Abstract
The gastrointestinal parasite Haemonchus contortus is an haematophagous parasitic nematode of veterinary interest and a model for the study of drug resistance mechanisms or host-parasite interactions. To understand its evolutionary history, and its ability to adapt in the face of climatic and drug pressure, we have performed an extensive survey of genome-wide diversity using single-worm whole genome sequencing of 223 individuals sampled from 19 isolates spanning five continents. The pattern of global diversity is driven by an African origin for the species, together with contemporary dispersal that is consistent with modern human movement, with evidence for parasites spreading during the transatlantic slave trade and colonisation of Australia presented. Strong selective sweeps were identified in independent populations each surrounding the β-tubulin locus, a target of benzimidazole anthelmintic drug treatment used widely to control H. contortus infections. These signatures of selection were further supported by signals of diversifying selection enriched in genes involved in response to drugs, as well as other anthelmintic-associated biological functions including pharyngeal pumping and oviposition. From these analyses, we identify some known, and previously undescribed, candidate genes that may play a role in ivermectin resistance. Finally, we describe genetic signatures of climate-driven adaptation, revealing a gene acting as an epigenetic regulator and components of the dauer pathway may play a role in adaptation in the face of climatic fluctuations. These results begin to define genetic adaptation to climate for the first time in a parasitic nematode, and provides insight into the ongoing expansion in the range of Haemonchus contortus, which may have consequences for the management of this parasite.
Introduction
Nematodes have evolved to exploit a wide diversity of ecological niches. Although many sustain a free-living lifestyle, parasitic nematodes rely on one or more hosts to complete their life cycle. Many parasitic nematodes undergo a complex series of morphological changes, linked to migration through their hosts to establish a mature infection1. Their complex life cycles may involve both intermediate hosts, vectors or time spent in the environment, where they face harsh and variable conditions such as frost or drought that they must withstand between infection of their hosts2. Parasitic nematodes have adapted to a wide range of threats, including predation, climate and the immune responses of a great diversity of both plant and animal host species34.
The evolutionary success of parasitic nematodes comes at a cost to humans, either directly as they significantly impact human health (amounting to a loss of 10 million disability-adjusted life-years)3, or indirectly via major economic losses in plant4 and livestock production5, and parasite control. The control of animal parasitic nematodes relies almost exclusively on anthelmintic drugs, administered on a recurrent basis in livestock6 and through mass-drug administration program in humans7. Although the success of such strategies was originally undeniable, the emergence of drug resistant veterinary parasites 5, or the reported lack of efficacy in human-infective species8, threatens ongoing control efforts for many parasitic infections. Vaccines offer an attractive alternate control strategy against these parasites: the extensive genetic diversity and immune-regulatory properties of parasites has, however, greatly hampered vaccine development9, and although two licensed vaccines are currently available for veterinary purposes10, transcriptomic plasticity of the parasite following vaccine challenge may contribute to circumvent the vaccinal response of their host10. It is therefore clear that novel, sustainable control strategies are required. The potential of helminths to adapt to – and thus escape – control measures lies in their underlying genetic diversity. A greater understanding of the extent of this diversity and the processes that shape it throughout their range should provide insight into the mechanisms by which they adapt, and may identify new targets which may be exploited for control.
The trichostrongylid Haemonchus contortus is a gastrointestinal parasite of ruminants in tropical and temperate regions throughout the world, and causes significant economic and animal health burden particularly on sheep husbandry. It is also emerging as a model parasitic nematode system for functional and comparative genomics, largely due to its rapid ability to acquire drug resistance, the relative tractability of its life-cycle under laboratory conditions11, the development of extensive genomic resources12,13, and its relatively close relationship with other clade V parasitic nematodes of both veterinary and medical importance (i.e, other gastro-intestinal nematodes of livestock and human hookworms)14. We have used whole genome sequencing of 223 individual H. contortus sampled from 19 isolates spanning five continents to characterise genome- and population-wide genetic diversity throughout its range. This survey of genome-wide diversity has revealed old and new genetic connectivity influenced by human history, and signatures of selection in response to anthelmintic exposure and local climatic variation.
Results
Haemonchus contortus isolates are genetically diverse, with large effective population sizes
Whole genome sequencing of 223 individuals from 19 isolates (Fig. 1a; Supplementary Table 1) revealed 23,868,644 SNPs with a genome-wide distribution of 1 SNP per 9.94 bp on average.
Only a proportion of the filtered SNPs were called in more than half the individuals (n = 3,338,155 SNPs), with only 411,574 SNPs segregating with a MAF > 5% in those individuals. Estimates of nucleotide diversity (π) in isolates with at least five individuals ranged from 0.44% (STA.2) to 1.3% (NAM; Supplementary Table 2). Variance in n across autosomes among isolates was partly explained by isolate mean coverage (F(1,84) = 9.49, P = 0.003; Supplementary note). To account for this bias, estimates were obtained from three subsets of isolates with the highest coverage (greater than 8× on average) from France (FRA.1 and FRA.2), Guadeloupe (GUA) and Namibia (NAM) that yielded slightly higher values that ranged between 0.65 and 1.14%. Overall, these data show equivalent diversity levels to Drosophila melanogaster (ranging between 0.53% and 1.71%)16 but represented approximately 1.6 to 45-fold greater diversity than two filarial nematode species for which similar statistics are available (0.02% for Wuchereria bancrofti larvae17; 0.01% and 0.4% for πS and πN in Onchocerca volvulus18 Supplementary Fig. 1).
To begin to explore the global diversity of H. contortus, we performed a principal component analysis (PCA) of genetic variation, which revealed three broad genetic clusters of isolates (Fig. 1b) that largely coincided with the geographic region from which they were sampled, including: (i) Sub-tropical African isolates (NAM, STA and ZAI), (ii) Atlantic isolates including Morocco (MOR), São Tomé (STO), Benin (BEN), Brazil (BRA) and Guadeloupe (GUA), and (iii) the remainder from the Mediterranean area (FRA, ACO) and Oceania (AUS.1, AUS.2 and IND). The greatest diversity among samples was identified in the African and South American isolates, with East African samples spread along PC1 and West African and South American samples along PC2.
Using estimates of nucleotide diversity estimates, together with the the C. elegans20 mutation rate and assuming a balanced sex ratio21, we inferred the current effective population size (Ne) of H. contortus to be between 0.60 and 1.05 million. MSMC analysis, which models past recombination events based on heterozygosity patterns along the genome, revealed the historical Ne has remained within a slightly lower range of values for most of the sampled time interval, i.e. from 2.5 to 500 thousand years ago (kya), with extreme estimates falling between 1.5 × 105 and 6.1 × 105 individuals for GUA (633 years ago) and NAM (415 years ago) isolates, respectively (Fig. 1c). Ne estimates remained relatively constant in most populations until 2.5 Kya; since this time, GUA and FRA.1 isolates suffered a more drastic reduction in Ne, which ranged for each isolate between 0.859 × 105 and 1.2 × 105 individuals approximately 633 and 452 years ago, respectively (Fig. 1c). (Fig. 1c).
Global population connectivity of Haemonchus contortus is characterised by old and new migration
To explore the global connectivity between isolates, we used a number of complementary approaches. Phylogenetic relationships determined by nuclear (Supplementary Fig. 2) and mitochondrial (Fig. 2a; PCA of mtDNA genetic diversity is presented in Supplementary Fig. 3) diversity broadly supported the initial PCA analysis (Fig. 1b), each revealing three main groups of samples partitioned by broad geographic regions, i.e. Africa, Oceania and Mediterranean groups. However, the mitochondrial data revealed further subdivision of the Oceanian and Mediterranean clades than nuclear data alone.
The presence of close genetic relationships between geographically distant isolates, resulting in a weak phylogeographic signal (Mantel’s test r = 0.10, P = 0.001), was inconsistent with a simple isolation-by-distance scenario. This observation was supported by, for example, little genetic differentiation (measured by FST) between geographically distant French (FRA) and Oceanian (AUS.1, AUS.2, IND) isolates (Fig. 2b).
