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Neuropeptide ACP facilitates lipid oxidation and utilization during long-term flight in locusts

  1. Li Hou
  2. Siyuan Guo
  3. Yuanyuan Wang
  4. Xin Nie
  5. Pengcheng Yang
  6. Ding Ding
  7. Beibei Li
  8. Le Kang  Is a corresponding author
  9. Xianhui Wang  Is a corresponding author
  1. State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, China
  2. CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, China
  3. Beijing Institutes of Life Science, Chinese Academy of Sciences, China
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Cite this article as: eLife 2021;10:e65279 doi: 10.7554/eLife.65279

Abstract

Long-term flight depends heavily on intensive energy metabolism in animals; however, the neuroendocrine mechanisms underlying efficient substrate utilization remain elusive. Here, we report that the adipokinetic hormone/corazonin-related peptide (ACP) can facilitate muscle lipid utilization in a famous long-term migratory flighting species, Locusta migratoria. By peptidomic analysis and RNAi screening, we identified brain-derived ACP as a key flight-related neuropeptide. ACP gene expression increased notably upon sustained flight. CRISPR/Cas9-mediated knockout of ACP gene and ACP receptor gene (ACPR) significantly abated prolonged flight of locusts. Transcriptomic and metabolomic analyses further revealed that genes and metabolites involved in fatty acid transport and oxidation were notably downregulated in the flight muscle of ACP mutants. Finally, we demonstrated that a fatty-acid-binding protein (FABP) mediated the effects of ACP in regulating muscle lipid metabolism during long-term flight in locusts. Our results elucidated a previously undescribed neuroendocrine mechanism underlying efficient energy utilization associated with long-term flight.

eLife digest

Flight allows insects to find food or seek a better environment. Some insects have developed the ability of ‘long-term flight’, which allows them to make continuous journeys over large distances. For example, one locust species regularly crosses the Red Sea which is up to 300 km wide – a spectacular feat for insects only a few inches long.

However, flight is an energy-intensive activity, and insects’ muscles need the right sort of chemical fuel to work properly. Previous work has shown that this ‘fuel consumption’ is highly dynamic and happens in two stages. First, immediately after take-off, the muscles rapidly consume carbohydrates (sugars); then, during the prolonged phase of the flight, muscles switch to exclusively consume lipids (fats).

How the flight muscles ‘know’ when to start using fats for energy remains largely unclear. It has been suggested that this switch may involve hormone-like chemicals made in the brain called neuroendocrine peptides. Hou et al. therefore set out to test this hypothesis, using the locust species Locusta migratoria as a representative migratory insect.

Initial experiments used an abundance detection technique to determine which of the neuroendocrine peptides were active in adult locusts. Further analysis, looking specifically at locusts that had just been flying, revealed that the gene for a peptide called ACP became much more active after one hour of continuous flight. Further evidence that the ACP hormone could indeed be helping to power long-term flight came from locusts with a mutated, ‘switched-off’ version of the gene. These insects could only fly for half the time, and half the distance, compared to locusts that did not have mutations in the gene for ACP.

Biochemical studies of the ACP mutant locusts confirmed that their flight muscle cells could not transport and break down fatty acids normally. These experiments also showed that ACP was acting through a type of carrier protein called FABP, which is present in many different insects and normally ‘ferries’ lipids to the places they are needed.

These findings shed new light on the biological mechanisms that control long-term flight in migratory insects. The ability to move over long distances is key to the outbreak of locust plagues, which in turn cause widespread crop damage around the world. Hou et al. therefore hope that this knowledge will one day help develop effective strategies for locust pest control.

Introduction

Flight is an extraordinary biological trait that has only evolved in several kinds of animals (e.g. insects, bats, and birds) and is notably effective in searching for food and mates, finding habitats, defending against predators, and adapting to seasonal changes in environment (Krause and Godin, 1996; Borgemeister et al., 1997; Chapman et al., 2010). Despite these adaptive advantages, flight is one of the most intense and energy-demanding physiological processes, especially for insects that possess long-term flight capacity, such as locusts and butterflies (Mentel et al., 2003; Zhan et al., 2014). Long-term flight is usually defined as sustained flight for seasonal and long-range migration toward a distinct direction in populations (Stefanescu et al., 2013; Juhász et al., 2021). Metabolic rates in the flight muscle can increase by 20- to 100-fold during flight (Harrison and Roberts, 2000; Suarez, 2000). To meet the high energy demands of long-term flight, migratory insects have evolved a suite of adaptive physiological traits (Arrese and Soulages, 2010) and exhibit highly efficient utilization of energy substrates, such as carbohydrates and lipids, in the flight muscle (Canavoso et al., 2003; Van der Horst and Rodenburg, 2010). Compared to short-term flight, which exclusively employs carbohydrates as energy substrates, many physiological activities subsequently occur in the flight muscle during long-term flight, including carbohydrate/lipid metabolism transition, fatty acid transport, lipid oxidation, and lipid mobilization in the fat body. Clearly, complex and precise spatial and temporal regulation of energy metabolism is essential for long-term fight performance. However, the mechanisms underlying the highly efficient energy utilization associated with long-term flight have not been elucidated to date.

A number of reports have emphasized the central roles played by neuropeptides in coordinating systematic energy metabolism during insect flight (Gade, 1992; Lorenz and Gäde, 2009). Most neuropeptides are produced by the central nervous system and perform distinct tasks through binding with their cognate G-protein-coupled receptors (GPCRs) (Nässel, 2002). By affecting energy metabolism either directly or indirectly, neuropeptides participate in a variety of biological events, such as flight, reproduction, diapause, and immune response (Gäde and Marco, 2009; Sim and Denlinger, 2013; Ling et al., 2017; Urbanski and Rosinski, 2018). Furthermore, a single behavior or physiological process is usually controlled by multiple neuropeptides that play distinct roles in energy metabolism (Waterson and Horvath, 2015; Toprak, 2020). For example, adipokinetic hormone (AKH) has been demonstrated to be a conserved regulator of flight-related energy metabolism by promoting glycolysis and lipid mobilization in the fat body in different insect species (Gäde et al., 2006; Kaufmann and Brown, 2008). Downstream signal transduction of AKH involved in lipid mobilization has been elucidated in insects (Gäde and Auerswald, 2003). In addition, other neuropeptides are also involved in either lipogenesis or lipolysis and play distinct roles in insect flight (Toprak, 2020).

The migratory locust, Locusta migratoria, which possesses a notable long-term flight capacity, is one of the most destructive agricultural pests (Wang and Kang, 2014) and has been employed as a useful study model for the neurohormonal regulation of flight-related energy metabolism (Jutsum and Goldsworthy, 1976; Van der Horst and Rodenburg, 2010; Bullard et al., 2017). The locust displays strong adaption to long-distance flight at both the physiological and morphological levels, exhibiting clear expansion of the energy gene family and high plasticity of muscle metabolism (Wang et al., 2014). In the locust, the patterns of energy metabolism display tissue-specific and time-dependent patterns during tethered flight. Usually, metabolism involved in long-term flight in insects contains two major phases: during the early stage of flight, carbohydrates in flight muscle and hemolymph significantly decrease, and lipid mobilization in the fat body gradually increases thereafter followed by utilization in the flight muscle during the prolonged flight phase (Worm and Beenakkers, 1980; Wegener et al., 1986), indicating a clear transition in energy consumption from carbohydrates to lipids during long-term flight. Distinct neuropeptide and neurotransmitter have been demonstrated to modulate lipid mobilization and transport in the fat body (Van der Horst, 2003) and carbohydrate catabolism in flight muscle at the beginning of flight (Mentel et al., 2003), and the relevant regulatory mechanisms have also been uncovered. Nevertheless, neuroendocrine mechanisms underlying energy utilization associated with prolonged flight remain to be explored.

In this study, we employed integrated multi-omics studies to screen potential neuropeptides involved in the long-term flight of locusts and elucidated relevant regulatory mechanisms by using the CRISPR/Cas9 method. Finally, we identified a novel flight modulator, the AKH/corazonin-related peptide (ACP), which plays an important role in prolonged flight by facilitating lipid utilization in the flight muscle in locusts.

Results

Identification of candidate neuropeptide regulators involved in the flight activity of locusts

Given that flight is a unique biological trait in adult locusts, we first performed comparative neuropeptidome analysis on the neuroendocrinal tissues brain (Br) and retrocerebral complex (main endocrine tissues of the locust, RC) between final instar nymphs (5th) and adult locusts through high-resolution and high-sensitivity MS (LTQ-Orbitrap Elite) to identify neuropeptides possibly related to flight. In total, 201 and 362 nonredundant peptides (including both mature neuropeptides and their potential degradation products) derived from 37 neuropeptide precursors were identified in Br and RC, respectively (Supplementary file 1). Tissue-specific analysis showed that neuropeptides from 20 precursors were considerably more abundant in the RC, whereas neuropeptides from 16 precursors were more abundant in the Br. The GPB5-derived peptides showed similar abundance levels in Br and RC (Figure 1—figure supplement 1).

The abundant levels of neuropeptides in Br and RC between 5th-instar nymphs and adult locusts were further compared by a label-free quantitative strategy. Compared to 5th-instar nymphs, there were 20 and 18 upregulated neuropeptides in the Br and RC of adult locusts, respectively (Figure 1—figure supplement 2), and 10 neuropeptides displayed significantly higher abundance (Log2FC > 1.5) in either Br or RC of adult locusts (Figure 1A and B). To validate whether these neuropeptides were closely related to flight activity, we examined the expression levels of the precursor genes of these ten neuropeptides in either Br or RC (depending on their tissue-specific expression patterns, Figure 1—figure supplement 3) after 1 h-sustained flight. The mRNA levels of four neuropeptide precursor genes, namely, AKH/corazonin-related peptide (ACP), adipokinetic hormone (AKH2), NPF1, and GPB5, changed significantly after flying treatment. Among these genes, three (ACP, AKH2, and NPF1) exhibited clearly increased expression levels, whereas GPB5 displayed decreased expression levels after 1 h- of sustained flight (Figure 1C and Figure 1—figure supplement 4).

Figure 1 with 7 supplements see all
Identification of AKH/Corazonin-related neuropeptide (ACP) as a potential neuroendocrine modulator for flight activity in the locust.

Volcano plot of neuropeptidomes from (A) brains (Br) and (B) the retrocerebral complex (RC) of the final instar nymphs (5th) and mature adult locusts. Each circle represents a neuropeptide. Differential peptides with a Log2(FC) >1.5 and p value < 0.05 are highlighted in red. Data are from three biological replicates. (C) Expression levels of four neuropeptide precursor genes (ACP, AKH2, NPF1, and GPB5) in Br or RC after 1 h-sustained tethered flight (Student’s t-test, p=0.011, t = 3.619, df = 6 for ACP; p=0.0003, t = 7.394, df = 6 for AKH2; p=0.0194, t = 3.166, df = 6 for NPF1; p=0.0007, t = 6.451, df = 6 for GPB5, respectively, n = 4 biological replicates for each treatment, *p<0.05, ***p<0.001). CK indicates control, FL 1 h indicates 1h-sustained flight. (D) Total flight time and (E) total flight distance after the knockdown of four candidate neuropeptide precursor genes, ACP, AKH2, NPF1, and GPB5, in adult locusts. Columns labeled with different letters indicate that there is a significant difference between the two groups, columns contain same letters indicate no significance observed between the two groups (one-way ANOVA for D, F = 4.658, df = 4, p=0.0016; for E, F = 5.48, df = 4, p=0.0004; n = 30 (dsGFP), 25 (dsACP), 24 (dsAKH2), 22 (dsNPF1), and 23 (dsGPB5), respectively). Each blot represents a single individual. The mean ± SEM are shown. See Figure 1—source data 1 for details.

Figure 1—source data 1

Raw data for comparisons of peptide contents, gene expression, and flight activity.

https://cdn.elifesciences.org/articles/65279/elife-65279-fig1-data1-v3.xlsx

To explore whether these four neuropeptides are involved in the modulation of flight performance in locusts, we performed gene knockdown by RNA interference (RNAi) for each gene in adult locusts. Of these four genes, only the ACP and AKH2 RNAi treatments exhibited significant effects on the flight activity of adult locusts, although the expression levels of all neuropeptide genes were successfully downregulated (Figure 1—figure supplement 5). Compared with the control, total flight time and total flight distance decreased by more than 50% after the knockdown of ACP or AKH2 (Figure 1D and E). However, the average flight velocity and maximum flight velocity of the locusts did not significantly change (Figure 1—figure supplement 6A and B). AKH family members have been determined to play conserved roles in flight activity by promoting the mobilization of lipids and carbohydrates stored in the fat body of locusts (Van der Horst, 2003). However, to date, few studies have investigated ACP functions in insects. Thus, we then validated the regulatory role of ACP in flight activity by injecting synthetic ACP peptide in adult locusts. After peptide injection, the locusts exhibited significantly enhanced total flight time and total flight distance, whereas average and maximum flight velocity were unaffected (Figure 1—figure supplement 7). These results confirm the essential regulatory role of ACP peptide in locust flight ability.

Mutant line of CRISPR/Cas9 confirmed the essential role of ACP in long-term flight

The ACP precursor gene exhibited a brain-specific expression pattern (Figure 2A). In addition, the mRNA level of ACP did not significantly change until sustained flight for 1 h, with a 100% increase being observed at this time point (Figure 2B), implying that the ACP peptide plays major roles in facilitating long-term flight in locusts. To further explore the functional roles played by ACP in long-term flight, we generated an ACP mutant line using a CRISPR/Cas9-mediated gene editing system. We injected Cas9 protein and a gRNA targeting the second exon of the ACP gene into eggs < 2 h after they were laid (Figure 2C). The ACP gRNA-induced mutation at high efficiency in the G0 generation, as well as their progeny (G1) (Figure 2—figure supplement 1A, Supplementary file 2). We finally successfully obtained a heritable homozygous ACP mutant line with a 13 bp deletion modification (ACP13/13, referred to as ACP-/- in the following text) by performing a series of crossing experiments (Figure 2D and Figure 2—figure supplement 1B). The ACP-/- locusts were predicted to produce a frameshift precursor unable to give rise to mature ACP peptide. Through immunohistochemistry analysis, we detected strong signals for ACP peptide in the neurons of the par intercerebralis and bilateral forebrain of wild-type locusts (WT) (Figure 2E and Figure 2—figure supplement 2), whereas the fluorescence signal was not observed in the brain of ACP-/- locusts (Figure 2E), which further confirmed the successful construction of ACP mutants of the migratory locust.

Figure 2 with 4 supplements see all
Functional role of ACP in long-term flight validated by CRISPR/Cas9 system-mediated gene knockout.

(A) Tissue-specific expression pattern of the ACP precursor gene in adult locusts analyzed by qPCR (n = 4 replicates, 6–8 locusts/replicate). Br, brain; RC, retrocerebral complex; FB, fat body; MS, muscle; Ov, ovary; Tg, thoracic ganglia. (B) Expression levels of the ACP gene in the brain during the time course of sustained flighting of adult locusts. The mRNA level of ACP significantly increased after 1 h of sustained flighting. Different letters indicate that there is a significant difference between the two groups (one-way ANOVA, F = 20.69, df = 3, p<0.0001, n = 4 replicates). (C) Gene structure and designed sgRNA-targeted site in exon 2 of the locust ACP gene. Three exons are represented in different colors. The sgRNA targeted site in exon two is highlighted in blue. The pink region in exon two indicates the mature ACP peptide-encoding region. (D) Genome PCR product sequencing of the wild-type (WT) and 13 bp-deletion ACP mutants (ACP13/13, referring to ACP-/- in the following section), which could not give rise to mature ACP peptide. (E) Detection of ACP peptide in WT and ACP-/- locusts by immunohistochemistry assay. The bar indicates 100 μm. (F) Morphology of females and males of WT and ACP-/- locusts. Both females and males of ACP mutant adults show increased body size compared to that of WT controls. Bars represent 1 cm. Measurement of (G) total flight time, (H) total flight distance, and (I) average flight velocity in females and males of WT and ACP-/- locusts. Each dot represents a single individual (Student’s t-test for G, p=0.8499, t = 0.1904, df = 44 for 15 min♀, p=0.0183, t = 2.460, df = 40 for 60 min♀; p=0.9504, t = 0.0626, df = 46 for 15 min♂, p=0.0088, t = 2.755, df = 40 for 60 min♂; For H, p=0.6416, t = 0.4687, df = 44 for 15 min♀, p=0.0097, t = 2.718, df = 40 or 60 min♀; p=0.7489, t = 0.332, df = 46 for 15 min♂, p=0.01, t = 2.704, df = 40 for 60 min♂; For I, p=0.6067, t = 0.5186, df = 44 for 15 min♀, p=0.654, t = 0.4516, df = 40 for 60 min♀; p=0.7644, t = 0.3015, df = 46 for 15 min♂, p=0.9122, t = 0.1109, df = 40 for 60 min♂; n = 23 (WT♀ 15 min), 23 (ACP-/-♀ 15 min), 22 (WT♀ 60 min), 20 (ACP-/-♀ 60 min), 24 (WT♂ 15 min), 24 (ACP-/-♂ 15 min), 21 (WT♂ 60 min), 21 (ACP-/-♂ 60 min), *p<0.05, **p<0.01, n.s. indicates not significant). See Figure 2—source data 1 for details.

Figure 2—source data 1

Raw data for gene expression and flight activity.

https://cdn.elifesciences.org/articles/65279/elife-65279-fig2-data1-v3.xlsx

In comparison with WT locusts, no significant difference in the survival rate of either females or males was observed (Figure 2—figure supplement 3), whereas ACP-/- locusts had a larger body size (Figure 2F). Next, we compared the flight performance of ACP mutants and WT locusts during the 60 min sustained tethered flight test. Within the first 15 min, there was no significant difference in total flight distance and total flight time between female ACP mutants and WT locusts. However, for the 60 min tethered flight test, both females and males of ACP mutants displayed significantly shorter flight times and flight distances compared with the WT locusts (Figure 2G and H). The average flight velocity and maximum flight velocity did not show any changes between ACP mutants and WT locusts in either 15 min or 60 min flight tests (Figure 2I and Figure 2—figure supplement 4). These results indicated that the neuropeptide ACP was involved in modulating long-term flight in locusts.

Loss of function of ACP receptor (ACPR) significantly impaired long-term flight in the locust

Distinct receptors play essential roles in mediating the functions of neuropeptides, and tissue-specific expression patterns of neuropeptide receptors have been proposed to be sources of critical information for exploring the regulatory mechanisms of neuropeptides (Garcia et al., 2015; Nässel and Vanden Broeck, 2016). Therefore, we further identified the ACP receptor by homolog searching the genome and transcriptome databases. Phylogenetic analysis showed that the putative locust ACPR was closely related to ACP receptors from other insects but was evolutionarily divergent from its structure-related neuropeptide receptors, AKH receptors and corazonin receptors (Figure 3A). Compared to other organs tested, ACPR was highly expressed in the fat body and flight muscle of adult locusts (Figure 3B). To validate the role of ACPR in regulating long-term flight, we generated an ACPR mutant locust line using the CRISPR/Cas9 system. Combined injection of designed ACPR gRNA and Cas nine protein induced multiple kinds of mutations around the target sequence in the G0 generation (Figure 3C and Supplementary file 3). Using the crossing strategy similar to ACP mutant line construction, we successfully obtained a homozygous ACPR mutant line with a 13 bp deletion (ACPR13/13). The ACPR13/13 locusts were predicted to produce truncated protein that losses the last four transmembrane domains (Figure 3D). Compared with the WT locusts, both ACPR female and male mutants showed significantly reduced flight time and flight distance during the 60 min tethered flight test (Figure 3E and F), although intense immunostaining signal of ACP peptide was detected in the brain of ACPR mutants (Figure 3—figure supplement 1). However, no significant changes in average flight velocity and maximum flight velocity were observed between ACPR mutants and WT locusts (Figure 3—figure supplement 2). The flight phenotypes caused by ACPR knockout was similar to that observed in ACP mutants, supporting that the essential role of ACP peptide system in modulating long-term flight in locusts.

