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Transcriptomic and proteomic analysis of pyrethroid resistance in the CKR strain of Aedes aegypti

['Haina Sun', 'School Of Basic Medicine', 'Biological Sciences', 'Soochow University', 'Suzhou', 'Jiangsu China', 'Department Of Entomology', 'Comstock Hall', 'Cornell University', 'Ithaca']

Date: 2021-12

There were 10 CYPs which had increased expression of both transcripts and proteins. These CYPs appeared to be largely trans-regulated, except for some CYPs for which we could not rule out gene duplication. We identified 65 genes and lncRNAs as potentially being responsible for elevating the expression of CYPs in CKR. Resistance was associated with multiple loci on chromosome 1 and at least one locus on chromosome 3. We also identified five CYPs that were overexpressed only as proteins, suggesting that stabilization of CYP proteins could be a mechanism of resistance. Future studies to increase the resolution of the resistance loci, and to examine the candidate genes and lncRNAs identified here will greatly enhance our understanding of CYP-mediated resistance in A. aegypti.

Aedes aegypti is an important vector of human viral diseases. This mosquito is distributed globally and thrives in urban environments, making it a serious risk to human health. Pyrethroid insecticides have been the mainstay for control of adult A. aegypti for decades, but resistance has evolved, making control problematic in some areas. One major mechanism of pyrethroid resistance is detoxification by cytochrome P450 monooxygenases (CYPs), commonly associated with the overexpression of one or more CYPs. Unfortunately, the molecular basis underlying this mechanism remains unknown. We used a combination of RNA-seq and proteomic analysis to evaluate the molecular basis of pyrethroid resistance in the highly resistant CKR strain of A. aegypti. The CKR strain has the resistance mechanisms from the well-studied Singapore (SP) strain introgressed into the susceptible Rockefeller (ROCK) strain genome. The RNA-seq and proteomics data were complimentary; each offering insights that the other technique did not provide. However, transcriptomic results did not quantitatively mirror results of the proteomics.

Aedes aegypti is an important vector of human viral diseases and is commonly controlled using pyrethroid insecticides. This has led to the evolution of insecticide resistance in this mosquito via two main mechanisms: increased detoxification of pyrethroids mediated by cytochrome P450 proteins (CYPs) and mutations in the voltage sensitive sodium channel (Vssc, gene for the target site of pyrethroids). While much is known about the Vssc mutations, the mutation(s) causing the CYP-mediated resistance has been elusive. We used a combined transcriptomic and proteomic approach to try to identify the molecular basis of CYP-mediated resistance in the highly resistant CKR strain. We identified CYPs that were overexpressed in the CKR strain, and for most of these the increased expression was due to a trans-acting factor. We identified 65 transcription factors or long non-coding RNAs (lncRNAs) that may play a role in the increased expression of these CYPs. We also found evidence that stabilization of CYP proteins could be a mechanism of resistance.

Funding: This study was funded in part by the National Institute of Allergy and Infectious Diseases (Award # R21AI133203 and R21AI149121, to JGS). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Data Availability: All transcriptomic data are available from Genbank Sequence Read Archive (BioProject ID: PRJNA753843, https://www.ncbi.nlm.nih.gov/search/all/?term=PRJNA753843 ) and the mass spectrometry proteomics data are available from ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD025994 ( http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD025994 ).