The greatest genetic dissimilarity was found between subtropical African isolates and the French and Australian isolates, with mean FST across comparisons of 0.21 (FST range according to isolate pairs: 0.06 – 0.42) and 0.24 (FST range: 0.17 – 0.33) respectively (Fig. 2b, Supplementary Fig. 4). The divergence of African isolates was likely due to the higher within-isolate diversity (+14% higher mitochondrial nucleotide diversity) relative to others (P = 0.004; mitochondrial nucleotide diversity of 0.63% ± 37%, 0.58% ± 0.31%, 0.66 ± 0.32%, 0.6% ± 0.35%, for Mediterranean, Oceanian, American and south-African isolates respectively).
The higher genetic differentiation (8.9% difference, F(1,134) = 11.36, P = 0.0009) between STO and other isolates relative to other pairwise comparisons, almost certainly reflects the isolation of an island population relative to continental populations (Fig. 2b). STO samples also displayed higher genetic dissimilarity (5.7% difference, F(1,7567) = 581, P < 10−4) to other other samples. Inference of the joint history of STO and other African populations supported this view. The best-fitting model (supplementary Table 3) was consistent with either ancient symmetrical gene flow followed by isolation or an early split followed by secondary contact before isolation with MOR population (supplementary Table 3). This latter demography would underpin the pattern of admixture detected between STO and MOR (Fig. 2c).
A closer inspection of GUA samples revealed a mixed ancestry, likely derived from West African and Mediterraneanheritage. A subset of GUA samples showed limited genetic differentiation to FRA isolates (FST range = 0.07 – 0.10; Fig. 2b), whereas the remaining GUA samples were genetically similar to isolates from the West African coast, i.e. ACO, BEN, MOR, STO (Fig. 2b & c), as indicated by lower FST estimates with these isolates (FST range = 0.07 – 0.13; Wilcoxon’s test, P = 0.07; Fig. 2b) and evidence of shared ancestry from the admixture analysis (Fig. 2c, Supplementary Fig. 5 and 6). A particularly close relationship was identified between STO and GUA (FST = 0.10; Fig. 2b). This conflicting genetic origin among GUA sub-isolates is responsible for the higher nucleotide diversity observed in GUA as a whole (Supplementary Table 2).
The patterns of genetic connectivity support at least three distinct migration events in time and space that we investigated using forward genetic simulations (supplementary Table 3). First, we detect an out-of-Africa scenario, whereby the greatest diversity was sampled within Africa, and that isolates outside of Africa represent a subset of this diversity. Consistent with this hypothesis, nuclear genome variation of non-African isolates experienced a genetic bottleneck that occurred between 2.5 and 10 kya that is not present in the African isolates sampled (Fig. 1c). Bayesian coalescent estimate of this initial divergence from mitochondrial genome data yielded an overlapping time range of between 3.6 kya and 4.1 kya (Supplementary Fig. 7). Genetic simulations of the joint demography between FRA.1 and African populations also favoured complex scenarios involving early split (maximum likelihood estimates ranging between 13 and 25 Kya) followed by ongoing gene flow with STA populations or more recent isolation with Namibia (supplementary Table 3). Secondly, the genetic connectivity of West African and American isolates is consistent with parasites spreading during the trans-Atlantic slave trade movement. The scenario linking GUA and STO was compatible with initial population division occurring around 1640 Common Era (CE) ± 167 years (supplementary table 3), consistent with migration associated with colonization and slave trade that occurred in the West Indies under French influence during that time22. The third pattern of connectivity likely reflects British colonisation of Australia in late 1700’s: the interweaving of Australian and South-African worms into the Mediterranean phylogenetic haplogroup (Fig. 2b) may mirror the foundation of Australian Merino sheep, which were first introduced into Australia from South Africa, before additional contributions from Europe and America were made23,24. The admixture pattern observed for worms from the two countries matched their shared ancestry (Fig. 2c), as well as a genetic connection between America and Australia (seen for the AUS.1 isolate; Fig. 2c). Although maximum likelihood estimates supported these scenarios with the isolation of European and South-African isolates occurring between 1794 and 1871 CE (supplementary Table 3), broad uncertainty limited our ability to specifically define the timings of these events (Supplementary Table 3). However, the split between Australian isolates occurred in 1895 CE ± 132 years, consistent with the initial foundation of the sheep industry in this country.These complex patterns reiterate that human movement has played an important role in shaping the diversity of this livestock parasite throughout the world.
Anthelmintic resistance has left distinct patterns of diversity in the Haemonchus contortus genome
The extensive use of anthelmintics has been and remains the primary means of H. contortus and other gastrointestinal control worldwide. This strategy has resulted in the independent emergence of drug-resistant isolates throughout the world, which now limits farming in some areas. Strong selection should impact the distribution of genetic variation within isolates; signatures of selection may reveal genes associated with drug resistance, knowledge of which may contribute to monitoring the emergence and spread of drug resistant isolates, and the design of new control strategies.
The genetic determinants of benzimidazole resistance is perhaps the best characterised of all anthelmintics, with either one of three amino acid residue changes – F167Y, E198A, and F200Y – in the beta tubulin isotype 1 (Hco-tbb-iso-1) protein capable of mediating phenotypic resistance. We identified indications of a selective sweep in the region surrounding the Hco-tbb-iso-1 locus in the resistant isolates analysed. Focusing on three isolates with the highest coverage, we found an average 2.31-fold reduction of Tajima’s D coefficient within 1 Mbp (Fig. 3a, Supplementary Fig. 8) and an average 33% reduction in nucleotide diversity (Supplementary Fig. 9) within this region relative to the rest of chromosome I.
This signature was most evident in the GUA and NAM isolates, but was weaker in the French isolate (FRA.1) due to both phenotypically susceptible and resistant individuals being present (Supplementary Table 4). Phased genotypic information over the whole Hco-tbb-iso-1 (Supplementary Fig. 10) revealed that French and Guadeloupian individuals had little divergence in their haplotypes, suggesting that gene flow of resistant haplotypes between mainland France and the West Indies had occurred (Supplementary Fig. 10). A topology analysis of a 100 Kbp region spanning the Hco-tbb-iso-1 locus supported the shared phylogenetic origin between FRA.1 and FRG (topology 3), whereas the surrounding region was in favour of topologies congruent with overall population structure (Fig. 3b). This finding is consistent when either Moroccan (Fig. 3b) or São Tomé populations are used as a population with close ancestry with FRG (Supplementary Fig. 11).
We analysed the frequency of the three well-characterised resistance-associated mutations affecting codon positions 167 (T to A), 198 (A to T) and 200 (T to A) (Supplementary Table 4; Supplementary Table 5; Supplementary Fig. 12 & 13). The F200Y homozygous genotype was the most common and widespread resistant genotype (n = 39), and accounted for all samples from the Guadeloupe (n = 14) and the White-River South-African (STA.3; n = 6) isolates. Variants at codons 167 and 198 were much less common, i.e. 13 mutant allelic carriers were observed at position 198 and only one F167Y mutant was identified in a French isolate. No double homozygous mutants were found, but three individuals from France, Australia and South-Africa were heterozygous at both positions 198 and 200. In each case, inspection of the sequencing reads revealed that the two mutations never appeared together in the same sequencing read, suggesting they cannot co-occur in cis (on the same chromosome copy). Genotype frequencies were consistent with isolate-level benzimidazole efficacies as measured by the percentage of egg excretion after treatment (Supplementary Table 5). Samples analysed from suspected susceptible isolates always presented with the susceptible genotype for each of the three positions considered (n = 20).
The genetic determinants of ivermectin resistance are largely unknown, but many genes have been proposed to be associated with resistance; although one interpretation of this observation is that ivermectin resistance is a multigenic trait, a major locus associated with resistance in Australian and South African H. contortus has recently been mapped to a region approximately 37-40 Mbp along chromosome V 12. To examine for the presence of ivermectin-mediated selection in our data, a pairwise differentiation scan was performed between known ivermectin-resistant isolates and other isolates sharing same genetic ancestry, i.e. STA.1 and STA.3 versus NAM and ZAI in Africa, AUS.1 against AUS.2 in Australia (Fig. 3c).
