Transgenic crops expressing insecticidal Cry proteins from Bacillus thuringiensis bacteria, known as Bt-crops, have become important tools for the management of insect crop pests (Carrière et al., 2003; Cattaneo et al., 2006; Hutchison et al., 2010; Shelton et al., 2002; Tabashnik et al., 2010). Planting of Bt-crops effectively suppresses the targeted insects, decreasing insecticide use and promoting biocontrol services (Bravo et al., 2011; Carrière et al., 2003; Cattaneo et al., 2006; Hutchison et al., 2010; Lu et al., 2010; Lu et al., 2012; Shelton et al., 2002; Tabashnik et al., 2002; Wu, 2010; Wu et al., 2008). We have previously shown that the commercialization of transgenic Bt-cotton in China brought significant changes in the ecology of insects utilizing the crop (Lu et al., 2010; Swiatkiewicz et al., 2014; Wu et al., 2008). However, the effect of Bt-crops on other organisms, such as microbes, which could be playing important roles in the life cycle of insect populations, remains largely unknown.

Recently, we showed in laboratory trials that infection with a densovirus, Helicoverpa armigera densovirus-1 (HaDV2, named as HaDNV-1 previously, GenBank accession number: NC_015718), was associated with significantly enhanced tolerance of cotton bollworm, H. armigera, to a baculovirus (H. armigera nucleopolyhedrovirus, HaNPV) (Xu et al., 2014), and there was some suggestion that the densovirus also increased tolerance to Cry1Ac toxin in a Bt-susceptible strain of H. armigera (Xu et al., 2014; Xu et al., 2017a; Xu et al., 2017b). HaDV2 was found to be widespread in wild populations of H. armigera adults (>67% prevalence between 2008 and 2012) (Xu et al., 2014). The densovirus was mainly distributed in the fat body of the insect and could be both horizontally and vertically transmitted. Moreover, HaDV2-positive individuals showed faster development and higher fecundity than non-infected individuals. There was no evidence for a negative effect of HaDV2 infection on H. armigera in relation to other fitness-related traits, suggesting a possible mutualistic interaction between the cotton bollworm and HaDV2 (Xu et al., 2014).

Here, we further explore the interaction between H. armigera, HaDV2, and Bt-cotton, to establish its relevance to field populations and to test the hypothesis that the widespread adoption of Bt-cotton in China has selected for cotton bollworm carrying the densovirus. Laboratory experiments show that HaDV2 infection in both Cry1Ac-resistant and Cry1Ac-susceptible strains of cotton bollworm enhances larval tolerance to Bt and overall fitness. Field experiments indicate that H. armigera populations infected with HaDV2 also have a higher tolerance to Bt-cotton relative to those not infected with HaDV2. Moreover, field monitoring over a 10-year period indicates that the frequency of HaDV2-infected cotton bollworm is significantly higher in regions planted with Bt-cotton than in those areas where Bt-cotton is not grown, and that in Bt-cotton-growing areas, the prevalence of HaDV2 infection increases with time since Bt-cotton adoption and with the proportion of cotton that is grown which is transgenic. Further, we found that increased Bt tolerance is associated with activated immune pathways to HaDV2 infection. These results indicate that increased HaDV2 infection in cotton bollworm is correlated with the wide-scale adoption of Bt-cotton in China. Our data are consistent with the notion that exposure to Bt-crops selects for beneficial interactions between the target pest and a microorganism that enhances their fitness in response to Bt exposure under field conditions.

Commercialization of transgenic Bt-crops has brought some significant benefits to farmers (Carrière et al., 2003; Cattaneo et al., 2006; Lu et al., 2012; Shelton et al., 2002; Wu et al., 2008). These Bt-plants successfully control target insect pests and have resulted in the reduced use of insecticides in the field (Bravo et al., 2011; Lu et al., 2012; Wu et al., 2008). Here, we show that HaDV2 infection enhances tolerance to Cry1Ac in both susceptible and resistant strains of H. armigera. We also show that there appears to have been a strong positive selection for HaDV2-infected H. armigera in populations exposed to Cry1Ac-cotton in the field, especially (but not exclusively) when this exposure has been for prolonged periods of time. Intensive monitoring in two provinces where Bt-cotton has been grown over a number of years showed a strong temporal increase in HaDV2 infection levels, from around 40% in 2007 to nearly 100% in 2016, with high levels of HaDV2 infection being associated with higher larval growth rates on Bt-cotton in the field. Indeed, at locations where Bt-cotton is not grown, H. armigera shows much lower rates of HaDV2 infection (12–44% vs. 81–90%), consistent with weaker selection for HaDV2-infected H. armigera in the absence of Bt-crops. Moreover, the prevalence of HaDV2 infection increased with the number of years since Bt-cotton was first introduced to a province and with the proportion of cotton grown that is transgenic, consistent with Bt-cotton being a key selection pressure. There were no significant relationships between HaDV2 prevalence and the proportional cover of any other main crops grown locally (cotton, rice, corn, wheat, beans, tubers, oil-producing crops, and vegetables) or with any of the environmental variables tested (rainfall, temperature, and altitude). Thus, our data suggest that the Cry1Ac-toxin selected for HaDV2-infected H. armigera following exposure to Bt-cotton and our RNA-Seq analysis suggests that the increased tolerance of HaDV2-infected insects is caused, in part at least, by the activation of a series of immune pathways and pathways that enhance development.

The emergence and rapid spread of a symbiont through a host population have been observed previously (Himler et al., 2011; Turelli and Hoffmann, 1991; Weeks et al., 2007). For example, the bacterial symbiont, Rickettsia sp. nr. bellii, swept through a population of invasive sweet potato whitefly, Bemisia tabaci, in just 6 years (Himler et al., 2011). Although we do not know when HaDV2 first infected H. armigera in China, in two areas where Bt-cotton is grown extensively, and where nearly 100% of all insects are currently infected with HaDV2, the prevalence of the densovirus was as low as 40% in 2007, when the earliest samples were collected (Figure 5). Moreover, extrapolation of the observed temporal trends (Figure 5A,B and Figure 7) would suggest that emergence of HaDV2 may have coincided with the introduction of Bt-cotton in China two decades ago. This apparently rapid spread is perhaps not surprising given the strong fitness benefits of carrying the densovirus (amounting to a ~40% increase in H. armigera R₀) (Figure 3). Consistent with this, based on these estimated increases in R₀, a simple population growth model (Himler et al., 2011) suggests that HaDV2 would go to near fixation in 20–30 generations in Bt-cotton-growing areas (Figure 3—figure supplement 1).

