Global climate change and other factors are placing increasing demands on available agricultural land to deliver efficient, reliable and sustainable food production. Insecticides are important tools in securing yields of all major crops but need to be continually replaced to overcome resistance in target species and reduce environmental impacts. In addition, new insecticides must have low toxicity to non-target species, particularly the major pollinators essential for agriculture. A large class of insecticide targets are neurotransmitter receptors such as the nicotinic acetylcholine receptors (nAChRs) located in synaptic plasma membranes (Ihara et al., 2020). These pentameric cys-loop ligand-gated ion channels consist of either only α-subunits or α- and β-subunits, with ligand-binding sites located between two α-subunits or between α- and β-subunits. Most insect genomes, including that of the highly tractable Drosophila melanogaster model, harbour 10 highly conserved subunit genes that assemble in various combinations to form the active receptors.

An essential pre-requisite for effective design of new insecticides targeting these receptors is an understanding of their distinctive binding properties. For many reasons, including low expression in endogenous tissues or difficulties in expressing insect receptors in heterologous systems, the characterisation of functional insect receptors has been challenging (Perry et al., 2021; Zuo et al., 2022; Salgado, 2021). Even in the tractable D. melanogaster insect model, there has been no systematic isolation of mutations in nAChR subunit genes, until recently, when Perry and colleagues described the generation of a new set of null mutations in nine out of the ten D. melanogaster subunit genes (Perry et al., 2021). These mutations, however, were generated in different genetic backgrounds necessitating additional work to assay background sensitive phenotypes such as neural or behavioural defects.

Several classes of insecticide, the most effective being those in the neonicotinoid and spinosad class, have been shown to bind insect nAChRs highly selectively to block their functions (Chambers et al., 2019; Houchat et al., 2019; Martelli et al., 2022; Lu et al., 2022). Recently, the binding affinity and the positive allosteric effects of ω-Hexatoxin-Hv1a (Hv1a) peptide on nAChRs has been demonstrated (Chambers et al., 2019) and this spider venom peptide is well known for its insecticidal effects. In addition, other peptide toxins, such as the snake venom constituent, α-Bungarotoxin (α-Btx), have been widely used to probe nAChR functions, however whether α-Btx harbours a selective insecticidal property is currently unknown. Alpha-Btx is a 74 amino acid peptide that binds irreversibly to nAChR α-subunits in different species, including D. melanogaster, although the exact binding targets of these receptors are not fully understood (Schmidt-Nielsen et al., 1977; Dellisanti et al., 2007; daCosta et al., 2015). Landsdell and co-workers have shown binding of α-Btx to D. melanogaster α5 (Dα5), Dα6, and Dα7 subunits in a heterologous S2 cell expression system (Lansdell and Millar, 2004; Lansdell et al., 2012). The amino acid sequences of these subunits show strong similarity across their ligand-binding domains (LBD), suggesting that at least three subunits with sequence similarity are implicated in α-Btx binding in vivo.

The lipid bilayer surrounding nAChRs is known to be essential for structural integrity, stability, and ligand-binding (daCosta et al., 2013). However, this lipid requirement can make analysis of membrane protein complexes challenging. The development of methods for extracting membrane proteins from lipid bilayers using detergents and introducing them into artificial lipid nanodiscs has facilitated a much better characterisation of receptor-ligand interactions (Denisov and Sligar, 2016). The use of detergents generally employed to solubilise membrane proteins, however, leads to destabilisation, aggregation and misfolding and their use is therefore not compatible with this type of analysis (Loo et al., 1996). Styrene maleic acid lipid particles (SMALPs) allow detergent-free extraction of membrane proteins in their local lipid environment and provide a promising technique for investigating receptor-ligand interactions under native conditions (Lee et al., 2016). This is particularly important since loss of lipids surrounding membrane proteins can lead to changes in measured binding affinities (Martens et al., 2018; Gault et al., 2020). The combination of detergent free SMALPs extraction coupled with mass spectrometry analysis provides a potential route for characterising native membrane receptor complexes (Sobotzki et al., 2018; Kalxdorf et al., 2021).

Here, we report the results from a combined genetic and biochemical analysis of D. melanogaster nAChRs in vivo. Using CRISPR/Cas9 genome engineering, we generated new null mutations for all ten receptor subunit genes in a uniform genetic background as well as introducing a fluorescent protein tag into the nAChRα6 locus. We show that the null mutants in all seven α-subunit genes and two of the three β-subunit genes are viable and fertile, although we find mild morphological defects and some neurological impairment. Mutation of the remaining subunit gene, nAChRβ1, is recessive lethal. All nine of the viable null mutants were used to demonstrate a novel selective insecticidal effect of α-Btx on the nAChRα5, nAChRα6 and nAChRα7 subunits. We also applied the insecticidal Hv1a peptide to the viable null mutants, showing resistance with two subunit gene mutants: nAChRα4 and nAChRβ2. In our biochemical studies, we analysed receptor-ligand interactions in native conditions using SMALPs to verify the in vivo receptor subunit α-Btx binding targets in adult neural tissue from wild-type and receptor subunit mutants. Our analysis revealed binding of α-Btx to receptors containing Dα5, Dα6 and Dα7 subunits with the analysis of mutants in these subunit genes indicating heterogeneity in α-Btx binding nAChRs. Furthermore, we have identified specific glycosylation sites in Dα5 and Dα7 subunits which are known from other studies to play a critical role in α-Btx-binding affinity (Dellisanti et al., 2007; Rahman et al., 2020). Localisation studies with the Dα6 subunit tagged at the endogenous locus with a fluorescent reporter shows expression at different developmental stages in specific neuronal cells, including the Kenyon cells of the mushroom bodies, a known site of α-Btx-binding (Su and O’Dowd, 2003).

Elucidation of complex insect nAChR heterogeneity will lead to a better understanding of selective insecticidal effects. We present a new set of null mutations in all D. melanogaster nAChR subunit genes and investigated insecticidal peptide toxin effects on wild-type and receptor subunit mutant larvae. Utilising biochemical approaches involving SMALP solubilisation to create nano-discs and pull-downs we characterised toxin binding of native nAChR complexes.

Our genome engineering approach generated viable and fertile mutations in nine out of the ten subunit genes encoded in the D. melanogaster genome and is largely concordant with the described work by Perry and colleagues (Perry et al., 2021). In both studies, null mutations in the nAChRβ1 gene were inviable as stocks. However, Christesen and colleagues report lethality primarily at the pre-pupal/pupal stage with a low percentage of individuals surviving to adulthood (Christesen et al., 2021). In contrast, we never recovered homozygous adults or pupae and when outcrossing to a 3rd chromosome balancer with a dominant larval marker did not find homozygous nAChRβ1 3rd instar larvae. It is possible that our DsRED insertion strategy somehow generated a stronger or neomorphic allele of nAChRβ1.

