To explore social workers’ and medical advisors’ accounts of genetic testing in adoption.A qualitative study using semi-structured interviews to gather in-depth accounts of retrospective cases. Data were analysed thematically to identify professionals’ knowledge and expectations.Twenty professionals working in adoption services  participated in this study. Social workers adopted an essentialist (single-gene) model to discuss genetic testing in relation to past cases. They assumed that testing was a generic procedure for detecting the presence or absence of a specific aetiology, the results of which were believed to be definitive and mutually exclusive. By contrast, medical advisors were circumspect and agnostic about the meaning of results, especially in relation to chromosomal microarray testing. Whereas social workers believed that genetic testing provided clarity in assessment and therefore assisted adoption, medical advisors emphasised the uncertainties of testing and the possibility that prospective adopters might be misled. Medical advisors also reported inappropriate requests to test children where there was a family history of a genetic condition, or to confirm or exclude a diagnosis of fetal alcohol spectrum disorder in children presenting with non-specific dysmorphic features.Recent advances in genetic technologies are changing the ways in which professionals understand and tolerate uncertainty in adoption. Social workers and medical advisors have different understandings and expectations about the clinical utility of genetic testing. These findings have implications for social work training about genetic testing and enabling effective communication between professional groups. Looked-after children are a diverse paediatric population that present with complex physical, developmental and/or health-related problems.While recent advances in genome-wide testing have increased diagnostic yield, they also pose new challenges to paediatrics and adoption.Clinical significance of chromosomal abnormalities is not well understood, which raises concerns that its detection among looked-after children can be misleading and potentially stigmatising.Interviews with social workers and medical advisors reveal different understandings and expectations about the clinical utility of genetic testing in adoption.Medical advisors were circumspect and agnostic about the meaning of results, while social workers believed that genetic testing provided clarity and therefore assisted adoption.This study suggests that targeted training would assist social work and medical professionals to communicate more effectively about the use of genetic technologies in adoption.Adopted children are a diverse paediatric population. Many present with complex physical, developmental and/or health-related problems.There are two circumstances in which assessment may involve clinical genetic assessment. First is when a child has a clinical problem or physical features that suggest the condition may be genetic in origin. Paediatricians and clinical geneticists may agree that a child can be tested to obtain a diagnosis to account for their problem. The second circumstance arises when a looked-after child has a family history of a genetic disorder that is not apparent but which that child might either develop later or transmit to their own children in the future. Such circumstances can be further divided into clinical assessment that may involve carrier or predictive genetic testing.Carrier testing is usually carried out on unaffected individuals at risk of recessive or sex-linked conditions, such as cystic fibrosis or haemophilia. While there may not be any direct health implications, a positive result will affect future reproductive decisions. Predictive testing applies to autosomal dominant conditions usually with late onset, such as Huntington’s disease. Testing positive means the individual will develop the disorder, although onset is variable. In such cases, the general consensus in the genetics community is that children should not be tested unless there is a clear medical benefit in doing so. If there is no medical benefit, then children should not be tested because it removes their autonomy and their right to an open future. The recommendation is that children should be given the choice of genetic testing when they are capable of making their own decisions.The recent introduction of next-generation sequencing technologies has substantially increased both the coverage and resolution of genetic information. While genetic testing may refer to relatively targeted investigations involving the search for mutations in one or more specific genes, ‘genome-wide’ testing can identify multiple variants across the entire genome. Recent advances in diagnostic testing pose challenges to paediatrics and adoption as new methods—microarray-based comparative genomic hybridisation (aCGH)—have largely replaced the light microscopy approach to chromosome analysis as the first-line investigation for identifying a likely cause of a child’s developmental, learning and behavioural difficulties.The clinical utility of diagnostic genetic testing is further clouded by the fact that a negative result does not unequivocally rule out a genetic cause because aCGH can only detect copy number changes—it cannot detect changes (variants) within genes.Fetal alcohol spectrum disorder (FASD) is a common embryopathy found more frequently among looked-after children.As chromosomal microarray testing becomes a mainstream diagnostic technology, there are concerns that non-genetics professionals, especially those working within social services, may not be fully informed about the limitations of a genetic test result.Twenty participants were recruited through purposive sampling of social workers (n=8), social work managers (n=4) and medical advisors/community paediatricians (n=8) working in adoption services in Wales. Social workers were contacted through the National Adoption Service, all of whom were self-selecting as having first-hand experience of cases that involved genetic testing. Medical advisors working in each of the local authorities were contacted individually via email. Snowball sampling techniques were used to contact specific participants who were singled out as having relevant experience; this widened our recruitment to England. Medical advisors were all mid-to-late career, social workers were more diverse, ranging from the newly qualified to those with over 30 years’ experience. Semi-structured qualitative interviews were used to gather detailed accounts of retrospective cases. Treating research interviews as ‘accounts’ recognises that interviews are more than representations of past events and experiences, but complex social activities oriented to performing certain actions in the present, such as explaining or defending the speaker’s conduct or practical reasoning.Transcripts were coded via an iterative process of reading and noticing relevant phenomena, allowing the analyst (first author) to arrange data according to differences, commonalities and structures. Coded selections of data were compiled into a subcorpus for group discussion . Data extracts were then selected in order to identify and illustrate ‘themes’ relating to implicit assumptions and expectations that underpin professionals’ accounts of genetic testing. Each of the major themes represent a range of statements about the reasons given for, and presumed outcomes of, genetic testing in adoption .Social workers often described ‘genetic testing’ as a generic technical procedure, and were generally unaware of recent advances in genome-wide (microarray) testing. A significant finding of the study was that social workers employed a single-gene model to discuss the outcomes of testing in relation to past adoption cases. They assumed that testing was a procedure for detecting the presence or absence of a specific aetiology, the outcomes of which were definitive and mutually exclusive.“no genetic abnormality found. So we had to sort of then look for a family who would take a child with developmental delay”.When social workers described cases of children referred for diagnostic testing, they interpreted the results of an investigation as a clear and definite outcome. For instance, a normal result was thought to discount genetic factors and imply non-genetic reasons for a child’s difficulties: “what is normal one day might not be normal the next”. A normal result was relative to the limitations or sensitivity of the technology. Only after considering family history and clinical judgement did medical advisors infer non-genetic causes.Several medical advisors reported that children referred for microarray testing often receive normal results. Rather than discounting a genetic explanation, advisors were more agnostic about the meaning of results: explained the child’s condition. While in some cases that would be true, in other cases social workers conceded that the clarity of the result was offset by variability of the phenotype, that is, it was uncertain “how the disorder would manifest itself”.Some social workers described cases in which a positive result offered clarity about the child’s genetic status. For instance, the detection of a chromosomal abnormality was described as a specific aetiology that definitive answer, as one advisor put it. Variants found in the general population do not mean that its detection in an individual was necessarily pathogenic: “there are lots of people in the general population that have these small changes and they do not impact on them in any way”. In other cases, variants of unknown significance were explained to prospective adopters as inherently uncertain , many believed that it can assist adoption by removing uncertainty about the child’s health. They explained that most adopters were averse to uncertainty, and described situations in which an impending genetic investigation had “put off” adopters. For these reasons, information was considered to be beneficial to adoption. One social worker described genetic testing as simply “more information”  genetic condition. However, social workers reported several cases in which diagnostic testing of unaffected children had occurred on the basis of family history alone. One social worker described a case in which adopters had pursued genetic testing because a chromosomal abnormality had been detected in an older sibling. Against the recommendations of the medical advisor, All the medical advisors we interviewed reported cases involving inappropriate requests for genetic testing. One advisor described how a social worker had initiated a court order to test an unaffected child based on unverified information that the birth father had Noonan syndrome. Another advisor described a similar case involving a family history of Charcot-Marie-Tooth. Implicit to these requests was the assumption that testing for a family mutation would confirm or exclude a genetic diagnosis in the child .Social workers indicated that FASD affecting looked-after children was a growing concern. For children presenting with non-specific dysmorphic features, establishing a definitive diagnosis was considered important for assessment and adoption. One social worker believed that genetic testing could be used to diagnose FASD .I think if the case is strong history of using alcohol heavily during pregnancy, if the child is showing from birth some indication that he or she would be foetal alcohol syndrome … why should I do an array CGH?”Microarray testing was described by several medical advisors as a procedure for excluding chromosomal abnormalities in order to assist diagnosis. Although they insisted that genetic testing should not replace clinical judgement, one medical advisor indicated that social workers were misled in thinking that there was a test for FASD . AnotherThe study found that social workers often described ‘genetic testing’ as a generic procedure for detecting the presence or absence of a specific aetiology, the results of which were believed to be definitive and mutually exclusive. In the absence of specific knowledge about genetic testing, social workers adopted an essentialist model  to make sense of past cases.It was not unexpected to find that medical advisors were better informed about genetic testing and therefore recognised that many genetic conditions are multifactorial. Indeed, they were far more circumspect and agnostic about the outcomes of genetic testing, especially the results of aCGH. Rather than reducing uncertainties about the child, many advisors explained that uncertainty was an unavoidable outcome of assessment, and often at odds with the knowledge and expectations of social workers and prospective adopters. Cases that involved reporting variants of unknown significance were described as unwelcome outcomes that required careful and sensitive communication.Medical advisors also reported multiple cases involving inappropriate requests for testing from social workers, special guardians, prospective adopters and legal professionals. There was apparently a general misconception that genetic testing was capable of confirming or excluding a genetic condition in the child. In some cases, testing was pursued for unaffected children on the basis of family history. Medical advisors expressed concerns that testing unaffected children was medically and ethically inappropriate as well as an unsuitable strategy for diagnosing complex, multifactorial conditions.The study also revealed cases in which social workers and legal professionals believed in the existence of a genetic test for FASD. This may arise from the fact that some medical advisors use microarray to exclude chromosomal abnormalities in children presenting with dysmorphic features. However, several medical advisors stressed that diagnostic testing for FASD should be approached with caution. Where there is clinical suspicion of FASD, children should receive formal clinical assessment as well as detailed assessment of the available family before deciding to pursue a genetic investigation. Even then, the outcomes may have limited clinical utility and may affect the child’s prospects of adoption. A negative aCGH result could be misinterpreted as confirming a diagnosis of FASD, while finding a microdeletion of reduced penetrance could be taken inappropriately as undermining the same diagnosis.This study explores the ways in which different professional groups make sense of genetic assessment in adoption. Social workers and medical advisors play a crucial role in ensuring that vulnerable children are properly assessed and supported to achieve permanency with an adoptive family. However, recent advances in genetic technologies, notably the shift from genetic to genomic testing, are changing the ways in which professionals understand and tolerate uncertainty. This study reveals cases in which social workers and medical advisors have different understandings and expectations about the clinical utility of genetic testing. The priority for social workers is to use health information to increase the transparency of information about the child in order to secure their placement with a suitable family. In many cases, information regarding medical and developmental uncertainties is construed as obstacles to the adoption process. The priority for medical advisors is to report these uncertainties as and when they apply, to place the risks of the child in context and to protect the child’s future autonomy from unnecessary testing. The findings suggest that social workers would benefit from specific training on the ethical and technical aspects of genetic and genomic testing, while multidisciplinary team meetings would provide a practical forum to discuss the contrasting priorities of adoption professionals.


