Rapid and cost-effective screening of genetic markers associated with pyrethroid resistance in Musca domestica using RNAse H2 PCR (rhPCR)

Rapid and cost-effective screening of genetic markers associated with pyrethroid resistance in Musca domestica using RNAse H2 PCR (rhPCR) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Rapid and cost-effective screening of genetic markers associated with pyrethroid resistance in Musca domestica using RNAse H2 PCR (rhPCR) Alden Estep, Neil Sanscrainte, Alexandra Pagac, Christopher Geden, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5239153/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Pyrethroid resistance, particularly knockdown resistance ( kdr ), is widespread in insect pest populations, but rarely has kdr been associated with field-level pest control failure. The prevailing understanding is that kdr contributes to a resistant phenotype, but this knowledge has remained largely an academic pursuit and has not translated to tools and strategies needed by agricultural producers to make rapid decisions for effective resistance management. As a first step in providing these operational tools, we developed robust assays using the high specificity of rhPCR to reduce kdr assessment time by approximately 80% and costs ~ 75% from the traditional Sanger based method used for Musca domestica . An important consideration for the use of an operational tool is the ability to get an accurate result on the first attempt, so we used Nanopore sequencing to confirm genotypes in a subset of samples and found the first pass genotyping accuracy of rhPCR method to be 75.0%, versus 41.2% with the traditional Sanger method. To demonstrate the broad applicability and comparability of screening for kdr SNPs using rhPCR, we conducted the largest assessment of kdr genotypes of M. domestica in United States dairy operations and found similar kdr patterns to other recent studies using traditional methods. Biological sciences/Biological techniques/Genetic techniques/Genotyping and haplotyping Biological sciences/Biological techniques/Genetic techniques/Pcr based techniques Musca domestica knockdown resistance RNAse H2 PCR voltage gated sodium channel insecticide resistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Pesticide resistance affects the control of numerous arthropod pests worldwide, including those relevant to crops 1 , 2 , animal production 3 – 5 , public health 6 , and the urban environment 7 . Consequences of pesticide resistance include the loss of crops 8 , reduced animal performance (reviewed in Brewer et al. 9 ), risk of outbreaks of vector-borne diseases (reviewed in Rivero et al. 10 ), and litigious infestations of urban pests 11 , to name a few. Pest surveillance, which includes pesticide resistance surveillance, is an important facet of an effective Integrated Pest Management program 12 . Numerous resistance mechanisms have been described across arthropod species, including metabolic detoxification, reduced cuticular penetration, and modifications of the pesticide target site (i.e., target site mutations). Of these, target site mutations are often implicated in strains with strong resistance phenotypes 13 , 14 . Target site mutations are single nucleotide polymorphisms (SNPs) resulting in changes in amino acid codons of the target ion channels or proteins (e.g., acetylcholinesterase), which, when translated into the protein, alters various properties of the target, including its susceptibility to pesticides 15 . Often target site SNPs act in ensembles with additive properties to create higher levels of phenotypic resistance. Common target site mutations include “ r esistance to d ie l drin”, or “ rdl ,” which is a mutation in the rdl subunit of arthropod GABA/glutamate-gated chloride channels, conferring resistance to dieldrin 16 , fipronil, abamectin 17 , 18 , but not fluralaner 19 . Another common set of mutations is found in the gene for acetylcholinesterase, an enzyme that hydrolyzes the neurotransmitter acetylcholine into choline and acetic acid 20 . These mutations alter affinity for organophosphates and carbamates. One of the most implicated target site mutations due the widespread use of pyrethroids in countless pest control systems all over the world is “ k nock d own r esistance,” or “ kdr ,” which was first described in house flies by Busvine 21 (1951) shortly after DDT was brought to market. All three of these target site mutations have been described in bed bugs 13 and importantly, in house flies 14 , as this offers an exemplary model for surveillance tool development for broad application. Commonly used methods for the detection of target site mutations include Sanger sequencing 4 , multiplex PCR 22 , and allele-specific PCR 23 (which includes melt curve analysis (MCA)). However, as with all methods, each of these has benefits and limitations. While Sanger sequencing is the traditional “gold standard” for target site mutation detection 4 , 14 , it is usually reliant on a remote facility (which may not be locally available) and cannot be multiplexed, thus making it challenging to use for large sample sizes. There is also a level of subjectivity in play when determining if two in-phase peaks can be “called” a heterozygote during inspection of electropherograms, and the threshold of the minimum signal needed to make a call is rarely identified. Multiplex PCR, as another method, is considered inexpensive and fast but can result in false positives if methods are not optimized properly 24 , 25 . Allele-specific PCR is also inexpensive and fast but relies on visual inspection of gels where faint bands pose a challenge 26 . The MCA approach with variously tailed SNP-specific primers, resulting in distinct melting temperatures, has become the predominant method used for kdr assessment in mosquitoes due to cost effectiveness, the rapidity of producing results for thousands of samples and the ease of implementation with existing laboratory infrastructure often used for mosquito-borne pathogen screening 27 – 29 . However, MCA is difficult when mutations of interest are tri-allelic due to side-by-side SNPs, such as the substitution of leucine for phenylalanine or serine at amino acid position 1014 (L1014 F/S) for the mosquito Culex quinquefasciatus and valine for glycine or isoleucine at position 1016 (V1016 G/I) in Aedes aegypti 30 , 31 . Notably, the 1014 mutation in house flies is also due to adjacent SNPs resulting in the wildtype leucine becoming a histidine or phenylalanine (L1014 H/F) 4 . An alternative form of allele-specific PCR that has not yet been explored for use in detecting SNPs implicated in insecticide resistance target site mutations is RNase H-dependent PCR (rhPCR) 32 . This method utilizes blocked primers, with an RNA base in the primer located complementary to the SNP of interest on the template DNA, with several DNA bases 3’ of the RNA base and a final blocking group that, when present, inhibits amplification by DNA polymerase. When the RNA base is correctly matched to the complimentary DNA base on the template, RNAse H2, derived from Pyrococcus abyssi (Thermococcales: Thermococcaceae), cleaves the primer at the 5’ side of the RNA:DNA heteroduplex. This cleavage relieves the steric hindrance caused by the blocked 3’ end of the primer allowing the DNA polymerase to amplify as in standard PCR. This SNP-specific limitation acts at each cycle to reduce spurious amplification. This allows for highly specific amplifications of targeted SNPs and is quickly being adopted for detection of critical SNPs in biomedical research 33 – 35 . The advantages of rhPCR are that it is relatively inexpensive, can be conducted in high throughput on common qPCR equipment and produces results quickly. The specificity of the RNAse H2 cleavage reaction enables discrimination among triallelic mutations and produces very low error rates when properly controlled and optimized. The products of rhPCR can also be visualized with simple gel electrophoresis and transillumination and even sent for Sanger sequencing with no additional steps required. To demonstrate the utility of rhPCR to rapidly and efficiently detect insecticide resistance target site mutations, we developed rhPCR assays to detect kdr SNPs in house fly collections from dairies located in eight states (Table 1 ). As a significant agricultural and public health pest, house flies have become a model organism for pesticide resistance research (reviewed in Geden et al. 36 ) due to their wide geographic distribution and pest status. House fly populations have long been challenging to control due to their rapid development of insecticide resistance 37 – 39 . Of the insecticides registered for use against house flies, pyrethroids are one of the most widely used and often fail due to resistance 4 , 40 – 42 . This resistance is due in part to well-characterized and numerous kdr mutations, including the triallelic L1014 F/H mutation, a methionine to threonine mutation at 918 (M918T), a threonine to isoleucine mutation at 929 (T929I), and a rare aspartic acid to asparagine mutation at 600 (D600N) 4 . Traditionally, the 1014F mutation is considered to be a gatekeeper that must be present before the 918, 929 or 600 mutations are found 4 , 14 , 40 , 43 – 45 . As the ability to get an accurate answer from the first test attempt is an important consideration for the use of an operational, rather than academic, research tool, we used Nanopore sequencing to confirm genotypes in a subset of samples. The rhPCR method adds a new tool to resistance monitoring programs and can extend to virtually all pests or arthropod disease vectors and any resistance mutation that is conferred by a SNP. Additionally, rhPCR can be scaled to a wide range of instrumentation, from simple PCR and gel electrophoresis to quantitative PCR and next-generation sequencing. The rhPCR method makes resistance genotyping accessible to a wider range of lab types, especially those in low and middle-income countries. Results Development of RNAse H-dependent PCR methods to assess knockdown resistance mutations in Musca domestica We developed a rhPCR based method to assess the well-described markers of pyrethroid insecticide resistance found in the voltage-gated sodium channel (VGSC) of the house fly Musca domestica , resulting in a tool that could improve operational fly management in dairy or livestock production operations. Relying upon the reported single base specificity of rhPCR and the design guide provided by the manufacturer (IDTDNA, Coralville, IA), we designed primers to detect the presence of the SNPs for the 1014 (Fig. 1 A), 918 (Fig. 1 B), 929 (Fig. 1 C) and 600 (Fig. 1 D) VGSC mutations based on previously reported sequences 43 , 44 (Table 1 ). For the 1014 SNPs, three reactions assess the presence of two possible SNPs in consecutive base pair positions (Fig. 1 A). The primers for the wildtype 1014 lysine and the kdr mutant 1014 histidine interrogate for the presence of the codons C T T (L) or C A T (H) (position 4371952 of NW026712250.1). The primer for the kdr mutant 1014 phenylalanine assesses the presence of the codon T TT (F) (position 4371953 of NW026712250.1). We observed that the specificity of the rhPCR primers prevented misamplification at the two adjacent SNPs at the site of the 1014 kdr mutations. Primers for the 918, 929 and 600 kdr mutations assess the presence of the following SNPs: 918 – wildtype methionine (A T G) to kdr mutant threonine (A C G) (Fig. 1 B, position 4376601 of NW026712250.1); 929 – wildtype threonine (A C A) to kdr mutant isoleucine (A T A) (Fig. 1 C, position 4376568 of NW026712250.1); and 600 – wildtype aspartic acid ( G AC) to kdr mutant asparagine ( A AC) (Fig. 1 D, position 4392528 of NW026712250.1). Comparison of samples with a specific kdr SNP to those without the SNP were discernable with a difference in cycle threshold (C t ) of at least 4 cycles. This allows for the genotyping of unknowns for specific kdr alleles by comparison to known controls. While we used the amplification curves produced by the higher capacity of standard qPCR equipment to call genotypes, it was also possible to make presence or absence calls using lower throughput visualization on agarose gels as commonly done with AS-PCR (Fig. 2 ), although the very short amplicon (~ 60bp) for the 600D or N was difficult to visualize. Comparison of genotyping methods We compared rhPCR genotyping and the traditional Sanger based method of genotyping to genotypes generated by Nanopore-based next generation sequencing. We evaluated the ability of each method to determine the complete genotype by evaluating success after only a single attempt, what we call the “first pass” accuracy of each method. First pass accuracy is critical for cost-effectively genotyping large sample numbers as in large MCA based mosquito insecticide resistance (IR) studies 28 , 29 , 31 . For this comparison, we selected 68 samples from the 1,209 tested by rhPCR. These included genotypes obtained by the rhPCR assays that were unexpected based on previous literature, including those with 918 or 929 mutations without the presence of the 1014F gatekeeper mutation as well as those with more common genotypes (Supplementary Table 1) and we did not consider the 600 kdr locus in our calculations because its presence in the samples tested was so rare 5 . To be considered a success, the 7 individual rhPCR reactions (3 for 1014, 2 for 918, 2 for 929) or the 2 individual Sanger reactions (one for 1014, 1 for 918/929) needed to match the result from nanopore sequencing. Three of the 68 samples resulted in a different genotype by each of the three methods (Table 2). The rhPCR assays resulted in successful genotyping for 75.0% of samples while individual assay success rate for each allele was greater than or equal to 89.7%. We obtained a complete and accurate genotype result from commercial Sanger sequencing (2 reactions) on the first pass for only 41.2% of the samples. We also assessed the time needed to generate genotypes by the three different methods based on a group of 96 samples (Fig. 3 ). Including sample preparation and homogenization time (Fig. 3 A), which was similar regardless of method, rhPCR was able to produce results in 6.5 hours (Fig. 3 B). Both Sanger based genotyping (Fig. 3 C) and nanopore based sequencing (Fig. 3 D), which shared much of the same procedure, both required more than 30 hours to generate results. We note that our timeline for Sanger includes only 24 hours for overnight shipment, purification and sequencing by a commercial provider which could certainly be longer depending on individual laboratory facilities. Based on this comparison, rhPCR reduced the time needed to produce results by 80%. We also considered the costs associated with each method to develop a complete genotype (Supplementary Table 2). Based on the same 96 sample group on one plate, rhPCR was able to produce results for $ 4.00 USD per sample or less than $ 400 USD for the plate. Sanger costs resulted in a per sample cost of $ 17.50 or $ 1,680 USD for the plate. Most of this cost was the purification and Sanger sequencing expense charged by the provider. We also calculated the costs for genotyping by nanopore and it was comparable to Sanger at $ 14.72 USD per sample with the major expenses being the sequencing flow cell and library prep reagents. Assessment of kdr mutations in wild populations of Musca domestica from dairy production facilities by rhPCR Using the rhPCR assays described above, we assessed the presence of kdr mutations in populations of M. domestica from eight states, which totaled 1,209 individual flies with nearly 77% (926/1,209) producing a result from each of the 9 individual genotyping reactions (File S1). Genotype calls were made for each wild individual by comparison to the results from laboratory strains with known kdr genotypes included in each assay. Negative controls, containing no DNA, were also included. Raw genotyping data is included in the data repository at DOI: 10.15482/USDA.ADC/25044329 . We observed 27 different kdr genotypes (abbreviated by summarizing the amino acids resulting at 1014, 918, 929 and 600) among all the locations (Supplementary Table 1). The three most common genotypes (described by a shortened genotype notation for 1014, 918, 929, and 600 respectively, LLMMTTDD, HHMMTTDD, FFMMTTDD) were homozygotes at position 1014 without any other kdr mutations (Fig. 4 A). These together accounted for nearly 50% of the successfully genotyped M. domestica . Heterozygotes at 1014 with no other kdr mutations accounted for 3 of the 4 (HFMMTTDD, LHMMTTDD, LFMMTTDD) next most common genotypes. Notably, the 1014F homozygote paired with heterozygous 929 (genotype FFMMTIDD) was found at similar frequency as the 1014-only heterozygotes. Together, these 7 genotypes represented more than 70% of the total genotypes detected. We did detect low frequencies of unexpected genotypes where kdr mutations were found without the 1014F, which is considered a gatekeeper for the other kdr mutations 4 . Notably, the 600N mutation was rare and only detected in a single sample from Minnesota. We observed considerable variation in allele frequencies between states (Fig. 4 B). The wildtype 1014L allele ranged from absent in the NC samples to nearly 0.7 in WI although the average frequency was 0.35. The frequency of the 1014H mutation averaged 0.328 (range: 0.082–0.726) and the 1014F frequency was similar at 0.326 (range: 0.095–0.609). Both the 918T and 929I mutations were identified in all states although not in all locations within a state (Figs. 4 B & 5 ). The 918T SNP frequency averaged 0.077 (range: 0.012–0.191) and was highest in MN and KS and less than 0.02 in the southeastern states of FL, GA, and NC. In contrast, the 929I mutation frequency, which averaged 0.102 (range: 0.016–0.316) across all states, was higher in FL, GA, and NC and low (< 0.061) in all other states including TX. As noted above, the 600N mutation was only identified in a single sample from MN. For multiple samples within a state, we observed a similar presence of genotypes although the relative frequencies of each genotype varied (Fig. 5 ). Assessment of genotype frequency variation by NMDS To formalize our observation that genotype frequencies varied by location, we conducted non-metric multidimensional scaling (NMDS) analysis using our genotype data. The original dissimilarities among sites, with regard to genotype composition, were well-preserved in reduced dimensions (non-metric R 2 = 0.993, linear R 2 = 0.946). Model stress was 0.085. Permutational ANOVA revealed a statistically significant difference among the states (Fig. 6 A; F = 2.34, df = 7, 14, P = 0.001). There was also a statistically significant difference among the regions (Fig. 6 B; F = 2.43, df = 2, 19, P = 0.002). Among regions, only the pairwise comparison of the South and Midwest regions was significant (F = 3.14, df = 1, P = 0.006; R 2 = 0.156). Discussion This study is the first to describe a method using rhPCR for resistance genotyping in insects. This robust tool will enable more rapid field-level sampling and could positively impact operational decision-making by allowing inclusion of IR information into pest control programs in livestock production operations. We introduced a series of rhPCR assays to detect key genotypes from large numbers of flies with a relatively high rate of success at the first testing event. Notably, the rhPCR-based genotyping we conducted in this study encompassed over 1,200 organisms from 22 locations across 8 states, making it one of the largest M. domestica IR genotyping studies conducted to date 5 , 45 – 48 . The rhPCR assays can be completed quickly and cost effectively; in this study only 12 days were needed to conduct testing at a consumable cost of less than $ 4,900 (in 2024 USD); this is significantly cheaper than obtaining the same data through Sanger sequencing which would have cost around $ 21,000, a savings of slightly more than 75%. Additionally, processing time for these rhPCR assays (qPCR performed on homogenized samples) was only about a quarter of the time required for a Sanger sequencing workflow, which requires multiple procedures for each sample (DNA purification, PCR assays, PCR cleanup, sequencing onsite or through a 3rd party). Traditionally, the Sanger based kdr assessment in flies has been applied in a manner to screen first for the mutations at 1014. If 1014F was detected, further reactions and Sanger were conducted to assess the presence of the 918, 929 and 600 SNPs. This reduced the overall costs by not running additional Sanger reactions for SNPs that were not expected to be present without the gatekeeper 1014F mutation. The rhPCR method described here was able to successfully identify several unexpected genotypes where the 918 and 929 mutations were not paired with 1014F. These initial genotyping results were also frequently obtained by both Sanger and nanopore-based methods, thus confirming that rhPCR correctly identified these unexpected genotypes. The rhPCR method described here has a few distinct advantages for expanding the operational utility of IR assessment in flies and other insects. At a minimum, we demonstrate that rhPCR can easily screen for SNPs and is highly specific even when dealing with adjacent variation (as in the 1014L, H, or F SNPs in M. domestica ). This method could conceivably be a useful improvement for genotyping other tri-allelic mutations such as those that exist in Ae. aegypti and Cx. quinquefasciatus . The same rhPCR method we applied to the kdr gene here could test for acetylcholinesterase or rdl mutations, which are indicative of IR to other modes of action in various insect taxa, including flies. A second advantage of the rhPCR method is the ability to screen ecologically relevant numbers of organisms at relatively low cost (1/5 the cost) and in a short time period compared to traditional methods. Screening larger sample sizes will allow for a better understanding of which genotypes survive sprays conducted in agricultural and urban settings, where both survivors and dead are screened for genotype and compared statistically 28 , 49 . The speed of rhPCR would allow for multiple assessments being conducted during a treatment to understand the dynamics of kdr spread in a population under selection. The rhPCR method could also be applied to understand the distribution of kdr genotypes over geographic space. Our NMDS analysis showed significant differences in genotype between geographically dispersed collection sites, but we did not have adequate sample density in any one area to use methods that can quantify the spatial distribution of resistance mutations, which would guide sampling strategies and operational interventions in adjacent areas not yet tested 29 . While there are clear advantages to the use of rhPCR for IR assessment, it has disadvantages that are common to surrogate methods. The rhPCR method provides a screening tool for rapid assessment only of known mutations; it is not a tool for discovery of novel mutations and so a foreknowledge of the SNP or SNPs of interest is required. Along with this, rhPCR, like other surrogate methods, requires that the SNP being assessed is a high value marker in order for the resulting phenotype to be meaningful. It is clear from work in mosquitoes that many mutations exist in the VGSC but that not every mutation results in much resistance; some act as part of an ensemble of SNPs and are found in both susceptible and resistant organisms. Third, proper use of rhPCR requires that controls for the alleles being tested must be included in every assay conducted, optimally including heterozygotes to ensure the assay is indicating adequate sensitivity. This study benefitted from the kindness of other researchers in the field who provided strains with the specific kdr mutations we required, but these strains may not be as accessible to other workers, especially those in developing countries. Though controls in the same format as the collections (i.e. legs, whole bodies, purified DNA, etc.) may not always be possible to source, this does not relieve the experimenter from including proper controls. Improperly controlled surrogate assays should never be used for operational decision making or in academic publishing. Long synthetic DNA oligos can be ordered and should be used as allele-specific controls when proper control strains are not available. In summary, we demonstrate an rhPCR based assay system that can move IR assessment