The previously described region on chromosome V12 appeared differentiated, particularly between pairwise comparisons involving the South-African resistant isolate (Fig. 3c). A second major differentiation hotspot spanned a 3 Mbp region of chromosome I and encompassed the Hco-tbb-iso-1 locus (Fig. 3c). Although non-synonymous mutations at codon positions 167, 198 and 200 of the β-tubulin isotype 1 are associated with resistance to benzimidazoles, it has been proposed that there may also be an association between this gene and ivermectin resistance25,26. Although our data seem to support this association, an attempt to narrow-down the differentiation signal in this region found significant differentiation in only two 10-kbp windows in two comparisons (NAM vs STA.1 and NAM vs. STA.3, and these two windows did not overlap the Hco-tbb-iso-1 locus. Furthermore, the fact that all of our ivermectin-resistant isolates were also benzimidazole-resistant suggests that this signal is confounded; pairwise FST analyses between ivermectin-resistant and – susceptible isolates from the field will always be biased toward strong differentiation around the Hco-tbb-iso-1 locus due to loss of diversity in benzimidazole-resistant isolates. As such, it was not possible to confirm the putative association between the Hco-tbb-iso-1 locus and ivermectin resistance. We note that the lack of QTL evidence in this region from controlled genetic crosses using ivermectin selection12 supports the conclusion that standing genetic variation at the Hco-tbb-iso-1locus is unlikely to be directly influenced by ivermectin.
To further investigate more subtle signatures of selection, we computed the XP-CLR coefficient, which simultaneously exploits within-isolate departure from neutrality and between-isolate allele-frequency differences 27. This analysis relies on called genotypes rather than genotype likelihoods, but is robust to SNP uncertainty27. Within continent pairwise comparisons of African (NAM, MOR, STA.1, STA.3, ZAI) and Australian isolates (AUS.1, AUS.2) yielded 1,740 hotspots of diversification, 48% of which were contained within a gene locus (Supplementary Fig. 14, Supplementary Table 6). Among these hotspots, two known candidate genes associated with ivermectin resistance were identified, namely an ivermectin sensitive glutamate-gated chloride channel28 (HCOI00617300, glc-4 ortholog) on chromosome and a P-glycoprotein coding gene already involved in ivermectin susceptibility in the equine ascarid Parascaris sp 29, (HCOI00233200, pgp-11 ortholog) on chromosome V. While the former showed indication of reduced genetic diversity in its vicinity in resistant isolates relative to others (0.48% ± 0.09% difference in nucleotide diversity between the two groups, t = 5.25, P < 10−4; Fig. 3d), the genetic diversity pattern in the 100 Kbp window surrounding the latter was similar across isolates (t = 0.004, P = 0.99; Fig. 3d), suggesting its role may not be specifically related to ivermectin resistance.
GO term enrichment analysis of all XP-CLR significant regions identified 26 significant terms (Supplementary Table 7). The top ten most significant GO terms encompassed enzymatic-related activity, neurotransmitter transporter activity (GO:0005326, P = 5.8 × 10−4) and response to drug (GO:0042493, P = 4.3 − 10−3). Additional significant biological process associated terms were related to phenotypes tightly linked to anthelmintic effects. For example, pharyngeal pumping30 (GO:0043051, P = 5.8 × 10−3) and oviposition (GO:0046662, P = 8.6 × 10−3) genes were enriched, both of which are phenotypes linked to ivermectin and its effect on parasite fecundity31. Neurotransmission is the primary target of anthelmintics such as macrocyclic lactones32 and levamisole33; genes with GO terms related to neurotransmission (GO:0001505, P = 9.1 × 10−3) were significantly enriched across comparisons. Anthelmintic-associated GO terms including “response to drug”, “regulation of neurotransmitter” and “neurotransmitter transporter activity” were significantly enriched in every comparison involving a South-African multi-resistant field isolates (STA.1, Supplementary Table 8). Four candidate genes (HCOI00389600, HCOI00032800, HCOI00243900, HCOI00489500) were found overlapping XP-CLR signals of selection and thus seem candidates for drug resistance loci in H. contortus, as supported by the functions of their C. elegans orthologs (snf-9, unc-24, B0361.4, aex-3 respectively). The snf-9 gene encodes a neurotransmitter:sodium symporter belonging to a family of proteins involved in neurotransmitter reuptake, and the latter three genes are expressed in neurons. Unc-24 and B0361.4 are involved in response to lipophilic compounds and aex-3 is critical in synaptic vesicle release34. A reduction in nucleotide diversity surrounding unc-24, B0361.4, aex-3 in the STA.1 isolate in comparison to others (Fig. 3d) may reflect evidence of drug selection.
Climatic adaptation has shaped genomic variation between isolates
In addition to putative anthelmintic-related GO terms, response to stress (GO:0006950, P = 5.9 × 10−3) was among the top ten significant biological process ontologies associated with genes under diversifying selection. Although anthelmintic exposure would be associated with significant stress on susceptible (and perhaps resistant or tolerant) parasites, all free-living stages of H. contortus will be exposed to and must tolerate abiotic factors such as temperature or humidity prior to infection of a new host. To evaluate the impact of such climatic stressors, a genome scan for genetic differentiation between isolates categorised by by the climactic conditions prevailing at their sampling locations, i.e. arid (Namibia), temperate (France mainland) and tropical (Guadeloupe), was performed (Fig. 4a).
A major signal of differentiation formed of two windows (6.925 – 6.995 Mbp and 7.165 – 7.205 Mbp on chromosome I) was shared by all pairwise comparisons. Six genes were found within these two regions (Supplementary Table 9), among which orthologs of C. elegans genes hprt-1, cpb-1, B0205.4 were identified. A highly differentiated 260 Kbp region of chromosome III also repeatedly occurred across comparisons. A window between 31.155 Mbp and 31.185 Mbp was common to comparisons involving isolates from arid areas (NAM vs. FRA.1 and NAM vs. GUA), and a second window (31.345 Mbp to 31.415 Mbp) was common to comparisons between tropical isolates and others (GUA vs. FRA.1 and GUA vs. NAM). While the two annotated genes in the latter region were not associated with any biological description, the first window overlapped a chromo-domain containing protein (HCOI01540500; Supplementary Table 89). This gene is an ortholog of Pc (FBgn0003042) in D. melanogaster, a chromo-domain subunit of the Polycomb PRC1 -complex that specifically recognizes trimethylated lysine7 of histone 3 (H3K27me3)35. Nucleotide diversity over these genomic windows revealed reduced genetic diversity in FRA.1 relative to isolates from hotter climates (Fig. 4b). The contrasted diversity downstream from HCOI01540500 (31.225 Kbp to 31.350 Kbp, Fig. 4b) found between GUA and NAM certainly contributed to higher FST in these windows. Additional differentiation analyses performed at the gene level with base-pair resolution highlighted a few discrete locations of elevated FST common to every comparison and overlapping intron 4 and exon 5 of this gene (Fig. 4b). Translated consensus exon 5 sequences revealed the highest divergence in the GUA isolate (92.1% identity with reference sequence; supplementary Fig. 15), characterized by multiple amino acid changes whose putative functional consequences remain unknown.
To further explore the impact of climatic conditions of genetic diversity, we used a random forest based statistical approach to quantify the relationship between genetic information along environmental gradients, and relative impact of specific bioclimatic variables on genetic diversity. Bioclimatic variables were derived from monthly temperature and precipitation records, with the aim to represent annual trends or seasonality over the 1970 to 2000 time period (Supplementary Fig. 16; Supplementary Table 10). This analysis randomly samples subsets of sites (encoded as SNP frequencies) that can be partitioned into groups based on differences in climactic variables to estimate the predictive ability of climatic variables. Annual precipitation (BIO12), and temperature annual range (BIO7) were revealed to be the most important bioclimatic variables impacting genetic variation (Fig. 4c).
