Dispersal is one of the main determining factors of population structure. In the marine habitat, well-connected populations with large numbers of reproducing individuals are common but even so population structure can exist on a small-scale. Variation in dispersal patterns between populations or over time is often associated to geographic distance or changing oceanographic barriers. Consequently, detecting structure and variation in dispersal on a fine-scale within marine populations still remains a challenge. Here we propose and use a novel approach of combining a clustering model, early-life history trait information from fish otoliths, spatial coordinates and genetic markers to detect very fine-scale dispersal patterns. We collected 1573 individuals (946 adults and 627 juveniles) of the black-faced blenny across a small-scale (2 km) coastline as well as at a larger-scale area (<50 kms). A total of 178 single nucleotide polymorphism markers were used to evaluate relatedness patterns within this well-connected population. In our clustering models we categorized SHORT-range dispersers to be potential local recruits based on their high relatedness within and low relatedness towards other spatial clusters. Local retention and/or dispersal of this potential local recruitment varied across the 2 km coastline with higher frequency of SHORT-range dispersers towards the southwest of the area for adults. An inverse pattern was found for juveniles, showing an increase of SHORT-range dispersers towards the northeast. As we rule out selective movement and mortality from one year to the next, this pattern reveals a complex but not full genetic mixing, and variability in coastal circulation is most likely the main driver of this fine-scale chaotic genetic patchiness within this otherwise homogeneous population. When focusing on the patterns within one recruitment season, we found large differences in temperatures (from approx. 17 °C to 25 °C) as well as pelagic larval duration (PLD) for juveniles from the beginning of the season and the end of the season. We were able to detect fine-scale differences in LONG-range juvenile dispersers, representing distant migrants, depending on whether they were born at the beginning of the season with a longer PLD, or at the end of the reproductive season. The ability to detect such fine-scale dispersal patchiness will aid in our understanding of the underlying mechanisms of population structuring and chaotic patchiness in a wide range of species even with high potential dispersal abilities. Although our understanding of larval dispersal in the marine environment has greatly improved over the last several decades2, much is still unknown. Most marine organisms exhibit a larval phase where small larvae can, in theory, travel large distances with ocean currents. Marine populations were thus generally thought to have elevated gene flow and little population structure3. This paradigm has been largely disproven as many recent studies have revealed that genetic population structure can exist across even small spatial scales and self-recruitment is often larger than previously believed5. In addition, direct measurements of dispersal using pedigree reconstruction analyses have revealed that self-recruitment and recruitment patterns are often heterogeneous and complex7.In marine ecosystems, larval dispersal and recruitment are the main processes determining population structure of species and understanding how they maintain connectivity is crucial for effective management8, reproductive output9, ocean currents10 or localized eddies and oceanographic retention patterns12. The duration of the larval period is known to influence larval dispersal distance13 and can have a direct relationship with connectivity14. Information on PLD has been used in numerous studies modelling dispersal capabilities, connectivity and for the establishment of networks of Marine Protected Areas17. Several studies have shown an influence of ocean temperature on PLD, decreasing its duration with increasing temperature13, thus a change in temperature could have a direct influence on population connectivity18. Considering the potential increase of sea temperatures due to global climate change19, studies analysing the relationships among temperature, PLD and connectivity are essential in marine ecology.Dispersal patterns depend on pelagic larval duration (PLD)23. Further factors determining patterns of dispersal are availability of habitat, environmental selection, behaviour or kinship24. The population structure of marine organisms can be influenced by the degree to which larvae from different populations or demes are mixed in the plankton26. Some studies have demonstrated the existence of familial structure with marked genetic distances between recruitment cohorts; thus the lack of larval mixing during the planktonic period can lead to chaotic genetic patchiness29. Other studies, however, showed that related individuals did not necessarily disperse cohesively, but limited dispersal was the cause of non-random patches of closely related individuals30. Hence, many questions remain to be answered about dispersal, settlement and survival patterns of related individuals, how these are affected by environmental conditions such as temperature and how in turn they affect the structure of populations in the marine environment. Particularly genetic structure on a very fine-scale has not been investigated and we here present a novel approach to understanding recruitment patterns leading to population structuring.In the last few years, studies of connectivity have used observations of kin, such as parent-offspring pairs or full siblings, to infer the trajectories and distances of larval movement at small scales, but require large sampling efforts and genotype datasets32, molecular ecology not being an exception. The existence of large-scale structured datasets with fine scale metrics related to environmental data, behavioural phenotypes, or genetic information, challenges traditional ways to analyze data structure. Unsupervised clustering methods36 are useful to get insights about data structuration when there is minimal or no information about it. Several first principled methods can be used to highlight structural properties, some of them particularly focusing on the interpretability of such structures37. Here, we apply novel and easy-interpretable clustering procedures to genetic relatedness information in order to infer fine-scale genetic structuring and dispersal in large populations.In the last decade, many fields of ecology have experienced a data revolutionTripterygion delaisi), using patterns of kinship inferred from single nucleotide polymorphism (SNP) genotypes. We used a novel approach of binary data clustering to consider these fine-scale dispersal patterns. Specifically, we combined larval traits, such as pelagic larval duration, and evaluated selective dispersal patterns via genetic marker heterogeneity in space. In addition, the detected fine-scale patterns allowed studying the effect of temperature on dispersal throughout the recruitment season with lower temperatures at the beginning and higher temperatures at the end. In summary, this approach is used to detect fine-scale patterns of dispersal and potential spatial assemblages of recruits, as well as differences between early and late season individuals in a well-connected population of T. delaisi, revealing patterns of relatedness that were not previous detected using traditional methods.We aim to detect fine-scale structure and dispersal patterns of a rocky shore fish species, the black faced blenny  of this species was estimated to be about 2–3 weeks, depending on the water temperature42. Therefore, this species is a good study organism to analyse the relationships between temperature and dispersal patchiness.T. delaisi is a small, common, rocky-shore fish from the Mediterranean Sea and the eastern AtlanticT. delaisi were collected on SCUBA from the coast of Blanes, Spain in the western Mediterranean Sea (41°40′30.7″N 2°48′14.4″E). Adults were caught with hand nets, body length was measured, and dorsal fin clips were taken non-lethally underwater, and then preserved in 95% ethanol. Adults were collected during the length of their breeding season from April 2010 to July 2010. Juveniles were also captured with hand nets on SCUBA between July and September 2010 and subsequently used for genetic and otolith analysis. Sea surface temperature readings were taken 5 m below the surface twice weekly with a Conductivity, Temperature and Depth probe (CTD) at Las Medes Marine Reserve . Collection and field procedures followed the Spanish Laws  for Animal Experimentation, in accordance with the European Union directive (2010/63/UE) and approved by the Consejo Superior de Investigaciones Cientificas (CSIC).Overall, 946 adult and 627 juvenile Two different spatial scales were studied. A ~2 km stretch of coastline in the vicinity of Blanes Fig.  is the s43. To verify the first growth increment of the otolith, fertilized T. delaisi eggs collected in the field were reared in aquaria. First growth bands were checked at day 1 and day 3 post-hatching in ten individuals. This yielded both the timing of formation of the first band and the daily pattern of band deposition and confirmed that band deposition took place daily from the day of hatching. Otolith marks in this species were always clear and belonged to Type Ia: an abrupt settlement mark with a sharp decrease in increment width across the settlement mark, completed within one increment44. The length of pelagic larval duration (PLD) for each individual was determined by counting the number of daily rings visible between the core and the settlement mark, and the age at sampling was determined by counting the total number of bands from core to margin. We analysed the otoliths along the longest radius from the centre to the edge and recorded otolith width during the pelagic larval period. We also recorded the size-at-hatching (radius from core to first increment) and size-at-settlement along the longest axis of the otolith. To minimize errors, all measurements were repeated three times. Trait differences were statistically evaluated in R 45.Juvenile otoliths (lapilli) were extracted and mounted on microscope slides using thermoplastic glue . To expose all daily increments, the otoliths were mounted