Abstract

   Fluoride tolerance is an important economic trait in sericulture,
   especially in some industrial development regions. Analyses of
   physiological changes involving structural damage to the insect body
   and molecular analyses of some related genes have focused on this area;
   however, the changes that occur at the metabolic level of silkworms
   after eating fluoride-contaminated mulberry leaves remain unclear.
   Here, metabonomic analysis was conducted using gas chromatography-mass
   spectrometry (GC-MS) to analyze the changes in midgut tissue after NaF
   stress using silkworm strains 733xin (susceptible stain) and T6 (strain
   resistant to fluoride), which were previously reported by our
   laboratory. Differential metabolomics analysis showed that both T6 and
   733xin strains displayed complex responses after exposure to 200 mg/kg
   NaF. The purine metabolism and arginine and proline metabolic pathways
   of fluoride-tolerant strains reached significant levels, among which
   3′-adenylic acid and hypoxanthine were significantly upregulated,
   whereas guanine, allantoic acid, xanthine, N-acetyl-L-glutamic acid,
   and pyruvate were significantly downregulated. These metabolic pathways
   may be related to the fluoride tolerance mechanism of NaF poisoning and
   tolerant strains.

   Keywords: fluoride tolerance, untargeted metabolomics, silkworm midgut,
   differential metabolites, metabolic pathways

1. Introduction

   Fluoride is one of the important factors causing environmental
   pollution. Fluoride-contaminated mulberry leaves are absorbed and
   accumulated in the midgut of the silkworm after being eaten [[34]1].
   The resulting fluorosis has resulted in sericulture farmers incurring
   huge economic losses, which has seriously damaged the stable
   development of the sericulture industry. After fluoride poisoning,
   differences in population development, reduced appetite, and slow
   production in silkworm are observed. Some internode membranes bulge and
   appear as ring-shaped spots connected by black spots. Some abdominal
   links have rough black-brown lesions on the back, which easily rupture
   and discharge pale yellow blood [[35]2].

   The fluoride ion is a protoplasmic toxicant that produces toxicity
   through the cell wall of mulberry leaves and binds with the protoplasm
   [[36]3,[37]4]. After eating fluoride-contaminated mulberry leaves, the
   silkworm accumulates fluoride in the midgut, which then enters the
   blood and other tissues and organs. The midgut not only serves the
   function of digesting and absorbing nutrients, it also prevents the
   invasion of toxic and harmful substances and pathogenic microorganisms.
   Electron microscopy showed that the structure of the midgut cell layer
   of the fluorosis silkworm is loose, and many fine particles can be
   observed in the cytoplasm of the inner wall. During fluorosis, the cell
   membrane is severely damaged and various organelles are severely
   degraded [[38]5].

   In response to the fluorosis of silkworm, researchers have adopted some
   control strategies, such as breeding fluoride-tolerant varieties and
   using drugs [[39]6,[40]7], and many explorations have been conducted
   into the fluoride-tolerance mechanism at the molecular level. Using
   fluorescent differential display technology, Zhou et al. [[41]8]
   obtained Bmcyp306a1 in the P450 family and another Bmbcl-2 gene.
   Real-time fluorescent quantitative PCR analysis showed that the
   expression of Bmcyp306a1 in different tissues of fluoride-tolerant and
   sensitive individuals was significantly different, which might be
   related to the mechanism of fluoride tolerance. Chen et al. [[42]9]
   identified five differentially expressed proteins in Bombyx mori N cell
   line (BmN) via two-dimensional electrophoresis. These include two
   upregulated proteins: mitochondrial aldehyde dehydrogenase (ALDH2) and
   prohibitin protein WPH, and two upregulated proteins: calreticulin
   precursor and DNA supercoiling factor. Li et al. [[43]5] found that
   fluoride stress could inhibit SOD activity and the cholesterol and
   triglyceride contents, and reduce the content of short-chain fatty
   acids in the intestinal tract by affecting the water content and pH
   value of the intestinal contents [[44]7]. A dominant fluoride-tolerant
   gene was mapped using an SSR marker in our previous research, and 30
   upregulated genes were screened from the constructed fluoride-tolerant
   near-isogenic line (DefNIL) by using RNA-seq analysis [[45]2]. The
   results showed that glutathione metabolism, glycerolipid metabolism,
   and steroid biosynthesis were the metabolic pathways for significant
   enrichment of differentially expressed genes (DEGs). These preliminary
   studies helped to understand the effects of fluoride on some
   physiological and biochemical indexes of silkworms with different
   tolerances, and to elucidate the mechanism through which fluoride
   stress affects silkworm mortality.

   Fluoride stress can activate the autoimmune response, including the
   synthesis of fluoride-tolerant metabolites, or signal molecules to
   regulate the metabolic process. Metabonomics has been used to further
   study the functions of some key metabolites by comparing the
   differences in metabolic patterns between two or more groups of
   samples. It has been widely used in functional gene research
   [[46]10,[47]11], metabolic pathway and mechanism analysis [[48]12], and
   key marker screening [[49]13,[50]14]. There have been reports on the
   transcriptome [[51]15,[52]16,[53]17], proteome [[54]9], and microbial
   diversity [[55]5] after NaF stress in silkworm. However, the
   differences in metabolic pathways of fluoride-tolerant and sensitive
   silkworm varieties under NaF stress have not been reported using
   metabonomics.

