Sagitta enflata in the northern South China Sea were investigated by 623 bp fragment of mtDNA COI gene sequence. A total of 146 individuals were collected from nine stations and 92 different haplotypes were obtained. 485 variable sites (210 were parsimony informative and 275 were singleton variable sites), and no insertion or deletion was found. An analysis of molecular variance (AMOVA) and conventional population statistics (FST) revealed a low level of genetic differentiation among nine populations , indicating no geographical patterning among nine populations. The present results were able to provide a reference for the phylogenetic relationships and assessment of the genetic structure of S. enflata in the northern South China Sea.In this study, genetic diversity and population genetic structure of  Chaetognaths (arrow worms) are small-sized and important predators in the marine ecosystem  is widely being used for elucidating molecular systematics studies. Moreover, cytochrome oxidase subunit I (COI) is considered as more rapid evolutionary rate and polymorphic than other mitochondrial genes, and therefore frequently used to study population genetic structure analysis and phylogeographic relationships of marine species  and HCO2198(5′-TAAACTTCAGGGTGACCAAAAAATCA-3′) . The PCR program was carried out under the following conditions: an initial denaturing at 94 °C for 4 min, followed by 30 cycles of denaturing at 94°C for 30 s, annealing at 50 °C for 50 s, and extension at 72 °C for 1 min, with a final extension at 72 °C for 10 min. The PCR products were visualized on 1% agarose gels, and purified with a Takara Agarose Gel DNA Purification Kit . Gene sequencing was performed on ABI 3730XL DNA Analyzer .We collected 146 hina Sea  during AS. enflata COI sequences as reference (GenBank accession no. KF977332). Nucleotide composition and variable sites were analyzed using MEGA3.1 , and the FST was examined using the Mantel test with 1000 permutations, performed by Arlequin version 3.01  of the COI gene fragment obtained for each specimen were aligned and edited by visual inspection using SeqScape version 2.5 (Applied Biosystems). Final alignments were optimized using BioEdit version 7.0.4.1 using Parasagitta elegans, Parasagitta setosa, Caecosagitta macrocephala, Eukrohnia hamata . Among all 92 haplotypes, there were 485 variable sites (210 were parsimony informative and 275 were singleton variable sites), and no insertion or deletion was found. The mean total nucleotide composition was 22.4% A, 19.3% G, 23.0% C, and 35.3% T.Molecular genetic studies have shown the existence of genetic differentiation corresponding to different water masses or biogeographic boundaries in several zooplankton species  is one of the most widely used methods of genetic data analysis (Excoffier et al. S. enflata for the present study region included continental shelf, continental slope, and deep-sea basins, it may be explained by the Kuroshio current intrusion through the Luzon Strait into the nSCS. In order to further study the genetic structure of this worldwide species, more molecular markers and populations will be needed in a comprehensive analysis.Compared to the previous reported studies on zooplankton, our estimates of genetic diversity within the


Eptatretus burgeri) is particularly important from the perspective of the eel-skin leather industry in the northwest Pacific. In order to reveal the genetic diversity and population structure of E. burgeri in the northwest Pacific, we analyzed partial nucleotide sequences of three mitochondrial DNA regions  based on specimens collected from six locations in Korea and Japan. The genetic diversities of E. burgeri were higher in Korean locations compared to Japanese ones. AMOVA showed that E. burgeri was completely separated into two groups (group A: southern coast of Korea and western coast of Japan vs. group B: eastern coast of Japan). Furthermore, groups A and B were divided into each two lineages . Our molecular results suggest that these two groups and lineages of E. burgeri may be different evolutionary significant unit and management unit, respectively.The fishery of inshore hagfish ( Eptatretus burgeri, which is major commercial species, is found in the restricted patch range around the northwest Pacific such as Korea, Japan, and Taiwan  has proven to be useful for population genetic studies  sequence markers.Hagfish has a high commercial value and significant economic importance from the perspective of the fisheries industry (eel-skin leather and food) in the northwest Pacific , Wando Island , Jeju Island  in Korean waters, and Kyoto , Tsushima Island , Shikoku Island  in Japanese waters between May 2014 and Sep 2017, caught by hook and line, and fishing trap . The specimens have been deposited in the Marine Fish Resource Bank of Korea (MFRBK) at Pukyong National University (PKU), Korea. We loaned the Kyoto specimens and received the Shikoku Island specimens from Kyoto University (FAKU).A total of 180 specimens of b (Cytb). PCR was then performed using an MJ Mini Thermal Cycler PTC-1148 (Bio-Rad) in mixtures consisting 1 μL of genomic DNA (ca. 100 ∼ 200 ng), 2 μL of 10× PCR buffer, 1.6 μL of 2.5 mM dNTPs, 1 μL of each primer (10 pmol), 0.1 μL of TaKaRa EX-Taq polymerase , and distilled water to bring the final volume to 20 μL. PCR products were amplified using universal and species-specific designed primers (Table S1). The PCR profile for the mtDNA COI consisted of initial denaturation at 94 °C for 4 min, followed by 34 cycles of denaturation at 94 °C for 30 s, annealing at 52 °C for 30 s, extension at 72 °C for 30 s, and a final extension at 72 °C for 8 min; for the mtDNA ND4 gene consisted of initial denaturation at 94 °C for 3 min, followed by 34 cycles of denaturation at 94 °C for 30 s, annealing at 60 °C for 1 min, extension at 72 °C for 5 min, and a final extension at 72 °C for 10 min; and for the mtDNA Cytb consisted of initial denaturation at 94 °C for 5 min, followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at 58–60 °C for 1 min, extension at 72 °C for 1 min, and a final extension at 72 °C for 7 min. The PCR products were purified using a Davinch™ PCR Purification Kit . The purified products of mitochondrial DNA were sequenced based on the Sanger sequencing method with an Applied Biosystems ABI 3730XL analyzer  using an ABI PRISM BigDyeTM Terminator Cycle Sequencing Ready Reaction Kit v3.1 (Applied Biosystems). The sequences were aligned using ClustalW . We analyzed three mitochondrial DNA markers: Cytochrome c oxidase subunit I (COI), NADH dehydrogenase subunit four (ND4), and cytochrome ΦST values were calculated with 10,000 permutations. Phylogenetic trees were constructed: Neighbor-joining (NJ) and Bayesian inference (BI). The genetic distances were calculated using the Kimura 2-parameter (K2P) model (Kimura b).Genetic diversities were evaluated using Arlequin 3.5.1.2 . Analyses of 1852 bp of the combined mtDNA  identified 43 haplotypes detected among 112 individuals from six locations (Table S2). In the combined dataset, HP1 and HP3 were the most abundant haplotypes, appearing 19 and 11 times, respectively. Ten haplotypes (HP34 to HP43) were shared by only Shikoku Island. Compared molecular diversities, the Korean E. burgeri generally showed higher haplotype diversity (0.8696–1.0000) and nucleotide diversity (0.001–0.003) than the Japanese one  based on combined mtDNA  showed high haplotype diversity and low nucleotide diversity, and revealed that the existence of four highly divergent allopatric groups within the Iberian Peninsula. The genetic diversity of E. burgeri in Kyoto was the lowest, but those of other locations showed higher molecular diversities. Density stratification in the water column occurred in the western coast of Japan during the LGM, and have resulted in serious anaerobic water columns at the sea bottom  in the northwest Pacific (b). Group A included E. burgeri collected from southern coast of Korea and western coast of Japan, whereas Group B included E. burgeri collected from the eastern coast of Japan. Group A is geographically divided into two lineages; lineage I which is mainly detected in west-southern coast of Korea, and lineage II which is mainly detected in east-southern coast of Korea and western coast of Japan. Group B also consists of two lineages, lineage III, and lineage IV ( Pacific . The genneage IV .ΦST population pairwise comparisons were significant (p < .01) except for the ΦST value between Tongyeong and Tsushima Island, and between Jeju Island and Wando Island (Table S3). The pairwise ΦST values between Shikoku Island and the other five locations were especially high than the values among the others (Table S3). The highest ΦCT value, which indicates the highest proportion of genetic variance able to be attributed to groups given the present data, was observed as 0.684 (p < .000) when the locations were grouped into following two groups: (TY&JJ&WD&TS&KT) and SK . The different proportion of lineage I to lineage II between the west and the east might have been caused by this low-salinity boundary. In the past, the incised-channel system of the paleo-Seomjin River has significantly affected the sedimentary processes of the southern sea of Korea  and SK . Shikoku) and SK  as well ) and SK . TherefoE. burgeri than the Japanese one. In demersal and deep-sea fish similar to the habitat trait of hagfish, another reason to affect gene flow may consider the biogeographical barrier. Lycodes matsubarai indicated the high genetic population structure because they have been restricted post-glacial migration between western coast of Japan and Sea of Okhotsk by the shallow strait (13–55 m depth)  and management unit (MU) Ryder . ConsideClick here for additional data file.


