Leucine-Induced Metabolome To Eliminate

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Metabolic mechanism for L-leucine-induced metabolome to eliminate Streptococcus iniae Chao-chao Du, Manjun Yang, Min-Yi Li, Jun Yang, Bo Peng, Hui Li, and Xuan-xian Peng J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00944 • Publication Date (Web): 07 Mar 2017 Downloaded from http://pubs.acs.org on March 9, 2017

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Journal of Proteome Research

Metabolic mechanism for L-leucine-induced metabolome to eliminate Streptococcus iniae

Chao-chao Du1, Man-jun Yang1, 2, Min-Yi Li1, Jun Yang1, Bo Peng1, Hui Li1, Xuan-xian Peng 1*

1

Center for Proteomics and Metabolomics, State Key Laboratory of Biocontrol,

School of Life Sciences, Guangdong Province Key Laboratory for Pharmaceutical Functional Genes, School of Life Sciences, Sun Yat-sen University, University City, Guangzhou 510006, People’s Republic of China. 2

Tibet Vocational Technical College, Lhasha 850000, People’s Republic of China

________________________________________________ Running title: Exogenous leucine-induced metabolome

*Corresponding author: Dr. Xuanxian Peng, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, University City, Guangzhou 510006, People’s Republic of China. Fax: +86-20-8403-6215; E-mail: [email protected].

1

ACS Paragon Plus Environment


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Abstract Crucial metabolites that modulate hosts’ metabolome to eliminate bacterial pathogens have been documented, but the metabolic mechanisms are largely unknown. The present study explores the metabolic mechanism for L-leucine-induced metabolome to eliminate Streptococcus iniae in tilapia. GC-MS based metabolomics was used to investigate tilapia liver metabolic profile in the presence of exogenous L-leucine. Thirty-seven metabolites of differential abundance were determined, and eleven metabolic pathways were enriched. Pattern recognition analysis identified serine and proline as crucial metabolites, which are the two metabolites identified in survived tilapias during S. iniae infection, suggesting the two metabolites play crucial roles in L-leucine-induced elimination of the pathogen by the host. Exogenous L-serine reduces mortality of tilapias infected by S. iniae, providing a robust proof for supporting the conclusion. Furthermore, exogenous serine elevates expression of genes Il-1β and Il-8 in tilapia spleen, but not TNFα, CXCR4 and Mx, suggesting the metabolite promotes a phagocytosis role of macrophages, which is consistent with the finding that L-leucine promotes macrophages to kill both Gram-positive and negative bacterial pathogens. Therefore, the ability of phagocytosis enhanced by exogenous L-leucine is partly attributed to elevation of serine. These results demonstrate a metabolic mechanism by which exogenous L-leucine modulates tilapias’ metabolome to enhance innate immunity and eliminate pathogens. Keywords: Metabolome; leucine; metabolic mechanism; Streptococcus iniae; tilapias

2


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1. Introduction Streptococcus iniae is a severe Gram-positive pathogen in aquaculture, which infects a diverse range of fish species including tilapia, rainbow trout, salmon, barramundi, yellowtail, flounder, red drum, golden pompano and hybrid striped bass.1-3 S. iniae infection is symptomized with severe septicemia and meningitis, and thus leads to acute and chronic mortality in marine and continental aquaculture.1,2 As such, S. iniae is one of the most important pathogens resulting in huge economic losses in aquaculture worldwide.3,4 S. iniae is also a zoonotic pathogen causing sepsis, toxic shock syndrome, soft tissue infections, intervertebral discs and orinner layer of the heart in humans.5 Vaccination and antibiotics are the routine ways in controlling bacterial infection including S. iniae. Although vaccination is an ideal way to prevent diseases outbreak, the development of effective vaccines is both time- and laborconsuming. More importantly, vaccination achieves limited access in aquaculture due to the various species and serotypes of pathogens, which could hardly been prevented by “one-shot”. On the other hand, the use of antibiotics leads to the severe consequences that the spread of antibiotic-resistant bacteria globally, and the accumulation of excessive antibiotics in the agricultural products,6.7 which impose burden to the society to cope with those drug-resistant bugs. Therefore, the development of novel strategies is especially required for the control of infectious diseases to ensure the aquatic product safety.

