Effects of cold storage on the chemical composition of Corcyra cephalonica eggs by 1H NMR spectroscopy

Biological Control 110 (2017) 25–32

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Effects of cold storage on the chemical composition of Corcyra cephalonica eggs by 1H NMR spectroscopy Yan-Chang Huang a,1, Han Wu a,1, Zi-Wei Song b, Dun-Song Li b, Gu-Ren Zhang a,⇑ a

State Key Laboratory for Biocontrol and Institute of Entomology, Sun Yat-Sen University, Guangzhou 510275, China Guangdong Provincial Key Laboratory of High Technology for Plant Protection/Plant Protection Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China b

h i g h l i g h t s
56 compounds were identified from rice moth eggs.
Cold storage resulted in changes to the chemical composition of rice moth eggs.
Alanine, glutamine, glucose and acetate contributed to the observed difference.

a r t i c l e

i n f o

Article history: Received 1 August 2016 Revised 22 February 2017 Accepted 23 February 2017 Available online 24 March 2017 Keywords: Rice moth eggs Cold storage Multivariable analysis

a b s t r a c t Rice moth Corcyra cephalonica eggs, frequently utilized as factitious hosts of Trichogramma wasps, are usually cold-stored for a period of time in order to meet the need of Trichogramma mass rearing. However, compounds in cold stored C. cephalonica eggs can influence the quality and quantity of Trichogramma. In the present study, we used Hydrogen-1 Nuclear Magnetic Resonance (1H NMR) spectroscopy and multivariate statistical analysis to investigate the effect of cold storage on the chemical composition of fresh rice moth eggs (U) and killed embryo eggs cold stored at 4 °C for 0 (CK), 15 (N15), 30 (N30), 45 (N45), and 60 (N60) days, respectively. A total of 56 compounds were identified and quantified for each treatment, including amino acids, nucleic acid components, organic acids, sugars, and amides. Principle component analysis (PCA) and partial least square discriminant analysis (PLSDA) demonstrated that cold storage had a pronounced effect on the chemical composition of rice moth eggs. Three groups, U and CK, N15 and N30, and N45 and N60, were classified by their components’ similarity. The primary components that contributed to difference among three groups were alanine, glutamine, glucose and acetate, the concentrations of which significantly changed with the increasing of cold storage days. Thus, cold storage elicits changes in the chemical compositions of rice moth Corcyra cephalonica eggs, which may affect the growth and development of Trichogramma. Ó 2017 Elsevier Inc. All rights reserved.

1. Introduction Trichogramma (Hymenoptera: Trichogrammatidae) wasp is one of the most successful biological control agents of agricultural and forest insect pests (Huffaker and Messenger, 1976; Li, 1994; Consoli et al., 2010). These wasps demonstrate a wide range of hosts, extensive distribution, abundant species resources, and easy mass rearing and are now used world-wide to control insect pests, especially for lepidopterous pests such as Ostrinia furnacalis, Mythimna separata and Chilo venosatus (Ebrahimi, 1999; Herz ⇑ Corresponding author. 1

E-mail address: [email protected] (G.-R. Zhang). These authors contributed equally to this work.

http://dx.doi.org/10.1016/j.biocontrol.2017.02.010 1049-9644/Ó 2017 Elsevier Inc. All rights reserved.

et al., 2007; Poorjavad, 2011). The host eggs are an important nutritional resource for parasitoids and, as such, are known to influence a wide range of physiological and behavioral aspects of egg parasitoids (Grenier et al., 1986; Farahani et al., 2016). The eggs of the rice moth Corcyra cephalonica (Stainton) (Lepidoptera: Galleriidae) are commonly used as a host for Trichogramma mass rearing in China (Wang et al., 2014). Amassing a sufficient quantity of rice moth eggs is a crucial part of mass rearing and of successful release of Trichogramma in the field. To meet the requirement of the large-scale industrialized production of Trichogramma and to reduce waste of biological material, ultraviolet ray (UV) and refrigeration are often used to prolong the storage period of host eggs. Eggs of C. cephalonica exposed to UV can be easily parasitized by Trichogramma and can