Copyright: © 2021 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

To gain a greater understanding of the molecular basis of CYP-mediated resistance we used the pyrethroid resistant CYP+KDR:ROCK (CKR) strain [ 14 ] of A. aegypti. The CKR strain has the resistance mechanisms from the well-studied Singapore (SP) strain [ 36 ] introgressed into the susceptible Rockefeller (ROCK) strain genome. CYP-mediated pyrethroid resistance in SP has been demonstrated by in vivo metabolism, in vitro metabolism (using microsomes) and by synergism with the CYP inhibitor piperonyl butoxide [ 36 ]. Although much is known about the principal mechanisms of pyrethroid resistance in this strain, its underlying molecular basis is still largely unknown (except for kdr). The goals of this study were to 1) use transcriptomics (RNA-seq) and proteomics to improve our understanding of the molecular basis of CYP-mediated resistance in A. aegypti, and 2) to determine if the levels of CYP transcripts correlate with levels of CYP proteins across strains. To enhance our chances of success, we compared the CKR, SP and ROCK strains. We found that CYP-mediated resistance in CKR (and SP) is associated with the increased expression level of 10 CYP proteins and we identified a list of candidate genes and long non-coding RNAs (lncRNAs) for future investigation. These candidates include overexpression of groups of CYPs (potentially due to gene amplification), transcription factors and lncRNAs. The lncRNAs were included because of the importance they play in gene regulation [ 37 ]. We also identify stabilization of CYP proteins as a potential mechanism of resistance. Future studies to increase the resolution of the resistance loci, and to examine the candidate genes identified here will help move our understanding of CYP-mediated resistance in A. aegypti forward.

Another challenge to understanding CYP-mediated resistance is that expression levels are often measured at the transcript level, rather than at the protein level. This has been commonly done, despite the fact that mRNA levels often do not accurately predict the protein levels of that gene [ 22 – 35 ]. Thus, an investigation of the correlation between CYP transcript and protein levels would be highly informative to the field of insect toxicology.

CYP-mediated resistance is commonly found in insects. This resistance mechanism is unequivocally demonstrated by isolation of endoplasmic reticulum (a centrifugal fraction termed microsomes, which contain CYPs) and demonstrating NADPH-dependent (i.e. CYP-mediated) metabolism of the insecticide is higher in the microsomes of the resistant strain [ 20 ]. In A. aegypti, CYP-mediated resistance is associated with increased levels of total cytochrome P450s and/or with increases in expression of one or more CYPs [ 8 ]. However, the mutation(s) responsible for this resistance has remained elusive. This is because there are ~160 CYPs in A. aegypti [ 21 ] and the factors regulating CYP expression in this species are largely unknown. Our inability to identify the mutations responsible for CYP-mediated resistance constrains our understanding of multiple aspects of this trait (population genetics, fitness costs, etc.).

Pyrethroid insecticides have been the primary means of controlling adult A. aegypti populations to suppress arbovirus outbreaks for decades, but resistance to pyrethroid insecticides has become a global problem [ 8 , 10 – 13 ]. Based on numerous studies, two mechanisms of pyrethroid resistance in A. aegypti are prevalent: cytochrome P450 (CYP)-mediated detoxification (the causal mutation(s) are not known) and mutations in the target site (voltage-sensitive sodium channel, Vssc) [ 8 , 14 – 19 ].

Due to their efficacy and fast action, the use of insecticides has often been the only feasible control strategy for vector-borne diseases. However, insecticide resistance is an increasing problem for vector control [ 8 – 10 ]. Detection and monitoring of resistance is important if we are to mitigate the evolution of insecticide resistance. Identification of the mutations responsible for resistance is a necessary first step for the development of sensitive, high-throughput assays for resistance detection.

Aedes aegypti vectors four important human disease viruses: dengue, yellow fever, Zika and chikungunya. Given that this mosquito has a wide global distribution, has high vector competence for several arboviruses, frequently bites humans and thrives in urban environments, it poses a serious risk to human health [ 1 ]. For example, dengue, the most devastating virus vectored by A. aegypti, is estimated to be a risk to over 50% of the world’s population [ 2 ], and Zika has generated a great deal of human health concern since it arrived in the Americas in 2014 [ 3 – 7 ].

Correlation of transcript abundance and protein levels were displayed graphically using ggplot2 in R [ 70 ]. The correlation coefficients were calculated using the Pearson method using R. The linear model function was used to create the regression line for correlation analysis and the statistics (such as r 2 and equation) was summarized using R.

