Plantago lanceolata plants as sentinels during a seasonal virus epidemic, we find non-random co-occurrence patterns of five focal viruses. Using joint species distribution modelling, we attribute the non-random virus occurrence patterns primarily to differences among host genotypes and local population context. Our results show that intraspecific variation among host genotypes may play a large, previously unquantified role in pathogen community structure.Infection by multiple pathogens of the same host is ubiquitous in both natural and managed habitats. While intraspecific variation in disease resistance is known to affect pathogen occurrence, how differences among host genotypes affect the assembly of pathogen communities remains untested. In our experiment using cloned replicates of naive  Plantago lanceolata and its viruses, showing that viral co-occurrences are driven predominantly by environmental context and host genotype rather than viral interactions.The factors that determine whether pathogens co-occur in a host are poorly understood, especially for plant viruses. Here the authors conduct field experiments with the plant  Host susceptibility, pathogen infectivity, and environmental favourability have been identified as the corner stones of disease within the disease triangle framework8. However, it is becoming increasingly clear that multiple infections within individuals are abundant3, and have the potential to change the evolutionary and epidemiological trajectories of pathogens9. Consequently, accounting for the diversity of infection is necessary to understand and predict disease dynamics and costs of infection for the host.Parasites constitute the majority of biological diversity on our planet11, others highlight the importance of environmental drivers, including stress and disturbance on community dynamics13. To date, disentangling biotic processes from the abiotic ones has remained challenging14. In recent years, pathogens are increasingly studied within a community ecological framework19. Environmental variables and wider landscape context, such as human management, are linked to infection load, parasite diversity, and coinfection prevalence across multiple spatial scales26. The composition of parasite communities has also been linked to pathogen transmission mode, degree of host specialty, and life-cycle complexity29, as well as host history, phylogeny, geographical range, longevity, and growth strategy38. High parasite prevalence itself is a strong predictor of coinfections39. For vector-borne diseases, positive co-occurrence is common for pathogens that share a vector or transmission site, or when vectors show preference for already infected individuals41.Understanding the determinants of the assembly and composition of pathogen communities is one of the key challenges in disease biology today. As a challenge it is analogous to the long-standing debate on the relative importance of biotic interactions versus external drivers of community dynamics. While some theories suggest species interactions to structure biological communities44. One of the key challenges is to determine how biotic interactions between hosts and their pathogens themselves shape these distributions. Under the community ecological framework, a host can be viewed as a resource patch and its resistance as a local filter that determines the pathogen community within that host18. Hosts are resistant against most pathogen species they encounter45, and even for pathogens capable of infecting a host species, there is often considerable variation among individuals in their susceptibility50. The effect of intraspecific variation in disease resistance on the dynamics of individual pathogens is well described54. However, the importance of intraspecific host resistance variation for community assembly and diversity of species that exploit the host is only beginning to gain attention55. Due to allocation costs associated with genetically-based resistance, a host resistant against a particular pathogen may be susceptible to others57. On the other hand, limited evidence suggests that the same resistance loci may provide protection against several different pathogens58. Pathogens attacking the same host may also compete for host resources (resource-mediated interaction), or interact via elicited host immune responses59. Induced immunity by a first arriving pathogen may change the resistance phenotype, as immuno-suppression of the host by the first arriving pathogen may facilitate establishment and replication of later arriving pathogens63. On the other hand, cross-reactive immune responses elicited by the first parasite have the potential to suppress the success of later arriving parasites64. These biotic interactions could result in non-random pathogen co-occurrence patterns across host genotypes. Variation in host resistance may be spatially structured with pronounced differences in resistance observed among host populations53 and regions65. Such spatially structured resistance variation may also drive spatially structured co-occurrence patterns of pathogens exploiting the same host. Whether the host genotype is indeed a strong determinant of within-host parasite communities in the wild, and what the consequences of these within-host parasite