Aedes albopictus has become the central stage of attention for deciphering multiple biological and evolutionary aspects at the root of its success as an invasive species. Its genome of 1,967 Mb harbours an unusual high number of non-retroviral integrated RNA virus sequences (NIRVS). NIRVS are enriched in piRNA clusters and produce piRNAs, suggesting an antiviral effect. Here, we investigated the evolutionary history of NIRVS in geographically distant Ae. albopictus populations by comparing genetic variation as derived by neutral microsatellite loci and seven selected NIRVS. We found that the evolution of NIRVS was far to be neutral with variations both in their distribution and sequence polymorphism among Ae. albopictus populations. The Flaviviral elements AlbFlavi2 and AlbFlavi36 were more deeply investigated in their association with dissemination rates of dengue virus (DENV) and chikungunya virus (CHIKV) in Ae. albopictus at both population and individual levels. Our results show a complex association between NIRVS and DENV/CHIKV opening a new avenue for investigating the functional role of NIRVS as antiviral elements shaping vector competence of mosquitoes to arboviruses.Since its genome details are publically available, the mosquito  Aedes albopictus  has become a public health concern owing to its ability to transmit several human pathogenic viruses such as Chikungunya virus (CHIKV), Dengue viruses (DENV) and Zika virus (ZIKV) [Ae. albopictus was confined for centuries to few regions in Asia. Starting in the eighteenth or nineteenth centuries, Ae. albopictus was introduced in the islands of the Indian Ocean by Asian immigrants. From the late 1970s, this species took advantage of the increase in global trade to invade most tropical and sub-tropical regions of the world [Ae. albopictus colonization into temperate regions [Aedes albopictus was first reported in Europe in 1979 [In less than four decades, the Asian tiger mosquito s Skuse, 894 has bs (ZIKV) ) and asts (ZIKV) ,4, thus he world . Additio regions . Aedes a in 1979 .Ae. albopictus to expand from its Asian cradle can be defined using population genetic approaches [Ae. albopictus is a direct consequence of increasing human activities with rapid irradiation of populations [Migratory routes used by proaches . Mosquitulations . Populatulations . Vector ulations . The levulations .Aedes mosquitoes can host NIRVS originated from different viruses related to arboviruses: mainly insect-specific viruses (ISVs) including insect-specific flaviviruses (ISFs) and other viruses belonging to the Mononegavirales order (such as rhabdoviruses) [Drosophila melanogaster, piRNAs are primarily produced in germline cells and target TE transcripts based on sequence complementarity to protect from heritable lesions [D. melanogaster strains to TE invasion [In the early 2000s, non-retroviral integrated RNA virus sequences (NIRVS) were discovered in different metazoans, including mosquitoes ,22. Aedeviruses) . Additioviruses) . piRNA c lesions . The laninvasion . The anainvasion . On the invasion ,33.Ae. albopictus populations that cover the geographical distribution of the species, where CHIKV and DENV were circulating, to study the evolutionary dynamics of seven selected NIRVS [Ae. albopictus are distributed into two different genetic clusters, one of them divided into four subclusters, without any correlation between genetic and geographical distances, (ii) the distribution of NIRVS in geographic populations and their polymorphism are not related to genetic divergences within and between populations as depicted by microsatellite markers, suggesting that NIRVS are not evolving neutrally, and (iii) all NIRVS studied, except AlbFlavi1 may have influence on vector competence to DENV and CHIKV, at the population level.In this study, we selected 19 ed NIRVS . The occMice were housed in the Institut Pasteur animal facilities accredited by the French Ministry of Agriculture for performing experiments on live rodents. Work on animals was performed in compliance with French and European regulations on care and protection of laboratory animals . All experiments were approved by the Ethics Committee #89 and registered under the reference APAFIS#6573-201606l412077987 v2.Ae. albopictus populations, 19 samples from different geographic locations were studied . Frozen mosquitoes (<13th generation) were used for population genetic and NIRVS analyses. For the pilot experiments for DENV and CHIKV infections, F1 mosquitoes from Tibati population (Cameroon) and F18 mosquitoes from Foshan laboratory strain were used. Larvae obtained after immersion in dechlorinated tap water of field-collected eggs were distributed in pans of 200 individuals. Immature stages were fed every two days with a yeast tablet dissolved in 1 L of dechlorinated tap water and incubated at 26 ± 1°C. Emerging adults were placed in cages and maintained at 28 ± 1°C with a light/dark cycle of 12/12 h at 80% relative humidity and supplied with a 10% sucrose solution. Females were exposed three times a week to anesthetized mice  as a source of blood for producing eggs.Thermo Scientific™, MA, USA) and a PCR was performed using histone h3 reference gene (NCBI: XM_019696438.1) as control. Eleven microsatellite loci were amplified using PCR specific primers flanking the repeated region [Genomic DNA was extracted from individual mosquito using the Nucleospin Tissue kit  according to manufacturer’s instructions. Briefly, mosquitoes were individually homogenized in 180 µL lysis buffer supplemented with 25 µL of Proteinase K. To bind total nucleic acids, homogenates were passed through columns. Silica membranes were further desalted and DNA was collected in 100 µL of elution buffer. Quality and quantity of DNA were then assessed using the Nanodrop 2000 Spectrophotometer . The number of alleles (AN), allelic richness (AR), expected (HE), observed (HO) heterozygosis, FIS by locus and FST by populations were obtained. The significance of FIS and FST were analyzed using FSTAT v.2.9.3.2 [Amounts of heterozygosity at various levels of population structure were explored by using software  and test.2.9.3.2 .FST distances were tested between populations using Mantel test implemented in GenAlEx v 6.5 [Departures from Hardy-Weinberg equilibrium, linkage disequilibrium between loci and molecular variance (AMOVA) over all populations were analyzed with Arlequin v3.5.2.2 to estimate intra- and inter-population variation . The meaEx v 6.5 .Genetic relationships between populations were estimated by using PHYLIP 3.69, as previously described . CavalliFST/(1−FST)).To test the hypothesis whether the geographical pattern of genetic differentiation is caused by isolation by distance (IBD), we ran Mantel tests for pairwise matrices between geographical distances (kilometres) and genetic differentiation (measured as http://taylor0.biology.ucla.edu/structureHarvester/). The results were then graphically displayed with DISTRUCT 1.1 [Genetic population structure was assessed with individual-based Bayesian clustering method implemented in the program STRUCTURE v.2.3.4 . The likRUCT 1.1 .Six NIRVS , plus CSA , were st2, 0.25 mM dNTP mix, 0.25 μM of each primer and 1.25 unit of Taq DNA polymerase (Invitrogen™). Amplifications were done using T100™ Thermal Cycler (Bio-Rad) according to the following cycle conditions: 95°C for 5 min, 35 cycles at 95°C for 15 s, 59–64°C for 90 s, 72°C for 90 s, and a final elongation step at 72°C for 10 min. PCR products were electrophoresed on 1.5% of agarose gels stained with ethidium bromide and visualized under UV light. All negative samples were confirmed with a second-round PCR.PCR primers flanking each NIRVS (Supplementary Table 1) were designed using PRIMER3 . PCR reaThe number of each NIRVS per population (frequency) was represented using Graph Pad Prism version 6 .NIRVS composition of each population was expressed in terms of relative abundance corresponding to the percentage of each NIRVS relative to the total number of tested mosquitoes. To assess variations of NIRVS abundance between populations, we calculated Bray Curtis dissimilarities, a metrics, widely used, including data standardization . A dendrMantel tests implemented in GenAlEx v 6.5 , were usAe. albopictus population level. P-values <0.05 were considered significant.Vector competence is assessed using several parameters . The disTOPO® TA Cloning®Kit (Thermo Fisher Scientific) and transformed into One Shot® TOP10 Chemically Competent E. coli (Invitrogen™). Sequences were aligned in Geneious® 10.1.3 using MUSCLE algorithm [Amplification products for AlbFlavi2 and AlbFlavi36 were purified by using NucleoSpin® Gel and PCR Clean-up kit (Macherey-Nagel) according to the manufacturer’s instructions and sequenced by Eurofins Genomics . If the quality of chromatogram profiles assessed with Geneious® 10.1.3 did not meet the standard required, amplicons were subsequently cloned into pCR™II-TOPO® vector using lgorithm . p-distaDNA sequences from AlbFlavi2 and AlbFlavi36 were aligned with MUSCLE algorithm . ExogenoAe. albopictus cells [P-values <0.05 were considered significant.Logistic linear regression models were used to test association between the presence of AlbFlavi2 and AlbFlavi36 and viral dissemination in single mosquitoes from the Foshan colony and a field-collected African population . Foshan and