However, lack of convergence of the estimated parameters and high standard errors of the rg are often experienced for beef cattle populations due to limited across-country genetic connections. Furthermore, using all available genetic connections to estimate rg is prohibitive due to computational constraints, thus sub-setting the data is necessary. Our objective was to investigate and compare the impact of strategies of data sub-setting on estimated across-country rg and their computational requirements.Cattle international genetic evaluations allow the comparison of estimated breeding values (EBV) across different environments, i.e. countries. For international evaluations, across-country genetic correlations  methodology, we estimated across-country rg (standard errors in parentheses) were on average equal to 0.79 (0.14) and 0.71 (0.19), respectively. Direct-maternal within-country and between-country rg were on average equal to − 0.12 (0.09) and 0.00 (0.14), respectively. Data sub-setting scenarios gave similar results: on average, estimated rg were smaller compared to using all data for direct (0.02) and maternal (0.05) genetic effects. The largest differences were obtained for the direct-maternal within-country and between-country rg, which were, on average 0.13 and 0.12 smaller compared to values obtained by using all data. Standard errors always increased when reducing the data, by 0.02 to 0.06, on average. The proposed sub-setting strategies reduced the required computing time up to 22% compared to using all data.Using all available data, direct and maternal rg that are required for beef cattle international evaluations, using a multi-trait MC EM REML approach, is feasible but involves long computing time. We propose four strategies to reduce computational requirements while keeping a multi-trait estimation approach. In all scenarios with data sub-setting, the estimated rg were consistently smaller  and had larger standard errors.Estimating all 120 across-country  International genetic evaluations of beef cattle performed by Interbeef allow the comparison of estimated breeding values (EBV) across countries. Current Interbeef evaluations involve up to ten countries, five breeds , and two trait groups: animal weaning weight (composed of age-adjusted weaning weight) and calving  . To estimate international EBV (IEBV), across-country estimated genetic correlations  to estimate across-country Interbeef January 2018 routine evaluation data for age-adjusted weaning weight (AWW) were available for eight Limousin populations, representing ten European countries: Switzerland (CHE), Czech Republic (CZE), Germany (DEU), Denmark, Finland and Sweden (DFS), Spain (ESP), France (FRA), Great Britain (GBR) and Ireland (IRL). The following data edits were applied to the submitted national datasets: (1) animals belonging to contemporary groups (CG) smaller than the defined national minimum size  were removed. Furthermore, using the RelaX2 software , the avaGenetic connections across countries are provided by animals having recorded offspring across two or more populations. First, an analysis was conducted to investigate and quantify the existing common bulls (CB), common dams and common maternal grandsires (CMGS) across-populations. Then, this information was used to compute the following measures of connectedness: coefficients of genetic similarity, coefficient of adjusted number of populations for sires, and the harmonic mean of a sire’s progeny size. Finally, these measures were used to identify the best-connected subsets of data for the estimation of across-country The concept of genetic similarity between two populations initially proposed by Rekaya et al. , 17 has The coefficient of genetic similarity ranges from 0 to 1 and can be interpreted as the proportion of offspring between two populations that originate from CB. Therefore, the closer the coefficient of genetic similarity between two populations is to 1, the larger is the number of genetic connections between two populations.The concept of genetic similarity was extended by Jorjani et al.  to take Following Jorjani et al. , the adjThe BOD coefficient ranges from 0 to 1 and the AN_POP coefficient ranges from 1 to The harmonic mean of a sire’s progeny size across two countries can be used as a measure to identify an unbalanced distribution of offspring. The harmonic mean of the progeny size for sire The use of the harmonic mean to measure the unbalanced distribution of offspring can be extended to the herd level as follows:The data sub-setting strategies were focused only on the French Limousin population, since it was the largest national dataset and, therefore, it has a large impact on computing time. Data sub-setting was not applied to the other Limousin populations since it could lead to a relatively large reduction in the number of observations for any of those populations . In order to minimize variation in data size across different scenarios, and allow a meaningful comparison of computational requirements, the FRA population was reduced in all sub-setting strategies such that the selected amount of data was close to the number of phenotypes for the GBR population, which is the second largest dataset. As a result, the total number of records retained across all populations in any of the data subsets was approximately 0.5 million.Scenario ALL: using the complete dataset, i.e. 3,115,598 AWW records and 3,431,742 animals in the pedigree.Scenario RND: selection of randomly composed groups of FRA herds. FRA herds were randomly divided into 20 subsets. For each subset, the coefficients of genetic similarity with all participating countries were computed. The three subsets with the highest coefficients of genetic similarity were analysed separately. In these subsets of data, 533,816, 521,077 and 556,100 phenotypes were retained, with 706,717, 692,205 and 729,778 animals in the pedigree, respectively.Scenario GSCB: selection of FRA herds based on herd-level coefficients of genetic similarity, defined as the average of the coefficients of genetic similarity computed between herd The final dataset included 506,080 phenotypes and the pruned pedigree included 654,841 animals across all involved countries. The amount of retained data for the FRA population corresponded to 1% of the FRA herds.Scenario GSTOT: selection of FRA herds based on the herd-level coefficient of genetic similarity that includes information from both CB and CMGS . Genetic similarity at the herd level was defined as the average of the coefficients of genetic similarity computed between herd The final dataset included 513,969 phenotypes and the pruned pedigree included 663,127 animals across all involved countries. The amount of retained data for the FRA population corresponded to the top 1% of the FRA herds ranked on their Scenario HM: selection of FRA herds based on the harmonic mean of sires’ progeny size. Based on the harmonic means computed at the herd level, FRA herds were selected until the FRA population was reduced to about 0.5 million records. The final dataset included 502,716 phenotypes, with a pruned pedigree of 649,081 animals across all involved countries.For the subsequent estimation of variance components, we considered different scenarios depending on which FRA records were selected for the analysis, whereas all the records of all other countries were included in all scenarios:When data reduction was applied to the FRA population , the RelaX2 software was used for pedigree pruning with the following options: “prediction” pruning method and no generation limit.In all scenarios, variance component estimation (VCE) was performed using an animal model accounting for across-country interaction (AMACI) . The AMAThe four random environmental effects refer to the national effects of herd-year-season in CZE, herd-year in DEU and CHE, and sire-herd in CHE  one simulated dataset for each REML round, and (4) a convergence criterion of 10−9 for the VCE. In all scenarios, the provided set of starting values were the most recent estimates available and currently in use for the 2018 Limousin Interbeef routine evaluations.The VCE estimated via MC EM REML was standardized for all scenarios using: (1) a maximum of 1000 PCG iterations, (2) a convergence criterion for PCG iterations equal to 10Approximated standard errors (SE) of the estimated VC can be obtained in an additional MC EM REML round . This ad Tsuruta .First, we present the results for the assessment of the available genetic connections in the whole dataset, followed by the estimated We assessed the available genetic connections between countries by quantifying the number of recorded offspring of CB. The number of CB varied with the country combination, with a minimum of 44 CB between ESP-CZE and a maximum of 396 for FRA-GBR Table . The aveSince CB can have recorded offspring in more than one bivariate country-combination, hereafter we will use the term “unique CB” to indicate an individual CB. The total number of unique CB in the available dataset was equal to 1436. Of these unique CB, 1053 (73.4%) had offspring in two populations, while 12.4, 5.2, 4.4, 2.2, 1.7 and 1.2% had offspring in three up to all eight populations, respectively. Moreover, the distribution of CB by number of connecting populations and by country of origin , which reflects the high proportion of French CB. Moreover, the IRL population had a high level of connections with all countries except with DFS and DEU. As a result, IRL and FRA were the two populations with the highest average coefficient of genetic similarity with other countries . GBR showed the lowest bivariate connections (GS < 0.05) with CZE, ESP, DEU, and CHE.The balanced offspring distribution and the adjusted number of population coefficients can be considered as two quantitative measures of the same quantity, i.e. balance in sires’ offspring records across country. Table Modelling the countries as different traits in international evaluations, as in the AMACI model, allows the genetic correlations between countries to be lower than 1, which accounts for genotype-by-environment interactions and possible differences in phenotypic distribution, trait and national model definition of the AWW. Descriptive statistics for each population-sex combination highlight differences in phenotypic mean for AWW across populations  Table . Howeverct Table . The SE In Scenario GSCB, Across-country estimated ct Table . Direct-LL Table . The SE In Scenario GSTOT, Direct and maternal LL Table , and theLL Table  and the The aim of Scenario HM was to select FRA herds based on their harmonic mean coefficient (HM). The average HM coefficient of all FRA herds was 965.7 but large variations were observed across herds, with HM coefficients ranging from 0  up to 15,116. For Scenario HM, the 37 selected herds had an average HM coefficient of 8942. Selecting FRA herds on their HM resulted in an average increase of 0.08 of the coefficient of genetic similarity at the population level for FRA compared to Scenario ALL.Across-country estimated ct Table . The dirly Table . SimilarUsing all the data, Scenario ALL took 43 days and 23 h to estimate across-country rg Table . The datrg Table , corresprg Table . Sub-setrg Table . CompareIn this study, we applied a multi-trait approach to estimate a 16 × 16 across-country all data , 23.Hereafter, first we discuss the genetic level of the connections available in the dataset used, then the impact of the sub-setting strategies on the estimated Direct connections varied greatly across countries as indicated by the number of CB Table . Most ofThe coefficient of genetic similarity provides a quantification of the number of genetic connections available between two countries. The coefficients of genetic similarity revealed that IRL and FRA are “link-provider” countries, as termed in dairy cattle international evaluations , 6, 29. The sire’s AN_POP coefficient provided further insights on the level of genetic connectedness available in the data. Jorjani et al.  showed tThe across-country estimated  studies . Estimatf − 0.01 ; the posIn cattle international evaluations, across-country onal EBV  and can onal EBV , which conal EBV . Second,mentclass2pt{minimonal EBV : an exammentclass2pt{minimSubmitted AWW were partly modelled differently by each participating country , the required computing time per REML round was reduced to ~ 17% (from 53 min in Scenario ALL to 9 min in non-random scenarios). However, the larger required number of REML rounds in sub-setting scenarios resulted in the total computing time not being proportional to the data reduction applied Table . A largeEstimated within-country genetic and environmental variances may differ from those reported by member countries, both when estimated using all international data (ALL Scenario) and subsets of data  , minimum, phenotypic mean, maximum and phenotypic standard deviation  and their standard errors  between RND and ALL. Table S5. Differences in estimated genetic correlations for direct and maternal genetic effects  and their standard errors  between GSCB and ALL. Table S6. Differences in estimated genetic correlations for direct and maternal genetic effects  and their standard errors  between GSTOT and ALL. Table S7. Differences in estimated genetic correlations for direct and maternal genetic effects  and their standard errors  between HM and ALL. Table S8. Average differences in GS between each random subset of FRA-herds of RND and ALL. in 2018 . Table SAdditional file 2: Figure S1. Adjusted Number of Populations (AN_POP) for all common bulls in function of their year of birth and country of first registration. Figure S2. Estimated (co)variance components per population across different scenarios. Figure S3. Estimated random contemporary group and sire by herd interaction variances across different scenarios.