To identify genes that might be impacted by these variables, we performed a genome-wide test for association between SNP variants, and each of these two environmental variables, accounting for genetic structure between isolates. In total, 17 and 25 significant associations (5% FDR; 49,370 SNPs tested) were found with BIO7 and BIO12 respectively (Fig. 4a bottom panel; Supplementary Table 11). Consistent with the initial differentiation scan, chromosomes I and III harboured most of the associations (n = 13 and n = 11, respectively). On chromosome I, eight (of the 13) associations with BIO12 fell within a 6 Kbp window (7,177,538 bp and 7,183,617 bp), overlapping the region universally differentiated between climatic areas. Chromosome III contained 11 significant associations for both BIO7 (n = 6) and BIO12 (n = 5). The two most significant associations were also found on this chromosome for BIO7 (positions 26,824,240 and 26,824,197 bp; P = 6.34 × 10−9 and P = 1.39 × 10−7, respectively). These two SNPs fell within the HCOI00198200 locus, which codes for a metallopeptidase M1, an ortholog of the C. elegans gene anp-1. Additional BIO7-associated SNPs were found within (16,544,889 bp) and in the vicinity (15,905,141 & 15,905,191 bp) of a solute carrier coding gene (HCOI00312500) on chromosome IV, whose orthologs in C. elegans are under the control of daf-12, a key player in dauer formation. Of note, two H. contortus orthologs of components of the dauer pathway in C. elegans, namely tax-4 (HCOI00661100) and daf-36 (HCOI00015800), were among genes under diversifying selection (Supplementary Fig. 14), but neither of these genes contained SNPs identified as being associated with the two most significant bioclimatic variables.
Discussion
The ecology and epidemiology of gastrointestinal nematodes have been well characterized and exploited to build mathematical models to guide treatment decision in the field36. On the contrary, knowledge about their genetic diversity remains limited; a better understanding of population structure and selective pressure applied by environmental factors would yield better predictive power of their range dispersion37. By exploiting a broad collection of samples from globally-distributed isolates, together with chromosome-scale assembly and extensive individual resequencing, we have performed an in-depth characterization of H. contortus genetic diversity, explored historical contributions to its current population structure and identified important drivers shaping the genome of this parasite.
H. contortus populations displayed high levels of nucleotide diversity, consistent with early estimates based on mitochondrial data39,40, and recent re-sequencing experiments of inbred isolates12. These extreme levels of genetic diversity are thought to arise from both a large census population42 and the high fecundity of H. contortus females21.
An early attempt based on a set of genome-wide AFLP markers obtained for 150 individual worms from 14 countries supported the first exploration of H. contortus population structure at the continent level 40. Analyses of these data identified three to four (Africa, South-East Asia, America and Europe) main phylogenetic clusters as well as evidence for the strong genetic connectivity between Australian, South-African and European isolates40. Our genome wide data corroborated these early results. However, the use of a chromosome-scale assembly and individual resequencing contributed to identify genome-wide patterns of genetic diversity in its chromosomal context, and in turn, provided sufficient resolution to identify genes likely associated with selective advantage against drug or climate selection pressures. This had not been possible in previous attempts with AFLP markers 40.
Major past human migrations and associated sheep movements have contributed to the mixing of parasite populations, which partly accounts for the limited genetic structure and extensive admixture between some of our globally distributed isolates. Our data supported an out of Africa expansion derived from ancestral populations from Western Africa, with a bottleneck dated back between 2.5 to 10 kya. Sheep domestication originally took place in the Middle East around 10 kya43, before introduction into Eastern Africa and subsequent spread, likely through cultural diffusion, toward southern Africa approximately 2 to 2.5 kya 44. The timing of the bottleneck identified in our data and occurring between 2.5 and 10 kya is compatible with major migrations of pastoralist populations that ultimately resulted in the import of small ruminants into Southern Africa 44. The simultaneous increase in rainfall in central Africa around 10.5 kya45 would have supported the population expansion and dispersal of H. contortus. In addition, early radiation towards Asia observed in our data was congruent with evidence obtained from the study of retroviral insertions within the sheep genome that suggested direct migration between Africa and southwest Asia46, and with the timing of Asian sheep expansion between 1.8 and 14 kya47.
The genetic congruence of parasites in Guadeloupe with those from both West Africa and the Mediterranean region is consistent with co-transportation of livestock, including sheep, during the discovery and subsequent colonization of America.. Woolly Churra sheep were originally brought to the Caribbean by Spanish conquistadors 49,50, before West African breeds more suited to tropical conditions were transported, resulting in an admixed Carribean sheep population 49,50. The timing of genetic admixture here overlaps with centuries of human and presumably livestock movement during the transport of slaves, most of whom originated in West Africa and were transported to colonies that included the French West Indies22. The Mediterranean ancestry of H. contortus isolates in Guadeloupe and the close relationship with French worm populations suggest additional sheep transport occurred between French mainland and its Guadeloupian overseas territory. Although these movements are difficult to track precisely, live sheep were shipped aboard slave trade vessels departing from French harbours51, and may have introduced European H. contortus to the island. Based on historical data49,50, a Spanish lineage of H. contortus on Guadeloupe would also be expected to exist, though data are missing to confirm this. It can be speculated that the shared ancestry between Guadeloupian and Moroccan worm isolates might result from the introduction of Spanish Churra sheep, as this breed emerged in Spain while the region was under Arab influence between the 8th and 13th century49,52. Introduction of contortus-infected sheep from the Maghreb may have been associated with the spreading of African worms in Spain, and ultimately, Guadeloupe.
The widespread use of anthelmintic drugs has been a major selective force shaping standing genetic variation in the isolates analysed. Adaptation to drug exposure was clearly illustrated in the genetic diversity surrounding the β-tubulin coding gene, a target of benzimidazole drugs; strong loss of diversity was observed at this locus in many distinct isolates phenotypically characterised to be resistant to this class of drug. The co-occurrence of the same resistant haplotypes in geographically disconnected isolates is almost certainly due to the independent evolution of benzimidazole resistance as has been described previously53; however, some evidence of shared ancestry between French and Guadeloupe resistant individuals emphasises the risk of spreading resistance without careful monitoring of parasite populations during livestock trade. Furthermore, this highlights the risk of evolution of resistance in other parasitic nematodes, for example, parasites of medical interest that are treated with benzimidazoles in mass-drug administration programs54.
Ivermectin is an important anthelmintic for parasite control in both veterinary and medical settings55. Worldwide emergence of ivermectin-resistant veterinary parasitic nematodes5 and evidence of reduced efficacy in human filarial nematodes56 underline the urgency and importance of a better understanding of the mechanisms involved55. Our data identified genes previously associated with ivermectin resistance in parasitic nematodes of either veterinary (pgp-11 in Parascaris sp.29) or medical (aex-3 in Onchocerca volvulus57) interest and uncovered new candidates. Additional validation study will determine whether these could serve as that may be useful markers in the field to monitor drug efficacy.. Of note, support for a major QTL for ivermectin resistance on chromosome V recently identified in an introgression experiment in two resistant isolates38 was also present in our genome-wide data and warrants further investigation.