from the academic laboratory to an operationally useful tool. This easy method can be used to better define important ecological parameters of IR in the field, by increasing sample sizes and increasing the number of locations that can be assessed for a given quantity of resources. This may allow more effective use of IR information in integrated pest management programs. Monitoring IR informs decision-making about pesticide usage by reducing use and expense when a formulation is likely to be ineffective. Because house flies in the US are widely resistant to all the products currently available for their management, the ability to monitor resistance as new products enter the market will be critical for their effective management. Improved IR monitoring will also be an emerging tool in parts of the world where resistance to current products is not as severe as in the US, and alternating insecticidal modes of action remains a viable option. Methods Musca domestica strains and samples Musca domestica strains with known kdr genotypes were kindly provided by Dr. Jeffrey Scott. The abyss (no 1014, 918, 929 or 600 mutations), ALkdr (1014F mutation), NChis (1014H), Type N (1014F, 918T, 600N), kdr1B (1014F, 929I) and JPSkdr (1014F, 918T) strains were used for initial assay development and as specific allele containing controls for rhPCR assays. The CAR21 strain (no 1014, 918, 929 or 600 mutations) was also used as a kdr negative control. This strain, which was originally obtained from Carolina Biological Supply, has been maintained at the USDA Center for Medical, Agricultural and Veterinary Entomology and is recognized as being insecticide-susceptible 50 . Maintenance and rearing of these strains was conducted as previously described 51 . Samples of field strains were collected from eight states by collaborators in Hatch Project S-1076. Samples were collected from livestock facilities using sweep nets. Collected samples were knocked down by freezing and placed into 50 mL tubes (Thermo Fisher, Waltham, MA) or 1 pint paper containers (WestRock, Atlanta, Georgia, USA) for overnight shipping on ice blocks and were maintained at -80 o C after arrival. Samples from CA and TX were delayed in shipping and spent more than 24 hours at room temperature causing significant degradation but were tested anyway. Sample preparation and homogenization Samples provided by collaborators were stored at -80°C until sorting and identification on ice. Flies were visually identified as M. domestica using morphological characters, and forceps were submerged in 100% ethanol between the handling of each fly. Musca domestica individuals had a mid and hind leg removed if present (if not, any two legs were chosen) with forceps where the femur meets the trochanter. The legs were then placed in a 96-deep well homogenization plate (Omni International, Kennesaw, GA) and the rest of the sample placed in a corresponding 96-well plate in the same well position. The homogenization plate containing the legs was prepared by adding 2.0 mm cubic zirconium beads (BioSpec Products, Bartlesville, OK) to each well using a LabTie bead dispenser (BioSpec Products, Bartlesville, OK), followed by the addition of 200 µL of deionized water. All plates were labeled with the relevant collection information to keep the leg samples for testing linked with the remainder of the sample. Once all samples from a collection event were processed, the 96-well plate containing the remainder of the sample was covered with a foil plate seal (USA Scientific, Ocala, FL) and stored at -80°C in case further testing was required. The homogenization plate containing the legs was then covered with a silicone sealing mat (Omni International, Kennesaw, GA) and stored at -20°C until homogenization. Sample preparation was conducted separately from sample testing. On the day of assay, homogenization plates containing legs, beads, and deionized water were defrosted in a 22 o C water bath and then homogenized for 2 minutes on a Beadruptor 96 (Omni International, Kennesaw, GA). After homogenization, 96-well plates were spun for 2 min at 805 x g then placed on ice until assay setup. rhPCR assays Master mixes for rhPCR were prepared for each allele using the primers (IDTDNA, Coralville, IA) and reaction quantities listed in Tables 2 & 3. The effective concentration of H2 RNAse (IDTDNA, Coralville, IA) was optimized for each allele-specific reaction as recommended by the manufacturer using laboratory strains with known genotypes 43 , 44 . Reactions were assembled in 384-well plates using an EpMotion 5750 liquid handling system (Eppendorf, Hamburg, Germany) in a final reaction volume of 10 µL. Eight microliters of each master mix was added by the liquid handling system followed by 2 µL of spun homogenate. Each 96-well plate of homogenates required three 384 well plates for all the assays needed: 1st – 1014L, 1014H, and 1014F; 2nd – 9198M, 918T, 929T, and 929I; and 3rd – 600D and 600N. Plates were sealed with clear optical covers (Thermo Fisher, Waltham, MA), mixed gently on a plate vortex (IKA Works, Inc., Wilmington, NC), spun to remove bubbles and then cycled on a QuantStudio 6 Flex system (Thermo Fisher, Waltham, MA) running QuantStudio Real-Time PCR Software V1.3 using standard FAST conditions of 95 o C for 20 sec, then 40 cycles of 95 o C for 3 sec and 60 o C for 20 sec. This was followed by a melt curve phase of 95 o C for 15 sec, 60 o C for 1min, and a ramp step from 60 o C to 95 o C at 0.05 o C /sec with continuous fluorescence recording to assess amplicon melting temperature as an extra check to ensure the specificity of the reaction. The presence of an allele in an organism was made using two metrics. First, the Ct of a curve had to be within 3 cycles of the known positive controls for the allele in question and at least two cycles above the Ct of the known negative controls. As a second factor, the melting temperature (Tm) of the resulting amplicon had to be ± 0.4 o C of the Tm of the known positive control. A sample that did not meet both metrics above was considered negative for the specific allele. A detailed version of the protocol for this assay has been deposited in the data repository. PCR amplification and Sanger sequencing of sodium channel mutations Reactions for PCR amplification of regions containing kdr mutations used a combination of previously published and novel primers (Table 2). All reactions were conducted in 25 µL volumes using 1 µL of fly homogenate or DNA with final concentrations of 1.5 mM MgCl 2 , 0.2 µM dNTPs, 0.4 µM forward and reverse primers, and 0.04 U/µl of Platinum Taq (Thermo Fisher, Waltham, MA). Reactions to amplify the 1014 containing region were conducted on an Eppendorf Mastercycler (Eppendorf, Hamburg, Germany) with the following conditions: 94 o C for 2 min 40 cycles of 94 o C for 30 sec, 60 o C for 25 sec and 72 o C for 30 sec; and 72 o C for 5 min. Reactions for the 918/929 and 600 kdr containing regions were amplified on the same equipment but with the following conditions: 94 o C for 2 min; 40 cycles of 94 o C for 30 sec, 55 o C for 30 sec and 72 o C for 1 min; and then 72 o C for 5 min. Select amplicons were verified on 2% agarose gels. ExoSAP purification and Sanger sequencing were conducted by PsomagenUSA (Rockville, MD). Visualization of rhPCR amplicons for Fig. 2 used 10 µl of PCR reaction with 1.1 µL of 10X Blue Juice. A 100 base pair ladder (Thermo Fisher, Waltham, MA) was included and the samples were electrophoresed at 102 V for 1 hr and then visualized on an iBright imaging system (Thermo Fisher, Waltham, MA). Nanopore sequencing of kdr amplicons and sodium channel coding region Nanopore sequencing was conducted according to the manufacturer provided protocol using R10 chemistry and Native Barcoding Kit V14 (SQK-NBD114.96; Oxford Nanopore Technologies, Oxford, England). The protocol version used was: NBA_9170_v114_revL_15Sep2022 updated 02/10/2023 and a marked up version is included in the data repository. Samples for sequencing kdr amplicons were prepared from unpurified PCR products by combining 0.34 µL of the 1014 amplicon PCR, 0.27 µL of the 918/929 amplicon PCR, and 0.27 µL of the 600 amplicon PCR with 11.37 µL of nuclease free water. Samples for VGSC sequencing used 12.25 µL of the unpurified VGSC PCR and were placed into a 96-well plate and end prepped using NEBNext Ultra II End Repair/dA-Tailing Module (New England BioLabs Inc, Ipswich, MA) per the protocol. Each sample was then affixed with a unique barcode and all barcoded samples were then combined, cleaned with 80% ethanol and then final sequencing adapters were ligated per the ONT protocol. The adapted, barcoded library was washed with standard fragment buffer, eluted in 15 µL of elution buffer and quantified on a Nanodrop 8000 spectrophotometer. Fifty femtomoles of eluted DNA was mixed with sequencing buffer, loading beads, and elution buffer before being loaded onto a prepared R10 flow cell. Sequencing was managed on a GridION x5 device by the MinKNOW software (version 23.07.12) and electrical signals were converted to basecalls by Guppy (version 7.1.4). Initial read quality control was managed by the MinKNOW software with a minimum quality score of 10. Reads at or above this threshold were binned into barcode specific folders as fastq files. Barcodes were not removed from raw reads to allow subsequent filtering at higher stringency. Two sequencing runs were conducted with 36 kdr amplicon samples in the first run and 32 kdr amplicon samples in the second run. Raw reads are available as aligned .bam files under accession: PRJNA1163914. Bioinformatic analysis Barcode binned raw reads that met initial quality filters were subsequently filtered at higher stringency to reduce spurious barcode misassignments. The sequencing summary file for each run was filtered using command line tools to require a barcode match of > 97% and more than 37 bases in length at each end of the amplicon. A maximum of 4k reads meeting these requirements were selected for each barcode using seqtk 52 and then subsequently mapped to the M. domestica VGSC using Minimap2 53 . The resulting pileups were examined using IGV 54 to determine the presence of SNPs for the 1014, 918, 929 and 600 kdr mutations. Statistics and Reproducibility Frequency of kdr genotypes was ordinated alone or as an aggregate representation of the trapping sites from which they were collected using non metric multidimensional scaling in the ‘vegan’ package 55 in R version 4.3.2 56 . Centroids (i.e., means) of genotype frequencies were summarized at state and regional levels as 95% confidence interval ellipses. Regions were defined according to the US Census Bureau as “Midwest” (Minnesota, Kansas, Wisconsin), “South” (Texas, Florida, North Carolina, Georgia), and “West” (California). The Jaccard dissimilarity index method was used with NMDS done in three dimensions (i.e., k = 3), which produced the lowest stress. Stress is an important indicator of ordination fit in the assigned number of dimensions, with values < 0.2 being acceptable representation, < 0.1 being good representation, and < 0.05 being excellent representation 57 . Stress plots were visually inspected and both non-metric and linear fit R 2 values are reported. Then, a permutational type II ANOVA was done using the ‘adonis.II’ function in the ‘RVAideMemoire’ package 58 . Posthoc comparisons of F statistics were done using the ‘pairwiseAdonis’ package 59 , with a Holm-Sidak P-value correction to account for familywise error rate. Due to the number of pairwise comparisons among states, relatively low sample size, and the associated P-value adjustment, we only report the global permutational type II ANOVA for the state comparisons. For all statistical comparisons, α = 0.05. Samples sizes from each of the 22 locations ranged from 32–89 individuals tested from each location. Individual samples that did not produce a clear genotype by rhPCR were excluded from further testing. Declarations Data Availability Experimental data not included in the manuscript are available from USDA AgData Commons at: doi: 10.15482/USDA.ADC/25044329. Sequencing data is available from NCBI under BioProject ID PRJNA1163914. Competing interests The author(s) declare no competing interests. Acknowledgements The Authors would like to acknowledge the assistance of the members of Hatch Project S-1076 for providing field collections and Dr. Jeffrey Scott for providing M. domestica strains with known kdr genotypes. Project support for ASE, NDS, AAC and CJG was provided by USDA National Program 104. Author Contribution Statement ASE and ERB conceived the project and performed data analysis. ASE, NDS and AAC conducted experiments. All authors contributed to the manuscript draft, edits and revisions. All authors have approved submission of the manuscript. References Zhang, L. et al. Genetic structure and insecticide resistance characteristics of fall armyworm populations invading China. Mol Ecol Resour 20 , 1682-1696 (2020). https://doi.org/10.1111/1755-0998.13219 Chen, Y. H., Cohen, Z. P., Bueno, E. M., Christensen, B. M. & Schoville, S. D. 