Aedes aegypti  as a model and two study sites in Singapore, we show how mosquito dispersal can be characterized by the spatial analyses of genetic relatedness among individuals sampled over a short time span without interruption of their natural behaviors.Hundreds of millions of people get a mosquito-borne disease every year and nearly one million die. Transmission of these infections is primarily tackled through the control of mosquito vectors. The accurate quantification of mosquito dispersal is critical for the design and optimization of vector control programs, yet the measurement of dispersal using traditional mark-release-recapture (MRR) methods is logistically challenging and often unrepresentative of an insect’s true behavior. Using Ae. aegypti across high-rise apartment blocks and genotyped them using genome-wide SNP markers. We developed a methodology that produces a dispersal kernel for distance which results from one generation of successful breeding , using the distance separating full siblings and 2nd- and 3rd-degree relatives (close kin). The estimated dispersal distance kernel was exponential (Laplacian), with a mean dispersal distance  of 45.2 m (95% CI 39.7–51.3 m), and 10% probability of a dispersal > 100 m (95% CI 92–117 m). Our genetically derived estimates matched the parametrized dispersal kernels from previous MRR experiments. If few close kin are captured, a conventional genetic isolation-by-distance analysis can be used, as it can produce σ estimates congruent with the close-kin method if effective population density is accurately estimated. Genetic patch size, estimated by spatial autocorrelation analysis, reflects the spatial extent of the dispersal kernel “tail” that influences, for example, the critical radii of release zones and the speed of Wolbachia spread in mosquito replacement programs.Using simple oviposition traps, we captured adult female We demonstrate that spatial genetics can provide a robust characterization of mosquito dispersal. With the decreasing cost of next-generation sequencing, the production of spatial genetic data is increasingly accessible. Given the challenges of conventional MRR methods, and the importance of quantified dispersal in operational vector control decisions, we recommend genetic-based dispersal characterization as the more desirable means of parameterization. Aedes aegypti, an urban-dwelling container-breeding mosquito, conventional control approaches include removal and treatment of larval habitats, as well as elimination of adults through insecticide application  and lethal trapping [R and Wolbachia-based population replacement and suppression [Mosquitoes’ ability to carry and transmit human pathogens  causes hundreds of millions of infections and nearly one million fatalities every year . Both prtrapping . New biopression –5.Ae. aegypti adults is typically conducted at and around the main and secondary residences of dengue cases. The radius of the area to be treated is informed by the average dispersal distance of potentially infected female Ae. aegypti [Wolbachia may fail if the release area of Wolbachia-infected Ae. aegypti is too small and too vulnerable to immigration by wild-type mosquitoes [Defining the optimal area for treatment or mosquito release is one of the key considerations when implementing a public health intervention or designing a field-trial for a new control approach. For example, to contain the spread of dengue virus during an outbreak, focal insecticide-based control of  aegypti . Underst aegypti . Additiosquitoes . For thesquitoes , well-chsquitoes , 11.Quantifying mosquito dispersal of both wild-type and introduced mosquito strains in any given landscape is, therefore, critical for the considerations of the size of the treatment area and the surrounding buffer zones. Those considerations complement practical operational deliberations of the availability of human and economic resources for implementation and the sample sizes required to capture epidemiological endpoints , 13.Ae. aegypti have reported the mean dispersal distance to range from tens to hundreds of meters [Mosquito dispersal characteristics have been typically studied using conventional mark-release-recapture (MRR) experiments utilizing powders and paints on trapped or laboratory-reared adult mosquitoes . Locatiof meters , and thif meters . AdditioHere we show how information on mosquito dispersal characteristics can be obtained from the spatial patterns of genetic distance and relatedness among sampled individuals, providing an alternative to the MRR experiments for informing the mosquito control programs. In contrast to conventional MRR approaches that require insect trapping or rearing, followed by mark, release, and recapture, the genetic approach requires only insect capture, utilizing the information from genetic markers and spatial location of individuals sampled continuously across an area over a limited time span and without manipulation or interruption of their natural behaviors.Ae. aegypti.In social insects like bumblebees, queen dispersal distance has been estimated by comparing the locations of workers (sampled in summer) and queens (sampled in the following spring) that were identified as sisters through sibling reconstruction analysis with genetic markers . InferenAe. aegypti.Our newly developed method decomposes the observed separation distances between close kin (sampled as breeding adults) to generate the distribution of potential effective dispersal distances and to parametrize the dispersal distance kernel. This dispersal kernel provides the density of probability that a dispersal event ends at a given distance away from the source, regardless of the direction. Importantly, it refers to the dispersal distance achieved over one generation of successful reproduction, such as distance between the birthplace and the ovipositing place of a female . We demonstrate the robustness of the method to produce dispersal kernel parameters consistent among different subsets of data (with one or multiple kinship categories) and congruent with the estimates of dispersal characteristics from the previously published MRR experiments in When few close kin are captured, the conventional genetic analysis of isolation-by-distance (IBD) between unrelated individuals can be used to estimate the spread of the dispersal kernel from the slope of the IBD relationship and the effective density, and we show that its results can be congruent with the new close-kin method. Finally, we show that the estimated genetic patch size  from the spatial autocorrelation analysis reflects the spatial extent of the effective dispersal distance kernel’s “tail” that cannot be ascertained with IBD analysis alone.Aedes aegypti individuals collected in two densely populated areas of Singapore with a homogeneous distribution of high-rise apartment buildings. Aedes aegypti is the primary vector of dengue virus in Singapore that, despite having a low Aedes house index (2%) and an extensive vector surveillance and control program [Ae. aegypti dispersal in a highly urbanized landscape with a prominent vertical dimension, but the spatio-genetic analytical approach  can be applied across different landscapes and vector species.We analyzed the genotyped and geo-referenced  program , continu program , 24. ThiAedes aegypti females were collected using the simple sticky traps (Gravitraps) [Adult vitraps)  that werAe. aegypti females were obtained using the double-digest RAD sequencing [Reduced representation genome sequences for individual quencing  followinquencing . Genotypquencing , 29 to dpo) pairs, and 26.3% (20/76) of close-kin pairs were found on the same floor or 4–5 floors apart (19/20 pairs). Nearly half (47%) of all detected full-sibling pairs were caught within a building, in comparison to 27.8% of all 2nd-degree relatives and 15.4% of all 3rd-degree relatives. The members of each close-kin pair were sampled within 8 days (median = 1 day apart).In the total dataset, we detected 76 close-kin pairs: 19 full-sibling pairs, 18 pairs of 2nd-degree relatives, and 39 pairs of 3rd-degree relatives. We did not detect any parent-offspring (p < 0.001).The median pairwise distance between close kin (CK) was 111.6 m (90th percentile = 264 m), with the maximum distance of 531 m, giving a positively skewed distribution of distances  of effective dispersal distance, i.e., effective dispersal distance kernel. A detailed description of the method is found in the “A distance separating close kin is a result of dispersal and successful breeding over multiple generations, and here we show how it can be used to infer a dispersal distance achieved over one generation of successful breeding. This distance is also known as the “effective dispersal distance”  and can k) was close to 1 , the Weibull distribution can be reduced to an exponential distribution with the estimated rate parameter λ = 0.022 (95% CI 0.020–0.025) (Table λ = 0.019 (95% CI 0.014–0.027), 2nd-degree relatives λ = 0.021 (95% CI 0.016–0.027), 3rd-degree relatives λ = 0.024 (95% CI 0.020–0.028) , and the inferred dispersal kernel gives the 95% probability of effective dispersal distance up to 136 m (95% CI 120–152 m).The “goodness of the fit” criteria  and Weibull scale λ of 111.63 (95% CI 110.50–112.70) .We repeated the same analytical procedure for the null distribution (random sampling of pairwise distances between non-close kin). The null distribution produced a distance kernel with the Weibull shape â [When the probability of dispersal declines with distance, a positive correlation between genetic and geographic distances between individuals is expected, and this relationship is known as isolation-by-distance (IBD) , 35. IBDâ , Loisellâ ) gave hir = 0.124, 95% CI 0.052–0.198; R2 = 0.015, p = 3.91 × 10−8) and Yishun   was detected between non-close kin in Tampines (Mantel b was 0.0037 (95% CI 0.0024–0.0050) for the Tampines data, and 0.0065 (95% CI 0.0051–0.0079) for the Yishun dataset , or theb and D), the estimated effective dispersal kernel spread (σ) for Tampines was 54.1 m , 122.3 m , and 66.8 m . For Yishun, the estimated σ was 44 m , 94 m , and 74.4 m  , where σ represents both standard deviation and the mean, the estimates of σ from the IBD analysis are equivalent to the mean effective dispersal distance and can be used to parametrize the mensions . Our resx-axis (x-intercept) between 200 and 300 m in Yishun and between 200 and 500 m in Tampines ) [kL(r)) that gives probabilities for the end locations of dispersers relative to the source locations [kD(r) = 2πrkL(r) in a two-dimensional habitat [kD(r) = 4πr2kL(r) in a three-dimensional habitat.Our newly developed method allows for the parametrization of the effective dispersal distance kernel, as it decomposes the observed distances between close kin to generate the distribution of potential effective dispersal distances (achieved over one generation of successful reproduction). It gives probabilities of dispersal distances in any direction, referred to as the “dispersal distance kernel” (kD(r)) , and it ocations . The loc habitat , and kD [Our method has some obvious parallels to the work of Jasper et al.  that alsr et al.  that arer et al. ), even tr et al. , 44, 45.r et al. . For exa kernel) . Second, kernel)  produces kernel)  produce Ae. aegypti males from a genetically engineered line OX513A [Ae. aegypti [, as well as the robustness of spatial genetic patterns in reflecting the dispersal characteristics of this mosquito.Using the close-kin data from the Tampines and Yishun districts in Singapore, our close-kin method produced the negative exponential (Laplacian) kernel that gives 50% probability (kernel median) of effective dispersal occurring within 32 m. This indicates that, in this landscape, we can expect half of the successfully breeding individuals to stay within the high-rise building where they emerged or move to the adjacent building. The mean effective dispersal distance  was estimated at 45.2 m (95% CI 39.7–51.3 m), with a 10% probability of a dispersal distance greater than 100 m (95% CI 92–117 m). Our genetic-based estimates match the parametrized dispersal kernel from