All MS and MS/MS raw spectra from each set of TMT10-plex experiments were processed and searched using the Sequest HT search engine within Proteome Discoverer 2.2 or 2.3 (PD2.2/3, Thermo Fisher). The A. aegypti NCBI protein database containing 35,399 entries downloaded on August 30, 2018 was used for an initial database search in PD2.2, revealing multiple alleles of some genes and four CYPs incorrectly annotated in the AaegL5.1 genome (these CYPs were identified from the Cytochrome P450 homepage [ 68 ]). The ROCK variant-informed protein database consisting of the longest transcript from each gene in AaegL5.1 was found to identify more proteins than a similar unmodified LVP database or SP-specific database. This ROCK-specific protein database, with the incorrectly annotated CYP sequences added, was used for the final database search using PD2.3. The default search settings used for TMT 10plex quantitative processing and protein identification were: two missed cleavages for full trypsin with fixed carbamidomethyl modification of cysteine, fixed TMT 10plex modifications on lysine and N-terminal amines and variable modifications of methionine oxidation, deamidation on asparagine/glutamine residues and protein N-terminal acetylation. The peptide mass tolerance and fragment mass tolerance values were 10 ppm and 0.02 Da, respectively. Identified peptides were filtered for maximum 1% FDR using the Percolator algorithm in PD 2.3 along with additional peptide confidence set to high. The TMT 10plex quantification method within PD2.3 software was used to calculate the reporter ion abundances that were corrected for isotopic impurities. Both razor (assigned to the protein with the largest number of peptide matches) and unique peptides were used for quantitation. Signal-to-noise (S/N) values of peptides, which were summed from the S/N values of the peptide spectrum matches (PSMs), were summed to represent the abundance of the proteins. For relative ratios between two groups, normalization on total peptide amount for each sample was applied. The search result including ratio, p-value, and peptide abundance for each sample was outputted to Microsoft Excel software for further data analysis. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [ 69 ] partner repository with the dataset identifier PXD025994.

The nanoLC-MS/MS analysis was carried out using an Orbitrap Fusion (Thermo Fisher) mass spectrometer equipped with a nanospray Flex Ion Source using high energy collision dissociation (HCD) similar to previous reports [ 64 – 66 ] and coupled with the UltiMate3000 RSLCnano (Dionex, Sunnyvale, CA, USA). Each reconstituted fraction (3 μL for global proteomics fractions) was injected onto a PepMap C-18 RP nano trap column (3 μm, 100 μm × 20 mm, Dionex) at 15 μL/min flow rate for on-line desalting, and separated on a PepMap C-18 RP nano column (2 μm, 75 μm x 25 cm). The labeled peptides were eluted with a 120 min gradient of 4% to 35% ACN in 0.1% FA at 300 nL/min, followed by an 8-min ramping to 95% ACN/0.1% FA and a 9-min hold at 95% ACN/0.1% FA. The column was re-equilibrated with 2% ACN/0.1% FA for 25 min prior to the next run. The Orbitrap Fusion was operated in positive ion mode with nanospray voltage set at 2.1 kV and source temperature at 275°C. External calibration for mass analyzers was performed. For global proteomics fractions, the instrument was operated in data-dependent acquisition (DDA) mode using FT mass analyzer for one survey MS scan for selecting precursor ions followed by 3 second “Top Speed” data-dependent HCD-MS/MS scans for precursor peptides with 2–8 charged ions above a threshold ion count of 20,000 with normalized collision energy of 40%. MS survey scans at a resolving power of 120,000 (fwhm at m/z 200), for the mass range of m/z 400–1600 with AGC = 4 x 10 5 and Max IT = 50 ms, and MS/MS scans at 60,000 resolution with AGC = 1 x 10 5 , Max IT = 86 ms and with Q isolation window (m/z) at 1.6 for the mass range m/z 105–2000. Dynamic exclusion parameters were set at 1 within 50 s exclusion duration with ± 10 ppm exclusion mass width. All data are acquired under Xcalibur 3.0 operation software and Orbitrap Fusion Tune 3.0 (Thermo Fisher).

