Assessment of metabolic changes in Acinetobacter johnsonii and Pseudomonas fluorescens co-culture from bigeye tuna (Thunnus obesus) spoilage by ultra-high-performance liquid chromatography-tandem mass spectrometry

LWT - Food Science and Technology 123 (2020) 109073

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Assessment of metabolic changes in Acinetobacter johnsonii and Pseudomonas fluorescens co-culture from bigeye tuna (Thunnus obesus) spoilage by ultrahigh-performance liquid chromatography-tandem mass spectrometry

T

Xin-Yun Wanga,b,c,d, Jing Xiea,b,c,d,∗ a

Shanghai Engineering Research Center of Aquatic Product Processing & Preservation, Shanghai Ocean University, Shanghai, China Shanghai Professional Technology Service Platform on Cold Chain Equipment Performance and Energy Saving Evaluation, Shanghai Ocean University, Shanghai, China c National Experimental Teaching Demonstration Center for Food Science and Engineering, Shanghai Ocean University, Shanghai, China d College of Food Science & Technology, Shanghai Ocean University, Shanghai, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Acinetobacter johnsonii Pseudomonas fluorescens Metabolic changes Ultrahigh performance liquid chromatography (UHPLC)

Acinetobacter johnsonii and Pseudomonas fluorescens are specific spoilage microorganisms (SSO) of aquatic products, and their metabolites are effective indicators for analyzing these two SSOs. However, the metabolome of a coculture of A. johnsonii and P. fluorescens is still unclear. The aim of this study was to investigate the metabolomic changes in A. johnsonii, P. fluorescens, and their co-culture by screening for metabolic markers. PCA and PLS-DA revealed that A, P, and AP groups were different from each other, indicating a significantly varying metabolic profile among them. Based on UHPLC information, 540 metabolites with significant differences, were identified in the ESI+ and ESI- modes, which covered 79 metabolic pathways. The different metabolites were mainly related to the pathways, such as taurine and hypotaurine metabolism:M, bile secretion:OS, and arginine biosynthesis:M were in the A vs AP group, while PPAR signaling pathway:OS, tropane, piperidine, and pyridine alkaloid biosynthesis:M, longevity regulating pathway-worm:OS, choline metabolism in cancer:HD, biosynthesis of phenylpropanoids:M, and amoebiasis:HD were in the P vs AP group. Therefore, this study may provide significant information regarding the metabolic mechanisms of A. johnsonii, P. fluorescens, and their co-cultures, which may provide insights on their role in deteriorating the quality of aquatic products.

1. Introduction Bigeye tuna is one of the most consumed and popular meats globally because of its high nutrient value and delicious taste (Wang & Xie, 2019). However, high protein and water content of bigeye tuna accelerate microbial growth, which can lower the shelf-life (Jaaskelainen et al., 2019). Fish spoilage is caused by endogenous enzymes and microbial activity (Xie, Zhang, Yang, Cheng, & Qian, 2018). The major spoilage organisms contributing to the spoilage in seafood are known as specific spoilage organisms (SSOs), such as Pseudomonas spp., Shewanella spp., Acinetobacter spp., and Aeromonas spp. (Carvalheira, Ferreira, Silva, & Teixeira, 2016; Parlapani, Mallouchos, Haroutounian, & Boziaris, 2014). Pseudomonas spp. and Acinetobacter spp. play significant roles in decomposing nitrogenous substances to produce ammonia, trimethylamine, and an off-odor (Edirisinghe, Graffham, & Taylor, 2007; Odeyemi, Burke, Bolch, & Stanley, 2018). These studies examined the spoilage potential and volatile organic compounds of one bacterial species under ideal laboratory conditions (Don, Xavier, Devi, ∗

