Abstract
The recent identification of isolates of D. immitis with confirmed resistance to the macrocyclic lactone preventatives presents an opportunity for comparative genomic studies using these isolates, and examining the genetic diversity within and between them. We studied the genomes of Wolbachia endosymbionts of five isolates of D. immitis maintained at the University of Georgia. Missouri and Georgia-2 are maintained as drug susceptible isolates, and JYD-27, Yazoo-2013 and Metairie-2014 are resistant to the macrocyclic lactone preventatives. We used whole genome amplification followed by Illumina-based sequencing from 8–12 individual microfilariae from each of the five isolates, obtaining a depth of coverage of approximately 40–75 fold for each. The Illumina sequences were used to create new genome assemblies for all the Wolbachia isolates studied. Comparisons of the Wolbachia sequences revealed more than 3,000 sequence variations in each isolate. We identified 67 loci specific in resistant isolates but not in susceptible isolates, including 18 genes affected. Phylogenetic analysis suggested that the endosymbionts of the drug-susceptible isolates are more closely related to each other than to those from any of the resistant parasites. This level of variation in the Wolbachia endosymbionts of D. immitis isolates suggests a potential for selection for resistance against drugs targeting them.
Keywords: Dirofilaria immitis, drug resistance, genome sequence, Wolbachia
Introduction
The existence of Dirofilaria immitis that are resistant to the macrocylic lactone anthelmintics used to prevent heartworm disease has been established and thoroughly documented (Bourguinat et al., 2015; Cima, 2013; Pulaski, 2014; Wolstenholme et al., 2015), although their capacity to spread and the extent of the threat they present to canine health remains uncertain and is the subject of debate (Atkins et al., 2014). The discovery of genome sequence variations that strongly correlate with, or cause, drug resistance would greatly help resolve unanswered questions about the origin, spread and threat of resistance in this species. Some success in the development of such markers has been reported (Ballesteros et al., 2018), but the discovery of additional variations would still be of value, especially if they proved to be related to the mechanism of resistance.
Many filarial nematodes, including D. immitis, harbor a Wolbachia endosymbiont. Many aspects of this symbiotic relationship remain poorly understood, including the potential genetic and genomic variation between Wolbachia in different heartworm isolates, and their biological consequences. Variations in the endosymbiont copy number have been associated with biological variation in Onchocerca volvulus (Armoo et al., 2017), but no sequence comparisons of the complete Wolbachia genomes have been made between heartworm isolates. The use of doxycycline, which targets Wolbachia, is a standard component of treatment regimens for heartworm disease (Nelson et al., 2018; Savadelis et al., 2017), so sequence variations in the endosymbiont could theoretically impact the effectiveness of this treatment. Doxycycline targets the bacterial ribosome, and, in other species, resistance can be caused by mutations within the ribosomal binding site, mobile genetic elements carrying tetracycline-specific resistance genes, or mutations leading to increased expression of intrinsic resistance mechanisms (Grossman, 2016). Targeting Wolbachia is also a strategy being used in the development of novel anthelmintics (Clare et al., 2019), especially for the discovery of novel adulticide drugs, and sequence variations in the endosymbiont may lead to differences in the efficacy of such compounds on parasite species and isolates. Here, we report the level of variation between the Wolbachia sequences present in five D. immitis isolates, some of which are resistant to macrocyclic lactones. This variation raises the possibility that environmental pressures, including drug treatment, could act on the endosymbiont and result in phenotypic changes, including drug resistance.
2. Materials and Methods
2.1. Parasites
The D. immitis isolates, Missouri, Georgia-2, JYD-27, Yazoo-2013 and Metairie-2014, used in these studies were described previously (Berrafato et al., 2019; Maclean et al., 2017). Missouri and Georgia-2 are susceptible to macrocyclic lactone preventatives, JYD-27, Yazoo-2013 and Metairie-2014 are resistant to them. Microfilariae (Mf) were isolated from the blood of infected animals as described previously (Maclean et al., 2017).
2.2. DNA extraction and sequencing
To extract the genomic DNA from single Mf of each isolate, the Mf were diluted in RPMI1640 to 0.05 Mf/mL so that a single Mf would be present in every 20μl. Individual Mf (12/isolate) were placed in a 0.2ml microcentrifuge tube and DNA extracted using the DNeasy Blood and Tissue Kit (Qiagen, Germantown, MD, USA). Whole Genome Amplification was performed using the TruPrime Whole Genome Amplification Kit (Lucigen, Middleton WI, USA). The isolated DNA from all single-Mf samples were sent to the Georgia Genomics and Bioinformatics Core (GGBC) at the University of Georgia to be sequenced via Illumina HiSeq using PE150. All sequence data are available from NCBI under Bioproject accession number PRJNA588995.
