up-regulated, for DNA synthesis from a DNA template), helicase (48.0% up-regulated, for the unwinding of double-stranded helical structure of nucleic acids), nuclease (46.2% up-regulated, for hydrolysis of nucleic acids), as well as peptidase (54.5% up-regulated, for the breakdown of protein peptides).

FIGURE 2.

Percentage of proteins with significant fold changes in each carbon source treatment compared to LB medium. (A) MMA; (B) MMC; (C) MMP; (D) MMS. Proteins are classified by functions, and the categories with numbers less than 10 are summed up as “others.”

Regarding biological processes, the percentages of up- and down-regulated proteins across different carbon source treatments are shown in Figure 3. Among them, proteins related to biosynthetic process, metabolic process, amino acid biosynthesis, and aromatic hydrocarbon catabolism showed similar patterns in all treatments. A significant repression effect on translation-related proteins was found in all treatments, in which 93.5% ± 1.9% of translation-related proteins were down-regulated. Proteins involved in ATP synthesis behaved differently between MMC (6.7% up-regulated and 40.0% down-regulated), MMA (40.0% up-regulated and 13.3% down-regulated), MMP (46.7% up-regulated and 13.3% down-regulated), and MMS (46.7% up-regulated and 20.0% down-regulated). Additionally, proteins associated with response in adverse conditions were mostly down-regulated, including proteins for stress response (30.4% ± 11.6%), virulence or antiviral defense (40.9% ± 5.3%), and antibiotic resistance (18.8% ± 12.5%).

FIGURE 3.

Percentage of proteins with significant fold changes in each carbon source treatment compared to LB medium. (A) MMA; (B) MMC; (C) MMP; (D) MMS. Proteins are classified by biological process category.

Different Protein Profiles Between Nutrient-Deficient and Nutrient-Rich Medium

Of the proteins with significant fold changes in comparison with LB, 174 and 210 proteins exhibited the same up- or down-regulation patterns across different carbon source treatments, respectively. Among them, 140 and 166 proteins were validated or had putative roles in metabolic processes (Table 2). Proteins associated with four types of functions were most significantly affected, including transferase (34 up-regulated and 27 down-regulated), hydrolase (11 up-regulated and 16 down-regulated), oxidoreductase (37 up-regulated and 12 down-regulated), and DNA/RNA-binding proteins (7 up-regulated and 60 down-regulated).

TABLE 2.

Up- and down-regulated proteins involved in each molecular functional category.

