Although genetic approaches have long been used in conservation research, it has only recently become tractable to generate genome-wide data at a scale that is useful for conservation. In this Review, we discuss how genome-scale data can inform species delineation in the face of admixture, facilitate evolution through the identification of adaptive alleles, and enhance evolutionary rescue based on genomic patterns of inbreeding. As genomic approaches become more widely adopted in conservation, we expect that they will have a positive impact on management and policy decisions. The human footprint on our planet is currently threatening biological diversity across habitats. Arguably the biggest threat to biodiversity across the planet is habitat degradation , 2. As tEfforts to stop mass extinctions and population declines include setting up protected areas ), international agreements to limit greenhouse gases to curb climate change , and legal frameworks to protect endangered species  and the US Endangered Species Act (ESA)). Genomic technologies can aid these efforts by identifying biodiversity “hotspots” to prioritize for protection, using predictive models to help build natural communities that are resilient to environmental change, and informing management actions that attempt to mitigate threats to endangered species.In this Review, we differentiate genetic approaches, which use a small number of neutral markers, from genomic approaches, which use complete genomes or genome-wide data. No standardized amount of data divides genetics from genomics; rather, this is a semantic distinction. We consider a study to have transitioned into the realm of genomics when a high density of markers is assayed from across the entire genome, usually in the order of thousands of markers.Although both genetic and genomic data sets can be used to estimate genetic diversity, population structure, and demographic history, genome-scale data, with an increased density of markers across the genome, can provide more accurate estimations of these parameters –12, someIn addition to the increased precision of estimates of traditional parameters, the transition to genomic approaches allows researchers to ask qualitatively different questions. This is because our capacity to examine different evolutionary mechanisms increases with the amount of the genome interrogated. In addition to assaying putatively neutral loci and protein-coding regions of the genome, whole-genome sequencing allows the identification of non-coding regulatory regions that control gene expression, and whole-transcriptome sequencing allows the quantification of gene expression differences.The limited use of genome-scale data in a conservation context is probably due to the additional challenges presented by these data sets. One important consideration is cost. Although the cost of sequencing continues to decrease, most conservation projects have limited budgets that allow genome-scale sequencing of only a small number of samples. The tradeoff between the number of samples and the number of loci sequenced is a critical consideration, and the best approach in each case will depend on the specific research question. Another important consideration is data analysis; that is, the specific resources and expertise that are available to analyze whole-genome data. Calling genotypes requires a reference genome, which may not be available for many non-model organisms, and analysis software is not always user-friendly. Finally, once a researcher obtains results from whole-genome analyses, it is often difficult to interpret the results and to translate them into conservation recommendations.In this Review, we discuss how conservation researchers and managers can use the power of genomic data to make decisions on the conservation of biodiversity. We focus on conservation topics where genome-scale data can provide valuable insights that are unattainable with traditional genetic techniques: delineating species in the face of admixture, identifying adaptive alleles through association mapping, and enhancing evolutionary rescue based on genomic patterns of inbreeding.The current conservation regulatory framework relies on defining distinct units of conservation to support law enforcement and to inform resource allocation. In conservation, the term “species” is often used to convey the idea of a unit of conservation and includes taxonomic levels below species, such as subspecies and distinct populations. Defining specific species is fraught with challenges and differing opinions . In consIdentifying and describing conservation units is often confounded by the lack of clear boundaries between management units. The most common categorization currently in use is the evolutionarily significant unit (ESU), which defines a group as distinct if it is “substantially reproductively isolated from other conspecific population units” and “represents an important component in the evolutionary legacy of the species” . An ESU In evolutionarily simple biological systems, traditional genetic techniques can delineate conservation units in a straightforward manner. However, the task is more complicated in complex evolutionary systems, such as those with a history that includes admixture and introgression . AdmixtuGenomic research has revealed a high frequency of admixture in natural systems, ranging from great apes to bears and butterflies –26. For Understanding the admixture histories of natural populations is important when delineating units for conservation, as admixture erodes the genetic distinctiveness on which conservation units are based. Historically, this has led admixture to be seen in conservation as a threat to the integrity of endangered species –35. MoreGiven that conservation legislation is based on the identification of distinct units, it is not surprising that regulations also vary with respect to how hybrid populations should be protected , 34. SomAlthough genomics will not solve the problem of discrete classification in an inherently non-discrete system, genome-scale data can provide researchers and managers with a more complete understanding of the spatial and temporal dynamics of admixture in evolutionarily complex systems. Much research in this realm has taken place in naturally occurring hybrid zones where one of the two parent species is protected. In both genetic and genomic approaches, the main goal is to identify ancestry-informative markers that are capable of distinguishing the two parent-species and estimating the proportion of ancestry of the protected parent species in hybrid individuals. For example, using a genetic approach, researchers used amplified fragment length polymorphisms (AFLPs) to determine parentage in hybrid garter snakes in Wisconsin . DespiteAs DNA-sequencing technologies advance, so does the ability to sequence markers more densely across the genome, which both improves parentage estimates and provides a means to identify patterns of genetic introgression, with potential conservation implications. For example, researchers used expressed sequence tags (ESTs) to identify SNPs that were fixed for different alleles between the threatened California tiger salamander and the intentionally introduced non-native barred tiger salamander . ResearcGenomic inference can, however, help to protect specific traits. For example, by combining fine-scale genomic data with phenotypic data, it is possible to connect particular genomic regions to ecologically important traits (as discussed below in the “Association mapping and adaptation” section). This would provide an improved understanding of the ecological consequences of introgression and may lead to targeted efforts to protect individuals that carry these traits. The ability to identify adaptive genetic variation raises the possibility of using this variation to delineate conservation units. For example, a proposal has been submitted to list spring-run Chinook in the Klamath River as endangered under the ESA based on a single allele that is strongly associated with the spring-run phenotype –45. ThisCanis lupus baileyi, Canis lupus nubilus, Canis lupus occidentalis, Canis lupus arctos, and Canis lupus lycaon (the eastern wolf) . Gray wolves in North America have been divided into several subspecies: lf) Fig.  48]. Th. ThCanisC. lupus lycaon, a separate species, C. lycaon, and means that the current listing for C. lupus is invalid as it includes 29 states that are occupied by C. lycaon rather than by C. lupus [C. lupus based on the new taxonomy, the USFWS found that C. lupus was neither threatened nor endangered, with the exception of the subspecies C. l. baileyi in the southwestern US and Mexico [The history of federal protection of the gray wolf in the US spans 50 years. Initially, individual gray wolf subspecies were protected separately. In 1978, the US Fish and Wildlife Service (USFWS) issued a ruling under the US Endangered Species Act (ESA) to reclassify the gray wolf as endangered at the species level, protecting gray wolves throughout the lower 48 states and Mexico. Some gray wolf populations have since recovered and six states have been removed from the 1978 listing. Gray wolves are currently protected in 42 states and Mexico . In 2013C. lupus . In addid Mexico .The 2013 taxonomic revision that led to the proposed delisting of the gray wolf has proved to be as controversial as other aspects of wolf protection and recovery in the US. Although there seems to be agreement that admixture is an important component in explaining patterns of genetic variation in eastern wolves, there is disagreement about the context of admixture and the implications for canid taxonomy. Chambers et al. (2012) argue that, based on a review of the existing literature, the eastern wolf evolved in North America from a common ancestor with coyotes and now hybridizes with gray wolves where their ranges overlap . They ciDetermination of the historical context of admixture has implications for conservation. An admixed species is viewed differently if admixture is a natural part of its evolutionary history versus for a species where admixture is recent and driven by human activities , 55. ForAdaptation is a genetic process that allows a species to persist for generations in a changing habitat. A central focus of traditional conservation genetics has been to ensure that populations maintain sufficient genetic variation to act as substrates for the process of adaptation. With the transition to modern high-resolution genomic data, conservation researchers can not only assay overall levels of genetic variation, but also identify specific alleles that may be adaptive. Such data can provide managers with useful information when they need to prioritize populations for protection or need to make decisions regarding which individuals to translocate so as to boost diversity in a declining population.Candidate loci underlying a particular phenotype can be determined through association mapping, which searches for an association between genotype and phenotype. Genome-wide association studies (GWASs) are commonly used to delineate the genetic basis of diseases in humans , 57. UsiAssociation mapping can be useful