Gaining insight into crop diversity, both at the genetic and phenotypic levels, is of prime importance for onion breeding with an enhanced yield and quality in combination with improved resistance to biotic and abiotic stresses. In the current study, 192 different onion plants, representing 16 ecotypes, were characterized using ISSR markers.H2) for the phenotypic traits associated with color  was the largest , demonstrating that color is conditioned primarily by genetic factors. In contrast, the H2 for yield was the lowest (41.8%), indicating that the environment has a substantial effect on yield. In addition, there was a significant association between the presence/absence of certain bands and various phenotypic traits.Based on the ISSR marker profile, there was a clear grouping of the plants into 16 different ecotypes. Though the 16 populations originated from the same geographic region in Morocco, a significant genetic diversity was detected. After a genomic characterization, field trials in three different environments in Morocco were laid out. The phenotypic characterization showed that there were always significant differences between ecotypes, and for most traits, there was also a significant environmental effect and a significant interaction between environment and ecotype. The broad-sense heritability (ISSR markers are a powerful tool in distinguishing onion ecotypes. In addition, significant associations between marker scores and phenotypic traits could be detected, representing particular importance for future breeding programs. Allium cepa L.) is one of the main vegetable crops grown in many regions of Morocco [With a size of 31,862 ha and an annual production of 828,901 tons, onion yl cysteine sulphoxides. These compounds have been reported to having powerful positive health effects, including anti-carcinogenic, anti-thrombotic, anti-asthmatic, and antibiotic effects . DespiteAccording to Walters et al. , crop doAs gaining insight into the genetic variability is of pivotal importance, modern breeding employs marker analysis to explore populations to tackle the loss of valuable genetic traits . The maiTo deal with this influence, genetic markers present a lot of advantages over phenotypic markers for two main reasons. Firstly, they are not influenced by the environment , 13. SecSeveral molecular techniques serve to characterize the genetic diversity of crop plants. The most used methods are random amplified polymorphic DNA (RAPD), restriction fragment length polymorphism (RFLP), single nucleotide polymorphism (SNP), amplified fragment length polymorphism (AFLP), inter-simple sequence repeat (ISSR), and microsatellite or simple sequence repeat (SSR) .Each molecular marker presents some advantages: e.g., the low cost and the simplicity of RAPD and ISSR, the high genome coverage and the large number of bands for AFLP, and the revelation of variability at the allelic level using RFLP and SSR markers , 14. DesInter-simple sequence repeats (ISSR) have been proven to be very useful for the analysis of genetic diversity in plant species. ISSR analysis is a fast, accessible technique with a high level of polymorphism and reproducibility due to the use of long primers 16–25 bp) . The dev–25 bp . Eleusine coracana [Oryza genus [Carthamus tinctorius [ISSR markers are widely used in plant genomic analysis. It is used for the estimation of genetic diversity within many crops like coracana , Oryza gza genus , and Carnctorius  and the nctorius .In Morocco, there are two main climatic zones: the Mediterranean northern coastal regions and the southern interior regions. This climatic separation results from the presence of the Atlas Mountain range in the center of Morocco. In the northern regions, the rainy season runs from November to March, with an average of 50 to 100 mm of rainfall per month. The temperature varies between 22 and 25 °C in the summer and between 10 and 12 °C in the winter. In the Atlas Mountains, especially at higher altitudes, the temperatures in the wet season are lower than in the coastal regions. However, in the southern and interior regions, the temperatures vary from 25 to 30 °C in the summer and drop to less than 15 °C in the winter .A. cepa L. ecotypes using ISSR markers, (ii) study the efficiency of ISSR markers to gain insight into Moroccan onion ecotypes structure, and (iii) gaining insight into the phenotypic diversity expressed under different growing conditions.Given the importance of onion and the lack of knowledge of its genetic diversity in Morocco, the main goals of the present study include (i) characterization for the first time of the genetic diversity within and between 16 Moroccan A. cepa L.) were collected as seeds from 14 different regions around the country  and the Agricultural Development Regional Office (ORMVA), 18 Moroccan onion ecotypes  for 10 min, stirring, and then rinsing them twice with sterile demineralized water for 5 min. To evaluate the seed quality and viability, a germination test was carried out according to the International Rules for Seed Testing (ISTA) . With thFrom each ecotype, 12- to 20-day-old seedlings were harvested, and the plant material (~ 30 mg/plant) was ground in liquid nitrogen using a pestle and a mortar. The genomic DNA extraction from the ground onion plant was done using the Invisorb® Spin Plant Mini Kit (Stratec Molecular). DNA concentrations were measured using the Quantus™ Fluorometer (Promega). The optical density was determined at 260 and 280 nm against TE buffer as blank, and the ratio of A260 to A280 was calculated to check the purity of the DNA. It is important to note that the DNA samples for the analysis were diluted to 50 ng/μL with the molecular-grade water.The isolated genomic DNA was amplified with ten University of British Columbia (UBC) ISSR primers Table . The amp2. The quantity of seed per unit per ecotype was calculated using the germination rate and the weight of 1000 seeds to have 1000 onion bulbs in each unit. Four months later, only onion plantlets with a good vegetative appearance were transplanted into a new unit of 2 m2 in randomized complete blocks with three replicates. The agricultural practices  were the same for all the units in the same location and were according to the crop husbandry practices of Moroccan farmers.Next to genetic screening, the differences between phenotypes of the various ecotypes were determined. Therefore, the different ecotypes were grown in three different regions: (i) Goulmima , an oasis region in the desert environment; (ii) Ifrane  in the middle of the Atlas Mountain environment; and (iii) Ras Ijerri  in the Saïs plateau. The planting started in the second week of November 2018 with a broadcast sowing of 16 onion ecotype seeds, in a randomized complete block design with three replicates, laid out in 48 experimental units of 1 mL*a*b* colorimetric model , in which a color is indicated by three values: L*, luminance, expressed as a percentage (0 for black to 100 for white); a* and b* two color ranges, from green to red and from blue to yellow, respectively, with values ranging from − 120 to 120.After harvesting (August 2019), various characteristics of the onion were measured for randomly selected onion bulbs from each unit for all ecotypes in each location. These characteristics include the diameter in two directions using a caliper, the weight of the bulbs, the total yield, and the number of onions per kilogram. In addition, the bulb shape index (BSI) was calculated as the ratio of polar diameter to the equatorial diameter. Furthermore, the coloration of the outer layers (the skin) was perceived using the KONICA MINOLTA, Chroma meter CR-5. It is based on the The R software package  was usedPIC). The latter was calculated as i is the polymorphic information content of marker i′, fi is the frequency of the amplified allele (band present), and 1 − fi is the frequency of the null allele [To gain an insight into the informativeness and discriminative power of the ISSR markers, several parameters of the banding pattern were determined, for example, the total number of bands, percentage of polymorphic bands, and the polymorphic information content  was applied to determine whether, based on the banding pattern of the different onion genotypes, a differentiation up to the ecotype level was possible. Based on LDA, linear combinations of ISSR bands that provide the best separation between populations can be found.H2) was calculated as H2 = sg2/(sg2 + sge2/n + se2/nr), in which sg2, sge2, se2, r, and n represent the estimated variances for the genetic effects, genotype-environment interaction effects, random errors, number of replicates, and number of environments, respectively.To analyze the effects of ecotypes and the environment on phenotype expression, a two-way analysis of variance (ANOVA) was used. Based on the output of the ANOVA, the broad-sense heritability  was used to detect the grouping of the phenotypes, and a Spearman rank correlation was calculated to reveal the association between molecular markers and the phenotypic traits.The germination test revealed that there were clear differences in seed viability between ecotypes. Genotypes of ecotypes 11 and 14 resulted in an average germination percentage of 12% and 10%, respectively . According to the AMOVA, 58% of the total variance could be attributed to variations between populations, whereas 42% of the variance was within-population variance. A pairwise post hoc comparison revealed that all the populations significantly differed from each other based on their ISSR profile . Furthermore, in Fig. The statistical significance of this subdivision was proven by a In Table The individuals belonging to populations 16 and 18 were most diverse since all bands are polymorphic, i.e., none of the bands was present in each individual of the population. In addition, these populations have the highest average within-population Jaccard distance, 0.72 and 0.75, respectively. In contrast, population 10 is characterized by the lowest frequency of polymorphic bands (74%) and the lowest within-population Jaccard distance (0.118).The informativeness and the discriminative power of the different markers are presented in Table In the next step, an LDA was applied to identify the bands that were characteristic of a population. In Fig. Based on the comparison between the geographic distance and the genetic distance of each individual, populations P1 and P2 are most similar Fig. .p-value 0.147).It appears also clear that populations P5, P6, and P7 cluster together based on the ISSR marker profile and based on the geographic distance. The similarity level between the genetic profile tree and the origin distance tree, based on the entanglement, which is the measurement of the similarity between two trees, is 0.53. However, based on a Mantel test, the correlation between the geographic distances and the genetic distances was not significant  , and the ecotypes react differently depending on the environment (interaction effect). In addition, the two-way ANOVA revealed that there were always significant differences between the ecotypes (1) Table .H2) for the phenotypic traits associated with color  was the largest (79–84%), demonstrating that onion color is primarily determined by genetic factors. In contrast, the H2 for yield (42%) was the lowest, indicating the environment has a substantial effect on the yield  showed a high correlation with the second principal component. In addition, a clear differentiation based on the region where the onions were cultivated can be observed .A Mantel test revealed that there was no significant correlation between the distances based on the ISSR pattern and the distances between the average phenotypes expressed