Recently, we have adopted GC-MS methodology to characterize the differential 3



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metabolomes between the dying and survival tilapias after sub-lethal dose of S. iniae challenge. These two groups have distinct metabolic characteristics, where the survival group was characterized as increased valine, leucine and isoleucine metabolism to the dying group. The abundance of leucine was significantly higher in survival group. Furthermore, exogenous L-leucine could be used as a metabolic modulator to elevate survival ability of tilapias infected by S. iniae, implying L-lecuine could be a promising metabolite in fending off S. iniae infection.8 Although the function of valine, leucine and isoleucine metabolism in bacterial infection has been reported previously,9 the underlying mechanisms are still unexplored. Thus, elucidating the mechanisms of L-leucine in promoting host survival would benefit our understanding of metabolite-enabled enhanced immunity.

To explore the metabolic reprogramming, we have developed a novel approach to thoroughly investigate how certain metabolite could change the metabolome associated with the change of phenotypes, which we termed as reprogramming metabolomics.10 We have successfully applied this approach in reverting antibiotic-resistant bacteria to -sensitive ones. Exogenous alanine, glucose and fructose reprogram the kanamycin-resistant metabolome to kanamycin-susceptible metabolome, which sensitizes the kanamycin-resistant bacteria to aminoglycoside antibiotics through elevating the TCA cycle, and promoting NADH and proton motive force production.11,12 Similarly, crucial metabolites identified from hosts may also program the hosts’ metabolome to combat bacterial pathogens.13-17 Thus, the 4


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metabolic modulation could be a promising strategy to manage pathogens in a antibiotic-free way.

Here we specifically address how L-leucine enhances immunity to eliminate S. iniae. We find that L-leucine promotes murine macrophages to clear several bacterial pathogens including S. iniae. Our study confirms the role of valine, leucine and isoleucine metabolism in the phagocytosis of macrophages, and also indicates that leucine plays critical roles in innate immunity. Furthermore, we specifically dissect the metabolic modulation in fish liver, which is the one of the major organs for cellular metabolism. We find that L-leucine increases not only valine, leucine and isoleucine biosynthesis but also glycine, serine and threonine metabolism, where serine is the crucial metabolite in the liver. L-serine has the similar action as L-leucine in elevating tilapias’ survival against infection caused by S. iniae. At last, we show that serine elevates expression of genes Il-1β and Il-8 in the spleen, an important site for immune response. These results indicate that serine is the effector by L-leucine in promoting innate immunity.

2. Materials and methods 2.1 Ethics statement. All work was conducted in strict accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University (Animal Welfare Assurance 5



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Number: I6).

2.2 Fish and rearing conditions. Juvenile tilapias with an average length of 4-5 cm and average weight of 2.0 g ± 0.2 g were purchased from the Guangzhou Tilapias Breeding Base, and were acclimatized to laboratory conditions in stock tanks (80 × 75 × 90 cm) for 7 days before experiments. The fish were fed with Jinfeng pellet twice a day, which has complete nutrient with no amino acid restriction (Table S2).

2.3 Bacterial strains and challenge dosage S. iniae were cultured in Brain Heart Infusion Broth (BHI) at 30 0C until 1.0 of OD600. The cultures were centrifuged, washed and resuspended to the same OD. To determine half lethal dose (LD50), tilapias were infected with four different doses of bacteria including 1×107, 2 ×107, 3 ×107 to 4×107 CFU/fish, 10 tilapias each dose. The LD50 was 3 ×107 CFU/fish. Tilapias usually showed signs of infection 36 h post-infection and died within 90 h. The survived tilapias were alive without any symptoms. Signs of infection, and mortality were record twice a day for 15 days.

Escherichia coli BL21 carrying gfp gene were grown overnight at 37 °C in 5 mL Luria-Bcrtani (LB) (tryptone 10 g/L, yeast extract 5 g/L, NaCl 10 g/L) broth with 50 µg/mL kanamycin. Then E. coli were re-inoculated at 1:100, cultured at 37 °C until OD600

at

0.6.