and polished using two different grained sandpapers  to obtain a thin section exposing the nucleus and the first growth ring. Reading of otolith bands was performed using a high-powered microscope with transmitted light  connected to a ProgRes C10 camera (Jenoptic) and an image analysis systemth of June and the end of the recruitment season, which is at the beginning of September . One additional comparison was made to be able to evaluate differences in possible selective mortality post-settlement. We could establish individuals that had just settled  named “recent settlers” and individuals that had survived a period of minimum of 30 days after settlement  considered “survivors”.A total of 200 randomly selected otoliths from juveniles collected in the small-scale area were read. Total body length of the juveniles correlated very well with the determined age by otolith reading  among adults as well as among juveniles were obtained by using the Queller and Goodnight equation48 implemented in the SPAGeDi software49. To find patterns of high or low relatedness amongst thousands of pairwise values, we used a relatedness ‘ratio’ to identify individuals with large numbers of high relatedness values. This is not a direct measurement of how these individuals recruited, but an attempt to categorize individuals according to their relatedness values to other samples and find patterns within this well-connected population. For each individual, the number of r-values above 0.1 was counted and divided by the total number of comparisons. The top 25% of individuals with the highest ratios were termed ‘locals’, as these individuals are showing the highest relatedness values to a larger portion of other individuals. The samples with the lowest ratios (bottom 25%), which have an elevated number of highly negative relatedness values with the rest of the individuals, were referred to as ‘recent migrants’. A cutoff of 10% was also used for comparison, however, patterns did not differ although the number of samples was small (data not shown). Differences in spatial structure of these groups of individuals were evaluated using their geo-referenced locations of collection and with a Kolmogorov-Smirnov test in R.All 1573 individuals were genotyped with 192 SNP markers developed for 35. Based on the overall depiction of the positions, we assumed a Gaussian mixture model over the spatial distribution of individuals and performed several runs of the algorithm with 4, 5 and 6 initial Gaussian components (10 runs each) picking the model with lowest AIC50 which resulted in 6 spatial clusters.To evaluate the spatial distribution of kinship we incorporated spatial structuring of samples into the analysis. Latitudes and longitudes for each individual’s sampling location were used to find spatial clusters within the small-scale (Blanes) area as well as in the large-scale sampling area and assigned individual geo-localized samples to the respective spatial clusters. To this end we used the R package EMCluster51 for calculation needs we converted each single relatedness value into a measure between 0 and 1 with the formula (d = 1 − (r − a)/(b − a)), where a and b are the minimum and maximum value of relatedness , respectively. Thus, for each individual we computed two distinct values based on mean pairwise-relatedness: (i) the mean relatedness value of an individual in a given spatial cluster to all the other members of the same cluster (rIN), and (ii) the mean relatedness value of that individual to the rest of individuals outside of its spatial cluster (rOUT). Finally, we characterized each individual with these two measures and performed an Expectation-Maximization binary clustering with the R package EMbC37. The EMbC splits the range of each variable into high and low values based on a Gaussian mixture model. The thresholds of these binary partitions are determined by the frontier lines of equiprobability between the different Gaussian components. This analysis allowed us to partition all individuals according to four categories  and late juveniles (−0.00027 ± 0.015) were not significantly different   nor between early and late juveniles  for all loci combined Fig. , nor bet58) Fig. . Of the All collected fish were geo-localized allowing the analysis of spatial structuring. We did not find differences in spatial distribution patterns between adults and juveniles representing two different years of recruitment  when only space was considered. Immigration of less-related individuals, possibly from more distant populations, into the small-scale area could potentially result in different spatial patterns for these ‘recent migrants’. Adult migrants were differently distributed when compared to the rest of adults  with some differences evident in the southernmost part of the distribution Fig. . HoweverT. delaisi, as we found no adults and only a few juveniles.With hundreds of pairwise relatedness comparisons per individual, it is difficult to detect fine-scale genetic structure, although there could be spatial clusters. To evaluate genetic clustering in space, we first found significant spatial clusters in the small-scale area for both adults and juveniles Fig. . We founThe proportion of individuals in each cluster that came from the SHORT-range disperser category varied from southwest to northeast along the coastline Table . For juvJuveniles born at the beginning of the season (early juveniles) and at the end of the season (late juveniles) have differences in pelagic larval duration Fig. , and difGenetic spatial clustering for juveniles across the whole large-scale sampling area, spanning a distance of almost 50 km, identified five distinct clusters, four of them including individuals within the small-scale area Fig. . Some ofThe proportion of LONG-range dispersers, and thus likely immigrants, was very low for the large-scale dispersal area Table . As the MIXED-range dispersers: individuals with high mean relatedness (r) with respect to individuals in their own spatial cluster (rIN) and high r with respect to individuals in other spatial clusters (rOUT). SHORT-range dispersers: individuals with high rIN and low rOUT. MEDIUM-range dispersers: individuals with low rIN and high rOUT. LONG-range dispersers: individuals with low rIN and low rOUT. Values above 0.5 are in bold.Tripterygion delaisi) on the northern Mediterranean coast of Spain, through the combined use of genetic relatedness indices and a novel approach of binary data clustering that incorporates spatial patterns. In such a well-connected population with large numbers of dispersing individuals, genetic homogeneity would be expected and no population structure or population-wide pairwise relatedness was found with traditional methods20. However, binary clustering of genetic relatedness indices mapped in space revealed a slight clustering of T. delaisi individuals with different dispersal patterns. More adult blennies (sampled in 2010 and recruited mostly in 2009) considered short distance dispersers, were found in the southwestern parts of the small-scale study area. In contrast, juvenile individuals that recruited and were sampled in the following year (2010), had more short distance dispersers in the northeastern part of the 2 km coastline. Previous population genetic studies of this species with microsatellite loci, in the same small-scale area and the surrounding areas (up to 50 km), identified only one genetic unit38. Sibship analysis of T. delaisi from the same location found that full siblings can recruit many km apart from each other, further indicating a genetically homogenous population20. Nonetheless, the binary clustering analysis across the large-scale area demonstrated that a large proportion of juveniles collected northeast of the small-scale sampling region were MEDIUM-range dispersers and thus more related to the juveniles of this sampling region. This could potentially be the result of a previously unknown directional movement of larvae from southwest to northeast. Although high site-fidelity is observed in this species40 we cannot completely rule out selective movement behaviour of adults to the southwestern part after having recruited the year before. However, there was no selective movement between recent settlers and survivors, individuals that survived a period after settlement, within one recruitment year. The detection of fine-scale clusters at both small and large-scale study areas shows that there is a complex but not full genetic mixing and suggests oceanic/coastal circulation as the main driver of the fine-scale chaotic genetic patchiness within this otherwise homogeneous population.In the present work, we document very fine-scale patterns of dispersal and recruitment in a population of the black-faced blenny  is very large, with more than 1000 individuals on a narrow 2 km stretch of coastline20. Reproductively active individuals include females and two types of males, the dominant nest-making male and the sneaker male40. So-called sweepstake reproduction has been shown to cause genetic patchiness in other species56 and for T.delaisi it could be that a few successful  males contribute to the majority of offspring. However, the testes of T. delaisi sneaker males are significantly larger than those of dominant males and gene expression analysis found genes involved in differentiation and development, suggesting that these males are reproductively active57. A decline in diversity would be expected if only a proportion of the population is contributing to the next generation58. However, in our analyses, we did not find any evidence of sweepstakes reproductive success as genetic diversity did not decrease between adults and juveniles. Furthermore, neither heterozygosity nor relatedness differed for juveniles recruiting at the beginning or at the end of the breeding season, or between recent settlers and survivors. Sweepstakes-driven patchiness thus seems unlikely, even though high variability in the number of contributing adults over time has been shown in other marine fishes59.The reproductive biology of T. delaisi migrants arrived early and late in the season. Nonetheless, at ‘La Roca’ , a submerged rocky habitat not continuous with the rocky shoreline habitat, all early juveniles collected (N = 29) were LONG-range dispersers and can be considered migrants. This could hint at collective dispersal, at least at the beginning of