by the ecotypes  Spearman correlations between the traits and the average marker scores are shown.Since both genotypic and phenotypic data were available, the possibility of finding any correlation between a band and a phenotypic trait was tested. The correlation analysis was carried out for the nine different traits and the 127 bands from the 10 ISSR molecular markers. In Table Remark that for yield, no significant correlations with the presence of a marker could be found. A positive correlation means that the value of the trait is higher in case a band is present, whereas a negative correlation indicates that the value of the trait increases with a lower average band score.Variations in genetic and phenotypic diversity are of pivotal importance for breeding. Under these circumstances and for the first time, the genetic and phenotypic diversity in onion cultivated in Morocco were investigated.Allium genus and also to characterize the genetic relationship between the species [Nowadays, molecular markers with a high level of DNA polymorphism contribute to the improvement of vegetable crops around the world . Several species , 26, 27. species .Our study addresses for the first time the diversity between onion genotypes from different regions in Morocco. Based on various approaches including genetic distances, AMOVA, LDA, and the genetic diversity between individual plants and between populations, valuable information concerning the available genetic resources was obtained.A. cepa L. [The obtained results showed that, based on the ISSR profile, there was a clear subdivision of the 192 onion plants into 16 ecotypes or populations. In addition, the ISSR primers revealed a 100% polymorphism within the onion plants, i.e., each onion plant was characterized by a unique banding pattern. The high level of genetic diversity within onions is confirmed by several researchers using different molecular markers like ISSR, RAPD, and SSR , 31 and  cepa L. . So, alt cepa L. , ISSR maThe results of the AMOVA revealed that most of the variation could be attributed to the differences between populations (58%). These results are in contrast with the results of Monteverde et al.  who founConcerning the discriminative power of the various markers, clear differences were observed. The ISSR markers revealed 8 to 18 polymorphic bands for the markers UBC 841 and UBC 840, respectively. The PIC value varied between 0.161 and 0.368. Studying the genetic diversity of selected Indian onion germplasm, the calculated PIC value of ISSR markers ranged between 0.13 and 0.40 . For theFrom the LDA analysis, it was concluded that a new onion plant can be assigned to the correct ecotype with an accuracy of 93% based on its specific ISSR banding pattern.A comparison of the clustering of the ecotypes/populations according to their genetic makeup and based on the distance between their regions of origin showed some similarities, which could be considered as a genetic breeding base of genotypes belonging to the same origin .b* and UBC 815–1, meaning the ecotypes in which this marker is primarily absent have a large value for b*.Apart from genetic differentiation, plants express phenotypic plasticity, i.e., expressing the optimal phenotype in a certain environment. To detect the phenotypic diversity and a possible association with the genotype, a multi-location field trial was set up. This trial revealed a significant environmental effect (and significant interaction between environment and ecotype) for most of the studied traits. The strong influence of the environment on phenotype is confirmed by Leino et al. . In addiA. cepa L. in Morocco consists of well-differentiated ecotypes. Despite the proximity of the geographic origin of some onion ecotypes, a significant genetic diversity was detected.Given the importance of the onion crop in Moroccan agriculture, the present study was carried out to evaluate and characterize, for the first time, the genetic potential of Moroccan onion ecotypes. The analysis of the genetic diversity of 192 Moroccan onion plants, belonging to 16 different ecotypes using ISSR markers profile, reveals a high genetic diversity between onion ecotypes, compared to the genetic variation within onion ecotypes. The clear aggregation of the onion plants into 16 different ecotypes, and the absence of the overlapping in the Neighbor joining tree, confirms that The study of the genetic diversity of Moroccan onions confirms the efficiency of ISSR markers to gain insight into the population structure. The potential of these molecular markers is represented by high discriminative power between the ecotypes and the high number of polymorphic produced bands. In addition, correct attribution of a new onion plant can be possible using a specific ISSR banding pattern.Significant phenotypical diversity was revealed through the characterization of the onion ecotype phenotype under three different environments in Morocco. Furthermore, the obtained results affirm a significant influence of the environment on the majority of phenotypical traits, and the genotype influence of all studied characters, especially the coloration components. Next to that, a strong interaction effect between environment and genotype was also confirmed. In addition, the present study showed that onion ecotypes are different essentially by the size traits, followed by the coloration characters. Finally, some significant associations between marker scores and phenotypic traits were detected. This combination of genetic and phenotypic data on onions is important to select breeding populations needed for future breeding programs.


Health of brain, heart and blood vessels are closely connected, which means that Alzheimer’s (AD) and cardiovascular (CVD) diseases may have overlapping etiologies including genetic component. Most of previous genetic association studies considered different traits  separately. The analysis of pleiotropic predisposition to these traits may shed light on the trait-specific mechanisms in protection against AD. We carried out pair-wise pleiotropic exome-wide association study (~250K common genetic variants) in predisposition to AD and each of 17 traits in a sample of 118K individuals from UK biobank. The analysis included seven qualitative traits , as well as their 10 risk factors . Fisher’s method and omnibus test were used in pleiotropic analyses. In addition to the APOE-TOMM40 locus, the analysis identified 12 genetic loci in which genetic polymorphisms demonstrated significant associations with AD at p≤5×10-4 and pleiotropic associations at genome-wide level, p≤5×10-8. The identified genes are involved in processes of phosphorylation; regulation of cell growth, differentiation, and brain development; signal transduction and neurotransmission; mitochondrial and cytoskeleton organization; regulation of gene expression, apoptotic processes, and adaptive immune response. Our results provide novel evidence supporting the hypothesis of complex pleiotropic mechanisms contributing to the development and progression of AD and provide more insights into understanding of underlying biological functions and regulatory mechanisms behind these effects.


Lutra lutra), environmental and legal protection has more recently led to population growth and recolonization of parts of their historic ranges. While heralded as conservation success, only few such recoveries have been examined from a genetic perspective, i.e. whether genetic variability and connectivity have been restored. We here use large‐scale and long‐term genetic monitoring data from UK otters, whose population underwent a well‐documented population decline between the 1950s and 1970s, to explore the dynamics of a population re‐expansion over a 21‐year period. We genotyped otters from across Wales and England at five time points between 1994 and 2014 using 15 microsatellite loci. We used this combination of long‐term temporal and large‐scale spatial sampling to evaluate 3 hypotheses relating to genetic recovery that (i) gene flow between subpopulations would increase over time, (ii) genetic diversity of previously isolated populations would increase and that (iii) genetic structuring would weaken over time. Although we found an increase in inter‐regional gene flow and admixture levels among subpopulations, there was no significant temporal change in either heterozygosity or allelic richness. Genetic structuring among the main subpopulations hence remained strong and showed a clear historical continuity. These findings highlight an underappreciated aspect of population recovery of endangered species: that genetic recovery may often lag behind the processes of spatial and demographic recovery. In other words, the restoration of the physical connectivity of populations does not necessarily lead to genetic connectivity. Our findings emphasize the need for genetic data as an integral part of conservation monitoring, to enable the potential vulnerability of populations to be evaluated.Numerous terrestrial mammal species have experienced extensive population declines during past centuries, due largely to anthropogenic pressures. For some species, including the Eurasian otter ( Muscle samples were taken from the hind leg and stored in ethanol at −20°C.Road‐killed otter carcasses from across Wales and England were collected and their locations recorded as part of the national Cardiff University Otter Project programme (n = 28), 1998–2000 (n = 100) and 2004 (n = 79). For 2009, 97 samples were genotyped and 7 existing genotypes were included from Stanton et al., Samples and associated metadata were selected to represent five time points between 1993 and 2014, separated by approximately 5‐year intervals . This maximized the overall temporal span  while providing a sufficient number of discrete time points to facilitate the interpretation of trends. A minimum of 3 years separation between time points ensured appropriate temporal independence, based on a typically assumed otter generation time of 3 years  following the manufacturer's guidelines. Samples were genotyped at 15 microsatellite loci using three multiplexes as in Hobbs et al. . The 15 2.3ST) therefore it is important to identify them if present. An exact test of Hardy–Weinberg equilibrium (HWE) and a test for linkage disequilibrium (LD) between all pairs of loci were conducted in ARLEQUIN 3.5 ; however, it is particularly important that standard deviations are reported and considered alongside estimates for smaller sample sizes  and private allelic richness (pAr) for each STG; initially, this was calculated using the smallest sample size at each individual time point, respectively. As sample size fluctuated both between regions and time points, we considered the bias that these fluctuations might introduce into each analysis. For analyses that were sensitive to such data fluctuations , we used the resampling approach implemented in HP‐RARE where the resample size was set to the smallest sample size in any RBD region for that year  to account for sample size bias in the estimations and allow comparisons to be made across all STGs. Additional statistical analyses of genetic diversity estimates were carried out using R; correlations between both allelic richness and private allelic richness under both resampling regimes were assessed using Kendall's Tau , as was the relationship between observed heterozygosity and RBD region land area.Genetic diversity was estimated for each STG using multiple diversity statistics. We used MICROSATELLITE TOOLKIT Park,  to estimIS) was initially estimated based on all individuals from each STG, using FSTAT version 2.9.3.2  using the Benjamini and Hochberg  assumption of a single, randomly mating population  and individual