It is also possible that the lethality is due to a second site mutation on the chromosome or that there is a genetic background effect. We have also added to previous work by generating a new null mutation in nAChRα5, which shows resistance to α-Btx and a moderate reduction in locomotor activity. We observed some minor morphological defects in some of the null mutants especially in nAChRα1, nAChRα2, nAChRα5 and nAChRβ3 as well as locomotor defects with some alleles, particularly severely in nAChRα3. The locomotor defects we observed are in agreement with previously reported neuronal phenotypes with nAChR subunit genes, including sleep disruption, defective jump response, memory impairment or locomotor defects (Fayyazuddin et al., 2006; Rohde et al., 2016; Somers et al., 2017; Tackenberg et al., 2020). Locomotor defects in climbing assays were previously shown in nAChRβ1 mutant strains (Homem et al., 2020; Christesen et al., 2021).

We used the nAChR null mutants to study insecticidal effects of the Hv1a peptide on viability after injection into larvae and investigated whether α-Btx has any insecticidal properties. As described by Chambers and colleagues, we confirm that Hv1a effects nAChRs (Chambers et al., 2019) and our analysis shows that the Dα4 and Dβ2 subunits are involved in the insecticidal response. We show for the first time that α-Btx has selective insecticidal effects against the Dα5, Dα6, and Dα7 subunits, which we further characterised at a biochemical level.

The pharmacology of Hv1a and α-Btx binding has been shown to be distinctive (Chambers et al., 2019), correlating with our demonstration that these two peptide toxins mediate their effects through different receptor subunits. Furthermore, resistance to neonicotinoid insecticides, which interact most strongly with Hv1a binding, has been associated with Dβ2 (Perry et al., 2008; Perry et al., 2021), consistent with the involvement of this subunit in the response to Hv1a. However, no resistance to neonicotinoids was seen in D. melanogaster carrying a nAChRα4 gene knockout (Perry et al., 2021), which could be explained if neonicotinoids act on multiple receptor classes. Multiple binding sites for the neonicotinoid imidacloprid can be resolved in equilibrium binding assays in many insect species (Xu et al., 2010) and by binding kinetics in flies (Liu and Casida, 1993).

The agonist actions of neonicotinoids were investigated with D. melanogaster Dα1/Dβ1, Dα1/Dα2/Dβ1, Dα1/Dβ1/Dβ2, and Dα1/Dα2/Dβ1/Dβ2 nAChRs expressed in Xenopus laevis oocytes, indicating that Dβ1 subunit is a key player in the recognition of these insecticides (Ihara et al., 2020). It would be interesting to know if Dβ1 also plays a role in the in vivo interaction with insecticidal peptide toxins such as α-Btx or Hv1a, which is difficult to fully investigate because of the deleterious effects of nAChRβ1 mutations. However, we note that while multiple Dβ1 peptides were recovered in our SMALP preparations without α-Btx enrichment, none were observed in the α-Btx pull-down experiments, suggesting that Dβ1 is not a major component of α-Btx binding receptors.

Resistance to spinosad is strongly associated with Dα6 (Perry et al., 2021), and spinosad binding is much more sensitive to the action of α-Btx than to the action of neonicotinoids (Chambers et al., 2019), again consistent with the involvement of this subunit with sensitivity to injected α-Btx and with the proposition that α-Btx and Hv1a act at distinct receptor classes. nAChR subunits are known to be difficult to purify due to solubilisation issues (Cheng et al., 2015; Maldonado-Hernández et al., 2020) and the requirement for a lipid environment for ligand-binding (daCosta et al., 2013) makes it challenging to study these receptors in native conditions.

We used the SMALPs extraction method for preparing membrane discs and enriched nAChRs via α-Btx affinity purification and our Western blot analysis showed that Dα6-mVenus is enriched by α-Btx resins. Electron microscopy analysis indicated that receptor-like particles were recovered and these were substantially enriched by α-Btx pull-down. Mass spectrometry analysis showed an enrichment for the Dα5, Dα6 and Dα7 subunits in these preparations, which is concordant with our in vivo injection results and previous studies that characterised aspects of α-Btx binding (Lansdell and Millar, 2004; Wu et al., 2005; Lansdell et al., 2012). This confirms that in vivo molecular interactions are similar to those described in the S2 cell expression system.

To our knowledge this is the first report of the identification of a native endogenous α-Btx binding nAChRs. We note however, that we cannot determine from our analysis whether all three identified subunits are part of the same complex or if there are different receptors containing a subset of these subunits.

Using chimeric receptors in a cell line system, Landsdell and colleagues reported that a combination of all three of these subunits show high-affinity acetylcholine binding but α-Btx binding varied depending on receptor combinations, with Dα5 and Dα6 binding most strongly (Lansdell et al., 2012). In a prior study, they implicated Dα6 and Dα7 (Lansdell and Millar, 2004). However, these assays were performed with 5HT3A-nAChR subunit fusions, here we provide strong evidence that these three subunits bind to α-Btx in vitro and in vivo.

We note that at least some of the α-Btx binding receptors are likely to contain at least two Dα6 subunits since our non-reducing Western blots showed the presence of a dimer in the pull-downs. In addition, glycopeptide enrichment showed site specific glycosylation modifications on the Dα5 and Dα7 nAChR subunit ligand-binding domains. The unique lipid environment and glycosylation sites of nAChR α-subunits from the electric ray, T. californica, were found to be important for α-Btx binding activities (Quesada et al., 2016; Rahman et al., 2020), and structural studies support this conclusion (Dellisanti et al., 2007).

Our work supports the view that there is a role for Dα5 and Dα7 glycosylation modifications in the recognition of α-Btx in D. melanogaster. We note that Lansdell and colleagues have shown that Dα3 is implicated in α-Btx binding if it is co-expressed together with a non α-subunit such as mammalian b2, γ or δ in S2 cells (Lansdell and Millar, 2000). We did observe single peptides from Dα3 and Dβ3 in our α-Btx enrichments and our glycoproteomic analysis with α-Btx binding pull-downs identified additional shared peptides from Dα5 and Dα7 but also Dα3 and Dβ3, suggesting that there may be at least some α-Btx binding to receptors containing Dα3 together with a non α-subunit like Dβ3.