The hpRP chromatography was carried out using a Dionex UltiMate 3000 HPLC system with the built-in micro fraction collection option in its autosampler and UV detection (Thermo Fisher) as reported previously [ 65 – 67 ]. Specifically, the TMT 10plex tagged tryptic peptides were reconstituted in 20 mM ammonium formate pH 9.5, and loaded onto an XTerra MS C18 column (3.5 μm, 2.1 x 150 mm) from Waters, (Milford, MA) with 20 mM ammonium formate pH 9.5 as buffer A and 80% ACN/20% 20 mM ammonium formate as buffer B. The LC was performed using a gradient from 10–45% of buffer B over 30 minutes at a flow rate of 200 μL/min. Forty-eight fractions were collected at 1 minute intervals and pooled into a total of 10 fractions based on the UV absorbance at 214 nm and with multiple fraction concatenation strategy [ 67 ]. Each of the 10 fractions was dried and reconstituted in 125 μL of 2% ACN/0.5% formic acid (FA) for nanoLC-MS/MS analysis.

Further processing of the proteins was then performed according to Thermo Fisher Scientific’s TMT Mass Tagging Kits and Reagents protocol with a slight modification [ 64 , 65 ]. A total of 50 μg protein of each sample was reduced with 20 mM tris (2-carboxyethyl) phosphine for 1 h at room temperature, alkylated with 20 mM iodoacetamide for 1 h in the dark and then quenched by addition of 20 mM dithiothreitol (DTT). The alkylated proteins were precipitated by adding 6 volumes of ice-cold acetone and incubating at −20°C overnight, and reconstituted in 50 μL of 100 mM triethylammoniumbicarbonate. Each sample was digested with 2.5 μg trypsin for 18 h at 35°C. The TMT 10plex labels (dried powder) were reconstituted with 25 μL of anhydrous acetonitrile (ACN) prior to labeling and added at a 1:2 ratio to each of the tryptic digest samples for labeling for 1 hour at room temperature. The peptides from the 10 samples (4 ROCK samples as controls, 3 SP samples and 3 CKR samples) were mixed with each tag as follows: ROCK (126, 127C, 127N, and 128C), SP (128N, 129C, and 129N), and CKR (130C, 130N, and 131). After label incorporation was checked using an Orbitrap Fusion (Thermo Fisher) by mixing 1 μL aliquots from each sample and desalting with SCX ziptip (Millipore, Billerica, MA), the 10 digested samples were pooled together. The pooled peptides were evaporated to 200 μL and subjected to cleanup by solid phase extraction (SPE) on Sep-Pak Cartridges (Waters, Milford, MA). The eluted tryptic peptides were evaporated to dryness, and ready for the first liquid chromatography (LC) fractionation via high pH reverse phase chromatography as described below.

Microsomes from all four ROCK replicates and three each of the SP and CKR replicates were submitted to the Biotechnology Resource Center at Cornell University for quantitative analysis with isobaric labels. Microsomes were denatured in a final concentration of 0.1 M phosphate buffer pH 7.4, 8 M urea, and 0.15% SDS. The protein concentration for each sample was determined by Bradford assay [ 63 ] using BSA as the standard, and further quantified by running on a precast NOVEX 10% Bis-Tris mini-gel (Invitrogen) along with a series of E. coli lysates (2, 5, 10, 20 μg/lane). The SDS gel was visualized with colloidal Coomassie blue stain (Invitrogen), imaged with a Typhoon 9400 scanner and ImageQuant Software version TL 8.1 (GE Healthcare, Chicago IL).