admixture proportions (using the largest assignment at K = 3 as our indicator), with each measure in turn assigned as the dependent variable. To ensure that our models were not confounded by uneven sampling across region/year groups, we used a GLM approach including both RBD region and year as independent terms and excluded 1994 due to data deficiency. For our models of individual Ho and individual admixture proportions, we also included Sex as a categorical variable, to test for sex bias. In preliminary testing for Ho and admixture, we incorporated the year:RBD region interaction to evaluate whether putative change over time differed between regions, but in all cases, the interaction was nonsignificant (p > 0.05) and in order to simplify model reporting this term was removed from all starting models. For all GLMs, model fit was evaluated by exploration of residuals to check assumptions of normality, homogeneity of variance and absence of leverage. Tukey tests were applied post hoc to test pairwise differences. For models of Ho and Ar, a Gaussian model with an identity link met all model assumptions. Models of admixture failed to meet necessary assumptions and no suitable error family/link function combination was found. We therefore used a univariate approach, testing region, year and sex separately using a Kruskal–Wallis test for differences in medians with region or sex, and a Kendall rank correlation to test for trend over time. Because our univariate tests were unable to control for uneven sampling within the model, we tested for year differences within regions and tested for regional differences within years. Any groups with N < 6 were excluded ’ option and 10,000 permutations and significance level set at 0.05. Analysis of Molecular Variance (AMOVA) was also performed in ARLEQUIN 3.5, with 10,000 permutations for significance and 1000 permutations for mantel test, using the ‘number of different alleles (FST‐like)’ option for molecular distance.Population differentiation was estimated between STGs in ARLEQUIN 3.5, using the pairwise Fockerham  with the2. On an irregularly shaped island like Great Britain, with multiple peninsulas separated by sea, land distance was deemed a more realistic measure of physical space between otters than Euclidean distance. Each land cell was given a resistance of 1 (sea cells were classified as ‘NoData’), such that the least‐cost resistance estimates calculated reflected the land distance between each pair of otters. For genetic distance, the proportion of shared alleles was calculated between pairs of otters using GenAlEx 6.5 . Firstly, GENEPOP v4.6 Rousset,  was usedTo determine the extent of population structure within UK otters we used two complementary approaches, one parametric and one nonparametric. The first was a Bayesian clustering algorithm implemented in STRUCTURE 2.3.4  was computed. An optimal K or a range of K values was then selected based on the lowest BIC value or the steepness of the gradient between K's on the graph , a multivariate approach  of null alleles at >2 of the 5 geographic regions, and therefore, all 15 loci were retained for further analysis. All 15 loci were polymorphic, with the number of alleles per locus ranging from 6 to 11, and observed heterozygosity per locus from 0.40 to 0.70 (Table Full genotypes (for 15 microsatellite loci) were obtained for all 193 samples newly analysed in this study (100% genotyping success rate), and re‐analysis of the 14 calibration samples allowed an additional 214 genotypes selected from previous studies  to 0.73  with an average of 0.61, while Ho ranged from 0.44  to 0.70 . Higher Ho was generally found in larger RBD regions (Northern and Eastern) while the lowest estimates were consistently found in the smallest RBD region . This was not reflective of sample size . GLM results for individual Ho  showed that RBD region explained a significant amount of variation in the data (p < 0.001), whereas year and sex were not significant (p > 0.05). Pairwise comparisons between regions showed significantly lower Ho in the Severn, South West and Western Wales regions when compared with Eastern and Northern regions (Tukey post hoc test: p < 0.001). There was no significant difference in Ho  between the Eastern and Northern regions, or between the Severn, South West and Western Wales regions, indicating a significant West–East divide in this genetic diversity estimate  than in the Western RBD regions , allelic richness (Ar) and private allelic richness (pAr). Ar and pAr were calculated using the smallest sample size across the regions for that time point as the resampling number and additionally calculated using the smallest (reasonable) sample size of any region at any time point (N = 6) as the resampling number, to allow comparisons unbiased by sample size. Although estimates were lower when the resampling size was smaller, as expected, there was a highly significant correlation between Ar and pAr using both resampling techniques , indicating that uneven sampling across time did not impact overall conclusions about regional genetic diversity. GLM results for Ar  showed the same pattern as those for Ho, i.e. differences between geographic regions were highly significant, but temporal differences were not; subsequent pairwise comparisons between regions showed an identical pattern of significance and spatial differences as for Ho, echoing the West–East divide in genetic diversity (see previous).Other measures of diversity followed a similar spatial pattern, of higher genetic diversity in the Eastern than the Western RBD regions Table . This waIS estimates suggested significant inbreeding in the majority of RBD regions at certain time points, but many incidents of apparent inbreeding were likely due to the Wahlund effect  to 0.02 . All pairwise comparisons showed high FST values between RBD regions, apart from Eastern‐Northern and Severn‐Western Wales comparisons. By 2014, pairwise FST estimates remained high between the majority of RBD regions, with significant differentiation remaining between these subpopulations. Global estimates of FST at each time point estimated from AMOVA slightly decreased over time where all 5 RBD regions were included.Tests of population differentiation showed that all STGs were significantly differentiated from each other apart from Severn‐West Wales in 2009 Table . The degMantel tests for signals of isolation by distance (IBD) at each time point were not significant, indicating that there is no consistent overall IBD pattern across the UK. However, when we tested for spatial autocorrelation in the data using mantel correlograms it was possible to observe both negative and positive autocorrelation in different distance classes. Across all time points the relationship between Mantel's correlation and distance class was similar Figure  with negEstimates of gene flow between RBD regions suggest an increase over time, whether using the nondirectional private alleles method , whereas trends in Northern and Severn RBD regions were not significant ; in all other years, admixture showed no difference between regions (p > 0.05). Differences in the degree of individual admixture between the sexes were also not significant (p > 0.05).Otters across the study area showed clear evidence of geographic substructuring. The value of K identified from the STRUCTURE runs using the Evanno ΔK method  using the lowest BIC score as the model evaluation criterion was K = 8; however, these clusters were very difficult to rationalize biologically or spatially (data not shown) and thus we explored other values of K, using the gradient of the decline between BIC values as a guide. Comparison of the clusters inferred by DAPC Figure  with thoST = 0.13, p < 0.001), more pronounced than at any subsequent time point. The major partitions exhibited significant genetic differentiation, as measured by pairwise FST across all three time points  showed that the strength of regional differentiation differed with time Figure . Despite4o and Ar) within regions despite increased gene flow and admixture. These results suggest that the UK otter population is less functionally connected than previously presumed, perhaps due to landscape or other ecological/behavioural barriers impeding effective genetic mixing. Understanding such limitations in recovering populations is important with respect to managing and quantifying conservation successes.Our study represents comprehensive population genetic tracking of a recovering carnivore population over a twenty‐one‐year period. Quantifying changes in genetic structure over time allowed us to test theoretical predictions about demographic population recovery and ensuing genetic changes. Using the Eurasian otter as our case study, we predicted a weakening of genetic structure as anthropogenically fragmented populations reconnected as part of population recovery. Our use (predominantly) of roadkill samples may have biased our dataset toward younger males, which are assumed to disperse longer distances than other demographics  that persisted across the 21‐year study period.Spatial differences in both Hpers. comm. Grace Yoxon, Director of International Otter Survival Fund, UK). The persistence of higher genetic diversity in Eastern and Northern regions (despite the lack of any subsequent or ongoing translocations) suggests that the demographic connectivity implied by recent national otter surveys  was high compared with other European studies, whereas observed heterozygosity was relatively low , an increase in overall admixture rates at all values of K and significant increases in individual admixture rates in most regions.Both clustering analyses of population structure (STRUCTURE and DAPC) showed that otters in the United Kingdom have maintained a highly genetically structured population despite increased connectivity between subpopulations through range expansion. At higher values of K, the two methods deviated slightly in their spatial‐clustering pattern, which could be due to the assumptions made of Hardy–Weinberg equilibrium and/or linkage equilibrium in the Bayesian clustering model implemented in STRUCTURE, which does not exist in the DAPC model  in Finland by Hagen et al. . Commonly used generation times in brown bears and otters are 11 years . Given the territorial nature of adult otters Erlinge, , it woul5Between surveys carried out in the late 1970s and 2010, the otter population in Great Britain expanded from small stronghold populations and near extinction in some areas (i.e. East Anglia) to an almost continuous distribution (Crawford, Demographic, behavioural and spatial barriers to the genetic mixing of subpopulations can lead to a time lag, where genetic recovery in terms of variability and connectivity requires much more time than a re‐occupation of range. Studies of spatial–temporal changes in genetic diversity and population structure during contemporary population expansions are still rare, perhaps due to the difficulties and costs of sampling over such large ranges, both spatially and temporally. However, such empirical studies are urgently needed to make predictions about population recovery progress and to set realistic goals for management and monitoring activities.Our findings illustrate that spatial recovery of formerly endangered species may not necessarily imply that genetic recovery has occurred as well. Genetic recovery may require much longer than is apparent from spatial data alone leaving populations more vulnerable than they first appear. Newly published evidence from the 6th Otter Survey for Wales (Kean & Chadwick, The authors declare no conflict of interest.Appendix S1.Click here for additional data file.Appendix S2.Click here for additional data file.