GFP

gene

expression

was

induced

6


with

0.2

mM

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isopropyl-b-D-thiogalactoside (IPTG) for 3 h. All other bacteria strains, Edwardsiella tarda, Vibrio anguillarum, Acinetobacter baumannii, Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus (MRSA), were from the collection of our laboratory. All of these bacteria were grown in LB medium, where E. tarda and V. anguillarum were kept at 30 °C, and A. baumannii, P. aeruginosa and MRSA were kept at 37 °C. To prepare fluorescence-labeled bacteria, all bacterial cultures were grown overnight, re-inoculated to fresh medium at 1:100 ratio, shaken at 200 rpm until the OD600 is 1.0. The bacteria were then harvested, washed with buffered saline and labeled with 0.1 mg/mL FITC (Sigma-Aldrich) for 1 h.

2.4 Quantitative phagocytosis assay Murine macrophage cell line RAW264.7 cells were grown in Dulbecco's modified Eagle's medium (DMEM; Gibco, USA) containing 10% fetal bovine serum (FBS; Gibco, USA), and cultured at 37 0C in a humidified atmosphere with 5% CO2. Quantitative phagocytosis assay was performed as described previously.18 RAW264.7 cells were harvested at 70% - 80% confluence, and re-plated at 5 × 106 cells/well in 6-well plate. Cells were rested in serum-free DMEM/0.5% serum for 12 h, followed by incubation with exogenous L-leucine for another 6 h. After the treatment, cells were incubated with FITC-conjugated bacteria at a cell: bacterial ratio of 1:100 and incubated for 1.5 h at 37 °C incubator for the indicated time points. Extracellular bacteria were washed away with cold PBS for five times. Cells were then fixed with 4% paraformaldehyde for 20 min, and analyzed on a BD BioSciences FACSCalibur 7



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flow cytometer using CellQuest software (BD BioSciences).

2.5 Collection of tilapia liver samples and measurement of total liver proteins After seven days of acclimation, 60 tilapias were randomly divided into control and experimental groups with equal number. Tilapias were intraperitoneally injected with 30 µL sterile saline in control group, and with 0.6 mg L-leucine in L-leucine group once daily for 5 days. No specific response including pro-inflammatory was detected after L-leucine administration. Livers were isolated and collected from 20 fish of each group, frozen immediately, and frozen in liquid nitrogen for further use. For measurement of total liver proteins, 20 tilapias were randomly divided into control and experimental groups, 10 each group, and then treated without and with L-leucine as described above, respectively. Tilapia livers were extracted and lysed in RIPA lysis buffer (Beyotime) containing 1 mM PMSF (Beyotime) at a ratio of 100 µL lysis buffer/ 20 mg liver. The liver samples were disrupted through intermittent sonic oscillation of the power output 20% for 2 s with intervals of 5 s on ice for a total of 2 min on ice. Undisrupted tissues were removed by centrifugation at 12,000×g at 4 0C for 10 min. Supernatant was transferred to a fresh tube, and the protein concentration was determined by bicinchoninic acid (BCA) assay (Thermo Scientific).

2.6 Sample preparation for GC-MS Samples were prepared as described previously.13,14 Briefly, livers removed from liquid nitrogen were quickly immersed into methanol based on the weight as 800 8


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mL/100 mg. After homogenization, samples were centrifuged at 12,000×g, 4 0C for 10 min. 100 µL supernatant of each sample was collected, and included 10 µL 0.1 mg/mL ribitol (Sigma-Aldrich) as an analytical internal standard. In addition, samples only with the reagents were used as a black control. The supernatants were concentrated in a rotary vacuum centrifuge device (LABCONCO). The dried extracts were used for GC-MS analysis.