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prevent the larva of C. cephalonica from eating the parasitic eggs (Tuncbilek et al., 2009). Host eggs stored at low temperatures can extend storage periods and meet the high demands for the production and release of Trichogramma. However, long-term storage of host eggs could lead to detrimental effects that can reduce host acceptance, host recognition, percent parasitism and percent emergence (Flanders, 1938; Leopold, 1998; Kostal et al., 2004, 2006; Krechemer and Foerster, 2016). In T. brassicae it was found that there was a negative relationship between the level of parasitism and cold storage time (Nazeri et al., 2015). Limiting the period of storage at low temperature was also a crucial factor to control the decline in host quality. Pan et al. (2011) had found that T. dendrolimi had a weaker parasitizing capacity and lower emergence rate in rice moth eggs stored at low temperature for a long time, which was also demonstrated in previous studies (Zhang et al., 2008). Some species of egg parasitoids showed no negative consequences when host eggs were exposed to 2–10 °C for one month (Greco and Stilinovic, 1998; Kivan and Kilic, 2005; Chen and Leopold, 2007; Mahmoud and Lim, 2007; Alim and Lim, 2010).To keep good quality levels in Trichogramma rearing, research has suggested that the storage period for rice moth eggs at 4 °C should not exceed 15 days (Zhang et al., 2008). The Trichogramma larvae absorb and digest nutrition from host eggs. Thus the composition of host eggs, the basic qualitative nutritional requirements of Trichogramma, has a great effect on their growth, development and reproductive behavior (Farahani et al., 2016). Extensive research has been carried out to investigate the nutrient requirements for the oviposition of egg parasitoid wasps (Ayvaz and Karabörklü, 2008; Barrett and Schmidt, 1991; Nettles et al., 1985). However, systematic research of composition of rice moth eggs has not been reported, and further studies are needed to elucidate the internal mechanism impacting host behavior. Increasing evidence showed that eggs in cold storage had critical effects on the reproduction and breeding of Trichogramma, but it is not clear what changes happen in the rice moth eggs when refrigerated. In this study, we comparatively investigated the contents of C. cephalonica eggs in fresh and cold storage over various time periods at 4 °C to reveal the effects of cold storage on the chemical compositions of rice moth eggs using Nuclear Magnetic Resonance (NMR) technology and multivariate statistical analysis. 2. Materials and Methods 2.1. Materials C. cephalonica was reared long-term on wheat bran in the Plant Protection Research Institute Guangdong Academy of Agricultural Science. C. cephalonica eggs were collected and, after removing impurities, exposed under a 400-w ultraviolet lamp for 30 min to kill the embryo inside (Yuan et al., 2013). 2.2. Cold storage treatments of C. Cephalonica eggs Twenty-five grams of C. cephalonica eggs with killed embryos were stored in a refrigerator at 4 °C. Six grams of eggs were taken every 15 days and equally divided into six samples. The treatments of egg stored for 15, 30, 45, and 60 days were marked as N15, N30, N45, and N60, respectively. Fresh eggs and killed embryo fresh eggs were used as controls and marked as U and CK, respectively.

at 80 °C until NMR analysis. The freeze-dried powder of each sample was dissolved in 1000 lL purified water and was sonicated 4 times for 4 s using in-solution ultrasonic extraction. The solution was then centrifuged at 13,000 rpm for 15 min at 4 °C. The aqueous layer was transferred to a 0.5 mL 3 kDa ultra filtration filter (Mimacon, USA), followed by further centrifugation (4 °C, 30 min, 13,000 rpm). A 450 lL aqueous layer was collected and transferred to a 2 mL centrifuge tube. DSS (4,4-dimethyl-4-silapentane-1-sulfo nic acid, 50 lL) was added as standard solution and then transferred to a 5 mm NMR tube for NMR analysis. 2.4. NMR and Chemometric analysis All spectra were acquired on a Bruker AV III 600 MHz spectrometer, which was equipped with an inverse cryoprobe at 298 K. 1H NMR spectra were collected using a 2D-1H, 1H-NOESY pulse sequence for the acquisition of 1H-NMR data and suppression of the solvent signal. A total of 64 scans were recorded using a spectral width of 8000.00 Hz, a recycle delay of 1.0 s, an frequency domain size of 65536, and a 100 ms mixing time along with a 990 ms pre-saturation (80 Hz gammaB1) over a period of 7 min. The collected Free Induction Decay (FID) signal was Fourier transformed in the processing module, carefully phased and baseline corrected by an experienced technician in Chenomx NMR Suite 8.0 (Chenomx Inc., Edmonton, Canada). All of the spectra data were identified by matching spectra signals to the Chenomx Compound Library. DSS was used as the internal standard for chemical shifts (set to 0 ppm) and all the spectra were quantified by reference to the standard. The quantification was calculated by comparing the integral of the known signal of DSS from the library of compounds to all of the compound chemical shifts and peak multiplicities. The result was exported into Excel and used in the multivariable analysis. Principle component analysis (PCA) and Partial least square discriminant analysis (PLSDA) were performed using the pca Methods bioconductor package (Stacklies et al., 2007) and pls package (Mevik and Wehrens, 2007) respectively. Variable Importance in Projection (VIP) scores were obtained from the PLSDA. VIP scores are weighted sum of squares of the PLS weight, which represent the importance of the variable to the whole module. Plots were made using the ggplot2 package (Wickham, 2009). The differences in composition concentration between treatments were evaluated using one-way ANOVA and Duncan’s multiple-range test with the SPSS software. Differences were considered significant at p < 0.05. 3. Results 3.1. 1H NMR spectra and metabolite composition The chemicals of C. cephalonica eggs cold stored for different times were analyzed by 1H NMR spectrum. Table 1 shows the 56 components and their absolute concentration in N15 and N30, 53 in U, N45 and N60, and 54 in CK, respectively. 53 of the total of 56 metabolites were detected in all treatments, including 24 amino acids and their derivatives, 9 nucleic acid components, 7 organic acids, 4 sugars, 5 amides and others. The ethanolamine, glucose1-phosphate and nicotinurate were not identified in U, N45 and N60 treatments, but glucose-1-phosphate was detected in CK. 3.2. Principal component analysis (PCA)