Figure 3 with 2 supplements see all
CRISPR/Cas9-mediated knockout of ACPR impairs long-term flight in the locust.

(A) Phylogenic relationship of AKHR, ACPR, CRZR from the migratory locust and their homologs from other representative insect species. The phylogenetic tree is constructed using the neighbor-joining method. The locust ACPR protein is evolutionarily divided into the insect ACPR cluster. (B) Tissue-specific expression pattern of ACPR in both female and male adult locusts. The data are presented as the mean ± SEM (one-way ANOVA, F = 27.08, df = 5, p<0.0001 for male; F = 73.02, df = 5, p<0.0001 for female, n = 4 replicates, 6–8 locusts/replicate). For both female and male adults, ACPR gene is highly expressed in fat body and muscle, compared with other tissues tested. Columns labeled with different letters in same color indicate that there is a significant difference between the two groups, columns contain same letters in same color indicate no significance observed between the two groups (red indicates female, green indicates male). Br, brain; RC, retrocerebral complex; FB, fat body; MS, muscle; Ov, ovary; Tg, thoracic ganglia. (C) Gene types of the ACPR mutation in the G0 generation determined by Sanger sequencing. (D) Sequencing of the wild-type (WT) and 13 bp-deletion homozygous ACPR mutants (ACPR13/13), which is predicted to produce a truncated protein that lacks the last four transmembrane domains. Measurement of (E) total flight time and (F) total flight distance in females and males of WT and ACP-/- locusts. Each dot represents a single individual (Student’s t-test for E, p=0.7536, t = 0.3153, df = 62 for 15 min♀, p=0.0157, t = 2485, df = 62 for 60 min♀; p=0.2481, t = 1.166, df = 63 for 15 min♂, p=0.0384, t = 2.115, df = 63 for 60 min♂; For F, p=0.0734, t = 1.821, df = 62 for 15 min♀, p=0.0016, t = 3.295, df = 62 or 60 min♀; p=0.1271, t = 1.546 df=63 for 15 min♂, p=0.0227, t = 2.336, df = 63 for 60 min♂; *p<0.05, **p<0.01, n.s. indicates not significant). See Figure 3—source data 1 for details.

Figure 3—source data 1

Raw data for gene expression and flight activity.

https://cdn.elifesciences.org/articles/65279/elife-65279-fig3-data1-v3.xlsx

Transcriptome analysis reveals significant downregulation of genes associated with lipid metabolism in the flight muscle of ACP-/- locust adults

Based on the tissue-specific expression pattern of ACPR, we hypothesized that fat body and flight muscle may be the main tissues targeted by ACP to participate in flight regulation. We then performed comparative transcriptome analysis of the fat body and flight muscle tissues in ACP-/- and WT locusts. The results showed that the number of differentially expressed genes (DEGs) in flight muscle was greater than that in fat body (520 in flight muscle and 318 in fat body, Log2 FC >1, FDR < 0.05, RPKM > 0.5). For ACP-/- locusts, there were 212 upregulated and 308 downregulated genes in the flight muscle and 200 upregulated and 118 downregulated genes in the fat body (Figure 4—figure supplement 1). The fat body and flight muscle had more tissue-specific DEGs and fewer overlapping DEGs after knockout of the ACP gene (Figure 4A). Several pathways associated with energy metabolism were significantly changed in the flight muscle of ACP-/- locusts (-Log2(P value)>10, enriched gene number >15), including oxidation phosphorylation, fatty acid degradation, valine, leucine and isoleucine degradation, cardiac muscle contraction, and fatty acid metabolism. However, only a small number of genes were enriched in the KEGG analysis of the fat body (-Log2(P value)<10, gene number <10) (Figure 4B), indicating that the gene expression profiles of the flight muscle were more strongly affected by ACP knockout.

Figure 4 with 4 supplements see all
Transcriptome analysis reveals significant downregulation of genes and pathways associated with lipid transport and oxidation in the flight muscle of ACP-/- locusts.

(A) Ven diagram of differentially expressed genes (DEGs) in flight muscle and fat body between WT and ACP-/- locusts. There were 212 upregulated and 349 downregulated genes in the flight muscle and 200 upregulated and 118 downregulated genes in the fat body of ACP mutants. (B) The representative enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) terms of the DEGs in the flight muscle and fat body in WT and ACP-/- locusts. (C) Heat map of DEGs related to fatty acid transport, beta-oxidation, and oxidation phosphorylation in the flight muscle of WT and ACP-/- locusts. (D) Volcano plot of RNA-seq data from flight muscle of WT and ACP-/- locusts. Blue dots indicate DEGs, and the red dot indicates fatty acid-binding protein (FABP) that shows highly basic expression and fold change after ACP gene knockout. Validation of FBAP expression in the flight muscle of WT and ACP-/- locusts via (E) qPCR and (F) western blot. (Student’s t-test for E, p=0.0007, t = 5.756, df = 7, n = 4–5 biological replicates, ***p<0.001). (G) Heat map of beta-oxidation-related genes in flight muscle after FABP knockdown. See Figure 4—source data 1 for details.

The expression levels of 4 genes responsible for fatty acid transport, 10 genes involved in beta-oxidation, and 19 genes associated with mitochondrial energy metabolism were clearly downregulated in the flight muscle of the ACP-/- locust (Figure 4C). A fatty acid binding protein (FABP), as the most highly expressed gene (Log10(mean expression)>4), exhibited an expression decrease of more than 80% (log2FC = −2.43) in ACP-/- locusts (Figure 4D). Reduced expression levels of FABP in the flight muscle of ACP-/-locusts were further confirmed by qPCR and western blot analyses (Figure 4E and F). We also validated decreased mRNA levels of eight other genes involved in fatty acid transport and beta-oxidation in the flight muscle of ACP mutants (Figure 4—figure supplement 2A). Reduced expression of representative genes that participate in mitochondrial fatty acid transport (CPT2) and beta-oxidation (ACDM) was also confirmed at the protein level (Figure 4—figure supplement 2B). Moreover, we found that the expression levels of genes related to fatty acid transport and oxidation were strongly enhanced in the flight muscle upon ACP peptide injection as well as 1 h-sustained flight of WT locusts (Figure 4—figure supplement 3), implying that the ACP peptide may facilitate long-term flight by promoting fatty acid utilization. Given that FABP serves as a primary transporter for fatty acid translocation through the aqueous cytosol to mitochondria, where the beta-oxidation process takes place (Haunerland and Spener, 2004), we further assessed whether the change in FABP could affect the expression levels of beta-oxidation-related genes. RNAi knockdown of FABP resulted in a 99.3% decrease in FABP mRNA levels (Figure 4—figure supplement 4) and clearly suppressed the expression levels of multiple beta-oxidation-related genes, including CROT, CPT2, ACDM, ACADS, ACADSB, ECH-6, ACAT1, and CRAT (Figure 3G). Taken together, these results indicated that the lipid metabolism pathway was significantly suppressed in the flight muscle of ACP-/- locusts and that FABP may serve as an important molecular target of ACP peptide signaling.

Metabolome analysis indicates decreased lipid utilization in the flight muscle of ACP-/- locusts

Based on the above results, we hypothesized that ACP knockout may affect energy utilization in the flight muscle of locust adults. To verify this possibility, we performed comparative metabolome analysis between ACP-/- and WT flight muscles by ultra-high-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS). In total, we identified 881 metabolites with confidence from all twelve samples (Figure 5—figure supplement 1). Locust samples of WT and ACP-/- can be clearly separated by unsupervised principal component analysis (PCA) and orthogonal projection to latent structures–discriminant analysis (OPLS-DA) (Figure 5A and Figure 5—figure supplement 2). The overall metabolite distribution was considerably more intense in WT samples than in ACP-/- samples (Figure 5A), suggesting reduced general metabolic activity in the flight muscle of ACP-/- locusts.

Figure 5 with 4 supplements see all
Metabolomic analysis reveals impaired lipid metabolism in the flight muscle of ACP mutants.

(A) Metabolite distribution in the flight muscle of WT and ACP-/- locusts, as determined by orthogonal partial least squares discriminant analysis (OPLS-DA). Red blocks indicate WT samples, blue blocks indicate ACP-/- samples. Each green dot indicates a single metabolite. (B) Volcano plot of the metabolome from flight muscle of WT and ACP-/- locusts. There were 31 upregulated and 204 downregulated metabolites in the flight muscle of ACP mutants compared to WT locusts. Metabolites with p values < 0.05 are highlighted in red (upregulated) and green (downregulated). (C) Chemical structure classification of differential metabolites between WT and ACP-/- samples. (D) Heat map of differential metabolites related to lipid metabolism, including acylcarnitine, triglyceride, phospholipid, and coenzyme. The contents of metabolites are highlighted in red (upregulated) and blue (downregulated). Abundance detection of (E) acetyl CoA, (F) NADH, (G) pyruvate, and (H) citric acid in the flight muscle of WT and ACP-/- locusts. (Student’s t-test for E, p=0.007, t = 3.472, df = 9, n = 6 (WT) and 5 (ACP-/-); for F, p=0.0022, t = 4.439, df = 8, n = 5 (WT) and 5 (ACP-/-); for G, p=0.0857, t = 1.959, df = 8, n = 5 (WT) and 5 (ACP-/-); for H, p=0.0001, t = 7.085 df=8, n = 5 (WT) and 5 (ACP-/-), **p<0.01, ***p<0.001). (I) Schematic diagram of the metabolic pathway combining the metabolomic and transcriptome analyses. The metabolites involved in fatty acid transport and subsequent oxidation are downregulated in the flight muscle of ACP mutants compared to WT locusts. Genes associated with lipid transport and oxidation are also presented (highlighted in purple). See Figure 5—source data 1 for details.

Figure 5—source data 1

Raw data for metabolic and metabolite contents.

https://cdn.elifesciences.org/articles/65279/elife-65279-fig5-data1-v3.xlsx

Statistically, we obtained 204 downregulated metabolites and 31 upregulated metabolites in ACP-/- samples (Student’s t-test, p<0.05) (Figure 5B). The differential metabolites identified between the two groups included many triacylglycerols (TG), glycerophosphatides (PC, PE, PG, LysoPC, LysoPE), amino acids, carbohydrates, acylcarnitine, and nucleotides (Figure 5C and Figure 5—figure supplement 3). Clustering analysis demonstrated that eight medium/long-chain acylcarnitines (AcCa18:1, AcCa16:0, AcCa18:0, AcCa16:1, AcCa14:1, AcCa14:0, AcCa17:1, AcCa16:2) were present at significantly reduced levels in the flight muscle of ACP-/- locusts. Meanwhile, several coenzymes involved in mitochondrial oxidative phosphorylation were decreased, such as coenzyme Q9, coenzyme Q10, nicotinamide ribotide, and flavin mononucleotide (Figure 5D). These data indicated that acyl carnitine-dependent fatty acid transport to the mitochondrion and subsequent metabolism were notably decreased in the flight muscle of ACP mutants.

We further evaluated beta-oxidation status between WT and ACP-/- samples by determining the relative amounts of two end metabolites of beta-oxidation, acetyl-CoA and NADH. Relative amounts of acetyl-CoA and NADH in the ACP-/- samples significantly decreased by 77% and 65%, respectively, relative to the levels observed in WT locusts (Figure 5E and F). Instead, the injection of ACP peptide could significantly enhance the relative amounts of acetyl-CoA and NADH in the flight muscle of WT locusts (Figure 5—figure supplement 4A). However, upon 1 h-tethered fight, the relative content of acetyl-CoA did not change but the NADH level was decreased (Figure 5—figure supplement 4B). As a key intermediate metabolite of energy metabolism, acetyl-CoA is primarily generated from fatty acid oxidation and oxidative decarboxylation of pyruvate, which is an important product of glycolysis (Rui, 2014). We next assessed whether the glycolysis process was affected by ACP knockout by measuring the pyruvate amount. We observed that the relative level of pyruvate showed no significant difference between WT and ACP-/- samples (Figure 5G). To confirm the changes in energy metabolism in ACP-/- locusts, we also determined the abundance of citric acid, a representative metabolite of the tricarboxylic acid (TCA) cycle produced from acetyl-CoA (Koubaa et al., 2013). The relative level of citric acid in the WT samples was 2.82-fold higher than that in ACP-/- samples (Figure 5H). Therefore, by combined analysis of gene expressions and metabolite contents, we inferred that the downregulation of genes and metabolites involved in lipid transport and beta-oxidation in the flight muscle contributes to the deteriorated flight ability of ACP-/- mutant (Figure 5I).

FABP mediates the regulatory effects of ACP on lipid metabolism and flight performance

We further verified whether FABP acts as a key downstream molecular target of ACP to regulate lipid metabolism in flight muscle during the long-term flight of locusts. First, we carried out a 60 min-tethered flight test after performing gene knockdown of FABP in locust adults. Compared to dsGFP-injected locusts, the total flight duration and total flight distance did not change within the first 15 min but were significantly decreased after 60 min of flight in dsFABP-injected locusts (Figure 6A and B). The average and maximus flight velocity did not change significantly, although a declining tendency was observed after FABP knockdown (Figure 6—figure supplement 1). Next, we measured the amount of acetyl-CoA and NADH because they indicate beta-oxidation status after dsFABP treatments. Compared to the dsGFP control, the relative levels of acetyl-CoA and NADH were strongly reduced by 70% and 44% in the dsFABP-injected samples (Figure 6C and D), respectively.

Figure 6 with 1 supplement see all
FABP mediates the regulatory effects of ACP on lipid metabolism and flight activity.

(A) Total flight time and (B) total flight distance after knockdown of FABP in adult locusts (Student’s t-test for A, p=0.451, t = 0.7609, df = 44, n = 24 (dsGFP) and 22 (dsFABP) for 15 min, p=0.021, t = 1.992, df = 48, n = 25 (dsGFP) and 25 (dsFABP) for 60 min; for B, p=0.337, t = 0.9713, df = 44, n = 24 (dsGFP) and 22 (dsFABP) for 15 min, p=0.007, t = 2.796, df = 48, n = 25 (dsGFP) and 25 (dsFABP) for 60 min, *p<0.05, **p<0.01). Measurement of (C) acetyl CoA and (D) NADH in the flight muscle after knockdown of FABP in adult locusts (Student’s t-test for C, p=0.0011, t = 4.499, df = 10, n = 6 (dsGFP) and 6 (dsFABP), for D, p=0.017, t = 3.124, df = 7, n = 5 (dsGFP) and 4 (dsFABP)). (E) Expression levels of beta-oxidation-related genes in flight muscle after FABP knockdown in ACP-/- locusts injected with ACP peptide. Unsupervised hierarchical clustering was performed using Clustal 3.0 under uncentered Pearson correlation and average linkage conditions; the results are presented by Java Treeview software. Measurement of (F) acetyl CoA, (G) NADH, and (H) acetyl carnitine in the flight muscle after knockdown of FABP in ACP-/- locusts injected with ACP peptide (one-way ANOVA for F, F = 7.143, df = 3, p=0.0026, n = 5, 5, 5, and six from left to right, respectively; for G, F = 8.673, df = 3, p=0.0012, n = 5, 5, 5, and five from left to right, respectively). Measurement of (I) total flight time and (J) total flight distance after knockdown of FABP in ACP-/- locusts preinjected with ACP peptide (one-way ANOVA for I, F = 6.274, df = 3, p=0.0007; for J, F = 5.93, df = 3, p=0.001, for both I and J, n = 20, 21, 21, and 20 from left to right, respectively). Different letters indicate that there is a significant difference between two groups. See Figure 6—source data 1 for details.

Figure 6—source data 1

Raw data for flight activity, metabolite contents, and gene expression.

https://cdn.elifesciences.org/articles/65279/elife-65279-fig6-data1-v3.xlsx

To demonstrate the key role played by FABP in mediating the effect of ACP on lipid metabolism in flight muscle, we performed a molecular rescue experiment by combined peptide injection and gene knockdown in ACP-/- locusts. When ACP peptide was injected in ACP-/- locusts, the expression levels of eight key beta-oxidation genes significantly increased in the flight muscle, whereas this stimulatory effect of ACP injection was remarkably abolished by FABP knockdown (Figure 6E). The knockdown of FABP also alleviated the upregulated amount of acetyl-CoA and NADH in the flight muscle (Figure 6F and G). In addition, the enhancement of the contents of multiple medium/long-chain acylcarnitines induced by ACP peptide administration also disappeared after FABP gene silencing (Figure 6H). In particular, the impaired prolonged flight performance in ACP-/- locusts, including reductions in both total flight duration and flight distance, could be efficiently recovered by ACP peptide injection. Moreover, this recovered flight activity induced by the ACP peptide was clearly blocked by dsFABP treatments (Figure 6I). Taken together, these results indicated that FABP acts as a key component of ACP signaling, regulating lipid metabolism of the flight muscle during long-term flight in locusts.

Discussion

The results of this study demonstrated the key roles played by the neuropeptide ACP in the modulation of long-term flight in locusts. ACP peptide is highly abundant in the retrocerebral complex of adult locusts and the ACP precursor gene in the brain displays strong transcription responses to prolonged flight. Mechanistically, ACP facilitates long-term flight by promoting intracellular fatty acid transport to the mitochondria by regulating FABP expression in the flight muscle of locusts (Figure 7). Our findings highlight a novel neuroendocrine regulator and help to elucidate relevant mechanism involved in long-term flight in insects.

Schematic model showing that the neuropeptide.

ACP regulates long-term flight by affecting FABP-mediated fatty acid transport and subsequent β-oxidation in the flight muscle. Neuropeptide ACP is produced from the brain and could be secreted into the circulation through CC-CA. Fatty acid transport and lipid utilization are significantly downregulated in the flight muscle of the ACP mutant, thereby resulting in decreased prolonged flight performance.

Neuropeptidome analysis reveals tissue- and development-specific abundance of neuropeptides in locusts

Through peptidome analysis, we obtained a lot of non-abundant neuropeptides as well as their potential degradation products produced by 37 precursors in the main neuroendocrine tissues, including brain and retrocerebral complex. The neuropeptides detected here including most of previously identified peptides (Clynen and Schoofs, 2009). Many of the peptides were abundant at adult stage, indicating their distinct roles in adult-related biology. However, several neuropeptides (e.g. AST-C, inotocin, and AKH4) identified from previous peptidomic study and transcriptome data-based prediction, were not found in the current study. The absence of these neuropeptides in the neuropeptidomic analysis may thanks to their low abundance in tested samples, relative short half-life period, unsuitable chromatographic condition or data acquisition setting. Different sample collection methods as well as multiple mass spectrometry methods may be helpful for systematically identification of all neuropeptides in future.

We also found that the neuropeptides displayed apparently tissue-specific distributions, with either brain- or retrocerebral complex-specific distribution in the locust. By comparing our results with previously peptidome study (Clynen and Schoofs, 2009), we found that most of the neuropeptides show similar tissue distribution in the two studies, except for sulfakinin and PVK. The discrepancy between the two studies may be attributed to different sample collection strategies. In the present study, the whole retrocerebral complex of mature adults was used for peptidomic analysis, in contrast to the study of Clynen and Schoofs, 2009 who analyzed the organs of the retrocerebral complex of immature adults separately.