in conservation when an identifiable phenotype has clear fitness consequences in the population of interest. Disease resistance is a particularly important target that may have implications for management decisions. For example, the Tasmanian devil is at risk of extinction due to devil facial tumor disease (DFTD)  Box 2, . DFTD isThis study highlights one of the challenges of using GWASs in endangered species—the very small sample sizes and the often close relatedness of individuals in the data sets. These characteristics reduce the power of GWASs and can lead to spurious results . AlthougA similar process can be applied to identifying adaptive genetic variation that reduces the susceptibility of coral species to bleaching, which can be used to increase the resilience of reef ecosystems to warming oceans. Corals are the foundation species of the reef ecosystem, so coral bleaching is a major threat to the entire ecosystem . Coral bThe Tasmanian devil is one of the iconic animals of Australia, but this carnivorous marsupial is at risk of extinction due to devil facial tumor disease (DFTD) . DFTD waGenomics research on both tumor and host genomes has greatly increased the understanding of the disease and has informed potential management actions. Comparison of host and tumor karyotypes found complex rearrangements to be present in all tumor samples but absent from the host samples, indicating that the tumors were all derived from the same source . In furtThe discovery that DFTD is a clonally transmissible cancer led to research to determine why the devils failed to reject the foreign cell line. Researchers examined the major histocompatibility complex (MHC), which plays an important role in the recognition of foreign molecules by the immune system. Sequencing the host MHC revealed low diversity, which was consistent with reduced immune function ; howeverGenetic rescue is a conservation tool used to increase the fitness of at-risk populations by introducing new genetic variation into the population. This is usually accomplished by translocating individuals from a closely related population (assuming that such a population exists) into an at-risk population. Genetic rescue is expected to be most useful for small, isolated populations that suffer from inbreeding . TheoretTo make decisions regarding genetic rescue, it is important to understand the level of inbreeding in the population, which depends on the size of the population and its demographic history. A common way to estimate the level of inbreeding is to calculate a genome-wide estimate using either genetic markers or a pedigree. Because genetic markers estimate realized inbreeding but pedigrees estimate expected inbreeding (including a large variation due to stochastic processes), estimates from genetic markers are more accurate –82. AddiIn addition to estimating the timing and the level of inbreeding, it is useful to estimate the deleterious fitness effects that result from an increase in homozygosity. The fitness effects of a particular genetic variant in a protein-coding sequence can be predicted from models of protein structure and by comparing the level of sequence conservation across species , 86. PreGenomic information about inbreeding and deleterious alleles can be valuable for managers who are considering a genetic rescue program. First, researchers should determine whether the population has reduced genetic diversity and a substantial amount of inbreeding. If so, they should then determine whether the inbreeding is predicted to have negative consequences on the fitness of the population and whether genetic rescue is predicted to increase fitness. If managers decide to establish a genetic rescue program, they should then decide which populations and which individuals will be used as the source of translocations into the at-risk population. All of these decisions, including the decision not to initiate a rescue program, rely on being able to predict the genomic consequences of the different available options.One classic example of a successful genetic rescue is the Florida panther, a subspecies of mountain lion . By the Genetic rescue, however, is not always successful, as seen with the wolves of Isle Royale National Park . It was Even in systems that are able to leverage the genomic resources of model systems, it has proved difficult to connect predicted high deleterious loads to decreased population fitness. The Channel Island fox, for example, occurs in only very small and isolated populations. Genomic analyses using genomic resources developed for domestic dogs revealed extremely low levels of genomic diversity and an increased load of deleterious mutations in Channel Island fox populations . These gIncreasing genomic resources in model and non-model systems, combined with improved prediction algorithms, should help researchers and managers to better identify at-risk populations and to understand the genomic and fitness consequences of different proposed management actions.Genomic sequencing is helping to inform conservation decisions by providing critical information regarding species of conservation concern. Although the current focus of conservation genomics is on monitoring and managing existing genomes of species, new genomic technologies will allow researchers to manipulate genomes to help achieve conservation goals. Genome-editing technologies such as CRISPR–Cas9 , 95 alloAgrobacterium-mediated transformation, has been used to incorporate fungal blight-resistant genes from wheat into the American chestnut tree, which is nearly extinct as a result of an introduced fungal pathogen [The ability to use genome editing to replace alleles might enable researchers to assist the evolution of species by improving disease resistance or by enhancing adaptation to changing climates. For example, an older gene transfer technology, pathogen . This mopathogen .A similar plan has been proposed to save the critically endangered black-footed ferret. The black-footed ferret was once widespread across the Great Plains, but a combination of factors, including habitat loss and disease, caused its extinction in the wild . A succeSimilarly, genetic-engineering techniques could be applied to help corals become more resistant to rising water temperatures. Heat-resistant alleles could be engineered from heat-tolerant corals and introduced into susceptible corals. To this end, work has already begun to develop CRISPR techniques in coral symbionts to increase the resilience of coral reefs to climate change-related stressors .Other applications of genome-editing technologies with potential use in conservation are gene drives. Genetically engineered gene drives increase the inheritance of the engineered allele to spread the desired trait through the population , 103. GeNew technologies also allow us to move beyond making small changes to the genome. For instance, cloning by somatic cell nuclear transfer has been proposed as an approach to reintroduce lost genetic material into the black-footed ferret using preserved cell lines from an extinct lineage . In the Whether using traditional conservation genetics or cutting-edge genomic engineering, any action taken—or not taken—comes with practical, legal, and ethical issues that need to be discussed with researchers, managers, and the public , 108. WiAs human activities drive our planet into its sixth mass extinction event, genomic technologies will be an important tool for conservation researchers, helping to provide valuable scientific information to managers and policy makers. Genetic approaches have a long history of use in conservation, but the transition to genomic technologies is only just beginning. By expanding available data sets to the genomic scale, researchers can ask and answer different questions and can thus gain valuable insights that will be applicable to conservation. As genomic technologies continue to advance, the potential for these technologies to impact conservation decisions increases. The knowledge we gain will hopefully enable us to mitigate our impact on the earth’s biota.


Understanding the genetic basis of local adaptation has long been the concern of biologists. Identifying these adaptive genetic variabilities is crucial not only to improve our knowledge of the genetic mechanism of local adaptation but also to explore the adaptation potential of species.P. bungeana could be subdivided into three groups. Redundancy analysis showed that this genetic divergence was caused by divergence selection from environmental variables related to the ecological habitats of “avoidance of flooding” and “avoidance of high temperature and humidity.” LFMM results indicated that Bio1, Bio5, Bio8, Bio12, Bio14, and Bio16, which are related to the ecological habitat of P. bungeana, were correlated with the highest numbers of environment-associated loci (EAL).Using 10 natural populations and 12 start codon targeted (SCoT) markers, a total of 430 unambiguous loci were yielded. The Bayesian analysis of population structure clearly demonstrated that the 10 populations of P. bungeana clearly supported the hypothesis that environmental variations related to the ecological habitat of species are the key drivers of species adaptive divergence. Moreover, a method to calculate the species landscape adaptation index and quantify the adaptation potential of species was proposed and verified using ecological niche modeling. This model could estimate climatically suitable areas of species spatial distribution. Taking the results together, this study improves the current understanding on the genetic basis of local adaptation.The results of EAL characterization in The online version of this article (10.1186/s12862-019-1489-x) contains supplementary material, which is available to authorized users. Understanding the genetic basis of local adaptation is one of the core issues in the adaptive evolution of species and has long been the concern of biologists , 2. LongOne of main goals of landscape genetics is to uncover the relationship between the adaptive genetic divergence in the genome and the environmental heterogeneity on a spatial scale . Recent Previous studies have identified a multitude of adaptive genes or loci from a large number of species –17. HowePinus bungeana Zucc. ex Endl. (Pinaceae), a coniferous evergreen tree that grows at 500–1800 m above sea level, is located in the warm temperate zone and subtropical margin of China [P. bungeana is a heliophilous plant; and it has a certain tolerance to cold and drought but is not resistant to flooding [P. bungeana across its distribution area were used to investigate the adaptive divergence of the species by using the landscape genetic approach.of China . This spflooding , 29. TheP. bungeana and identify adaptive loci by analysis of associations with environmental data. The objectives of the study are to (i) examine the adaptive loci of P. bungeana, (ii) evaluate environmental variations associated with adaptive differentiation in P. bungeana, and (iii) establish a suitable LAI to measure the adaptation potential of species.Start codon targeted (SCoT) polymorphism, a molecular marker with bias toward candidate functional genes, was