Bathymodiolus brooksi mussels constitute genetically isolated subpopulations. The reconstruction of core genome-wide strains from high-resolution metagenomes revealed distinct phylogenetic clades. Nucleotide diversity and strain composition vary along the mussel life span and individual hosts show a high degree of genetic isolation. Our results suggest that the uptake of environmental bacteria is a restricted process in B. brooksi, where self-infection of the gill tissue results in serial founder effects during symbiont evolution. We conclude that bacterial colonization dynamics over the host life cycle is thus an important determinant of population structure and genome evolution of horizontally transmitted symbionts.Eukaryotes are habitats for bacterial organisms where the host colonization and dispersal among individual hosts have consequences for the bacterial ecology and evolution. Vertical symbiont transmission leads to geographic isolation of the microbial population and consequently to genetic isolation of microbiotas from individual hosts. In contrast, the extent of geographic and genetic isolation of horizontally transmitted microbiota is poorly characterized. Here we show that chemosynthetic symbionts of individual  Bacteria inhabit most eukaryotes where their presence has consequences for key aspects of the host biology , such asVibrio fischeri colonizing the squid light organ, where different light organ crypts are infected by a specific strain [Microbiota dispersal over the host life cycle depends on the level of fidelity between the host and its microbiota. In faithful interactions, vertically transmitted bacteria are transferred from adults to their progeny during early host developmental stages, while in less faithful interactions, horizontally transmitted bacteria are acquired from the environment throughout the host life cycle . Strictlc strain . The degBathymodiolus brooksi deep-sea mussels. Bathymodiolus mussels live in a nutritional symbiosis with the chemosynthetic sulfur-oxidizing (SOX) and methane-oxidizing (MOX) bacteria. The symbionts are acquired horizontally from the seawater and are harbored in bacteriocytes within the gill epithelium [B. brooksi [Bathymodiolus species from hydrothermal vents in the mid-Atlantic ridge showed the presence of different SOX strains with differing metabolic capacity [B. brooksi are colonized predominantly by environmental bacteria or by symbionts from older filaments of the same host remains unknown. These two alternative scenarios are expected to impose different degrees of geographic isolation on the symbiont population: in continuous environmental acquisition, the level of inter-host dispersal is high while self-infection limits the symbiont dispersal. Here we studied the impact of tissue colonization dynamics of horizontally transmitted intracellular symbionts on the degree of symbiont genetic diversity. Furthermore, we quantified the level of genetic isolation among communities across individual mussels and its impact on symbiont genome evolution. For that, we implemented a high-resolution metagenomics approach that captures genome-wide diversity for both symbionts in multiple B. brooksi individuals from a single site.Here we study the microbiota strain composition of horizontally transmitted endosymbionts across individual ithelium , 23. Mos brooksi . Neverthcapacity . Mussel capacity . HoweverBathymodiolus mussels we used a high-resolution metagenomics approach. Twenty-three adult B. brooksi individuals of shell sizes ranging between 4.8 and 24.3 cm were sampled from a single location at a cold seep site in the northern Gulf of Mexico. Shell size correlates with mussel age [To study the evolution of the SOX and MOX genomes in ssel age ; thus, aThe mussels were sampled from three separate mussel ‘clumps’  that were at most 131 m apart . Our analysis revealed two MGSs that comprise the SOX and MOX core genomes  that were detected in the core genomes of the two symbionts. In this analysis, we considered SNVs that are fixed in a metagenome as well as polymorphic SNVs, i.e., SNVs, where both the reference and the alternative allele are observed in a single metagenome. We found 18,070 SNVs in SOX  and 4652 SNVs in MOX . The number of polymorphic SNVs per sample ranges from 162 (0.9%) to 11,064 (61%) for SOX and from 27 (0.58%) to 3026 (65%) for MOX . The abTo study the community assembly at the strain level, we examined the strain distribution across individual mussels. Each SOX strain could be identified in between three and eight samples  of each mussel. A visual inspection of the derived allele frequency spectra revealed multimodal distributions for both symbiont populations. The modes reach high allele frequencies and are associated with the main phylogenetic clades; this suggests that the clades constitute cohesive genetic units Fig. . The preBathymodiolus species [Overall, our results revealed that the symbiont populations are composed of strains that cluster into few clades, which appear to be maintained by strong cohesive forces. In addition, the strains are shared among multiple mussels and multiple strains are capable of dominating different hosts. This suggests that stochastic processes are governing the symbiont community assembly, as previously proposed for other  species .Bathymodiolus, we examined the selection regimes that have been involved in the formation of cohesive genetic SOX and MOX units. The core genome-wide ratio of pN/pS is higher in MOX (pN/pS of 0.425) in comparison with SOX (pN/pS of 0.137), which indicates that the strength of purifying selection is higher for SOX. In addition, we estimated pN/pS for each of the symbiont core genes. This revealed that MOX genes are characterized by large pN/pS and small pS values, while SOX genes have small pN/pS and large pS values . The NI is used to distinguish between divergent and polymorphic SNVs and to quantify the departure of a population from the neutral expectation. An excess of divergent nonsynonymous mutations (NI < 1) indicates that the population underwent positive selection or an important demographic change in the past [MOX (<1), which suggests that MOX evolved under a neutral (NI ~ 1) or positive selection regime. NIMOX increased when considering the clades as diverged, which suggests that the low NIMOX observed at the strain level is the result of an excess of nonsynonymous SNVs within the strain clades that may constitute transient polymorphisms. Thus, the excess of nonsynonymous mutations observed for MOX is biased by the low level of divergence; hence, similar to the pN/pS ratio, it cannot serve as an indication for positive selection. On the other hand, we found that purifying selection is in action for SOX (NISOX > 1), i.e., the divergent SNVs are enriched for synonymous SNVs in comparison with the polymorphic SNVs. Similar to MOX, when using the clades as divergent, NISOX slightly increases. This increase indicates that the SNVs that differ between clades are more likely to be substitutions in comparison with those that differ among within-clade strains.To circumvent the bias in the past . We estions NI <  indicateBathymodiolus mussels is considered to be ancient in chemosynthetic deep-sea mussels, whereas the MOX association is thought to have evolved secondarily during Bathymodiolus diversification [Prochlorococcus spp. [Bathymodiolus species [Altogether, these results suggest differences in the selection regimes during the evolution of the SOX and MOX strains. While the SOX core genome is shaped by purifying selection, we cannot detect deviation from the neutral expectation in the MOX core genome. These differences likely stem from the different divergence levels among the strains of both symbiont species populations. The association of SOX with fication . This agfication , where acus spp.  and in S species .Bathymodiolus symbiosis, we next studied the degree of genetic isolation. Our sample collection of mussels covering a range of sizes (and thus ages) enabled us to compare symbiont genome diversity among individual hosts of different age within a single sampling site, thus minimizing the putative effect of biogeography on population structure. The host species B. brooksi is ideal for such an analysis as it grows to unusually large sizes and possibly lives longer than many other Bathymodiolus species. To study differences in genome diversity of the two symbionts across individual mussels, we estimated the intra-sample nucleotide diversity (π) and the ecological measure α-diversity at the resolution of the SOX and MOX strains.The association with the host limits the dispersal of bacterial populations where the association across generations is likely maintained by symbiont dispersal between host individuals. If symbionts are not continuously taken up from the environment, each individual host constitutes an isolated habitat over its lifetime . GeograpSOX among different mussels  and large mussels (14.6–24.1 cm) have a low πSOX and harbor one to two strains. Medium-sized mussels (7.2–14.1 cm) have a high πSOX and harbor two to nine strains. The community in the largest mussel is an exception, as it has a high πSOX, similar to medium-sized mussels, which can be explained by the presence of three strains from two clades.We found a high variability of πMOX between 5.6 × 10−6 and 7.0 × 10−4, Table p = 0.015, Fig. MOX  , while the other group habors MOX strains from both clades and is characterized by high MOX nucleotide diversity . These groups are not associated with mussel size. Taken together, we observed a strong correlation between the nucleotide diversity π and α-diversity for both symbionts. Notably, π is based on all the detected SNVs, whereas the α-diversity is based only on the strain composition and relatedness. Thus, the strong correlation demonstrates that the strain diversity captures most of the core genome-wide nucleotide diversity.The MOX nucleotide diversity is significantly lower in comparison with SOX (intra-sample πon) Fig. . One groSOX have been observed in other Bathymodiolus species (mean between 2.2 × 10−3 and 3.9 × 10−3) [Solemya velum microbiome where the symbiont transmission mode is thought to be a mixture of vertical and horizontal transmission [A comparison of the π values estimated here to other microbiome studies shows that higher π × 10−3) . The avesmission . Furthersmission .ST and the ecological measure β-diversity at the strain resolution across the metagenomic samples for the two symbionts. Small FST indicates that the samples stem from the same population, whereas large FST indicates that the samples constitute subpopulations.Symbiont transmission mode is an important determinant of the community assembly dynamics . For horST values, indicating a strong genetic isolation between individual mussels . We observed groups of mussels that are characterized by a low pairwise SOX.Our results revealed generally high pairwise Fentclass1pt{minimaentclass1pt{minimaity Fig. . NotablyMOX while the other group is characterized by lower MOX , which can be explained by the high proportion of invariant SNVs in MOX. Unlike SOX, the analysis of The distribution of pairwise MOX Fig. . These cST values and the population structure we observed here reveal population stratification that is especially pronounced for SOX. One possible factor that influences symbiont population structure is host genetics, whose