Nayak, & Kannuchamy, 2018; Parlapani et al., 2019). However, some samples have more than one species of bacteria in a natural ecosystem (Chanos & Mygind, 2016). Therefore, it is essential to study the activity, metabolism, and interactions of the two bacterial species in co-culture, as both may show mutualistic or antagonistic interactions to regulate metabolism and enzyme activities (Niu et al., 2018). In this regard, limited information is available on co-culturing Acinetobacter johnsonii and Pseudomonas fluorescens. Currently, ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) is a powerful and valuable emerging tool for analyzing the metabolites of aquatic products (Holl, Behr, & Vogel, 2016), vegetables (Loznjak, Striegel, Diaz De la Garza, Rychlik, & Jakobsen, 2019), diseases (Liu et al., 2018), and microorganisms (Jaaskelainen et al., 2019; Liu et al., 2020). Non-targeted metabolomics involves the simultaneous detection of a variety of endogenous metabolites and provides an unbiased and global metabolite profile of the microorganisms by UHPLC-MS/MS (Lu, Bennett, & Rabinowitz, 2008). Separation of a vast range of metabolites with

Corresponding author. Shanghai Engineering Research Center of Aquatic Product Processing & Preservation, Shanghai Ocean University, Shanghai, China. E-mail address: [email protected] (J. Xie).

https://doi.org/10.1016/j.lwt.2020.109073 Received 6 December 2019; Received in revised form 17 January 2020; Accepted 20 January 2020 Available online 23 January 2020 0023-6438/ © 2020 Published by Elsevier Ltd.

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X.-Y. Wang and J. Xie

provide a set of data from which repeatability could be evaluated.

excellent quantitative analysis is achieved for small molecular metabolites, including amino acids, peptides, complex carbohydrates, lipids, organic acids, and nucleotides (Want et al., 2010). Using UHPLC-MS/ MS, Wright, Shalom, Matthews, Greene, and Cock (2019) identified several compounds from Terminalia ferdinandiana (Kakadu plum) that could inhibit Shewanella spp. growth, which contributed to delayed spoilage and extended the shelf life of fish (Wright et al., 2019). Yogendrarajah et al. (2015) studied Aspergillus flavus and Aspergillus parasiticus strains isolated from black pepper, and their secondary metabolite production was assessed by UHPLC-MS/MS (Yogendrarajah et al., 2015). However, many of these previous studies focused on microbial pathogenicity from different bacteria and detected metabolites and biosynthetic pathways, or investigated the inactivation kinetics or risk assessment (Li et al., 2019; Liao et al., 2019; Niu et al., 2018). To date, the sensitive, reliable and powerful technique of UHPLC-MS/MS has been limited in studying the co-culturing of two species of SSOs. Therefore, the aim of this study was to investigate the metabolite composition and pathways of A. johnsonii, P. fluorescens and their coculture by UHPLC-MS/MS-based non-targeted metabolomics approach. Statistical analysis was performed to explore the substantial differences in A. johnsonii (A group), P. fluorescens (P group), and A. johnsonii + P. fluorescens (AP group). Furthermore, we hypothesized that dynamic changes in the key metabolites occured because of the bacterial interaction in the AP group during co-culture, thereby providing novel insights into the interaction of SSOs, as well as scientific guidance for future research.

2.3. UHPLC–MS metabolomics equipment and parameters Metabolism analysis was performed using a Progenesis QI software (Waters ultra performance liquid chromatography, Milford, USA) system coupled with a Triple TOF Mass spectrometer (ABSCIEX-Triple TOF 5600, AB SCIEX, USA). UHPLC–MS conditions were identified based on the method described by Liu et al. (2019). The parameters of chromatography were as follows: column: BEH C18 (100 mm × 2.1 mm i.d., 1.7 μm; Waters, Milford, USA), flow rate: 0.4 mL/min, sample injection volume: 5 μL, gradient mobile phase: water with 0.1% formic acid as solvent A and 0.1% formic acid and methanol as solvent B, column temperature: 40 °C, and ion mode: positive ion mode (ESI+). The gradient mobile phase program applied is as follows: t = 0 min, 95% A; t = 3 min, 80% A; t = 9 min, 5% A; t = 13.1 min, 95% A; t = 16 min, 95% A. The conditions of MS/MS included the ion source temperature: 500 °C, declustering potential: 80 V, collision energy: 5 V, and MS/MS collision energy: 20–60 V. 2.4. Statistical analysis Multivariate data analysis included principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The data represented at least six independent parallel experiments. T test (Student's test) combined with the multivariate analysis of PLS-DA was used to evaluate the differential metabolites or potential marker (variable importance in the projection (VIP) > 1, p value < 0.05), to reveal the differences in the metabolic composition and pathway of A vs AP and P vs AP. Correlation analysis was performed using Pearson correlation test coefficient, and p value < 0.05 was considered significant between A vs AP and P vs AP. Moreover, the metabolite molecules were further identified by using the HMDB (http://www.hmdb. ca/) and METLIN (http://metlin.scripps.edu/) databases (Liu et al., 2017), and metabolism pathways were revealed by the iPath 3.0 (http://pathways.embl.de) and KEGG pathway (http://www.genome. jp/kegg/). For analysis software, the online platform of Majorbio ISanger Cloud platform (https://cloud.majorbio.com/) was used.

