High-throughput phenotyping assay

Strains were propagated for four generations to reduce transgenerational effects of starvation and bleach-synchronized before transfer to 96-well growth plates (∼ 1 embryo/μl in K medium). Hatched L1 larvae were fed HB101 bacterial lysate (5 mg/ml) and incubated for 48 hours at 20°C. L4 larvae were sorted into 96-well drug plates (three animals/well) using the COPAS BIOSORT large particle sorter (Union Biometrica). Drug plates contained anthelmintics dissolved in K medium at the desired final concentrations along with 1% DMSO, 10 mg/ml HB101 bacterial lysate, and 31.25 μM kanamycin. These cultures were incubated for 96 hours at 20°C to allow development to the adult stage and the maturation of deposited embryos. Animals were fed a solution of 1 mg/ml bacterial lysate and 0.01 μM red fluorescent microspheres (Polysciences, cat. 19507-5) for five minutes prior to scoring. Animals were immobilized with 50 mM sodium azide, and the COPAS BIOSORT large particle sorter was used to measure a range of animal fitness traits including length, pharyngeal pumping (red fluorescence), and brood size [19, 20].

Trait generation for dose responses and linkage mapping

Raw phenotype data collected from the COPAS BIOSORT large particle sorter were processed with the R package easysorter [21]. The function read_data() was used to distinguish animals from bubbles using a support vector machine (SVM). The functions remove_contamination() and sumplate() were used to mask contaminated wells and to calculate summary statistics across measured parameters. Parameters included time-of-flight (animal length), extinction (optical density), fluorescence (pharyngeal pumping), and total object count (brood size). Summary statistics included the mean and quantiles (10th, 25th, 50th, 75th, and 90th) for each of these parameters. Brood size was normalized to the sorted number of animals per well (n), and fluorescence was normalized to animal length (TOF). The regress(assay = TRUE) function was used to fit a linear model to account for differences in assays carried out on different days. Outliers were defined as observations that fall outside the IQR by at least twice the IQR and that do not group with at least 5% of the observations. Outliers were removed using the bamf_prune() function and regress(assay = FALSE) was used to fit a linear model (phenotype ∼ control phenotype) to calculate drug effects with respect to control (DMSO solvent) conditions.

Dose responses and selection of mapping doses

The dose-dependent phenotypic effects of benzimidazoles were assayed in technical quadruplicate across four genetically diverged C. elegans (N2, CB4856, DL238, and JU258) and C. briggsae (AF16, HK104, VT847, and ED3035) strains. Phenotypes were measured using the high-throughput assay [19] and trait generation pipeline described above. Drug concentrations for linkage mapping experiments were selected based on broad-sense heritability calculations for traits of interest and with the goal of maximizing differences in sublethal drug effects between the parental strains used to generate recombinant lines (C. elegans: N2 and CB4856; C. briggsae: AF16 and HK104). We did not calculate LC50 and EC50 values based on dose response data given that this assay is generational and we are simultaneously measuring the effects of prolonged drug exposure on both the seeded animals and their progeny. We could consider brood size the parameter most closely linked to lethality, however, this trait likely conflates suppression of fecundity (sterility in the adults) and true drug-induced lethality (i.e., “killing” of eggs and young larvae). The dose response data are replicated within assay and not performed in large block replicates. Therefore, absolute trait differences among the strains are not definitive data as compared to the highly replicated parent and near-isogenic line (NIL) values discussed below.

Linkage mapping of fitness traits

Benzimidazole exposure phenotypes were measured for a population of 292 unique C. elegans recombinant inbred advanced intercross lines (RIAILs) resulting from an advanced intercross of N2 and CB4856 [19, 22], as well as 153 unique C. briggsae recombinant inbred lines (RILs) created using AF16 and HK104 [23]. These phenotypic data were collected and processed as described above. R/qtl [24] was used to carry out marker regression on 1454 C. elegans markers and 1031 C. briggsae markers. QTL were detected by calculating logarithm of odds (LOD) scores for each marker and each trait as −n(ln(1 − r²)/2ln(10)), where r is the Pearson correlation coefficient between RIAIL genotypes at the marker and phenotype trait values [25]. Significance thresholds for QTL detection were calculated using 1000 permutations and a genome-wide error rate of 0.05. The marker with the maximal LOD score exceeding significance was retained as the peak QTL marker for each of three mapping iterations. QTL confidence intervals were defined by a 1.5 LOD drop from peak QTL markers. The reproducibility of traits across divergent individuals and/or in parent-offspring relationships is often referred to as heritability. Broad-sense heritability is calculated as the amount of phenotypic variance that is caused by genetic differences across a population. We collected repeated measures of parental and recombinant strain phenotypes in independent assays and then fitted a linear mixed model to calculate broad-sense heritability, as described previously [26].

