Genetic variation is fundamental to population fitness and adaptation to environmental change. Human activities are driving declines in many wild populations and could have similar effects on genetic variation. Despite the importance of estimating such declines, no global estimate of the magnitude of ongoing genetic variation loss has been conducted across species. By combining studies that quantified recent changes in genetic variation across a mean of 27 generations for 91 species, we conservatively estimate a 5.4%–6.5% decline in within‐population genetic diversity of wild organisms since the industrial revolution. This loss has been most severe for island species, which show a 27.6% average decline. We identified taxonomic and geographical gaps in temporal studies that must be urgently addressed. Our results are consistent with single time‐point meta‐analyses, which indicated that genetic variation is likely declining. However, our results represent the first confirmation of a global decline and provide an estimate of the magnitude of the genetic variation lost from wild populations. Genetic variation is also a component of the planetary boundaries that humanity must operate within for our own survival , meaning that 99 independent temporal estimates of genetic variation were available. Metrics of genetic variation, including expected heterozygosity  , were collected from each species within a study (i.e. each independent estimate). Eighty‐eight estimates were based on microsatellite markers, nine on single nucleotide polymorphisms, and two on restriction enzyme‐based markers. Studies spanned an average of 97 years (±439 SD) or 27 generations (±44 SD). Eighty‐four estimates spanned more than one generation.We searched Google Scholar (20/02/2018–24/05/2018 and 25/02/2019) for peer‐reviewed publications that repeatedly sampled nuclear variation in the same population. Keywords used were "museum sequencing," "genetic diversity time," "ancient DNA," "temporal diversity genetics," "historic specimens," "museum specimens," "museum specimens genomics," "temporal genetic dynamics," and "ghost alleles." We examined 5970 peer‐reviewed publications and identified 88 publications on 91 species that met our criteria. Five species were independently studied multiple times . Finally, we also recorded if any markers in the historic and modern time points deviated from Hardy–Weinberg equilibrium proportions.We recorded the mean genetic variation metrics for two time point, henceforth “historic” and “modern,” from each species within a study. Historical time points, defined as the age of the oldest samples, were made possible through archival or ancient DNA specimens. Many of the oldest historical samples were collected towards the end of the industrial revolution (~1840), though the earliest sample was from 2.3 kY BC. The average historical sample was from 1908. Modern samples were defined as the youngest samples, and the average modern sample was from 2005. Though multiple time points would be ideal for pinpointing when change in genetic variation began, many publications had only two time points and we were forced to limit the analysis to two time points. For each species within a study, we took an average of each metric of genetic variation per time point. Consequently even if diversity metrics were measured at the population level, we use only an average across all populations. This was necessary because many studies only report historical genetic variation in this way. To maintain consistency, only populations that were sampled in both the historic and modern time points were included in our average. An exception was made if a population from the historic time point went extinct before modern samples were taken. Such extinct populations were included in the historic time‐point average only. For each species in a study, we recorded the length of time separating the oldest and newest sample in generations, as well as the start date of the study. If multiple time points were reported, we took the two furthest apart. If multiple estimates of generation time were given for a species, we used the median. If no generation time was given in the study reporting the genetic estimates, we used estimates for the same species from peer‐reviewed literature. To account for potential research bias towards bottlenecked or inbred species, which would inflate estimates of diversity loss, we recorded whether the paper discussed if the time series began before or after a major population crash or “bottleneck” (hereafter bottleneck status), and 2.1t tests to examine whether the mean observed heterozygosity, expected heterozygosity, and allelic diversity differed between time points. Pairs consisted of the modern and historic time‐point values of allelic diversity, expected heterozygosity or observed heterozygosity for each species in each independent study that spanned more than one generation. Thus, species that were studied multiple times were initially considered as independent pairs. Paired analyses were necessary because they preserved the vital connection between the time points from the same study. This ensured that we assessed changes in diversity using the unique historical baseline for each modern estimate. A paired analysis is also technically necessary because each marker set will suffer from different ascertainment biases and study‐specific limitations. Furthermore, paired data points are not independent and should not be separated. To meet the assumption of normality, we log‐transformed the values for each time point (results were stable without transformation). Due to multiple allelic diversity metrics, it was also necessary to standardize the values of allelic diversity by dividing both the historic and modern values by the historic value. The difference in variance that causes between historic and modern values does not violate the assumptions of the paired t test, because it is the difference between the paired values that the test assumptions apply to . These values were not initially excluded and were treated independently because the studies are independent and the populations were different evolutionary significant units. However, to control for potential bias due to unequal taxonomic representation, we averaged across each taxonomic class and repeated the paired t tests on this reduced data set.We used three separate paired 2.2t value from each nested model, followed by confirming the variable's nonsignificance using a likelihood ratio test (chi‐squared). This was repeated until the final model contained only terms that resulted in significant change in the deviance of the two nested models . Both models only examined studies spanning more than one generation. To ensure the data frame did not change with variable removal, species without a clear Red List status or bottleneck status were removed from the data set. Values of percentage change reported from the linear mixed‐effects models are for this reduced data set .To examine factors influencing the total change between time points in expected heterozygosity and allelic diversity, we used linear mixed‐effects models  global decrease in mean expected heterozygosity  = −0.590, mean of modern time point (log) = −0.672, mean of differences = 0.081, 95% confidence interval (CI) = 0.022–0.141, df = 69, p = 0.008, paired t test). A slightly higher decrease of 6.5% (±17.8% SD) was seen for mean allelic diversity  = 0, mean of modern time point (log) = −0.094, mean of differences = 0.094, CI = 0.040–0.148, df = 72, p = 0.0009, paired t test). In contrast to expected heterozygosity and allelic richness, observed heterozygosity did not change significantly  = −0.7, mean of modern time point (log) = −0.713, mean of differences = 0.013, CI = −0.047–0.072, df = 60, p = 0.67, paired t test). Across taxonomic averages, we also identified a significant decline in expected heterozygosity of 3.8%  = −0.560, mean of modern time point (log) −0.620, mean of differences = 0.04, CI = 0.0033–0.0771, df = 9, p = 0.0358, paired t test) and decline in allelic diversity of 3.7% (±10.1% SD). However, this was nonsignificant  = 0, mean of modern time point (log) = −0.05, mean of differences = 0.049, CI = −0.0342–0.1340, df = 8, p = 0.2084, paired t test). Trends in observed heterozygosity remained nonsignificant  = −0.664, mean of modern time point (log) = −0.649, mean of differences = −0.015, CI = −0.0375–0.0078, df = 8, p = 0.1686).We documented an average 5.4%  and whether the populations occurred on an island or mainland . The final model contained only these two explanatory variables and taxonomic class as a random factor. All other variables in the model were nonsignificant, including the IUCN Red List of Threatened Species status , bottleneck status  and number of generations a study spanned . There was a negative relationship between the total expected heterozygosity lost and age of the oldest sample  relative to an average loss of 2.1% (±15.8 SD) in mainland populations.The magnitude of change in expected heterozygosity was significantly associated with the age of the oldest sample in a study  or bottleneck status . In contrast to expected heterozygosity, there was also no significant effect of the age of the oldest sample . Instead, the number of generations a study spanned had a significant association with loss of allelic diversity . However, the trends across the two metrics were similar, and studies spanning more generations showed greater loss of allelic diversity  also showed substantially greater loss of allelic diversity relative to those on a mainland (4.6% ±12.3 SD) . Finally, we found that deviations from Hardy–Weinberg equilibrium proportions were not more common in either time point .The analysis of allelic diversity  yielded similar results to those for expected heterozygosity. No significant effect was found for 44.1We identified a significant loss in allelic diversity (6.5%) and expected heterozygosity (5.4%) of modern populations based on data from a comprehensive literature review. The overall trends of decline in expected heterozygosity and allelic diversity are consistent with previous single time‐point studies showing a putative global decline in genetic variation , amphibians (n = 3) or reptiles (n = 2) were found. Plant‐based studies were also not in this data set because they often failed to meet our inclusion criteria. A strong taxonomic skew towards commercially important fish, where stocking has been used over the last century, could be biasing the observed trends in genetic diversity and decreasing the loss we observe. Thus, we also compared variation trends averaged across taxonomic classes. The previously identified trends remained reasonably stable: expected heterozygosity declined by an average of 3.8% (significantly) and allelic diversity declined by 3.7% (nonsignificantly). We recommend that the taxonomic gaps be addressed, given the importance of understanding diversity trends in different taxa, for instance, in crop pollinators and nonagricultural plants.The studies that met our criteria were taxonomically biased. For instance, commercially important fish from the class 4.2IUCN Red List of Threatened Species status . Recent research has shown that there is similarly little correlation between Red List ranking and contemporary genome‐wide heterozygosity, where historical bottlenecks and species‐specific life history traits were speculated to be more important  of species in the data set were from North America and Europe. This regional bias is similar to, though more extreme than, that previously found for mitochondrial haplotype diversity estimates  forced the exclusion of extinct species. Consequently, the species that have undergone the most severe population declines are not in this analysis. Conservative estimates suggest extinction rates are now 100 times the historical baseline rate (Ceballos et al., None declared.DML conducted the literature and statistical analysis, and wrote the paper. AH and EVD commented on the analyses and text. VF mentored the work and commented on the analyses and text. All authors contributed to the original idea.