   In this study, metabonomic analysis was conducted using gas
   chromatography-mass spectrometry (GC-MS) to analyze the changes in
   midgut of fluoride-tolerant T6 strain and the fluoride-sensitive 733xin
   strain at the metabolomic level after NaF stress, to screen the
   differential metabolites, and to further explore the relevant metabolic
   pathways. Our present study will provide useful information for
   understanding the fluoride-tolerance mechanism in silkworm.

2. Materials and Methods

2.1. Silkworm Rearing and Material Treatment

   The silkworm was a fluoride-tolerant strain T6 and a fluoride-sensitive
   strain 733xin preserved in our laboratory. The T6 silkworm strain has
   high tolerance to fluoride, its larvae can survive with few effects
   even when they are fed with mulberry leaves treated with 200 mg/Kg of
   NaF solution. While, the high degree of change in growth in 733xin
   strain when fed with mulberry leaves even treated with 50 mg/Kg.
   Fluoride poisoning is a cumulative process. The larvae can recover from
   early instars fluoride toxicity and convalescence from fluorosis by
   feeding of fresh fluoride free mulberry leaves in subsequent stages. In
   order to prevent the early larvae from being too susceptible to
   poisoning and death, and to facilitate the extraction of tissues, we
   carried out the present experiment starting from the fourth instar.
   Silkworms reared to the fourth instar (25 °C, 80% relative humidity)
   were divided into control and experimental groups. Commercially
   available anhydrous sodium fluoride (NaF) AR grade produced from Sangon
   Biotech Ltd., Shanghai, China was used as a toxicant in this research.
   The control group was fed with fresh mulberry leaves dried naturally
   after 5 min immersion in clear water, labeled T6_C and 733xin_C,
   respectively, whereas the experimental group was fed fresh mulberry
   leaves dried naturally after 5 min immersion in 200 mg/kg NaF solution,
   labeled T6_F and 733xin_F, respectively. The midgut tissues of
   different silkworm varieties were dissected and stored in liquid
   nitrogen. We performed eight replicates per sample.

2.2. Sample Preparation

   Firstly, put the samples at room temperature after take out from the
   −80 °C refrigerator, and then added to water of the same volume as the
   samples. Then, 2 small steel beads were added in turn, placed in the
   refrigerator at −80 °C for 2 min and ground in a grinder (60 Hz, 2
   min). Then, the samples were moved from 100 μL to 1.5 mL tubes. A
   quantity of 20 μL internal standard (L-2-chlorophenylalanine, 0.3
   mg/mL, methanol configuration) was added, followed by whirlpool
   oscillation for 10 s. Then, 300 μL of methanol-acetonitrile (2:1, v/v)
   mixed solution was added, followed by eddy oscillation for 1 min.
   After, ultrasonic extraction in an ice water bath was performed for 5
   min. After storing for 10 min at −20 °C and centrifuging for 10 min
   (12,000 rpm, 4 °C), 300 μL supernatant was placed in a glass derivative
   bottle. Quality control samples (QC) were prepared by mixing the
   extracts of all samples in equal volume. The volume of each QC was the
   same as that of the samples. The samples were dried using a
   freeze-concentration centrifugal dryer. The oxime reaction occurred at
   37 °C for 90 min in an oscillating incubator after adding 80 μL
   methoxyamine hydrochloride pyridine solution (15 mg/mL), followed by
   swirling oscillation for 2 min. A quantity of 80 μL BSTFA (containing
   1% TMCS) derivative reagent and 20 μL hexane was then added to samples.
   After 2 min of swirl oscillation, the samples were reacted at 70 °C for
   60 min. Finally, the samples were collected and placed at room
   temperature for 30 min for GC-MS metabonomics analysis [[56]18].

2.3. GC-MS Analysis Conditions

   Analysis was conducted with a 7890B-5977A GC/MSD (Agilent Technologies
   Inc., San Jose, CA, USA).

   The chromatographic conditions were as follows: DB-5MS capillary column
   (30 m × 0.25 mm × 0.25 μm, Agilent J&W Science, Folsom, CA, USA);
   carrier gas was high0purity helium (purity not less than 99.999%); flow
   rate was 1.0 mL/min; and temperature of the inlet was 260 °C. The
   injection volume was 1 μL not diverted and the solvent was delayed for
   5 min. For programmed heating, the initial temperature of the column
   temperature box was 60 °C, and programmed temperature was 8 °C/min to
   125 °C, 5 °C/min to 210 °C, 10 °C/min to 270 °C, and 20 °C/min to 305
   °C for 5 min.

   The mass spectrometry conditions were as follows: electron bombardment
   ion source (EI), ion source temperature 230 °C, fourth-stage rod
   temperature 150 °C, and electron energy 70 eV. Scanning mode was full
   scan mode (SCAN), with quality scanning range of m/z 50 to 500.

