The inference of genetic ancestry plays an increasingly prominent role in clinical, population, and forensic genetics studies. Several genotyping strategies and analytical methodologies have been developed over the last few decades to assign individuals to specific biogeographic regions. However, despite these efforts, ancestry inference in populations with a recent history of admixture, such as those in Brazil, remains a challenge. In admixed populations, proportion and components of genetic ancestry vary on different levels: (i) between populations; (ii) between individuals of the same population, and (iii) throughout the individual's genome. The present study evaluated 1171 admixed Brazilian samples to compare the genetic ancestry inferred by tri-/tetra-hybrid admixture models and evaluated different marker sets from those with small numbers of ancestry informative markers panels (AIMs), to high-density SNPs (HDSNP) and whole-genome-sequence (WGS) data. Analyses revealed greater variation in the correlation coefficient of ancestry components within and between admixed populations, especially for minority ancestral components. We also observed positive correlation between the number of markers in the AIMs panel and HDSNP/WGS. Furthermore, the greater the number of markers, the more accurate the tri-/tetra-hybrid admixture models. Since the 1970s, owing to the pioneering work by Richard Lewontin, it has been understood that most human genetic variation occurs between individuals of the same population group, while genetic variation between individuals of distant populations is restricted to a small proportion of the human genome. This study is one of the first to refute the use of social races in biological studies and draw attention to the fact that genetic information is more accurate for biological issues than social groups or ethno-racial self-declaration4.Understanding how human genetic diversity is distributed and its implications has been a recurrent focus in clinical, population, and forensic genetics studies6. Therefore, the categorization of human groups by current geo-political regions are arbitrary choices and not true biological clusters. Thus, these studies clarify, that there is only a small set of genetic polymorphisms with a distinct allelic frequency between human populations or continents.Since then, several studies have subsequently confirmed these observations and revealed that the distribution of genetic diversity and population differentiation is a continuous gradient within and between populations across continents7, hypertension8, and inflammatory bowel disease9. Identifying associated genetic variants and the correct assignment of individuals in these groups helps in the development of personalized medical care10. In forensic genetics, when identifying the individual via CODIS (the Combined DNA Index System), it is often necessary to provide additional information such as phenotypic characteristics  and/or the most probable continental origin11.As such, these small genetic differences have been widely studied to address specific biological issues. For example, in clinical genetics, some diseases are recognized as having different incidences among population groups, for example: chronic kidney disease12. One of the first strategic approaches applied was to identify sets with a few dozen ancestry informative markers  in order to compose informative ancestry panels with the purpose of clustering individuals into continental and subcontinental population groups15. The development of AIM panels attempts to select a group of genetic markers that compose a small, accurate, low-cost, and highly informative set. In general, AIM panels vary in some characteristics, including: specific loci, number of loci, genotyping strategies, and parental reference populations18. Studies usually use only one AIM panel or even a complementary set, for example: PIMA + 34-plex19, Pacifiplex and 34-plex20, and KiddLab + Seldin + 34-plex21.Over the last few decades, several methods and strategies have been developed in an attempt to assign an individual´s geographical origin based on DNA variations. This is what geneticists often refer to as genetic ancestry23. In this scenario, there is not only a significant increase in the number of genetic markers evaluated, the number of genotyped individuals per study grows from hundreds to thousands26. Consequently, some analytical approaches have been adapted, in addition to the development of new analytical strategies29.On the other hand, the emergence of high-throughput genotyping technologies, such as high-density SNPs array (HDSNPs), whole-exome-sequence (WES) and whole-genome-sequence (WGS) enabled high horizontal genome coverage studies for genomic ancestry inference30.Additionally, in both the AIM and high-throughput genotyping strategies, proper genetic ancestry inference is dependent on the existence of a reference population panel for each ancestral component under study. Genetic ancestry inference remains deficient in some population subgroups due to lack of reference data collection, in addition to some inconclusive or non-validated studies. For example, there is an effort to develop reference panels for Asians and Native Americans, in addition to increasing reference genome data for Latin Americans32 and more recently East Asians33. The genomes of admixed individuals are a redistribution of genetic variation observed in parental populations, which produces new genomic combinations of pre-existing genetic variants. This leads to a paradigm shift, in which the geographic origin of the admixed individual becomes a secondary issue, the primary objective being to identify each ancestral component, its distribution and proportion in the individual's admixed genome.Admixed populations, such as those in America, pose a peculiar case for genetic ancestry inference as, they originated over the last 500 years through a complex admixture process with population sources of individuals from different continents: Native Americans, Europeans, Africans34. This has a direct impact on the reproducibility and transposition of study results in these populations. Thus, nowadays, genetic ancestry inference in an admixed population is an essential tool to control the effect of population stratification in association studies35, for the identification of disease-associated genes36, precision medical care11 and to reveal population history38.Furthermore, in admixed populations, the proportion and components of genetic ancestry vary at different levels: (i) between populations; (ii) between individuals of the same population, and (iii) throughout the individual's genome40.Therefore, the correct assignment of each genetic ancestral component is essential for studies with admixed populations. Concerning this issue, some studies compared AIM panels in American admixed populations, observing differences in ancestry proportion inference between panels, which may be related to both the number of markers and parental reference populations of each panel37. Approximately 3 million Native Americans (indigenous people) lived in the current Brazilian territory in the fifteenth century who, after contact with Europeans, declined in number by at least 90%41. Recently, in the twentieth century, Brazil continues to receive millions of immigrants, especially from East Asia33.Of the Latin American countries, Brazil was the only Portuguese colony, which resulted in peculiarities in the admixture process. Brazil received more than 4.5 million African slaves whose origin in the African continent may differ in terms of place and time from those of non-Portuguese colonies in Latin America40. Herein, we present for the first time a comparison of ancestry estimates between different genetic marks sets—AIMs, high-density array of SNPs and Whole Genome Sequencing—in the Brazilian population using the tri-hybrid  and tetra-hybrid admixture models .Today Brazil is the most populous country in Latin America and the seventh most populated country in the world, with more than 215 million inhabitants. Understanding how different marker sets are assigned to Brazilian genetic ancestry is of extreme relevance to both historical and public health issues. The first comparative studies to address this issue were only performed with AIMs or pharmacogenomic high-density SNP array panels and are based on the tri-hybrid admixture model 23, which we compared to (i) 5 AIMs panels: 34AISNP42 + PIMA19; 55AISNP18; 128AISNP43; 170AISNP44, and 446AISNP45, selected by both the tri- and tetra-hybrid admixed models. In addition, we evaluated the combination of the 5 aforementioned panels, referred to herein as 665AISNP; (ii) a high-density SNPs array (HDSNP), specially developed for population genetics studies and therefore without biased markers such as the GWAS SNP array or pharmacogenomics, and (iii) whole genome sequencing (WGS) data.This study analyzed 1,171 admixed samples from Brazil46 and 1000 Genomes Project phase III (1KGP)24  which contributed most to the Brazilian population. For the parental populations, we used samples of the Human Genomic Diversity Panel (HGDP)46 into each of the 4 continental groups. Only a small proportion of samples were not assigned correctly  , ranging from 0.98 to 1 for the African and European samples , Europeans (EUR), East Asians (EAS), and Native Americans (NAM) from the HGDP, only including samples with z-score values < |3| . We subsequently performed a pairwise comparison to verify the correlation between the ancestry inferences obtained by the same panel from the tri- and tetra-hybrid models. We observed that the inferred AFR and EUR ancestral component correlation coefficient ranged from 0.97 to 0.99, and the NAM component between 0.46 and 0.67 ; EUR ; EAS (r2tetra = 0.80 to 1), and NAM component  ; Afro-American (ASW); Colombian (CLM); Mexican (MXL); Peruvian (PEL), and Puerto Rican (PUR). The admixed populations evaluated herein have different proportions of parental ancestry, ranging from those with proportions of mostly African ancestry (ACB and AWS), mostly European ancestry , and mostly Native American ancestry (MXL and PEL)  , all panels showed a high accuracy rate 33.We know that the EAS contribution is less than 1% for most of the Latin American admixed populations. Therefore, the choice of tri- or tetra-hybrid model will depend on the admixture profile of the population. Our motivation to analyze the tetra-hybrid model lies in the fact that in recent decades there has been a growing migratory flow of East Asian populations to large urban centers in the USA and Brazil. East Asian immigration to Brazil began in 1908 with the Japanese and today, according to the Ministry of Foreign Affairs of Japan, more than 2 million Japanese descendants live in Brazil. São Paulo, the city where the Brazilian samples of the present study were collected, is home to one of the largest Japanese communities outside Japan. The Brazilian cohort has 33 samples with 100% East Asian ancestral component that are direct descendants of the first Japanese immigrantsBased on this scenario, using WGS data from 1171 Brazilian individuals, we evaluated how different admixed models and sets of panels behave to infer ancestry in the Brazilian population. First, we checked for differences in the inferences of each ancestral component according to the tri- or tetra-hybrid admixture model. The population average inferred