A pilot metabolic profiling study in hepatopancreas of Litopenaeus vannamei with white spot syndrome virus based on 1H NMR spectroscopy

Journal of Invertebrate Pathology 124 (2015) 51–56

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A pilot metabolic profiling study in hepatopancreas of Litopenaeus vannamei with white spot syndrome virus based on 1H NMR spectroscopy Peng-fei Liu a,b, Qing-hui Liu a,⇑, Yin Wu b, Huang Jie a a Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, PR China b Dalian Ocean University, Dalian, PR China

a r t i c l e

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Article history: Received 26 June 2014 Accepted 26 September 2014 Available online 14 October 2014 Keywords: WSSV NMR 1 H NMR spectroscopy Metabolomics

a b s t r a c t White spot syndrome virus, which was a pathogen first found in 1992, had emerged globally affecting shrimp populations in aquaculture. Here, we comprehensively analyzed the metabolic changes of hepatopancreas from Litopenaeus vannamei which were infected with white spot syndrome virus by 1H nuclear magnetic resonance (NMR). Through the NOESYPR1D spectrum combined with multi-variate pattern recognition analysis, including principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) models, significantly metabolic changes were observed in WSSV-infected groups compared with the control groups. In the first 48 h, a-glucose and b-glucose were higher in the WSSV-infected group. Meanwhile, acetate, lactate, N-acetyl glycoprotein signals, lysine, tyrosine and lipid were significantly decreased in the WSSV-infected group. These results suggest that WSSV caused absorption inhibition of amino acids and disturbed protein metabolism as well as cell metabolism in favor of its replication. Our findings could also contribute to further understanding of disease mechanisms. Ó 2014 Elsevier Inc. All rights reserved.

1. Introduction White spot syndrome virus (WSSV) is a serious infectious pathogen of cultured shrimp that appeared in Southeast Asia at the beginning of the 1990s. It often leads to large scale mortalities and results in diminished shrimp production and severe huge economic losses in the past decades to farmers worldwide. The virus has a broad host range and is reported to infect a wide range of aquatic animals including marine and brackish water crustaceans, penaeids, crabs, freshwater prawns and crayfish, aquatic arthropods and planktons (Lo et al., 1996; Wang et al., 1998; Paz, 2010; Hossain et al., 2001). WSSV acts as a global virus because it can infect its host by both horizontal and vertical mode (Flegel, 1997). So far, exploring shrimp molecular response to WSSV infection is very important. Recently, considerable progress has been made in the detection of genes, proteins, biochemical and physiological changes related to WSSV infection in shrimp (Yoganandhan et al., 2003; Pan et al., 2005; Leu et al., 2007; Wang et al., 2007; Zhao et al., 2007; Chai et al., 2010; Li et al., 2013, 2014). ⇑ Corresponding author. E-mail address: [email protected] (Q.-h. Liu). http://dx.doi.org/10.1016/j.jip.2014.09.008 0022-2011/Ó 2014 Elsevier Inc. All rights reserved.

Furthermore, apoptosis related to WSSV infection has been explored (Leu et al., 2013; Watthanasurorot et al., 2013). Moreover, how WSSV modulated the host cell metabolism has induced more attention. Chen reported that WSSV induces metabolic changes which resemble the Warburg effect in shrimp hemocytes in the early stage of infection (Chen et al., 2011). Further research indicated that WSSV-induced Warburg effect appears to be essential for successful viral replication (Su et al., 2014). However, information about the metabolic profiling that may be related to WSSV infection remains limited. Nuclear magnetic resonance spectroscopy (NMR) is a suitable instrumental platform to probe dose-dependent alterations in metabolites, which provides a rapid, steady, reproducible, and high-throughput methodology for monitoring altered biochemistry and the metabolic analysis of biofluid (Coen et al., 2008). Metabolomics, a new but rapidly growing ‘‘omic’’ in system biology, has the ability to detect a large number of small-molecule metabolites from body fluids or tissues in parallel. Due to its high reproducibility, non-destructiveness, non-selectivity in metabolite detection and the ability to simultaneously quantify multiple classes of metabolites, NMR technology has grown in popularity for metabolic profiling. Recently, NMR spectroscopy in combination with pattern