Generation of near-isogenic lines

Near-isogenic lines (NILs) were generated by backcrossing N2xCB4856 RIAILs to either parental strain for six generations, followed by six generations of selfing to homozygose the genome. Primers were optimized to genotype N2xCB4856 insertion-deletion variants immediately flanking introgression regions of interest. These lines were whole-genome sequenced at low coverage to confirm genotypes at all loci. NIL reagents, primers, and PCR conditions are detailed in S1 Methods.

Mutant strains

Existing C. elegans mutants (alg-4(tm1184); alg-3(tm1155), ergo-1(tm1860), prg-1(n4357), ben-1(tm234)) were propagated alongside N2 and CB4856 to assay albendazole-response phenotypes. The prg-1 mutant strain was backcrossed to N2 for 10 generations. Independent prg-1 deletion strains were generated by CRISPR/Cas9-mediated gene editing using Cas9 ribonucleoprotein [27]. A co-CRISPR strategy targeting dpy-10 was used to improve efficiency of screening for edits [28]. All Cas9 reagents were purchased through IDT (Skokie, IL). Alt-R tracrRNA (IDT, 1072532), dpy-10 crRNA, and each prg-1 crRNA (oECA2002 and oECA2003) were combined and incubated at 95°C for five minutes. Cas9 nuclease (IDT, 1074181) was added, and the mix was incubated at room temperature for five minutes. Finally, the dpy-10 repair template was added, and the final volume was brought to 5 μL with nuclease-free water. The final concentrations are as follows: tracrRNA 13.6 μM, dpy-10 crRNA 4 μM, each prg-1 crRNA 9.6 μM, Cas9 23.8 μM, and dpy-10 repair construct 1.34 μM. The mix was centrifuged at maximum speed for five minutes, mouth pipetted into a pulled injection needle (World Precision Instruments, 1B100F-4), and injected into gravid adult N2 and CB4856 animals. Each injected animal was placed onto an individual 6 cm NGM plate approximately 18 hours post-injection. Rol F1 animals were placed onto individual 6 cm NGM plates when they reached the L4 stage, or when the Rol phenotype was apparent, and allowed to lay embryos. These F1 animals were then screened for large deletion events with PCR using primers oECA2004 and oECA2042. Non-Dpy, non-Rol progeny from edited F1 animals were propagated until homozygous and verified with Sanger sequencing. Primers and other reagents used to genotype backcross progeny and to generate CRISPR alleles are outlined in S1 Methods. Reagents, primers, and PCR conditions are detailed in S1 Methods.

Mutant and NIL strain phenotyping assays

NIL and mutant phenotyping assays were carried out with the high-throughput pipeline described above with at least two independent biological replicates carried out for each strain panel.

Statistical analyses

Phenotype data are shown as Tukey box plots. Analyses were performed using R by one or two-tailed t-test (for two groups and specific hypotheses about direction of effect) or one-way ANOVA with Tukey’s multiple comparison test (for more than two groups). P-values less than 0.05 were considered significant. P-values for all statistical tests are provided in S9 Table.

Gene interval annotation and parasite orthology analysis

C. elegans variants distinguishing N2 and CB4856 [29] were used to annotate QTL intervals with respect to existing gene annotations. C. briggsae variants distinguish AF16 and HK104 as well as their estimated functional consequences were produced from genomic sequence and annotation data using SnpEff [30]. For all C. elegans QTL identified in this study, parasite orthologs of C. elegans genes with variants predicted to be of ‘moderate’ or ‘high’ impact were extracted from Wormbase (WS255) using custom Python scripts. Caenorhabditis orthologs from the clade IV gastro-intestinal parasite Stronygloides ratti and the clade III human filarial parasite Brugia malayi were extracted from WormBase [31]. Orthologs are reported across all QTL for completeness, but S8 Table provides orthologs specific to each QTL mapped.