Our broad sampling throughout the global range of H. contortus has enabled the first analyses into climate-driven adaptation in a parasitic nematode. Understanding how climate shapes parasite genetic variation is of primary importance to foresee consequences of climate change on parasite phenology and range dispersion. Parasite dispersal is largely driven by their hosts but H. contortus free-living stages experience climatic conditions that affect their development2 and constrain their spatio-temporal dispersal58. Observations in Northern Europe suggest that climate change has already altered H. contortus winter phenology 59. Our results suggest that adaptation toward annual precipitation was mostly under the control of variation on chromosome I, however, no obvious candidate genes could be identified. In addition, the Hco-tbb-iso-1 locus was close to the region of interest, and linked variation in allelic frequency at this locus as a result of benzimidazole selection cannot be ruled out. A second region of chromosome III was associated with both temperature- and precipitation-related variables, within which biologically relevant genes could be identified; first, the strongest genetic associations with annual temperature range were found in a metallopeptidase with zinc ion binding function (anp-1 ortholog), an enzyme linked to drought stress tolerance in Drosophila 60, and second, an ortholog of Pc displayed strong genetic differentiation between arid- and wetter temperate or tropical environmental conditions. This gene has been linked to putative epigenetic regulation of xeric adaptation in Drosophila melanogaster, where Pc mutants display lower resistance to desiccation stress61. This finding is also corroborated by observations in plants showing Polycomb-mediated regulation of climate-induced phenotypes62. Further links to climatic adaptation include identification members of the dauer pathway63; tax-4 and daf-36 orthologs were under diversifying selection and a daf-12-respondent solute carrier was associated with annual temperature range. Dauer is a developmentally arrested stage in C. elegans that is triggered by environmental stress and mediates tolerance to unfavourable conditions until better conditions are met63, and can occur in parasitic nematodes like H. contortus under semi-arid conditions64. Evidence for climatic adaptation suggests that adaptation in the face of climate change will be constrained by available genetic variability at temperature-selected loci, that may both limit or enable range expansion depending on the region. By a better understanding of the interaction between climatic conditions and phenotypes such as hypobiosis (a temporary developmental arrest during unfavorable conditions), optimisation of treatment timing may be possible to maximise control efficacy.
In summary, our data describes the extensive global and genome-wide diversity of the blood-feeding parasitic nematode H. contortus, and how this diversity has been shaped by adaptation to its environment and to drug exposure. Understanding the mechanism(s) by which parasites adapt to fluctuating environmental conditions both within and outside their hosts will have important implications for field management of parasitic nematodes in both veterinary and medical settings. Further characterisation of these putative strategies, together with genetic covariation of drug resistance genes, should contribute to refining epidemiological models and guide treatment decision-trees for a more sustainable management of worm populations in the face of a changing climate.
Legends to Figures
Figure 1. Global diversity of Haemonchus contortus.
(a) Global distribution of Haemonchus contortus isolates. Isolates sampled are coloured by geographical region (sand: South-America, brown: Western-Africa, dark green: Mediterranean area, black: Subtropical Africa, red: Australia). Shape indicates the anthelmintic resistance status of each isolate to fenbendazole (resistant =squares; susceptible =circles) or resistant to both ivermectin and fenbendazole (triangles).
(b) Principal component analysis based on genotype likelihood inferred from whole genome sequences of 223 individual males (243,012 variants considered). Samples are coloured by geographic region described in panel 1a.
(c) MSMC effective population size across time for 6 isolates. Coloured shaded area represents range of values estimated from a cross-validation procedure with five replicates, computed by omitting one chromosome at a time.
Figure 2. Global connectivity of Haemonchus contortus isolates.
(a) Unrooted maximum likelihood phylogenetic tree constructed using 3,052 SNPs from 223 individual mitochondrial genomes. Circles indicate bootstrap support for each branch, blue if support was higher than 70%, red elsewhere. Branches leading to a sample identifier are coloured by geographic region described in Figure 1a. Mitogroups are annotated with constitutive sample populations.
(b) Matrix showing pairwise dissimilarity between individuals (upper right) and pairwise FST between isolates (lower left).
(c) Admixture analysis of 223 individuals. A cluster size of K = 3 is presented, determined from sites with minor allele frequency above 5% and call rate higher than 50%. Admixture pattern for other values of K is provided in supplementary Figure 5. Isolates are presented sorted along their longitudinal range, and samples sorted by assignment to the 3 clusters.
Figure 3. Anthelmintic-mediated selection is a major driver of genetic variation
(a) Analysis of Tajima’s D surrounding the Hco-tbb-iso-1 locus on chromosome I. A total of 10-Mbp surrounding was considered (pink), of which 1 Mbp nearest to Hco-tbb-iso-1 is highlighted (blue). Isolates compared included benzimidazole-resistant French (FRA.1), Guadeloupian (GUA), Namibian (NAM) or benzimidazole susceptible Australian (AUS.2) and Zaire (ZAI) isolates. Mean expected Tajima’s D (solid grey line) and 99% confidence interval (dotted line) were estimated from a 1,000 simulated 10-Kbp wide sequences following MSMC-inferred demography.
(b) Topology weighing analysis of a 100-Kbp window centred on Hco-tbb-iso-1. At each position, the weight of each of the three possible topologies inferred from 50 Kbp-windows are overlaid. Topology 2 (blue) corresponds to an isolation-by-distance history, while topology 3 (brown) would agree with shared introgressed material between worm isolates from French mainland into Guadeloupe. The Hco-tbb-iso-1 locus is indicated by vertical dashed lines.
(c) Genome-wide differentiation scan between pairs of ivermectin-resistant (AUS.1, STA.1, STA.3) and isolates with no evidence for ivermectin resistance. Chromosomes are coloured and ordered by name, i.e. from I (pink) to V (forest green). Horizontal line represents the 0.5% FST quantile cut-off. Vertical line on chromosome I points at Hco-tbb-iso-1 locus.
(d) Nucleotide diversity over 100 Kbp windows surrounding positional candidate genes for ivermectin resistance. Candidate gene position is highlighted by vertical dashed lines. Ivermectin-resistant isolates (IVM-R) appear as triangles and squares indicate susceptible isolates (IVM-S).
Figure 4. Genomic signal of climate adaptation
(a) Top three panels represent FST values plotted against genomic position (Mbp), colored and ordered by chromosome name (from I to V). Horizontal dashed line represents the 0.5% quantile. Bottom panel shows genomic positions of significant associations between annual precipitation (circle) and temperature annual range (triangle).
(b) A Polycomb group protein coding gene underpins major differentiation signal between populations from arid, temperate and tropical areas. Top panel shows nucleotide diversity estimates for 10 Kbp genomic windows spanning windows of chromosome III with high genetic differentiation for climate. Dashed lines indicate the Pc ortholog boundaries. Within these boundaries, FST estimates along every base-pair of the gene sequence are represented below (circle size proportional to FST coefficient), with predicted exon model shown as grey rectangles. The bottom panel shows translated consensus sequences of exon 5 for populations of interest with asterisks marking mutations.
(c) Proportion of genetic variance explained by precipitation- (blue) and temperature-associated (orange) climatic variables. Variants from eight isolates with no record of ivermectin resistance (AUS.2, FRA.1, GUA, IND, MOR, NAM, STO, ZAI) were analysed against a set of nine variables (from a total of 19), selected to minimize correlation between variables. Annual precipitation (BIO12) and temperature annual range (BIO7) are main contributors of genetic variance.
Materials and methods
Sample DNA extraction and sequencing
A total of 267 individual male H. contortus were obtained from a collection held at INRA16 (metadata for all samples is presented in detail in Supplementary Table 1). The sampling regime was motivated to delineate the contribution of major evolutionary forces, i.e. migration and selection (drug and climate) but also constrained by the material available in the collection. Because migration was likely to match human history, isolates from western African countries and southern America were selected to address the contribution of slave trade history to the structuring of H. contortus populations; isolates from former colonies of the British Empire (South-Africa, Australia) were sampled to establish the connectivity between worm populations from Europe and these countries. Ivermectin-resistant isolates (AUS.1, STA.1, STA.3) were also retained to evaluate how anthelmintics had shaped H. contortus genomic variability (drug efficacy data have been provided in supplementary Table 4). Isolates were selected based on available material to ensure minimal sample size (n = 9) per isolate and proper allele frequency estimation. Following these criteria, 19 isolates from 12 countries were available (Supplementary Table 1). Note that the second Australian population (AUS.2) was obtained from an Italian laboratory (labelled ITA_NAP, supplementary Table 1). Samples were gathered between 1995 and 2011 (Supplementary Table 1), and stored in liquid nitrogen upon collection. Four isolates were fenbendazole susceptible (Fig. 1 and supplementary table 4; triangles; FRA.2, FRA.4, STO, ZAI).