2.3. Sample preparation The sampled rice moth eggs were ground to a fine powder with a mortar and pestle in liquid nitrogen. The powder was then lyophilized by using vacuum freeze-drying system and storaged

PCA is an unsupervised recognition and used to reveal the components similarities of the C. cephalonica eggs cold stored for different times. The PCA score plots originating from the NMR spectra of C. cephalonica eggs with five different cold storage periods are

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Y.-C. Huang et al. / Biological Control 110 (2017) 25–32 Table 1 Chemicals identified in rice moth Corcyra cephalonica eggs from U, CK, N15, N30, N45 and N60 treatments. Amino acids

Varieties (n = 6) U (mmol/L)

CK (mmol/L)

N15 (mmol/L)

N30 (mmol/L)

N45 (mmol/L)

N60 (mmol/L)

3-Hydroxykynurenine Alanine Arginine Asparagine Glutamate Glutamine Glycine Histidine Isoleucine Leucine Lysine Methionine O-Acetylcarnitine Ornithine Phenylalanine Pyroglutamate Proline Serine Taurine Threonine Tryptophan Tyrosine Valine b-alanine

0.0035 ± 0.00 0.1594 ± 0.00 0.2406 ± 0.05 0.3310 ± 0.01 0.5005 ± 0.04 1.5872 ± 0.09 0.4044 ± 0.01 0.6874 ± 0.02 0.1074 ± 0.00 0.0665 ± 0.00 0.6096 ± 0.04 0.0948 ± 0.00 0.0018 ± 0.00 1.2156 ± 0.05 0.0861 ± 0.00 0.0705 ± 0.01 0.3088 ± 0.00 0.3011 ± 0.02 0.2369 ± 0.01 0.1663 ± 0.01 0.0144 ± 0.00 0.3159 ± 0.01 0.1623 ± 0.00 0.0092 ± 0.00

0.0056 ± 0.00 0.1624 ± 0.01 0.3083 ± 0.02 0.3782 ± 0.01 0.6051 ± 0.05 1.7151 ± 0.09 0.4721 ± 0.00 0.7572 ± 0.03 0.1853 ± 0.01 0.1387 ± 0.01 0.6765 ± 0.06 0.1052 ± 0.01 0.0058 ± 0.00 1.2471 ± 0.08 0.1234 ± 0.00 0.0748 ± 0.01 0.3672 ± 0.00 0.2787 ± 0.02 0.3114 ± 0.01 0.2449 ± 0.01 0.0225 ± 0.00 0.3423 ± 0.00 0.2829 ± 0.00 0.0095 ± 0.00

0.2148 ± 0.00 1.4519 ± 0.07 0.5372 ± 0.07 0.5571 ± 0.01 0.7766 ± 0.09 2.1835 ± 0.08 0.7616 ± 0.02 0.8295 ± 0.04 0.3767 ± 0.01 0.1959 ± 0.01 0.9260 ± 0.04 0.1651 ± 0.01 0.0133 ± 0.00 1.1355 ± 0.15 0.2265 ± 0.00 0.4238 ± 0.05 0.4895 ± 0.01 0.8642 ± 0.08 0.2949 ± 0.02 0.4498 ± 0.01 0.0338 ± 0.00 0.4815 ± 0.01 0.5255 ± 0.01 0.0221 ± 0.00