Protein	Gene name	ACIAD No.	MMA:LB	MMC:LB	MMP:LB	MMS:LB
Up-regulated
Transferase
Acetyltransferase	argA	ACIAD0039	1.61	1.53	1.74	1.49
2-isopropylmalate synthase	leuA	ACIAD0530	6.49	11.07	6.55	6.08
Phosphate acetyltransferase	pta	ACIAD0540	1.82	3.87	3.16	1.75
Acetate kinase (propionate kinase)	ack	ACIAD0541	1.84	4.02	3.53	2.07
Branched-chain amino acid aminotransferase	ilvE	ACIAD0597	2.54	1.5	1.31	1.2
Glutamate synthase subunit alpha	argJ	ACIAD0650	5.5	7.52	7.8	7.59
Glutamate synthase subunit beta	argJ	ACIAD0650	5.75	8.47	10.67	8.87
UDP-N-acetylglucosamine 1-carboxyvinyltransferase	murA	ACIAD0660	1.33	1.25	1.32	1.29
O-methyltransferase protein	bioC	ACIAD0858	2.4	1.96	1.77	1.32
Acyl carrier protein	acpP	ACIAD0872	3.87	3.87	4.21	5.11
Glycerol kinase	glpK	ACIAD0930	2.33	3.63	3.53	1.39
Dihydrolipoamide dehydrogenase (E3 component of pyruvate and 2-oxoglutarate dehydrogenase complexes)	acoC	ACIAD1019	2.86	5.5	3.77	3.8
B12-dependent methionine synthase	metH	ACIAD1045	1.85	1.46	1.66	1.26
Sulfate adenylyltransferase subunit 2	cysD	ACIAD1072	1.61	2.09	1.94	2.11
Aspartate carbamoyltransferase catalytic subunit	pyrB	ACIAD1270	4.53	4.83	4.7	2.68
Aspartate carbamoyltransferase	pyrB	ACIAD1270	2.94	2.94	2.44	2.19
NADH oxidase	putA	ACIAD1646	2.36	2.23	1.74	1.94
3-oxoadipate CoA-transferase	pcaJ	ACIAD1705	1.53	1.29	1.49	1.41
3-oxoadipate CoA-transferase subunit A	pcaI	ACIAD1705	2.47	3.7	2.56	1.56
Beta-ketoadipyl CoA thiolase	pcaF	ACIAD1706	1.58	2.68	1.91	1.28
Phospho-2-dehydro-3-deoxyheptonate aldolase	aro	ACIAD1878	10.96	9.38	11.07	7.66
Phosphoglycerate kinase	pgk	ACIAD1927	1.94	2.05	4.17	1.91
Aminotransferase AlaT	AlaT	ACIAD2087	2.33	2.03	1.58	1.64
Malate synthase G	glcB	ACIAD2335	4.66	2.94	4.66	2
Acetyl-CoA acetyltransferase	atoB	ACIAD2516	1.47	3.94	1.58	1.28
Sulfate ABC transporter periplasmic substrate-binding protein	cysP	ACIAD2591	5.4	2.75	4.74	4.29
Acyl-CoA transferase/carnitine dehydratase	−	ACIAD2821	1.87	6.03	2.38	1.51
Acetolactate synthase 3 catalytic subunit	ilvH	ACIAD3103	6.37	5.86	3.73	4.53
Acetolactate synthase 3 regulatory subunit	ilvH	ACIAD3103	3.87	2.88	2.33	1.96
Homocysteine synthase	metY	ACIAD3382	2.17	4.74	3.22	2.65
Dihydrolipoamide acetyltransferase	aceF	ACIAD3506	2.38	6.31	4.13	3.53
UDP-N-acetylmuramate–L-alanine ligase	murC	ACIAD3516	1.47	1.84	1.89	2.11
5-methyltetrahydropteroyltriglutamate–homocysteine methyltransferase	metE	ACIAD3523	28.31	3.37	14.19	14.86
NADH dehydrogenase II	ndh	ACIAD3633	1.84	2.25	4.33	2.75
DNA/RNA-binding
AraC family transcriptional regulator	ntrC	ACIAD0194	1.27	1.67	1.54	1.49
Curved DNA-binding protein	cbpA	ACIAD0406	1.54	1.96	1.96	1.5
Magnesium and cobalt efflux protein	corC	ACIAD0416	1.87	1.37	1.25	1.6
Transcriptional regulator	baeR	ACIAD0627	5.5	3.84	3.7	5.45
AraC family transcriptional regulator	glnG	ACIAD1368	1.26	3.94	2.73	1.26
Holliday junction DNA helicase RuvB	ruvB	ACIAD2615	1.51	1.29	1.32	1.32
Two-component regulatory system response regulator	ompR	ACIAD3388	1.92	2	1.82	2.13
Electron transfer
Tryptophan synthase beta chain	trpB	ACIAD0636	1.46	2.01	1.27	1.31
Ferredoxin 1	fpr	ACIAD2244	1.54	1.67	2.01	1.91
Electron transfer flavoprotein beta-subunit	etfB	ACIAD2655	1.41	1.64	1.87	2.03
Synthase
Cysteinyl-tRNA synthetase	cysS	ACIAD1481	1.42	1.43	1.69	1.42
Citrate synthase	gltA	ACIAD2886	3.53	2.09	4.61	1.79
Transporter
Response regulator	gacA	ACIAD0260	1.29	1.45	1.21	1.54
Glutamate/aspartate ABC transporter substrate-binding protein	gltK	ACIAD2058	0.7	7.52	1.89	1.36
Outer membrane protein (ComQ)	comQ	ACIAD3355	4.17	3.87	3.31	3.98
Activator
Transcriptional regulator	qseB	ACIAD2961	5.5	3.84	3.7	5.45
Ligase
Cysteinyl-tRNA synthetase	cysS	ACIAD1481	1.42	1.43	1.69	1.42
Medium-chain-fatty-acid-CoA ligase	alkK	ACIAD1818	1.39	5.6	3.16	1.31
Protein kinase	pyrG	ACIAD2003	2.15	1.36	1.89	1.58
Glutamine synthetase	glnA	ACIAD2458	2.25	2.86	2.63	2.56
Carbamoyl phosphate synthase, large subunit	carB	ACIAD2861	3.5	3.37	1.66	1.84
Acetyl-CoA synthetase	acs	ACIAD3463	2.23	5.86	3.98	2.96
Peptidase
Aminopeptidase P	pepP	ACIAD1126	1.82	1.58	1.87	1.49
Hydrolase
Thiol: disulfide interchange protein (DsbC-like)	dsbA	ACIAD0045	2.29	3.22	3.4	2.75
Bifunctional phosphoribosyl-AMP cyclohydrolase/phosphoribosyl-ATP pyrophosphatase	hisIE	ACIAD0380	1.54	1.39	1.29	1.29
SlyX protein	slyX	ACIAD0488	1.5	1.71	1.67	1.24
Hydrolase	–	ACIAD0886	3.8	2.96	2.81	2.96
Multidrug resistance secretion protein	emrA	ACIAD0926	1.46	1.22	1.63	1.24
ATP-binding protease component	clpA	ACIAD1363	1.49	1.91	1.33	1.57
Salicylate esterase	SalE	ACIAD1426	2.65	3.19	2	2.11
3-oxoadipate enol-lactonase 2	catD	ACIAD1451	1.54	1.69	1.72	1.37
Fructose-1,6-bisphosphate aldolase	fda	ACIAD1925	2.23	1.82	1.61	1.38
Copper transporting ATPase	copA	ACIAD2400	1.49	2.19	2.05	2
Toluene tolerance efflux ABC transporter periplasmic substrate-binding protein	ttg2C	ACIAD3242	1.33	1.72	1.32	1.61
Protease
SlyX protein	slyX	ACIAD0488	1.5	1.71	1.67	1.24
Aminopeptidase P	pepP	ACIAD1126	1.82	1.58	1.87	1.49
ATP-binding protease component	clpA	ACIAD1363	1.49	1.91	1.33	1.57
ATP-binding
Twitching motility protein	pilG	ACIAD0786	3.4	3.66	5.55	3.94
Twitching motility protein	pilH	ACIAD0787	2.31	2.94	2.99	2.58
Twitching motility protein	pilI	ACIAD0788	1.26	1.41	1.26	1.2
Metal binding
Bacterioferritin	bfrA	ACIAD0852	2.03	2.38	2.38	2.56
Bacterioferritin	bfrB	ACIAD3330	2.65	3.37	2.86	2.7
Chaperone
Fimbrial chaperone protein	–	ACIAD0388	2.61	2.05	2.49	2.07
Fimbrial protein	–	ACIAD0390	2.65	2.19	2.7	2.47
ATP-binding protease component	clpA	ACIAD1363	1.49	1.91	1.33	1.57
Lipase chaperone	lifO	ACIAD3308	1.39	3.19	2.25	1.54
Isomerase
Peptidyl-prolyl cis-trans isomerase	fklB	ACIAD0065	1.33	2.05	1.85	1.56
UDP-N-acetylglucosamine 1-carboxyvinyltransferase	murA	ACIAD0660	1.33	1.25	1.32	1.29
Peptidylprolyl isomerase	ppiD	ACIAD1409	2.33	2.38	1.5	1.69
Peptidyl-prolyl cis-trans isomerase precursor (PPIase)	surA	ACIAD2372	1.74	1.29	1.98	1.21
Lyase
Threonine synthase	thrC	ACIAD0263	2.47	1.71	1.38	1.92
Thiamine biosynthesis protein ThiC	thiC	ACIAD0276	20.7	28.31	30.2	27.54
Isopropylmalate isomerase large subunit	leuC	ACIAD0463	23.33	21.68	29.38	19.77
Isopropylmalate isomerase small subunit	leuD	ACIAD0466	16.14	14.32	20.7	12.82
Fumarate hydratase	fumA	ACIAD0538	5.7	4.33	3.47	3.6
Isocitrate lyase	aceA	ACIAD1084	22.91	4.88	22.28	3.08
Dihydroxy-acid dehydratase 1	ilvD1	ACIAD1266	3.98	3.94	2.96	2.42
4-carboxymuconolactone decarboxylase	pcaC	ACIAD1710	2.49	1.94	2.05	2.13
Catabolic 3-dehydroquinate dehydratase	aroD	ACIAD1713	2.4	4.97	2.75	2.33
Aconitate hydratase	acnA	ACIAD3090	1.94	1.51	1.56	1.91
Dihydroxy-acid dehydratase 2	ilvD2	ACIAD3636	3.08	6.55	7.24	4.13
Oxidoreductase
Flavoprotein monooxygenase acting on aromatic compound	–	ACIAD3540	1.41	1.43	1.98	2.01
NADH-flavin reductase	–	ACIAD3290	1.69	1.58	1.21	2.01
Magnesium and cobalt efflux protein	corC	ACIAD0416	1.87	1.37	1.25	1.6