Populus deltoides) is one of the keystone forest tree species, and has become the main breeding parents in poplar hybrid breeding. However, the genetic diversity and population structure of the introduced resources are not fully understood.Black cottonwood  were detected; the expected heterozygosity (He) per locus ranged from 0.070 to 0.905, and the average polymorphic information content (PIC) was 0.535. The provenance ‘Was’ had a relatively low genetic diversity, while ‘Que’, ‘Lou’, and ‘Ten’ provenances had high genetic diversity, with Shannon’s information index (I) above 1.0. The mean coefficient of genetic differentiation (Fst) and gene flow (Nm) were 0.129 and 1.931, respectively. Analysis of molecular variance (AMOVA) showed that 84.88% of the genetic variation originated from individuals. Based on principal coordinate analysis (PCoA) and STRUCTURE cluster analysis, individuals distributed in the Mississippi River Basin were roughly classified as one group, while those distributed in the St. Lawrence River Basin and Columbia River Basin were classified as another group. The cluster analysis based on the population level showed that provenance ‘Iow’ had a small gene flow and high degree of genetic differentiation compared with the other provenances, and was classified into one group. There was a significant relationship between genetic distance and geographical distance.In the present study, five loci containing null alleles were excluded and 15 pairs of SSR (simple sequence repeat) primers were used to analyze the genetic diversity and population structure of 384 individuals from six provenances , and Quebec in Canada) of P. deltoides resources have high genetic diversity and there is a moderate level of genetic differentiation among provenances. Geographical isolation and natural conditions may be the main factors causing genetic differences among individuals. Individuals reflecting population genetic information can be selected to build a core germplasm bank. Meanwhile, the results could provide theoretical support for the scientific management and efficient utilization of P. deltoides genetic resources, and promote the development of molecular marker-assisted breeding of poplar. Populus trichocarpa was the first to be sequenced [P. nigra L. [P. simonii [P. tremuloides [P. balsamifera [P. cathayana Rehd [P. euphratica [P. tomentosa [P. szechuanica var. tibetica [Genetic diversity is an important component of biodiversity, and is the basis of ecosystem and species diversity –3. Underequenced ; therefoequenced , 11. In nigra L. –14, P. s simonii , P. tremmuloides , P. balssamifera , P. cathana Rehd , P. euphphratica , P. tomeomentosa  and P. stibetica .P. simonii [P. tomentosa [Many methods and techniques have been developed to advance population genetic diversity research, among which molecular tools play an important role in the management and utilization of genetic resources. In particular, simple sequence repeat (SSR) molecular marker technology is an ideal method because of its simple operation, co-dominance, high resolution, polymorphism, and repeatability . Many sp simonii , 24 and omentosa –27 by anP. deltoides is widely distributed from the Mississippi River to southern Canada in North America, and is one of the keystone forest tree species with important ecological value. This species has been widely used in forest breeding research and its genetic resources have become the main gene donors of poplar cultivars. These cultivars are mainly used to provide feedstocks for pulp, fiber, and bioenergy industries [P. deltoides germplasm resources through international exchange and cooperation in 2009, and a germplasm bank of P. deltoides was established in China. Production practice showed that the black poplar has a dominant position in the poplar plantation; thus, P. deltoides and its hybrid with P. nigra L. have become the main commercial poplar trees in China.dustries . FurtherP. deltoides. Fahrenkrog et al. [P. deltoides distributed in the Mississippi River Basin to determine its genetic diversity and adaptation potential. In the present study, we analyzed 384 P. deltoides individuals preserved in the germplasm bank, which were originally distributed in 27 collection sites for 6 provenances. SSR molecular marker technology was used to study their genetic diversity and population structure. Some of the selected primers were developed based on functional genes sequences, which could be used to study gene function of P. deltoides. In this way, more information could be provided for marker-assisted breeding in combination with phenomics. Meanwhile, other  primers were obtained from the International Populus Genome Consortium  and Washington University . Using this strategy, the genetic diversity of the population could be reflected objectively and comprehensively. In addition, based on the results, a core germplasm bank of P. deltoides could be built, allowing P. deltoides resources to be protected, managed, and utilized more scientifically and rationally.Little research has been performed on the genetic diversity and population structure of g et al.  reportedP. deltoides.In this study, 20 polymorphic SSR primer pairs were preliminary selected from 145 poplar SSR primer pairs for further analysis  per locus ranged from 2 (SSR85) to 19 (SSR126), with a mean of 7.2. The average effective number of alleles (Ne) per locus was 3.48, ranging from 1.08 at SSR85 to 10.40 at SSR126. Locus SSR126 provided relatively large amounts of genetic information and its Shannon’s information index (I) was 2.533. The means of the observed heterozygosity (Ho) and expected heterozygosity (He) were 0.509 and 0.579, respectively. In addition, except for locus SSR104, the false-homozygous phenomenon existed in the other loci (Ho > He). The polymorphic information content (PIC), as one measurement of genetic diversity, was between 0.068 (SSR85) and 0.896 (SSR120), with an average of 0.535. Six loci showed significant deviations from the Hardy-Weinberg equilibrium (HWE), which indicted that there was genetic differentiation among the provenances . The I values of the ‘Que’, ‘Lou’, and ‘Ten’ populations were 1.032, 1.131, and 1.175, respectively, and their genetic diversities were relatively high. Meanwhile these three populations had private alleles. The HWE results showed that the ‘Que’ and ‘Ten’ populations deviated significantly from the equilibrium.Based on the result of the population genetic diversity analysis  and inter-population (Fit) inbreeding coefficients were used as indicators to evaluate the degree of population neatness. The inter-population genetic fraction coefficient (Fst) was used as an indicator to evaluate the level of genetic differentiation of the populations. The existence of large gene flow (Nm > 1) among populations weakened the possibility of genetic drift, which would decrease the degree of genetic differentiation among populations. By contrast, when Nm < 1, the genetic differentiation among populations increases [Fis and Fit values of P. deltoides were 0.058 and 0.182, respectively, indicating that there was a loss of heterozygosity in the population, and there was inbreeding among the populations. The Fst between populations ranged from 0.062 to 0.205, with an average of 0.129, indicating that there was moderate genetic differentiation among the populations  showed that 84.88% of the total genetic variation originated from individuals, while 11.49% came from populations, and only 3.64% was ascribed to differences among individuals within the populations Table . The res2 = 0.1135, P = 0.001, Fig. 2 = 0.1867, P = 0.001, Fig. Analysis using the Mantel test showed that there was a very significant relationship between genetic distance and geographical distance between points  showed that the 384 individuals of P. deltoides, we carried out PCoA and cluster analysis between the populations, based on the unweighted pair-group method with arithmetic means (UPGMA) on the provenance populations ; however, the genetic diversity was high [P. deltoides, and single-nucleotide polymorphisms were detected by targeted resequencing of 18,153 genes in a population of 391 unrelated individuals. The authors found that both common and low frequency variants need to be considered to understand the genetic regulation of complex traits [P. deltoides from six provenances in the Saint Lawrence River Basin , Columbia River Basin  and Mississippi River Basin  were analyzed using the SSR marker technique. The results showed that the P. deltoides had abundant genetic diversity and moderate genetic differentiation. In addition, the degree of genetic differentiation was relatively high, probably because the materials in this study were from three basins in the main distribution area of P. deltoides, and the materials in Fahrenkrog’s study were mainly from the Mississippi River Basin [A better understanding of the genetic diversity, genetic variation, and genetic regulation of a population is essential for its proper management and conservation, especially in the face of current climate change, and the genetic evaluation of groups such as forests is particularly important . Fahrenkwas high . Furtherer Basin , 39. OurP. deltoides at different levels. At the individual level, PCoA and STRUCTURE cluster analysis were performed on 384 unrelated individuals, which divided them into two groups. Most of the individuals distributed in the Mississippi River Basin were grouped together  markers and isozyme systems to estimate the extent of duplication and the genetic diversity within and between banks. To better manage and utilize P. tomentosa [P. simonii [P. deltoides, we may identify the key polymorphic loci associated with the traits through correlation analysis, which is conducive to the development of molecular marker-assisted breeding or detection of target genes in the near future. In addition, a core germplasm bank could be built to rationally manage, preserve, and utilize the P. deltoides resources.The management of germplasm resources is a complex task. We should have a full understanding of their genetic information, morphological diversity, and adaptability. Meanwhile, germplasm resources need to be effectively identified to prevent redundancy between resources , 43. Gerermplasm  and Discermplasm . Reyes-Vermplasm . Storme ermplasm  analyzedomentosa  and P. s simonii  resourceP. deltoides germplasm resources were analyzed. The results showed that they had abundant genetic diversity. However, there were also some differences in genetic diversity among different provenances, with moderate genetic differentiation  that was established through international exchange and cooperation of germplasm resources in 2009, among which the resources came from its main distribution areas. The materials have been formally identified by the State Forestry and Grassland Administration, People’s Republic of China, under the identification number 2009–56. In this study, 384 unrelated individuals were randomly selected from six provenances. When collecting the materials, several collection sites were selected in each provenance, and then individual materials were collected near the collection sites. The geographical distance between individuals was more than 100 m. Six individuals were selected from two sampling sites in Missouri (‘Mis’). Eight sampling sites were selected in Louisiana (‘Lou’) and five in Tennessee (‘Ten’); 108 individuals and 75 individuals were selected, respectively. Eighteen unrelated individuals were from Iowa (‘Iow’). The four provenances were located in Mississippi River basin. Seventeen unrelated individuals were from Washington (‘Was’), which is located in the Columbia River basin. In addition, 160 unrelated individuals were from ten sampling sites in the Saint Lawrence River basin   amplification.The plant materials were cultured in the greenhouse of the Chinese Academy of Forestry (40°0′10″N 116°14′38″E), and the leaves were collected during the growing season and stored in a refrigerator at − 40 °C for DNA extraction. Total genomic DNA was extracted according to the highly efficient modified cetyltrimethylammonium bromide (CTAB) method . The quaP. deltoides were selected randomly for SSR primer screening, and 145 pairs of primers were used for screening. Seventy-five of the primer pairs were developed by researchers based on the analysis of functional gene sequences in poplar [http://www.ornl.gov/sci/ipgc/ssr_resource.htm) and Washington University , which have used by other researchers in related studies [In this study, DNA of 20 unrelated individuals n poplar –27, 51.  