ACP has been identified as a novel neuroendocrine player modulating locust flight

Our results suggested that the ACP peptide acts as a neuroendocrine hormone to regulate locust flight capacity. To the best of our knowledge, this study is the first to clearly demonstrate the biological function of ACP in insects. In fact, the ACP peptide was initially isolated from the storage lobes of the CC of migratory locusts and was named locust hypertrehalosemic hormone (Lom-HrTH) because of its activity in the induction of hemolymph trehalose levels in cockroaches but not in locusts (Siegert, 1999). Further MS analysis shows that the peptide is highly distributed in CC, hypocerebral ganglion, frontal ganglion, protocerebrum, pars intercerebralis, tritocerebrum, as well as thoracic ganglia of immature Africa migratory locust (Clynen and Schoofs, 2009). However, the biological significance of ACP has not been described in detail. Our results show that the ACP precursor gene in the brain displays strong transcription responses to prolonged flight. The regulatory roles played by ACP in locust flight are clearly supported by tethered flight experiments after knockdown and knockout of its precursor gene, as well as reduced extended flight ability of ACPR mutants. Based on ours and previous finding, ACP mature peptide is abundantly detected in the CC of locusts, indicating that ACP may be synthesized in neurosecretory cells of the brain and transported to the storage lobe of the CC via nervi corporis cardiaci I and/or II (Hekimi and O'Shea, 1987). Thus, the ACP peptide may be involved in locust flight through secretion into the circulation, to modulate distinct physiological activities in target tissues, such as the flight muscle and fat body, where its receptor is expressed.

The ACP peptide is an insect structural intermediate of corazonin and AKH hormones, all of which belong to the vertebrate gonadotropin-releasing hormone (GnRH) family (Hansen et al., 2010). ACP and its cognate receptor have been found in various insects, although they are selectively lost in several insect species, such as the fruit fly (Diptera), the honey bee (Hymenoptera), the pea aphid (Hemiptera), and the body louse (Phthiraptera) (Hansen et al., 2010). The role played by ACP in energy metabolism has been suggested in several insect species; for example, this gene has been suggested to regulate hemolymph carbohydrate and lipid levels in Gryllus bimaculatus (Zhou et al., 2018) and to play a possible role in glycogen hydrolysis in Platypleura capensis (Gäde and Janssens, 1994). Thus, ACP may play species-specific roles in modifying metabolic activity in insects. Furthermore, it appears that all three GnRH neuropeptides (AKH, ACP, and corazonin) are related to energy embolism to some extent (Andreatta et al., 2020), although they have distinct tissue or cellular distributions and do not share overlapping detailed biological roles (Patel et al., 2014).

ACP is involved in the control of lipid transport during long-term flight

Our results demonstrate that ACP modulates long-term flight by primarily affecting lipid transport and utilization in the flight muscle of locusts. This finding is strongly supported by a significant decrease in the levels of genes (e.g. FABP, CPT2, CRAT, ACDs, ECHs, HADHs, ACATs) and metabolites (carnitine and acylcarnitine) related to lipid transport and beta-oxidation (Rubiogozalbo et al., 2004) in the flight muscle of ACP mutant locusts, as well as the enhanced metabolism-related gene expressions and fatty acid oxidation activity in WT locusts upon ACP peptide administration. Integrating energy metabolism related to long-term flight is a complex and multistep physiological process (Auerswald and Gäde, 2006). Generally, the initial flight primarily consumes carbohydrates as an energy substrate, whereas subsequent prolonged flight depends largely on highly efficient lipid utilization as an energy supply (van der Horst et al., 1993; Van der Horst and Rodenburg, 2010). We show that ACP strongly affects lipid transport and oxidation, not carbohydrate metabolism, in the locust. The changes in lipid metabolism in the flight muscle upon ACP manipulations (both gene knockout and peptide injection) are closely in line with the alteration of long-term flight performance in the parallel treatments, demonstrating the distinct functional roles played by ACP during long-term flight. Despite the inconsistent effects on beta-oxidation products in the flight muscle upon ACP peptide injection (stimulatory effect) and sustained flight (partially inhibitory effect), both two treatments could significantly enhance the expressions levels of gene related to lipid utilization in the flight muscle, implying that the ACP peptide may facilitate lipid utilization in response to prolonged flight. The decreased beta-oxidation products may reflect rapid energy utilization in subsequent mitochondrial metabolism during sustained flight.

FABP acts as a key molecule for flight-related energy metabolism underlying ACP modulation

We showed that FABP serves as a key molecular target mediating the regulatory effects of ACP on lipid metabolism during locust long-term flight. Although multiple FABP family members are predicted in the locust genome (Wang et al., 2014), the FABP identified in this study is specifically expressed in the flight muscle of adult locusts with notably high abundance (Haunerland et al., 1992). The mediating role played by FABP in ACP-controlled flight-related lipid metabolism was further confirmed by knockdown and rescue experiments at the molecular, metabolic, and behavioral levels. The significant role of FABP in prolonged flight has also been reported in the desert locust (Rajapakse et al., 2019), indicating that FABP plays a conserved role in lipid metabolism and long-term flight in locust species.

FABP has been characterized as an evolutionarily conserved fatty acid carrier that plays essential roles in lipid utilization by affecting mitochondrial beta-oxidation (Luxon, 1993; Luxon et al., 1997; Binas and Erol, 2007). Usually, tissues with high fatty acid oxidative capacities possess more FABP than those that use carbohydrates as an energy source. The locust flight muscle is structurally and functionally closely related to the mammalian heart muscle, which also depends mostly on fatty acids to fuel its continuous contractions (Neely and Morgan, 1974). Therefore, FABP is suggested to be equally important for insect flight muscle and mammalian heart muscle. It has been shown that FABP gene expression in muscle can be upregulated by sustained flying (Chen and Haunerland, 1994) and extended physical exercise (Lammers et al., 2012). However, the regulatory mechanisms underlying FABP transcription by upstream molecules have not been fully characterized. The regulation of FABP expression by ACP during long-term locust flight thus presents a typical case showing the modulation of FABP expression by neuroendocrine factors and may help to elucidate the common molecular mechanisms that participate in the modulation of muscle FABP expression in different species. Further studies are warranted to decipher signaling pathways mediating the regulatory effect of ACP on FABP expression, and the results of this research may help to elucidate the precise metabolic mechanisms regulating high energy-demanding activities.

Multiple neuromodulators are involved in the regulation of flight-related energy metabolism

The central roles played by neuropeptides in glycogen hydrolysis and lipid mobilization have been extensively demonstrated in various insect species (Arrese and Soulages, 2010; Andreatta et al., 2020). However, few studies have examined the regulatory mechanism underlying flight-related lipid utilization in flight muscle. The transition of substrate utilization from carbohydrates to lipids in the flight muscle has been proposed to be crucial for maintaining long-term flight (Van der Horst and Rodenburg, 2010). Glycolysis in flight muscle during early-stage flight is controlled by octopamine (Mentel et al., 2003). In comparison, lipid mobilization and glycogen hydrolysis in the fat body during prolonged flight are directly modulated by AKH, whose receptor primarily localizes in the fat body (Van der Horst, 2003). The expression levels of all three AKH genes strongly increase upon sustained flight in the locust (Bogerd et al., 1995). However, enhanced expression after sustained flight was observed only for AKH2 in our experiments. The difference between the two studies may be attributed to the different sample collection strategy and detection methods used retrocerebral complex for expression analysis examined by qPCR in our study, whereas only CC tissue for expression analysis detected by norther blot in previous work. A strong reduction in lipid metabolites, such as acylcarnitines, acyl-CoA, NADH, triglycerides, and phosphoglycerides, was observed in the flight muscle of ACP mutants, whereas pyruvic acid generated from glycolysis did not change, suggesting that ACP primarily affects lipid metabolism, rather than glycolysis, in the flight muscle of locusts. We infer that these three flight-related regulators have functional differentiation in either a temporally or spatially dependent manner in the modulation of flight metabolism. A coordinated regulatory network involving AKH, ACP, and octopamine is thus proposed to modulate the cooperation of substrate mobilization, transport, and utilization in different tissues during long-term flight. Here, we also revealed that knockdown of either ACP or AKH2 induced similarly suppressed effects on locust flight performance. Further work is warranted to investigate the potential interaction among these neuroendocrine factors in energy regulation associated with flight activity.

Through phenotype examination, we also observed a larger body size in ACP mutants. Usually, the body size of locusts is stable after adult eclosion thanks to its hard exoskeleton. Therefore, the effects of ACP on body size could not be assessed through RNAi of the gene after adult eclosion in the current study. It has been suggested that the growth state for an organism can be negatively affected by other physiological traits, such as locomotion, reproduction, or life span (Lee et al., 2010). Therefore, the increased body size of ACP mutants may be attributed to the continuous metabolism changes associated with trade-off effects between flight activity and body growth. Similarly, the loss of function of the AKH peptide results in adult-onset obesity in Drosophila (Gáliková et al., 2015). These findings may reflect a common role played by ACP and AKH in governing the energy balance of insects. It will be an interesting work to explore the molecular and metabolic basis for body size determination on the basis of established ACP mutant locust line.

In summary, we demonstrate that the ACP peptide acts as a novel neuroendocrine regulator controlling lipid transport and utilization associated with long-term flight in locusts. The ACP-FABP axis involved in long-term flight may serve as an effective molecular target for the prevention of locust plagues and may provide insights into metabolic hemostasis related to sustained locomotion.

Materials and methods

Key resources table
Reagent type
(species)
or resource
DesignationSource
or reference
IdentifiersAdditional
information
Gene (Locust migratoria)ACPYang et al., 2019http://www.locustmine.org:8080/locustmine
(LOCMI05723)
Gene (Locust migratoria)ACPRYang et al., 2019http://www.locustmine.org:8080/locustmine
(comp340809_c0_seq1)
Genetic reagent (Locust migratoria)GeneArtPrecision gRNA Synthesis KitThermoFisherA29377
Genetic reagent (Locust migratoria)Cas9 proteinInvitrogenA36496
Antibody(anti-ACP rabbit polyclonal)This paperProduced by ABclone, China(1:200)
AntibodyAlexa Fluor-488 goat anti-rabbit IgGLife TechnologiesCat. A-11008(1:500)
Antibodyanti-FABPThis paperDeveloped by ABclone, China(1:5000)
Antibodyanti-CPT2Abcamab153869(1:1000)
Antibodyanti-ACDMAbcamab92461(1:1000)
AntibodyGoat anti-rabbit IgG secondary antibodyEASYBIOBE0101-100(1:5000)
AntibodyPolyclonal antibody against tubulinThis paperProduced by ABclone, China(1:5000)
Peptide, recombinant proteinACP peptideABclonepQVTFSRDWSPGamide
Commercial assay or kitAcetyl-CoA assay kitSigma-AldrichMAK039
Commercial assay or kitNAD/NADH Assay KitAbcamab65348
Commercial assay or kitPyruvate Colorimetric/Fluorometric Assay KitBioVisonK609
Commercial assay or kitCitric acid content detection kitSolarbioBC2150
Chemical compound, drugLightCycler 480 SYBR Green I MasterRoche04887352001
Software, algorithmGraphPad Prism 5GraphPad SoftwareRRID:SCR_002798

Insect rearing

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Locusts used in these experiments were obtained from a colony maintained at the Institute of Zoology, Chinese Academy of Sciences, Beijing, China. Both nymphs (300–400 insects per cage) and adult locusts (~100 per cage) were reared under a 14:10 light:dark photocycle regime at 30 ± 2°C. The locusts were fed with fresh wheat seedlings and bran (Hou et al., 2017). For age definition, adult locusts between 0 and 12 hafter molting were referred as day 0 post-adult eclosion (PAE 0 day).

Quantitative neuropeptidome analysis

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For sample preparation, the brain tissues containing only the protocerebrum, deuterocerebrum, and tritocerebrum and the retrocerebral complex (including corpora cardiaca, corpora allata, hypocerebral ganglion, and small neuronal structures) from 35 fifth-instar female nymphs (the third day after molting) and 35 mature female adults (PAE 10 days) were carefully microdissected and frozen in liquid nitrogen. All samples were stored at −80°C. Three independent biological replicates were prepared for each sample.

Tissues were homogenized in 200 μl lysis buffer (methanol/ddH2O/formic acid = 90/9/1) through sonication on ice followed by centrifugation at 12,000 × rpm for 20 min at 4°C to remove insoluble fractions. The supernatants were ultrafiltered with a 10 kDa ultrafiltration column and freeze-dried for peptide collection. The peptide pellets were resuspended in 20 μl 0.1% formic acid prior to MS analysis (Han et al., 2015). LC-MS/MS analysis was performed on an Easy-nLC 1000 (Thermo Fisher Scientific, Bremen, Germany) coupled LTQ-Orbitrap Elite (Thermo Fisher Scientific) hybrid mass spectrometer. Peptides were separated on a column packed with 2 μm C18 (100 Å, 75 μm x 50 cm, Thermo Fisher Scientific) using a 130 min gradient from 3–30% acetonitrile (0.1% formic acid) with a flow of 250 nL/min. The eluted neuropeptides were injected into the mass spectrometer via a nano-ESI source (Thermo Fisher Scientific). Ion signals were collected in a data-dependent mode and run with the following settings: full scan resolution at 70,000, automatic gain control (AGC) target 3E6; maximum inject time (MIT) 20 ms; scan range m/z 300–1800; MS/MS scans resolution at 17,500; AGC target 1E5; MIT 60 ms; isolation window 2 m/z; normalized collision energy 27; loop count 10; charge exclusion: unassigned, 1, 8,>8; peptide match: preferred; exclude isotopes: on; dynamic exclusion: 30 s; dynamic exclusion with a repeated count: 1. The MS/MS data were acquired in raw files using Xcalibur software (version 2.2, Thermo Fisher Scientific).

The extracted MS/MS spectra were searched against a composite database of Locust migratoria (3286 protein sequences, download from NCBI, 2019) and a protein database (containing 17,307 protein sequences, http://www.locustmine.org:8080/locustmine) (Yang et al., 2019) using in-house PEAKS software (version 7.0, Bioinformatics Solutions, Waterloo, Canada). The database search parameters: parent ion mass tolerance is 15 ppm, and fragment ion mass tolerance is 0.05 Da; enzyme specificity, none. The following modifications were applied: C-terminal amidation (A, −0.98) and pyroglutamination from Q (P, −17.03), maximum missed cleavages per peptide: 2, and maximum allowed variable PTM per peptide: 2. A fusion target and decoy approach was used to for the estimation of the false discovery rate (FDR) and controlled at ≤1.0% at the peptide level. Identified neuropeptides were further validated by comparison with predicted neuropeptide precursors and previous neuropeptidome analysis in the locust (Clynen and Schoofs, 2009; Hou et al., 2015).

Relative quantification of the neuropeptidome was performed by the label-free approach in the PEAKS Q module (Han et al., 2015). Feature detection was performed separately on each sample by using the expectation-maximization algorithm. The features of the same peptide from different samples were reliably aligned together using a high-performance retention time alignment algorithm. The identification results were chosen to attach as the last step of the label-free quantification. Peptides were determined to be significantly changed between different samples if Student’s t-test yielded p-values<0.01.

RNA extraction and qPCR

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Total RNA of experimental samples was isolated using TRIzol reagent (Invitrogen). RNA quantification and reverse transcription were performed as previously described (Hou et al., 2017). Transcript levels of target genes were detected by SYBR Green kit on a LightCycler 480 instrument according to the manufacturer’s instructions (Roche). RP49 was used as an internal reference. Dissociation curves were determined for each gene to confirm unique amplification. At least four biological replicates were performed for gene expression level analysis. The primers are shown in Supplementary file 4.

Tissue-specific expression pattern analysis

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Tissues including the brain containing only the protocerebrum, deuterocerebrum, and tritocerebrum, thoracic ganglion, retrocerebral complex, fat body, flight muscle, and ovary (or testis) of adult locusts at PAE 7 days were dissected and frozen in liquid nitrogen. All samples were stored at −80°C. Four independent biological replicates were prepared for each sample. Tissues of six to eight individuals were collected for each replicate. RNA extraction and qPCR analysis were performed as described above.

RNA interference

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Double-stranded RNAs of target genes were synthesized using the T7 RiboMAXTM Expression RNAi system (Promega, USA). The dsRNA was first injected into the hemolymph of adult locusts at day one post-adult eclosion (PAE 1 day, 6 μg/locust). A second injection was performed at PAE 4 days. dsGFP was used as the control. Flight performance was measured 3 days after the second injection (PAE 7 days).

Tethered flight assay

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The flight activities of individual locusts were measured by using a computer‐aided flight‐mill device modified from previous studies (Beerwinkle et al., 1995). The arm length of the fight‐mill is 12 cm, and the flight circumference is 0.75 m. One plastic rod forms the upper, free‐turning body of the flight mill that supports the mill arm and serves as a bearing on the pivot pin. The locust was tethered at the end of the lightweight arm, which allowed it to fly along the cycle derived from the arm. The interruption of an infrared beam by a rotating arm generated an electrical signal that was recorded by the computer. Flight was induced by a fan placed above the flight mill (blow for 2 s with an interval of 30 s, 1.5 m/s wind speed). During the assay, four flight parameters were obtained for each locust, including total flight distance and duration and average flight velocity and maximum flight velocity. The flight mill device was positioned in a room under a photoperiod of 14:10 (L:D) h at 30 ± 1°C. Locusts that did not flight in this assay were excluded. At least 20 locusts were used in each treatment. Individuals were randomly allocated into experimental group and control group, and no restricted randomization was applied.

For sample preparation of flight treatment, the locusts were forced to sustain flight for 15, 30, 45, and 60 min, respectively. Insects that stopped flying were artificially stimulated to continue flight. Brains of tested locusts were collected, rapidly frozen in liquid nitrogen and stored at −80°C. Four independent biological replicates were prepared for each time point. Tissues of six to eight individuals were collected for each replicate. Individuals who could not finish sustained flight were discarded.

Generation of ACP and ACPR mutant locusts using the CRISPR/Cas9 system

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The establishment of mutant locusts using the CRISPR/Cas9 system was performed as previously described (Li et al., 2016). The gRNAs containing 20 bases adjacent to a PAM sequence for both ACP and ACPR were designed using the CasOT tool. The gRNAs were synthesized using the GeneArt Precision gRNA Synthesis Kit (ThermoFisher, A29377). In brief, a 13.8 nl mixture of purified Cas9 protein (Invitrogen, A36496, Massachusetts, USA) and guide RNA of target genes (final concentrations: 300 and 150 ng/μl, respectively) was injected into the newly collected embryos (2 hr after production) using a microinjector. The injected embryos were then placed in a 30°C incubator until the nymphs hatched. The hatched nymphs were reared as described above. For genotype analysis, part of the middle foot of each adult locust was collected and lysed with 45 μl NAOH buffer (50 mM) at 95°C for 30 min and then neutralized by adding 5 μl Tris-HCl (1 M, pH = 8.0). The supernatant (3 μl) was used as the PCR template to amplify the targeted DNA fragment. Primers for gRNA synthesis were shown in Supplementary file 4.

Whole-mount immunohistochemistry assay

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The brains of adult locusts were fixed in 4% paraformaldehyde overnight. After being washed with 1 × PBS buffer, the samples were blocked with 5% BSA for 1 h and then incubated with affinity-purified polyclonal rabbit antibody against ACP (produced by ABclone, China, 1: 200) at 4°C for 24 h. Alexa Fluor-488 goat anti-rabbit IgG (Cat. A-11008, 1: 500; Life Technologies) was used as the secondary antibody. Fluorescence was detected using an LSM 710 confocal laser-scanning microscope (Zeiss). Negative controls were imaged under the same detection conditions as positive staining.

Survival rate measurement

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Adults of WT and ACP-/- locusts at PAE 1 day were reared under a 14:10 light/dark cycle at 30°C. The numbers of dead insects were assessed every day. The survival curves were drawn using GraphPad Prism five software. Differences in the survival rate of females or males between WT and ACP mutant locusts were compared by using the log-rank (Mantel-Cox) test method. At least 50 individuals were assayed in each group to determine the survival rate.