Tibati mosquitoes were experimentally infected with DENV  and CHIus cells , and  with FST being highly significant , suggesting a significant genetic structure. To identify genetic clusters among tested individuals, we implemented 25 independent simulations in the software Structure according to the method of Evanno; the uppermost level of structuring in the model was observed at K = 2 ). The best assignment of individuals made by Structure led to two clusters: (i) cluster 1, which includes populations from Brazil (i.e. PMNI and Manaus), Northern Africa (i.e. Rabat) and a population from Albania, and (ii) cluster 2, which includes the remaining populations . Additionally, these two genetic clusters were found to deviate significantly  from Hardy-Weinberg proportions, with FIS values of 0.23 and 0.36, respectively after implementing 104 permutations on allele frequencies in the software GENETIX. This result suggested a Wahlund effect which indicated a genetic substructure. As a consequence, we re-analyzed the two clusters separately and detected substructures in Cluster 2 .When considering the genetic differentiation according to geographical distances using Mantel test, only 21% of the genetic variability was explained by the geographical distance , showing that NIRVS composition of populations was not related to the genetic structure caused by random genetic drift .To define whether NIRVS evolve under selective pressures or randomly, we compared (i) the relatedness between populations based on microsatellite polymorphism  of DENV and CHIKV in corresponding or geographically close populations selected from the literature (Supplementary Table 2). On this basis, we classified our tested populations depending on their frequencies of each NIRVS using the median. Several associations between NIRVS and DENV DEs were found. Whereas high AlbFlavi2 and CSA-JJL frequencies were significantly associated with high DEs, high AlbFlavi10, AlbFlavi36, AlbFlavi41 and CSA-NS3 frequencies were however correlated to low DENV DEs . OppositAe. albopictus at the individual level.Besides its association to high DENV DEs, we further focused on AlbFlavi2 located in a piRNA cluster and presenting a large geographical distribution encompassing all continents. Moreover, AlbFlavi36 located in an intergenic region was not found in populations from South America and presented an opposite pattern of association to DENV DEs; it was used for comparison to AlbFlavi2. Therefore, sequence polymorphism, evolution and potential association to vector competence to arboviruses were assessed in Ae. albopictus populations studied . All AlbFlavi2 sequences underwent deletion events that were sometimes shared by several individuals from different populations or observed in a specific The p-distance values calculated between AlbFlavi36 sequences were low and varied only from 0% to 0.9% (data not shown). Therefore, AlbFlavi36 sequences mapping to an intergenic region  appearedWe further performed phylogenetic analysis to describe the evolutionary history of AlbFlavi2 and AlbFlavi36 . One of AlbFlavi2 from Brazilian populations  appeared clearly polyphyletic. Three clusters represented only sequences from Brazil: an older cluster of sequences sharing a 405 bp insertion and two other clusters of sequences sharing respectively a 46 bp and a 6–65 bp deletions. Only AlbFlavi2 from Rabat population formed a monophyletic group, a unique cluster of sequences characterized by a 248 bp deletion.One cluster contained most of the sequences from Tirana (Albania), Vero Beach (USA), Alessandria  and Binh Duong (Vietnam). However, some sequences from Binh Duong, Vero Beach and Alessandria populations, were also isolated or found in other clusters. Moreover, half of the AlbFlavi2 sequences from Foshan formed a unique cluster whereas the other half shared the heterogeneous cluster of sequences described above.Ae. albopictus populations and all the sequences showed close relationships with each other, without any significant bootstrap values . However, sequences from African mosquitoes  in branching deeply in the tree may be more ancient than sequences of Asian  and European mosquitoes.The polymorphism of AlbFlavi36 was low in Collectively, phylogenetic studies revealed different histories of NIRVS in mosquito populations and particularly, complex history of Albflavi2 evolving by both mutation and deletion events.p-value <10−4; data not shown). However, the presence of AlbFlavi2 or AlbFlavi36 in mosquitoes from Foshan or Tibati was not significantly associated to DENV dissemination  were infected with DENV-2 and CHIKV. At 14 days post-infection, mosquito dissemination status and the presence of both AlbFlavi2 and AlbFlavi36 were determined for 313 mosquitoes . When coAedes spp. mosquitoes and their enrichment in piRNA clusters support the hypothesis that viral integrations, at least some, are not simply viral fossils, but could have a biological role [Ae. albopictus populations and all of them (except AlbFlavi1) were significantly associated to vector competence to DENV and CHIKV.The presence of sequences with similarities to Flaviviruses in the genome of cal role ,25,54. HAe. albopictus populations contribute to variations of genome size among mosquitoes [NIRVS are endogenous viral sequences located in protein-coding gene exons, intergenic regions, and PIWI-interacting RNA (piRNA) clusters . Contrarsquitoes . Our stuAe. aegypti (unpublished data), the presence of the tested NIRVS was highly variable among worldwide populations, with the lowest number being detected in mosquitoes from Brazil. This suggests that these NIRVS have not reached fixation in any of the Ae. albopictus populations tested . Interestingly, Brazilian populations  displayed the lowest number of NIRVS studied with a total absence of AlbFlavi10, AlbFlavi36 and CSA-NS3. Considering the ancient origin of NIRVS, estimated between 6.5 thousands to 2.5 million years ago [Contrary to NIRVS deriving from flaviviruses in ears ago , we hypoWe showed that the microsatellite polymorphism of populations did not match with the NIRVS abundance profiles. Therefore, because microsatellites are sequences that neutrally evolve in the genome, we speculate that the evolution of NIRVS is far from neutral and NIRVS could provide benefits to the host. NIRVS may have been produced for specific purposes in the host rather than being the consequences of random endogenization of exogenous viral fragments.Ae. albopictus populations. Whereas AlbFlavi36 appeared monophyletic with highly conserved sequences among populations, the phylogenetic analyses showed a particularly complex history for Albflavi2 evolving by both mutation and deletion events. We postulate that owing to its high variability, AlbFlavi2 may act similarly to TE fragments and piRNA genes, and evolve according to exposures to related exogenous virus burden.The focus on NIRVS from piRNA cluster (AlbFlavi2) and intergenic region (AlbFlavi36) revealed different sequence polymorphism and evolutionary histories despite their relatively wide distribution among Ae. albopictus mosquitoes. Because NIRVS located in piRNA clusters, such as AlbFlavi2, produce piRNAs, are differentially distributed among populations and appeared older integrations than NIRVS located in codons [Similar to some endogenous retroviruses (ERVs) that have an effect on viruses from different subfamilies and genera , we quesn codons , they weAe. albopictus populations. The frequency of some of them appears correlated (positively or negatively) to DENV dissemination. Whereas some NIRVS were associated with high viral dissemination (i.e. AlbFlavi2 and CSA-JJL), others were significantly related to low viral dissemination , suggesting different functions of NIRVS deriving from ISFs. Moreover, fewer NIRVS were positively (i.e. AlbFlavi4) and negatively (i.e. AlbFlavi36 and CSA-NS3) correlated to CHIKV dissemination. These results are consistent with the contradictory results obtained in studies assessing the impact of exogenous ISFs on arbovirus fitness in mosquitoes. Indeed, whereas some ISFs have shown to repress arboviral replication [At the population level, many NIRVS were present in lication , others lication . FurtherAt the level of individuals, our pilot experiment based on mosquitoes from both laboratory colony and field did not show any significant association between dissemination of DENV and CHIKV, and NIRVS, AlbFlavi2 or AlbFlavi36. while this does not seem surprising for the alphavirus CHIKV considering that NIRVS examined are homologous to flavivirus sequences, it is still questionable for DENV. Since it appears that they do not evolve neutrally and were preserved from purifying selection, NIRVS such as CSA has demonstrated to produce a transcript of 4671 nt long ,69; its Ae. albopictus follow processes different from that of neutral genes such as microsatellites and most NIRVS are far from reaching fixation. Flaviviral integrations are differentially distributed among Ae. albopictus populations and are here suggested to be associated with the vector competence to arboviruses by mechanisms that remain to be elucidated. Finally, this study opens the way to new perspectives on evolution and biological functions of NIRVS, in part on vector competence.To conclude, our results clearly show that NIRVS in Click here for additional data file.