Rather than being a recent introduction to H. armigera in China, it is possible that the association between the HaDV2 and its host is more ancient, but that the increase in HaDV2 prevalence in recent years is because it has only recently evolved from a parasitic or commensal relationship into one that is more mutualistic (Weeks et al., 2007), perhaps under direct or indirect selection from Bt-cotton. Indeed, most of the densoviruses that have been studied thus far have tended to be at the parasitic end of the symbiotic spectrum and some of these have even been promoted as potential biological pesticides (El-Far et al., 2012). However, the observation that densoviruses tend to be parasitic in nature is likely biased by the fact that viruses with notable pathological effects on their hosts are more likely to be reported (Roossinck, 2011; Roossinck, 2015; Webster et al., 2015; Xu et al., 2020). With the advent of modern molecular methods, such as suppression subtractive hybridization and RNA-seq, it has become apparent that there are many ‘good viruses’ that have failed to be discovered due to their relatively benign, or even beneficial, effects on their hosts (Roossinck, 2011; Roossinck, 2015; Webster et al., 2015; Xu et al., 2020), including HaDV2 (Xu et al., 2014). Although there is an increasing number of studies suggesting that symbiotic microbes can impact an insect host’s ability to combat pathogens and parasites (Graham et al., 2012; Hedges et al., 2008; Oliver et al., 2003; Xu et al., 2020), as far as we are aware, this is the first field study to provide support for the notion that a symbiotic virus could protect a crop pest from Bt toxin.

Regardless of whether or not larvae are exposed to Cry1Ac, HaDV2 appears to enhance the fitness of both Bt-resistant and Bt-susceptible H. armigera (Xu et al., 2012; Figure 3). Theoretically, the infection rate should naturally increase in the H. armigera population both in Bt-cotton and non-Bt-cotton regions because of efficient transmission with horizontal and vertical modes and the mutualistic relationship between HaDV2 and its host, especially increasing its reproduction (Xu et al., 2014). We monitored HaDV2 infection rates at 38 locations across 16 provinces during the period 2014–2016. Indeed, our data showed that HaDV2 infection levels increased both in Bt-cotton and non-Bt-cotton areas. However, with the exception of samples from Changde (Hunan Province), the other 37 locations showed consistent trends that HaDV2 infection rates were significantly higher in Bt-cotton areas than in non-Bt-cotton areas. Previously, we also quantified the infection rate of HaDV2 in samples collected from Changde in 3 years (Xu et al., 2012). The final infection rate in samples from Changde in the 4 years was 45%, which is higher than the infection rate of samples from non-Bt-cotton planting regions (26%). Changde is located at the southernmost point of Bt-cotton-growing range, where the planted cotton included both non-Bt- and Bt-cotton and the fluctuation of HaDV2 infection rate in Changde might be due to both the lower pressure of Bt-cotton and the migration of H. armigera between Bt- and non-Bt-cotton-growing areas. Previously, we showed that there was evidence for a significant decline in HaDV2 prevalence from 2008 to 2012, especially in the migrating moths collected in Yantai (Beihuang Island), which is located in Bohai Sea between Bt- and non-Bt-cotton areas and where there were no resident populations of H. armigera (Feng et al., 2004; Feng et al., 2005; Feng et al., 2009; Xu et al., 2014). If we exclude the data from Yantai, however, there was no obvious temporal trend in HaDV2 prevalence between 2008 and 2012 (85%, 77%, 83%, 73%, and 75%, respectively). Other factors might also result in variation in HaDV2 infection rates. For example, there are four generations of H. armigera per year in northern China and the fourth generation overwinters as diapausing pupae, and if H. armigera grows too fast due to HaDV2 infection, it will overwinter at an inappropriate state which may lead to higher mortality of HaDV2-positive individuals (Mu et al., 1995; Zhang and Li, 2001).

The climate and crop structures of the locations where our samples were collected have been relatively constant over the last few decades and data analyses supported the notion that HaDV2 prevalence was not correlated with environmental variables or the areas of any other crops being grown. Using the PCR method, it is not possible to discount the possibility that HaDV2 was widespread in field populations of H. armigera as very low-titer covert infections, and that the higher incidence of virus in areas of Bt-cotton was due to the stress associated with larvae feeding on plants expressing Bt toxin. To test the hypothesis, using larvae with low dose of HaDV2 from single pair, we quantified the copy numbers of HaDV2 within the cotton bollworms feeding on artificial diets with or without Bt toxin. Interestingly, the results showed that feeding on the Bt toxin resulted in lower titers of HaDV2, which might be due to the Bt suppressing the growth of H. armigera larvae. These results suggest that the higher infection rate of HaDV2 might be due to lower mortality by higher Bt tolerance and the increased reproduction of the cotton bollworms with HaDV2 than those without. These findings support the notion that Bt-cotton has played an important role in increasing HaDV2 infection rates in field populations of H. armigera, and suggest the possibility that HaDV2 may threaten the future control of H. armigera by Bt.

We found some evidence to support this hypothesis from LC₅₀-bioassays using synthetic diets containing Cry1Ac protoxin, with the benefits of hosting HaDV2 being greatest for the most Bt-resistant strains (Figure 1), suggesting synergistic effects of Bt-resistance and HaDV2 infection. However, laboratory trials using non-Bt- and Bt-cotton plants suggested that the relative fitness benefits of harboring HaDV2 were independent of exposure to Bt-cotton and of Bt-resistance levels (at least in terms of larval growth rates) (Supplementary file 1f). This is at odds with the observation that HaDV2 infection levels in the field are higher when populations are exposed to Bt-cotton for prolonged periods, and may indicate that LC50-bioassays are a more reliable measure of relative fitness than larval growth on Bt-cotton plants in the laboratory. Alternatively, it is possible that there are added benefits to HaDV2 infection for H. armigera exposed to Bt-cotton in the field that our laboratory bioassays cannot fully capture, such as the enhanced capacity to resist predators and parasitoids, or reduced exposure to chemical pesticides. In this latter regard, there may be parallels here with the increase in outbreaks of mirid bugs (Heteroptera: Miridae) following the wide-scale adoption of Bt-cotton in China and the subsequent reduction in pesticide application rate on crops (Lu et al., 2010; Zhang et al., 2018).