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Voltage-gated sodium channel intron polymorphism and four mutations comprise six haplotypes in an Aedes aegypti population in Taiwan. PLoS Negl Trop Dis 13 , e0007291 (2019). https://doi.org/10.1371/journal.pntd.0007291 Aldridge, R. L. et al. Point Protection with Transfluthrin against Musca domestica L. in a Semi-Field Enclosure. Insects 15 (2024). https://doi.org/10.3390/insects15040277 Morales-Ramos, J. A. et al. in Mass Production of Beneficial Organisms 101-155 (Elsevier, 2023). Li, H. GitHub-lh3/seqtk: toolkit for processing sequences in FASTA/Q formats , (2021). Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34 , 3094-3100 (2018). https://doi.org/10.1093/bioinformatics/bty191 Thorvaldsdottir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14 , 178-192 (2013). https://doi.org/10.1093/bib/bbs017 Oksanen, J. et al. _vegan: Community Ecology Package_. 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Supplementary Files Tables.docx SupplementaryInformationMdomkdr.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Dec, 2024 Reviews received at journal 28 Nov, 2024 Reviewers agreed at journal 28 Nov, 2024 Reviews received at journal 11 Nov, 2024 Reviews received at journal 07 Nov, 2024 Reviewers agreed at journal 27 Oct, 2024 Reviewers agreed at journal 22 Oct, 2024 Reviewers invited by journal 22 Oct, 2024 Editor assigned by journal 22 Oct, 2024 Editor invited by journal 10 Oct, 2024 Submission checks completed at journal 10 Oct, 2024 First submitted to journal 10 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5239153","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":364596811,"identity":"fd6fc5fa-7718-49b8-81a7-45b04f6fb184","order_by":0,"name":"Alden Estep","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACdgYDGJPxAQMDMxFamKFaeIBMA5K1sEkQpYW/mXnj4wIGG3t79t5j1Tx/rOUZ+Bcfk8CnReIwW7HxDIa0xB6ec2m3edvSDRsknqXh1cJwmMdMmofhcAKPRI7Zbd6Gw4wNEmeMDfDpkD/MY/6bh+G/PY/8G7Ninj+H7QlqMQDawszDcICxRwLEYDuc2MDfY/gAnxZDoF+keQySE3vO5CVLzm1LT26TYEvEq0XuePPGzzwVdvbs7WcPfnjzx9q2n//wgQP4tECdByJ4IGw2iQTCGqAAqoWBnwg7RsEoGAWjYEQBANSwP53h2DUJAAAAAElFTkSuQmCC","orcid":"","institution":"US Department of Agriculture Agricultural Research Service","correspondingAuthor":true,"prefix":"","firstName":"Alden","middleName":"","lastName":"Estep","suffix":""},{"id":364596815,"identity":"f0ceb3c1-49ae-4b5f-b96d-8354e002d9f5","order_by":1,"name":"Neil Sanscrainte","email":"","orcid":"","institution":"US Department of Agriculture Agricultural Research Service","correspondingAuthor":false,"prefix":"","firstName":"Neil","middleName":"","lastName":"Sanscrainte","suffix":""},{"id":364596818,"identity":"934f485a-0c36-4757-8a2d-3b36551f9606","order_by":2,"name":"Alexandra Pagac","email":"","orcid":"","institution":"US Department of Agriculture Agricultural Research Service","correspondingAuthor":false,"prefix":"","firstName":"Alexandra","middleName":"","lastName":"Pagac","suffix":""},{"id":364596820,"identity":"713c38bf-c126-4b15-961d-355ea344a927","order_by":3,"name":"Christopher Geden","email":"","orcid":"","institution":"US Department of Agriculture Agricultural Research Service","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Geden","suffix":""},{"id":364596822,"identity":"44732c41-c3e7-469b-9c52-e4754347e882","order_by":4,"name":"Edwin Burgess IV","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Edwin","middleName":"Burgess","lastName":"IV","suffix":""}],"badges":[],"createdAt":"2024-10-10 11:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5239153/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5239153/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66592129,"identity":"c6b23aa5-361d-4d8d-9f1f-826c157c1c87","added_by":"auto","created_at":"2024-10-14 15:07:11","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78444,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of rhPCR assays. Genomic locations, amplicon sizes, and base coordinates of the SNP for the rhPCR assays for house flies for the (a) 1014L, 1014H and 1014F alleles, (b) 918M and 918T alleles, (c) 929T and 929I alleles and (d) the 600D and 600N alleles.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5239153/v1/3324212c00c6e5749893d37f.jpeg"},{"id":66592126,"identity":"94da8e82-ef96-4e29-93c2-45dcde717253","added_by":"auto","created_at":"2024-10-14 15:07:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":146881,"visible":true,"origin":"","legend":"\u003cp\u003eVerification of specificity of rhPCR assays on \u003cem\u003eMusca domestica\u003c/em\u003e. The rhPCR reactions for each of the 9 kdr alleles were run on the same 16 samples with the genotypes shown. This includes laboratory strains ALkdr (1014F), NCHis (1014H), TypeN (1014F \u0026amp; 918T \u0026amp; 600N) and Kdr1B (1014F \u0026amp; 929I) kindly provided by Dr. Jeffrey Scott. CAR21 (no \u003cem\u003ekdr \u003c/em\u003emutations) was provided by Carolina Biological Supply. Samples were amplified using the standard conditions as specified in Table 3. Five microliters of each standard 10 µl reaction was visualized on a 1.5% agarose gel with 1:10000 GelRed. Gel images were trimmed for presentation. Full images including MW standards are available in the Supplemental Information as Supplemental Images 1-4.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5239153/v1/948620622f5a9d47c10a80f4.png"},{"id":66592139,"identity":"b693326b-6d7c-41ba-ba61-041c23ae9817","added_by":"auto","created_at":"2024-10-14 15:07:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":332264,"visible":true,"origin":"","legend":"\u003cp\u003eVisualized comparison of the time and major steps in the methods for assessing knockdown resistance SNPs. Initially homogenization (a) is similar for all three procedures but diverge for (b) rhPCR , (c) Sanger, and (d) Nanopore based methods for 96 samples used to determine knockdown resistance genotypes in Musca domestica. Created in BioRender. Estep, A. (2024) BioRender.com/h16l713\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5239153/v1/b4f5ad44e15ac270dc6690a7.png"},{"id":66593323,"identity":"c0d229f1-9d51-4572-97be-30423861686b","added_by":"auto","created_at":"2024-10-14 15:15:12","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":222588,"visible":true,"origin":"","legend":"\u003cp\u003eAssessment of\u003cem\u003e kdr\u003c/em\u003e genotypes in US dairies. (a) Fifteen most common genotypes across all sites. Remaining genotypes combined into “OTHER.” Genotypes are abbreviated as the coding amino acid at positions 1014, 918, 929, and 600. (b) Allele frequencies by state for the 1014, 919, 929 and 600 \u003cem\u003ekdr\u003c/em\u003e mutations. Total number of samples genotyped is 1209 from 22 locations. Samples per location and site information is found in the data repository.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5239153/v1/6018167d27aed65f55ea046f.jpeg"},{"id":66593324,"identity":"3a7db935-59a2-40c2-b70e-f2b46d13acd9","added_by":"auto","created_at":"2024-10-14 15:15:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":260158,"visible":true,"origin":"","legend":"\u003cp\u003eAlluvial plot of genotypes (frequency greater than 0.01%) for individual locations. Genotypes present at a frequency of less than 0.01 are included in the “OTHER” category. Alluvial plot was constructed in R using ggplot2 and ggalluvial\u003csup\u003e60,61\u003c/sup\u003e. Total number of samples for all sites is 1209.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5239153/v1/8c813a6f8590d52767c0cfbe.png"},{"id":66592146,"identity":"55df4caf-0002-4332-9eea-a3a756286d1c","added_by":"auto","created_at":"2024-10-14 15:07:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":112870,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Non-metric multidimensional scaling (NMDS) of Jaccard dissimilarity among house fly \u003cem\u003ekdr \u003c/em\u003esample sites. (a) Ellipses represent 95% confidence intervals of sample sites organized by state. The first two letters of each site represent the two-letter abbreviation of each state, with the number at the end to distinguish between different sites within states. (b) Ellipses represent 95% confidence intervals of sample sites organized by region according to the US Census Bureau. The first two letters of each site represent the two-letter abbreviation of each state, with the number at the end to distinguish between different sites within states. (c) Non-metric multidimensional scaling (NMDS) of Jaccard dissimilarity among house fly \u003cem\u003ekdr \u003c/em\u003egenotypes. Ellipses represent 95% confidence intervals of genotypes in relation to states. Arrows and numbers correspond to the following homozygous \u003cem\u003ekdr\u003c/em\u003e type according to Freeman et al.\u003csup\u003e4\u003c/sup\u003e: 1) “wildtype no \u003cem\u003ekdr\u003c/em\u003e” 2) “\u003cem\u003ekdr\u003c/em\u003e” 3) “\u003cem\u003e1B\u003c/em\u003e \u003cem\u003ekdr\u003c/em\u003e” 4) “\u003cem\u003ekdr-his\u003c/em\u003e” 5) “\u003cem\u003esuper-kdr\u003c/em\u003e.” Very little “\u003cem\u003eType N kdr\u003c/em\u003e” (i.e., LLMMTTD\u003cu\u003eN\u003c/u\u003e) was observed and was most closely associated with Minnesota. \u0026nbsp;(d) Non-metric multidimensional scaling (NMDS)\u0026nbsp; of Jaccard dissimilarity among house fly \u003cem\u003ekdr \u003c/em\u003egenotypes. Ellipses represent 95% confidence intervals of genotypes in relation to geographical regions according to the US Census Bureau. Arrows and numbers are as described in (c).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5239153/v1/cecb350559b52d3385047387.png"},{"id":66593327,"identity":"2fbeca68-efb5-4b77-9d70-870ff7b9c9c4","added_by":"auto","created_at":"2024-10-14 15:15:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1647353,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5239153/v1/dbd0140a-2da8-42f5-a0c9-3a641fe8c429.pdf"},{"id":66592124,"identity":"c3b5b22e-c99a-49fd-b5f7-eb94992dd144","added_by":"auto","created_at":"2024-10-14 15:07:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19192,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5239153/v1/4ec4f1749e68c139b5610fb1.docx"},{"id":66592142,"identity":"d217d967-dbc1-477c-9f7f-ea031c4e05b7","added_by":"auto","created_at":"2024-10-14 15:07:12","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9056096,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationMdomkdr.docx","url":"https://assets-eu.researchsquare.com/files/rs-5239153/v1/bb7e4508c568aa25614b98af.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rapid and cost-effective screening of genetic markers associated with pyrethroid resistance in Musca domestica using RNAse H2 PCR (rhPCR)","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePesticide resistance affects the control of numerous arthropod pests worldwide, including those relevant to crops\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, animal production\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, public health\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, and the urban environment\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Consequences of pesticide resistance include the loss of crops\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, reduced animal performance (reviewed in Brewer et al.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e), risk of outbreaks of vector-borne diseases (reviewed in Rivero et al.