Meloidogyne species cause great crop losses worldwide. Although genetic host plant resistance is an effective control strategy to minimize damage caused by Meloidogyne, some resistant genes are ineffective against virulent species such as Meloidogyne enterolobii. Detailed knowledge about the genetic composition of Meloidogyne species is thus essential. This study focused on genotyping-by-sequencing (GBS) and Pool-Seq to elucidate the genetic relation between South African M. enterolobii, M. incognita and M. javanica populations. Hence, 653 common single nucleotide polymorphisms (SNPs) were identified and used to compare these species at genetic level. Allele frequencies of 34 SNPs consistently differed between the three Meloidogyne species studied. Principal component and phylogenetic analyses grouped the M. enterolobii populations in one clade, showing a distant relation to the M. javanica populations. These two species also shared genetic links with the M. incognita populations studied. GBS has been used successfully in this study to identify SNPs that discriminated among the three Meloidogyne species investigated. Alleles, only occurring in the genome of M. enterolobii and located in genes involved in virulence in other animal species (e.g. a serine/threonine phosphatase and zinc finger) have also been identified, accentuating the value of GBS in future studies of this nature. Meloidogyne) are polyphagous, obligate pests that are distributed worldwide and parasitize almost all the higher plant species, resulting in great economic losses1. Meloidogyne incognita is generally considered as the most damaging root-knot nematode species worldwide2. Since this species can infect Arabidopsis thaliana, it is also a key model system to study metazoan adaptations to plant parasitism, hence its genome has already been elucidated3. However, Meloidogyne enterolobii listed as a threat species, can be confused with M. incognita and other thermophilic species due to it exhibiting similar morphological characteristics4. Of more significance is that M. enterolobii has the ability to overcome resistance genes that are effective against its thermophilic counterparts Meloidogyne arenaria, M. incognita and Meloidogyne javanica8. Meloidogyne enterolobii for example had been reported to reproduce optimally on tomato and pepper that exhibit the Mi-1, N and Tabasco resistance genes, respectively8, while M. arenaria failed to reproduce on such resistant plants9. This phenomenon has far reaching implications for the management of this species.Root-knot nematodes  orchards15. Its established occurrence in South Africa fits the hypothesis of the late Dr Kent Kleynhans and Mr Piet Willers that the occurrence and host range is wider than the initial localities and hosts around Mbombela, Mpumalanga . It has hence been reported from other crop production areas, infecting green pepper (Capsicum annuum); potato (Solanum tuberosum) and tomato (Solanum lycopersicum)17. This scenario justified a more detailed genetic study of South African Meloidogyne populations to determine if genomic differences linked with virulence exhibited by M. enterolobii could be found between M. incognita and M. javanica.Management of root-knot nematodes has been done traditionally by means of chemical control. This approach generally keeps the nematode population under the economic threshold level since eradicating these pests is considered impossibleMeloidogyne populations by using different molecular techniques, e.g. random amplified polymorphic DNA (RAPD), restriction fragment length polymorphisms (RFLP), PCR based on sequences of rDNA, mtDNA, ITS and IGS, or satellite DNA probe markers26. However, most of these methods are expensive, time-consuming, require several PCR analyses and many nematode individuals27. The ultimate drawback of these techniques are that they are targeting only a small part of the genome of a nematode and are hence not optimal for pan-genomic comparison. It is therefore necessary to apply novel and rapid molecular genotyping tools to obtain more detailed information about the genetic diversity between Meloidogyne species.Several studies have been conducted to elucidate the genetic diversity of 29. Significant advances in sequencing technologies are providing lots of information at relatively low cost30. Genotyping by sequencing (GBS)31 is a simple protocol based on next generation-sequencing (NGS) of genomic fragments of organisms (e.g. nematodes) obtained by specific restriction enzymes followed by a bioinformatics pipeline30. This enzyme-based reduction of complexity, combined with the use of barcodes for multiplexing considerably reduces sequencing cost while providing genome-wide information32. This technique has proven to be useful and accurate to characterize nematode species even when no information about the genome was available27. Currently, good reference genomes are available for M. hapla33 and M. incognita34. However, no annotated reference genomes exist for M. enterolobii and M. javanica, although some assemblies from whole genome sequencing (WGS) data were published recently35. Genotyping by sequencing has, for example successfully been applied in combination with Pool-Seq by Mimee et al.27, to investigate the genetic diversity among populations of the golden cyst nematode Globodera rostochiensis. Pool-Seq is a method described by Futschik and Schlötterer36, which instead of sequencing isolated individuals directly uses a population . When using a sufficiently big pool size, Pool-Seq even showed to be more appropriate for estimating allele frequencies and is more cost effective than sequencing the DNA of individuals36.Single nucleotide polymorphisms (SNPs) are popular and common molecular markers used to study the entire genome of nematodesMeloidogyne species viz. M. enterolobii, M. incognita and M. javanica using GBS in order to highlight relationships among these species and loci putatively involved in virulence.This study aimed to investigate the genetic diversity of three different Meloidogyne populations digested with PstI/MspI restriction enzymes generated 83 038 291 reads. After initial quality control, 77 095 925 good barcoded reads were kept for further analysis. The UNEAK pipeline identified 2,786 SNPs before filtering. The final dataset contained from 59 to 929 SNPs depending on filtering stringency  grouped in a first cluster, while M. javanica populations  were placed in a second. This distinction was very clear and the differences between these two species (first dimension in the PCA) explained 89.7% of the variation in the dataset  exhibited different genetic relation on this first dimension, this species was clearly separated from M. enterolobii and M. javanica in the second dimension of the PCA explaining 4.1% of the total variation  which removed the fastidious step of isolating and extracting DNA from single juveniles. The bioinformatics pipeline allowed the rapid de novo identification of SNPs, without the need of a reference genome. When aligning the sequencing reads to the closest reference species, M. incognita, we found less good quality SNPs (no missing data and good coverage) than when using the GBS pipeline. This indicates that significant differences exist between the two species. This was confirmed by the eight-fold increase in the number of SNPs when the pipeline was run with M. enterolobii populations alone. As UNEAK will only keep the SNPs that are present in all populations , this approach indicates that the three species are probably more different than anticipated. On the other hand, some genetic variants were found upon alignment with the M. incognita genome and not by the UNEAK pipeline. This is explained by the high stringency of the pipeline that only tolerates one mismatch by read and rejects sequences with multiple SNPs or other kind of variants. Therefore, combining the two approaches will maximize the discovery rate of SNPs in the genome of Meloidogyne species. Also, it was hypothesized that several mitotic parthenogens Meloidogyne species had acquired pairs of divergent gene copies during past hybridisation event35. This will result in an excess of heterozygosity that could complicate classical phylogenetic analyses. One of the advantage of GBS is that it is not affected by ploidy as the UNEAK pipeline compare all the sequences. Thus, if the same sequence is present in many versions in an organism due to ploidy or gene duplication, the pipeline will compute all versions together and output the allele frequency for that sequence and not for each physical locus in the genome. Furthermore, using Pool-Seq, we theoretically captured all the allelic diversity of each population.The GBS method used in this study proved to be useful in identifying diagnostic SNPs for the discrimination of three of the highly damaging thermophilic M. enterolobii, M. javanica and M. incognita. This result was expected since the distinction of these species has been reported using different markers37. The analysis also revealed a close genetic proximity of the four M. enterolobii populations when the 277 SNPs dataset  was used. This is in agreement with results obtained by Tigano et al.22 that described M. enterolobii as a geographic homogenous species with low diversity between populations. Similarly, Onkendi and Moleleki38 showed 100% homology between sequences of IGS and COII from South African M. enterolobii populations. However, when the GBS approach was used for M. enterolobii only, more variation was observed and suggests that this species could be more diverse than initially thought. As genetic diversity is an important driver of adaptation to new environmental conditions or host plants, it will be interesting to explore this diversity by studying more M. enterolobii populations.PCA analyses confirmed the genetic separation between M. incognita investigated in this study showed substantial genetic difference, one being related to M. enterolobii and the other to M. javanica. A possible explanation for this phenomenon may be that in this analysis only loci sequenced in all Meloidogyne populations were conserved, meaning that the sequences that were unique to one or two species only (not present or not sequenced in the other) were discarded even if they contain SNPs. Thus, this place the emphasis on the differences that exist in terms of SNPs contained in the genomes of these three Meloidogyne species.When comparing allele frequencies of 277 SNPs distributed all across the genome, the two populations of M. enterolobii genome will give us a better understanding of why the species is more aggressive and overcome resistance genes that are effective against M. incognita and M. javanica. For now, 19 SNPs identified by UNEAK and 14 by the alignment on the M. incognita reference genome were located in exons of predicted genes. These variants were specific to M. enterolobii and could affect nematode-host interactions. Some of these genes have already been reported to be involved in parasitism. For example, two SNPs were located in genes coding for a serine/threonine phosphatase. A protein with a similar sequence is a known effector in the Hessian fly, Mayetiola destructor. This plant-galling arthropod uses effectors to modify host cells in a way that is superficially similar to root-knot nematodes. The type-2 serine/threonine protein phosphatase (PP2C) domain was shown to be associated with the ability of the fly to survive and parasite wheat seedlings in susceptible plants. This protein is also recognized as an avirulence factor in cultivars carrying the H24 resistant genes. Interestingly, a single loss-of-function mutation in that gene is sufficient to overcome resistance in virulent population42.This study also highlighted allelic differences between the species that exists in specific genes that could modify protein sequence and function. Ultimately, in depth characterization of the M. enterolobii and the other species in a sequence coding for a zinc finger, C2H2 domain. These small protein motifs are known to interact with different molecules and are involved in multiple functions from gene transcription and mRNA trafficking to protein folding and apoptosis43. Wang et al.44, reported that the C2H2 zinc finger PsCZF1 was involved in pathogenesis in Phytophthora