The evolution of tolerance to Bt-toxins could endanger the utility of this GM technology (Gahan et al., 2001, Gahan et al., 2007; Gahan et al., 2010; Heckel, 2012; Tabashnik, 2015; Tabashnik et al., 2008; Tabashnik et al., 2013). However, as observed here, resistance to Cry toxins is often associated with significant fitness costs that are likely to delay the establishment of Bt-resistant populations under field conditions (Gassmann et al., 2009; Horikoshi et al., 2016; Liu et al., 2017; Raymond et al., 2005; Santos-Amaya et al., 2017). Interestingly, we show here that Bt-resistant strains with different resistance mechanisms, such as mutations affecting ABCC2 (strain LF60) or cadherin (strain 96CAD) (Liang et al., 2008; Xiao et al., 2014), incurred significantly reduced fitness costs when they were infected with HaDV2. The reduction of fitness costs by HaDV2 infection, especially increasing the reproduction of their hosts, could speed up the establishment of resistant populations. The Bt resistance induced by ABCC2 and cadherin is due to genomic heritable variation (Liang et al., 2008; Xiao et al., 2014), while the Bt tolerance induced by HaDV2 appears to be due to the regulation of immune and other pathways. ABCC2 and cadherin can directly mediate high levels of resistance to Bt toxin in H. armigera, but with significant fitness costs (e.g., lower reproduction) (Cao et al., 2014); however, HaDV2 infection could enhance host reproduction and tolerance to Cry1Ac in both susceptible and resistant strains of H. armigera, increasing larval survival rate. Susceptible strains infected with HaDV2 showed only 1.5 times greater tolerance to Cry1Ac toxin. In the case of the Cry1Ac-resistant strains, infection with HaDV2 again showed a significant increase in their resistance relative to the corresponding strains without HaDV2 infection, ranging between 30% and 130% enhanced resistance. Thus, a potential implication of these results is that the increased prevalence of HaDV2 infections under field conditions could impact the evolution of H. armigera populations resistant to Bt-cotton, which might decrease the control efficacy by Bt-cotton expressing Cry1Ac toxin and promote the development of novel strategies for H. armigera control.

The temporal increase in HaDV2 infection levels observed in Xiajin and Anci, two cotton-growing counties >300 km apart in north-east China, was associated with parallel temporal increases in relative growth rates (RADR) (Figure 5C,D). In a previous study of these two populations, the temporal increase in RADR between 2002 and 2014 was ascribed to evolved genetic resistance to Cry1Ac in H. armigera, despite the failure to identify the key genetic locus conferring resistance in these populations (An et al., 2015). The relatively strong correlation between the annual prevalence of HaDV2 in H. armigera and their mean annual RADR in each of these populations (Figure 5E,F) suggests that the putative increase in genetic resistance over this period may, at least in part, be due to the phenotypic expression of HaDV2 infection. The fact that the densovirus is mostly located in the insect’s fat body suggests that it may play a role in H. armigera development (Xu et al., 2014). Lepidopteran larvae tend to become more tolerant to pathogens and toxins as they age and grow, a phenomenon known as developmental resistance (Engelhard and Volkman, 1995). HaDV2-infected H. armigera not only accumulate more fat body than non-infected individuals, but they also grow faster and larger (Xu et al., 2012). Therefore, developmental resistance could provide a potential mechanism to explain the enhanced tolerance of densovirus-infected insects. HaDV2-infected larvae have more fat body than non-infected insects (Xu et al., 2014), and the fat body plays an important role in insect immunity as it is a major site for the production and secretion of antimicrobial peptides (Hoffmann, 2003; Lemaitre and Hoffmann, 2007), so immunological resistance is another potential mechanism by which HaDV2 may enhance resistance to both baculovirus and Cry1Ac.

To explore further the molecular interaction between the HaDV2 and its host, we performed an RNA-seq experiment using H. armigera larvae. Due to the observed effects of HaDV2 on its host, we focused on molecular pathways related to immunity and development (Jindra et al., 2013; Kingsolver and Hardy, 2012; Kingsolver et al., 2013; Lin and Smagghe, 2019; Shields, 2017). In H. armigera larvae at 24 and 48 hr, the Jak-STAT pathway was significantly enriched by upregulation in HaDV2-positive individuals. In fruit flies and mosquitoes, this pathway controls the expression of genes in response to infection with a range of viruses and bacteria, including Drosophila C virus, dengue virus, West Nile virus, Escherichia coli, and Micrococcus luteus (Barillas-Mury et al., 1999; Marques and Imler, 2016). The Jak-STAT pathway was not significantly enriched in larvae at 72 hr, however, pathways related to other antimicrobials were, for example, the lysosome and MAPK signaling pathways (Takano et al., 2019; Watts, 2012). Previously, we found that HaDV2-positive individuals developed faster than non-infected insects (Xu et al., 2014). Interestingly, the transcriptome data suggest that several pathways related to development are also significantly enriched, including the insect hormone synthesis, insulin, mTOR, and AMPK signaling pathways (Bland et al., 2010; Jindra et al., 2013; Lin and Smagghe, 2019; Saxton and Sabatini, 2017). We did not observe the differential expression of genes playing pivotal roles in Bt resistance of H. armigera, for example, cadherin, APN, and ALP (Chen et al., 2015; Gahan et al., 2001; Zhang et al., 2009), suggesting that HaDV2 may not enhance resistance to Bt using traditional molecular resistance pathways, though we did observe that the ABC transporter pathways, which has been reported to be associated with Bt-resistance (Bretschneider et al., 2016; Dermauw and Van Leeuwen, 2014), were significantly upregulated at 48 hr post challenge.

Generally, the mode of action of Bt toxins agrees that Bt protoxins are first converted to activated toxins by midgut proteases, then the toxins bind to the midgut receptors, for example, CAD, APN, and ALP, finally leading to insect death (Xiao and Wu, 2019). A change in any of these steps will lead to Bt resistance, such as mutations in genes for toxin activation, toxin-binding, and changes in insect immunity (Xiao et al., 2014; Zhang et al., 2009). To understand the responses of H. armigera to Bt toxins with or without HaDV2, we conducted RNA-seq after larvae had been exposed to Cry1Ac. In insects lacking HaDV2, Cry1Ac increased the activation of Bt protoxins by increasing the expression level of proteases, such as trypsin. In a defensive response to Cry1Ac, receptor genes of H. armigera, such as APN and ALP, were significantly downregulated to reduce the toxin-binding capacity. Carboxylesterase genes and most of ABC transporters genes were significantly upregulated to improve insect immunity, while other pathways related to antimicrobials, for example, MAPK signaling pathways, were significantly downregulated, reducing resistance to Bt toxin (Yang et al., 2020). In contrast, when H. armigera larvae were infected with HaDV2, these genes and pathways were generally not significantly enriched after Cry1Ac exposure: one trypsin gene was downregulated and some genes involved in drug metabolism were upregulated to increase the metabolism of xenobiotics, improving Bt tolerance. Through RNA-seq analysis, we did not find significant upregulation or downregulation of genes known to be related to typical Bt tolerance. Thus, it is difficult to perform further experiments, e.g. CRISPR. In spite of this, our data still support the notion that HaDV2 increased the Bt tolerance level of H. armigera via related immune mechanisms.