Lactobacillus plantarum FPS 2520 and/or Bacillus subtilis N1 fermented soybean meal (SBM) in rats fed a high-fat diet (HFD) were investigated by assessing plasma glucose levels, insulin resistance, and oxidative stress-induced organ damage. Supplementation with FPS 2520- and/or N1-fermented SBM (500 and 1000 mg/kg of body weight per day) to HFD-induced obese rats for 6 weeks significantly down-regulated the concentration of plasma glucose during the oral glucose tolerance test (OGTT), as well as the concentration of fasting plasma glucose, insulin, and the value of the homeostasis model assessment of insulin resistance (HOMA-IR). In addition, plasma and hepatic levels of malondialdehyde (MDA) were alleviated in rats fed fermented SBM, especially SBM fermented by mixed strains. Moreover, fermented SBM treatment reduced HFD-exacerbated increases in plasma aspartate aminotransferase (AST), alanine aminotransferase (ALT), creatinine, and uric acid levels. Based on these results, we clearly demonstrate the effect of fermented SBM on improving insulin resistance and oxidation-induced organ damage. Therefore, it is suggested that fermented SBM has the potential to be developed as functional foods for the management of obesity-induced hyperglycemia and organ damage.The hypoglycemic and antioxidant activities of  Obesity has become a prominent public health issue, as it increases the risk of cardiovascular disease, diabetes, oxidative organ damage, non-alcoholic liver disease, and kidney failure ,2. It isLactobacillus plantarum 128/2 and Bacillus subtilis natto NTU-18 with the activities of β-glucosidase production and isoflavone deglycosylation [Soybean meal (SBM), a defatted soybean by-product of the food industry, contains high protein content and a variety of nutrients, such as isoflavones, dietary fiber, and oligosaccharides . Soy isosylation ,10. per se may exert beneficial bioactivity. In HFD-fed C57BL6 mice, oral treatment with heat-killed L. plantarum OLL2712 (109 CFU) for 12 weeks reduced the concentration of blood glucose in response to insulin [B. subtilis SPB1 biosurfactant (10 mg/kg of body weight per day for one month) significantly decreased the concentration of blood glucose in diabetic rats [L. plantarum and B. subtilis are reported to exhibit antioxidant activity [L. plantarum- and/or B. subtilis-fermented products possibly exert hypoglycemic activity.Moreover, bacteria  insulin . With reactivity ,14. AccoL. plantarum FPS 2520 and/or B. subtilis N1 was established [Although some studies investigated the impacts of microbial fermented soybean products and soybean-derived components of protein and genistein on plasma glucose and oxidative stress ,15,16, lablished . Oral adablished .As soy isoflavones have beneficial effects on improving metabolism , the conSince obesity is closely related to lipid peroxidation and oxidative damage, we further investigated the protective effects of fermented SBM products against HFD-induced lipid peroxidation and damage to the liver and kidney. As shown in Although the influence of soybean products on ameliorating obesity has been investigated in previous studies, the impacts of soybean products on obesity-associated hyperglycemia and oxidative stress are controversial. As our previous study demonstrated the anti-obesity effect of fermented SBM in HFD-fed rats, the objective of the current study is to explore the hypoglycemic and antioxidant effects of fermented SBM in the same animal model. Based on the data obtained from this study, dietary supplementation with fermented SBM for 6 weeks diminished the concentration of glucose in the plasma of HFD-fed rats during the OGTT, as well as the concentrations of fasting plasma glucose and insulin, and the value of HOMA-IR. In addition, the antioxidant effect of fermented SBM was evidenced by the attenuation of MDA concentration in both plasma and liver homogenates. In parallel, fermented SBM treatment also down-regulated the levels of ALT, AST, creatinine, and uric acid. Notably, the protection provided by fermented SBM products against hyperglycemia and oxidative damage was dose-dependent, with SBM fermented by mixed strains being the most potent product. Bacillus licheniformis, fed to HFD-treated mice for 13 weeks, decreased concentrations of blood glucose and insulin [B. subtilis [Bacillus subtilis-fermented soybean consumption also improved glucose tolerance when it was employed with and without resistance exercise [The hypoglycemic and antioxidant effects of fermented soybean products have been previously reported. For example, a dietary supplement of cheonggukjang, a soybean paste fermented with  insulin . Extract insulin . In addisubtilis . Bacilluexercise . It has exercise . Adminisexercise . Neverthexercise . CompareAlthough soy isoflavones, such as daidzein and genistein, are considered to be the major bioactive components contributing to the anti-obesity effects of soybean products, controversial results were observed in previous studies investigating the hypoglycemic and antioxidant effects of soybean-derived isoflavones. For example, soy isoflavones treatment improved insulin sensitivity among postmenopausal women . Soy isoL. plantarum SS18–5 markedly improved the blood glucose of, and diminished oxidative stress in, diabetic rats [L. plantarum ZJUFB2 from insulin resistance via modulating enteric microbiota and bile acids in HFD-treated mice [L. plantarum [B. subtilis is limited. However, the antidiabetic potential of metabolites from B. subtilis has been reported. Oral administration of B. subtilis SPB1 biosurfactant (10 mg/kg of body weight per day) to diabetic rats for one month could inhibit α-amylase and protect the pancreas’ β-cells [B. subtilis [Except for isoflavones, the hypoglycemic activity of other bioactive soybean components, such as protein, peptide, dietary fiber, pectin, and pinitol, has been reported ,36,37. Itic rats . Zhong eted mice . Inhibitlantarum ,41,42. O β-cells . Moreovesubtilis ,44. BecaAll chemicals were purchased from Sigma-Aldrich Chemical Co.  unless otherwise stated. Reagents for microbial culture were purchased from Becton, Dickinson and Company . Kits for glucose determination and biochemistry tests were purchased from Eagle Diagnostics  and Randox Laboratories, Ltd. . Soybean meal was obtained from HUNG YANG Foods Co., Ltd. . The normal rodent diet (D12450H) and high fat diet (D12451i) were purchased from Research Diets Inc. .L. plantarum FPS 2520 and B. subtilis N1 were isolated from the intestine of Pacific saury and a natto product , respectively, and the culture, storage, and preparation of bacteria for fermentation were conducted as described in the previous study [v/v), respectively, at −80 °C. Before inoculation, the strains were subcultured twice in fresh broth without glycerol at 37 °C for 24 h. To prepare the SBM fermentation medium, 30 g of SBM and 240 mL of distilled water were mixed in a 500 mL volume baffled flask and then autoclaved. For single strain fermentation, a sterile 10% (w/w) SBM suspension was inoculated with either strain (104 cfu/mL) and fermented at 37 ℃ for 72 h [The strains of us study . Brieflyfor 72 h . For mixv/v) and incubated in a 70 °C water bath for 30 min. After centrifugation at 18,000× g for 30 min, the supernatant was isolated and passed through a 0.22 μm pore size filter. Separation of isoflavones was performed using a Mightysil RP-18 GP column . Solvent A of the mobile phase was acetonitrile, and solvent B was 1% (v/v) acetic acid in water. Flow rate was set to 0.6 mL/min. The gradient elution was 85% solvent A for the first 5 min. The gradient elution was changed from 85 to 55% of solvent A for the following 5 to 45 min. Finally, the column was washed with solvent B for 5 min. HPLC chromatograms were detected using an HPLC detector  at 260 nm. The fermented SBM samples were mixed with methanol  rats were purchased from BioLASCO Taiwan Co., Ltd.  and acclimatized for 1 week at the Terrestrial Animal Experimental Center of National Taiwan Ocean University (NTOU) with diet and water provided ad libitum. All animal experiments were conducted in accordance with the guidelines of the Terrestrial Animal Experimental Center of the NTOU under the approval protocol of the Institutional Animal Care and Use Committee of the NTOU, with approval number NTOU-107042. The experimental design and protocol of this animal study was conducted in accordance with our previous study demonstrating the anti-obesity effect of fermented SBM in HFD-fed rats . As showThe plasma levels of glucose, insulin, AST, ALT, creatinine, as well as uric acid levels were determined using glucose detection kits, insulin ELISA kits, and Enzymatic Kits , respectively, according to the manufacturers’ instructions. The HOMA-IR was calculated using the following equation :HOMA-IR g, 4 ℃ for 10 min, the samples were kept at room temperature for 30 min, and then the supernatants were collected and measured using a Luminescence Spectrometer  with excitation at 520 nm and emission at 535 nm.Based on the protocol of Uchiyama and Mihara , the samp  <  0.05. Multiple comparisons of means were analyzed using Duncan’s multiple range test. The results are expressed as mean ± standard deviations.SPSS Version 12.0  and one-way analysis of variance (ANOVA) were employed to analyze the data and to determine the statistical differences between each group, respectively, with the level of significance set at L. platarum FPS 2520 and B. subtilis N1 on attenuating obesity-associated hyperglycemia and oxidative damage. These data also reveal that the product fermented by mixed strains was the most potent fermented SBM product. The levels of isoflavone glycosides and aglycones in fermented SBM products were comparable; therefore, it is suggested that isoflavone and microbial metabolites produced by both strains exert a synergistic effect in alleviating hyperglycemia and lipid peroxidation. How the interaction between microbial fermentation metabolites and SBM-derived bioactive components may contribute to these effects remains elusive. This issue is intriguing and warrants further investigation. Owing to the promising activity of fermented SBM against glucose intolerance, insulin resistance and oxidative stress, we suggest that fermented SBM products can prospectively be developed as functional foods or additives for preventing obesity-associated hyperglycemia and oxidative damage.This is the first study to demonstrate and compare the effects of SBM fermented by single strain and mixed strains of