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recognition (PR) analytical techniques has been reported as being successfully applied to the diagnosis and prognosis of many human diseases (Ludwig et al., 2009; Tiziani et al., 2009), confirming that NMR-based metabolomics provides a powerful platform for clinical research and diagnostic applications, including early diagnosis, therapy monitoring, and understanding the pathogenesis (Tiziani et al., 2009; Maher et al., 2008; Sinclair et al., 2010). Therefore, we intended to applied 1H NMR-based metabolomics spectrum to examine the Litopenaeus vannamei which infected by WSSV. In the present study, a metabolite profile study in aquatic animals was presented, and the observed profile in healthy controls injected PBS buffer as well as shrimps infected by WSSV was compared. For better understanding of the mechanism of WSSV, we applied 1H NMR based spectroscopy to detect the metabolites. From the large amount of data represented by the spectra, relevant information was extracted by the method of the PR analysis. In WSSV infected shrimps, metabolic profiling of hepatopancreas composition was investigated and analyzed comprehensively. Using these information, we aim to reveal the characteristic metabolic phenotype and identify valuable biomarkers for disease diagnosis. Enhancement of our current understanding of a shrimp metabolic response to WSSV infection is a promising approach for biomarker identification, virus surveillance and virus disruption. 2. Materials and methods 2.1. Extraction of WSSV Procambarus clarkii was used to amplify WSSV particles. The infection of healthy crayfish P. clarkii and the purification of virus were performed as described previously (Xie and Yang, 2005). Briefly, the tissues of infected crayfish, excluding the hepatopancreas, were homogenized in TNE buffer (50 mM Tris–HCl, 400 mM NaCl, 5 mM EDTA, pH 8.5) and then centrifuged at 3500g for 5 min at 4 °C. After being filtered by nylon net (400 mesh), the supernatant was centrifuged at 30,000g for 30 min at 4 °C. Then, the upper loose pellet was rinsed out carefully and the lower white pellet was suspended in 10 ml TN buffer (20 mM Tris–HCl, 400 mM NaCl, pH 7.4). After centrifugation at 3500g for 5 min, the virus particles were sedimented by centrifugation at 30,000g for 20 min at 4 °C, then resuspended and kept in 1 ml TN buffer. The purified WSSV was observed under electron micrograph and virions were calculated according to previous method (Zhou et al., 2007). 2.2. Shrimp infected by WSSV and tissue sampling The experiment breeding shrimp L. vannamei (body weight 8.05–9.90 g) (body length 8.0–10.0 cm) were obtained from a larval production laboratory located in Qingdao, Shandong Province, China, and cultured temporarily in cycling-filtered plastic tanks for 2 weeks (25 °C). Each tank was aerated continuously using an aeration stone. During the acclimation period, water quality parameters were measured daily. Shrimp were daily fed with diet and water was changed twice a day. After 2 days, ten shrimps were randomly chosen, and five shrimp were intramuscularly injected with 20 ll of the WSSV inoculum (106 virios/shrimp) using a 1 ml tuberculin syringe into the third abdominal somite, and then isolated in plastic tanks connected to a recirculating seawater system. Others were injected with 20 ll PBS buffer solution which passed through a 0.22 lm membrane filter in the same manner as injected animals and isolated in plastic tanks connect to a separate recirculating seawater system. Gills and hepatopancreas of five shrimp from the WSSV-injected group and PBS-injected group were taken after 48 h post-injection. Gills were used for quantity diagnosis of WSSV and hepatopancreas were carefully dissected for NMR analyses.