Localisation studies with fluorescence tagged endogenous Dα6 subunit, along with preliminary data from further three subunits indicates that a strategy generating C-terminal tagged subunits may be effective in following endogenous nAChR expression and opens the possibility of exploring receptor subunit composition using subunits with different fluorescent tags. Our analysis of Dα6 localisation showed relatively restricted expression in the brain and ventral nerve cord, with prominent expression in the Kenyon cells of the mushroom body. Our observations are largely consistent with recent reports of nAChRα6 expression derived from expression reporters, particularly the strong mushroom body expression (Perry et al., 2015; Martelli et al., 2022), although we do observe weaker expression in the larval optic lobes, prefacing the later expression in the adult brain.

Work generating in locus Gal4 fusions for a variety of neurotransmitter receptors (Kondo et al., 2020) would seem to indicate wider adult brain expression than we and others observed, perhaps reflecting a degree of translational control or, more likely, limitations in the sensitivity of our live imaging compared to Gal4 driven reporters. The expression of Dα6 in Kenyon cells across development is in line with a proposed role for this subunit in memory plasticity, along with other α-subunits including Dα5, in mushroom body output neurons (Barnstedt et al., 2016). While our nAChRα6 mutant shows significant resistance to α-Btx we still find α-Btx binding in the absence of Dα6 in our pull-down assays, likely a reflection of its restricted localisation. In contrast, it is clear that Dα6 plays a major and specific role in binding to the insecticide spinosad in D. melanogaster since mutations in this subunit are highly resistant to the toxin (Perry et al., 2015).

Translating our neurotoxin binding results to other target species, there are several studies showing that nAChRs are sensitive to α-Btx in other insects, including the cockroach Periplaneta americana, the moth Manduca sexta and the housefly Musca domestica (Salgado and Saar, 2004; Eastham et al., 1998; Jones et al., 1981). The exact α-Btx binding mechanism in these organisms is unknown or under investigation (Salgado, 2021). This indicates broader implications for our in vivo study determining the α-Btx binding nAChR subunits in the D. melanogaster, with our results potentially extrapolated to other target species that have similar nAChR subunits.

In conclusion, we identified ligand-binding subunit sites for a D. melanogaster nAChR antagonist with newly insecticidal effects. Our findings contribute to a better understanding of the role of nAChR subunits that interacts with insecticidal peptide toxins.

The mass spectrometry data from this publication have been deposited to PRIDE (http://www.ebi.ac.uk/pride/archive/) with the data set identifier PXD028484 (DOI: https://doi.org/10.6019/PXD028484).

1. 1. Benedict D

   (2021) ProteomeXchange

   Drosophila nicotinic acetylcholine receptor subunits and their native interactions with insecticidal peptide toxins.

   https://doi.org/10.6019/PXD028484

1. 1. Alexander JK
   2. Govind AP
   3. Drisdel RC
   4. Blanton MP
   5. Vallejo Y
   6. Lam TT
   7. Green WN

   (2010) Palmitoylation of nicotinic acetylcholine receptors

   Journal of Molecular Neuroscience: MN 40:12–40.

   https://doi.org/10.1007/s12031-009-9246-z

   • Google Scholar
2. 1. Barnstedt O
   2. Owald D
   3. Felsenberg J
   4. Brain R
   5. Moszynski JP
   6. Talbot CB
   7. Perrat PN
   8. Waddell S

   (2016) Memory-Relevant Mushroom Body Output Synapses Are Cholinergic

   Neuron 89:1237–1247.

   https://doi.org/10.1016/j.neuron.2016.02.015

   • Google Scholar
3. 1. Blanc M
   2. David F
   3. Abrami L
   4. Migliozzi D
   5. Armand F
   6. Bürgi J
   7. Goot FG

   (2015) SwissPalm: Protein Palmitoylation database

   F1000Research 4:261.

   https://doi.org/10.12688/f1000research.6464.1

   • Google Scholar
4. 1. Bologna G
   2. Yvon C
   3. Duvaud S
   4. Veuthey AL

   (2004) N-terminal myristoylation predictions by ensembles of neural networks

   Proteomics 4:1626–1632.

   https://doi.org/10.1002/pmic.200300783

   • Google Scholar
5. 1. Carroll J
   2. Altman MC
   3. Fearnley IM
   4. Walker JE

   (2007) Identification of membrane proteins by tandem mass spectrometry of protein ions

   PNAS pp. 14330–14335.

   https://doi.org/10.1073/pnas.0706817104

   • Google Scholar
6. 1. Chambers C
   2. Cutler P
   3. Huang YH
   4. Goodchild JA
   5. Wang CK
   6. Bigot A
   7. Kaas Q
   8. Craik DJ
   9. Sabbadin D
   10. Earley GF

   (2019) Insecticidal spider toxins are high affinity positive allosteric modulators of the nicotinic acetylcholine receptor

   FEBS Letters 593:1336–1350.

   https://doi.org/10.1002/1873-3468.13435

   • Google Scholar
7. 1. Cheng H
   2. Fan C
   3. Zhang S
   4. Wu Z
   5. Cui Z
   6. Melcher K
   7. Zhang C
   8. Jiang Y
   9. Cong Y
   10. Xu HE

   (2015) Crystallization scale purification of α7 nicotinic acetylcholine receptor from mammalian cells using a BacMam expression system

   Acta Pharmacologica Sinica 36:1013–1023.

   https://doi.org/10.1038/aps.2015.34

   • PubMed
   • Google Scholar
8. 1. Christesen D
   2. Yang YT
   3. Chen W
   4. Batterham P
   5. Perry T

   (2021) Loss of the Dβ1 nicotinic acetylcholine receptor subunit disrupts bursicon-driven wing expansion and diminishes adult viability in Drosophila melanogaster

   Genetics 219:iyab112.

   https://doi.org/10.1093/genetics/iyab112

   • PubMed
   • Google Scholar
9. 1. daCosta CJB
   2. Dey L
   3. Therien JPD
   4. Baenziger JE

   (2013) A distinct mechanism for activating uncoupled nicotinic acetylcholine receptors

   Nature Chemical Biology 9:701–707.

   https://doi.org/10.1038/nchembio.1338

   • PubMed
   • Google Scholar
10. 1. daCosta CJB
    2. Free CR
    3. Sine SM

    (2015) Stoichiometry for α-bungarotoxin block of α7 acetylcholine receptors

    Nature Communications 6:8057.

    https://doi.org/10.1038/ncomms9057

    • PubMed
    • Google Scholar
11. 1. Dellisanti CD
    2. Yao Y
    3. Stroud JC
    4. Wang ZZ
    5. Chen L

    (2007) Crystal structure of the extracellular domain of nAChR α1 bound to α-bungarotoxin at 1.94 Å resolution

    Nature Neuroscience 10:953–962.