RNA-seq pipeline

C. elegans strains N2 and CB4856 were bleach-synchronized and grown at 20°C for isolation of total RNA from young adult animals (60 hours post-embryo plating) using a liquid N2 freeze-cracking protocol with TRIzol (Life Technologies). RNA was collected from four independent biological replicates per strain. Sample RNA concentration and quality were assessed via Agilent Bioanalyzer. mRNA libraries were prepared from RNA samples using the TruSeq Stranded mRNA Library Prep Kit with oligo-dT selection (Illumina). All samples were sequenced using the Illumina HiSeq 2500 platform with a single-end 50 bp read setting (University of Chicago Genomics Facility) and demultiplexed for downstream analyses. Mean per-sample yield was 10.0 Gb for mRNA-seq samples. Reads were adapter and quality trimmed using Trimmomatic [32]. HiSAT2 and StringTie [33] were used to align reads to the N2 reference genome (WormBase.org [31] release WS255) and to produce raw and TPM (transcripts per million) read counts for annotated genes, respectively. DESeq2 [34] was used to identify differentially expressed genes. The complete RNA-seq pipeline was implemented with Nextflow [35] and is publicly available through GitHub (www.github.com/AndersenLab/BZRNA-seq-nf).

Natural variation in C. elegans responses to benzimidazole anthelmintics

We examined natural variation in C. elegans responses to four benzimidazoles (albendazole, fenbendazole, mebendazole, and thiabendazole) that are widely used in human and veterinary medicine. Dose responses were performed on a set of four genetically diverged strains using a flow-based, large-particle analysis device (COPAS BIOSORT, Union Biometrica) for high-throughput quantification of anthelmintic effects on animal fitness traits, including offspring production and growth rate. This platform enables trait measurements at unprecedented scales in a metazoan system [19] (S1 Fig). In brief, strains are synchronized and dispensed into 96-well microtiter plates containing anthelmintic drugs or no drug (DMSO). Conditions are strictly controlled for temperature, humidity, food source, and culture mixing. The COPAS BIOSORT measures the length, optical density, and fluorescence (green, yellow, and red) of every nematode from populations grown in 96-well microtiter plates. This high-throughput platform enables the measurement of animal size, fecundity, and feeding behavior. As C. elegans grows, animals get longer [36], so length measurements are a proxy for developmental stage and growth rate. Fecundity is assessed by counting the number of offspring produced by a defined number of parent animals in each well. Feeding rate is quantified by exposing animals to fluorescent microspheres (Fluoresbrite Fluorescent Microspheres, Polysciences Inc.), which are the same size as bacterial food, and then measuring fluorescence after a defined period of time. Anthelmintic drugs reduce growth rate, offspring production, and muscle activity, so these traits are directly relevant to anthelmintic mechanisms of action in parasitic nematodes [37, 38]. Assay measurements with longer animals, more offspring, and/or more fluorescence indicate that that strain is more resistant to an anthelmintic than strains with shorter animals, fewer offspring, and/or less fluorescence. All drugs showed dose-dependent effects for at least one of these major phenotypic categories (S2 Fig). Concentrations that exhibited high broad-sense heritability and strain-specific differences were chosen for quantitative genetic mappings. These dose response data were used to quickly assess doses for mapping potential, but due to lack of biological replication, do not support robust and statistically meaningful claims about trait differences between individual strains. Single concentrations of albendazole (12.5 μM) and mebendazole (20 μM) were selected. To assess the potential effects of dose on the genetic architecture of drug sensitivity, two concentrations of fenbendazole (15 μM and 30 μM) and thiabendazole (62.5 μM and 125 μM) were selected. These sublethal concentrations fall within the range of previous studies in C. elegans [11, 39] and likely correspond to pharmacologically relevant drug accumulation levels in parasitic nematodes. This conclusion is supported by observations that the C. elegans cuticle is generally less permissive and that exogenous drug concentrations required to elicit effects are often orders of magnitude higher in this model system [40–42].