2.7 GC-MS analysis GC-MS analysis was performed with a variation on the two-stage technique as described previously.13,19 In brief, liver samples were derivatized and used to firstly protect carbonyl moieties through methoximation which was a 90 min, 37 0C reaction with 80 µL of 20 mg/mL methoxyamine hydrochloride (Sigma-Aldrich) in pyridine, followed by derivatization of acidic protons through a 30 min 37 0C reaction with the addition

of

80

µL

nmethyl-N-trimethylsilyltrifluoroacetamide

(MSTFA,

Sigma-Aldrich). The resulting derivatized sample of 1 µL was injected into a 30 m×250 µm i.d. × 0.25 µm DBS-MS column by splitless sampling, and analysis was carried out by Trace DSQ II (Thermo Scientific). The initial temperature of the GC oven was held at 85 0C for 5 min followed by a fast increase to 270 0C at a rate of 15 0

C min-1 then held for 5 min. Electron impact ionization was applied at 70 eV. Helium

was used as carrier gas and flow was kept constant at 1 mL min-1. The MS was operated in a range of 50-600 m/z. Two technical replicates were prepared for each sample to confirm the reproducibility of the reported procedures. 9



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2.8 Data processing for GC-MS Data processing was conducted on AMDIS. The internal standard ribitol was used for spectral deconvolution and calibration,13,19 and the black control was utilized to filter noisy signals. A retention time (RT) correction was performed for all the samples, and the RT was used as reference against which the remaining spectra were queried. A film integrating abundance information from each metabolite in all the samples was assembled. All of the metabolites from GC-MS spectra were determined in National Institute of Standards and Technology (NIST 08) Mass Spectral Library, where enantiomers were not recorded due to the lack of specialty, so the same metabolites were emerged.

The raw data were transformed to the standard format data in Excel

for statistic analysis by Agilent Technologies MSD Productivity ChemStation software (E. 02. 02. 1431, 2011). Among the detected peaks of all the chromatograms, 239 peaks were identified as endogenous metabolites excluding the internal standard, ribitol. The resulting data matrix was normalized by the concentrations of ribitol and was scaled by the total intensity. Normalized peak intensities formed a single matrix with RT-area pairs for each file in the dataset. This file was then used for subsequent analysis. There is no outlier in this study. A model was set up in PCA and OPLS-DA analysis, where R2Y and Q2 are more than 95%.

Statistical analyses. Metabolites which excluded the median metabolites were scaled by the quartile range in the sample. According to a reference distribution, Z-score analysis scaled each metabolite and was calculated based on the mean and standard 10


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deviation of reference.20 Hierarchical clustering was completed in the R platform ( https://cran.r-project.org/ ) with the function “heatmap. 2” of “gplots library”. In the function “heatmap. 2”, the default value “distfun = dist” was used for the Euclidean distance.

Pattern recognition. Multivariate statistical analysis included principal component analysis (PCA) (SIMCA-P+12.0.1), which was used to discriminate sample patterns, and to identify the metabolites associated with L-leucine infection.21 SPSS 13.0 and Prism v5.01 (GraphPad, La Jolla, CA, USA) were used to draw the histogram and the scatter plot. There is no excluded outlier in this study, and a good model was set up in PCA and OPLS-DA analysis, where R2Y and Q2 are more than 95%.

2.9 Exogenous addition of L-serine, spleen extraction and bacteria challenge For intraperitoneal injection, fish were randomly divided into control and test groups, with 40 fish each group. Fish were intraperitoneally injected with 30 µL 0.85% sterile saline in control group and with 0.5 mg L-serine which was dissolved in 30 µL of sterile saline in the test group once a day for 5 days. Then the spleens were isolated from 10 random fish of each group, and RNA was extracted for qRT-PCR. For oral administration, fish were also randomly divided into control and test groups, 30 fish each group. L-serine was diluted with sterile saline to a working solution at 16.7 mg/mL and then was administered 40 µL directly into gastral cavity using a 100 µL syringe as the test group once daily for 5 days. The control tilapias were treated with 11



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the same volume of sterile saline. No specific response including pro-inflammatory was detected after L-serine administration. These fish were intraperitoneally challenged with S. iniae (3×108 CFU/fish), 30 fish each group. The animals were observed twice daily for 15 days.