mark-release-recapture (MRR) experiments performed in Brazil and Malaysia with  aegypti  producedNe estimates from the PWoP method [Ne estimate [Nb, [Ne) [Ae. aegypti from Tampines and Yishun  rather Nb, [Ne) . We obseAe. aegypti females marked with rubidium via spiked blood meal were released from middle floors and moved readily towards the top or bottom of multi-storey buildings in search of oviposition sites [In Tampines and Yishun, that are largely homogeneous landscapes with multi-storey apartment buildings, 47% of all detected full siblings were found on the same floor or 4–5 floors apart, and the inferred dispersal kernel predicts high level of effective dispersal within a building or between adjacent buildings. This agrees with a previous study in Singapore where on sites . In Trinon sites , again sAe. aegypti to remain close to the birthplace is not unexpected. The tail of the dispersal kernel, however, provides insight into rarer long-range dispersal events that are consequential for the control strategies that rely on the releases of modified mosquitoes for population suppression or replacement. For example, the Wolbachia spread is expected to be slower in Ae. aegypti populations with longer-tail dispersal kernels, but this dispersal pattern also allows the initiation of the Wolbachia spread with smaller local releases [Wolbachia  could be initiated if Wolbachia-infected Ae. aegypti are released in an area with a radius of at least 100–130 m (2.51σ for Laplacian kernel [x-intercept = 200–300 m).Given that high-rise apartment blocks can provide an abundance of hosts, oviposition, and resting sites, the tendency of releases . In a poreleases  predictsn kernel ). The diAe. aegypti was achieved with very high release numbers of Wolbachia-infected males [x-intercept) suggest this buffer zone to be ~ 250 m.Accurately estimated dispersal range is also critical when determining the size of the area targeted by sterile or incompatible mosquito male releases for population elimination (SIT or IIT programs). In Fresno County, California, a high suppression (~ 95%) but not local elimination of ed males . The inaed males . In thised males . TherefoWolbachia-based mosquito replacement [The theoretical approximation of the conditions for lacement  and simulacement , 55–58 hAe. aegypti. Knowledge of the dispersal kernel parameters enables operational teams to design and implement optimal surveillance, control, and release strategies in a given landscape, facilitating the efficient utilization of resources and maximizing the impacts of interventions. We show that spatial genetic analyses can provide robust estimates of mosquito dispersal patterns. Our newly developed method for the construction of the effective dispersal distance kernel through close-kin analysis enables the most comprehensive estimation of relevant parameters. The indirect inference from the IBD framework that requires less intensive sampling than close-kin analysis can also provide estimates of dispersal kernel spread; however, this approach is sensitive to the inaccurate estimates of effective population size and is uninformative about the probabilities of long-range dispersal that have important implications for control programs. Spatial autocorrelation analysis can complement the IBD analysis to ascertain the spatial extent of the effective dispersal kernel tail through estimation of the genetic patch size. With the decreasing cost of next-generation sequencing, acquisition of spatial genetic data is increasingly accessible. Given the complexities and criticisms of conventional MRR methods, and the central role of dispersal measures in essential vector control programs, we recommend genetic-based dispersal characterization as the more desirable means of parameterization.Accurate and precise characterization of dispersal in the field is critical for the optimization of surveillance and control of disease vectors like Aedes aegypti females were collected using the sticky traps developed by the Environmental Health Institute, National Environment Agency of Singapore , known as the Gravitraps—simple, hay infusion-filled cylindrical traps with a sticky inner surface that preferentially catch gravid females in search of suitable ovipositing sites [Ae. aegypti were transferred to 2-mL tubes according to their collection date, residential block, and floor and stored in 100% ethanol at 4 °C until further processing.Adult ng sites . The GraAe. aegypti genome assembly [Total genomic DNA was extracted from individual mosquitoes using Dneasy Blood and Tissue DNA extraction kit  according to the manufacturer’s instructions. Individual mosquitoes were homogenized manually in 180 μl of ATL buffer using sterile plastic pestles, and the proteinase digestion (with 20 μl of proteinase K) was carried out overnight. The DNA was quantified by using the Qubit High Sensitivity DNA kit , and only the specimens that yielded a DNA concentration of at least 4 ng/μL were included in RADseq libraries. Reduced-genome representation sequence data were generated for each individual using the double-digest RAD sequencing approach by Peterson et al. , with thassembly  using Boassembly . Unambigassembly  to generassembly . The finRelationships between individuals were determined using the recently developed approach by Waples et al.  as impleAe. aegypti females rarely mate more than once [For the spatial analyses, we considered close kin as pairs with an inferred category of 1st-, 2nd-, or 3rd-degree relatives. Kinship categories were determined based on the combination of three statistics , which allows the distinction between the parent-offspring and the full-sibling relationship within the category of 1st-degree relatives . Second-han once , 67. We â [We used different individual-based genetic distances among individuals within each area (Yishun or Tampines). The PCA-based genetic distance was estimated by first performing the principal component analysis (PCA) from genotype data in the R package “adegenet”  and thenâ  and Loisâ  in the pâ .X/Y (long/lat) and Z (height) coordinates of their collection point, here called the Euclidean 3D distance, representing a linear geographic distance in meters (m). Natural logarithm (ln) of this distance was used in the analyses where Rousset’s â or Loiselle’s kinship coefficient was applied (see below), given that both of these genetic coefficients exhibit approximate linear relationship with ln-geographic distance [Pairwise spatial (geographic) distance between mosquitoes was calculated as the shortest straight line (Euclidean) distance in three dimensions, based on the distance .po), full siblings (fs), 2nd-degree relatives (2nd) , 3rd-degree relatives (3rd) , and non-close kin. Every close-kin category contains information about the number of possible effective dispersal events. For example, a pair of full siblings (fs) could have originated from the same breeding site from which each sibling moved into a gravitrap (two events), or they could have originated from different breeding sites . Therefore, the corresponding number of possible dispersal events, for each case, can be calculated as the number of such breeding sites (n) plus one (n + 1). Based on this, we constructed the sets with elements that represent the number of possible dispersal events for each case as {nmin + 1,.., nmax + 1}. For the fs category, this set is FS = {2, 3}. In the case of a parent-offspring (po) pair, the minimum and maximum number of breeding sites is nmin = nmax = 1, giving a set PO = {2}. For the kinship category of 2nd-degree relatives, we have the following subsets: half siblings HS = {2, 3, 4, 5}, avuncular AV = {3, 4}, and grandparent-grandoffspring GO = {2, 3}. We constructed the full set for 2nd-degree relatives as the union of these three subsets (containing unique elements): 2ND = HS ∪ AV ∪ GO = {2, 3, 4, 5}. In the case of 3rd-degree relatives (first cousins fc), the minimum number of contributing breeding sites is nmin = 1 , while the maximum is nmax = 4 (first cousins and their mothers each originate from a unique breeding site). Therefore, for the category of 3rd-degree relatives, we can construct a set as 3RD = FC = {2, 3, 4, 5}.In our sampling scheme, adult females were caught after landing on a lethal ovipositing site (Gravitrap), and we assume that this is a result of their active flight . We consider pairs of females caught in different traps (non-zero separation distance) that could be genetically assigned to one of the following kinship categories: parent-offspring (d) with the corresponding set element. For example, if a collected pair AB, separated by distance dAB, falls into the category of 3rd-degree relatives, then a set of possible effective dispersal distances for this pair would be d3rd,AB = {dAB/2, dAB/3, dAB/4, dAB/5}. For a full-sibling pair BC separated by spatial distance dBC, the set of possible effective dispersal distances will be dfs,BC = {dBC/2, dBC/3}.We then created a set of possible effective dispersal distances for each detected close-kin pair based on their distance and assigned kinship category. This set of distances contains the same number of elements as the corresponding set of possible effective dispersal events (described above), and its values are obtained by dividing the detected spatial distance between a pair (pdf) of effective dispersal distance  by fitting different functions  using the R package “fitdistrplus” [By combining the values from all pairwise sets of possible effective dispersal distances into one dataset, we characterized the resulting distribution of possible effective dispersal distances. This dataset was used to generate a probability density function , we simulated 100 datasets where pairs had a randomly assigned kinship category and a distance randomly sampled from the recorded distances between non-close kin. The number of simulated pairs in each kinship category matched the number of observed pairs in the empirical dataset. We then applied the analytical procedure described above on all simulated datasets and compared the simulated (random) and empirical distributions in the R package “sm” [To estimate a age “sm”  using thIBD analysis can be used even when few or no close kin are captured. In fact, highly related individuals should be removed from the IBD analysis in order for it to reflect the long-term population processes , and we Db), where b is the slope of the linear regression and D is the effective density of reproducing individuals.IBD is best illustrated by the regression of pairwise genetic distances onto geographic distances among individuals . The sloâ or Loiselle’s kinship coefficient was tested against the matrix of ln-transformed geographic distances, given that both genetic estimators are expected to vary approximately linearly with the natural logarithm of the distance [The slope of the linear relationship was estimated using the lm function in R (R Core Team) for three different sets of genetic and geographic matrices. The first set included a matrix of PCA-based genetic distances against the matrix of linear geographic distances. A matrix of Rousset’s distance .D is defined as Ne/study area, where Ne is the effective population size. We estimated Ne using two genetic methods based on a single sample. The first method is Ne estimation by Waples and Waples [. The median and the 95% confidence interval were calculated using 100 resamples with a random replacement of one individual. The second method represents Ne estimation by Waples and England [e), with the 95% confidence interval calculated using the jackknifing procedure over loci implemented in the program Ne estimator v.2.1 [Ae. aegypti population [Ne and density than the smaller patch 2  in Aedes aegypti from Singapore. Table S6. Mantel and partial Mantel tests for non-close-kin data in Tampines and Yishun. Table S7. Entomological data used for the estimation of effective population density in Aedes aegypti (method 3).Additional file 2: Fig. S1. Spatial network of close kin in Tampines (A) and Yishun (B). Fig. S2. Frequency histogram of separation distance for each kinship category. Fig. S3. Distribution fitting  analysis.