sojae by demonstrating that PsCZF1-deficient mutants lost virulence on different soybean cultivars. Another study on Alternaria brassicicola showed that a knockdown mutation in the zinc finger transcription factor AbVf19 resulted in a 90% decrease in virulence45. Interestingly, Gross and Williamson46 identified a novel transposable element in root-knot nematodes that contained a C2H2 zinc-finger motif and that could be involved in the ability of some populations to bypass resistance in tomato mediated by the Mi-1 gene.Results from this study also highlighted differences between 47. Root-knot nematodes are actively remodeling the cytoskeleton of infected plants during feeding cell and gall formation48. This reprogramming is induced by secreted effectors, several being homologous to plant proteins49. In this study, we have identified mutations in several M. enterolobii genes that code for proteins required for microtubule and cytoskeleton formation: Cytoskeleton-associated 5, spectrin and plectin repeat.Finally, the plant cytoskeleton is hypothesized to play a crucial role in host defense response and a target for virulenceMeloidogyne species. The phylogenetic comparison of these variants generated valuable and novel knowledge about the genetic diversity of Meloidogyne species. Candidate genes associated with virulence were also highlighted and should be further explored to evaluate whether they are involved in M. enterolobii pathogenicity.In this paper, the GBS technique proved to be a powerful and accurate technique to obtain detailed information about the diversity that exists among root-knot nematode species. The bioinformatic pipeline allowed the identification of high-quality diagnostic SNPs from the South African Meloidogyne populations, representing M. enterolobii, M. incognita and M. javanica (Table 50 to ensure species purity. Subsequent mass rearing of pure populations was done in the glasshouse on tomato seedlings (cultivar Floradade) planted in individual pots that were filled with sandy-loam soil  of 7.47) previously fumigated with Telone II . An ambient temperature range of 21 (min) – 26 °C (max) and 14L:10D photoperiod were maintained in the glasshouse. Sixty days after inoculation, approximately 100 females of each population were randomly dissected from infected tomato roots of individual plants and DNA extracted by using DNeasy Blood & Tissue kit  according to the protocol supplied. The DNA of each Meloidogyne population was quantified using the Invitrogen Quant-iT Qubit dsDNA HS Assay Kit , and 30 ng of each sample was used for sequencing library construction.Eleven ca Table , were obMeloidogyne species used in this study were identified using morphological and molecular approaches. Morphological identification was done based on female oesophagus and perineal patterns. The molecular confirmation was done by sequencing the 28S rDNA (D2-D3)51, COI52 and NADH537 mtDNA, regions and comparing to NCBI database, and by the amplification of a species specific SCAR marker53.All populations of et al.31, using PstI/MspI restriction enzymes. The library was sequenced on one Ion Proton chip (ThermoFisher Scientific) at IBIS.Sample preparation and sequencing were done by the Genomics Analysis Core Facility at the Institute for Integrative and Systems Biology  according to the GBS method developed by Elshire 54 was used to analyse Pool-Seq data and create the SNP list for the 11 Meloidogyne populations. To eliminate sequencing errors, a tolerate rate of 0.03 was used for UNEAK running. Filtering of SNPs was done by setting the minimum call rate (number of populations in which a locus must have been scored) at 0.8 (i.e. <20% missing data) or 1.0 (no missing data). The minimum minor allele frequency (MAF) threshold was set to 0.01. The effect of locus coverage on the final SNP number was also compared by setting the minimum number of reads at a given locus in each population required to accept a SNP (minCov) to 5, 20, or 50. The UNEAK pipeline was also run on the M. enterolobii populations alone with the minimum call rate set at 0.7 or 1.0 and the minimum coverage at 5, 20 and 50.The Universal Network Enabled Analysis Kit (UNEAK) pipelineM. incognita genome3 by using burrows-wheeler aligner (BWA)55 after trimming with Trimmomatic  (http://www.usadellab.org/cms/?page=trimmomatic) and demultiplexing with Sabre (https://github.com/najoshi/sabre). Then, variants and annotations were called with freebayes56 and SnpEff57 respectively using the gene-finding format (GFF) of M. incognita.Raw sequences were also aligned on the prcomp function from the stats package.A neighbor-joining phylogenetic tree was elaborated by using Gendist and Neighbor modules in PHYLIP v3.695 with the genome-wide allele frequencies obtained from UNEAK. PCA was done in R by using the same sets of allele frequencies with the M. incognita genome by means of BLASTN with the default parameters, except for a smaller word size of 4, with the Blast2GO application58.The SNPs of interest from UNEAK were retrieved from the Supplementary Table 1


Salmo salar) populations were established with individuals of both European and North American origins. These populations are expected to be highly genetically differentiated due to evolutionary history and poor gene flow between ancestral populations from different continents. The extent and decay of linkage disequilibrium (LD) among single nucleotide polymorphism (SNP) impacts the implementation of genome-wide association studies and genomic selection and provides relevant information about demographic processes of fish populations. We assessed the population structure and characterized the extent and decay of LD in three Chilean commercial populations of Atlantic salmon with North American (NAM), Scottish (SCO), and Norwegian (NOR) origin. A total of 123 animals were genotyped using a 159 K SNP Axiom® myDesignTM Genotyping Array. A total of 32 K SNP markers, representing the common SNPs along the three populations after quality control were used. The principal component analysis explained 78.9% of the genetic diversity between populations, clearly discriminating between populations of North American and European origin, and also between European populations. NAM had the lowest effective population size, followed by SCO and NOR. Large differences in the LD decay were observed between populations of North American and European origin. An r2 threshold of 0.2 was estimated for marker pairs separated by 7,800, 64, and 50 kb in the NAM, SCO, and NOR populations, respectively. In this study we show that this SNP panel can be used to detect association between markers and traits of interests and also to capture high-resolution information for genome-enabled predictions. Also, we suggest the feasibility to achieve similar prediction accuracies using a smaller SNP data set for the NAM population, compared with samples with European origin which would need a higher density SNP array.Chilean Farmed Atlantic salmon ( Salmo salar) is one of the species of farmed fish with the highest commercial value in aquaculture . Chile is the second largest producer, generating nearly 532,000 tons of this specie in 2016 . All of the Atlantic salmon populations farmed in Chile were introduced from three main geographical origins (i) North America, (ii) Scotland, and (iii) Norway. These populations also represent the main origins of cultured Atlantic salmon worldwide. Breeding programs for Atlantic salmon were first established in Norway during the early 1970s  and r2 are the most widely used. D’ = 1, indicates no recombination between loci and complete LD, while values less than 1 indicate that loci have been separated by recombination. D’ estimations are overestimated in small sample sizes and low frequencies of minor allele, therefore, high values of D’ can be obtained even when markers are in linkage equilibrium , River Bolstad in the Vosso watercourse, River Årøy and Maurangerfjord area . Ova of ® myDesign Custom Array as described by S. salar reference genome  was performed using PLINK software v1.09, and assessed separately for each population. SNPs with minor allele frequency (MAF) lower than 5%, significantly deviating from Hardy–Weinberg Equilibrium (HWE) (p < 1e-6), and a SNP call rate of lower than 95% were excluded. Samples with more than 5% of missing genotypes were also excluded. All subsequent analyses were done using the common SNPs along the three populations after QC.Fin clip samples from individuals from the three populations were obtained for genomic DNA extraction and further genotyping. Genotyping was carried out using a 200 K Affymetrix Axiome genome . QualityTo investigate genetic structure among populations, we performed a principal component analysis (PCA) implemented in PLINK v1.09. Visualization of the first two PCA were plotted along two axes in R. Additionally, we used a hierarchical Bayesian modeling implemented in STRUCTURE software, using a burn-in of 20,000 iterations, and running 50,000 iterations with three replicates. Subsequently, we computed the posterior probability of each K value according r2) was chosen over |D’| to predict the LD between each pair of molecular markers. This statistic is less sensitive to bias caused by differences in allelic frequencies .Linkage disequilibrium as Pearson’s squared correlation coefficient  was estimated using SNeP v1.1 . A total of 40,316 (27.02%), 113,282 (75.92%), and 136,446 (91.46%) SNP markers passed the QC criteria for the NAM, SCO and NOR populations, respectively. Filtered SNPs differed significantly between populations of North American or European origin. 106 K SNPs were excluded from the NAM population by a low MAF, representing 70% of the total available markers in the array. The markers excluded by MAF in SCO and NOR populations reached 23 and 7.8%, respectively. A summary of the number of fish genotyped from each population, number of SNPs excluded by HWE, MAF and the final number of SNPs per population are shown in Table r2 and Ne estimates among SNPs for each chromosome and population are shown in Table S. salar genome size of 2.96 Gb based on the last assembly GCA_000233375.4). Average r2 between adjacent SNPs reached up to 0.26 ± 0.28 in the NAM population. These values were higher than for SCO and NOR populations . The average LD, measured as r2, between adjacent markers across the 29 chromosomes, ranged from 0.16 to 0.35, 0.08 to 0.14, and 0.05 to 0.09 in NAM, SCO, and NOR populations, respectively , followed by the SCO and NOR . Average r2 for SNP pairs with a mean distance of 1.0 Mb was 0.31, 0.13, and 0.08 for the NAM, SCO, and NOR populations, respectively. These values decreased to 0.19, 0.08, and 0.06, when average distance between SNPs reached up to 10.0 Mb for NAM, SCO, and NOR, respectively.The LD decay was estimated for each population as a function of physical distance. SNP pairs were sorted in 100 kb-bins based on the distance between pairs. Average  animals , genome-Ne up to 85  in Nellore cattle (p < 0.05). Like It has been suggested that the minimum number of individuals needed for accurate LD estimations using for |D’| . Howeverividuals . Because mapping . Signifie cattle . We onlyr2 threshold value of 0.2, as suggested by The current results compared the LD decay between Chilean Atlantic salmon breeding populations originating from different geographic regions. The SNP panel used in the current study has one SNP every 14 kb . Based oNe that wa1s observed in NAM is consistent with the hypothesis that American salmon colonization from European fish favored only a few individuals. The high long range LD in NAM indicates the feasibility of achieving better prediction accuracies in this population with a smaller SNP data set than European populations.The current study reveals different LD decay between three Atlantic salmon farmed populations. The highest extent of LD was estimated for the NAM population, followed by the SCO and NOR populations. A lower level of LD in NOR was consistent with its population history. Specifically, this population comes from a farmed strain established with samples from several rivers in Norway. Therefore, subsequent genetic bottlenecks associated with strain formation have been less severe in comparison with the other two populations used in