impact on the composition of horizontally transmitted microbiota has been debated in the literature. Studies of the mammal gut microbiome showed that the host genotype had a contribution to the microbiome composition in mice [ST and symbiont FST for any of the two symbionts , which implies a weaker genetic isolation in these vents [B. brooksi data revealed SOX subpopulations with low genetic isolation that are observed using both FST, which takes all SNVs into account, and β-diversity at the level of strains. In contrast, only β-diversity disclosed subpopulations for MOX. Thus, strain-resolved metagenomics resolves similarities between individual mussel microbiomes below the species level.Our results provide evidence for a strong genetic isolation between the symbiont subpopulations associated with individual mussels. This finding is consistent with the observed individual-specific symbiont strain composition. In contrast, much lower Fse vents . Our anaOur analysis revealed strong genetic isolation among subpopulations of symbiotic bacteria found in individual mussel hosts, indicating geographic isolation between mussels. The genetic isolation is independent of host genetics and of the mussel location in clumps. We hypothesize that the geographic isolation occurs through restricted uptake of SOX and MOX symbionts from the environment over time. The lack of evidence for strong adaptive selection in SOX and MOX strains suggests that the inter-host population structure is the result of neutral processes rather than host discrimination against different strains. Here, we propose a neutral model for symbiont community assembly that explains how restricted symbiont uptake and colonization impose barriers to the symbiont dispersal, which can, over time, lead to inter-host population structure and contribute to the formation of cohesive genetic units within the symbiont population Fig. . In our Bathymodiolus species from hydrothermal vents, which concluded that SOX populations from individual mussels of the same site intermix [Notably, our results are in contrast to a recent study on other intermix . This coYersinia pestis [Listeria monocytogenes [The colonization of new filaments over the mussel life span via self-infection entails serial founder events on the bacterial population. Throughout this process, new mutations arising in the symbiont population during the lifetime of the mussel can reach fixation due to genetic drift following population bottelnecks. This process is expected to lead to a reduction of symbiont genetic diversity over the mussel life time. Thus, individual mussels develop into independent habitats that harbor individual symbiont subpopulations, which are genetically isolated from other mussel-associated subpopulations and from the environmental pool. The evolution of vertically transmitted endosymbiont populations is similarly affected by serial founder effects , as we sa pestis  and Listytogenes . Our modB. brooksi mussels were collected during a research cruise with the E/V Nautilus from the cold seep location GC853 at the northern Gulf of Mexico in May 2015. The mussel distribution at the cold seep was patchy and mussel individuals were collected from three distinct clumps within a radius of 131 meters . The gills from each mussel individual were dissected immediately after retrieval and homogenized with sterilized stainless steal beads, 3.2 mm in diameter . A subsample of the homogenate for sequencing analyses was preserved in RNA later  and stored at −80 °C. DNA was extracted from these subsamples as described by Zhou et al. [Twenty-three individuals of u et al. . TruSeq Illumina paired-end raw reads from the samples were trimmed for adapters and filtered by quality using BBMap tools . Only ree-value < 10−6). To reduce the potential inflation caused by the single-linkage clustering, we applied two additional filters to discard hits: the maximum ratio allowed between the two compared sequence lengths must be 4 and hits between partial and nonpartial genes are discarded. These filters are meant to remove spurious links between sequences due to the presence of commonly spread protein domains. This clustering was performed in two successive steps; first, we obtained sample-specific gene catalogs by performing intra-sample clustering. This is meant to reduce sequence redundancy, resulting in an average of ~676,000 nonredundant genes per sample  and obtaining the best hit. Genes were annotated as MOX-related if their best hit is Bathymodiolus platifrons methanotrophic gill symbiont (NCBI Taxonomy ID 113268) or Methyloprofundus sedimenti (NCBI Taxonomy ID 1420851). For SOX, the genomes of thioautotrophic symbionts belonging to four different Bathymodiolus species were used for annotation . In addition, the gene catalog was screened for mitochondrial genes using best blastp hits against the B. platifrons mitochondrial protein sequences (NC_035421.1) [e-values < 10−40). The gene catalog was also screened for symbiont marker genes by best blastp hits to a published protein database for Bathymodiolus azoricus symbionts [Taxonomic annotation of the gene catalog was performed by aligning the translated genes to the nonredundant protein NCBI database (date: 24/05/18) using diamond  e-value35421.1)   and three MGSs (≥700 genes). An additional filter was applied to the MGSs to obtain final bins by removing outlier genes based on their coverage  is then given as follows, where S1 and S2 correspond to the two samples compared:SNV data are used for calculating intra-sample and inter-sample nucleotide diversity (π) as applied before to human gut microbiome species . Intra-sST), which measures genetic differentiation based on the intra and inter-sample nucleotide diversity.Finally, these diversity measures are used to estimate the fixation index (FST) across all inter-sample comparisons from pooled SNV data have been deposited at https://github.com/deropi/BathyBrooksiSymbionts.The scripts to calculate genome-wide inter and intra-sample nucleotide diversity (π) and fixation index  indicates that the population underwent positive selection or a large demographic change in the past [The NI accounts for differences in the ratio of nonsynonymous to synonymous variants between divergent and polymorphic SNVs in order to quantify the departure of a population from neutral evolution . \documethe past .pN/pS and NI have been deposited at https://github.com/deropi/BathyBrooksiSymbionts. Statistics and plotting were done in R [Here we used the NI to analyze if differences in selection have been involved in the evolution of SOX and MOX strains. Different strains are typically found in more than one sample and this supports the notion that SNVs that characterize the strains constitute substitutions. We estimated NI by considering two different levels of divergence and polymorphism. First, we defined as divergent all those SNVs that have two possible states among the strains and as polymorphic all the invariant SNVs, i.e., the SNVs that do not differentiate among strains. Second, we used a more restrictive level of divergence. To this end, we excluded putative recently acquired SNVs from the set of divergent SNVs by discarding those that have multiple states among strains from the same clade. Polymorphic SNVs are all the remaining. The scripts to calculate the allele frequency spectra, one in R .The core genomes from the two bacterial species were screened for the presence of ribosomal proteins. To this end, the amino acidic translations of the largest coding sequence found in a gene cluster have been functionally annotated with eggNOG . A ribosSupplementary material


Candida tropicalis is a globally distributed human pathogenic yeast, especially prevalent in tropical and sub-tropical regions. Over the last several decades, a large number of studies have been published on the genetic diversity and molecular epidemiology of C. tropicalis from different parts of the world. However, the global pattern of genetic variation remains largely unknown. Here we analyzed the published multilocus sequence data at six loci for 876 isolates from 16 countries representing five continents. Our results showed that 280 of the 2677 (10.5%) analyzed nucleotides were polymorphic, resulting in a mean of 82 (a range of 38–150) genotypes per locus and a total of 633 combined diploid sequence types (DSTs). Among these, 93 combined DSTs were shared by 336 strains, including 10 by strains from different continents. Analysis of Molecular Variance (AMOVA) showed that 89% of the observed genetic variations were found within regional and national populations while < 10% was due to among-country separations. Pairwise geographic population analyses showed overall low but statistically significant genetic differentiation between most geographic populations, with the Singaporean and Indian populations being the most distinct from other populations. However, the Mantel test showed no significant correlation between genetic distance and geographic distance among the geographic populations. Consistent with high genetic variation within and limited variations among geographic populations, results from STRUCTURE analyses showed that the 876 isolates could be grouped into 15 genetic clusters, with each cluster having a broad geographic distribution. Together, our results suggest frequent gene flows among certain regional, national, and continental populations of C. tropicalis, resulting in abundant regional and national genetic diversities of this important human fungal pathogen. Candida tropicalis is an ascomycete yeast and an important opportunistic human pathogen  at six gene fragments  the high success rates of PCR amplification and sequencing using the same primers; (ii) their high-levels of single nucleotide polymorphisms (SNPs) within and among strains; and (iii) when sequences at the six loci are combined, their high genotypic discriminating power among strains  of the MEGA Version 6 software . To analm manual .C. tropicalis using the program STRUCTURE version 2.3.4  was identified by following the method developed by Aside from the above population genetic analyses, we also estimated the putative number of genetic clusters K in the global sample of on 2.3.4 . STRUCTUC. tropicalis MLST database. The metadata and DNA sequences at the six MLST loci of all 876 strains were retrieved from the database. Below we summarize the observed data and the results of our analyses.Up to the end of May 2018, a total 876 isolates were deposited in the C. tropicalis MLST database are shown in The geographic distributions of these strains in the Among the 876 strains, 760 had information about their ecological niche or anatomic body site while the remaining 106 had no ecological niche or anatomic body site information . Among tICL1, MDR1, SAPT2, SAPT4, XYR1, and ZWFa1) for all 876 isolates. Information about these loci and the levels of DNA sequence polymorphisms within this global sample are summarized in C. tropicalis is estimated at about 14.63 Mb, containing 6,441 genes . Indeed, the fragment  with the highest number of SNPs (at 64) was the second shortest sequenced fragment, at 390 bp. At present, the reason for the different levels of polymorphisms among the sequenced gene fragments is not known but may be related to the different environmental and/or functional constraints that these genes have experienced during the evolution