by either admixed model only differed for the NAM ancestral component , it was unexpected to visualize individuals with this ancestral component as a minority in their genome. Thus, we evaluated the remaining 1138 samples as probably not possessing the EAS ancestral component. Our results showed that for the tetra-hybrid model, especially for the AIMs panels, there was more noise in the EAS component inference  at the time of collection  for which there are no historical records of large migratory flows from EAS (except for Peru). Therefore, it is unusual to analyze the tetra-hybrid model in this dataset and we only performed it in order to better explore the patterns. Unlike the Brazilian cohort, we did not observe significant differences in the population averages of the components between the admixed models. However, Figs. Therefore, choosing an admixed model is not a simple decision, as each model has advantages and disadvantages in each population. The decision of which model to apply will depend on the question the investigator wants to ask and whether there is interest in the population average or ancestry of each individual in the sample. If it is to decipher specific admixture components, for example to learn about an individual’s family migratory patterns, then all possible parental populations involved should be included. If they are simply trying to determine the major ancestral component, for example exclusion purposes, then a smaller model with the key continental groups may suffice.The second objective of our study was to compare ancestry inferences with different sets of panels . In the Brazilian sample, we observed significant differences for the EUR ancestral component averages in the tri-hybrid model (34AISNP + PIMA and 55AISNP × HDSNP and WGS) , and among those with the highest number of markers . Again, it is important to point out that the choice of panel will depend on the purpose and needs of the study. For example, in forensic genetics, sometimes samples with quantity and quality are not available, which limits the genotyping methodologyThe admixed populations of America are being increasingly studied in terms of population history, clinical and forensic studies. Therefore, nowadays, it is essential to discuss and understand how methodological advances, both in genotyping and in analysis, help to improve the inference of genetic ancestry in admixed populations. In the present study, we analysed data from WGS, HDSNP and AIMs in a Brazilian samples, through different admixture models and compared with other admixed populations of the American continent. We showed that heterogeneity within and between admixed populations still poses methodological challenges. Therefore, it is fundamental when defining the research question, to be aware of the advantages and limitations of each admixture model and set of panels for the populations of interest.46; (ii) 1000 Genomes Project phase III (1KGP)24, and (iii) Brazilian Cohort of Health, Well-being and Aging (Saúde e Bem Estar—SABE)23.Samples from 3 datasets were analyzed: (i) Human Genomic Diversity Panel (HGDP)Based on the American admixture history, analyses were performed with parental samples from African, European, East Asian and Native American populations of HGDP  and 1KGP  as described in Table https://www.internationalgenome.org/data—HGDP and 1KGP; https://ega-archive.org/studies/EGAS00001005052—SABE). All individuals enrolled in the SABE cohort signed written consent forms to participate in this study approved by local and national institutional review boards: COEP/FSP/USP OF.COEP/23/10, CONEP 2044/2014, and CEP HIAE 1263-10.All samples were genotyped by WGS and are publicly available  was selected as a representative of high-density SNP arrays. This genotyping panel was optimized for population genetic studies and developed from genomic markers identified in 11 human populations: France, China, Papua New Guinea, San, Yoruba, Mbuti pygmies, Karitiana, Italy-Sardinia, Melanesia, Cambodia, and Mongolia, avoiding confounding biases introduced using GWAS SNP arrays.AISNP panels: 672 SNPs comprise the 6 AISNP panels selected for the present study. Of these, 5 SNPs  are not detected in all datasets  WGS data: the original datasets with more than 60 million variants described. For the present study, we excluded SNPs: (a) MAF < 1%; (b) missing data per SNP > 1%; (c) Hardy–Weinberg p-value < 1 × 10–8, and (d) filter for LD coefficient  55. Correction for multiple testing was made according to Bonferroni correction.Allelic frequency inferences, Hardy–Weinberg and Fisher's exact test was performed using the PLINK v.1.9 software56 was used to perform global ancestry inference. Analyses were performed in an unsupervised manner when considering only parental populations, and in a supervised manner when considering admixed populations. Parameters used: 4 clusters (K = 4) and 2000 bootstrap replicates. Each analysis was repeated 10 runs and the results combined using the CLUMP software (v.1.222)57.ADMIXTURE v.1.32 = 0.01, 0.05, 0.1, 0.3 and 0.5), assuming a distance not closer than 200 Kb between adjacent markers56.Information redundancy may occur in high-density SNP data (HDSNP and WGS). Therefore, to minimize and evaluate background linkage disequilibrium in the analyses, we also tested some disequilibrium linkage coefficients (r58.Comparisons of ancestry inference were performed by correlation analysis and z-score test by R scripts using package stats v.4.1.1Supplementary Information 1.Supplementary Tables.


Sharko et al. Nature Communications 10.1038/s41467-021-22567-5 (2021)“Steller’s sea cow genome suggests this species began going extinct before the arrival of Paleolithic humans”, Sharko et al.1 use novel genomic methods to infer the demographic history of this species. Based on a single specimen from the Commander Islands, the authors conclude that the species suffered a single catastrophic population decline approximately 400,000 years ago and was thus already on the verge of extinction well before human arrivals in the Late Pleistocene. Here we suggest their demographic assumptions warrant reinterpretation given the ecological barriers that likely structured sea cow populations along the North Pacific Rim. Our preliminary range simulations suggest that the Commander Is. population may have been physically isolated from others, making it unsuitable as a demographic inference for the entire sea cow North Pacific range. Under these assumptions, Sharko et al.’s findings are more likely indicative of the time since the isolation of this remnant population from the rest of the sea cow range, rather than representative of the population contraction of the species. This perspective highlights the importance of considering historical ecology and paleobiogeography when interpreting genomic data to infer past demographic histories.In their recent paper entitled 2 published their milestone paper on the development of pairwise sequentially Markovian coalescent (PSMC) models to infer species demographic histories from a single diploid genome, their approach has become extremely popular with iconic endangered  and extinct species . Although there are significant methodological questions, such as the effects that natural selection can have on vertebrate genomes6, and separating population size variation from population structure7, we believe this method will contribute in impactful ways to our understanding of species demographic histories, especially given the possibility of using few, or single specimens.Since Li and Durbin8. In this sense, although we were pleased to see these novel genetic methods applied to the demographics of marine megafauna in the North Pacific, particularly to Steller’s sea cow1, we believe an examination of this species’ ecology and biogeography lends clarity to the interpretation of their results.However, to appropriately substantiate assumptions, demographic analyses derived from such models must carefully consider long-term range fluctuations, as well as ecological and behavioural characteristics that would influence the species’ dispersal, distribution and consequently gene flow dynamics10), have all contributed to the idea that they existed as one large panmictic population. However, different lines of evidence indicate the existence of ecological barriers that could have significantly altered sea cow dispersal and population structure, including both periodic  and permanent reproductive barriers, such as several wide and deep channels separating the Aleutian Islands.The North Pacific-wide presence in the fossil record, the absence of visible physical barriers for dispersal and migration, and the sheer size of Steller’s sea cow  without a shoreline to navigate. The 330 km distance between Medny (the easternmost of the Commander Islands) and Attu (the closest of the Aleutian Islands) would have posed an even greater barrier to genetic exchange. Given the predominant oceanographic conditions, it is possible that an occasional vagrant, or a carcass, as mentioned above, could have floated, or been forced by storms or tsunamis from the Commanders to the Kamchatkan mainland. However, the opposite would have been unlikely given the predominant direction of winds and currents and the low probability of a straggler (floating or swimming) from the continent hitting such a small island on the open ocean.The assessments conducted by Krasheninnikov and Steller in Kamchatka and the Kurile Is., supported by interviews with Indigenous Itelmen and Kurile peoples, did not find ethnobiological evidence of living sea cows outside the Commanders, except for anecdotal reports of carcasses washed ashore13—has likely been genetically impaired from significant contributions from other regions of the Pacific Rim for probably most of its history, possibly for as long as it first colonised the islands, except for the occasional immigrant. In light of these possibilities, we believe that the discussions related to the population history of Steller’s sea cows made by Sharko et al. may be more relevant if related to the context of the Commander Islands population and its peculiar biogeographic trajectory. At the very least, we argue it is not justified to extrapolate results from a single specimen from the Commander Islands population to the rest of the North Pacific given the uncertainty in when they diverged and the degree to which it was genetically isolated. PSMC models may well be able to provide supporting evidence to address these uncertainties, determining when this founding population first established itself in the Commanders, or help elucidate some of the complex dispersal possibilities discussed above.These assumptions have led us to conclude that the Commander archipelago’s sea cow population—a notoriously relict, stunted population21, finding comparable demographic trajectories as Sharko et al. Similarly, since their analyses were only based on individuals from the Commander Islands, their data do not address the question of whether the Commander Islands population accurately represents the demographic history of the species across its former geographic distribution.Another recent paleogenomic study focusing on Steller’s sea cow skin phenotype and cold adaptation has also used PSMC methods to estimate effective population size over timeUnfortunately, known specimens from outside the Commander Islands are few, and perhaps not able to provide genetic material to allow the level of refinement achieved by Sharko et al. However, the continued development of underwater archaeology and zooarchaeological research may provide future guiding evidence regarding sea cow biogeography and extinction dynamics. In the meantime, similar genetic analyses with existing specimens from California, Alaska  and Japan, may provide a broader perspective on the spatial and demographic dynamics of Steller’s sea cow across the Pacific Rim. In any case, ground-breaking methods such as those used by Sharko et al.—when interpreted in the context of reconstructed species life histories and dispersal dynamics—may constitute one of the best available tools to infer past demographic histories, especially when specimens are rare.Further information on research design is available in the Dataset 1Reporting Summary