the season, which Broquet and coauthors54 suggested as the second source for chaotic genetic patchiness and greater than expected levels of kinship as an indicator have been found in other marine organisms60. However, a previous study only found one full sibling pair of T. delaisi settling together in the same area20 and the fine-scale patchiness pattern might be due largely to seasonal variation in current patterns.Patchiness could also be due to recruitment differences related to the conditions encountered by larvae. Pelagic larval duration (PLD) varied significantly with the hatching date of the larvae, with PLD up to 10 days shorter at the end of the season than at the beginning. Larvae that spend more time in the water column have more opportunities to disperse further and might include more immigrants from other populations. Yet, in the whole sampling area a similar proportion of T.delaisi was the sea temperature. In our study area, the difference in sea surface temperature between the beginning (April) and the end of the reproductive period (July) is approximately 8 °C. This increase in temperature resulted in a decrease of PLD. Consequently, increased ocean temperatures, due to global climate change, could decrease connectivity between populations and increase self-recruitment due to a general decline in PLD at higher temperature63. The effect of temperature on larval connectivity has also been modelled and results suggested a considerable change in larval recruitment and connectivity64. Such changes in dispersal potential and connectivity in T. delaisi could have detrimental effects on population genetic diversity. Nonetheless, the seasonal increase is larger than the predicted temperature increase in the western Mediterranean Sea (ca. 0.25 °C per decade) towards the end of the present century due to climate change65. Mean PLD of the black-faced blenny clearly decreases with increased temperature, but in the small-scale area studied in the present work this had little effect on the overall connectivity of the population since the proportion of individuals arriving from distant localities was similar regardless of PLD or water temperature. Even with a possible reduction in PLD over time due to climate change, intra-annual variation in conditions encountered by larval T. delaisi may provide a buffer and allow maintenance of generally well-connected populations.One factor influencing the PLD in Tripterygion delaisi on the Mediterranean coast of Spain is a well-connected unit but appears to have fine-scale structure in both space and time. The combination of geo-localized individual locations, hatching date information, and genotypic SNP data allowed detection of patches of larvae with particular dispersal histories. Yet, very fine-scale genetic patchiness across space and time was only apparent when using binary clustering techniques, previously used primarily for animal movement trajectory segmentation37. The ability to detect such fine-scale dispersal patchiness will aid in our understanding of the underlying mechanisms of population structuring and chaotic patchiness in a wide range of species, including those with high potential dispersal abilities.The species Supplementary Figures & Table


Clostridium perfringens, which cause fatal enterotoxaemia in sheep, goats and cattle. Previous studies showed that only a restricted number of cell lines are sensitive to ETX and ETX-induced hemolysis has not previously been reported. In this study, the hemolytic ability of ETX was examined using erythrocytes from 10 species including murine, rabbit, sheep, monkey and human. We found that ETX caused hemolysis in human erythrocytes (HC50 = 0.2 μM) but not erythrocytes from the other test species. Moreover, the mechanism of ETX-induced hemolysis was further explored. Recent studies showed that some bacterial toxins induce hemolysis through purinergic receptor (P2) activation. Hence, the function of purinergic receptors in ETX-induced hemolysis was tested, and we found that the non-selective P2 receptor antagonists PPADS inhibited ETX-induced lysis of human erythrocytes in a concentration-dependent manner, indicating that ETX-induced hemolysis requires activation of purinergic receptors. P2 receptors comprise seven P2X (P2X1–7) and eight P2Y  receptor subtypes. The pattern of responsiveness to more selective P2-antagonists implies that both P2Y13 and P2X7 receptors are involved in ETX-induced hemolysis in human species. Furthermore, we demonstrated that extracellular ATP is likely not involved in ETX-induced hemolysis and the activation of P2 receptors. These findings clarified the mechanism of ETX-induced hemolysis and provided new insight into the activities and ETX mode of action.Epsilon-toxin (ETX) is produced by types B and D strains of  Clostridium perfringens is a Gram-positive, spore-forming, anaerobic, rod-shaped bacterium that carries genes encoding more than 17 exotoxins. It is conventionally categorized into five toxinotypes, A to E, based on carriage of one or more of the major toxin genes  [C. perfringens epsilon toxin (ETX) is mainly produced by C. perfringens types B and D [Clostridium botulinum and Clostridium tetani neurotoxins [50) of ETX in mice is 50 ng·kg−1 [ B and D . The tox B and D . ETX is rotoxins . The fac ng·kg−1 .C. perfringens λ-protease [In vivo, the toxin appears to target the brain and kidneys, but a relatively restricted number of cell lines are susceptible to the toxin, with most work having been carried out in Madin Darby canine kidney (MDCK) cells [in vitro model for the study of ETX [The relatively inactive secreted prototoxin is converted to the fully active mature toxin by proteases of the host, such as trypsin and chymotrypsin , or by tprotease . Active protease  and enteK) cells . Over thy of ETX . The Cauy of ETX , human ky of ETX , and mury of ETX  were idey of ETX .+, an increase in Na+ and Cl−, and a delayed increase in Ca2+ [Escherichia coli [Staphylococcus aureus [ETX is considered to be a pore-forming toxin. It was reported that ETX formed a heptameric membrane complex that created ~2-nm-wide pores in the MDCK cell membrane and led to membrane permeabilization, in turn leading to a rapid decrease in intracellular K in Ca2+ . Many pohia coli  and α-tos aureus . HoweverE. coli [S. aureus [+, K+, Ca2+, and organic ions [Previous studies showed that purinergic (P2) receptor activation is involved in some bacterial toxin-induced hemolysis of erythrocytes. For example, hemolysis induced by HlyA from E. coli  and α-to. aureus  requires. aureus –14. Puri. aureus ,16. P2 r. aureus . P2X recnic ions . P2Y recnic ions . P2 recenic ions . It is rIn the present study, we investigated the characteristics of ETX-induced hemolysis. Also, the function of purinergic receptors in ETX-induced hemolysis of human erythrocytes was tested using various antagonists. Furthermore, the role of ATP in the activation of P2 receptors was determined and consequently the mechanism of ETX-induced hemolysis was clarified.E. coli BL21 (DE3) strain and purified using a Ni2+ chelating affinity chromatography resin column. A high-purity of rETX protein was obtained. No unwanted band was detected in the gel . The hemolytic ability of rETX was examined using erythrocytes from various sources including murine, rabbit, sheep, goat, cattle, equine, dog, monkey, and human. The erythrocyte suspension  from various sources was incubated with purified rETX . The result showed that rETX lysed human erythrocytes, but not erythrocytes from the other test species (Recombinant ETX (rETX) with 6× His-tag was expressed in the  species . The eff species . The max species . Complet species .10.1080/E. coli strain, we tested the effect of the attenuated rETX mutant rETXF199E  [F199E was expressed and purified using the same method as rETX. rETXF199E exhibited an attenuated hemolytic ability (To verify that the observed hemolysis was induced by rETX and not contaminating proteins from the engineered ic acid)  on the h ability , indicat50) after 60 min incubation at 37°C was 0.2 μM. In all subsequent experiments, 0.2 μM of rETX was therefore used. ETX-induced hemolysis is also time-dependent, with the level of hemolysis increasing with time. It took approximately 30 min for human erythrocytes to reach stable phase  value for PPADS was 519 μM. This finding was substantiated for a wide range of ETX concentrations , suggesting that a P2X receptor is involved in ETX-induced hemolysis. Of the P2X-receptors expressed in erythrocytes, we regarded P2X7 and P2X1 as the most likely mediators of ETX-induced hemolysis according to previous studies [Having tested P2Y receptor antagonists we then moved onto testing non-selective and selective P2X receptor antagonists.  studies ,16. P2X1 studies –14.10.10We tested the P2X1 selective antagonists NF449 and NF023 . NF449, 2+ [50 value of 90 μM , ATP-2′, 3′-dialdehyde (OxATP), MRS2159 , KN-62 , Mg2+ [22+ , and A432+ . BBG decof 90 μM . The proof 90 μM . MoreoveBy contrast to BBG, other P2X7 receptor antagonists OxATP and KN-62 slightly decreased ETX-induced hemolysis by 20% at the maximal concentrations tested . The nov50 of at least 1 log lower than that of A438079 [50: ~349 μM)  .2+ ions were effective in suppressing the P2X7 current component in P2X1/7 co-expressing oocytes, whereas the P2X1 current component remained relatively unaltered [2+ ions were used to be a high selective antagonist of P2X7. Our results showed that Mg2+ ions inhibited the ETX-induced hemolysis in a concentration-dependent manner (EC50: ~252 mM)  , indicatE. coli HlyA and Aggregatibacter actinomycetemcomitans leukotoxin A induce ATP release from RBCs by forming toxin pores, which then acts on P2X1 and P2X7 to mediate hemolysis [Recent findings indicated that emolysis . TherefoAdditionally, we found that PPADS (1.0 mM) could abolish ATP release by ETX-treated RBCs , suggestWe hypothesized that ATP release is likely the result of hemolysis and that ATP is released when erythrocytes are disrupted by activation of P2 receptors. Many antagonists such as MRS2159, MRS2211, A438079, BBG, and OxATP, were employed to investigate the relationship between ATP release and hemolysis of erythrocytes . FurtherThe P2X7 receptor has been reported to interact with the channel protein pannexin 1 , and thiETX was first described by Wilsdon over 70 years ago . ETX hasThe characteristics of ETX-induced