3-isopropylmalate dehydrogenase	leuB	ACIAD0469	10.67	12.82	15.56	10
Aldehyde dehydrogenase	calB	ACIAD0503	8.17	11.8	9.38	8.39
Peptide methionine sulfoxide reductase	msrA	ACIAD0510	1.61	2.65	2.31	2.15
Dihydrofolate reductase	folA	ACIAD0514	1.49	1.22	1.47	1.21
Acyl-CoA dehydrogenase	acdB	ACIAD0624	1.64	1.94	1.46	2.23
NADH-quinone oxidoreductase subunit K	nuoK	ACIAD0740	1.41	1.56	1.46	1.61
Thioredoxin reductase 1	trxB	ACIAD0890	2.13	1.91	2.09	1.58
Acetoin:2,6-dichlorophenolindophenol oxidoreductase alpha subunit	acoA	ACIAD1017	2.78	5.3	3.44	2.44
Diacetyl reductase	budC	ACIAD1022	1.82	6.79	2.99	1.8
Isocitrate dehydrogenase	icd	ACIAD1190	2.68	3.91	2.56	3.05
Aryl-alcohol dehydrogenase	areB	ACIAD1429	2.81	8.24	2.47	3.31
Linalool 8-monooxygenase	linC	ACIAD1575	1.25	1.27	1.31	1.36
Methylmalonate-semialdehyde dehydrogenase	mmsA	ACIAD1604	2.33	5.7	3.19	3.44
3-hydroxyisobutyrate dehydrogenase	mmsB	ACIAD1605	2.44	3.44	2.7	1.47
NADH oxidase	putA	ACIAD1646	2.36	2.23	1.74	1.94
Protocatechuate 3,4-dioxygenase subunit beta (3,4-PCD)	pcaH	ACIAD1711	1.96	2.25	1.75	1.56
Protocatechuate 3,4-dioxygenase subunit alpha (3,4-PCD)	pcaG	ACIAD1712	3.31	3.19	2.65	3.1
Hydroxybenzaldehyde dehydrogenase	hcaB	ACIAD1725	1.69	3.53	1.45	1.94
Dihydropteridine reductase	nfnB	ACIAD1923	1.69	1.25	1.82	1.41
Glutathione peroxidase	gpo	ACIAD2085	1.2	2.88	2.49	2.01
Ferredoxin-NADP+ reductase	fpr	ACIAD2244	2.03	1.89	1.98	1.96
Malic enzyme	–	ACIAD2287	2.56	4.41	3.44	2.61
Signal peptide	lepB	ACIAD2583	2.63	1.71	2.11	1.77
Glutamate dehydrogenase	gdh	ACIAD2680	3.94	5.65	4.66	3.34
Succinate dehydrogenase flavoprotein subunit	sdhA	ACIAD2880	3.28	1.82	1.85	2.96
Glucose dehydrogenase	gcd	ACIAD2983	1.43	1.2	1.41	1.22
Pyridine nucleotide transhydrogenase (proton pump), alpha subunit (part1)	pntA	ACIAD3079	1.31	1.34	6.03	1.84
Ketol-acid reductoisomerase	ilvC	ACIAD3102	4.21	2.38	2.07	2.78
Malate dehydrogenase	mdh	ACIAD3155	1.77	1.49	2.65	1.8
Alcohol dehydrogenase	adhA	ACIAD3339	6.73	18.54	10	7.73
Fatty acyl-CoA reductase	acr1	ACIAD3383	1.47	2.68	1.87	2.29
ssDNA exonuclease, 5′– > 3′ specific, Mg dependent	recJ	ACIAD3500	1.61	1.32	1.6	1.46
Pyruvate dehydrogenase (acetyl-transferring), homodimeric type	aceE	ACIAD3507	1.49	1.96	1.89	1.66
NADH dehydrogenase II	ndh	ACIAD3633	1.84	2.25	4.33	2.75
GTP binding
Peptide chain release factor 3	prfC	ACIAD3095	1.58	2.7	2.38	1.32
Hydratase
Bifunctional aconitate hydratase 2/2-methylisocitrate dehydratase	acnB	ACIAD2395	5.35	1.69	3.31	1.47
Kinase
Acetate kinase (propionate kinase)	ack	ACIAD0541	1.84	4.02	3.53	2.07
Glycerol kinase	glpK	ACIAD0930	2.33	3.63	3.53	1.39
Phosphoglycerate kinase	pgk	ACIAD1927	1.94	2.05	4.17	1.91
Sulfate ABC transporter periplasmic substrate-binding protein	cysP	ACIAD2591	5.4	2.75	4.74	4.29
UDP-N-acetylmuramate–L-alanine ligase	murC	ACIAD3516	1.47	1.84	1.89	2.11
Porin activity
Porin	hcaE	ACIAD1722	3.05	3.37	2.58	2.73
Down-regulated
Transferase
Porphobilinogen deaminase	hemC	ACIAD0286	0.69	0.14	0.51	0.63
DNA-directed RNA polymerase subunit beta	rpoB	ACIAD0307	0.49	0.72	0.55	0.58
Queuine tRNA-ribosyltransferase	tgt	ACIAD0590	0.59	0.74	0.58	0.67
UDP-N-acetylglucosamine 1-carboxyvinyltransferase 2	murA	ACIAD0660	0.81	0.74	0.8	0.74
Gamma-glutamyltranspeptidase precursor	ggt	ACIAD0929	0.34	0.77	0.5	0.41
Bifunctional succinylornithine transaminase/acetylornithine transaminase	argD	ACIAD1284	0.4	0.32	0.15	0.26
Arginine N-succinyltransferase	astA	ACIAD1286	0.48	0.63	0.76	0.63
Succinylglutamate desuccinylase	astE	ACIAD1289	0.17	0.82	0.69	0.66
Amidase	amdA	ACIAD1618	0.68	0.06	0.58	0.27
Acetyl-CoA acetyltransferase	dcaF	ACIAD1689	0.43	0.69	0.54	0.53
Polyphosphate kinase	ppk	ACIAD1782	0.77	0.51	0.36	0.48
Tellurium resistance protein	terZ	ACIAD1952	0.68	0.65	0.81	0.7
Tellurium resistance protein	terY	ACIAD1969	1.91	1.58	1.72	1.75
DNA polymerase III beta	dnaX	ACIAD1970	0.78	0.37	0.6	0.73
1-phosphofructokinase	fruK	ACIAD1992	0.53	0.69	0.63	0.62
Superoxide dismutase	sodM	ACIAD2072	0.7	0.41	0.52	0.61
3-oxoacyl-(acyl carrier protein) synthase III	–	ACIAD2101	0.62	0.67	0.66	0.72
16S rRNA methyltransferase GidB	gidB	ACIAD2368	0.59	0.48	0.56	0.52
4-diphosphocytidyl-2C-methyl-D-erythritol kinase	ipk	ACIAD2903	0.56	0.35	0.73	0.51
Valyl-tRNA synthetase	valS	ACIAD2950	0.81	0.65	0.63	0.69
Xanthine phosphoribosyltransferase	xpt	ACIAD3164	0.44	0.52	0.44	0.59
Glycerol-3-phosphate acyltransferase	plsB	ACIAD3232	0.6	0.53	0.52	0.69
Guanylate kinase	gmk	ACIAD3324	0.6	0.47	0.6	0.63
23S rRNA m(2)G2445 methyltransferase	rrm	ACIAD3362	0.5	0.44	0.68	0.44
4-aminobutyrate aminotransferase, PLP-dependent	gabT	ACIAD3446	0.1	0.08	0.38	0.19
Histidine phosphatase	phoR	ACIAD3558	0.52	0.3	0.59	0.59
Hypoxanthine phosphoribosyltransferase	hpt	ACIAD3669	0.47	0.57	0.57	0.51
DNA RNA-binding
GntR family transcriptional regulator	lldR	ACIAD0107	0.57	0.6	0.52	0.55
50S ribosomal protein L11	rplK	ACIAD0302	0.64	0.63	0.33	0.5
50S ribosomal protein L1	rplA	ACIAD0304	0.56	0.69	0.5	0.7
50S ribosomal protein L10	rplJ	ACIAD0305	0.74	0.77	0.69	0.61
Translation initiation factor IF-2	infB	ACIAD0369	0.48	0.74	0.3	0.69
30S ribosomal protein S15	rpsO	ACIAD0401	0.58	0.75	0.5	0.69
Endoribonuclease	rne	ACIAD0438	0.55	0.73	0.67	0.74
DNA-binding transcriptional regulator HcaR	hcaR	ACIAD0448	0.38	0.49	0.22	0.3
50S ribosomal protein L33	rpmG	ACIAD0501	0.45	0.77	0.33	0.4
50S ribosomal protein L28	rpmB	ACIAD0502	0.52	0.65	0.39	0.53
30S ribosomal protein S12	rpsL	ACIAD0881	0.37	0.49	0.3	0.51
BetI family transcriptional regulator	betI	ACIAD1010	0.67	0.69	0.81	0.63
DNA-binding ATP-dependent protease La	lon	ACIAD1115	0.55	0.77	0.55	0.48
30S ribosomal protein S21	rpsU	ACIAD1331	0.54	0.53	0.44	0.65
Protein RecA	recA	ACIAD1385	0.57	0.51	0.59	0.45
30S ribosomal protein S20	rpsT	ACIAD1389	0.21	0.35	0.25	0.29
tRNA (uracil-5-)-methyltransferase	trmA	ACIAD1645	0.28	0.52	0.41	0.34
Tellurium resistance protein	terX	ACIAD1968	0.68	0.65	0.81	0.7
DNA polymerase III beta	dnaX	ACIAD1970	0.78	0.37	0.6	0.73
30S ribosomal protein S2	rpsB	ACIAD2269	0.55	0.64	0.38	0.53
30S ribosomal protein S1	rpsA	ACIAD2347	0.69	0.59	0.36	0.47
Chromosome partitioning protein	spoOJ	ACIAD2366	0.56	0.67	0.71	0.81
Chromosome partitioning protein	soj	ACIAD2367	0.66	0.45	0.73	0.82
30S ribosomal protein S18	rpsR	ACIAD2431	0.52	0.68	0.55	0.49
50S ribosomal protein L9	rplI	ACIAD2432	0.55	0.8	0.77	0.61
LysR family transcriptional regulator	cbl	ACIAD2597	0.6	0.55	0.74	0.6
50S ribosomal protein L25	rplY	ACIAD2908	0.74	0.79	0.65	0.63
50S ribosomal protein L27	rpmA	ACIAD2938	0.51	0.69	0.48	0.65
50S ribosomal protein L21	rplU	ACIAD2939	0.54	0.64	0.36	0.5
50S ribosomal protein L13	rplM	ACIAD3012	0.53	0.6	0.47	0.7
Transcription termination factor Rho	rho	ACIAD3038	0.74	0.77	0.6	0.65
50S ribosomal protein L20	rplT	ACIAD3046	0.32	0.56	0.44	0.44
50S ribosomal protein L35	rpmI	ACIAD3047	0.28	0.42	0.44	0.47
30S ribosomal protein S4	rpsD	ACIAD3195	0.44	0.59	0.41	0.58