studies , 52–54. 2+ plus), 1.8 μL of dNTP mixture, 1 μL of forward primer (10 μmol·L− 1), 1 μL of reverse primer (10 μmol·L− 1), 0.25 μL of Taq polymerase (5 U·μL− 1), 1 μL of DNA template (50 ng·μL− 1), and 17.45 μL of ddH2O. The PCR amplification procedure comprised 94 °C for 3 min; 35 cycles of 94 °C for 30 s, annealing at the annealing temperature of each primer pair for 30 s, elongation at 72 °C for 45 s; and a final extension at 72 °C for 10 min.The PCR amplification reaction system for all SSR markers comprised a 25 μL mixed system containing 2.5 μL of 10 × buffer (MgThe forward primers for the preliminarily screened polymorphic loci were labeled with fluorescent dyes (5-HEX or 5-FAM) and their PCR products were separated by capillary electrophoresis using an ABI 3730xl DNA analyzer . A peak size map of the amplified fragments was obtained.PIC of each locus.The Gene-Marker 2.2.0 software  was used to read the peak maps of the amplified fragments of each polymorphic locus. The null allele frequencies of each locus were detected using Cervus 3.0.7  and MicrNa), the effective number of alleles (Ne), Shannon’s information index (I), observed heterozygosity (Ho), expected heterozygosity (He), the number of private alleles, gene flow (Nm), and the F-Statistics . The HWE tests across all provenances were performed using Genepop 4.7.0 [www.omicshare.com/tools). With the help of the Arlequin 3.5.2.2 [The GeneAlEx 6.503  softwareop 4.7.0 . The hea 3.5.2.2  and GeneWe further calculated the genetic distance between individuals and populations using the GeneAlEx 6.503 software for PCoA. A cluster analysis between populations, based on UPGMA, was also developed using the NTSYS-pc 2.10e  softwareK), 10 independent runs for each value of K, ranging from 2 to 5, were conducted. Each run consisted of a burn-in period of 100,000 steps followed by 1000,000 MCMC iterations. The ΔK parameter, which was based on the rate of change in the log probability of data between successive K values, was estimated to determine the best K, based on the model developed by Evanno et al. [The population genetic structure was analyzed using STRUCTURE 2.3.4  softwareo et al. .www.movable-type.co.uk/scripts/latlong-vincenty.html).The Mantel test was performed using the GeneAlEx 6.503 software to analyze the correlation between Nei’s genetic distance and geographical distance. The geographical distance between different sites was calculated according to the latitude and longitude using Vincenty’s formula . Table S6. Material Information for P. deltoides germplasm resources. Table S7. Genetic information of polymorphic loci.


Mytilicola intestinalis and Mytilicolaorientalis) by combining an extensive literature survey covering the reported putative native regions and the present-day invaded regions with a global phylogeography of both species. The population genetic analyses based on partial COI sequences revealed significant population differentiation for M. orientalis within the native region in Japan, while introduced populations in North America and Europe could not be distinguished from the native ones. Thus, M. orientalis’ invasion history resembles the genetic structure and recent spread of its principal host, the Pacific oyster, Crassostrea gigas, while M. intestinalis lacks population genetic structure and has an overall low genetic diversity. Therefore, the native origin of M. intestinalis remains unclear. With this study, we demonstrate that even highly related and biologically similar invasive species can differ in their invasion genetics. From this, we conclude that extrapolating invasion genetics dynamics from related invasive taxa may not always be possible.Invasive species, and especially invasive parasites, represent excellent models to study ecological and evolutionary mechanisms in the wild. To understand these processes, it is crucial to obtain more knowledge on the native range, invasion routes and invasion history of invasive parasites. We investigated the consecutive invasions of two parasitic copepods ( Invasive species can also be parasites, which may be introduced with or without their hosts. Parasite invasions can have wide-ranging effects on host-parasite interactions with strong ecological and evolutionary implications6. To examine the effects of invasive parasites, a detailed understanding of their origins and invasion pathways is important. To this extent, the traditional approach of using literature records can be fruitfully complemented by population genetic analyses. Phylogeographic studies of species in their putative native and invaded regions (“invasion genetics studies”)7 can provide more detail to clarify the origin, invasion routes, population genetic structure and genetic diversity of invasive parasites8. However, invasive parasites are greatly understudied in this respect, as well as with regard to their ecological and evolutionary impacts9.Biological invasions (whether recognized or cryptic) can alter ecosystem functioning and servicesMytilicola intestinalis Steuer 1902 and Mytilicola orientalis Mori 1935 along the European Atlantic shores. Both of these natural experiments have been proven to be particularly useful to study their ecological consequences and coevolution with the blue mussel, Mytilus edulis, one of their host species. Mytilicola intestinalis invaded European waters first in the 1930s presumably through ship hull fouling and relaying of infected mussels (e.g.12). Its invasion into the western European Wadden Sea was recently used as a model system to study host-parasite coevolution in the wild14. Cross-infections of hosts and parasites from separate invasion fronts where the parasite had invaded both locations at a similar time  showed that parasite invasion led to different evolutionary trajectories, with differences in the parasite’s infectivity and the host’s resistance and tolerance13. These trajectories were characterized by specific molecular interactions between host and parasite14. Mytilicola orientalis was co-introduced with the invasive Pacific oyster, Crassostrea gigas, through aquaculture transports16, and now also infects a suite of native hosts, including blue mussels, M. edulis18. Thus, the native host, M. edulis, is now shared between M. intestinalis and M. orientalis.Two exceptions are the consecutive invasions of the parasitic copepods Mytilicola species that could, in turn, lead to dissimilarities in ecological effects and evolutionary responses in M. edulis. However, while the major invasion route of the principal host of M. orientalis, the Pacific oyster, is known20, the invasion route of its hitchhiking parasite has yet to be validated. Similarly, the dispersal routes of Mytilus galloprovincialis are better known than those of its parasitic copepod M. intestinalis22. To fully understand the parasites’ invasion histories and their consequences for ecological and evolutionary processes, the genetic structure in the native ranges and along invasion routes of both parasite species needs to be investigated.The reported invasion histories already suggest substantial differences in the mode of invasion between these two Mytilicola is a genus of cyclopoid copepods that have a direct life cycle involving one pelecypod host. Their free-living larval stages are short and have mainly been studied in M. intestinalis (e.g.24). The duration of the free-living and parasitic larval stages of the other Mytilicola species are thought to be similar25. The two free-living larval stages, nauplius and metanauplius, last 36 to 48 hours at a laboratory temperature of 18 °C23. The next larval stage, the first copepodite, is infective and will search for a suitable host to infect. It may live for 11 to 15 days under laboratory conditions, but it is doubtful whether it lives that long in the field23. Once Mytilicola has infected a host, its dispersive capacity depends on its bivalve host, which is sessile and does not move across long distances unaided. Therefore, the dispersal capacity of Mytilicola was considered to be limited26.c-oxidase I (COI) sequences. We specifically wanted to verify their putative areas of origin , and determine the genetic diversity and population differentiation between putative native and invaded regions for both invasive Mytilicola species.In this study, we aimed to (1) use the available literature to reconstruct the invasion history of both parasites, and (2) test whether the reported invasion routes could be confirmed by a global phylogeography based on partial cytochrome-Mytilicola intestinalis” resulted in 72 hits on the Web of Science/Web of Knowledge  and 934 hits on Google Scholar (on 15 December 2017). A search for “Mytilicola orientalis” resulted in 15 hits on the Web of Science/Web of Knowledge , and 373 hits on Google Scholar (on 15 December 2017). More literature was found by snowballing (see Methods). Our literature survey led to the inclusion of 173 publications on occurrences of M. intestinalis  and of 47 publications on occurrences of M. orientalis . The literature included peer-reviewed publications, fishery leaflets, reports, book sections and dissertations. A total of 41 publications with potentially relevant information could not be read because the full texts were not available online, neither accessible via Dutch and German national libraries, nor by attempts to contact the original authors, and thus, could not be included.A search for “Mytilicola intestinalis before the 1930s was limited to the Mediterranean Sea Basin, where the parasite was recorded from a few locations near large ports and aquaculture areas . It is, thus, unclear from which Mediterranean basin (or basins) M. intestinalis was introduced, and whether this happened repeatedly, which might lead to different expectations regarding signatures of bottlenecks and genetic diversity in the introduced region.The distribution of M. orientalis in its putative native range has not been the subject of many studies, which is reflected in our literature survey , 2658 