2. Materials and methods 2.1. Culture of the bacterial strains Two types of SSOs, A. johnsonii and P. fluorescens, were isolated from spoiled bigeye tuna muscle (Zhejiang Fenghui Ocean Fishing Company Ltd., China), and were identified by 16S rRNA gene sequencing using a VITEK ® 2 CompactA system (BIOMÉRIEUX, France). Bacterial stock cultures contained 25% glycerine and were stored at −80 °C. Before use, A. johnsonii and P. fluorescens were pre-cultured individually for two successive periods of 18 h in brain-heart infusion (BHI, Qingdao Hope Bio-Technology Co., Ltd., China) broth at 30 °C and then cultured in tryptose soya broth (TSB, Qingdao Hope Bio-Technology Co., Ltd., China) until the maximal concentration (108 CFU/mL) was reached. The bioreactors were inoculated with overnight cultures at 1% (v/v) inoculation level. The co-culture was defined as a mixture of equal amounts (v/v) of A. johnsonii and P. fluorescens. All the bacterial strains were cultured overnight at 30 °C, collected in a 15 mL centrifuge tube, and then centrifuged at 10,000 g for 10 min, for removing medium. The precipitate was washed three times with 10 mL of pre-cooled 1x PBS solution. The obtained precipitate was transferred into a new 1.5-mL centrifuge tube and immediately stored at −80 °C.

3. Results and discussion 3.1. Metabolic variations among Acinetobacter johnsonii (A group), Pseudomonas fluorescens (P group), and co-culture samples (AP group) The 18 bacterial samples were clustered into three groups samples (Table 1). A PCA score plot based on the first two principal components was responsible for 54.5% (PC1 35.7%, PC2 18.8%) in positive mode and 56.3% (PC1 30.3%, PC2 26%) in negative mode of the overall variance of the metabolite profiles, explaining a difference in the distribution of A, P, and AP groups. The two component PCA score plots were constructed with the data from both positive (R2X = 0.633, Q2 = 0.41; Fig. 1a) and negative modes (R2X =0.663, Q2 = 0.466; Fig. 1b) respectively, indicating a good fitness ability of the PCA model (Fig. 1a,b). Therefore, the metabolite profiles of A, P, and AP groups

2.2. Sample preparation for metabolomics A total of 18 bacterial strain samples from three groups were obtained for the metabolomics study using the UHPLC-MS/MS platform (Thermo, Ultimate 3000LC, Q Exactive). Six biological replicates were used per sample. The stored bacteria (at −80 °C) were thawed at room temperature. To 50 mg of each sample, 400 μL of methanol extraction solution (methanol-water, 4:1, v/v) was added. The cells were lysed by crushing with a high-throughput tissue crusher at −20 °C for 6 min followed by homogenizing for 30 s. The solution was ultrasonically extracted for 30 min at 5 °C and incubated for 30 min at −20 °C, then centrifuged for 15 min at 13,000 g at 4 °C. The supernatant was transferred to a sample bottle for UHPLC-MS analysis. The quality control (QC) samples were prepared by mixing aliquots, to obtain a pooled sample, and were analyzed accordingly. They were injected at regular intervals (every 6 samples) throughout the analytical run to

Table 1 Significance values for the PCA model and PLS-DA models (A group, P group and AP group). Abbreviations: A. johnsonii, A; P. fluorescens, P; A. johnsonii + P. fluorescens, A + P.