this study, that were established using fish from only one location. Also, the highest level of LD and lowest 10.6084/m9.figshare.7144631.Raw genotype data for each population is available from the online digital repository Figshare, accession number doi: The sampling protocol was previously approved by The Comité de Bioética Animal, Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile (certificate N° 29–2014).Ne analyses, and wrote the initial version of the manuscript. ML performed populations structure analysis, first quality control of genomic data and contributed with discussion and writting. GY contributed with LD analysis and discussion. RC and JL contributed with analysis and discussion. JY and JL conceived and designed the study. JY supervised work of AB and contributed to the analysis, discussion, and writing. All authors have reviewed and approved the manuscript.AB performed LD and The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Piezodous guildinii is an emerging pest of soybean in the southern region of the United States, and increasingly important in Brazil in recent years. However, the reasons P. guildinii is gradually becoming more of a problem are questions still mostly unanswered. Here, we have genotyped P. guildinii samples and discovered 1,337 loci containing 4,083 variant sites SNPs that were used to estimate genetic structure and to identify gene candidates under natural selection. Our results revealed the existence of a significant genetic structure separating populations according to their broad geographic origin, i.e., U.S. and Brazil, supported by AMOVA (FGT = 0.26), STRUCTURE, PCA, and FST analyses. High levels of gene flow or coancestry within groups  can be inferred from the data, and no spatial pattern was apparent at the finer scale in Brazil. Samples from different seasons show more heterogeneous compositions suggesting mixed ancestry and a more complex dynamic. Lastly, we were able to detect and successfully annotated 123 GBS loci (10.5%) under positive selection. The gene ontology (GO) analysis implicated candidate genes under selection with genome reorganization, neuropeptides, and energy mobilization. We discuss how these findings could be related to recent outbreaks and suggest how new efforts directed to better understand P. guildinii population dynamics.Unravelling the details of range expansion and ecological dominance shifts of insect pests has been challenging due to the lack of basic knowledge about population structure, gene flow, and most importantly, how natural selection is affecting the adaptive process.  Piezodorus guildinii (Westwood), or the ‘percevejo-verde-pequeno’ as it is known in Brazil, is one of the most important pests of soybean crops [Glycinia max (L.) Merr.], and along with Nezara viridula (L.) and Euschistus heros (F.) is responsible for significant damage in most cultivated areas of the New World2. Piezodorus guildinii was first discovered on the Caribbean island of St. Vincent, and its geographical distribution currently extends from Uruguay and northern Argentina to the southeastern region of the United States along the Gulf of Mexico, as well as Arkansas and South Carolina5. In contrast to Argentina and Uruguay where this insect is listed as the most important pest of soybean5, it is only sporadically reported as a problem in Brazil, and historically had limited impact in North America10. In recent years, however, P. guildinii has expanded its range and is becoming a more significant economic pest of crops in the U.S.2.The red-banded stink bug, P. guildinii population dynamics in North America are still not fully elucidated, but well-established populations and breeding sites in Florida and Mexico have been reported since the 1960s9. In recent years, great yield losses caused by the stink bugs complex —in which P. guildinii was the most abundant species—have been reported in Mississippi and Arkansas soybean fields11. The reason behind this apparent range expansion and recurrent outbreaks are not clear, and more information regarding the movement dynamics will be vital to understanding the recent appearance in southern states12. Interestingly, P. guildinii was not reported before the year 2000 in Louisiana, but now it is one of the major concerns for soybean producers4. One of the main hypotheses to explain recent attacks in Arkansas and Missouri is the seasonal movement from Louisiana, and possibly the influence of mild winters building larger populations13. However, these hypotheses have not been tested, and more data are needed to provide supporting evidence. It is possible that movement and adaptations to cold temperature are primary drives and can explain P. guildinii success colonizing and displacing other stink bug species14. Another hypothesis is that global climate change might affect the migration pattern of this species15.Specific aspects of P. guildinii has been directly linked to the agriculture technological revolution during the 1970s and subsequent soybean area expansion to central and northern lands8. Drastic changes in natural ecosystems and, to a large extent, substitution for extensively cultivated fields, correlates with first reports of N. viridula and P. guildinii in soybean2. During the following years, P. guildinii displaced N. viridula and became the primary pest in some areas of Brazil, but the reasons underlying this ecological dominance shifts are also not clear. Nowadays E. heros is undoubtedly the most abundant stinkbug species in soybean crops in Brazil, but P. guildinii damage is more severe to the soybean buds causing greater economic losses when they reach high population levels. Understanding more about P. guildinii populations in South America may provide insight on how to improve and develop new long-term sustainable pest control tactics. It can also improve our understanding of what could be causing range expansion and the dominance shifts within the stink bug complex in North and South America.In South America, particularly in Brazil, the rise in importance of P. guildinii populations collecting in five locations in Brazil, and seven locations in the U.S , generated by the genotyping-by-sequencing (GBS) methodology, to study U.S Fig. . Our priA total of 556,039 loci were genotyped in 159 individuals from 12 locations across the U.S. during four different crop seasons  and in Brazil during the crop season of 2016. Effective coverage per-sample was 12.2x  per locus and the mean number of sites per locus of 100 sites. After filtering loci according to its minimum required frequency in a population, minor allele frequency (MAF), and maximum observed heterozygosity (MOH), a total of 1,337 loci containing 4,083 variant sites were retained for downstream population analyses. For structure analysis, only one SNP per locus was retained to avoid linkage bias.O = 0.002 ± 0.000) and expected heterozygosity (HE = 0.003 ± 0.000), nucleotide diversity (π = 0.003 ± 0.00), and inbreeding coefficient (FIS(US) = 0.005 ± 0.013 vs. FIS(BR) = 0.002 ± 0.008). Comparing locations within Brazil, the genetic diversity values showed no significant variation across the geographic range , estimates for U.S. and Brazilian population have shown similar values for observed heterozygosity , PCA, and FST results  mixed at varies part of the sampling range in the U.S. The results indicate that the samples were collected in an area with an influx of individuals from different genetic backgrounds . Most samples collected in Mississippi were likely to result in multiple sources infestation Fig. . The mosfsthet , Bayescan , and pcadapt . Additionally, Bayescan detected 2,470 markers under balancing selection. Considering all methods, a total of 485 (~12%) outlier SNPs, out of which all programs concurred on a single marker (0.02%)  while Lositan (303 outliers) was had a high rate of uniquely flagged SNPs . A total of 58 were successfully mapped and 30 annotated. The only outlier identified concomitantly by the four methods was not successfully mapped. A great number of sequence descriptions were related to the genome reorganization such as retrotransposable elements, transposase, translocon, mobile elements, and reverse transcriptases. A small number of sequence descriptions was related to neuropeptides, and energy mobilization such as allatostatin-A receptor-like, neuropeptide CCHamide-1 receptor, and octopamine receptor.BLASTX analysis based on 485 candidate GBS loci containing outliers has successfully identified 123 loci under positive selection (10.5%) corresponding to known proteins in the NCBI database  as putative loci under selection.Considering only Blastx results from P. guildinii in North and South America16. By sequencing hundreds of markers, we were able to make inferences about population divergence, genetic structure, and the nature of the adaptative process shaping P. guildinii populations in the U.S and Brazil. Considering exclusively neutral markers, we gathered evidence that U.S. and Brazilian populations constitute two well differentiated gene pools that have remained allopatric separated with limited admixture throughout the years. Given the geographic distance, the presence of physical barriers, and differences in environmental conditions, the existence of well-differentiated genetic clusters was within our previous expectations. However, that is not always the case when it comes to insect pests that often reach global distribution with a history of recurrent invasions as is the case for the Nezara viridula and Halyomorpha halys19. A few locations, however, have showed signs of gene pool admixture (e.g. APRO and MSYA), which could suggest that the two gene pools are not completely isolated. It is nonetheless a fundamental step to study pest insect populations gather more information about their evolutionary history and to monitor the changes over the time, in particular in scenarios where range expansion and risk of invasion are present21.In the present study, we have used genotyping-by-sequencing to examine the relation between and within populations of P. guildinii has been restricted to America, even though, the Piezodorus genus can be found distributed worldwide22. Notably, other species of the genus are also considered important pests of cultivated crops, but the evolutionary aspects of other species of the group are also poorly understood1. Even though at this point we have no information about morphological similarities or the degree of mating compatibility between the two populations, the differentiation between the two populations should be considered in further studies involving the Piezodorini group in Neotropical and Nearctic regions. Looking more closely at the chronology of reports of P. guildinii occurrence, data suggest that the increase in abundance is linked to human occupation of the continent and more recently to the transformation of the environment related to soybean crops. Piezodorus guildinii was initially described using material collected from the St. Vincent island in 1837 in the Caribbean Sea but rarelly reported in Brazil until the early 1970s2. In the U.S., the first reports about P. guildinii dated the early 1960s, but just recently this species became a significant problem8. We do not have details of the location and time where the P. guildinii diverged from other species of the group, but the chronology of reported dates suggests that the Central American and the Caribbean region might be the center of origin/differentiation of this group. If that is the case, then we can expect highly diverse populations in that region. However, this hypothesis still needs to be supported by data and more research is needed to access the levels of genetic diversity of insects from that region.Until now, the range of the geographic distribution of P. guildinii have been outlined in Florida and Texas for a much longer time than in Louisiana, Mississippi, and Arkansas, which may indicate the presence of more isolated gene pools in other areas of the U.S.23 Our analysis of structure in finer scales considering four different crop seasons give support to the idea that the Coastal plain regions are indeed a region with high levels of genetic admixture, and we can, therefore, hypothesize that more ancestral populations could be found at the extremes