of C. tropicalis.We retrieved the DNA sequences at the six MLST loci (41 genes . All sixICL1) to 150 (locus MDR1), with a mean of 81.67. Similar to that in SNP density, the number of genotypes per locus was not significantly correlated with either fragment length or SNP density. For example, the Pearson correlation coefficient between the number of SNPs and number of genotypes at the six loci was 0.042, with a P-value of 0.937. In contrast to our expectation, there was a negative correlation between the sequenced fragment length and the number of genotypes among the six loci (Pearson correlation coefficient = -0.688). However, this seemingly negative correlation was statistically insignificant (P = 0.132). Regardless, these results are consistent with the distinctiveness of these six loci in the C. tropicalis genome  was shared by 25 strains, all of which were from Taiwan . The secC. tropicalis in terms of the diversity of geographic locations and the number of strains within a country. In contrast to the skewed distribution at the local and within a country-level, the multilocus genotypes shared among countries were geographically more broadly represented, with multilocus genotype #13 from Europe, #90 from South America, and #394 and #426 from Asia.The 56 multilocus genotypes exclusively shared among strains within a region were distributed in all continents, including most of the countries represented in the database: Belgium, Brazil, China, Colombia, Germany, India, Singapore, United Kingdom, and the United States . Most muC. tropicalis MLST database.Ten multilocus genotypes were shared by strains from different continents. Genotype #12 was shared between Belgium and the United States; genotype #23 between China and the Netherlands; genotype #27 between China and the United States; genotypes #45, #114, #172 between China and the United Kingdom; genotype #80 between Brazil and the United States; genotype #90 among Brazil, Colombia, and China, and genotypes #98 and #99 between Colombia and China. Though countries from Asia, Europe, South America and North America were all represented here, there was a bias for strains from China, with eight of the 10 multilocus genotypes having representative strains from China. Again, the bias was most likely related to the differences in sample sizes in the C. tropicalis strains.Together, the above results suggest that short-, medium-, and long- distance dispersals were all possible for C. tropicalis. To quantify the effects of geographic separation at regional, national and continental levels on the observed genetic variation, we conducted an analysis of molecular variance (AMOVA). At the global level, our analyses revealed that the majority (89%) of the SNP variations was found within individual countries while that among countries contributed the remaining 11% . Together, these results are consistent with an overall low but statistically significant differentiation among regional populations within China and among national populations in the world.The sharing of multilocus genotypes between regional, national and continental populations suggests that gene flow might be common in ning 11% . Interesp < 0.05 . The other two pairs had low and statistically insignificant PhiPT values: 0.006 between Campinas and Recife (p = 0.261), and 0.031 between Campinas and São Paulo (p = 0.132). Within the United Kingdom, the three geographic populations  were genetically indistinguishable from each other with a PhiPT value of 0.000 for both between Aberdeen and Leeds (p = 0.398) and between Leeds and London (p = 0.352). The PhiPT value between Aberdeen and London was slightly higher than 0.000, at 0.017 and statistically also insignificant (p = 0.299).The extent of genetic differentiation between pairs of regional populations were estimated for those in three countries . Among the three regional samples in Brazil , that between Recife and São Paulo had a statistically significant pairwise PhiPT value of 0.132 , the amount of gene flow between most regional and national populations is insufficient to obscure the genetic differences among most of these populations. However, despite the observed significant genetic differentiation between most geographic populations, there is no evidence of genetic isolation by geographic distance .C. tropicalis sample, we investigated the likely number of genetic clusters using the STRUCTURE software. Our analyses revealed an optimal number of 15 genetic clusters. The geographic distributions and sample sizes of these genetic clusters are presented in C. tropicalis have been deposited in the MLST database.To identify potentially distinct genetic populations within the global C. tropicalis and that most genetic clusters are broadly distributed, with some showing geographic bias. The results are overall consistent with the geographic analyses of multilocus genotype distributions, pairwise PhiPT, and AMOVA.At the individual country-level, most countries with sample sizes greater than 10 contained strains belonging to at least two different genetic clusters. The two countries with the most strains in the database also had the highest numbers of genetic clusters. Specifically, all 15 genetic clusters are well-represented in China, the country with the highest number of strains in the database. Indeed, over 90% of the strains in three of the 15 genetic clusters  came from China. Similarly, 12 of the 15 genetic clusters are found in the United Kingdom sample. Taken together, the STRUCTURE analyses indicate that there are multiple distinct genetic clusters in the global sample of C. tropicalis. Our analyses revealed that over 10% of the 2677 nucleotides at the six sequenced gene fragments were polymorphic in this global sample. These SNPs generated a large number of genotypes at each locus. In total, the DNA sequence variation at the six loci allowed the identification of 633 multilocus genotypes among the 876 strains. Among these genotypes, 93 were shared by 336 strains while the remaining 540 were represented by one strain each. The majority of the 93 shared multilocus genotypes were between strains from the same geographic region. However, strains from different regions within a country, from different countries, and from different continents have also been found to share identical multilocus genotypes. These results are consistent with short- medium-, and long- distance dispersals of C. tropicalis strains.This study analyzed the geographic patterns of genotype variation in a global sample of C. tropicalis sample belonged to 15 genetic clusters, with most genetic clusters distributed in multiple regions and countries. Overall, these results suggest that the global C. tropicalis population have likely been historically differentiated but recent gene flows have brought strains with divergent genotypes together into the same geographic regions. A similar result has been reported in another human fungal pathogen Aspergillus fumigatus. In that study, the analyses of genotype data at nine microsatellite loci for over 2000 strains revealed eight distinct genetic clusters with most clusters being broadly distributed in Asia, Europe, Australia, and South and North America , source of infection, route of transmission, and the potential effects of antifungal drug treatments on genotypic changes within individual patients and hospital environments  contributed only 0.5% to the total genetic variation (detailed data not shown). Together, the results suggest that at the global level, there is very little evidence for human anatomic site-specific adaptation of C. tropicalis MLST genotypes and that most genotypes and genetic clusters are capable of colonizing a diversity of human body sites and causing infections. We would like to emphasize that our current study focuses on the geographic patterns of C. tropicalis genetic variation based on the published and publicly available MLST data. Our study differs from previous studies by taking a broad population genetic approach to examine the distributions of genotypes and genetic variations across large geographic scales. While several broad and interesting patterns have emerged through our analyses, the amount of genetic diversity as well as the patterns of variation as revealed here are likely incomplete and biased. Below we discuss two areas of research that could help enhance our understanding of the global C. tropicalis population.Previous studies of ts e.g., . In genens e.g., . In one ns e.g., . Our humC. tropicalis in the MLST database. At present, the samples in the MLST database are geographically highly skewed at the regional, national, and continental levels. Less that 10% of the countries are represented in the database and literature. There are few or no isolates from most countries, including those in the entire African continent. Though the total number of C. tropicalis strains analyzed and deposited in the MLST database so far is large (876), over two-thirds of the strains were from one country, China. The second biggest source of the analyzed strains was the United Kingdom. However, over 50% (67/124) of the United Kingdom strains was from the first MLST study of C. tropicalis that established the consensus MLST loci and the database  by medical mycologists from different countries and geographic regions over the last decade. As evidenced by a recent study in the journal Mycopathologia, studies of medical fungi have undergone a notable shift, with an increasing number of papers coming from emerging economies such as China, India, and Brazil etc. , these samples were from geographically very diverse and distant provinces within China and most provincial populations only had one or two body sites with reasonable sample sizes  in Hamilton, ON, Canada were different (C. tropicalis genetic variation and the relationships between environmental and human samples, future studies should focus on obtaining and analyzing diverse ecological samples from each geographic region and from many different regions and countries using the same MLST system.The second area of future research is to integrate natural ecological studies of n humans , soil (Yn humans , trees (n humans , aquaticn humans , and anin humans , horses n humans , rheas (n humans  as well n humans . Howeverle sizes . As a reifferent . To accuC. tropicalis consists multiple genetic clusters with most of the clusters broadly distributed geographically. Our analyses showed that most observed genetic variations were found at local and regional levels and rejected the hypothesis that the global populations of C. tropicalis were structured based on geographic distances. Evidence for both medium- and long- distance dispersals was found. This study reaffirmed that the C. tropicalis MLST database provided a powerful platform not only for sharing strain and genotype information among investigators but also for helping us identifying broad epidemiological and population genetic patterns. Our analyses revealed several knowledge gaps from which future studies of C. tropicalis ecology, population genetics and molecular epidemiology could be focused on.Our study revealed that the global population of J-YW retrieved and analyzed the data. JX guided the data analyses and drafted the manuscript. D-YZ, FM, and YZ participated in data analyses. All authors read and approved the manuscript.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.