2.10 Analysis of gene expression Total RNA was isolated from tilapia spleen tissues using TRIZOL regent (Invitrogen Life Technologies) according to the manufacturer's protocol. Electrophoresis in 1% (w/v) agarose gels and staining with ethidium bromide were performed to check the quality of extracted RNA. Reverse transcription-PCR was carried out on 1 µg of total RNA, by using a PrimeScriptTM RT reagent Kit with gDNA eraser (Takara, Japan) according to manufacturer's instructions. qRT-PCR was performed in 384-well plates with a total volume of 10 µL containing 5 µL 2× SYBR Premix Ex Taq™, 2.6 µL PCR-grade water, 2 µL diluted cDNA template and 0.2 µL each of forward and reverse primers (10 µM) shown in Table 1. All the actions were run on LightCycler 480 system (Roche, Germany) according to the manufacturer’s instructions. The cycling parameters were listed as follows: 95 0C for 30 sec to activate the polymerase; 40 cycles of 95 0C for 10 sec; 58 0C for 30 sec; Fluorescence measurements were performed at 70 0C for 1 sec during each cycle. Cycling was terminated at 95 0C with a calefactive velocity of 5 0C/sec to obtain a melting curve. Data are shown as relative expression of mRNA expression compared to control group injected with only saline and standardized with the endogenous reference gene beta-actin. 12


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Tab. 1. Primers used for qRT-PCR Gene

Primer

Primer sequence

β-actin

Forward

5'-GCCCCACCTGAGCGTAAATA-3'

Reverse

5'-TGCGCCTGAGTTGTGTATGA-3'

Forward

5'-AGCAGGGATGAGATTGAGTG-3'

Reverse

5'-ATAGAGGTTTGTGCCTTTGA-3'

Forward

5'-TTGATTCTTCCCAACTCCCA-3'

Reverse

5'-CACGGTGATTTCAGTGATTTTC-3'

Forward

5'-CGTCGTGGCTCTTTGTTT-3'

Reverse

5'-CTTGGCTTTGCTGCTGAT-3'

Forward

5'-CGGCAACCATTCTGACCATA-3'

Reverse

5'-CCGTGTCCTCGCTTTCTTCT-3'

Forward

5'-GAGGACTTTGGGTATCTTG-3'

Reverse

5'-GTAGCCCTTATTCAGGTAGA-3'

IL-1β

IL-8

TNFα

CXCR4

Mx

3. Results 3.1. Exogenous L-leucine promotes phagocytosis of macrophages We previously showed that exogenous L-leucine was a metabolic modulator to elevate the survival of tilapias during S. iniae infection,8 which was also demonstrated in this study (Fig. 1A). The present study further showed that L-leucine promoted phagocytosis of macrophages in the following experiments. First, L-leucine enhanced phagocytosis of murine macrophages (RAW264.7) to E. coli expressing green fluorescent protein (GFP) in a dose-dependent manner, and reached to the plateau at 13



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20 mM of L-leucine (Fig. 1B). Then, several clinical-isolated pathogens, including fish-derived E. tarda (EIB202, LTB4, WY37 and ET47), V. anguillarum, and human-derived A. baumannii, P. aeruginosa, MRSA, were also included. Nevertheless, L-leucine increased phagocytosis of macrophages to all the pathogens tested (Fig. 1B). These results indicate that L-leucine enhances phagocytosis as a way to promote hosts to clear pathogens.

3.2. Exogenous L-leucine induces changes in metabolome. As an amino acid, exogenous L-leucine may alter the metabolome of host cells, which could be associated with immunity. To test this, we performed the study as shown in Fig. 2A to explore the mechanism. L-lecuine didn’t obviously affect tilapias activity and amount of total liver proteins (Figure S1). After the treatment, livers were removed from both groups, and no difference on the weight of total liver proteins was detected (Table S2). The metabolome of livers were profiled by GC-MS. After the removal of internal standard, ribitol, and any known artificial peaks, 60 metabolites with reliable signal were detected for each sample. Representative total ion current chromatograms from the two groups were displayed in Fig. 2B. The correlation coefficient of two technical repeats was 0.998 - 0.999, indicating the reliability of the data (Fig. 2C). According to KEGG, biological roles of these metabolites were defined as carbohydrates (56.25%), amino acids (23.44%), lipids (14.06%), nucleotides (1.56%) (Fig. 2D). Metabolomic profiles of the two groups were displayed as heat map (Fig. 2E). These results indicate that exogenous L-leucine 14


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modulates liver metabolome.