Mediterranean mussels , more than 1595 blue mussels (Mytilus edulis), 104 bay mussels (Mytilus trossulus) and 90 mussels (Brachidontes pharaonis) were dissected in this study. Dissections of Pacific oysters and mussels from 20 locations along Mediterranean coasts, 13 locations along Japanese coasts, 7 locations along Pacific American and Canadian coasts, and 21 locations along European Atlantic, North Sea and Baltic coasts resulted in detection of M. intestinalis and M. orientalis at 20 and 30 locations, respectively. In Japan, M. orientalis has only been previously recorded in Hiroshima29 and Takehara30, both in the Hiroshima Prefecture, and furthermore in Hiwasa, the Tokushima Prefecture31 – the other locations, including those outside the Seto Inland Sea are, thus, new records. Also, Tavira  is a new location for M. orientalis. The Mediterranean locations Malinska, Marjan (both Croatia) and Cassis (France) are new records for M. intestinalis, although Cassis is close to locations where M. intestinalis was already known from .A total of 5604 Pacific oysters  and FST = 0.00848 (n.s.), with an overall population differentiation in the Mediterranean Sea Basins of ΦST = 0.01531 (p = 0.0323) and FST = 0.02397 (p = 0.0325), and in the invaded region of ΦST = 0.01081 (n.s.) and FST = −0.00544 (n.s.)  and FST = 0.03002 (p < 0.00001), with an overall population differentiation in Japan of ΦST = 0.10287 (p < 0.00001) and FST = 0.07019 (p < 0.00001), and in the introduced region of ΦST = 0.02243 (p = 0.0303) and FST = 0.01005 (n.s.) , ΦSC = 0.01247 (n.s.) and ΦST = 0.0162 (p = 0.0480) , FSC = 0.00825 (n.s.) and FST = 0.02878 (n.s.)  between the putative native regions and the introduced regions   and from 0.699 to 0.839 in its introduced region (Supplementary Table π) between the putative native region and the invaded regions . Nucleotide diversity for M. intestinalis ranged from 0.000276 to 0.00179 in the putative native region, and from 0.000572 to 0.00267 in the introduced region . The interaction term in the linear models (species × region) was not significant for haplotype diversity h or nucleotide diversity π , indicating that genetic diversity differed consistently between species.We did not detect significant differences in haplotype diversity (02) Fig. . Haplotyion Fig. , and M. ons Fig. . HoweverMytilicola orientalis match the classical pattern of a recent invasion described in the literature, while the phylogeography of Mytilicola intestinalis did not match our literature-based expectations due to low mtDNA genetic diversity. While several scenarios could explain the low genetic diversity, we cannot identify the origin of the species, which potentially also makes it a cryptogenic species in the Mediterranean Sea Basins.Our study utilized the consecutive invasions of two congeneric invasive parasites to align the invasion history derived from their reported spread in the literature with their phylogeography based on molecular markers. Based on mitochondrial haplotype diversity, we found that the native range and the invasion route of M. orientalis, because of the significant population genetic structure between the Seto Inland Sea and Iwaya in the Sea of Japan, and the stronger overall population differentiation in Japan than in the invaded regions. This was expected based on the invasion history derived from our literature survey. The invasion of M. orientalis was extremely long-distance, crossing both the Pacific and Atlantic Ocean via aquaculture transports, followed by a relatively rapid spread through the invaded regions .Our study confirmed the coastal seas of Japan as the native region of ons Fig.  that is 35 contributing smaller amounts35. Oyster seed is regularly transported between the regions largely homogenizing the genetic diversity and restricted gene flow has only been reported for the Nagasaki and Kumano regions36. Mytilicola orientalis hitchhiked with oyster transports to North America16 and to Europe38, and our population genetic analyses support these patterns in the native range and invaded regions: the North American and European samples could not be distinguished from the Seto Inland Sea samples from Japan, while the sample from Iwaya in the Sea of Japan was significantly different.Pacific oysters have been imported from Japan to locations across the globe, with Miyagi Prefecture as the main overseas export area and Kumamota and Hiroshima Prefectures including the Seto Inland SeaM. orientalis showed more resemblance in the MDS plots between North America and the northern Wadden Sea invasion: Belfair (representing North America) and Puan Klent (representing the northern Wadden Sea oyster invasion). This pattern represents a small mismatch with the phylogeography of the host20 and might suggest that other vectors than oyster transports contributed to M. orientalis’ secondary spread in the Wadden Sea. For example, the wider host range of M. orientalis and spillover to native bivalves18 could have further sped up and modified its spread. In the Mediterranean, the distribution of M. orientalis is disjunctive, with presence at the Thau lagoon in France since 197939 and in the northern Adriatic40, but an absence in between . Likewise, genetic breaks were found near Marseille, separating the Ligurian Sea and Gulf of Lion, especially for species with lower dispersal ability46. As the short-lived pelagic larvae of Mytilicola limit the potential for natural dispersal23, population differentiation among the long-established Mediterranean populations should have been maintained. Especially, since breaks in the distribution of mussel hosts introduce additional barriers for M. intestinalis larval dispersal. This limited dispersive ability of M. intestinalis can also be observed in the the literature-based invasion data  contests this. The number of parasites is usually proportional to the shell length of mussels52 and very young mussels are rarely infected12. Moreover, Mediterranean commercial mussels are usually grown in hanging cultures, which are considerably less vulnerable to parasite infections than mussels grown on the benthos. These mussels are also replaced yearly by a new parent stock, which makes it difficult for M. intestinalis to build up a dense population (reviewed in12). Nevertheless, transportation of spat between Mediterranean regions does occur and could be contributing to or have contributed in the past to the homogenization of its parasite fauna. Other human activities related to mussel dispersal are ballast water and ship hull fouling53. Ship hull fouling was seen as the primary route by which M. intestinalis invaded the German and British coasts (e.g.23) and explained its disjunctive distribution there (reviewed in12). Higher resolution of more independent genetic markers might allow for detecting differentiation. Nevertheless, while anthropogenic shuffling could explain the lack of population genetic structure in the Mediterranean and Adriatic Seas, it cannot explain the overall low genetic diversity compared to M. orientalis.Alternatively, anthropogenic shuffling of native genetic diversity might explain the lack of population genetic structure in M. intestinalis may be an introduced species in the Mediterranean Sea Basins, which would explain the lack of population genetic structure and the indications for population expansion. A few hints supporting this scenario can be found in literature. Mytilicola intestinalis was first described in 1902 and 1903 from two important ports for shipping, Trieste and Gravosa (both Adriatic Sea)55 – around 40 years after the opening of the Suez Canal (November 1869), an event that led to the invasion of many marine species into the Mediterranean . In 1902, nearly all dissected mussels at those Adriatic sites were infected by M. intestinalis, with up to 50 parasites in one host55. Prevalences and intensities that high are typically seen in mussel populations that have recently invaded – e.g., maximum intensity of up to 14 parasites in Germany10, up to 59 in the UK58 and up to 70 in the Netherlands59. In 1953, M. intestinalis could not be found in the same locations where A. Steuer initially described the species60, and we also did not find M. intestinalis in > 200 mussels from Trieste  was found in 195362 but in 1996, M. intestinalis was absent (n = 31)63. The cases of Trieste and Taranto both may be interpreted as outbreaks upon initial invasion followed by low infestation over longer time scales, resembling the typical boom-bust dynamics of biological invasions before populations enter the comparatively stable adjustment phase64. Moreover, Adriatic harbors that were rarely visited by large vessels had no or low M. intestinalis prevalences, while mussels at large ports were all heavily infested65.Finally, M. intestinalis is native in the Mediterranean Sea Basins, we suggest that M. intestinalis should be regarded as a cryptogenic species . Along European Atlantic and North Sea coasts, however, M. intestinalis is clearly an invasive species, as there are records of times when M. intestinalis was not present and later invaded. The absence of such data in the Mediterranean neither proves nor disproves presence or absence of M. intestinalis in the Mediterranean Sea Basins before its taxonomic description. The higher prevalence around shipping ports and aquaculture areas could indicate that M. intestinalis is invasive, or that its distribution has been influenced by humans also in the Mediterranean. Such a scenario would also imply that its native host species may not be Mytilus galloprovincialis. Solving the puzzle whether M. intestinalis is native or is an invader in the Mediterranean would need explorative, basic parasitological work in potential native regions such as the southern and eastern Mediterranean Sea Basins, the Red Sea and potentially further, in combination with a population genetic study.Because our data may not be consistent with the idea that M. orientalis were validated with the sampling and choice of marker in the current study: the native region of M. orientalis is Japan, and the parasite hitchhiked with its host to North America and Europe. Whether M. orientalis was introduced to Europe via Canada, or directly from Japan, or both, and how M. orientalis spread throughout Europe remains to be answered with the use of higher resolution nuclear genetic markers. In contrast, the native region of M. intestinalis could not be validated, because of low genetic diversity and lacking population structure within and among the Mediterranean Sea basins, which may point to M. intestinalis being an invader in the Mediterranean Sea Basins (cryptogenic species). The invasion histories and demographics thus differ between the two Mytilicola species, and this may have profound impacts on their further invasion and evolutionary trajectories, as well as their competition in the invaded regions where they now co-occur.Our study shows that despite the large body of literature existing on these two common marine parasites of commercially important bivalves, hypotheses derived from literature-based invasion histories reports do not necessarily match conclusions based on population genetic demography. This indicates that some basic understanding of the invasion processes is lacking, and highlights the usefulness of population