developed based on short conserved sequences of start codons in plant genes . This maP. bungeana from 10 natural populations were successfully scored using the 12 SCoT primers, and 430 unambiguous fragments were identified with sizes ranging from 60 bp to 1200 bp. The number of loci in 12 primers ranged from 17 (SCoT24) to 48 (SCoT25). The lowest number and percentage of polymorphic alleles  were found in the Gaopinghui, Sichuan  population and while the highest  were found in the Nannao Mt., Shanxi  population. The level of Nei’s genetic diversity (HE) of each population ranged from 0.069 in Lijiazhai, Hubei  to 0.143 in SXNN (P1). Overall summary statistics of the genetic diversity analyses of 10 populations of P. bungeana are presented in Table P. bungeana , the second group is the north group (P5 − P8), and the third group is the south group (P9 − P10). Despite the significant genetic subdivision observed in the 10 populations of P. bungeana, only a small percentage of the genetic variation existed among the three subgroups  was performed to detect the roles of several environmental variables in this genetic subdivision, as well as the relative contribution of each variable to the population genetic structure, and the results are presented in Fig.  2 Table . Among tP. bungeana  above the 95% threshold were identified  were significantly smaller than those for C. coggygria  and F. suspensa . This result could be matched to the LAIs calculated earlier.Based on the LAI calculation method introduced in Methods, the LAI of P. bungeana across its entire distribution range to provide insights into the complex interaction between environment variables and the adaptive genetic divergence of this conifer species. This work raises our understanding of the interaction of evolutionary processes, such as natural selection, gene flow, and local adaptation. Landscape genetics research has proven to be an effective approach to reveal this interaction [P. bungeana in response to a heterogeneous landscape.Our study investigated the spatial distribution of genetic variations in natural populations of eraction , 5. In tP. bungeana. Ten populations from P. bungeana are significantly clustered into three genetic groups, namely, the north group (P5 − P8), the middle group (P1 − P4), and the south group (P9 − P10).Revealing the role of environmental variables in the formation of the spatial population genetic structure of species is a major concern of landscape genetics , 32. AltP. bungeana is the result of redistribution from three glacial refugia during Quaternary glaciation. Second, genetic division may occur due to environmental or geographical isolation. Geographical or environmental barriers existent among the three groups may hinder gene flow and lead to significant genetic differentiation. Finally, genetic division may arise from natural selection. Given significant differences in the habitat environments of the species considered, the selection pressure caused by such heterogeneity could result in the divergent selection of species, leading to significant divergence of the three groups. Assuming that the first explanation is true, populations with the highest genetic diversity can be considered as glacial refugia, whereas populations located far from the refugia can show a gradually reduction in genetic diversity due to the founder effect. The results of the middle group (P1 − P4) support this hypothesis, but those of the north group (P5 − P8) clearly do not meet expectations. We do not consider the results of the south group because it only has two populations. Furthermore, previous phylogeographic studies of P. bungeana have confirmed that it has only one or two refugia and that these refugia are located in the western areas of the Qinling-Daba and Lvliang Mountains [P. bungeana. By assuming that genetic divergence of P. bungeana is caused by geographical or environmental barriers, we would expect to find significant genetic differences among these three groups. The results of hierarchical AMOVA  seem to support this hypothesis. In fact, the distribution of populations from the three groups of P. bungeana is continuous, especially for the north and middle groups. No obvious geographic barrier, i.e., mountains or rivers, separates the three groups. Thus, we may consider an alternative explanation: the genetic divergence among the three groups of P. bungeana is caused by environmental barriers. However, according to the results of our simulation of the climatically suitable areas of P. bungeana, these climatically suitable areas are distributed continuously, and no ecological isolation among the three groups could be observed  based on neutral loci was consistent with this expectation. RDA was performed to verify whether the genetic divergence of the three groups of P. bungeana is caused by divergence selection from heterogeneous environments. The RDA results demonstrated that the 12 environmental variables unambiguously divided 10 populations of P. bungeana into the south group, the middle group, and the north group  has the highest adaptive ability among the three species whereas P. bungeana (LAI = 0.607) has the lowest. This finding raises an important question of whether the index is reasonable. Thus, we sought a valid indicator to test this index. The adaptation potential of species is mainly manifested in two aspects: the ability of species to cope with climate change and the ability of species to distribute in space. As predicting and quantifying the ability of species to adapt to climate change is difficult, we used ecological niche modeling to calculate climatically suitable areas to represent the ability of spatial distribution of species. The results showed that C. coggygria  has the largest climatically suitable areas and that the climatically suitable areas of F. suspensa  and P. bungeana  are smaller than that of C. coggygria. This result clearly supports the LAIs of these three species. Although previous studies have suggested that climate change and human disturbance are the main reasons for the endangerment of P. bungeana [P. bungeana.Moving beyond identification of adaptive genetic variation, landscape genetics provides researchers an opportunity to assess species adaptive potential. The adaptation potential of species depends mainly on two factors: the ability to produce adaptive genes and the ability to spread these genes out . Thus, wbungeana , our resDespite its benefits, a number of problems related to our LAI that may lead to an underestimation of species adaptation potential must be noted. For instance, we only considered the current status of differentiation of adaptive genes, which might produce more differentiated genotypes in the past. In addition, increases in human activities in the warm temperate and subtropical regions of China could seriously interfere with gene flow among species populations , 38. TheP. bungeana. Our results showed that environmental variables related to ecological habitat play a key role in the adaptive genetic divergence of species. We also proposed a method to calculate the LAI of a species and quantify its adaptation potential and then verified this index by using ecological niche modeling to estimate the climatically suitable areas of species spatial distribution. Our results showed that P. bungeana has a low LAI, which suggests that low adaptive differentiation ability is also responsible for its endangerment. We believe that, although it requires a larger number of species for verification, the LAI proposed in this work will enhance the current understanding of the adaptation potential of various species.In this study, SCoT markers were adopted to investigate the adaptive genetic divergence of P. bungeana in China . To minimize false scoring, peaks within 60–1200 bp and relative fluorescent units above 500 were scored. The following population genetic analyses were conducted on the basis of the 1/0 matrix of SCoT markers.NA), Nei’s measure of gene diversity (HE) [PPA), and gene frequencies per allele were estimated using AFLPSURV 1.0 [P. bungeana was implemented using the Bayesian-based program STRUCTURE 2.3.4 [K values from 1 to 10 using the no-admixture model with independent allele frequencies. Ten replicate runs of 100,000 Markov chain Monte Carlo iterations and a burn-in period of 100,000 repetitions were performed for each value of K. The optimal K that best represented the major population structure was assessed according to the method introduced by Evanno et al. [http://www.diva-gis.org/climate) from 1950 to 2000 at 2.5 arcmin resolution were downloaded and extracted using DIVA-GIS 7.5 [Genetic parameters, including polymorphic allele number (ity (HE) , percentSURV 1.0 . AnalysiRE 2.3.4 . STRUCTUo et al. , which wo et al. . CLUMPP o et al. . Histogro et al. , and hieo et al.  to infer-GIS 7.5 . In this-GIS 7.5 . Twelve -GIS 7.5 . Here, wP values below 0.001. Linkage disequilibrium analysis were performed in ARLEQUIN 3.5 [2 values above than 0.8.Two approaches were used to identify outlier loci and reduce the false discovery rate. In the first approach, the hierarchical island model in Arlequin 3.5 was used . The advQUIN 3.5 , and cutFST) based on adaptive loci and gene flow (Nm) based on neutral loci to indicate these two abilities. Here, we devised a new index, i.e., LAI, to measure the adaptability of species. LAI = FST(E) × Nm = FST(E)/4 (1/FST(N) − 1), where FST(E) is the FST value of EAL, Nm is the gene flow among populations, and FST(N) is the FST value of neutral loci. Although the value of Nm is not exactly equal to 1/4 (1/FST(N) − 1) [P. bungeana was based on a set of 51 presence points covering the entire distribution range: 16 points were obtained from Yang et al. [C. coggygria and F. suspensa. The geographic distribution coordinates of these two species were obtained from Wang et al. [Considering that the adaptive potential of a species is mainly determined by its ability to produce adaptive genes and transmit them to other populations , 25, we (N) − 1) , it can (N) − 1) . Informag et al. , 25 fromg et al. , and 10 g et al.  and Fu eg et al. , and molg et al.  and Yangg et al. . The rung et al. , but logg et al. .Additional file 1:The outlier loci identified by BayeScan and Arlequin. (DOC 110 kb)Additional file 2:Gene frequencies per allele of 430 alleles for each population. (DOCX 57 kb)Additional file 3:Environmental variables for each location from the WorldClim database. (DOCX 16 kb)