In this study, we serially passaged populations of rhesus rotavirus. We demonstrated that populations with reduced chlorine sensitivity emerged at random and independently of chlorine exposure. Sequencing analysis revealed that compared with sensitive populations, less-sensitive ones had higher non-synonymous genetic diversity of the outer capsid protein gene, suggesting that changes in the amino acid sequences of the outer capsid protein were the main factors influencing chlorine sensitivity. No common mutations were found among less-sensitive populations, indicating that rather than specific mutations, the diversity of the outer capsid protein itself was associated with the disinfection sensitivity and that the disinfection sensitivity changed stochastically. Simulation results suggest that the disinfection sensitivity of a genetically diverse population is destabilized if cooperative viral clusters including multiple sequences are formed. These results advocate that any prevention measures leading to low intrapopulation genetic diversity are important to prevent the spread and evolution of pathogenic RNA viruses in society.RNA virus populations are not clonal; rather, they comprise a mutant swarm in which sequences are slightly different from the master sequence. Genetic diversity within a population (intrapopulation genetic diversity) is critical for RNA viruses to survive under environmental stresses. Disinfection has become an important practice in the control of pathogenic viruses; however, the impact of intrapopulation genetic diversity on the sensitivity of disinfection, defined as –log Although RNA virus populations are not clonal, they comprise mutant swarms that have different genomic sequences from the master sequence and are, therefore, called quasispecies . MutantsIncreases in genetic diversity are usually restricted by some forces: natural selection and genetic drift owing to a bottleneck . AdvantaEnteric viruses such as rotavirus and norovirus are RNA viruses and spread from human fecal matter to various environments. Wastewater, which contains pathogenic RNA viruses, is often reused for irrigation after disinfection treatments  or is reIn this study, we used populations of rotavirus as a representative of pathogenic RNA viruses and assessed how the virus populations altered their sensitivity to disinfection. Rotavirus is a double-stranded RNA (dsRNA) virus with 11 genome segment coding structural proteins  and non-structural proteins , and its virion is composed of three capsid layers , a spike protein (VP4), and an enzymatic complex of VP1 and VP3. We identified the mechanism underlying the change in disinfection sensitivity. We demonstrated that the disinfection sensitivity of rotavirus randomly changed and that its change was associated with the diversity of non-synonymous mutations (non-synonymous genetic diversity) of the outer capsid protein gene (VP7). There were no specific mutations in less-sensitive populations, indicating that the genetic diversity may be correlated with the disinfection sensitivity of the rotavirus population. Viral phenotypes such as infectious titer, cell-binding ability, specific growth rate, and lag period were not different between the less-sensitive and sensitive populations. Since non-synonymous mutations on the protein surfaces can change various physical properties such as electrostatic potential and hydrophobic interaction, we hypothesized that virions with different sequences tended to gather each other (cooperative virus cluster) within a genetically diverse population, resulting in the reduction of disinfection sensitivity. Based on this hypothesis, we performed an evolutionary simulation and observed that the disinfection sensitivity was likely determined by cooperation capable of maintaining the genetic diversity and by the stochasticity of successful transmission of cooperative virus clusters to the next generation.L-glutamine, 1% penicillin–streptomycin (GIBCO by Life Technology), and 1.125 g/L sodium bicarbonate  in a T75 flask. The average cell numbers were approximately 5.0 × 106 cells/flask. The original rhesus rotavirus (RRV) population was passaged thrice using the MA104 cell line. Trypsin from porcine pancreas type IX-S  at a final concentration of 40 μg/ml was added to a 1 ml RRV suspension to facilitate viral entry into the MA104 cells; the incubation conditions were 37°C for 30 min under 5% CO2. After incubation and discarding of the cell culture medium in a flask in which MA104 cells were confluent, the RRV suspension was inoculated onto the cells at a multiplicity of infection (MOI) of 0.1 and incubated at 37°C for a further 60 min under 5% CO2 to allow the viruses to bind the cell receptor. Then, 32 ml of serum-free Eagle’s MEM was added to the infected culture and maintained for 2 or 3 days. At the time of harvest, the flask was frozen and thawed thrice to recover not only the viral particles released from the cells but also those that remained within the cells. The resulting suspension was centrifuged at 12,600 × g for 10 min at 4°C to remove cell debris. The same procedure was performed in duplicate on the original RRV population to generate two ancestral populations for serial passage experiments. The population structure, nucleotide diversity, and sensitivity of these ancestral populations to chlorine were different from each other (MA104 cells (ATCC:CRL-2378.1™) were cultured in Eagle’s minimal essential medium (MEM) containing 10% fetal bovine serum, 2 mM ch other .N,N-diethyl-p-phenylenediamine method. An RRV suspension was disinfected using chlorine  without purification because drinking water, wastewater, and environmental surfaces polluted by feces are usually associated with disinfectant-consuming organic matter. Then, 0.5 ml of the RRV suspension (106 PFU/ml) was added to 4.5 ml of chlorine solution . The mixed suspension was sampled after 0, 0.5, 2, 5, 10, and 15 min, and the residual chlorine in the sampled suspension was neutralized using 1% sodium thiosulfate of which concentration properly neutralizing the residual chlorine was determined on the basis of the reaction’ stoichiometry of chlorine and sodium thiosulfate. The concentration of sodium thiosulfate used for the neutralization did not prevent cell growth and virus propagation. We also measured the total (free and combined) chlorine concentration using the N,N-diethyl-p-phenylenediamine method because combined chlorine is easily generated via the reaction of free chlorine with organic matter in virus suspensions and can inactivate the viruses. Due to the consumption of free chlorine species (HClO and ClO–) by organic components, the free chlorine concentration decreased immediately, but some residual free chlorine remained (0.06–0.08 mg/L) (Nt to that before disinfection N0 “Log10 (Nt/N0)”} was fit to the efficiency of the Hom factor model to yield an inactivation curve (N = 3).Sodium hypochlorite  was diluted with Dulbecco’s phosphate-buffered saline (PBS) (–)  to adjust the concentration of free chlorine. The free chlorine concentration was measured using the 08 mg/L) . In conton curve . The inaon curve . In addion curve ; therefovia the serial passaging of the two initial populations with/without disinfection.Each serial passage experiment had three steps: disinfection (or dilution) of an RRV population, propagation of the disinfected (or just diluted) population using the MA104 cell line, and evaluation of the phenotypes . In thi2. After the cells were washed twice with Dulbecco’s PBS (–), 2.5% agar mixed with the same volume of phenol red free-MEM  was overlayed, and the plate was incubated for 2 days. MA104 cells were stained with 0.015% neutral red for 3 h at 37°C for 90 min under 5% CO2, and then, neutral red was removed. After 1 or 2 days, the number of plaques was counted. Chlorine sensitivity was taken as LRV (–log10 [postdisinfected/predisinfected infectious titer]) and measured at the same disinfection conditions as that in the serial passage experiments. The infectious titer and chlorine sensitivity were measured thrice for all experiments using the plaque assay.Infectious titer and chlorine sensitivity were determined by performing the plaque assay. In line with our previous report , seriallGt of the virus particles attached to cell surfaces to those inoculated to cells G0 was calculated as t/G0G.A cell-binding assay was conducted thrice following the steps outlined in our previous report . Confluevia which the quantitative parameters of viral growth were obtained  ,  period) . As in tN0 and Nt are the virus titer values (PFU/ml) at 0- and t-h after inoculation, A is the asymptotic value [10(N∞/N0)log], μ is the specific growth rate [hour–1], λ is the lag time [hour], and e is the Napier’s constant. These parameters  were determined using the ordinary least-squares method.where As compared with conventional RNA-seq, the fragmented and primer-ligated dsRNA sequencing (FLDS) method for dsRNA sequencing enables higher sequence coverage of the entire genomic region that includes terminal sequences , 2018. IEF583006.1, EF583007.1, EF583008.1, M18736.1, HQ665465.1, EU636929.1, AY065842.1, EU636931.1, M21650.1, EU636933.1, and EU636934.1), and the consensus sequence was determined using CLC Genomics Workbench version 11 . Previously determined consensus sequences were used as reference sequences for each RRV genome segment . The threshold value of sequencing error frequency was set to 1.0% to avoid the detection of false-positive variants, according to the results of previous studies  is a classification algorithm that explores a boundary line to classify samples into two distinct groups . In thisω is the vector for coefficients  of x that is also a vector of the frequency of non-synonymous SNPs ; b is an intercept; and n is the number of SNPs on the VP7 genome segment (N = 62). Then, we denoted another function for binary classification g(t), which is 1 if f(x) ≧ 0; otherwise, it is –1. To determine f(x), the algorithm seeks data points belonging to different classes nearest to the boundary line. The ω and b are then calculated to maximize the distance of the boundary line from support vectors. Classification into less-sensitive groups could have been determined by only a few SNPs; thus, we introduced linear SVM into the L1 regularization term. L1 regularization mathematically cuts the coefficient of non-essential variables, which is estimated as zero. In contrast, L2 regularization employs all variables that are shrunk as small as possible. The total dataset was separated into the train (80%) and test datasets (20%), and the boundary line was estimated using only the train dataset. The test dataset was then predicted by the estimated model, and the prediction performance was evaluated on the basis of the accuracy defined below:where TP, TN, FP, and FN are the numbers of true positives, true negatives, false positives, and false negatives, respectively. Linear SVM was conducted using the scikit-learn library in Python version 3.71.where 10 population size of subpopulation i (vi) as follows:Simultaneous ordinary differential equations, which have been originally established for simulating the population dynamics of the human immune deficiency virus under immune pressures specific to each strain, were applied  and modiri is the overall growth ability and p is the coefficient of a removal term associated with practical inactivation efficiency (ivipx). The response of subpopulation i to chlorine (xi) is proportional to the log10 virus population size (cvi) but is restrained by the subpopulation-specific sensitivity (ixib), which is expressed as follows:where i positively interacts with others, the terms of the subpopulation-specific sensitivity of other subpopulations are added to the equation of xi:If subpopulation We further assumed that bottlenecks exerted on this viral population and excluded some cooperative subpopulations, which resulted in random changes in the number of positive interactions:i + 1θ is 0, subpopulation i loses the interaction with subpopulation i + 1. In this case, the subpopulation i + 1 decreases its population size due to the bottleneck. The equations with the binomial distribution were run 100 times. We ran the simulation using R software version 3.5.02.The parameter θ followed binomial distribution, and it took 0 or 1 to express the bottleneck. If F-test if the dataset followed a normal distribution. For chlorine sensitivity, both two-sided and lower-tailed one-sample t-tests were applied. The two-sided Welch’s t-test was also conducted to compare the cell-binding ratio and specific growth rate between the less-sensitive and sensitive populations. Statistical differences in the infectious titer, lag period, and non-synonymous and synonymous diversities were assessed using the Wilcoxon rank-sum test. To confirm the correlation of LRV with non-synonymous and synonymous nucleotide diversities, robust linear regression, which can minimize the effect of outliers when datasets are fitted, was conducted using the scikit-learn library in Python version 3.7 (see footnote 1). Pearson product-moment correlation coefficient and Spearman’s rank correlation coefficient were estimated to find a correlation between LRV and nucleotide diversities and the frequency of VP7 SNPs identified by linear SVM. Except for the robust regression analysis, all statistical tests were performed using R software version 3.5.0.Before confirming statistical differences with reference values or between two groups, the Shapiro–Wilk test was conducted to test whether the variables were normally distributed. Moreover, the homogeneity of variance was validated using the t-test with a 0.5 LRV, which was 50% of the expected disinfection sensitivity (90% reduction ≒ 1.0 LRV). As a result, the disinfection sensitivity of 17 populations, including 12 Test and 5 Control populations, did not achieve the expected disinfection sensitivity . We defined these 17 populations as “less-sensitive populations A; LA” (sensitive populations A were expressed as SA). To discover populations that were more resistant to chlorine from LA, the mean LRVs of each population were compared with the hypothetical LRVs ranging from 0.1 to 1.0 in the lower-tailed one-sample t-test ; thus, we defined these three populations as “less-sensitive populations B; LB” (sensitive populations were expressed as SB).Two ancestral RRV populations, which had different disinfection sensitivities and genetic diversities , were see t-test . When ththan 0.5 . When thP-value for the lag time was observed for LB (P = 0.02 for LB vs. SB). However, low values of the lag time were also found in some of the SA and SB populations, indicating that the chlorine sensitivity was not attributed to these virological phenotypes.We also tested some virological phenotypes of each population to explore clues to the cause of chlorine sensitivity reduction. There were no significant differences in infectious titer, specific growth rate, and cell-binding efficiency between the less-sensitive and sensitive populations , whereasSD ± 1131)   and exce ± 1131) . Since a ± 1131) , these s ± 1131) , suggestP < 0.05 for both LA and LB). The nucleotide diversity is composed of non-synonymous (Nπ) and synonymous (Sπ) nucleotide diversities. By calculating Nπ and Sπ, we can identify which types of nucleotide diversity are more important for disinfection sensitivity. Compared with the sensitive populations, both LA and LB showed significantly higher Nπ of the VP7 gene (P < 0.05 for both LA and LB). The Sπ of the VP1 and NSP4 genes of LA were also higher, whereas those of LB was not different from that in sensitive populations  but no correlation between the disinfection sensitivity and the Sπ .Nucleotide diversity, as an indicator of intrapopulation genetic diversity, denotes the probability that two sequences randomly sampled from a gene pool are different from each other. Both LA and LB exhibited significantly higher nucleotide diversities of the gene coding outer capsid protein VP7  . As for the LB result, however, not all less-sensitive populations had T281I. If the disinfection sensitivity is determined by specific mutants, advantageous mutations must persist in even less-sensitive populations, and the low sensitivity must be maintained according to the principle of natural selection. Thus, the reduction in chlorine sensitivity is unlikely to be associated with any specific mutations.Correlation analysis between the extracted SNPs and chlorine sensitivity revealed significant correlation coefficients for D267N (Pearson’s  < 0.01) . However < 0.01) . K99N wa < 0.01) . As for via coinfection with intact virions within a cluster . Recently, it has been reported that viral phenotypes are modified by positive interactions among variants  , which promoted the formation of virus clusters comprising not only major but also minor mutants with an affinity toward each other. Such clusters were assumed to avoid damage to the whole region of capsid protein and/or enable the virion that could not infect alone to replicate eration) . To confr  to maintain dominance in a population and 10 subpopulations that did not have cooperative interactions with others. In this case, log10 subpopulation sizes were converged to a lower level (approximately 1) (The simulation was run for one major subpopulation having a higher fitness ately 1) , 5. To rately 1) , 5. ThesThe relationship between the population size and chlorine sensitivity of a cooperative subpopulation was confirmed using four-phase plots in which the number of cooperators varied . WithoutIn this study, RRV populations less-sensitive to chlorine disinfection randomly emerged from serial passages  . As reduFirst, one must confirm whether the RRV populations used in this study indeed exist as quasispecies. The two ancestral populations included distinct SNPs and nucleotide diversities . The nucThe populations less sensitive to chlorine disinfection (LA and LB) were selected from 42 populations according to two scenarios in which different reference values of statistical tests were used. Both LA and LB had higher genetic diversities of the outer capsid protein gene VP7 ; thus, tvia the exclusion of major sequences and allows the hidden rare sequences to get some space . The strength of the bottleneck determines whether the bottleneck performs its exploration  can be found below: S-SK, S-IU, TNu, SO, ONa, and DS designed the research. S-SK, performed the experiments related to the evaluation of the rotavirus phenotypes, the evolutionary simulation, and statistical analyses. ONa and TNa designed the experimental system using rotavirus and prepared the initial rotavirus populations. S-IU, MH, and TNu developed and performed the NGS processing. MK, SO, and DS developed the approach for phenotypic evaluation and conducted disinfection experiments. S-SK, S-IU, and YT conducted the population genetic analyses. ONi participated in data and statistical analyses. DS coordinated and supervised this study. S-SK, S-IU, TNu, and DS drafted the manuscript. All the authors have read and agreed to this version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Parachanna obscura. Our study aimed: (1) to develop a set of highly polymorphic microsatellite markers suitable for the analysis of genetic diversity among P. obscura and (2) to study the genetic diversity and structure of P. obscura populations from the West Africa region and mostly from Côte d'Ivoire, with respect to the effects of climate region and geographical distance on the genetic differentiation. A total of 259 specimens from 15 locations of P. obscura were collected over the West Africa region reflecting a high variability of pairwise geographical distances and variability of hydrological connectivity of the area. We developed a set of 21 polymorphic microsatellite markers for studying population genetics of the fish. The results showed relatively low intragenetic diversity for all the 15 locations, certainly attributable to confinement of fish in segregated catchments, resulting in limited gene flow. We also found evidence for local adaptation processes, suggested by five out of 21 microsatellite loci being under putative selection, according to BAYESCAN analysis. In turn, there was strong genetic differentiation (FST > 0.5) among fish from most locations, reflecting the allopatric development in watersheds without hydraulic connectivity. Neighbor‐joining dendrogram, Principal Coordinate Analysis, and analysis of ancestral groups by STRUCTURE suggested that the 15 locations can be divided into three clusters, mainly matching the dominant climate zones and the segregation of the watersheds in the region. The distinct genetic structure of the fish from the 15 locations obtained in this study suggests that conservation and sustainable management actions of this fish resource should avoid genetic mixing of potentially locally adapted populations.An essential factor for aquatic conservation is genetic diversity or population divergence, which in natural populations reflects the interplay between geographical isolation with restricted gene flow and local evolution of populations. The long geological history of Africa may induce stronger among‐population divergence and lower within‐population divergence in fish populations of African watersheds. As an example, we studied population structure of the African snakehead fish  Parachanna obscura. We found relatively low intragenetic diversity and high genetic differentiation, resulting in limited gene flow, a dominance of local adaptation processes and an allopatric development in watersheds without hydraulic connectivity.The long geological history of Africa may induce stronger among‐population divergence and lower within‐population divergence in fish populations of African watersheds. The study developed a set of 21 highly polymorphic microsatellite markers suitable for the analysis of genetic diversity among  According to estimates, there are over 3000 species of freshwater fish in