genetic studies to test anecdotal evidence of invasion histories. The native region and invasion route of Mytilicola species through time in putative native and invaded regions, we searched the Web of Science/Web of Knowledge and Google Scholar with the keywords “Mytilicola intestinalis” and “Mytilicola orientalis”. Further publications, including grey literature, were found by checking the references of each relevant publication . All publications were screened for recorded absence or presence of Mytilicola species per host species, and for coordinates of sampling sites. This resulted in data sets for Mytilicola intestinalis and for Mytilicola orientalis (available upon request) which were used for plotting occurrence maps with the R packages rworldmap67 and ggplot68. If latitude and longitude were not reported in a publication, we inferred approximate coordinates from maps so that the data could be plotted.To describe the distribution of both invasive M. orientalis’ invasion in Europe has been cryptic because of difficulties with reliable species identification69. In the literature survey, we used the identification presented by the authors, but that may be incorrect when studies are based on morphology, as the distinguishing characteristics for M. intestinalis and M. orientalis are not fully reliable69. For instance, M. intestinalis has been reported from Crassostrea gigas71, although it was shown not to be a suitable host for M. intestinalis73; these observations are more likely to be of M. orientalis instead.A possible caveat in the literature survey is that Mytilus edulis, Mytilus galloprovincialis, Mytilus trossulus and Brachidontes pharaonis), and Pacific oysters (Crassostrea gigas) were collected in the intertidal zone during low tide at 61 locations along European coasts , along American and Canadian Pacific coasts  and along Japanese coasts  or with the help of a magnifying glass (10×) in order to screen for the presence of Mytilicola individuals. Parasites were isolated from their host and stored separately by individual host in 100% ethanol.Different mussel species  or the Zymo Research genomic DNA kit . One to five M. intestinalis and one to eight M. orientalis per host were sequenced. All extractions were performed according to the protocols provided by the manufacturers. DNA concentrations were measured on a Nanodrop to confirm DNA quality and quantity. A 534 base pair (bp) fragment of the mitochondrial cytochrome-c-oxidase I region (COI) was amplified using the primer pair MOICOIf and MOICOIr for M. intestinalis69 and a newly developed primer pair for M. orientalis (MoriCOI50f: 5′-TTG ATC GGG CTT AAT TGG-3′ and MoriCOI50r: 5′-GAT CGG TTA ARA GCA TGG T-3′). The M. orientalis primers were developed in CLC Genomics Workbench 8.5.1  and were based on M. orientalis COI sequences from Elsner et al.74.DNA was extracted from individual M. intestinalis, polymerase chain reaction (PCR) and Sanger sequencing using both forward and reverse primers were performed according to Goedknegt et al.69 at Baseclear B.V. . For M. orientalis, PCR was performed with HotStarTaq  in 30 μl reaction volumes, adding 15 μl HotStarTaq Mastermix, 1.5 μl of each 10 μM primer, 11 μl H2O and 1 μl of sample for each reaction. Amplification started with a heat-activation of 15 min at 95 °C, followed by 38 cycles of 95 °C for 30 s, annealing at 50 °C for 30 s and elongation at 72 °C for 60 s. The final extension step was at 72 °C for 10 min. Amplification success of all M. orientalis samples was checked with a QIAxcel electrophoresis system . PCR purification and Sanger sequencing of M. orientalis samples were performed on both the forward and reverse strand at the Institute of Clinical Molecular Biology .For M. intestinalis or M. orientalis divided by the total number of mussels or oysters examined per location, and mean intensity as the mean number of M. intestinalis or M. orientalis within an infected mussel or oyster per location . Part of the M. orientalis sample from Tavira  was acquired through an anti-Mytilicola treatment of mussels using Dichlorvos Pestanal 76. Fifteen live Mytilicola specimens escaped the mussels during the recovery period shortly after the 4 h treatment. Prevalence and mean intensity  or BioEdit 7.2.577 , after which forward and reverse reads were assembled. We screened all chromatograms for double peaks and at least one of the forward and reverse sequences had no double peaks at any base in the sequence. Some of the North American samples failed to amplify or sequence entirely and were not considered further. COI sequences were cropped to 483 base pairs (bp) for M. intestinalis and to 476 bp for M. orientalis. Unique haplotypes were determined using DnaSP 5 78 or using a custom Python 3.6 script  (GitHub: https://github.com/pluttik/collarl). Arlequin 3.5.1.279 was used to calculate pairwise population comparisons , population differentiation based on the mitochondrial F-statistic ΦST and conventional F-statistics FST , Analyses of MOlecular VAriance (AMOVA), molecular diversity indices, Tajima’s D statistic80 and to perform Fu’s Fs tests82 for selective neutrality , mismatch distributions  and minimum spanning networks for both species. For neutral markers, Tajima’s D and Fu’s Fs can be used to detect changes in population size. Significantly negative D and Fs values can be interpreted as signatures of population expansion or, unless another independent marker has the same pattern, a past selective sweep82. We calculated both ΦST and FST to detect any signs of possible population differentiation, as well as for easier comparison to other studies that use either or both fixation indices. In our analyses we only included locations with more than 15 Mytilicola individuals, with the exception of calculations for minimum spanning networks, in which all individuals were included. In addition to overall AMOVAs, we also calculated AMOVAs within putative native and introduced regions for both species, and hierarchical AMOVAs for Mediterranean M. intestinalis with two designs to test whether the overall population differentiation could be attributed to different regions within the Mediterranean Sea Basins. The first design tested the Gulf of Lion  vs. the Ligurian Sea (Livorno) vs. the Adriatic Sea . In the second design, the location Cassis was added to the Ligurian Sea group instead of the Gulf of Lion group. This was done to account for the possibility that the location Cassis, which is east of Marseille and a known location for genetic breaks46, belongs to the Italian group rather than to the group with more proximate locations within France. To test for differences in genetic diversity between species and regions, we fitted a linear model testing haplotype diversity (h) and nucleotide diversity (π) as a function of species , region  and the interaction term species × region in the R statistical environment version 3.4.383.Quality control of the sequences was performed in CLC Genomics Workbench 8.5.1 (for Online Resource 1Online Resource 2Supplementary Information


Transcription factors are proteins able to bind DNA and induce the transcription of specific genes. Consequently, they play a pivotal role in multiple cellular pathways and are frequently over-expressed or dysregulated in cancer. Here, we will focus on a specific “signal transducer and activator of transcription” (STAT3) factor that is involved in several pathologies, including cancer. For long time, the mechanism by which STAT3 exerts its cellular functions has been summarized by a three steps process: (1) Protein phosphorylation by specific kinases, (2) dimerization promoted by phosphorylation, (3) activation of gene expression by the phosphorylated dimer. Consequently, most of the inhibitors reported in literature aimed at blocking phosphorylation and dimerization. However, recent observations reopened the debate and the entire functional mechanism has been revisited stimulating the scientific community to pursue new inhibition strategies. In particular, the dimerization of the unphosphorylated species has been experimentally demonstrated and specific roles proposed also for these dimers. Despite difficulties in the expression and purification of the full length STAT3, structural biology investigations allowed the determination of atomistic structures of STAT3 dimers and several protein domains. Starting from this information, computational methods have been used both to improve the understanding of the STAT3 functional mechanism and to design new inhibitors to be used as anticancer drugs. In this review, we will focus on the contribution of structural biology to understand the roles of STAT3, to design new inhibitors and to suggest new strategies of pharmacological intervention. Transcription factors  are nodal points in signaling pathways and among the most frequently affected genes in cancer ,2. SignaIn this review, we will focus on structural biology studies of a specific member of the STAT family, namely STAT3, and its implications for comprehension of the protein functional mechanism and the development of novel anticancer therapies. This protein plays a pivotal role in the regulation of several genes involved in proliferation, differentiation, apoptosis, angiogenesis and immuno-inflammatory processes. Additionally, it has been recently demonstrated that STAT3 also localizes into mitochondria and that it can influence their function ,6,7.c-myc, bcl-XL, mcl-1 [Over the years, enhanced and dysregulated STAT3 activity has been observed in a large number of cancer cell lines , indicatL, mcl-1 ,14. FurtL, mcl-1 . FinallyL, mcl-1 ,17,18.A search in the Web of Science database for papers with the word “STAT3” and “inhibitor” in the title revealed more than 500 articles published in the last twenty years. This data makes explicit the great effort made by the scientific community to develop pharmacological therapies based on the modulation of STAT3 functions.Despite the significant efforts made, the tendency of the STAT3 to aggregate prevented, until now, the determination of the structure of the entire protein in both monomeric and dimeric form. However, several recombinant proteins not prone to aggregation have been expressed and their structure solved by X-ray crystallography . These iDifferent splicing results in two main STAT3 isoforms (α and β) that differ for the length of the TAD (∼50 residues in STAT3α and ∼7 residues in STAT3β). The biological roles of the two isoforms have been debated since their discovery. However, because the high disorder that marks this protein region it has been scarcely characterized from the structural point of view. Therefore, in this review we will only discuss the other structured domains that are common in both isoforms.Cytokine receptors and growth factor receptors are the main drivers of STAT3 activation. Moreover, it has been shown that also environmental factors such as smoking cigarettes, infections and stress can lead to STAT3 triggering by toll-like receptors (TLR), adrenergic receptors and nicotinic receptors .The interaction of the physiological ligands with their receptors starts the so-called “canonical” STAT3 activation pathway that involves phosphorylation of a specific tyrosine residue (Tyr705) in the TAD . This po3 amino group, stabilizes the p-Tyr705 