0.0228 ± 0.00 3.6423 ± 0.38 1.0341 ± 0.07 0.5909 ± 0.03 0.6284 ± 0.05 1.8544 ± 0.18 0.8968 ± 0.03 0.7553 ± 0.07 0.4363 ± 0.03 0.2636 ± 0.03 1.2070 ± 0.08 0.2022 ± 0.01 0.0411 ± 0.01 0.7395 ± 0.12 0.2716 ± 0.01 0.7164 ± 0.05 0.4681 ± 0.01 1.2412 ± 0.10 0.3267 ± 0.03 0.5199 ± 0.01 0.0385 ± 0.00 0.5261 ± 0.01 0.6313 ± 0.02 0.0209 ± 0.00

0.0187 ± 0.00 7.3694 ± 0.18 0.7882 ± 0.09 0.5199 ± 0.01 0.6144 ± 0.04 1.6495 ± 0.07 0.8517 ± 0.01 0.3905 ± 0.03 0.4687 ± 0.01 0.2567 ± 0.01 1.2546 ± 0.03 0.2133 ± 0.01 0.0347 ± 0.00 0.8673 ± 0.05 0.3058 ± 0.01 0.8806 ± 0.05 0.4872 ± 0.01 1.1841 ± 0.03 0.3216 ± 0.01 0.5694 ± 0.01 0.0489 ± 0.00 0.6357 ± 0.01 0.6548 ± 0.01 0.0162 ± 0.00

0.0030 ± 0.00 8.6192 ± 0.18 0.3227 ± 0.05 0.5044 ± 0.01 0.7722 ± 0.04 1.1478 ± 0.04 0.9058 ± 0.01 0.2495 ± 0.02 0.4203 ± 0.01 0.4006 ± 0.01 1.2225 ± 0.03 0.1953 ± 0.00 0.0253 ± 0.00 1.1312 ± 0.09 0.2815 ± 0.00 0.9304 ± 0.04 0.2578 ± 0.02 0.9720 ± 0.03 0.2768 ± 0.01 0.5184 ± 0.01 0.0457 ± 0.00 0.7161 ± 0.02 0.5677 ± 0.01 0.0115 ± 0.00

Nucleic acid components 20 -Deoxyinosine Cytidine Guanosine IMP Inosine NAD+ Thymidine Uracil Uridine

0.0016 ± 0.00 0.0187 ± 0.00 0.0664 ± 0.01 0.0902 ± 0.01 0.0840 ± 0.00 0.0093 ± 0.00 0.0086 ± 0.00 0.0091 ± 0.00 0.0370 ± 0.00

0.0017 ± 0.00 0.0177 ± 0.00 0.0744 ± 0.00 0.0607 ± 0.01 0.0527 ± 0.01 0.0126 ± 0.00 0.0064 ± 0.00 0.0092 ± 0.00 0.0284 ± 0.00

0.0020 ± 0.00 0.0156 ± 0.00 0.0744 ± 0.01 0.0645 ± 0.01 0.0819 ± 0.01 0.0103 ± 0.00 0.0118 ± 0.00 0.0234 ± 0.00 0.0176 ± 0.00

0.0037 ± 0.00 0.0323 ± 0.00 0.1244 ± 0.01 0.1071 ± 0.01 0.1176 ± 0.02 0.0241 ± 0.00 0.0202 ± 0.00 0.0696 ± 0.00 0.0419 ± 0.00

0.0050 ± 0.00 0.0530 ± 0.00 0.1795 ± 0.01 0.0304 ± 0.00 0.2848 ± 0.01 0.0101 ± 0.00 0.0225 ± 0.00 0.1677 ± 0.00 0.0693 ± 0.00

0.0039 ± 0.00 0.0741 ± 0.00 0.1722 ± 0.01 0.0093 ± 0.00 0.3713 ± 0.01 0.0030 ± 0.00 0.0144 ± 0.00 0.1863 ± 0.00 0.0720 ± 0.00

Organic acids Acetate Formate Fumarate Lactate Pyruvate Succinate 3-Hydroxyisovalerate

0.1228 ± 0.02 0.0124 ± 0.00 0.0083 ± 0.00 0.0158 ± 0.00 0.2435 ± 0.04 0.0124 ± 0.00 0.3723 ± 0.01

0.1136 ± 0.01 0.0227 ± 0.00 0.0140 ± 0.00 0.0296 ± 0.00 0.2119 ± 0.04 0.0201 ± 0.00 0.3975 ± 0.01