Studies on metagenomic data of environmental microbial samples found that microbial communities seem to be geolocation-specific, and the microbiome abundance profile can be a differentiating feature to identify samples’ geolocations. In this paper, we present a machine learning framework to determine the geolocations from metagenomics profiling of microbial samples.Our method was applied to the multi-source microbiome data from MetaSUB (The Metagenomics and Metadesign of Subways and Urban Biomes) International Consortium for the CAMDA 2019 Metagenomic Forensics Challenge . The goal of the Challenge is to predict the geographical origins of mystery samples by constructing microbiome fingerprints.First, we extracted features from metagenomic abundance profiles. We then randomly split the training data into training and validation sets and trained the prediction models on the training set. Prediction performance was evaluated on the validation set. By using logistic regression with L2 normalization, the prediction accuracy of the model reaches 86%, averaged over 100 random splits of training and validation datasets.The testing data consists of samples from cities that do not occur in the training data. To predict the “mystery” cities that are not sampled before for the testing data, we first defined biological coordinates for sampled cities based on the similarity of microbial samples from them. Then we performed affine transform on the map such that the distance between cities measures their biological difference rather than geographical distance. After that, we derived the probabilities of a given testing sample from unsampled cities based on its predicted probabilities on sampled cities using Kriging interpolation. Results show that this method can successfully assign high probabilities to the true cities-of-origin of testing samples.Our framework shows good performance in predicting the geographic origin of metagenomic samples for cities where training data are available. Furthermore, we demonstrate the potential of the proposed method to predict metagenomic samples’ geolocations for samples from locations that are not in the training dataset.The online version contains supplementary material available at (doi:10.1186/s13062-020-00278-z). With approximately 30% of urban populations using subways and buses, public transportation becomes one of the most shared spaces of the urban environment. Within this shared space, there exists a rich and diverse world of unseen organisms in constant interaction with human. The MetaSUB Consortium studies genetic interactions and community compositions of mass-transit biomes through DNA sequencing and computational modeling. It is well-known that environmental microbial communities differ in composition and functions across different geographical locations, possibly due to variations in climate, rainfall, altitude, soil, as well as the metabolic properties of their host or energy sources available in the environment. The MetaSUB Consortium is aiming at improving city utilization and planning through the detection, measurement, and design of metagenomics within urban environments [The advance of Next Generation Sequencing (NGS) technologies has made it possible to generate metagenomic sequencing data from microbiome samples at reasonable costs. The vast amount of sequencing data and the modern machine learning tools for data analytics are driving the advances in microbial ecology. Of the world’s 7.4 billion people, more than half (57%) live in urban areas , which is a more unusual setting for machine learning. In such a case, we first use the prediction model from the first scenario to calculate the probabilities of the samples from cities in the training set (sampled city). The predicted probabilities were then used as anchor points to interpolate the probabilities of the sample from unsampled cities based on the continuity assumption on the geographic distribution of microbial samples on the map.p, small n” problem with a few data points and many features. Therefore, feature extraction is a critical step to avoid the potential overfitting problem that deteriorates the location prediction performance on testing data. It is intuitive to use bacteria abundance profiles extracted from the metagenomic data for geolocation prediction. Many metagenomic profiling tools can be used for this purpose, and some of these tools are reviewed and evaluated in [k-NN [Previous works on geolocation prediction of metagenomic samples in the first problem can be found in –5. Geolouated in . Furtheruated in , differeuated in , sequencuated in , functiouated in , t-distrin [k-NN , random in [k-NN , logistiin [k-NN , XGBoostin [k-NN , multi-lin [k-NN , etc.http://camda2018.camda.info/) were used to evaluate the similarity of microbial samples between cities using bacteria abundance as features. Results show the tendency that samples cluster on the feature space based on their geographic locations, but outliers exist. The results suggest that geographical distance is an important factor, and it may be possible to infer cities-of-origin of samples based on their geographical distances to the cities in the training set. However, the geographical distance may not accurately reflect the biological distance between samples from different cities by itself. Hence, further modifications are necessary to mitigate the gap between geographical and biological distances for more reliable prediction results.To determine the geolocation in the second scenario is more challenging as machine learning algorithms can only assign the probabilities of a testing sample to the cities in the training set. Relatively fewer works have been done in this scenario. In , testinghttp://camda2019.camda.info/) provided by CAMDA in partnership with MetaSUB International Consortium. The training dataset composes of 305 samples from 16 cities  with unbalanced distribution, and the testing dataset contains 61 mystery samples from unsampled cities that do not occur in the training dataset. These datasets provide a unique resource for the study of biodiversity within and across geographic locations for metagenomic forensics. All data were Illumina-sequenced at variable depth values and were provided in compressed FASTQ format with DSRC tool [We downloaded data from the CAMDA 2019 Metagenomics Forensic Challenge (SRC tool . Detail https://github.com/OpenGene/fastp) [First of all, the raw metagenomic sequencing data were preprocessed by fastp   for qualhttps://github.com/lh3/bwa.git) [https://github.com/samtools/samtools.git) [After quality control, the reads are filtered against human reference genome to remove possible contamination. First, the reads are aligned to human reference genome hg19  using bwbwa.git)  with optols.git)  with opthttp://huttenhower.sph.harvard.edu/metaphlan) [https://ccb.jhu.edu/software/kraken2/) [For each sample, we estimated its clade-abundance using MetaPhlAn2   and Krakraken2/) . Each taNote that the number of samples per city varies between 10 and 26  to perform RFE [Secondly, we applied Recursive Feature Elimination (RFE) to select a subset of features that provides good differentiation power for target identification from the original abundance profile. RFE selects features by repeatedly constructing prediction models with decreasing number of features, and then choose the best model based on the prediction performance on the validation set. In this work, we used the REF function provided by the scikit-learn package of Python with step size 5  is given by:In some cases, people may be interested to check the probability of a testing sample from a city not sampled before if they know a priori that the city-of-origin of that sample is not included in the training set. The multi-class classifier only gives the probabilities of a testing sample from sampled cities. To derive its probabilities from unsampled cities, we propose to interpolate from predicted probabilities on sampled cities under a continuity assumption on the distribution of abundance profile on the map. For this purpose, we used Kriging interpolation  originatpi denotes the predicted probability that the sample is from city i, and λo,i denotes the weight for city i calculated w.r.t. target coordinate . λo,i can be obtained by solving the following contrained optimzation problemwhere subject toE(·) denotes statistical expectation. The detailed mathematical derivation of the solutions can be found in [http://pykriging.com) in our implementation.Here, found in , and we By using Kriging interpolation to determine the probabilities of testing samples from unsampled cities, we assumed that the spatial distribution of abundance profile from different cities follows Tobler’s First Law of Geography, i.e., “everything is related to everything else, but near things are more related than distant things” . Howeverc as the biological coordinates of city c, i.e.,The biological coordinate system was derived as follows. First, we performed PCA on the abundance profiles of all samples from the training set, which gave us a 2D vector n is the total number of samples from city c in the training set, and where Finally, based on the biological coordinates of all sampled cities, the biological coordinates of unsampled cities can be derived by applying affine transformation  between x,y) and m’s and t’s are the affine transform parameters obtained from the anchor points based on least squares fit [Here, (ares fit . Krigingmentclass2pt{minimData preprocessing is applied to all training samples as described in “We observed that classification performance increases with an increasing number of selected features from RFE Fig. . HoweverSalmonella enterica subsp. enterica serovar Choleraesuis, Staphylococcus capitis subsp. capitis, Fusobacterium hwasookii, Bacillus flexus and Streptococcus salivarius. Other prevalent bacterial clades are involved in carbon and nitrogen of metabolism, including Lactobacillus delbrueckii subsp. bulgaricus, Rubrobacter xylanophilus DSM 9941, Cellulomonas flavigena and Comamonas aquatica.We performed unsupervised clustering of samples in the data based on the top 50 Kraken2 features derived using RFE. In general, samples from one city cluster into one distinct group, and different cities like Sacramento, Offa, Tokyo, Stockholm, New York, form separate groups with distinct species patterns Fig. . In addiWe also observed that when the same set of