30S ribosomal protein S11	rpsK	ACIAD3196	0.6	0.67	0.51	0.66
30S ribosomal protein S13	rpsM	ACIAD3197	0.55	0.48	0.5	0.64
50S ribosomal protein L36	rpmJ	ACIAD3198	0.48	0.82	0.61	0.68
50S ribosomal protein L15	rplO	ACIAD3200	0.6	0.59	0.47	0.53
50S ribosomal protein L30	rpmD	ACIAD3201	0.77	0.73	0.62	0.73
30S ribosomal protein S5	rpsE	ACIAD3202	0.51	0.63	0.72	0.76
50S ribosomal protein L18	rplR	ACIAD3203	0.45	0.44	0.43	0.53
50S ribosomal protein L6	rplF	ACIAD3204	0.44	0.62	0.56	0.63
30S ribosomal protein S8	rpsH	ACIAD3205	0.59	0.79	0.44	0.52
30S ribosomal protein S14	rpsN	ACIAD3206	0.51	0.58	0.54	0.67
50S ribosomal protein L5	rplE	ACIAD3207	0.49	0.75	0.48	0.48
50S ribosomal protein L24	rplX	ACIAD3208	0.67	0.63	0.43	0.71
50S ribosomal protein L14	rplN	ACIAD3209	0.58	0.54	0.57	0.64
30S ribosomal protein S17	rpsQ	ACIAD3210	0.39	0.67	0.3	0.55
50S ribosomal protein L29	rpmC	ACIAD3211	0.66	0.53	0.34	0.62
50S ribosomal protein L16	rplP	ACIAD3212	0.47	0.45	0.45	0.49
30S ribosomal protein S3	rpsC	ACIAD3213	0.56	0.55	0.41	0.61
50S ribosomal protein L22	rplV	ACIAD3214	0.62	0.63	0.49	0.59
30S ribosomal protein S19	rpsS	ACIAD3215	0.41	0.65	0.36	0.46
50S ribosomal protein L2	rplB	ACIAD3216	0.49	0.44	0.43	0.66
50S ribosomal protein L23	rplW	ACIAD3217	0.44	0.57	0.42	0.6
50S ribosomal protein L4	rplD	ACIAD3218	0.56	0.57	0.48	0.6
30S ribosomal protein S10	rpsJ	ACIAD3220	0.71	0.77	0.65	0.7
50S ribosomal protein L19	rplS	ACIAD3310	0.32	0.58	0.35	0.4
30S ribosomal protein S16	rpsP	ACIAD3313	0.62	0.75	0.58	0.6
50S ribosomal protein L34	rpmH	ACIAD3684	0.46	0.61	0.46	0.37
Synthase
Arginyl-tRNA synthetase	argS	ACIAD0164	0.58	0.6	0.56	0.69
Aspartyl-tRNA synthetase	aspS	ACIAD0609	0.45	0.71	0.39	0.54
Lysyl-tRNA synthetase	lysS	ACIAD1069	0.51	0.72	0.61	0.56
Glutamyl-tRNA synthetase	gltX	ACIAD3371	0.76	0.67	0.79	0.82
Activator
Transcription termination factor Rho	rho	ACIAD3038	0.74	0.77	0.6	0.65
Ligase
GMP synthase	guaA	ACIAD0151	0.54	0.39	0.45	0.56
Arginyl-tRNA synthetase	argS	ACIAD0164	0.58	0.6	0.56	0.69
Aspartyl-tRNA synthetase	aspS	ACIAD0609	0.45	0.71	0.39	0.54
Lysyl-tRNA synthetase	lysS	ACIAD1069	0.51	0.72	0.61	0.56
Cyanophycin synthetase	–	ACIAD1279	0.19	0.12	0.23	0.23
Amidase	amdA	ACIAD1618	0.68	0.06	0.58	0.27
CTP synthetase	pyrG	ACIAD2003	0.82	0.74	0.62	0.7
Bifunctional biotin carboxylase/biotin carboxyl carrier protein	bccA	ACIAD2517	0.52	0.48	0.67	0.59
Pantoate–beta-alanine ligase	panC	ACIAD3060	0.79	0.82	0.71	0.64
Glutamyl-tRNA synthetase	gltX	ACIAD3371	0.76	0.67	0.79	0.82
Peptidase
Aminopeptidase N	pepN	ACIAD2008	0.4	0.24	0.56	0.53
Hydrolase
Endoribonuclease	rne	ACIAD0438	0.55	0.73	0.67	0.74
ATP-dependent dsDNA exonuclease (suppression of recBC)	sbcC	ACIAD0916	0.72	0.82	0.54	0.6
DNA-binding ATP-dependent protease La	lon	ACIAD1115	0.55	0.77	0.55	0.48
Phosphate transporter ATP-binding protein	pstB	ACIAD1215	0.58	0.3	0.3	0.55
Succinylglutamic semialdehyde dehydrogenase	astD	ACIAD1287	0.47	0.51	0.37	0.38
Succinylarginine dihydrolase	astB	ACIAD1288	0.33	0.26	0.35	0.2
Uridylate kinase	pyrH	ACIAD1372	0.67	0.79	0.81	0.75
DNA polymerase III beta	dnaX	ACIAD1970	0.78	0.37	0.6	0.73
Aminopeptidase N	pepN	ACIAD2008	0.4	0.24	0.56	0.53
Formyltetrahydrofolate deformylase	purU	ACIAD2554	0.62	0.54	0.57	0.59
GTPase	obgE	ACIAD2561	0.52	0.6	0.76	0.7
Major intrinsic multiple antibiotic resistance efflux outer membrane protein precursor	macB	ACIAD3110	0.59	0.74	0.42	0.75
Oligoribonuclease	orn	ACIAD3118	0.55	0.76	0.74	0.69
Oligopeptidase A	prlC	ACIAD3182	0.63	0.72	0.76	0.51
Inositol-1-monophosphatase (IMPase) (inositol-1-phosphatase) (I-1-Pase)	suhB	ACIAD3246	0.59	0.65	0.52	0.54
DNA/RNA non-specific endonuclease G protein	–	ACIAD3408	0.47	0.42	0.52	0.53
Protease
DNA-binding ATP-dependent protease La	lon	ACIAD1115	0.55	0.77	0.55	0.48
Oligopeptidase A	prlC	ACIAD3182	0.63	0.72	0.76	0.51
ATP-binding
ATP-dependent protease ATP-binding subunit ClpX	clpX	ACIAD0535	0.82	0.47	0.73	0.72
Phosphate starvation-inducible protein (PhoH-like)	phoL	ACIAD3159	0.42	0.45	0.62	0.54
ATPase
High affinity Zn ABC transporter ATP-binding protein	znuB	ACIAD0174	0.78	0.49	0.74	0.8
ATP-dependent protease ATP-binding subunit ClpX	clpX	ACIAD0535	0.82	0.47	0.73	0.72
Cell cycle
Cell division protein	ftsZ	ACIAD3511	0.66	0.42	0.54	0.45
Chaperone
Heat shock protein 90	htpG	ACIAD0316	0.51	0.51	0.55	0.66
Heat shock protein HSP33	hslO	ACIAD0407	0.36	0.72	0.44	0.64
Porin precursor	quiX	ACIAD1715	0.17	0.05	0.33	0.46
Chaperonin GroEL	groEL	ACIAD2838	0.63	0.29	0.63	0.72
Isomerase
Alanine racemase 2, PLP-binding, catabolic	dadX	ACIAD0116	0.55	0.52	0.47	0.33
Fatty acid oxidation complex subunit alpha	fadB	ACIAD0335	0.76	0.74	0.7	0.77
Glutamate-1-semialdehyde aminotransferase	hemL	ACIAD1201	0.72	0.72	0.6	0.63
Porin precursor	quiX	ACIAD1715	0.17	0.05	0.33	0.46
Triphosphoribosyl-dephospho-CoA synthase	mdcB	ACIAD1754	0.25	0.17	0.16	0.19
UDP-N-acetylenolpyruvoylglucosamine reductase	murB	ACIAD1945	0.25	0.82	0.61	0.52
Phosphoglucosamine mutase	glmM	ACIAD3502	0.64	0.61	0.77	0.58
Phosphoserine phosphatase	serB	ACIAD3567	0.59	0.44	0.65	0.59
Lyase
Fatty acid oxidation complex subunit alpha	fadB	ACIAD0335	0.76	0.74	0.7	0.77
Aspartate ammonia-lyase	aspA	ACIAD1744	0.02	0.06	0.03	0.03
Fumarate hydratase	fumC	ACIAD1890	0.49	0.47	0.56	0.42
Phosphoenolpyruvate carboxylase	ppc	ACIAD3627	0.76	0.53	0.46	0.77
Oxidoreductase
Signal peptide	lspA	ACIAD0021	0.81	0.47	0.54	0.79
L-lactate dehydrogenase	lldD	ACIAD0108	0.39	0.45	0.44	0.57
D-lactate dehydrogenase	dld	ACIAD0109	0.55	0.64	0.64	0.58
D-amino acid dehydrogenase small subunit	dadA	ACIAD0115	0.11	0.06	0.12	0.16
Fatty acid oxidation complex subunit alpha	fadB	ACIAD0335	0.76	0.74	0.7	0.77
Acyl-CoA dehydrogenase	acdA	ACIAD0623	0.49	0.81	0.7	0.76
Malate: quinone oxidoreductase	mqo	ACIAD1007	0.2	0.56	0.17	0.58
Catechol 1,2-dioxygenase	catA	ACIAD1442	0.65	0.65	0.44	0.28
Acyl-CoA dehydrogenase	dcaA	ACIAD1693	0.55	0.63	0.6	0.58
Tellurium resistance protein	terZ	ACIAD1968	0.68	0.65	0.81	0.7
Pyruvate dehydrogenase (cytochrome)	poxB	ACIAD3381	0.52	0.33	0.42	0.62
NADP + -dependent succinate semialdehyde dehydrogenase	gabD	ACIAD3445	0.09	0.11	0.33	0.23
Helicase
ATP-dependent RNA helicase	rhlB	ACIAD1314	0.56	0.6	0.42	0.52
Nuclease
Endoribonuclease	rne	ACIAD0438	0.55	0.73	0.67	0.74
DNA/RNA non-specific endonuclease G protein	cafA	ACIAD0830	0.47	0.42	0.52	0.53
ATP-dependent dsDNA exonuclease (suppression of recBC)	sbcC	ACIAD0916	0.72	0.82	0.54	0.6
DNA polymerase III beta	dnaX	ACIAD1970	0.78	0.37	0.6	0.73
Oligoribonuclease	orn	ACIAD3118	0.55	0.76	0.74	0.69
DNA polymerase
DNA polymerase III beta	dnaX	ACIAD1970	0.78	0.37	0.6	0.73
Kinase
Polyphosphate kinase	ppk	ACIAD1782	0.77	0.51	0.36	0.48
1-phosphofructokinase	fruK	ACIAD1992	0.53	0.69	0.63	0.62
Superoxide dismutase	sodM	ACIAD2072	0.7	0.41	0.52	0.61
4-diphosphocytidyl-2C-methyl-D-erythritol kinase	ipk	ACIAD2903	0.56	0.35	0.73	0.51
Guanylate kinase	gmk	ACIAD3324	0.6	0.47	0.6	0.63