binding. Phosphorylated STAT3 is retained in the nucleus, where it binds to DNA in order to promote and activate transcription of a wide array of genes controlling cell differentiation, proliferation and survival in various cell types.Phosphorylation changes the propensity of STAT3 molecules to form homo-dimers ,29, whilYear after year, is becoming clearer that the phosphorylation–dimerization–activation scheme does not completely recapitulate the complexity of the STAT3 functional mechanisms and a novel more complicated picture, involving unphosphorylated STAT3 and other post-transcriptional modifications, is emerging . For exaThe NTD domain is not directly involved in the formation of phosphorylated STAT3 dimers or in their interaction with DNA, therefore it was not considered central for the STAT3 functions for a long time. However, recent studies indicated that NTD has an important role at low cytokine concentration, i.e., when the activated STAT3 concentration is small. In fact, the NTD domains seem to facilitate the STAT3 binding to weak STAT3-binding sites by forming tetramers composed of a pair of phosphorylated dimers . MoreovePhosphorylation of tyrosine residues located in the SH2 and TAD domains of STAT proteins by protein tyrosine kinases (PTKs) has been identified as a critical event for modulating their functions ,41. HoweFor example, STAT3 is subject to phosphorylation on serine 727 (that lacks in STAT3β) by serine protein kinases, as well as, lysine acetylation and methylation by protein acetyltransferase and methyltransferases ,42. MultConcerning acetylation, recent studies supported the hypothesis that it modulates different functions and properties of STAT3  such as Regarding methylation, Lee and coworkers recently demonstrated that Enhancer of Zest Homolog 2 (EZH2), a histone-lysine N-methyltransferase, interacts with and methylates STAT3 increasing its activity .Finally, in 2014 Mariotto and coworkers reported that the S-Glutathionylation of Cys328 and Cys542, located in the DNA-binding domain and in the linker domain, respectively, severely impair STAT3 phosphorylation .As already pointed out, structural studies on STAT3 and other STAT proteins, have been slowed down by problems with protein expression and purification due to the tendency of STATs to form aggregates. As consequence, only domains of the STAT proteins have been solved and the structure of full length STAT3 is not available in the protein data bank (PDB).The first structure (PDB code 1BG1) of mouse phosphorylated STAT3β core fragment  in complex with DNA see  was solvIn 2008 Chen and coworkers solved by X-ray crystallography the structure of the monomeric core fragment of USTAT3 (PDB code 3CWG) at a resolution of 3.05 Å . This stThe same core fragment, with the NTD replaced by green fluorescent protein (GFP), was solved in complex with DNA (PDB code 3E68) at a resolution of 2.65 Å by Nkansah et al., in 2013 [Finally, in 2015 the first structure of the NTD was published by Frank and coworkers . During +-mediated”  the tendency of STAT3 to form paracrystals [+-mediated” interface could be necessary for some physiological, still not well characterized, effects.Concerning the interaction between multiple NTD, the asymmetric unit of the crystal is formed by five NTDs and two different protein-protein interaction surfaces were observed, one indicated as ‘“handshake” interface and the other as “Ni2ediated” . It is icrystals ,55 or nucrystals , also thInterestingly, in the previously discussed study , the NTDComputational studies also have been carried out to investigate the structure of STAT3 monomer and dimer. In 2012, Husby et al.  investigMore recently, some of us used protein-protein docking and molecular dynamics simulations to investigate the structure of USTAT3 dimers not bound to DNA . In partOver the years, experimental information about the structure of STAT3 and computer simulations have been used to (1) identify new inhibitors, (2) to better understand the binding mode of existing inhibitors and (3) to improve their affinity for the target. A list of papers published in the last five years, together with a summary, is reported in The goal of this section is to give an overview about how structural biology influenced the discovery of new biologically active molecules. The medicinal chemistry efforts to discover STAT3 inhibitors have been extensively reviewed elsewhere ,64,65,6650 of 150 nM  [cis-3,4-methanoPro-Gln-NHBn) that showed an IC50 of 150 nM in FP assays [50 162 nM [Among the STAT3 inhibitors designed to compete for p-Tyr705 binding site a large group is formed by phosphopeptides. This approach was firstly attempted by Turkson et al. in 2001 . Startiny, EMSA)  or 290 ny, EMSA)  . The structure with the 20-residues long peptide was not determined because it induced protein aggregation, probably due to NTD unfolding. Also, the experiments on the 12-residues long peptide, analog of helix 2 , suggestFinally, starting from the helix 2 analog, a library of mutant peptides was synthetized and their ability to suppress STAT3 signaling in cancer cells was evaluated. To note, these peptides were fused with penetratin, a peptide able to permit the entrance in the cells of non-cell penetrating molecules .Some of the synthetized peptides demonstrated STAT3 inhibitory activity in the gamma activation sequence (GAS)-luciferase reporter assays and inhibition of proliferation of Michigan Cancer Foundation (MCF)-7 breast cancer cells, confirming that the NTD domain is a protein region suitable for the development of new anticancer drugs.In summary, these studies provide clear evidence of the crucial role of structural biology information for the identification and optimization of STAT3 inhibitors and indicate that future studies cannot disregard the use of computational and experimental structural techniques.In summary, despite difficulties in the expression and crystallization of the protein, structural biology investigations have been of great help in improving our understanding of the STAT3 structure and its functional mechanism.The structures available in the PDB have been the starting point for a large number of studies in which computer simulations have been used to identify new drugs, improving their affinity for the target or simply to understand their biding mode.However, a lot of issues, in particular concerning the structure of USTAT3 dimers, the interaction with specific drugs but also the role of NTD domains in the phosphorylated dimers and other molecular events, are still open and should be the subjects of new studies.Hopefully, in the next future the integration between computational, structural and biophysical techniques will help to better characterize from the structural point of view the drug protein complexes and those STAT3 species that have eluded the attempts of characterization carried out so far.


Genomic global positioning system (GPS) applies the multilateration technique commonly used in the GPS to genomic data. In the framework we present here, investigators calculate genetic distances from their samples to reference samples, which are from data held in the public domain, and share this information with others. This sharing enables certain types of genomic analysis, such as identifying sample overlaps and close relatives, decomposing ancestry, and mapping of geographical origin without disclosing personal genomes. Thus, our method can be seen as a balance between open data sharing and privacy protection.The online version of this article (10.1186/s13059-019-1792-2) contains supplementary material, which is available to authorized users. It is crucial to balance privacy protection and data sharing in genomics –3. Full genomic GPS that aims to achieve a balance between data sharing and privacy protection. It allows sharing of information to a degree sufficient for approximating relative genetic distance of an individual from either another individual or a group. Identification of closer relatives and population genomic analyses, such as ancestry decomposition and geographical origin mapping, are possible. Importantly, though, the shared information conceals individual genotypes, making it extremely difficult to reconstruct the personal genomes.Here we present a method called multilateration, a localization technique for wireless sensor networks in which spatial coordinates of a node with an unknown position are inferred by measuring the distances from the node to several reference nodes at known positions [Our method builds upon ositions . For exaN-dimensional space, and with K reference nodes with known positions, an unknown node’s coordinates can be unequivocally identified if K > N . Then, we define a statistic that compares two distance vectors, vt and vu:vt − vu. We showed th element of Σ ispn is the population allele frequency of SNP n. The statistic soverlap follows a χ2 distribution with K degrees of freedom if t and u are unrelated  to obtain the top two eigenvectors (PCs) in a 2D space, K references based on the genetic relatedness metric. Then, we can construct the GRM of the K + 1 individuals (the references and the target) by appending the distance vector to the GRM. We decompose this (K + 1) × (K + 1) GRM to obtain a PC map of K + 1 individuals in a new 2D space, K references in The second application for sharing a distance vector is population genomic analyses. The distance vector contains information that can infer the genetic spatial structure of individuals. Recent studies showed that genetic data enabled the geographical origin of an individual to be located on a two-dimensional (2D) map , 9. Novementclass2pt{minimmization . After rTo evaluate the performance of our method, we used the POPRES data  Additioa closelyr2 = 0.98, Fig. r2 = 0.86, Fig. Another application for sharing distance vectors in population genomics is the inference of an individual’s ethnic admixture proportions. We designed a procedure to estimate the admixture proportion of an individual only using the distance vector