2.3. Genome assembly and analysis
The genome assembly for all samples was performed using a reference-based assembly method using as reference the D. immitis Wolbachia (wDi) version 2.2 described in Godel et al. (2012), available at http://genoweb.toulouse.inra.fr/~formation/M2_Phylogenomique/data/Wolbachia/Additional/. To perform this analysis all Illumina reads from each sample were aligned against the reference genome by using BWA mem (Li & Durbin, 2009). To assess the mapping percentage to the reference genome, SAMtools (v1.6) (Li and Durbin, 2009) flagstat tool was used. SAMtools mpileup (v1.6) (Li and Durbin, 2009) and BCFtools (v1.8) (https://github.com/samtools/bcftools) call and consensus tools were used to assemble the wDi genome, which was then checked with the quality assessment online tool QUAST (Gurevich et al., 2013). Gene annotation was performed using Prokka (v1.13) (Seemann, 2014) and a text-based format FASTA file for protein sequences was produced for each wDi isolate.
2.4. Wolbachia SNPs analysis
To identify single nucleotide polymorphism (SNP), all previously mapped, aligned and merged aligned files (BAM) were pre-processed with Picard (v2.16.0) (http://broadinstitute.github.io/picard/) and SAMtools (v1.6) (Li and Durbin, 2009). The Picard ValidateSamFile module was used before running GATK (v3.8–0) HaplotypeCaller recommended by GATK Best Practices (Van der Auwera et al., 2013). A hard filter was performed to remove false positive variants. Filter parameters were set at QD (variant confidence/quality by depth) < 2.0, FS (Phred-scaled p-value using Fisher’s Exact Test to detect strand bias) > 25.0, MQ (Root Mean Square of mapping quality) < 40.0, SOR (StrandOddsRatio is used to detect strand bias) > 3.0, MQRankSum (Z-score from Wilcoxon rank sum test for mapping quality) < −7.5, ReadPosRankSum (Z-score from Wilcoxon rank sum test of alternative vs. reference read position bias) < −6.5, and DP (read depth) < 10 for single nucleotide variant (SNV), and QD (variant confidence/quality by depth) < 2.0, FS (Phred-scaled p-value using Fisher’s Exact Test to detect strand bias) > 100.0, ReadPosRankSum (Z-score from Wilcoxon rank sum test of alternative vs. reference read position bias) < −15.0, SOR (StrandOddsRatio is used to detect strand bias) > 10.0, and DP (read depth) < 10 for indel separately. The GATK CombineVariants module was used to identify SNPs specific in the resistant (JYD-27, Metairie-2014, and Yazoo-2013) and susceptible isolates (Georgia-2 and Missouri). SNP impact was predicted using SnpEff (v4.3t) (Cingolani et al., 2012). SNPs density of wDi genome was calculated with the window set at 500 bp using BEDTools (v2.26.0) (Quinlan and Hall, 2010). The SNPs density for each wDi isolate and the specific loci in resistant isolates were plotted using circos (v0.69–6) (Krzywinski et al., 2009). The specific genes in the resistant and susceptible isolates were blasted using NCBI BLAST (http://blast.ncbi.nlm.nih.gov/), and GO term for genes was found using Blast2GO InterPro module (Conesa and Gotz, 2008).
2.5. Wolbachia phylogeny
To construct the phylogenetic tree for wDi from five D. immitis isolates, Wolbachia from O. ochengi (wOo) (accession number: HE660029.1 in the NCBI nucleotide database), Wolbachia in O. volvulus (wOv) (accession number: HG810405.1 in the NCBI nucleotide database) and Wolbachia from B. malayi (wBm) (accession number AE017321) in the supergroup C were selected as the outgroups. A dataset of 564 single-copy orthologous genes, shared among the eight Wolbachia strains was obtained using OrthoFinder (v2.1.2) (Emms and Kelly, 2015). According to the protein FASTA files generated from Prokka (v1.13) (Seemann, 2014), corresponding amino acid sequences for those orthologs were aligned. An amino acid supermatrix of the 8 Wolbachia was concatenated and aligned, and then generating a single phylip format alignment file with MAFFT (v7) (Katoh and Standley, 2013). Maximum-likelihood analysis of the amino acid supermatrix was performed with PhyML (v3.0) and the substitution model was selected by Modeltest-NG (Darriba et al., 2020) using the AIC criteria, and the robustness of nodes was assessed with bootstrap 1,000 replicates (Guindon et al., 2009). PhyML (v.3) was used to construct the final Maximum Likelihood (ML) tree (Guindon et al., 2010).