community assembly processes are for host populations, remain unanswered66.Co-occurrence of pathogens among host individuals is often non-random and coinfections can reach unexpectedly high levels68, and visually asymptomatic infections are common in wild plants3. Using cloned replicates of naive Plantago lanceolata plants as sentinel traps placed in natural populations during a seasonal epidemic of viruses, we can tease apart the role of the host genotype from drivers that affect the distribution of viruses within the local population context, which may include environmental variation, the local disease pool, host population structure and history, as well as local vector communities. Moreover, we aim to understand how biotic interactions among the viruses64 influence their community assembly.Here, we study the importance of the host genotype in determining the structure of within-host virus communities. Viruses are in principle obligate parasites as they require a host for reproduction. A growing body of evidence has demonstrated that consequences of virus infection can shift along the pathogenic–mutualistic-continuum, even for the same interaction69. We first test whether the viruses occur in the same sentinel plant more often than would be expected based on their frequencies alone. In other words, we test whether virus co-occurrence patterns differ from expectations of a random distribution. We then employ a joint species distribution modelling (JSDM) framework70, that allows us to tease apart the effect of local population context  on virus (co-)occurrences from host plant characteristics and host genotype. We can account for the shared environmental responses of the target species, which makes the model a robust method also for sparse data71. Using this approach, we are also able to capture signals of possible biotic community assembly processes from virus‐to‐virus association matrices after controlling for shared environmental responses of the viruses. The performance of JSDMs in relation to traditional, single-species distribution modelling (SDM) methods has recently been validated72. The application of these kinds of multivariate statistical tools— typically used in community ecological analysis—to parasite data has the potential to reveal new insights of the determinants of parasite community assembly and composition74.We characterize the establishing virus communities using PCR detectionAfter accounting for the aforementioned effects (2–3) of the local population context and plant host characteristics (including the host genotype), is there evidence of residual virus co-occurrence patterns across the entire data indicative of competitive or facilitative virus interactions?; and (5) Do these residual co-occurrence patterns vary among host genotypes indicating genotype-specific resistance responses affecting virus community structure? Our results indicate that while the population context also drives virus community assembly, host genotypes vary in the virus communities they acquire.In this study, we ask: (1) Do we see more  co-occurrences between the viruses than what would be expected solely based on their frequencies?; (2) Does the local population context affect the virus community composition?; (3) Do host genotypes differ in the virus communities they acquire, suggesting genotype-level variation in overall sensitivity to infection?; (4) Out of the 320 sentinel host plants, 68% were hosts to at least one virus over the study period. Three viruses were clearly more common in the sentinel plants: closterovirus in 120 individuals, betapartitivirus in 102 individuals, and capulavirus in 84 individuals; while caulimovirus and enamovirus were rare: in 10 and 5 individuals, respectively Figs.  and 2. OWe found significant non-random positive co-occurrences between species pairs capulavirus and caulimovirus as well as betapartitivirus and caulimovirus, when we analysed the complete data set Fig. . When weThe model variants 2 and 3 performed almost equally well, as seen from their performance Table . Model vThe variance partitioning conducted for the model variant 2 revealed sentinel plant genotype to be the most important determinant for virus community composition . However, further inspection revealed that none of the residual correlations between virus species gained strong statistical support. Hence, we see no signal of potential biotic interactions between viruses after taking into account the effects of fixed explanatory variables, i.e. the sentinel plant genotype, size, signs of herbivory and local population context.As expected, the predicted coinfections based on model variant 2 show similar patterns to what we can see in the raw data occurrences of the viruses can be attributed to some unmeasured abiotic or individual-related variables, which may influence the (co-)occurrences of the viruses.Of the total amount of variation explained with our best model variant, the population context explained within-host virus communities to a large extent, although the proportion of explained variation varied among the viruses Fig. . Drivers82, to our knowledge the effect of host genotype on the assembly has rarely been tested experimentally in wild systems, or with multiple parasites simultaneously. In our