of the range, but our data does not allow us to draw any conclusion in this aspect. The heterogeneity seems to be more associated with the geographical location than the time of the collection or to the crop type. That fact that locations more to the east and the south of Mississippi had a more mixed genetic composition might be additional support for a possible greater genetic differentiation when a broader scale in the U.S. is considered. A broader sampling scheme that includes Mexico, Texas, Georgia, and Florida would be necessary to address the specific question regarding the source of migrants and the spatial structure within the U.S.Resident populations of 26. One hypothesis to explain the presence of P. guildinii in higher latitudes  is the seasonal movement in years with mild winters from breeding sites in lower latitudes 2. Apart from previous expectation, the intraspecific distribution of the genetic diversity did not have a clear relationship with the latitude . A well-differentiated cluster and more homogenous sampling composition such as the ones observed for the MSLE and LAHA might be explained by a founding event or a single source of the infestation. Piezodorus guildinii are unlikely to establish a permanent population in those geographic locations due to the cold weather25. Higher values of genetic admixture and the presence of multiple coancestry groups might indicate movement from different directions instead of from a single source in the southern region.We have not found significant differences in the population-level genetic diversity values. The overall nucleotide diversity estimated harbored moderate levels of polymorphism (~3%) across the genome regardless of the sampling location and was comparable in both countries. The analysis of genetic partition within clusters  and coancestry analysis suggested differences in the local dynamics even when we considered only insects collected in 2016. It is likely that demographic structure to be stable in Brazil with persistent populations over the time but unstable in the U.S. compromise of a great number of populations that last for only a few generations in certain cold areas2. No apparent pattern of spatial structure could be drawn from our data, which could initially be attributed to a high dispersal rate, the absence of physical barriers or simply the common ancestry of the sampled insects. Many important soybean regions  are not represented in our sampled effort because 2016 was a year with very few reported incidences of P. guildinii in soybean fields in Brazil. Samples from Paraná, Mato Grosso, and Bahia must be included in future efforts as those represent key areas of soybean production that could provide a broader picture of the distribution of diversity across the geographic range in Brazil.In Brazil, in places where the insect can be found, its distribution is often associated with the presence of soybean fields27. It is also likely the case that the absence of gene flow allowed the allopatric populations to evolve independently in many aspects, such as regarding physiological and behavioral attributes. Therefore, natural selection can both prevent or establish local differences more effectively than genetic drift27, which can, at least in part, explain why FST values calculated based on outlier SNPs were substantially higher compared to estimates based on neutral markers. However, it is challenging to pointing out which aspect of the environment—if climate, host distribution, or pesticide usage—is the most important driver of the adaptive response of P. guildinii populations. Understanding how pest populations are changing over time may help us explaining outbreaks or recent changes in ecological dynamics, including the ability to compete with other pest species28.One of the questions we were interested here was to ask of the divergence between the two populations was primarily driven by stochastic processes such as genetic drift or due to adaptive evolution . Our results clearly show that both neutral and adaptive evolution are components present in the history of this species. Gene flow, in certain cases, can be a constraint for local adaptation as the continuous influx of alleles from other populations drives the average population fitness down to suboptimum levels29. In practical terms, the directional selection will cause populations to diverge  whereas in balancing selection the distance between population might be shortened30. When we consider only loci putatively under directional selection, only one non-annotated marker was concurrently listed by all four methods used. The outlier detection methods vary in the ability to detect true positives, and the most appropriate method will depend on the degree of structure, true populations, and the degree of gene flow between populations31. Methods based on Bayesian approach  have shown least type I error, but methods that take in account genetic structure  can also be powerful tools to detect candidates34. In our data set, Bayescan and pcadapt were the methods with the greatest overlap suggesting good concordance between the two. Serine/threonine-protein kinase and lipase 1-like isoform X2 were the two successfully annotated loci candidates listed by the two methods, but functional studies are necessary to validate and to demonstrate the role of those candidates for the two populations.The annotation of outlier-containing sequences revealed several functional genes under directional and balancing selection based on four different methods. A simple distinction can be made about the two selection regimes; while in directional selection one phenotypic form in a population has a higher probability of advancing in following generations, in balancing selection more than one form can be favored 35. We can pose the following questions: what are the ecological processes involved in genetic differentiation of the U.S. and Brazilian populations? Is the differentiation primarily driven by environmental conditions such as temperature and humidity or by resources such as host plants and pesticides? The annotation of putative loci suggests that the differentiation of the two populations involves a myriad of processes ranging from regulation of cellular processes, signaling, energy mobilization, and the regulation of primary metabolism. The annotation of the candidates provides evidence for the possible ecological importance of those processed in herbivory and movement. However, only a small portion of the outlier SNPs was successfully detected, compared to the NCBI database, and annotated, and therefore, we cannot disregard factors related to climate as a potential driver of the population differentiation.Sequences were often involved in the genome reorganization, neuropeptides, and energy mobilization associated with lipid metabolism. Processes related to the genome reorganization can cause a further divergence between the groups even though the adaptive importance of those processes are still not clear to this momentIn conclusion, current findings show that at least two well-structured populations occupy areas in North and South America. The differentiation was both caused by random drift and adaptive evolution. At the fine scale, high levels of population admixture were observed in the U.S., but not in Brazil suggesting a more heterogeneous and of mixed ancestry in the northern hemisphere. A broader sampling is needed to address the specific question of movement at a finer scale in both in Brazil and the U.S. Our data also suggests that natural selection is acting upon a variety of metabolic processes and it is likely related to genome reorganization and energy metabolism  rather than climate adaptation. Further studies are necessary to conclude a causal association between what we have found here with broader ecological processes such as range expansion and ecological dominance shifts.Red-banded stink bugs were sampled Fig. , Table 136. DNA quality and approximate quantity were assayed by agarose electrophoresis gel (1% w/v) stained with SYBR Safe DNA Gel Stain (Invitrogen), visually compared with lambda DNA standards (Invitrogen), and used to construct GBS libraries, similar to those previously described by Poland et al.16. DNA concentrations were determined more precisely using picogreen  and a Synergy HT  microplate readers, and adjusted to approximately 50 ng/μl. Five microliters (~250 ng) were pipetted into a new set of 96-well plates containing 2.5 μl 0.1 μM specific DNA barcoded (5–10 bp) PstI adaptors. The restriction enzymes PstI  and Msp1 (New England Biolabs) were used to digest the DNA and reduce genome complexity. The barcoded PstI adapters (CTGCAG) and a common non-barcoded MspI adapter (CCGG) were ligated to the digested DNA and amplified by PCR to create a sequencing library following Poland et al.16. The resulting library was single-end sequenced to 150 nucleotides on a single lane using the Illumina HiSeq. 4000 sequencing kit v1 , and the fastq files were demultiplexed with the bcl2fastq v2.17.1.14 (Illumina) by the Roy J. Carver Biotechnology Center at the University of Illinois at Urbana-Champaign producing over 133 million raw reads.We extracted DNA from the head and legs from randomly selected individuals using cetyltrimethylammonium bromide (CTAB)-based protocol adjusting proteinase K concentrationsprocess-radtags in STACKS v.2.437. The original Illumina data consisted of 133,097,998 reads pooled in a single file. Process-radtags uses information from the individual barcodes, and the RAD cut site to demultiplex sequence data assigning reads to the correct sample ID. Reads with uncalled bases and low-quality sequence reads were removed. RAD-tags and barcodes were rescued during demultiplexing, allowing a single mismatch while rescuing single-end barcodes. A total of 284,641 reads were discarded as they were low quality or contained ambiguous barcode or RAD-Tags. Sequence reads were truncated to 100 nucleotides long.We processed the Illumina raw single-end reads using the program de novo approach to assembly loci and call SNPs. The number of mismatches allowed between stacks while merging them into putative loci  and during the construction of stacks catalog  have shown to be critical steps in de novo analyses and are dependent on the natural levels of polymorphism found in the sample  and the technical approach used to produce libraries38. We investigated the impact of M and n is the number of retained loci and SNPs while varying the percentage of individuals required to possess a certain locus  in STACKS population module. First, we assumed M = n and considered only loci with a minimum depth of coverage of 3 × (m = 3) to build putative RAD-loci39. Loci and SNP distribution plateaus between Mn3 and Mn4, therefore M and n were set to 4. To minimize the impact of missing while still trying to extract reliable biological information from available samples, we investigated retained SNP and loci distribution for different values of percentage of individuals required to have a locus for it to be processed in population module (r). Our criteria to select r was based on the maximum possible values until it reaches an approximate value of ~1,500 loci, which in this case was r55.After demultiplexing, we used the 40. However, steps during library preparation such as PCR, library sequencing, and bioinformatic treatment to the data can generate errors and create biases for the population genetics estimations40. To prevent biases created by the excess of singletons, we investigated the impact of the parameter that controls minor allele frequency (MAF) to the number of retained SNPs. No clear sign of an excess of singleton was detected in our dataset, but to ensure that no ‘artificial alleles’ would interfere in our estimates, we applied a 1% MAF filter (MAF < 0.01)41. Moreover, to minimize the influence of paralogs from the final dataset, we have limited the maximum observed heterozygosity (MOH) to 45%, after investigating the distribution of the parameter in our samples.Both common and rare alleles can help us detecting important demographic and ecological phenomena in natural systems and therefore, be treated with great care. While common alleles can be used to detect bottlenecks, purifying selection, and population structure, high