In Australia and New Zealand, by contrast with much of the developed world, insurance companies can use genetic test results to refuse cover or increase premiums for mutually-rated insurance products, including life, income protection and disability insurance. Genetics professionals regularly discuss insurance implications with clients and report the issue as a clinical challenge, yet no studies have examined clinical practices or opinions. This study surveyed genetic counsellors and clinical geneticists from Australia and New Zealand to (i) investigate variability in professional practice across the Australasian clinical genetic workforce relating to the insurance implications of genetic testing, and (ii) ascertain views regarding current regulation of the issue. There was considerable variability in training and clinical policies, especially around the communication of insurance implications. Almost half of participants reported receiving no training on the insurance implications of genetic testing, and almost 40% were unsure whether they could adequately advise clients. A number of deficits in professional knowledge and understanding of the issue were identified. Widespread concerns regarding regulation of this area were reported, with < 10% of Australian participants considering current Australian regulations as adequate to protect clients from genetic discrimination. The findings from this study highlight scope for greater education, consistency and professional training on the issue of genetics and insurance in Australasia, and strong agreement about the need for regulatory reform. In Australia and New Zealand, insurance companies can use genetic test results to refuse cover, increase premiums or exclude aspects of cover for mutually-rated life insurance products, including life, income protection and total disability insurance. Genetic test results cannot be used for health insurance in Australia, which is community rated , but canMany countries, including Canada, the UK and much of Europe, have banned or restricted the use of genetic information by life insurance companies , 3. In AThe use of genetic test results by life insurers is particularly relevant for individuals who are unaffected by disease and undergoing clinical predictive genetic testing . Emerging research demonstrates that some at-risk individuals are deterred from having predictive genetic testing , 6 and cLife insurers in Australia and New Zealand are currently self-regulated , without government oversight . It can Clinical genetics professionals are in a unique position to inform clients about insurance implications of genetic testing before testing takes place . GuidancThis study was designed to determine if variability exists in workplace trends, training policies and opinions related to the issue of genetic testing and insurance, and its current regulation.Genetics professionals were recruited through the HGSA by email to members of the Australasian Society of Genetic Counsellors and the Australasian Association of Clinical Geneticists, the HGSA newsletter, and the 2017 HGSA Annual Scientific Meeting. Although the focus of the project was on Australian practice, the HGSA includes Australian and New Zealand practitioners and any interested participants were encouraged to participate. A screening question was used to include only genetics professionals who see clients considering genetic testing.The study utilised an online survey , which was developed and refined through consultation with statistical and subject matter experts, including genetic counsellors, geneticists, and law and ethics experts. The survey aimed to measure (1) presence and adequacy of training and policies held by genetics services regarding communication of insurance issues with clients; (2) knowledge and practice of genetics professionals; and (3) attitudes regarding regulation of the area. The published literature was reviewed and relevant validated scales were considered, however, no scales were suitable for the topics in the survey.The survey was open for data collection from 7 June 2017 until 18 August 2017. Data were collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools hosted at Murdoch Children's Research Institute . Online This study was completed in partial fulfilment of the requirements for the Master of Genetic Counselling, University of Melbourne, Victoria, Australia, and was supported by the Victorian Government's Operational Infrastructure Support Program. Approval for the project was granted by the Human Ethics Advisory Committee, Department of Paediatrics, University of Melbourne on 12 May 2017.n value) is reported. The demographics of the survey participants are set out in Table Eighty-seven genetics professionals participated in the online survey. The number of participants who completed each question  of participants reported the genetics service where they work had not provided training about the insurance implications of genetic testing  felt this training was inadequate. Sixty-one percent (n = 53/87) of participants stated that either their genetics service did not have a policy  or they were unsure whether there was a policy  regarding communicating with clients about the insurance implications of genetic testing  indicated their genetics service has one standard consent form for all types of genetic testing, and 53% (n = 19/36) of these do not include a statement about insurance implications  of participants did not have  or were unsure of having  sufficient knowledge about the insurance implications of genetic testing to properly advise clients  had < 10 years' professional experience. Of participants with more than 10 years' experience, 27% (n = 10/36) did not have or were unsure of having sufficient knowledge. Fifteen percent of Australian participants (n = 11/74) believe that genetic information could be used for health insurance policies in Australia,n = 74/79) of participants stated it could be used for life insurance, 68% (n = 54/79) for disability insurance, 91% (n = 72/79) for income protection insurance and 42% (n = 33/79) for travel insurance, indicating variability in knowledge of current regulation. Travel insurance is a mutually rated insurance product,Thirty-nine percent (n = 68/80) of participants had read the Centre for Genetics Education (CGE)'s Fact Sheet 20 titled, “Life insurance products and genetic testing in Australia”  or somewhat important (n = 19/79), and 99% (n = 78/79) consider that this communication is their responsibility  of participants always discuss insurance implications with unaffected adults considering predictive genetic testing. Practice differed for other kinds of testing . Participants were next most likely to discuss insurance implications in predictive testing in unaffected children  and least likely in prenatal testing .Ninety-four percent (n = 16/73) of participants have had direct experience with a client/s who had an adverse policy outcome on the basis of genetic test results  sometimes (n = 19/74), often (n = 24/74) or always (n = 27/74) discuss with clients the option to go away and organise their insurance before having genetic testing  agreed with the suggestion that this practice may amount to fraud  of survey participants considered insurers should not be allowed to ask whether applicants are considering having a genetic test, while 21% (n = 15/43) thought it should be allowed. Twenty percent (n = 15/73) were unsure  considered current Australian regulations adequate to protect clients from genetic discrimination. Fifty-two percent (n = 36/69) felt they were inadequate and 39% (n = 27/69) were unsure  considered Australia should have separate legislation regulating the use of genetic information by insurers. Two percent (n = 1/69) of participants answered no to this question, and 36% (n = 25/69) were unsure  of participants considered Australia should ban the use of genetic information by insurers, while 23% (n = 16/69) did not agree with a ban and 38% (n = 26/69) were unsure  and the Australian Genomics Health Alliance (AGHA) could collaborate to build on existing precedents and develop nationally consistent training modules and resources, and model consent clauses that could be adapted to each clinic's needs. However, while a collaborative approach to the insurance implications of genetic testing will assist with national consistency, each genetics service must ultimately take final responsibility for maintaining appropriate policies, communicating them to staff and ensuring knowledge and practice are up-to-date. Given the potential financial implications of misinformation in this area for clients, action is needed to address this situation. Encouragingly, the findings of this study have already led to the implementation of some training initiatives in Victoria, and prompted the development of a patient brochure in conjunction with the Centre for Genetics Education.A major finding of the study is the considerable professional concern regarding Australian regulation. Very few participants considered Australian regulations adequate, consistent with the HGSA's position statement regarding genetic testing and personal insurance products , which uThere was no clear consensus among participants regarding what type of regulation should be implemented, though more participants agreed with the implementation of legislation than a moratorium (ban) on the use of genetic test results by insurers. It is argued elsewhere that a ban should be implemented (with the exception of mutation-negative results used to counter a family history of disease), along with longer term regulatory reform .The model of a self-regulated life insurance industry does not compel a rigorous standard of evidence regarding which genetic test results have a sufficient evidence base for use in underwriting . A key rRegulatory reform on the use of genetic test results in life insurance underwriting must be considered by the Australian government to allow individuals to access genetic testing without fear of insurance implications. An inquiry into the life insurance industry has been conducted by the Joint Parliamentary Committee on Corporations and Financial Services. The Committee report, tabled in March 2018 , highligOur recommendations for addressing the issues identified in this paper are summarised in Table The study has a number of limitations. Its relatively small sample size of 87 limits its generalisability, providing an indication of the issues that could be found in the broader professional workforce with further investigation. Participant demographics were skewed towards inexperienced professionals, with 59% having 0–10 years' experience and senior professionals (>20 years' experience), constituting only 8% of the sample. It is difficult to determine whether these percentages accurately represent the current workforce distribution, given the lack of publicly available data on this. All participants' experiences and attitudes were given equal weight, where participants with many years of experience and knowledge may have a more informed view. The survey questions could have better encompassed the New Zealand regulatory system to allow for more meaningful comparison.The findings indicate an emerging clinical issue, highlighted by a lack of knowledge and/or training by a considerable proportion of genetics professionals. Individuals and health service organisations could better address the inconsistency of training provision and knowledge limitations in this area. Genetics services are responsible for developing appropriate policies and ensuring staff are adequately equipped in this regard. However, collaboration with other genetics services, the HGSA, and other relevant bodies, such as state governments, the National Health and Medical Research Council (NHMRC) and the Centre for Genetics Education (CGE), to develop a nationally consistent training programme, should be encouraged.Future research could focus on exploring these issues in a larger cohort, as well as considering the content of genetic counselling sessions by direct transcript analysis, further investigation of the differences between clinical services in various Australasian locations, and consumer views and experiences regarding genetic testing and insurance issues.JT wrote the initial draft of the manuscript. PL substantially revised the manuscript. SW and LK provided research input to the development of the project and reviewed the manuscript and suggested revisions. MO and MD reviewed the manuscript and suggested revisions.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.


