Abstract

   Azadirachtin exhibits excellent bioactivities against several hundred
   arthropods. However, current knowlege of its biochemical effect on B.
   dorsalis larvae is not deep enough. In this study, integrated LC-MS and
   GC-MS-based untargeted metabolomics were used to analyze the changes of
   endogenous metabolites and the biochemical effects of azadirachtin on
   B. dorsalis larvae. Azadirachtin has excellent bioactivities against B.
   dorsalis larvae in this study, leading to a longer developmental
   duration, lower survival rate, and low pupa weight. The effect of
   azadirachtin was investigated on a total of 22 and 13 differentially
   abundant metabolites in the LC–MS and GC–MS-based metabolomics results,
   are selected respectively. Pathway analysis indicated that 14
   differentially enriched metabolic pathways, including seven influential
   pathways, are worthy of attention. Further integrated key metabolic
   pathway analysis showed that histidine metabolism, d-glutamine and
   d-glutamate metabolism, biotin metabolism, ascorbate and aldarate
   metabolism, pentose and glucuronate interconversions, and alanine,
   aspartate and glutamate metabolism in B. dorsalis larvae are
   significantly relevant pathways affected by azadirachtin. Although
   extrapolating the bioactivity results in this study to the practical
   project of B. dorsalis pest management in the field has limitations, it
   was found that azadirachtin has a significant effect on the primary
   metabolism of B. dorsalis larvae.

   Subject terms: Metabolomics, Entomology

Introduction

   Bactrocera dorsalis is a destructive polyphagous and invasive insect
   pest of tropical and subtropical fruits and vegetables; this oriental
   fruit fly has been found to attack many types of commercial fruits and
   a wide variety of agricultural products^[36]1. Azadirachtin exhibits
   excellent bioactivities against agricultural, forestry, medical, and
   veterinary arthropods^[37]2–[38]4. However, studies on the effects of
   azadirachtin on B. dorsalis are scarce. Azadirachtin is the main active
   ingredient in neem. It was reported that neem derivatives are
   ineffective when used as toxic bait for tephritid fruit flies^[39]5.
   Several studies reported that neem seed kernel extracts and
   azadirachtin deters oviposition of B. dorsalis adults^[40]6,[41]7. Neem
   leaf dust significantly reduced the longevity and fertility of B.
   dorsalis adults by blocking ovarian development^[42]8. Neem extract
   could effectively reduce the fecundity, fertility, and post-embryonic
   development of freshly emerged B. dorsalis flies^[43]9. However, we
   found no previous studies in the literature on the activity of
   azadirachtin against the larvae of B. dorsalis.

   The biological effects of azadirachtin include impacts on egg-laying
   behavior, feeding behavior, growth and metamorphosis, reproduction,
   activity, and histopathology^[44]10. The mode of action of azadirachtin
   against lepidopteran insects can be explained, in part, by effects on
   digestive enzymes, NADPH cytochrome reductase, and
   cholinesterase^[45]11. The physiological effects of azadirachtin
   include direct inhibition of cell division and protein
   synthesis^[46]12. The indirect effects of blocking the release of
   morphogenetic peptide hormones (PTTH and allatostatins) causes
   disruption of molting hormone from the prothoracic glands and juvenile
   hormone from the corpora allata^[47]12. Transcriptomics analysis to
   investigate growth inhibition in Drosophila larvae after exposure to
   azadirachtin showed that 28 genes are significantly up or down
   regulated, with genes involved in starch and sucrose metabolism,
   defense response, signal transduction, instar larval or pupal
   development, and chemosensory behavioral processes were
   affected^[48]13. The 2DE proteomics analysis of azadirachtin showed
   that 21 proteins were differentially expressed, which involved
   cytoskeletal organization, transcription and translation, hormonal
   regulation, and energy metabolism^[49]14.

   Azadirachtin can also have effects at the biochemical level by
   impacting insect endogenous metabolites. Azadirachtin could interfere
   with serotonin pools in the neuroendocrine system of locusts^[50]15. It
   significantly decreased the lipids levels in the fat body, hemolymph,
   and ovary of Atractomorpha crenulata and the amino acid content in the
   fat body, testes, seminal vesicle, and MARGs of Odontopus
   varicornis^[51]16,[52]17. Azadirachtin was also found to severely
   reduce protein, glycogen, and lipid contents of Plodia
   interpunctella^[53]18. The levels of cholesterol, uric acid, urea, and
   glucose decreased in azadirachtin-treated larvae of Hyphantria cunea
   compared with the control^[54]19. Quantities of fatty acids and their
   relative composition in Asian corn borer larvae were significantly
   affected by azadirachtin at 0.1–10 ppm^[55]20. The protein, lipid, and
   glucose contents decreased, whereas uric acid increased when Glyphodes
   pyloalis larvae were fed with neem-treated mulberry leaves^[56]21.
   These studies clearly showed that azadirachtin can affect various
   biochemical compounds, such as carbohydrates, fatty acids, amino acids,
   cholesterol, uric acid, and urea. Such studies focused mainly on a few
   biochemical metabolites, and no further analysis was performed to
   determine the biological importance of these molecular changes.

   Metabolomics, an important part of systems biology, identifies the
   entire profile of detectable metabolites contained in a biological
   system; it has also been used to reveal alterations in the endogenous
   metabolite levels that may result from disease processes, drug
   toxicity, or gene function^[57]22. In addition, metabolomics is a
   powerful bioanalytical tool and has been widely used in insect science,
   such as in the discovery of pesticide modes-of-action^[58]23, the pupal
   diapause of the cotton bollworm^[59]24, radiation-induced insect
   sterility technique^[60]25, and research on the Asian citrus psyllid
   Diaphorina citri^[61]26.

