Anal Bioanal Chem DOI 10.1007/s00216-015-8648-8

NOTE

Metabolomics reveals insect metabolic responses associated with fungal infection Yong-Jiang Xu 1 & Feifei Luo 1 & Qiang Gao 1 & Yanfang Shang 1 & Chengshu Wang 1

Received: 28 December 2014 / Revised: 9 March 2015 / Accepted: 17 March 2015 # Springer-Verlag Berlin Heidelberg 2015

Abstract The interactions between insects and pathogenic fungi are complex. We employed metabolomic techniques to profile insect metabolic dynamics upon infection by the pathogenic fungus Beauveria bassiana. Silkworm larvae were infected with fungal spores and microscopic observations demonstrated that the exhaustion of insect hemocytes was coupled with fungal propagation in the insect body cavity. Metabolomic analyses revealed that fungal infection could significantly alter insect energy and nutrient metabolisms as well as the immune defense responses, including the upregulation of carbohydrates, amino acids, fatty acids, and lipids, but the downregulation of eicosanoids and amines. The insect antifeedant effect of the fungal infection was evident with the reduced level of maclurin (a component of mulberry leaves) in infected insects but elevated accumulations in control insects. Insecticidal and cytotoxic mycotoxins like oosporein and beauveriolides were also detected in insects at the later stages of infection. Taken together, the metabolomics data suggest that insect immune responses are energy-cost reactions and the strategies of nutrient deprivation, inhibition of host immune responses, and toxin production would be jointly employed by the fungus to kill insects. The data obtained in this study will facilitate future functional studies of genes and pathways associated with insect–fungus interactions. Electronic supplementary material The online version of this article (doi:10.1007/s00216-015-8648-8) contains supplementary material, which is available to authorized users. * Chengshu Wang [email protected] 1

Key Laboratory of Insect Developmental and Evolutionary Biology, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200032, China

Keywords Insect metabolomics . Beauveria bassiana . Nutrient deprivation . Immunity inhibition . Insect–fungus interaction

Introduction Insect metabolic dynamics are coupled with insect developments, metamorphosis, and immune responses including the up- and downregulation of many biochemical factors including small molecules [1]. Insect pathogenic fungi play important roles in maintaining insect populations in nature and species like Beauveria bassiana and Metarhizium robertsii have been developed as promising insect biocontrol agents [2, 3]. The interactions between the insects and pathogenic fungi are complex. Molecular biology studies have revealed the mechanisms of insect immunological responses and fungal evasion of host immunities [4–6]. However, there is a lack of investigation on the metabolic responses during the in vivo interactions between the insects and pathogenic fungi. Metabolomics is a high-throughput technique that could potentially offer novel insights into biological processes involving small molecules [7–9]. A gas chromatography–mass spectrometry (GC/MS)-based metabolomic analysis revealed the alterations of carbohydrates and amino acids in the bee hemolymph after infection by the microsporidian Nosema ceranae, which demonstrated the nutritional and energetic stresses imposed by the pathogen [10]. A metabolomic investigation has shown that differential arrays of secondary metabolites are biosynthesized by the fungal pathogens M. brunneum and B. bassiana during their growth in insect tissues and saprophytic medium, respectively [11]. Integrated metabolomics and proteomic approaches also revealed the varied metabolic profiles and proteomics in pea aphid

Y.-J. Xu et al.

(Acyrthosiphon pisum) in response to the elimination of symbiotic bacterium [12]. In this study, we employed metabolomic techniques that combined GC/MS and liquid chromatography–mass spectrometry (LC/MS) to investigate insect metabolic profiles after infection with the pathogenic fungus B. bassiana (Bb) for different times. Our findings revealed that, relative to the controls, substantially altered metabolites of amino acids, lipids, eicosanoids, and carbohydrates occurred in fungal infected silkworm (Bombyx mori) larvae, suggesting that the strategies of nutrient deprivation, inhibition of host immunities, and toxicity caused by fungal insecticidal metabolites were jointly employed by the fungus to kill insect hosts.

Materials and methods Chemicals and reagents Analytical standards of alanine, glycine, lysine, threonine, glutamine, heptadecenoic acid, glucose, trehalose, 17hydroxyeicosatetraenoic acid (17-HETE, ≥99 %), cytosine, N-(9-fluorenylmethoxycarbonyl)-glycine (≥98 %), and Nmethyl-N-trimethylsilyl- trifluoroacetamide (≥97 %) were purchased from the J & K Scientific Company (Shanghai, China). Formic acid (≥95 %), methoxyamine, pyridine, high-performance LC-grade methanol, and ethanol were obtained from the Shanghai ANPEL Scientific Company (Shanghai, China). Fungal culture and maintenance The pathogenic fungus B. bassiana strain Bb13 (RCEF0013, from the Research Center for Entomogenous Fungi, Hefei, China) used in this study was originally isolated from a mycosed cadaver of Masson’s pine caterpillar (Dendrolimus punctatus) [13]. Fungal conidia were obtained by growing the fungus on potato dextrose agar (PDA, Difco) at 25 °C for 14 days. Insect infection and collection of hemolymph samples The last instar silkworm larvae were injected from the second proleg with 10 μL of 0.05 % Tween-20 or its suspensions containing 1 × 10 6 , 1 × 10 7 , and 1 × 10 8 conidia/mL of B. bassiana, respectively. There were three replicates (10 insects each) for each treatment. Insects were fed with fresh mulberry leaves after treatments and insect survival was recorded every 12 h. The median lethal time (LT50) for each treatment was estimated as we described before [14]. For metabolomics analysis, additional insects were injected with 1×107 spores/mL and five sets (three insects per set) of hemolymph were collected on ice at the time points of 12, 24,