Cities are both embedded within and ecologically linked to their surrounding landscapes. Although urbanization poses a substantial threat to biodiversity, cities also support many species, some of which have larger populations, faster growth rates, and higher productivity in cities than outside of them. Despite this fact, surprisingly little attention has been paid to the potentially beneficial links between cities and their surroundings. We identify five pathways by which cities can benefit regional ecosystems by releasing species from threats in the larger landscape, increasing regional habitat heterogeneity and genetic diversity, acting as migratory stopovers, preadapting species to climate change, and enhancing public engagement and environmental stewardship. Increasing recognition of these pathways could help cities identify effective strategies for supporting regional biodiversity conservation and could provide a science-based platform for incorporating biodiversity alongside other urban greening goals. Cities are embedded in and connected to their surrounding landscapes. Energy, resources, and species all flow across political and geographic boundaries, with impacts on landscape-scale biodiversity. Although urbanization poses a substantial threat to biodiversity  populations have grown dramatically in cities over the past five decades, likely in response to decreased persecution by humans and winter bird feeding  and red foxes (Vulpes vulpes) in Madison, Wisconsin, in the United States, revealed that both species’ home ranges spanned urbanized and natural areas  in Vezprem, Hungary, dispersal between urban and rural sites may be extremely limited, and individuals may be mostly restricted to either urban or rural areas  as stopover sites in a matrix of residential backyards and woodlands  has higher breeding densities in the city center, but lower reproductive success compared with suburban breeding individuals  are attracted to exotic fruiting trees in cultivated in gardens where they are also vulnerable to high rates of electrocution from nearby power lines in Sri Lanka , peregrine falcon, burrowing owl (Athene cunicularia), hooded crow (Corvus cornix), bronzed cowbird (Molothrus aeneus), and black-and-rufous warbling finch  were found to have higher reproductive success in urban sites compared with surrounding agricultural sites, although there was little evidence of active selection of urban sites , despite higher abundance in forested sites and little evidence of active selection of urban sites  that are restricted entirely to small urban habitat patches figure . HoweverDrawing on a range of examples from around the world, we identify five primary categories of benefits cities can provide to species figure . Each caUrsus americanus) in Colorado, in the United States, moved into urban areas during food-poor years and out of them during food-rich years  suffered a massive die-off in an exurban wildlife sanctuary during an El Niño Southern Oscillation-related drought event. Adjacent urban populations in the city of Jodhpur were supported through the drought by irrigated vegetation and human feeding, and suffered no corresponding die-off  in Canada  undergoing species recovery in the UK , peregrine falcons, crested goshawks (Accipiter trivirgatus), and Mississippi kites  in Argentina  in Poland (Łopucki and Kiersztyn Coptotermes gestroi) in Southeast Asia , the Mission Blue (Icaricia icarioides missionensis), and the San Bruno elfin  butterflies , a species of conservation concern  had higher reproductive success in urban sites compared with agricultural sites  compared with grasslands and woodland countryside habitats in the UK  had more diverse gut microbiomes than rural sparrows, likely because of higher land cover diversity and variation in tree cover in urban sites compared with rural sites dominated by a single shrub habitat type  in Highbanks Park (15.5 ha) Columbus, Ohio, in the United States , support over 100 species of migrating songbirds  in the western United States  in Germany  in northern Poland show evidence of extensive gene flow and little differentiation between urban and rural populations, likely because of the high dispersal ability, colonial life history, and migratory behavior of the species . A few recent studies document adaptations that confer tolerance to the hotter conditions found in cities. Urban populations of lesser pepperwort (Lepidium virginicum) in the northern United States bolt earlier and have a longer period between bolting and flower production—adaptations that are beneficial in hotter drier conditions and under water stress  have higher heat tolerance than their rural counterparts  are experiencing severe population declines in North America. Numerous organizations are promoting the planting of milkweed (Asclepias spp.), the Monarch caterpillar's host plant, in private gardens . Planting of native milkweed in butterfly gardens has been found to be an effective strategy. In Omaha, Nebraska, in the United States, for instance, similar recruitment, survival, and parasitism was found for Monarch butterflies in tall grass prairie conservation areas and suburban gardens, suggesting that private yards with milkweed maintained for Monarchs can contribute to their conservation , and ponds are managed to support a wide range of successional stages. These actions can promote biodiversity, and urban ponds support similar alpha diversity of aquatic macroinvertebrates, as well as higher prevalence of some taxonomic groups than nonurban ponds , and enabling and bolstering public engagement and stewardship (figure Cities can benefit some species by releasing them from threats in the larger landscape, increasing regional habitat heterogeneity, acting as migratory stopovers, enhancing regional genetic diversity and providing selective forces for species to adapt to future conditions under climate change (e.g., a phenomenon we are calling p figure . Althougp figure . Other sResponses to urbanization are highly species-specific and depend on a combination of species traits and the characteristics of both the city and its surrounding landscape. In many cases, species responses to cities have changed over time as the habitat and resource context shifts. This implies that actions that we take focused on urban biodiversity conservation can broaden the suite of species that are able to take advantage of the resources cities have to offer while reducing their negative impacts. Although a small subset of species clearly benefit from cities, for many, the true population and demographic outcomes of urban living remain unclear. A recent meta-analysis of urban raptors found that several species that are more abundant in urban landscapes compared with rural have lower reproductive success in cities, highlighting that abundance alone is a poor proxy for gauging the population-level implications of cities (Kettel et al. An expanded research agenda could fill this gap, deepening our understanding of the ways cities can support regional biodiversity conservation and providing information to guide the planning and design of urban green spaces. For example, urban greening actions can be informed by design guidance derived from biodiversity-focused research in cities (Spotswood et al. Expanding our understanding of these positive impacts can help support a growing recognition that urban ecosystems are a necessary component of landscape-scale biodiversity conservation (Soanes and Lentini


P < 0.001), field of study (P < 0.001), and status of heard or unheard of genetic testing (P < 0.001). The perception of genetic testing was generally positive and influenced by the respondent's differences in age (P < 0.016), educational level (P < 0.001), field of study (P < 0.001), and status of heard or unheard of genetic testing (P < 0.001). Although positive responses were obtained, ~20.2% of the respondents had never heard of genetic testing. Of the respondents, 24.5% were unwilling to undergo genetic testing, with 25.1% believing that genetic testing tampers with nature and 18% believing that it opposes religion and their beliefs. Such attitude calls for the need to conduct programs to eliminate any misconception, as well as to educate the public to lessen any perceived misunderstanding of the concepts of genetic testing.Genetic testing aids patients in making important decisions in the prevention, treatment, or early detection of hereditary disorders. Low awareness of the importance of genetic testing contributes to the increase in the incidence of hereditary disorders. This study aims to explore the knowledge, awareness, and perception of genetic testing for hereditary disorders among local residents of the Klang Valley, Malaysia, and the potential variables that influence their understanding of genetic testing. A survey was conducted in different municipalities of the Klang Valley through self-administered questionnaire assessing the public's knowledge, awareness, and perception of genetic testing. Overall, the results revealed adequate knowledge and positive awareness of genetic testing, in which both were influenced by the respondent's educational level ( Genetic disorder is a health condition that is inherited by a person, usually caused by mutations in the deoxyribonucleic acid (DNA) or changes in the number or overall structure of chromosomes , providing a confidence level of 95%, with a margin of error of 5%  version 22.0 software. Descriptive data analysis, Cronbach's alpha test, Mann–Whitney A scoring system was used to analyze the data, which were coded and inputted into the SPSS software. For questions regarding knowledge of genetic testing, a score of 2 was given for “yes,” a score of 1 was given for “maybe,” and a score of 0 was given for “no.” Then, the total score was summed and classified according to the following categories: inadequate = 0–53%, moderate = 54–66%, and adequate = 67–100% . Of the respondents, 56.2% graduated with a Bachelor's degree. A significant number (37.6%) of respondents were from science-related fields of study. Moreover, of the respondents, 63.6% was single during the duration of this study. In this study, 239 (53.1%) respondents declared having a history of genetic or hereditary disorders in their family, where hypertension (58.2%) was the most prevailing disorder, followed by diabetes (53.6%) and hypercholesterolemia (40.2%) .In this study, 359 (79.8%) respondents have heard of genetic testing, and the Internet was their key source of information (68.0%) . Table 1P-value of <0.001 (The majority of the respondents (83.1%) have adequate knowledge of genetic testing . As showf <0.001 .As shown in P-value of <0.001  and opposes religion and their beliefs (37.7%). Many respondents disagreed (44.3%) with the statements compared with those who agreed .P = 0.016), ethnicity (P = 0.031), educational level, field of study, and whether they have heard of genetic testing or not, with each having a P-value of <0.001  showed willingness to undergo genetic testing, whereas 24.5% of the respondents gave a negative response and 25.3% of the respondents gave a neutral response. This finding was supported by a similar study in the U.S. population where a large portion (43%) of the general public lacks awareness toward genetic testing (Krakow et al., Moreover, there is strong support from the respondents that genetic testing should be performed exclusively in hospitals with a doctor's prescription. Furthermore, the majority of the respondents were against the idea of genetic testing kits being sold in stores or through the Internet. Although no similar studies in Malaysia were found, these results were in line with those reported in a survey conducted among the Dutch and Belgium public (Vermeulen et al., More than half of the respondents recognized the importance of genetic testing and its use for preventive care purposes . They alNonetheless, most of the respondents remained neutral when asked whether genetic testing tampers with nature and opposes religion and their beliefs. A minority of respondents agreed to these statements, and this can be related to their ethnicity. A significant difference between ethnicity and perception of genetic testing can be observed. As reported in a previous study in New York city, ethnic and racial identities are associated with the perception of the benefits of and barriers to the application of genetic testing (Sussner et al., Nevertheless, with the high recognition of the importance of genetic testing, the majority of the respondents believe that it is necessary to increase the awareness of this topic as they feel that the lack of education and knowledge of genetics and tests available leads to ethical issues concerning the practice of genetic testing . Thus, mIn general, in terms of the scope of this research, only Malaysians resided in the Klang Valley were included in the survey. Therefore, the findings obtained may not be considered a direct representation of the population of Malaysia as a whole, although the residents of the Klang Valley comprised people from all states of Malaysia. Despite achieving a full response rate of 100%, inaccuracies and biases may be detected because of the choice of location and the respondents chosen. The respondents chosen are those within the age range of 18–50 years old and considered to have a higher capability to understand the topic of the study. Correspondingly, a lower response is obtained from those aged 43 years and above because of language barriers as the questionnaire was developed only in the English language. However, despite the limitations regarding the choice of respondents, this survey was conducted in a large scale, i.e., accounting for the entire Klang Valley, compared with other almost similar research that was conducted in a small scale. Furthermore, this survey provides a first update of Malaysians' knowledge, awareness, and perception of genetic testing for hereditary disorders. Thus, this study is set as the foundation for further similar studies that may be conducted in a larger scale to obtain a better assessment and a more accurate scenario of the knowledge, awareness, and perception of genetic testing of Malaysians.The practice of genetic screening for genetic or hereditary disorders is becoming more acceptable worldwide, likewise in Malaysia. In spite of the majority of the residents of the Klang Valley having adequate knowledge and relatively good and positive awareness and perception of genetic testing, there is still a minority of respondents with negative awareness and perception. Thus, the public's knowledge, awareness, and perception of genetic testing should be improved to decrease any stigma or taboo that one may have. For this, media campaigns and government and non-government programs, such as seminars, should be arranged to further educate the public on the beneficial health consequences of genetic testing. Furthermore, the government should implement laws and policies to ensure the safe and efficient use of genetic testing.The datasets generated for this study are available on request to the corresponding author.This study was approved by the SEGi Ethics Committee, with project number SEGi/RIMC/FOP/26/2018. Before the survey, informed written consent was obtained from each respondent, and they were informed that the research was voluntary, confidential and purely for academic purposes.J-JC and H-WT conceived, designed the study, and drafted the manuscript. J-JC acquired and analyzed the data. H-WT critically revised and finalized the manuscript. All authors approved the final version of the manuscript. All authors contributed to the article and approved the submitted version.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.