2.3. Quantification of WSSV by real-time PCR The DNA was isolated from gills of ten shrimps using DNA extraction kit (Tiangen, China) following the instructions. Extracted DNA was quantified by NanoDrop spectrophotometer (Thermo Fisher Scientific Inc., USA). Quantitative real-time PCR was performed to determine the WSSV viral loads in shrimp tissues samples collected at different days of post infection (dpi). The viral copy number was estimated by using real-time PCR kit (TaKara, China). The primer 50 -CTCTTGTGGTTCATCAGGG-30 and 50 -CTGGATTTTCTCTCAGGGTCTTTAGT-30 was used to amplify a 221-bp fragment (NCBI No: AF332093) of WSSV. The PCR parameters consisted of 30 cycles of denaturation at 94 °C for 30 s, annealing at 61 °C for 30 s, and extension at 72 °C for 30 s. A standard curve was obtained using serial dilutions of plasmid (221 bp fragment of WSSV was cloned into T-vector) to quantify the WSSV viral genomic copy number (Chen et al., 2010). Each assay was carried out in triplicate. Quantitative data were expressed as means ± SD (standard deviation). 2.4. Metabolic profile of hepatopancreas by 1H NMR NMR analyses for hepatopancreas samples were collected at a frequency of 599.925 Hz by using a Varian Unity INOVA NMR spectrometer. Spectra were acquired by using a conventional presaturation pulse sequence with solvent suppression NOESYPR1D [RD-90°-t1-90°-tm-90°-ACQ] (relaxation delay = 2.5 s, mixing time = 100 ms, accumulated times = 64 during the relaxation delay and mixing time solvent presaturation was applied). We processed all samples randomly and masked the operator during the clinical diagnosis. The Carr–Purcell–Meiboom–Gill (CPMG) sequences were used in crosswise relaxation weighted experiments with solvent suppression, relaxation delay = 4 s, accumulated times = 256. 2.5. Statistical analysis All free induction decays were multiplied by an exponential function to a 1.0 Hz line-broadening factor prior to Fourier transformation. 1H NMR spectra were manually phased and baselinecorrected using TopSpin (V3.0 Bruker Biospin, Germany). For data analysis and pattern recognition, we applied SIMCA-Pt software (V11.0 Umetrics AB, Umea, Sweden) to analyze imported normalized data. As an unsupervised PR method, principal component analysis (PCA) was applied. After the NMR, the spectral data of the WSSV-infected and control groups were explored by PCA to identify the intrinsic variation (Trygg, 2002; De Meyer et al., 2010). The spectral variation was reduced to a series of principal components (PC), each representing correlated spectral change. In the score plot, the most efficient information was included into the first two PCs. The data was subjected to orthogonal partial leastsquares discriminant analysis (OPLS-DA) (Trygg, 2002; Trygg and Wold, 2003) after the overview of the NMR data using PCA analysis. PLS-DA was used to build a metabolic profile model which can identify varying metabolites between diagnostic groups as a supervised analysis technique (Trygg et al., 2007). Otherwise, a model was built and utilized to identify marker metabolites that accounted for the differentiation of all groups (Trygg et al., 2007). OPLS-DA, as an extension of PLS-DA featuring an integrated Orthogonal Signal Correction (OSC) can remove variability not relevant to class separation. Both PLS-DA and OPLS-DA were based on unit variance scaling strategy. Q2 and R2 values were obtained by a 7-fold cross-validation, which represent the predictive ability of the model and the explained variance, respectively. R2 is defined as the proportion of variance in the data explained by the models and indicates goodness of fit. Q2 is defined as the proportion of variance in the data predictable by the model and indicates

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predictability. To further validate the quality of the PLS-DA model, permutation tests consisting of a randomly permuting class membership and running 100 iterations were carried out (Ni et al., 2008). Results about the rate of OPLS-DA (percentage of samples correctly classified) models were then depicted and expressed as sensitivity, specificity, and classification. 3. Results 3.1. Detection of WSSV infection To confirm shrimp infection with WSSV, WSSV copy numbers in gill samples (0 h and 48 h post-injection) of WSSV-injected group and PBS-injected group were quantified by real-time PCR. The WSSV amount in WSSV-injected group reached 1.7  107 copies/lg DNA, while that in PBS-injected group was only 5.4  103 copies/lg DNA at 48 h post-injection. There was a significant difference of viral load between WSSV-injected and PBS-injected group using SPSS17.0 for statistical analysis (p < 0.05) (Fig. 1).

Fig. 1. Quantification of WSSV copy numbers by real-time PCR.

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3.2. 1H NMR spectroscopy of hepatopancreas samples We used the 1H NMR spectra to get the average signals from WSSV-infected and healthy control groups. The signal peaks of WSSV-infected and healthy controls are depicted in Fig. 2. The metabolite resonances were assigned based on the literature and results from 2D NMR experiments based on the existing literature (Nicholson et al., 1995), TOCSY. The spectra identified several endogenous compounds and a great number of amino acid signals such as lysine and tyrosine and signals from acetate, N-acetyl glycoprotein, a-glucose and b-glucose. In order to further investigate the discrimination between diseases and controls, we conducted more analyses with the NMR spectra data in the following sections.

3.3. Multivariate analysis PCA was carried out and the score plot (Fig. 3. R2X = 79.6%, Q2 = 0.701) was obtained with the first two PCs which represented 58.0% and 21.6% variance respectively. The PCA clustered two groups, which was used by supervised analysis techniques such as PLS-DA or OPLS-DA, because it can maximize the difference between two groups and aid in the screening of the marker metabolites responsible for class separation by removing systematic variations unrelated to pathological status (Ni et al., 2008). In order to further distinguish the WSSV-infected groups and control groups, the PLS-DA models were analyzed. We discriminated the infected and control subjects with an R2X of 24.1%, an R2Y of 93.9% and a Q2 of 58.1%; R2 and Q2 indicate high goodness of fit and high percentage of prediction, respectively. The PLS-DA models with cluster of 100 Y-permutated models were visualized in validation plots (Fig. 4). The negative intercept of Q2 regression and permuted R2 values on the right was higher than the original point on the left, indicating that we can utilize the model for further examination. After PLS-DA analysis, the OPLS-DA models was constructed subsequently by the first principal component (t[1]P) and the second orthogonal component (t[2]O). The quality of the models was