Clostridium perfringens, is responsible for the pathogenesis of enterotoxaemia of ruminants and has been suggested to play a role in multiple sclerosis in humans. Etx is a member of the aerolysin family of β-PFTs (aβ-PFTs). While the Etx soluble monomer structure was solved in 2004, Etx pore structure has remained elusive due to the difficulty of isolating the pore complex. Here we show the cryo-electron microscopy structure of Etx pore assembled on the membrane of susceptible cells. The pore structure explains important mutant phenotypes and suggests that the double β-barrel, a common feature of the aβ-PFTs, may be an important structural element in driving efficient pore formation. These insights provide the framework for the development of novel therapeutics to prevent human and animal infections, and are relevant for nano-biotechnology applications.Epsilon toxin (Etx), a potent pore forming toxin (PFT) produced by  Epsilon toxin (Etx) is a potent pore forming toxin (PFT) produced by Clostridium perfringens. Here authors show the cryo-EM structure of the Etx pore assembled on the membrane of susceptible cells and shed light on pore formation and mutant phenotypes. Clostridium perfringens type B and D strains and plays an important role in the pathogenesis of enterotoxaemia, a severe neurological disease of domestic ruminants, particularly sheep. More recently, Etx has been implicated in the development of multiple sclerosis (MS) in humans3.Epsilon toxin (Etx) is a pore-forming toxin (PFT) produced by 4. The protoxin is converted to the active, mature toxin after carboxy- and amino-termini peptides are removed by proteolytic cleavage in the gut, either by digestive proteases of the host, such as trypsin, or protease produced by C. perfringens5. Toxin activation generates a more acidic protein (isoelectric point of 5.4 vs. 8.3) that is nearly 1000 times more toxic than its progenitor7. By a mechanism unknown, Etx crosses the gut wall, enters the blood stream and accumulates preferentially in the brain and kidneys9.Etx is typically secreted by the bacterium in the gut as a relatively inactive, water-soluble monomer, called protoxin (P-Etx), with an estimated molecular weight of 32.9 kDa11. Other bacterial toxins from the aβ-PFTs that play a role in disease include aerolysin from the human pathogen Aeromonas hydrophila12, enterotoxin (CPE) from C. perfringens13, α-toxin from Clostridium septicum14 and monalysin from Pseudomonas entomophila15. Other members of the aβ-PFTs serve in defence, such as lysenin produced by immune cells of the earthworm Eisenia fetida16, assist in prey digestion, such as hydralysins from Cnidaria17, or have cytocidal activity against human cancer cells, such as parasporin-2 from Bacillus thuringiensis18.Etx is a member of the aerolysin family of β-PFTs (aβ-PFTs). Members of the aβ-PFTs are found in all kingdoms of life and can be used for either attack or defence11. They typically contain one or more receptor-binding domains (RBDs) and a pore-forming module (PFM), a well-conserved structural element of the aβ-PFTs19. The PFM, rich in serine and threonine residues, contains a flexible membrane insertion loop flanked by pre-insertion strands that refold into an amphipathic β-hairpin during pore formation to create the transmembrane β-barrel of the pore20.As evidenced by their crystal structures, members of the aβ-PFTs share an overall similar domain arrangement21 and revealed an elongated molecule composed mainly of β-sheets, with two strands traversing the entire molecule. The suggested RBD at the amino terminus contains a cluster of surface-exposed aromatic amino acids critical for cell binding23, and the PFM at the carboxy terminus is suggested to play a role in oligomerization and pore formation19. The PFM also contains a C-terminal peptide (CTP), proteolytic removal of which is essential for toxin activation6 as interaction of the CTP with the pre-insertion strands restricts their movement, thus preventing pore formation20. A glycan (β-octyl-glucoside (β-OG)) binding site has also recently been identified in the PFM, suggesting that Etx may have two distinct receptor-binding sites22.The crystal structure of the water-soluble, monomeric P-Etx was solved by Cole et al. in 200450) in mice after intravenous administration of the toxin is reported to be 100 ng kg−1 24, which is equivalent to 7 μg per 70 kg human. This makes Etx the third most potent clostridial toxin after botulinum and tetanus neurotoxins24. For this reason, Etx is classified as a potential biological weapon in some countries and as a category B biological agent by the Centres for Disease Control and Prevention (CDC), the leading public health institute of the United States25. Among the many cell lines tested, only a few are susceptible to Etx. Most in vitro studies on Etx, including this study, have been performed using the Madin–Darby Canine Kidney (MDCK) cell line as this cell line is the most sensitive to the toxin29. The cell specificity of Etx suggests that the toxin binds to a specific receptor in the membrane of target cells and this is likely to explain why the toxin is over 1000 times more potent compared to other aβ-PFTs.Etx is unique amongst aβ-PFTs as it shows high potency and high cell specificity. The 50% lethal dose (LD23 and the tetraspan membrane proteolipid myelin and lymphocyte protein (MAL)2. The molecular basis for the interaction of these putative receptors with Etx remains unknown, although it is thought that the binding site for Etx on the glycoprotein HAVCR1 includes one of the sugar components, while in MAL the second extracellular loop is critical for binding and cytotoxicity of Etx. Etx can also interact with artificial lipid bilayers and form functional pores without the help of receptors, albeit with less efficiency32.To date, two putative Etx receptors have been identified: the O-linked glycoprotein hepatitis A virus cellular receptor 1 (HAVCR1)33. Finally, the heptamer, with a molecular mass of approximately 155–174 kDa34, inserts into the membrane, which is triggered by significant structural rearrangements within the PFM of each protomer in the complex. Pore formation disrupts the membrane and is central to the damage caused to intoxicated cells, and consequently to the symptoms of disease, due to the leakage of water and small ions through the pores that may lead to cell death.Both inactive P-Etx and activated toxin can bind to cells, but only activated toxin with CTP removed can form heptameric pre-pores on the membrane surface in the next step of pore formation35. A 3.9 Å structure of the aerolysin pre-pore and a 7.5 Å structure of aerolysin pore have also been determined36.Whilst atomic resolution structures of the water-soluble, monomeric forms have been determined for many members of the aβ-PFTs over the past few decades, their corresponding pore structures have only recently been revealed. The lysenin pore was the first high-resolution structure of the aβ-PFTsThe cryo-electron microscopy (cryo-EM) structure of Etx pore determined here provides further insights into the molecular details of structural rearrangements during pore formation of the aβ-PFTs. These results will have important implications for the development of novel therapeutics aimed at the functional disruption of pore formation to prevent human and animal diseases, and are relevant for nano-biotechnology applications.38, Etx does not spontaneously oligomerize into sodium dodecyl sulfate (SDS)-resistant species when activated in aqueous buffer, and negatively stained EM samples do not reveal regular particles other than elongated monomers and small clumps  assay. The average dose of recombinant Etx required to kill 50% of Super Dome cells (CT50) was 2.47 ± 0.47 nM (SEM) or 78.27 ± 14.89 ng ml−1 (SEM)  as a suitable detergent, which has previously been successfully used to solubilize lysenin and Etx pores35  Fig. . Etx monEM) Fig. , consist8 cells) meant that we could not pursue additional clean-up steps, such as size-exclusion chromatography. This heterogeneity hindered particle identification and selection using manual or automated picking procedures  fold, a common feature of the aβ-PFTs41. The RBD (residues 1–62 and 199–234) is unique to each aβ-PFT and determines the binding specificity of the toxin.Each protomer in the pore structure can be divided into three domains Fig. : the β-h20, members of the aβ-PFTs form β-barrels that span the height of their pores. This domain includes the insertion loops that have been hypothesized to form the membrane-spanning regions (Etx residues 123–148)43. As in lysenin, in fact the β-barrel recruits polypeptide from the flanking β-strands on either side of the insertion loop so that the β-hairpin domain actually comprises residues 95–173. The lumen of the pore is mostly lined with serine and threonine residues, yet another characteristic of the aerolysin family, but asparagine and glutamine residues are also present. Additionally, a number of salt bridges are present  and Phe148 ~35 Å from the tip.The external surface of the Etx inner β-barrel reveals a number of interesting features Fig. . In the 21. Interestingly, histidine residues were also found in the detergent micelle region of lysenin above the standard aromatic belt20. Towards the membrane-distal part of the inner β-barrel, His162 (H149 in Etx without propeptide) is another important residue for Etx activity45. H162 (Nδ1) is within hydrogen bonding distance of D250 (Oδ2), which lies in the cap domain of the same chain , which were identified in earlier studies as important in receptor binding46, are located directly above the membrane in a position that would facilitate interaction with a receptor  has previously been reported to have reduced, but not abolished, toxicity45, and the crystal structure of P-Etx-H162A closely resembles that of wild-type P-Etx22, while purified recombinant Etx-D250A is completely inactive towards Super Dome cells, as shown in Supplementary Fig. 34) . Further analysis of oligomer formation by native-PAGE revealed a much larger complex of ∼624 kDa relative to the SDS-resistant oligomers of ∼155 kDa when Super Dome cells were incubated with activated wild-type Etx ) relative to 0.05% (w/v) used in ref. 34 maximized pore yield for subsequent purification, and thus were critical for structural determination of the Etx pore complex. Furthermore, due to the very small quantities of material that can be isolated from the membrane of cultured cells and the heterogeneity of the purified samples, cryo-EM technique was the only structural method that could be employed. Use of the VPP proved essential in our studies to achieve high resolution, owing to the challenging sample rather than its molecular mass (∼225 kDa). First, particles could be identified by the automatic and manual picking procedures more efficiently, resulting in more particles  to alanine resulted in a significant decrease in activity of the recombinant toxin both in vitro and in vivoExamination of the Etx pore structure reveals that H162 may also play a role in stabilizing the DBB fold by forming a hydrogen bond with D250 in the cap domain. This interaction will stabilize the DBB fold further in addition to the hydrophobic interactions and make it more rigid. In agreement with this hypothesis, substitution of aspartate with alanine at position 250 results in a completely inactive Etx that displays ∼95% reduction in SDS-resistant oligomers relative to wild-type Etx. The reduced oligomerization of Etx-D250A and its complete inactivity towards Super Dome cells could be a result of either oligomer instability or a reduction in oligomer formation. Since D250 does not participate in any inter-chain interactions in the oligomer, we cannot explain the latter. The abolished cytotoxicity of Etx-D250A suggests that the SDS-resistant oligomers resembling the wild-type ones may correspond to pre-pores