3.3. Differential metabolome associated with exogenous L-leucine We further identified differential metabolites between the two groups with or without exogenous L-leucine. Thirty-seven metabolites of differential abundance were obtained. For visualizing the relationship among the abundance of metabolites, hierarchical clustering was used to arrange the metabolites on the basis of their relative levels across samples (Fig. 3A). Z-score with value -5.61~148.30 displayed variations of these metabolites based on control. Among the perturbed metabolites, 23 metabolites were increased, and 16 were decreased in the experimental group (Fig. 3B). Category of these differential metabolites was listed in Fig. 3C, showing carbohydrates 53.85% (21), amino acids 30.77% (12), lipids 12.82% (5), nucleitide 2.56% (1). Number of differential metabolites increased mainly in carbohydrates and amino acids (Fig. 3D).

3.4. Differential pathways associated with exogenous L-leucine Then, ingenious network analysis was performed for detailed analysis of metabolic pathways associated with L-leucine. The significantly differential abundance of metabolites to their respective biochemical pathways were analyzed, and outlined in the

Kyoto

Encyclopedia of Genes and Genomes (KEGG,

release 41.1,

http://www.genome.jp/kegg) and MetPA (http://metpa.metabolomics.ca). Eleven pathways are enriched, and shown in Fig. 4A. The first six most impactful pathways 15



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are valine, leucine and isoleucine metabolism, glycine, serine and threonine metabolism, methane metabolism, cysteine and methionine metabolism, alanine, aspartate and glutamate metabolism, and aminoacyl-tRNA biosynthesis, and arginine and proline metabolism. The metabolites in the pathways valine, leucine and isoleucine metabolism, aminoacyl-tRNA biosynthesis, alanine, aspartate and glutamate metabolism, glycine, and serine and threonine metabolism are elevated. In the cysteine and methionine metabolism, serine is elevated and methionine and cystathionine are decreased (Fig. 4B).

3.5. Metabolic biomarkers in the liver by exogenous L-leucine To identify biomarkers in response to exogenous L-leucine, OPLS-DA was carried out for the multivariate analysis. The control and experimental groups are clearly separated in two different quadrants of the PCA scores plot (Fig. 5A). Loading plots displayed variables positively correlated with score plots. The loading plots represented the impact of the significantly differential metabolites on the clustering results. The metabolites of variance were demonstrated by their distance from the control as highlighted by red mark in the score plot (Fig. 5B). In the plots of predictive correlation p(corr)[1] and p(corr)[2] (>0.5 or < -0.5), the red triangles indicate that the differential metabolites have larger weight and higher relevance. These results indicate that exogenous L-leucine affects bacterial cell metabolism, where alanine, serine, proline, stearic, palmitic acid, pyroglutamic acid, glycine, pinitol and malic acid are biomarkers to distinguish the L-leucine treatment (Fig. 4 16


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C).

3.6. Features of L-leucine-induced metabolome Collectively, the carbon metabolism and amino acid metabolism affected by L-leucine is described in Fig. 6A. The results highlight on elevating abundances of metabolites from valine, leucine and isoleucine metabolism, glycine, serine and threonine metabolism, and alanine, aspartate and glutamate metabolism. These findings show that increased amino acid metabolism is a characteristic feature to exogenous L-leucine. These results indicate that L-leucine promotes biosynthesis of proline, alanine, serine and threonine. Compared with the differential metabolomes between the dying and survival tilapias in the previous report,8 only serine and proline are common between these two studies (Fig. 6B), suggesting the two amino acids are two crucial biomarkers. Then, serine is selected for further investigation of mechanism and function.

3.7. Effect of exogenous L-leucine on survival and innate immunity The above results suggest that one of the mechanisms by which exogenous L-leucine potentiates host against bacterial infection is to promote serine biosynthesis and then serine plays a role in innate immunity. To demonstrate this, first exogenous L-leucine was injected into tilapias and then challenged with S. iniae, which increased the tilapia survival by 32 - 45.94% upon infection (Fig. 7A). Then the expression of key cytokines in immune response including IL-1β, IL-8, TNFα, CXXR4 and Mx genes 17



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were measured with QPCR. Significantly increased transcriptional levels of IL-1β and IL-8 genes were detected (Fig. 7B).