frequency of rare alleles may indicate population expansion, abnormal mutation rates, and gene flow in fine-scaleE), nucleotide diversity (π), and FIS) and pairwise FST values were calculated using the population module of STACKS. A heatmap was produced to illustrate FST relations using the R package RColorBrewer. Results were saved in genepop, variant call format (VCF) and structure files and converted to other formats using PGDSPIDER 2.042.All descriptive diversity statistics  and all significant outliers for diversifying selection flagged by Lositan, fsthe, and pcadapt removed (described in the next section). To run STRUCTURE, we assumed admixture and allele frequency correlation models, and the program was set to run 1.5 × 105 burn-in followed by another 5 × 106 Markov Chain Monte Carlo (MCMC) interactions. We simulated the number of clusters ranging from 1 to 15 (K = 1 to K = 15) with 15 repetitions for each value. The optimal K value was selected using the Evanno method implemented in Structure Harvest45. Multiple runs were averaged using CLUMPP 1.1.2 software46 and plotted using DISTRUCT47. To offer an additional support for the number of genetic clusters, we also used a non-model method based on principal component analysis (PCA) using Adegenet package in R. The mean of allele frequencies at a certain locus was used to when genotype data was missing with missing. Finally, the test the hypothesis of two genetic clusters  we used an analysis of molecular variance (AMOVA) using all and exclusively neutral loci48. To estimate AMOVA’s FST, we used pairwise differences distance matrix and 10,000 permutations to determine the significance of the parameter.A model-based Bayesian method was implemented to inferred population structure using the program STRUCTURE v.2.3.4fineRADstructure51. The approach is based on nearest neighbor haplotype ‘coancestry’ in which a coancestry matrix is first created in RADpainter following the assignment individuals to putative populations using fineSTRUCTURE. Markov Chain Monte Carlo (MCMC) clustering algorithm ran for 1 × 106 MCMC generations following an initial 1 × 106 generation burn-in and figures were plotted using R script fineRADstructurePlot.R and FinestructureLibrary.R (https://github.com/millanek/fineRADstructure).To investigate genetic structure in fine scale in the U.S., we used ST-based tests to detect outlier loci candidates that could be associated with patterns of differentiation between populations given neutral expectations. The tests contrasted Brazil and U.S. populations, except for Bayescan that considered all locations separately. The first method was implemented in LOSITAN52 that is based on the relation between FST values and heterozygosity of each marker, comparing it to data simulation. Beaumont and Nichols (1996), and it is based on the infinite island model53. LOSITAN was set to run 1 × 106  simulations using a confidence interval of 0.99 and a false discovery rate of (FDR) 0.01. Neutral mean and force mean FST options were selected. However, LOSITAN has a comparatively high rate of false positives compared to Bayesian methods and can produce unusual pattern derived from FST and HE mathematical relation32. Incline and skewed patterns often emerge when few independent genetic clusters with limited gene flow are present31. To mitigate this problem, we used an alternative method available in fsthe package in R that uses the raw empirical data to generate smoothed outlier plot. In addition to the two described methods, we use a PCA based method that takes into account population structure and can handle well high rates of missing data and admixture34. Lastly, we used a Bayesian approach in Bayescan using default parameters: 20 short pilot runs with 5,000 integration, burn-in set to 5 × 104 and thinning interval and prior odds of 10. To select candidates, we used FDR of 1%54.We investigated loci putatively under selection using F55.All RAD-loci containing the outlier SNP by the different methods were used as a query in nucleotide search BLASTx against the National Center for Biotechnology Information (NCBI) using Blast2Go suite following mapping and annotation steps using default configurationsSampling was carried out following standard regulation and with the proper research permits issued by the Brazilian Federal Government Institution (Ministério do Meio Ambiente, Instituto Chico Mendes de Conservação da Biodiversidade – ICMBio/SISBIO: No. 44402 e 63459. The collection made during this study did not involve endangered species.Supplementary information


Adaptation often proceeds from standing variation, and natural selection acting on pairs of populations is a quantitative continuum ranging from parallel to divergent. Yet, it is unclear how the extent of parallel genetic evolution during adaptation from standing variation is affected by the difference in the direction of selection between populations. Nor is it clear whether the availability of standing variation for adaptation affects progress toward speciation in a manner that depends on the difference in the direction of selection. We conducted a theoretical study investigating these questions and have two primary findings. First, the extent of parallel genetic evolution between two populations rapidly declines as selection changes from fully parallel toward divergent, and this decline is steeper in organisms with more traits . This rapid decline happens because small differences in the direction of selection greatly reduce the fraction of alleles that are beneficial in both populations. For example, populations adapting to optima separated by an angle of 33° might have only 50% of potentially beneficial alleles in common. Second, relative to when adaptation is from only new mutation, adaptation from standing variation improves hybrid fitness under parallel selection and reduces hybrid fitness under divergent selection. Under parallel selection, genetic parallelism from standing variation reduces the phenotypic segregation variance in hybrids, thereby increasing mean fitness in the parental environment. Under divergent selection, larger pleiotropic effects of alleles fixed from standing variation cause maladaptive transgressive phenotypes when combined in hybrids. Adaptation from standing genetic variation therefore slows progress toward speciation under parallel selection and facilitates progress toward speciation under divergent selection. In addition, evolutionary biologists are increasingly embracing the fact that the difference in the direction of natural selection on pairs of populations is a quantitative continuum ranging from completely parallel to completely divergent. In this article, we ask two questions. First, how does the degree of genetic parallelism—here, adaptation using the same alleles in allopatric populations—depend on the differences in the direction of natural selection acting on two populations, from parallel (0°) to divergent (180°)? And second, how does adaptation from standing variation affect progress toward speciation, and does its effect depend on the direction of natural selection? We develop theory to address these questions. We first find that small differences in the direction of selection (angle) can largely preclude genetic parallelism. Second, we find that adaptation from standing variation has implications for speciation that change along the continuum from parallel to divergent selection. Under parallel selection, high genetic parallelism causes interpopulation hybrids to have high mean fitness when their parents adapt from standing variation. As selection tends toward divergent, adaptation from standing variation becomes less beneficial for hybrid fitness and under completely divergent selection causes interpopulation hybrids to have lower mean fitness than when adaptation was from new mutation alone. Our results provide general insight into patterns of genetic parallelism and speciation along the continuum of parallel to divergent natural selection.Much of adaptation, especially that which occurs rapidly, proceeds from the sorting of ancestral standing variation and does not rely completely on de novo mutations , thereby reducing gene flow upon secondary contact. When a pair of populations adapts in response to divergent natural selection, hybrids might have an intermediate phenotype that is unfit in either parental environment Schluter . When a θ = 0°) to completely divergent (θ = 180°). Our specific goal was to characterize the pattern of decline in parallelism. We also hypothesized that adaptation from standing variation would reduce the evolution of reproductive isolation under parallel selection because parental populations would fix more of the same alleles and therefore evolve fewer incompatibilities , which are initially fixed for alleles with an effect of 0 on all m traits. We primarily present results from simulations with five phenotypic dimensions (m = 5) in the main text. Results for alternative parameter combinations can be found in the supplementary figures . Each tes Figs. –S7.z – o||, and the strength of selection, σ .. u Kimura ). Mutatiu Kimura ), with aNanc = 10,000) under stabilizing selection (σanc = 0.01) at the origin  for 100,000 generations. All other parameters in the ancestor  were identical to those of parental populations (Table anc = 1) that puts it into the multivariate “House‐of‐Cards” regime . Each parental individual received the mutant  allele at each of these n loci with a probability equal to the allele's frequency in the ancestor. Loci fixed in the ancestral population were also fixed in the parental population but were not considered when quantifying parallelism. This admittedly artificial sampling procedure allowed us more control over the amount of standing genetic variation across simulations with different parameter values. Further control was achieved by making the second parental population initially identical to the first, so that each possessed the exact same collection of genotypes and there were therefore no founder effects. Populations adapted from only new  mutation when n = 0. Within each parameter combination, we began each replicate simulation from a unique realization of the ancestor . After initialization, parental populations adapted to their respective phenotypic optima without interpopulation gene flow  and new mutation simultaneously.In simulations with standing genetic variation, a parental population was established by first randomly choosing low Fig. B, and add . More distant optima yield a greater amount of genetic and phenotypic change. In the main text, we set d = 1, which is equivalent to 10 times the SD of mutation effect size (α). The second key feature of the new optima is the angle of divergence, θ, between vectors that originate at the origin and each pass through one of the parental optima  to divergent (θ = 180°) and is explicitly invoked in most empirical metrics that quantify phenotypic parallelism .Two properties of the new phenotypic optima are the key. The first is the Euclidean distance between each optimum and the origin, T generations (T = 2000 in the main text), at which time all populations had reached their phenotypic optima  and the number of alleles that fixed in both populations  during the adaptation phase. We then calculated our metric of “genetic parallelism” as:To quantify parallel genetic evolution between parental populations, we first determined the number of alleles that fixed in each population ]/[ max (Pg)− min (Pg)], in order to facilitate comparison of simulations conducted with different parameters.Values of one indicate complete genetic parallelism  and values of 0 indicate complete genetic nonparallelism . We use this metric because of its ease of interpretation and note that it is highly correlated with other metrics of genetic divergence between populations  or alleles of individually larger effect   Castle . SegregaAn individual hybrid's fitness in a given parental environment was calculated from its phenotype in the same manner as the fitness of parental populations Fig. C. We detPg, the average fraction of fixed alleles that were also fixed in the other population—changes with the angle of divergence (θ) when adaptation is from standing variation. Genetic parallelism is highest under completely