Phylogenetic tree construction

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The protein sequences of ACPR from Tribolium castaneum and Aedes aegypti were used as seed sequences to search their homologs in the locust genome and transcriptome database using the tblastn algorithm. Three kinds of structure-related neuropeptide receptor proteins, adipokinetic hormone receptor (AKHR), ACPR, and corazonin receptor (CRZR), from locusts and several representative insect species were used to construct their phylogenetic relationship by using MEGA software (Tamura et al., 2011).

Western blot analysis

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Total proteins from flight muscles of WT and ACP-/- locusts were extracted using TRIzol reagent, as previously described (Hou et al., 2019). The protein extracts (50 μg) were electrophoresed on 4–20% Biofuraw precast gels (Tanon, China) and then transferred to polyvinylidene difluoride (PVDF) membranes (Millipore). The membrane was incubated with polyclonal antibody against target protein (anti-FABP, developed by ABclone, China, 1:5000; anti-CPT2, Abcam, 1:1000; anti-ACDM, Abcam, 1:1000). Goat anti-rabbit IgG (EASYBIO, 1:5000) was used as the secondary antibody. Polyclonal antibody against tubulin was used as an internal control. Protein bands were detected by chemiluminescence (ECL kit, Thermo Scientific).

RNA-seq and analysis

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The flight muscle and fat body tissues were dissected from WT and ACP-/- female locusts under resting state at PAE 7 days. Total RNA of these samples with three biological replicates was extracted using TRIzol (Invitrogen) and treated with DNase I following the manufacturer’s instructions. RNA quality was assessed using an Agilent 2100 Bioanalyzer (Agilent) to verify RNA integrity. cDNA libraries were prepared according to Illumina’s protocols. The adaptor sequences in the raw sequencing data were filtered using Trimmomatic-0.30. Clean reads were mapped to the locust genome sequence using Tophat software. The number of total reads was normalized by multiple normalization factors. Transcript levels were calculated using the reads per kb million mapped (RPKM) reads criteria. The differences between the test and control groups were based on P values with false discovery rate (FDR) correction. Differentially expressed genes with FDR < 0.1, Log2 (FC) >1, and RPKM > 0.5 in each comparison were enriched. The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2017) in National Genomics Data Center, Beijing Institute of Genomics (China National Center for Bioinformation), Chinese Academy of Sciences, under accession number CRA003348 that are publicly accessible at https://bigd.big.ac.cn/gsa.

Metabolome analysis

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The flight muscle was dissected from WT and ACP-/- female locusts under resting state at PAE 7 days. The metabolomic profile analysis contained extraction, separation, and detection of metabolites together with metabolomic data processing. For metabolite extraction, 20 mg flight muscle was homogenized in 600 μl cold extraction buffer (Vmethanol/VddH2O, 8/2). The 300 μl homogenate was transferred to a new tube containing 900 μl methyl tert-butyl ether (MTBE) and 250 μl ddH2O. After 10 min of shaking, the mixture was centrifuged at 13,000 × g at 4°C for 10 min. Lipid extract in the upper layer and polar metabolites in the lower layer were collected for further freeze drying. Lipid pellets and polar metabolites were thus resuspended using acetonitrile/isopropanol and acetonitrile/ddH2O for subsequent chromatographic separation, respectively.

For polar extract separation, untargeted metabolomics analysis was conducted on an Ultimate 3000 ultra-high-performance liquid chromatograph coupled with a Q Exactive quadrupole-Orbitrap high-resolution mass spectrometer UPLC-HRMS system (Thermo Scientific, USA). The polar metabolome extracts were profiled on reversed-phase chromatographic separation with positive and negative ionization detection, respectively. Metabolites were separated by using an Acquity HSS C18 column (Waters Co., USA, 2.1 × 100 mm) and eluted by 0.1% formate/water and acetonitrile using linear gradient ramping from 2% organic mobile phase to 98% in 10 min. Furthermore, other mobile phases consisting of water and ammonium acetonitrile/methanol both containing ammonium bicarbonate buffer salt were employed to elute metabolites separated on an AcquityTM BEH C18 column (Waters Co., USA, 1.7 μm, 2.1 × 100 mm), the gradient was used as follows: 0 min 2% organic phase ramped to 100% in 10 min, and another 5 min was used for column washing and equilibrating. The flow rate, injection volume and column temperature were all set at the same conditions, 0.4 ml/min, 5 μl and 50°C, respectively.

MS of polar extracts

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The quadrupole-Orbitrap mass spectrometer was operated under identical ionization parameters with a heated electrospray ionization source except ionization voltage including sheath gas 45 arb, aux gas 10 arb, heater temperature 355°C, capillary temperature 320°C and S-Lens RF level 55%. The metabolome extracts were profiled with full scan mode under 70,000 FWHM resolution with AGC 1E6 and 200 ms max injection time. A 70 ~ 1000 m/z scan range was acquired. QC samples were repeatedly injected into the acquired Top 10 data-dependent MS2 spectra (full scan-ddMS2) for comprehensive metabolite and lipid structural annotation. The 17,500 FWHM resolution settings were used for full MS/MS data acquisition. Apex trigger, dynamic exclusion and isotope exclusion were turned on, and the precursor isolation window was set at 1.0 Da. Stepped normalized collision energy was employed for collision-induced disassociation of metabolites using ultrapure nitrogen as the fragmentation gas. All the data were acquired in profile format.

Separation of lipid extraction

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The chromatographic separation of untargeted lipidomics was performed under positive and negative ionization detection modes, respectively, as described above. An Accucore C30 core-shell column was utilized for lipid molecule separation at 50°C, which was eluted with 60% acetonitrile in water (A) and 10% acetonitrile in isopropanol (B), with both containing 10 mM ammonium formate and 0.1% formate. The separation gradient was optimized as follows: initial 10% B ramping to 50% in 5 min and further increasing to 100% in 23 min, the other 7 min for column washing and equilibration using 0.3 mL/min flowrate.

The ionized lipid molecules were detected using the same parameters as previously described. Lipid extracts (300–2000 m/z) were profiled with the same parameters as the metabolome used. Lipids were structurally identified by acquiring data-dependent MS2 spectra, and the key settings included 70,000 FWHM full scan resolution, 17,500 FWHM MS/MS resolution, loop count 10, AGC target 3e6, maximum injection time 200 ms and 80 ms for full scan and MS/MS, respectively, and dynamic exclusion 8 s. Stepped normalized collision energy 25% + 40% and 35% were employed for positive and negative mode after optimization.

Data processing

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The full scan and data-dependent MS2 metabolic profile data were further processed with Compound Discoverer software for comprehensive component extraction. The polar metabolites were structurally annotated by searching acquired MS2 against a local proprietary iPhenome SMOL high resolution. The MS/MS spectrum library was created using authentic standards, the NIST 17 Tandem MS/MS library (National Institute of Standards and Technology), the local version MoNA (MassBank of North America), and the mzCloud library (Thermo Scientific, USA). In addition, the exact m/z of MS1 spectra was searched against a local KEGG and HMDB metabolite chemical database. For metabolite identification or structural annotation, the mass accuracy of the precursor within ±5 ppm was a prerequisite; meanwhile, isotopic information including at least 1 isotope within 10 ppm and a fit score of a relative isotopic abundance pattern of 70% were introduced to confirm the chemical formula in addition to the exact mass. Furthermore, retention time information as well as high resolution MS/MS spectra similarity was employed to strictly confirm the structural annotation of metabolites. The area under curve values were extracted as quantitative information of metabolites with XCalibur Quan Browser information, and all peak area data for the annotated metabolites were exported into Excel software for trimming and organization before statistical analysis (Microsoft, USA). On the other hand, untargeted lipidomics data were processed with LipidSearch software, including peak picking and lipid identification. The acquired MS2 spectra were searched against in silico predicted spectra of various compounds, including phospholipids, neutral glycerolipids, spingolipids, neutral glycosphingolipids, glycosphingolipids, steroids, and fatty esters. The mass accuracies for the precursor and MS/MS product ion searches were 5 ppm and 5 mDa, respectively. The MS/MS similarity score threshold was set at 5. The potential ionization adducts include hydrogen, sodium, and ammonium for positive ion and hydrogen loss, as well as formate and acetate adducts for negative mode. Lipid identification was strictly manually checked and investigated one-by-one to eliminate false positives chiefly based on peak shaking, adduct ion behavior, fragmentation pattern, and chromatographic behavior.

Data statistics

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The metabolome and lipidome data derived from different measurements were normalized to the sample weight used prior to further processing. Then, the resultant quantitative information from the foregoing methods was merged, and those detected with multiple methods were excluded to guarantee the uniqueness of metabolites and lipids. Log10 was then transformed for final statistical analysis. Principal component analysis was conducted with SIMCA-P software (Umetrics, Sweden), and other univariate analyses, including independent sample t-test and p value FDR adjustment, as well as metabolic pathway analysis, were conducted on the MetaboAnalyst website.

Measurement of metabolites

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For measurement of acetyl-CoA, NADH, pyruvate, and citric acid, tissues (20–40 mg) of 3–4 individuals were dissected out and homogenized in distinct extraction buffer. The extraction solution was centrifuged at 10,000 × g at 4°C for 10 min. Of these supernatants, 6 μl was aspirated for protein determination, and the rest was deproteinized with a 10 kDa molecular weight cutoff spin filter to remove proteins prior to the reaction according to the manufacturer’s procedures. Intracellular acetyl-CoA levels were measured by the acetyl-CoA assay kit (Sigma, MAK039) following the manufacturer’s instructions. A 50 μl sample was added to the reaction mixture and incubated for 10 min at 37°C. Fluorescence intensity (λexcitation = 535/λ emission = 587 nm) was measured for acetyl-CoA detection. The NADH content was measured using the NAD/NADH Assay Kit (Abcam, ab65348). Samples were incubated at 60°C for 30 min to remove NAD+. The 20 μl sample was added to the reaction mixture and incubated for 1 h- at room temperature. The absorbance at 450 nm was measured for NADH detection. Pyruvate was detected by using the Pyruvate Colorimetric/Fluorometric Assay Kit (BioVison, K609) according to the manufacturer’s instructions. The 20 μl sample was added into the reaction mix and incubated at room temperature for 30 min. The absorbance at 570 nm was measured in a microplate reader. Citric acid was detected using the citric acid content detection kit (Solarbio, BC2150) following the manufacturer’s instructions. A 50 μl sample was used in the reaction. The absorbance at OD545 nm was detected for citric acid measurement using SpectraMax Plus 384. For all metabolite measurements, the background was corrected by subtracting the blank standard value from all readings. All data were normalized to the protein concentration, which was measured by using the BCA method. At least five biological replicates were performed for each treatment.

Peptide injection assay

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For both WT locusts and ACP-/- mutants, commercially synthetic ACP peptide (20 pmol, 2 μL, ABclone) was injected into the hemolymph of female ACP-/- adults every two days beginning at PAE 1 day. To validate the involvement of FABP in the regulation of energy metabolism and flight activity by ACP peptide, metabolites in muscles and flight performance were measured in ACP-/- locusts injected with ACP peptide or combined with injection of ACP peptide and dsFABP. For ACP peptide and dsFABP dual treatments, dsFABP (6 μg/μL) was mixed with ACP peptide (40 pmol) and injected into the ACP-/- adults at PAE 3 days. Tissue collection and flight performances of tested insects were both conducted at PAE 7 days.

Measurement of acyl carnitines

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For measurement of acyl carnitine, muscle tissues (20 ± 0.5 mg) were homogenized in 600 μL prechilled methanol-water (8/2, v:v) solution containing deuterium-labeled internal standards (200 ng/mL AcCa(16:0)-D3, 20 ng/mL AcCa(12:0)-D3, 10 ng/mL AcCa(8:0)-D3, 200 ng/mL AcCa(2:0)-D3, 50 ng/mL Carnitine-D9). The homogenates were vortexed and centrifuged at 13,000 × g for 10 min at 4°C. 250 μL aliquots of supernatant were transferred into another EP tubes and dried in vacuum using the CentriVap Concentration Systems (Labconco Corporation, USA). The extracts were dissolved in 150 μL methanol-water (5:5, v:v) for further chromatography analysis.

Statistical analysis

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Statistical methods for omic analysis were performed as described above. The data that do not meet normal distribution was excluded for the analysis of gene expression, biochemistry assay, and flight activity. All data are presented as the mean ± SEM and statistically analyzed using GraphPad Prism five software. Two-tailed unpaired student’s t-test was used for two-group comparisons, and one-way ANOVA followed by Tukey’s post hoc test was used for multigroup comparisons. Differences were considered statistically significant at p<0.05.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 1, 2, 3, 4, 5, and 6.

The following data sets were generated
    1. Yang P
    (2020) National Genomics Data Center
    ID gsa. RNA-Seq of fat body and muscle tissues with mutant ACP neuropeptide.

References

    1. Gade G
    (1992)
    The hormonal integration of insect flight metabolism
    Zool Jahrb Allg Zool 96:211–225.
    1. Van der Horst DJ
    (2003) Insect adipokinetic hormones: release and integration of flight energy metabolism
    Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology 136:217–226.
    https://doi.org/10.1016/S1096-4959(03)00151-9

Decision letter

  1. Raghu Padinjat
    Reviewing Editor; National Centre for Biological Sciences, India
  2. K VijayRaghavan
    Senior Editor; National Centre for Biological Sciences, Tata Institute of Fundamental Research, India
  3. Raghu Padinjat
    Reviewer; National Centre for Biological Sciences, India
  4. Aravind Ramanathan
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This study provides an insight into the rewiring of metabolism in insect muscles to support the energy requirements of long-range flight. It demonstrates the molecular mechanisms by which signaling neuropeptides released by the insect brain act via cell-surface receptors on muscle cells to regulate metabolism during long-range flight.

Decision letter after peer review:

Thank you for submitting your article "Neuropeptide ACP facilitates lipid oxidation and utilization during long-term flight in locusts" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Raghu Padinjat as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by K VijayRaghavan as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Aravind Ramanathan (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Summary:

This paper addresses the metabolic adaptations required to sustain long term flight in insects and the potential of the neuropeptide ACP to regulate these processes. This is a scientific problem which is interesting and important for a metabolic and neuroendocrinology perspective. The paper is of general importance because locusts are important pest insects. This study is expected to have an important impact for understanding how locusts (and numerous other animals) modulate muscle metabolism in response to physiological needs. The study is done elegantly and provides interesting new insights into the above process.

Essential Revisions:

1) While a potential ACP receptor is presented on the basis of informatics and expression analysis, there is no functional evidence of a role for this protein in mediating the actions of ACP. The authors can show this in vitro by expressing the receptor in a cellular expression system and challenge the receptor with synthetic ACP.

and/or to substantiate the mechanism of action of ACP in vivo, the authors should compare the phenotype of the RNAi-knockdown of the ACP receptor ( or Crispr-Cas9 knockout) with the flight phenotypes of WT and FABP knockdowns.

2) A key strategy in this study is to compare phenotypes, transcriptomes and metabolomes between wild type and ACP-/- animals. A body size defect is reported for ACP-/- locusts that is also seen with the RNAi. This is an important point as in insects adult body size is determined during development and will also imply effects of metabolism. In general, studying rapidly adaptive processes like metabolism (that change based on seasons and nutritional availability) using knockout lines can lead to alternative metabolic adaptations that will be difficult to map. This is a concern in an otherwise elegant study.

The authors should strengthen the conclusion that enhanced ACP activity in adult locusts is responsible for the reported transcriptome and metabolome changes leading long flight modulation. Can they provide data that:

a) Please show if there is altered staining of ACP in the brain during long flight conditions ?

b) Does the increased ACP expression actually modulate fatty acid oxidation in response to extended flight as suggested. Can injection of ACP peptide alter FAO capacity and map to extended flight capability?

c) If the changes in transcriptome and metabolome seen in ACP-/- are a direct reflection of the function of this peptide is modulating metabolism in long-flight, then are the changes in these transcripts and metabolites also seen in wild type locust tissue when comparing resting with tissue after 1 hr flight ?

3) Clarity on methodology

Please provide clarity on some methodology issues to make the work more informative and accessible. The following points to be addressed:

i) Hou et al. analysed the brain and CC-CA neuropeptidome. For this purpose, they prepared samples of brains and CC-CA. To be able to understand the tissue distribution of the neuropeptides, it is important to know what is meant by 'brain' samples and by CC-CA samples. Does the brain also contain the optic lobes, frontal ganglion, suboesophageal ganglion, the NCC or only the proto-, deutero-, and tritocerebrum? Do the CC-CA samples only contain the CC and the CA or also the hypocerebral ganglion and other small neuronal structures that are closely attached to the CC-CA?

ii) Although the neuropeptidomics / mass spectrometry data do not directly influence the conclusions of the manuscript, it is crucial that the authors clarify the trypsin setting in their MS analysis. Because this may be a major mistake. It is hard to believe that the neuropeptidomics data were generated by this method. Explanation: Trypsin generates theoretical peptide spectra that end in R or K. So it is impossible that these theoretical tryptic peptide spectra were used for the identification of naturally occurring neuropeptide spectra (as these do not by definition end in R or K).

iii) Please revise the annotation of the peptidomic data in line with the conventions or existing literature for the field so that this work can be used in conjunction with existing literature. The authors should compare their data with the existing neuropeptidomic tissue distribution literature, for e.g in Clynen and Schoofs, (2009). (detailed comments on this point provided in the individual reviews below)

iv) It is not clear if the transcriptome analysis was performed on resting muscle tissue or on tissue collected after 1 hr flight. Same applies to metabolomics analysis.

v) In all figure notations such as a, ab, b, bc , abc have been used. It is apparently related to statistical analysis but this is very confusing and not well explained. The statistical analysis in all figure should be explained much more clearly.

Reviewer #1:

To identify neuropeptides that might regulate muscle metabolism during sustained flight, the authors carried out mass spectrometry to identify peptides that are expressed in the brain and neuroendocrine tissues using high-resolution mass spectrometry. From the peptides found in these tissues, they shortlisted those (ACP, AKH2 and NPF1) whose levels were elevated during long-term flight; of these, when depleted by RNAi, ACP and AKH2. A role for AKH2 in controlling metabolism and flight has been previously described. Here the authors study the role of ACP in this process.

To study the role of ACP the authors deleted the gene using CRISPR/Cas9 gene editing and demonstrate loss of the peptide in edited animals by immunohistochemistry. Knockout animals were larger than controls and showed shorter flight times and distances travelled. This suggests a role for ACP in regulating long term flight. Using bioinformatics analysis, the authors identify the likely candidate receptor for ACP (ACPR) that could mediate the actions of ACP. It is highly expressed in muscle and fat body. However the functional significance of this receptors in terms of ACP action remains to be established.

To understand the mechanism by which ACP supports flight a transcriptome analysis was carried out on flight muscle and fat body tissue comparing wild type and ACP-/-. From a statistical analysis the authors conclude that the likely target is muscle although this remains to be established. Pathway analysis of transcript changes indicate alteration in molecules involved in metabolism. Perhaps an analysis of transcriptional changes in wild type muscle will help to identify transcript changes relevant to adaptation to long term flight. A key molecule identified in this analysis was FABP, a key transporter of fatty acids. The functional importance of this molecule was determined by the RNAi depletion of FABP in muscle which resulted in defects in a tethered flight assay.

To determine the impact of these transcriptional changes ACP-/- muscles on metabolism, a metabolome analysis was performed on muscle tissue using LC-MS. A key finding was the downregulation of several molecular species of acylcarnitines with no change in the levels of pyruvate, the end product of glycolysis. Together with the transcriptome analysis, this study concludes that ACP signalling during long flight remodels metabolism to enhance mitochondrial metabolism.

The definition of long-term flight as used in the context of this study and the phenomenon being discussed have not been clearly defined. This should be done early on in the manuscript.