Varroa destructor, is the most severe biotic threat to honeybees (Apis mellifera) globally, usually causing colony death within a few years without treatments. While it is known that a few A. mellifera populations survive mite infestations by means of natural selection, the possible role of mite adaptations remains unclear. To investigate potential changes in mite populations in response to host adaptations, the genetic structure of V. destructor in the mite-resistant A. mellifera population on Gotland, Sweden, was studied. Spatio-temporal genetic changes were assessed by comparing mites collected in these colonies, as well as from neighboring mite-susceptible colonies, in historic (2009) and current (2017/2018) samples. The results show significant changes in the genetic structure of the mite populations during the time frame of this study. These changes were more pronounced in the V. destructor population infesting the mite-resistant honeybee colonies than in the mite-susceptible colonies. These results suggest that V. destructor populations are reciprocating, in a coevolutionary arms race, to the selection pressure induced by their honeybee host. Our data reveal exciting new insights into host-parasite interactions between A. mellifera and its major parasite.The ectoparasitic mite,  The western honeybee, Apis mellifera, is a perfect example of a species that has experienced a rapid expansion in geographical distribution due to international trade and globalization and in this process has aquired many novel parasites and pathogens5.Anthropogenic movement of species can threaten biodiversity, agriculture, ecosystem functioning and can facilitate the spread of harmful pathogensA. mellifera is the propagation of the invasive ectoparasitic mite, Varroa destructor. This mite is inarguably the most severe threat to A. mellifera globally, practically exterminating wild colonies and severly affecting the management and profitability of beekeeping in the wake of its global spread during the 1980’s and 1990’s6. The damage this parasite causes to its new host by feeding on adults and brood is amplified by the multiple viruses it carries and transmits9.The most dramatic consequence of the global spread of V. destructor, originally restricted to the Asian continent, has a non-lethal relationship with its natural host, the eastern honey bee, Apis cerana10. However, due to the transportation and introduction of A. mellifera in Asia, the mite managed to switch host and has successfully established itself as a harmful parasite in A. mellifera honeybee colonies11. Without a long-term coadaptive evolution, as is shared between the mite and its natural host, A. mellifera colonies are ill-equiped to cope with this new invasive parasite.V. destructor infestations are costly, time-consuming, can harm the host and alter the quality of bee products6. Despite these drawbacks, most colonies of A. mellifera require treatment to have a chance for surviving the parasite infestation for more than 1–2 years. Yet, a few populations of A. mellifera exhibit resistance or tolerance traits that allow them to survive extended periods without treatments12.Current treatment strategies used in apiculture for the control of 17. One such well-studied isolated honeybee population on the island of Gotland, Sweden, has been living treatment-free for almost two decades12. This population was established in the late 1990’s as an isolated natural selection experiment, allowing A. mellifera bees to naturally adapt to V. destructor parasitism18. This experiment resulted relatively quickly in improved survival rates through naturally adapted traits that limit the reproductive success of V. destructor and are genetically inheritable within the population19.To understand how these colonies deal with the invasive parasite, surviving honeybee populations have been studied extensively in the past decades. This research has highlighted that a wide range of individual or colony-level mechanisms are involved in their survival and that many of these traits are inheritable21. This ultimately results in an arms race with a series of adaptations and counter-adaptations between the host and the parasite22. In most senarios, the parasite, usually with a shorter generation time than its host, would have an advantage in this arms race24. However, in this particular system, V. destructor is at a disadvantage due to an important factor: the invasive mite population in Europe has a low level of genetic diversity25. This is in part due to a founder effect abolishing most genetic diversity during only a few invasion occasions from its original host, but also due to the reproductive biology of the mite with frequent incestuous mating25. Within the sealed worker brood cells of developing A. mellifera pupae, the mother mite produces a single haploid male offspring followed by 4–5 diploid females26. The adult male copulates multiple times with its adult sisters before the parasitized bee emerges as an adult. At which point, the male dies in the cell and the fertilized daughters along with their mother will enter the honeybee colony’s mite population growth cycle with on average 10–15 generations per year6. This incestuous reproductive system generates high inbreeding levels. Nevertheless, occasionally two mites may enter a brood cell together and mating can then occur between lineages introducing genetic admixing27.Host-parasite coevolutionary theory predicts that adapted host resistance traits, in response to parasitic pressure, are expected to induce a reciprocated selection pressure on the parasite, strong enough to drive counter adaptations towards a fitness optima29. The aim of this study was therefore to analyse temporal genetic structure changes of V. destructor mites infesting the naturally adapted mite-resistant honeybee population on Gotland, Sweden, to investigate whether the parasite population shows signs of reciprocated adaptations in response to the resistant adaptations of its host. The mites from the mite-resistant population on Gotland were compared with a geographically neighboring mite population from managed mite-susceptible honeybees that have not experienced natural selection pressures. The changes in the genetic diversity and genetic structure was compared over time using microsatellites to compare historic mite samples collected in 2009 with current mite samples collected from the same populations in 2017 and 2018.To date, it is unclear if or how mites reciprocate with antagonistic adaptations to their adapted host. Antagonistic coevolution (such as that between hosts and parasites) is expected to drive molecular evolution leading to genetic divergence between populations at a much faster rate compared to selection pressures of environmental changeV. destructor adult females were genotyped at nine polymorphic microsatellite markers to study the temporal genetic structure of the mites in the mite-resistant colonies and local susceptible colonies from 2009 (“historic samples”) and 2017–18 (“current samples”) located on the island of Gotland, Sweden , confirming the independence of the markers used.A total of 432 V. destructor groups  . Notably, a few private alleles were detected in all groups, but these alleles had a very low frequency (<3%). Finally, a rarefaction analysis to assess the impact of sample size on the estimates of genetic diversity showed that the level of allelic richness reaches a plateau after about 20 mites are included, irrespective of the group considered  Fig. . In addired Fig. .ST and Dest). These tests indicated different patterns between the groups compared . In contrast, the level of differentiation between these two groups was more marked and significant in the current samples . In parallel, when comparing mites from the same host population between the two sampling dates, more elevated and significant levels of population differentiation were obtained. Notably, this difference was about three times higher in the samples from the resistant colonies  compared to the susceptible ones . Finally, the comparison between the samples from the mainland and the two current Gotland populations indicated low but highly significant genetic differentiation  in our samples. Overall, 64 MLGs were detected Table . The mai30. Here, the samples from the mainland were excluded to focus on the changes due to the host colony phenotype (resistant vs susceptible) and exclude any background noise from a potential island effect. This analysis revealed that the most likely number of genetic clusters in this mite sample was two (ΔK2 = 269.46). Displaying the individuals according to these two clusters  and the groups with less (i.e. the current groups), with a plateau phase being reached after about 20 individuals are analysed. This result is in line with the findings of another study using the same method31. Here, the lowest number of samples analysed in a group was well above this threshold (N = 41 in the S-current group). Additionally, populations of V. destructor are homogenous at the apiary level32. Therefore, clearly neither the difference in the number of colonies nor the difference in sample size used across the five groups had any significant impact on the genetic diversity estimates in our results.Despite marked differences in the number of individuals screened between the historic and the current groups, the markers used were able to accurately grasp the overall diversity in the populations studied here. For instance, when comparing the level of genetic diversity between the five V. destructor populations infesting A. mellifera throughout the world, suggesting that strong bottlenecks have taken place after this parasite’s host switch and further dispersal of the infested colonies25. Yet, more recent studies have revealed that the diversity of V. destructor is not as limited as previously thought in populations of Europe27, the USA32 and Asia34. The discrepancies between the earlier work on the varroa mite’s population genetics and the more recent investigations could be due to (i) the average low number of samples of the populations initially screened by Solignac et al. (2005) (avg = 12.44 mites per location), (ii) the degree of polymorphism of the markers screened, and/or (iii) an increased diversification of the parasite populations over the ten years separating the studies. Discriminating between the first two possibilities is not possible given the lack of investigations using the same markers as Solignac et al. (2005). Nevertheless, the results of this study suggest that the possibility (iii) is not a valid one, since we did not detect significant changes in the genetic diversity between our historic and current sample groups.Low levels of genetic diversity have initially been reported from a large number of V. destructor invaded A. mellifera colonies of Europe a few decades ago only35. Yet, despite this unique origin, significant genetic variation can now be observed, and the number of individuals belonging to the two genetic clusters detected here differed significantly across time and across the resistant and susceptible colonies in the current sampling period but not in the historic one. This temporal population structure was confirmed by the analysis of Multi-Locus Genotypes. The latter analysis permitted to assess the diversity and structure of V. destructor populations in a subtler way. Moreover, the differences of magnitude between the two indexes of population differentiation (FST and Dest) reported also reflect these changes.Despite this lack of differences in the level of genetic diversity over time, significant changes in population structure between the historic and current samples were detected. The low number and low frequency of private alleles detected demonstrate that these changes in population structure were mainly driven by variation in the frequency of the alleles