Discovery of quantitative trait loci (QTL) associated with benzimidazole responses in C. elegans

From our dose response assays of four genetically diverse C. elegans strains, we found that the laboratory strain from Bristol, England (strain N2) and a wild strain from Hawaii, U.S.A. (strain CB4856) differed in responses to benzimidazoles. We can use the natural differences between these two strains to identify the variants that contribute to divergent anthelmintic responses. Previously, these two strains were crossed to create a collection of recombinant inbred advanced intercross lines (RIAILs) to facilitate linkage mapping approaches [19, 22]. Using 292 RIAILs, we identified 15 quantitative trait loci (QTL) that each explain greater than 5% of trait variation in benzimidazole susceptibility (Fig 1A, S1 Table). Many of these QTL span multiple animal fitness traits (S3 Fig). The trait groupings can be observed by the correlation structure of measured parameters and are robust across summary statistics (mean, median, 75th quantile, and 90th quantile) for these parameters (S4 Fig). Benzimidazole sensitivity involved the contributions of multiple loci for many drug-trait combinations. Within this complex trait landscape, we identified QTL that are common but also some that are unique across drugs and doses. Drug-specific QTL provide new leads to explain observed differences in in vitro bioactivity [43, 44] and clinical efficacy [45–48] among benzimidazole derivatives. Although mammalian host factors are likely a major source of variation in clinical efficacy among benzimidazoles, drug-specific QTL may help explain variation resulting from factors that affect drug bioactivity in the nematode context (e.g., uptake, metabolism, and potency). By contrast, dose-specific QTL reveal the engagement of different genetic determinants of benzimidazole response as a function of drug exposure.

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Fig 1. Discovery of benzimidazole response QTL in C. elegans.

(A) Results of C. elegans linkage mapping experiments are shown for the five drug-dose conditions tested. QTL peak markers (circles) and confidence intervals (lines) are depicted. Fill color corresponds to the QTL significance (LOD) score. Overlapping QTL for a given condition are represented by the trait with the highest significance score. In total, 15 non-overlapping QTL were identified across these conditions. (B) Linkage mapping plots for the QTL of highest significance for each drug condition. The plots show genomic position on the x-axis and significance (maximum LOD score across mapping iterations) on the y-axis. QTL confidence intervals (e.g., chromosome IV: 15.47–15.91 Mb for 12.5 μM albendazole) are shaded in gray and the red dashed line represents the genome-wide significance threshold for the first mapping iteration (LOD score for 12.5 μM albendazole QTL peak marker = 19.93; LOD threshold = 2.99). (C) Tukey box plots showing the phenotypic split of RIAILs that have either the N2 or CB4856 genotype at the peak QTL marker position(s).

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We discovered a major QTL associated with the effects of albendazole on animal size traits. This QTL is localized to a 441 kb interval on chromosome IV (15.47–15.91 Mb) and explains 36% of albendazole-induced variation in animal length among the strains (Fig 1B). This major-effect albendazole QTL extends to pharyngeal pumping and is also associated with small differences in brood size (S3 Fig). An overlapping QTL associated with animal size and pumping behavior was identified for 30 μM fenbendazole and 20 μM mebendazole. A distinct size and pumping-associated QTL was mapped for 20 μM mebendazole on the left arm of chromosome V that does not overlap with loci identified for other benzimidazole derivatives. Another highly significant QTL on chromosome V (14.15–14.72 Mb) explains 29% of thiabendazole (125 μM)-induced variation in brood size between the strains. This QTL extends to length and pharyngeal pumping traits for both 62.5 μM and 125 μM thiabendazole, and partly overlaps with a QTL associated with differences in pumping behavior in 15 μM fenbendazole. A number of unique loci with smaller effect sizes were mapped for fenbendazole sensitivity at 15 and 30 μM, with only one overlapping QTL apparent across the two drug concentrations. Plots of phenotype as a function of peak QTL marker genotype (Fig 1C) show that the direction of effect varies across drugs and traits, indicating that both N2 and CB4856 strains possess alleles contributing to both benzimidazole sensitivity and resistance.

Near-isogenic lines recapitulate and narrow major-effect albendazole QTL

The albendazole QTL confidence interval contains 17 protein-coding genes with coding variants and falls within the large Piwi-interacting RNA (piRNA) cluster on chromosome IV (13.5–17.2 Mbs) [49]. In the linkage mapping experiment, recombinant strains with the N2 genotype at this chromosome IV QTL locus (15.47–15.91 Mb) were more resistant to incubation in 12.5 μM albendazole than those strains with the CB4856 genotype, with CB4856 animals exhibiting a significantly greater decrease in length (Fig 1C, top). To narrow this interval to a smaller region, we measured the albendazole response phenotypes of near-isogenic lines (NILs) with QTL genomic regions derived from either the N2 or CB4856 strains introgressed into the opposite genetic background (Fig 2A). NILs with the N2 genotype spanning the 15.57–15.65 Mb interval of chromosome IV exhibited greater resistance to albendazole compared to the parental CB4856 strain and, conversely, a NIL with the CB4856 genotype in this region was more sensitive to albendazole when compared to the parental N2 strain (Fig 2A, bottom). These results are consistent with the QTL direction of effect, and we expect that the variant(s) of largest effect fall within this narrowed interval. Significant differences among NILs within the resistance and sensitive groupings suggest that additional variants and epistatic interactions within the complete QTL interval influence albendazole susceptibility.