    https://doi.org/10.1038/nn1942

    • PubMed
    • Google Scholar
12. 1. Denisov IG
    2. Sligar SG

    (2016) Nanodiscs for structural and functional studies of membrane proteins

    Nature Structural & Molecular Biology 23:481–486.

    https://doi.org/10.1038/nsmb.3195

    • PubMed
    • Google Scholar
13. 1. Depner H
    2. Lützkendorf J
    3. Babkir HA
    4. Sigrist SJ
    5. Holt MG

    (2014) Differential centrifugation-based biochemical fractionation of the Drosophila adult CNS

    Nature Protocols 9:2796–2808.

    https://doi.org/10.1038/nprot.2014.192

    • PubMed
    • Google Scholar
14. 1. Eastham HM
    2. Lind RJ
    3. Eastlake JL
    4. Clarke BS
    5. Towner P
    6. Reynolds SE
    7. Wolstenholme AJ
    8. Wonnacott S

    (1998) Characterization of a nicotinic acetylcholine receptor from the insect Manduca sexta

    The European Journal of Neuroscience 10:879–889.

    https://doi.org/10.1046/j.1460-9568.1998.00095.x

    • PubMed
    • Google Scholar
15. 1. Eng JK
    2. McCormack AL
    3. Yates JR

    (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database

    Journal of the American Society for Mass Spectrometry 5:976–989.

    https://doi.org/10.1016/1044-0305(94)80016-2

    • PubMed
    • Google Scholar
16. 1. Fang P
    2. Ji Y
    3. Silbern I
    4. Doebele C
    5. Ninov M
    6. Lenz C
    7. Oellerich T
    8. Pan KT
    9. Urlaub H

    (2020) A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics

    Nature Communications 11:5268.

    https://doi.org/10.1038/s41467-020-19052-w

    • PubMed
    • Google Scholar
17. 1. Fayyazuddin A
    2. Zaheer MA
    3. Hiesinger PR
    4. Bellen HJ

    (2006) The nicotinic acetylcholine receptor Dalpha7 is required for an escape behavior in Drosophila

    PLOS Biology 4:e0040063.

    https://doi.org/10.1371/journal.pbio.0040063

    • PubMed
    • Google Scholar
18. 1. Gault J
    2. Liko I
    3. Landreh M
    4. Shutin D
    5. Bolla JR
    6. Jefferies D
    7. Agasid M
    8. Yen H-Y
    9. Ladds MJGW
    10. Lane DP
    11. Khalid S
    12. Mullen C
    13. Remes PM
    14. Huguet R
    15. McAlister G
    16. Goodwin M
    17. Viner R
    18. Syka JEP
    19. Robinson CV

    (2020) Combining native and “omics” mass spectrometry to identify endogenous ligands bound to membrane proteins

    Nature Methods 17:505–508.

    https://doi.org/10.1038/s41592-020-0821-0

    • PubMed
    • Google Scholar
19. 1. Geladaki A
    2. Kočevar Britovšek N
    3. Breckels LM
    4. Smith TS
    5. Vennard OL
    6. Mulvey CM
    7. Crook OM
    8. Gatto L
    9. Lilley KS

    (2019) Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics

    Nature Communications 10:331.

    https://doi.org/10.1038/s41467-018-08191-w

    • PubMed
    • Google Scholar
20. 1. Ginestet C

    (2011) ggplot2: Elegant Graphics for Data Analysis

    Journal of the Royal Statistical Society 174:245–246.

    https://doi.org/10.1111/j.1467-985X.2010.00676_9.x

    • Google Scholar
21. 1. Gobom J
    2. Nordhoff E
    3. Mirgorodskaya E
    4. Ekman R
    5. Roepstorff P

    (1999) Sample purification and preparation technique based on nano-scale reversed-phase columns for the sensitive analysis of complex peptide mixtures by matrix-assisted laser desorption/ionization mass spectrometry

    Journal of Mass Spectrometry 34:105–116.

    https://doi.org/10.1002/(SICI)1096-9888(199902)34:2<105::AID-JMS768>3.0.CO;2-4

    • PubMed
    • Google Scholar
22. 1. Goddard TD
    2. Huang CC
    3. Meng EC
    4. Pettersen EF
    5. Couch GS
    6. Morris JH
    7. Ferrin TE

    (2018) UCSF ChimeraX: Meeting modern challenges in visualization and analysis

    Protein Science 27:14–25.

    https://doi.org/10.1002/pro.3235

    • PubMed
    • Google Scholar
23. 1. Gratz SJ
    2. Ukken FP
    3. Rubinstein CD
    4. Thiede G
    5. Donohue LK
    6. Cummings AM
    7. O’Connor-Giles KM

    (2014) Highly specific and efficient CRISPR/Cas9-catalyzed homology-directed repair in Drosophila

    Genetics 196:961–971.

    https://doi.org/10.1534/genetics.113.160713

    • PubMed
    • Google Scholar
24. 1. Hägglund P
    2. Bunkenborg J
    3. Elortza F
    4. Jensen ON
    5. Roepstorff P

    (2004) A new strategy for identification of N-glycosylated proteins and unambiguous assignment of their glycosylation sites using HILIC enrichment and partial deglycosylation

    Journal of Proteome Research 3:556–566.

    https://doi.org/10.1021/pr034112b

    • PubMed
    • Google Scholar
25. 1. Homem RA
    2. Buttery B
    3. Richardson E
    4. Tan Y
    5. Field LM
    6. Williamson MS
    7. Emyr Davies TG

    (2020) Evolutionary trade-offs of insecticide resistance - The fitness costs associated with target-site mutations in the nAChR of Drosophila melanogaster

    Molecular Ecology 29:2661–2675.

    https://doi.org/10.1111/mec.15503

    • PubMed
    • Google Scholar
26. 1. Houchat JN
    2. Dissanamossi BM
    3. Landagaray E
    4. Mathé-Allainmat M
    5. Cartereau A
    6. Graton J
    7. Lebreton J
    8. Le Questel JY
    9. Thany SH

    (2019) Mode of action of sulfoxaflor on α-bungarotoxin-insensitive nAChR1 and nAChR2 subtypes: Inhibitory effect of imidacloprid

    Neurotoxicology 74:132–138.

    https://doi.org/10.1016/j.neuro.2019.06.003

    • PubMed
    • Google Scholar
27. 1. Huang DW
    2. Sherman BT
    3. Lempicki RA

    (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

    Nature Protocols 4:44–57.

    https://doi.org/10.1038/nprot.2008.211

    • PubMed
    • Google Scholar
28. 1. Ihaka R
    2. Gentleman R

    (1996)

    R: A Language for Data Analysis and Graphics

    Journal of Computational and Graphical Statistics 5:299–314.