Whilst data from the field are inevitably correlational, when combined with the laboratory experiments, we feel that the most parsimonious explanation for the observations reported here is that HaDV2 directly or indirectly enhances tolerance of H. armigera to Bt in the field, probably via increasing larval growth rates and via priming of immune defenses. However, demonstrating causation conclusively in the field will prove difficult. One possibility would be to plant some Bt-cotton in areas that are currently Bt-free and to compare larval growth patterns and HaDV2 infection levels in neighboring Bt and non-Bt areas, mirroring the ‘natural’ field experiments we report here. As was found with the rapid spread of mutualistic Rickettsia in whiteflies (1–97% prevalence in just 6 years or around 80 field generations; Himler et al., 2011), we envisage that HaDV2 infection levels will eventually go to fixation (or close to it) in both Bt and non-Bt areas, due to the growth benefits associated harboring this mutualist, but that the time to get to this point will take longer in non-Bt areas, due to the lower fitness benefits. Likewise, we envisage that the spread of HaDV2 infection would slow down in Bt areas if Bt-cotton were to be replaced by conventional cotton or a GM variety that did not rely on Bt toxins. Another possibility would be to conduct laboratory evolution experiments using populations of H. armigera infected or not with HaDV2 growing on either Bt- or non-Bt-cotton over several generations. We envisage that HaDV2 infection levels would increase over time on both Bt- and non-Bt-cotton due to the fitness benefits of HaDV2 infection, but that the rate of spread would be faster on Bt-cotton. We also envisage that the fitness of H. armigera (R₀ and cumulative population size after several generations) would be determined by the interaction between HaDV2 infected-status and cotton plant Bt-status. A similar experiment to this was conducted by Himler et al., 2011 for laboratory cages of whitefly populations infected with Rickettsia, who found that over five generations, the prevalence of Rickettsia increased from 14% in the parental generation to 40–70% on three different host plant species. Repeating such experiments with H. armigera is feasible but logistically much more difficult due to their much larger size. In summary, here, we report the rapid increase in the prevalence of a mutualistic virus in a major crop pest and show that its spread is greatest for field populations exposed to Bt-cotton for prolonged periods. Our findings support a novel mechanism by which insects may cope with changes in engineered host plant defenses, namely via infection with a mutualistic virus that enhances their tolerance to the toxins expressed by Bt-cotton crops. Our study has significant implications for understanding the co-evolution of host-pathogen-symbiont interactions. Although we show that HaDV2 increases the tolerance of its host to Bt, it is not feasible to manipulate HaDV2 infection status in the field in order to enhance the capacity of Bt to manage H. armigera because of the lack of suitable antivirals (against HaDV2) and because of the efficient vertical and horizontal transmission of this virius in the field.

RNA-seq data have been deposited in NCBI, see Supplementary file 8. All data generated or analysed during this study are included in the manuscript and supplementary file.

1. 1. Xiao YT
   2. Li WJ
   3. Yang XM
   4. Xu PJ
   5. Jin MH
   6. Yuan H
   7. Zheng WG
   8. Soberón M
   9. Bravo A
   10. Wilson K
   11. Wu KM

   (2021) NCBI BioProject

   ID PRJNA638220. Rapid spread of a symbiotic virus in a major crop pest following wide-scale adoption of Bt-cotton in China.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA638220
2. 1. Xiao YT
   2. Li WJ
   3. Yang XM
   4. Xu PJ
   5. Jin MH
   6. Yuan H
   7. Zheng WG
   8. Soberón M
   9. Bravo A
   10. Wilson K
   11. Wu KM

   (2021) NCBI BioProject

   ID PRJNA638972. Rapid spread of a symbiotic virus in a major crop pest following wide-scale adoption of Bt-cotton in China.

   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA638972

1. 1. An J
   2. Gao Y
   3. Lei C
   4. Gould F
   5. Wu K

   (2015) Monitoring cotton bollworm resistance to Cry1Ac in two counties of northern China during 2009-2013

   Pest Management Science 71:377–382.

   https://doi.org/10.1002/ps.3807

   • PubMed
   • Google Scholar
2. 1. Barillas-Mury C
   2. Han YS
   3. Seeley D
   4. Kafatos FC

   (1999) Anopheles gambiae Ag-STAT, a new insect member of the STAT family, is activated in response to bacterial infection

   The EMBO Journal 18:959–967.

   https://doi.org/10.1093/emboj/18.4.959

   • PubMed
   • Google Scholar
3. 1. Bland ML
   2. Lee RJ
   3. Magallanes JM
   4. Foskett JK
   5. Birnbaum MJ

   (2010) AMPK supports growth in Drosophila by regulating muscle activity and nutrient uptake in the gut

   Developmental Biology 344:293–303.

   https://doi.org/10.1016/j.ydbio.2010.05.010

   • PubMed
   • Google Scholar
4. 1. Bravo A
   2. Likitvivatanavong S
   3. Gill SS
   4. Soberón M

   (2011) Bacillus thuringiensis: a story of a successful bioinsecticide

   Insect Biochemistry and Molecular Biology 41:423–431.

   https://doi.org/10.1016/j.ibmb.2011.02.006

   • PubMed
   • Google Scholar
5. 1. Bretschneider A
   2. Heckel DG
   3. Vogel H

   (2016) Know your ABCs: characterization and gene expression dynamics of ABC transporters in the polyphagous herbivore Helicoverpa armigera

   Insect Biochemistry and Molecular Biology 72:1–9.

   https://doi.org/10.1016/j.ibmb.2016.03.001

   • PubMed
   • Google Scholar
6. 1. Cao G
   2. Feng H
   3. Guo F
   4. Wu K
   5. Li X
   6. Liang G
   7. Desneux N

   (2014) Quantitative analysis of fitness costs associated with the development of resistance to the bt toxin Cry1Ac in Helicoverpa armigera

   Scientific Reports 4:5629.

   https://doi.org/10.1038/srep05629

   • PubMed
   • Google Scholar
7. 1. Carrière Y
   2. Ellers-Kirk C
   3. Sisterson M
   4. Antilla L
   5. Whitlow M
   6. Dennehy TJ
   7. Tabashnik BE

   (2003) Long-term regional suppression of pink bollworm by Bacillus thuringiensis cotton

   PNAS 100:1519–1523.

   https://doi.org/10.1073/pnas.0436708100

   • PubMed
   • Google Scholar
8. 1. Cattaneo MG
   2. Yafuso C
   3. Schmidt C
   4. Huang CY
   5. Rahman M
   6. Olson C
   7. Ellers-Kirk C
   8. Orr BJ
   9. Marsh SE
   10. Antilla L
   11. Dutilleul P
   12. Carrière Y

   (2006) Farm-scale evaluation of the impacts of transgenic cotton on biodiversity, pesticide use, and yield

   PNAS 103:7571–7576.

   https://doi.org/10.1073/pnas.0508312103

   • PubMed
   • Google Scholar
9. 1. Chen W
   2. Liu C
   3. Xiao Y
   4. Zhang D
   5. Zhang Y
   6. Li X
   7. Tabashnik BE
   8. Wu K

   (2015) A toxin-binding alkaline phosphatase fragment synergizes bt toxin Cry1Ac against susceptible and resistant Helicoverpa armigera

   PLOS ONE 10:e0126288.