hemolysis were determined. The morphological effects of ETX-induced hemolysis in RBCs appeared to resemble those in MDCK cells. Similar sags and crests were observed on the surface of RBCs as those on ETX-treated MDCK cells . A stablThe preferred hemolysis in human RBCs indicated that ETX likely causes hemolysis in a receptor-dependent manner. This was substantiated by an ETX binding assay. The binding assay showed that rETX could bind to the surface of human erythrocytes compared with sheep erythrocytes . Taken together, these data suggested that there was a receptor exclusively expressed on the surface of human RBCs. ETX is a pore-forming toxin; therefore, changes in plasma osmolality commonly induced by toxin-formed pores are likely to be the main reason for ETX-induced hemolysis. However, we found that a high concentration of sucrose did not affect hemolysis, indicating that osmolality plays little role in ETX-induced hemolysis. This raises a new question: what is the mechanism of ETX-induced hemolysis? Therefore, we further investigated whether P2 receptors are involved in ETX-induced hemolysis.We found that ETX-induced hemolysis is blocked by various inhibitors of purinergic signaling. PPADS, a non-selective purinergic antagonist, entirely eliminates ETX-induced hemolysis. Suramin, another non-selective P2 antagonist, reduces ETX-induced hemolysis with less efficiency. Taken together, these data imply a central role for P2 receptors in ETX-induced hemolysis. This finding led us to test a series of antagonists to find out which P2 receptor(s) are involved in ETX-induced hemolysis. The results showed that P2Y13 and P2X7 are mainly involved in ETX-induced hemolysis.Functional P2Y13 is present on RBCs, where it negatively regulates ATP release from these cells . Activat2+ ions were effective in suppressing the P2X7 current component but left the P2X1 current component relatively unaltered [The P2X1 and P2X7 receptors are known to undergo transition to a greater permeability state, which eventually leads to lysis in certain cells . P2X1 annaltered . Taken tE. coli and leukotoxin A from A. actinomycetemcomitans induce ATP release from RBCs by forming toxin pores. Released ATP then acts on P2X1 and P2X7 to mediate hemolysis [Studies reported that HlyA from emolysis ,14,16. Aemolysis .10.1080/The mechanism by which P2 receptor activation plays a role in ETX-induced hemolysis remains unclear. One possibility is that ETX binds to and activate P2 receptors. Another possibility is that ETX interacts with P2 receptors after binding to cells, which is mediated by another receptor. This could explain why ETX exclusively lyses human RBCs, since P2 receptors are widely expressed in the RBCs of many species that are resistant to ETX-induced hemolysis. An inferred mode of ETX-induced hemolysis is shown in +, influx of Na+, Ca2+, and Cl− ions, and finally rapid cell death [2+, which could be mostly inhibited by an antagonist of P2 receptors . This indicated that activation of P2 receptors was responsible for the majority of the influx of Ca2+. P2X7 receptors are most highly expressed in immune and epithelial cells [+, and the influx of Na+ and Ca2+, are likely the result of activation of P2X receptors since P2X receptors are non-selective cation channels [Previous studies suggested that in MDCK cells exposed to ETX, a large membrane complex formed on the cell surface , leadingll death ,11. In Eal cells . We therchannels . Howeverchannels . This phchannels . In factchannels . Taken tOur results suggested that pannexin 1 does not play a significant role in P2X7-mediated hemolysis. A previous study showed that pannexin 1 is part of the pore-forming unit of the P2X7 receptor death complex . FurtherLittle is known about the effect of ETX on human health. There are only two case reports of illness in humans related to the epsilon toxin and these were reported in 1955 . ConsideIn conclusion, we show that ETX induces hemolysis in human erythrocytes but not in erythrocytes from the 10 animals tested in our study. We also confirmed that ETX-induced hemolysis in human erythrocytes requires the activation of P2 receptors, mainly including P2Y13 and P2X7. Moreover, we demonstrated that ATP is likely not involved in the activation of P2 receptors, and that ATP is the result of ETX-induced hemolysis rather than a mediator of ETX-induced hemolysis. These findings clarified the mechanism of ETX-induced hemolysis and provided new insights into the mode of action of ETX. The mechanism of ETX-induced hemolysis is different from hemolysis reported previously for other pore-forming toxins. However, the mechanism by which ETX activates P2 receptors and the mechanism of ETX-induced hemolysis remains unclear.P2 receptor antagonists: Pyridoxal phosphate-6-azo  tetrasodium salt hydrate (PPADS), MRS2211, MRS2179, MRS2159, A438079, Brilliant Blue G, ATP-2′, 3′-dialdehyde (OxATP), niflumix acid, carbenoxolone disodium salt, and apyrase were purchased from Sigma-Aldrich . NF449, NF023, and Evans Blue tetrasodium salt were purchased from Tocris Bioscience . Suramin hexasodium salt was purchased from Aladdin . Anti-His monoclonal antibody was purchased from EARTHOX Life Sciences . Anti-ETX monoclonal antibody was developed previously by colleagues in our laboratory. HRP-coupled goat anti-mouse IgG antibody was purchased from TransGen Biotech . The ATP assay kit was purchased from Calbiochem-Merck . MTS  -5(3-carboxymethoxyphenyl) -2-(4-sulfopheny)-2H-tetrazolium, inner salt) was purchased from Promega Corporation .Human blood was collected by venopuncture from healthy volunteers using evacuated blood collection tubes containing EDTA-2K. The blood of mice, rats, sheep, bovine, rabbits, monkeys, horses, dogs and goats were collected, then the erythrocytes were washed three times in 0.01 M PBS . The “buffy coat” was removed and isolated erythrocytes were packed by centrifugation at 1,000 g for 10 min at 4°C.E. coli BL21 (DE3) cells. The bacteria were grown in 5.0 ml of sterile lysogeny broth (LB) medium and incubated for 6 h with constant swirling . Then the culture was transferred to 500 ml of sterile LB medium with Amp+ (100 μg/ml) and incubated for 4.5 h at 37°C with constant swirling (180 rpm) to exponential growth phase (measured as the OD600). IPTG at a final concentration of 0.5 mM was used to induce the expression of rETX. Then, the medium was cultured in a shaker  overnight. The following morning, cultures were centrifuged  to pellet the bacteria.Our laboratory constructed the recombinant plasmid pTIG-his-etx, which expressed rETX  and were transferred into 2+ affinity chromatography column  as previously described [To purify the ETX, which contained a C-terminal His-Tag, the pellet was re-suspended in lysis buffer and the bacteria were lysed by sonication on ice. The lysate was centrifuged at 11,000 rpm for 15 min at 4°C. The clarified supernatant was purified using an Niescribed ,44. The Washed RBCs were resuspended in 0.01 M PBS to produce a 5% erythrocyte solution. The hemolytic activity was measured spectrophotometrically at 540 nm. In the hemolytic assay, purified rETX (0 ~ 33 μM) was added to the 5% red blood cell solution  and incubated up to 1 h at 37°C with constant swirling (300 rpm), or the purified rETX (0.2 μM) was added at a concentration leading to 50% hemolysis after 60 min with or without antagonists. Then the 1.5 ml tubes were centrifuged at 1,000 g for 10 min at 4°C, and the optical density at 540 nm of the supernatants was determined as a measure of the released hemoglobin. Relative hemolysis was shown in each figure. Relative hemolysis means the relative value compared to a control, which is defined as 1. The control of each figures varied. In general, the value of the maximal hemolysis of each figure is defined as 1.For scanning electron microscopy observations, 5% erythrocyte solution was incubated with 300 μl of toxins (0.2 μM) at 37°C over a time course from 0 to 40 min. The samples were immediately fixed in 0.1 M phosphate buffer (pH 7.4) containing 2.5% glutaraldehyde for 4 h. Then the samples were rinsed, dehydrated in graded alcohol and acetone, and baked, plated with gold. Finally, the cells and untreated control were analyzed and photographed by S-3400N scanning electron microscopy .The cellular ATP content was determined using an ATP assay kit  via luminescence according to the manufacturer’s protocol. The erythrocytes were incubated with rETX and different antagonists for 5 min or 30 min at 37°C and 400 rpm. After incubation, the compound was centrifuged at 1,000 g for 10 min at 4°C, and the supernatant was aspirated. To estimate the release of ATP from erythrocytes, the supernatant of the RBCs (50 μl) was mixed with 50 μl of nucleotide releasing buffer and 1 μl of ATP monitoring enzyme. The sample was read using a Varioskan Flash Multimode Reader  and the software SkanIt in luminescence mode.Human erythrocytes were isolated and washed with 0.01 M PBS, then lysed four times with 0.01 M PB. After each wash, the suspension was centrifuged at 15,000 g for 15 min at 4°C and the supernatant was gently removed. The pellet was incubated with rETX at 37°C for 1 h and then spun as above. The pellet was dissolved in 1% TritonX-100, separated by electrophoresis using 12% SDS-PAGE, and transferred to a PVDF membrane . Nonspecific protein binding was reduced by incubation with 3% skim milk powder in 0.01 M PBS at room temperature for 3 h. The PVDF membrane was washed four times using PBS with 0.05% Tween-20 and incubated with primary antibody  at 4°C overnight. The PVDF membrane was washed three times and incubated for 1 h at room temperature with a HRP-coupled goat anti-mouse IgG antibody.1.25% erythrocyte solution was incubated for 15 min at 37°C with 1.0 μM mScarlet or mScarlet-rETX. Then, the RBCs were washed three times with 0.01 M PBS, the washed pellets were dissolved in 100 μl 0.01 M PBS. The fluorescence signals of erythrocytes were determined by Varioskan Flash  with excitation at 569 nm and emission was detected at 600 nm.4 cells/well) were grown in 96-well plates with Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and cultured in a 5% CO2 incubator for 24 h at 37_C. The toxin protein used in this assay was freshly purified. MTS  -5(3-carboxymethoxyphenyl) -2-(4-sulfopheny)-2H-tetrazolium, inner salt)  assay was performed as described previously [MDCK cells (3–4 × 10eviously .