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e), and litigious infestations of urban pests\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, to name a few. Pest surveillance, which includes pesticide resistance surveillance, is an important facet of an effective Integrated Pest Management program \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNumerous resistance mechanisms have been described across arthropod species, including metabolic detoxification, reduced cuticular penetration, and modifications of the pesticide target site (i.e., target site mutations). Of these, target site mutations are often implicated in strains with strong resistance phenotypes\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Target site mutations are single nucleotide polymorphisms (SNPs) resulting in changes in amino acid codons of the target ion channels or proteins (e.g., acetylcholinesterase), which, when translated into the protein, alters various properties of the target, including its susceptibility to pesticides\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Often target site SNPs act in ensembles with additive properties to create higher levels of phenotypic resistance. Common target site mutations include \u0026ldquo;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003er\u003c/span\u003eesistance to \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ed\u003c/span\u003eie\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003el\u003c/span\u003edrin\u0026rdquo;, or \u0026ldquo;\u003cem\u003erdl\u003c/em\u003e,\u0026rdquo; which is a mutation in the \u003cem\u003erdl\u003c/em\u003e subunit of arthropod GABA/glutamate-gated chloride channels, conferring resistance to dieldrin\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, fipronil, abamectin\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, but not fluralaner\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Another common set of mutations is found in the gene for acetylcholinesterase, an enzyme that hydrolyzes the neurotransmitter acetylcholine into choline and acetic acid\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These mutations alter affinity for organophosphates and carbamates. One of the most implicated target site mutations due the widespread use of pyrethroids in countless pest control systems all over the world is \u0026ldquo;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ek\u003c/span\u003enock\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ed\u003c/span\u003eown \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003er\u003c/span\u003eesistance,\u0026rdquo; or \u0026ldquo;\u003cem\u003ekdr\u003c/em\u003e,\u0026rdquo; which was first described in house flies by Busvine\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e (1951) shortly after DDT was brought to market. All three of these target site mutations have been described in bed bugs\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and importantly, in house flies\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, as this offers an exemplary model for surveillance tool development for broad application.\u003c/p\u003e \u003cp\u003eCommonly used methods for the detection of target site mutations include Sanger sequencing\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, multiplex PCR\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and allele-specific PCR\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e (which includes melt curve analysis (MCA)). However, as with all methods, each of these has benefits and limitations. While Sanger sequencing is the traditional \u0026ldquo;gold standard\u0026rdquo; for target site mutation detection\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, it is usually reliant on a remote facility (which may not be locally available) and cannot be multiplexed, thus making it challenging to use for large sample sizes. There is also a level of subjectivity in play when determining if two in-phase peaks can be \u0026ldquo;called\u0026rdquo; a heterozygote during inspection of electropherograms, and the threshold of the minimum signal needed to make a call is rarely identified. Multiplex PCR, as another method, is considered inexpensive and fast but can result in false positives if methods are not optimized properly\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Allele-specific PCR is also inexpensive and fast but relies on visual inspection of gels where faint bands pose a challenge \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The MCA approach with variously tailed SNP-specific primers, resulting in distinct melting temperatures, has become the predominant method used for \u003cem\u003ekdr\u003c/em\u003e assessment in mosquitoes due to cost effectiveness, the rapidity of producing results for thousands of samples and the ease of implementation with existing laboratory infrastructure often used for mosquito-borne pathogen screening\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, MCA is difficult when mutations of interest are tri-allelic due to side-by-side SNPs, such as the substitution of leucine for phenylalanine or serine at amino acid position 1014 (L1014 F/S) for the mosquito \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e and valine for glycine or isoleucine at position 1016 (V1016 G/I) in \u003cem\u003eAedes aegypti\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e. Notably, the 1014 mutation in house flies is also due to adjacent SNPs resulting in the wildtype leucine becoming a histidine or phenylalanine (L1014 H/F)\u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn alternative form of allele-specific PCR that has not yet been explored for use in detecting SNPs implicated in insecticide resistance target site mutations is RNase H-dependent PCR (rhPCR)\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. This method utilizes blocked primers, with an RNA base in the primer located complementary to the SNP of interest on the template DNA, with several DNA bases 3\u0026rsquo; of the RNA base and a final blocking group that, when present, inhibits amplification by DNA polymerase. When the RNA base is correctly matched to the complimentary DNA base on the template, RNAse H2, derived from \u003cem\u003ePyrococcus abyssi\u003c/em\u003e (Thermococcales: Thermococcaceae), cleaves the primer at the 5\u0026rsquo; side of the RNA:DNA heteroduplex. This cleavage relieves the steric hindrance caused by the blocked 3\u0026rsquo; end of the primer allowing the DNA polymerase to amplify as in standard PCR. This SNP-specific limitation acts at each cycle to reduce spurious amplification. This allows for highly specific amplifications of targeted SNPs and is quickly being adopted for detection of critical SNPs in biomedical research\u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The advantages of rhPCR are that it is relatively inexpensive, can be conducted in high throughput on common qPCR equipment and produces results quickly. The specificity of the RNAse H2 cleavage reaction enables discrimination among triallelic mutations and produces very low error rates when properly controlled and optimized. The products of rhPCR can also be visualized with simple gel electrophoresis and transillumination and even sent for Sanger sequencing with no additional steps required.\u003c/p\u003e \u003cp\u003eTo demonstrate the utility of rhPCR to rapidly and efficiently detect insecticide resistance target site mutations, we developed rhPCR assays to detect \u003cem\u003ekdr\u003c/em\u003e SNPs in house fly collections from dairies located in eight states (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As a significant agricultural and public health pest, house flies have become a model organism for pesticide resistance research (reviewed in Geden et al.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e) due to their wide geographic distribution and pest status. House fly populations have long been challenging to control due to their rapid development of insecticide resistance\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Of the insecticides registered for use against house flies, pyrethroids are one of the most widely used and often fail due to resistance\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. This resistance is due in part to well-characterized and numerous \u003cem\u003ekdr\u003c/em\u003e mutations, including the triallelic L1014 F/H mutation, a methionine to threonine mutation at 918 (M918T), a threonine to isoleucine mutation at 929 (T929I), and a rare aspartic acid to asparagine mutation at 600 (D600N)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Traditionally, the 1014F mutation is considered to be a gatekeeper that must be present before the 918, 929 or 600 mutations are found\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. As the ability to get an accurate answer from the first test attempt is an important consideration for the use of an operational, rather than academic, research tool, we used Nanopore sequencing to confirm genotypes in a subset of samples.\u003c/p\u003e \u003cp\u003eThe rhPCR method adds a new tool to resistance monitoring programs and can extend to virtually all pests or arthropod disease vectors and any resistance mutation that is conferred by a SNP. Additionally, rhPCR can be scaled to a wide range of instrumentation, from simple PCR and gel electrophoresis to quantitative PCR and next-generation sequencing. The rhPCR method makes resistance genotyping accessible to a wider range of lab types, especially those in low and middle-income countries.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDevelopment of RNAse H-dependent PCR methods to assess \u003cem\u003eknockdown resistance\u003c/em\u003e mutations in \u003cem\u003eMusca domestica\u003c/em\u003e\u003c/p\u003e \u003cp\u003eWe developed a rhPCR based method to assess the well-described markers of pyrethroid insecticide resistance found in the voltage-gated sodium channel (VGSC) of the house fly \u003cem\u003eMusca domestica\u003c/em\u003e, resulting in a tool that could improve operational fly management in dairy or livestock production operations. Relying upon the reported single base specificity of rhPCR and the design guide provided by the manufacturer (IDTDNA, Coralville, IA), we designed primers to detect the presence of the SNPs for the 1014 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), 918 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), 929 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) and 600 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD) VGSC mutations based on previously reported sequences\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the 1014 SNPs, three reactions assess the presence of two possible SNPs in consecutive base pair positions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The primers for the wildtype 1014 lysine and the \u003cem\u003ekdr\u003c/em\u003e mutant 1014 histidine interrogate for the presence of the codons C\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eT\u003c/span\u003eT (L) or C\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA\u003c/span\u003eT (H) (position 4371952 of NW026712250.1). The primer for the \u003cem\u003ekdr\u003c/em\u003e mutant 1014 phenylalanine assesses the presence of the codon \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eT\u003c/span\u003eTT (F) (position 4371953 of NW026712250.1). We observed that the specificity of the rhPCR primers prevented misamplification at the two adjacent SNPs at the site of the 1014 \u003cem\u003ekdr\u003c/em\u003e mutations.\u003c/p\u003e \u003cp\u003ePrimers for the 918, 929 and 600 \u003cem\u003ekdr\u003c/em\u003e mutations assess the presence of the following SNPs: 918 \u0026ndash; wildtype methionine (A\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eT\u003c/span\u003eG) to \u003cem\u003ekdr\u003c/em\u003e mutant threonine (A\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC\u003c/span\u003eG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, position 4376601 of NW026712250.1); 929 \u0026ndash; wildtype threonine (A\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eC\u003c/span\u003eA) to \u003cem\u003ekdr\u003c/em\u003e mutant isoleucine (A\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eT\u003c/span\u003eA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, position 4376568 of NW026712250.1); and 600 \u0026ndash; wildtype aspartic acid (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eG\u003c/span\u003eAC) to \u003cem\u003ekdr\u003c/em\u003e mutant asparagine (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA\u003c/span\u003eAC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, position 4392528 of NW026712250.1).