Genome-wide association studies have identified hundreds of single nucleotide variations (formerly single nucleotide polymorphisms) associated with several cancers, but the predictive ability of polygenic risk scores (PRSs) is unclear, especially among non-Whites.PRSs were derived from genome-wide significant single-nucleotide variations for 15 cancers in 20,079 individuals in an academic biobank. We evaluated the improvement in discriminatory accuracy by including cancer-specific PRS in patients of genetically-determined African and European ancestry.Among the individuals of European genetic ancestry, PRSs for breast, colon, melanoma, and prostate were significantly associated with their respective cancers. Among the individuals of African genetic ancestry, PRSs for breast, colon, prostate, and thyroid were significantly associated with their respective cancers. The area under the curve of the model consisting of age, sex, and principal components was 0.621 to 0.710, and it increased by 1% to 4% with the inclusion of PRS in individuals of European genetic ancestry. In individuals of African genetic ancestry, area under the curve was overall higher in the model without the PRS (0.723-0.810) but increased by <1% with the inclusion of PRS for most cancers.PRS moderately increased the ability to discriminate the cancer status in individuals of European but not African ancestry. Further large-scale studies are needed to identify ancestry-specific genetic factors in non-White populations to incorporate PRS into cancer risk assessment. Polygenic risk scores (PRSs) are a weighted sum of multiple disease associated alleles to identify individuals at high risk for a specific disease or phenotype.12 For example, PRS can stratify women above and below a lifetime breast cancer risk threshold of 20% that is used to justify the incorporation of breast magnetic resonance imaging into cancer surveillance protocols.13Incorporation of common genetic factors could vastly improve the current risk prediction models for cancer. Genome-wide association studies (GWAS) have identified a large number of common genetic variants associated with multiple cancers,4 however, PRS is less studied in other cancer types.12 In addition, because non-European patients are historically underrepresented in GWAS,14 the weights derived from European GWAS and used for PRS models may not be appropriate for non-White patients.15 Current commercial genetic tests using PRS are only available for individuals of European and/or Ashkenazi Jewish ancestry, which may worsen already existing cancer health disparities.16PRS improves the accuracy of breast cancer risk prediction models;We therefore sought to evaluate the improvement in discriminatory ability with the inclusion of PRS in predicting cancer risk in genetically determined African and European individuals from an academic biobank for 15 cancers  that had at least 1 GWAS within the past 10 years.17 Participants were recruited into PMBB between 2004 and 2020 to a University of Pennsylvania institutional review board approved study at the time of medical appointments in the University of Pennsylvania Health System . Genotyping was ongoing as samples were recruited. As of December 2020, 20,079 unique participants (some samples were genotyped multiple times) had genome-wide DNA array-based genotyping using the Infinium Global Screening Array (GSA) chip (Illumina) in 3 batches: (1) 5676 samples on Illumina GSA V1 chip , (2) 2972 samples on Illumina GSA V2 chip at Children’s Hospital of Philadelphia Center for Applied Genomics , and (3) 16,940 samples on Illumina GSA V2 chip at Regeneron Genetics Center. All individuals who were recruited for the PMBB were patients of clinical practice sites of the University of Pennsylvania Health System. Appropriate consent was obtained from each participant regarding storage of biological specimens, genetic sequencing, access to all available EHR data, and permission to recontact for future studies. The study was approved by the Institutional Review Board of the University of Pennsylvania.The Penn Medicine BioBank (PMBB) is a genomic and precision medicine cohort comprising participants who actively consent for biospecimen collection and linkage of their biospecimen to their electronic health record (EHR) data.18Quality control of the data set consisted of removing individuals with sex mismatch errors  or had a sample call rate of <90% and removing palindromic variants or variants with a call rate of <95%. Genotyping data of unique samples were phased (using EAGLE v2.4.1 software) and imputed to the Trans-Omics for Precision Medicine reference panel (Freeze 5) on the Trans-Omics for Precision Medicine imputation server. Eigenstrat principal components (PCs) analysis was used to identify the genetic substructure of the entire PMBB population, and quantitative discriminant analysis was performed on all samples to determine their genetically informed ancestry. For these analyses, 1000 Genomes samples were used as training set with labels and PMBB samples were used as a testing set to determine their ancestry. Individuals from genetically-determined African and European ancestry were included in the analysis. The first 10 ancestry-specific PCs for the African and European individuals were used as covariates to account for genetic ancestry.We evaluated 19,935 individuals in PMBB with available genotyping data who had passed genotyping quality control and had at least 1 physician encounter, who together had 4.9 million health care encounters. Cancer cases were identified from the EHR using International Classification of Diseases (ICD)-9 or ICD-10 billing codes. To determine the number of instances of ICD-9/10 codes needed to identify cancer cases, manual chart review was performed for 2365 individuals with at least 1 ICD-9/10 billing code for prostate cancer. The positive predictive value of 1 ICD-9/10 prostate cancer billing code was 94% . On the 19 for each of the 15 cancers in this study  on the basis of pairwise genotypic correlation at 2r = 0.1 21 and prostate cancer22 in our biobank. For the previously validated prostate cancer PRS, we calculated PRS using weights from individuals of European (Prostate-EUR) and African (Prostate-AFR) genetic ancestry separately.Summary statistics were obtained using the most recent study and largest GWAS available in the GWAS catalogis study . All of r2 = 0.1  using th23 All statistical analyses were performed using R 4.0.3.PRS for each individual was calculated using PLINK 1.9 by summing the LD-pruned SNV variants and weighing their corresponding effect sizes using the odds ratios (ORs) or beta values reported in the original GWAS. We standardized each cancer PRS to a mean of 0 and SD of 1 and used logistic regression to test for the association between cancer PRS and cancer phenotype and to compare the top versus bottom quintiles of polygenic risk, controlling for age, sex, and the first 10 within-ancestry PCs as covariates. We performed a primary analysis for cancers with >100 cases and included a secondary analysis of the remaining cancer types. Area under the curve (AUC) for primary phenotype was determined using the package pROC.Of the 9759 individuals of European genetic ancestry  with complete information , 1735 (17.8%) had at least 1 ICD-9/10 code for at least 1 of the 15 different types of cancer  . There wP = 3.4 × 10−4), colorectal , melanoma , and prostate cancer  were significantly associated with their respective cancer phenotypes , colon , and prostate  were significantly associated with their respective cancer phenotypes (P = .06). For individuals of European genetic ancestry, previously published PRSs for breast21 and prostate cancers22 were significantly associated with their respective cancers in individuals of African genetic ancestry  in individuals of African genetic ancestry, the AUC for the full model ranged from 0.729 to 0.817 . Among iIn this large retrospective case-control study, we evaluated the performance of PRSs calculated using GWAS-identified cancer risk variants for 15 major cancers in a hospital-based biobank from a large academic medical center. Among the individuals of European genetic ancestry, the PRSs for breast, colorectal, melanoma, and prostate cancers were significantly associated with their respective cancer phenotypes. Among the individuals of African genetic ancestry, the PRSs for breast, colorectal, prostate, and thyroid cancers were significantly associated with their cancer phenotype. For both individuals of European genetic ancestry and individuals of African genetic ancestry, age contributed to the highest proportion of the AUC , whereas the contribution of PRSs to the existing model was higher in individuals of European genetic ancestry than in individuals of African genetic ancestry. Restricting to the cancers with >100 cases, the average AUC difference was 0.018 in individuals of European genetic ancestry compared with 0.008 in individuals of African genetic ancestry.24 The PRS could further identify the patients of European genetic ancestry who were at higher risk of developing certain cancers. For example for breast cancer, the further discriminatory ability of PRSs in our prediction model was similar to the baseline breast cancer prediction models with addition of established clinical risk factors such as mammographic breast density,13 BMI,25 and family history.11Although the discriminatory ability of PRS to identify patients with cancer versus controls was moderate when measured using AUCs, this was consistent with past literature in the UK Biobank.26 It is notable that use of well-validated SNVs22 and ancestry-specific weights22 led to improved performance of PRSs in individuals of African genetic ancestry. A previous meta-analysis on all PRS studies from 2008-2017, which included phenotypes such as body mass index, schizophrenia, and high-density lipoprotein, also showed that among the individuals of African genetic ancestry, the median effect size of PRSs was only 42% of that among the individuals of European genetic ancestry.15 It is well known that three-quarters of all GWAS have been performed exclusively in individuals of European genetic ancestry.14 The lack of variant representation, loci diversity, and LD patterns between populations among individuals of African genetic ancestry likely explains the smaller improvement of the model prediction compared with the genetic PCs alone. We predict that as GWAS for cancers in populations of African genetic ancestry approach the size of those currently reported in populations of European genetic ancestry, it will be possible to construct African ancestry-specific PRS with a discriminative capability similar to what we currently see in populations of European ancestry. Multiethnic PRSs have shown promise in patients of African genetic ancestry with prostate cancer.27In contrast, the discriminatory ability of PRS in individuals of African genetic ancestry were extremely small for nearly all cancers tested in our study, consistent with attenuated performance of PRS in non-White patients with breast cancer.P-value significance