Africa, which is comparable to the number of species in Asia (over 3600) and South America  to develop a set of highly polymorphic microsatellite markers suitable for the analysis of genetic diversity among P. obscura fish and (2) to study the genetic diversity and structure of P. obscura fish from 15 locations from the West Africa region and mostly from Côte d'Ivoire. According to the geological history of the region with potentially long periods of geographical isolation, we expected low genetic diversity and high genetic differentiation for this species. Furthermore, we expected that the genetic differentiation was affected by geographical isolation caused by the interplay between connectivity barriers, landscape structure, and geographical distances among locations.Previous studies on 22.1P. obscura from 15 locations were collected over the West Africa region from Côte d'Ivoire (14 locations) and Benin republic  GenoSat library preparation using 5 μg DNA from an equimolar pool of 10 DNA samples followed by high throughput DNA sequencing run on Nano 2x250 v2—MiSeq Illumina and bioinformatic analysis and primer design. (2) Biological validation of 142 primer pairs on eight P. obscura DNA samples from different populations including PCR amplification and analysis of the obtained profiles on QIAxcel (Qiagen). GenoScreen usually considers as validated those primer pairs with a specific PCR product at the expected size for at least 5 samples. This was the case for 120 (or 84.5%) of the tested 142 primer pairs. Based on the delivered list of validated primers with comments for each pair and migration profiles generated by QIAxcel ScreenGel 1.6.0, the number of potentially suitable primer pairs was further reduced by excluding those with weak, very weak, or no visible PCR product for one or more samples and focusing on those indicating polymorphism on the migration profiles. This screening resulted in 29 primer pairs selected for testing on a larger number of individuals using the PCR protocol described below. However, only 21 primer pairs (Appendix Genomic DNA from fin clippings was isolated using the DNeasy Blood & Tissue Kit (Qiagen) following the manufacturer protocol. The development of microsatellite markers suitable for the analysis of population genetics of Each forward primer of the 21 loci was synthesized with either DY751 or Cyanine 5 or BMN‐6 fluorescent dyes attached to its 5′ end. A set of eight multiplex reactions were conducted  was estimated in genepop_in_R. The significance of this coefficient was evaluated based on 95% confidence intervals calculated using bootstrapping (N = 100) in hierfstat 0.5–11 package in R , which is a multidimensional metric scaling, in order to visualize the distribution of individuals and location centroids in reduced space. This analysis was carried out in ade4 v1.7–19  values and the natural logarithm (ln) of the pairwise straight‐line geographic distances (km) to assess the effect of geographic isolation or distance on the genetic structure among P. obscura sample locations. The genetic distance matrix was calculated in the genepop package of R, while the geographical distance matrix was calculated with QGIS 3.22.6 using the global positioning system (GPS) coordinates of the sampling locations. Mantel correlation tests with 1000 permutations were performed by the vegan v 2.6–2 package  per locus ranged from two found at Para038, Para128, Para136, and Para137 loci to 12 found at Para027 locus , while the lowest variability was detected at locus Para042 . Following maximum likelihood estimates , KRINB  and BIN  had a higher genetic diversity than the other locations. The estimated inbreeding coefficient (FIS) value was low for all locations, and the confidence intervals showed that the FIS coefficients were non‐significant for all locations. Except for SASA (Para 107), SASB (Para027) and KRINA (Para104) locations where deviations from HWE at single loci were found  recorded relative to the other location pairs. Estimation of the pairwise FST was extremely low (close to zero) between SASA, SASB, NZOA, NZOB, and BAHO from Sassandra river basin and between KRINA and KRINB from Bia river.The pairwise rt Table . BAG wasp < .05) high genetic variation among locations (66.7%) and within individuals (33.5%), while no significant variance was detected among individuals within locations ) and the natural logarithm of the pairwise Euclidean geographical distance between the 15 locations. Although there was a significant positive trend between genetic and geographical distances, there were strong deviations from the linear pattern at 5.5 and 6.5 ln geographical distances, caused by the strong genetic distances between BAG and SASA, SASB, NZOA, NZOB, BAHO, and BAHO locations  was recorded at K = 9, suggesting that nine ancestral groups would be an alternative splits worth exploring. With K = 3, the STRUCTURE clustering diagram corresponded to differentiation among locations into three clusters as proposed previously in the distance‐based and Principal coordinate analysis. However, individuals of SANP and SIK locations displayed a more complex genetic structure with shared membership of ancestral origins from clusters I and II. Also, ABIN defined as belonging to cluster III shared memberships with clusters I and II. Considering K = 9, the STRUCTURE clustering diagram showed that the locations of the previously defined cluster II were clearly differentiated from the other locations, with two admixed ancestral genetic groups (yellow and orange). The other locations were unique with dominance of one ancestral group in each location, except KRINA and KRINB which belong to the same ancestral group.The fish from the 15 locations could be divided into three ancestral genetic groups  was putatively under balancing selection, while the other four loci  were under putatively directional selection , also for those with q > 0.05 , using the set of polymorphic microsatellite markers developed in the current study. Among the 120 produced and validated markers, a group of 21 polymorphic microsatellites were deemed suitable for the analysis of genetic differentiation among the sampling locations. However, some of these markers had a slightly increased frequency of null alleles. Microsatellite null alleles have been documented in studies of population genetics and PCR primer characterization  using only 19 microsatellites showed no difference in the value compared to the original data including null alleles. Jaisuk and Senanan  and Oreochromis niloticus  populations in eastern Africa. Mehner et al. (Coregonus cisco populations (AR = 3.4–6.3). Many studies on the genetic diversity of other species from the Channidae family such as Channa striata  showed highly significant differences among most of the locations of P. obscura except among locations from connected river networks. This result suggests that the fish from most of the different locations belong to distinct populations, except those collected from interconnected water networks, which may form one unique population together. In our study, SASA, SASB, NZOA, NZOB, and BAHO are samples from interconnected rivers  and therefore had low values of pairwise genetic differentiation (FST close to zero). Nzo River, Baho and Glo streams are tributaries of Buyo lake, which is an artificial lake created by dam construction on Sassandra river. Thus, fish from these five locations can be considered as belonging to one population. KRINA and KRINB are both from Bia River, with samplings only a few km apart, and therefore likewise can be considered belonging to the same population.The estimation of the pairwise genetic differentiation  populations from western North America.Aquatic species, and freshwater fishes in particular, frequently display strong population structure probably as a result of their confinement to the network structure of aquatic systems in the landscape  and the high genetic variation obtained among locations (66.7%) from AMOVA in our study, suggest that the populations have been isolated for long period of time, probably matching with the paleogeographic history of the formation of the hydrological systems on the African continent. Indeed, various paleogeographic  and paleoclimatic (such as wet‐dry cycles) events have had a significant impact on the African ichthyofauna from the Miocene to the Pleistocene by altering the connectivity between the various hydrographic systems , situated in the north Cote d'Ivoire, characterized by a tropical climate. As seen on the map, the watersheds of Bagoue River are completely disconnected from the watersheds of the other populations analyzed, making geographical separation very likely. This separation is likely induced by the geography of the area, which is characterized by a mountain ridge south of Bagoue River that prevents the river flowing to the south, as most of the other rivers in the southern part. Similar observations were taken on salmonid fishes for which it has been suggested that landscape characteristics such as the complexity of the drainage network and differences in channel gradients between habitats are likely to limit dispersal between their populations  but showed that ABIN shared a higher admixture with those populations from the sub‐equatorial climate areas, which form cluster I. Therefore, it is more likely that ABIN location also belongs to cluster I. The partly match between clusters and climate zones suggests that regional climate conditions may have contributed to the patterns of genetic diversity of P. obscura in West Africa. However, more appropriate types of markers  should be applied in future for confirming this hypothesis of regional climate as driver of the pattern of genetic diversity in P. obscura. In contrast, the population structure at higher resolution with classification into nine ancestral groups (K = 9) reflects the geographical configuration of the network of watersheds with populations from the same watersheds belonging to the same cluster. Hence, hydraulic connectivity or isolation have shaped gene flow among the habitats, facilitating differing evolution of the populations.The classification of 5P. obscura populations from West Africa, representing an important baseline for further exploration of the population dynamics in this species. Understanding the genetic diversity of wild populations can help establishing aquaculture breeding programs as well as conservation initiatives to preserve fish stocks and their unique genetic identities ; formal analysis ; investigation ; methodology ; validation ; writing – review and editing . Asja Vogt: Data curation ; formal analysis ; investigation ; methodology ; validation ; writing – review and editing . Klaus Kohlmann: Formal analysis ; investigation ; methodology ; validation ; writing – review and editing . Essetchi Paul Kouamelan: Conceptualization ; supervision ; writing – review and editing . Thomas Mehner: Conceptualization ; formal analysis ; funding acquisition ; methodology ; project administration ; resources ; software ; supervision ; validation ; writing – review and editing . Amien Isaac Amoutchi: Conceptualization ; data curation ; formal analysis ; investigation ; methodology ; project administration ; software ; validation ; visualization ; writing – original draft ; writing – review and editing .The authors have no conflicts of interest to declare.Appendix S1Click here for additional data file.