initially found in the mite populations and not because of the addition or deletion of alleles. The results of the genetic structure analysis using the software Instruct did not show a pure segregation of the two genetic clusters across locations or dates, which can be explained by the fact that a unique lineage of V. destructor populations have changed within the eight years separating the collection of the current and historical samples. These changes led to a higher level of differentiation and an increased diversity of mite genotypes in the resistant colonies compared to the susceptible ones. These variations of population structure and diversity may reflect host-parasite interactions between A. mellifera and V. destrutor, but could also be caused by an influx of foreign mite genotypes in the current samples, the development of resistance to acaricide treatments and/or genetic drift. First, the comparison of the mainland mite population with the two populations located on Gotland suggest that an influx of mites was not responsible for the differences detected. Despite the common origin of the mites infesting both locations, the significant barrier to gene flow observed between the island and the mainland and the high proportion of private MLGs in the mainland and island mites in the current sampling period  indicate that these populations are isolated. Second, both susceptible populations  are treated with oxalic acid, a compound that does not select specific resistant mites36. Thus, the changes of genetic structure detected is not likely be caused by the treatment of the susceptible colonies. Lastly, genetic drift, a process causing the allele frequencies of populations to vary from one generation to the next as a result of chance37, is probably not responsible for the integrality of the changes in population structure we observed within the time frame of our study. With a strong genetic drift effect, a reduction of diversity is expected38. In this study, there was no significant difference in the number of alleles and heterozysogity levels between the different mite populations from Gotland and from the mainland. In contrast, we found an increased diversity of MLGs in the resistant colonies of Gotland between 2009 and 2018. Moreover, if genetic drift was responsible for the increased differentiation between the two sampling dates, the most prevalent MLGs in the historical sampling period would be more likely to prevail in the current period38. Instead, the patterns of changes of mite MLGs between the two dates show that the most common MLG, representing more than half the mites sampled during the historical collection, was not sampled in the resistant group later on, but was still infesting the susceptible population. This observation suggests that a strong selection against the most common mite genotypes is occurring in the resistant colonies. To conclude, the above-mentioned evidences strongly suggest that the main factor responsible for the temporal changes in the genetic structure of V. destructor measured here are caused by the strong selection pressures induced through the co-evolution of the mite with its host, rapidly leading to observable genetic changes in the parasite population, potentially aiding their survival in adapted mite-resistant A. mellifera colonies.Hence, these independent but complementary analyses clearly indicate that V. destructor adaptation in response to its host or environment is not far fetched. Examples such as the ability of the mite to adapt to acaridice treatments39 show how quickly this parasite’s populations can react to selection pressures, despite it’s apparent lack of genetic diversity. In our study, given an average of ten generations of mites per year in the Northern-European climate of Gotland40, the time separating the collection of samples from the historic and current group is more than enough to assess the impact of strong selective forces guided by antagonistic coevolution29. V. destructor genetic variation has been linked to phenotypic variation associated with fertility and virulence differing between the two parasite haplotypes that spread globally from Japan and Korea41. When V. destructor was first reported in Brazil, the fertility and the virulence of the parasite were low, causing little damage to A. mellifera colonies42. However, only a few years later, the virulence of  V. destructor had increased in the region similar to what was experienced in European populations at the time. Genetic analyses demonstrated that the initial avirulent Japanese V. destructor haplotype had been replaced by the more virulent Korean haplotype43. The fact that variability in the consequences of mite infestation so clearly exists within the V. destructor haplotypes, having a significant effect on colony survival, suggests that disregarding the possibility of, for example, reduced virulence adaptions of the mite influencing host survival, is no longer reasonable. Indeed reduced virulence in mites has been proposed previously to explain the long term survival of another isolated honeybee population in the Arnot Forest in Ithaca, NY44.The notion of 45. In their study, only 3 years before the historical samples of this study were taken, they found that mite source had no effect on the colonies and that Gotland colonies had significantly reduced mite infestation rates. Since their study was published, there has been a consensus in the scientific community to regard the mite as a “fixed factor” in the studies of underlying mechanisms to explain the long-term survival of naturally adapted honeybee populations, supported by the limited genetic diversity in the mite population25. The historic mite samples of this study confirm the results of Fries and Bommarco’s in 2006 that the mite source did not influence the bees, since the mites in 2009, only 3 years later, did not significantly differ genetically between groups. However, by looking over a decade after Fries and Bommarco’s study it is evident that a change has occurred in the genetic structure of the mite populations between surviving and treated colonies. Performing a similar cross-infection experiment testing the current mite genotype in the Gotland resistant and susceptible colonies will show whether the genotypic differences observed in the current samples of this investigation are associated with phenotypic differences and will help understanding how the diversity of V. destructor populations observed at the genetic level may impact the survivability of their hosts. If the genetic changes on the neutral markers of this study are confirmed by phenotypic changes, the recently updated version of the genome of the mite46 could be used to unravel the genetic bases of the parasite adaptations. Inhibiting mite reproductive success is a well defined genetically inheritable trait of the Gotland mite-resistant honeybee population, even if the mechanisms explaining how the bees are capable of this are still not completely clear16. Whether this host trait could explain the differences in mite genotype distribution we observed here remains to be studied.In 2006, Fries and Bommarco performed a cross-infection experiment with the Gotland resistant honeybee population to test for varying host responses to mites, sourced from either the resistant or susceptible populationsV. destructor suggest adaptations of the parasite, in a host-parasite coevolutionary arms race, most likely in response to selection pressures applied by the adapted resistant traits of the host. The magnitude of these changes between the historic and current samples of this study demonstrate a relatively fast response. Model systems for experimental host-parasite coevolution research has been largely restricted to microbes and short generation hosts47. The natural selection experiment on Gotland has been, and continues to provide, a unique oportunity to study host-parasite coevolution in real time between two larger eukaryote organisms.In conclusion, the observed changes over time in the genetic structure of V. destructor adult females were collected from the brood of eight honeybee colonies in the naturally adapted mite-resistant honeybee population on the island of Gotland Sweden as well as from eleven mite-susceptible colonies of A. mellifera that require regular mite-treatement with oxalic acid to survive, located approximately ten km away from the resistant colonies . In addition to the samples collected on Gotland, mites were collected on the mainland in Sweden from four mite-susceptible honeybee colonies at the Swedish University of Agricultural Sciences in Uppsala during the summer of 2017 as a secondary control group to account for a possible island effect. All mite samples were kept at −20 °C until DNA extraction. From hereinafter, the 2017 and 2018 samples will be refered to as the temporally “current” samples while the 2009 samples are refered to as the “historic” samples for streamlining the presentation and discussion of the data  and polymorphism on a subset of 24 individuals from three colonies of the susceptible historic group. From these 20 markers, nine were polymorphic and worked well under our lab conditions  and the date of sampling (historic or current). The mite samples from Uppsala were included in the analyses and labeled as “mainland”. Only individuals with genotypes available for five or more loci were kept in this dataset. Along these lines, a total of 432 mites were kept in the analyses Table .49. Then, to compare the genetic diversity across the five groups of mites, the number of alleles (NA) and the level of observed heterozygosity (Ho) were calculated using the Microsatellite Toolkit50 and compared using Kruskal-Wallis tests in R v. 3.5.251. We used the software ADZE52 to conduct a rarefaction analysis on the allelic richness over markers for every mite groups to estimate whether the lowest sample size we used was sufficient to grasp accurately the genetic diversity of the group considered.First, the linkage disequilibrium between pairs of markers was tested along with the Hardy-Weinberg equilibrium for each group using Fstat v 2.9.353 was then used to assess genetic differentiation in the populations of V. destructor of in the four groups from Gotland and between the current mainland and Gotland samples by calculating two distinct estimates of population differentiation (FST and Dest) as recommended by54. FST reflects the probability that two alleles drawn at random from within a group are identical by descent55 and is commonly used to compare the genetic differentiation between two or more group of samples56. Generally, FST values below 0.05 suggest low genetic differentiation across groups, whereas when this estimates ranges between 0.05 to 0.25, moderate genetic differentiation is suggested37. In parallel, Dest complements this estimate and better reflects differences in allele frequencies when markers are highly polymorphic57.The software GenAlex v. 6.5V. destructor genotypes, the package poppr58 for R v. 3.5.251 was used to compare the prevalence of Multi-Locus Genotypes (MLGs) in the five mite groups. All individuals with missing data were excluded from this analysis  from Gotland . The methods from Evanno et al. (2005) was then used to determine the most likely K in the overall dataset. The results of the Instruct analysis were pooled using the software CLUMPP60 and a figure corresponding to the most likely number of K was prepared with the software Distruct61. In order to test whether the probabilities of belonging to a cluster differed among the different groups, a Chi-squared test was used to evaluate the relationship between the inferred clusters and the mite groups. To do so, the number of individuals in the different genetic clusters for each group was obtained from the ancestry values of the populations obtained with the CLUMPP software and was compared using a contingency table. In parallel, the differences between naturally-surviving and treated colonies for each sampling date was tested using the same method63.Finally, an analysis of genetic structure without Suppl. Info