or post pre-pores. This may explain why Etx-D250A oligomers resembling the wild-type ones were not observed by native-PAGE immunoblot. Pre-pores or post-pre-pores are expected to migrate differently to pores on native-PAGE as they will lack any associated detergents or lipids.48. Y84A completely abolishes pore formation and is only recoverable by substitution to another aromatic residue, whilst F105A has reduced cytotoxic activity towards MDCK cells at low toxin concentrations48. In the pore structure, these two residues form a π-π stacking interaction  to the insertion of the inner β-barrel into the membrane. By stabilizing the top end of the inner β-barrel as it is being formed by the unfolding of the β-hairpin domain, the insertion into the membrane will be analogous to a drill, which is forced into the ground by applying force at the top. Similarly, the RBDs with their hydrophobic tips may keep the complex anchored to the membrane during membrane insertion, analogous to the stabilizing feet of the drill, by either an interaction with the receptor or with the lipid bilayer itself. The pre-pore structure of aerolysin by cryo-EM22. In the pore form of Etx, the β-OG binding site is disrupted and the interacting residues  are scattered amongst the inner β-barrel and the cap domain. Similar to aerolysin49, in order for Etx to cross the thick layer of glycocalyx covering the apical side of the plasma membrane of gut epithelial cells, Etx may initially bind to surface oligosaccharides with low affinity, possibly through the β-OG binding site at the carboxy terminus, before it binds with high affinity to its receptor on the membrane surface through the RBD at the amino terminus. This hypothesis is supported by the finding that polarized epithelial cells are more sensitive to Etx from their apical side, which is more abundant in oligosaccharides, than from their basolateral side50. Binding of Etx to a double receptor in the nervous system has also been suggested previously9.Monomeric P-Etx has previously been crystallized bound to β-OG in the PFM of the carboxy terminusInsights into the molecular details of structural rearrangements and monomer–monomer interactions established upon pore formation can be applied to structure-based drug design where high-affinity compounds are identified to block pore formation, thus fighting bacterial infections. Further studies on Etx will have important implications for developing novel therapeutics aimed to disrupt pore formation, thus preventing serious human and animal infections, and are relevant for nano-biotechnology applications.51. After electrophoretic separation purified Etx pore was detected by SimplyBlue staining . Protein concentrations were determined using the Pierce™ BCA Protein Assay Kit . Synthetic oligonucleotides were sourced from Eurofins Genomics, Germany.Chemicals were purchased from Sigma, UK, unless otherwise stated. The polyclonal antibody against Etx mutant Y30A-Y196A was raised in rabbits as described in ref. etxB, encoding epsilon protoxin (P-Etx) was PCR amplified using synthetic oligonucleotides Etx-NcoI-F and Etx-XhoI-R . This fused the amino-terminal end of P-Etx without the 14 amino-terminal residues (KEISNTVSNEMSKK) to the 26 amino acid residue-long peptide (MSYYHHHHHHDYDIPTTENLYFQGAM), which contains an amino-terminal polyhistidine (6× His) affinity tag upstream of a spacer region (DYDIPTT) and the rTEV protease cleavage site (ENLYFQG). The fidelity of PCR amplification was verified by automated DNA sequencing . The recombinant plasmid expressing P-Etx is termed pHis-Parallel1-P-Etx. Amino acid numbering corresponds to P-Etx with the 13 amino acids amino-terminal peptide sequence, unless otherwise stated.The gene, E. coli Rosetta 2 (DE3) cells  and expression of P-Etx was induced using the autoinduction system as described before22 but with modifications. In brief, cells (100 ml) were grown at ACDP/ACGM (Advisory Committee on Dangerous Pathogens/Advisory Committee on Genetic Modification) containment level 3 in ZYM-5052-autoinducing medium supplemented with 50 μg ml−1 carbenicillin and 34 μg ml−1 chloramphenicol and cultured at 37 °C for 3 h at 300 rpm, then for a further 24 h at 20 °C at 300 rpm. Cells were harvested by centrifugation at 10,000 × g for 10 min at 4 °C and the cell pellet was lysed by 10 ml BugBuster™ Protein Extraction Reagent  containing rlysozyme™ (0.5 µl at 30 KU µl−1)  and Benzonase® Nuclease (10 µl at 25 U µl−1) . The cell suspension was incubated on a rotating mixer for 25 min at room temperature and centrifuged at 16,000 × g for 20 min at 4 °C to separate soluble and insoluble fractions. The supernatant was loaded onto His GraviTrap columns  following the manufacturer’s guidelines. In brief, His-tagged P-Etx was bound to the affinity column using a buffer composed of 20 mM sodium phosphate, 500 mM NaCl and 20 mM imidazole, pH 7.4. The column was washed with a buffer composed of 20 mM sodium phosphate, 500 mM NaCl and 60 mM imidazole, pH 7.4. Recombinant P-Etx was eluted in a buffer composed of 20 mM sodium phosphate, 500 mM NaCl and 500 mM imidazole, pH 7.4. All purification steps were carried out at 4 °C. For buffer exchange and sample clean-up, P-Etx-containing eluate was applied to a PD-10 Desalting Column  and eluted in Dulbecco’s phosphate-buffered saline (DPBS), pH 7.0–7.2 (Invitrogen). Protein concentrations were determined by BCA assay.For expression of P-Etx, recombinant plasmid pHis-Parallel1-P-Etx was transformed into l-(tosylamido-2-phenyl) ethyl chloromethyl ketone) treated from bovine pancreas, which removes the CTP sequence. Trypsin was prepared in DPBS and added to recombinant P-Etx at 1:100 (w/w) ratio and incubated at room temperature for 1 h. The reaction was stopped by adding trypsin inhibitor (0.66 mg per 1 mg trypsin) to the digest. Removal of the CTP sequence was assessed by SDS-PAGE.Purified recombinant P-Etx was activated with trypsin, TPCK  with 240× SYPRO Orange protein gel stain. Fluorescence was monitored using a QuantStudio™ 6 Real-Time PCR System  with a 1% thermal gradient from 25 °C to 99 °C. The fluorescence data obtained was analysed using the Protein Thermal Shift Software  to calculate the melting temperature (Tm) using the Boltzmann method.Thermostability of purified recombinant P-Etx-D250A was assessed by mixing purified protein , USA) cells, a canine epithelial-like cell line established by cloning from the MDCK (ATCC® CCL-34™) cell line, were routinely cultured in Dulbecco’s modified Eagle’s medium/Ham’s F12 medium  with 2.5 mM l-glutamine, 15 mM HEPES, 0.5 mM sodium pyruvate and 1.2 g l−1 sodium bicarbonate supplemented with 0.05 mM nonessential amino acids and 10% foetal bovine serum Gold  at 37 °C in a humidified atmosphere of 95% air/5% CO2. The culture medium was replaced every 2 to 3 days. Cells were routinely detached by incubation in trypsin/EDTA and split as appropriate . For dome formation, Super Dome cells were fed twice a day by a complete medium change.Super Dome (ATCC® CRL-22864 cells per well). Following incubation at 37 °C for 3 h, the cell culture medium (50 µl) was harvested from cell monolayers, transferred to a fresh 96-well enzymatic assay plate and 50 µl of reconstituted substrate mix was added to each well. The plate was incubated for 30 min at room temperature, protected from light. Absorbance was measured at 490 nm using a Tecan Infinite 200 PRO Microplate Reader . The absorbance values for each sample were normalized by subtracting the absorbance value obtained for the culture medium from untreated cells. The toxin dose required to kill 50% of the cell monolayer (CT50) was determined by nonlinear regression analysis, fitting a variable slope log(dose) vs. response curve, constraining F to a value of 50 (log CT50 = log CTF − (1/HillSlope) × log(F/(100 − F)).The cytotoxic activity of trypsin-activated Etx toward Super Dome cells was determined by measuring the amount of LDH released from the cytosol of lysed cells into the cell culture medium using the CytoTox 96 Nonradioactive Cytotoxicity Assay Kit  according to the manufacturer’s protocol. In brief, a 2-fold dilution series of each activated toxin (ranging from 139 to 0.067 nM) was prepared in DPBS and added to cells seeded into 96-well plates  at 37 °C for 1 h to allow binding and pore formation. Lysed cells were centrifuged at 1500 × g for 15 min at 4 °C to pellet cellular debris and unbound Etx was removed by ultracentrifugation at 100,000 × g for 1 h at 4 °C using a Beckman Type 70Ti rotor. Solubilization of toxin complex from Super Dome cells was carried out by resuspending the pellet in DPBS (pH 7.0–7.2) containing 137 mM NaCl and 2% (w/v) n-dodecyl-β-d-maltoside (DDM) and incubation at 4 °C overnight, a modification of the method described by Shimada et al.34 where the toxin complex from MDCK cells was solubilized by resuspending the pellet in 20 mM sodium phosphate buffer (pH 7.4) containing 0.05% (w/v) DDM and incubation at 25 °C for 1 h. After incubation, insoluble material was removed by ultracentrifugation at 75,000 × g for 45 min at 10 °C using a Beckman Type 70Ti rotor. Solubilized Etx oligomers were bound to Ni-NTA Agarose resin (Qiagen), washed with 2× 20 volumes of DPBS, 0.02% (w/v) DDM and eluted in three volumes of DPBS, 200 mM imidazole and 0.02% (w/v) DDM.Super Dome cells were grown to confluency in 30× T175 flasks. Each flask of cells was incubated with 5 ml cell culture medium containing trypsin-activated Etx  DDM, 1% (w/v) DM , 0.5% (w/v) C12E8, 1% (w/v) FC-12, 2% (w/v) β-OG, 2% (w/v) C8E4, 1% (w/v) CYMAL-5, 1% (w/v) LDAO (lauryldimethylamine-N-oxide) or 1% (w/v) LMNG ) and incubated overnight at 4 °C with occasional shaking. After incubation, 10 µl aliquots were removed from each tube for Western blot analysis  and insoluble material was removed by ultracentrifugation, as before. The supernatants, which contained solubilized Etx pore, were collected and 10 µl aliquots were removed from each tube to assess Etx pore solubilization by Western blot analysis using 6×-His Tag Monoclonal Antibody (4A12E4)  and IRDye® 800CW Goat  Anti-Mouse IgG (H + L), Highly Cross Adsorbed secondary antibody  at 1:500 and 1:5000 dilutions, respectively. The solubilization yields for each detergent were determined by calculating the % of Etx pore in the supernatant relative to its amount in the total protein sample using Image Studio ver 5.2 software .To screen for a suitable detergent that could efficiently extract and maintain Etx pores soluble, Super Dome cell membrane was incubated with trypsin-activated Etx for 1 h at 37 °C and membrane incubations were distributed between nine tubes. After ultracentrifugation at 100,000 × For DDM-extracted samples, solubilized Etx oligomers were bound to Ni-NTA resin (Qiagen), washed with 20 volumes of DPBS containing 20 mM imidazole and 0.02% (w/v) DDM, transferred to a Bio-Rad gravity-flow column, washed again with 20 volumes of DPBS containing 20 mM imidazole and 0.02% (w/v) DDM, and eluted in three volumes of DPBS containing 200 mM imidazole and 0.02% (w/v) DDM.6 cells per well), and after washing cells three times with DPBS to remove residual trypsin, each well was incubated with 2 ml pre-warmed DMEM-F12 medium