Dione (A.) dodona Lamas and Farfán , a recently described butterfly from xerophytic environments of the western slopes of the Central Andes, associated with Malesherbiatenuifolia (Passifloraceae). Searches for additional host-plant species were carried out throughout its known distribution. In total, seven species of Malesherbia have been reported as host plants for D. (A.) dodona, extending its distribution as far north as 50 km. We obtained samples of the last larval instar in six localities on different host-plant species that have disjointed distributions. We inferred the genetic diversity and structure between populations by using microsatellites and mitochondrial COI markers. The results showed a weak genetic differentiation between the evaluated populations, suggesting a continuum of gene flow through high dispersal, along with the disjoint occurrence of the host plants.In this study, we analyzed the population genetics of Dione (Agraulis) dodona  is a butterfly restricted to the western slopes of the Andes of Peru and Chile and is associated with Malesherbia tenuifolia in xerophytic environments. In this study, we found six additional species of host plants for D. (A.) dodona belonging to the genus Malesherbia (Passifloraceae). We used mitochondrial DNA sequences (COI) and microsatellites to screen genetic variation and investigate population structure in six geographic disjointed populations of D. (A.) dodona associated with distinct Malesherbia species. The PCoA analysis based on allele frequencies evidenced a lack of differentiation among populations and a low FST. The Bayesian cluster analyses revealed the existence of three genetically distinct groups, but almost all individuals present an admixture ancestry. An absence of isolation by distance pattern was observed. Possible scenarios are discussed: a bottleneck or recent colonization from interconnected populations from the south, and ongoing gene flow among local populations by high dispersal through a landscape formed of isolated populations of Malesherbia. Dione Hübner  includes a newly described species, Dione (Agraulis) dodona Lamas & Farfán, restricted to xeric slopes of the Andes in Peru and Northern Chile, associated with the host plant Malesherbia tenuifolia D. Don (Passifloraceae) [D. (A.) dodona oviposit on flowers or leaves near them, a behavior that is unique among Heliconiines, because the first instar feed on the internal structures of the flowers [Malesherbia R. and P. is a genus distributed in the Central and Southern Andes in Peru, Chile, and Argentina, mostly in the lower parts of the western slopes on rocky and sandy soils, in arid and semi-arid habitats [Malesherbia comprises five sections, of which only the Malesherbia section is found in Peru and Northern Chile, with M. tenuifolia being a member, along with thirteen other species [D. (A.) dodona appears to be narrowly based on the description records besides the type locality; this butterfly has been considered rare [The Neotropical butterfly genus oraceae) . The fem flowers . Maleshehabitats ,3,4,5. T species , mostly  species . The geored rare ,7, with red rare .D. (A.) dodona in the Peru–Chile desert [M. tenuifolia [Malesherbia between the Pacific Coast and the Andes of South–Central Peru and Northern Chile provides a potential for (i) wider geographic and potential additional host-plant ranges for this butterfly and (ii) the occurrence of isolated populations due to a discontinuous distribution of host plants. Accordingly, to verify the entire occurrence of D. (A.) dodona (considered a rare species) and to assess its host-plant range, it should be screened in habitats where Malesherbia species occur. Considering the patchy distribution of Malesherbia throughout extensive arid areas in the desert landscape of Western South–Central Peru and Northern Chile, together with the highly specialized egg-laying-site selection of the females, some degree of genetic differentiation might be expected for putative populations of D. (A.) dodona.The scale at which intraspecific gene flow occurs is an important property of a species, with implications for conservation. However, the spatial scale of genetic connectivity is often unknown since its assessment either requires large-scale mark-recapture studies or genetic data [Heliconius [Dione [Dione (Agraulis) vanillae [D. (A.) dodona.As the northernmost occurrence of e desert  exceeds nuifolia , and thetic data . A simpltic data ,10. Howetic data ; that isliconius ,13 and os [Dione . Since cvanillae , we consIn phytophagous insects, the distribution of intraspecific genetic diversity can be determined by the intrinsic and extrinsic factors of the organism. Life history can be of significant importance , but envD. (A.) dodona [M. tenuifolia [Malesherbia species were recorded by Beltrán et al. [D. (A.) dodona and evaluated the levels of genetic differentiation between populations along the corresponding latitudinal gradient in the western slopes of the Central Andes (Considering that the distribution of ) dodona  is much nuifolia ,5, one wn et al.  for the al Andes .Malesherbia plants along the roadways, covering a latitudinal gradient of seven degrees (∼1200 km), from Central Peru to Northern Chile, on western slopes of the Andes, between 1000 and 3000 m elevation (D. (A.) dodona on flowers of Malesherbia spp. was carried out in each locality where plants were found. Larvae of the fifth instar of D. (A.) dodona were collected and preserved in 70% ethanol. Samples of Malesherbia from each locality were also collected and identified at the species level, based on the literature [Fieldwork was performed from November 2020 to May 2022, screening for levation . An inteterature ,5.D. (A.) dodona  feeding on flowers of Malesherbia spp. were used for population genetics analysis: (1) LIM—Peru, Lima Department (n = 8), Canta; (2) ARE—Peru, Arequipa Department (n = 10), Pacaychacra, type locality; (3) MOQ—Peru, Moquegua Department (n = 10), Torata; (4) TAC—Peru, Tacna Department (n = 10), Chululuni; (5) CH1—Chile, Arica Region (n = 9), Cuesta El Águila; and (6) CH2—Chile, Arica Region (n = 5), Las Peñas . PCRs were performed by using the following conditions: 95 °C for 2 min, followed by 35 cycles of 30 s of 94 °C, specific annealing temperature for 30 s, 1 min of 72 °C, and a final extension of 72 °C for 4 min [We surveyed 14 microsatellites developed in the genus Agraulis . An M13 O), and the heterozygosity expected from Hardy–Weinberg equilibrium (HE) [p = 0.05 to adjust the statistical significance levels. Standard genetic diversity indices, such as the number of COI haplotypes and haplotype diversity per population, were estimated for mtDNA sequences, using ARLEQUIN 3.5. The topological relationship between haplotypes was estimated by using the median-joining approach [Genetic diversity within each population was estimated by using the mean number of alleles per locus ), the percentage of polymorphic loci (%P), observed heterozygosity (Hium (HE) , using tium (HE) . Genotypium (HE) . Linkageium (HE)  were tesium (HE) . Sequentium (HE) , with p approach  implemenFST [FST) and geographic  distances between populations were calculated in GenAlEx 6.5 and Google Earth 7.3, respectively. Pairwise genetic and geographic distance matrices were correlated by using a Mantel test, with a test for a significant relationship by 9999 random permutations that was also implemented in GenAlEx 6.5. To investigate the existence of hierarchical levels of population structure, an analysis of molecular variance (AMOVA) was performed by using allele frequencies with ARLEQUIN 3.5, following a geographic pattern of north  to south (CH1 and CH2) structure.Genetic differentiation between the six populations was characterized by exact tests  and by eFST , within FST . We inveD. (A.) dodona, a principal component analysis (PCoA) was applied by using gene frequencies of all six variable microsatellite loci. The frequencies of all the alleles at a single locus were considered independent variables. Accordingly, correlation matrices were computed from the gene frequencies of all the loci, as well as the eigenvalues of all the principal components, the proportions of individual eigenvalues to the total variance (contribution rates of components), and the raw scores of every individual for each of the principal components. PCoA was performed by using the program PCAGEN 1.2.1.As an alternative approach to represent the genetic relationship among the six populations of K clusters was set from one to six . MCMC simulations were performed by using the admixture model and correlated genetic frequencies among individuals. Simulations ran for 1,000,000 iterations, using 100,000 iterations as a burn-in period. We evaluated the length of burn-in, as well as the stabilization of the parameters of the analysis, using the plot function present in STRUCTURE 2.3.4 software. Then we ran results through STRUCTURE HARVESTER  [K as the selected value of K (Evanno method) [A Bayesian clustering procedure implemented in STRUCTURE v 2.3.3  was useday 2022) , using t method) .D. (A.) dodona by using COI sequences. We performed Fu’s Fs test by comparing Fs statistics against a distribution generated from 1000 random samples, using Arlequin v.3.5. The statistic Fs tend to be negative under an excess of recent mutations, and a significant negative value was then taken as evidence of population growth. We also estimated Tajima’s D statistics, and the significance of the D statistic was tested by generating random samples under the hypothesis of selective neutrality and population equilibrium, using a coalescent algorithm. A mismatch distribution analysis was also performed for the species, using the population growth–decline model in the software DNAsp 6.12.03 [Finally, we looked for a genetic signature of past demographic events  in populations of  6.12.03 .D. (A.) dodona were recorded in seven localities  dodona) on the western slopes of the Andes in Peru and Chile  J. St.