In conclusion the gene pool of Central Italy seems to be rather consistent and highly differentiated compared with other European studies (FST = 0.147). The importance of safeguarding this diversity and the impact on the management of the germplasm living collection is discussed.In the last 50 years, intensive farming systems have been boosted by modern agricultural techniques and newly bred cultivars. The massive use of few and related cultivars has dramatically reduced the apple genetic diversity of local varieties, confined to marginal areas. In Central Italy a limited spread of intensive fruit orchards has made it possible to preserve much of the local genetic diversity, but at the same time the coexistence of both modern and ancient varieties has generated some confusion. The characterization and clarification of possible synonyms, homonyms, and/or labeling errors in old local genetic resources is an issue in the conservation and management of living collections. 175 accessions provided by 10 apple collections, mainly local varieties, some of unknown origin, and well-known modern and ancient varieties, were studied by using 19 SSRs, analyzed by STRUCTURE, Ward’s clustering and parentage analysis. We were able to identify 25 duplicates, 9 synonyms, and 9 homonyms. As many as 37 unknown accession were assigned to well known local or commercial varieties. Polyploids made up 20%. Some markers were found to be significantly correlated with morphological traits and the  Malus × domestica Borkh., family Rosaceae, tribe Pyreae, 2n = 2x = 34) is one of the most ancient and widespread fruit crops in temperate regions. Almost certainly the domesticated apple is the result of a long evolutionary process extending over thousands of years, and it seems several species have contributed to its gene pool. On the basis of genetic data, of fruit and tree morphology, the wild Asian species M. sieversii M. Roem is actually considered the main contributor to the Malus × domestica gene pool, and the Tian Shan Mountains  the center of origin (M. baccata (L.) Borkh. in Siberia, M. orientalis Uglitz. in Caucasus and M. silvestris (L.) Miller in Europe, have produced the diversity currently present in the domesticated apple , Parco Tecnologico Agroalimentare dell’Umbria , the Department of Agriculture, Forestry and Food Science of the University of Torino , the Archeologia Arborea private collection (c05), the Department of Agricultural, Food and Environmental Sciences of the Polytechnic University of Marche , Malva Rinaldi School in Torino (c07), the Department of Agricultural Science, University of Bologna , the Giardino Armonico of Bevagna private collection (c09) and Azienda Ortofrutticola Sett’Olmi, Perugia (c10). Details are reported in Table 1. Many of the 175 accessions were well documented by reliable historical sources. Those lacking of several reliable information were coded as Unknown. Therefore, based on the initial information, the 175 accessions used in this study were classified into 17 commercial varieties (CV) used as control, 99 local varieties (LV), and 59 unknown accessions (UA).One hundred and seventy five accessions of Table 2). Primer sequence and allele range for validated loci were analyzed by Multiplex Manager  according to manufacturer’s protocol. Twenty one apple SSR primer pairs  distribu Manager  to deterPCRs were carried out in a final volume of 25 μl using 1 × Type-it Microsatellite PCR Master Mix (Qiagen), 0.2 μM of each fluorescent forward primer labeled with 6-FAM or ROX dyes (Sigma) and reverse unlabeled primer and 20 ng of template DNA. All amplifications were performed in a GeneAmpPCRSystem 9700  consisting of a denaturing step of 5 min at 95°C followed by 30 cycles of 95°C for 30 s, 57°C for 90 s and 72°C for 30 s, and a final elongation step of 30 min at 60°C.PCR products were separated and analyzed on a 3130 XL DNA Analyzer (Applied Biosystems). The size of the amplified products was determined on internal standard DNA  and the scorable peaks were assigned by GeneMapper software v.4.0 (Applied Biosystems).Supplementary Table S1).Phenological and morphological traits of interest were scored as follows: time of eating maturity ; fruit shape ; fruit ground color ; hue over color of fruit ; fruit rustiness , pulp color . The scores were assigned by 3 operators who exchanged opinions before eventually providing individual scores, which were averaged prior to statistical analysis. Data were available only for 130 accessions and were therefore used primarily to ascertain doubts  which were therefore discarded. The statistical analysis was then based on the remaining 19 loci (Supplementary Table S2).For each r v2.2.3  based onlocus, the effective number of alleles (Ne), the percentage of rare alleles , of the observed (Ho) and expected (He) heterozygosity  ((ID)sib   and the ((ID)sib , calculai and pj are the frequencies of the ith and jth alleles and i ≠ j.where pID)  .K inferred clusters, each characterized by a set of allele frequencies at Hardy-Weinberg equilibrium, based on estimates of the corresponding probabilities of membership to each group. The analyses were run on an admixture ancestral model with correlated allele frequencies, and the number of K clusters was determined firstly by simulating a range of K-values from 1 to 21 with 10 independent runs each. Since after K = 5 there were no other appreciable peaks, STRUCTURE was run again with twenty runs for K-values ranging from 1 to 11, using a burn-in and a run length of the Monte Carlo Markov Chain (MCMC) of 300,000 and 500,000 iterations for data collection. The best K-value was determined through the ΔK method (qI) was ≥ 0.80  was ≥ 0.80 were grouped into K = 5 subset of 7, 9, 29, 11, and 36 individuals. The goodness of fit of these 5 groups was investigated by the F statistics (FIS and FST) (The 92 accessions whose probability (and FST) , and theand FST) .Supplementary Figure S1). For polyploid accessions a FaMoz control analysis was run on a 0–1 data matrix.The software FaMoz  was usedMoreover, in order to look for correlations between molecular data and morphological traits, a non-parametric correlation analysis (Spearman) was carried out using SAS 9.1 .locus was 14.6, ranging from 5 (Hi22f12) to 26 (CH05e03), but the number of effective alleles per locus was significantly lower (Ne = 5.94) (Table 3). With the exception of locus Hi22f12, all loci showed at least one genotype with three alleles. Loci CH02b03b and CH01h01 identified as many as 28 individuals with three alleles, while the other loci identified between 8 and 22 individuals. Even if several individuals showed one locus with a third allele, only those showing a third allele in at least 3 loci (Table 1), as they showed a third allele from 4 up to 13 loci.The 19 nuclear SSRs were all polymorphic and produced scorable amplicons with a total of 278 alleles. The average number of alleles per t 3 loci  were conloci and were more common as the number of alleles per locus increased. Rare alleles ranged from 12.5%  to 57% (at locus CH-Vf1 they were 8 out of 14). No rare allele were found at the loci Hi22f12 and AU223657, where the range of allele size were the lowest, 16 and 13 bp, respectively (Table 3).Rare alleles were found in 17 loci were not in Hardy-Weinberg equilibrium (P ranging from 0.05 to less than 0.001) and this was expected as the 175 individuals do not belong to a panmictic population. Mean observed heterozygosity (Ho) was 0.78, ranging from 0.16 (locus Hi22f12) to 0.91 (CH01g12 and CH04c07) (Table 3). Mean expected heterozygosity (He) was 0.81, denoting high variability and ranging from 0.65 (CH-Vf1) to 0.89 (Hi07h02 and Hi03a10). F coefficients ranged from - 0.154 (CH-Vf1) to + 0.784 (Hi22f12), but the latter was the only value significantly departing from the others, thus denoting high homozygosity.Except for CH-Vf1 and CH01c06, all other loci showed PIC values ranging from 0.65 (CH-Vf1) to 0.89 (Hi07h02 and Hi03a10), which were all higher than 0.5 and therefore very informative (The 19 ormative .ID) of a locus is the probability that two individuals share the same genotype at that locus, while the power of discrimination (PD = 1-PID) is the probability that two individuals have different genotypes at that locus. An overall mean value of PD = 0.98 (ranging from 0.924 to 0.997) indicates that the loci are polymorphic enough in discriminating individuals. By considering the profile of the 19 loci at the same time, the probability to find two identical individuals is indeed remote (PID = 2.2 × 10-38); therefore, two individuals with the same profile at 19 loci are expected to be clones of the same genotype.The probability of identity . Table 4 lists 38 accessions that were found to be identical. Among these, accessions #049, #012, #058, #147, and #013 were identical to #087, #010, #059, #118, and #043, respectively; they confirmed the correctness of the procedure as they had the same names, even if some of them were provided by different collections. Moreover, five unknown accessions, namely #076, #173, #148, #151, and #093 were identical to known genotypes and could therefore be named Annurca, Fuji, Coccianese, Rosciola, and Reinette du Canada, respectively. Some other accessions found to be identical can be considered synonyms: #002 of 001_Cerina, #134 of 126_Limoncella, #015 of 011_Sona, #028 and #030 of 016_Gelata, #041 of 039_Ciocarina Bianca, #053 of 080_ Ducale, #112 of 146_Gelata, #035, and #036 of 034_Panaia. Lastly, there were 5 other pairs of identical accessions , all of unknown origin. In order to proceed with the statistical analysis accessions #054, #060, #103, #140, and #160 were removed.The Ward’s clustering method, based on the Euclidean distance matrix, highlighted several individuals grouped at zero distance and, according also to the Figure 1 and are aligned in order to compare and, eventually, validate accession membership and population structure. On Ward’s dendrogram and at a distance of 60 units we found two main groups (P < 0.001). The upper group includes all local varieties, while the other includes several commercial varieties that were used in this study as controls: Annurca, Reinettes, Abbondanza, Golden Delicious, Golden Gala, Fuji, Cripps Pink, and Stark Delicious. Moreover, at a distance of 35 the dendrogram showed essentially 5 clusters, in agreement with K = 5 of STRUCTURE (see later paragraph).After the removal of 25 duplicates, the 150 accessions were reanalyzed by Ward’s clustering and analyzed by STRUCTURE. The results are reported in Cluster 1 is characterized by a group of local varieties which refer to ‘Gelata’ , common in central Italy where it is known with different names: Cerina, Oleata, Cera  . In thisCluster 2 includes several ancient accessions from Umbria, such as Ruzza (#066 and #010) or Roggia (#128 and #018), which had already been described in the 16th century . In thisCluster 3, with 45 accessions, includes the subgroups of Spoletina , of Limoncella , of several polyploids (red labeled beside the name), the local varieties Sona (#011), Coccianese (#059) and several unknown accessions. Limoncella is an ancient variety common in the South of Italy whose name means ‘small lemon’; Sona is common in Valnerina (Umbria) and its name derives from the rattling sound of the seeds inside the carpel detaching at maturity.P < 0.06), both collected in Umbria.Although apparently uniform, in Cluster 4 it is possible to detect two subgroups, one including all diploids accessions that refer to Annurca, an ancient cultivar of Campania whose origins were already described by Pliny the Elder , and theCluster 5 is the most numerous. It includes 54 accessions and it