To maximize peptide detection and gain a higher proteome coverage, a strain-specific protein database was established for ROCK and SP starting from the proteins predicted from the LVP genome (AaegL5.1) and then substituting the variants (strain-specific sequence differences) that were identified in the transcriptomic analysis. The bam files with mapped reads generated from the TopHat aligner for each RNA-seq library were merged for each strain, and variants were called for both strains using samtools (v1.8, http://www.htslib.org/ ) and bcftools (v1.8, http://samtools.github.io/bcftools/bcftools.html ). Alternate fasta sequences for each strain were generated by FastaAlternateReferenceMaker tool in GATK (v4.0.1.1), which replaced the reference bases with the variants supplied by a variant call format (VCF) file. Protein fasta files were generated from the strain-specific transcriptomes using gffread (v0.9.12). The longest isoform of each protein sequence was extracted from the reference genome LVP, and ROCK- or SP-specific protein fasta files. Ultimately, LVP-, ROCK- and SP-specific protein databases containing the longest isoform of each protein were established for protein database searches.

Microsomes were assayed for total cytochrome P450s and cytochrome b 5 [ 60 ] using a Molecular Devices SpectraMax Plus 384 (San Jose, CA) with a 1 cm path length quartz cuvette and spectra were collected from 400–500 nm. All cytochrome P450 and b5 assays were performed in triplicate for each biological replicate. Statistical analyses were carried out with ANOVA followed by a TukeyHSD test using R [ 61 ]. Cytochrome b 5 is important in CYP mediated metabolism because it can act as the second electron donor or may be a required co-factor [ 43 , 62 ].

Microsomes (centrifugation fraction containing smooth and rough endoplasmic reticulum) from the SP strain were found to metabolize permethrin at a more rapid rate than a susceptible strain [ 36 ]. Thus, microsomes were used as our source for proteomic analysis. In addition, microsomes provide a feasible number of proteins for a proteomic study [ 56 , 57 ]. Microsomes were isolated using a protocol originally developed for house flies [ 58 ] and adapted for mosquitoes [ 59 ]. Female abdomens were collected as described above and at the same time (i.e. same mosquito cohort used for both RNA extraction and microsome preparation). Intact abdomens were immediately placed in ice cold homogenization buffer (0.1 M sodium phosphate pH 7.5, 10% glycerol (v/v), 1 mM EDTA, 1 mM PMSF, 1 mM PTU, and 0.1 mM DTT). Approximately 1200 abdomens were collected for each replicate of SP and CKR, and 1400 abdomens were used for each replicate of ROCK (anticipating a lower total CYP content). Abdomens were homogenized in 10 mL of homogenization buffer using a 15 mL glass/Teflon homogenizer until all abdomens were disrupted. The homogenate was filtered through cheesecloth into a chilled centrifuge tube, then the solid material was added back to the homogenizer with 10 mL fresh homogenization buffer. After 5–10 additional passes with the pestle the homogenate was filtered through cheesecloth into the same centrifuge tube. The homogenizer was then rinsed with 5 mL homogenization buffer and filtered through cheesecloth into the centrifuge tube. The filtered homogenate was centrifuged at 10,000 g at 4°C for 20 minutes. The supernatant was filtered through cheesecloth and then centrifuged at 100,000 g at 4°C for 1 hour. The supernatant was discarded and the pellet transferred to a chilled 1 mL glass/Teflon homogenizer with 0.5 mL resuspension buffer (0.1 M sodium phosphate pH 7.5, 20% glycerol (v/v), 1 mM EDTA, 1 mM PMSF, and 0.1 mM DTT). This was homogenized with 5–10 passes or until fully resuspended and transferred to a cryovial. Another 0.5 mL resuspension buffer was added to the homogenizer and passed 5–10 times to resuspend and rinse out any remaining material, then this was added and mixed into the cryovial. The protein content of the microsomes was quantified using the Bio-Rad Protein Assay Dye Reagent (Hercules, CA) as per the manufacturer’s instructions and the microsomes were stored at -70°C.