threshold of 10−6. Alternative methods for creating PRS were not studied herein, including using pruning and thresholds below genome-wide significance as well as using LDpred, a Bayesian approach that takes into account the LD among various SNVs, which can improve the performance of PRS beyond pruning and thresholding.28 However, these methods require full summary statistics, which are not publicly available for many of the cancers. In addition, recent cancer risk models have incorporated genetic data into a polygenic hazard score30 to associate with age at cancer onset and death. Notably, polygenic hazard score performance was again better in individuals of European genetic ancestry, highlighting the need for multiancestry analyses to further identify African ancestry specific genetic loci to incorporate into genetic risk prediction. Genetic diversity needs to be prioritized in future studies to avoid systematically underperforming prediction in non-White populations.16We acknowledge limitations of our analysis. First, our cancer-free group was obtained from an academic biobank at a tertiary care academic center, where patients may have higher comorbidities and not necessarily reflect the general population. Second, currently, our full model contains age, sex, PCs, and PRSs, but the precise predictive capability of PRSs in combination with other previously validated clinical attributes, such as family history, BMI, and breast density, is unknown. Follow-up of this study population with additional clinical factors could improve the performance of each cancer PRS. Furthermore, given that most were prevalent in the PMBB, we did not have enough sample size in our data set to perform separate sensitivity analyses on only incident cases. In addition, we created our PRSs from SNPs with a GWAS We showed that genetic liability for cancer is associated with its corresponding cancer phenotypes as classified by the EHR, with a more robust predictive capability of PRSs in individuals of European genetic ancestry than in individuals of African genetic ancestry. Expansion of GWAS in non-White populations is critically important to improve estimates of genetic risk, further tailoring PRSs as a reliable future tool for clinical cancer risk prediction across ancestry groups.Supplemental FileSupplemental Tables


FST) was 0.08 for all studied markers, which is indicative of the low genetic differentiation of the Danubian horse population. Our analysis also confirmed the low level of inbreeding and heterozygous deficiency among the animals selected from the six paternal lineages of the Danubian horse. The present research could be helpful for the development of breeding and conservation programs for the Danubian horse, as well as for making informed decisions on the management of paternal lines.The Danubian horse was created on the former Klementina stud farm near Pleven to satisfy the demands of the Bulgarian Army for light draft horses and to improve the working capacity of the local horse population. The privatization of the Klementina stud farm in the late 1990s and the lack of economic activity have led to a sharp reduction in the number of mares and stallions and their sale to private owners. At present, only six of the main paternal lines which participated in the creation of the Danubian horse breed are preserved: Zdravko, NONIUS XVII-30, Torpedo, Lider, Kalifa, and Hrabar. This is the first study on the genetic and population diversity of the Danubian horse paternal lines based on microsatellite markers (STRs). The results showed that the studied Danubian horse population was characterized by a high level of genetic diversity with a medium value of 0.84. The fixation index . In total, 166 individuals from the six genealogical lines were investigated, based on 15 STR markers . In total, 184 alleles were found in the six populations, using 15 microsatellite loci. The mean number of alleles, the effective number of alleles, and the polymorphic information content (PIC) values per locus were 12.28, 9.48, and 0.73, respectively. In a comparison of the allelic diversity among sire lineages, the highest genetic diversity (Na) was observed in Lider and Kalifa (14.60 ± 0.21), while the lowest value of this parameter was observed in the Zdravko lineage 4.20 ± 0.35. The largest genetic diversity was found in loci HMS3 and HMS7, with 13 alleles, and the smallest polymorphism was noted for the locus ASB17, with 10 alleles. The level of observed heterozygosity was in the range of 0.65 ± 0.069 for the Zdravko lineage to 0.93 ± 0.01 for the Torpedo lineage. The expected heterozygosity level range was from 0.57 ± 0.048 to 0.91 ± 0.01 for all horse lineages. Structure analysis revealed three main gene pools in the study population. The first pool included the Zdravko lineage; the second had the NONIUS XVII-30, Torpedo, Lider, and Kalifa lineages; and the third defined the Hrabar lineage, which was significantly differentiated from the other genealogical lineages. Considering the ever-changing dynamics of today’s reality, it is hard to anticipate what kind of animal breeds will be necessary tomorrow, what their productivity will be, what human needs for food or other demands will be, and what will be required by society and the common people thereof ,2,3. ForOne of the modern horse breeds in Bulgaria is the Danubian horse .This horse was officially recognized as a breed in 1951 . The DanThe population of the Danubian horse in Bulgaria is on the verge of the critical minimum , which threatens its existence. This requires urgent plans in order to prepare a selection program for an adequate approach to reproduction and the avoidance of inbreeding processes. This determined the purpose of the present study, namely genotyping of representatives of the Danubian horse from six paternal genealogical lines , based on 15 microsatellite markers, for clarification of the genetic structure and biodiversity of the breed.All experimental procedures were reviewed and approved by the Animal Research Ethics Committee of the Bulgarian Food Safety Agency (BFSA) (Art. 154 of the Law on Veterinary Activity), in accordance with European Union Directive 86/609.Hair samples from the mane and/or tail  were collected from 166 animals from Danubian horse representatives of six paternal genealogical lineages . The samples were collected in a way to ensure that each extracted hair contained a follicle and a sufficient length for the shaft samples. Most of the samples were from private tribal horse farms in the Bulgaria stud and the Klementina stud farm in the village of Pobeda .For all Danubian horse individuals analyzed in this study, to trace the genealogical structure, the genealogical data were obtained from records in studbooks located in the Executive Agency for Selection and Reproduction in Animal Husbandry, the pedigree lists of the Klementina stud farm, the breeding program for the Danubian horse breed, and the database of the National Horse Breeding Association. We eliminated all samples with a doubtful or excluded pedigree. The information gathered showed that currently, the largest number of licensed and certified stallions for breeding is for the Torpedo line—with 6 stallions, followed by the lines of Kalif, Hrabar, and Lider, with 5 stallions each. Regarding the Zdravko line, there is only a single stallion left: Zahir. In the genetic bank of the Executive Agency for Selection and Reproduction in Animal Husbandry, there is cryopreserved semen from the stallion Zaher (from the Zdravko lineage), which will be used for the development of the lineages.Total DNA was extracted from hair follicle and hair shaft samples, using a GeneMATRIX Tissue DNA purification kit , according to the manufacturer’s instruction. Briefly, 1–3 hair follicles or 3 pieces of hair or, in cases when no hair follicles were available, shafts of approximately 0.5 cm in length were cut and placed in an Eppendorf tube. After that, they were mixed with 350 μL of the buffer Lyse T, 20 μL of 1 M DTT, and 20 μL of proteinase K and incubated overnight at 56 °C with shaking. The quality and quantity of the isolated DNA were checked by 1% agarose gel electrophoresis and then visualized om UV transilluminator gel documentation systems after staining with SimpliSafe™ . The isolated DNA was stored at −20 °C before analysis.We analyzed a total of 15 ISAG  recommenAll samples were sent to the GeneControl GmbH laboratory , where PCR amplification and fragment analysis were performed.FST matrix, was also computed with GenAlEx 6.5. The polymorphic information content (PIC) of each locus was calculated using PICcalc [FIT, FIS, and FST) were obtained by Wright’s F-statistics [T), the diversity between populations (DST), and the coefficient of gene differentiation (GST) values were calculated with FSTAT 2.9.4 [GenAlEx 6.5   was used PICcalc . The fixatistics  using POatistics . Nei’s gAT 2.9.4 .http://clumpak.tau.ac.il/, accessed on 13 June 2022) was was used to identify most probable values of K [Population structure was evaluated using STRUCTURE 2.3.4 software . The numues of K . The DISues of K .In total, 184 alleles were identified in the studied 166 animals within six paternal genealogical genotypes at 15 microsatellite loci. All markers were found to be polymorphic in the population of the Danubian horse breed . The meaFIS value was −0.043 (p = 0.002) , showing that there was no excess of or deficiency in heterozygotes over the total population (FIT was nearly zero). The mean FST index, which is used for measuring the degree of genetic differentiation among the six lineages, was 0.078, thus indicating that genetic diversity was significantly higher within the genealogical lineages than among them.The average = 0.002) . There wDST value, which describes the diversity among the six paternal lineages, was 0.46. The general mean of the GST value determining the genetic differentiation was 0.061, indicating that 6.1% of the genetic variation existed among the lineages and 93.9% of the genetic variation existed within them. Nei’s average gene diversity (HT) was in the range of 0.82 and 0.93  and Kalifa (p  <  0.01) lineages. Deviations were also observed in the ASB17 marker in the Zdravko (p  <  0.05) and Hrabar (p  <  0.001) lineages. The presence of a heterozygous deficit was observed in the loci HTG6 , ASB23 , and HTG10 .Hardy–Weinberg equilibrium tests (HWS tests) were performed for all genealogical paternal lineages, based on 15 STRs markers . The obtThe number of different alleles with a frequency of ≥5% showed the lowest value in the Zdravko lineage , while in the Torpedo lineages, the number was the largest .FST values of the studied paternal lineages as a measure of their genetic differentiation (A) values, it was observed that two pairs of genealogical paternal lineages Lider and Kalifa, and