3. Results
After merging the HiSeq genomic data and mapping to the wDi genome reference, the mean read depth of coverage of the wDi genome was approximately 40–75 X (Table 1). We used the wDi genome assembly data from each isolate to generate a text-based format FASTA file for protein sequences to construct phylogenetic tree in the following step. To build data for the following SNP effect prediction, gene annotation of the wDi reference (version 2.2) (Godel et al., 2012) was performed. The results are shown in Table 2. To compare the wDi gene annotation results, wOo and wOv reference genomes were annotated with the same pipeline.
Table 1.
Depth of coverage of wDi genome sequences
| wDi isolate | Genome Length (bp) | Mean Depth of Coverage |
|---|---|---|
| Georgia 2 | 920663 | 49 X |
| Missouri | 920577 | 38 X |
| JYD-27 | 920683 | 77 X |
| Metairie-2014 | 920720 | 64 X |
| Yazoo-2013 | 920763 | 69 X |
Table 2.
The number of sequence variations between the wDi sequences of the isolates studied here and the reference sequence (Godel et al., 2012b).
| wDi isolate | SNPs | SNPs in coding regions | Indels | Total Variations |
|---|---|---|---|---|
| Georgia 2 | 3311 | 2937 | 661 | 3972 |
| Missouri | 2944 | 2579 | 509 | 3453 |
| JYD-27 | 2222 | 1931 | 429 | 2651 |
| Metairie-2014 | 2736 | 2501 | 611 | 3347 |
| Yazoo-2013 | 2858 | 2518 | 607 | 3465 |
3.1. Wolbachia SNPs analysis
We used GATK (v3.8–0) (Van der Auwera et al., 2013), which primarily focuses on variant discovery and genotyping purposes, to find variations between the wDi reference and each wDi isolate. The generated text-based variant call format (VCF) file from GATK contains different parameters indicating input data quality, which allowed us to assess the results and remove the false positive variants. By combining these variants from different wDi isolates, we found positions in the wDi genome specific to resistant or susceptible wDi isolates. The generated VCF file from GATK is compatible with SnpEff, a software which can annotate the variants and predict their effects. Following this pipeline, we found that more than 3,000 raw variants were generated for each isolate using the GATK HaplotypeCaller module. Hard filter results showed an average of 86% of the variants are passed, which means these variants are confident and can be included for the following analysis (Table 3). To show the SNP density in the wDi genome, we set the window size at 500 bp and constructed the line plots for each wDi isolate. In Figure 1, the five rings stand for the SNPs density for each wDi isolate and the red dots on the outmost scale represent the positions showed variation only in the resistant isolates, JYD-27, Metairie-2014, and Yazoo-2013, but not in the susceptible isolates. The distribution of these positions is not even. Around positions 255 kb, 265 kb, 360 kb, 450 kb, and 710 kb, there are more than 6 variations at each defined region. GATK CombineVariants and SnpEff results revealed, excluding upstream and downstream gene variants, 67 SNPs specific to the resistant isolates with 18 genes affected (Table 3). Among these 67 SNPs, there are 26 SNPs where the coverage was less than 10 from all three resistant isolates (marked with * and ** in Table 3). We kept them in our data because either coverage of two of the isolates were higher than 10 or the same gene was affected by many SNPs. Of these variations, 69% are non-synonymous variants resulting in protein sequence changes or premature stop codon. Blast2GO was used to further investigate the functions of the affected genes and the gene ontology relationships. The molecular function results from Blast2GO showed that, in the resistant isolates, there are 5 genes related to ATP binding (GO:0005524), which can further affect drug binding (GO:0008144). The five affected genes in the resistant isolates are the type I secretion system ATP-binding protein PrsD (prsD), transcription termination factor Rho (rho), phosphoribosylformylglycinamidine synthase subunit PurL (purL_2), arginyl-tRNA synthetase (argS), and isoleucyl-tRNA synthetase (ileS). There are four genes, rpoBC, rho, purL_2, and rplQ, with variations in both resistant and susceptible isolates. The effects of SNPs resulting in rpoBC mutation in resistant isolates is one non-synonymous variant, and in susceptible isolates are five non-synonymous and one synonymous variants. For rho mutation, three non-synonymous and four synonymous variants in the resistant isolates whereas only one synonymous variant in the susceptible isolates. The SNPs affecting purL_2 gene in the resistant and susceptible isolates are both non-synonymous variants. For the SNPs in the resistant and susceptible isolates affecting rplQ are synonymous and non-synonymous variants respectively. The allelic frequency of these variants is shown in Supplementary Table 1.