Single nucleotide polymorphism (SNP) analysis was carried out using the transcriptomic data with the LVP genome (AaegL5.1) as the reference. The Sentieon Genomic Tool (v201704.01) [ 54 ] was used for variant calling from the merged bam file of four biological replicates of each strain. The variants with phred-scaled confidence lower than 30 (QUAL score < 30) were filtered out. The single nucleotide polymorphisms (SNPs) and indels were separated using SelectVariants tool in GATK (v4.0.1.1). The software snpEff (4.3t) was used to predict the effects of SNPs and indels on genes [ 55 ]. To search for resistance loci present in both the SP and CKR strains, we filtered resistance associated homozygous SNPs (in both SP and CKR) (using 10 Mb windows) that were different from both ROCK and the genomic reference LVP strains.

Sequencing was performed on an Illumina NextSeq 500 sequencer in one lane of 75-bp single-end-read runs in the Biotechnology Resource Center of Cornell University and the sequences were generated by Illumina pipeline software v2.18 in a fastq format. The raw data were quality checked using FastQC (version 0.11.5, https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ). TopHat (v2.1.1, [ 49 ]) was used to align the Illumina reads against the A. aegypti [susceptible Liverpool (LVP) strain] genome (version AaegL5.1) with Bowtie2 (v2.2.8.0) [ 50 ], allowing a maximum of two mismatches. The edgeR package [ 51 ] was used for multidimensional scaling (MDS) plot and analysis of differential gene expression of the RNA-seq data using the edgeR_count.xls file generated from mapping [ 51 , 52 ]. The gene expression profile was analyzed with edgeR’s generalized linear models. Only genes with a count per million (CPM) value greater than 1 in at least 4 samples were used for the relative expression levels between two strains. To obtain a list with a feasible number, the genes were counted as differentially expressed if the log 2 fold-change (FC) was ≥ 1 and had a false discovery rate (FDR) ≤ 0.01. Volcano plots were generated with the log 2 (FC) value and -log 10 (FDR) for three comparisons between two different strains using R [ 53 ] software.

There were four biological replicates for each strain. For each biological replicate, the eggs of each strain were hatched once a week for a four-week period (one replicate done each week), and larvae were reared under the laboratory conditions described above. Adult A. aegypti 4–7 days old were anesthetized by chilling to 4°C and then kept on an ice-cold surface. Total RNA was extracted from 20 female (mated, but not blood fed) abdomens that were separated using forceps and the intact abdomens were immediately placed in 0.5 mL TRIzol (Invitrogen, Carlsbad, CA, USA). Abdomens were used because they were the body region used to make microsomes and demonstrate increased CYP-mediated resistance in the SP strain [ 36 ]. Total RNA was extracted as previously described [ 14 ]. RNA was quantified with a NanoDrop 2000 spectrophotometer (Thermo Fisher, San Jose, CA, USA) and a Qubit Fluorometer (Invitrogen). Strand-specific RNA-seq libraries were prepared for sequencing by Polar Genomics (Ithaca, NY, USA).

In order to understand the molecular basis of CYP-mediated resistance we considered six testable hypotheses ( Table 1 ). One hypothesis (#1) was that resistance was due to a polymorphism in a CYP resulting in an enzyme with a higher rate of detoxification of pyrethroids (this was previously shown for Cyp6a2 and metabolism of DDT [ 40 ] and for CYP9A186 and abamectin metabolism [ 41 ]). Two hypotheses involved increased expression of one CYP due to a mutation in the CYP “promoter” (hypothesis #2, as found for CYP6D1 and metabolism of pyrethroids, [ 42 – 44 ]) or due to gene duplication (hypothesis #3, as found for resistance to neonicotinoids due to duplication of CYP6CY3 [ 45 ]). Two hypotheses involved increased expression of multiple CYPs due to a mutation in a “switch” (i.e. transcription factor or lncRNA, hypothesis #4) or a mutation in the “promoter” of a “switch” (hypothesis #5). We also hypothesize (#6) that resistance could be due to stabilization of one or more CYP proteins, as this has been found as a mechanism of CYP overexpression due to disruption of P450 proteolytic turnover via endoplasmic reticulum-associated degradation (ERAD) pathway in mammals [ 46 – 48 ]. It would also be possible that resistance could be due to stabilization of a CYP transcript although this would be impossible to distinguish from hypotheses #3, 4 or 5 with our RNA-seq and proteomics data. A schematic of the approaches taken in this study is shown in S1 Fig .