For biological processes, most up- or down-regulated proteins were related to metabolite pathways and amino acid synthesis. Phospho-2-dehydro-3-deoxyheptonate aldolase (Aro, biosynthetic process) and 5-methyltetrahydropteroyltrigluta mate-homocysteine methyltransferase (MetE, methionine formation) in all treatments were most up-regulated, showing an average relative abundance of 15.2 ± 10.2 and 9.8 ± 1.6, respectively. In contrast, enzymes responsible for biosynthesis, e.g., L-lactate dehydrogenase (LldD), pyruvate dehydrogenase (cytochrome, PoxB), and malate synthase G (GlcB), were remarkably down-regulated. Additionally, proteins associated with amino acid metabolism varied significantly across different carbon sources, such as alanine (MurC, PanC, and DadX), aspartate (PyrB, GltK, and AspA), and glutamate (ArgJ, MetE, GltK, Gdh, AstE, and HemL). Proteins involved in DNA replication and stress response showed a diverse profiling change. The up-regulated proteins included DNA polymerase Pol III (DnaX), DNA replication regulator (GlnG, BaeR), and Holliday junction DNA helicase (RuvB), whereas DNA replication regulator (LldR, Cbl), two-component regulatory system response regulator (OmpR), and heat shock proteins (HtpG and HslO) were obviously down-regulated. It is worth mentioning that the relative abundance of all identified ribosomal proteins, including twenty-eight 50S ribosomal proteins and eighteen 30S ribosomal proteins, remarkably decreased in comparison with those in LB treatments (Table 2).