Ceratophyllus gallinae, exploiting two hosts that are sympatric in our study population, the great tit Parus major and the collared flycatcher Ficedula albicollis. Previous experimental studies have indicated that the hen flea is both locally maladapted to great tit populations and composed of subpopulations specialized on the two host species, suggesting limited parasite dispersal in space and among hosts, and a potential interaction between these two structuring factors.In host-parasite systems, relative dispersal rates condition genetic novelty within populations and thus their adaptive potential. Knowledge of host and parasite dispersal rates can therefore help us to understand current interaction patterns in wild populations and why these patterns shift over time and space. For generalist parasites however, estimates of dispersal rates depend on both host range and the considered spatial scale. Here, we assess the relative contribution of these factors by studying the population genetic structure of a common avian ectoparasite, the hen flea C. gallinae fleas were sampled from old nests of the two passerine species in three replicate wood patches and were genotyped at microsatellite markers to assess population genetic structure at different scales . As expected, significant structure was found at all spatial scales and between host species, supporting the hypothesis of limited dispersal in this parasite. Clustering analyses and estimates of relatedness further suggested that inbreeding regularly occurs within nests. Patterns of isolation by distance within wood patches indicated that flea dispersal likely occurs in a stepwise manner among neighboring nests. From these data, we estimated that gene flow in the hen flea is approximately half that previously described for its great tit hosts.Our results fall in line with predictions based on observed patterns of adaptation in this host-parasite system, suggesting that parasite dispersal is limited and impacts its adaptive potential with respect to its hosts. More generally, this study sheds light on the complex interaction between parasite gene flow, local adaptation and host specialization within a single host-parasite system.The online version of this article (10.1186/s12862-018-1205-2) contains supplementary material, which is available to authorized users. Long-term interactions between hosts and parasites can shape the evolution of their life history traits, their behavior and their physiology, and can alter the way they interact with other organisms in the environment . BecauseThe host range of a parasite is also an important determinant of coevolutionary outcomes. Indeed, a parasite which infests a broad range of hosts will be subject to diffuse selective pressures from each host type compared to a more specialist parasite, resulting in a lower probability of fixing beneficial alleles for generalists compared to specialists , 7, 8. HCeratophyllus gallinae, infesting hole-nesting passerine species [Parus major, Paridae) in a fragmented habitat composed of distinct wood patches on Gotland (Sweden), suggested that hen fleas are locally maladapted to their hosts, i.e. local fleas have higher fitness when exploiting non-local compared to local tit hosts [Ficedula albicollis, Muscicapidae), also suggested the presence of distinct flea populations associated with each host species in some localities [The present study aimed at characterizing the population structure of a “generalist” ectoparasite at different spatial scales, and among different host species, in order to examine the link between dispersal, patterns of local adaptation and host specialization. We focused on a common bird ectoparasite, the hen flea  species . This ne species –19. Memb species . An expeit hosts . This obcalities . This obParus caeruleus), a closely related species with similar ecology, was possible in our sample. As blue tits are about half as abundant as great tits in our patches and frequently use feathers as nest material in addition to fur, few blue tit nests were likely included among the sampled nests. However, to remain conservative, we only make the distinction between “flycatcher nests” (collared flycatchers) and “tit” nests in our study.In March 2013, we sampled old nests of great tits and collared flycatchers in three wood patches (Fleringe (FL), Hall (HL) and Hammarsänget (HM)) on the northern part of the Swedish island of Gotland , 29 nests were selected at random for genotyping, with a balance between patches and host species . We genotyped 19 to 22 fleas from each selected nest. Fleas sampled within a nest are considered as an infrapopulation.Maps summarizing the sampling locations and methods Figs.  and 2 weWe individually extracted DNA from fleas in 300 μL of an extraction mix prepared with 234 μL of Nuclei Lysis Solution , 56 μL of 0.5 M Ethylenediaminetetraacetic acid  and 10 μL of Proteinase K . We then performed DNA purification using magnetic beads , following the manufacturer’s protocol.Polymerase Chain Reactions were performed in a 10 μL volume, containing 2 μL of the DNA extract, 5 μL of QIAGEN Multiplex PCR Master Mix , 1 μL of fluorescent primer mix (2 to 6 μM), and 2 μL of molecular grade water. DNA amplifications were carried out using a Geneamp 9700 Thermocycler . PCR conditions were the same for all targeted markers: initial denaturation at 95 °C for 15 min, followed by 35 cycles of 30s at 94 °C for denaturation, 90s at 57 °C for primer annealing and 60s at 72 °C for elongation and ending with a final elongation step of 72 °C for 10 min.C. gallinae genotyping, focusing on the most reliable : Cga2, Cga3As, Cga6, Cga9, Cga11, Cga14, Cga26, Cga28, Cga31, Cga32, Cga42, Cga45 and Cga46. We assessed allele lengths using the genetic analyzer ABI 3100 (Applied Biosystems) and the software Genemapper v3.7 (Applied Biosystems).We targeted 13 of the 23 markers previously described by Binz et al.  for C. gis and Fst for each locus over all populations using Weir and Cockerham’s unbiased estimator f and θ, respectively [In order to verify marker independence, we tested for linkage disequilibrium among the 13 markers over all infrapopulations using the software Genepop v. 4.3  with defectively , and calWe tested for flea population genetic differentiation within and among wood patches and investigated the relative genetic structure at the different spatial levels of the system Fig. .Fig. 2Hist at the among patch level using Weir and Cockerham’s unbiased estimator θ [st max as suggested by Hedrick and Goodnight [st standardized = Fst obs / Fst max [We characterized Fimator θ  and calcoodnight , using toodnight  and FSTA Fst max . Populat Fst max  for the  Fst max . This an Fst max , 32 withst and Fst max among nests as described above. Second, we tested for genetic differentiation between hosts in each patch using AMOVAs to decompose molecular variance from the host species category to the intra-individual level. Third, we investigated clustering among nests within each patch using DAPC as described above (with 50 components and 2 discriminant functions). Fourth, in order to test if the nest level was the most relevant minimal hierarchical scale for our study and to identify potential genetic groups shared by different nests, we also carried out a clustering analysis for each patch using the software STRUCTURE v.2.3.4 [st/(1-Fst)) and the natural logarithm of the geographic distance between nests (in meters) using Mantel tests . Where host-associated differentiation among flea populations was found, this test was carried out separately for each host species. Finally, we used Teriokhin’s generalized binomial procedure implemented in the software Multitest v1.2 [p-values was used rather than just half [We examined the population genetic structure of fleas within each of the three patches, both among nests and between host species. First, for each patch, we calculated F v.2.3.4 , 34. We  v.2.3.4  using th v.2.3.4 . Fifth, est v1.2 , 38 to cust half . For thris (f) for each patch with FSTAT v.2.9.3.2. We also calculated mean relatedness within each nest with Queller and Goodnight’s coefficient [In this last analytical step, we examined the population genetic structure at the infrapopulation level (within nests). We tested for a departure from panmixia within nests by calculating the Ffficient  using Geis and relatedness within infrapopulations (see within-nest section) and suggests that the markers may be physically linked. We therefore removed marker Cga31 from the marker set.Significant linkage disequilibrium was observed between two marker pairs: Cga31 and Cga46 (one nest in Fleringe), and Cga31 and Cga32 . This result could be due in theory to consanguinity within some nests, but this explanation was not supported by the observed values of Fis estimates were also variable among markers, but most estimates were significantly different from zero . Results did not differ with or without these markers, except for the estimation of global Fis, which was lower with the set of 8 compared to 12 markers, as expected. However, the infrapopulation estimates of Fis remained significantly higher than zero even with the reduced marker set . Deviations from Hardy-Weinberg proportions therefore originated from biological factors rather than technical issues (see below).Genetic diversity and allelic richness were variable among markers, ranging from 0.42 to 0.88 on average for genetic diversity, and from 5 to 32 over all populations for allelic richness, but did not vary much among nests or patches  ± SE =0.044 ± 0.006, p = 0.0001). Given that the maximum Fst max value calculable with this dataset is 0.265 instead of 1, the actual value of differentiation is Fst standardized = 0.166. At this large scale, differentiation was mostly driven by spatial components, and the DAPC separated infrapopulations into three groups corresponding to the three patches  ± standard error: Fleringe: 0.035 ± 0.006, Hall: 0.021 ± 0.004, Hammarsänget: 0.044 ± 0.006; all p-values< 0.0001). Once standardized for polymorphism, these values of Fst were Fst standardized = 0.138, 0.085 and 0.166 for Fleringe, Hall and Hammarsänget, respectively.Within each wood patch, overall among-nest differentiation was significantly differently from zero (FP ≤ 0.3689), the overall pattern of between-host differentiation was significant.The DAPC analysis revealed no obvious pattern of population structure in relation to host type Fig. . The decEvanno’s Delta k method suggested an optimal number of clusters of k = 17, 2 and 5 for Fleringe, Hall and Hammarsänget respectively. We also compared the patterns of assignment to clusters corresponding to the second highest Delta k value  to test how robust the genetic structure was with respect to the number of assumed clusters. Regardless of the k-value considered, the results of STRUCTURE indicated shared group memberships among nests and no obvious correspondence with host species , where between host differentiation was not significant at the 0.05 threshold. In the two other patches, isolation by distance signals differed between host species,  suggesting within nest substructure and/or inbreeding. For all three patches, mean pairwise relatedness within each nest indicated that individuals within a nest were frequently more closely related than individuals selected at random in the patch  is approximately twice as high as that previously estimated for great tits on Gotland at the among patch level . Althouax value , the levax value , 48 and ax value , could tax value . Althougax value . As an eax value , which cax value  reducingax value ; in bat ax value  and reprax value  which ca, a local Wahlund effect, [We found that flea infrapopulations  deviated significantly from Hardy-Weinberg proportions. This result may be due to either inbreeding (i.e. mating with relatives) and/or the presence of different family groups within nests ; both p effect, ). Given  effect, , which sPopulation differentiation in relation to host species was observed here, suggesting that despite small scale gene flow, population divergence is occurring between fleas exploiting tits and flycatchers. This result matches fitness differences observed in a cross-infestation experiment of hen fleas between great tits and collared flycatchers on the same island . In thisBecause fleas sampled in the nest of a given host species were suggested to perform less well when infesting the alternative host , host spHen flea populations on Gotland could either be at an equilibrium between selection and gene flow, such that the between-host genetic structure observed here is maintained across generations, or they could be undergoing specialization that could lead to increasing population structure between the different host species over time . The saHen flea populations were genetically structured across the study area and the observed patterns of spatial and host-associated differentiation fell in line with the results of previous experiments in this multi-host-parasite system. Our results suggest that the observed hen flea maladaptation to its main host, the great tit, could be explained by low parasite dispersal compared to its host. Limited dispersal may also account for the between-host patterns we found, where low gene flow favors host-associated selection and adaptation. The results of our population genetic study therefore provide essential new elements for understanding the ecology and coevolutionary trajectories of the hen-flea passerine system. Because this system represents a popular model system to study host-parasite interactions and evolution in the wild , 64–67, More generally, our study sheds empirical light on the relationship between the two evolutionary phenomena acting here: local adaptation and host specialization. We suggest that there may be a feedback loop between local maladaptation and host specialization; low dispersal of the parasite can prevent local adaptation of the parasite population, but it can also prevent parasites from shifting hosts within their lifetime, therefore enhancing host-associated selective pressures on the parasite. Parasite specialization to particular host types may then, in turn, reduce the dispersal potential of the parasite  and enhance parasite maladaptation. Modelling and experimental studies with fast-evolving organisms would be helpful to test this relationship, and would provide exciting new perspectives for understanding the evolutionary ecology of host-parasite systems.Additional file 1:Table S1. Genetic diversity at each locus for each population & Table S2. Allelic richness at each locus for each population (PDF 162 kb).This file contains two tables: Additional file 2:This file contains figures representing the probability of individual flea assignment to the k clusters computed by STRUCTURE, for each patch (PDF 202 kb).Additional file 3:This file contains graphs representing the isolation by distance within each patch, not considering host-associated structure (PDF 86 kb).