Signal transducer and activator of transcription (STAT) 3 is one of the most complex regulators of transcription. Constitutive activation of STAT3 has been reported in many types of tumors and depends on mechanisms such as hyperactivation of receptors for pro-oncogenic cytokines and growth factors, loss of negative regulation, and excessive cytokine stimulation. In contrast, somatic STAT3 mutations are less frequent in cancer. Several oncogenic targets of STAT3 have been recently identified such as c-myc, c-Jun, PLK-1, Pim1/2, Bcl-2, VEGF, bFGF, and Cten, and inhibitors of STAT3 have been developed for cancer prevention and treatment. However, despite the oncogenic role of STAT3 having been widely demonstrated, an increasing amount of data indicate that STAT3 functions are multifaced and not easy to classify. In fact, the specific cellular role of STAT3 seems to be determined by the integration of multiple signals, by the oncogenic environment, and by the alternative splicing into two distinct isoforms, STAT3α and STAT3β. On the basis of these different conditions, STAT3 can act both as a potent tumor promoter or tumor suppressor factor. This implies that the therapies based on STAT3 modulators should be performed considering the pleiotropic functions of this transcription factor and tailored to the specific tumor type. Signal transducer and activator of transcription 3 (STAT3) is a multifunctional transcription factor involved in multiple biological processes. It was identified in 1993 by Wegenka et al.  describeSTAT3 belongs to the STAT family of cytoplasmic transcription factors that mediate signal transduction from the plasma membrane to the nucleus in various cellular activities .There are seven STAT proteins: STAT 1, 2, 3, 4, 5A, 5B, and 6 encoded by genes clustered on different chromosomes. Each of them consists of six regions: (1) a helical N-terminal domain (ND) for protein–protein interactions between adjacent STAT dimers on DNA; (2) a coiled-coil (CC) domain for interactions with regulatory proteins that positively or negatively modulate the transcriptional activity; (3) a DNA-binding domain (DBD) for the recognition of specific DNA-sequences of target genes; (4) a helical linker (LK) domain involved in nuclear export and DNA binding; (5) an Src homology 2 (SH2) domain for receptor binding and dimerization; (6) a C-terminal transactivation domain (TAD) that contains specific residues that are phosphorylated upon transcriptional activation [STATs are activated in the cytoplasm by Janus kinases (JAKs), a family composed of four different intracellular non-receptor tyrosine kinases, that transduce cytokine-mediated signals ,8. JAKs STATs recognize DNA motifs with a consensus sequence of 5′-TTCN3/4GAA-3′ that are located in promoter and enhancer regions and in the first introns of target genes .STAT3 is one of the most complex transcription regulators and is involved in many biological functions such as cell proliferation, maturation, and survival. STAT3 is activated by the entire family of IL-6-type cytokines comprised of IL-6, IL-11, IL-22, IL-27, IL-31, oncostatin M (OSM), cardiotrophin 1 (CT-1), ciliary neurotrophic factor (CNTF), cardiotrophin-like cytokine factor 1 (CLCF1), and leukemia inhibitory factor (LIF) . These cA main role in the IL-6 cytokine family/STAT3 axis is played by a transmembrane—cytokine receptor associated—protein of 130 kD called gp130. In fact, this protein mediates, as a common signal transducer, the pleiotropic but overlapping functions of the IL-6 cytokine family. When a member of the IL-6-type cytokine family binds to its receptor, it induces the recruitment of gp130 and the formation of a complex of three proteins (IL-6-type cytokine/IL-6 member receptor/gp130). After complexing, gp130 is phosphorylated on tyrosine residues. The phosphorylation leads to the association with JAK, which, in turn, activates STAT3, which leads to the activation of downstream genes. Animals lacking gp130 are not viable, indicating the importance of this receptor associated protein [While gp130 expression is relatively ubiquitous in a wide variety of tissues and organs, receptors of the IL6-cytokine family are more restricted and cell-type specific. These receptors include IL-6R (IL-6 receptor), IL-11R (IL-11 receptor), IL-27Rα , OSMR (OSM receptor), LIFR (LIF receptor), and CNTFRα .Many studies have demonstrated the critical role of aberrant STAT3 in malignant transformation, and several STAT3 oncogenic targets have been identified. However, recent evidence has shown that STAT3 can have opposite functions in cancer, and based on different conditions, STAT3 can act as both a potent tumor promoter and a tumor suppressor factor. This appears to depend on several factors such as the integration of multiple signals, the oncogenic environment, and the alternative splicing into different isoforms.STAT3 is currently considered an oncogene, and aberrant regulation of STAT3 has been reported in nearly 70% of cancers ,15. AberThe first study demonstrating the involvement of STAT3 in cancer was published in 1995 by Yu et al. They showed that oncoprotein Src can activate the STAT3 signaling pathway, raising the possibility that this transcription factor contributes to oncogenesis by Src . In 1996It is noteworthy that Bromberg et al. reported that the substitution of two cysteine residues within the C-terminal loop of the SH2 domain of STAT3 produces a molecule that dimerizes spontaneously, binds to DNA, and activates transcription, causing cellular transformation, thus demonstrating that the activated STAT3 molecule by itself can mediate cellular transformation .Hyperactivation of STAT3 has been reported in many types of tumors and can depend on the following mechanisms: (i) hyperactivation of receptors for pro-oncogenic cytokines and growth factors; (ii) elevated activity of cytoplasmic non-receptor tyrosine kinases, such as Src, Janus kinases (JAKs), and Abelson (Abl) kinase; (iii) loss of negative STAT3 regulation; (iv) excessive stimulation by cytokines such as IL-6 or EGF. These mechanisms can induce uncontrolled cell growth, malignant cell transformation, angiogenesis, metastasis, invasion, and immune escape. For example, the growth and survival of human myeloma cells is dependent on an IL-6 autocrine loop that induces a constitutive activation of STAT3 . LikewisIn contrast, somatic STAT3 mutations are less frequent in cancer. For example, sixty percent of inflammatory hepatocellular adenomas have IL-6 signal transducer mutations that cause hyperactivation of IL-6/STAT3 signaling, while 12% of these tumors lacking IL-6 signal transducer mutations have somatic STAT3 mutations .However, some types of cancers frequently have STAT3 mutations. For example, STAT3 mutations were found in 40% of patients with large granular lymphocytic leukemia. In this leukemia, all mutations were located in exon 21, encoding the Src homology 2 (SH2) domain, which mediates the dimerization and activation of STAT3 .STAT3 activation in normal and tumor cells is dependent on the phosphorylation of a tyrosine residue (Y705), which is located between the SH2 domain and TAD . PhosphoAfter cytokine treatment, STAT3 is also acetylated on a single lysine (K) residue, K685 . AcetylaOther post-translational STAT3 modifications are p300-mediated acetylation of K87 at the STAT3 NH2 terminus, which is required for IL-6-induced target gene activation , dimethySTAT3 regulates multiple biological functions in the initiation of malignant transformation, and it is the convergence point of several major oncogenic signaling pathways. Moreover, STAT3 modulates host immunity against tumor cells leading to tumor-induced immunosuppression. This depends on the ability of STAT3 to suppress anticancer immune cells and to activate cancer promoting immune cells. Therefore, therapies based on STAT3 modulators can not only directly inhibit the growth of tumors, but also enhance antitumor immunity.Activated STAT3 can upregulate the mRNA levels of many genes involved in cell growth and apoptosis such as cyclins D1, D2, D3, A, and B, Cdc25A, Cdc2, c-Myc, PLK1, Pim-1/2, Cten, survivin, Bcl-xL, IAPs, and Mcl-1. These upregulated genes cooperate in inducing the oncogenic transformation of cells. In addition, STAT3 downregulate genes encoding cell cycle checkpoint proteins such as p21, p27, and p53. Other genes, such as c-Jun, c-Fos, and FGFR, interact with STAT3 in inducing cancer transformation .STAT3 plays a key role in the G1 to S phase cell cycle transition through the upregulation of cyclins D1, D2, D3, A, and Cdc25A and the concomitant downregulation of p21 and p27. Moreover, STAT3 participates in modulating the G2–M phase checkpoint by regulating gene expressions of cyclin B1 and Cdc2 via E2F . Cyclin c-Myc is another important downstream effector of STAT3 signaling involved in cell growth and transformation. Disruption of STAT3 signaling by using dominant-negative STAT3β protein in NIH 3T3 fibroblasts suppresses c-Myc expression. Moreover, fibroblasts with c-Myc gene knockout are refractory to transformation by v-Src, and disruption of STAT3 signaling in normal cells inhibits PDGF-induced mitogenesis in a manner that is reversed by ectopic c-Myc expression .Zhang et al. reported that there is an interaction between a region within c-Jun and specific sites within STAT3 . c-Jun wTwo other factors implicated in cell growth and apoptosis and modulated by STAT3 are PLK-1 and Pim 1 and 2. Serine/threonine-protein kinase PLK1, also known as polo-like kinase 1 (PLK-1), is an enzyme that in humans promotes in cells the G2/M transition. It is considered a proto-oncogene, whose overexpression is often observed in tumor cells. The oncogenic properties of PLK1 are believed to be due to its role in driving cell cycle progression. In addition, PLK1 can inhibit the transactivation and pro-apoptotic functions of p53. STAT3 and PLK1 control each other’s transcription in a positive feedback loop that contributes to the development of cancer such as esophageal squamous cell carcinoma . The PIMSTAT3 activation upregulates factors implicated in cell motility and malignant transformation such as COOH terminal tensin-like (Cten). Tensin is a signaling molecule that binds to actin filaments and localizes to focal adhesions. Cten is an atypical tensin family member lacking the actin-binding domain that promotes colon cancer tumorigenicity and cell motility ,68. In aA novel pathway that contributes to tumor