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Fig 2. Narrowing of the albendazole QTL interval using near-isogenic lines.

(A) NIL genotypes and corresponding Tukey box plots of NIL phenotypes are shown with respect to their parental strain. NILs constructed via introgression of N2 into the CB4586 background can be classified into albendazole-sensitive (LJ1206 and LJ1207) and resistant groups (LJ1204, LJ1209, LJ1210, and LJ1212). This phenotypic separation allows for the tentative narrowing of the QTL interval (chromosome IV: 15.47–15.91 Mb) to a much smaller region (chromosome IV: 15.57–15.65 Mb) that contains one or more causative variants. Solid and dashed-vertical lines are used to mark the original QTL interval and the NIL-narrowed interval, respectively. Statistically significant differences among NILs in the resistant group suggest that additional variants outside of this narrowed interval contribute to phenotypic variance through epistatic interactions. Comparison of the N2 parental strain and LJ1203 are consistent with at least one causal variant contained within the narrowed interval. ANOVA with a post hoc Tukey HSD test was used to compare NILs used in these assays (p-values reported in S9 Table). (B) Variants distinguishing N2 and CB4856 within the NIL-narrowed interval are highlighted with respect to gene annotations. Candidate albendazole resistance variants include nonsynonymous and splice-donor variants in protein-coding genes (blue circles). No variants occur within genes encoding many non-coding RNA biotypes (tRNAs, miRNAs, and snoRNAs), though at least 276 variants occur within annotated piRNAs within the narrowed interval (S3 Table, not plotted).

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The NIL interval was annotated with known variants distinguishing N2 and CB4856 and their estimated functional consequences (Fig 2B, S2 and S3 Tables). The presence of large numbers of annotated piRNAs added to the complexity of analyzing genome variation in both the complete QTL interval and the NIL-narrowed interval. We considered protein-coding and non-coding RNA (ncRNA) variation as potentially causal to the albendazole-resistance phenotype. Only three proteins in the narrowed interval are predicted to have altered functions as a result of single nucleotide variants (SNVs). However, the plausibility of these specific candidate genes was dampened by a number of factors. Y105C5A.508 is curated as a short and likely pseudogenic transcript, which shares no homology with proteins in species with available sequence data. Additionally, expression of this transcript was barely detectable via RNA-seq in either the N2 or CB4856 strains (S4 Table). The gene pqn-79 has a predicted coding variant (Thr194Ala) but belongs to a highly redundant gene family that has > 99.5% nucleotide sequence identity with at least three other homologs. None of these homologs exhibit coding variation or differences in expression across strains (S4 Table), but the possibility of a dominant-negative or gene dosage effect cannot be excluded. The gene Y105C5A.8 codes for a protein of unknown function that is predicted to contain a splice-donor variant, but we found no differences in overall expression (S4 Table) or splice form abundance (S5 Fig) between N2 and CB4856 for this gene. We therefore originally hypothesized that albendazole resistance is more likely a function of variation in the non-coding RNA (ncRNA) complement, specifically the piRNA-encoding genes. 1,684 SNVs occur in known piRNAs in the complete QTL interval, and 276 of these fall within the NIL-narrowed interval (S3 Table).