DNA was extracted with the NucleoSpin Tissue XS kit (Macherey-Nagel GmbH&Co, France) following the manufacturer’s instruction. Sequencing libraries were prepared as previously described12. DNA libraries were sequenced with 125 bp paired-end reads on an Illumina Hiseq2500 platform using V4 chemistry (Supplementary Table 1). A second round of sequencing was performed with 75 bp paired-end reads to increase the coverage of 43 samples (Supporting Fig. 15). After sequencing, two samples were identified to be heavily contaminated by kraken-0.10.6-a2d113dc8f65 and were discarded. In total, 18 sequencing lanes consisting of 4,152,170,256 reads were sequenced, the raw data of which are archived under the ENA study accession PRJEB9837.
Sequencing data processing
Read mapping to both the mitochondrial and nuclear genomes (v3.0, available at ftp://ngs.sanger.ac.uk/production/pathogens/Haemonchus_contortus) was performed using SMALT (http://www.sanger.ac.uk/science/tools/smalt-0) with a median insert size of 500 bp, k-mer length of 13 bp, and a stringency of 90%. For samples that had two or more BAM files (when split across multiple sequencing lanes), the BAM files were merged using samtools v.0.1.19-4442866, and duplicated reads removed using Picard v.2_14_0 (https://github.com/broadinstitute/picard) before performing realignment around indels using Genome Analysis Toolkit (GATK v3.6)67 RealignerTargetCreator. Mean coverage of the mitochondrial and genomic genomes were estimated using GATK DepthOfCoverage, revealing coverage lower than the estimated target coverage (original 8x) for most samples. Individuals with more than 80% of their mitochondrial sequence with at least 15 reads and a mean mitochondrial genome coverage of at least 20x were retained for population genetic inferences (n = 223 individuals).
Nuclear genome SNP calling
To call SNPs, we used GATK HaplotypeCaller in GVCF mode, followed by joint genotyping across samples (GenotypeGVCFs) and extraction of variants (SelectVariants), resulting in a total of 30,040,159 unfiltered SNPs across the five autosomes. Sex determination in H. contortus is based on an XX/XO system; as only male worms were sequenced, their hemizygous X chromosome would have revealed limited phylogenetic information relative to the autosomes, and was henceforth excluded from further analysis.
Low coverage sequencing will inadvertently bias allele sampling at heterozygous SNPs, resulting in excess homozygous genotypes particularly if stringent filtering is applied during SNP calling. To circumvent this issue, we applied the GATK Variant Quality Score Recalibration (https://gatkforums.broadinstitute.org/gatk/discussion/39/variant-quality-score-recalibration-vqsr), which first uses a reference (truth) SNP set to estimate the covariance between called SNP quality score annotations and SNP probabilities, followed by application of these probabilities to the raw SNPs of interest.
The reference “truth” SNP database was generated from the intersection of variants called from samples with at least a mean of 10x coverage (n = 13) using three independent SNP callers: (i) samtools mpileup (-q20 −Q20 −C50 −uD), (ii) Freebayes68 v.9.9.2-13-gad718f9-dirty (--min-mapping-quality 20 --min-alternate-count 5 --no-indels --min-alternate-qsum 40 --pvar 0.0001 --use-mapping-quality --posterior-integration-limits 1,3 --genotype-variant-threshold 4 --use-mapping-quality --site-selection-max-iterations 3 --genotyping-max-iterations 25 --max-complex-gap 3), and (iii) GATK HaplotypeCaller followed by hard filtering (--QD<2, --DP>10000, --FS>60, --MQ<40, --MQRS <-12.5, --RPRS<-8). The three variant call sets were merged (GATK CombineVariants with --genotypeMergeOptions UNIQUIFY), resulting in an intersecting set of 794,606 SNPs (extracted with GATK SelectVariants). The GATK VariantRecalibrator model was trained with this reference SNP database with a 90% prior likelihood, before being subsequently applied to the raw set of SNPs (n = 30,040,159). The estimation was run for several truth sensitivity threshold values ranging from 90 to 99.9%. After visual inspection of the additional number of SNPs by using sensitivity tranche curves (Supplementary Fig. 16a), a 97% sensitivity threshold was applied to the raw SNP set with GATK ApplyRecalibration, resulting in a total set of 23,868,644 SNPs spanning the five autosomes. Variant depth of coverage (DP) and strand bias (FS) were the main drivers of SNP removal (Supplementary Fig. 16b).
Called SNPs were used for particular analyses (Ne trajectory through time, cross-population composite likelihood-ratio) that could not be performed under the probabilistic framework that relied on genotype likelihoods as implemented in ANGSD69 v. 0.919-20-gb988fab (Supplementary note). These data were also used to estimate average differentiation between isolates. However, within isolate diversity and admixture were analysed using ANGSD69 (Supplementary note).
Mitochondrial DNA data processing
The mitochondrial genome exhibited an average coverage depth of 322× (ranging from 24× to 5,868×) per sample (Supplementary Table 1). Mitochondrial reads were extracted and filtered from poorly mapped reads using samtools view (-q 20 -f 0×0002 -F 0×0004 -F 0×0008) and from duplicated reads using Picard v.2_14_0 MarkDuplicates (https://github.com/broadinstitute/picard). Realignment around indels was applied with GATK70 and SNP were subsequently called using samtools71 mpileup using only reads that achieved a mapping quality of 30 and base quality of 30. The occurrence of heterozygous sites in mtDNA, known as heteroplasmy, has been described across vertebrate species72 and in other nematode species, including C. briggsae73 and C. elegans74. However, heterozygous sites may also occur as technical artefacts as a result of genetically similar sequences shared between the nuclear and mitochondrial genomes, i.e., numts75. To exclude sites prone to heterozygous signals from further phylogenetic inference analysis, a SNP calling procedure was implemented with the HaplotypeCaller tool to apply hard filtering parameters on the raw SNP sets (QD>=10, FS<=35 MQ>=30 MQRankSum>=−12.5 and ReadPosRankSum >=−8) with a minimum depth of 20 reads. This procedure excluded 1,354 putative heterozygous sites, and retained 72% of the putative SNP sites (3,052 out of 4,234 SNPs). Nucleotide diversity and Tajima’s D were computed by sliding-windows of 100 bp using vcftools v.0.1.1576. A principal component analysis (PCA) was performed on genotypes using the SNPrelate package77 in R version 3.578. A consensus fasta sequence was subsequently generated with GATK FastaAlternateReferenceMaker for each sample using the filtered variant set, which was used for the phylogenetic analyses.
Diversity and divergence analysis
Genome-wide nucleotide diversity (π) was computed for each isolate with at least five individuals using ANGSD69. Using genotype likelihoods (GLs) from samtools71 (option GL=1) as an input, variants were included that had a minimal supporting evidence of 5 reads, and base and mapping quality phred scores of at least 20. As π values were biased by population mean coverage (Supplementary Fig. 17), π was also calculated from a subset of isolates containing individuals with a minimum mean coverage of 5×: this was limited to France (FRA.1, n = 5, mean coverage of 7.66×), Guadeloupe (n = 5, mean coverage of 12.75×) and Namibia (n = 6, mean coverage of 9.85×).
FST was estimated from the VQSR-called genotypes between isolates with at least five individuals using the Weir-Cockerham estimator79 in vcftools v0.1.1576. To prevent artifactual signal linked to variation in coverage between isolates, FST was calculated on subsets of SNPs, binned based on their minor allele frequency (MAF) in 10% increments. The maximum FST value calculated was retained for comparison (Supplementary Fig. 4) and the maximal value was considered as reported elsewhere80. The resulting FST estimates were not biased by coverage, as measured by negligible correlation between pairwise FST coefficients and associated population cross-coverage (Pearson’s r(136) = −0.05, P = 0.55; Supplementary Fig. 18).