2

Mode

Type

A

N

R2X(cum)

ESI+ ESI+ ESIESI-

PCA PLS-DA PCA PLS-DA

3 4 3 4

18 18 18 18

0.633 0.784 0.663 0.772

R2Y(cum)

0.988 0.989

Q2(cum) 0.41 0.947 0.466 0.958

R2

Q2

0.813

−0.0532

0.773

−0.145

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Fig. 1. Principal components analysis (PCA) score plots of Acinetobacter johnsonii, Pseudomonas fluorescens, and Acinetobacter johnsonii + Pseudomonas fluorescens in positive ion mode (a) and negative ion mode (b) Validation of partial least squares discriminant analysis (PLS-DA) models of pairwise comparation among A. johnsonii, P. fluorescens, and A. johnsonii + P. fluorescens in positive ion mode (c, d) and negative ion mode (e, f). Abbreviations: A. johnsonii, A; P. fluorescens, P; A. johnsonii + P. fluorescens, A + P.

degree (Q2 < 0.05) (Fig. 1 (d),(f)), indicating higher reliability of these models (Jiang, Kang, & Yu, 2017). The clear clusters revealed metabolite differences among A, P, and AP groups; however, to further identify the related metabolites responsible for the group separation, pairwise comparative method PLS-DA should be used to show co-culture metabolomic differences (Chen et al., 2020).

were different from each other. Moreover, A and AP groups were negatively affected by PC1 and located on the opposite sides of PC2, while P group was postively affected by PC1 and located in PC2. There was a great degree of separation between A group and the other groups by a large distance along PC1, indicating that the metabolite profiles of A. johnsonii were prominently different from those of P. fluorescens, and their co-culture. A previous study had shown that A. johnsonii and P. fluorescens showed spoilage characteristics (Carvalheira et al., 2016; Parlapani et al., 2019). These results further revealed their similarities at the metabolic level. Based on the PCA of metabolite differences for A, P, and AP groups, the present study revealed metabolic diversity. To further explore the differences in metabolic profiles among these bacteria, PLS-DA was carried out, which showed some differences in the metabolites between the A, P, and AP groups under both ionization modes. The PLS-DA models displayed a strong goodness of fit (R2X) and high predictability (Q2), which were calculated as 0.784 and 0.947 (in positive mode, Table 1), and 0.772 and 0.958 (in negative mode, Table 1) for the comparison of the A, P, and AP groups, respectively. Metabolites were identified by UHPLC–MS, an important and significant method. All the PLS-DA models were validated by response permutation testing (RPT), which revealed the absence of overfitting and false positives in the experimental data. Furthermore, permutation test cross validation was performed 200 times to ensure the suitability of the model. The validation of the PLS-DA models presented prediction

3.2. Correlation analysis of metabolites in A. johnsonii (A group), P. fluorescens (P group), and co-culture samples (AP group) A correlation matrix analysis was performed to investigate the correlation analysis of metabolites in A, P, and AP groups by Pearson correlation analysis; the detailed results are presented in Fig. 2 using each compound as a variable. N-Acetyltyramine, vignatic acid B, alliospiroside D, kanamycin, obacunone, and chavicol had no correlation with daidzein and emodin. Threoninyl-tyrosine, except soyasaponin I, oxypurinol and gonyautoxin II, was negatively correlated with the other metabolites (p < 0.05). Threoninyl-tyrosine and N-acetyltyramine may affect the amino acid and peptide degradation of the bacteria, in which N-acetyltyramine is the active compound in cell density-dependent mechanism (Reina, Perez-Victoria, Martin, & Llamas, 2019). 27-Odemethylrifabutin had high positive correlations with beta-casomorphin (1–6), 3,6,7-Trihydroxy-4\'-methoxyflavor, N-Acetyltyramine, vignatic acid B, alliospiroside D, kanamycin, obacunone, chavicol, 313

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Fig. 2. Pearson correlation analysis of A. johnsonii, P. fluorescens, and their co-culture. Abbreviations: A. johnsonii, A; P. fluorescens, P; A. johnsonii + P. fluorescens, A + P. Levels of significance are defined as *p < 0.05, **p < 0.01 and ***p < 0.001.