Gasterosteus aculeatus) across 19 semi‐interconnected ponds within a small geographic region (~7.5 km2) using comparisons of multivariate phenotypic divergence (PST), neutral genetic (FST), and environmental (EST) variation. We found phenotypic divergence across the ponds in a suite of functionally relevant phenotypic traits, including feeding, defense, and swimming traits, and body shape (geometric morphometric). Comparisons of PSTs with FSTs suggest that phenotypic divergence is predominantly driven by neutral processes or stabilizing selection, whereas phenotypic divergence in defensive traits is in accordance with divergent selection. Comparisons of population pairwise PSTs with ESTs suggest that phenotypic divergence in swimming traits is correlated with prey availability, whereas there were no clear associations between phenotypic divergence and environmental difference in the other phenotypic groups. Overall, our results suggest that phenotypic divergence of these small populations at small geographic scales is largely driven by neutral processes , although environmental determinants  may play a role.Divergence in phenotypic traits is facilitated by a combination of natural selection, phenotypic plasticity, gene flow, and genetic drift, whereby the role of drift is expected to be particularly important in small and isolated populations. Separating the components of phenotypic divergence is notoriously difficult, particularly for multivariate phenotypes. Here, we assessed phenotypic divergence of threespine stickleback ( NaturalStudies assessing adaptive divergence and the mechanisms of phenotypic divergence are most commonly conducted across large spatial  and narrow sense heritability (h2), typically using quantitative genetic breeding designs, which are often not measurable for natural populations. PST, a phenotypic variance‐based measure of divergence, provides a field‐based proxy for QST  on phenotypic variation, within and among natural populations, is to compare quantitative trait (QST) and neutral genetic (FST) variation  inhabiting a small geographic area in Iceland  as well as body shape using geometric morphometric. Second, to infer whether selective or neutral processes influenced phenotypic divergence, we assessed the relationship between each set of phenotypic traits and neutral genetic divergence using PST‐FST comparisons. Third, we investigated the potential effects of environment  by comparing PSTs to environmental differences (ESTs).22.12) on a small geographic scale (~7.5 km2). The system was formed within the last 2,300 years, after lava from the Þrengslaborgir eruption flowed over a large part of the former Lake Mývatn  indicated small populations , which was correlated with landscape connectivity associated with periodic flooding events Figure . Specifi2.2N = 670). Removal of deformed animals was done to avoid errors in subsequent analyses that would not reflect natural population variation.Threespine stickleback Figure  were col2.310 transformed for statistical analyses (see below). To assess pond size, surface area of each pond was estimated from high‐resolution  SPOT‐5 satellite imagery obtained from the National Land Survey of Iceland for the years 2002/2003 and ARCGIS 10 .To characterize the environment that threespine stickleback experience, we recorded water chemistry parameters, pond size, and macroinvertebrate community diversity. Water chemistry was assessed for each pond using a YSI 600 XLM multiprobe sonde (YSI Incorporate) to measure pH (±0.2 units) and total dissolved solids . Chemistry was measured once in late June 2009 at each study pond. Total dissolved solids (TDS) were log2.4To describe the feeding environment of threespine stickleback within each pond, invertebrate samples were collected just below the water surface and above the pond bottom by moving a hand net (153 micron mesh) for three minutes back and forth while moving forward at a slow pace. Samples were then rinsed and stored in 70% ethanol for later identification. All invertebrates retained in these samples were identified to various taxonomic levels  using a Leica MZ12 (Nussloch) stereo microscope. If a given sample consisted of 200 or fewer individuals, all individuals were identified. If a given sample consisted of more than 200 individuals, the sample was thoroughly mixed and divided into aliquots. All individuals within an aliquot were then counted and the number of individuals in that aliquot multiplied by the total number of aliquots. If the number of individuals in the first aliquot was less than 200, the next aliquot was counted, and so on, until there were more than 200 individuals prior to calculating the total number. For statistical analyses, invertebrates were classified into three groups: limnetic , benthic , and “other”  following . Stomachs were opened and contents scraped out with forceps to insure all contents were removed. All invertebrates retained in these samples were counted and identified to the lowest possible taxonomic level, using the same groups as above, using a Leica MZ12 stereo microscope. Partially digested items were not always identifiable and softer bodied prey (such as copepods or worms) were rarely found, whereas shells of Cladocera, Mollusca, and Chironomidae larvae, which take longer to digest, were more frequent. For statistical analyses, prey items were classified into limnetic, benthic, and other using the same groupings as for the pond invertebrate sampling.2.62O2 and 1% KOH) and stained (Alizarin red 1% KOH) to aid measurement of traditional morphometric and meristic traits.Threespine stickleback phenotypic variation was characterized by measuring linear morphometric traits or counts of meristic traits, as well as using geometric morphometrics to assess body shape. For phenotypic analyses, threespine stickleback were thawed, their stomachs removed, sexed by examining gonad morphology, and preserved in 70% ethanol, and subsequently fixed in 5% buffered formalin for three weeks. Prior to further processing, the formalin‐fixed fish were rinsed with water and transferred to 70% ethanol. For geometric morphometric analyses of shape, each formalin‐fixed fish was pinned onto a wax board, with spines spread out and mouth closed, and photographed on the left side using a Nikon  Coolpix 4500 digital camera , the first gill arch from each stained fish was removed, placed between two glass plates to assure a straight position, and photographed using a Nikon Coolpix 4500 (Nikon) mounted onto Leica MZ12 stereo microscope. Total gill raker number (GRN) across the long and short gill raker arcs, the length of the first five gill rakers on the long arc (GRL) and the width of the gaps between these five gill rakers (GW) were measured (to the nearest 0.1 mm) from the gill raker pictures using the public domain ImageJ software  using an ocular micrometer mounted on a Leica MZ12 stereo microscope and counting the number of fin rays on each fin.2.6.3Defense traits were characterized by counting the number of lateral plates from the left side of the fish and measuring the length of both long dorsal spines (to the nearest 0.1 mm) using an ocular micrometer. Average spine length was used in subsequent statistical analyses.2.6.4N = 643). Digitized fish were aligned using generalized Procrustes superimposition using the geomorph package in R  to assess dispersal patterns in relation to landscape cover as previously reported . Morphometric measures that correlated with body size  were size corrected following Reist  by calcu2.8.1lm function in R. Because of the small sample size for males or females in some populations, we did not test for the population and sex interactions. Subsequently, we assessed individual trait variation among populations using ANOVAs, which are a special case of linear regression, via the ANOVA function in R, with pond identity and sex as explanatory variables. Response variables included body length, pectoral fin length, dorsal fin length, caudal fin ray number, pectoral fin ray number, GRN, GRL, GW, dorsal spine length, and number of lateral plates. Length measurements were size corrected as previously mentioned. Geometric morphometric shape variation across the ponds was assessed using Procrustes ANOVAs with the procD.lm function in the geomorph package, with Procrustes coordinates as response variables and pond identity and sex as the explanatory variables, with 1,000 residual randomized permutations. Procrustes ANOVA allows for statistical assessment of the term (here: pond identity and sex) using Procrustes distances among specimens and is equivalent to distance‐based ANOVA, allowing comparison with the analyses of the morphometric/meristic variables stated prior  and pond identity (p < 0.01), we also found a significant effect of centroid × pond identity interaction (p = 0.036), indicating that allometric relationships are not the same in all the ponds. Therefore, we elected to not to include size as a co‐variant for the final analysis due to the nongeneral allometric trends across the ponds and underlying importance of the allometric variation in describing the observed phenotypic variation . For each MANOVA, we assessed the effect of pond identity (19 levels) and sex  on the response trait matrix  using the 2.8.2δGB2 is the variation between ponds, δGW2 is the variation within ponds, h2 is trait heritability, and c is a unit‐less scalar variable  as the response and FST/PST group as the explanatory. We removed two outlier points  for the defense PST versus FST significant test to maintain the assumption of normality for the ANOVA and to ensure the outliers were not influencing the test.We first calculated the mean PST and FST for each pond from the initial pairwise PST or FST calculations (see above) to avoid violating the assumption of independence in the subsequent linear models. These pond mean PSTs and FSTs were then compared, using the lm function in R to perform a linear regression, with FST as the response variable and PST as the explanatory variable. Given that our study utilizes a set of sampling sites that is large enough to compare means across all populations, we opted for a mean‐based comparison of PST‐FST and PST‐EST. Assessing population means is a conservative approach compared to using analyses of pairwise distance matrices, such as Mantel test  here refers to observed ecological gradients  The dietary analysis showed lower proportions of limnetic to benthic prey across ponds, with mean proportion of limnetic per pond being 0.43; SD = 0.21.Total dissolved solids ranged from 0.042 to 0.306 ppm, pH from 7.26 to 9.38 , and pond surface area from 608 to 105,000 m3.2SD = 6.4) and females  and ranged from 44 to 57 mm across the ponds (mean = 50.3 mm SD = 3.6).Average body size of threespine stickleback varied between males  and sex . Population mean GRN ranged from 12.3 to 14 , size‐corrected GRL from 0.76 to 1.02 mm , size‐corrected GW from 0.18 to 0.22 mm , and size‐corrected arc length from 1.40 to 1.72 mm . GRN, GRL, and arc length differed also between sexes , whereas GW did not  and sex . Size‐corrected pond mean pectoral fin length ranged from 6.00 to 6.53 mm , average dorsal fin ray numbers ranged from 11.1 to 11.8 , and average caudal fin ray numbers ranged from 11.9 to 12.5 . Pectoral fin length, dorsal fin ray number, and caudal tail fin ray number differed among populations  .Multivariate swimming traits varied significantly among ponds  and sex . Size‐corrected pond mean spine length ranged from 2.82 to 3.33 mm  and lateral plate number from 0.1 to 3.11 . Spine length as well as lateral plate number differed significantly among ponds (both p < 0.01) and sex .Procrustes ANOVA of the Procrustes coordinate matrix indicated threespine stickleback differed in body shape among ponds  generally greatly altered the magnitude and variation of the PST calculations, whereas altering c at h2 < 1 had little effect on any of the PST calculations , for swimming morphology from <0.001 to 0.060 , for defense morphology from <0.001 to 0.056 , and for body shape from 0.007 to 0.091 . Pond‐specific FSTs ranged from 0.007 to 0.141 , with defense PST significantly greater compared to FST . We found nonsignificant PST‐FST relationships for feeding , swimming , and shape  traits (Table p < 0.01) and body shape , and a positive relationship between PSTs and EST as limnetic prey . Phenotypic divergence (population pairwise PSTs) in defense traits was greater than neutral genetic differentiation (population pairwise FSTs), indicating divergent selection or phenotypic plasticity. There were no clear difference between phenotypic divergence and neutral genetic differentiation for swimming traits, feeding traits, or body shape, suggesting that divergence in these traits may be mostly influenced by genetic drift or stabilizing selection. We found little evidence for the role of environmental divergence (EST) in phenotypic divergence, which may partly reflect relative environmental similarities of the ponds across the study area. The exception was that divergence in swimming traits positively correlated with pond differences in the proportion of limnetic invertebrates in the environment and in stickleback diet. Overall, our results suggest that phenotypic divergence in these small populations is driven by a combination of neutral processes  and either natural selection or phenotypic plasticity in response to small‐scale environmental variation.Podiceps auritus), red‐breasted merganser (Mergus serrator), and great northern diver (Gavia immer), which migrate to the area for breeding. Fish predation is expected to be rare in these ponds due to their small size. Larger ponds may contain Arctic charr  and brown trout  or under divergent selection (PST > FST). Commonly for QST‐FST comparisons, trait heritability is assumed to be 0.5  in the diet and the availability of limnetic prey in the ponds. Threespine stickleback morphology is often linked to availability of limnetic versus benthic prey (Bell & Foster, 5Ne = 12–86, Seymour et al., Here, we examined the phenotypic divergence of threespine stickleback, across small populations under partially dispersal limited conditions and over a small geographic scales. Specifically, we examined whether different evolutionary processes may account for the observed variation in different sets of traits. Our findings show that genetic drift is likely a key player for a large extent of the observed phenotypic diversification. At the same time, environmental differences were correlated with small spatial scale phenotypic divergence in swimming morphology, although PST‐FST comparisons indicated that these traits were under stabilizing selection. Due to the small effective population sizes (Authors declare no competing interest.BKK, KR, and MS designed the study and wrote the paper. MS conducted the fieldwork, performed the laboratory and data analyses, and lead the writing. All authors commented on manuscript drafts.