Fig. 2. Representative 600 MHz CPMG 1H NMR spectra (d0.5–5.5 and d6.5–9.0) of A and B. The region of d6.5–9.0 (in the dashed box) was magnified 16 times compared with corresponding region of d0.5–5.5 for the purpose of clarity. Keys: 1-MH: 1-Methylhistidene; 3-HB: 3-Hydroxybutyrate; AA: Acetoacetate; Ace: Acetate; Act: Acetone; Ala: Alanine; All: Allanotin; Ci: Citrate; Cr: Creatine; Gln: Glutamine; Glu: Glutamate; Gly: Glycine; GPC: Glycerolphosphocholine; Ileu: Isoleucine; L1: LDL, CH3A(CH2)nA; L2: VLDL, CH3A(CH2)nA; L3: LDL, CH3A(CH2)nA; L4: VLDL, CH3A(CH2)nA; L5: VLDL, ACH2ACH2AC@O; L6: Lipid, ACH2ACH@CHA; L7: Lipid, ACH2ACH@CHA; L8: Lipid, ACH2AC@O; L9: Lipid, @CHACH2ACH@; L10: Lipid, ACH@CHA; Lac: Lactate; Leu: Leucine; Lys: Lysine; Met: Methionine; NAG: N-acetyl glycoprotein signals; OAG: O-acetyl glycoprotein signals; PC: Phosphocholine: Phe: Phenylalanine; Py: Pyruvate; Suc: Succinate; TMA: Trimethylamine; Tyr: Tyrosine; Val: Valine.

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P.-f. Liu et al. / Journal of Invertebrate Pathology 124 (2015) 51–56 Table 1 OPLS-DA coefficients derived from the NMR data of metabolites in hepatopancreas obtained from shrimps infected by WSSV. Metabolites

ra WSSV-infected R2X = 24.1% Q2 = 58.1%

Acetate: 1.92(s)

0.713 0.702

Lipid, ACH2ACH@CHA: 2.02(br) Lactate: 1.33(d), 4.11(q) Lysine: 1.72(m), 1.91(m), 3.01(m), 3.76(t) N-Acetyl glycoprotein signals: 2.04(s) Tyrosine: 6.90(d), 7.19(d) a-Glucose: 3.42(t), 3.54(dd), 3.71(t), 3.73(m), 3.84(m), 5.23(d) b-Glucose: 3.25(dd), 3.41(t), 3.46(m), 3.49(t), 3.90(dd), 4.65(d) Fig. 3. PCA scores plot based on 1H NOESYPR1D NMR spectra of serum obtained from two groups. WSSV infected: black box; health control: red dot. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

described by the cross-validation parameters Q2, and the R2X values show the total number of the variation in the X matrix explained by the model, and the values are tabulated in Table 1. It was evident that the distinction between the WSSV infected groups and control groups (Fig. 5) was evident in OPLS-DA score plots. In the first 48 h, a-glucose and b-glucose were higher in the WSSV-infected group. Meanwhile, acetate, lactate, N-acetyl glycoprotein signals, lysine, tyrosine and lipid were significantly decreased in the WSSV-infected group (Table 1).

4. Discussion In this paper, we obtained NMR data and used metabolomics analysis technology to identify the characteristics of metabolite profiling from hepatopancreas of L. vannamei infected by WSSV. This study indicated that the WSSV-infected shrimps have an altered hepatopancreas metabolite profile. Given the complexity of pathophysiology of WSSV, it is conceivable that a combination rather than a single biomarker may detect some early changes and be suggestive of the risk disease evolution in crustaceans. In hepatopancreas, some metabolites have been found to be altered. In this study, major altered endogenous metabolites in hepatopancreas contained a-glucose and b-glucose, the amino acids of lysine and tyrosine, and N-acetyl glycoprotein signals. Despite the relatively small number of metabolites detected in hepatopancreas, the ratios of these metabolites were highly discriminatory,

t [2]

0.870

Multiplicity: s, singlet; d, doublet; t, triplet; q, quartet; dd, doublet of doublets; m, multiplet. a Correlation coefficients, positive and negative signs indicate positive and negative correlation in the concentrations, respectively. The correlation coefficient of |r| > 0.666 was used as the cutoff value for the statistical significance based on the discrimination significance at the level of p = 0.05 and df (degree of freedom) = 7. ‘‘–’’ means the correlation coefficient |r| is less than 0.666.