and 36 h post injection. Total proteins in each sample were determined using a Bradford method as we described previously [14]. The collected hemolymph samples (50 μL) were treated with 200 μL ice-cold methanol spiked with 10 μg/mL N-(9-fluorenylmethoxycarbonyl)-glycine as an internal standard (IS) [15]. After centrifugation at maximum speed for 10 min at 4 °C, the supernatants were transferred into new tubes and stored at −80 °C until further analysis. GC/MS analysis A 50-μL aliquot of supernatant was dried under nitrogen and derivatized with 75 μL methoxyamine (5 mg/mL in pyridine, 37 °C for 2 h) and followed by addition of 75 μL of N-methylN-trimethylsilyltrifluoroacetamide for 16 h. After centrifugation (4 °C, 6000 rpm for 1 min), 1 μL of sample was injected in the splitless mode for analysis. GC/MS analysis was performed on a Thermo DSQ GC/MS system (Thermo Fisher Scientific, Waltham, USA). A fused-silica capillary column HP-5MSI (30 m×0.25 mm i.d., 0.25 μm film thickness) was used. The injector was kept at 250 °C. Helium was used as the carrier gas with a constant flow rate of 1 mL/min through the column. The GC oven temperature was maintained at 70 °C for 1 min, and then increased to 250 °C at a rate of 10 °C/min and further increased at 25 °C/min to 300 °C and held for 6 min. The transfer line temperature was kept at 280 °C. Electron impact mode (70 eV) and full scan monitoring (m/z 50 to 550) were used in MS detection. The temperature of the ion source was set at 230 °C, and the quadrupole was set at 150 °C. The compounds were identified by comparison of mass spectra and retention time with those of reference standards, and those available in libraries (NIST 0.5; Thermo Fisher Scientific). LC/MS analysis LC/MS analysis was performed on a high-performance LC system 1200 (Agilent Technologies, Santa Clara, USA) coupled to a 6520 quadrupole time of flight mass detector equipped with an electrospray ionization (ESI) source. The samples were analyzed in ESI positive and negative ion modes. The separation was performed on an Agilent rapid resolution HT Zorbax SB-C18 column (2.1 × 50 mm, 1.8 μm, Agilent) at a column temperature of 50 °C. The mobile phases consisted of phase A (water with 0.1 % formic acid) and B (acetonitrile with 0.1 % formic acid). The gradient program was 0–9 min, 5–45 % B; 9–15 min, 45–100 % B; 15– 18 min, 100 % B; 18–20 min, 100–5 % B. The flow rate was set at 0.4 mL/min. A 10-μL aliquot of supernatant was injected for each individual analysis. Mass data were collected between m/z 100 and 1000 at a rate of 2 scans/s. The ion spray voltage was set at 4000 V, and the heated capillary temperature was maintained at 350 °C. The drying gas and nebulizer

Insect metabolic changes associated with fungal infection

nitrogen gas flow rates were 12.0 L/min and 50 psi, respectively. The compounds showing significant differences between samples were searched against the databases of NMD (National Microbiological Database) and Mass Bank (High Quality Mass Spectral Database) using mass-to-charge ratio (m/z) or identified by MS/MS fragmentation patterns. In the MS/MS analysis, the collision energy was set to 10, 20, or 40 V according to the situation. Method validation The proposed method was validated by calibration, limit of quantification (LOQ), and recovery, using available standard compounds. The calibration curves were generated from three replicate measurements of six concentrations of each standard sample. A linear regression with r2 >0.995 was obtained in all ranges. The LOQ was defined as a signal-to-noise ratio (S/N) of 10 [Table S1 in the Electronic Supplementary Material (ESM)]. The recoveries were evaluated by spiking defined amounts of standards into the aliquots of pooled hemolymph sample. The recoveries were calculated by comparing peak areas extracted from hemolymph against the mean peak areas of the three equal amounts of unprocessed compounds prepared in solution. The obtained recovery rates ranged from 63.4 % to 110.9 % (see ESM, Table S2). Data processing and statistical analysis Each chromatogram obtained from GC/MS or LC/MS analysis was pre-processed using the program MZmine 2.0 to generate discrete peak lists containing m/z, retention time (RT), and ion abundance (peak intensity). The results were combined into a single matrix by aligning peaks with the same mass and retention time for GC/MS and LC/MS data, respectively. Normalization was based on the standard compound normalizer module. The internal standard peaks (m/z 178 in GC/MS and 179 in LC/MS) were detected in all samples. Then each peak area was normalized by weighting the contribution to the standard peak area. Missing values were replaced with a half of the minimum value found in the data set. Statistical t tests were performed on the basis of the value of metabolite peak intensity between the controls and infected insect samples collected at the same time points. The processed GC/MS and LC/MS data were exported to soft independent modeling of class analogy (SIMCA-P 11.0, Umetrics AB, Umea, Sweden) for analysis and visualization by multivariate statistical methods. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) was performed to find the optimal separation of clusters. Prior to OPLS-DA analysis, all data were mean-centered and scaled for unit variance. In OPLS-DA analysis, the value of R2Y describes how well the data in the training set are

mathematically reproduced, ranging between 0 and 1, where 1 indicates a model with a perfect fit. Models with a Q2 value greater than or equal to 0.5 are generally considered to have good predictive capability. The variable importance in the projection (VIP) values reflects the importance of terms in the OPLS-DA model with respect to Y (its correlation to all the responses) and X (the projection). Thus, metabolites with VIP values greater than 1 were selected for a further test and validation using the Kruskal–Wallis test (P

Metabolomics reveals insect metabolic responses associated with fungal infection.

The interactions between insects and pathogenic fungi are complex. We employed metabolomic techniques to profile insect metabolic dynamics upon infect...
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