To test this hypothesis, we generated NILs encompassing the broader piRNA-enriched region on chromosome IV (13.5–17.2 Mbs). These NIL strains robustly recapitulated the QTL direction of effect (S6A Fig). Next, we analyzed the responses of small RNA pathway mutants to albendazole in an effort to perturb the large number of diverse piRNAs. 21U-RNAs/piRNAs are regulated by the Piwi Argonaute PRG-1 [50]. We hypothesized that albendazole sensitivity should be dependent on prg-1 function. We tested mutants in all three Argonaute genes that encode proteins that interact with primary small RNAs immediately upstream of WAGO-associated 22G RNA generation (ergo-1, alg-4; alg-3, and prg-1) [51]. ERGO-1 and ALG-3/4 engage 26G RNAs, and PRG-1 coordinates the processing of piRNAs. Of the mutations in these genes, a back-crossed prg-1 N2 strain conferred greater albendazole sensitivity compared to N2 (S6B Fig). Concerned with the relative sickness of this mutant strain in control (DMSO) conditions, we generated and tested two independent prg-1 loss-of-function alleles in both the N2 and CB4865 backgrounds using CRISPR/Cas9 genome editing. These new prg-1 knock-out strains were normal in DMSO and not significantly different in albendazole from the wild-type parents in the expected direction of effect across three replicate assays (S7 Fig). Although we have not identified the causal genetic locus, it is possible that the factors underlying the albendazole resistance phenotype are a mixture of coding or regulatory variants that interact and affect gene function. Numerous expression QTL (eQTL) have also previously been mapped to the major-effect albendazole QTL region, potentially connecting candidate variants to local (3 cis eQTL) or distant (78 trans eQTL) regulatory targets that affect drug sensitivity [52] (S5 Table).

Discovery of quantitative trait loci (QTL) associated with benzimidazole responses in C. briggsae

To compare benzimidazole-resistance loci across nematode species, we examined variation in the responses of Caenorhabditis briggsae strains to the same set of benzimidazole compounds that we studied in C. elegans. Dose responses and heritability calculations (S8 and S9 Figs) were used to select concentrations for linkage mapping experiments. Single concentrations of albendazole (25 μM), fenbendazole (30 μM), mebendazole (50 μM), and thiabendazole (40 μM) were chosen. Linkage mapping was carried out with a collection of 153 recombinant inbred lines (RILs) created using the parental strains AF16 and HK104 [23], which led to the discovery of four QTL for the tested drugs (Fig 3A, S10 Fig and S6 Table). The C. briggsae QTL of largest effect is found on the left arm of chromosome IV (2.56–3.45 Mb) and explains approximately 18% of fenbendazole-induced variation in fecundity between the parental strains (Fig 3B). QTL of smaller effect were identified on the center of chromosomes V for thiabendazole and on the left arms of chromosomes V and X for albendazole. No QTL were identified for mebendazole across the examined set of fitness traits.

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Fig 3. Discovery of benzimidazole response QTL in C. briggsae.

(A) Results of C. briggsae linkage mapping experiments are shown for three tested drugs as grouped by trait category. QTL peak markers (circles) and confidence intervals (lines) are depicted. Fill color corresponds to the QTL LOD score. In total, four non-overlapping QTL were identified across conditions. (B) Linkage mapping plots are shown for the QTL of highest significance for each drug, including a major QTL associated with the effect of fenbendazole on brood size, and to a lesser extent, length and pumping traits. Plots show genomic position along the x-axis and significance (maximum LOD score across mapping iterations) along the y-axis. The QTL confidence intervals (chromosome IV: 2.56–3.45 Mb for 30 μM fenbendazole) are shaded in gray and the red dashed line represents the genome-wide correction significance threshold for the first mapping iteration (LOD score for fenbendazole QTL peak marker = 9.97; LOD threshold = 2.75). The fenbendazole QTL explains 18% of trait variation (effect size = 0.70). (C) Tukey box plots showing the phenotypic split of RIAILs that have either the AF16 or HK104 genotype at the peak QTL marker positions. AF16 animals are significant more resistant to fenbendazole exposure than HK104 animals, exhibiting much larger brood sizes in the presence of drug.

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Strikingly, the major fenbendazole QTL falls within the primary C. briggsae piRNA cluster (Chr IV: 0–6.9 Mb). Despite tens of millions of years of evolutionary distance [53], the most significant C. elegans and C. briggsae benzimdazole QTL were found to occur in genomic regions that are syntenic between species [54] (Fig 4A). The C. briggsae fenbendazole QTL region contains 84 protein-coding genes with variants (S7 Table). However, no orthologs were identified across species that both contained variants and fell within these specific QTL confidence intervals. This result suggests that different gene(s) and mechanisms account for the drug effects mapped to these syntenic loci across species. piRNA-encoding genes that densely cover this locus could potentially underlie the fenbendazole-resistance phenotype in C. briggsae, but no piRNAs are shared between C. elegans and C. briggsae.

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Fig 4.