    • Google Scholar
29. 1. Ihara M
    2. Furutani S
    3. Shigetou S
    4. Shimada S
    5. Niki K
    6. Komori Y
    7. Kamiya M
    8. Koizumi W
    9. Magara L
    10. Hikida M
    11. Noguchi A
    12. Okuhara D
    13. Yoshinari Y
    14. Kondo S
    15. Tanimoto H
    16. Niwa R
    17. Sattelle DB
    18. Matsuda K

    (2020) Cofactor-enabled functional expression of fruit fly, honeybee, and bumblebee nicotinic receptors reveals picomolar neonicotinoid actions

    PNAS 117:16283–16291.

    https://doi.org/10.1073/pnas.2003667117

    • PubMed
    • Google Scholar
30. 1. Jones SW
    2. Sudershan P
    3. O’Brien RD

    (1981) Alpha-bungarotoxin binding in house fly heads and Torpedo electroplax

    Journal of Neurochemistry 36:447–453.

    https://doi.org/10.1111/j.1471-4159.1981.tb01613.x

    • PubMed
    • Google Scholar
31. 1. Jones AK
    2. Davis P
    3. Hodgkin J
    4. Sattelle DB

    (2007) The nicotinic acetylcholine receptor gene family of the nematode Caenorhabditis elegans: an update on nomenclature

    Invertebrate Neuroscience 7:129–131.

    https://doi.org/10.1007/s10158-007-0049-z

    • PubMed
    • Google Scholar
32. 1. Kaji H
    2. Kamiie JI
    3. Kawakami H
    4. Kido K
    5. Yamauchi Y
    6. Shinkawa T
    7. Taoka M
    8. Takahashi N
    9. Isobe T

    (2007) Proteomics reveals n-linked glycoprotein diversity in Caenorhabditis elegans and suggests an atypical translocation mechanism for integral membrane proteins

    Molecular & Cellular Proteomics: MCP 6:2100–2109.

    https://doi.org/10.1074/mcp.M600392-MCP200

    • Google Scholar
33. 1. Kalxdorf M
    2. Günthner I
    3. Becher I
    4. Kurzawa N
    5. Knecht S
    6. Savitski MM
    7. Eberl HC
    8. Bantscheff M

    (2021) Cell surface thermal proteome profiling tracks perturbations and drug targets on the plasma membrane

    Nature Methods 18:84–91.

    https://doi.org/10.1038/s41592-020-01022-1

    • Google Scholar
34. 1. Kanehisa M
    2. Furumichi M
    3. Sato Y
    4. Ishiguro-Watanabe M
    5. Tanabe M

    (2020) KEGG: integrating viruses and cellular organisms

    Nucleic Acids Research 49:D545–D551.

    https://doi.org/10.1093/nar/gkaa970

    • Google Scholar
35. 1. Knight PJ
    2. Carroll J
    3. Ellar DJ

    (2004) Analysis of glycan structures on the 120 kDa aminopeptidase N of Manduca sexta and their interactions with Bacillus thuringiensis Cry1Ac toxin Insect

    Biochem Mol Biol 34:101–112.

    https://doi.org/10.1016/j.ibmb.2003.09.007

    • Google Scholar
36. 1. Koenig T
    2. Menze BH
    3. Kirchner M
    4. Monigatti F
    5. Parker KC
    6. Patterson T
    7. Steen JJ
    8. Hamprecht FA
    9. Steen H

    (2008) Robust prediction of the MASCOT score for an improved quality assessment in mass spectrometric proteomics

    Journal of Proteome Research 7:3708–3717.

    https://doi.org/10.1021/pr700859x

    • Google Scholar
37. 1. Kondo S
    2. Takahashi T
    3. Yamagata N
    4. Imanishi Y
    5. Katow H
    6. Hiramatsu S
    7. Lynn K
    8. Abe A
    9. Kumaraswamy A
    10. Tanimoto H

    (2020) Neurochemical Organization of the Drosophila Brain Visualized by Endogenously Tagged Neurotransmitter Receptors

    Cell Reports 30:284–297.

    https://doi.org/10.1016/j.celrep.2019.12.018

    • PubMed
    • Google Scholar
38. 1. Korona D
    2. Koestler SA
    3. Russell S

    (2017) Engineering the Drosophila Genome for Developmental Biology

    Journal of Developmental Biology 5:16.

    https://doi.org/10.3390/jdb5040016

    • PubMed
    • Google Scholar
39. 1. Korona D
    2. Nightingale D
    3. Fabre B
    4. Nelson M
    5. Fischer B
    6. Johnson G
    7. Lees J
    8. Hubbard S
    9. Lilley K
    10. Russell S

    (2020) Characterisation of protein isoforms encoded by the Drosophila Glycogen Synthase Kinase 3 gene shaggy

    PLOS ONE 15:e0236679.

    https://doi.org/10.1371/journal.pone.0236679

    • PubMed
    • Google Scholar
40. 1. Krogh A
    2. Larsson B
    3. von Heijne G
    4. Sonnhammer EL

    (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes

    Journal of Molecular Biology 305:567–580.

    https://doi.org/10.1006/jmbi.2000.4315

    • PubMed
    • Google Scholar
41. 1. Lansdell SJ
    2. Millar NS

    (2000) The influence of nicotinic receptor subunit composition upon agonist, alpha-bungarotoxin and insecticide (imidacloprid) binding affinity

    Neuropharmacology 39:671–679.

    https://doi.org/10.1016/s0028-3908(99)00170-7

    • PubMed
    • Google Scholar
42. 1. Lansdell SJ
    2. Millar NS

    (2004) Molecular characterization of Dalpha6 and Dalpha7 nicotinic acetylcholine receptor subunits from Drosophila: formation of a high-affinity alpha-bungarotoxin binding site revealed by expression of subunit chimeras

    Journal of Neurochemistry 90:479–489.

    https://doi.org/10.1111/j.1471-4159.2004.02499.x

    • PubMed
    • Google Scholar
43. 1. Lansdell SJ
    2. Collins T
    3. Goodchild J
    4. Millar NS

    (2012) The Drosophila nicotinic acetylcholine receptor subunits Dα5 and Dα7 form functional homomeric and heteromeric ion channels

    BMC Neuroscience 13:73.

    https://doi.org/10.1186/1471-2202-13-73

    • PubMed
    • Google Scholar
44. 1. Lee SC
    2. Knowles TJ
    3. Postis VLG
    4. Jamshad M
    5. Parslow RA
    6. Lin YP
    7. Goldman A
    8. Sridhar P
    9. Overduin M
    10. Muench SP
    11. Dafforn TR

    (2016) A method for detergent-free isolation of membrane proteins in their local lipid environment