While the manuscript makes a case for the function of ACP, what is the role of AKH2. Clearly not sufficient to account for loss of ACP. What is the phenotype of a depletion of both AKH2 and ACP. AKH2 Can RNAi for AKH2 be done in ACP-/- animals and if so what would be the flight defect ?

A body size defect is reported for ACP-/-. Is this also seen with the RNAi. This is an important point as in insects adult body size is determined during development and will also imply effects of metabolism. Please clarify this point.

What is the effect of RNAi depletion, in muscle, of ACPR on flight duration and length. Can it phenocopy the ACP depletion or knockdown ? At the moment this data is of limited value.

It is not clear if the transcriptome analysis was performed on resting muscle tissue or on tissue collected after 1 hr flight. Same applies to metabolomics analysis.

Also what are the transcriptome changes in wild type muscle tissue comparing resting and 1 hr flight. This information will help identify the changes relevant to long term flight in the ACP-/- and separate out transcriptional changes that are developmental compared to wild type.

In all figure notations such as a, ab, b, bc , abc have been used. It is apparently related to statistical analysis but this is very confusing. The statistical analysis in all figures should be explained much more clearly.

Reviewer #2:

– The authors aim to understand how locusts modulate skeletal muscle metabolism to support long flights that is a well known aspect of the animal's natural behavior. The authors hypothesize that a neuropeptide directly signals to flight muscles and promote a metabolic switch (fatty acid β oxidation) that can support long flights. The authors aimed to identify one or more neuropeptides that can perform this metabolic switch.

– In order to identify the neuropeptide the authors used a combination of mass spectrometry based peptidomics, mRNA profiling and RNAi mediated knockdown studies and immunofluorescence staining. This part of the study identified 2 neuropeptides ACP and AKH2 that were upregulated in response to sustained flight. Since ACP was less studied in the context of this study, the authors proceeded with this peptide for functional studies.

– Using CRISPR knockout lines, the authors showed the functional dependency of this peptide on sustained flight.

– They identified the role of this peptide in promoting β oxidation of fatty acids (using metabolomics and mRNA profiling) as judged by impaired β oxidation in ACP knockout animals. This effect was recued by peptide treatment.

– The authors next showed a specific FABP protein as a mediator of the effects of ACP in promoting fatty acid transport and β oxidation in response to this neuropeptide.

The systems analysis of metabolism is a major strength of this paper.

In general, studying rapidly adaptive processes like metabolism (that change based on seasons and nutritional availability) using knockout lines can lead to alternative metabolic adaptations that will be difficult to map. This is a concern in an otherwise elegant study. Though not highlighted here the studies reveal that ACP is actually involved in metabolic homeostasis. The model proposed here is that in response to extended flight, ACP levels are increased in order to cause a metabolic switch in flight muscle. The ability of ACP to perform this switch directly needs to be more comprehensively and quantitatively tested.

The analytical methods are thoughtfully done. The peptidomics works is well designed and successfully analyzes hundreds of neuropeptides in an unbiases manner. This data set will be extremely valuable to researchers.

As the authors point out in their discussion, a metabolic switch to β oxidations is a conserved mechanism in numerous animals especially in the heart during stress. Therefore molecular regulators of this process will have broad impact in various animal models.

A wonderful question about adaptation to extend flights in locusts is asked and a mechanism is suggested via the ACP neuropeptide and its direct actions on flight muscle. The analytical methods are very thoughtful and will be broadly impactful.

This reviewer is appreciative of this elegant piece of work, and has the following questions and suggestions to the authors-

1. The reviewer believes that the genetic model used in this study shows the role of ACP in flight muscle metabolic homeostasis. Therefore as expected the already impaired fatty acid β oxidation can cause the inability to perform extended flights. Does the increased ACP expression actually modulate fatty acid oxidation in response to extended flight as suggested. Please show if there is increased staining of ACP in response to flight.

2. The reviewer also suggests an additional experiment to take a quantitative approach in order to map the causative role of ACP in fatty acid oxidative capacity. This can be done using transgenic animals with inducible expression of ACP and correlation with measuring FAO capacity.

This should also map directly with ability for extended flight strengthening the hypothesis.

Reviewer #3:

Neuropeptides are small signalling molecules in the central nervous system that control many physiological and behavioural processes. Hou et al., show that the ACP neuropeptide modulates long-term flight in adult locusts. They first performed a mass spectrometry analysis aiming at identifying all neuropeptides in the locust brain and in its associated neurohaemal organs, the corpora cardiaca-corpora allata. Next, they show that the expression of four neuropeptide-encoding genes changes after 1 hour of sustained flight. Knockdown by RNA interference revealed that down-regulation of two of these genes, respectively encoding for ACP and AKH-2, affect flight time and flight distance. The role of AKH-2 in flight regulation has already been demonstrated, but a possible role of ACP has so far remained elusive. Therefore, the authors made use of CRISPR/Cas9 gene editing to provide compelling evidence that ACP knockouts fail in normal long-term flight activity when compared to wild type controls.

Based on homology searches, the authors then predicted the ACP receptor and found by means of transcriptome analysis that this receptor is highly expressed in the fat body and in-flight muscles of locusts. Comparative transcriptome analysis between wild type and ACP knockout locusts revealed a differential expression of 520 genes in flight muscle and 318 genes in the fat body. The highly expressed FABP gene in flight muscle was downregulated by more than 80% in ACP knockouts, as confirmed by qPCR and Western blotting. RNAi knockdown of the fatty acid binding protein, FABP, suppressed the expression levels of multiple β-oxidation-related genes.

In addition, a comparative metabolite analysis showed that 204 metabolites were downregulated in flight muscles of ACP knockouts, including acetyl-CoA and NADH, two end metabolites of β-oxidation.

RNAi knockdown of FABP resulted in a similar phenotype with decreased flight activity as well as decreased levels of acetyl-CoA and NADH.

Injecting synthetic ACP in ACP knockouts could (partially) rescue expression levels of eight β-oxidation genes as well as metabolite contents and flight activity, but not in FABP knockdown animals, suggesting that FABP may act downstream of ACP.

These data convincingly show that the neuropeptide ACP regulates long-term flight activity in locusts and that the fatty acid transport protein FABP may act downstream of ACP.

A weakness of the study is that it has not been experimentally demonstrated that ACP acts through its predicted ACP receptor in flight muscles. An alternative explanation – namely that ACP induces the release of a flight-controlling hormone from the CC or possibly from the CA into the haemolymph – is still possible.

1) Hou et al., analysed the brain and CC-CA neuropeptidome. For this purpose, they prepared samples of brains and CC-CA. To be able to understand the tissue distribution of the neuropeptides, it is important to know what is meant by 'brain' samples and by CC-CA samples. Does the brain also contain the optic lobes, frontal ganglion, suboesophageal ganglion, the NCC or only the proto-, deutero-, and tritocerebrum? Do the CC-CA samples only contain the CC and the CA or also the hypocerebral ganglion and other small neuronal structures that are closely attached to the CC-CA? The authors should clarify this and compare their data with the neuropeptidomic tissue distribution analysis as described in Clynen and Schoofs, (2009).

2) line 113: In total, 177 and 341 non-redundant neuropeptides derived from 36 precursors were identified in the brain and CC-CA respectively. Does this refer to number of ion peaks, of which several can be derived from the same mature neuropeptide? Or are all distinct endogenously occurring neuropeptides?

3) Figure 1 supplement 1:

- is each of the 36 peptides a representative for each of the 36 neuropeptide precursors? This should be indicated, because there are more than 36 neuropeptides.

- What is Mys-B; Is it locustamyosuppressin? Then it should be abbreviated as MS.

- Ryamine should be RYamide.

- Hou et al., 2015 identified 4 AKH precursor genes. Why is AKH-4 not identified in this analysis?

- Sulfakinin is more abundant in CC-CA compared to brain, which contrast the neuropeptidedome analysis of Clynen and Schoofs, (2009). Explain.

- Allatostatin B should be named as MIP, as this is the now commonly accepted name for the precursor genes of this bilaterian peptide family, of which the first member was identified in Locusta as Lom-MIP.

- Procolin should be Proctolin

- Explain why a number of Locusta neuropeptides, known to be present in the brain and/or CC-CA, were not identified in this neuropeptidome analysis, including (the precursors of) corazonin, AST-C, inotocin.

4) Source data supplementary Table 1.

This table shows the peptide ions corresponding to the neuropeptide precursor genes.

Remarks: Please consult the Locusta neuropeptidomics papers of Clynen and Schoofs and name the peptide precursors according to the originally given neuropeptide names.

– Accession number 12049.1 has 2 names: DH/PBAN and tryptopyrokinin. This is, however, the precursor of locustapyrokinin-1, which was identified by neuropeptidome analysis by Clynen and Schoofs and before. It is confusing to rename the genes encoding pyrokinin peptides. AYC12049 definitely is a locustapyrokinin precursor, and should be named according to the original discovery of its encoding neuropeptides, i.e. PK, and not renamed into DH/PBAN. PK-encoding genes have expanded in the evolutionary lineage leading to locusts, when compared to other insects. Naming them as PK1, PK 2, etc. reflects better this expansion (instead of giving them different names).

– AYC12051.1 seems to be the precursor of the periviscerokinins and should be named PVK or CAPA.

– AKN21242.1 is the precursor for LOM-MIP or locustamyoinhibiting peptide and should not be renamed into AST-B.

– Procolin should be proctolin in the source table of neuropeptides.

– SNPF should be sNPF.

5) Line 191 and following: The authors did not experimentally demonstrate that the ACP neuropeptide (or HrTH) acts through the predicted ACP receptor. The authors can show this in vitro by expressing the receptor in a cellular expression system and challenge the receptor with synthetic ACP.

In addition, to substantiate the mechanism of action of ACP in vivo, the authors should compare the phenotype of the RNAi-knockdown of the ACP receptor (or Crispr-Cas9 knockout) with the flight phenotypes of WT and FABP knockdowns.

6) line 308: The ACP neuropeptide is abundant in the CC (not in the CA) as shown by Clynen and Schoofs (not in this manuscript). The expression of the gene is confined to cells in the brain as shown in this manuscript. Line 308 is about the neuropeptide. Transcription responses refer to the gene expression, which is brain-specific according to Figure 2. Rephrase.

7) line 322: "Our results show that the ACP precursor gene is specifically produced in the brain" This phrasing is incorrect: all cells/tissues contain the same genome. No single gene is specifically produced in any tissue". Should be: specifically expressed.

8) line 329: it is not quite similar. AKH is synthesized in the CC, more particular by cells in the CC's glandular lobes. JH is synthesized in the CA. ACP is synthesised in the brain and transported via the NCC to the storage lobe of the CC, where it is stored (until release) or where it may influence the release of other substances.

6) line 317: The ACP neuropeptide or HrTH was isolated from the storage lobes of the corpora cardiaca. These storage lobes contain peptide-storing nerve endings of the NCC coming from the brain.

7) line 332: rephrase: where its receptor is expressed. (A circulating neurohormone will act on all its receptor molecules on the surface of cells (and not only on tissue displaying a high receptor expression), while circulating through the body).

8) line 365: "Based on transcriptome profile analysis, the flight muscle appears to be the main tissue targeted by ACP, although the ACP receptor also has a relatively high expression level in the fat body".

This is speculation. ACP will also target its receptor in the fat body, where it will elicit a distinct functional response.

Targets of neuropeptides cannot be (exclusively) identified in the context of altered gene expression upon neuropeptide receptor activation. In fact, most neuropeptides act on GPCRs and the responses are usually very rapid (within seconds), while altered gene expression is a process that takes much more time.

Immediately downstream of the neuropeptide-receptor interaction, fast processes are more likely to be the immediate result of this interaction. These fast responses include increased/decreased secretion, change of membrane permeability, opening/closing of ion channels etc. The presence of the ACP receptor in the fat body suggests that – like the flight muscles – also the fat body is a target of ACP, if the predicted ACP receptor is the in vivo cognate receptor of ACP.

The manuscript only provides very weak evidence (the presence of a predicted ACP receptor) that ACP directly acts on flight muscles.

I suggest to delete lines 365 to 380: many interpretations, wrong phrasing. Does not contribute much to the story.

9) line 442: ACP has not been identified in this manuscript. The peptide was identified by Siegert long before and the ACP precursor was already identified in the study of Hou et al., 2015.

Quantitative neuropeptidome analysis:

10) line 476: provide the source of the protein database. Where can it be consulted?

11) why was the enzym trypsin set as a parameter in a peptidomics analysis? This is done for tryptic peptide analysis in a classical proteome analysis, but it is not the optimal way of analysing the neuropeptidome. This parameter does not yield the theoretical spectra of mature neuropeptides. Instead, it yields theoretical spectra of theoretic tryptic peptides, which aid in the identification of the corresponding protein. Spectra of tryptic peptides do not reveal the endogenously occurring neuropeptides, which are endogenously processed by other specific processing enzymes.

12) line 482: what is meant by this? At least two spectra in one sample?

13) What is the mass tolerance for fragmented peptides? Modifications should also include C-terminal amidation, N-terminal pGlu.

14) Figure 3 supplement 2: Explain abbreviations. The expression of ACPR is only tissue-specific for the organs/tissues tested. (The gut, Malpighian tubules, trachea, hypodermis, etc were not analysed). I suggest to rephrase in manuscript text as: Compared to the other organs tested, ACPR is highly expressed in flight muscles and fat body.

15) line 567: include reference or source of MEGA software.

16) Bogerd et al., (1995) showed that flight activity increased steady-state levels of the AKH I and II mRNAs (approximately 2.0 times each) and the AKH III mRNA (approximately 4.2 times) in the corpora cardiaca. Explain why an altered expression of AKH-1 and AKH-III precursor genes is not seen in the present manuscript.

17) Many (supplemental) Figures contain abbreviations that are not explained in the Figure legends.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for submitting your article "Neuropeptide ACP facilitates lipid oxidation and utilization during long-term flight in locusts" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by K VijayRaghavan as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Aravind Ramanathan (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential Revisions:

Thank you for clarifying the peptidomic analysis in your revised version.

1. However it is essential that your analysis makes clear the source of brain region used for the peptidomic analysis. Therefore "CC-CA" should be replaced by "retrocerebral complex" in the entire manuscript.

2. Also, rewrite lines 370-375, by stating that in the present manuscript the whole retrocerebral complex was used for peptidomic analysis, in contrast to the study of Clynen who analysed the organs of the retrocerebral complex separately.

Delete "a little bit different" (it is not a little bit).

3. An important methodology consideration, that is key to interpreting your results has been raised. Please address this in your revision.

– The authors have adapted the methods section on mass spectrometry. It is much clearer now, but one major issue remains. It regards the sentence "Neuropeptide identification were used only if at least two spectra were identified in one sample".

This is a criterion that is used for proteomics analyses; it is not at all suited for neuropeptidomics studies. The reason is that neuropeptides are produced by cleavage from larger preproteins, whereby the mature cleaved peptide is kept and the remainder of the protein is degraded. So, if the preprotein contains only one mature neuropeptide sequence, this neuropeptide cannot be identified by the restricted rule of "two spectra in one sample". The authors refer in their comments to the publication of Han et al. in J. Protein Research. But in the discussion of this article on page 4389, Han et al. clearly state that the 19 neuropeptides that they were unable to identify might be caused by the stricter FDR threshold they used (two spectra in one sample). That is the reason that they missed 19 well known bee neuropeptides in their study.

Regarding the present revised manuscript one may wonder in fact how the authors were even able to identify the ACP neuropeptide by using this restrictive threshold. Because the MS-spectra were not provided in the MS (I could not find them), I cannot check what other peptide from the same ACP-precursor they identified. This would be strange, because the remainder of the protein is normally broken down upon cleavage of the ACP peptide out of it.

So the question is: did the authors use this restrictive threshold? (which might also explain why they were unable to identify some well known locust peptides residing as single peptides in their respective precursors).

If yes, then this reviewer would like to know what other peptide they identified from the ACP precursor, as this would contradict with textbook knowledge about neuropeptide processing. In this aspect, the term "lots of new peptide fragments" is unclear. What is meant by "peptide fragments"? Are these fragments of the same mature peptide or fragments of the remainder of the precursor protein, which should in principle have been degraded.

Or did they not use this restrictive threshold?

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Neuropeptide ACP facilitates lipid oxidation and utilization during long-term flight in locusts" for further consideration by eLife. Your revised article has been evaluated by K VijayRaghavan (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

Reviewer #1:

Once Reviewer #3 approves the responses of the authors, I am fine with it. Reviewer #3 is the expert in regard to these specific points

Reviewer #3:

I still have a major concern regarding the neuropeptidome analysis, which I explain in detail below.

The authors state in their rebuttal : " Based on the reviewer's comments, we realize that our description of neuropeptide identifications is inaccuracy in the sentence "Neuropeptide identifications were used only if at least two spectra were identified in one sample". To avoid misunderstanding, we have revised the description as: The criterion of peptide fragment identifications was used only if at least two spectra were formed in one sample [...] Generally, a single peptide fragment could be detected for several times by the MS. A spectrum will be formed each time when the peptide fragment is detected by MS. The criterion of peptide fragment identifications was used only if at least two spectra were formed in one sample"

In the text, the authors wrote: ""The threshold used in the current study (two spectra in one sample) could significantly improve the reliability of peptides identified in the MS. "

Reply:

1) This statement is wrong. One MSMS spectrum of good quality is sufficient for the identification of a neuropeptide. An MSMS fragmentation spectrum of good quality is a spectrum where the series of y-ions and b-ions are sufficiently complete to cover the whole amino acid sequence of the peptide (which is the case for the spectra nrs 1 and 4 provided by the authors, but not for spectrum 5).

You can never state that at least two MSMS spectra of the same neuropeptide are needed. The argument of the authors "more peptide spectra = more reliability" is also not in line with the way data are collected on the mass spec.

In data-dependent acquisition, which is how the mass spec collects data, you have to take into account the dynamic exclusion parameter, which ensures that an ion with a particular m/z value will not be selected for fragmentation for a number of seconds to avoid that the instrument detects and measures the same ion over and over again during an elution peak. The normal situation with a dynamic exclusion parameter setting at 15s, is that the mass spectrometer detects a single peptide ion, because peptides typically elute over 30s. The detection of many identical ions of the same peptide is the exception rather than the normal situation and should not be set as a rule of confidence. The dynamic exclusion parameter cannot be found in the methods section.

So, in the normal situation, only one precursor ion is selected for MSMS fragmentation. Only when the elution peak of that peptide ion is broad, the peptide ion can be selected multiple times over the period of the elution peak. That is why the dynamic range parameter is important. This parameter avoids that the same ion is selected for fragmentation over and over again at the expense of other – less abundant – peptide ions that elute in the very same region.

The "more than one spectrum in one sample" argument of the authors is thus a reflection of the elution and abundance of a particular peptide ion. It is not at all a criterion for reliability or confidence regarding the MSMS peptide identification. One spectrum of good quality is as reliable as two or three good spectra of the same ion that just elutes over a longer period of time.

The omission of neuropeptides represented by a single MSMS spectrum of good quality is thus a mistake.

A second mistake is that all the measured neuropeptide degradation products are counted as bona fide neuropeptides (see below under nr 2).

The four ACP spectra provided in the rebuttal are all spectra of the same ACP sequence. The excel file also contains degradation products of this peptide. However, there is no reason to assume that the measurement of multiple spectra in one sample or the measurement of peptide degradation products provide a higher reliability. The measurement of degradation products can be due to a technical issue (sample preparation) or a biological issue (in vivo degradation). It does not tell you anything about the reliability of the measurement of the intact neuropeptide.

2) Results section

The authors state: "In total, 177 and 341 nonredundant neuropeptides derived from 36 precursors were identified in Br and RC, respectively (Supplementary File 1).