features were used, cities can be separated better with the binning operation. We visualized all the training samples based on the selected features using t-SNE Fig. , which sk-fold stratified shuffle split for cross validation in model training with k=100. We used 70% of the training data for training and the remaining 30% for validation with random split. In each random split, the top 50 Kraken2 features derived from the training data were used as the prediction features. The average accuracy on validation sets reaches 86% by Logistic Regression, 81% by Random Forest [k-NN. Based on the performance, the final model was built using Logistic Regression classifier.We applied several widely used multi-class classification algorithms on the binned features to predict the probabilities of metagenomic samples belonging to cities from the training data. For quantitative evaluation, we performed m Forest , 83% by To calculate the probability of a mystery sample from a city not sampled before, we performed Kriging interpolation based on its predicted probabilities on sampled cities. As the underlying principle behind Kriging interpolation assumes certain geographic proximity among observed values, we used only sampled cities from Europe for interpolation as other cities are scattering around the world. Similarly, we used only mystery samples from European cities in our evaluation. Since there are only 6 European cities in the training set which is far from sufficient for interpolation, we combined with all samples from the European cities in the provided testing dataset, resulting in 10 European cities in total. Then, for each city, we interpolated the probabilities of testing samples from this city based on their predicted probabilities on the other 9 European cities. Note that in predicting the probability of a given testing sample, only data from other cities were used in training the multi-class classifier and performing Kriging interpolation with affine transform. Therefore, there was no information leaking on the ground truth geolocation when individual samples were tested in our validation.We observed that our geolocation prediction framework successfully assigned a certain number of samples to their cities-of-origin with high probabilities (illustrated in Fig. SupplementaryThe results also show that the algorithm performs poorly on some testing samples, which could be possibly due to the limited number of cities from the training set compared to the size of the geographic coverage of those cities, as well as the small number of available training samples for each city, which may not cover all potential microbial from the city. To further illustrate this limitation, we showed the predicted and the interpolated probabilities of four different samples from Stockholm in Fig. In this work, we developed a framework using abundance profiles to predict the geolocation of metagenomic samples. The proposed method provides accurate predictions of geolocation of microbial samples using selected abundance profiles as features when the ground truth cities are sampled in the training dataset. For more challenging tasks where the ground truth cities are not sampled, the framework interpolates the probabilities of the testing samples from different cities based on their predicted probabilities on sampled cities. Note that although the interpolated probabilities may not be decisive as other surrounding cities may have the same or even higher probabilities than the city-of-origin, the derived probabilities are still useful for forensic applications where evidence from other sources could be combined to identify the true location under Bayesian framework. The effectiveness of the proposed framework was validated using the CAMDA 2019 dataset, while only partially due to limited available training data. It is envisioned that the performance of the proposed framework could be further validated once larger databases with more microbial samples and denser distribution of sampling cities become available in the future.Additional file 1 Supplementary material for “A Machine Learning Framework to Determine Geolocations from Metagenomic Profiling”.


Small ruminant lentiviruses (SRLVs) are endemic in most areas of Europe, causing a chronic infection and a multisystemic disease affecting the udder, carpal joints, lungs, and central nervous system. Due to the lack of treatments and protective vaccination strategies, infection control is focused on the identification of infected animals through serological or molecular techniques. However, antigenic and genetic heterogeneity of SRLVs represent a clear drawback for diagnosis. Infected animals may present lower animal production parameters such as birth weight or milk production and quality, depending on productive systems considered and, likely, to the diagnostic method applied. In this study, four sheep flocks dedicated to dairy or meat production were evaluated using three different ELISA and two PCR strategies to classify animal population according to SRLV infection status. Productive parameters were recorded along one whole lactation or reproductive period and compared between positive and negative animals. SRLV was present in 19% of the total population, being unequally distributed in the different flocks. Less than half of the infected animals were detected by a single diagnostic method, highlighting the importance of combining different diagnostic techniques. Statistical analysis employing animal classification using all the diagnostic methods associated lambing size, lamb weight at birth, and daily weight gain with SRLV infection status in meat flocks. Milk production, somatic cell count, fat, and protein content in the milk were associated with SRLV infection in dairy flocks, to a greater extent in the flock showing higher seroprevalence. A multi-platform SRLV diagnostic strategy was useful for ensuring correct animal classification, thus validating downstream studies investigating production traits. Small ruminant lentiviruses (SRLVs) cause chronic infection in sheep and goats that results in the development of a multisystemic disease that may affect animal production depending on a myriad of factors including breed susceptibility –3, virulAntibody production in response to infection can be detected by serological tests, while integrated provirus in circulating monocytes can be targeted by primers in PCR strategies. Despite the important contribution of ELISA approaches in control programs established so far , a descrQuantification of productive losses due to SRLV infection remains controversial, while some studies claim for a role of SRLV infection in decreasing quantity and quality of animal productions in both dairy and meat farms –14, otheFurthermore, diagnostics sensitivity may have significantly influenced studies aiming at evaluating production losses derived from SRLV infection. After initial assessment of antibody production for the detection of infected animals by agar gel immunodiffusion (AGID), later studies have demonstrated that ELISA methods are more sensitive . Beyond In this study, four sheep flocks belonging to two different production systems (dairy and meat) and breeds (Raza Navarra and Latxa Navarra) were classified as SRLV infected or uninfected using three different commercial ELISA methods, a home-made PCR kit, and a commercial PCR kit. The different commercial ELISA methods globally detected a similar number of infected animals; however, the combination of the three methods identified a significantly greater infected population in all the flocks analyzed. Furthermore, commercial PCR was more sensitive than ELISA in some cases and clearly added value to SRLV diagnosis and animal classification. Different production parameters in meat and dairy flocks were recorded during one lactation or reproductive period and, after final classification according to the different tests, were negatively (birth weight and weaning weight) or positively [somatic cell count (SCC)] associated with SRLV infection.These results highlight the importance of using a multi-platform strategy to detect the humoral response to, as well as the presence of, different circulating strains in order to unequivocally identify infected and uninfected individuals, thereby influencing downstream studies such as production losses estimation, accreditation schemes, or control programs.A total of 1,497 animals belonging to four different flocks dedicated to dairy or meat production were used . Flocks 3+ tubes. After centrifugation, plasma samples were stored at −20°C until use in ELISA. Buffy coats were washed, erythrocytes lysed, resuspended in PBS, and stored at −20°C until DNA extraction.Whole blood was obtained by jugular puncture and placed in EDTA-KPlasma samples were tested for the presence of SRLV antibodies with three commercial ELISA kits: ELISA#1 uses an Eradikit™ SRLV screening test  that includes capsid recombinant proteins from different genotypes; ELISA#2 uses ELITEST™ MVV/CAEV  that uses recombinant a capsid protein and synthetic peptide of the TM region; and ELISA#3 uses INgezim Maedi screening™  that includes synthetic peptides from the Env protein. All tests were performed following manufacturers' instructions.Data were analyzed by considering each ELISA individually and the combinations of diagnostic kits. “Total ELISA” results were built by summarizing all positive samples to at least one of the ELISAs tested. Diagnostic efficiency was determined for each of the ELISAs in samples analyzed by the three methods, in comparison with the Total ELISA result.Genomic DNA was extracted from buffy coat samples with and E.Z.N.A. tissue/blood kit . DNA was quantified , and real-time PCRs were performed using 250 ng of DNA  using the commercial kit EXOone Maedi Visna CAEV oneMix  following manufacturer's instructions.A total of 341 samples randomly distributed across all the flocks were analyzed by Gag PCR using primers described elsewhere –30. AmplAn animal was finally considered as infected when at least one commercial ELISA test or one PCR method revealed a positive result .In meat production farms, the following factors were studied during one production period: lambing size, weaning date, birth and weaning weight, and daily weight gain (DWG).In dairy production farms, total volume of produced milk, fat, and protein content, and SCC were parameters quantified in each monthly quality control