    Nature Protocols 11:1149–1162.

    https://doi.org/10.1038/nprot.2016.070

    • PubMed
    • Google Scholar
45. 1. Liu MY
    2. Casida JE

    (1993) High Affinity Binding of [3H]Imidacloprid in the Insect Acetylcholine Receptor

    Pesticide Biochemistry and Physiology 46:40–46.

    https://doi.org/10.1006/pest.1993.1034

    • Google Scholar
46. 1. Liu W
    2. Xie Y
    3. Ma J
    4. Luo X
    5. Nie P
    6. Zuo Z
    7. Lahrmann U
    8. Zhao Q
    9. Zheng Y
    10. Zhao Y
    11. Xue Y
    12. Ren J

    (2015) IBS: an illustrator for the presentation and visualization of biological sequences

    Bioinformatics (Oxford, England) 31:3359–3361.

    https://doi.org/10.1093/bioinformatics/btv362

    • PubMed
    • Google Scholar
47. 1. Loo RR
    2. Dales N
    3. Andrews PC

    (1996) The effect of detergents on proteins analyzed by electrospray ionization Methods

    Mol Biol 61:141–160.

    https://doi.org/10.1385/0-89603-345-7:141

    • Google Scholar
48. 1. Lu W
    2. Liu Z
    3. Fan X
    4. Zhang X
    5. Qiao X
    6. Huang J

    (2022) Nicotinic acetylcholine receptor modulator insecticides act on diverse receptor subtypes with distinct subunit compositions

    PLOS Genetics 18:e1009920.

    https://doi.org/10.1371/journal.pgen.1009920

    • Google Scholar
49. 1. Madeira F
    2. Park YM
    3. Lee J
    4. Buso N
    5. Gur T
    6. Madhusoodanan N
    7. Basutkar P
    8. Tivey ARN
    9. Potter SC
    10. Finn RD
    11. Lopez R

    (2019) The EMBL-EBI search and sequence analysis tools APIs in 2019

    Nucleic Acids Research 47:W636–W641.

    https://doi.org/10.1093/nar/gkz268

    • Google Scholar
50. 1. Maldonado-Hernández R
    2. Quesada O
    3. Colón-Sáez JO
    4. Lasalde-Dominicci JA

    (2020) Sequential purification and characterization of Torpedo californica nAChR-DC supplemented with CHS for high-resolution crystallization studies

    Analytical Biochemistry 610:113887.

    https://doi.org/10.1016/j.ab.2020.113887

    • Google Scholar
51. 1. Martelli F
    2. Hernandes NH
    3. Zuo Z
    4. Wang J
    5. Wong CO
    6. Karagas NE
    7. Roessner U
    8. Rupasinghe T
    9. Robin C
    10. Venkatachalam K
    11. Perry T
    12. Batterham P
    13. Bellen HJ

    (2022) Low doses of the organic insecticide spinosad trigger lysosomal defects, elevated ROS, lipid dysregulation, and neurodegeneration in flies

    eLife 11:e73812.

    https://doi.org/10.7554/eLife.73812

    • Google Scholar
52. 1. Martens C
    2. Shekhar M
    3. Borysik AJ
    4. Lau AM
    5. Reading E
    6. Tajkhorshid E
    7. Booth PJ
    8. Politis A

    (2018) Direct protein-lipid interactions shape the conformational landscape of secondary transporters

    Nature Communications 9:4151.

    https://doi.org/10.1038/s41467-018-06704-1

    • PubMed
    • Google Scholar
53. 1. Mitchell AL
    2. Attwood TK
    3. Babbitt PC
    4. Blum M
    5. Bork P
    6. Bridge A
    7. Brown SD
    8. Chang HY
    9. El-Gebali S
    10. Fraser MI
    11. Gough J
    12. Haft DR
    13. Huang H
    14. Letunic I
    15. Lopez R
    16. Luciani A
    17. Madeira F
    18. Marchler-Bauer A
    19. Mi H
    20. Natale DA
    21. Necci M
    22. Nuka G
    23. Orengo C
    24. Pandurangan AP
    25. Paysan-Lafosse T
    26. Pesseat S
    27. Potter SC
    28. Qureshi MA
    29. Rawlings ND
    30. Redaschi N
    31. Richardson LJ
    32. Rivoire C
    33. Salazar GA
    34. Sangrador-Vegas A
    35. Sigrist CJA
    36. Sillitoe I
    37. Sutton GG
    38. Thanki N
    39. Thomas PD
    40. Tosatto SCE
    41. Yong SY
    42. Finn RD

    (2019) InterPro in 2019: improving coverage, classification and access to protein sequence annotations

    Nucleic Acids Research 47:D351–D360.

    https://doi.org/10.1093/nar/gky1100

    • PubMed
    • Google Scholar
54. 1. Mitchell J
    2. Smith CS
    3. Titlow J
    4. Otto N
    5. van Velde P
    6. Booth M
    7. Davis I
    8. Waddell S

    (2021) Selective dendritic localization of mRNA in Drosophila mushroom body output neurons

    eLife 10:e62770.

    https://doi.org/10.7554/eLife.62770

    • Google Scholar
55. 1. Mulcahy MJ
    2. Paulo JA
    3. Hawrot E

    (2018) Proteomic Investigation of Murine Neuronal α7-Nicotinic Acetylcholine Receptor Interacting Proteins

    Journal of Proteome Research 17:3959–3975.

    https://doi.org/10.1021/acs.jproteome.8b00618

    • PubMed
    • Google Scholar
56. 1. Perry T
    2. Heckel DG
    3. McKenzie JA
    4. Batterham P

    (2008) Mutations in Dalpha1 or Dbeta2 nicotinic acetylcholine receptor subunits can confer resistance to neonicotinoids in Drosophila melanogaster

    Insect Biochemistry and Molecular Biology 38:520–528.

    https://doi.org/10.1016/j.ibmb.2007.12.007

    • PubMed
    • Google Scholar
57. 1. Perry T
    2. Somers J
    3. Yang YT
    4. Batterham P

    (2015) Expression of insect α6-like nicotinic acetylcholine receptors in Drosophila melanogaster highlights a high level of conservation of the receptor:spinosyn interaction

    Insect Biochemistry and Molecular Biology 64:106–115.

    https://doi.org/10.1016/j.ibmb.2015.01.017

    • PubMed
    • Google Scholar
58. 1. Perry T
    2. Chen W
    3. Ghazali R
    4. Yang YT
    5. Christesen D
    6. Martelli F
    7. Lumb C
    8. Bao Luong HN
    9. Mitchell J
    10. Holien JK
    11. Parker MW
    12. Sparks TC
    13. Batterham P