organoheterocyclic compounds, 43 organic oxygen compounds, 41 phenylpropanoids and polyketides, 19 benzenoids, 13 nucleosides, nucleotides, and analogues, 9 alkaloids and derivatives, 7 organic nitrogen compounds, 4 organooxygen compounds, 2 organosulfur compounds, and 1 mixed metal/non-metal compound. The number of differential metabolites between A and AP groups was higher than the number of those between P and AP groups. There were clearly more upregulated or downregulated differential metabolites for AP group when compared with the A group or P group. In the ten selected classifications, shown in Fig. 3 (a, b), there were more predominantly accumulated lipids and lipid-like molecules, organic acids and derivatives, and organoheterocyclic compounds, and less predominantly accumulated organic nitrogen compounds, alkaloids and derivatives, and organooxygen compounds in each group. Increasing evidence and data suggest that lipid-like molecules, organic acids, and organoheterocyclic compounds were considered significant metabolites of microorganism growth (Chen et al., 2020). To further investigate the metabolite correlation among the bacteria, the samples were separately compared between the A and AP groups or P and AP groups. The key differential metabolites with biological activities in each classification are shown in Fig. 3(c–d). A heat map was plotted for the A vs AP and P vs AP chemical compositions to show the changes in the metabolite concentrations (Fig. 3(c–d)). The differences between the two pairs of microorganism samples indicated

Hydroxy rifabutin, CDP-DG (18:2(9Z,11Z)/i-14:0), jubanine A, methyl 10-undecenoate, riesling acetal, p-tolualdehyde, benzoquinoneacetic acid, deoxypyridinoline, schidigerasaponin B1, 2-octenyl butyrate, and N-desmethylclarithromycin (p < 0.05). These findings suggested differences in the metabolic fluxes controlled by interactions with some macrolide of 27-O-demethylrifabutin among bacterial samples (Iatsimirskaia et al., 1997). 3.3. Qualitative and quantitative analysis of A. johnsonii, P. fluorescens, and its co-culture samples A comparison of the relative abundance of metabolites in A vs AP group and in P vs AP group is shown in Fig. 3, which reflects the content and intensity distribution in positive ion mode and negative ion mode. A total of 540 metabolites were identified in the ESI+ and ESI- modes, based on the metabolome databases of HMDB (http://www.hmdb.ca/; https://metlin.scripps.edu/). As shown in the Supplementary Table S1, the metabolite intensity of each group in positive ion mode was obviously higher than in the negative ion mode. The numbers of the HMDB compound metabolites that accumulated with the greatest frequency in each bacteria group for some of these chemical classifications are shown in Fig. 3 (a, b). Five hundred and forty metabolites were present in high amounts and were divided in chemical categories as 209 lipids and lipid-like molecules, 138 organic acids and derivatives, 54 4

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Fig. 3. Relative abundance of metabolites of A. johnsonii, P. fluorescens, and their co-culture in positive ion mode (a) and in negative ion mode (b) Comparison of the relative abundance of metabolites in A vs AP group (c) and in P vs AP group (d). Levels of significance are defined as *p < 0.05, **p < 0.01 and ***p < 0.001.

carbohydrates in these microorganisms could produce acetic acid as the main short chain fatty acid (Rurangwa et al., 2009). Significant differences were observed between the P and AP groups. Some metabolites, such as phenylpropanoids and polyketides, terpene, and vaprolactams lactones showed the strongest differences between P and AP groups (p < 0.05), particularly including (−)-shinpterocarpin (VIP = 4.4266), annuolide E (VIP = 3.497), and epsilon-caprolactam (VIP = 3.5318), respectively, among the few differential metabolites, which had major contribution to the internal communication between A. johnsonii and P. fluorescens. It is possible that these metabolites, (−)-shinpterocarpin, annuolide E, and epsilon-caprolactam are potential biomarkers in co-culturing condition of A. johnsonii and P. fluorescens that were not reported to be produced by these bacteria previously. (−)-shinpterocarpin inhibits the activity of adenosine 3′, 5cyclic monophosphate phosphodiesterase (Azimova & Vinogradova, 2013). Organoheterocyclic compounds, lipids and lipid-like molecules, and organic oxygen compounds existed in A vs AP group and P vs AP group (p < 0.05), which contained shinflavanone, perindopril, decyl alcohol, diacetylfusarochromanone, hordatine a glucoside, annuolide E, s-(formylmethyl)glutathione, nicotinate d-ribonucleoside, pseudoecgonine, (−)-shinpterocarpin, melleolide, crispolide, and orciprenaline. There were higher intensities in annuolide E, (−)-shinpterocarpin and nicotinate d-ribonucleoside, s-(formylmethyl) glutathione and crispolide (p < 0.05) in A vs AP group and P vs AP group, while they exhibited significantly lower intensities of diacetylfusarochromanone in A vs AP group and shinflavanone in P vs AP group (p < 0.05), respectively. The co-culturing condition may also be related to the upregulated or downregulated metabolites in the bacteria, which need further exploration for the specific effects. In previous studies, some