during a whole lactation.A generalized linear model (GLM) was used to assess the effect of each diagnostic strategy on each productive parameter recorded in meat flocks, taking into account the effect of the flock and the age of the mother included as covariables. Statistical results were shown as mean and standard error (SE). Production parameters were analyzed in dairy flocks by Mann Whitney's test due to significant heterogeneity in terms of SRLV prevalence, animal management, and production values between the flocks. Medians and interquartile range (IR) are shown.The kappa coefficient was calculated to assess the agreement between tests using the methods of Cohen–Fleiss (weighted) and Pearson–Fleiss.Statistical analyses were carried out with IBM SPSS Statistics 19.0 for Windows, and alpha error was set at 0.050.Serological diagnosis strongly depended on the flock and the test used. While Flocks A, B, and C presented low seroprevalence rates of up to 10%, Flock D showed a moderate rate of seropositive animals of around 30%.Seroprevalence values in meat flocks ranged from 2.5 to 7.5% depending on the ELISA used, showing an average value of 5% with similar ELISA efficiencies .However, seroprevalence in dairy flocks differed not only depending on the ELISA used but also depending on the flock. While Flock C showed low seroprevalence values, up to 10% (range 1.9–10%), seropositive animals in Flock D ranged from 24 to 34.1% depending on the test used. Average values considering both flocks ranged from 14.9 to 22%. ELISA efficiencies differed between flocks and ELISA tests, ranging from 10 to 84% .Overall, ELISA#1 showed better results in terms of reactivity, taking into account the whole population, followed by ELISA#3 and ELISA#2. However, when considering data within flocks, ELISA#1 detected a higher number of animals in Flocks A and C, whereas ELISA#2 and ELISA#3 were more accurate in detecting infected animals in Flocks D and B, respectively. Considering samples positive to any of the kits tested , a single ELISA only detected from 19 to 57% of the seropositive animals in Flock A and from 22 to 54% in Flock B; and the range in Flock C was from 10 to 53%. Single ELISA reactivity in Flock D ranged from 61 to 84.1% in comparison with Total ELISA results .Indeed, agreement between serological tests calculated in samples analyzed with the three methods was very good between ELISA#1 and ELISA#2 only in Flock D. Values in Flocks A and C were not calculated since no samples were found to be simultaneously positive to more than one ELISA. In addition, association was minimal or low in Flock B (data not shown).PCR analysis included a commercial kit that employs real-time PCR to detect and quantify different genotypes of SRLV and a home-made PCR based on previous publications. Commercial PCR detected more positive animals than did the classical home-made procedure, and both detected a total percentage of positive animals of 0, 36.8, 3.3, and 38.5% in Flocks A, B, C, and D, respectively. These values were higher than those obtained by serological methods in all flocks, except for Flock A in which the number of samples analyzed by PCR was significantly lower. In addition, an average of 10% of the seronegative animals rendered PCR-positive reactions. Consistently, PCR and ELISA agreement was minimal .Sequence characterization of the Gag region in Flocks B, C, and D showed a mixed virus population between genotypes A and B2, the latter being prevalent, with high similarity in respect to previous deposited sequences. Gag immunodominant region alignment revealed no significant changes compared to sequences described (data not shown).Lambing size, birth body weight, weaning date, and weight gain per day were affected depending on animal classification using single or combined ELISA results as well as PCRs. Lambing size was not affected when animal classification was conducted with single ELISAs; however, a combination of the three ELISAs and Total ELISA data revealed a significant positive association. A combination of PCRs also associated SRLV infection status with higher lambing size. However, a combination of serological and molecular tests associated SRLV infection with lower lambing size .Birth weight was also associated when animals were classified according to ELISA combinations but not when considering the Total Infected result. SRLV infection influenced weaning date when considering a combination of the serological tests or the Total Infected data. Finally, gain per day was not associated when animals were classified according to single considered methods; however, the Total Infected result showed significant association with lower DWG values .Total ELISA-positive animals were further classified into PCR negative or positive, and production losses were evaluated . Lower bp < 0.01). Milk yield was reduced by 6% in infected animals classified by single or combined ELISA results, as well as by commercial PCR (p < 0.05). Fat and protein contents were also related to SRLV serodiagnosis, while fat percentage was higher in milk from infected animals, protein content was reduced  intended for meat production, and one milk flock of the Latxa Navarra breed (Flock C) presented low seroprevalence values. However, the remaining milk flock from the Latxa Navarra breed presented a moderate seroprevalence of around 30% excluding breed or production system influence.Antigenic heterogeneity of SRLVs seems to be at the basis for this relatively low individual sensitivity. Consistently, the antigenic preparations included in the different ELISAs used may account for this different performance, since antigenic spectrum of the circulating strains in the studied population is also a key point to consider . PrelimiIn spite of including antigenic preparations from different genotypes, ELISA failed at detecting a proportion of infected animals that were evidenced by molecular techniques. Infection by divergent SRLV strains in these animals is unlikely since primers used in PCR were designed based on known genotypes. Instead, low or fluctuating antibody titers may account for this discrepancy between serological and molecular techniques , 37. IndThe use of more than one diagnostic technique allowed the evaluation of different animal classifications according to single ELISA, combined Total ELISA results, and the combination of these with PCRs, resulting in a Total Infected classification. Proper classification enabled determination of the real effect SRLV infection had on production traits. Of note, the use of a single ELISA may represent the detection of roughly the 61–84% of the seropositive animals as shown in Flock D of moderate seroprevalence. Furthermore, in low-seroprevalence flocks, a single ELISA may only detect 10% of the seropositive animals. ELISA performance differed among flocks, while results from Flocks A, B and C showed low seroprevalence and ELISA efficiency; in Flock D, seroprevalence and efficiency reached 32 and 84%, respectively. Breed, age, production system, nutrition, or animal management cannot be argued as important factors, since Flock C shared these features. Instead, circulation of a more prototypic SRLV in Flock D could explain this better performance.in utero transmission  was evidenced by PCR. Thus, PCR analysis has improved the results presented here due to detection of incipient infections that may mask SRLV influence. Chronic infections and especially SRLV show a long asymptomatic period in which ewe's body condition may inadvertently diminish, likely determining a reduced nutrient transfer to the fetus , 44. SusIn dairy flocks, the application of single ELISA already identified higher SCC and fat content as well as lower milk yield and protein in milk from SRLV-seropositive sheep. A combination of ELISAs and PCR further confirmed this finding. Total Infected animals showed lower milk production (up to 3%) and elevated SCCs (60% increment). Augmented SCC has been already linked to SRLV infection due to epithelial cell desquamation derived from microscopic alterations in the mammary gland , 49 and Among ELISA-positive animals, the PCR-negative population showed lower production losses as compared to PCR-positive animals in meat and dairy flocks. Higher viral load implies higher PCR sensitivity as well as increased disease severity –54. ThesInterestingly, lower weight at weaning presented by lambs from seropositive ewes in meat farms could be explained by the lower milk production observed in infected sheep from dairy flocks. However, milk production parameters were not evaluated in meat flocks.Exhaustive estimation of production losses derived from infections, especially those chronic, should be evaluated after proper infection status evaluation. The multi-platform strategy applied here to classify more than 1,000 animals into SRLV infected vs. uninfected enabled the analysis of different production parameters in meat and milk-oriented semi-extensive production systems. Proper diagnosis was achieved when three different ELISA methods and two different PCRs were used. Both meat and dairy flocks showed diminished production parameters in infected animals, mainly affecting birth and weaning weights as well as milk production together with an increased number of somatic cells counts. These results highlight the crucial importance of proper SRLV infection status determination in sheep production studies and help to clarify previous colliding results obtained by other authors.All datasets generated for this study are included in the article/supplementary material.Ethical review and approval was not required for the animal study because Samples were obtained for institutional control campaings and later used in this study. Written informed consent was obtained from the owners for the participation of their animals in this study.IE, RR, and LD were involved in processing of the samples and DNA extraction. IE, RD, and LD performed ELISA and PCR. AB, IE, and RD contributed to real-time PCR analysis. IE, RD, and ID performed statistical analysis of data. IE, RD, and RR drafted the manuscript. ID, DD, LL, IG, and RR revised the manuscript and wrote the final version. DD, LL, and RR obtained funds and coordinated and supervised the study. All co-authors approved the final version of the manuscript.AB is Head of Molecular and Cell Biology Department in Exopol. The remaining 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.