    (2021) Role of nicotinic acetylcholine receptor subunits in the mode of action of neonicotinoid, sulfoximine and spinosyn insecticides in Drosophila melanogaster

    Insect Biochemistry and Molecular Biology 131:103547.

    https://doi.org/10.1016/j.ibmb.2021.103547

    • PubMed
    • Google Scholar
59. 1. Pierleoni A
    2. Martelli PL
    3. Casadio R

    (2008) PredGPI: A GPI-anchor predictor

    BMC Bioinformatics 9:392.

    https://doi.org/10.1186/1471-2105-9-392

    • Google Scholar
60. 1. Port F
    2. Chen HM
    3. Lee T
    4. Bullock SL

    (2014) Optimized CRISPR/Cas tools for efficient germline and somatic genome engineering in Drosophila

    PNAS 111:E2967–E2976.

    https://doi.org/10.1073/pnas.1405500111

    • PubMed
    • Google Scholar
61. 1. Queiroz RML
    2. Smith T
    3. Villanueva E
    4. Marti-Solano M
    5. Monti M
    6. Pizzinga M
    7. Mirea DM
    8. Ramakrishna M
    9. Harvey RF
    10. Dezi V
    11. Thomas GH
    12. Willis AE
    13. Lilley KS

    (2019) Comprehensive identification of RNA-protein interactions in any organism using orthogonal organic phase separation (OOPS

    Nature Biotechnology 37:169–178.

    https://doi.org/10.1038/s41587-018-0001-2

    • PubMed
    • Google Scholar
62. 1. Quesada O
    2. González-Freire C
    3. Ferrer MC
    4. Colón-Sáez JO
    5. Fernández-García E
    6. Mercado J
    7. Dávila A
    8. Morales R
    9. Lasalde-Dominicci JA

    (2016) Uncovering the lipidic basis for the preparation of functional nicotinic acetylcholine receptor detergent complexes for structural studies

    Scientific Reports 6:32766.

    https://doi.org/10.1038/srep32766

    • PubMed
    • Google Scholar
63. 1. Rahman MM
    2. Teng J
    3. Worrell BT
    4. Noviello CM
    5. Lee M
    6. Karlin A
    7. Stowell MHB
    8. Hibbs RE

    (2020) Structure of the Native Muscle-type Nicotinic Receptor and Inhibition by Snake Venom Toxins

    Neuron 106:952–962.

    https://doi.org/10.1016/j.neuron.2020.03.012

    • PubMed
    • Google Scholar
64. 1. Rappsilber J
    2. Mann M
    3. Ishihama Y

    (2007) Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips

    Nature Protocols 2:1896–1906.

    https://doi.org/10.1038/nprot.2007.261

    • PubMed
    • Google Scholar
65. 1. Ren X
    2. Sun J
    3. Housden BE
    4. Hu Y
    5. Roesel C
    6. Lin S
    7. Liu LP
    8. Yang Z
    9. Mao D
    10. Sun L
    11. Wu Q
    12. Ji JY
    13. Xi J
    14. Mohr SE
    15. Xu J
    16. Perrimon N
    17. Ni JQ

    (2013) Optimized gene editing technology for Drosophila melanogaster using germ line-specific Cas9

    PNAS 110:19012–19017.

    https://doi.org/10.1073/pnas.1318481110

    • PubMed
    • Google Scholar
66. 1. Rohde PD
    2. Madsen LS
    3. Neumann Arvidson SM
    4. Loeschcke V
    5. Demontis D
    6. Kristensen TN

    (2016) Testing candidate genes for attention-deficit/hyperactivity disorder in fruit flies using a high throughput assay for complex behavior

    Fly 10:25–34.

    https://doi.org/10.1080/19336934.2016.1158365

    • PubMed
    • Google Scholar
67. Software

    1. Rstudio Team

    (2020) RStudio: Integrated Development for R

    Rstudio, PBC.

    https://www.rstudio.com/
68. 1. Salgado VL
    2. Saar R

    (2004) Desensitizing and non-desensitizing subtypes of alpha-bungarotoxin-sensitive nicotinic acetylcholine receptors in cockroach neurons

    Journal of Insect Physiology 50:867–879.

    https://doi.org/10.1016/j.jinsphys.2004.07.007

    • PubMed
    • Google Scholar
69. 1. Salgado VL

    (2021) Selective actions of insecticides on desensitizing and non-desensitizing nicotinic acetylcholine receptors in cockroach (Periplaneta americana) neurons

    Pest Management Science 77:3663–3672.

    https://doi.org/10.1002/ps.6396

    • PubMed
    • Google Scholar
70. 1. Schmidt-Nielsen BK
    2. Gepner JI
    3. Teng NN
    4. Hall LM

    (1977) Characterization of an alpha-bungarotoxin binding component from Drosophila melanogaster

    Journal of Neurochemistry 29:1013–1029.

    https://doi.org/10.1111/j.1471-4159.1977.tb06505.x

    • PubMed
    • Google Scholar
71. 1. Shevchenko A
    2. Tomas H
    3. Havlis J
    4. Olsen JV
    5. Mann M

    (2006) In-gel digestion for mass spectrometric characterization of proteins and proteomes

    Nature Protocols 1:2856–2860.

    https://doi.org/10.1038/nprot.2006.468

    • PubMed
    • Google Scholar
72. 1. Sobotzki N
    2. Schafroth MA
    3. Rudnicka A
    4. Koetemann A
    5. Marty F
    6. Goetze S
    7. Yamauchi Y
    8. Carreira EM
    9. Wollscheid B

    (2018) HATRIC-based identification of receptors for orphan ligands

    Nature Communications 9:1519.

    https://doi.org/10.1038/s41467-018-03936-z

    • PubMed
    • Google Scholar
73. 1. Somers J
    2. Luong HNB
    3. Mitchell J
    4. Batterham P
    5. Perry T

    (2017) Pleiotropic Effects of Loss of the Dα1 Subunit in Drosophila melanogaster: Implications for Insecticide Resistance

    Genetics 205:263–271.

    https://doi.org/10.1534/genetics.116.195750

    • PubMed
    • Google Scholar
74. 1. Sormanni P
    2. Aprile FA
    3. Vendruscolo M

    (2015) The CamSol method of rational design of protein mutants with enhanced solubility

    Journal of Molecular Biology 427:478–490.

    https://doi.org/10.1016/j.jmb.2014.09.026

    • PubMed
    • Google Scholar
75. 1. Sormanni P
    2. Amery L
    3. Ekizoglou S
    4. Vendruscolo M
    5. Popovic B

    (2017) Rapid and accurate in silico solubility screening of a monoclonal antibody library