growth, involving fibroblast growth factor receptors (FGFRs), STAT3, and hyaluronan (HA) synthesis, has been recently described by Bohrer et al. . FGFR isFinally, STAT3 inhibits apoptosis by modulating five important factors implicated in apoptosis regulation: survivin, Bcl-xL, IAPs, Mcl-1, and p53.Survivin is an inhibitor of apoptosis. It acts by inhibiting caspase activation, thereby leading to the negative regulation of apoptosis. Survivin protein is overexpressed in most human tumors and fetal tissues. Survivin gene has been identified as a STAT3-regulated gene in breast cancer cells . Direct Bcl-xL is another important anti-apoptotic factor, and its expression level is upregulated by STAT3 other than by other factors such as NF-κB and tumor necrosis factor-alpha (TNF-α). Inhibition of STAT3 leads to a decrease in the expression of Bcl-xL . In a stActivation of STAT3 increases the transcription and expression of the IAP (inhibitor of apoptosis proteins) family proteins and Mcl-1 . IAPs arSTAT3 is able to modulate the expression of p53, one of the most important oncosuppressor proteins, which plays a crucial role in many tumors. In fact, the p53 gene is frequently mutated in cancer, but p53 mutations generally occur at a late stage in tumor development. In contrast, in the early stage, many clinically detectable cancers have only reduced p53 expression. STAT3 seems to play an important role in reducing p53 transcription by binding to the p53 promoter. Site-specific mutation of an STAT3 DNA-binding site in the p53 promoter partially abrogates STAT3-induced p53 inhibition, and the blocking of STAT3 in cancer cells upregulates expression of p53, leading to p53-mediated tumor cell apoptosis .All these data indicate the complex role of STAT3 in modulating cell growth, apoptosis, and tumorigenesis. However, many tumors express pro-survival and growth genes in a non-STAT3-dependent manner. In other tumors, STAT3 activation alone appears insufficient to induce tumorigenesis, but acts by conferring greater malignancy to neoplastic cells. This was demonstrated in two different studies. In a mouse model of tumorigenesis study, STAT3 did not alter mammary tumor initiation but affected metastatic progression. In fact, only a few mice bearing STAT3-null tumors showed lung metastasis and lung lesions compared with the wild-type cohort. Tumors from STAT3-null mice were less vascularized than tumors from the wild-type animals and showed a significant reduction of the proangiogenic factor, VEGF. Moreover, a gene expression analysis showed that STAT3-null mice expressed lower levels of both cebpd and osmr, two genes that are induced by STAT3. These genes code for C/EBPδ and the oncostatin M receptor, two factors that are known to potentiate the acute-phase response of inflammation .Similar results were obtained by Babieri et al. in mice expressing the activated rat ErbB-2 (neu) but lacking STAT3 in the mammary epithelium. STAT3 was apparently not required for neu-driven mammary tumorigenesis. However, STAT3 silencing in a neu-overexpressing tumor-derived cell line completely abolished both neu-driven anchorage-independent growth and lung metastasis .These data indicate that STAT3 confers tumor great malignancy by promoting tumor invasion, migration, metastasis, and angiogenesis. Moreover, it plays an important role in cancer immune escape.The formation of metastases depends on the ability of cancer cells to degrade the basal membrane and extracellular matrix and to move within. STAT3 regulates both of these two processes. RhoA-Rho-associated protein kinase (ROCK) is a kinase belonging to the AGC family of serine-threonine kinases. It is involved mainly in regulating the shape and movement of cells by acting on the cytoskeleton. The ROCK-myosin axis is the most well-known mechanism of cell contractility and is the major signaling pathway that induces amoeboid movement ,83. AmoeAfter degradation of the basement membrane, tumor cells enter the circulatory or lymphatic system, forming metastasis in lymph nodes and in distant organs. STAT3 activation has an important role in protecting metastatic cells from the immune system by modulating the secretion of various inflammatory factors such as IL6 and TNFα  and reduAnother essential step for tumor growth and metastasis formation is angiogenesis, which is mediated by the vascular endothelial growth factor (VEGF) . STAT3 iThe immune system can suppress or promote cancer depending on the type of immune cells. Innate immune cells  and cells from the adaptive immune system (Th1 cells) can destroy tumor cells when activated . In contT helper cells (Th cells), also known as CD4+ cells, play a central role in cancer immune response by activating antigen-specific effector cells and recruiting cells of the innate immune system such as macrophages and mast cells. Once activated, Th cells proliferate rapidly and secrete cytokines to regulate immune response .Th cells are activated and develop into effector T cells by two costimulatory signals from antigen presenting cells (APCs). The first costimulatory signal is the stimulation with antigens presented by major histocompatibility complex class-II (MHC-II) molecules, expressed on the surface of APCs. The second costimulatory signal is mediated by the linkage between CD80 or CD86 on the APCs and CD28 on the T cells.Activated STAT3 in cancer cells induces the release of factors that block APCs maturation/activation and inhibits the generation of antigen-specific T cells, causing immune tolerance . FurtherNaive CD4 + T cells that develop into effector T cells differentiate into five major subtypes of cells known as Th1, Th2, Threg, Th17, and Th9.Th1 cell differentiation is induced in the presence of IL-12. Th1 cells produce the proinflammatory cytokine IFN-γ and are associated with a good prognosis in several cancer types. They are primarily responsible for activating and regulating the development and persistence of cytotoxic T cells (CTLs). In addition, Th1 cells activate APC. Stat1 is the key transcription factor for the IFN-γ-mediated gene , and NF-Th2 and Th17 cells are two other major T cell subsets found within the tumor microenvironment. Th2 cells produce anti-inflammatory cytokines, whereas Th17 cells produce mainly proinflammatory cytokines including IL-17 and IL-22. These two cell types appear to be overall pro-tumoral.The Th2 anti-inflammatory cytokine IL-10 is involved in cancer development and is transcriptionally upregulated by STAT3 in tumors. IL-6 promotes STAT3 recruitment in both colon cancer cells and T cells, which, in turn, upregulates IL-10 secretion . IL-10 dIL-6, transforming growth factor-β (TGFβ), and IL-23 are involved in the activation of Th17 immune response. In contrast to the tumor suppressor role of Th1 immune response, Th17 response and IL-23 play a cancer-promoting role. STAT3 is involved in Th17 development because it acts both as the major IL-6R-dependent transcription factor in T cells and as a transcriptional activator of Il-23a that encodes part of IL-23 . FurtherTreg cells are characterized by the production of major suppressive cytokines, including IL-10 and TGF-β. The presence of Treg cells in the tumor correlates mainly with a bad prognosis. Treg cells secrete IL-10 and TGF-β. TGF-β in turn induces the expression of Forkhead box P3 (FOXP3), a mediator of Treg cells that converts naive CD4+ T cells into CD4+CD25+ FOXP3+ Treg cells ,110. STAFinally, STAT3 signaling is required for the immunosuppressive and tumor-promoting effects of myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages , and in Although STAT3;s role as a tumor promoter has been widely demonstrated, several studies indicate that STAT3, under specific conditions, can act as a tumor suppressor factor in many different tumors .The first dual function of STAT3 was described in the nervous system during the development of cells along the glial lineage. The different cell types of the mammalian central nervous system  are derived from common neural precursor cells (NPCs) ,118. In These opposite functions could be determined by the interaction of STAT3 with different signals in the oncogenic environment. For example, STAT3 has a tumor suppressive activity in glioma cells with an intact PTEN function and an oncogenic role in EGFRvIII-expressing gliomas and glioblastomas .PTEN is an oncosuppressor protein, and STAT3 plays a key role in the PTEN pathway to suppress malignant transformation of astrocytes. In this pathway, IL8 plays a critical role. IL8 is not expressed in normal brain but is expressed in human glioblastoma tumors where it promotes proliferation and invasiveness. Gene-profiling analyses of glioblastoma cells have shown that the IL8 gene is directly repressed by STAT3 .In cells with intact PTEN function, the protein kinase AKT is directly inhibited by PTEN. AKT is an inhibitor of FOXO3, a transcription factor involved in several biological functions including apoptosis. This transcription factor activates the transcription of the LIFRβ gene, which, in turn, activates STAT3, causing the repression of the IL8 gene, thereby inhibiting glioma cell proliferation and invasiveness. Conversely, in PTEN-deficient tumor cells, Akt is constitutively active, thus inhibiting FOXO3 function and leading to LIFRβ downregulation. In this scenario, STAT3 is not active and cannot repress the IL8 gene. This leads to an overregulation of IL8, which drives glioma cell proliferation and invasiveness .In glioblastoma tumors that are not deficient in PTEN, STAT3 can play an oncogenic role in response to the expression of the oncogenic truncated protein EGFRvIII . EGFRvIIThese findings indicate that STAT3 plays distinct roles in cell transformation depending on the mutational background of the tumor.Hyperactivation of the IL-6/STAT3 axis is frequently observed in prostate cancer cell lines. However, the blocking of the IL-6/STAT3 axis did not result in a survival advantage in patients with advanced prostate cancer. About 70% of metastatic prostate cancers show deletions or mutations in the PTEN gene. The loss of PTEN function in these tumors is associated with the activation of the oncosuppressor ARF–p53 pathway, which likely acts as a oncosuppressor compensatory mechanism . In factThe genetic background of tumor plays an important role in determining the activity of STAT3 as a pro-oncogenic or anti-oncogenic factor also in lung cancer. STAT3 is considered to play an oncogenic role in lung cancer ,129; conAnother