reflecting the central nodes that many of these metabolites have in different metabolic pathways. In conjunction with statistical models, we hope to evaluate the technique as a potential diagnostic tool for the WSSV for identifying characteristic metabolites of shrimps. The hepatopancreas of shrimp is a vital organ involved in several physiological and metabolic key activities (Sánchez-Paz et al., 2007). Compared with healthy controls, two important metabolites, a-glucose and b-glucose, are increased in the hepatopancreas of WSSV group. Since these two metabolites composed of the total glucose content, we inferred that the content of glucose should also increase in the WSSV-infected group. Yoganandhan et al. previously reported a significant increase in plasma glucose concentration of WSSV-infected shrimp (Yoganandhan et al., 2003). Chen studied the changes of glucose and lactate in shrimp plasma of WSSV group by bio-chemical assay. They found that WSSV infection significantly affects the levels of glucose and lactate in shrimp plasma. Glucose level was significantly lower than those in the PBS controls at 12 hpi and was significantly higher at 24 hpi. Lactate was higher in the WSSV group at 12 hpi and lower at 24 hpi (Chen et al., 2011). Moreover, in WSSV-injected shrimp hemocytes at 12 hpi, there was a significant increase (p < 0.05) in the glycolytic pathway metabolites glucose, etc. (Su et al., 2014). Galván-Alvarez suggested that shrimp infected with WSSV may need to use glucose as an energy source to immediately

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0.832 0.666 0.688 0.692 0.888

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t [1] Fig. 4. Validation plots of PLS-DA models (WSSV-infected compared with control) obtained from 100 permutation tests.

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40 20 0 -20 -40 -60

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Fig. 5. OPLS-DA scores plots (left panel) derived from 1H NMR spectra of hepatopancreas and the corresponding coefficient loading plots (right panel) obtained from the group WSSV infected (black) as compared with the control (red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

impulse the activation and maintenance of the immune response to control this rapidly replicating virus (Galván-Alvarez et al., 2012). The changes of glucose and lactate may point to alteration of metabolic pathways in the hepatopancreas. Under normal biochemical circumstances, lactate is shuttled into gluconeogenesis and thus is not maintained at low concentration in the plasma (Solanky et al., 2003). The drop of the content of lactate indicated that power of hydrogen had been altered in the infected shrimps and it contributed to the gluconeogenic supply of glucose for energy needs. Previous research (Hohnke and Scheer, 1970) has established glucose as the major sugar in the circulating hemolymph in crustaceans. Abdel Rahman et al. (1979) and Lynch and Webb (1973) reported that efficient mechanism of glucose homeostasis is absent in crustaceans and they tolerate large variations of glucose in blood. Hall and van Ham (1998) indicated that blood glucose served as a simple and reliable index for biological stress for shrimp. Telford (1968) and Lynch and Webb (1973) also reported that stress affected qualitative and quantitative nature of circulating carbohydrates. The high level of glucose suggests that WSSV will manipulate host cell glycolysis metabolic homeostasis to favor pathogen biosynthesis and fulfill the pathogen’s energy requirements during the early stage of viral infections. From Table 1, we can see that lysine and tyrosine concentrations are low in the WSSV group. Lysine is the essential amino acid. Tyrosine could be degraded to produce acetoacetate and fumerate. The significantly decreased of lysine and tyrosine implied that WSSV caused absorption inhibition of some amino acids and disturbed protein metabolism. The catabolism of tyrosine is probably disrupted by WSSV. Su reported that WSSV infection induced an increase in several of the detected amino acids, including alanine, histidine, tryptophan, glutamate, proline and aspartate in hemocytes at the early stage (12 hpi). They inferred that the strongly up-regulates amino acid metabolites (12 hpi) may useful for protein synthesis and benefit viral genome replication (Su et al., 2014). But the pattern had changed at the late stage (24 hpi). Otherwise, viral infection had resulted in significant reduction of short-chain fatty acids acetate and lipid content (Table 1). The acetate and lipid provides an energy source for metabolism. This indicated that WSSV disturb the cell metabolism for fit its replication. In this study, major altered endogenous metabolites in hepatopancreas of shrimps infected by WSSV contained products of glucose (a-glucose, b-glucose), N-acetyl glycoprotein and amino acids (lysine, tyrosine), as well as lipid (ACH2ACH@CHA) and acetate. From the results of this study that compare the WSSVinfected subjects with the healthy subjects, we concluded that

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