parallel natural selection (θ = 0°) and rapidly decreases toward its minimum value as θ increases , indicating that populations fix some deleterious alleles. Our conclusion that genetic parallelism rapidly decreases with θ is generally robust to variation in population size and selection strength, except for when small populations are under weak selection , in agreement with the findings of Chevin et al.  because the fraction of alleles that are beneficial in both parental populations declines as θ increases. For a given population, beneficial alleles bring populations closer to the middle of a hypersphere centered at the phenotypic optimum . Perhaps even more surprisingly, the fraction of overlap is not expected to change over the course of an “adaptive walk” (sensu Orr [d), the fraction of overlap is expected to remain constant throughout adaptation. Simulations conducted for four different dimensionalities  qualitatively capture the predicted pattern of decreasing parallelism with increasing dimensionality , if the distance between the ancestral phenotype and the phenotypic optimum of population 2 is twice that of the ancestor‐optimum distance for population 1 , less than 5% of the alleles beneficial to population 2 are also beneficial to population 1 . Even iθ increases. In Figure In this section, we evaluate the effect of standing variation on hybrid fitness across the continuum from parallel to divergent natural selection. The most readily observable pattern is that the mean relative fitness of hybrids is lower under divergent selection than under parallel selection regardless of whether adaptation proceeds with standing variation Fig. A. This pθ = 0°)—segregation variance is universally deleterious. When parental populations adapt to identical optima from only new mutation, hybrids vary considerably around the parental optimum and thus have relatively low mean fitness. When populations have access to a common pool of standing variation, parallel genetic evolution leads to lower segregation variance around the optimum and therefore higher mean fitness under parallel selection compared to when populations adapt from only new mutation , at larger angles hybrids increasingly fall into a “fitness valley.” In this case, some variation along the axis of divergence can be beneficial see Fig. C. Secondθ = 180°, standing genetic variation reduces hybrid variation along the axis connecting parental optima but slightly increases variation along all other axes  differs between simulations where populations adapted under parallel versus divergent selection. Under parallel selection, standing variation's effect on hybrid fitness is caused largely by parallel genetic evolution and therefore adaptation from standing variation is most likely to have an effect if populations adapting in parallel are founded with the same standing variation. Under divergent selection, standing variation's effect on hybrid fitness is not caused by genetic parallelism but rather by cryptic genetic differences—cryptic because they don't reveal themselves until after hybridization—that evolve between parental populations. Therefore, our predictions about the effect of adaptation from standing variation on hybrid fitness under divergent selection should hold regardless of whether populations have the same or different initial standing variation. A simple prediction—testable theoretically and empirically—resulting from our study is that founder effects should have a greater effect on hybrid fitness under parallel selection than under divergent selection.We emphasize that the mechanism through which adaptation from standing variation affects hybrid fitness , suggesting that adaptation from standing variation might have a role in adaptive radiation beyond simply increasing the rate at which adaptation proceeds.Associate Editor: Z. GompertAppendixFig. A1. Cartoon illustration of why divergence among populations does not affect whether an allele is beneficial in both of them.Figure S1. Genetic parallelism across the continuum of parallel to divergent natural selection (N = 100).Figure S2. Genetic parallelism across the continuum of parallel to divergent natural selection (N = 1000).Figure S3. Genetic parallelism across the continuum of parallel to divergent natural selection (N = 5000).Figure S4. Effect of standing genetic variation on hybrid fitness across the continuum of parallel to divergent natural selection (N = 100).Figure S5. Effect of standing genetic variation on hybrid fitness across the continuum of parallel to divergent natural selection (N = 1000).Figure S6. Effect of standing genetic variation on hybrid fitness across the continuum of parallel to divergent natural selection (N = 5000).Figure S7. Properties of fixed mutations under a variety of parameter combinations (N = 1000).Figure S8. Simulations under various fitness functions.Figure S9. Mutation‐selection balance and mutation effect sizes in ancestral populations.Figure S10. Mutation‐selection balance and mutation effect sizes in ancestral populations under stronger selection (σanc = 1).Figure S11. The effects of standing genetic variation on genetic parallelism and phenotypic segregation variance in hybrids under parallel and divergent natural selection.Figure S12. Effect of standing variation on the pace of adaptation and attainment of mutation‐selection‐drift balance.Figure S13. Relationship between genetic parallelism and (A) segregation variance and (B) expected heterozygosity.Figure S14. Alternative presentation of simulation results across environments: distance between optima (δ).Figure S15. The effect of population size on the rate of divergence between populations due to drift.Figure S16. Effect of dimensionality on net segregation variance.Figure S17. Fraction of overlap of beneficial mutations with parallel selection (θ = 0°) but unequal distance (d1 ≠ d2).Figure S18. The effect of standing genetic variation (SGV) on relative maximum hybrid fitness across environments.Figure S19. The relationship between segregation variance and θ for different dimensionalities.Click here for additional data file.


Our findings emphasize the need to develop carefully considered management strategies for genetic conservation.We examined genetic diversity of the wild Tsushima leopard cat—a regional population of the Amur leopard cat—using microsatellite markers. In addition, we compared genetic diversity of the Tsushima leopard cat with that of the Korean population of Amur leopard cat. Although bias should be considered when applying cross-species amplification, the Tsushima leopard cat showed a lower index of molecular genetic diversity than did the Korean population. These results were consistent with those obtained using other genetic markers, such as mitochondrial DNA and Y chromosome sequences. This low genetic diversity of the wild Tsushima leopard cat may be derived from the founding population. Furthermore, our results suggest that the captive populations held in Japanese zoos may show extremely low genetic diversity, leading to difficulties in genetic management of the Tsushima leopard cat. Moreover, the two regional populations were clearly separated using these marker sets. In the present study, we demonstrated that the genetic diversity of the Tsushima leopard cat is extremely low compared with that of the continental regional population. Importantly, the Japanese captive population for  Prionailurus bengalensis euptilurus) is a wild feline, which is restricted to the Tsushima Island in Japan —a subspecies of the leopard cat . The wild population of the Tsushima leopard cat has been declining, mainly because of habitat fragmentation, prey base depletion (rodents), and road kills. Their estimated number in the wild is less than 100  has been conducted, and the results of such investigations have indicated that the Tsushima leopard cat shows lower genetic variation than do other populations of the Asian leopard cat . MicrosaIn the present study we aimed to assess the diversity of Tsushima leopard cat populations. We analyzed genetic polymorphism at 12 microsatellite loci identified in the population of the Korean Amur leopard cat and compared genetic variation indices between the Tsushima and Korean populations. Moreover, we evaluated genetic differentiation between the two populations using structure analysis and principal coordinate analysis (PCoA).. All muscle samples were obtained from animals that died in road accidents or after the death of rescued individuals and were stored in ethanol until DNA extraction. DNA was extracted from the samples using the QIAGEN DNeasy Blood and Tissue Kit .This study was conducted in strict accordance with the guidelines for the ethics in animal research established by the Wildlife Research Center of Kyoto University. Kyoto City Zoo approved the research (#20170426). We obtained muscle samples from 28 Tsushima leopard cats, which had been killed in road accidents or rescued during 2004–2012Primers were re-designed to amplify repeated regions according to the reference sequences  using Prn = 30)  . The coin = 30)  and thatn = 30) .Ar) per locus was calculated using the HP-Rare software ver. 1.1 (Na) and private alleles (Np), expected heterozygosity (He) and observed heterozygosity (Ho), probability of identity (Pid), and Pid among siblings (Pid-sib) were calculated using GenALEx ver. 6.5  were cited data from the previous study , FIT , and FST (population differentiation between populations) were calculated using GENEPOP ver. 4.0.10  of 1–10 that were run for 500,000 generations after an initial burn-in of 250,000 generations. The most probable number of clusters (K) was determined by calculating the second order rate of change considering the likelihood of K between successive K values  of the Tsushima and Korean populations were 8.2 (0.8–infinity) and 115.1 (33.3–infinity), respectively.Genetic diversity indices of the population of the Tsushima leopard cat are summarized in FIS, FST, and FIT between the Tsushima and Korean populations across all loci were −0.0504, 0.4803, and 0.4541, respectively. Clustering of the two populations observed in the STRUCTURE ; therefore, more individuals and loci should be analyzed to establish a more precise estimate of population size.Cumulative STRUCTURE analysis and PCoA using the developed markers clearly distinguished between the two regional populations, except for one individual. These results indicate that the two populations are genetically separated. The leopard cat has been classified into four subspecies based on recent molecular genetic studies, and the Tsushima leopard cat is classified as the Amur leopard cat similar to the Korean population. The Amur leopard cats have no subdivision among their habitat . In our Collectively, these results provided insights into initiating a captive conservation breeding program for Tsushima leopard cats. For many endangered species, conservation breeding programs have been established with the primary goal of maintaining the genetic diversity of populations to prevent the negative effect of inbreeding and preserve the adaptive potential of the species . TraditiThe present study showed that the Tsushima leopard cats have an extremely low genetic diversity compared with the Korean population, and this marker set could distinguish between the two regional populations. However, in the present study, we analyzed only a small fraction of the genome, which does not necessarily provide an accurate estimate of individual genetic diversity. Genome-wide data are already used for research and conservation of endangered species, such as gorillas  and Visa1. The Tsushima leopard cat population has a lower genetic diversity in nuclear DNA, as evidenced by molecular genetic analysis, than does the regional Korean continental population, which belongs to the same subspecies: the Amur leopard cat.2. STRUCTURE and PCoA analyses revealed that the two regional populations are clearly divided.3. Our findings suggest that captive populations may have extremely low genetic diversity, and better planned genetic management is required for the conservation of the Tsushima leopard cats.10.7717/peerj.7297/supp-1Table S1These data were obtained from row typing score of multiple genotyping experiments.Click here for additional data file.