Reply:

Now that the authors have explained how they have interpreted the MSMS data, this sentence is completely wrong and will lead to confusion. The authors have not identified that high number of neuropeptides, because this number reflects multiple MSMS spectra resulting from the same neuropeptide (and its degradation products). A neuropeptide is defined as the amino acid sequence located between the dibasic cleavage sites within the neuropeptide precursor. The shorter versions presented in the excel file are not novel neuropeptides but degradation products. It is unclear what the origin of these degradation products is. They may be artifacts resulting from the sample preparation, or they may represent in vivo degradation fragments resulting from the inactivation of the neuropeptide.

The authors should rearrange their excel file by clustering the degradation products of the same neuropeptide within a neuropeptide precursor. Here an example for a pyrokinin (PK1)-precursor derived neuropeptide.

GAVPAAQFSPRLamide

GAVPAAQFSPRL

AVPAAQFSPRLamide

AVPAAQFSPR

VPAAQFSPRL

VPAAQFSPR

PAAQFSPRLamide

AQFSPRLamide

3) The authors also state: " In addition, only several of known neuropeptides, not too many, were not detected under the current threshold in our study. The absence of these neuropeptides may be attributed to their low abundance in tested samples, relative short half-life period, and unsuitable chromatographic conditions. We think that the criterion used in the current study should be suitable."

Reply:

The identification of the neuropeptides is also dependent on the used database for spectrum scanning, as the authors did not rely on de novo identification. Thus, the identification can only be as good as the database against which the spectra are scanned. If the used locust database does not contain the (protein precursor) sequences, or the derived theoretical spectra, particular experimental spectra will not lead to identification, even if they are abundantly present;

The second reason is due (i) to their data acquisition setting (parameter not mentioned in the methods) and to (ii) the "two spectra in one sample" rule that the authors wrongly used, a rule that merely reflects long elution times of very highly abundant peptides (see above). This rule is wrong and excludes less abundant neuropeptides with clear but shorter chromatographic elution peaks.

4) In the excel files, CC-CA has still not been replaced by retrocerebral complex.

https://doi.org/10.7554/eLife.65279.sa1

Author response

Essential Revisions:

1) While a potential ACP receptor is presented on the basis of informatics and expression analysis, there is no functional evidence of a role for this protein in mediating the actions of ACP. The authors can show this in vitro by expressing the receptor in a cellular expression system and challenge the receptor with synthetic ACP.

AND/OR

to substantiate the mechanism of action of ACP in vivo, the authors should compare the phenotype of the RNAi-knockdown of the ACP receptor ( or Crispr-Cas9 knockout) with the flight phenotypes of WT and FABP knockdowns.

We accept the reviewer’s comments. In fact, we have successfully constructed the homozygous ACPR mutant locust line using the CRISPR/Cas 9 system. As expected, the ACPR mutant (ACPR13/13) indeed displayed significantly reduced long-term flight activity, similar to the flight phenotype caused by ACP knockout and FABP knockdown. The experimental evidence about molecular and functional identifications of ACPR has been provided in the revised Figure 3.

2) A key strategy in this study is to compare phenotypes, transcriptomes and metabolomes between wild type and ACP-/- animals. A body size defect is reported for ACP-/- locusts that is also seen with the RNAi. This is an important point as in insects adult body size is determined during development and will also imply effects of metabolism. In general, studying rapidly adaptive processes like metabolism (that change based on seasons and nutritional availability) using knockout lines can lead to alternative metabolic adaptations that will be difficult to map. This is a concern in an otherwise elegant study.

We understand the reviewer’s concern. However, the body size of locusts actually does not change after adult eclosion thanks to its hard exoskeleton. Therefore, the effects of ACP on body size could not be assessed through RNAi of the gene after adult eclosion. So, we have clarified this point by providing additional discussion in the revised manuscript. Details were shown as: “Through phenotype examination, we also observed a larger body size in ACP mutants. Usually, the body size of locusts is stable after adult eclosion thanks to its hard exoskeleton. Therefore, the effects of ACP on body size could not be assessed through RNAi of the gene after adult eclosion in the current study. It has been suggested that the growth state for an organism can be negatively affected by other physiological traits, such as locomotion, reproduction, or life span (Lee et al., 2010). Therefore, the increased body size of ACP mutants may be attributed to the continuous metabolism changes associated with trade-off effects between flight activity and body growth. Similarly, the loss of function of the AKH peptide results in adult-onset obesity in Drosophila (Galikova et al., 2015). These findings may reflect a common role played by ACP and AKH in governing the energy balance of insects. It will be an interesting work to explore the molecular and metabolic basis for body size determination on the basis of established ACP mutant locust line” (lines 503-515).

The authors should strengthen the conclusion that enhanced ACP activity in adult locusts is responsible for the reported transcriptome and metabolome changes leading long flight modulation. Can they provide data that:

a) Please show if there is altered staining of ACP in the brain during long flight conditions?

We appreciated the reviewer’s suggestion. Actually, we have tried to determine the intensity of ACP staining in response to 60 min-sustained flight by performing IHC experiments before. However, it seems that the IHC method was unsuited to do quantitative analysis of ACP staining. Several reasons might be taken into considerations: (1) There are many ACP-positive neurons (>30 neurons in the pars intercerebralis and > 8 neuron in each lateral forebrain), and some of them showed overlapped staining, which could lead to inaccurate intensity analysis; (2) Although the cellular localization of ACP peptide is in similar brain regions, numbers of ACP-positive staining neurons varied among different individual brains thanks to the complex stereoscopic neuronal structures (see Author response image 1, CK indicates control locust brains, FL indicates brains from locusts after 1 h-sustained flight). In fact, the evidence from the significant increase of ACP transcription after 1h-sustained flight (in Figure 2B) indicates the sensitive response of ACP to flight activity. We have discussed this point in the Discussion: “Our results show that the ACP precursor gene in the brain displays strong transcription responses to prolonged flight. The regulatory roles played by ACP in locust flight are clearly supported by tethered flight experiments after knockdown and knockout of its precursor gene, as well as reduced extended flight ability of ACPR mutants” (lines 390-394).

Author response image 1

b) Does the increased ACP expression actually modulate fatty acid oxidation in response to extended flight as suggested. Can injection of ACP peptide alter FAO capacity and map to extended flight capability?

Thanks for the reviewer’s suggestions. According to this suggestion, we added a new experiment to examine the flight performance and β-oxidation of WT locusts through the injection with ACP peptide. The results showed that the injection of ACP peptide could significantly promote locust flight activity including both flight duration and flight distance (new Figure 1—figure supplement 7). Moreover, the expression levels of lipid oxidation-related genes and the abundance of β-oxidation products in the flight muscle were also significantly enhanced upon ACP peptide injection (new Figure 4—figure supplement 3A and Figure 5—figure supplement 4A). Therefore, these new data further support that increased ACP level can promote fatty acid oxidation and facilitate sustained flight in turn.

c) If the changes in transcriptome and metabolome seen in ACP-/- are a direct reflection of the function of this peptide is modulating metabolism in long-flight, then are the changes in these transcripts and metabolites also seen in wild type locust tissue when comparing resting with tissue after 1 hr flight ?

We understand the reviewer’s concern. In the revised manuscript, we performed additional experiment to examine the expression levels of genes associated with fatty acid transport and oxidation and the contents of end metabolites of β-oxidation (acetyl-CoA and NADH) in the flight muscle after 1 h-sustained flight as well as ACP peptide injection. We found both 1 h- sustained flight and ACP peptide injection could significantly enhance the expressions levels of gene related to lipid transport and oxidation in the flight muscle of WT locusts (Figure 4—figure supplement 3). Moreover, injection of ACP peptide could strongly increase the relative amounts of muscle acetyl-CoA and NADH those reflecting fatty acid oxidation activity (Figure 5—figure supplement 4A), further supporting the regulatory role of ACP peptide in lipid metabolism. However, upon 60 min-tethered fight, the relative content of acetyl-CoA did not change but the NADH level was decreased (Figure 5—figure supplement 4B). The decreased β-oxidation products may reflect rapid energy utilization in subsequent mitochondrial metabolism during sustained flight. The consistent expression changes in FAO-related genes upon ACP peptide injection and sustained flight could further support the tight regulation of ACP on flight-related lipid oxidation. We have provided additional discussion on this point in the revised manuscript (see lines 433-440).

3) Clarity on methodology

Please provide clarity on some methodology issues to make the work more informative and accessible. The following points to be addressed:

i) Hou et al. analysed the brain and CC-CA neuropeptidome. For this purpose, they prepared samples of brains and CC-CA. To be able to understand the tissue distribution of the neuropeptides, it is important to know what is meant by 'brain' samples and by CC-CA samples. Does the brain also contain the optic lobes, frontal ganglion, suboesophageal ganglion, the NCC or only the proto-, deutero-, and tritocerebrum? Do the CC-CA samples only contain the CC and the CA or also the hypocerebral ganglion and other small neuronal structures that are closely attached to the CC-CA?

We appreciated the reviewer’s helpful suggestions. In the current study, brain samples containing only the protocerebrum, deuterocerebrum, and tritocerebrum and the corpora cardiaca-corpora allata (CC-CA) complex including attached small neuronal structures and hypocerebral ganglion were used for neuropeptidome analysis. We have provided detailed information in the method part (lines 532-534).

ii) Although the neuropeptidomics / mass spectrometry data do not directly influence the conclusions of the manuscript, it is crucial that the authors clarify the trypsin setting in their MS analysis. Because this may be a major mistake. It is hard to believe that the neuropeptidomics data were generated by this method. Explanation: Trypsin generates theoretical peptide spectra that end in R or K. So it is impossible that these theoretical tryptic peptide spectra were used for the identification of naturally occurring neuropeptide spectra (as these do not by definition end in R or K).

We really appreciate the reviewer’s comments and apologize for this mistake. We have corrected it in describing the neuropeptidome method. Detailed methods for neuropeptidome were shown as: “parent ion mass tolerance is 15 ppm, and fragment ion mass tolerance is 0.05 Da; enzyme specificity, none. The following modifications were applied: C-terminal amidation (A, −0.98) and pyroglutamination from Q (P, −17.03), maximum missed cleavages per peptide: 2, and maximum allowed variable PTM per peptide: 2. A fusion target and decoy approach was used to for the estimation of the false discovery rate (FDR) and controlled at ≤1.0% at the peptide level. Neuropeptide identifications were used only if at least two spectra were identified in one sample. Peptides were further validated by comparison with predicted neuropeptide precursors and neuropeptidome analysis in the locust (Clynen and Schoofs, 2009, Hou et al., 2015)” (lines 558-567).

iii) Please revise the annotation of the peptidomic data in line with the conventions or existing literature for the field so that this work can be used in conjunction with existing literature. The authors should compare their data with the existing neuropeptidomic tissue distribution literature, for e.g in Clynen and Schoofs (2009).(detailed comments on this point provided in the individual reviews below).

Thanks, we have carefully compared our peptidome data with that reported by Clynen and Schoofs, (2009). The annotation of neuropeptides has been revised in corresponding figures and tables according to the reviewer’s suggestion.

iv) It is not clear if the transcriptome analysis was performed on resting muscle tissue or on tissue collected after 1 hr flight. Same applies to metabolomics analysis.

In the current study, the transcriptome analysis was performed on resting muscle tissue. We have added detailed information in the method part: “The flight muscle and fat body tissues were dissected from WT and ACP-/- female locusts under resting state at PAE 7 days” (lines 671-672).

v) In all figure notations such as a, ab, b, bc , abc have been used. It is apparently related to statistical analysis but this is very confusing and not well explained. The statistical analysis in all figure should be explained much more clearly.

Multiple comparison between groups were analyzed using one-way ANOVA method. Columns labeled with different letters indicate that there is a significant difference between the two groups, columns contain same letters indicate no significance observed between the two groups. We have explained the meaning of letters labeled on different columns in the corresponding figure legends (please see lines 1051-1053, 1098-1101).

Reviewer #1:

To identify neuropeptides that might regulate muscle metabolism during sustained flight, the authors carried out mass spectrometry to identify peptides that are expressed in the brain and neuroendocrine tissues using high-resolution mass spectrometry. From the peptides found in these tissues, they shortlisted those (ACP, AKH2 and NPF1) whose levels were elevated during long-term flight; of these, when depleted by RNAi, ACP and AKH2. A role for AKH2 in controlling metabolism and flight has been previously described. Here the authors study the role of ACP in this process.

To study the role of ACP the authors deleted the gene using CRISPR/Cas9 gene editing and demonstrate loss of the peptide in edited animals by immunohistochemistry. Knockout animals were larger than controls and showed shorter flight times and distances travelled. This suggests a role for ACP in regulating long term flight. Using bioinformatics analysis, the authors identify the likely candidate receptor for ACP (ACPR) that could mediate the actions of ACP. It is highly expressed in muscle and fat body. However the functional significance of this receptors in terms of ACP action remains to be established.

To understand the mechanism by which ACP supports flight a transcriptome analysis was carried out on flight muscle and fat body tissue comparing wild type and ACP-/-. From a statistical analysis the authors conclude that the likely target is muscle although this remains to be established. Pathway analysis of transcript changes indicate alteration in molecules involved in metabolism. Perhaps an analysis of transcriptional changes in wild type muscle will help to identify transcript changes relevant to adaptation to long term flight. A key molecule identified in this analysis was FABP, a key transporter of fatty acids. The functional importance of this molecule was determined by the RNAi depletion of FABP in muscle which resulted in defects in a tethered flight assay.

To determine the impact of these transcriptional changes ACP-/- muscles on metabolism, a metabolome analysis was performed on muscle tissue using LC-MS. A key finding was the downregulation of several molecular species of acylcarnitines with no change in the levels of pyruvate, the end product of glycolysis. Together with the transcriptome analysis, this study concludes that ACP signalling during long flight remodels metabolism to enhance mitochondrial metabolism.

The definition of long-term flight as used in the context of this study and the phenomenon being discussed have not been clearly defined. This should be done early on in the manuscript.

We accepted the reviewer’s suggestion. We have provided the definition of long-term flight in the introduction. Details were presented as: “Long-term flight is usually defined as sustained flight for seasonal and long-range migration towards a distinct direction in populations (Stefanescu et al., 2013, Juhasz et al., 2020)” (lines 50-52).

While the manuscript makes a case for the function of ACP, what is the role of AKH2. Clearly not sufficient to account for loss of ACP. What is the phenotype of a depletion of both AKH2 and ACP. AKH2 Can RNAi for AKH2 be done in ACP-/- animals and if so what would be the flight defect ?

Yes, this is an interesting question to explore the interaction of AKH2 and ACP in modulating the flight activity of locusts. In fact, previous documents have demonstrated the role of AKH members in locust flight by lipid mobilization of fat body (Van der Horst, 2003; Bogerd et al., 1995). It is reasonably hypothesized that there would be the coordination between the two peptides in the regulation of long-term flight of locusts. We are willing to test this hypothesis in the future work. In the revised manuscript, we have enriched the discussion about this point. The details were presented as: “Here, we also revealed that knockdown of either ACP or AKH2 induced similarly suppressed effects on locust flight performance. Further work is warranted to investigate the potential interaction among these neuroendocrine factors in energy regulation associated with flight activity” (lines 499-502).

A body size defect is reported for ACP-/-. Is this also seen with the RNAi. This is an important point as in insects adult body size is determined during development and will also imply effects of metabolism. Please clarify this point.

Thanks for the reviewer’s comments. Actually, the body size of locusts is stable after adult eclosion thanks to its hard exoskeleton. Because our RNAi treatments were conducted in adult locusts, the effects of ACP on body size could not be seen in the current study. We have clarified this point by providing additional discussion in the revised manuscript (lines 503-515).

What is the effect of RNAi depletion, in muscle, of ACPR on flight duration and length. Can it phenocopy the ACP depletion or knockdown ? At the moment this data is of limited value.

We accepted the reviewer’s concern. We have recently successfully constructed the homozygous ACPR mutant locust line using the CRISPR/Cas 9 system. The ACPR mutant (ACPR13/13) indeed induced similar phenotypes as ACP mutants, and displayed significantly reduced flight time and flight distance. The experimental evidence related to molecular and functional identification of ACPR were presented in the revised Figure 3.

It is not clear if the transcriptome analysis was performed on resting muscle tissue or on tissue collected after 1 hr flight. Same applies to metabolomics analysis.

The muscle tissues used in both transcriptome and metabolomics analysis were collected from WT and ACP-/- locusts under resting state. We have clarified this information in the method part in the revised manuscript (lines 671-672, 690-691).

Also what are the transcriptome changes in wild type muscle tissue comparing resting and 1 hr flight. This information will help identify the changes relevant to long term flight in the ACP-/- and separate out transcriptional changes that are developmental compared to wild type.

We thanked the reviewer’s valuable suggestion. In the current study, we examined the transcriptome changes in the flight muscle of ACP-/- locusts compared to WT locusts. In the revised manuscript, to further clarify the reviewer’s concern, we systematically examined the expression levels of genes associated with fatty acid transport and oxidation in the flight muscle after 1 h-sustained flight using qPCR methods. We found the expressions levels of genes related to lipid oxidation in the flight muscle were significantly up-regulated upon sustained flight. These results suggest that transcriptional changes in metabolism related genes are tightly correlated with flight activity. Relative experimental data were show in (Figure 4—figure supplement 3B).

In all figure notations such as a, ab, b, bc , abc have been used. It is apparently related to statistical analysis but this is very confusing. The statistical analysis in all figure should be explained much more clearly.

Thanks for the reviewer’s suggestion. One-way ANOVA method was used in our multiple comparisons between groups. Columns labeled with different letters indicate that there is a significant difference between the two groups, columns contain same letters indicate no significance observed between the two groups. To more clarify this concern, we have explained the meaning of letters labeled on different columns in the corresponding figure legends.

Reviewer #2:

– The authors aim to understand how locusts modulate skeletal muscle metabolism to support long flights that is a well known aspect of the animal's natural behavior. The authors hypothesize that a neuropeptide directly signals to flight muscles and promote a metabolic switch (fatty acid β oxidation) that can support long flights. The authors aimed to identify one or more neuropeptides that can perform this metabolic switch.

– In order to identify the neuropeptide the authors used a combination of mass spectrometry based peptidomics, mRNA profiling and RNAi mediated knockdown studies and immunofluorescence staining. This part of the study identified 2 neuropeptides ACP and AKH2 that were upregulated in response to sustained flight. Since ACP was less studied in the context of this study, the authors proceeded with this peptide for functional studies.

– Using CRISPR knockout lines, the authors showed the functional dependency of this peptide on sustained flight.

– They identified the role of this peptide in promoting β oxidation of fatty acids (using metabolomics and mRNA profiling) as judged by impaired β oxidation in ACP knockout animals. This effect was recued by peptide treatment.

– The authors next showed a specific FABP protein as a mediator of the effects of ACP in promoting fatty acid transport and β oxidation in response to this neuropeptide.

The systems analysis of metabolism is a major strength of this paper.

In general, studying rapidly adaptive processes like metabolism (that change based on seasons and nutritional availability) using knockout lines can lead to alternative metabolic adaptations that will be difficult to map. This is a concern in an otherwise elegant study. Though not highlighted here the studies reveal that ACP is actually involved in metabolic homeostasis. The model proposed here is that in response to extended flight, ACP levels are increased in order to cause a metabolic switch in flight muscle. The ability of ACP to perform this switch directly needs to be more comprehensively and quantitatively tested.

We appreciate the reviewer’s comments. In the revision, we have provided addition experimental data to support the regulatory role in lipid metabolism related to prolonged flight. We examined the flight performance and β-oxidation activity of WT locusts after injection with ACP peptide (new Figure 1—figure supplement 7, new Figure 4—figure supplement 3A, and Figure 5—figure supplement 4A). The enhanced prolonged flight activity, increased expressions levels of gene related to lipid utilization, as well as the elevated abundance of β-oxidation products in the flight muscle upon ACP peptide injection could further support the essential role of ACP in muscle lipid utilization during flight.