Gekko swinhonis) populations in its endemic region, 60 individuals were sampled from Lushan, Qi, and Linying counties in Henan Province, China. Through PCR amplification and Sanger sequencing, 120 sequences with lengths of 652 bp (COI) and 739 bp (Cyt b) were obtained, and nine haplotypes were detected for each gene. Overall, results indicated that Peking gecko populations in China have high genetic diversity and significant genetic differentiation. This study provides necessary scientific basis for the protection of Peking gecko germplasm resources.To understand the genetic diversity of Peking gecko ( Gekko swinhonis) is a reptile within the family Gekkonidae. It feeds mainly on small insects and lives in crevices of buildings, trees, and rocks. This species is unique in China, and, in 2010, it was listed as vulnerable in the International Union for Conservation of Nature red list of endangered species. Gekko swinhonis is nocturnal, good at climbing, and, when attacked, it can break its tail to escape. It is traditionally used in medicine and to catch a variety of insects  genes are suitable for the detection of population level differences, and have been widely used to assess the genetic variation and phylogeny of several animal groups  is an ideal material for solving molecular evolution and phylogenetic issues. Compared with nuclear genes, mitochondrial genes have a simpler structure and lower molecular weight, faster evolutionary rate, and no tissue specificity. Therefore, mitochondrial genes are widely used in vertebrates’ phylogeny, biogeography, and conservation genetics studies Boore . Among mThe Peking gecko individuals were collected from Lushan County (LS), Qi County (QX), and Linying County (LY), all in Henan Province, China, from August to September 2018 . The taib  genes were designed and synthesized by Nanjing Tsingke Biotechnology Co., Ltd. The two PCR reactions were performed in a total volume of 30 μL, including 15 μL 2 × PCR Premix TaqTM, 0.9 μL upstream and downstream primers, 4 μL DNA template, and 9.2 μL double-distilled water. The optimized PCR reaction profile was as follows: pre-denaturation at 95 °C for 5 min; 35 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 1.5 min; further extension at 72 °C for 10 min. The amplicons were then detected in 1.5% agarose gels, and those of the correct size were excised and sent to Nanjing Tsingke Biotechnology Co., Ltd. for purification and sequencing.Muscle samples were rinsed in distilled water, dried, and shredded. DNAiso Reagent  was used to extract DNA, which was eluted with 50 μL EB buffer and stored at –20 °C. Specific primers for COI  and Cyt Fst and the analysis of molecular variance (AMOVA) were both calculated in Arlequin 3.5  were analyzed and compared. The average percentages of T, C, A, and G nucleotides were 32.7%, 24.8%, 24.8%, and 17.7%, respectively, in COI sequences and 29.1%, 27.7%, 28.5%, and 14.6%, respectively, in Cyt b sequences. In both gene sequences, the content of A + T was higher than that of G + C.Using SeqMan, 120 sequences with 652 bp (COI) and 739 bp  in the COI sequences of the three populations, and these included three single-variable sites and 102 parsimony-informative sites. The COI sequences obtained encoded 217 amino acids and defined nine haplotypes. There were 140 variable sites  in the Cyt b, respectively. Overall, LS and LY were the most genetically distant populations. The AMOVA results are displayed in b genes was 0.83355 and 0.74915, respectively, with p = 0.00. The results of the Fst test showed that there were significant differences among the three populations (p < 0.001). The inheritance among populations accounted for 83.35% and 74.91% of the total genetic variation, suggesting that most of the population variation in Peking gecko was due to genetic variation among populations.The average genetic distance among the three populations was analyzed based on the two gene sequences. The average genetic distances between LS and QX, LS and LY, and QX and LY were 0.127, 0.135, and 0.135 for COI, and 0.145, 0.143, and 0.035 for Cyt Previous studies have shown that Peking geckos are morphologically different among areas, but morphological data alone cannot fully reflect the phylogenetic relationships between groups. Combining morphological and molecular data can better explain biological evolution and potential related factors. In the absence of background data pairs, multiple sets of sequence data of different gene regions are used to obtain more objective information.b genes contained the best phylogenetic information. The evolutionary rate of COI is relatively slow, which is suitable for the analysis of closely related taxa, and the evolutionary rate of Cyt b is moderate, which is suitable for the analysis of intraspecific to interspecific genetic information. Therefore, at present many scholars combine COI and Cyt b information to analyze genetic differences within and between species. Here, 652-bp COI and 739-bp Cyt b gene sequences were obtained for three Peking gecko populations. The content of A + T was higher than that of G + C, in agreement with the heterogeneity of the distribution of the four nucleotides in the mitochondrial genome (Lian-Wei et al. The number of individuals sampled from each geographic location in the present study was larger than that sampled by Yan et al. , but fewFolmer et al.  considerb genes as analytical tools, the genetic diversity and differentiation of Peking gecko individuals from three geographical locations were studied. The results showed that there is significant genetic differentiation among the three populations, and that their genetic diversity is high. Due to the sampling constraints, this study does not fully reflect the population differentiation and genetic diversity of Pecking gecko in China. However, results indicate that human factors have had little influence on the diversity of this species. The present results also provide scientific basis for the protection of Peking gecko germplasm resources and basic information for further research on the evolution of this species.In summary, using mitochondrial COI and Cyt


Invasive species are increasingly affecting agriculture, food, fisheries, and forestry resources throughout the world. As a result of global trade, invasive species are often introduced into new environments where they become established and cause harm to human health, agriculture, and the environment. Prevention of new introductions is a high priority for addressing the harm caused by invasive species, but unfortunately efforts to prevent new introductions do not address the economic harm that is presently manifested where invasive species have already become established. Genetic biocontrol can be defined as the release of organisms with genetic methods designed to disrupt the reproduction of invasive populations. While these methods offer the potential to control or even eradicate invasive species, there is a need to ensure that genetic biocontrol methods can be deployed in a way that minimizes potential harm to the environment. This review provides an overview of the state of genetic biocontrol, focusing on several approaches that were the subject of presentations at the Genetic Biocontrol for Invasive Species Workshop in Tarragona, Spain, March 31st, 2019, a workshop sponsored by the OECD’s Co-operative Research Program on Biological Resource Management for Sustainable Agricultural Systems. The review considers four different approaches to genetic biocontrol for invasive species; sterile-release, YY Males, Trojan Female Technique, and gene drive. The different approaches will be compared with respect to the efficiency each affords as a genetic biocontrol tool, the practical utility and cost/benefits associated with implementation of the approach, and the regulatory considerations that will need to be addressed for each. The opinions expressed and arguments employed in this publication are the sole responsibility of the authors and do not necessarily reflect those of the OECD or of the governments of its Member countries. As global trade increases the transfer of goods and commodities around the world, it leads to the movement of species from their native ranges to new locations where they become invasive. According to United States Executive Order 13112 of February 3, 1999 (Invasive Species), invasive species are defined as “an alien species whose introduction does or is likely to cause economic or environmental harm or harm to human health” . Whether or not a species becomes invasive depends on the characteristics of both its life history traits and the environment in which it is introduced. Not all new species introductions become invasive, however, species that become invasive in a new environment can have profound effects on industry, agriculture, and conservation lands within the new location where they become established .Eradication of invasive species is often impractical once they have become established . In someWolbachia) constitute genetic biocontrol techniques that would not be considered genetic engineering.Genetic biocontrol provides opportunities for the control and potential eradication of invasive species. The term “genetic biocontrol” refers to techniques that alter the genetic material of an organism to control invasive species in the environment. Some, but not all, of these techniques involve knowledge or manipulation of the genome. This includes utilization of naturally occurring genotypes; parasitic microbes that distort sex ratios; the use of traditional methods such as irradiation, hormonal sex reversal to generate sterile, or sexually incompatible genotypes; and the use of modern genetic engineering technologies. Because genetic biocontrol describes a wide variety of methods that take advantage of species biology in order to achieve control, it is important to note that genetic biocontrol is not a synonym for the use of genetically engineered organisms. Existing technologies that use naturally occurring genetic alleles, irradiated organisms, chromosomal segregation techniques, or endoparasitic bacteria  for invasive species control, examining the attributes of four approaches; sterile-release, YY Males, Trojan Female Technique, and gene drive. The review compares techniques regarding the mechanism of control used in each method, and the regulatory hurdles that must be overcome for the genetic biocontrol methodology to be put into practice. Because gene drive is a fundamentally new approach to invasive species control that presents new possibilities for efficient suppression of invasive species populations (and may therefore pose new risk as well), research efforts to test containment for gene drives  are also reviewed. Lastly, the paper presents a review of classical biocontrol to provide context around ongoing practices for invasive species management and to highlight existing regulatory frameworks involving the release of a new organism into the environment that are likely to be relevant to the use of newer genetic biocontrol methods.Cochliomyia hominivorax (Coquerel) as a means of producing sterile individuals that could be generated in large numbers and distributed within a target population in order to suppress reproduction. The technique, known as Sterile Insect Technique (SIT) successfully eradicated screw worms from the southeastern United States (Cydia pomonella (L.)) (Pectinophora gossypiella (Saunders)  (Aedes aegypti (L.) (Glossina spp.) ) ], the piaunders) ], and th ]. Prelimpti (L.) ] and TseInsects are irradiated for SIT using a dose of gamma radiation sufficient to cause chromosomal breaks within the germ line. For some insect species, SIT does not substantially alter mating competitiveness. However, there are limitations to this technique in that some species have incomplete sterilization, causing a serious decrease in competitiveness and an insufficient sterile population, amongst other issues . If it iAlthough SIT has been a successful approach to control some insect pests, there are disadvantages associated with its use. In order to be successful in suppressing a target population, an overwhelming number of irradiated organisms must be