containing trypsin-activated recombinant Etx wild-type, Etx-D250A or Etx-H162A  at 37 °C for 1 h to allow binding and/or oligomerization. Cells were subsequently washed three times with DPBS to remove unbound Etx, lysed by three cycles of freezing and thawing in 100 µl DPBS supplemented with Halt™ Protease Inhibitor Cocktail (100×)  at a final concentration of 1× and harvested by scraping. The collected cells were transferred to a microcentrifuge tube and solubilized in DPBS containing 1% (w/v) DDM by incubation at 4 °C overnight with occasional shaking. After incubation, insoluble material was removed by centrifugation at 20,000 × g for 30 min at 4 °C and the concentration of solubilized protein was determined by BCA assay.Super Dome cells were grown to confluence in 6-well plates  using Surelock Xcell apparatus . For protein gel electrophoresis under denaturing running conditions NuPAGE™ Tris-Acetate SDS Running Buffer  and NuPAGE LDS sample buffer  were used. For protein gel electrophoresis under native running conditions Novex™ Tris-Glycine Native Running Buffer  and Novex™ Tris-Glycine Native Sample Buffer  were used. Denaturing gels were electrophoresed at 150 V for 1 h at room temperature, while native gels were electrophoresed at 150 V for 2 h in ice cold running buffer. For SDS-PAGE the molecular weight standard Perfect Protein™ Marker, 10–225 kDa  was used. For native-PAGE, the molecular weight marker NativeMark™ Unstained Protein Standard  was used.51 at 1:500 dilution and IRDye® 800CW goat anti-rabbit IgG (H + L) secondary antibody  at 1:5000 dilution. For loading control, the mouse monoclonal Na+/K+-ATPase α1 (C464.6): sc-21712 primary antibody  and the IRDye® 680RD goat  anti-mouse IgG (H + L) secondary antibody  were used at 1:200 and 1:5000 dilutions, respectively. Antibodies were diluted in PBS containing 0.1% Tween-20 and 3% skim milk. Immuno-reactive bands were detected using the Odyssey CLx infrared imaging system . Quantitative analyses were performed using the Image Studio ver 5.2 software and the Housekeeping Protein Normalization Protocol . The oligomer yield for each Etx variant was determined by calculating the percentage of fluorescence intensity relative to wild-type toxin.After electrophoresis, proteins in the gel were transferred to a nitrocellulose membrane using the Trans-Blot® Turbo™ Transfer System (Bio-Rad). The membrane was blocked with PBS containing 0.1% Tween-20 and 5% skim milk and bound P-Etx and/or oligomeric complexes were detected with rabbit anti-Etx polyclonal primary antibody−1 and 3 μl was added to freshly glow-discharged carbon-coated grids for 60 s before staining with 20 μl of 2% (w/v) aqueous uranyl acetate. Grids were then imaged on a Philips CM200 FEG transmission electron microscope operating at an accelerating voltage of 200 kV. Images were recorded at a calibrated pixel size of 2 Å on a TVIPS F224HD CCD camera.Purified recombinant P-Etx variants were trypsin activated as described above. After activation, samples were diluted to 0.02 mg ml−1) was diluted 3-fold in elution buffer (DPBS containing 20 mM imidazole and 0.02% (w/v) DDM) and deposited on Quantifoil R1.2/1.3 grids overlaid with graphene oxide, as described in ref. 20. Grids were then blotted and plunge frozen using a custom-made device and stored in LN2 until further use. Data were collected on an FEI Titan Krios operating at an accelerating voltage of 300 kV. All datasets were collected on an FEI Falcon 3EC direct electron detector at a nominal magnification of ×75K  and a dose rate of 0.6e− per pixel per second in counting mode over 60 s and 75 fractions. Conventionally defocused and phase plate datasets were collected using the EPU software (Thermo Fisher Scientific). In brief, grid squares and suitable holes were selected and microscope alignments were performed. Just prior to data collection, the phase plate was inserted and the un-scattered beam was made parallel by observing the Ronchigram in the back focal plane of the objective lens over an empty specimen area. Condenser astigmatism was corrected by reducing the smearing of the Ronchigram near the blow-up point. Due to the extremely low screen current used (~0.014 nA), the 150 μm C2 aperture was used to aid in this process and then switched back to the 50 μm aperture. Coma and astigmatism were corrected using AutoCTF. Defocused data were collected by defocusing between −2 and −3.5 μm every 15 μm, and phase plate data between −0.3 and −0.8 μm with focusing at every hole.Etx eluate fraction 4  and then extending this using Coot. Refinement of the atomic coordinates was performed using PhenixData were analysed using the Prism v7 software . Data are expressed as the mean value ± SEM.Further information on research design is available in the Supplementary InformationSupplementary Movie 1Reporting SummaryDescription of Additional Supplementary FilesPeer Review FileSource Data File


AbstractMyxocyprinusasiaticus) has been conducted for years in China. To prevent loss of genetic variation in wild populations, it is important to assess and monitor genetic diversity of broodstocks before release of offspring. Three broodstocks , Yibin Base of Sichuan Fisheries Research Institute, China (YBS) and Yibin Rare Aquatic Animal Research Institute, China (YRA)) were investigated using mitochondrial control region and 12 microsatellites. The relatively low genetic diversities of these broodstocks were detected  = 0.877, observed heterozygosity (Ho) = 0.416; YBS, h = 0.812, Ho = 0.392; YRA, h = 0.818, Ho = 0.365). PBS showed higher Ho than YBS and YRA (P < 0.05). Genetic divergence (FST) based on microsatellites between PBS and YRA was significant , the same situation happened between YBS and YRA . However, divergence between PBS and YBS was not significant . Structure analysis revealed that YRA were distinct from PBS and YBS. Based on these results, it is important to propose some suggestions of genetic management for artificial propagation of Chinese sucker, such as broodstock exchange among hatcheries and broodstock supplement from wild.The releasing program of Chinese sucker ( Myxocyprinusasiaticus , an endangered freshwater fish in China and the only representative of the family Catostomidae in Asia  to assess the genetic diversity and relationship of the broodstocks; (ii) to propose suggestions of genetic management for artificial propagation, and (iii) to provide necessary information for genetic conservation for implementation hatchery release program in the future.ze River . HoweverM.asiaticus were used in this study, including 53  sampled from the Yibin Base of Sichuan Fisheries Research Institute, China (YBS), 60  from the Pixian Base of Sichuan Fisheries Research Institute, China (PBS) and 21  from the Yibin Rare Aquatic Animal Research Institute, China (YRA) . Polymerase chain reaction (PCR) was used to amplify the mitochondrial control region with the primers DL1  and DH2   and then directly sequenced on an ABI 3730 Genetic Analyzer .Total genomic DNA was extracted from the fin clips of a: 61oC) . The PCRPCR reaction volume was 25 μL contained 1 μL genomic DNA (50 ng/μL), 3 μL 10×PCR buffer (plus Mg2+), 1 μL dNTPs (10 mmol/L each), 0.5 μL for each primer (10 μmol/L), 0.5 U Taq DNA polymerase , and 18.7 μL ddH2O. PCR was programmed as follows: 94 °C for 5 min, followed by 35 cycles at 94 °C for 30 s, 54–63 °C for 40 s, 72 °C for 30 s, and a final extension at 72 °C for 10 min. Electrophoresis, fluorescent microsatellite detection and determination of genotypes were as described in Twelve microsatellite loci  were useML) by PAUP version 3.1.1 (BI) by MRBAYES version 3.1.2 (Catostomuscommersonii (GenBank No. AB127394) and Cycleptuselongates (GenBank No. EF062437) were obtained from GenBank and used as outgroups. The relationships among the haplotypes were evaluated by NETWORK version 4.2.0.6 , allelic richness within individuals (Ai), expected and observed heterozygosities (Ho and He) were calculated using the software AUTOTET which is especially developed for autotetraploid species . A test for bottleneck assessment was conducted using the BOTTLENECK version 1.9 (MCMC) steps. Finally, the Delta K method in STRUCTURE HARVESTER  was used to infer the optimal K value.To estimate genetic diversity of the three broodstocks and genetic differentiation among them, numbers of alleles  in length. The number of variable sites was 82 . Most polymorphic sites were transitional mutations, and only a few were transversions or inserts/deletions. The average nucleotide difference was 24.1%. The average base composition was A = 28.5 %, T = 31.4 %, C = 22.4 % and G = 17.7 %. The haplotype diversity and nucleotide diversity was 0.864 and 0.028 across all samples, respectively (Table PBS showed higher haplotype diversity (h = 0.877) than that of YBS and YRA  (Table YRA possessed higher nucleotide diversity (0.0278) than that of PBS and YBS  was monomorphic in YRA. The number of amplified alleles per locus ranged from 1 (MA61) to 16 (MA27) with an average of 8.1, and allele richness per locus varied from 1.00 at locus MA61 in PBS to 2.00 at locus MA64 in YBS (Table Ho) ranged from 0.365 in YRA to 0.416 in PBS. Under both chromosome segregation and chromatid segregation, average values of Ho were lower than those of expected heterozygosities (He) in all broodstocks  and almost half rare alleles (11)  was monomorphic in all broodstocks, two  were monomorphic in He ranged from 0.443 to 0.523 . M.asiaticus  in the broodstocks were found in present study .The broodstocks from middle Yangtze River were investigated based on same microsatellites, and Average to 0.523 , which wsiaticus , lower gP > 0.05) and significant variance among individuals within hatcheries . Significant divergence was observed between PBS and YRA , and the same situation occurred between YBS and YRA . But no significant divergence was observed between PBS and YBS . The analysis based on mtDNA control region showed congruent results derived from microsatellites. The genetic distances between PBS and YRA, YBS and YRA were larger than that between PBS and YBS. Furthermore, PBS and YBS broodstocks were clustered together in neighbor-joining tree based on FST values  were shared among all three samples while 8 haplotypes were shared between PBS and YBS, and most haplotypes in YRA were clustered together. Hap1 was most widespread, and included 28 individuals  when K = 3 Figure , which iS Figure .On the basis of individual genetic distance, individual neighbor-joining tree was divided into two main branches, with one of them further divided into two branches. Individual distributions were widespread in neighbor-joining tree and did not cluster together based on broodstocks Figure .IS was calculated using 12 microsatellite loci. Insignificant FIS was found in all broodstocks (P > 0.05). Under the infinite allele model (IAM) heterozygosity excesses were detected in YBS and YRA. However, under stepwise mutation model (SMM) and the two-phase model (TPM), all broodstocks showed insignificant heterozygosity excess, and did not suffer from bottleneck or founder effects in the past  (Table The Ft) Table .M.asiaticus have been used as parental fishes and usually, one