4. Discussion The emergence of antibiotic resistance in bacterial pathogens poses a big threat to human health and animal breeding. Thus, enhancement of innate immunity to fight against bacterial infection without antibiotics becomes a hot research area. Recently, we have shown that hosts has distinct metabolomes, which contribute to the outcome of bacterial infections, either survived or being conquered as the survival group and dying group, respectively.8, 13-15, 22 According to the differential metabolomes, crucial biomarkers were identified to differentiate the two different groups. The compensation of the crucial metabolites that are increased in survival group but decreased in dying group to tilapia would dramatically increase their survival following bacterial challenge8, 13-15, 22, which we called reprogramming metabolomics. We propose that the programmed metabolomics represents a metabolic strategy by identifying crucial biomarkers that are used as exogenous complement of metabolites to modulate the metabolic status for enhancement of innate immunity.10, 23 Therefore, further investigation is required for understanding of the mechanisms.

In our previous report, we identified L-leucine in fish liver was increased but decreased in the survival and dying groups, respectively, indicating a potential functional role of L-leucine on promoting host survival. When exogenous L-leucine 18


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was administrated to tilapia, it increased tilapia’s survival,8 but the mechanisms are unknown. Phagocytosis of macrophage is a key response of innate immunity, playing a crucial role in combating bacterial pathogens in both the fish and mammals.24 We speculate that the phagocytosis may contribute to the survival. Thus, we test the phagocytosis of murine macrophages to several pathogens in the presence of L-leucine. Our results show that L-leucine increases phagocytosis of macrophages to all the fish-derived and human-derived pathogens tested, suggesting a possible metabolic mechanism by which L-leucine activates or enhances macrophages to kill pathogens for host’s survival. Then, we demonstrate that exogenous L-leucine modulates tilapia’s metabolome and thereby enhances anti-infective metabolome that elevates the host’s survival ability. Pattern recognition analysis identifies several biomarkers. Among them, serine and proline are two increased metabolites that overlapped with elevated metabolites found in the survival groups reported previously.8 These results together suggest that serine and proline contribute to L-leucine-promoted elimination of bacterial pathogens.

Further, the function of serine is investigated. First, our results show that administration of serine by either oral or i.p. injection significantly elevates survival of tilapia, which works as effective as leucine did.8 Then we investigate effect of serine on expression of cytokines. Genes Il-1β, Il-8, TNFα, CXXR4 and Mx are selected. Among these cytokines, IL-1β is rapidly produced by macrophages in response to inflammatory stimuli, and is able to induce the expression of several 19



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genes and the synthesis of proteins that in turn induce inflammation.18,25 IL-8 is generated by several types of cells including macrophages in response to a wide variety of stimuli, including proinflammatory cytokines.25 TNFα is a key mediator of inflammation.26 CXXR4 is a chemokine receptor as an important mediator in development, homeostatic and inducible leukocyte trafficking.27 Mx protein belongs to small GTPase family, which has been implicated in antiviral activity involved in interferon.28

Our results that serine elevates expression of Il-1β, Il-8, but not TNFα,

CXXR4 and Mx indicate serine is an ideal mediator of macrophages activation, which is consistent with the current finding that L-leucine promotes macrophages to kill both Gram-positive and -negative bacteria tested. Therefore, ability of phagocytosis enhanced by exogenous L-leucine is partly attributed to the increased abundance of serine. Amino acids promote innate immunity has been reported.29, 30 However, the mechanisms that serine potentiates host to kill bacterial pathogen and induces macrophages to secret IL-1β and IL-8 are yet unexplored. The finding that exogenous L-leucine elevates serine production provides a clue that macrophage may utilize leucine from the environment or diet to promote serine synthesis, which in turn enhances the immune function of macrophages. Thus, further work is required to understand the metabolic pathways by which exogenous leucine metabolism promotes serine biosynthesis, and the mechanisms that how serine regulates macrophage functions. More importantly, whether other crucial biomarkers like proline also play roles in the regulation of immune response waits further investigation.