released into the environment, thus temporarily increasing the potential impacts caused by the pests. To produce the required numbers of sterile insects, a dedicated facility to rear and irradiate organisms for release is required, which can be costly to construct and operate. Some species are not suitable for sterile release because of limitations in rearing, thus the technique may be unsuitable for control of some species. The effects of radiation on insect mating competitiveness can also be a disadvantage, requiring that even greater numbers of irradiated insects are released in order to compensate for reduced mating efficiency.As an alternative to SIT, genetic engineering has also been used to produce sterile insects for release to suppress a target population . This teLithobates catesbeianus (Shaw)] in Europe are not amenable to mass sterilization by radiation, therefore require an alternative strategy to produce sterile individuals for sterile-release. As demonstrated in a recent pilot study, induced triploidy can reliably produce sterile bullfrogs in sufficient numbers to eradicate a small target population under containment conditions  or the Trojan Female Technique  may offeAlthough the method of sterile-release may offer some utility for reducing or eradicating small populations of invasive species, more efficient genetic biocontrol methods are currently being explored that do not require the production of large numbers of individuals for release into the environment, and are not limited with respect to the size of the target population that can be targeted. Three of those approaches; YY Males, Trojan Female Technique, and gene drive; are considered in the following sections.Oreochromis niloticus (L.) population , by the Idaho Department of Fish and Game (IDFG) in November 2008  mutations that cause reductions to male, but not female fertility .The TFT concept is enabled because of an evolutionary loophole that is common to most eukaryotic life. mtDNA is overwhelmingly inherited maternally thus mtDNA mutations that affect only males will not be subject to natural selection . Theory A variety of naturally occurring mtDNA mutations that reduce male, but not female, fertility have now been identified in fruit fly , mouse , and EurModeling studies suggest that the TFT has the potential to achieve pest control under a wide range of conditions . Single Drosophila melanogaster) supports the view that the TFT can reduce populations . Both strategies require that a steady influx of Trojan individuals are added to a target population to cause eradication over time. However, by ceasing the addition of Trojan individuals, eradication efforts can be terminated. Having a means of terminating an eradication program is a feature that is important to natural resource managers as it reduces the risk of making mistakes that permanently change the population. Neither method involves the introduction of transgenic organisms or GMO’s into the environment , which wMus musculus L.), the recent advent of CRISPR/Cas9 gene editing technology has enabled generation of synthetic gene drives that in theory could be adapted for use in any sexually reproducing species . Importantly, this cassette is inserted into a chromosome at the genomic site that is cut by the endonuclease. Once the cassette has been integrated, the chromosome becomes immune to cleavage. Thus, a cell that is heterozygous for a gene drive cassette contains one allele that is susceptible to digestion by the endonuclease [the wild type (WT) allele] and one allele that is not . Expression of the gene drive endonuclease in a heterozygous cell will generate a double stranded break in the WT allele . Repair In silico modeling demonstrated that a Y-CHOPE gene drive can eradicate a pest vertebrate population. However, simulations indicate that, for polygynous species such as mice, Y-shredding efficiencies must be greater than ∼90% to produce high probabilities of eradication success  strategy . This ap success . Y-CHOPEIn silico modeling indicates that gene drives targeting female fertility genes and embryonic viability genes may be useful strategies for invasive mouse population suppression, and that a novel Y-shedding gene drive strategy has eradication potential , possibly affecting non-target populations or species. For species which are not “global targets” , appropriate measures should thus be taken to reduce this risk, if these technologies are to be trialed in the field .Transgene escape can occur in one of two ways. The first of these occurs at the spatial level in which the gene drive could move into a non-target population of the target species, for example from an area where the species is an invasive pest, back into the native range of that species. This is termed “intra-specific transgene escape” . SecondlRegardless of their mechanism, all gene drive systems are vertically transmitted technologies requiring gene flow to spread. Therefore, the level of gene flow between target and non-target populations a critical parameter in determining whether a gene drive transgene will escape a particular target population. Intra-specific transgene escape must therefore involve migration of individuals from the release area to non-target populations and subsequent introgression of their genomes into those populations. If sufficiently isolated from non-target populations, islands  provide high levels of ecological gene drive containment. The degree of containment offered will generally grow with increasing geographic distance between two populations, increasing ecological inhospitality of the intervening area and decreasing size of the population at the release site. In summary, small target populations surrounded by large distances of inhospitable terrain (including ocean if the target is non-marine) are the least likely to escape. Of course, what is deemed to be a sufficiently small population, or a great enough distance will be highly dependent on the ecological characteristics of the target species such as its reproductive rate and dispersal abilities. Additionally, possible human-mediated dispersal must be considered and should be minimized by careful trial site selection, biocontainment, and biosecurity.Wolbachia-based gene drives spreading through Aedes aegypti mosquito populations in Australia  to that particular target population, if a sequence can be identified which is fixed in the target population but not in non-target populations. This “genetic gene drives containment” remains robust even if the targetable sequence is present in non-target populations .Another potential route for transgene escape is through hybridization between the target species and a closely related species at the release site, followed by introgression of gene drive alleles by hybrids back into the non-target species. As with intra-specific transgene escape, there are several factors which can aid in reducing inter-specific transgene escape at island site locations. First, for hybridization to occur, there must be a closely related species (most likely a congenic species) at the release site. The risk of inter-specific transgene escape can thus be drastically reduced by choosing locations which are devoid of species congenic with the target. In some cases, the nature of hybridization events and the mechanics of the drive may also preclude transgenic hybrids being formed, even if congeners are present. For example, if hybridization is unidirectional with regards to sex and does not involve target species males, then drive systems which convert genotypic females to phenotypic males (such as Y-drive)  will be Secondly, there are factors which can act cumulatively to limit or prevent the risk of a drive spreading even if it does enter a non-target species. For example, if hybridization events do occur, they must result in fertile and competitive individuals that are able to introgress the drive into the non-target species. If hybrids are competitive, but hybridization events are rare, it is possible that stochastic loss of the drive will occur prior to the hybrid individuals being able to introgress the transgene into the non-target species. Similarly, for drive designs which require a minimum population frequency in order to spread,  the rateGene drive system development is underway in several pest vertebrates. If promising technologies are to be trialed in the open field, there are advantages to conducting these trials on islands – whether oceanic or purely ecological. Small, ecologically simple, geographically isolated and genetically distinct island locations can aid in reducing the risk of unintended movement of the drive system into a non-target population. Islands offer the additional advantage of potential genetic containment through the exploitation of locally-fixed alleles.In order to reduce the uncertainties associated with the spread of gene drives, modifications to gene drives have been suggested that eliminate their capacity to propagate in a self-sustaining fashion, and instead allow them to persist for only a limited duration or in a limited area such as suggested by the LFA approach . Daisy-cInvasive mice on islands, which cause ecological destruction to sensitive island ecosystems , are a gMathematical models can be used to assess the effectiveness of a gene drive associated with an island population of locally-fixed alleles. Further, they can be used to test the spread of a drive in a continental population where the islands locally-fixed alleles may occur in low frequency. For gene drives to be effective, the target allele must be fixed in the island population meaning they are found at 100% frequency on the island. It is important to test this event as the possibility exists of a gene drive mouse getting transported to the continent as humans have long managed to transport commensal rodents unintentionally. Mathematical models demonstrate that, for a gene drive associated with an island locally-fixed allele, escape of a drive-bearing individual to from an island that was introduced into a continental population with that allele in low frequency would cause only a temporary decline in the continental population with a subsequent rebound in population numbers . This suMus populations, N = 3) of M. musculus helgolandicus, a subspecies found only on the German island of Heligoland, where it evolved from anthropogenic introductions of M. m. domesticus (M. m. domesticus (N = 8) collected near Cologne-Bonn, Germany (1 we first estimated genome-wide diversity as expected single-nucleotide polymorphism heterozygosity (SNP-He)  compared to continental mice (SNP-He = 0.315) in this new work. To assess population genetic differentiation, we calculated the fixation index (FST) using the poolfstat R package (FST = 0.136) despite a relative short time (ca. 400 years) since island colonization and likely in the presence of persistent gene flow   based on package . Estimatene flow , which bene flow . More imene flow . Our anaene flow . Thus, t effects .3, improving the power and speed of the original pool-seq pipeline  may provide greater precision in allele frequency estimates compared to deep sequencing of a small number of individuals . To enabpipeline . With acBoth genetic biocontrol and classical biocontrol involve the introduction of a new organism into the environment. Classical biocontrol is the release of a natural enemy to manage an introduced invasive species. Classical biocontrol agents are released with the understanding that the newly introduced organism will become permanently established in the environment. Because biocontrol effects of an introduced biocontrol organism are permanent and cannot be reversed, risk assessments for classical biocontrol agents must carefully consider risks that the new organism may pose to the environment. It is thus worthwhile to examine the risk assessment strategies used for classical biocontrol organisms and consider this risk assessment framework as a possible model for genetic biocontrol organisms.Historically, classical biocontrol has been successfully applied to manage invasive species all over the world . Host spWhen successfully implemented, classical biocontrol is an environmentally