Changes of Proteins Involved in Metabolism and Energy Production

As carbon source participates in metabolism and energy production, proteins involved in glycolysis, pyruvate metabolism, and tricarboxylic acid (TCA) cycle were investigated (Figure 4 and Table 2). Except for two enzymes in glycolysis and TCA cycle that were down-regulated, fruK and fum encoding enzymes for the conversion of fructose-6-P to fructose-1,6-BP and fumarate to malate, other enzymes were all up-regulated, including fructose-1,6-bisphosphate aldolase (Fda), phosphoglycerate kinase (Pgk), pyruvate dehydrogenase (AceE), isocitrate dehydrogenase (Icd), succinate dehydrogenase flavoprotein (SdhA), malate dehydrogenase (Mdh), and citrate synthase (GltA).

FIGURE 4.

Proteins involved in glycolysis/gluconeogenesis and TCA cycle with significant fold changes in nutrient-deficient media compared to nutrient-rich LB medium. The down- and up-regulated proteins are highlighted in green and red, respectively. Glucose-1-P, glucose 1-phosphate; glucose-6-P, glucose 6-phosphate; fructose-6-P, fructose 6-phosphate; G-3-P, glyceraldehyde 3-phosphate; DHAP, dihydroxyacetone phosphate; 1,3-BP-glycerate, 1,3-bisphospho-D-glycerate; 3-P-glycerate, 3-phospho-D-glycerate; 2-P-glycerate, 2-phospho-D-glycerate; PEP, phosphoenolpyruvate.

Protein Network Analysis

The protein–protein interaction network for up-regulated proteins was primarily composed of two clusters (marked by red circles in Figure 5A), both closely related to metabolite processes. Among them, core proteins located in Cluster I were citrate synthase (GltA), malic enzyme (MaeA), methylmalonate-semialdehyde dehydrogenase (MmsA), NADH oxidase (PutA), etc. In Cluster II, core proteins included acetolactate synthase (IlvH), dihydroxy-acid dehydratase (IlvD1), ketol-acid reductoisomerase (IlvC), branched-chain amino acid aminotransferase (IlvE), isopropylmalate isomerase (LeuC and LeuD), bifunctional phosphoribosyl-AMP cyclohydrolase/phosphoribosyl-ATP pyrophosphatase (HisI), etc. In contrast, the protein–protein interaction network for down-regulated proteins had only one cluster (Figure 5B). Proteins related to translation process functioned in this cluster, including ribosomal proteins of rpl, rpm, and rps families.

FIGURE 5.

Protein–protein interaction networks of proteins with fold change across carbon sources under mitomycin C-induced DNA damage predicted by STRING. (A) The protein–protein interaction network of up-regulated proteins in nutrient-deficient media and core proteins were mainly involved in metabolic pathways. (B) The protein–protein interaction network of down-regulated proteins in nutrient-deficient media and core proteins were involved in translation processes. The red circle labels the peptides clustered together in the interaction network with close biological process or similar functions.

Protein Profiles Involved in Stress Response

As A. baylyi ADP1 cells are commonly used as bioreporter hosts for evaluating genotoxicity in environmental samples, the relative abundance and profiles of stress-related proteins across the four defined media were investigated. Heatmap listed the relative abundance of 80 proteins involved in stress response (Figure 6). It is worth noting that these 80 proteins might show different up- and down-regulation pattern across all four carbon source treatments, differing from those listed in Table 2. These proteins are classified into seven categories according to a previous study (Onnis-Hayden et al., 2009), including 12 general stress-related proteins responsible for keeping steady the biochemical and biophysical homeostasis of cells, 9 antibiotic resistance/detoxification-related proteins responsive to chemical/drug-induced stress or killing, 6 protein stress-related proteins related to oxidative stress, and heat- or chemical-induced protein damage, 3 electron transport/transport-related proteins involved in electron transport, 5 activators/repressors involved in stress response, 2 redox stress-related proteins associated with conditions altering the redox potential of cells, and 43 response/DNA repair-related proteins.

FIGURE 6.

Fold changes of proteins involved in stress response network in relation to different stress response categories. Columns from left to right represent the ratio of proteins of MMA-to-LB, MMC-to-LB, MMP-to-LB, and MMS-to-LB, respectively. (A) General stress. (B) Antibiotic resistance/detoxification. (C) Protein stress. (D) Electron transport/transport. (E) Activator/repressor. (F) Redox stress. (G) SOS response/DNA repair. Red arrows refer to significantly down-regulated proteins with relative abundance < 0.83 (p < 0.05); green arrows represent significantly up-regulated proteins with relative abundance > 1.20 (p < 0.05); yellow arrows refer to proteins with insignificant relative abundance change from 0.83 to 1.20 (p > 0.05).

Among them, only 20 proteins were not influenced by carbon source, including 5 associated with general stress (activator of morphogenic pathway, universal stress protein, catabolite repression control protein, membrane protease subunit, small ubiquitous protein required for normal growth), 2 related to antibiotic resistance/detoxification (aminoglycoside phosphotransferase and tellurium resistance protein), 1 electron transport/transport (transthyretin-like protein), 1 activator/repressor (alternative sigma factor Rpoh), and 12 involved in SOS response/DNA repair [DNA starvation/stationary phase protection protein Dps, excinuclease ABC subunit B, holliday junction DNA helicase (RuvA), helicase, formamidopyrimidine-DNA glycosylase, transferase, DNA polymerase III subunits tau and gamma, DNA polymerase III subunit delta, recombination protein RecJ, enzyme in methyl-directed mismatch repair, protein used in recombination and DNA repair, and chaperone protein DnaJ].