    Scientific Reports 7:8200.

    https://doi.org/10.1038/s41598-017-07800-w

    • PubMed
    • Google Scholar
76. 1. Su H
    2. O’Dowd DK

    (2003) Fast synaptic currents in Drosophila mushroom body Kenyon cells are mediated by alpha-bungarotoxin-sensitive nicotinic acetylcholine receptors and picrotoxin-sensitive GABA receptors

    The Journal of Neuroscience 23:9246–9253.

    https://doi.org/10.1523/JNEUROSCI.23-27-09246.2003

    • PubMed
    • Google Scholar
77. 1. Tackenberg MC
    2. Giannoni-Guzmán MA
    3. Sanchez-Perez E
    4. Doll CA
    5. Agosto-Rivera JL
    6. Broadie K
    7. Moore D
    8. McMahon DG

    (2020) Neonicotinoids disrupt circadian rhythms and sleep in honey bees

    Scientific Reports 10:17929.

    https://doi.org/10.1038/s41598-020-72041-3

    • PubMed
    • Google Scholar
78. 1. The Gene Ontology Consortium

    (2019) The Gene Ontology Resource: 20 years and still GOing strong

    Nucleic Acids Research 47:D330–D338.

    https://doi.org/10.1093/nar/gky1055

    • Google Scholar
79. 1. Tsirigos KD
    2. Elofsson A
    3. Bagos PG

    (2016) PRED-TMBB2: improved topology prediction and detection of beta-barrel outer membrane proteins

    Bioinformatics (Oxford, England) 32:i665–i671.

    https://doi.org/10.1093/bioinformatics/btw444

    • PubMed
    • Google Scholar
80. 1. Wang H
    2. Yu M
    3. Ochani M
    4. Amella CA
    5. Tanovic M
    6. Susarla S
    7. Li JH
    8. Wang H
    9. Yang H
    10. Ulloa L
    11. Al-Abed Y
    12. Czura CJ
    13. Tracey KJ

    (2003) Nicotinic acetylcholine receptor alpha7 subunit is an essential regulator of inflammation

    Nature 421:384–388.

    https://doi.org/10.1038/nature01339

    • PubMed
    • Google Scholar
81. 1. Wu P
    2. Ma D
    3. Pierzchala M
    4. Wu J
    5. Yang LC
    6. Mai X
    7. Chang X
    8. Schmidt-Glenewinkel T

    (2005) The Drosophila acetylcholine receptor subunit D alpha5 is part of an alpha-bungarotoxin binding acetylcholine receptor

    The Journal of Biological Chemistry 280:20987–20994.

    https://doi.org/10.1074/jbc.M409639200

    • PubMed
    • Google Scholar
82. 1. Xu X
    2. Bao H
    3. Shao X
    4. Zhang Y
    5. Yao X
    6. Liu Z
    7. Li Z

    (2010) Pharmacological characterization of cis-nitromethylene neonicotinoids in relation to imidacloprid binding sites in the brown planthopper, Nilaparvata lugens

    Insect Molecular Biology 19:1–8.

    https://doi.org/10.1111/j.1365-2583.2009.00923.x

    • PubMed
    • Google Scholar
83. 1. Zuo YY
    2. Xue YX
    3. Wang ZY
    4. Ren X
    5. Aioub AAA
    6. Wu YD
    7. Yang YH
    8. Hu ZN

    (2022) Knockin of the G275E mutation of the nicotinic acetylcholine receptor (nAChR) α6 confers high levels of resistance to spinosyns in Spodoptera exigua

    Insect Science 29:478–486.

    https://doi.org/10.1111/1744-7917.12922

    • PubMed
    • Google Scholar

Author details

1. Dagmara Korona

   Department of Genetics, University of Cambridge, Downing Street, Cambridge, United Kingdom

   Contribution

   Formal analysis, Investigation, Validation, Visualization, Project administration, Resources, Supervision

   Contributed equally with

   Benedict Dirnberger and Carlo NG Giachello

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5988-3894

2. Benedict Dirnberger

   1. Department of Genetics, University of Cambridge, Downing Street, Cambridge, United Kingdom
   2. Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
   3. Syngenta, Jealott's Hill International Research Centre, Bracknell, United Kingdom

   Contribution

   Formal analysis, Investigation, Validation, Visualization, Project administration, Resources, Supervision, Writing – review and editing

   Contributed equally with

   Dagmara Korona and Carlo NG Giachello

   For correspondence

   bd415@cam.ac.uk

   Competing interests

   is affiliated with Syngenta. The author has no other competing interests to declare

   "This ORCID iD identifies the author of this article:" 0000-0002-0772-5923

3. Carlo NG Giachello

   Syngenta, Jealott's Hill International Research Centre, Bracknell, United Kingdom

   Contribution

   Formal analysis, Investigation, Validation, Visualization, Project administration, Resources, Supervision

   Contributed equally with

   Dagmara Korona and Benedict Dirnberger

   Competing interests

   No competing interests declared

4. Rayner ML Queiroz

   Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Formal analysis, Investigation, Visualization, Project administration

   Competing interests

   No competing interests declared

5. Rebeka Popovic

   MRC Toxicology Unit, Gleeson Building, University of Cambridge, Tennis Court Road, Cambridge, United Kingdom

   Contribution

   Formal analysis, Validation, Visualization, Project administration, Resources

   Competing interests

   No competing interests declared

6. Karin H Müller

   Cambridge Advanced Imaging Centre, Department of Physiology, Development and Neuroscience/Anatomy Building, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Validation, Visualization, Resources

   Competing interests

   No competing interests declared

7. David-Paul Minde

   Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Investigation, Visualization

   Competing interests

   No competing interests declared

8. Michael J Deery

   Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Formal analysis, Visualization, Supervision

   Competing interests

   No competing interests declared

9. Glynnis Johnson

   Department of Genetics, University of Cambridge, Downing Street, Cambridge, United Kingdom

   Contribution

   Visualization

   Competing interests

   No competing interests declared

10. Lucy C Firth

    Syngenta, Jealott's Hill International Research Centre, Bracknell, United Kingdom

    Contribution

    Writing – original draft

    Competing interests

    is affiliated with Syngenta. The author has no other competing interests to declare

11. Fergus G Earley

    Syngenta, Jealott's Hill International Research Centre, Bracknell, United Kingdom

    Contribution

    Data curation, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Project administration, Resources, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1943-0724

12. Steven Russell

    Department of Genetics, University of Cambridge, Downing Street, Cambridge, United Kingdom

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

13. Kathryn S Lilley

    Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, United Kingdom

    Contribution

    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

• 2,760

  views

• 469

  downloads

• 8

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.