safe and cost-effective alternative to chemical pest control . New tecIn order to establish whether an organism created using genetic biocontrol would pose a risk to the environment, it is necessary to perform an environmental risk assessment, a systematic procedure for predicting the possible harm that a hazard may pose to human health or the environment. A well-established framework exists for assessing the risk associated with the introduction of genetically modified plants into the environment  as well For release of genetic biocontrol agents, the first step in risk assessment will be problem formulation, where entities of value within the environment  that could be harmed by a gene drive are identified. Potential pathways leading to harm of the protection goals by the gene drive are then hypothesized (resulting in a risk hypothesis) and experimental data is collected to either validate or disprove the risk hypothesis. If experimental data suggests that no harm is likely to occur, this would provide information to decision-makers that the environmental risk posed by the new organism is low. Alternatively, the experimental data could indicate that the risk of harm is high, leading decision-makers to prohibit release of the new organism (or seek mitigation measures to reduce the risk to an acceptable level).Wolbachia infected insects, as part of such strategies . Although physical and genetic containment of gene drive mice on islands seems feasible, it may be difficult to find appropriate test conditions for gene drives constructed in other species. Mathematical modeling may therefore be an important means of providing data to inform risk assessments for gene drives for some invasive species. It should be noted that the regulatory framework noted above for classical biocontrol may not apply to genetic biocontrol and management of invasive fish and wildlife. In particular, in the United States, with the exception of migratory waterfowl and ESA listed species, wildlife (whether native or invasive) in the United States is owned, protected and managed by each of the states . This riAlthough scientific and regulatory hurdles exist for the practical use of genetic biocontrol to control invasive species, perhaps the greatest hurdle to be overcome will be public acceptance of the technology. Gaining public trust will also be an essential component in the development of new genetic biocontrol methods (and was identified in the workshop as the major barrier to implementation any genetic biocontrol).The prospects for the development of genetic biocontrol to control invasive species will likely hinge on public perception of whether the use of such new technologies is sufficiently warranted to solve the problems being addressed. In a recent review of genetic control methods for invasive species, YY Male (TYC) was identified as the method least likely to generate public controversy  though TWhether the general public would consider the eradication of invasive species a problem that warrants the use of genetic engineering is yet undetermined. The review by A landscape analysis on the use of gene drives in mice showed that the research community was concerned that gene drives would only receive public support if they could effectively eradicate the species, with a general concern that if implementation of gene drive were only partially successful, the public support for the strategy and even research on gene drive would be greatly diminished . In a laThis public perception is complicated by the fact that the science involved in invasive species eradication is often complex and is not currently well understood by or communicated to the general public. In a survey of the public on gene drive in agriculture, 85% of respondents were not aware of the existence of gene drive technology prior to receiving the survey . Public Eradication of invasive species continues to be a challenge in a variety of ecosystems, ranging from heavily managed agricultural environments to wilderness areas. Tools for effective control are often inefficient and costly, making eradication of invasive species impractical once they have become established. While the potential exists to manage a range of pest with genetic biocontrol, hurdles remain with the implementation of these techniques. For some genetic biocontrol methods, one hurdle is the production of the large numbers of organisms that must be released into the environment. Gene drive, a genetic biocontrol method that is increasingly the focus of public attention, has the potential to spread without the need for sustained human intervention. This theoretically reduces the requirement for mass rearing and release of large numbers of organisms and would allow genetic biocontrol efforts to be applied to remote areas that are difficult to access or lack the infrastructure to support mass rearing efforts. Gene drive could also potentially be used to target invasive species that have become established over very large geographical areas  . AlthougMany genetic biocontrol methods, such as YY Males, require a steady influx of individuals with the biocontrol trait into a target population to cause eradication over time. While costly in terms of mass rearing requirements, these mechanisms offer the advantage of allowing for termination of releases if undesirable effects are observed. This can be very attractive to resource managers and to the general public. In contrast, at least some gene drive methods are intended to be self-sustaining. Consequently, the primary concern associated with the use of gene drive as a genetic biocontrol tool for invasive species is that it could spread to a non-target population causing unintended harm . BecauseSterile-release and gene drive represent two extremes within a continuum of genetic biocontrol approaches that vary with respect to efficiency vs. control afforded to natural resource managers, while YY Males and the Trojan Female Technique represent an intermediate in this continuum see . SterileTogether with certain technologies, including genetically engineered gene drive constructs or genetically engineered sterility systems genetic biocontrol options exist on a continuum and provide opportunities for the control and potential eradication of invasive species based on our knowledge of inheritance. While the techniques themselves are all unique, they fit under the broader category of genetic biocontrol, and all should be considered in the context of existing biocontrol and invasive species control programs. In considering how best to move forward with the development and deployment of genetic biocontrol methods, it is informative that public surveys on genetic biocontrol techniques reveal, perhaps unsurprisingly, that the general public simply are not educated on the techniques or even aware of them. This suggests that any program intending release of such organisms should also be associated with some form of educational campaign.We suggest that resource managers, regulators and researchers should work together to ensure that several of these methods be deployed in the future to control invasive species while minimizing the impact on non-target species and the environment.The findings and conclusions in this publication have not been formally disseminated by the U.S. Department of Agriculture and should not be construed to represent any agency determination or policy.JT, LA, SD, ME, OE, NG, TH-S, RM, KO, AP, JS, DS, PT, TS, and AR all made significant contributions to the drafting of the manuscript. KO and AP conducted original data analysis for the manuscript. JT and AR organized the workshop. LA, NG, RM, AR, DS, and PT developed and created the figures. RM and AR lead the editorial efforts to develop the manuscript.DS was employed by Fisheries Management Solutions, Inc. JS received travel support from the Outreach Network for Gene Drive Research Secretariat. The remaining 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.


Olive is one of the oldest cultivated species in the Mediterranean Basin, including Tunisia, where it has a wide diversity, with more than 200 cultivars, of both wild and feral forms. Many minor cultivars are still present in marginal areas of Tunisia, where they are maintained by farmers in small local groves, but they are poorly characterized and evaluated. In order to recover this neglected germplasm, surveys were conducted in different areas, and 31 genotypes were collected, molecularly characterized with 12 nuclear microsatellite (simple sequence repeat (SSR)) markers, and compared with 26 reference cultivars present in the Tunisian National Olive collection. The analysis revealed an overall high genetic diversity of this olive’s germplasm, but also discovered the presence of synonymies and homonymies among the commercialized varieties. The structure analysis showed the presence of different gene pools in the analyzed germplasm. In particular, the marginal germplasm from Ras Jbal and Azmour is characterized by gene pools not present in commercial (Nurseries) varieties, pointing out the very narrow genetic base of the commercialized olive material in Tunisia, and the need to broaden it to avoid the risk of genetic erosion of this species in this country. Olea europaea var. sativa Hoffm. and Lk.), with 715 million olive trees covering an area of more than 7 million hectares, is one of the most important fruit trees in all the countries overlooking the Mediterranean Sea [O. europaea subspecies, allowing the olive germplasm to be continuously diversified [Olive  the recovery of new germplasm from marginal areas; ii) the genetic identification of this germplasm, solving cases of homonyms and synonyms; iii) the definition of an allelic consensus list; iv) the improvement of knowledge about the genetic variability of Tunisian germplasm; v) the enrichment of the reference collection of Tunisian olive varieties.The SSR analysis produced a total of 124 alleles, ranging from the minimum of 4 at locus DCA15 to 19 at locus DCA16  . Values −15)  . This reThe estimation of pairwise relatedness revealed three cases of synonymy at the Lynch and Ritland estimator LRM value of 0.50 : GERBOUI1/RKHAMI; MESKI2/NIB2/BESBESSI2/BESBESSI3/UNKNOWN1; ZALMATI/CHEMLALI_SFAX2 . These iAmong the 77 genotypes, seventeen showed private alleles , with thTo make some additional observation on the different collections, an AMOVA analysis was performed, assigning 89% of the molecular variance to differences within groups and 11% among the four groups . Thus, tThe genetic relationships of the Tunisian olive cultivars and genotypes were highlighted in the principal coordinate Analysis, based on Nei’s unbiased genetic distance matrix . The firIn order to confirm the results, a cluster analysis was carried out based on Ward’s method to maximize the between-cluster variance. The obtained dendrogram is shown in Application of the Bayesian clustering model implemented in STRUCTURE software with genotyping data generated by 12 SSR markers, yielded K = 3 as the best number of subpopulations (SP) for the data . Thus, tST analysis was conducted on the three groups obtained in STRUCTURE; the results indicated great genetic differentiation between group SP1 and both SP2 (FST = 0.21) and SP3 (FST = 0.21) (An F = 0.21) . To better understand the structure of the collection, it was divided into four a priori defined groups based on the sampling area of the collections. In each group, the mean q determined by structure analysis was calculated, resulting in a different stratification of the population . In partOlive is a very important crop in Tunisia, which is the fourth biggest producer in the Mediterranean area, generating around 800,000 olive tons/year, mostly from two highly productive varieties: Chemlali and Chetoui , both foTo achieve these goals, in 2018, the Tunisian Gene Bank and other public scientific Tunisian institutions carried out a project to collect marginal olive germplasm throughout the country and from plant material commercialized by nurseries, performing the molecular fingerprint of these genotypes to characterize/identify them through comparison with the reference cultivars available at the IO collection. The evaluation