-Hil. Each of the seven localities presented a distinct species of Malesherbia, except for CH2, which showed M. tenuifolia, the same species recorded in type locality (ARE). An additional species, M. fatimae, was found in the population of D. (A.) dodona of the type locality. The environments where Malesherbia spp. occurs correspond to xerophytic slopes or dry ravines with very little vegetation (Seven host-plant species for nd Chile  Table S1getation .D. (A.) dodona (LIM) was recorded 50 km north of the northernmost known record of the species; therefore, we expanded its distribution to the north in the Department of Lima, in the Chillon river basin in the locality of Canta, with immature stages associated with M. tubulosa. Additionally, two Malesherbia species were found in two localities without immature stages of D. (A.) dodona: M. laraosensis H. Beltrán and M. Weigend ; and M. tenuifolia .A population of p = 0.05), thus suggesting that loci are independent. Significant positive departures from the Hardy–Weinberg equilibrium were found in two loci (Dione9 and Dione27) for two populations (LIM and CH2)   Table S3As two of our six loci were possibly affected by null alleles, we performed all statistics twice: once for all loci, and a second time for only the four loci without null allele signals. In the following analysis, we only show the values based on all six loci  of the total variance component was explained by the north (LIM + ARE + MOQ + TAC) and south (CH1 + CH2) groups of populations (see p = 0.000) was identified within populations. The FST based on allele frequencies between pairs of populations ranged from 0.10 to 0.30  and south (CH1 and CH2) groups were observed. However, Mantel tests did not show a significant correlation between geographic distance and estimates of gene flow, thus failing to support a pattern of isolation by distance  , the differences among groups were not significant. Accordingly, 13.5%  b.K = 3)  . All thrD. (A.) dodona  are presented in The results of Fu’s and Tajima’s neutrality tests for three populations of Malesherbia and D. (A.) dodona is intimate, both in space and time, considering the overlap in their distribution and origin of the clades (around 5 Mya) [Malesherbia [D. (A.) dodona. In fact, radiation of heliconiine butterflies occurred predominantly on the eastern slopes of the Andes in Colombia, Ecuador, and Peru, as well as in the upper/middle Amazon basin [D. (A.) dodona larvae–Malesherbia plants. Such a co-evolutionary scenario predicts a progression from heliconiine species that feed on woody, long-lived, and presumably ancient food plants to those that specialize in herbaceous recent passion vines [Malesherbia is a fragile herbaceous plant, not showing a scandent growing habitat. Furthermore, ecological opportunism and host and habitat shifts also seem to have played a major role in Heliconiinae speciation  dodona and associated Malesherbia plants—which, in most cases, are geographically isolated—where populations of both seem to be very localized, restricted to single valleys on the western slopes of the Andes, offers a unique opportunity for future studies on heliconiine evolution of host-plant usage.The evolutionary history of d 5 Mya) ,34. The d 5 Mya) ,36,37. Tesherbia . From a on basin . Thus, ton basin  were theon vines . Howeverferences ,41). TheDione (A.) dodona uses seven distinct Malesherbia species as host plants, revealing it as an oligophagous species. This oligophagy was evidenced through the field collection, with each population presenting larvae that use distinct plants. Some of these individuals were brought to the laboratory and reared, exchanging the host plants. Flowers of M. ardens from Torata locality (MOQ) were offered to the first instar collected on M. tenuifolia from the type locality (ARE); the larvae accepted the flowers and fed during the following days without any negative behavior (unpublished data). All Malesherbia species reported here belong to the Malesherbia section, a lineage distributed from Northern Chile to Central Peru [D. (A.) dodona (7 vs. 13 spp.). Although the search for Malesherbia species during this study was restricted to the altitude range from 1000 to 3000 m elevation , it is not ruled out that D. (A.) dodona can use as host plant Malesherbia section species that are found at lower elevations, as is the case of M. tocopillana, which is only known from its locality type (Tocopilla) in Chile, at an altitude range of 150 to 400 m elevation [M. haemantha, M. scarlatiflora, M. splendens, and M. turbinea.We found that ral Peru ; more thD. (A.) dodona is strongly associated with the presence of Malesherbia, since all known records of adults match the distribution of this plant genus on the western slopes of the Central Andes [D. (A.) dodona in a given area seems to relate to the availability of plant population belonging to the section Malesherbia, which is mostly allopatric. Changes in host plants among localities allow the occurrence of this butterfly in a vast area in the xeric western slopes of the Andes. However, the genus Malesherbia has a much wider distribution. Thus, in a broader sense, D. (A.) dodona may be limited in expansion due to the absence of Malesherbia species accepted as hosts, a subject which should be further explored. Heliconiinae, in general, is selective regarding intra- and inter-specific attributes of plants used as hosts, particularly regarding oviposition behavior and larval feeding, which may lead to local specializations [Dione (Dione) moneta and Heliconius nattereri are monophagous and, thus, have narrow geographic distributions in a given area of occurrence, which are limited by that of their host plants [The occurrence of al Andes . Interesizations ,43,44,45t plants ,42,46.D. (A.) dodona contain relatively little genetic variation and are not genetically subdivided. The overall haplotype and nucleotide diversity in the sister species Dione (A.) vanillae was 0.68 and 0.0143, respectively [D. (A.) dodona. Considering that the D. (A.) vanillae study included subspecies (further considered species) in addition to the population level, and that variation in sampling size and length of sequences likely influence such differences between studies, we looked to the microsatellites within the genus. Similarly, the other species of Dione showed little variation  [Our study shows that populations of ectively , which iD. (A.) dodona migrates or stays in a kind of dormancy when plants are not in active growth. Given that one congeneric species, D. (D.) moneta, is putatively migratory [D. (A.) dodona could have a migratory potential. This aspect should be explored in conjunction with variation in phenology of Malesherbia species used as hosts, since females are highly selective regarding oviposition, laying eggs on flowers [D. (A.) dodona could result from lower historical population sizes and perhaps more recent colonization and/or bottleneck, as is supported by neutrality tests and mismatch distribution.It remains unknown whether igratory , we migh flowers . Thus, t flowers . AccordiDione (Agraulis) dodona is the oldest lineage among the extant Heliconiinae, ca. 8.59–2.8 My according to estimates of Nuñez et al. [Malesherbia section species  are relatively young (~5 My), with the genus Malesherbia having only recently colonized hyperarid habitats in deserts (~10 My) [D. (A.) dodona originated in the north of Chile and spread to Central Peru, together with the species of the Malesherbia section, being relatively recent colonization that does not allow us to detect genetic divergence between the populations. There exists a slight difference in the genetic structure about the southernmost population evaluated of D. (A.) dodona in the Arica region (CH2); however, this could be studied in greater detail in future studies including more southern populations.z et al. ; and sev(~10 My) , thus poD. (A.) dodona  is promoted by the distribution of the Malesherbia species. Historically, the species of Malesherbia recorded in Peru were considered isolated populations in their majority, due to the few records [Malesherbia species were absent. An example is the Department of Ica, which connects ARE and LIM populations, where no Malesherbia species were recorded. However, probably due to the lack of access to areas where this plant genus grows, we assume that if a species of Malesherbia is recorded in the future in this Department, it could be the link between the populations of D. (A.) dodona from Lima and Arequipa that are currently separated by approximately 600 km, without a record of species.In the second scenario, probably connectivity between the different populations of  records . HoweverMalesherbia species, based on the individual admixture of alleles and shared haplotypes of D. (A.) dodona, allows us to infer a high dispersal capability in this species. Likely using a stepwise pattern, individuals keep an ongoing gene flow between populations along 1200 km.The evidence of connectivity between populations using different D. (A.) dodona populations are almost completely devoid of vegetation, representing potential barriers to the dispersal of flying insects, as recently reported for a leaf miner micromoth whose geographic range partially overlaps that of this butterfly [D. (A.) dodona, its dispersal ability may not be enough to maintain population connectivity. Accordingly, habitats in which this butterfly and its host plants occur must be protected as a first step toward contributing to the conservation of the still insufficiently known biodiversity of the arid environments of the western slopes of the Andes.Desert areas separating the currently documented utterfly . High coutterfly . HoweverDione (Agraulis) dodona is an oligophagous species which uses at least seven different species of Malesherbia (Passifloraceae) as host plants within its distribution range on the western slopes of the Andes of Peru and Northern Chile. Such a distribution coincides with that of species located within the section Malesherbia, recorded as the only host plant group, suggesting that these are selected for oviposition, thus conditioning the geographic distribution of this butterfly. We did not find significant genetic differentiation between population samples from different host-plant species. Our results indicate high genetic exchange among populations between the different localities and host plants. It remains unknown how such a gene exchange is made among such geographically isolated populations located in the valleys of the Andes.