\u003c/p\u003e \u003cp\u003eComparison of samples with a specific \u003cem\u003ekdr\u003c/em\u003e SNP to those without the SNP were discernable with a difference in cycle threshold (C\u003csub\u003et\u003c/sub\u003e) of at least 4 cycles. This allows for the genotyping of unknowns for specific \u003cem\u003ekdr\u003c/em\u003e alleles by comparison to known controls. While we used the amplification curves produced by the higher capacity of standard qPCR equipment to call genotypes, it was also possible to make presence or absence calls using lower throughput visualization on agarose gels as commonly done with AS-PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), although the very short amplicon (~\u0026thinsp;60bp) for the 600D or N was difficult to visualize.\u003c/p\u003e \u003cp\u003eComparison of genotyping methods\u003c/p\u003e \u003cp\u003eWe compared rhPCR genotyping and the traditional Sanger based method of genotyping to genotypes generated by Nanopore-based next generation sequencing. We evaluated the ability of each method to determine the complete genotype by evaluating success after only a single attempt, what we call the \u0026ldquo;first pass\u0026rdquo; accuracy of each method. First pass accuracy is critical for cost-effectively genotyping large sample numbers as in large MCA based mosquito insecticide resistance (IR) studies\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. For this comparison, we selected 68 samples from the 1,209 tested by rhPCR. These included genotypes obtained by the rhPCR assays that were unexpected based on previous literature, including those with 918 or 929 mutations without the presence of the 1014F gatekeeper mutation as well as those with more common genotypes (Supplementary Table\u0026nbsp;1) and we did not consider the 600 \u003cem\u003ekdr\u003c/em\u003e locus in our calculations because its presence in the samples tested was so rare\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. To be considered a success, the 7 individual rhPCR reactions (3 for 1014, 2 for 918, 2 for 929) or the 2 individual Sanger reactions (one for 1014, 1 for 918/929) needed to match the result from nanopore sequencing.\u003c/p\u003e \u003cp\u003eThree of the 68 samples resulted in a different genotype by each of the three methods (Table\u0026nbsp;2). The rhPCR assays resulted in successful genotyping for 75.0% of samples while individual assay success rate for each allele was greater than or equal to 89.7%. We obtained a complete and accurate genotype result from commercial Sanger sequencing (2 reactions) on the first pass for only 41.2% of the samples.\u003c/p\u003e \u003cp\u003eWe also assessed the time needed to generate genotypes by the three different methods based on a group of 96 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Including sample preparation and homogenization time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), which was similar regardless of method, rhPCR was able to produce results in 6.5 hours (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Both Sanger based genotyping (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) and nanopore based sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), which shared much of the same procedure, both required more than 30 hours to generate results. We note that our timeline for Sanger includes only 24 hours for overnight shipment, purification and sequencing by a commercial provider which could certainly be longer depending on individual laboratory facilities. Based on this comparison, rhPCR reduced the time needed to produce results by 80%.\u003c/p\u003e \u003cp\u003eWe also considered the costs associated with each method to develop a complete genotype (Supplementary Table\u0026nbsp;2). Based on the same 96 sample group on one plate, rhPCR was able to produce results for \u003cspan\u003e$\u003c/span\u003e4.00 USD per sample or less than \u003cspan\u003e$\u003c/span\u003e400 USD for the plate. Sanger costs resulted in a per sample cost of \u003cspan\u003e$\u003c/span\u003e17.50 or \u003cspan\u003e$\u003c/span\u003e1,680 USD for the plate. Most of this cost was the purification and Sanger sequencing expense charged by the provider. We also calculated the costs for genotyping by nanopore and it was comparable to Sanger at \u003cspan\u003e$\u003c/span\u003e14.72 USD per sample with the major expenses being the sequencing flow cell and library prep reagents.\u003c/p\u003e \u003cp\u003eAssessment of \u003cem\u003ekdr\u003c/em\u003e mutations in wild populations of \u003cem\u003eMusca domestica\u003c/em\u003e from dairy production facilities by rhPCR\u003c/p\u003e \u003cp\u003eUsing the rhPCR assays described above, we assessed the presence of \u003cem\u003ekdr\u003c/em\u003e mutations in populations of \u003cem\u003eM. domestica\u003c/em\u003e from eight states, which totaled 1,209 individual flies with nearly 77% (926/1,209) producing a result from each of the 9 individual genotyping reactions (File S1). Genotype calls were made for each wild individual by comparison to the results from laboratory strains with known \u003cem\u003ekdr\u003c/em\u003e genotypes included in each assay. Negative controls, containing no DNA, were also included. Raw genotyping data is included in the data repository at DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15482/USDA.ADC/25044329\u003c/span\u003e\u003cspan address=\"10.15482/USDA.ADC/25044329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. We observed 27 different \u003cem\u003ekdr\u003c/em\u003e genotypes (abbreviated by summarizing the amino acids resulting at 1014, 918, 929 and 600) among all the locations (Supplementary Table\u0026nbsp;1). The three most common genotypes (described by a shortened genotype notation for 1014, 918, 929, and 600 respectively, LLMMTTDD, HHMMTTDD, FFMMTTDD) were homozygotes at position 1014 without any other \u003cem\u003ekdr\u003c/em\u003e mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). These together accounted for nearly 50% of the successfully genotyped \u003cem\u003eM. domestica\u003c/em\u003e. Heterozygotes at 1014 with no other \u003cem\u003ekdr\u003c/em\u003e mutations accounted for 3 of the 4 (HFMMTTDD, LHMMTTDD, LFMMTTDD) next most common genotypes. Notably, the 1014F homozygote paired with heterozygous 929 (genotype FFMMTIDD) was found at similar frequency as the 1014-only heterozygotes. Together, these 7 genotypes represented more than 70% of the total genotypes detected. We did detect low frequencies of unexpected genotypes where \u003cem\u003ekdr\u003c/em\u003e mutations were found without the 1014F, which is considered a gatekeeper for the other \u003cem\u003ekdr\u003c/em\u003e mutations\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Notably, the 600N mutation was rare and only detected in a single sample from Minnesota.\u003c/p\u003e \u003cp\u003eWe observed considerable variation in allele frequencies between states (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The wildtype 1014L allele ranged from absent in the NC samples to nearly 0.7 in WI although the average frequency was 0.35. The frequency of the 1014H mutation averaged 0.328 (range: 0.082\u0026ndash;0.726) and the 1014F frequency was similar at 0.326 (range: 0.095\u0026ndash;0.609). Both the 918T and 929I mutations were identified in all states although not in all locations within a state (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB \u0026amp; \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The 918T SNP frequency averaged 0.077 (range: 0.012\u0026ndash;0.191) and was highest in MN and KS and less than 0.02 in the southeastern states of FL, GA, and NC. In contrast, the 929I mutation frequency, which averaged 0.102 (range: 0.016\u0026ndash;0.316) across all states, was higher in FL, GA, and NC and low (\u0026lt;\u0026thinsp;0.061) in all other states including TX. As noted above, the 600N mutation was only identified in a single sample from MN. For multiple samples within a state, we observed a similar presence of genotypes although the relative frequencies of each genotype varied (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAssessment of genotype frequency variation by NMDS\u003c/p\u003e \u003cp\u003eTo formalize our observation that genotype frequencies varied by location, we conducted non-metric multidimensional scaling (NMDS) analysis using our genotype data. The original dissimilarities among sites, with regard to genotype composition, were well-preserved in reduced dimensions (non-metric R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.993, linear R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.946). Model stress was 0.085. Permutational ANOVA revealed a statistically significant difference among the states (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA; F\u0026thinsp;=\u0026thinsp;2.34, df\u0026thinsp;=\u0026thinsp;7, 14, P\u0026thinsp;=\u0026thinsp;0.001). There was also a statistically significant difference among the regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB; F\u0026thinsp;=\u0026thinsp;2.43, df\u0026thinsp;=\u0026thinsp;2, 19, P\u0026thinsp;=\u0026thinsp;0.002). Among regions, only the pairwise comparison of the South and Midwest regions was significant (F\u0026thinsp;=\u0026thinsp;3.14, df\u0026thinsp;=\u0026thinsp;1, P\u0026thinsp;=\u0026thinsp;0.006; R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.156).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to describe a method using rhPCR for resistance genotyping in insects. This robust tool will enable more rapid field-level sampling and could positively impact operational decision-making by allowing inclusion of IR information into pest control programs in livestock production operations. We introduced a series of rhPCR assays to detect key genotypes from large numbers of flies with a relatively high rate of success at the first testing event. Notably, the rhPCR-based genotyping we conducted in this study encompassed over 1,200 organisms from 22 locations across 8 states, making it one of the largest \u003cem\u003eM. domestica\u003c/em\u003e IR genotyping studies conducted to date\u003csup\u003e \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e \u003c/sup\u003e. The rhPCR assays can be completed quickly and cost effectively; in this study only 12 days were needed to conduct testing at a consumable cost of less than \u003cspan\u003e$\u003c/span\u003e4,900 (in 2024 USD); this is significantly cheaper than obtaining the same data through Sanger sequencing which would have cost around \u003cspan\u003e$\u003c/span\u003e21,000, a savings of slightly more than 75%. Additionally, processing time for these rhPCR assays (qPCR performed on homogenized samples) was only about a quarter of the time required for a Sanger sequencing workflow, which requires multiple procedures for each sample (DNA purification, PCR assays, PCR cleanup, sequencing onsite or through a 3rd party).\u003c/p\u003e \u003cp\u003eTraditionally, the Sanger based \u003cem\u003ekdr\u003c/em\u003e assessment in flies has been applied in a manner to screen first for the mutations at 1014. If 1014F was detected, further reactions and Sanger were conducted to assess the presence of the 918, 929 and 600 SNPs. This reduced the overall costs by not running additional Sanger reactions for SNPs that were not expected to be present without the gatekeeper 1014F mutation. The rhPCR method described here was able to successfully identify several unexpected genotypes where the 918 and 929 mutations were not paired with 1014F. These initial genotyping results were also frequently obtained by both Sanger and nanopore-based methods, thus confirming that rhPCR correctly identified these unexpected genotypes.