0.1037 ± 0.01 0.0985 ± 0.00 0.0080 ± 0.00 0.0477 ± 0.01 0.4868 ± 0.03 0.0488 ± 0.01 0.7460 ± 0.03

0.6905 ± 0.06 0.1058 ± 0.00 0.0048 ± 0.00 0.1544 ± 0.02 0.6566 ± 0.03 0.0710 ± 0.01 0.8260 ± 0.04

1.2216 ± 0.02 0.0547 ± 0.00 0.0018 ± 0.00 0.2319 ± 0.01 0.7197 ± 0.03 0.0429 ± 0.00 0.7112 ± 0.03

1.3000 ± 0.03 0.0147 ± 0.00 0.0005 ± 0.00 0.2836 ± 0.02 0.8775 ± 0.02 0.0392 ± 0.00 0.6073 ± 0.01

Sugars Glucose Glucose-1-phosphate Maltose Trehalose

0.4435 ± 0.02 0 0.3547 ± 0.02 0.0297 ± 0.00

0.3294 ± 0.03 0.0013 ± 0.00 0.3564 ± 0.02 0.0441 ± 0.00

0.5678 ± 0.03 0.0124 ± 0.00 0.3653 ± 0.01 0.1026 ± 0.00

1.2568 ± 0.10 0.0313 ± 0.00 0.3843 ± 0.01 0.1442 ± 0.00

1.8791 ± 0.04 0 0.2216 ± 0.01 0.0667 ± 0.01

1.613 ± 0.10 0 0.0711 ± 0.00 0.0475 ± 0.00

Amides Acetamide Ethanolamine Nicotinurate N-Nitrosodimethylamine Putrescine

0.0046 ± 0.00 0 0 0.0069 ± 0.00 0.2542 ± 0.02

0.0045 ± 0.00 0 0 0.0080 ± 0.00 0.2526 ± 0.03

0.0118 ± 0.00 0.0593 ± 0.00 0.0030 ± 0.00 0.0301 ± 0.00 0.3304 ± 0.01

0.0283 ± 0.00 0.1013 ± 0.01 0.0046 ± 0.00 0.0509 ± 0.01 0.3597 ± 0.02

0.0722 ± 0.00 0.1947 ± 0.01 0 0.0369 ± 0.00 0.3393 ± 0.01

0.0814 ± 0.00 0.3478 ± 0.01 0 0.0304 ± 0.00 0.3590 ± 0.01

Others Choline Nicotinate O-Phosphocholine O-Phosphoethanolamine Pantothenate Theophylline 2-Phosphoglycerate

0.2124 ± 0.01 0.0122 ± 0.00 0.9520 ± 0.03 0.5665 ± 0.03 0.0524 ± 0.00 0.0072 ± 0.00 0.1473 ± 0.02

0.2394 ± 0.00 0.0130 ± 0.00 0.9497 ± 0.02 0.5621 ± 0.02 0.0509 ± 0.00 0.0046 ± 0.00 0.1093 ± 0.00

0.2595 ± 0.01 0.0176 ± 0.00 0.9794 ± 0.04 0.5818 ± 0.06 0.0514 ± 0.00 0.0090 ± 0.00 0.2689 ± 0.01

0.2852 ± 0.01 0.0219 ± 0.00 1.1122 ± 0.05 0.6316 ± 0.03 0.0608 ± 0.00 0.0118 ± 0.00 0.3532 ± 0.02

0.4559 ± 0.01 0.0342 ± 0.00 1.0041 ± 0.02 0.5338 ± 0.05 0.0636 ± 0.00 0.0220 ± 0.00 0.2419 ± 0.01

0.6399 ± 0.01 0.0203 ± 0.00 1.1300 ± 0.02 0.3345 ± 0.02 0.0656 ± 0.00 0.0269 ± 0.00 0.1406 ± 0.00

N15, N30, N45, and N60 represent the eggs stored for 15, 30, 45, and 60 days, respectively. U and CK mean fresh eggs and killed embryo fresh eggs, respectively. The data in the table are mean ± SE. ‘‘0” represents the substance can’t be detected in the sample.

shown in Fig. 1A. PC1 and PC2 in the present study explained 88.8% and 10% of the total variance, respectively. Samples from six treatments were clustered into five groups, showing a preferably dipartite effect. U and CK were closely clustered into a group. The treatments, N15, N30, N45 and N60, clearly differed from U and CK, were also distinguished from each other. There was a slight

overlap in the regions between N15 and N30, N45 and N60, respectively. Treatments (N15 and N30) with no more than 30 days and treatments (N45 and N60) with more than 30 days had similar metabolic characteristics (Fig. 1A). PCA loading plots showed that many metabolites, including alanine, glutamine, ornithine, O-phosphocholine, O-phospoethanolamine, histidine, lysine, and