Table 3.
Summary of 67 sequence variations specific to the resistant isolates.
| Position | Reference allele | Alternative allele | SNP effect | Gene | Protein | Predicted amino- acid change |
|---|---|---|---|---|---|---|
| 798* | T | TC | Upstream gene variant | tlyC | Hemolysin C | None |
| 127027 | T | G | Missense variant | Hypothetical protein | Lys476Gln | |
| 218531 | C | T | Missense variant | rpoBC | Bifunctional DNA-directed RNA polymerase subunit beta-beta’ | Val1338Ile |
| 237140* | C | G | Missense variant | prsD | Type I secretion system ATP-binding protein PrsD | Pro440Arg |
| 237170* | C | T | Missense variant | prsD | Type I secretion system ATP-binding protein PrsD | Thr450Ile |
| 248697** | G | T | Synonymous variant | acnA | Aconitate hydratase A | None |
| 248717** | G | A | Missense variant | acnA | Aconitate hydratase A | Gly226Asp |
| 248737** | G | T | Stop gained | acnA | Aconitate hydratase A | Glu233* |
| 248745** | A | G | Synonymous variant | acnA | Aconitate hydratase A | None |
| 255515 | G | T | Missense variant | nusA | Transcription termination/antitermination protein NusA | Lys200Asn |
| 255516 | G | A | Missense variant | nusA | Transcription termination/antitermination protein NusA | Val201Ile |
| 255537 | G | A | Missense variant | nusA | Transcription termination/antitermination protein NusA | Val208Ile |
| 255541 | A | G | Missense variant | nusA | Transcription termination/antitermination protein NusA | Lys209Arg |
| 255557 | A | G | Synonymous variant | nusA | Transcription termination/antitermination protein NusA | None |
| 255559 | G | A | Missense variant | nusA | Transcription termination/antitermination protein NusA | Arg215His |
| 264389* | A | G | Synonymous variant | rho | Transcription termination factor Rho | None |
| 264392* | A | G | Synonymous variant | rho | Transcription termination factor Rho | None |
| 264407* | T | C | Synonymous variant | rho | Transcription termination factor Rho | None |
| 264409* | C | T | Missense variant | rho | Transcription termination factor Rho | Ser283Phe |
| 264413* | G | A | Synonymous variant | rho | Transcription termination factor Rho | None |
| 264418* | A | T | Missense variant | rho | Transcription termination factor Rho | Asp286Val |
| 264424* | C | T | Missense variant | rho | Transcription termination factor Rho | Pro288Leu |
| 301681* | TC | T | Frame-shift | rpe | Ribulose-phosphate 3-epimerase | Asp13fs |
| 301684* | G | A | Missense variant | rpe | Ribulose-phosphate 3-epimerase | Ala12Val |
| 301685* | C | T | Missense variant | rpe | Ribulose-phosphate 3-epimerase | Ala12Thr |
| 301698* | A | G | Synonymous variant | rpe | Ribulose-phosphate 3-epimerase | None |
| 333916 | C | T | Missense variant | purL_2 | Phosphoribosylformylglycina midine synthase subunit PurL | Pro71Ser |
| 348374 | A | AC | Frame-shift | virD4 | Protein VirD4 | His7fs |
| 355534 | A | T | Stop gained | nusB | Transcription antitermination protein NusB | Tyr126* |
| 363850* | G | A | Missense variant | prmC | Release factor glutamine methyltransferase | Gly122Ser |
| 363851* | G | A | Missense variant | prmC | Release factor glutamine methyltransferase | Gly122Asp |
| 363873* | G | A | Synonymous variant | prmC | Release factor glutamine methyltransferase | None |
| 363882* | G | A | Synonymous variant | prmC | Release factor glutamine methyltransferase | None |
| 363886* | A | G | Missense variant | prmC | Release factor glutamine methyltransferase | Ser134Gly |