Lider and Torpedo were genetically the closest among the studied lineages . The highest genetic distance was determined between Zdravko and NONIUS XVII-30 lineages (1.142).We further compared Nei’s genetic distances and the ntiation . From thFST values between the studied lineages as a measure of their genetic differentiation , possibly due to the different genetic structure of the Zdravko lineage. The other genealogical lineages showed low genetic differentiation, with FST values between 0.008 and 0.0354, suggesting a homogenous structure within these lineages.We also compared the ntiation . The FSTThe genetic population structure of each genealogical paternal lineage was determined on the basis of the admixture level for each individual, using a correlated allele frequency model implemented in STRUCTURE software v0.6.94 . The results of Delta K indicated that the optimal number of genetic clusters representing the most similar ancestral breeds was at K = 3 a,b. FiguThe clustering observed by means of STRUCTURE software was supported by a principal coordinate analysis (PCoA) . The PCoThe main aim of the breeding policy of the Danubian horse was to create a horse with a good exterior and constitution to improve the speed traits and movement of horses ,35. The The results of the analysis of the population of the Danubian horse in Bulgaria showed a high level of polymorphism in all 15 studied microsatellite loci . In totaThe values of the observed heterozygosity (Ho) ranged from 0.75 at the AHT4 locus to 0.96 at the ASB23 marker . The expFST index and Nei’s genetic distance  in all microsatellite loci, thus indicating that inbreeding depression is not a threat in the Danubian horse population. The inbreeding coefficient (FIS) is an indicator of inbreeding among individuals in a population and is considered to be the main reason for deviation from Hardy–Weinberg equilibrium. The values of the FIS coefficient vary from −1 to +1, and positive values are an indicator of heterozygous deficiency in a given locus in the whole population [FIS value was negative for most loci , thus confirming the low level of inbreeding and heterozygous deficiency among the animals selected from the six paternal lineages of the Danubian horse  . The smaThe Ho showed the highest value in Torpedo (0.94) and the lowest value in Zdravko (0.65). In contrast, the Lider and Kalifa lineages had the highest He (0.91), indicating that they were the most genetically variable lineages. This was supported by the high value of the I index in the Lider (2.54) and Kalifa (2.53) lineages. The Ho and the He coefficients were also high (above 0.5), indicating a good level of variability across all lineages.Structure and admixture analyses were used in earlier studies involving different horse populations to provide an appropriate approach to determine ancestral, pure, and hybrid populations ,46,47. AThe results of the STRUCTURE analysis showed that the six genealogical lineages could be clustered into three gene pools . All indThe results from the PCoA plot analysis support the data from the STRUCTURE analysis . Figure FST value of 0.08 in the overall population of the Danubian horse paternal lines suggests that this local breed is not differentiated enough. The specific grouping of the main paternal lineages into three separate cluster suggests the different origin of the Zdravko and Hrabar lineages, and the common ancestry of the NONIUS XVII-30, Torpedo, Lider, and Kalifa lineages. Due to the small number of typical representatives of the Danubian horse, the genetic structure of the breed must be monitored, as certain paternal lines now have an uneven representation. However, if we objectively consider the question of the number of representatives in each male line by stallions, on the one hand, and through mares, on the other, in severely reduced population numbers, there is a negative outlook for the future development of the breed.This study evaluated the genetic diversity and population structure of Danubian horse individuals selected by the main paternal lines. The low


Litsea cubeba essential oil (LCEO) were evaluated. The minimum inhibitory concentrations of LCEO against Candida albicans and Streptococcus mutans were 1.25% v/v and 10% v/v, respectively. However, when LCEO was incorporated into the three soft lining materials , 10% v/v and 30% v/v of LCEO could inhibit the growth of C. albicans and S. mutans, respectively. The extracts of soft lining materials with 10% and 30% v/v LCEO, 2% chlorhexidine, 30% v/v nystatin and no additive were used for cytotoxicity tests on a human gingival fibroblast cell line. There was no significant difference in cell viability in all groups with additives compared to the no additive group (p > 0.05). Surface hardness increased significantly between 2 h and 7 day incubation times in all groups, including the controls (p < 0.05). A higher LCEO concentration had a dose-dependent effect on the surface hardness of all soft lining materials (p < 0.05). However, the surface hardness of materials with additive remained in accordance with ISO 10139-1. LCEO could be used as a natural product against oral pathogens, without having a negative impact on soft lining materials.The antimicrobial properties, cell cytotoxicity and surface hardness of soft lining materials  incorporated with various concentrations of  Candida albicans, Staphyloccocus aureus and Streptococcus mutans [Soft lining materials are used in various clinical treatments, such as relieving inflamed tissue, covering sharp atrophic ridges and improving denture fitting . Soft lis mutans . Antimics mutans . Due to Litsea cubeba, an evergreen tree from the Lauraceae family, is widely found in China, Japan and Southeast Asia. Litsea cubeba essential oil (LCEO) is extracted from various parts of the tree. It is reported that LCEO has the following pharmaceutical advantages: anti-inflammatory, anti-cancer and antimicrobial properties [L. cubeba on yeast and fungi  was revealed. In an agar disc diffusion assay, L. cubeba oil with citronellal as the main component completely inhibited this growth. The oil also affected the growth of Gram-positive bacteria (Bacillus subtilis and S. aureus), but it showed no effect on Gram-negative bacteria (Pseudomonas aeruginosa and Escherichia coli) [P. aeruginosa and Klebsiella pneumoniae) are more resistant to citral than others [operties ,6,7,8. Cn others .Ephedra foeminea was changed by the use of various solvent extracts [Additionally, depending on the extraction procedure, traditional plants may have a variety of active chemical capabilities. In a previous work, the antioxidant capacity of extracts . Anotherextracts .Nevertheless, adding substances into soft lining materials may adversely affect the mechanical properties of the materials. Therefore, it is crucial to maintain the most favorable condition in which antimicrobial activity is improved without compromising the other properties. Surface hardness is one of the required properties of soft lining materials for clinical use. Moreover, the cytotoxic effect on human gingival fibroblast cells is of concern, and the cytotoxicity of soft lining materials is attributed to leachable products .LCEO that could be used as an antimicrobial additive incorporated into soft lining materials while not significantly altering the surface hardness of the materials.Therefore, the aim of this study was to determine the optimum concentration of LCEO  containing citral as an active component was obtained by the steam distillation extraction technique from fruits. Nystatin oral suspension 100,000 IU/mL or 33.3 mg/mL  was selected to represent a conventional antifungal drug against C. albicans. Moreover, 2% chlorhexidine gluconate mouthwash  was used as a positive control against S. mutans.GC soft liner , Coe comfort  and Viscogel  were used in this study. The chemical compositions are listed in C. albicans  and S. mutans  were cultured on Sabouraud dextrose agar  and brain heart infusion agar , respectively. C. albicans and S. mutans were subcultured in Sabouraud dextrose media and brain heart infusion media, respectively, at 37 °C for 24 h before testing.LCEO on C. albicans and S. mutans was assessed by using the agar disc diffusion assay. Inoculum suspensions were standardized by adjusting the turbidity to 1.0 and 0.5 McFarland standards using a McFarland densitometer  for C. albicans and S. mutans, respectively. Then, 100 µL of diluted inoculum was used to form a lawn culture. LCEO was serially diluted in dimethyl sulfoxide  to obtain a final concentration of 0.675, 1.25, 2.5, 5 and 10% v/v. The negative control was DMSO, and the positive control was nystatin oral suspension and 5% v/v of 2% chlorhexidine gluconate for C. albicans and S. mutans, respectively. Then, paper filter discs with a diameter of 6 mm were filled with 20 µL of the tested conditions. The plates were inoculated at 37 °C for 48 h. A digital caliper was used to measure the inhibition zone. The average inhibition zones were computed. Each experimental group (n = 5) was replicated five times independently.The antimicrobial activity of LCEO incorporated into GC soft liner, Coe comfort and Viscogel was assessed by using the agar well diffusion assay. Different concentrations of LCEO were added into the liquid components of soft lining materials before the powder was added and mixed. The final concentrations of the LCEO in the soft lining materials were 5%, 10%, 20% and 30% v/v. The final concentration of positive control in the materials against S. mutans was 5% v/v of 2% chlorhexidine gluconate (CHX), while soft lining materials with 30% v/v nystatin oral suspension (NYS) were used as a positive control against C. albicans. The negative control was soft lining materials without additives. All specimens were incubated at 37 °C for 48 h; the inhibition zone of each specimen was measured by using a digital veneer caliper, and the mean inhibition zones were calculated. Each experimental group (n = 5) was replicated five times independently.The antimicrobial activity of ® CRL-2014, Manassas, VA, USA) was adapted from ISO 10993-5 [2 /mL extracted ratio), was created with a stainless-steel mold. Soft lining materials without additives and soft lining materials with 10%, 30% v/v LCEO, NYS and CHX were tested. All specimens were exposed to UV light for 15 min per side. Subsequently, each sample was immersed in a microcentrifuge tube containing 1.0 mL Dulbecco’s modified Eagle’s medium high-glucose  with 10% fetal bovine serum and 1% antibiotics penicillin–streptomycin. The samples were incubated in a shaking incubator at 37 °C for 24 h. Culture medium without sample was also incubated under the same conditions to serve as a negative control. HGF cells were cultured in DMEM supplemented with 10% v/v FBS and 1% v/v