Proteins responsive to protein stress were mainly down-regulated (62.5%) or unchanged (33.3%). In this category, the relative abundance of heat shock protein 90 (0.6 ± 0.1) and heat shock protein HSP33 (0.5 ± 0.2) remarkably decreased in all treatments. Similarly, 40.0% and 55.0% of activators/repressors were down-regulated or unchanged, respectively. The relative abundance of DNA-binding transcriptional regulator HcaR declined to only 0.4 ± 0.1.

In contrast, proteins belonging to electron transporter/transporter were mostly up-regulated, e.g., electron transfer flavoprotein beta-subunit (1.7 ± 0.3), tryptophan synthase beta chain (1.5 ± 0.3), and oxidoreductase related to nitroreductase (2.0 ± 0.3). This was in accordance with the accelerated energy production rates in nutrient-deficient media (Figure 4), as electron transporters/transporters play critical roles in aerobic respiration and serve as the most typical way for bacteria to gain energy (Bjerg et al., 2018; Naradasu et al., 2019).

Across carbon source treatments, profiles of stress-related proteins in MMC treatment behaved significantly different in comparison with others. Nine proteins were down-regulated only in MMC treatment, which are general stress-related protein (Protease I), antibiotic resistance/detoxification-related proteins (tellurium resistance protein and RND efflux membrane fusion protein precursor), SOS response/DNA repair-related proteins (ATP-dependent helicase, endonuclease III, DNA ligase, DNA uptake protein, and/or related DNA-binding protein and rhombosortase), protein stress-related protein (bacteriolytic lipoprotein entericidin B), and redox stress-related catalase (hydroperoxidase II).

Forty-six proteins involved in SOS response/DNA repair were down-regulated in nutrient-deficient treatments (Figure 6G). Among them, three proteins were all down-regulated in all carbon source treatments, including DNA polymerase III subunit beta, protein RecA, and ATP-dependent dsDNA exonuclease (suppression of RecBC). Holliday junction DNA helicase (RuvB) was up-regulated in all nutrient-deficient treatments. The relative abundance of other proteins remained unchanged, e.g., DNA starvation/stationary phase protection protein Dps, excinuclease ABC subunit B, Holliday junction DNA helicase (RuvA), helicase, formamidopyrimidine-DNA glycosylase, transferase, DNA polymerase III subunits tau and gamma, DNA polymerase III subunit delta, recombination protein (RecJ), enzyme in methyl-directed mismatch repair, protein used in recombination and DNA repair, and Chaperone protein (DnaJ).

FIGURE 7.

Continued

Discussion

Different Behavior of Protein Profiles in Different Carbon Sources

As a model soil microorganism with strong natural transformation competence, A. baylyi ADP1 has been fully sequenced (Barbe et al., 2004) and studied for decades (Hare and Gregg-Jolly, 2003; Metzgar et al., 2004; Zheng and Gregg-Jolly, 2004; Buchan and Ornston, 2005). Although A. baylyi ADP1 cells have been previously constructed as hosts for DNA damage responsive whole-cell bioreporters in environmental toxicity assessment (Ammerman and Azam, 1987; Barbe et al., 2004; Song et al., 2009; Jiang et al., 2014, 2017; Jia et al., 2016), the DNA damage response network in A. baylyi ADP1 is still not well-established. Additionally, previous studies addressing Acinetobacter-based bioreporters have demonstrated that carbon sources have significant impacts on their performance. For instance, MMC and MMS were preferred carbon sources to enhance the responsive ratio of ADPWH_recA after DNA damage, whereas MMA and rich medium (LB) did not achieve the optimal performance (Jiang et al., 2015). However, the underlying mechanisms influencing the behavior of ADP1 in response to toxins cultivated with different carbon sources, particularly on translational level, are still not clear and require further study.

From Figures 1, 2 and Supplementary Figure S1, carbon sources significantly affected the protein profiles in A. baylyi ADP1 responding to DNA damage by iTRAQ-based proteomics analysis, particularly those involved in metabolism, biosynthesis, transport, energy utilization, and stress response. Previous studies have reported some correlations between carbon source and proteomic profiles. Vandera et al. (2015) conducted a metabolic study on the cultivation of Arthrobacter phenanthrenivorans Sphe3 with phenanthrene, phthalate, glucose, or their combinations, suggesting that the influence of aromatic substrates in shaping the protein abundance was related to substrate and amino acid metabolism, as well as stress response. Giardina et al. investigated the influence of glucose starvation and re-feeding on the proteomic profiles of Saccharomyces cerevisiae, demonstrating that up-regulated proteins in response to glucose re-feeding included ribosomal subunits and plasma membrane ATPase, whereas those down-regulated included small heat shock proteins, mitochondrial proteins, and gluconeogenic enzymes (Licausi et al., 2013).

Particularly, across different carbon sources, the protein profiles in MMA, MMP, and MMS treatments were similar, all behaving different with those in MMC treatment (Table 2 and Figure 1). To be more precise, more proteins associated with DNA replication and stress were down-regulated in MMC treatment (Figures 2, 6). It might be explained by the ubiquitous roles of citrate as a key cellular intermediate readily assimilated through the central metabolic pathway. This possibly explains the previous observation in Jiang’s work that ADPWH_recA bioreporter cells had the best performance in responding to genotoxins in MMC treatment (Jiang et al., 2015).

The citrate-related proteomic profiles were also previously reported. By exploring the proteome of Pseudomonas putida F1, Mandalakis et al. (2013) found the enhancement of the Na⁺/H⁺ antiporter and carbonic anhydrase in citrate treatments compared to benzoate treatments, suggesting that citrate poses more challenges in maintaining pH homeostasis. Our work, for the first time, addressed the critical roles of citrate in shaping the DNA damage responsive proteomic profiles in A. baylyi ADP1.

As it is a key technique for exploring gene changes at the level of translation, proteomics has always been applied to reveal the responses of microbes to environmental stresses (McNair et al., 2007; Xu et al., 2007; Chuang et al., 2010; Tian et al., 2013). Transcriptome is another widely used technique in the investigation of microbial responses under stress, which is useful for identifying novel transcripts and analyzing gene expression (Jonas et al., 2007; Sun et al., 2015). The transcriptome of A. baylyi ADP1 in response to DNA damage was also explored. Hare et al. applied RNA-Seq to evaluate the DNA damage transcriptome in A. baylyi ADP1 induced by mitomycin C with MMS as the medium. Although 2% of genes (66) in the genome of A. baylyi ADP1 were inducible by DNA damage, only a few of them were differentially regulated, and most of them were repressed or absent (Hare et al., 2014). To be more precise, 38.4% of all genes in A. baylyi ADP1 were repressed in case of DNA damage induced by mitomycin C (Hare et al., 2014). Another study by Aranda et al. (2013) applied DNA microarray to investigate the transcriptional response of Acinetobacter baumannii to mitomycin C, confirming the roles of UmuDAb as a direct regulator of DNA damage response instead of RecA. Other studies addressed the transciptomic changes in A. baylyi postexposure to environmental stresses, such as low temperature (Ma et al., 2019), pesticides (Pi et al., 2017), and antibiotics (Heo et al., 2014). However, many studies have suggested a weak correlation or discrimination between the transcriptomic and proteomic changes (Barker et al., 2012; Lackner et al., 2012; Su et al., 2016; Fan et al., 2017; Bathke et al., 2019), which is possibly due to the key role that post-transcriptional processes play in the adaptation to stresses (Fan et al., 2017). Therefore, the investigation on the proteomic profiles of A. baylyi ADP1 responsive to DNA damage in different carbon sources provides significant molecular information and clue for further interpretation of the mechanism of DNA damage response in A. baylyi ADP1.