   Hence we tested the bioactivities of azadirachtin against B. dorsalis
   larvae in this study. Thereafter, we introduced an approach of
   integrated untargeted metabolomics using UPLC–QTOF-MS and GC–Q-MS to
   explore the changes in endogenous metabolites and the potential
   biological implications.

Results

Azadirachtin bioactivities against B. dorsalis larvae

   Azadirachtin was found to exhibit significant bioactivities towards B.
   dorsalis larvae (Fig. [62]1d). As shown in Fig. [63]1a, with regard to
   the developmental duration, 9.59 ± 0.27 days in the treatment (Tr)
   group was significantly longer than 8.23 ± 0.11 days in the control
   (CK) group (P < 0.01). As shown in Fig. [64]1b, in terms of survival,
   19.78 ± 1.5% in the Tr group was significantly lower than 88.56 ± 1.4%
   in the CK group (P < 0.001). As shown in Fig. [65]1c, pupal weight,
   0.084 ± 0.007 mg in the Tr group was significantly lower than
   0.112 ± 0.003 mg in the CK group (P < 0.05).

Figure 1.

   [66]Figure 1
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   Bioactivities of azadirachtin against B. dorsalis larvae. Data were
   expressed as the mean ± SE. * Indicates P < 0.05, ** indicates
   P < 0.01, and **** indicates P < 0.001.

Metabolic profiles analyzed by GC–MS and LC–MS

   The unsupervised principal component analysis (PCA) was used to check
   the quality of the data from the GC–MS and LC–MS analyses. They showed
   that all CK, Tr, and QC (quality control) samples were within the 95%
   Hotelling’s T-squared ellipse and significantly separated into
   clusters. That is no outlier was found among these samples. In the
   GC–MS analysis, the first principle component (PC1) and second
   principle component (PC2) explained 28.4% and 27% of the total variance
   of all samples (Fig. [68]2a). In ESI− mode, the PC1 and PC2 explained
   45.6% and 13.8% of the total variance of all samples (Fig. [69]2b). In
   ESI+ mode, the PC1 and PC2 explained 35.9% and 23.9% of the total
   variance (Fig. [70]2c).

Figure 2.

   [71]Figure 2
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   PCA score plots derived from (a) GC–MS, (b) negative ion mode (ESI−)
   and (c) positive ion mode (ESI+) in LC–MS metabolite profiles of B.
   dorsalis larvae.

   The supervised partial least squares discrimination analysis (PLS-DA)
   was performed to identify the metabolites responsible for the
   separation between control and azadirachtin-exposed groups. The CK and
   Tr groups in these PLS-DA models were inside the 95% Hotelling’s
   T-squared ellipse and showed clear separation (Fig. [73]3). The 7-folds
   internal cross validation and 200 times permutation test were further
   conducted to assess these models’ predictive accuracy and statistical
   significance. In the GC–MS analysis, the parameters of PLS-DA model’s
   predictive accuracy were R^2X[cum] = 0.48, R^2Y[cum] = 0.985, and
   Q^2Y[cum] = 0.843; with its corresponding statistical significance were
   R^2 = 0.793 and Q^2 = −0.0593 (Fig. [74]3a). In ESI− mode, the
   parameters of PLS-DA model’s predictive accuracy were
   R^2X[cum] = 0.576, R^2Y[cum] = 0.999, and Q^2Y[cum] = 0.96; with its
   corresponding statistical significance were 0.844 and 0.0944
   (Fig. [75]3b). In ESI+ mode, the parameters of PLS-DA model’s
   predictive accuracy were R^2X[cum] = 0.573, R^2Y[cum] = 0.992, and
   Q^2Y[cum] = 0.959; with its corresponding statistical significance were
   0.859 and 0.107 (Fig. [76]3c). According to the criteria that if all
   blue Q^2 values to the left are lower than the original points to the
   right or if the blue regression line of the Q^2 points intersects the
   vertical axis at or below zero^[77]27, these PLS-DA models exhibited a
   low risk of overfitting. The above results indicated that these PLS-DA
   models could identify the differentially enriched metabolites between
   CK and Tr groups.

Figure 3.

   [78]Figure 3
   [79]Open in a new tab

   PLS-DA score plots (left) with corresponding permutation test plots
   (right) derived from (a) GC–MS, (b) negative ion mode (ESI−) and (c)
   positive ion mode (ESI+) in LC–MS metabolite profiles of B. dorsalis
   larvae.

Changed metabolites in B. dorsalis larvae between the CK and the Tr groups

   The representative GC–MS and LC–MS total ion chromatograms (TICs) of B.
   dorsalis larvae tissue samples are shown in Fig. [80]4. The shape and
   quantity of peaks between the CK and the Tr groups varied.
   Approximately 7328 and 13,375 metabolite peaks were deconvoluted in
   ESI− and ESI+ mode of LC–MS. By contrast, 415 metabolite peaks were
   deconvoluted in GC–MS. These deconvoluted data were further processed
   through missing value imputations, filtering, and normalization in
   MetaboAnalyst 4.0^[81]28. A total of 1979 and 3904 remaining peaks in
   ESI− and ESI+ modes in LC–MS and 235 remaining peaks in GC–MS were
   further annotated using references in existing databases. After the