   https://doi.org/10.1371/journal.pone.0126288

   • PubMed
   • Google Scholar
10. 1. Dermauw W
    2. Van Leeuwen T

    (2014) The ABC gene family in arthropods: comparative genomics and role in insecticide transport and resistance

    Insect Biochemistry and Molecular Biology 45:89–110.

    https://doi.org/10.1016/j.ibmb.2013.11.001

    • PubMed
    • Google Scholar
11. 1. El-Far M
    2. Szelei J
    3. Yu Q
    4. Fédière G
    5. Bergoin M
    6. Tijssen P

    (2012) Organization of the ambisense genome of the Helicoverpa armigera Densovirus

    Journal of Virology 86:7024.

    https://doi.org/10.1128/JVI.00865-12

    • PubMed
    • Google Scholar
12. 1. Engelhard EK
    2. Volkman LE

    (1995) Developmental resistance in fourth instar Trichoplusia ni orally inoculated with Autographa californica M nuclear polyhedrosis virus

    Virology 209:384–389.

    https://doi.org/10.1006/viro.1995.1270

    • PubMed
    • Google Scholar
13. 1. Feng HQ
    2. Wu KM
    3. Cheng DF
    4. Guo YY

    (2004) Northward migration of Helicoverpa armigera (Lepidoptera: noctuidae) and other moths in early summer observed with radar in northern China

    Journal of Economic Entomology 97:1874–1883.

    https://doi.org/10.1093/jee/97.6.1874

    • PubMed
    • Google Scholar
14. 1. Feng HQ
    2. Wu KM
    3. Ni YX
    4. Cheng DF
    5. Guo YY

    (2005) Return migration of Helicoverpa armigera (Lepidoptera: noctuidae) during autumn in northern china

    Bulletin of Entomological Research 95:361–370.

    https://doi.org/10.1079/BER2005367

    • PubMed
    • Google Scholar
15. 1. Feng H
    2. Wu X
    3. Wu B
    4. Wu K

    (2009) Seasonal migration of Helicoverpa armigera (Lepidoptera: noctuidae) over the bohai sea

    Journal of Economic Entomology 102:95–104.

    https://doi.org/10.1603/029.102.0114

    • PubMed
    • Google Scholar
16. 1. Gahan LJ
    2. Gould F
    3. Heckel DG

    (2001) Identification of a gene associated with bt resistance in Heliothis virescens

    Science 293:857–860.

    https://doi.org/10.1126/science.1060949

    • PubMed
    • Google Scholar
17. 1. Gahan LJ
    2. Gould F
    3. López JD
    4. Micinski S
    5. Heckel DG

    (2007) A polymerase chain reaction screen of field populations of Heliothis virescens for a retrotransposon insertion conferring resistance to Bacillus thuringiensis toxin

    Journal of Economic Entomology 100:187–194.

    https://doi.org/10.1603/0022-0493(2007)100[187:APCRSO]2.0.CO;2

    • PubMed
    • Google Scholar
18. 1. Gahan LJ
    2. Pauchet Y
    3. Vogel H
    4. Heckel DG

    (2010) An ABC transporter mutation is correlated with insect resistance to Bacillus thuringiensis Cry1Ac toxin

    PLOS Genetics 6:e1001248.

    https://doi.org/10.1371/journal.pgen.1001248

    • PubMed
    • Google Scholar
19. 1. Gassmann AJ
    2. Carrière Y
    3. Tabashnik BE

    (2009) Fitness costs of insect resistance to Bacillus thuringiensis

    Annual Review of Rntomology 54:147–163.

    https://doi.org/10.1146/annurev.ento.54.110807.090518

    • Google Scholar
20. 1. Grabherr MG
    2. Haas BJ
    3. Yassour M
    4. Levin JZ
    5. Thompson DA
    6. Amit I
    7. Adiconis X
    8. Fan L
    9. Raychowdhury R
    10. Zeng Q
    11. Chen Z
    12. Mauceli E
    13. Hacohen N
    14. Gnirke A
    15. Rhind N
    16. di Palma F
    17. Birren BW
    18. Nusbaum C
    19. Lindblad-Toh K
    20. Friedman N
    21. Regev A

    (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome

    Nature Biotechnology 29:644–652.

    https://doi.org/10.1038/nbt.1883

    • PubMed
    • Google Scholar
21. 1. Graham RI
    2. Grzywacz D
    3. Mushobozi WL
    4. Wilson K

    (2012) Wolbachia in a major African crop pest increases susceptibility to viral disease rather than protects

    Ecology Letters 15:993–1000.

    https://doi.org/10.1111/j.1461-0248.2012.01820.x

    • Google Scholar
22. 1. Heckel DG

    (2012) Learning the ABCs of bt: abc transporters and insect resistance to Bacillus thuringiensis provide clues to a crucial step in toxin mode of action

    Pesticide Biochemistry and Physiology 104:103–110.

    https://doi.org/10.1016/j.pestbp.2012.05.007

    • Google Scholar
23. 1. Hedges LM
    2. Brownlie JC
    3. O'Neill SL
    4. Johnson KN

    (2008) Wolbachia and virus protection in insects

    Science 322:702.

    https://doi.org/10.1126/science.1162418

    • PubMed
    • Google Scholar
24. 1. Himler AG
    2. Adachi-Hagimori T
    3. Bergen JE
    4. Kozuch A
    5. Kelly SE
    6. Tabashnik BE
    7. Chiel E
    8. Duckworth VE
    9. Dennehy TJ
    10. Zchori-Fein E
    11. Hunter MS

    (2011) Rapid spread of a bacterial symbiont in an invasive whitefly is driven by fitness benefits and female Bias

    Science 332:254–256.

    https://doi.org/10.1126/science.1199410

    • PubMed
    • Google Scholar
25. 1. Hoffmann JA

    (2003) The immune response of Drosophila

    Nature 426:33–38.

    https://doi.org/10.1038/nature02021

    • PubMed
    • Google Scholar
26. 1. Horikoshi RJ
    2. Bernardi O
    3. Bernardi D
    4. Okuma DM
    5. Farias JR
    6. Miraldo LL
    7. Amaral FS
    8. Omoto C

    (2016) Near-Isogenic Cry1F-Resistant strain of Spodoptera frugiperda (Lepidoptera: noctuidae) to investigate fitness cost associated with resistance in Brazil

    Journal of Economic Entomology 109:854–859.

    https://doi.org/10.1093/jee/tov387

    • PubMed
    • Google Scholar
27. 1. Hutchison WD
    2. Burkness EC
    3. Mitchell PD
    4. Moon RD
    5. Leslie TW
    6. Fleischer SJ
    7. Abrahamson M
    8. Hamilton KL
    9. Steffey KL
    10. Gray ME
    11. Hellmich RL
    12. Kaster LV
    13. Hunt TE
    14. Wright RJ
    15. Pecinovsky K
    16. Rabaey TL
    17. Flood BR
    18. Raun ES