   A quality control (QC) sample was inserted into every 16 analysis
   samples to examine the repeatability of the whole analysis process.

2.4. Metabonomics Data Preprocessing

   The original GC-MS data (D format) was transformed into general format
   (CDF format) using ChemStation (version E.02.02.1431, Agilent, CA, USA)
   analysis software, and then imported into Chroma TOF (version 4.34,
   LECO, St Joseph, MI, USA) software for pre-processing, including peak
   extraction, noise removal, and deconvolution. Qualitative analysis of
   metabolites used NIST and Fiehn databases. Finally, peak alignment was
   performed to derive the three-dimensional (3D) data matrix (original
   data matrix) in CSV format.

   The internal standard was used for data quality control. The internal
   standard peaks in the original data matrix and any known false positive
   peaks (including noise, column loss, and derivative reagent peaks) were
   removed and missing values replaced by 0. In each sample, all peak
   signal intensities (peak area) were normalized; the relative intensity
   of each peak signal intensity was transformed into the relative
   intensity of the spectrum. After data were normalized and multiplied by
   10,000, the data matrix was obtained by de-redundancy and peak merging.

2.5. Multivariate Statistical Analysis

   After log2 conversion (0 was replaced by 0.000001, then converted), the
   values in the data matrix were imported into the SIMCA software package
   (version 14.0, Umetrics, Ume, Sweden). Unsupervised principal
   components analysis (PCA) was used to observe the overall distribution
   of samples and the stability of the whole analysis process. Then,
   supervised (orthogonal) partial least squares discriminate analysis
   (OPLS-DA) was used to distinguish the overall differences of metabolic
   profiles among groups and to find the different metabolites among
   groups.

2.6. Screening of Differential Metabolites

   Multidimensional analysis and one-dimensional analysis were used to
   screen different metabolites between groups. The criteria for screening
   were a VIP value of the first principal component (PC1) of the OPLS-DA
   model > 1 and p-value returned by Student’s t-test < 0.05. The fold
   change (FC) of differential metabolites was calculated, in which the
   change multiple was the ratio of the average content of the
   differential metabolites in the two groups. FC > 1 indicated that the
   content of metabolites was upregulated; FC < 1 indicated that the
   content of metabolites was downregulated.

2.7. Metabolic Pathway Analysis of Differential Metabolites

   The pathway enrichment analysis of differential metabolites helps with
   understanding the mechanism of metabolic pathway change in different
   samples. Common pathway analysis is based on the KEGG metabolic pathway
   ([57]http://www.genome.jp/KEGG/pathway.html). Mapping differential
   metabolites to the KEGG database can be used to obtain the enrichment
   results of their metabolic pathways.

3. Results and Analysis

3.1. Analysis of GC-MS Results

   GC-MS can be used to identify metabolites with low polarity, such as
   silane derivatives and esters. It provides good coverage of amino
   acids, organic acids, and carbohydrates [[58]11,[59]19]. Visualization
   of total ion current (TIC) of all samples showed that, all samples had
   a strong signal, large peak capacity, and good reproducibility of
   retention time. The original data of all samples were processed by the
   analysis software to obtain the data matrix. A total of 1367 peaks were
   detected, and 484 peaks were detected.

3.2. Overall PCA of Metabolic Phenotypes

   PCA can reflect the overall metabolic differences and the variability
   in samples between groups as a whole. As shown in [60]Figure 1, the
   abscissa of the PCA score chart represents the first principal
   component, PC1, with t [[61]1], and the ordinate represents the second
   principal component, PC2, with t [[62]2]. The quality control sample is
   located in the middle, and the machine works well. Samples are
   relatively concentrated and reproducible, except for eight samples of
   733xin_F, which are separated to a certain extent. The unsupervised PCA
   of all the samples in this experiment showed that R^2X = 0.48, which
   indicated that the established model had good fitting accuracy and
   could be reliably used to explain the metabolic differences between the
   two groups.

Figure 1.

   [63]Figure 1
   [64]Open in a new tab

   PCA score plots based on the GS-MS data of the Bombyx mori midgut
   samples.

3.3. Orthogonal Partial Least Squares Discriminate (OPLS-DA) Analysis

   To eliminate noise information unrelated to classification (e.g., the
   control group and processing) and obtain more reliable metabolite
   information that results in significant differences between the two
   groups of samples, orthogonal partial least squares discriminate
   analysis (OPLS-DA) was used to filter the unrelated signals with model
   classification, i.e., orthogonal, to obtain the OPLS-DA model
   ([65]Figure 2). We found significant differences (spectral separation)
   between the two groups on the OPLS-DA score map: R^2Y = 0.993, Q^2 =
   0.903 between T6_F and T6_C; R^2Y = 0.989, Q^2 = 0.805 between 733xin_F
   and 733xin_C; R^2Y = 0.996, Q^2 = 0.967 between T6_C and 733xin_C; and
   R^2Y = 0.998, Q^2 = 0.929 between T6_F and 733xin_F. These four sets of
   data indicated that the OPLS-DA model has good explanatory and
   predictive abilities for metabolic phenotypic differences among groups.
   The 733xin strain has greater metabolic differences after NaF stress,
   whereas the T6 strain has higher fluoride tolerance, indicating that
   the metabolic differences after NaF stress are smaller than 733xin.