| 363892* | T | C | Missense variant | prmC | Release factor glutamine methyltransferase | Tyr136His |
| 455084 | C | T | Synonymous variant | argS | Arginine--tRNA ligase | None |
| 455085 | T | C | Missense variant | argS | Arginine--tRNA ligase | Lys486Arg |
| 455092 | A | G | Synonymous variant | argS | Arginine--tRNA ligase | None |
| 455120 | A | G | Synonymous variant | argS | Arginine--tRNA ligase | |
| 455135 | AAG | A | Frame-shift | argS | Arginine--tRNA ligase | Leu469fs |
| 455138 | G | GAA | Frame-shift | argS | Arginine--tRNA ligase | Leu469fs |
| 455145 | C | T | Missense variant | argS | Arginine--tRNA ligase | Ser466Asn |
| 455152 | G | A | Missense variant | argS | Arginine--tRNA ligase | Leu464Phe |
| 455169 | C | T | Missense variant | argS | Arginine--tRNA ligase | Arg458His |
| 460335 | C | T | Synonymous variant | ubiE | Ubiquinone/menaquinone biosynthesis C-methyltransferase UbiE | NOne |
| 460336 | A | G | Missense variant | ubiE | Ubiquinone/menaquinone biosynthesis C-methyltransferase UbiE | Leu142Pro |
| 460343 | AT | A | Frame-shift | ubiE | Ubiquinone/menaquinone biosynthesis C-methyltransferase UbiE | Leu140fs |
| 460356 | C | T | Synonymous variant | ubiE | Ubiquinone/menaquinone biosynthesis C-methyltransferase UbiE | None |
| 636639 | T | G | Upstream gene variant | lolE | Lipoprotein-releasing system transmembrane protein LolE | None |
| 636699 | AGCACC | A | Upstream gene variant | lolE | Lipoprotein-releasing system transmembrane protein LolE | None |
| 636706 | C | CTGA | Upstream gene variant | lolE | Lipoprotein-releasing system transmembrane protein LolE | None |
| 636813 | ACC | A | Upstream gene variant | lolE | Lipoprotein-releasing system transmembrane protein LolE | None |
| 643957 | G | T | Synonymous variant | rplQ | 50S ribosomal protein L17 | None |
| 690200 | C | T | Upstream gene variant | pipB2 | Secreted effector protein PipB2 | None |
| 713047 | C | A | Upstream gene variant | ctaA | Heme A synthase | None |
| 713054 | G | A | Upstream gene variant | ctaA | Heme A synthase | None |
| 713065 | A | G | Upstream gene variant | ctaA | Heme A synthase | None |
| 713077 | G | A | Upstream gene variant | ctaA | Heme A synthase | None |
| 713091 | C | T | Upstream gene variant | ctaA | Heme A synthase | None |
| 713113 | TA | T | Upstream gene variant | ctaA | Heme A synthase | None |
| 713143 | T | C | Upstream gene variant | ctaA | Heme A synthase | None |
| 713148 | T | TG | Upstream gene variant | ctaA | Heme A synthase | None |
| 775024 | C | G | Missense variant | fumC | Fumarate hydratase class II | Lys211Asn |
| 809307 | ATT | A | Frame-shift | gph | Phosphoglycolate phosphatase | Ile179fs |
| 823794* | T | C | Upstream gene variant | Hypothetical protein | None | |
| 823798* | A | G | Upstream gene variant | Hypothetical protein | None | |
| 848154 | A | C | Missense variant | ileS | Isoleucine--tRNA ligase | Tyr651Asp |
indicates that coverage in one of the resistant isolates was <10 for this polymorphism.
indicates that coverage for this polymorphism was <10 for all three resistant isolates.
Figure 1. The density of sequence variations in the wDi genome from each D. immitis isolate.
The outermost scale indicates coordinates in kilobase pairs (kbp) in the wDi genome. These five rings represent the SNPs density in wDi from five D. immitis isolates using a window size of 500 bp. The red dots on the scale show the loci found specific to the resistant isolates. (Green: Yazoo-2013, orange: Metairie-2014, blue: JYD-27, purple: Missouri, yellow: Georgia-2).