male was used for propagation with five or more females. These contributed to the lower genetic diversities in the broodstocks than that in wild populations of M.asiaticus  between them. However, the genetic differentiations between PBS and YRA, and between YBS and YRA were significant. The genetic distances between YRA and PBS or YBS were larger than that between PBS and YBS. Great genetic differences between YRA and PBS or YBS might be attributed to that they originated from different cohorts in upper Yangtze River. Also, it was probably caused by a small number of wild founders which possessed limited gene pool and easily resulted in genetic drift (PBS came from the first generation of offspring of YBS, which therefore weakened the genetic differentiation between them. There are more haplotypes shared between PBS and YBS than between one of them and YRA. Structure analysis indicated that some individuals from PBS and YBS belonged to one cluster. Therefore, PBS and YBS of M.asiaticus have a very close genetic relationship, and YRA was a separated broodstock.A previous study revealed that there was insignificant genetic differentiation in most broodstocks of siaticus . The preic drift . AccordiM.asiaticus. First, although hatchery-release program has not much affected the genetic diversity (YRA might have important values in propagation or adaption, so it is necessary to do genetic exchange between YRA and PBS or YBS. Because the broodstocks of three hatcheries originated from neighboring ranges of the Yangtze River, genetic exchange does not mean mixing different genetic populations and would not affect the genetic characteristics of the wild populations through hatchery-release program. Hatcheries also need to record all details of each individual including body weight and length, gender, ID and genetic information. This precise information of existing broodstocks will be helpful for effective management (M.asiaticus in China.According to the analysis of genetic diversity and relationship of the three broodstocks, we can propose some implications for artificial propagation and releasing program of iversity  and inbriversity . Unfortunagement . Third,


Aceria tosichella, a major pest of the world’s grain industry, to identify the factors behind its extensive diversification.Understanding the mechanisms that underlie the diversification of herbivores through interactions with their hosts is important for their diversity assessment and identification of expansion events, particularly in a human-altered world where evolutionary processes can be exacerbated. We studied patterns of host usage and genetic structure in the wheat curl mite complex (WCM), We expanded on previous phylogenetic research, demonstrating deep lineage diversification within the taxon, a complex of distinctive host specialist and generalist lineages more diverse than previously assumed. Time-calibrated phylogenetic reconstruction inferred from mitochondrial DNA sequence data suggests that lineage diversification pre-dates the influence of agricultural practices, and lineages started to radiate in the mid Miocene when major radiations of C4 grasses is known to have occurred. Furthermore, we demonstrated that host specificity is not phylogenetically constrained, while host generalization appears to be a more derived trait coinciding with the expansion of the world’s grasslands. Demographic history of specialist lineages have been more stable when compared to generalists, and their expansion pre-dated all generalist lineages. The lack of host-associated genetic structure of generalists indicates gene flow between mite populations from different hosts.Our analyses demonstrated that WCM is an unexpectedly diverse complex of genetic lineages and its differentiation is likely associated with the time of diversification and expansion of its hosts. Signatures of demographic histories and expansion of generalists are consistent with the observed proliferation of the globally most common lineages. The apparent lack of constrains on host use, coupled with a high colonization potential, hinders mite management, which may be further compromised by host range expansion. This study provides a significant contribution to the growing literature on host-association and diversification in herbivorous invertebrates.The online version of this article (10.1186/s12862-018-1234-x) contains supplementary material, which is available to authorized users. Niche breadth varies significantly among species, with life history traits, physical tolerances, and evolutionary adaptations directly influencing the range of habitats which species can successfully colonize and thrive within , 2. ConsRhagoletis pomonella being the most well-known example )  tulipae , and thiWe identified some discrepancies in lineage delineation between the mitochondrial and nuclear DNA datasets, a phenomenon frequently reported in many animal taxa –86. The Our research has characterized WCM as a complex of at least seven specialist, six generalist, and two semi-specialist taxa, with specificity levels yet to be assigned for several other distinct genetic lineages. We expect that semi-specialist lineages MT-12 and MT-13 are in fact host specialists, given they occur predominantly on single grass host species, and only sporadically on other hosts. This is in concordance with previous research on host-related variation in population density of WCM lineages, which has indicated that MT-12 and MT-13 exhibit preferences toward one host species , although the lineages have been found on the other hosts sporadically .Our phylogenetic reconstructions also indicate that neither host generalization nor specialization is a phylogenetically constrained trait, as the tree topology provides clear evidence of their convergent evolution in unrelated taxa. Shifting patterns of host generalization to specialization between closely-related lineages suggest that host range expansion and contraction is likely to be dynamic through time. The phenomenon has been well documented and can be attributed to environmental changes, including anthropogenic influence , 12. AltVanessa cardui [Related phytophagous insect species tend to feed on plants from similar taxonomic groupings –92. Howea cardui , leaf bea cardui  and monoa cardui . These fWCM is inherently associated with grass hosts which are intensively farmed across multiple continents. Therefore, the global distribution of WCM has been previously attributed to the extension of agriculture around the globe , and herOur analyses suggest that some generalist WCM lineages have undergone major historical demographic expansion, but again the timing of these events precedes the potential influence of agriculture. Generalist lineages associated with wild grasses appear to have underwent much earlier demographic expansions during the Pleistocene 160,000–290,000 years ago. In contrast, the most widespread generalists MT-1 and MT-8, which nowadays predominate on cereal hosts, appear to have undergone expansion events more recently, approximately 27,000 and 86,000 years ago, respectively. It is likely that during the time of recent expansion MT-1 and MT-8 lineages infested wild grasses, since many cereal hosts are no older than 11,000 years old . It is pIn contrast, the demographic history of specialists MT-7  and MT-9 (associated with smooth brome) appears to have been more stable with evidence of expansion events predating all generalist lineages . Expansions seem to be more likely in generalists rather than in specialists, probably given their enhanced dispersal capacities and abilities to capitalize on host availability on greater geographic scales , 101.High host-specificity is a prevalent feeding strategy in eriophyoid mites , therefoBemisia tabaci [Our research focuses on patterns of genetic diversity and host association in obligatory phytophagous wheat curl mite (WCM), which is a major agricultural pest in cropping regions of the world. We revealed that lineage diversity within the taxon is significantly greater than was assumed, and consists of at least 29 genetically divergent lineages with distinctive host-use patterns, ranges from host generalists to specialists. The timing of WCM diversification events pre-dates agriculture, instead appears to be associated with timing of global diversification events within Poaceae during the mid-Miocene and the Pliocene. These novel findings provide insight into co-evolutionary processes among herbivores and their hosts, and revoke hypothesis that WCM lineage diversity is primarily linked to human activity, similarly like it has been shown for whitefly a tabaci .Furthermore, our results shed new light on the mechanisms associated with specialization and generalization, each appearing to have evolved convergently in unrelated WCM lineages on numerous occasions. Such a pattern supports the notion that host use strategy can be highly dynamic in herbivores , 105, alAdditional file 1:Sequence and sampling information. (DOCX 99 kb)Additional file 2:Phylogenetic trees for wheat curl mite sequence datasets. (DOCX 776 kb)Additional file 3:Statistics for genetic diversity of WCM genetic lineages. (DOCX 738 kb)Additional file 4:Mismatch distributions of WCM genetic lineages. (DOCX 747 kb)Additional file 5:Haplotype networks for the mtDNA Cox1 wheat curl mite (WCM). (DOCX 950 kb)


Ambystoma maculatum) and wood frogs (Lithobates sylvaticus), across a large  landscape in Maine, USA, containing numerous natural and anthropogenic gradients. Isolation‐by‐distance (IBD) patterns differed between the species. Spotted salamanders showed a linear and relatively high variance relationship between genetic and geographic distances , whereas wood frogs exhibited a strongly nonlinear and lower variance relationship . Scale dependence analysis of IBD found gene flow has its most predictable influence (strongest IBD correlations) at distances up to 9 km for spotted salamanders and up to 6 km for wood frogs. Estimated effective migration surfaces revealed contrasting patterns of high and low genetic diversity and gene flow between the two species. Population isolation, quantified as the mean IBD residuals for each population, was associated with local urbanization and less genetic diversity in both species. The influence of geographic proximity and urbanization on population connectivity was further supported by distance‐based redundancy analysis and multiple matrix regression with randomization. Resistance surface modeling found interpopulation connectivity to be influenced by developed land cover, light roads, interstates, and topography for both species, plus secondary roads and rivers for wood frogs. Our results highlight the influence of anthropogenic landscape features within the context of natural features and broad spatial genetic patterns, in turn supporting the premise that while urbanization significantly restricts interpopulation connectivity for wood frogs and spotted salamanders, specific landscape elements have unique effects on these two sympatric species.Metapopulation‐structured species can be negatively affected when landscape fragmentation impairs connectivity. We investigated the effects