Figure 2.

   [66]Figure 2

   [67]Figure 2
   [68]Open in a new tab

   OPLS-DA model diagram. (A) OPLS-DA model diagram of T6_F-T6_C; (B)
   OPLS-DA model diagram of 733xin_F-733xin_C; (C) OPLS-DA model diagram
   of T6_C-733xin_C; (D) OPLS-DA model diagram of T6_F-733xin_F.Model
   parameter R^2Y (the parameter value of this model does not refer to
   R^2X value) represents the ability to interpret the model, and Q^2
   represents the ability to predict the model.

3.4. Screening of Differential Metabolites

   A total of 173 differential metabolites were screened and identified
   between T6_F and T6_C; 70 were upregulated and 103 were downregulated.
   The differential metabolites included guanine, 3′-adenylic acid,
   N-acetyl-L-glutamic acid, L-glutamic acid, pyruvic acid, nicotinamide,
   trehalose, and 3-indoleacetonitrile, indicating that these compounds
   might be involved in the tolerance reaction of T6 strains to NaF. A
   total of 136 differential metabolites were screened and identified
   between 733xin_F to 733xin_C; 75 were upregulated and 61 were
   downregulated. The differential metabolites included fumaric acid,
   phosphate, urea, indole-3-acetamide, 2-aminophenol, glycine, and
   raffinose, suggesting that these compounds might be related to the
   sensitivity of the 733xin strain to NaF. T6_C–733xin_C screened a total
   of 209 differential metabolites; 117 were upregulated and 92 were
   downregulated. We found 182 differential metabolites between T6_F and
   733xin_F; 71 were upregulated and 111 downregulated. The greater the
   VIP value in the OPLS-DA model, the more significant the difference
   [[69]10,[70]13]. Statistical analysis of the data from the four groups
   showed that 22 metabolites had significant differences among the four
   groups ([71]Table 1). These can be classified into carbohydrates,
   amines and amides, amino acids, fatty acids, organic acids, sterols,
   bases, etc. Among the four control groups, N-methyl-DL-alanine,
   phosphate, D-alanyl-D-alanine, and melatonin were significantly
   upregulated, and 5-hydroxyindole-2-carboxylic acid and
   cytidine-monophosphate were significantly downregulated.

Table 1.

   Common differential metabolites in four control groups.
   Metabolite 733xin-F vs. 733xin-C T6-F vs. T6-C T6-C vs. 733xin-C T6-F
   vs. 733xin-F
   VIP p FC VIP p FC VIP p FC VIP p FC
   raffinose 1.9730 <0.0001 2.6702 1.1615 0.0270 0.7653 1.2283 0.0008
   1.9180 1.6267 <0.0001 0.5497
   tricetin 1.9949 <0.0001 0.4461 1.1013 0.0379 1.3371 1.2692 0.0008
   0.6576 1.4342 0.0009 1.9712
   arachidic acid 1.8459 0.0001 0.6691 1.3336 0.0054 1.1608 1.6063 <0.0001
   0.4817 1.1006 0.0224 0.8358
   androstanediol 1.7021 0.0001 0.6277 1.3489 0.0016 1.3559 1.2781 <0.0001
   0.6977 1.2835 0.0010 1.5070
   prostaglandin E2 1.7541 0.0005 1.2606 1.1868 0.0145 0.7736 1.0471
   0.0082 1.3228 1.3324 0.0018 0.8118
   N-methyl-DL-alanine 1.6075 0.0008 1.5624 1.3262 0.0092 1.1077 1.2269
   0.0002 1.6073 1.1278 0.0161 1.1396
   nicotinamide 1.2384 0.0028 0.5651 1.0728 0.0489 1.0777 1.1909 0.0043
   1.2455 1.3247 <0.0001 2.3752
   indole-3-acetamide 1.7442 0.0075 2.5556 1.7658 0.0001 0.5501 1.4274
   0.0001 1.9831 1.3437 0.0099 0.4269
   xylose 1.4202 0.0144 0.3171 1.5321 0.0013 0.4759 1.5303 <0.0001 8.0269
   1.7642 <0.0001 12.0456
   phosphate 1.3474 0.0149 2.0608 1.4143 0.0063 1.4409 1.3344 0.0002
   2.1808 1.0803 0.0229 1.5248
   D-alanyl-D-alanine 1.3563 0.0160 1.1455 1.3713 0.0093 1.0976 1.2162
   0.0014 1.1518 1.0003 0.0402 1.1036
   N-methyl-L-glutamic acid 1.4813 0.0164 2.0773 1.8072 <0.0001 0.4503
   1.3420 0.0007 1.7325 1.3931 0.0048 0.3756
   glucosaminic acid 1.3853 0.0191 1.3027 1.6018 0.0004 0.7394 1.2018
   0.0025 1.3776 1.1969 0.0084 0.7820
   guanine 1.4256 0.0207 1.5516 1.6091 0.0010 0.6318 1.1917 0.0091 1.6934
   1.4074 0.0014 0.6895
   5-methoxyindole-3-acetic acid 1.3218 0.0268 1.4926 1.0875 0.0289 1.4566
   1.2071 0.0010 0.5024 1.4787 0.0011 0.4903
   stigmasterol 1.2893 0.0412 0.6565 1.2993 0.0055 1.5196 1.2808 0.0015
   1.9553 1.7558 <0.0001 4.5257
   melatonin 1.1751 0.0416 1.1508 1.3098 0.0166 1.3538 1.2652 0.0010
   1.2440 1.3180 0.0061 1.4634
   methyl-beta-D-galactopyranoside 1.1474 0.0426 1.7359 1.7925 <0.0001
   0.5967 1.0187 <0.0001 8.9987 1.8350 <0.0001 3.0935
   alpha-D-glucosamine 1-phosphate 1.2684 0.0449 1.5270 1.8635 <0.0001
   0.3464 1.2883 0.0012 1.7887 1.5467 0.0013 0.4058
   5-hydroxyindole-2-carboxylic acid 1.8575 0.0041 0.3829 1.4027 0.0182
   0.5151 1.3143 0.0051 0.3682 1.8083 <0.0001 0.4954
   nicotinic acid 1.4744 0.0064 1.3137 1.7465 <0.0001 0.6829 1.1216 0.0043
   0.7923 1.8168 <0.0001 0.4119
   cytidine-monophosphate 1.8111 0.0068 0.4009 1.3625 0.0285 0.4958 1.2371
   0.0124 0.4089 1.8005 <0.0001 0.5057
   [72]Open in a new tab