metabolite changes using a VIP value > 1. The numbers of shinflavanone, perindopril, and glycyl-arginine (VIP value > 1) in A samples that predominantly accumulated phenylpropanoids and polyketides, amino acids, peptides, and analogues, were the majority (Fig. 3). The result is in agreement with those obtained in a previous study, in which the amino acids and peptides played significant roles in A. johnsonii 210A; the β-lactamases from A. johnsonii showed a peculiar amino acid and peptide hydrolysis profile to effectively promote amino acid synthesis (Figueiredo, Bonnin, Poirel, Duranteau, & Nordmann, 2012). In this study, as compared to the A and AP groups, the abundance of lipids and lipid-like molecules was remarkably different (VIP > 2, p < 0.05), including retinoyl b-glucuronide, N-acetylserotonin glucuronide, annuolide E, trigoneoside Xb, crispolide, PE (18:1(9Z)/0:0). This indicated that some lipid content changed dramatically in the coculture of A. johnsonii and P. fluorescens, which directly improved the interactions between A. johnsonii and P. fluorescens by lipid metabolite. The co-culture of A. johnsonii and P. fluorescens possibly indicated that their fluidity and permeability may change by changing the composition of the lipids and the interactions between the lipids and proteins (Aguilar-Galvez et al., 2020; Niu & Xiang, 2018). As depicted in Fig. 3 (d), the P groups exhibited high content of carbohydrates and carbohydrate conjugates, including N-acetylgalactosamine, hordatine a glucoside, and nicotinate D-ribonucleoside (VIP > 2, p < 0.05). N-acetylglucosamine is a major component of the peptidoglycan, capsular polysaccharides, and of the outer membrane lipopolysaccharides of gram-negative bacteria (Brinkkötter, Klöß, Alpert, & Lengeler, 2000). Some fermentable carbohydrates and prebiotics could promote the growth of bacteria in aquaculture. Moreover, the fermentation 5

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Fig. 4. KEGG maps of pentose and glucuronate interconversions (a); protein digestion and absorption (b); linoleic acid metabolism (c).

hormones, glucosinolate biosynthesis, tryptophan metabolism, purine metabolism, chemical carcinogenesis, ABC transporters, neuroactive ligand-receptor interaction, biosynthesis of alkaloids derived from shikimate pathway, amino sugar and nucleotide sugar metabolism, arginine biosynthesis, central carbon metabolism in cancer, tropane, piperidine and pyridine alkaloid biosynthesis, biosynthesis of alkaloids derived from ornithine, lysine and nicotinic acid, phenylpropanoid biosynthesis, biosynthesis of phenylpropanoids, metabolism of xenobiotics by cytochrome P450, ascorbate and aldarate metabolism, biosynthesis of plant secondary metabolites, phenylalanine metabolism, bile secretion, and pentose and glucuronate interconversions (Supplementary Fig. S1, Supplementary Table S2). During the co-culture of A. johnsonii and P. fluorescens, specific metabolites were mainly produced as a result of the pentose and glucuronate interconversions, phenylalanine metabolism, and amino acid changes (Fig. 4). Microbial growth requires energy, and energy sources are obtained by nutrients through metabolic pathways (Liu et al., 2018; Wang et al., 2019). Previous studies suggested that the energy metabolism and pentose interconversions may be different among bacteria that have evolved to use diverse carbon and energy sources under different culture conditions (Fabich et al., 2008; Kouremenos, Beale, Antti,