A considerable amount of evidence has favored ecological host-fitting, rather than coevolution, as the main mechanism responsible for trypanosome divergence. Nevertheless, beyond the study of human pathogenic trypanosomes, the genetic basis of host specificity among trypanosomes isolated from forest-inhabiting hosts remains largely unknown.Trypanosoma terrestris, a recently described trypanosome species isolated from lowland tapirs in the Brazilian Pantanal and Atlantic Forest biomes.To test possible scenarios on ecological host-fitting and coevolution, we combined a host capture recapture strategy with parasite genetic data and studied the genetic variation, population dynamics and phylogenetic relationships of T. terrestris population structure at three possible sources of genetic variation: geography, tapir hosts and ‘putative’ vectors. We found evidence of a bottleneck affecting the contemporary patterns of parasite genetic structure, resulting in little genetic diversity and no evidence of genetic structure among hosts or biomes. Despite this, a strongly divergent haplotype was recorded at a microgeographical scale in the landscape of Nhecolândia in the Pantanal. However, although tapirs are promoting the dispersion of the parasites through the landscape, neither geographical barriers nor tapir hosts were involved in the isolation of this haplotype. Taken together, these findings suggest that either host-switching promoted by putative vectors or declining tapir population densities are influencing the current parasite population dynamics and genetic structure. Similarly, phylogenetic analyses revealed that T. terrestris is strongly linked to the evolutionary history of its perissodactyl hosts, suggesting a coevolving scenario between Perissodactyla and their trypanosomes. Additionally, T. terrestris and T. grayi are closely related, further indicating that host-switching is a common feature promoting trypanosome evolution.We made inferences of This study provides two lines of evidence, both micro- and macroevolutionary, suggesting that both host-switching by ecological fitting and coevolution are two important and non-mutually-exclusive processes driving the evolution of trypanosomes. In line with other parasite systems, our results support that even in the face of host specialization and coevolution, host-switching may be common and is an important determinant of parasite diversification. A clear understanding of host–parasite relationships and the mechanisms by which parasites are adapted to their hosts is of paramount importance to public health, because emerging infectious diseases are frequently the result of host-switching, i.e. evolutionary change of host specificity of a parasite –3. In coParasites possess a great potential for evolutionary studies, having short generation times, complex life-cycles, and experiencing high fluctuations in population sizes, which in turn affect their population genetic structure , 9. Thervs host shifts) was solved in an empirical and theoretical framework by a process called ‘ecological fitting’, whereby host shifts can occur rapidly in ecological terms and the parasite should retain the capacity to use both the ancestral and novel host     in Asia . For insal hosts , episodeT. terrestris and T. grayi clades is not surprising ]. 95% highest posterior density (HPD) is the analogue of confidence intervals in Bayesian statistics. Units are in substitutions/site/million years.Additional file 4: Figure S1. a Extended Bayesian skyline plot illustrating the entire posterior distribution of demographic trends for T. terrestris isolates after removing CBTs 94-97-98 [= haplotype 9 (H9)]. Dotted lines indicate median effective population sizes, whereas the solid ones belong to 95% HPD limits. The time is in units of million years before present and population is at a logarithmic scale. b Extended Bayesian skyline plot illustrating the entire posterior distribution of demographic trends in the absence of time calibration points. X-axis indicates mutation rates (substitutions/site) and Y-axis corresponds to population size × mutation rate .Additional file 5: Figure S2. Evanno plot derived from STRUCTURE HARVESTER depicting the most likely number of genetic clusters. a No-admixture model. b Admixture model.Additional file 6: Figure S3. Bayesian phylogeny estimated in MrBayes v.3.1.2 by using V7V8 SSU rDNA sequences and depicting the evolutionary relationships of T. terrestris clade and its close relatives. Blue dashed lines represent the T. terrestris/T. grayi monophyletic clade. Numerical values represent the Bayesian posterior probabilities ranging from 0.0 to 1.0.


The origins of the first peoples to colonize the Caribbean Islands have been the subject of intense debate for over 30 years. Competing hypotheses have identified five separate migrations from the mainland with a separate debate concerning the colonization of The Bahamas. Significant differences in the facial morphology of the pre-Columbian inhabitants of Hispaniola and Cuba led to the present study of Lucayan skulls from The Bahamas. The goal was to determine which group the native Lucayans more closely resembled to resolve this long-standing dispute. The results indicate that they are related to groups from Hispaniola and Jamaica and not to Cuban inhabitants. This study clarified the larger picture of Caribbean migrations and supports evidence for a Carib invasion of the Greater Antilles around AD 800. This is the first of ten allusions to Carib raids during the first voyage2. Archaeologists have questioned this assertion based on the possible confusion of Caribe and Caniba (the Asiatic subjects of the Grand Khan), content with the knowledge that the true Caribs never advanced further than Guadeloupe in the Lesser Antilles4.The defining image of the Columbian encounter is ravenous cannibals descending upon unsuspecting peaceful Arawak villages, whence they ate the men and took the women as wives Fig. . From th5. The Caribbean’s stepping stone arrangement facilitated human and animal dispersals into the islands throughout prehistory.The Caribbean archipelago extends almost 3,000 km from the mouth of the Orinoco River in northern South America to Florida and the Yucatán enclosing the Caribbean Sea with three major island groupings consisting of the Lesser Antilles, the Greater Antilles, and The Bahamas7 that identified a “Lithic Age” (flaked-stone technology) migration as crossing the Yucatán passage from Central America to Hispaniola and Cuba between 4000–5000 BC, an “Archaic Age” (ground-stone technology) migration from Trinidad to the Greater Antilles through the Lesser Antilles around 2500 BC, and a “Ceramic Age”  migration from the mouth of the Orinoco River (Venezuela/Guiana) through the Lesser Antilles to Puerto Rico around 500 BC. After reaching Puerto Rico there was a 1,000-year hiatus before Ceramic Age expansion resumed with the colonization of Hispaniola, Jamaica, Cuba, and The Bahamas.The first and still popular framework for the peopling of the Caribbean by small groups of hunters, fishers, and foragers most likely infiltrated the archipelago by canoe was based on technological differences13. The Bahamas was the last of the Caribbean archipelagoes to be settled by humans. The earliest evidence of human arrival is the well-dated Coralie site on Grand Turk, Turks & Caicos Islands indicate initial occupation in the early 8th century AD14. Pottery analysis showed different materials mixed in the clay that was not endemic to the Bahamas suggest it was brought to the Turks & Caicos from Hispaniola15. This evidence supported the conclusion that the archipelago was first colonized by people from Hispaniola17. The name originally used by the Spanish to describe the Bahamas was used to argue that the Lucayans originally came from Cuba18. Early radiocarbon dates obtained from three Lucayan sites in the central Bahamas  supported this Cuban connection20.To date, the different proposed migration routes have been largely based on archaeological evidence of similarity and dissimilarity of pottery styles, which has provoked heated debate within the discipline21 yet it is based on a single individual and should be viewed with caution. Craniofacial morphology  has been empirically demonstrated as a suitable genetic proxy for examining population structure as cranial data have a greater spatial and temporal coverage than ancient DNA from pre-Contact New World populations23. The cranial base, facial and neurocranial modules are integrated systems that maintain stability throughout the whole complex during growth and development with the neurocranium being more susceptible to phenotypic plasticity24.Ancient DNA data suggests a northern South American origin for initial dispersal into the Caribbean29. Here, we propose evidence for multiple dispersal events and ancestral origins of Pre-Contact Caribbean Amerindians employing facial morphology as a genetic proxy.Studies of population diffusion into the Caribbean have been neglected until recently, partly due to the scarcity of available skeletal material resulting from unfavorable preservation environments and thus, there are significant gaps in knowledgeThree migration routes are evident in a sample of 103 individuals from 10 localities using 16 homologous anatomical landmarks  matrix, a measure of biological distance, shows that the two closest groups are Hispaniola and Jamaica, followed by Puerto Rico and Venezuela, Hispaniola and the Bahamas, with the most dissimilar groups being Panama and The Bahamas, Hispaniola, and Jamaica.  = 2.27, p =  < 0.0001) and centroid size  = 737.92, p =  < 0.0001). The canonical variates analysis (CVA) produced nine significant canonical axes and the eigenvalues indicate that 35% of the shape variation is accounted for on the first canonical axis, 21% on the second canonical axis, and 14% on the third axis for 70% of the total shape variation  PanI by kilometers) for shape (PC1) shows no spatial structure while the correlogram for centroid size shows the autocorrelations decreasing with increasing distance  and The Bahamas (circa AD 1000) during population expansion into essentially unoccupied territories. Meillacoid pottery also reaches eastern Cuba around AD 100039. Diffusion into Cuba is different because it reflects the infiltration of long-established Archaic Age communities, which would explain why individuals in Cuba are morphologically different from their neighbors. Meillacoid pottery is identical to the pottery associated with the Carib expansion13. Commencing in the northwestern Amazon basin around AD 500, the timing is appropriate for the sudden appearance around AD 800 of this new pottery style in Hispaniola11. Their arrival clarifies why population expansion suddenly resumed after a 1,000-year hiatus. The Carib invaders were on the move, as attested to their rapid expansion across South America and also resolves Columbus’ often perplexing descriptions of fierce raiders in The Bahamas, Jamaica, and western Hispaniola. Raiders that Las Casas would identify in a marginal note to Columbus’ Diario as “Caribes”1.Contacts between Florida and the islands have long been proposed40, and a high degree of genetic variability has been reported for the northwest Amazon homeland of the Carib migrants42. Third, previous studies have looked only at the two generally accepted Archaic Age and Arawak/Taíno expansions, with some reference to the centuries later Carib migration into the Lesser Antilles27. If future studies do not look for Carib influences, they will produce incomplete results.Genetic evidence eventually may help to verify or refute our proposed Carib migration hypothesis. However, there are currently three main problems. First, too few studies have been completed, although we would note that our Bahamas samples are part of an ongoing study of genetic relationships in the circum-Caribbean. Second, community-level identifiers are not yet available, it is difficult to evaluate the impacts of admixture over time27, although genetic contributions in Puerto Rican individuals that are more closely related to northwestern South America has been recognized40; and the study of dental morphology identified a distinct Cuban cluster25. These results independently confirm the Puerto Rico-Venezuela and Cuba-Yucatán clusters identified here. Finally, “marked haplogroup variation seems to be present among the three neighboring Caribbean islands ,” such that colonization of the Caribbean was mainly due to successive migration movements from mainland South America in different time periods26. Haplogroup diversity among the three islands and successive waves of migrations are exactly what our data predict. Finally, guinea pigs (Cavia porcellus) first appeared in the Antilles “sometime after AD 500,” and their origins were genetically traced to northwestern South America43. Their translocation occurred at the same time and from the same place that we propose for the Carib expansion into the Caribbean islands.Nevertheless, several studies support our conclusions. The DNA analysis of Saladoid individuals strongly suggest a common origin and genetic continuity over time4 was adamant that there was only one Ceramic Age population expansion, archaeologists were fixated on the shortest geographical water crossings, especially the “stepping-stone” Lesser Antilles44, and interest in Island Caribs was focused on the 17th century testimony of French missionaries living in the eastern Caribbean. It is only in the past decade that archaeologists have recognized the pre-Columbian capacity to directly cross the Caribbean Sea45. Looking at faces, instead of islands, we see three distinct populations from three distinct places  were imputed for any given individual. Data were imputed using the software Morpheus 50. Centroid size is a measure of geometric scale that is mathematically independent of shape50. The GPA procedure was performed using MorphoJ, which is freely available for downloading and developed by Klingenberg51. A principal component analysis (PCA) of the covariance matrix was conducted on the GPA transformed coordinates to reduce the dimensionality of the data for subsequent multivariate statistical analyses50. To examine shape and size (e.g. Centroid Size) variation among the groups, a Procrustes ANOVA was performed using principal component scores calculated from the PCA51. A canonical variates analysis (CVA)  was performed to account for the maximum amount of among-group variance relative to within-group variance, which is also uncorrelated within and among groups. MorphoJ uses the standard equation for calculating the number of pairwise comparisons, which is k(k − 1)/2 with k = number of groups. CVA is used to examine variation for more than two groups known a priori that presents the most variation with the least dimensions possible51. Mahalanobis distance or generalized distance, which considers the correlations among variables when computing the distance between means, was used to examine group relatedness  was used that calculates separate pairwise comparisons (with 1000 permutation runs) between groups using crossvalidation or n-1 method.Coordinate data must first undergo a Generalized Procrustes analysis or GPA transformation before subsequent statistical analyses can be performed. GPA translates, rotates, and scales each specimen and brings all individuals into a common coordinate system. Shape is defined as all of the geometric information that remains after the effects of location, scale, and rotational effects are removedss Table 51. To vi31. Hierarchical clustering begins with every sample in a single cluster, then in each successive iteration, it merges the closest pair of clusters  until all the data is in one cluster. The cluster analysis was performed in JMP ® Pro 1452.An average linkage hierarchical (or agglomerative) cluster analysis was conducted using the generalized distance matrix to examine group similarity and are commonly used in population history and structure studiesI, a product-moment coefficient, and Geary’s c, a distance-type coefficient, were used to measure the spatial autocorrelation of shape (PC1) and centroid size. The autocorrelation is a measure of genetic similarity between individuals with reference to geographic separation (latitude/longitude). This procedure was performed using the Proc Variogram procedure in SAS 9.4, which reports Moran’s I and Geary’s c as a standardized z-score with a positive autocorrelation indicated by a ZI > 0 and Zc < 053. Spatial correlograms were computed for shape (PC1) and centroid size to evaluate the spatial autocorrelation coefficients for all pairs of localities at specified geographic distance classes54. The correlograms were performed using the freeware software GeoDa v1.14.055.Moran’s Supplementary Information


Fusarium species and trichothecene genotypes associated with FHB on wheat were monitored in Hebei province during the periods 2005–2006 and 2013–2016. Fusarium species determination was carried out by morphological identification, species-specific amplification and partial translation elongation factor (TEF-1α) gene sequencing. Trichothecene genotype prediction was carried out by primers 3CON/3NA/3D15A/3D3 or Tri13F/Tri13R, Tri303F/Tri303R and Tri315F/Tri315R. A total of 778 purified Fusarium isolates were recovered from 42 sampling sites in 17 counties during the period 2005–2006 and 1002 Fusarium isolates were recovered from 122 sampling sites in 65 counties during the period 2013–2016. F. graminearum was the predominant pathogen recovered during the periods 2005–2006 and 2013–2016. However, the pathogen composition differed slightly between the two periods. In 2005–2006, 752 out of 778 (96.7%) of the isolates belonged to F. graminearum. Two were identified as F. culmorum. Five other Fusarium species were also recovered, F. equiseti, F. verticillioides, F. proliferatum, F. subglutinans and F. chlamydosporum, with lower recoveries of 0.4%, 0.8%, 0.8%, 0.1% and 1.0%, respectively. Trichothecene genotype prediction showed that all the 752 F. graminearum isolates were of the 15-ADON genotype. Five Fusarium species were recovered from samples collected over the period 2013–2016. F. graminearum was again the predominant pathogen with an isolation frequency of 97.6%. F. pseudograminearum, F. asiaticum, F. culmorum and F. negundis were also isolated at a recovery of 1.4%, 0.7%, 0.2% and 0.1%, respectively. For the 2013–2016 isolates, 971 of the 978 F. graminearum strains were 15-ADON whereas seven isolates were of the 3-ADON type. All seven F. asiaticum isolates were of the NIV type and fourteen F. pseudograminearum isolates were classified as 3-ADON. F. pseudograminearum was first isolated from FHB in Hebei in 2013. Although the recovery of F. pseudograminearum is still low, it represents a small shift in the pathogen composition and trichothecene genotypes associated with FHB in Hebei province. As Fusarium crown rot of wheat caused by F. pseudograminearum is an increasing problem in Hebei province, it is appropriate to monitor the role of F. pseudograminearum in FHB in the future.To clarify the changes in field populations of Fusarium head blight (FHB) pathogens over a decade,  Fusarium species, is a devastating disease affecting wheat and other small grains in many regions throughout the world [Fusarium head blight (FHB), caused by a complex of the he world . In addihe world . This dihe world . During he world .In recent decades, FHB has again become a major problem worldwide and epidemics have occurred in Asia, Australia, Europe, southern Africa and North America ,5,6. In Fusarium graminearum species complex (FGSC) being the most common causal agent in many countries of the world including the USA, Canada, Australia, and in parts of central Europe [F. culmorum predominates [F. pseudograminearum was also found to cause FHB in wheat and triticale in the northern wheatbelt of New South Wales in some years at low levels [F. graminearum being the most widely distributed species causing FHB around the world [F. asiaticum is the main FHB pathogen present in Asia [Fusarium species and subspecies were found to be associated with FHB but F. graminearum (FGSC) was the most important species in most provinces. However, F. culmorum was common in Guizhou province and the Ningxia Hui autonomous region [F. graminearum is the predominant species in the north of China where the annual average temperature is 15 °C or lower, whereas F. asiaticum is predominant in the south where the annual average temperature is above 15 °C and FHB epidemics occur most frequently [Over 17 species have been reported to be associated with FHB, with l Europe ,11, wherominates ,13. In Aw levels . Recentlhe world ,15, whil in Asia ,17,18,19s