is possible to distinguish the group of Reinettes, the group of Golden, that of Appiola, that of Abbondanza and that of Fuji, Cripps Pink, and Stark Delicious. The Reinette accessions  and those known as ‘Mele Grigie’ , provided by the Department of Agriculture of Torino, are very common in the North-West of Italy and are characterized by an acidic pulp and by a shrunken skin at maturity.Ks ranging from 1 to 21 and the distribution of ΔK-values . With this threshold as many as 58 of 150 individuals were classified as admixture. At K = 5 STRUCTURE was able to define 5 groups, identified by the colors green, yellow, blue, red, and gray, respectively, which are represented side by side with those detected by Ward’s clustering . By comparing the two grouping methods, and apart from the admixtures, it can be stated that there was a similar trend and correspondence. In Group 1 (green) only 7 out of 20 accessions were not classified as admixtures, and they all belong to Gelata and Cera . In Group 2 (yellow) only 7 out of 18 accessions were classified at P(qI)≥ 0.80; of these, some belong to the group of Ruzza  ≥ 0.80). In Group 4 (red) 11 out of 13 accessions were correctly classified: the Annurca’s  and a set of triploids, all belonging to #034_Panaia. Group 5 (gray) includes several commercial varieties  and here as many as 35 out of 54 accessions were accurately classified. Overall, by comparing the two methods, only two misclassified accessions emerged: the unknown #157 found in Ward’s Cluster 3 but ascribed by STRUCTURE in Group 2 (yellow), and 039_Ciocarina Bianca in Cluster 5 attributed by STRUCTURE in Group 3 (blue).The plot of the average log-likelihood values for K-values  accordinTable 5) and F-Statistics (Table 6). However, the mean number of alleles across the 19 loci and for all 92 accessions, as revealed by STRUCTURE, was 13.1, very similar to the population based on 150 accessions (14.6); likewise were the values of He (0.82 for both) and Ho . As expected, the mean number of alleles per locus in Groups 1, 2, and 4 was consistently lower than those for Groups 3 and 5, since heavily dependent on the number of accession forming the groups. Interestingly, in the former, less numerous groups, there was at least one locus with a fixed allele (Table 5), hence the indexes of diversity (He) were lower than those of Group 3 and 5. As expected, the overall FIS of the 5 groups was slightly negative (outbreeding), consistent for the majority of the loci (14), except for CH05e03  and especially for Hi22f12 , highly homozygous compared with the expected values. The overall loci FST-value  is to be considered rather high, meaning that the 92 accessions were well structured and close to a value (0.15), generally considered to indicate a threshold limit between a moderate and a great differentiation  denoted a little differentiation. All the results reported above were confirmed by AMOVA, where the variation within groups was 75.9% and among was 24.1%, values different from those reported in literature  to investigate the origins of the 49 unknown accessions after the removal of 10 found identicals to well-known genotypes and (ii) to look for concordance with the results from STRUCTURE and Ward’s clustering.Table 7 reports the 38 unknown accessions significantly related (LOD score > 5) to parents of known origin (LV and CV). As many as 27 of them showed full concordance with STRUCTURE and Ward’s clustering. The most likely parents of 10 unknown accessions, classified by STRUCTURE into the Admixture group, were classified also by Ward’s in the same group. Lastly, the 144_Unknown was included by STRUCTURE in group 5, whereas the most likely parent, 033_Panaia, was classified differently by the other two analytic procedures (Ward’s Cluster3 and Admixture in STRUCTURE).In particular, 051_Unknown was classified by STRUCTURE as admixture, showing a probability of 0.38 to be assigned to Group1 and of 0.61 to Group2; likely parents were 001_Cerina and 062_Piattuccia, belonging to Cluster1/Group1 and to Cluster2/Group2, respectively. Since Ward’s clustering assigned #051 to Cluster 1, it can be stated that STRUCTURE was more efficient to infer its mixed genomic configuration.vs. fruit rustiness . Eleven out of 19 SSR loci revealed that 15 alleles out of 278 were significantly (P < 0.001) correlated with five morphological traits (Table 8). Four alleles showed a significant negative correlation with time of eating maturity, so that the presence of these alleles was related with early maturity. CH03d12_128 and Hi03a10_199 were positively correlated with fruit shape. Similarly, pulp color revealed two alleles with significant positive correlations with two SSR loci related to aroma compounds  to - 0.36308 (for CH_Vf1_127), meaning that their presence is by some means related to light colors . Interestingly, the majority of the marker-alleles detected for the over color were related to QTLs for resistance to biotic stresses, aroma compounds, stiffness, and acidity, indicating a possible correlation among these traits  out of eleven The rapid spread of modern, intensive agricultural techniques during the last century was also accompanied by a rapid spread of newly bred cultivars characterized by greater productivity and uniformity. Although this trend was more intensive in annual crops, it did not spare fruit orchards. The oversimplification of the agricultural systems in favorable areas caused parallel changes in the agricultural economy, farming assets, rural culture, and agricultural landscapes as well. From a biological point of view this determined a significant reduction of crop biodiversity and a progressive genetic erosion with the loss of many ancient, well adapted local varieties. For this reason, over the last 50 years, the need to develop effective strategies for the conservation and management of genetic resources has become a fundamental issue. At this purpose germplasm banks have been established all over the world, operating at international, national and local levels. Italy, in compliance with EU directives, has promoted several Regional germplasm collections, with the aim of maintaining and preserving the autochthonous diversity. In Italy fruit germplasm, and apple genetic resources in particular, are conserved by National and Regional institutions , several of whom kindly provided accessions that were used as controls in this study.The area of Central Italy is characterized by hills, mountains, small valleys, a variety of soil types  and of eMany of these local varieties are well adapted to specific agro-climatic conditions and often express some diversity with respect to the originals in terms of morphological and physiological traits, thus assuming different names. Often, the names were assigned on the base of phenotypic traits, strongly influenced by the environment and agricultural practice, thus increasing the existing confusion about local genetic resources and their correct denomination. This gives rise to the importance of characterizing the germplasm present in the national and regional germplasm banks, by identifying duplicates and redundant accessions, hence simplifying the management and reducing costs of living collections.loci (Hi23g02 and Hi03g02) were discarded. The remaining 19 showed a high degree of polymorphism and discriminating power and allowed us to meet our objectives.For this purpose, molecular markers and in particular SSR have been widely used in genetic diversity studies and clarified cases of synonymy and homonymy in core collections . FollowiThe pool of accession studied here showed a percentage of polyploids of 20%, a value intermediate between 8% reported by Table 4) and 9 homonyms. Six accessions from the living collection of 3A-PTA Pantalla and 3A-PTA Casalina  were duplicates of the same genotype, because the SSR profiles were identical throughout the 19 loci. Also San Giovanni (c02_013) and Ruzza (c02_012) turned out to be identical to the corresponding accessions provided by the Department of Agricultural Science, University of Bologna (c08) and the National Center of Fruit Tree Germplasm (c01), thus confirming the goodness of the analysis.In brief, our study showed that 25 accessions were duplicates, 9 had to be considered synonyms , independently of other traits, one is diploid  while the other two were polyploids . Other examples of homonymy include #008_Roggia (Cluster3), genetically different from the two Roggia listed in Cluster2; Appiola Rossa #052 (diploid) vs. Appiola Rossa #077 (polyploid) in Cluster 5, and Bianchina #057  vs. Bianchina #124 .Another significant result of our study was the genetic identification of several unknown accessions. Ten of them were excluded as duplicates of well-known accessions. By using different statistical approaches  it was possible to assign 37 more accessions to known commercial or local varieties.K = 5, where the FST-value indicates a high differentiation among subpopulations, much higher than those reported in the literature (ST at some loci were very contrasting (see Table 6). The low FST-values at loci CH04c07, CH01h01, CH-Vf1, CH01f03b and CH01h02 suggests that homogenizing selection across subpopulations reduces differentiation, whereas the high FST-values at loci CH05c06, Hi22f12, and AU223657 suggest that selection for local adaptation is creating differentiation. In these cases we found that the allele 128 of CH-Vf1 is correlated with fruit over color and allele 137 of locus CH01f03b with fruit ground color, while allele 106 of CH05c06 and allele 235 of AU223657 are correlated with time of eating maturity (Table 8).Lastly, it is worth mentioning that the 92 accessions found by the Bayesian analysis were well-structured at terature , 2016, ilocus Hi22f12 was significantly lower than the values at the other 18 loci (0.13 vs. 0.78), meaning that almost all individuals at this locus are homozygous. Actually, this is also the locus with the lowest number of alleles. The sequence of the Hi22f12 SSR locus was then used in the BLAST program analysis against the NCBI nr database, and we found that Hi22f12 is located inside the transcription factor IIE subunit 1-like gene (XM_008376494.2) of Malus × domestica, at the position 1187–1242. The expectation and the identity of the query against the reference gene was 2 × 10-6 and 85%, respectively. The stability needed by this gene explains this low polymorphism, perhaps confined to introns. It would be interesting to extend this investigation to other germplasm collections.Unexpectedly, the observed heterozygosity at the loci resulted fixed; in particular, loci CH05C06 and CH02c09 showed a fixed allele in group 2 of STRUCTURE. Of these, CH05C06 is of particular interest, associated with a major QTL for fruit titratable acidity (TA) detected in the Ma region , coding for the aromatic compound β-damascenone  associated with anise and licorice descriptors on the LG 15(locus CH01c06 and the 218 at locus Hi22f12, were also fixed in Group 1 and Group 4, respectively, but no information was found in the Malus database.In the present study Hi22f12 and some other SSR a region . In the a region . The Ma r fruits . Indeed,r fruits  and is tr fruits . Moreoveascenone , a potenascenone  and otheascenone . In our the LG 15. Two othIn conclusion, this paper highlights the presence of considerable genetic variability among the apple accessions recovered in Central Italy and the information obtained can be used to better manage large living collections of a fruit tree of great nutritional interest such as the apple.EA, LC, and FV conceived the study. EA and GM designed and coordinated the experiments. EA, GM, and LC chose and provided the germplasm. GM and NF performed the lab experiments. GM, LR, and NF conducted the data analysis and wrote the manuscript, while EA and FV critically reviewed it.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.