metabolites could induce secondary metabolites associated with chemical communication signals in co-culture since microbial interactions shifted from competition to cooperation under environmental stress (Velez et al., 2018), which is similar to our study. 3.4. Metabolic pathway analysis based on the KEGG database The metabolic pathways were identified according to the KEGG database, to determine the most evident and vital metabolic or biosynthetic pathways related to the metabolites (Fig. 4). It is important for A. johnsonii and P. fluorescens to produce metabolites, and the differences in the metabolites between individual culture and co-culture are also significant. Sventy-nine metabolic pathways were identified and 22 remarkably significant metabolic pathways were annotated by the KEGG database. The differential metabolites among A. johnsonii, P. fluorescens, and their co-culture participated in the following metabolic or biosynthetic pathways: drug metabolism-cytochrome P450, biosynthesis of terpenoids and steroids, taurine and hypotaurine metabolism, mineral absorption, linoleic acid metabolism, cyanoamino acid metabolism, aminoacyl-tRNA biosynthesis, protein digestion and absorption, vitamin digestion and absorption, biosynthesis of plant 6

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Fig. 5. The 20 most enriched ratios of pathway terms of KEGG enrichment in A vs AP group (a) and in P vs AP group (b).

AP group were phenylpropanoid biosynthesis:M, PPAR signaling pathway:OS, tropane, piperidine and pyridine alkaloid biosynthesis:M, longevity regulating pathway-worm:OS, choline metabolism in cancer:HD, biosynthesis of phenylpropanoids:M, and amoebiasis:HD (p < 0.05). The enrichment ratio of phenylalanine metabolism:M was the highest contributor to the microorganism metabolism in P vs AP group. A previous report revealed that P. fluorescens R124 strain, isolated from a nutrient-limited cave, encoded phenylalanine, and could develop novel nitrogen fixation and transformation pathways to enrich the possibility of nitrogen metabolism (Csuka et al., 2018). The enrichment ratio of metabolites annotated PPAR signaling pathway:OS was the highest in P vs AP group. PPAR expression could stimulate lipolysis and inhibit fatty acid synthesis as a regulator (Tu et al., 2019). The remarkable changes in PPAR signaling pathway could promote the lipid metabolism in P. fluorescens and the interactions between P. fluorescens and A. johnsonii.

& Palombo, 2014). This result was supported by the dramatical pathway changes in arginine biosynthesis, phenylalanine metabolism, tryptophan metabolism, protein digestion and absorption, and histidine metabolism. Meanwhile, metabolites associated with alkali metabolism, including the biosynthesis of alkaloids derived from ornithine, lysine, and nicotinic acid, biosynthesis of alkaloids derived from shikimate pathway and alanine, and biosynthesis of alkaloids derived from terpenoid and polyketide, were also greatly affected in the co-culturing process. Furthermore, some metabolites produced by the biosynthetic pathways were influenced by the microorganism co-culturing process. Many pathways, such as microbial metabolism in the diverse environments, protein digestion and absorption, pentose and glucuronate interconversions, and linoleic acid metabolism influenced the changes in some metabolites, including UDP-D-glucuronate, β-D-Glucuronoside, 9(s)-Hode, 9,12,13-Tnhome, intracellular peptidases in A. johnsonii, P. fluorescens, and its co-culture. The interactions between A. johnsonii and P. fluorescens were examined by pairwise comparison, and the detailed relationship between different metabolic pathways and microorganisms was studied. The 20 most enriched pathway terms of KEGG enrichment are shown in Fig. 5. The significantly differential metabolite profile found in the A vs AP group was primarily observed in the following metabolic or biosynthetic pathways: aurine and hypotaurine metabolism:M; bile secretion:OS; and arginine biosynthesis:M (p < 0.01). Previous studies have reported that the essential factor for the growth of A. johnsonii is the ATP molecule bound to these motifs, which is effectively employed by the bacterial protein to autophosphorylate on tyrosine (Doublet, Vincent, Grangeasse, Cozzone, & Duclos, 1999) and was described in detail in our study, such as arginine biosynthesis:M. The enrichment ratio of lysosome:CP was the highest in A vs AP group. A previous study investigated the carbon metabolism and TCA cycle of the gram-negative bacterium invading a variety of host cell types and replicating within a unique vacuole derived from the host cell lysosome (Kuba et al., 2019). Therefore, lysosome plays an important role in the regulation of microbial metabolism. There were significantly higher amounts in A vs AP group in central carbon metabolism in cancer:HD, lysosome:CP, metabolism of xenobiotics by cytochrome P450:M, foxO signaling pathway:EIP, pentose and glucuronate interconversions:M, D-Alanine metabolism:M, biosynthesis of alkaloids derived from ornithine, lysine and nicotinic acid:M, ropane, piperidine and pyridine alkaloid biosynthesis:M, GABAergic synapse:OS, and furfural degradation:M (p < 0.05). The enrichment ratio of PPAR signaling pathway:OS was the highest, indicating that PPAR signaling pathway:OS played a central role in the A group and AP group. The most significant changes were observed for the specific metabolite pathway of phenylalanine metabolism:M (p < 0.001). The most enriched pathway terms for P vs