of the samples was based on a panel of SSR markers used at the international level ,41. All Tamri Douiret, from the GR collection, and Sayali3, from the IO collection, with four and three private alleles, respectively. Interestingly, several private alleles were also present in the natural “Azmour” collection. This result points out a peculiarity of the marginal “Azmour” germplasm from the perspective of a search for new and beneficial alleles; this could be important for facing incoming needs . The genetic indices calculated within each single group, IO, nurseries, Ras Jbal, and Azmour, allowed additional considerations to be made about the composition of the Tunisian germplasm. Indeed, the IO reference collection appeared to be the richest in alleles, together with that of the “nurseries” collection, which includes the “foreign” cultivars such as the Italian varieties Ascolana, Bella di Cerignola, and Carolea that are commonly commercialized in Tunisia. The two reference and commercial collections displayed most of the private alleles, in particular, genotype The genetic relationships of the Tunisian olive cultivars and genotypes were highlighted in the PCo analysis, where several samples from the Ras Jbal collection formed a group far from the rest of the genotypes, underlining its genetic distance from the rest of the germplasm. In addition, several Chemlali samples  appeared well scattered and far away one from another, suggesting they are phenotypically similar but genetically different. This result points out the problem of the clonal variants in the Tunisian olive germplasm, notably for the Chemlali variety. In Tunisia, Chemlali is a generic name to indicate a genotype with a good oil production and small fruits. It is probable that this variety has many genetic variants specific to different geographical regions  that can be confused during the intense exchanges of germplasm. Structure analysis clustered most of the Chemlali variants together with other good oil producer cultivars, such as Chetoui and Zalmati, with which it is often mistaken due to the high similarity for morphological and chemical characters , confirmThe cluster analysis, coupled with the LRM analysis confirmed the presence in the Tunisian germplasm of several synonymies and misnaming cases, such as that between genotypes GERBOUI1/RKHAMI; MESKI2/NIB2/BESBESSI2/BESBESSI3/UNKNOWN1; and ZALMATI/CHEMLALI_SFAX2. These samples all originate from the region of Ras Jibal, and it is probable that these synonymies are due to erroneous appellation of a single genotype in this marginal area. Out of 10 unknown genotypes, only UNKNOWN1_RJ was identified as a BESBESSI; the other unknown genotypes were found to be similar but not perfectly matched to known varieties; thus, they are worth investigating further to see if they can be considered as new varieties.The cluster analysis indicated a strong relationship between the Tunisian reference varieties of the National Olive Institute of Sfax with those mainly marketed in Tunisia (nurseries). This evidence confirms the genetic correspondence among the commercial material and the reference varieties, including the Italian varieties widely spread in Tunisia. These results will be very useful to start the plant material certification process in Tunisia, following the procedures commonly adopted . On the Plant genetic resources will be essential to adapting crops to the effects of climate changes; their recovery and valorization are a first step towards the enhancement of the Tunisian olive genetic resources, which have proven to be rich and worthy of preservation. Our results indicate the need to better characterize the Tunisian germplasm in the different areas of the country, emphasizing the crucial need to proceed with the realization of a national varietal certification system for the Tunisian olive germplasm to guarantee the genetic authenticity for the commercial varieties. The usefulness of SSRs was once again confirmed in the genotyping of the Tunisian germplasm, providing highly informative data for multilocus discrimination of individuals, and shedding light on their composition and structure. In the future, the Tunisian germplasm could be better explored with innovative techniques, coupling the use of SSRs with the more performant, high throughput technologies that use next generation sequencing ,50,51,52Surveys were conducted on Tunisian farms in cooperation with local agricultural authorities and international olive experts in northern, central, and southern regions of Tunisia . SeventyThree young leaves of each olive sample were lyophilized and finely ground; 50 mg of tissue was used for genomic DNA extraction following the protocol described in Spadoni et al. . In orde2O were added in each well containing 50 ng of DNA. PCR amplifications were performed in a C1000TM Thermal Cycler , and the products were checked in 1.5% agarose gel. PCR products were detected by the automatic capillary sequencer ABI PRISM 3100 Avant Genetic Analyzer  with the internal molecular weight standard GeneScan Liz 600 dye . GeneMapper genotyping software v.3.7  was used in order to carry out the sample analyses.A set of 12 microsatellite markers, previously proven to be highly performant for genetic olive characterization, were used  54,55,5,555,56. http://biology-assets.anu.edu.au/GenAlEx) [The estimation of the following genetic indices was achieved by using GenALEx software v.6.5 (GenAlEx) : number GenAlEx) , observeGenAlEx) . GenALExGenAlEx) , the marGenAlEx) , and theGenAlEx) . It was GenAlEx)  with CerGenAlEx) , as wellhttp://darwin.cirad.fr), with bootstrapping of 1000 replicates to determine the support for each node [The genetic relationships between the 77 olive samples were also estimated by using the Ward’s hierarchical clustering method based on a dissimilarity matrix using DARWIN software v.6.0.010 (ach node . Population genetic structure was assessed by using the Bayesian model-based clustering analysis  implemen


Bottlenecks reduce the size of the gene pool within populations of all life forms with implications for their subsequent survival. Here, we examine the effects of bottlenecks on bacterial commensal‐pathogens during transmission between, and dissemination within, hosts. By reducing genetic diversity, bottlenecks may alter individual or population‐wide adaptive potential. A diverse range of hypermutable mechanisms have evolved in infectious agents that allow for rapid generation of genetic diversity in specific genomic loci as opposed to the variability arising from increased genome‐wide mutation rates. These localised hypermutable mechanisms include multi‐gene phase variation (PV) of outer membrane components, multi‐allele PV of restriction systems and recombination‐driven antigenic variation. We review selected experimental and theoretical  models pertaining to the hypothesis that localised hypermutation (LH) compensates for fitness losses caused by bottlenecks and discuss whether bottlenecks have driven the evolution of hypermutable loci. Bottlenecks produce dramatic reductions in the genetic diversity of bacterial populations. Bottleneck events can be selective or non‐selective and occur frequently, particularly during transmission between hosts. Multiple mutational strategies have evolved in bacteria that can regenerate genetic diversity within surviving populations following a major reduction in population size. A key on‐going paradigm shift in our understanding of genetic variability in pathogenic bacteria was the demonstration that localised hypermutation (LH) due to hypermutable DNA elements was responsible for the phase variation (PV) phenomenon of rapid ON and OFF switches in surface antigens and restriction‐modification systems (RM). We now recognise that LH and PV are widely distributed across bacterial pathogens and commensals but with significant diversity in the numbers of loci and in the types of functions encoded by these loci Figure . The imp2Bottlenecks are a short‐hand to describe the phenomenon of a rapid reduction in the number of organisms in a population leading to loss of genetic variants Figure . Concept2.1Early evidence of infection‐associated bottlenecks came from studies of pneumococcal pathogenesis and the observation that intravenous injection was followed by a major reduction in population size prior to establishment of high‐level bacteraemia Wright, . Gerlini2.2Neisseria meningitidis to a bactericidal monoclonal antibody specific for a phase‐variable LOS epitope  in a population has an independent probability to initiate an infection without any cooperative or synergistic interactions from other individuals in the population so that infections become progressively more likely to occur for every increase in the infecting population size.Using  Meynell  first prSalmonella cells have inferred the presence of multiple within‐host bottlenecks. Han Lim and Colleagues ; stochastic, reversible, high frequency alterations in the flagella phenotypes of A key early observation was that the majority of contingency loci encode bacterial surface antigens or indirect modifiers of these antigens Figure . SubsequA final generic observation is that LH is not only widespread but exhibits variations in prevalence between and within species Figure . Some sp4In order to support the generic view of the previous section, we review some recent findings on distributions of LH and how hypermutable switches contribute to within‐host evolution.4.1It is a new programme , TRD switching is facilitated by conserved regions flanking the TRD sequences , had evolved longer SSRs as compared to the parental clone during expansion and persistence within the UK of a serogroup W cc11 sub‐clone of 4.7N. meningitidis was associated with reductions in the combined expression states of OMP‐encoding PV loci. High levels of serum antibodies to the capsule and one of these OMPs were present in the carriers studied suggesting that antibody‐mediated selection drives these changes. Using WGS of multiple longitudinal non‐typeable Haemophilus influenzae (NTHi) isolates from 61 persistently chronic obstructive pulmonary disease (COPD) patients, Pettigrew et al. found that increases in repeat number were positively correlated with length of persistence  but also niche specialisation limiting the adaptive benefits of hypermutability. Despite these advances, studies of how different types of bottlenecks are impacted by LH are still a rarity and have yet to explicitly demonstrate that LH enables adaptation to, or survival of, both the initial bottleneck and of a subsequent, alternative selective event.Despite the uncertainties about real‐world bottlenecks and the new findings on LH diversity, mathematical modelling has established a strong basis for unravelling the evolutionary forces acting on LH. The key roles of selection strength, epoch length, bottleneck frequency, bottleneck stringency and compartmentalisation have firm foundations with models that can be applied or adapted to specific situations. There are, of course, still many gaps in our understanding of key processes that mainly stem from a lack of real‐world knowledge; how do we measure selection strength?; are real‐world bottlenecks highly selective for variation in a single genetic locus or multiple loci?; are some bottlenecks completely, or evolutionarily, non‐selective? We also need to be aware of whether specific events are of evolutionary importance or are accidental effects of the existence of certain virulence attributes and LH mechanisms. This latter point is a key concern as theoretical modelling of evolutionary processes needs to focus on attributes that contribute to evolution and not on evolutionary dead ends such as bacterial meningitis. Perhaps unsurprisingly, we still await a definitive theoretically underpinned experimental demonstration that differences in mutability of an LH locus, or even loss of mutability, provide a competitive advantage during multiple cycles of transmission of an infectious agent.The authors declare no conflicts of interest.MDSC, JH, JJW, ERM, MRO and CDB have all made major contributions to the drafting of the manuscript. MDSC, JJW and CDB made and refined figures.