penicillin–streptomycin solution. HGF cells were plated into 96-well plates with the seeding density of 10,000 HGF cells/well/100 μL and incubated in a 5% CO2 incubator at 37 °C. After 24 h, old medium was discarded, and 200 μL aliquots of prepared extracted solutions were subsequently added into the 96-well plate, and cells were incubated for 24 h. Cell viability was assessed by using the MTT assay . The absorbance was read at 570 nm using a microplate spectrophotometer . Each experimental group (n = 3) was replicated three times independently.The test of the effects of different additives incorporated into GC soft liner, Coe comfort and Viscogel on a human gingival fibroblast (HGF) cell line  were tested.Surface hardness was evaluated using a Shore durometer according to ISO 10139-1:2018 . Cylindrh sample . After tα = 0.05.All statistical analyses were performed using PASW Statistics software . The significance level was set at S. mutans and C. albicans towards LCEO incorporated in different soft lining materials, one-way ANOVA and Games–Howell multiple comparison were performed to analyze the significant differences in the agar well diffusion assay of LCEO.To assess the susceptibility of To assess the HGF cell viability after 24 h exposure to the extracts, one-way ANOVA was performed for the GC soft liner and Coe comfort groups and Kruskal–Wallis analysis was used for Viscogel groups.For the surface hardness testing, two-way repeated-measures ANOVA was applied.LCEO was demonstrated as the mean inhibition zone, as listed in C. albicans was noticeably observed in the 1.25% v/v LCEO group, whereas the inhibitory zone against S. mutans was detected in the 10% v/v LCEO group . Moreover, the inhibition zones of the groups of the three soft lining materials with 30% v/vLCEO demonstrated significantly larger values than the 30% v/v NYS groups (p < 0.05). Meanwhile, all three soft lining materials with 5–20% v/v of LCEO did not show any inhibitory effect against S. mutans. Only in the 30% v/vLCEO group, the inhibition zone could be detected around the samples. However, when compared to the CHX positive control groups, the inhibition zones of the 30% v/vLCEO groups were significantly smaller (p < 0.05).The antimicrobial activity of soft lining materials with various concentrations of v/v LCEO, CHX and NYS) compared to their control materials (no additive). Within the same material, no cell cytotoxicity was detected when the groups of various additives were compared to the control group (p > 0.05).As shown in LCEO could significantly affect the Shore AO hardness of these three soft lining materials (p < 0.05), as shown in LCEO concentration. Briefly, 10%, 20% and 30% v/v LCEO incorporated into soft lining materials markedly decreased hardness in both stages (p < 0.05).The hardness results were illustrated in terms of Shore AO hardness values , with a v/v of LCEO could significantly affect the hardness (p < 0.05) but no significant difference in surface hardness among control, NYS, CHX and 5% v/v LCEO was found (p > 0.05). Regarding Viscogel, in both the 2 h and 7 day incubation periods, there was no significant difference in surface hardness among the control, NYS, CHX and 5% v/v LCEO groups (p > 0.05). However, the higher concentrations from 10% to 30% v/v of LCEO could significantly reduce the hardness (p < 0.05) at 2 h and 7 days. Lastly, regarding Coe comfort, only CHX had no effect on the material’s hardness at either incubation time, while NYS and all LCEO concentrations could reduce the surface hardness at both 2 h and 7 days.Nevertheless, when various additives were added, the surface hardness of each soft lining material showed different results. In the case of the GC soft liner, all additives could lower the material’s hardness at the 2 h initial stage. At the late stage, only 10% to 30% LCEO and was used as an antimicrobial agent against C. albicans and S. mutans in the present study. In another study, citral showed antimicrobial properties against Gram-negative and Gram-positive bacteria, fungi and protozoa, and the authors found that Gram-negative bacteria were the most resistant strains, requiring a high minimum inhibitory concentration (MIC) of approximately 18,000 μg/mL to inhibit Pseudomonas aeruginosa growth. Meanwhile, a lower MIC of 750 μg/mL worked against Enterobacter species [v/v citral could inhibit the growth of S. aureus, E. coli, C. albicans and Microsporum gypseum [C. albicans strains, while the minimum fungicidal concentration (MFC) of citral ranged from 64 to 256 μg/mL depending on the candida strain [LCEO against C. albicans and S. mutans were 1.25% v/v (11.1 mg/mL) and 10% v/v (89 mg/mL), respectively. The MIC of LCEO against C. albicans was in a range consistent with the results of Saikia et al. [LCEO extracted from fruit against C. albicans ranged from 2.5 to 20 mg/mL. Conversely, Yang et al. reported that the minimum microbicidal concentration of LCEO from fruit against S. mutans was 0.75 mg/mL [L. cubeba sources could affect LCEO’s antimicrobial property.Citral, the main component of fruit-derived essential oil, is the active compound of  species . A previa strain . In the a et al. . They us75 mg/mL . TherefoLCEO into all soft lining materials, the minimum concentration of LCEO against C. albicans and S. mutans was 10% and 30% v/v, respectively, indicating that LCEO incorporated into soft lining materials could inhibit C. albicans more effectively than S. mutans. However, higher concentrations of LCEO were required to inhibit these oral microbes when incorporated into soft lining materials. The findings are similar to a previous study that tested Melaleuca alternifolia oil loaded into various tissue conditioners against C. albicans. A higher concentration of M. alternifolia oil was required to inhibit the fungus [When incorporating e fungus . This woe fungus .LCEO into soft lining materials may affect the material properties or the gingival tissue surrounding them. The in vitro cytotoxicity of GC soft liner, Viscogel and Coe comfort that contained various additives towards HGF cell lines was evaluated using the MTT assay on 24 h eluate extracts. No significant cytotoxicity towards the HGF cell line was found in any additive group, including nystatin and chlorhexidine. Correspondingly, Luo et al. [LCEO in the mouse and Sprague-Dawley (SD) rat, reporting a negative result for LCEO genetic toxicity both in vitro and in vivo. The oral LD50, dermal LD50 and inhalation LC50 were listed as 4000 mg/kg, 5000 mg/kg and 12,500 ppm, respectively. The essential oil was classified as Class 5 in the globally harmonized system of classification and labeling of chemicals (GHS), which is the least toxic class [LCEO may contribute to unfavorable cell viability. A previous study, for example, reported a less than 6% survival rate of human fibroblasts, myoblasts and osteoblasts when exposed to more than 0.02% chlorhexidine gluconate [LCEO induced apoptosis and cell cycle arrest in human non-small cell lung cancer cells , according to a recent study [As previously stated, the incorporation of luconate . Furtherluconate . Moreovent study .LCEO resulted in lower Shore AO hardness both in 2 h and 7 day incubation periods, which indicates that adding LCEO also softened the materials in a dose-dependent manner. One possible factor associated with the hardness of materials would be a plasticizing effect. A plasticizer is an additive added to polymers to soften or make them more pliable. A good plasticizer is a compound that has a high degree of solvent power, is similar to the polymer chain in size and shape, as well as polarity, and can be retained within the polymer chain [LCEO could potentially act as a plasticizer to reduce the hardness of soft lining materials. Furthermore, not only the plasticizer concentration but also the molecular weight could affect the plasticizer’s efficiency [v/v nystatin oral suspension to 30% v/v LCEO as plasticizers, nystatin has a higher molecular weight than LCEO, so the Shore AO hardness of the 30% v/v LCEO group was lower than that of the 30% NYS group (p < 0.05).In addition, incorporating substances into the polymer network chains of materials may affect their hardness. This present study revealed that when compared to the control groups, all additives incorporated into soft lining materials lowered Shore AO hardness. The higher concentration of er chain . Therefoer chain ,30,31,32ficiency . A high-The addition of the PMMA polymer to the powder also greatly influenced the plasticizer leachability of soft lining materials. Previous studies found that when the PMMA polymer concentration was less than 10% by weight, the leaching of the plasticizer decreased as the PMMA polymer concentration increased. The soft lining materials containing 10% by weight PMMA polymer had the lowest amount of plasticizer leaching. Meanwhile, PMMA concentrations greater than 15% by weight may result in increased plasticizer leaching out because PMMA may impede the penetration of external unreacted plasticizer . AccordiAccording to ISO:10139-1:2018, soft and extra-soft lining materials are classified by using Shore AO hardness after the aging process. After 2 h incubation, the hardness of a soft material should be between 30 and 50 units, and it should not exceed 60 units after 7 days. Meanwhile, the hardness of an extra-soft material should be less than 30 units after 2 h incubation and no more than 60 units after 7 days . As a reHowever, the main limitation of this in vitro investigations was that, in the oral cavity, there are many other factors affecting the antimicrobial properties. Furthermore, in clinical situations, the biocompatibility of materials of significant concern. For example, saliva acts as a rinsing agent, and this may impact the release of compounds from materials, or the materials themselves may have adverse effects on the tissue underneath when using them for a period of time. Therefore, further research into the release profiles of leaching products over time and their cytotoxicity should be conducted, and a clinical study may be more beneficial.LCEO could possibly be used as a novel antimicrobial additive incorporated into soft lining materials against oral pathogens, with an optimal concentration at 10% v/v for C. albicans and 30% v/v S. mutans. The soft lining materials with LCEO demonstrated no cytotoxicity towards the HGF cell line compared to materials without additives. The surface hardness of materials was dose-dependently related to the oil concentration; nonetheless, all investigated materials were classified as extra-soft types according to ISO 10139-1: 2018. Therefore, LCEO is a medicinal herb that has potential to treat denture stomatitis and reduce secondary caries, with no harmful effect when incorporated into denture soft lining materials.Based on this in vitro study,