(A) Synteny between major-effect C. elegans albendazole QTL and C. briggsae fenbendazole QTL on chromosome IV. Circos [55] plot showing synteny of orthologous gene pairs on chromosome IV. The outer track shows linkage plots for the C. elegans albendazole and C. briggsae fenbendazole mappings (red line: LOD scores; blue line: QTL confidence intervals; black line: significance thresholds). Both QTL intervals fall within major piRNA clusters in both species (piRNA cluster beginning and end sites shaded in blue). Inner green links represent all orthologs that occur outside of the QTL intervals, yellow links represent protein-coding orthologs with at least one member inside either QTL interval but with no detected variants (58 pairs), and black links represent orthologs with at least one member inside either QTL interval and with a detected variant (38 pairs). No orthologs were identified across species that both contained variants and fell within their respective QTL confidence intervals. This result suggests that different genes and mechanisms account for the drug effects mapped to these loci. (B) Conservation of candidate C. elegans benzimidazole resistance genes in the parasitic nematodes S. ratti and B. malayi. Venn diagram showing that a substantial proportion of the QTL-contained protein-coding genes with predicted functional variants (5,434 total) are conserved in representative clade IV and clade III parasites. Approximately 40% of the candidate resistance genes have identifiable orthologs in the clade III human filarial parasite Brugia malayi (2,163 of 5,434) and the clade IV model gastro-intestinal parasite Strongyloides ratti (2,149 of 5,434), and one-third are conserved across all three species (1,685 of 5,434).

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Discovery of candidate benzimidazole resistance genes in parasitic nematode species

To explore the potential translation of these loci to parasites, we looked at putative conservation of candidate C. elegans resistance genes(s) in parasitic nematodes. Specifically, we examined conservation of protein-coding genes that fall within benzimidazole QTL confidence intervals and contain predicted functional variants. Substantial fractions of these candidate genes have identifiable preliminary orthologs in representative parasites from other clades, although conservation varies across individual QTL (S8 Table). Approximately 40% of the 5,434 candidate resistance genes across all identified QTL have orthologs in the clade III human filarial parasite Brugia malayi and the clade IV model gastro-intestinal parasite Strongyloides ratti, and 31% are conserved across all three species (Fig 4B and S8 Table). It is reasonably likely that gene(s) validated in this system as modulators of benzimidazole response have counterparts in parasite genomes. The narrowing of these QTL in Caenorhabditis species and the validation of the independent effects of genes on phenotypes has the potential to discover novel benzimidazole modes of action and resistance. It will be necessary to carry out more comprehensive parasite ortholog identification and analyses once QTL have been narrowed to smaller numbers of candidate genes.

A number of considerations can potentially hamper the translation of these data to parasitic nematodes. The linkage mapping approach is limited to sampling genetic variation between two parental strains. Genome-wide association (GWA) studies that explore species-wide variation in anthelmintic response would improve the potential scope of variant discovery and the prospects of translating results to parasite species. This study is focused on standing genetic variation and therefore does not speak to de novo mutations that might be selected through drug pressure in field populations of parasites. Many of the QTL identified span large confidence intervals and explain little variation, making them difficult to systematically narrow. Although it is reasonable to expect conservation of many underlying mechanisms that account for drug sensitivity, it is difficult to precisely map phenotypes and effect sizes between free-living and parasitic nematodes. Despite these caveats, we report a number of QTL associated with smaller confidence intervals and more significant effects. The systematic narrowing of these QTL is likely to lead to the discovery of causal genes with an appreciable likelihood of conservation in parasitic nematodes.

Ultimately, anthelmintic resistance gene variants validated in the genetically tractable Caenorhabditis species can be evaluated with respect to effect size and mutation type (e.g., gain or loss-of-function). Although efforts to narrow QTL may lead to the discovery of variants with modest drug effects, it is conceivable that small effects can additively build to significant resistance in parasites and that other potential variation at the same loci can produce much larger effects. For genes with parasite orthologs, the effects of gene loss-of-function on parasite drug response can be assayed using targeted genetic perturbation techniques (e.g., RNA interference) established in various helminth species. The expansion of the helminth genetic toolkit to CRISPR/Cas9 genome editing [56, 57] will pave the way for more precise mappings of mutations and effects. These Caenorhabditis data complement efforts to improve the resolution of parasite population genomics data [58], as well as efforts to carry out genetics studies in helminth species where feasible [59, 60]. More broadly, we expect that these approaches and data can hasten better models and markers for the development of anthelmintic resistance in human and animal parasite control.