case in which the oncosuppressor role of STAT3 is not dependent on its transcription function but the interaction with specific factors is colorectal cancer. The development of human colon adenomas is initially induced by mutations in tumor suppressor protein APC (adenomatous polyposis coli). APC is involved in the β-catenin/Wnt signaling pathway. The transition from adenoma to adenocarcinoma occurs only if two other oncosuppressor genes are mutated: SMAD4  and EPHB3 (ephrin type-B receptor 3) genes. SMAD4 is a transcription factor that acts as a mediator of TGF-β signal transduction. When the structure of SMAD4 is altered, the expression of the genes involved in cell growth is deregulated, and cells proliferate without any inhibition . EPHB3 iSTAT3 is a modulator of glycolysis and mitochondrial respiration, and constitutive activation of STAT3 induces aerobic glycolysis and downregulates mitochondrial activity both in primary fibroblasts and in STAT3-dependent tumor cell lines . STAT3 iThese data are consistent with the observation of high levels of activated phospho-STAT3 (Y705) in benign follicular thyroid adenomas and human primary papillary thyroid carcinoma without evidence of distant metastasis. Furthermore, a positive correlation between the expression of activated phospho-STAT3 (Y705) and smaller human primary papillary thyroid carcinoma tumor sizes has been observed. Finally, STAT3 knockdown resulted in the downregulation of multiple transcripts, including the tumor suppressor insulin-like growth factor binding protein 7 (IGFBP7) . All theSTAT3 is a regulator of epithelial cell death during mammary gland involution. Removal of the suckling stimulus and the consequent milk stasis lead to an increased expression of leukemia inhibitory factor (LIF), which is the initial activator of STAT3 during gland involution . ActivatIn contrast to this function, STAT3 acts as a promoter of survival in breast cancer cells ,140. In However, an analysis of 346 node-negative breast cancer showed that the expression of nuclear activated phospho-STAT3 (Y705) in the biopsied samples was correlated with a significant improved short-term (five years) and long-term (20-year) survival .By studying the distribution of mutations in breast cancer metastases matched with their primary lesions, it was observed that mutations in synchronous metastases  were similar to the those of the primary breast tumor. These mutations typically interested genes frequently mutated in breast cancer, such as TP53, PIK3CA, and GATA3. In contrast, metachronous distant metastases had additional mutations, in particular mutations that caused loss of JAK2 and STAT3 functions . This suConstitutive activation of STAT3 has been described in head and neck squamous cell cancers (HNSCCs), where it is involved in deregulation of the cell cycle, increased cell growth, and inhibition of apoptosis ,149,150.STAT3 has four isoforms: STAT3α, STAT3β, STAT3γ, and STAT3δ. STAT3α, STAT3β are generated by alternative splicing and differ structurally in their C-terminal transactivation domains because the β isoform lacks the C-terminal transactivation domain . STAT3γ STAT3α is the full-length version of STAT3. It regulates essential STAT3 target genes involved in cell proliferation, migration, and survival, and the oncogenic functions of STAT3 have also been associated with constitutively active STAT3α .Conversely, STAT3β inhibits cancer progression acting as a repressor of STAT3. Several studies have shown that STAT3β can inhibit cell proliferation by impairing STAT3α-driven activation of Bcl-xL, p21, and cyclin D1 ,155,156.Since constitutive activation of STAT3 plays a central role in many tumors, small and non-small-molecular STAT3 inhibitors have been developed as novel cancer therapeutics ,167,168.SH2 domain interactions are critical for molecular activation and nuclear accumulation of phosphorylated STAT3 dimers to drive transcription. The SH2 domain mediates STAT3 dimerization via intermolecular phosphorylated tyrosine–SH2 interactions. Therefore, molecules able to block the SH2 domain of STAT3 have been considered for the treatment of different tumors. Many STAT3 inhibitors that block the SH2 domain were identified through a structure-activity relationship (SAR) study based on the previously identified inhibitor S3I-201. The key structural feature of these inhibitors is a salicylic acid moiety, which, by acting as a phosphotyrosine mimetic, facilitates binding to the STAT3 SH2 domain. S3I-201 and derivatives disrupt active STAT3:STAT3 dimers suppressing the expression levels of STAT3 target genes, such as Bcl-xL, survivin, cyclin D1, and MMP-9 and causing inhibition of cell growth, migration, and invasion of human cancer cells ,170,171.OPB-31121 and OPB-51602 are two STAT3 inhibitors that interact with the SH2 domain with high affinity. Cell culture assays showed that OPB-31121 and OPB-51602 inhibited the phosphorylation of both Tyr705 and Ser727. OPB-31121 is a STAT3 inhibitor with potent antitumor effects against various human liver cancer cell lines . In addiAnother group of inhibitors targeting the SH2 domain of STAT3 are derivatives from natural compounds. Curcumin, a naturally-derived phytochemical from plants, has been shown to inhibit STAT3 phosphorylation and block the STAT3 signaling pathway in various tumor cell lines ,181,182.DBD interacts with the promoter of STAT3 targeting genes and has a relatively high specificity. The small molecule C48 was the first inhibitor targeting the DBD. It acts by alkylating glutathione sulfhydryl on cysteine (C468) in the STAT3 DBD region. C48 blocks the accumulation of activated STAT3 in the nucleus in tumor cell lines that overexpress active STAT3, leading to impressive inhibition of tumor growth in mouse models . Using aFinally, several platinum compounds, including IS3-295, CPA-1, CPA-7, and platinum (IV) tetrachloride, have shown the ability to inhibit the STAT3 DBD, suggesting potential new applications for platinum complexes as modulators of the STAT3 pathway for cancer therapy ,194.The N-terminal domain plays an important role in the interaction of dimers of STAT-3 and in the attachment of STAT-3 dimers to the sites of DNA. Therefore, in the last few years, compounds able to interact and block this domain have been developed. ST3-H2A2 is a highly selective STAT3 ND inhibitor. This compound induces apoptotic death in cancer cells associated with potent activation of proapoptotic genes. In particular, it activates the expression of the proapoptotic gene CHOP, causing apoptosis in tumor cells . ST3-HelSynthetic antibody mimetics, termed monobodies, have been developed to interfere with STAT3 N-terminal and coiled-coil domain signaling. These monobodies are highly selective for STAT3 and bind to STAT3 with nanomolar affinity. The crystal structure of STAT3 in complex with monobody MS3-6 reveals bending of the coiled-coil domain, resulting in diminished DNA binding and nuclear translocation. MS3-6 expression strongly inhibits STAT3-dependent transcriptional activation and disrupts STAT3 interaction with the IL-22 receptor .STAT3 indirect inhibitors prevent STAT3 activation by inhibiting the tyrosine kinase events at the receptor level. Receptor-associated and non-receptor tyrosine kinases are critical upstream regulators of STAT3 activation, so targeting these kinases has attractive potential to block STAT3 activation. Molecules that indirectly target upstream activators of STAT3 include KDI1, a short peptide that specifically binds to the intracellular domain of the EGFR , PD15303Numerous JAK2 inhibitors have been developed that are able to inhibit the JAK2/STAT signaling pathways, and ruxolitinib and tofacitinib have been approved by the FDA for the treatment of myelofibrosis and rheumatoid arthritis. However, apart from myelofibrosis, no satisfactory results have been obtained with JAK2 inhibitors in cancer patients, and clinical trials with JAK1/2 inhibitors and Src inhibitors have shown limited efficacy or excessive toxicity in advanced solid tumors .Despite the large amount of STAT3 inhibitors studied in vitro, only a few compounds have entered clinical trials. AZD9150, napabucasin (BBI608), TTI-101, and WP1066 are the most studied compounds in different trials.AZD9150 is a 16 oligonucleotide antisense molecule (ASO) targeting the 3’ untranslated part of STAT3 . ClinicaNapabucasin (BBI608) is a newly-developed small molecule inhibitor of STAT3  currentlTTI-101 is an orally bioavailable, binaphthol-sulfonamide-based inhibitor of STAT3 that specifically targets and binds to the phosphotyrosyl peptide binding site within the SH2 domain of STAT3 . It is iWP1066 has been designed to target the STAT3 pathway in tumor cells , and phaSTAT3 is a transcription factor that has been extensively studied over the past 20 years due to its implication in many biological processes and diseases, including cancer. It is one of the most complex transcription regulators involved in multiple cellular processes, and its hyperactivation has been reported in many types of tumors. This has led to this transcription factor being considered an important target for the development of a new class of drugs. Despite a large number of STAT3 inhibitors having been developed and studied in vitro, only a few of them are currently in clinical trials, and at present, there are no clinically approved drugs directly targeting STAT3. This is, in part, caused by the serious toxic effects observed with the use of most STAT3 inhibitors. In fact, STAT3 is involved in many biological processes, and its inhibition can induce important alterations in normal cells. Furthermore, complete inhibition of the STAT3 signaling pathway may require very high drug concentrations as compared to inhibitors of ligands or cell surface receptors. In fact, cellular signaling transmitted by the latter is always amplified downstream, while STAT3 translates the signal without amplification. Finally, several studies have raised questions and doubts about the use of STAT3 inhibitors based on the tumor suppressor role of STAT3 in several cancers. This role does not depend on the transcription function of STAT3 but on its ability to interact with other biological factors. Therefore, the goal could be the discovery of high-quality targeted selective agents against STAT3 that do not affect the functions of healthy cells and the identification of patients who are likely to receive actual benefit from the treatment.