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Fig. 1. Principle component analysis (PCA) scores (A) and corresponding loading plots (B) derived from spectra of Corcyra cephalonica eggs cold-stored in different days. N15, N30, N45, and N60 represent the eggs stored for 15, 30, 45, and 60 days, respectively. U and CK mean fresh eggs and killed embryo fresh eggs, respectively. PCA transforms the original variables into a smaller number of mutually orthogonal variables named principal components (PCs), in which PC1 represents the maximum amount of variance and PC2 explains lesser amount of variance.

glutamate, made important contributions to the differences between the 6 treatments (Fig. 1B). 3.3. Partial least square discriminant analysis (PLS-DA)
A partial least square discriminant analysis (PLS-DA) model was structured to reveal the compounds responsible for differences among C. cephalonica eggs cold-stored for different time periods

(Fig. 2). The R2 value describing the fitness of the model was 0.74 and the Q2 0.29, demonstrating the repeatability and predictive ability of the data model. The thirty-six samples distributed in the PLS score scatter plot were different from that in PCA, in which the positions of clusters were changed, while the pattern was similar. The eggs of C. cephalonica with cold storage treatments were clearly separated from the fresh eggs in the PLS-DA score plot (Fig. 2A). The loading plot shows the

Fig. 2. Partial least square (PLS) scores (A) and corresponding loading plots (B) derived from spectra of Corcyra cephalonica eggs cold-stored for different days. N15, N30, N45, and N60 represent the eggs stored for 15, 30, 45, and 60 days, respectively. U and CK mean fresh eggs and killed embryo fresh eggs, respectively. The points away from the origin in the right side of the loading plot were mostly contributed to distinguishing from each other among treatments in score plot.

Y.-C. Huang et al. / Biological Control 110 (2017) 25–32

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Fig. 3. Important features identified by partial least square discriminant analysis (PLSDA). The colored boxes on the right indicate the relative concentration of the corresponding component in Corcyra cephalonica eggs cold-stored for different time periods. N15, N30, N45, and N60 represent the eggs stored for 15, 30, 45, and 60 days, respectively. U and CK mean fresh eggs and killed embryo fresh eggs, respectively.

Fig. 4. Quantification of four variable importances in the projection (VIP) components identified from extracts of Corcyra cephalonica eggs cold-stored for different days. The four VIP components concentration are expressed as the mean ± SE (n = 6). The different lowercase letters on the bar indicate a significant difference from each other (p < 0.05, One-way ANOVA and Duncan’s multiple-range test).

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variables related to the supervision through comparison to the score plot and can show the correlation between variables (Fig. 2B). The PLS loading plot showed that alanine was situated in the right quadrant position of the plot, which is similar to the location of N30 and N45 in the score plot. In a similar way, arginine, glutamine and serine are also connected with N30 (Fig. 2B). Variable importance in the projection (VIP) confirmed the overall contribution to cold storage and showed the main component that plays a very important role in distinguishing from each sample. The variables with VIP values greater than 1.0 were supposed to be the major candidate contributors. Fig. 3 showed the VIP values of the contribution of each component for the separation in the score plots originated from the PLS model. A total of four variables with VIP values more than 1.0, Alanine, Glucose, Acetate and Glutamine, were selected as candidate biomarkers. The absolute concentrations of the 4 VIP components in six treatments are shown in Fig. 4. Besides glutamine, the concentrations of the other three VIP components showed similar trends, increasing with the increase of cold storage time. The concentration of alanine increased significantly with the increase of cold storage time and reached the highest absolute concentration in N60, while no significant difference was observed between CK and U (Fig. 4A). Glucose, a main energy substance, was also significantly increased after cold storage. Notably, its concentration in N30 was twice that in N15, from 0.5678 mmol/L to 1.2568 mmol/ L (Fig. 4C). However, the concentration of glucose in N60 was significantly lower than that in N45. Acetate, an organic acid, significantly increased in N30 compared to U, CK, and N15. There was no significant difference between N45 and N60, which were significantly higher than that in N30 (Fig. 4D). As for Glutamine, its concentration peaked in N15, significantly higher than that in U and CK, and then decreased significantly in N30, N45 and N60 (Fig. 4B). A new model of partial least square discriminant analysis (PLSDA) was established to reveal the difference between the N15 and N30 treatments, because N15 and N30 had a similar composition and previous studies indicated that the storage time for host eggs at 4 °C should not exceed 15 days. The R2 and Q2 value, respectively, describing the goodness of fit and predictive ability of the PLS model was 0.78 and 0.68. The result showed that N15 and N30 could be clearly separated in a PLS score scatter plot (Fig. 5A). The loading plot demonstrated that six components, that is, alanine, glucose, acetate, arginine, ornithine and serine, played a vital role in distinguishing between the N15 and N30 treatments (Fig. 5B).