Mechanism of Carbon-Dependent Protein Profiles in A. baylyi ADP1

The present work addressed the different protein profiles in rich (LB) and nutrient-deficient media (MMA, MMC, MMP, and MMS), possibly explained by carbon catabolite repression (CCR). CCR was first reported in E. coli and defined as the repression of pathways or enzyme activities related to the use of secondary carbon sources in the presence of a preferred carbon source, which is an important mechanism allowing efficient carbon source utilization (Ishizuka et al., 1993; Gosset et al., 2004; Görke and Stülke, 2008). A recent study demonstrated that driven by CCR, E. coli showed time-series protein profile changes and 96 proteins were remarkably affected, including those responsible for amino acid biosynthesis, cell division and DNA replication, translation, transcription, and central carbon metabolism (Borirak et al., 2015).

For A. baylyi ADP1, Huang et al. found that some components in LB medium, e.g., yeast extract, aspartic acid, and asparagine, repressed the expression of Pu promoter, explained by CCR (Huang et al., 2008). In addition, CCR is previously reported in A. baylyi during the polycyclic aromatic hydrocarbon (PAH) degradation process, in which acetate and succinate exhibited the repression effect and catabolite repression control (Crc) protein was identified (Fischer et al., 2008; Bernard and Habash, 2009; Zimmermann et al., 2009). Accordingly, CCR could potentially influence the performance of Acinetobacter-based bioreporters owing to the down-regulated proteins related to adverse conditions in nutrient-deficient media.

Protein Profiles Related to Translation

It is worth pointing out that almost all translation-related proteins were down-regulated in nutrient-deficient treatments comparing to LB medium (Figure 3). This was consistent with the location of ribosomal proteins in the center of protein–protein interaction network for down-regulated proteins (Figure 5). Ribosomal proteins are reported to conjunct with rRNA and make up the ribosomal subunits involved in the cellular process of translation (Rodnina and Wintermeyer, 2011; Rainer Nikolay et al., 2015), and therefore are positively related to the protein synthesis. Our results indicated that the translation process in A. baylyi ADP1 was remarkably inhibited in nutrient-deficient medium, while ribosomal and translation-related proteins were up-regulated, resulting in increased protein synthesis and nutrient uptake in nutrient-rich medium, e.g., LB.

Similar to our findings, previous studies also reported that translation-related proteins were significantly affected by carbon sources. However, no unique conclusion has been drawn whether the translation was promoted by nutritional downshift from nutrient-rich to nutrient-deficient medium. For instance, proteomic analysis deciphering the tacrolimus-overproduction mechanism of Streptomyces tsukubaensis revealed that soybean oil addition tuned the pathways of transcriptional regulation, translation, and stress response (Wang et al., 2017). Giardina et al. (2012) studied the proteomic changes in S. cerevisiae after transferring from glucose depletion to glucose-rich medium, and the proteins involved in translation including ribosomal subunits and plasma membrane ATPase were up-regulated in glucose-rich medium. A recent iTRAQ-based proteomic study investigated the adaptive strategies of Rubrivivax benzoatilyticus JA2 to glucose and demonstrated the down-regulation of proteins involved in DNA replication, translation, electron transport, and photosynthetic machinery (Gupta et al., 2019). These findings suggested that different carbon sources resulted in significant fold changes of translation-related proteins.

Protein Profiles Involved in Stress Response

As A. baylyi ADP1 is commonly used as a bioreporter for genotoxicity assessment, the patterns of DNA damage responsive proteins were comprehensively analyzed. There were three down-regulated proteins (subunit β of DNA polymerase III, RecA, and ATP-dependent dsDNA exonuclease) and one up-regulated protein (Holliday junction DNA helicase RuvB) among all the proteins related to DNA damage response (Figure 6G).

Among the down-regulated proteins, DNA polymerase III is a primary enzyme complex involved in prokaryotic DNA replication, with proofreading capabilities to correct replication mistakes by means of exonuclease activity working at 3′ to 5′ (Kelman and O’Donnell, 1995). The subunit β of DNA polymerase III acts as sliding DNA clamps, keeping the polymerase bound to the DNA (Leshinsky, 2008). RecA is a highly conserved protein in prokaryotic organisms, and its role in SOS response in E. coli is binding to single-strand DNA and helping recombinational repair and cleavage of LexA (Michel, 2005; Schlacher et al., 2006; Chen et al., 2008). Although evidence has suggested that RecA is also involved in DNA repair in Acinetobacter, possibly responsible for the cleavage of LexA-like UmuDAb (Aranda et al., 2011; DeBruyn et al., 2012), its mechanisms in Acinetobacter are not clear. ATP-dependent dsDNA exonuclease participates in DNA replication process when forming the blockage of replication forks. The only up-regulated protein Holliday junction DNA helicase (RuvB) involves the removal of Holliday junction, created by the annealing of newly synthesized strands, from the end of DNA double strands (Fainstein, 2005).

As most proteins (55.3%) involved in SOS response/DNA repair were down-regulated (Figure 6G) in nutrient-deficient media, less DNA damage was repaired and therefore led to a higher induction ratio of ADPWH_recA to genotoxins. Thus, our results provided proteomic explanation that LB was not an ideal medium for genotoxicity assessment in Acinetobacter-based bioreporters (Jiang et al., 2015), offering in-depth clues on the roles of carbon sources in post-transcriptional regulation during DNA damage response in A. baylyi ADP1.

Conclusion

In this work, the impacts of carbon source on the proteomic profiles of A. baylyi ADP1 responding to DNA damage response were comprehensively explored and discussed by iTRAQ for the first time. Our results unraveled the significant difference in proteomic patterns between nutrient-rich and nutrient-deficient media. Transferase, hydrolase, oxidoreductase, and DNA/RNA-binding proteins were significantly affected by carbon sources, and they were mainly involved in the biological processes of metabolite pathways and amino acid synthesis. Particularly, 80 proteins involved in stress response were significantly altered, hinting the strong influence of carbon source on microbial response to DNA damage and other stresses. These findings provide important mechanistic insights into the adaptation of A. baylyi ADP1 to DNA damage stress under nutrient-deficient conditions, suggesting that nutrient-deficient medium, instead of LB, was ideal for genotoxicity assessment in Acinetobacter-based bioreporters. Our findings also offer technical tools and theoretical clues to unravel the SOS response network and influential factors in other model microorganisms.

Data Availability Statement

The datasets generated for this study can be found in the http://www.ncbi.nlm.nih.gov/bioproject/549090.

Author Contributions

BJ and DZ contributed to the conception and design of the study. BJ, NZ, LL, and GS did the lab work. DZ, YX, GL, and BJ performed the data analysis. DZ, BJ, NZ, and LL performed the statistical analysis. BJ wrote the first draft of the manuscript. DZ, GL, and YX revised the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2019.02906/full#supplementary-material