region . In receequently ,21,22,23Fusarium species causing FHB, F. graminearum, also causes stalk and cob rot of maize, and produces abundant perithecia on wheat and maize residues. Presumably, these changes were conducive to an increase in the inoculum levels of F. graminearum, and other species that also cause FHB such as F. culmorum [F. pseudograminearum in Hebei. Thus, it is not surprising that five isolates of F. pseudograminearum have also been detected from FHB samples [Hebei province is located in the north of the North China Plain, an important agricultural region with biannual crop rotation of wheat and maize. In the last two decades, there have been major changes in farming systems in Hebei province. These changes include the adoption of continuous wheat–maize cropping systems with residue retention, and the introduction of high-yielding cultivars which lack resistance to FHB ,24. The culmorum . Most im samples . Fusarium species associated with FHB in Hebei province, as management decisions need to be made on the basis of such information. Considering the major changes in the status of FHB and farming systems over the last decade, we aimed to assess possible changes in the pathogen composition of FHB, including the chemotypes of each species and their geographical distribution in Hebei province during the years 2005–2006 and 2013–2016. It is important to clarify the nature and proportion of the Fusarium isolates were recovered from rachis tissue affected by FHB collected from 42 sampling sites across 17 counties, and seven Fusarium species were identified after cultures were purified. F. graminearum was the most common species of the FHB complex recovered during the period 2005–2006. The percentage of isolates identified as F. graminearum ranged from 86.2% to 100% across the sampling sites, with an average of 96.7%  isolates were identified as F. graminearum. The percentage of isolates identified as F. graminearum was 100% in Baoding and Langfang sampling sites .A total of 1002 purified ng sites . F. asiaectively . Seven FFusarium isolates from 2005–2006 and 999 Fusarium isolates from 2013–2016 were chemotaxonomically classified into 15-ADON, 3-ADON and NIV types by primers 3CON/3NA/3D15A/3D3 or Tri13F/Tri13R, Tri303F/Tri303R, and Tri315F/Tri315R. All F. graminearum isolates from 2005–2006 were classified as 15-ADON type  isolates were 15-ADON type, whereas only seven out of 978 (0.7%) isolates were classified as 3-ADON, which were isolated from the mid-south of Hebei province, Shijiazhuang, Xingtai and Handan. Moreover, all the isolates of F. asiaticum and of F. pseudograminearum were NIV and 3-ADON types, respectively.Seven hundred and fifty-two DON type . During F. graminearum, F. culmorum, F. avenaceum, and Microdochium nivale [F. poae [F. pseudograminearum (=F. graminearum Group 1 (Francis and Burgess)) [F. equiseti [F. equiseti is generally considered a saprophyte that can colonize diseased or senescent tissue. The relative contribution of these species to FHB will depend upon a range of variables, particularly temperature, rainfall and humidity [Fusarium species were confirmed to be associated with FHB in Hebei province. However, F. graminearum was found to be the dominant pathogenic species associated with FHB in the surveys over two consecutive periods of time, seven years apart. This finding is consistent with reports from North America and parts of Europe as well as other provinces of the northern part of China [F. graminearum in Hebei. F. graminearum persists in maize and wheat residues and produces abundant perithecia on these residues. The perithecia release ascospores under cool wet conditions which act as the primary inoculum causing initial infection of the wheat heads [FHB can be caused by a number of fungal species. The most common pathogens are m nivale . Other f[F. poae , F. pseuurgess))  and F. eequiseti . F. equihumidity . In the of China  where maat heads . Ascospoat heads ,31. F. culmorum was recovered at a low level but was found during the periods 2005–2006 and 2013–2016. This finding also agrees with earlier findings which indicated that the species is more common in cooler conditions in areas where maize is less common such as cooler, maritime climate regions of Europe as well as Ningxia Hui autonomous region and Guizhou province of China [F. graminearum to produce abundant perithecia on maize stubble and then release ascospores as the primary inoculum of FHB. Climatic conditions also favor F. graminearum over F. culmorum, F. avenaceum and F. poae, as the former species has a higher optimum temperature, whereas the latter species prefer cooler conditions [Fusarium species were recovered during the period 2005–2006. In 2005–2006, the samples were collected slightly later after the onset of FHB. Presumably, this enabled more secondary colonizers to invade the heads prior to sampling and led to relatively higher isolation of a wider range of secondary colonizers. The isolation of F. verticillioides, F. proliferatum and F. subglutinans at low levels is not surprising as these are commonly associated with maize, as endophytes and stress-related stalk and cob rot pathogens. The remaining two Fusarium species, F. equiseti and F. chlamydosporum, are commonly associated with cereals as saprophytic species [In contrast, of China ,20. In Hnditions . Several species ,33. ThesF. asiaticum and F. pseudograminearum both emerged as causal agents of FHB in Hebei during the period 2013–2016. They were not recovered during the surveys in the period 2005–2006. F. asiaticum was isolated mainly from the regions with rice cultivation. This result is consistent with earlier findings from China, Korea and Brazil, in which F. asiaticum dominated in regions where rice is grown in rotation with wheat, whereas F. graminearum was more common in wheat–maize rotation systems [F. pseudograminearum was also obtained from FHB-infected spikes in the mid-south part of Hebei province where FCR caused by F. pseudograminearum has emerged as a new destructive disease of wheat and the disease incidence and severity has been increasing year by year [F. pseudograminearum has also been reported to cause FHB in Australia under favorable conditions, but usually at low levels [F. pseudograminearum and F. graminearum caused FCR and FHB in surveys of wheat crops in Australia, though F. pseudograminearum was more frequently isolated from the crown, whereas F. graminearum was mostly recovered from the head [F. pseudograminearum, to infect the new host. Crop residues that favor persistence of Fusarium mycelia in infested residues also favor FCR [F. pseudograminearum produced on diseased wheat debris might also favor infection of wheat heads through water splash of macroconidia [There were more pathogenic species detected during the period 2013–2016 than the period 2005–2006.  systems ,34,35,36 by year . F. pseuw levels . Both F.the head . In the avor FCR , while toconidia .F. culmorum has previously been reported to be the dominant pathogen of FHB, evidence shows that climatic and agricultural changes have favored the propagation of F. graminearum over F. culmorum, as the former species has a higher optimum temperature. Rising temperatures and an increase in minimum tillage and maize cropping have also favored F. graminearum. Similarly, there has been a decline in the presence of F. culmorum and an increase in F. graminearum in the UK [F. pseudograminearum from wheat heads has so far been identified in a limited amount in Hebei province, its potential development into a common pathogen of FHB is of concern in FCR and FHB co-existence regions. Thus, there is a need for ongoing monitoring of the incidence and distribution of F. pseudograminearum causing FHB in Hebei province. Studies need to span the entire FHB and FCR continuum to address key questions on pathogen biology and genetics in the local farming systems. Such information would be a valuable basis for clarifying the epidemiology and mycotoxin issues relating to FHB. It will also be important for the formulation of integrated disease management strategies for the management of the diseases caused by this fungus.The composition of the pathogens causing FHB is believed to be determined, in part, by climatic factors such as temperature and moisture, as well as agronomic factors such as soil tillage and previous crops ,43,44. In the UK , Netherln the UK , Denmarkn the UK  and Polan the UK  in the lDiseased wheat spikes with characteristic symptoms of FHB were collected from farmers’ fields (42 sampling sites in 17 counties during the period 2005–2006 and 122 sampling sites in 65 counties during the period 2013–2016) in Hebei province. The sampling sites represented the different latitudes from the north to the south of Hebei and were recorded by a Global Positioning System (GPS) locator eTrex . The map indicating the sample sites was generated by the software ArcGIS10  . Each saFusarium species [Fusarium isolates which developed from the rachis segments were sub-cultured to carnation leaf-piece agar (CLA) [Fusarium isolates were purified by single-spore isolation. Single-spore isolates were cultured on potato dextrose agar (PDA) and CLA. Isolates producing purple pigment and microconidia on PDA were also cultured on Spezieller Nahrstoffarmer Agar (SNA) [Discolored segments of floral rachis were surface sterilized with 70% ethanol and plated onto Peptone PCNB Agar (PPA), a selective medium for  species , and inc species . All Fusar (CLA) , and incar (SNA) . Isolatear (SNA)  and Leslar (SNA) .® Spin Kit . The mycelium of 5- to 7-day old cultures grown on PDA was scraped and added to Lysis Matrix A tubes and run in a FastPrep® Cell Disrupter  for 20 s at speed level 5.5. The extraction was then carried out according to the manufacturer’s instructions.DNA of purified isolates was extracted using the commercial Fast-DNAFusarium species-specific PCR was performed using primers Fg16F/Fg16R, Fp1-1/Fp1-2 and Fc01F/Fc01R for the detection of FGSC, F. pseudograminearum and F. culmorum  for 30 s, annealing  gene sequences (culmorum . Fg16F/Fsiaticum ,54. Amplnnealing  for 30 sequences . The seqF. pseudograminearum, respectively (For trichothecene genotype prediction, Tri3 multiplex amplification was performed in 20 µL volumes using primers 3CON/3NA/3D15A/3D3 for FGSC and Tri13F/Tri13R, Tri303F/Tri303R, and Tri315F/Tri315R for ectively . The rea