\u003c/p\u003e \u003cp\u003eThe rhPCR method described here has a few distinct advantages for expanding the operational utility of IR assessment in flies and other insects. At a minimum, we demonstrate that rhPCR can easily screen for SNPs and is highly specific even when dealing with adjacent variation (as in the 1014L, H, or F SNPs in \u003cem\u003eM. domestica\u003c/em\u003e). This method could conceivably be a useful improvement for genotyping other tri-allelic mutations such as those that exist in \u003cem\u003eAe. aegypti\u003c/em\u003e and \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e. The same rhPCR method we applied to the \u003cem\u003ekdr\u003c/em\u003e gene here could test for acetylcholinesterase or \u003cem\u003erdl\u003c/em\u003e mutations, which are indicative of IR to other modes of action in various insect taxa, including flies. A second advantage of the rhPCR method is the ability to screen ecologically relevant numbers of organisms at relatively low cost (1/5 the cost) and in a short time period compared to traditional methods. Screening larger sample sizes will allow for a better understanding of which genotypes survive sprays conducted in agricultural and urban settings, where both survivors and dead are screened for genotype and compared statistically\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The speed of rhPCR would allow for multiple assessments being conducted during a treatment to understand the dynamics of \u003cem\u003ekdr\u003c/em\u003e spread in a population under selection. The rhPCR method could also be applied to understand the distribution of \u003cem\u003ekdr\u003c/em\u003e genotypes over geographic space. Our NMDS analysis showed significant differences in genotype between geographically dispersed collection sites, but we did not have adequate sample density in any one area to use methods that can quantify the spatial distribution of resistance mutations, which would guide sampling strategies and operational interventions in adjacent areas not yet tested\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile there are clear advantages to the use of rhPCR for IR assessment, it has disadvantages that are common to surrogate methods. The rhPCR method provides a screening tool for rapid assessment only of known mutations; it is not a tool for discovery of novel mutations and so a foreknowledge of the SNP or SNPs of interest is required. Along with this, rhPCR, like other surrogate methods, requires that the SNP being assessed is a high value marker in order for the resulting phenotype to be meaningful. It is clear from work in mosquitoes that many mutations exist in the VGSC but that not every mutation results in much resistance; some act as part of an ensemble of SNPs and are found in both susceptible and resistant organisms. Third, proper use of rhPCR requires that controls for the alleles being tested must be included in every assay conducted, optimally including heterozygotes to ensure the assay is indicating adequate sensitivity. This study benefitted from the kindness of other researchers in the field who provided strains with the specific \u003cem\u003ekdr\u003c/em\u003e mutations we required, but these strains may not be as accessible to other workers, especially those in developing countries. Though controls in the same format as the collections (i.e. legs, whole bodies, purified DNA, etc.) may not always be possible to source, this does not relieve the experimenter from including proper controls. Improperly controlled surrogate assays should never be used for operational decision making or in academic publishing. Long synthetic DNA oligos can be ordered and should be used as allele-specific controls when proper control strains are not available.\u003c/p\u003e \u003cp\u003eIn summary, we demonstrate an rhPCR based assay system that can move IR assessment from the academic laboratory to an operationally useful tool. This easy method can be used to better define important ecological parameters of IR in the field, by increasing sample sizes and increasing the number of locations that can be assessed for a given quantity of resources. This may allow more effective use of IR information in integrated pest management programs. Monitoring IR informs decision-making about pesticide usage by reducing use and expense when a formulation is likely to be ineffective. Because house flies in the US are widely resistant to all the products currently available for their management, the ability to monitor resistance as new products enter the market will be critical for their effective management. Improved IR monitoring will also be an emerging tool in parts of the world where resistance to current products is not as severe as in the US, and alternating insecticidal modes of action remains a viable option.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cem\u003eMusca domestica\u003c/em\u003e strains and samples\u003c/p\u003e \u003cp\u003e \u003cem\u003eMusca domestica\u003c/em\u003e strains with known \u003cem\u003ekdr\u003c/em\u003e genotypes were kindly provided by Dr. Jeffrey Scott. The abyss (no 1014, 918, 929 or 600 mutations), ALkdr (1014F mutation), NChis (1014H), Type N (1014F, 918T, 600N), kdr1B (1014F, 929I) and JPSkdr (1014F, 918T) strains were used for initial assay development and as specific allele containing controls for rhPCR assays. The CAR21 strain (no 1014, 918, 929 or 600 mutations) was also used as a \u003cem\u003ekdr\u003c/em\u003e negative control. This strain, which was originally obtained from Carolina Biological Supply, has been maintained at the USDA Center for Medical, Agricultural and Veterinary Entomology and is recognized as being insecticide-susceptible\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Maintenance and rearing of these strains was conducted as previously described\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSamples of field strains were collected from eight states by collaborators in Hatch Project S-1076. Samples were collected from livestock facilities using sweep nets. Collected samples were knocked down by freezing and placed into 50 mL tubes (Thermo Fisher, Waltham, MA) or 1 pint paper containers (WestRock, Atlanta, Georgia, USA) for overnight shipping on ice blocks and were maintained at -80 \u003csup\u003eo\u003c/sup\u003eC after arrival. Samples from CA and TX were delayed in shipping and spent more than 24 hours at room temperature causing significant degradation but were tested anyway.\u003c/p\u003e \u003cp\u003eSample preparation and homogenization\u003c/p\u003e \u003cp\u003eSamples provided by collaborators were stored at -80\u0026deg;C until sorting and identification on ice. Flies were visually identified as \u003cem\u003eM. domestica\u003c/em\u003e using morphological characters, and forceps were submerged in 100% ethanol between the handling of each fly. \u003cem\u003eMusca domestica\u003c/em\u003e individuals had a mid and hind leg removed if present (if not, any two legs were chosen) with forceps where the femur meets the trochanter. The legs were then placed in a 96-deep well homogenization plate (Omni International, Kennesaw, GA) and the rest of the sample placed in a corresponding 96-well plate in the same well position. The homogenization plate containing the legs was prepared by adding 2.0 mm cubic zirconium beads (BioSpec Products, Bartlesville, OK) to each well using a LabTie bead dispenser (BioSpec Products, Bartlesville, OK), followed by the addition of 200 \u0026micro;L of deionized water. All plates were labeled with the relevant collection information to keep the leg samples for testing linked with the remainder of the sample. Once all samples from a collection event were processed, the 96-well plate containing the remainder of the sample was covered with a foil plate seal (USA Scientific, Ocala, FL) and stored at -80\u0026deg;C in case further testing was required. The homogenization plate containing the legs was then covered with a silicone sealing mat (Omni International, Kennesaw, GA) and stored at -20\u0026deg;C until homogenization. Sample preparation was conducted separately from sample testing.\u003c/p\u003e \u003cp\u003eOn the day of assay, homogenization plates containing legs, beads, and deionized water were defrosted in a 22 \u003csup\u003eo\u003c/sup\u003eC water bath and then homogenized for 2 minutes on a Beadruptor 96 (Omni International, Kennesaw, GA). After homogenization, 96-well plates were spun for 2 min at 805 x g then placed on ice until assay setup.\u003c/p\u003e \u003cp\u003erhPCR assays\u003c/p\u003e \u003cp\u003eMaster mixes for rhPCR were prepared for each allele using the primers (IDTDNA, Coralville, IA) and reaction quantities listed in Tables\u0026nbsp;2 \u0026amp; 3. The effective concentration of H2 RNAse (IDTDNA, Coralville, IA) was optimized for each allele-specific reaction as recommended by the manufacturer using laboratory strains with known genotypes\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Reactions were assembled in 384-well plates using an EpMotion 5750 liquid handling system (Eppendorf, Hamburg, Germany) in a final reaction volume of 10 \u0026micro;L. Eight microliters of each master mix was added by the liquid handling system followed by 2 \u0026micro;L of spun homogenate. Each 96-well plate of homogenates required three 384 well plates for all the assays needed: 1st \u0026ndash; 1014L, 1014H, and 1014F; 2nd \u0026ndash; 9198M, 918T, 929T, and 929I; and 3rd \u0026ndash; 600D and 600N.\u003c/p\u003e \u003cp\u003ePlates were sealed with clear optical covers (Thermo Fisher, Waltham, MA), mixed gently on a plate vortex (IKA Works, Inc., Wilmington, NC), spun to remove bubbles and then cycled on a QuantStudio 6 Flex system (Thermo Fisher, Waltham, MA) running QuantStudio Real-Time PCR Software V1.3 using standard FAST conditions of 95 \u003csup\u003eo\u003c/sup\u003eC for 20 sec, then 40 cycles of 95 \u003csup\u003eo\u003c/sup\u003eC for 3 sec and 60 \u003csup\u003eo\u003c/sup\u003eC for 20 sec. This was followed by a melt curve phase of 95 \u003csup\u003eo\u003c/sup\u003eC for 15 sec, 60 \u003csup\u003eo\u003c/sup\u003eC for 1min, and a ramp step from 60 \u003csup\u003eo\u003c/sup\u003eC to 95 \u003csup\u003eo\u003c/sup\u003eC at 0.05 \u003csup\u003eo\u003c/sup\u003eC /sec with continuous fluorescence recording to assess amplicon melting temperature as an extra check to ensure the specificity of the reaction.\u003c/p\u003e \u003cp\u003eThe presence of an allele in an organism was made using two metrics. First, the Ct of a curve had to be within 3 cycles of the known positive controls for the allele in question and at least two cycles above the Ct of the known negative controls. As a second factor, the melting temperature (Tm) of the resulting amplicon had to be \u0026plusmn;\u0026thinsp;0.4 \u003csup\u003eo\u003c/sup\u003eC of the Tm of the known positive control. A sample that did not meet both metrics above was considered negative for the specific allele. A detailed version of the protocol for this assay has been deposited in the data repository.\u003c/p\u003e \u003cp\u003ePCR amplification and Sanger sequencing of sodium channel mutations\u003c/p\u003e \u003cp\u003eReactions for PCR amplification of regions containing \u003cem\u003ekdr\u003c/em\u003e mutations used a combination of previously published and novel primers (Table\u0026nbsp;2). All reactions were conducted in 25 \u0026micro;L volumes using 1 \u0026micro;L of fly homogenate or DNA with final concentrations of 1.5 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 0.2 \u0026micro;M dNTPs, 0.4 \u0026micro;M forward and reverse primers, and 0.04 U/\u0026micro;l of Platinum Taq (Thermo Fisher, Waltham, MA). Reactions to amplify the 1014 containing region were conducted on an Eppendorf Mastercycler (Eppendorf, Hamburg, Germany) with the following conditions: 94 \u003csup\u003eo\u003c/sup\u003eC for 2 min 40 cycles of 94 \u003csup\u003eo\u003c/sup\u003eC for 30 sec, 60 \u003csup\u003eo\u003c/sup\u003eC for 25 sec and 72 \u003csup\u003eo\u003c/sup\u003eC for 30 sec; and 72 \u003csup\u003eo\u003c/sup\u003eC for 5 min. Reactions for the 918/929 and 600 \u003cem\u003ekdr\u003c/em\u003e containing regions were amplified on the same equipment but with the following conditions: 94 \u003csup\u003eo\u003c/sup\u003eC for 2 min; 40 cycles of 94 \u003csup\u003eo\u003c/sup\u003eC for 30 sec, 55 \u003csup\u003eo\u003c/sup\u003eC for 30 sec and 72 \u003csup\u003eo\u003c/sup\u003eC for 1 min; and then 72 \u003csup\u003eo\u003c/sup\u003eC for 5 min. Select amplicons were verified on 2% agarose gels. ExoSAP purification and Sanger sequencing were conducted by PsomagenUSA (Rockville, MD). Visualization of rhPCR amplicons for Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e used 10 \u0026micro;l of PCR reaction with 1.1 \u0026micro;L of 10X Blue Juice. A 100 base pair ladder (Thermo Fisher, Waltham, MA) was included and the samples were electrophoresed at 102 V for 1 hr and then visualized on an iBright imaging system (Thermo Fisher, Waltham, MA).