Three strains of mosquitoes were used in this study: ROCK is a laboratory susceptible strain [ 38 ], SP is a field-collected and laboratory selected permethrin resistant strain [ 36 ] and CKR is a permethrin resistant strain that is congenic to ROCK, but has knockdown resistance (kdr) (S989P + V1016G mutations in Vssc) plus CYP-mediated resistance mechanisms from the SP strain [ 39 ]. We confirmed that the strains had the previously reported levels of resistance (permethrin resistance ratios were 360 and 110 for SP and CKR, respectively) [ 14 , 15 ] prior to starting our experiments. Mosquitoes were reared in a room with temperatures ranging from 25–30°C (median and average = 28), 3–37% (median = 14, average = 16) relative humidity, and a 14L:10D photoperiod. All three strains were reared in parallel for each of the biological replicates for the RNA-seq libraries and microsome preparations (proteomics). About 400 first instar larvae were transferred into a plastic container (29.5 x 23 x 8.4 cm) (Lock&Lock Co., Ltd., Seoul, Korea) in which ~ 1/3 of the lid was cut out and covered with nylon tulle. Each container had 2 L distilled water and larvae were fed with Cichlid Goldfish food pellets (Hikari, Hayward, CA, USA) as needed. Adults were released in cages (35 x 25 x 25 cm) and provided with 10% sugar water. For colony maintenance, female mosquitos were blood fed using membrane-covered water-jacketed glass feeders containing cow blood from a local butcher (Owasco Meat Co., Moravia, NY, USA).

Results

Transcriptomic analysis The RNA-seq libraries generated a total of 428,778,534 reads (S1 Table). Each library ranged from 24–42 million reads. Overall, 89.4% to 90.4% of the total reads from each library mapped to the reference genome at least once. Among those reads that mapped to the genome, 92.1%-92.7% mapped uniquely. A multidimensional scaling (MDS) plot was used to evaluate the level of similarity between different strains and biological replicates. Based on the MDS plot, ROCK and SP were separated along dimension 1 (the x axis), and CKR was in between ROCK and SP in dimension 1, but was separated from ROCK and SP in dimension 2 (the y axis) (S2 Fig), indicating that the strain differences were greater than the batch effects (i.e. replicates) among our libraries. There were >15,500 genes detected in each strain (S2 Table).

Utility of multiple and/or congenic strains in “omic” analyses of resistance Studies on the molecular basis of resistance that employ unrelated strains make resolution of the causes of resistance problematic, because too many differences are found and there is no way to distinguish those that are relevant vs. irrelevant to resistance. One approach to improve the resolution of molecular studies is to compare congenic strains. Another approach is to compare multiple strains, assuming the same molecular basis of resistance in all the resistant strains. Herein, we used both approaches and this allows for a comparison of what was gained. As expected, use of a congenic strain resulted in a 1.7- to 2.4-fold increase in resolution (i.e. all genes or proteins identified) relative to using the SP strain alone (Table 4), but a greater improvement in resolution (3.0- to 5.0-fold) was obtained by using both the CKR and SP strains. These gains in resolution were more modest when only CYPs were considered (Table 4). These results indicate that congenic strains improve resolution in RNA-seq and proteomic experiments, but that a greater resolution was found using both SP and CKR.

[END]

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