    (2010) Areawide suppression of european corn borer with bt maize reaps savings to non-Bt maize growers

    Science 330:222–225.

    https://doi.org/10.1126/science.1190242

    • PubMed
    • Google Scholar
28. 1. Jindra M
    2. Palli SR
    3. Riddiford LM

    (2013) The juvenile hormone signaling pathway in insect development

    Annual Review of Entomology 58:181–204.

    https://doi.org/10.1146/annurev-ento-120811-153700

    • PubMed
    • Google Scholar
29. 1. Kingsolver MB
    2. Huang Z
    3. Hardy RW

    (2013) Insect antiviral innate immunity: pathways, effectors, and connections

    Journal of Molecular Biology 425:4921–4936.

    https://doi.org/10.1016/j.jmb.2013.10.006

    • PubMed
    • Google Scholar
30. 1. Kingsolver MB
    2. Hardy RW

    (2012) Making connections in insect innate immunity

    PNAS 109:18639–18640.

    https://doi.org/10.1073/pnas.1216736109

    • PubMed
    • Google Scholar
31. 1. Klopfenstein DV
    2. Zhang L
    3. Pedersen BS
    4. Ramírez F
    5. Warwick Vesztrocy A
    6. Naldi A
    7. Mungall CJ
    8. Yunes JM
    9. Botvinnik O
    10. Weigel M
    11. Dampier W
    12. Dessimoz C
    13. Flick P
    14. Tang H

    (2018) GOATOOLS: a Python library for gene ontology analyses

    Scientific Reports 8:10872.

    https://doi.org/10.1038/s41598-018-28948-z

    • PubMed
    • Google Scholar
32. 1. Langmead B
    2. Trapnell C
    3. Pop M
    4. Salzberg SL

    (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

    Genome Biology 10:R25.

    https://doi.org/10.1186/gb-2009-10-3-r25

    • PubMed
    • Google Scholar
33. 1. Lemaitre B
    2. Hoffmann J

    (2007) The host defense of Drosophila melanogaster

    Annual Review of Immunology 25:697–743.

    https://doi.org/10.1146/annurev.immunol.25.022106.141615

    • PubMed
    • Google Scholar
34. 1. Li B
    2. Dewey CN

    (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

    BMC Bioinformatics 12:323.

    https://doi.org/10.1186/1471-2105-12-323

    • PubMed
    • Google Scholar
35. 1. Liang GM
    2. Wu KM
    3. Yu HK
    4. Li KK
    5. Feng X
    6. Guo YY

    (2008) Changes of inheritance mode and fitness in Helicoverpa armigera (Hübner) (Lepidoptera: noctuidae) along with its resistance evolution to Cry1Ac toxin

    Journal of Invertebrate Pathology 97:142–149.

    https://doi.org/10.1016/j.jip.2007.09.007

    • PubMed
    • Google Scholar
36. 1. Lin X
    2. Smagghe G

    (2019) Roles of the insulin signaling pathway in insect development and organ growth

    Peptides 122:169923.

    https://doi.org/10.1016/j.peptides.2018.02.001

    • PubMed
    • Google Scholar
37. 1. Liu L
    2. Gao M
    3. Yang S
    4. Liu S
    5. Wu Y
    6. Carrière Y
    7. Yang Y

    (2017) Resistance to Bacillus thuringiensis toxin Cry2Ab and survival on single-toxin and pyramided cotton in cotton bollworm from China

    Evolutionary Applications 10:170–179.

    https://doi.org/10.1111/eva.12438

    • PubMed
    • Google Scholar
38. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
39. 1. Lu Y
    2. Wu K
    3. Jiang Y
    4. Xia B
    5. Li P
    6. Feng H
    7. Wyckhuys KA
    8. Guo Y

    (2010) Mirid bug outbreaks in multiple crops correlated with wide-scale adoption of bt cotton in China

    Science 328:1151–1154.

    https://doi.org/10.1126/science.1187881

    • PubMed
    • Google Scholar
40. 1. Lu Y
    2. Wu K
    3. Jiang Y
    4. Guo Y
    5. Desneux N

    (2012) Widespread adoption of bt cotton and insecticide decrease promotes biocontrol services

    Nature 487:362–365.

    https://doi.org/10.1038/nature11153

    • PubMed
    • Google Scholar
41. 1. Marques JT
    2. Imler JL

    (2016) The diversity of insect antiviral immunity: insights from viruses

    Current Opinion in Microbiology 32:71–76.

    https://doi.org/10.1016/j.mib.2016.05.002

    • PubMed
    • Google Scholar
42. 1. Mu J
    2. Cui L
    3. Chen T

    (1995)

    Testing biological constants of generation of the cotton bollworm, Heliothis armigera hubner, by the method of “thermal sums”

    Journal of Shandong Agricultural University 26:280–284.

    • Google Scholar
43. 1. Oliver KM
    2. Russell JA
    3. Moran NA
    4. Hunter MS

    (2003) Facultative bacterial symbionts in aphids confer resistance to parasitic wasps

    PNAS 100:1803–1807.

    https://doi.org/10.1073/pnas.0335320100

    • PubMed
    • Google Scholar
44. Software

    1. R Development Core Team

    (2017) R: A language and environment for statistical computing, version 2.6.2

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org/
45. 1. Raymond B
    2. Sayyed AH
    3. Wright DJ

    (2005) Genes and environment interact to determine the fitness costs of resistance to Bacillus thuringiensis

    Proceedings of the Royal Society B: Biological Sciences 272:1519–1524.

    https://doi.org/10.1098/rspb.2005.3103

    • PubMed
    • Google Scholar
46. 1. Roossinck MJ

    (2011) The good viruses: viral mutualistic symbioses

    Nature Reviews Microbiology 9:99–108.

    https://doi.org/10.1038/nrmicro2491

    • PubMed
    • Google Scholar
47. 1. Roossinck MJ

    (2015) Move over, Bacteria! viruses make their mark as mutualistic microbial symbionts

    Journal of Virology 89:6532–6535.

    https://doi.org/10.1128/JVI.02974-14

    • PubMed
    • Google Scholar
48. 1. Santos-Amaya OF
    2. Tavares CS
    3. Rodrigues JV
    4. Campos SO
    5. Guedes RN
    6. Alves AP
    7. Pereira EJ

    (2017) Fitness costs and stability of Cry1Fa resistance in brazilian populations of Spodoptera frugiperda

    Pest Management Science 73:35–43.

    https://doi.org/10.1002/ps.4312

    • PubMed
    • Google Scholar
49. 1. Saxton RA
    2. Sabatini DM

    (2017) mTOR signaling in growth, metabolism, and disease

    Cell 168:960–976.

    https://doi.org/10.1016/j.cell.2017.02.004

    • PubMed
    • Google Scholar
50. 1. Shelton AM
    2. Zhao J-Z
    3. Roush RT

    (2002) Economic, ecological, food safety, and social consequences of the deployment of bt transgenic plants

    Annual Review of Entomology 47:845–881.

    https://doi.org/10.1146/annurev.ento.47.091201.145309

    • Google Scholar
51. Book

    1. Shields VDC

    (2017)

    Insect physiology and ecology

    In: Rosales C, editors. Cellular and Molecular Mechanisms of Insect Immunity. London: IntechOpen. pp. 179–212.