3.2. Wolbachia phylogeny
To construct the rooted phylogenetic tree for wDi from the five D. immitis isolates, wBm, wOo and wOv were used as outgroups. A total of 564 orthologous single-copy genes among these eight Wolbachia were identified using Orthofinder (v2.1.2) (Emms and Kelly, 2015). For each Wolbachia isolate, the corresponding amino acid sequences for those orthologs were concatenated and aligned. We then generated a single phylip alignment format file containing an amino acid supermatrix of the 8 Wolbachia, and then constructed a phylogenetic tree with the maximum likelihood method. The best substitution model predicted by Modeltest-NG for this dataset was Jones-Taylor-Thornton with 5 categories of discrete gamma distribution and frequencies estimated from the data (JTT+G+F). The resulted phylogenetic tree confirmed the separation of different wDi isolates and showed that wDi from two susceptible D. immitis isolates are indeed genetically different from the wDi from three resistant isolates (Figure 2). In addition, compared to the resistant isolates, the relatedness of wDi from the two susceptible isolates is closer to each other. The results of the maximum likelihood method showed bootstrap values presented in percent at the nodes are high, 93–100%, to support the phylogenetic tree (Figure 2).
Figure 2. Phylogenetic tree showing the relationships between the Wolbachia sequences.
The topology and branch lengths are based on the construction of the maximum likelihood method. Values at nodes are bootstrap values calculated from the maximal likelihood methods and given in percent as numbers. wBm, wOo and wOv are used as outgroups to root the phylogenetic tree. (GA2: Georgia-2; MO: Missouri; MET: Metairie-2014; JYD: JYD-27; Yazoo: Yazoo-2013; wBm: Wolbachia from B. malayi; wOo: Wolbachia from O. ochengi; wOv: Wolbachia from O. volvulus)
4. Discussion
We determined the sequence of the Wolbachia genome from five isolates of D. immitis all of which were isolated from the South-eastern United States during the current century. The sequences were compared with other and with that deposited by Godel et al (2012). These comparisons revealed over 3,000 variations between the isolates, about 0.3% of the genome. Some of these variations were specific to the drug-resistant isolates studied, though the number of such isolates was too small to draw any inferences from this. However, the Wolbachia sequences from the two drug-susceptible isolates were more closely related to each other than to those from any of the resistant ones, despite a considerable geographical and temporal separation in their original isolation. Several of the variations we discovered could have a significant impact on the functions of the proteins that would be produced by the endosymbiont, in particular the premature stop codons in nusB and acnA, and the frameshift mutations observed in gph, ubiE, rpe and virD4. Interestingly, O. volvulus parasites that responded sub-optimally to ivermectin did carry reduced Wolbachia loads (Armoo et al., 2017), though the authors did not report if any sequence variations correlated with either the copy-number of the endosymbiont or the drug response of the parasite, so it is possible that variations in Wolbachia are associated with drug resistance. However, this level of sequence variation does indicate that the Wolbachia genome has the potential to evolve in response to selection pressures; one such pressure could be the application of antibiotics as therapy for heartworm or other filarial diseases. Doxycycline is an established component of treatment for heartworm-infected dogs; in this instance selection for resistance may be limited as the drug is one component of the three recommended in the American Heartworm Society guidelines (Nelson et al., 2018). However, if only a combination of a macrocyclic lactone, such as moxidectin, and the antibiotic are used to treat infections with macrocyclic lactone-resistant parasites (Savadelis et al., 2017) then the potential for the evolution of double-resistant heartworms may exist. Resistance to tetracyclines, including doxycycline, is established in mycoplasmas, which are intracellular bacterial pathogens related to Wolbachia (Yatoo et al., 2019). In human filarial disease, Wolbachia have been proposed as a good target for the development of adulticidal drugs (Slatko et al., 2010; Taylor et al., 2019) and in this case a sole reliance on such drugs could also theoretically promote the development of resistance.
Supplementary Material
Acknowledgments
This work was supported by grants from the American Heartworm Society and the National Institutes of Health (R01AI103140 & R21AI125899). None of the funders had any influence on the study design or the decision to publish. We should like to thank Andrew Moorhead, Pablo Jimenez Castro and the staff of the Filarial Research Reagent Resource Center for their help in maintaining the heartworm infections and in Mf isolation. We should also like to thank the anonymous reviewers for their helpful and constructive comments.
Footnotes
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Declaration of Interest
The animal work was conducted under AUP A2017 05–002 from the University of Georgia Institutional Animal Care and Use Committee. The authors declare no financial conflicts of interest.
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