   The differential metabolites were selected on the basis of the
   combination of a statistically significant threshold of variable
   influence on projection (VIP) values obtained from the OPLS-DA model
   and p-values from a two-tailed Student’s t-test on the normalized peak
   areas, where metabolites with VIP values larger than 1 and p-values
   less than 0.05 were included, respectively.

3.5. Metabolic Pathway Analysis of Differential Metabolites

   Through the MBRole ([73]http://csbg.cnb.csic.es/mbrole/) pathway
   analysis function, the KEGG ID of differential metabolites was used for
   pathway enrichment analysis, and screened differential metabolites were
   enriched into related pathways. Differential metabolites of 733xin were
   enriched to 46 metabolic pathways after NaF stress, and differential
   metabolites of T6 were enriched to 56 metabolic pathways after NaF
   stress. The metabolic pathway enrichment maps are drawn with the
   metabolic pathway name as the abscissa and the log (p-value) as the
   ordinate; the maps of the four control groups are shown in [74]Figure
   3. In the enrichment map of the metabolic pathway, the red line p-value
   is 0.01 and the blue line p-value is 0.05. When the top of the column
   is higher than the blue line or the red line, the signal pathway it
   represents significant. The common metabolites of T6 and 733xin under
   NaF stress include fumaric acid and phosphate. Among the significant
   metabolic pathways of T6, fumaric acid participates in arginine and
   proline metabolism; nicotinate and nicotinamide metabolism; oxidative
   phosphorylation; and alanine, aspartate, and glutamate metabolism.
   Fumaric acid is involved in oxidative phosphorylation, nicotinate, and
   nicotinamide metabolism; arginine and proline metabolism; and the TCA
   cycle in the 733xin metabolic pathway. Phosphate participates in
   oxidative phosphorylation and ABC transporters in the significant
   metabolic pathways of T6. It participates in a significant metabolic
   pathway of 733xin, namely, oxidative phosphorylation ([75]Table 2). The
   distinct metabolic pathways of T6 are alanine, aspartate, and glutamate
   metabolism, and ABC transporters, which indicate that the
   fluoride-tolerant strain T6 effects some metabolic pathway changes
   through its own unique differential metabolites, thus helping to resist
   NaF damage. The metabolic pathways of T6 are mainly related to
   nucleotide metabolism, amino acid metabolism, and energy metabolism.

Figure 3.

   [76]Figure 3
   [77]Open in a new tab

   Categories of pathways in which the identified metabolites are
   involved. (A) metabolic pathway enrichment map of T6_F-T6_C; (B)
   metabolic pathway enrichment map of 733xin_F-733xin_C; (C) metabolic
   pathway enrichment map of T6_C-733xin_C; (D) metabolic pathway
   enrichment map of T6_F-733xin_F.

Table 2.