of urbanization on genetic diversity and gene flow for two sympatric amphibian species, spotted salamanders  patterns is empirically investigated, revealing how IBD inferences vary depending on the spatial extent considered. For instance, landscape fragmentation and habitat loss can lead to reduced connectivity among wildlife populations, consequently disrupting demographic support from metapopulation dynamics that would otherwise improve population stability via immigration Andrén, . ReductiMetapopulation‐structured species are especially vulnerable to negative effects of habitat fragmentation associated with urbanization  and wood frogs (Lithobates sylvaticus), in an area of overlap between their native ranges. Several characteristics make these species excellent subjects for evaluating the effects of urbanization with a landscape genetics approach. For instance, the species have small annual home ranges  relationships for these two species has supported this hypothesis through observations of clear positive correlations between geographic and genetic distances for wood frogs . Landguth et al.  and anthropogenic  landscape gradients. Sampling effort was concentrated in areas of urbanization to ensure adequate power to detect effects of relatively recent urban‐associated landscape features  was estimated using PopGenReport, and allelic richness , expected heterozygosity (HE), and Wright's inbreeding coefficient (FIS) were estimated using the R package hierfstat  summarizes the amount of diversity contained among populations relative to the diversity of all populations combined ) as suggested by Slatkin . Therefore, the regression slopes we report should be considered a broad approximation of observed increases in genetic differentiation with geographic distance (Dσ2) and useful only for comparisons between species within this specific study. Associations between the distance matrices were tested using regression and Mantel tests  of variable intercept IBD regressions that were performed repeatedly using expanding datasets based on distance between sampling sites, which generated IBD scaling profiles for each species. For example, the first iteration of the analysis was conducted using the 20 shortest pairwise geographic distances, the next iteration with the 21 shortest, and so on, until all pairwise comparisons were included. We performed 1,000 bootstrapped replicates of each regression using the “Boot” function in the R package car ; impervious surface extent was based on the Maine Department of Inland Fisheries and Wildlife's July 2016 impervious surface dataset ; and percent canopy cover data were drawn from the 2011 National Land Cover Database , we quantified isolation using only the GST‐based IBD relationships. Genetic diversity relationships were assessed using HE and AR. We conducted three statistical analyses to test hypotheses concerning the relationship between these three factors: multiple regression between each measure of genetic diversity and all retained urbanization variables, multiple regression between urbanization variables and degree of site isolation, and simple linear regression between each measure of genetic diversity and degree of isolation.We quantified the degree of isolation experienced at each sampling site by averaging the residuals of the IBD data points that include that site. This approach is similar to the decomposed pairwise regression analysis to detect outlier populations described by Koizumi, Yamamoto, and Maekawa  and esseGST values in the response matrix, the above‐described urbanization‐related metrics in an explanatory matrix, and a conditional matrix containing the latitude and longitude of each site in decimal degrees. Model terms were eliminated using a backward optimization procedure where nonsignificant terms were removed, and a simplified model was tested until all remaining terms were significant. MMRR provides a multivariate method for examining the relationships between a response matrix  and multiple explanatory matrices  while accounting for interpopulation geographic distances  and multiple matrix regression with randomization (MMRR). We conducted dbRDA using the “capscale” function and examined the significance of individual model terms using 10,000 permutations with the “anova.cca” function in vegan. Our global dbRDA model included pairwise es Wang, . With daCasgrain .2.6https://nhd.usgs.gov/, access Feb. 18, 2016) into two classes based on the Strahler numbering system. Medium rivers included order 4 and 5 streams; large rivers included order 6 and 7 streams; and lower order waterways were not considered. Railway data were based on the Maine Department of Transportation's RailRouteSys dataset  while controlling for effects of geographic distance between sites. The candidate resistance cost values that explained the most variation  were selected as optimal. Four to seven resistance values were tested for each landscape feature. These values were selected based upon the results of Richardson . Resampled sites were never found to be unoccupied in any particular year. In total, we collected and genotyped 2,862 spotted salamander eggs and 2,935 wood frog eggs and larvae. Following removals of individuals based on genotype completeness, sample size, and sibship, 2,413 spotted salamanders from 90 sites and 2,439 wood frogs from 87 sites were included in our analyses  for spotted salamanders and 0.03 (±0.0041 SE) for wood frogs  and were therefore excluded from further analyses. Null allele frequencies for the remaining eight spotted salamander loci ranged from 0.005 to 0.027. Tests of nonrandom assortment of genotypes indicated 16 of 2,578 tests  and HE averaged 0.72 (±0.024 SE), whereas wood frog AR averaged 5.15 (±0.233 SE) and HE averaged 0.83  were significant. For wood frogs, global GST was 0.032 and G″ST was 0.189. Pairwise GST values ranged from −0.002 to 0.068, and G″ST ranged from −0.029 to 0.583. Following FDR correction of alpha levels, 3,639 of 3,741 tests (97.3%) were significant. All pairwise G′ST and G″ST values are provided in Appendix Average genetic differentiation among sites was relatively low for both species but varied widely. For spotted salamanders, global GST: r = .196, p < .001; and G″ST: r = .203, p < .001), as well as following log transformation . IBD patterns without the geographic distance log transformation were stronger for wood frogs for GST  and G″ST  and were marginally weakened following transformation for both GST  and G″ST . Because the IBD relationship for the wood frogs appeared nonlinear, we also fit a quadratic rather than linear model to the data. Due to similar patterns between the genetic distance measures, only plots based on GST are shown , and the slope estimate for spotted salamanders (β = 0.0018) was less than that for wood frogs (0.0064).Isolation‐by‐distance patterns differed between the two species. Despite relatively weak correlation using each genetic distance, IBD relationships were statistically significant for spotted salamanders based on nontransformed geographic distances . Sampled sited represented a broad range of the measured environmental attributes  than spotted salamanders  experienced by a site was significantly greater for locations with more nearby light roads for both species, with this effect being stronger for wood frogs , but statistical support for the spotted salamander model was marginal . Variance partitioning based on the RDAs' adjusted R2 values revealed a much better overall model fit for wood frogs than spotted salamanders, mostly attributable to a stronger IBD signal for the wood frogs. For spotted salamanders, the light roads explained 2.4%, geography explained 5.4%, and the terms collectively explained 12.7% of the variation. For wood frogs, the amount of variation explained was 3.6% for light roads, 47.6% for geography, and 53.7% for the combined terms. MMRR further supported the existence of a significant relationship between interpopulation genetic divergence and both geographic distance and density of nearby light roads for both species , interstates, light roads, and terrain ruggedness  had varying responses to waterways and forest cover in different areas of its range  of analyses. Previous work has demonstrated a relationship between the grain size and estimated resistance values of environmental variables ; however, very high sensitivity was uncommon and even when observed, it remains difficult to determine which grain sizes are most ecologically meaningful. Our grain size was 90 m, which is reasonable given the home range of these species. Moreover, the grain size was determined by the resolution of our most coarse raster dataset and represented a trade‐off with the large study extent to maintain computational tractability. The effects of study extent on resistance estimates are less well resolved  and found that less connectivity among populations resulted in significantly lower heterozygosities and allelic richness. Similarly, Cosentino, Phillips, Schooley, Lowe, and Douglas (A. tigrinum) that were smaller and more isolated had less genetic diversity than populations that were more connected to one another. This lack of a direct effect between genetic diversity and urbanization is not theoretically surprising considering some gene flow likely still occurs, and a lag is likely present between the time for urbanization to generate isolation and the subsequent effects of that isolation on loss of diversity through drift. Along those lines, it is reasonable to hypothesize that much of the reduced genetic variation with isolation in our dataset comes from natural isolation patterns on the landscape. However, genetic diversity losses associated with urbanization have been observed in other species  experienced reduced allelic richness as nearby road density increased, although the effect was absent for southern leopard frogs  in the same landscape  would benefit from considering the extent over which the data were collected to generate equitable comparisons. Quantifying such variation can help provide an understanding of the heterogeneity in a species' dispersal propensity, consequently producing a more realistic understanding of how species interact with their surrounding environments. Overall, this research provides a strong example of the capacity of urbanization to shape species' interpopulation dynamics. A deeper understanding of the causes and consequences of these effects will provide a robust foundation for identifying and mitigating current and future risks to biodiversity.This research also underscores the importance of scale dependency when considering spatially explicit interpopulation relationships, as highlighted by our IBD scaling profiles that revealed strong spatial variation in isolation‐by‐distance relationships. Research geared toward quantifying variation in gene flow across other studies (e.g., using IBD None declared.JJH, CSL, and MTK designed the study. JJH collected samples, performed laboratory work, and conducted data analyses. JJH led the writing of the manuscript. All authors contributed critically to the drafts and approved of the final manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.