4. Conclusions UHPLC-MS-based non-targeted metabolomics revealed 540 metabolites with significantly differential amounts in A. johnsonii, P. fluorescens, and their co-culture, including 209 lipids and lipid-like molecules, 138 organic acids and derivatives, 54 organoheterocyclic compounds, 43 organic oxygen compounds, 41 phenylpropanoids and polyketides, 19 benzenoids, 13 nucleosides, nucleotides, and analogues, 9 alkaloids and derivatives, 7 organic nitrogen compounds, 4 organooxygen compounds, 2 organosulfur compounds, and 1 mixed metal/ non-metal compound. Pearson correlation analysis showed that threoninyl-tyrosine, except soyasaponin I, qxypurinol, and gonyautoxin II, was negatively correlated with the other metabolites (p < 0.05). Phenylpropanoids and polyketides, amino acids, peptides, and analogues were altered dramatically during co-culture conditions with the growth of microorganism, as explored in pairwise comparative analysis. The chemical categories of the differential metabolites were rich among the A. johnsonii, P. fluorescens, and their co-cultures, which covered a total of 79 pathways based on the KEGG database. The highest enrichment ratio was lysosome:CP in A vs AP group (p < 0.05) and PPAR signaling pathway in P vs AP group as metabolic markers (p < 0.05), respectively. Thus, further studies could be performed to assess how the different amounts of these metabolites and pathways affect the interactions between A. johnsonii, P. fluorescens, and their co-culture. Funding This research was funded by the National Key R&D Program of China (2016YFD0400106) and the China Agricultural Research System 7

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(CARS-47), and was also supported by the Shanghai Municipal Science and Technology Project to enhance the capabilities of the platform (grant number: 19DZ2284000).

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Xie, J., Zhang, Z., Yang, S. P., Cheng, Y., & Qian, Y. F. (2018). Study on the spoilage potential of Pseudomonas fluorescens on salmon stored at different temperatures. Journal of Food Science and Technology-Mysore, 55(1), 217–225. https://doi.org/10. 1007/s13197-017-2916-x. Yogendrarajah, P., Devlieghere, F., Njumbe Ediage, E., Jacxsens, L., De Meulenaer, B., & De Saeger, S. (2015). Toxigenic potentiality of Aspergillus flavus and Aspergillus parasiticus strains isolated from black pepper assessed by an LC-MS/MS based multimycotoxin method. Food Microbiology, 52, 185–196. https://doi.org/10.1016/j.fm. 2015.07.016.

Wang, X.-Y., & Xie, J. (2019). Evaluation of water dynamics and protein changes in bigeye tuna (Thunnus obesus) during cold storage. Lwt- Food Science and Technology, 108, 289–296. https://doi.org/10.1016/j.lwt.2019.03.076. Want, E. J., Wilson, I. D., Gika, H., Theodoridis, G., Plumb, R. S., Shockcor, J., et al. (2010). Global metabolic profiling procedures for urine using UPLC-MS. Nature Protocols, 5(6), 1005–1018. https://doi.org/10.1038/nprot.2010.50. Wright, M. H., Shalom, J., Matthews, B., Greene, A. C., & Cock, I. E. (2019). Terminalia ferdinandiana exell: Extracts inhibit Shewanella spp. growth and prevent fish spoilage. Food Microbiology, 78, 114–122. https://doi.org/10.1016/j.fm.2018.10.006.

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