   Partial identified differential metabolites of T6_F vs. T6_C and
   733xin_F vs. 733xin_C.
   Metabolic Pathway Metabolite 733xin-F vs. 733xin-C T6-F vs. T6-C
   VIP p FC VIP p FC
   Purine metabolism 3′-adenylic acid 1.4021 0.0146 1.5897 1.1089 0.0260
   1.2742
   guanine 1.4256 0.0207 1.5516 1.6091 0.0010 0.6318
   xanthosine 1.0500 0.0224 0.6018 1.1667 0.0279 0.8304
   guanosine 1.5426 0.0010 0.6137
   allantoic acid 1.4377 0.0034 0.6627
   adenosine 1.4556 0.0038 0.7421
   hypoxanthine 1.4200 0.0048 1.1328
   Arginine and proline metabolism N-acetyl-L-glutamic acid 1.6081 0.0167
   1.8397 1.8881 <0.0001 0.3537
   glutamic acid 1.8040 <0.0001 0.7483
   putrescine 1.5495 0.0007 0.8533
   pyruvic acid 1.3513 0.0167 0.5493
   fumaric acid 2.0018 <0.0001 1.2941 1.018 0.0414 0.9117
   Urea 1.7167 0.0004 1.7151
   Alanine, aspartate and glutamate metabolism L-glutamic acid 1.8040
   <0.0001 0.7483
   succinic acid 1.4135 0.0076 0.6709
   pyruvic acid 1.3513 0.0167 0.5493
   fumaric acid 2.0018 <0.0001 1.2941 1.018 0.0414 0.9117
   Nicotinate and nicotinamide metabolism nicotinic acid 1.4744 0.0064
   1.3137 1.7465 <0.0001 0.6829
   pyruvic acid 1.3513 0.0167 0.5493
   fumaric acid 2.0018 <0.0001 1.2941 1.018 0.0414 0.9117
   nicotinamide 1.2384 0.0028 0.5651 1.0728 0.0489 1.0777
   Oxidative phosphorylation phosphate 1.3474 0.0149 2.0608 1.4143 0.0063
   1.4409
   succinic acid 1.4135 0.0076 0.6709
   fumaric acid 2.0018 <0.0001 1.2941 1.018 0.0414 0.9117
   pyrophosphate 1.5484 0.0054 1.6668
   Tryptophan metabolism indoleacetonitrile 1.7692 <0.0001 0.4340
   indole-3-acetamide 1.7442 0.0075 2.5556 1.7658 0.0001 0.5501
   aminophenol 1.2944 0.0150 0.0892 1.0252 0.0025 0.4543
   5-methoxytryptamine 1.3455 0.0130 0.7748
   hydroxyanthranilic acid 1.3539 0.0141 <0.0001
   melatonin 1.1751 0.0416 1.1508 1.3098 0.0166 1.3538
   indolelactate 1.7985 0.0013 0.7070
   N-acetylisatin 1.2651 0.0152 1.4572
   Glutathione metabolism L-glutamic acid 1.8040 <0.0001 0.7483
   putrescine 1.5495 0.0007 0.8533
   L-cysteine 1.4476 0.0172 0.8256
   glycine 1.3452 0.0253 0.8108
   [78]Open in a new tab

   The differential metabolites were selected on the basis of the
   combination of a statistically significant threshold of variable
   influence on projection (VIP) values obtained from the OPLS-DA model
   and p-values from a two-tailed Student’s t-test on the normalized peak
   areas, where metabolites with VIP values larger than 1 and p values
   less than 0.05 were included, respectively.

4. Discussion

   GC-MS technology has high detection sensitivity and resolution,
   identifying different metabolites between treatment and control groups.
   In the present study, OPLS-DA analysis showed that after NaF treatment,
   the fluoride-sensitive strain showed a clear separation trend. The
   lower effects of fluoride on T6 strain compared with 733xin strain
   indicated is lower affinity towards T6, suggesting that the T6
   silkworms have tolerance and may metabolize the fluoride to a greater
   extent than 733xin strain. Pathway enrichment analysis of the screened
   differential metabolites can help understand the trends in metabolic
   pathway in different strains.

4.1. Energy Metabolism

   Fluoride poisoning in the silkworm is closely related to energy
   metabolism in tissues and cells [[79]20,[80]21]. Oxidative
   phosphorylation is the main metabolic pathway through which the energy
   released during the oxidative decomposition of organic substances is
   used to produce ATP. The activity of ATPase is related to the secretion
   of digestive juices and the absorption of nutrients in the midgut
   tissue [[81]22]. Fluoride can inhibit the activity and affect the
   distribution of ATPase in the midgut, which results in the obstruction
   of energy supply and the serious destruction of the absorption function
   of midgut cells. The main mode of absorption of mesenteric epithelial
   cells is active transport, which is a process of converse concentration
   gradient transport that requires a significant amount of energy. The
   phosphatic content of T6_F–T6_C significantly increased, while fumarate
   and succinate contents significantly decreased, as shown in [82]Figure
   4. The fumarate and phosphate contents significantly increased in
   733xin_F–733xin_C. The differential metabolites of the two control
   groups were enriched in the metabolic pathways, and the results showed
   that oxidative phosphorylation changed significantly, resulting in
   abnormal energy metabolism. We speculate that fluoride can act on
   enzyme block the process of oxidative phosphorylation in the
   fluoride-sensitive strain 733xin, resulting in an increase in the
   content of raw materials for ATP production and a decrease in the
   amount of ATP production. In the fluoride-tolerant strain T6, an
   oxidative phosphorylation tolerance mechanism and the increase of
   enzyme activity exist, so the raw materials required for oxidative
   phosphorylation process are significantly reduced. ABC transports
   energy from ATP hydrolysis to the plasma membrane, and some members of
   the ABC family are the main active transporters [[83]23]. This process
   significantly impacts substance transport, drug tolerance, metabolism,
   and growth [[84]24]. The ABC transporters metabolic pathway in the T6
   strain changed significantly after NaF stress compared with 733xin,
   suggesting that the significant change in ABC transporters is also
   related to the fluoride tolerance mechanism of silkworm.
   Glycometabolism is one of the most important metabolisms in the body.
   The calcium ion transmits signals from the central nervous system to
   the organs of the silkworm, and can participate in hormone secretion
   and other activities. Fluoride ions can combine with calcium and
   magnesium ions to form more stable compounds, which accumulate in the
   midgut and exert effects on acid phosphatase, catalase, and other
   enzymes, thus hindering the normal process of glycometabolism and
   regulating energy metabolism in the silkworm. This study highlights the
   significant changes due to the effect of fluoride ion on enzyme in the
   glucose metabolic pathways in each control group, mainly in the
   galactose metabolism and TCA cycle of 733xin_F–733xin_C and pentose and
   glucuronate interconversions of T6_C–733xin_C.