Quantifying variation of genetic traits over the geographical range of species is crucial for understanding the factors driving their range dynamics. The center-periphery hypothesis postulates, and many studies support, the idea that genetic diversity decreases and genetic differentiation increases toward the geographical periphery due to population isolation. The effects of environmental marginality on genetic variation has however received much less attention.Lilium pomponium in the southern Alps.We tested the concordance between geographical and environmental gradients and the genetic predictions of center-periphery hypothesis for endemic We found little evidence for concordance between genetic variation and both geographical and environmental gradients. Although the prediction of increased differentiation at range limits is met, genetic diversity does not decrease towards the geographical periphery. Increased differentiation among peripheral populations, that are not ecologically marginal, may be explained by a decrease in habitat availability that reduces population connectivity. In contrast, a decrease of genetic diversity along environmental but not geographical gradients may be due to the presence of low quality habitats in the different parts of the range of a species that reduce effective population size or increase environmental constraints. As a result, environmental factors may affect population dynamics irrespective of distance from the geographical center of the range. In such situations of discordance between geographical and environmental gradients, the predictions of decreasing genetic diversity and increasing differentiation toward the geographical periphery may not be respected. Quantifying variation of genetic traits over the geographical range of species is crucial for understanding the factors driving their range dynamics and in particular the ecological and evolutionary processes that act on populations on the periphery of a species range . As climA major theory that has been developed to enhance our understanding of traits and genetic variation among-population across the species range is the Centre-Periphery Hypothesis (hereafter CPH). The CPH postulates that species’ abundance and performance decrease from the geographical center of the range toward the periphery due to the deterioration of environmental conditions , 2017. AHowever, a concordance between “geographical periphery” and “environmental marginality”  might noFinally, when different factors are examined , they seldom all follow CPH predictions . This inLilium pomponium L. provides an ideal species to test the CPH predictions for genetic variation along geographical and ecological gradients. This plant species has a geographical distribution that is endemic to the Maritime and Ligurian Alps where it occurs along a wide altitudinal gradient. In the study area, a heterogeneous impact of glaciations and topographic complexity probably minimized population extinction during Quaternary glaciations  is a perennial herb (geophyte) endemic to the Maritime and Ligurian Alps—a region that represents a major regional hotspot of endemism and plant diversity in the Mediterranean Basin (2 (http://www.silene.eu/index.php?cont=accueil) and LiBiOss . Overall, a final dataset of 881 occurrence records was used to assess the global distribution of the species and to determine central and peripheral sites and those that may be environmentally marginal. A total of 197 individuals were sampled from 20 populations that cover the entire geographical and altitudinal range of the species .AFLP genotyping was based on the protocol described by One Hundred times diluted pre-amplification products were used to perform selective amplification in a final volume of 20 μL (15 μL + 5 μL of diluted pre-amplification products). For the selective amplification thermocycle we used 94 °C for 2 min, 10 cycles of 94 °C for 30 s, 65 °C for 30 s (step −1 °C per cycle), 72 °C for 1 min, followed by 22 cycles at 94 °C for 30 s, 56 °C for 30 s, 72 °C for 1 min, 72 °C for 5 min and 4 °C for 2h. The fragment length produced by the amplification was separated and quantified by electrophoresis using an ABI 3730xl DNA analyzer  with GS600 LIZ size marker. Peaks were scored in Peak Scanner V 1.0  as present (1), absent (0), or “no data” (NA). We utilized Raw Geno 2.0  to selecL. pomponium. Following this, geographical peripheral populations were identified by calculating for each occurrence its distance from the geographical center, its shortest distance to the edge of the convex hull and the ratio between these two distance measures.We used different measures to describe the geographical and environmental center-periphery gradients. To identify geographically central and peripheral populations we calculated the geographical center of distribution by averaging the coordinates of the occurrences and the distributional limits by calculating the minimum convex hull polygon that included all the known occurrences of www.chelsa-climate.org) at 30-s (c. 1 km) spatial resolution. We extracted values of bioclimatic variables from cells where the species occurs and defined the climatic space occupied by the species on the basis of the first two axes of a principal component analysis (PCA) using all cell values. Environmental marginality was assessed by calculating the Euclidean distance from the centroid of the climatic space and the Mahalanobis distance from the mean of the climatic space.To assess environmental marginality nineteen bioclimatic variables representative of the period 1979–2013 were downloaded from the Chelsa climate database website (h) and Shannon’s information index (I) using POPGENE v. 1.32  with distance from the geographical center  were not correlated with measures of geographical marginality while Rho index was significantly correlated with measures of geographical marginality, increasing toward the geographical periphery  assumptions of concordance between geographical and environmental gradients are not supported neither is the prediction of decreasing genetic diversity towards the geographical periphery. Our results nevertheless provide support for the prediction of increasing differentiation towards the range periphery.In this study on L. pomponium occurs, environmental factors change over very short distances because of high topographic complexity  explain the occurrence of a patchy distribution  shrubland or forest expansion in mid-elevation of high elevation sites  fragmented distribution of populations rather than the result of a decline in habitat quality and population size . In facton sites  and/or ; BIO4 = temperature seasonality; BIO5 = max temperature of warmest month; BIO6 = min temperature of coldest month; BIO7 = temperature annual range (BIO5-BIO6); BIO8 = mean temperature of wettest quarter; BIO9 = mean temperature of Driest quarter; BIO10 = mean temperature of warmest quarter; BIO11 = mean temperature of coldest quarter; BIO12 = annual precipitation; BIO13 = precipitation of wettest month; BIO14 = precipitation of driest month; BIO15 = precipitation seasonality; BIO16 = precipitation of wettest quarter; BIO17 = precipitation of driest quarter; BIO18 = precipitation of warmest quarter; BIO19 = precipitation of coldest quarter.Click here for additional data file.


Apis mellifera as the major crop pollinator. Commercial and research-based breeding programs have made significant improvements of favorable genetic traits (e.g. production and disease resistance). The variety of bees produced by artificial selection provides an opportunity to characterize the genetic diversity and regions of the genome undergoing selection in commonly managed stocks.The population genetics of U.S. honey bee stocks remain poorly characterized despite the agricultural importance of Varroa mite. Few variants were identified as being specific to any one stock, indicating potential admixture among the sequenced stocks. Juxtaposing the underlying genetic variation of stocks selected for disease- and parasite-resistance behavior, we identified genes and candidate regions putatively associated with resistance regulated by hygienic behavior.Pooled sequencing of eight honey bee stocks found strong genetic similarity among six of the stocks. Two stocks, Pol-line and Hilo, showed significant differentiation likely due to their intense and largely closed breeding for resistance to the parasitic This study provides important insights into the distinct genetic characteristics and population diversity of honey bee stocks used in the United States, and provides further evidence of high levels of admixture in commercially managed honey bee stocks. Furthermore, breeding efforts to enhance parasite resistance in honey bees may have created unique genetic profiles. Genomic regions of interest have been highlighted for potential future work related to developing genetic markers for selection of disease and parasite resistance traits. Due to the vast genomic similarities found among stocks in general, our findings suggest that additional data regarding gene expression, epigenetic and regulatory information are needed to more fully determine how stock phenotypic diversity is regulated. Apis mellifera) are the predominant managed insect pollinator for a majority of food crop species and depend on high genetic diversity within colonies and between populations. High intracolony diversity in honey bees promotes fitness , 43ST atST values and extracted only the unique SNPs. Within the top 1.0% FST value SNPs we found a range of 5–405 SNPs that are putatively stock-specific ; and UniProtKB keywords transmembrane, transmembrane helix, and membrane associated clustering.A total of 46 haplotype blocks containing the most significant Composite Selection Signal (CSS) scores top 0.1%) among generalized Italian, Minnesota Hygienic, Pol-line, Hilo, and Russian stocks with more than five SNPs were identified. Several regions demonstrated overlap with regions previously associated with general hygienic behavior or VSH  than they are from Pol-line (FST = 0.016). Additionally, these findings may reflect a paternal effect as the relationship between these stocks is through some sharing of patrilineal lineages. Other stocks that were either selected for hygienic behavior (Minnesota Hygienic) or express it at a high level without targeted selection for the trait (Russian) did not show a similar degree of differentiation from the common Italian background, potentially highlighting that VSH behavior has at least some partially unique genetic features. This may be a function of increased independent selection for hygienic behavior by bee breeders [Differences in genetic variation among stocks identified three general clusters which principally grouped as Hilo-Pol-line, Italian1-Italian3-Russian-Carniolan, and Italian2-Minnesota Hygienic. This provides further evidence of the genetic differentiation of Hilo and Pol-line from other stocks. We posit this was largely driven by a combination of continued selective pressure for a specific, shared mite-resistant phenotype (VSH) along with relative reproductive isolation. Interestingly, though Hilo and Italian2 stocks have some shared ancestry , Hilo primarily clusters with Pol-line. This may indicate a greater contribution of the Pol-line ancestry to Hilo, with Fbreeders  in the In = 405) and 0.1% (n = 12) cutoffs, respectively. This further highlights the genetic differentiation of these populations compared to other stocks, likely in response to targeted selection focused largely on one trait (VSH). The strong genetic similarity among Italian, Russian, Minnesota Hygienic, and Carniolan stocks yielded few unique SNPs that could effectively segregate the stocks at the highest FST. Interestingly, Carniolan and Russian stocks had very few SNPs differentiating them from the generalized Italian stock despite the controlled nature of their breeding programs. This supports the case that introgression from external Italian honey bee populations exists either from their population of origin (e.g. [The majority of SNPs identified in this study did not demonstrate a significant degree of differentiation between stocks. Hilo and Pol-line had the most SNPs at the 1.0% (in (e.g. ) or aftein . The extraction method for each sample is listed in Supplementary File Table Pool-seq was used to widely sample each population and identify conserved patterns of genetic variation within each of the 8 sampled stocks. To sample, 96 worker bees were collected from 12 breeder colonies of each stock. The Carniolan, Minnesota Hygienic and three Italian stocks were randomly sampled by the bee breeders to be sent for sequencing. Given that Hilo, Pol-line, and Russian stocks are either maintained by the USDA-ARS Baton Rouge research unit or close collaborators, they were carefully sampled to ensure independent sampling within each of the populations so as to minimize relatedness between colonies. Colonies from the Russian stock were selected from 12 of the 18 breeding lines, Pol-line colonies represented 12 different lines and Hilo represents 10 different lines. Eight pools of 12 heads per colony were prepared by first excising the heads using sterile surgical scalpels. The compound eye lenses were then removed to limit introduction of magnesium residues into downstream reactions . Sampleshttp://broadinstitute.github.io/picard/) on the trimmed fastq files followed by SortSam to sort reads. Sorted reads were then processed by GATK’s BaseRecalibrator to correct for patterns of systematic errors in base quality scores using base settings [Sequencing was performed at the Institute for Genome Science at the University of Maryland School of Medicine using the Illumina HiSeq4000 150 paired-end reads. The 96 pools of samples were spread across eight lanes for a mean coverage depth of 30x per sample. Raw sequence files were trimmed using Trim Galore v0.6.0 and then aligned to the honey bee reference genome (Amel_HAv3.1) using bwa v0.7.16a to generate bam files . Duplicasettings –61.SNPs were derived for our data set by using using Lofreq’s “call” function  which prA joined set of genetic variants with at least one overlapping SNP was used to conduct a principal component analysis (PCA) to summarize the covariation in allele frequencies between samples. The code used to generate the overlapping SNP set is provided in Supplemental File 1. An iterative k-means hierarchical analysis was applied to the resulting principal components (PC) to identify the optimal number of genetic clusters in our data set agnostic of population membership . The AkaST [ST [ST was > 0.65 [Genome-wide patterns of differentiation between the sequenced stocks was estimated with the population genetic parameters pi (π), theta (θ), and FST –67. For ST  and PopoST [ST . Both ths > 0.65 .ST values from each of the pairwise comparisons of stocks. These most highly differentiated SNPs were then compared among the six stocks  to identify SNPs unique to that stock among these top candidates.Stock specific SNPs were identified using the top 1.0 and 0.1% FST calculations for Minnesota Hygienic-Italian, Pol-line-Italian, Hilo-Italian, and Russian-Italian comparisons were calculated and used to derive a Z-score. The Z-score was averaged for each corresponding SNP, compared to a standard distribution to derive a P value, and transformed using a -Log10 representing the CSS [P-value as the cutoff using the Apis mellifera background. All statistical analyses were performed with R v3.6.0 [A composite selection signal (CSS) score was used to refine and detect signatures of selection . This me the CSS . CSS out the CSS . These h the CSS , 71, 72 R v3.6.0 .