Puccinia striiformis f. sp. tritici (Pst), is one of the most important fungal diseases affecting wheat (Triticum aestivum L.) worldwide. In this study, the genetic diversity and population structure of Pst isolates were analyzed using 15 microsatellite markers. Isolates were collected from five wheat cultivars with different levels of resistance from Yanting county and Fucheng district, Mianyang city, Sichuan province, China. The aim of this study was to investigate whether Pst populations are differentiated by wheat genotype or geographic origin. Seventy-six multilocus genotypes (MLGs) were identified from all 289 single uredinial isolates. In general, the genotypic diversity of Pst populations from five wheat cultivars in Fucheng was higher than that in Yanting. In addition, the genetic diversity was highest in the Pst populations from Mianmai 367, a cultivar considered to be highly resistant. The unweighted pair group method with arithmetic mean (UPGMA) phylogenetic tree, Bayesian clustering analysis, and minimum spanning network for the MLGs revealed two major genetic clusters based on geographical location. Greater differentiation was observed between the populations from the two sampling locations than between the populations from different hosts in the same location. The results suggest that geographic and environmental differences could partially explain the genetic differentiation of Pst more than wheat genotype. This study provides novel insight into the interactions between Pst populations and their hosts. The results could be helpful in designing more effective management strategies for stripe rust in wheat production.Stripe rust, caused by  Puccinia striiformis f. sp. tritici (Pst), is one of the most important fungal diseases affecting wheat (Triticum aestivum L.) worldwide [2 each. These resulted in total yield losses of up to 13.8 million metric tons [Stripe rust, caused by orldwide ,2, especorldwide . China iric tons ,5.Pst pathogen usually lead to cultivars that previously possessed resistance against a single major gene becoming susceptible within a few years [In agricultural production systems, different disease management practices are used to control wheat stripe rust, including fungicides and genetic resistance. As an alternative to the use of fungicides, developing and growing resistant cultivars is considered a more economical and environmentally sound approach for the control of stripe rust ,6. Henceew years ,8. TherePst populations have traditionally been elucidated using avirulence/virulence analyses. In China, since the 1940s, the identification of stripe rust races has been conducted based on the avirulence or virulence responses of isolates inoculated on host cultivars or genotypes [Pst populations in China have been analyzed on a regular basis [Pst that have been described in China using the current set of wheat differentials [Pst populations, which mediate the virulence of the pathogen, is limited. To understand the genetic structure and diversity of Pst populations, as well as the evolutionary processes that shape pathogen populations, reliable molecular evidence is needed [Studies on population genetics have enabled plant pathologists to gain a better understanding of the genetic structure of pathogen populations, as well as the evolutionary processes that shape pathogen populations ,10. The enotypes . More rear basis ,13,14. Trentials ,12. Thesrentials ,15. Howes needed . Pst, such as amplified fragment length polymorphisms (AFLPs) [Pst populations have focused on the differences and correlations of Pst populations over time (different years and epidemic periods) and space . Thus, the studies have revealed the origin, sources of inoculum, routes of introduction, migration pathways, and reproductive biology of Pst. Recently, several studies have examined the effect of wheat cultivars on population structure of Pst [Historically, numerous DNA-based tools have been applied in population studies of  (AFLPs) ,18,19,20 (AFLPs) ,22,23,24e of Pst ,26.Pst populations from 19 cultivars of wheat. The genetic diversity was highest in a population found in Tian 863-13, a highly resistant wheat cultivar. In contrast, the genetic diversity was lowest in a population found in Huixianhong, a highly susceptible cultivar. Recently, Chu et al. [Pst. Their results showed that Pst genotypic diversities from resistant cultivars may be higher than or as low as those from susceptible cultivars, but the frequency of the Pst dominant genetic group from the highly susceptible cultivar was generally higher than those from resistant cultivars.In agricultural systems, host genotype and diversity can affect pathogen population genetic diversity and structure ,25,26. ZHow plant pathogen populations and their genetic structures respond to the deployment of host resistance is still poorly understood. This knowledge is important for the effective use of plant resistance and sustainable disease management. ,27. In tThe experiments were conducted in Yanting county  and Fucheng district , Mianyang city, Sichuan province, in the 2014–2015 growing season  with a nPuccinia striiformis West.) (GB/T 15795-2011). The disease index (DI) was calculated as: I is the disease incidence and L is the total number of diseased leaves and iS and il are the disease severity and the number of diseased leaves with disease severity in percentages of 1%, 5%, 10%, 20%, 40%, 60%, 80% or 100%. At each site, five wheat cultivars, namely Mingxian 169 (MX), Xiaoyan 22 (XY), Zhengmai 9023 (ZM), Xikemai 4 (XK), and Mianmai 367 (MM), were planted in the experimental fields in a randomized block design (RBD) with three replications on 3 November 2014. Each plot was 5 m long and 6 m wide. Plots were separated by 0.5 m. The fields in Yanting (Yt) and Fucheng (Fc) consisted of 15 plots each. In mid-March 2015, the disease incidence and severity were evaluated at each experimental site during the period of occurrence of the stripe rust. In the fields, five subplots (approximately 30 × 30 cm) were randomly selected from each plot. In each subplot, 100 leaves were randomly sampled to assess disease incidence and severity. Disease incidence and severity were measured using the Rules for Monitoring and Forecasting Wheat Stripe Rust (Infection types (ITs) were visually scored on a 0–4 scale, as described by Chen et al. . IT scorPst followed the Rules for Resistance Evaluation of Wheat to Diseases and Insect Pests—Part 1: Rule for Resistance Evaluation of Wheat to Yellow Rust (NY/T 1443.1-2007).The evaluation of wheat cultivars for resistance to Pst were collected from experimental plots in Yanting and Fucheng after disease assessment on the same day. Approximately 60 leaves were obtained from each wheat cultivar and were considered as a population at each experimental site. The leaf samples were packaged in an absorbent paper bag and kept in a desiccator at 4 °C for later use.Wheat leaves naturally infected by Isolates were purified and multiplied from the collected samples by inoculating single pustules separately on 10-day-old seedlings of universally susceptible Mingxian 169, as previously described by Liang et al. . Inocula® UV-Vis Spectrophotometer . The DNA concentration was adjusted to 100 ng/μL by adding distilled water for use in the PCR amplification. The DNA of each isolate was extracted using a modified cetyltrimethylammonium bromide (CTAB) protocol, as described by Wang et al. . The conPst SSR primers were screened for useful markers. Among the 15 primers, RJ3, RJ13, and RJ21 were developed by Enjalbert et al. [Fifteen pairs of t et al. ; RJ3N ant et al. ; PstP03,t et al. ; and WSRt et al. . These p2+ free), 0.8 μL of Mg2+ (25 mM), 1.0 μL of dNTP (2.5 mM), 0.1 µL of Taq DNA polymerase (5 U/µL), and 5.1 μL of ddH2O. The reaction programs were set at 94 °C for 4 min; followed by 35 cycles of 30 s at 94 °C, 30 s at 52 °C, and 1 min at 72 °C, with a final extension at 72 °C for 10 min in a thermal cycler. PCR was conducted with a volume of 10 μL, which included 1.0 μL of DNA (100 ng/μL), 0.5 μL of each primer (10 mM), 1.0 μL of 10 × reaction buffer  by Beijing Tsingke Biotech Co., Ltd. The lengths of the SSR amplicons were scored using GeneMarker software  and compared to a GS500 (35–500 bp) internal standard . Since PPst isolates were defined as different populations according to the geographic locality or host cultivar. Locus-based statistics were calculated using GenAlEx software [Na) and number of effective alleles (Ne). The R package poppr 2.2.0 [eMLGs), the Shannon–Wiener index H [5E [expH [eMLG) considers the sample size and is equivalent to the genotypic richness of a population [eMLGs was estimated based on rarefaction curves using 1000 bootstrapped samples [5E [5E ranges from 0 to 1, with 0 indicating that the population is dominated by a single MLG and 1 indicating that all genotypes occur with the same frequency.All software  and inclpr 2.2.0  was used index H , the genex H [E5 ,38, and E5 [Hexp . Multilopulation . The num samples . Genotypples [E5 ,38. E5 rrbarD) [poppr [rbarD = 0) was assessed by permuting alleles between individuals independently for each locus (999 permutations). If the observed rbarD value was located outside the distribution of the randomized dataset at p < 0.001, the population was considered clonal [In order to determine the degree of clonality and the potential for recombination in the population, linkage disequilibrium was measured by the standardized index of association (rbarD)  using th) [poppr . Departud clonal .poppr was used to calculate the Nei’s distances between populations using the clone-corrected data and build the phylogenetic tree using the unweighted pair group method with arithmetic mean (UPGMA) [K) were evaluated from 1 to 10. For each K, we performed 10 independent runs with 500,000 iterations per run and a burn-in period of 100,000. The optimal K value was estimated using the method described by Evanno et al. [To infer between-population relationships, the R package  (UPGMA) . Populat (UPGMA) . The dato et al.  with STRo et al. . Pst isolates from different host populations, pairwise STF values were estimated, and significance levels were tested with 999 permutations using GenAlEx software [To test for differentiation between software . An analsoftware  was alsosoftware . poppr [To test evolutionary relationships between genotypes, microsatellite-based genetic distances were calculated using the Bruvo genetic distance . A minimpoppr  based onThe analysis of variance (ANOVA) of the obtained data was performed with the software package SPSS v. 20.0 . The least significant difference (LSD) at a 5% level of significance was used to compare the treatment means (disease index of stripe rust among wheat cultivars).Pst of the five wheat cultivars, the occurrence and severity of the stripe rust were surveyed before sampling in mid-March 2015. Results revealed that the disease index of stripe rust varied significantly among the five wheat cultivars, both in Yanting and Fucheng   .Pst populations from the five wheat cultivars in Fucheng was higher than the host cultivars from Yanting (H: 2.94 vs. 2.39), and the 5E was also higher (0.46 vs. 0.25) (H) ranged from 1.48 for FcZM to 2.04 for FcMM (H) (1.94), followed by YtXK (1.84), YtXY (1.69), YtMX (1.67), and YtZM (1.50) (expH) also revealed that the Fucheng population was more diverse than the Yanting population (expH: 0.30 vs. 0.24) at the region level (expH = 0.305) than other populations (expH = 0.309) than the other populations  . In Fuchfor FcMM . In YantM (1.50) . The genon level . In Yantulations . Similarulations .Pst populations was tested using the standardized index of association rbarD, with 999 permutations of the genotyping dataset. For each host population from Yanting and Fucheng, and the overall population, the hypothesis of no linkage among markers was rejected (p < 0.001) (Pst populations. The hypothesis of random mating within < 0.001) , supportPst populations could be divided into two major clusters by sampling location (K) calculated for Bayesian modeling was K = 2 , 9.20% to the differences among individuals (p = 0.001), and 61.08% to the differences within individuals  between collections of Pst from five different wheat cultivars also showed low levels of genetic differentiation between collections from the same location  than the other populations, and they were also more diverse according to gene diversity (expH)  . The mosPst populations originating from different wheat cultivars in Yanting and Fucheng. However, in previous studies [Pst populations from Sichuan Basin had rbarD values that were not significantly different from zero, indicating a lack of linkage disequilibrium. Indeed, recent studies have confirmed the possibility of recombination in Pst populations [Pst sexual stage has been observed with barberry (Berberis spp.) as an alternate host [Pst sexual reproduction has been reported to have occurred in China [When the reproductive mode was investigated, it was not possible to find evidence of recombination between genetically distinct individuals in the  studies ,23, Pst ulations . Additioate host , and a vin China .Pst from five different wheat cultivars in two different geographic locations. Significant differences in genetic diversity among Pst populations from different wheat cultivars were detected, whereas wheat cultivars did not significantly shape the population structure of the Pst populations. With the genetic characterization of Pst populations from different wheat cultivars in mind, framing control strategies should be emphasized for developing cultivars with non-specific and durable resistance that are effective against Pst.In this study, we analyzed the population genetics of