Figure 4.

   [85]Figure 4
   [86]Open in a new tab

   Differential metabolites between T6_F vs. T6_C are located in oxidative
   phosphorylation. Red dots indicate upregulated in oxidative
   phosphorylation and blue dots indicates downregulated in oxidative
   phosphorylation. The originated figure from KEGG pathway database.

4.2. Amino Acid Metabolism

   The silkworm excretes uric acid. Excess amino acids produced from food
   intake or metabolism can be decomposed into carbon shelf structures and
   ammonia. Most are excreted or accumulated in the epidermis through the
   malpighian tube in the form of ammonium salts, but small amounts are
   metabolized by purine compounds to produce uric acid or urea
   [[87]25,[88]26]. The metabolic pathways of different metabolites in
   four control groups were enriched and analyzed. We found that amino
   acid metabolism changed significantly. The melatonin content, a
   differential metabolite related to tryptophan metabolism between T6_F
   and T6_C, significantly increased, whereas the 3-indoleacetonitrile,
   indole-3-acetamide, 2-aminophenol, 5-methoxytryptamine, and
   3-hydroxyanthranilic acid contents significantly decreased. The
   indole-3-acetamide, N-acetyllysine, and melatonin contents, the
   different metabolites of 733xin_F–733xin_C, significantly increased,
   whereas the indolelactate and 2-aminophenol contents significantly
   decreased. Considerable differences exist in the mobilization of
   metabolites between T6 and 733xin strains. We presume that different
   tolerance mechanisms to fluoride may be responsible for the
   differences, and the specific reasons need to be further studied.

   Fluoride poisoning can reduce the body’s antioxidant ability.
   Glutathione is an antioxidant synthesized by glutamic acid, cysteine,
   and glycine. It can scavenge free radicals and inhibit the initiation
   and development of peroxidation [[89]27]. Glutathione S-transferases
   (GSTs) are detoxifying enzymes that widely exist in the midgut of
   silkworm [[90]28]. They can catalyze the electrophilic group of harmful
   substances to bind with the sulfhydryl group of reduced glutathione to
   form more soluble and non-toxic derivatives [[91]29]. That is, GSTs
   function in detoxification and antioxidation [[92]30,[93]31]. Previous
   studies showed that increased GST activity in insects can produce
   tolerance to toxic substances. With the accumulation of NaF
   concentration in wilkworm, the activity of GSTs in fluoride-tolerance
   varieties increased, while the acitivity of GSTs in
   fluoride-susceptible varieties fluctuated greatly, and finally showed a
   downward trend, which indicated that fluoride was more toxic to
   sensitive varieties. We speculate that the fluoride tolerance of the
   silkworm may be related to the GSTs activity [[94]32]. The L-cysteine
   and glycine contents decreased significantly between 733xin_F and
   733xin_C, and L-glutamic acid and putrescine contents decreased
   significantly between T6F and T6C. The enriched metabolic pathways
   showed that glutathione metabolism of the 733xin_F–733xin_C group
   changed significantly. The results showed that fluoride could change
   the activity of GSTs, which led to a significant decrease in the
   synthetic enzyme, and finally affected silkworm resistance to fluoride.

   Arginine plays an important role in regulating physiological metabolism
   and nutrition in silkworm. Arginine produces NO through metabolism; NO
   is involved in mediating substrate metabolism as a signal molecule
   [[95]33]. Transamination is an important reaction in amino acid
   metabolism. Arginine and proline can generate putrescine and pyruvic
   acid through transamination and enzyme catalysis. Similarly, alanine
   can also produce pyruvic acid under the action of transaminase. Pyruvic
   acid can enter the citrate cycle through glycolysis to provide energy
   for the body. There is a direct deamination of amino acids in insects.
   Asparate can be directly deaminated to form fumaric acid under the
   catalysis of asparaginase. Glutamate is finally formed succinic acid by
   decarboxylase. Both fumaric acid and succinic acid can enter the
   citrate cycle to oxidize and decompose to provide energy for the body,
   as shown in [96]Figure 5. After fluoride stress, the contents of
   fumaric acid, urea, and N-acetyl-L-glutamic acid (the differential
   metabolites of 733xin_F–733xin_C) increased significantly, whereas the
   contents of N-acetyl-L-glutamic acid, L-glutamic acid, putrescine,
   fumaric acid, succinic acid, and pyruvic acid (the differential
   metabolites of T6F–T6C) decreased significantly. The results of
   differential metabolite enrichment showed that arginine and proline
   metabolism in 733xin_F–733xin_C, and arginine, proline, alanine,
   aspartate, and glutamate metabolism in T6_F–T6_C, underwent significant
   changes.