4. Discussion 4.1. The cold storage effect on the chemical compositions of rice moth eggs Our results clearly indicate that the chemical composition of rice moth eggs differed significantly between fresh condition (U and CK) and cold storage at 4 °C for different time periods. PCA identified 56 chemicals from different treatments and demonstrated that cold storage had a pronounced effect on the chemical compositions and concentrations of rice moth eggs; similar observations were also obtained from a PLS-DA model. Different refrigeration times accounted for the differing chemical shifts of rice moth eggs. Based on the PCA and PLS-DA model in this study, all of the treatments can be divided into three groups, CK and U, N15 and N30, N45 and N60, indicating that the cold storage time had an important effect on the compositions of C. cephalonica eggs. U

Fig. 5. Partial least square discriminant analysis (PLSDA) model derived from spectra of Corcyra cephalonica eggs cold-stored for15 days and 30 days. A: PLS score plot; B: PLS loading plot; C: A variable importance plot showing the contribution of each component by PLSDA. N15 and N30 represent the eggs stored for 15 and 30 days, respectively.

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and CK were clustered closely together, indicating that U has similar biochemical composition with CK, and UV killing of the embryo has only a weak influence on the component of C. cephalonica eggs. Our result showed that the concentrations of 56 kinds of components changed with the storage time, leading to the separation of all treatments according to multivariable analysis. It is worth mentioning that there were four metabolic markers, that is, alanine, glutamine, glucose and acetate, contributing to the separation of the three groups on the basis of the VIP value. Earlier researches had suggested that the storage period for host eggs at 4 °C should not exceed 15 days, and the Trichogramma would hardly parasitize the eggs refrigerated more than 50 days, as the percent parasitism and percent emergent were significantly decreased with the increase of cold storage period (Zhang et al., 2008; Pan et al., 2011; Yi et al., 2014). Refrigeration for longer times may result in deterioration of the yolk, resulting in poor nutritional quality for Trichogramma embryos (Pratissoli et al., 2003; de Carvalho Spinola-Filho et al., 2014). Furthermore, PCA and PLS model suggested that 30 days may probably be the boundary, as the composition of N15 and N30 was similar as well as N45 and N60. As a matter of fact, C. cephalonica eggs should not be coldstored more than 15 days in most practical application and industrial processes, which may imply that the changes in eggs refrigerated more than 15 days may not be suitable for Trichogramma development. In this study, N15 and N30 just had slight overlap, although they clustered together in both PCA and PLS-DA model, implying that the chemical composition of N15 and N30 differed. In addition, we found N15 and N30 could be clearly separated when a new model of partial least square discriminant analysis (PLS-DA) was established. It is important to mention that alanine, glucose, acetate, arginine, ornithine and serine, were shown to play a vital role in distinguishing N15 and N30, and that alanine, glucose and acetate also acted in successfully separating all treatments from each other. Furthermore, the N30 cluster distribution was widely dispersed compared with other treatments, implying that cold storage at 30 days causes a large variation that essentially existed in the groups. Therefore, we infer that cold storage at 30 days is a turning point to prevent the sharp change of egg constituents. 4.2. Potential effect of host eggs stored at low temperature on the Trichogramma In this study, cold storage-induced chemical shifts in rice moth eggs revealed that four compounds, alanine, glutamine, glucose and acetate, which were supposed to be candidate biomarkers, accounted for the separation of different groups. The concentrations of these four compounds were generally evaluated as a function of the cold storage time. Metabolomics revealed that alanine, glutamine and glucose were also affected by rapid cold-hardening in insects (Michaud and Denlinger, 2007). These results may imply that the increasing concentration of these compounds is a universal characteristic for biological organisms exposed to a lower temperature. Consequently, the change of concentration of the four metabolic markers we tested and analyzed could be the main reason hampering Trichogramma parasitism and development in refrigerated C. cephalonic eggs. Previous research proved that composition of host eggs had an impact on stimulating oviposition of parasitic wasps (Grenier et al., 1986; Thompson, 1986; Barrett and Schmidt, 1991). Rajendram demonstrated that oviposition of T. californicum was affected by the concentration of sugars, vitamins and amino acids (Rajendram and Hagen, 1974; Rajendram, 1978). In the study of artificial eggs for Trichogramma pretiosum, a dilute solution of KC1and MgSO4 was a superior ovipositional stimulant (Nettles et al., 1982, 1983). However, the parasitoid oviposition was either