    • Google Scholar
52. 1. Swiatkiewicz S
    2. Swiatkiewicz M
    3. Arczewska-Wlosek A
    4. Jozefiak D

    (2014) Genetically modified feeds and their effect on the metabolic parameters of food-producing animals: a review of recent studies

    Animal Feed Science and Technology 198:1–19.

    https://doi.org/10.1016/j.anifeedsci.2014.09.009

    • Google Scholar
53. 1. Tabashnik BE
    2. Dennehy TJ
    3. Sims MA
    4. Larkin K
    5. Head GP
    6. Moar WJ
    7. Carrière Y

    (2002) Control of resistant pink bollworm (Pectinophora gossypiella) by transgenic cotton that produces Bacillus thuringiensis toxin Cry2Ab

    Applied and Environmental Microbiology 68:3790–3794.

    https://doi.org/10.1128/AEM.68.8.3790-3794.2002

    • PubMed
    • Google Scholar
54. 1. Tabashnik BE
    2. Gassmann AJ
    3. Crowder DW
    4. Carriére Y

    (2008) Insect resistance to bt crops: evidence versus theory

    Nature Biotechnology 26:199–202.

    https://doi.org/10.1038/nbt1382

    • Google Scholar
55. 1. Tabashnik BE
    2. Sisterson MS
    3. Ellsworth PC
    4. Dennehy TJ
    5. Antilla L
    6. Liesner L
    7. Whitlow M
    8. Staten RT
    9. Fabrick JA
    10. Unnithan GC
    11. Yelich AJ
    12. Ellers-Kirk C
    13. Harpold VS
    14. Li X
    15. Carrière Y

    (2010) Suppressing resistance to bt cotton with sterile insect releases

    Nature Biotechnology 28:1304–1307.

    https://doi.org/10.1038/nbt.1704

    • PubMed
    • Google Scholar
56. 1. Tabashnik BE
    2. Brévault T
    3. Carrière Y

    (2013) Insect resistance to bt crops: lessons from the first billion acres

    Nature Biotechnology 31:510–521.

    https://doi.org/10.1038/nbt.2597

    • PubMed
    • Google Scholar
57. 1. Tabashnik BE

    (2015) ABCs of insect resistance to bt

    PLOS Genetics 11:e1005646.

    https://doi.org/10.1371/journal.pgen.1005646

    • PubMed
    • Google Scholar
58. 1. Takano M
    2. Takeuchi T
    3. Kuriyama S
    4. Yumoto R

    (2019) Role of peptide transporter 2 and MAPK signaling pathways in the innate immune response induced by bacterial peptides in alveolar epithelial cells

    Life Sciences 229:173–179.

    https://doi.org/10.1016/j.lfs.2019.05.042

    • PubMed
    • Google Scholar
59. 1. Trapnell C
    2. Williams BA
    3. Pertea G
    4. Mortazavi A
    5. Kwan G
    6. van Baren MJ
    7. Salzberg SL
    8. Wold BJ
    9. Pachter L

    (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation

    Nature Biotechnology 28:511–515.

    https://doi.org/10.1038/nbt.1621

    • PubMed
    • Google Scholar
60. 1. Turelli M
    2. Hoffmann AA

    (1991) Rapid spread of an inherited incompatibility factor in California Drosophila

    Nature 353:440–442.

    https://doi.org/10.1038/353440a0

    • PubMed
    • Google Scholar
61. 1. Wang R
    2. Tetreau G
    3. Wang P

    (2016) Effect of crop plants on fitness costs associated with resistance to Bacillus thuringiensis toxins Cry1Ac and Cry2Ab in cabbage loopers

    Scientific Reports 6:20959.

    https://doi.org/10.1038/srep20959

    • PubMed
    • Google Scholar
62. 1. Watts C

    (2012) The endosome-lysosome pathway and information generation in the immune system

    Biochimica Et Biophysica Acta (BBA) - Proteins and Proteomics 1824:14–21.

    https://doi.org/10.1016/j.bbapap.2011.07.006

    • PubMed
    • Google Scholar
63. 1. Webster CL
    2. Waldron FM
    3. Robertson S
    4. Crowson D
    5. Ferrari G
    6. Quintana JF
    7. Brouqui JM
    8. Bayne EH
    9. Longdon B
    10. Buck AH
    11. Lazzaro BP
    12. Akorli J
    13. Haddrill PR
    14. Obbard DJ

    (2015) The discovery, distribution, and evolution of viruses associated with Drosophila melanogaster

    PLOS Biology 13:e1002210.

    https://doi.org/10.1371/journal.pbio.1002210

    • PubMed
    • Google Scholar
64. 1. Weeks AR
    2. Turelli M
    3. Harcombe WR
    4. Reynolds KT
    5. Hoffmann AA

    (2007) From parasite to mutualist: rapid evolution of Wolbachia in natural populations of Drosophila

    PLOS Biology 5:e114.

    https://doi.org/10.1371/journal.pbio.0050114

    • PubMed
    • Google Scholar
65. 1. Wu KM
    2. Lu YH
    3. Feng HQ
    4. Jiang YY
    5. Zhao JZ

    (2008) Suppression of cotton bollworm in multiple crops in China in Areas with bt toxin-containing cotton

    Science 321:1676–1678.

    https://doi.org/10.1126/science.1160550

    • PubMed
    • Google Scholar
66. 1. Wu K

    (2010) No refuge for insect pests

    Nature Biotechnology 28:1273–1275.

    https://doi.org/10.1038/nbt.1718

    • PubMed
    • Google Scholar
67. 1. Xiao Y
    2. Zhang T
    3. Liu C
    4. Heckel DG
    5. Li X
    6. Tabashnik BE
    7. Wu K

    (2014) Mis-splicing of the ABCC2 gene linked with bt toxin resistance in Helicoverpa armigera

    Scientific Reports 4:6184.

    https://doi.org/10.1038/srep06184

    • PubMed
    • Google Scholar
68. 1. Xiao Y
    2. Dai Q
    3. Hu R
    4. Pacheco S
    5. Yang Y
    6. Liang G
    7. Soberón M
    8. Bravo A
    9. Liu K
    10. Wu K

    (2017) A single point mutation resulting in cadherin mislocalization underpins resistance against Bacillus thuringiensis Toxin in Cotton Bollworm

    Journal of Biological Chemistry 292:2933–2943.

    https://doi.org/10.1074/jbc.M116.768671

    • PubMed
    • Google Scholar
69. 1. Xiao Y
    2. Wu K

    (2019) Recent progress on the interaction between insects and Bacillus thuringiensis crops