Figure 5.

   [97]Figure 5
   [98]Open in a new tab

   Differential metabolites between T6_F vs. T6_C are located in Arginine,
   Proline, Alanine, Aspartate, and Glutamate metabolism. Blue color
   metalites indicates downregulated.

4.3. Nucleotide Metabolism

   Ammonia is extremely toxic to the midgut cells of the silkworm, so it
   must be removed quickly for detoxification. Uric acid can be produced
   by purine metabolism in the midgut of the silkworm to excrete excess
   ammonia. Purine is deaminated by deaminase and hypoxanthine is produced
   by deamination of adenine. Then, hypoxanthine is oxidized to xanthine
   by xanthine oxidase, whereas xanthine is directly produced by guanine
   deaminase and further oxidized to uric acid [[99]34]. The hypoxanthine
   and 3′-adenylic acid contents, the different metabolites of T6_F–T6_C,
   significantly increased, whereas the guanine, guanosine, adenosine,
   xanthine, and allantoic acid contents significantly decreased, as shown
   in [100]Figure 6. The 3′-adenylic acid and guanine contents, the
   differential metabolites of 733xin_F–733xin_C, increased significantly,
   whereas the xanthine content decreased. Purine metabolism enriched by
   different metabolites changed significantly, and fluoride was able
   reduce the amount of uric acid excreted by both cultivars, possibly
   because fluoride can inhibit the conversion of xanthine to uric acid.
   Pyrimidine metabolism of T6_F–T6_C and T6_F–733xin_F also changed
   significantly. The uric acid content in the two control groups
   decreased significantly, indicating that the uric acid production
   pathway in fluoride-tolerant strain T6 was also inhibited due to the
   presence of fluoride.

Figure 6.

   [101]Figure 6
   [102]Open in a new tab

   Differential metabolites between T6_F vs. T6_C are located in Purine
   metabolism. Red indicates upregulated in purine metabolism and blue
   indicates downregulated in purine metabolism.

4.4. Other Metabolism

   Nicotinic acid in the midgut of the silkworm becomes nicotinamide to
   play a role. Nicotinamide can form coenzyme I and coenzyme II with
   ribose, phosphoric acid, and adenine. These are coenzymes of many
   dehydrogenases that dehydrogenate and hydrogenate in the process of
   biological oxidation. They are closely related to many metabolic
   processes, including glycolysis, fatty acid metabolism, pyruvate
   metabolism, and formation of high energy phosphate bonds. The nicotinic
   acid and fumaric acid contents (the differential metabolites of
   733xin_F–733xin_C) significantly increased and nicotinamide was
   downregulated. The nicotinamide content, the differential metabolite of
   T6F–T6C, significantly increased, and the nicotinic acid, pyruvic acid,
   and fumaric acid contents were significantly downregulated. From the
   changes in differential metabolites, we found that fluoride can inhibit
   the conversion of nicotinic acid to nicotinamide in the 733xin
   fluoride-sensitive strain but not in the T6 fluoride-tolerant strain.
   We speculate that the mechanism of fluoride tolerance in the midgut of
   the silkworm is related.

5. Conclusions

   Metabolome analysis showed that both T6 and 733xin silkworm strains
   induced complex responses under 200 mg/kg NaF stress. The infection of
   NaF can lead to significant levels of metabolic pathways such as
   oxidative phosphorylation, purine metabolism, and glutathione
   metabolism in fluorine-sensitive strain. Among the significantly
   upregulated differential metabolites are fumaric acid, phosphate and
   guanine, while the significantly downregulated differential metabolites
   are L-cysteine, glycine, and xanthosine. It was speculated that
   fluoride-susceptible strains could respond to NaF infection by changing
   these metabolic pathways. Metabolic pathways such as purine metabolism,
   arginine and proline metabolism, ABC transporters, and oxidative
   phosphorylation of fluoride-tolerant strain were induced changed, and
   reached a significant level, among which 3′-adenylic acid and
   hypoxanthine were significantly upregulated, whereas guanine, allantoic
   acid, xanthine, N-acetyl-L-glutamic acid, pyruvate, fumaric acid, and
   succinic acid were significantly downregulated. We hypothesized that
   these metabolic pathways may be related to the fluoride tolerance
   mechanism of NaF poisoning in tolerant strains. The results can provide
   some reference for exploring the metabolic mechanism of fluoride
   regulating the midgut tissue of the silkworm.

Acknowledgments