\u003c/p\u003e \u003cp\u003eNanopore sequencing of \u003cem\u003ekdr\u003c/em\u003e amplicons and sodium channel coding region\u003c/p\u003e \u003cp\u003eNanopore sequencing was conducted according to the manufacturer provided protocol using R10 chemistry and Native Barcoding Kit V14 (SQK-NBD114.96; Oxford Nanopore Technologies, Oxford, England). The protocol version used was: NBA_9170_v114_revL_15Sep2022 updated 02/10/2023 and a marked up version is included in the data repository. Samples for sequencing \u003cem\u003ekdr\u003c/em\u003e amplicons were prepared from unpurified PCR products by combining 0.34 \u0026micro;L of the 1014 amplicon PCR, 0.27 \u0026micro;L of the 918/929 amplicon PCR, and 0.27 \u0026micro;L of the 600 amplicon PCR with 11.37 \u0026micro;L of nuclease free water. Samples for VGSC sequencing used 12.25 \u0026micro;L of the unpurified VGSC PCR and were placed into a 96-well plate and end prepped using NEBNext Ultra II End Repair/dA-Tailing Module (New England BioLabs Inc, Ipswich, MA) per the protocol. Each sample was then affixed with a unique barcode and all barcoded samples were then combined, cleaned with 80% ethanol and then final sequencing adapters were ligated per the ONT protocol. The adapted, barcoded library was washed with standard fragment buffer, eluted in 15 \u0026micro;L of elution buffer and quantified on a Nanodrop 8000 spectrophotometer. Fifty femtomoles of eluted DNA was mixed with sequencing buffer, loading beads, and elution buffer before being loaded onto a prepared R10 flow cell. Sequencing was managed on a GridION x5 device by the MinKNOW software (version 23.07.12) and electrical signals were converted to basecalls by Guppy (version 7.1.4). Initial read quality control was managed by the MinKNOW software with a minimum quality score of 10. Reads at or above this threshold were binned into barcode specific folders as fastq files. Barcodes were not removed from raw reads to allow subsequent filtering at higher stringency. Two sequencing runs were conducted with 36 \u003cem\u003ekdr\u003c/em\u003e amplicon samples in the first run and 32 \u003cem\u003ekdr\u003c/em\u003e amplicon samples in the second run. Raw reads are available as aligned .bam files under accession: PRJNA1163914.\u003c/p\u003e \u003cp\u003eBioinformatic analysis\u003c/p\u003e \u003cp\u003eBarcode binned raw reads that met initial quality filters were subsequently filtered at higher stringency to reduce spurious barcode misassignments. The sequencing summary file for each run was filtered using command line tools to require a barcode match of \u0026gt;\u0026thinsp;97% and more than 37 bases in length at each end of the amplicon. A maximum of 4k reads meeting these requirements were selected for each barcode using seqtk\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e and then subsequently mapped to the \u003cem\u003eM. domestica\u003c/em\u003e VGSC using Minimap2\u003csup\u003e53\u003c/sup\u003e. The resulting pileups were examined using IGV\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e to determine the presence of SNPs for the 1014, 918, 929 and 600 \u003cem\u003ekdr\u003c/em\u003e mutations.\u003c/p\u003e \u003cp\u003eStatistics and Reproducibility\u003c/p\u003e \u003cp\u003eFrequency of \u003cem\u003ekdr\u003c/em\u003e genotypes was ordinated alone or as an aggregate representation of the trapping sites from which they were collected using non metric multidimensional scaling in the \u0026lsquo;vegan\u0026rsquo; package\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e in R version 4.3.2\u003csup\u003e56\u003c/sup\u003e. Centroids (i.e., means) of genotype frequencies were summarized at state and regional levels as 95% confidence interval ellipses. Regions were defined according to the US Census Bureau as \u0026ldquo;Midwest\u0026rdquo; (Minnesota, Kansas, Wisconsin), \u0026ldquo;South\u0026rdquo; (Texas, Florida, North Carolina, Georgia), and \u0026ldquo;West\u0026rdquo; (California). The Jaccard dissimilarity index method was used with NMDS done in three dimensions (i.e., k\u0026thinsp;=\u0026thinsp;3), which produced the lowest stress. Stress is an important indicator of ordination fit in the assigned number of dimensions, with values\u0026thinsp;\u0026lt;\u0026thinsp;0.2 being acceptable representation, \u0026lt; 0.1 being good representation, and \u0026lt;\u0026thinsp;0.05 being excellent representation\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Stress plots were visually inspected and both non-metric and linear fit R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e values are reported. Then, a permutational type II ANOVA was done using the \u0026lsquo;adonis.II\u0026rsquo; function in the \u0026lsquo;RVAideMemoire\u0026rsquo; package\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Posthoc comparisons of F statistics were done using the \u0026lsquo;pairwiseAdonis\u0026rsquo; package\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, with a Holm-Sidak P-value correction to account for familywise error rate. Due to the number of pairwise comparisons among states, relatively low sample size, and the associated P-value adjustment, we only report the global permutational type II ANOVA for the state comparisons. For all statistical comparisons, α\u0026thinsp;=\u0026thinsp;0.05. Samples sizes from each of the 22 locations ranged from 32\u0026ndash;89 individuals tested from each location. Individual samples that did not produce a clear genotype by rhPCR were excluded from further testing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperimental data not included in the manuscript are available from USDA AgData Commons at: doi: 10.15482/USDA.ADC/25044329. Sequencing data is available from NCBI under BioProject ID PRJNA1163914.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Authors would like to acknowledge the assistance of the members of Hatch Project S-1076 for providing field collections and Dr. Jeffrey Scott for providing \u003cem\u003eM. domestica\u003c/em\u003e strains with known kdr genotypes. Project support for ASE, NDS, AAC and CJG was provided by USDA National Program 104.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eASE and ERB conceived the project and performed data analysis. ASE, NDS and AAC conducted experiments. All authors contributed to the manuscript draft, edits and revisions. All authors have approved submission of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang, L.\u003cem\u003e et al.\u003c/em\u003e Genetic structure and insecticide resistance characteristics of fall armyworm populations invading China. \u003cem\u003eMol Ecol Resour\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1682-1696 (2020). https://doi.org/10.1111/1755-0998.13219\u003c/li\u003e\n\u003cli\u003eChen, Y. H., Cohen, Z. P., Bueno, E. M., Christensen, B. M. \u0026amp; Schoville, S. D. 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Frequencies of pyrethroid resistance-associated mutations of Vssc1 and CYP6D1 in field populations of Musca domestica L. in Turkey. \u003cem\u003eJ Vector Ecol\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 239-247 (2011). https://doi.org/10.1111/j.1948-7134.2011.00164.x\u003c/li\u003e\n\u003cli\u003eWang, Q.\u003cem\u003e et al.\u003c/em\u003e Diversity and frequencies of genetic mutations involved in insecticide resistance in field populations of the house fly (Musca domestica L.) from China. \u003cem\u003ePesticide biochemistry and physiology\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 153-159 (2012). \u003c/li\u003e\n\u003cli\u003eChung, H. 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C. ggalluvial: Layered Grammar for Alluvial Plots. \u003cem\u003eJ Open Source Softw\u003c/em\u003e\u003cstrong\u003e5\u003c/strong\u003e (2020). https://doi.org/10.21105/joss.02017\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Musca domestica, knockdown resistance, RNAse H2 PCR, voltage gated sodium channel, insecticide resistance","lastPublishedDoi":"10.21203/rs.3.rs-5239153/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5239153/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePyrethroid resistance, particularly knockdown resistance (\u003cem\u003ekdr\u003c/em\u003e), is widespread in insect pest populations, but rarely has \u003cem\u003ekdr\u003c/em\u003e been associated with field-level pest control failure. The prevailing understanding is that \u003cem\u003ekdr\u003c/em\u003e contributes to a resistant phenotype, but this knowledge has remained largely an academic pursuit and has not translated to tools and strategies needed by agricultural producers to make rapid decisions for effective resistance management. As a first step in providing these operational tools, we developed robust assays using the high specificity of rhPCR to reduce \u003cem\u003ekdr\u003c/em\u003e assessment time by approximately 80% and costs\u0026thinsp;~\u0026thinsp;75% from the traditional Sanger based method used for \u003cem\u003eMusca domestica\u003c/em\u003e. An important consideration for the use of an operational tool is the ability to get an accurate result on the first attempt, so we used Nanopore sequencing to confirm genotypes in a subset of samples and found the first pass genotyping accuracy of rhPCR method to be 75.0%, versus 41.2% with the traditional Sanger method. To demonstrate the broad applicability and comparability of screening for \u003cem\u003ekdr\u003c/em\u003e SNPs using rhPCR, we conducted the largest assessment of \u003cem\u003ekdr\u003c/em\u003e genotypes of \u003cem\u003eM. domestica\u003c/em\u003e in United States dairy operations and found similar \u003cem\u003ekdr\u003c/em\u003e patterns to other recent studies using traditional methods.\u003c/p\u003e","manuscriptTitle":"Rapid and cost-effective screening of genetic markers associated with pyrethroid resistance in Musca domestica using RNAse H2 PCR (rhPCR)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 15:07:06","doi":"10.21203/rs.3.rs-5239153/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-10T18:22:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-29T02:11:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284942549172164770842264839140678514783","date":"2024-11-28T21:24:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-11T20:57:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-07T14:27:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260749702796291187761260128017237401949","date":"2024-10-28T00:58:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115863604770299108735514610783079014036","date":"2024-10-22T17:58:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-22T15:07:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-22T10:32:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-10T13:45:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-10T12:03:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-10T11:05:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92015370-5a11-46cb-be39-7862f1a7642c","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":38792043,"name":"Biological sciences/Biological techniques/Genetic techniques/Genotyping and haplotyping"},{"id":38792044,"name":"Biological sciences/Biological techniques/Genetic techniques/Pcr based techniques"}],"tags":[],"updatedAt":"2026-02-04T16:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-14 15:07:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5239153","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5239153","identity":"rs-5239153","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}