The pairwise sequence divergence between individual samples was calculated using the Hamming distance, i.e. 1-IBS, using PLINK81, considering the only individuals with a mean coverage above 2.5× as failure to do so yielded biased estimates (Supplementary note, Supplementary Fig. 19). A neighbour-joining tree of Hamming distances calculated from the nuclear DNA genotypes was built using the R package ape82.
PCA on genotypes inferred from genotype likelihoods of the 223 samples was generated using ANGSD ngsCovar, filtering for sites with base and mapping quality phred scores less than 30, minimum depth of 5 reads, and a SNP p-value (as computed by ANGSD) below 10−3. Clustering was robust to coverage variation and closely matched the PCA from the VQSR-called SNP genotypes (Supplementary Fig. 20).
Phylogenetic inference
To determine the phylogenetic structure of the cohort, the 223 consensus mitochondrial fasta sequences were first aligned using Muscle v3.8.3183, followed by stringent trimming of sequence alignments using Gblocks84. The most likely evolutionary model, GTR substitution model with rate heterogeneity modelled by a gamma distribution with invariable sites, was determined using modelgenerator v.0.85185. A maximum-likelihood tree was subsequently generated using PhyML86 v.20120412, with branch supports computed using 100 bootstraps.
Admixture analysis
Admixture was determined using NGSAdmix87. This tool relies on genotype likelihoods to account for data uncertainty, and has been shown to produce robust inferences about population ancestry from low coverage samples alone, or a mixture of low and higher coverage samples87. This analysis was performed on 223 samples, for K ranging from 2 to 10 clusters (Supplementary Fig. 5 and 6), retaining sites with less than 50% missing data across individuals and minor allele frequency (MAF) above 5%. Five iterations were run omitting one autosome out at a time, and the best K was chosen as the first value that would minimize the median absolute deviation across runs (Supplementary Fig. 6). Sample coverage did not affect the results (Supplementary Fig. 21).
Effective population size and population divergence dating
The effective population size (Ne) trajectory through time, and the cross-divergence time between populations, were estimated using MSMC288,89. This approach uses patterns of heterozygosity along the genome to identify past recombination events modelled as Markov processes90. Mutation density along the sequence mirrors either recent (long tract of limited diversity) or older (enrichment in heterozygosity over short distances) events. According to coalescent theory91, at any given time, the amount of recombination is proportional to Ne.
MSMC2 was applied to individuals with a mean coverage above 10x, limiting the analysis to six isolates (FRA.1, GUA, IND, MOR, NAM, STA.1), and considering four haplotypes per isolate. Beagle v4.1 was used to impute missing genotypes and to establish phase in VQSR SNP calls. Imputation accuracy analysis revealed a 7.2% and 9.0% discordance rates at the individual and site levels respectively (Supplementary Fig. 22). Input files were created following MSMC recommendations and available msmc-tools (https://github.com/stschiff/msmc-tools). Briefly, the reference fasta sequence was masked with SNPable (http://lh3lh3.users.sourceforge.net/snpable.shtml) to extract regions of unambiguous read mapping in chromosome-specific bed files (using the available msmc_create_map_mask.py python script). Negative bed files indicating regions with sufficient coverage at the individual level were created from samples bam files with the bamCaller.py script and filter out sites with coverage below genome-wide average depth. Finally, MSMC2 input files were created for each chromosome with the generate_multihetsep.py script and concatenated into a single input file. For each isolate, estimates were averaged across five runs, leaving one chromosome out at a time for cross-validation, using rho/mu parameter value of 6.22 (average recombination rate of 1.68 cM/Mbp12 and considering a mutation rate similar to that of C. elegans mutation rate, 2.7 × 10−9 per site per generation20). MSMC2 times and coalescent rates were scaled to real time and population sizes by assuming the same mutation rate20, a balanced sex-ratio21 and an inferred generation interval of 40 days (the sum of 10 days to reach mature free-living infective larvae from the egg stage, and a 30-day prepatent period for fully mature egg-laying females)92.
Migratory scenarios between populations were determined using the forward simulation framework implemented in δaδi93. Under a given evolutionary scenario, this software models the expected joint site frequency spectrum between multiple populations using a diffusion equation. These expected values are then used to compute the most likely demographic parameters knowing the observed site frequency spectrum. For each model, four rounds of forward simulations were run with 10, 20, 30 and 40 replicates respectively using previously published python scripts94 (https://github.com/dportik/dadi_pipeline). Model Akaike Information Criterion (AIC) were compared for ranking scenarios, from which the lower the score, the more likely the outcome.
We first compared a divergence scenario without migration against models including symmetrical and asymmetrical gene flow before isolation. In case migration was the most likely, more complex models (involving split with ancestral (a)symmetrical gene flow, with or without population size change, or models involving secondary contact with/without gene flow and population size change) were tested. However, initial exploration indicated a likely lack of power in our design to accurately estimate parameters of more complex demographic models than the split and isolation model. Nevertheless, these models still provide the most likely scenario and their output have been listed in supplementary Table 3.
Parameters were scaled to real time using same parameters as for MSMC2 inference. Standard deviations of timing estimates for the simple split and isolation models were obtained using the Godambe Information Matrix95 applied to 100 simulated site frequency spectra produced with the ms software96 under the most likely demographic model.
Additional support to the estimates from the nuclear genome were obtained from a phylogenetic analysis of coding sequences using BEASTv1.1097. Mitochondrial coding sequences were extracted from the consensus sequence of every individual, concatenated per individual, and aligned using Musclev3.8.3183. A Bayesian skyline model98 was used, with a HKY substitution model and a strict clock model, as other modalities yielded weak effective sample size (ESS) and unstable parameter values. Clock rate was set to C. elegans mitochondrial mutation rate, i.e. 1 × 10−7 per site per generation74, as variation in sampling date was not sufficient to estimate molecular rate. Parameters showed sufficient sampling (effective size above 200) after 50,000,000 iterations and a burn-in of the first 20 million steps. Node ages were scaled to years assuming a generation interval of 40 days. A maximum clade credibility tree was generated with TreeAnnotator v1.10.1 (http://beast.community/treeannotator).
Diversifying selection scan
To further characterize the genetic diversity in H. contortus populations, we identified genomic regions under diversifying selection using XP-CLR27. This approach takes advantage of both within-population distortion of the allele frequency spectrum, and between-population differences in allele frequencies in the vicinity of selective sweeps27. Although XP-CLR is robust to SNP ascertainment bias27, the analysis was restricted to isolates with at least five individuals with a minimum mean coverage of 2× per individual. Unphased VQSR-derived genotypes were filtered to retain SNPs with a within-isolate call rate >80% and MAF >5%. The analysis was run on every one of the 22 possible within-continent pairwise comparisons of the retained isolates (Australia and Africa) with the following options: -w1 0.0001 500 2000 -p0 0. This fit a grid of putatively selected points every 2 Kbp along the genome, with a sliding window size around grid points of 0.01 cM, interpolating SNP position in Morgans from the average recombination rate for each chromosome12. Down-sampling was applied to windows where more than 500 SNPs were found to keep SNP numbers comparable between regions, and no LD-based down-weighing of the CLR scores was applied. A selection score was subsequently computed at every position as the root mean square of XP-CLR coefficients, and the highest 0.1% of selection scores were deemed significant, as reported elsewhere80.