    Philosophical Transactions of the Royal Society B: Biological Sciences 374:20180316.

    https://doi.org/10.1098/rstb.2018.0316

    • PubMed
    • Google Scholar
70. Website

    1. Xu P
    2. Liu Y
    3. Graham RI
    4. Wilson K
    5. Wu K

    (2012) Adoption of genetically engineered crops in the US

    United States Department of Agriculture. Accessed April 2, 2015.

    http://www.ers.usda.gov/data-products/adoption-of-genetically-engineered-crops-in-the-us.aspx
71. 1. Xu P
    2. Liu Y
    3. Graham RI
    4. Wilson K
    5. Wu K

    (2014) Densovirus is a mutualistic symbiont of a global crop pest (Helicoverpa armigera) and protects against a baculovirus and bt biopesticide

    PLOS Pathogens 10:e1004490.

    https://doi.org/10.1371/journal.ppat.1004490

    • PubMed
    • Google Scholar
72. 1. Xu P
    2. Graham RI
    3. Wilson K
    4. Wu K

    (2017a) Structure and transcription of the Helicoverpa armigera Densovirus (HaDV2) genome and its expression strategy in LD652 cells

    Virology Journal 14:23.

    https://doi.org/10.1186/s12985-017-0691-y

    • PubMed
    • Google Scholar
73. 1. Xu P
    2. Yuan H
    3. Yang X
    4. Graham RI
    5. Liu K
    6. Wu K

    (2017b) Structural proteins of Helicoverpa armigera Densovirus 2 enhance transcription of viral genes through transactivation

    Archives of Virology 162:1745–1750.

    https://doi.org/10.1007/s00705-017-3253-0

    • PubMed
    • Google Scholar
74. 1. Xu P
    2. Yang L
    3. Yang X
    4. Li T
    5. Graham RI
    6. Wu K
    7. Wilson K

    (2020) Novel partiti-like viruses are conditional mutualistic symbionts in their normal lepidopteran host, african armyworm, but parasitic in a novel host, fall armyworm

    PLOS Pathogens 16:e1008467.

    https://doi.org/10.1371/journal.ppat.1008467

    • PubMed
    • Google Scholar
75. 1. Yang X
    2. Deng S
    3. Wei X
    4. Yang J
    5. Zhao Q
    6. Yin C
    7. Du T
    8. Guo Z
    9. Xia J
    10. Yang Z
    11. Xie W
    12. Wang S
    13. Wu Q
    14. Yang F
    15. Zhou X
    16. Nauen R
    17. Bass C
    18. Zhang Y

    (2020) MAPK-directed activation of the whitefly transcription factor CREB leads to P450-mediated imidacloprid resistance

    PNAS 117:10246–10253.

    https://doi.org/10.1073/pnas.1913603117

    • PubMed
    • Google Scholar
76. 1. Zhang S
    2. Cheng H
    3. Gao Y
    4. Wang G
    5. Liang G
    6. Wu K

    (2009) Mutation of an aminopeptidase N gene is associated with Helicoverpa armigera resistance to Bacillus thuringiensis Cry1Ac toxin

    Insect Biochemistry and Molecular Biology 39:421–429.

    https://doi.org/10.1016/j.ibmb.2009.04.003

    • PubMed
    • Google Scholar
77. 1. Zhang W
    2. Lu Y
    3. van der Werf W
    4. Huang J
    5. Wu F
    6. Zhou K
    7. Deng X
    8. Jiang Y
    9. Wu K
    10. Rosegrant MW

    (2018) Multidecadal, county-level analysis of the effects of land use, Bt cotton, and weather on cotton pests in China

    PNAS 115:E7700–E7709.

    https://doi.org/10.1073/pnas.1721436115

    • PubMed
    • Google Scholar
78. 1. Zhang J
    2. Li Y

    (2001)

    Three climatic stages of cotton bollworm living through winter in Xinjiang

    Chinese Journal of Agrometeorology 22:44–47.

    • Google Scholar

Author details

1. Yutao Xiao

   1. The State Key Laboratory for Biology of Plant Disease and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
   2. Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

   Contribution

   Formal analysis, Validation, Methodology

   Contributed equally with

   Wenjing Li, Xianming Yang and Pengjun Xu

   Competing interests

   No competing interests declared

2. Wenjing Li

   1. The State Key Laboratory for Biology of Plant Disease and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China
   2. Institute of Plant Protection and Soil Fertility, Hubei Academy of Agricultural Sciences, Wuhan, China

   Contribution

   Data curation, Investigation

   Contributed equally with

   Yutao Xiao, Xianming Yang and Pengjun Xu

   Competing interests

   No competing interests declared

3. Xianming Yang

   The State Key Laboratory for Biology of Plant Disease and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China

   Contribution

   Resources, Formal analysis, Supervision

   Contributed equally with

   Yutao Xiao, Wenjing Li and Pengjun Xu

   Competing interests

   No competing interests declared

4. Pengjun Xu

   1. Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao, China
   2. Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Yutao Xiao, Wenjing Li and Xianming Yang

   Competing interests

   No competing interests declared

5. Minghui Jin

   Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

   Contribution

   Software, Methodology

   Competing interests

   No competing interests declared

6. He Yuan

   Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

   Contribution

   Software, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

7. Weigang Zheng

   Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

   Contribution

   Data curation, Software

   Competing interests

   No competing interests declared

8. Mario Soberón

   Instituto de Biotecnología, Universidad Nacional Autónoma de México, Morelos, United States

   Contribution

   Resources, Methodology, Writing - original draft

   Competing interests

   coauthor of a patent on modified Bt toxins, "Suppression of Resistance in Insects to Bacillus thuringiensis Cry Toxins, Using Toxins that do not Require the Cadherin Receptor" (patent numbers: CA2690188A1, CN101730712A, EP2184293A2, EP2184293A4, EP2184293B1, WO2008150150A2, WO2008150150A3).

9. Alejandra Bravo

   Instituto de Biotecnología, Universidad Nacional Autónoma de México, Morelos, United States

   Contribution

   Resources, Methodology, Writing - original draft

   Competing interests

   coauthor of a patent on modified Bt toxins, "Suppression of Resistance in Insects to Bacillus thuringiensis Cry Toxins, Using Toxins that do not Require the Cadherin Receptor" (patent numbers: CA2690188A1, CN101730712A, EP2184293A2, EP2184293A4, EP2184293B1, WO2008150150A2, WO2008150150A3).

10. Kenneth Wilson

    1. Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
    2. Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom

    Contribution

    Data curation, Software, Funding acquisition, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5264-6522

11. Kongming Wu

    The State Key Laboratory for Biology of Plant Disease and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China

    Contribution

    Resources, Supervision, Funding acquisition, Validation, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    wukongming@caas.cn

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3555-4292

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