The analytical methods are thoughtfully done. The peptidomics works is well designed and successfully analyzes hundreds of neuropeptides in an unbiases manner. This data set will be extremely valuable to researchers.

As the authors point out in their discussion, a metabolic switch to β oxidations is a conserved mechanism in numerous animals especially in the heart during stress. Therefore molecular regulators of this process will have broad impact in various animal models.

A wonderful question about adaptation to extend flights in locusts is asked and a mechanism is suggested via the ACP neuropeptide and its direct actions on flight muscle. The analytical methods are very thoughtful and will be broadly impactful.

This reviewer is appreciative of this elegant piece of work, and has the following questions and suggestions to the authors-

1. The reviewer believes that the genetic model used in this study shows the role of ACP in flight muscle metabolic homeostasis. Therefore as expected the already impaired fatty acid β oxidation can cause the inability to perform extended flights. Does the increased ACP expression actually modulate fatty acid oxidation in response to extended flight as suggested. Please show if there is increased staining of ACP in response to flight.

We appreciated the reviewer’s concern. Actually, we have tried to determine the intensity of ACP staining in response to 1 h-sustained flight by performing IHC experiments before. However, it seems that the IHC method was unsuited to do quantitative analysis of ACP peptide. Several reasons might be taken into considerations: (1) There are many ACP-positive neurons (>30 neurons in the pars intercerebralis and > 8 neuron in each lateral forbrain), and some of them showed overlapped staining, which could lead to inaccurate intensity analysis; (2) Although the cellular localization of ACP peptide is in similar brain regions, numbers of ACP-positive staining neurons varied among different individual brains thanks to the complex stereoscopic neuronal structures (see Author response image 1). In fact, the evidence from the significant increase of ACP transcription after 1h-sustained flight (in Figure 2B) indicates the sensitive response of ACP to flight activity. We have discussed this point in the Discussion “Our results show that the ACP precursor gene in the brain displays strong transcription responses to prolonged flight. The regulatory roles played by ACP in locust flight are clearly supported by tethered flight experiments after knockdown and knockout of its precursor gene, as well as reduced extended flight ability of ACPR mutants.”

2. The reviewer also suggests an additional experiment to take a quantitative approach in order to map the causative role of ACP in fatty acid oxidative capacity. This can be done using transgenic animals with inducible expression of ACP and correlation with measuring FAO capacity.

This should also map directly with ability for extended flight strengthening the hypothesis.

We appreciated the reviewer’s helpful suggestion. So far, it is not available to overexpress target genes using the transgenic technology in locusts. Instead, we have provided another experimental data to map the causative role of ACP in fatty acid oxidative capacity. We have tested the flight performance and fatty acid oxidative capacity in the fight muscle by artificially injecting synthetic ACP peptide in the ACP mutants. The results showed that the impaired prolonged flight performance and fatty acid oxidation (at both gene expression level and metabolite level) in ACP mutants could be significantly recovered by ACP peptide injection. Furthermore, we have provided addition experimental evidence showing that the artificial injection of ACP peptide in WT locusts could also significantly enhance flight performance (new Figure 1—figure supplement 7) as well as fatty acid oxidation capacity (new Figure 4—figure supplement 3A and Figure 5—figure supplement 4A).

Reviewer #3:

Neuropeptides are small signalling molecules in the central nervous system that control many physiological and behavioural processes. Hou et al., show that the ACP neuropeptide modulates long-term flight in adult locusts. They first performed a mass spectrometry analysis aiming at identifying all neuropeptides in the locust brain and in its associated neurohaemal organs, the corpora cardiaca-corpora allata. Next, they show that the expression of four neuropeptide-encoding genes changes after 1 hour of sustained flight. Knockdown by RNA interference revealed that down-regulation of two of these genes, respectively encoding for ACP and AKH-2, affect flight time and flight distance. The role of AKH-2 in flight regulation has already been demonstrated, but a possible role of ACP has so far remained elusive. Therefore, the authors made use of CRISPR/Cas9 gene editing to provide compelling evidence that ACP knockouts fail in normal long-term flight activity when compared to wild type controls.

Based on homology searches, the authors then predicted the ACP receptor and found by means of transcriptome analysis that this receptor is highly expressed in the fat body and in-flight muscles of locusts. Comparative transcriptome analysis between wild type and ACP knockout locusts revealed a differential expression of 520 genes in flight muscle and 318 genes in the fat body. The highly expressed FABP gene in flight muscle was downregulated by more than 80% in ACP knockouts, as confirmed by qPCR and Western blotting. RNAi knockdown of the fatty acid binding protein, FABP, suppressed the expression levels of multiple β-oxidation-related genes.

In addition, a comparative metabolite analysis showed that 204 metabolites were downregulated in flight muscles of ACP knockouts, including acetyl-CoA and NADH, two end metabolites of β-oxidation.

RNAi knockdown of FABP resulted in a similar phenotype with decreased flight activity as well as decreased levels of acetyl-CoA and NADH.

Injecting synthetic ACP in ACP knockouts could (partially) rescue expression levels of eight β-oxidation genes as well as metabolite contents and flight activity, but not in FABP knockdown animals, suggesting that FABP may act downstream of ACP.

These data convincingly show that the neuropeptide ACP regulates long-term flight activity in locusts and that the fatty acid transport protein FABP may act downstream of ACP.

A weakness of the study is that it has not been experimentally demonstrated that ACP acts through its predicted ACP receptor in flight muscles. An alternative explanation – namely that ACP induces the release of a flight-controlling hormone from the CC or possibly from the CA into the haemolymph – is still possible.

We accepted the reviewer’s concern. To clarify this concern, we have provided new experimental data and evidence to support the essential role of ACP peptide system using ACPR mutant. We have recently successfully constructed the homozygous ACPR mutant locust line using the CRISPR/Cas 9 system. The ACPR mutant (ACPR13/13) indeed displayed significantly reduced long-term flight activity, which phenocopied the flight performance caused by ACP knockout. The experimental evidence related to molecular and functional identification of ACPR was presented in the revised Figure 3.

1) Hou et al., analysed the brain and CC-CA neuropeptidome. For this purpose, they prepared samples of brains and CC-CA. To be able to understand the tissue distribution of the neuropeptides, it is important to know what is meant by 'brain' samples and by CC-CA samples. Does the brain also contain the optic lobes, frontal ganglion, suboesophageal ganglion, the NCC or only the proto-, deutero-, and tritocerebrum? Do the CC-CA samples only contain the CC and the CA or also the hypocerebral ganglion and other small neuronal structures that are closely attached to the CC-CA? The authors should clarify this and compare their data with the neuropeptidomic tissue distribution analysis as described in Clynen and Schoofs, (2009).

We appreciated the reviewer’s helpful suggestions. We have provided detailed information in the method part (lines 532-534). Moreover, we have carefully compared tissue distribution of neuropeptides identified in current study with the previously work described by Clynen and Schoofs, (2009) according to the suggestion. We found that most of the neuropeptides show similar tissue distribution in the two studies, except for sulfakinin and PVK. The discrepancy in neuropeptidome analysis between the two studies may be attributed to different sample collection strategies. In neuropeptidome analysis of our study, the brain tissue contains protocerebrum, deuterocerebrum and tritocerebrum, and the corpora cardiaca-corpora allata (CC-CA) complex attached some of around neuronal tissues, a little bit different with previous study reported by Clynen and Schoofs, in which distinct neuronal regions (protocerebrum, pars intercerebralis, tritocerebrum, CC, CA) were separately collected for neuropeptidome analysis. We have added detailed information in the Discussion (See lines 352-377).

2) line 113: In total, 177 and 341 non-redundant neuropeptides derived from 36 precursors were identified in the brain and CC-CA respectively. Does this refer to number of ion peaks, of which several can be derived from the same mature neuropeptide? Or are all distinct endogenously occurring neuropeptides?

The numbers of non-redundant neuropeptides refer to distinct endogenously occurring neuropeptides identified in the current study.

3) Figure 1 supplement 1:

- is each of the 36 peptides a representative for each of the 36 neuropeptide precursors? This should be indicated, because there are more than 36 neuropeptides.

In Figure1-supplement 1, each line represents the content of neuropeptides produced from a single precursor. We have revised the figure legend as: “the abundant level of neuropeptides generated from 36 neuropeptide precursors were log transformed for heat map drawing” (lines 1190-1191).

- What is Mys-B; Is it locustamyosuppressin? Then it should be abbreviated as MS.

Thanks, we have revised Mys-B as MS according to the reviewer’s suggestion (see Figure 1—figure supplement 1).

- Ryamine should be RYamide.

Thanks, we have revised Ryamine as RYamide.

- Hou et al., 2015 identified 4 AKH precursor genes. Why is AKH-4 not identified in this analysis?

We previously identified 4 AKH precursor genes based on the locust genome and transcriptome sequences (Hou et al., 2015). However, the predicted AKH4 peptide (QVTFSRDWSP) has not been identified by mass spectrometry analysis so far. One possible explanation is that the peptide abundance is very low and beyond the detection threshold of mass spectrometry. We have added more information in the results (Lines 358-365).

- Sulfakinin is more abundant in CC-CA compared to brain, which contrast the neuropeptidedome analysis of Clynen and Schoofs, (2009). Explain.

We accepted the reviewer’s concern. The discrepancy in the tissue distributions of sulfakinin between the two studies may be resulted from different sample collection strategies. In the current study, we used brains containing protocerebrum, deuterocerebrum and tritocerebrum, as well as corpora cardiaca-corpora allata complex (CC-CA) including attached neuronal structures for neuropeptidome analysis, whereas protocerebrum, pars intercerebralis, tritocerebrum, CC and CA were separately collected for neuropeptidome analysis in the other study. Moreover, the developmental stages of experimental insects used are different in the two studies: mature female adults for our study, whereas immature adults for Clynen and Schoofs’s work. We have added more information in the discussion to clarify this concern (lines 366-377).

- Allatostatin B should be named as MIP, as this is the now commonly accepted name for the precursor genes of this bilaterian peptide family, of which the first member was identified in Locusta as Lom-MIP.

We have revised Allatostatin B as MIP in the relative figures and tables as the reviewer’s suggestion.

- Procolin should be Proctolin

Thanks, we have corrected the word in relative figures and texts.

- Explain why a number of Locusta neuropeptides, known to be present in the brain and/or CC-CA, were not identified in this neuropeptidome analysis, including (the precursors of) corazonin, AST-C, inotocin.

We accepted the reviewer’s query. Actually, the detection of small peptides by mass spectrometry could be affected by multiple physiological and experimental conditions. The absence of some neuropeptides (e.g. corazonin, AST-C, inotocin) in the neuropeptidome analysis may thanks to their low abundance in tested samples, relative short half-life period, and unsuitable chromatographic conditions. Different sample collection methods as well as multiple mass spectrometry methods may be helpful for systematically identification of all neuropeptides. We have added more information in the Discussion to clarify this concern (lines 358-365).

4) Source data supplementary Table 1.

This table shows the peptide ions corresponding to the neuropeptide precursor genes.

Remarks: Please consult the Locusta neuropeptidomics papers of Clynen and Schoofs and name the peptide precursors according to the originally given neuropeptide names.

– Accession number 12049.1 has 2 names: DH/PBAN and tryptopyrokinin. This is, however, the precursor of locustapyrokinin-1, which was identified by neuropeptidome analysis by Clynen and Schoofs and before. It is confusing to rename the genes encoding pyrokinin peptides. AYC12049 definitely is a locustapyrokinin precursor, and should be named according to the original discovery of its encoding neuropeptides, i.e. PK, and not renamed into DH/PBAN. PK-encoding genes have expanded in the evolutionary lineage leading to locusts, when compared to other insects. Naming them as PK1, PK 2, etc. reflects better this expansion (instead of giving them different names).

– AYC12051.1 seems to be the precursor of the periviscerokinins and should be named PVK or CAPA.

We accepted the reviewer’s comments. We have carefully compared the peptide sequences identified in our study to that in the previously reported work (Clynen and Schoofs, 2009). We have revised the peptide annotation one by one. And AYC12051.1 has been annotated as PVK, AYC12049 has been annotated as Pyrokinin 1, DH/PBAN has been revised as Pyrokinin 4 (see supplement table 1 for details).

– AKN21242.1 is the precursor for LOM-MIP or locustamyoinhibiting peptide and should not be renamed into AST-B.

We have revised AST-B as MIP as suggested.

– Procolin should be proctolin in the source table of neuropeptides.

We have corrected the spelling mistake in the relative tables and figures.

– SNPF should be sNPF.

We have revised the word as suggested.

5) Line 191 and following: The authors did not experimentally demonstrate that the ACP neuropeptide (or HrTH) acts through the predicted ACP receptor. The authors can show this in vitro by expressing the receptor in a cellular expression system and challenge the receptor with synthetic ACP.

In addition, to substantiate the mechanism of action of ACP in vivo, the authors should compare the phenotype of the RNAi-knockdown of the ACP receptor (or Crispr-Cas9 knockout) with the flight phenotypes of WT and FABP knockdowns.

We accepted the reviewer’s concern. To further support the essential role of ACP peptide system in regulating long-term flight, we have provided new experimental data for the molecular and functional characterization of ACPR in the revised manuscript. We have recently successfully constructed the homozygous ACPR mutant locust line using the CRISPR/Cas 9 system. The ACPR mutant (ACPR13/13) indeed displayed significantly reduced long-term flight activity, which phenocopied the flight performance caused by ACP knockout and FABP knockdown. The experimental evidence related to molecular and functional identification of ACPR was presented in the revised Figure 3.

6) line 308: The ACP neuropeptide is abundant in the CC (not in the CA) as shown by Clynen and Schoofs (not in this manuscript). The expression of the gene is confined to cells in the brain as shown in this manuscript. Line 308 is about the neuropeptde. Transcription responses refer to the gene expression, which is brain-specific according to Figure 2. Rephrase.

We understand the reviewer’s concern. In the current study, we performed the neuropeptidome analysis in the CC-CA complex instead of CC alone, and the abundance of ACP peptide is much higher in CC-CA complex than in the brain. So, we wrote the sentence as “ACP peptide is highly abundant in the CC-CA of adult locusts”. However, we described the finding shown by Clynen and Schoofs in the subsequent discussion to clarify the tissue distribution of ACP peptide (lines 382-389). Besides, we have revised the inaccurate description as “the ACP precursor gene in the brain displays strong transcription responses to prolonged flight.” (lines 346-347).

7) line 322: "Our results show that the ACP precursor gene is specifically produced in the brain" This phrasing is incorrect: all cells/tissues contain the same genome. No single gene is specifically produced in any tissue". Should be specifically expressed.

Thanks, we have revised the inaccurate description as “the ACP precursor gene in the brain displays strong transcription responses to prolonged flight.” (lines 390-391).

8) line 329: it is not quite similar. AKH is synthesized in the CC, more particular by cells in the CC's glandular lobes. JH is synthesized in the CA. ACP is synthesised in the brain and transported via the NCC to the storage lobe of the CC, where it is stored (until release) or where it may influence the release of other substances.

We accepted the reviewer’s comments. We have removed the sentence to avoid misunderstanding.

6) line 317: The ACP neuropeptide or HrTH was isolated from the storage lobes of the corpora cardiaca. These storage lobes contain peptide-storing nerve endings of the NCC coming from the brain.

We have revised the sentence as “the ACP peptide was initially isolated from the storage lobes of the CC of migratory locusts and was named locust hypertrehalosemic hormone (Lom-HrTH)” (lines 382-384).

7) line 332: rephrase: where its receptor is expressed. (A circulating neurohormone will act on all its receptor molecules on the surface of cells (and not only on tissue displaying a high receptor expression), while circulating through the body).

Thanks, we have revised the description as reviewer’s suggestion (line 400).

8) line 365: "Based on transcriptome profile analysis, the flight muscle appears to be the main tissue targeted by ACP, although the ACP receptor also has a relatively high expression level in the fat body".

This is speculation. ACP will also target its receptor in the fat body, where it will elicit a distinct functional response.

Targets of neuropeptides cannot be (exclusively) identified in the context of altered gene expression upon neuropeptide receptor activation. In fact, most neuropeptides act on GPCRs and the responses are usually very rapid (within seconds), while altered gene expression is a process that takes much more time.

Immediately downstream of the neuropeptide-receptor interaction, fast processes are more likely to be the immediate result of this interaction. These fast responses include increased/decreased secretion, change of membrane permeability, opening/closing of ion channels etc. The presence of the ACP receptor in the fat body suggests that – like the flight muscles – also the fat body is a target of ACP, if the predicted ACP receptor is the in vivo cognate receptor of ACP.

The manuscript only provides very weak evidence (the presence of a predicted ACP receptor) that ACP directly acts on flight muscles.

I suggest to delete lines 365 to 380: many interpretations, wrong phrasing. Does not contribute much to the story.

We agree with the reviewer’s comments. We have removed this part of discussion as the reviewer’s suggestion.

9) line 442: ACP has not been identified in this manuscript. The peptide was identified by Siegert long before and the ACP precursor was already identified in the study of Hou et al., 2015.

We agree with the reviewer’s comments. We have revised the sentence as “we demonstrate that the ACP peptide acts as a novel neuroendocrine regulator controlling lipid transport and utilization associated with long-term flight in locusts” (lines 516-518).

Quantitative neuropeptidome analysis:

10) line 476: provide the source of the protein database. Where can it be consulted?

We have provided the source of protein database used. Details were shown as: “The extracted MS/MS spectra were searched against a composite database of Locust migratoria (3,286 protein sequences, download from NCBI, 2019) and a protein database (containing 17,307 protein sequences, http://www.locustmine.org:8080/locustmine)(Yang et al., 2019) using in-house PEAKS software”(lines 553-555).

11) why was the enzym trypsin set as a parameter in a peptidomics analysis? This is done for tryptic peptide analysis in a classical proteome analysis, but it is not the optimal way of analysing the neuropeptidome. This parameter does not yield the theoretical spectra of mature neuropeptides. Instead, it yields theoretical spectra of theoretic tryptic peptides, which aid in the identification of the corresponding protein. Spectra of tryptic peptides do not reveal the endogenously occurring neuropeptides, which are endogenously processed by other specific processing enzymes.

We have corrected the wrong description for peptidome analysis. Detailed methods were descripted as: “parent ion mass tolerance is 15 ppm, and fragment ion mass tolerance is 0.05 Da; enzyme specificity, none.

12) line 482: what is meant by this? At least two spectra in one sample?

We used the parameter to improve the reliability of identified peptides according to the neuropeptidome analysis (Han et al., 2015, J Proteome Res)

13) What is the mass tolerance for fragmented peptides? Modifications should also include C-terminal amidation, N-terminal pGlu.

We have revised the methods to make it easy to be understood. It has been shown as: “parent ion mass tolerance is 15 ppm, and fragment ion mass tolerance is 0.05 Da; enzyme specificity, none. The following modifications were applied: C-terminal amidation (A, −0.98) and pyroglutamination from Q (P, −17.03), maximum missed cleavages per peptide: 2, and maximum allowed variable PTM per peptide: 2. A fusion target and decoy approach was used to for the estimation of the false discovery rate (FDR) and controlled at ≤1.0% at the peptide level” (lines 558-567).

14) Figure 3 supplement 2: Explain abbreviations. The expression of ACPR is only tissue-specific for the organs/tissues tested. (The gut, Malpighian tubules, trachea, hypodermis, etc were not analysed). I suggest to rephrase in manuscript text as: Compared to the other organs tested, ACPR is highly expressed in flight muscles and fat body.

We have added the explanation for abbreviations in the figure legend and revised the description for tissue-specific expression analysis of ACPR as “Compared to other organs tested, ACPR was highly expressed in the fat body and flight muscle of adult locusts (Figure 3B)” (lines 200-201).

15) line 567: include reference or source of MEGA software.

We have provided the reference for MEGA software used as the reviewer’s suggestion (line 659).