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inhibited or not effected when glucose, protein hydrolyzates and free amino acids were present in KC1and MgSO4 solutions (Nettles et al., 1985). Qin and Wu (1988) found that a solution of three amino acids, isoleucine, leucine, and phenylalanine, could promote T. californicum to lay eggs. Host eggs that contain alanine and glycerol to resist cooling are rejected by Parasitoid females (Rivers et al., 2000). In the present study, the concentration of non-stimulating oviposition components, especially alanine, were violently changed when the refrigerated period was extended, which may be the main factor preventing Trichogramma from laying eggs on the refrigerated eggs. The change of level of chemicals in refrigerated eggs may break the internal environment of the host egg and thus cause them to be unsuitable for Trichogramma embryo development. Our previous study has demonstrated that cold storage of Corcyra cephalonica eggs reduced the fitness of T. chilonis offspring (Yi et al., 2014). The changes made by the chemicals that affected the internal environment were mainly manifested in two index, pH and osmotic pressure. Longer storage times result in increasing levels of free chemical composition, especially alanine and glucose, which can increase osmotic pressure, adverse to the Trichogramma embryo absorbing nutrition. The embryo could only absorb a small amount of nutrients, or even none from the host, which may eventually elevate the rate of malformed progeny and mortality. Acetate is important for regulating osmotic pressure and pH in the insect body. The concentration of free acetic acid in cells should be kept at a low level to avoid disrupting the control of the pH in the cell contents. Increasing the level of acetate may break the balance of the pH of the cell content. Thus, cold storage of C. cephalonica eggs for long times could lead to internal environments unsuitable for embryo development and survival. Further in-depth investigations are necessary to figure out what roles these four components play in embryo development of Trichogramma, as well as the physiological and biochemical change of host eggs when refrigerated. Acknowledgments We thank Xin-Xia Feng of Plant Protection Research Institute, Guangdong Academy of Agricultural Sciences for rearing insects. This work was supported by the project (2013CB127602) of National Program on Key Basic Research Project (973 Program) and Dean Fund of Guangdong Academy of Agricultural Sciences (Grant number 201405). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. References Alim, M.A., Lim, U.T., 2010. Biological attributes of Ooencyrtus nezarae Ishii (Hymenoptera: Encyrtidae) reared on refrigerated eggs of Riptortus pedestris (=clavatus) Fabricius (Hemiptera: Alydidae). J. Asia-Pac. Entomol. 13, 139–143. Ayvaz, A., Karabörklü, S., 2008. Effect of cold storage and different diets on Ephestia kuehniella Zeller (Lepidoptera: Pyralidae). J. Pest. Sci. 81, 57–62. Barrett, M., Schmidt, J.M., 1991. A comparison between the amino acid composition of an egg parasitoid wasp and some of its hosts. Entomol. Exp. Appl. 59, 29–41. Chen, W.L., Leopold, R.A., 2007. Progeny quality of Gonatocerus ashmeadi (Hymenoptera: Mymaridae) reared on stored eggs of Homalodisca coagulata (Hemiptera: Cicadellidae). J. Econ. Entomol. 100, 685–694. Consoli, F.L., Parra, J.R., Zucchi, R.A., 2010. Egg Parasitoids in Agroecosystems with Emphasis on Trichogramma. Springer Science & Business Media, New York. de Carvalho Spinola-Filho, P.R., Demolin Leite, G.L., Soares, M.A., Alvarenga, A.C., de Paulo, P.D., Tuffi-Santos, L.D., Zanuncio, J.C., 2014. Effects of duration of cold storage of host eggs on percent parasitism and adult emergence of each of ten Trichogramma tidae (Hymenoptera) species. Florida Entomologist 97, 14–21. Ebrahimi, E., 1999. Morphological and enzymatic studies of the genus Trichogramma Westwood (Hymenoptera: Trichogrammatidae) in Iran. Vol. Ph.D. Tarbiat Modares University. Farahani, H.K., Ashouri, A., Zibaee, P., Alford, L., 2016. The effect of host nutritional quality on multiple components of Trichogramma brassicae fitness. B. Entomol. Res. 106, 633–641.

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