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Conclusions Taken together, our results demonstrate a novel strategy of combating bacterial pathogens in an antibiotic-free way that is through the identification of crucial metabolites from survived hosts during infection. Elucidation of the mechanisms by which the metabolites like amino acids in this case modulate the metabolome would benefit our idea of development of novel method to boost host’s immunity to cope with bacterial invasions in a vaccine- and antibiotic-independent manner.

Associated Content available Table S1 The components of Jinfeng pellet. Table S2 Tilapia liver weight in the presence or absence of exogenous L-leucine. Figure S1 Total liver proteins of tilapias in the presence or absence of L-leucine.

Acknowledgements This work was sponsored by grants from NSFC projects (31572654, 31672656), and Science and Technology Program of Guangzhou (201504010025), Guangdong (2015A030308009) and the National Key Research and Development Plan (2016YFD0501307).

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Figure 1 L-leucine potentiates phagocytosis of macrophages to bacterial pathogens. (A). Percent survival of tilapias challenged by S. iniae in the presence or absence of exogenous leucine by injection (i. p.) or oral (o.p.) administration. Control, sterile saline. Significantly higher protection was observed in the two groups compared the control (p < 0.01, by Chi-squared test). (B). Mean fluorescence intensity (MFI) of GFP-E. coli by RAW264.7 cells in the presence of the indicated concentrations of L-leucine. (C). MFI of the FITC-conjugated pathogens indicated by RAW264.7 cells in the presence of the indicated concentrations of L-leucine. Results

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(B and C) are displayed as mean ± SEM, and three independent biological repeats were carried out. Significant differences are indicated by asterisk (*p < 0.05; **p < 0.01) and were determined by Student’s t test.

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Figure 2 Design outline, GC-MS reproducibility and tilapia liver metabolome. (A) Design outline. Two groups, L-leucine treatment and control, are compared for percent survival of tilapia infected by S. iniae, GC-MS based metabolomics in livers, 29



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and expression of immune genes in spleens. Biomarkers were identified from differential metabolomics. The most key biomarker is used to modulate the metabolome to combat the infection caused with S. iniae by injection or oral administration. Spleen samples are collected for detection of immune gene expression by QRT-PCR. (B) Representative total ion current chromatogram from control and leucine treatment groups. (C) Reproducibility of metabolic profiling platform used in the discovery phase. Metabolite abundances quantified in cell samples over two technical replicates are shown. Correlation coefficient between the two technical replicates varies between 0.998 and 0.999. (D) Category of metabolites detected. (E) Heat map of the metabolites detected. Black and grey indicate increase and decrease of metabolites relative to the median metabolite level, respectively (see color scale).

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Figure 3 Differential abundance of metabolites in the presence or absence of L-leucine. (A) Heat maps for relative abundance of differential metabolites in addition of leucine compared with the control. Black and grey indicate increase and decrease of metabolites relative to the median metabolite level, respectively (see color scale). (B) Z-score plot of differential metabolites based on control corresponding to data in (A). Each point represents one metabolite in one technical repeat and colored by sample types (grey, control; black, leucine treatment). (C) Pie for distribution of

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differential abundance of metabolites in four categories. (D) Number of metabolites increased and decreased in four categories. Black, increase; grey, decrease.

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Figure 4 Enriched pathways and analysis. (A) Pathway enrichment of differential metabolites. (B) Integrative analysis of metabolites in significantly enriched pathways. Number shows the relative value of differential metabolites. Arrows indicate increase and decrease of metabolites.

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Figure 5 Pattern recognition and identification of crucial metabolites. (A) PCA analysis of control and leucine-treatment groups according to the treatments set. Each dot represents the technological replicate analysis of samples in the plot. t[1] and t[2] used in this plot explain 98.5% of the total variance which allows confident interpretation of the variation. (B) S-plot generates from OPLS-DA. Predictive component p[1] and correlation p(corr)[1] differentiate the treatment from the control. Predictive component p[2] and correlation p(corr)[2] separate variation within the groups. Triangle represents metabolites and candidate biomarkers, which are p[1]>0.1,0.5,

Leucine-Induced Metabolome To Eliminate

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