16) Bogerd et al., (1995) showed that flight activity increased steady-state levels of the AKH I and II mRNAs (approximately 2.0 times each) and the AKH III mRNA (approximately 4.2 times) in the corpora cardiaca. Explain why an altered expression of AKH-1 and AKH-III precursor genes is not seen in the present manuscript.

In the current study, we first screened the top ten adult-abundant neuropeptides (including AKH2 and AKH3) for further expression analysis of their precursor gene in response to flight. The expression level of AKH2 gene significantly increase after 1 h-sustained flight, the result is consistent with previous findings descripted by Bigerd et al., (1995). However, there is no significant changes in AKH3 gene expression upon sustained flight, although an increased intendency was observed. The discrepancy between the two studies may be attributed to the different sample collection strategy and detection methods used. In the current study, we collected the CC-CA complex for RNA extraction and measured the gene expression levels by qPCR, whereas only CC tissue was used for RNA isolation and northern blot was used for mRNA level analysis in previous work performed by Bigerd et al. We have added more information in the Discussion to clarify this concern (lines 484-490).

17) Many (supplemental) Figures contain abbreviations that are not explained in the Figure legends.

We have provided the explanation for abbreviation in the corresponding figure legends.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential Revisions:

Thank you for clarifying the peptidomic analysis in your revised version.

1. However it is essential that your analysis makes clear the source of brain region used for the peptidomic analysis. Therefore "CC-CA" should be replaced by "retrocerebral complex" in the entire manuscript.

Thanks, we accept the reviewer’s suggestions. Now we have replaced “CC-CA” with “retrocerebral complex” in the entire manuscript during revision.

2. Also, rewrite lines 370-375, by stating that in the present manuscript the whole retrocerebral complex was used for peptidomic analysis, in contrast to the study of Clynen who analysed the organs of the retrocerebral complex separately.

Delete "a little bit different" (it is not a little bit).

Thanks, we have revised the description as: “In the present study, the whole retrocerebral complex of mature adults was used for peptidomic analysis, in contrast to the study of Clynen and Schoofs, (2009) who analyzed the organs of the retrocerebral complex of immature adults separately” (lines 372-375).

3. An important methodology consideration, that is key to interpreting your results has been raised. Please address this in your revision.

– The authors have adapted the methods section on mass spectrometry. It is much clearer now, but one major issue remains. It regards the sentence "Neuropeptide identifications were used only if at least two spectra were identified in one sample".

This is a criterion that is used for proteomics analyses; it is not at all suited for neuropeptidomics studies. The reason is that neuropeptides are produced by cleavage from larger preproteins, whereby the mature cleaved peptide is kept and the remainder of the protein is degraded. So, if the preprotein contains only one mature neuropeptide sequence, this neuropeptide cannot be identified by the restricted rule of "two spectra in one sample". The authors refer in their comments to the publication of Han et al. in J. Protein Research. But in the discussion of this article on page 4389, Han et al. clearly state that the 19 neuropeptides that they were unable to identify might be caused by the stricter FDR threshold they used (two spectra in one sample). That is the reason that they missed 19 well known bee neuropeptides in their study.

Regarding the present revised manuscript one may wonder in fact how the authors were even able to identify the ACP neuropeptide by using this restrictive threshold. Because the MS-spectra were not provided in the MS (I could not find them), I cannot check what other peptide from the same ACP-precursor they identified. This would be strange, because the remainder of the protein is normally broken down upon cleavage of the ACP peptide out of it.

So the question is: did the authors use this restrictive threshold? (which might also explain why they were unable to identify some well known locust peptides residing as single peptides in their respective precursors).

If yes, then this reviewer would like to know what other peptide they identified from the ACP precursor, as this would contradict with textbook knowledge about neuropeptide processing. In this aspect, the term "lots of new peptide fragments" is unclear. What is meant by "peptide fragments"? Are these fragments of the same mature peptide or fragments of the remainder of the precursor protein, which should in principle have been degraded.

Or did they not use this restrictive threshold?

Thanks for the reviewer’s nice comments. Based on the reviewer’s comments, we realize that our description of neuropeptide identifications is inaccuracy in the sentence “Neuropeptide identifications were used only if at least two spectra were identified in one sample”. In fact, this criterion is used for the identification of peptide fragments. Generally, a single peptide fragment could be detected for multiple times by the MS. A spectrum will be formed each time when the peptide fragment is detected by MS. To avoid misunderstanding, we have revised the description as: “The criterion of peptide fragment identifications was used only if at least two spectra were formed in one sample.” The criterion does not mean that two different peptide fragments derived from the same precursor were detected in one sample. Instead, it means that distinct peptide fragment detected for more than two times were identified in the MS. Despite the differences in modification selection, the sequence characterization of peptide fragments in neuropeptidome study is similar to that of proteomics. The threshold used in the current study (two spectra in one sample) could significantly improve the reliability of peptides identified in the MS. In addition, only several of known neuropeptides, not too many, were not detected under current threshold in our study. The absence of these neuropeptides may be attributed to their low abundance in tested samples, relative short half-life period, and unsuitable chromatographic conditions. We think that the criterion used in the current study should be suitable.

To further clarify the reviewer’s concerns, we have provided the spectra for ACP peptide identified in the peptidomic study (see Author response image 2). In detail, six spectra for ACP peptide were formed in the MS analysis of retrocerebral complex. Based on these spectra, we can clearly confirm the existence of ACP peptide in this sample, and no other peptides were identified for ACP precursor. To avoid misunderstanding, we have provided detailed explanations for the criterion used in the neuropeptidome analysis in the method. The details were shown as: “Generally, a single peptide fragment could be detected for several times by the MS. A spectrum will be formed each time when the peptide fragment is detected by MS. The criterion of peptide fragment identifications was used only if at least two spectra were formed in one sample. Neuropeptides were further validated by comparison with predicted neuropeptide precursors and neuropeptidome analysis in the locust (Clynen and Schoofs, 2009, Hou et al., 2015)” (lines 562-567).

New peptide fragments obtained in the peptidome are mostly newly identified peptides, and degraded products of mature peptides, as well as a few remainders of the precursor proteins (see details in supplementary File 1). All peptide fragments were identified using the same threshold.

Author response image 2

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

Reviewer #3:

I still have a major concern regarding the neuropeptidome analysis, which I explain in detail below.

The authors state in their rebuttal : " Based on the reviewer's comments, we realize that our description of neuropeptide identifications is inaccuracy in the sentence "Neuropeptide identifications were used only if at least two spectra were identified in one sample". To avoid misunderstanding, we have revised the description as: The criterion of peptide fragment identifications was used only if at least two spectra were formed in one sample. Generally, a single peptide fragment could be detected for several times by the MS. A spectrum will be formed each time when the peptide fragment is detected by MS. The criterion of peptide fragment identifications was used only if at least two spectra were formed in one sample"

In the text, the authors wrote: "The threshold used in the current study (two spectra in one sample) could significantly improve the reliability of peptides identified in the MS. "

Reply:

1) This statement is wrong. One MSMS spectrum of good quality is sufficient for the identification of a neuropeptide. An MSMS fragmentation spectrum of good quality is a spectrum where the series of y-ions and b-ions are sufficiently complete to cover the whole amino acid sequence of the peptide (which is the case for the spectra nrs 1 and 4 provided by the authors, but not for spectrum 5).

You can never state that at least two MSMS spectra of the same neuropeptide are needed. The argument of the authors "more peptide spectra = more reliability" is also not in line with the way data are collected on the mass spec.

We appreciate the reviewer’s valuable comments. After careful consideration, we agree and accept the reviewer’s suggestions. The two peptide spectra criterion could be used for proteomics analyses, but is not suitable for neuropeptidomic studies. Some neuropeptides with lower abundance may be omitted under the stricter criterion. Given this, we have re-analyzed our peptidomic data with the criterion suggested by the reviewer (using one MS/MS spectrum for neuropeptide identification). Under the new criterion, two additional neuropeptides, Pyrokinin 2 and sulfakinin, were detected in the brain samples, and four additional neuropeptides, Corazonin, natalisin, Ryamide, as well as kinin, were identified in the retrocerebral complex samples. Based on these new findings, we have updated relative figures (Figure 1, Figure 1-S1, and Figure 1-S2) and revised the results description as: “In total, 201 and 362 nonredundant peptides (including both mature neuropeptides and their potential degradation products) derived from 37 neuropeptide precursors were identified in Br and RC, respectively (Supplementary File 1). Tissue-specific analysis showed that neuropeptides from 20 precursors were considerably more abundant in the RC, whereas neuropeptides from 16 precursors were more abundant in the Br. The GPB5-derived peptides showed similar abundance levels in Br and RC (Figure 1—figure supplement 1). The abundant levels of neuropeptides in Br and RC between 5th-instar nymphs and adult locusts were further compared by a label-free quantitative strategy. Compared to 5th-instar nymphs, there were 20 and 18 upregulated neuropeptides in the Br and RC of adult locusts, respectively (Figure 1—figure supplement 2), and 10 neuropeptides displayed significantly higher abundance (Log2FC > 1.5) in either Br or RC of adult locusts (Figure 1A and B)” (lines 115-128). Since the newly identified neuropeptides were not included in the top-ten adult-abundant neuropeptide list, the re-analysis of peptidome data did not affect the screening of flight-related neuropeptides and subsequent results and conclusions in this manuscript.

In data-dependent acquisition, which is how the mass spec collects data, you have to take into account the dynamic exclusion parameter, which ensures that an ion with a particular m/z value will not be selected for fragmentation for a number of seconds to avoid that the instrument detects and measures the same ion over and over again during an elution peak. The normal situation with a dynamic exclusion parameter setting at 15s, is that the mass spectrometer detects a single peptide ion, because peptides typically elute over 30s. The detection of many identical ions of the same peptide is the exception rather than the normal situation and should not be set as a rule of confidence. The dynamic exclusion parameter cannot be found in the methods section.

So, in the normal situation, only one precursor ion is selected for MSMS fragmentation. Only when the elution peak of that peptide ion is broad, the peptide ion can be selected multiple times over the period of the elution peak. That is why the dynamic range parameter is important. This parameter avoids that the same ion is selected for fragmentation over and over again at the expense of other – less abundant – peptide ions that elute in the very same region.

Thanks for the reviewer’s comments. Although we did not provide the detailed information in the last version of our manuscript, in fact, we have taken into account the dynamic exclusion parameter as 30 s. In this revised version, we have added the detailed description in the Method and materials of our manuscript as followed: “The eluted neuropeptides were injected into the mass spectrometer via a nano-ESI source (Thermo Fisher Scientific). Ion signals were collected in a data-dependent mode and run with the following settings: full scan resolution at 70,000, automatic gain control (AGC) target 3E6; maximum inject time (MIT) 20 ms; scan range m/z 300–1800; MS/MS scans resolution at 17,500; AGC target 1E5; MIT 60 ms; isolation window 2 m/z; normalized collision energy 27; loop count 10; charge exclusion: unassigned, 1, 8, >8; peptide match: preferred; exclude isotopes: on; dynamic exclusion: 30 s; dynamic exclusion with a repeated count: 1. The MS/MS data were acquired in raw files using Xcalibur software (version 2.2, Thermo Fisher Scientific)” (lines 535-543).

The "more than one spectrum in one sample" argument of the authors is thus a reflection of the elution and abundance of a particular peptide ion. It is not at all a criterion for reliability or confidence regarding the MSMS peptide identification. One spectrum of good quality is as reliable as two or three good spectra of the same ion that just elutes over a longer period of time.

We accept the reviewer’s valuable suggestions. As described above, we have re-analyzed our peptidome data.

The omission of neuropeptides represented by a single MSMS spectrum of good quality is thus a mistake.

A second mistake is that all the measured neuropeptide degradation products are counted as bona fide neuropeptides (see below under nr 2).

The four ACP spectra provided in the rebuttal are all spectra of the same ACP sequence. The excel file also contains degradation products of this peptide. However, there is no reason to assume that the measurement of multiple spectra in one sample or the measurement of peptide degradation products provide a higher reliability. The measurement of degradation products can be due to a technical issue (sample preparation) or a biological issue (in vivo degradation). It does not tell you anything about the reliability of the measurement of the intact neuropeptide.

We appreciate the reviewer’s suggestions. Under the current condition, we cannot determine whether these shorter peptides are endogenous or degradation products of mature neuropeptides. Therefore, we have revised our description as: “In total, 201 and 362 nonredundant peptides (including both mature neuropeptides and their potential degradation products) derived from 37 neuropeptide precursors were identified in Br and RC, respectively (Supplementary File 1)” (lines 115-122).

As described above, we have re-analyzed the peptidome data using one MS/MS spectrum for neuropeptide identification. By comparison, neuropeptides from 37 precursors (36 precursors in the previous results) were identified after adjusting the peptide identification criterion and only neuropeptide Corazonin was newly identified. In detail, two additional neuropeptides (Pyrokinin2 and sulfakinin) were detected in the brain samples, and four additional neuropeptides (Corazonin, natalisin, Ryamide, and kinin) were detected in the retrocerebral complex samples. Besides, degradation products of several neuropeptides were also detected under new identification criterion. Detailed peptide information was shown in Supplementary file 1. Generally, the alteration of peptide identification criterion does not affect the results of comparative neuropeptidomic analysis (newly identified neuropeptides were not contained in top-ten adult-abundant neuropeptides).

2) Results section

The authors state: "In total, 177 and 341 nonredundant neuropeptides derived from 36 precursors were identified in Br and RC, respectively (Supplementary File 1).

Reply:

Now that the authors have explained how they have interpreted the MSMS data, this sentence is completely wrong and will lead to confusion. The authors have not identified that high number of neuropeptides, because this number reflects multiple MSMS spectra resulting from the same neuropeptide (and its degradation products). A neuropeptide is defined as the amino acid sequence located between the dibasic cleavage sites within the neuropeptide precursor. The shorter versions presented in the excel file are not novel neuropeptides but degradation products. It is unclear what the origin of these degradation products is. They may be artifacts resulting from the sample preparation, or they may represent in vivo degradation fragments resulting from the inactivation of the neuropeptide.

The authors should rearrange their excel file by clustering the degradation products of the same neuropeptide within a neuropeptide precursor. Here an example for a pyrokinin (PK1)-precursor derived neuropeptide.

GAVPAAQFSPRLamide

GAVPAAQFSPRL

AVPAAQFSPRLamide

AVPAAQFSPR

VPAAQFSPRL

VPAAQFSPR

PAAQFSPRLamide

AQFSPRLamide

We appreciate the reviewer’s suggestion. We have revised our description as: “In total, 201 and 362 nonredundant peptides (including both mature neuropeptides and their potential degradation products) derived from 37 neuropeptide precursors were identified in Br and RC, respectively (Supplementary File 1) (lines 115-122). In the discussion, the relative content was revised as: “we obtained a lot of non-abundant neuropeptides as well as their potential degradation products produced by 37 precursors in the main neuroendocrine tissues, including brain and retrocerebral complex. The neuropeptides detected here including most of previously identified peptides (Clynen and Schoofs, 2009)”(lines 349-353). In addition, we have re-arranged the excel file as the reviewer’s suggestion (see Supplementary File 1).

3) The authors also state: " In addition, only several of known neuropeptides, not too many, were not detected under the current threshold in our study. The absence of these neuropeptides may be attributed to their low abundance in tested samples, relative short half-life period, and unsuitable chromatographic conditions. We think that the criterion used in the current study should be suitable."

Reply:

The identification of the neuropeptides is also dependent on the used database for spectrum scanning, as the authors did not rely on de novo identification. Thus, the identification can only be as good as the database against which the spectra are scanned. If the used locust database does not contain the (protein precursor) sequences, or the derived theoretical spectra, particular experimental spectra will not lead to identification, even if they are abundantly present;

The second reason is due (i) to their data acquisition setting (parameter not mentioned in the methods) and to (ii) the "two spectra in one sample" rule that the authors wrongly used, a rule that merely reflects long elution times of very highly abundant peptides (see above). This rule is wrong and excludes less abundant neuropeptides with clear but shorter chromatographic elution peaks.

We appreciate the reviewer’s comments. In fact, it is impossible to identify all neuropeptides in a single peptidome study. Now, we have considered all three aspects concerned by the reviewer during peptidome analysis.

1) Both biochemically identified and bioinformatically predicted neuropeptides as well as downloaded protein database were used for peptidome analysis, which may be helpful for better peptide identification. (2) We have taken into account the dynamic exclusion parameter as 30 s. The detailed description was added in the Method and materials of our revised manuscript (lines 535-543). (3) As described above, we have revised our manuscript by data re-analysis using one MS/MS spectrum for neuropeptide identification. Moreover, we have enriched discussion related to peptide identification as: “However, several neuropeptides (e.g. AST-C, inotocin, and AKH4) identified from previous peptidome study and transcriptome data-based prediction, were not found in the current study. The absence of these neuropeptides in the neuropeptidome analysis may thanks to their low abundance in tested samples, relative short half-life period, unsuitable chromatographic condition or data acquisition setting” (lines 354-359).

4) In the excel files, CC-CA has still not been replaced by retrocerebral complex.

We apologize for the mission. We have revised CC-CA as retrocerebral complex in the excel files (see Figure 2-source data, Figure 3-source data, also supplementary file 1).

https://doi.org/10.7554/eLife.65279.sa2

Article and author information

Author details

  1. Li Hou

    State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
    Contribution
    Conceptualization, Data curation, Funding acquisition, Methodology, Writing - original draft
    Competing interests
    No competing interests declared
  2. Siyuan Guo

    State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
    Contribution
    Software, data analysis
    Competing interests
    No competing interests declared
  3. Yuanyuan Wang

    1. State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
    2. CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing, China
    Contribution
    Data curation, Methodology
    Competing interests
    No competing interests declared
  4. Xin Nie

    State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  5. Pengcheng Yang

    Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China
    Contribution
    Data curation, data analysis
    Competing interests
    No competing interests declared
  6. Ding Ding

    State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
    Contribution
    Data curation, Methodology
    Competing interests
    No competing interests declared
  7. Beibei Li

    State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  8. Le Kang

    State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
    Contribution
    Conceptualization, Funding acquisition, Writing - review and editing
    For correspondence
    lkang@ioz.ac.cn
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4262-2329
  9. Xianhui Wang

    State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
    Contribution
    Conceptualization, Funding acquisition, Writing - original draft, Writing - review and editing
    For correspondence
    wangxh@ioz.ac.cn
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8732-829X

Funding

National Natural Science Foundation of China (31930012)

  • Xianhui Wang

National Natural Science Foundation of China (32070497)

  • Li Hou

Chinese Academy of Sciences (152111KYSB20180036)

  • Le Kang

Youth Innovation Promotion Association of the Chinese Academy of Sciences (2021079)

  • Li Hou

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Weichan Cui for helpful work in IHC experiment, Liya Wei for sample collection of peptidome experiment, Bin Han for neuropeptidome analysis, and Baozhen Du for helpful discussion on this work. This study was supported by the National Natural Science Foundation of China (Grant NO. 31930012 and 32070497) and grants from Chinese Academy of Sciences (nos. 152111KYSB20180036) and Youth Innovation Promotion Association CAS (No. 2021079).

Senior Editor

  1. K VijayRaghavan, National Centre for Biological Sciences, Tata Institute of Fundamental Research, India

Reviewing Editor

  1. Raghu Padinjat, National Centre for Biological Sciences, India

Reviewers

  1. Raghu Padinjat, National Centre for Biological Sciences, India
  2. Aravind Ramanathan

Publication history

  1. Received: November 29, 2020
  2. Accepted: June 18, 2021
  3. Accepted Manuscript published: June 21, 2021 (version 1)
  4. Accepted Manuscript updated: July 1, 2021 (version 2)
  5. Version of Record published: July 30, 2021 (version 3)

Copyright

© 2021, Hou et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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