Analysis of β-tubulin isotype 1 (Hco-btub-iso-1) and the genetic architecture of benzimidazole- and ivermectin-resistance
The genomic coordinates of Hco-btub-iso-1 were determined by blasting the gene coding sequence from WormBase Parasite99 against current genome assembly (BLASTN, e-value < 10−50). The SNP positions associated with codons 167, 198 and 200 was determined to be at 7027535, 7027755, and 7027758 bp, respectively, along chromosome I after aligning the Hco-btub-iso-1 consensus sequence and published sequence100 (GenBank accession FJ981629.1) using muscle v3.8.3183. Genotype and GLs at these positions were determined with ANGSD69. Genotypes were only considered for GL >60% (n = 74) as coverage bias occurred otherwise (Supplementary Table 6 and Supplementary Fig. 12). For these samples, phased and imputed genotypes from VQSR SNPs spanning the Hco-btub-iso-1 locus were used to compute pairwise number of allele differences. Selection in the vicinity of the Hco-btub-iso-1 locus was assessed by computing Tajima’s D with ANGSD101. For benzimidazole-resistant isolates, neutral state was built with ms102 by simulating 10-Kbp wide isolate-specific sequences (n=1,000) following the same coalescent scenario as predicted by MSMC2 for chromosome I (considering a recombination rate of 1.83 cM/Mbp12). Suitable ms input parameters were derived from MSMC2 output files using the msmc2ms.py script from msmc-tools. This approach was implemented for isolates with sufficient mean depth of coverage, i.e. three benzimidazole-resistant isolates. In case of susceptible isolates, the lack of significant departure in the Hco-btub-iso-1 vicinity relative to the rest of chromosome I was tested.
The introgression of resistant haplotypes from France mainland into Guadeloupian worm isolates was tested by a topology weighting analysis implemented with TWISST103. This method computes phylogenetic trees between a set of isolates, using genetic information from short sliding windows (50 Kbp) and by sampling with replacement individuals from each isolate. At each window, a weight is subsequently computed for each of the possible tree topology, ultimately providing inference of introgression events in discrete locations where tree topology connects phylogenetically distant isolates. Analyses were run using individuals with sufficient mean depth of coverage (5× and more) from French (FRA.1; n = 5; mean depth = 7.62×) and Guadeloupian (GUA; n = 5; mean depth = 12.75×) isolates, adding Namibian isolate (n = 2; mean depth = 10.9×) as an outgroup. A fourth isolate was chosen for its common ancestry with Guadeloupian isolates: first, an analysis was run with worms from São Tomé (n = 2; mean depth = 7.01×), followed by a second analysis using Moroccan samples (n = 2; mean depth = 10.6×), to ensure that evidence of introgression was consistent in both cases.
To investigate the genetic architecture of ivermectin resistance, a differentiation scan was run between ivermectin-resistant isolates and isolates of unknown status sharing same ancestry, i.e. STA.1 and STA.3 versus NAM and ZAI in Africa, AUS.1 against AUS.2 in Australia. Windowed FST estimates79 were calculated every 10 Kbp with a 1 Kbp overlap along the genome with ANGSD69, retaining sites with minimal depth of 5 reads, mapping and base quality phred scores of at least 30, missing rate below 50%, and windows with at least 1000 sites. Genomic coordinates of the top 0.5% most differentiated windows were extracted and analysed by BLASTN (minimum e-value = 10−50) against the published V1 H. contortus assembly13, and annotated gene identifiers were inferred from the corresponding GFF file from WormBase Parasite99.
Identification of SNPs under environmental selection
To identify SNPs putatively influenced by environmental selection, we first defined the climatic conditions of each isolate using the Köppen-Geiger classification104, and inferred from isolate geographical coordinates105. Three isolates with the best coverage, i.e. Namibia, France, and Guadeloupe, were used to contrast dry, temperate and tropical conditions, respectively. We performed a genome-wide scan using pairwise comparisons of FST using ANGSD69, of which test values greater than the 0.5% quantile in at least two comparisons were analysed further.
To further explore the contribution of environmental climatic variables on standing genetic variation, we applied a machine-learning gradient forest algorithm106 to quantify changes in genetic variation (fit as population SNP MAF) along environmental gradients. The gradients consisted of 19 bioclimatic variables107 summarizing rainfall and temperature information recorded between 1970 and 2000(supplementary table 9). For each SNP, a random forest of 500 trees was grown. For each tree, bootstrapped SNP MAFs were regressed against a random subset of bioclimatic variables to determine the variable that best partitioned the data, thereby building the first node that partitions the data into two sets of homogeneous observations. Iterations follow to determine subsequent nodes by resampling a random subset of bioclimatic variables until no observations are left. The proportion of variance explained by each bioclimatic variable is then averaged across SNPs, and the function of SNP frequency modification along bioclimatic variable is built.
The analysis was performed on isolates with at least 5 individuals, a mean coverage of 2×, and no record of ivermectin resistance (AUS.2, FRA.1, GUA, IND, MOR, NAM, STO, ZAI). SNP MAFs were estimated from genotype likelihoods with ANGSD, and subsequently filtered to ensure a within-isolate MAF >10%, at least 90% within-isolate call-rate, and shared across the eight considered isolates, resulting in 3,758 SNPs retained for further analysis.
Environmental variables were highly correlated (Supplementary Fig. 15a), resulting in instability in the predictor’s importance. To minimise this effect, the gradient forest analysis was restricted to the 11 environmental variables showing least redundancy as assessed by a PCA (Supplementary Fig. 15b). Pair-wise Euclidean distances between variables was computed from their respective coordinates on the first two PCA axes (supplementary Fig. 15a). We selected variables with higher distances (mean distance > 0.85) with any others (BIO4, 7, 2, 15, 5, 10). Other variables defined three clusters (Supplementary Fig. 15b). Within each cluster, we picked the variable with closest distance from every other in the cluster, i.e. the variable summarizing others’ contributions (BIO9, BIO13, BIO19). For the BIO12, 13, 16, 18 cluster, we chose to pick BIO12 (annual precipitation) which is more relevant toward parasite life-cycle across climatic areas. Under temperate areas, quarter-based (BIO16, BIO18) or wettest month (BIO13) statistics would match seasons where hosts are housed. SNP-environment associations were further investigated by focusing on the top two environmental predictors of genomic variations and using a Latent Factor Mixed Model analysis. This analysis was implemented with the lfmm R package108 on genotype calls from the VQSR pipeline, considering SNPs with call rate of at least 70% across isolates and minor genotype frequency above 5%, using K = 3 for the latent factor accounting for underlying population structure. Analyses were run 5 times and P values were combined and adjusted as recommended108.
Gene identification and Gene Ontology enrichment analysis
Annotation of the revised H. contortus genome is ongoing. Therefore, genes underlying major differentiation signals were inferred by blasting the region of interest (10 Kbp window for FST analyses, 2000 bp region around XP-CLR hits) against the published V1 H. contortus genome assembly13. Any genes falling within most probable blast hit coordinates were retrieved from the H. contortus published assembly available from WormBase Parasite99. This database was also used to retrieve C. elegans orthologs of positional candidate genes and H. contortus Gene Ontology (GO) terms. GO term enrichment analysis was run with the R topGO109 package, considering nodes with at least 10 annotated genes. The “weight01” algorithm was used to account for existing topology between GO terms. This framework makes P-value of one GO term conditioned on its neighbours, thereby correcting for multiple testing. Enrichment was tested by the Kolmogorov-Smirnov statistic applied to gene selection or FST score accordingly. GO terms with P values below 1% were deemed significant.
Data availability
Raw sequencing data are archived under the ENA study accession PRJEB9837. Sequencing data were analysed with publicly available script and software as mentioned in main text. Outputs were analysed using an R script available at: https://github.com/guiSalle/Haemonchus_diversity. Reference assembly used in this project is available at: ftp://ngs.sanger.ac.uk/production/pathogens/Haemonchus_contortus.
Author contributions
GS, JAC designed the experiment. GS, JAC, SD drafted the manuscript. JCa and JCo sampled and prepared parasite materials. GS performed DNA extraction and data analyses. SD built the reference genome. MB and NH managed and supervised parasite sequencing and the project.
Competing interests
Authors declare they have no competing interests.
Acknowledgements
JAC, MB, NH, and SD are supported by the Wellcome Trust via their core funding of the Wellcome Trust Sanger Institute (grant 206194). GS has received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills (grant agreement n° 267196) and AgreenSkills+ fellowships (grant agreement n°609398). Authors are grateful to Pr. Beech and Gilleard for insightful discussions.
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