data, sentinel plant genotype accounted for most of the variation in virus occurrences of the total variation explained in the JSDM model. Indeed, both virus occurrence, and the acquired virus communities varied among the four P. lanceolata genotypes. In particular, sentinel plant genotype 609_19 had greater infection prevalence and diversity of viruses than the other genotypes  that confer resistance against a broad range of pathogens, have been characterized in P. lanceolata . Uncovering both phenotypic and molecular level virus resistance in this system is an important avenue of future research. Spatially structured variation in resistance is characteristic of natural host-parasite systems87, and based on our findings, intraspecific variation in disease resistance in a host population may play a large, previously unquantified role, in the non-random distribution of co-occurring pathogens that have been detected in the previous studies46.While there are multiple studies investigating within-host parasite communities89. Vector preference for infected hosts90 could also influence virus co-occurrence patterns. Transmission mode is often found to be critical for how pathogen communities are formed92, and reciprocally, the amount of genotypic variation within a host population may explain the abundance and composition of herbivory community present89.Intraspecific variation in traits other than resistance could also generate the differences we observe. To confirm which traits are involved, future studies should explore in more detail the ecological outcomes of these interactions, and their molecular underpinning. It is highly plausible that the host genotype could indirectly affect virus occurrences via their attractiveness or resistance against vector herbivores94. Although we find both positive and negative co-occurrence patterns among the viruses, these are largely explained by local population context and host genotype. After controlling for these in our model, we do not find strong statistical support for signals of associations among the viruses, as would be expected if arrival by one would decrease or increase the arrival probability of another. Hence, our results do not support the hypothesis that virus–virus interactions—either direct or those mediated by host immunity—would be the key drivers of virus community assembly at the within-host level in this system. However, our sample size could be insufficient to detect such interactions as some of our viruses are rare, and their arrival probability to the sentinel plants is also subject to random processes. In addition, we only accounted for a subset of all possible pathogens infecting plants in this system, thereby potentially missing some influential members of the community. Furthermore, the effects of induced immunity triggered by a first arriving pathogen may be short-lasting95 and, therefore, undetectable with the timescale of this experiment. Induced immunity could play a more important role among viruses of the same genus or strains of the same virus species, where the famous phenomenon of cross-protection is more often recorded96 and as is predicted by theory75. Given that the variants with less conservative priors detected a significant positive interaction between betapartitivirus and caulimovirus, we conclude that our study design was successful in capturing the effects of the host genotype, but larger-scale investigations would be required to detect signals of virus–virus interactions.A community of pathogens could be shaped by both direct and indirect pathogen–pathogen associations: reaction triggered by an earlier arrival could either induce or suppress resistance against later arriving pathogens, or within-host competition could favour one pathogen over the other. Evidence for both negative and positive pathogen–pathogen interactions have been reported in studies of multiple infections90. Nonetheless, transmission of all five focal viruses to the sentinel plants did occur. Whether the virus prevalences we detected with our approach are in line with infections of wild plants is difficult to assess, given that virus prevalences vary greatly among populations in the Åland Islands (0–64%)69. Overall, our study does not only highlight the importance of the host genotype, but also the need for further research on other aspects of virus ecology. Although we have placed the current work into a context of pathogens, viruses may also have neutral or positive effects on the hosts despite their parasitic lifestyle67. While knowledge of virus diversity and roles of viruses in wild populations is increasing67, research at the community scale remains scarce18.In our experimental design we kept the plants in their pots which meant these plants experienced different rooting environments than the wild plants but allowed us to standardize some factors . However, this approached allowed us to control for this level of variation in our data. Our approach may have affected vector preferences as visual presentation of the plants, in addition to other cues, is important for vector dynamicsHere, we have quantified the importance of intraspecific host plant variation on how within-host virus communities assemble by using sentinel plants in natural populations during a seasonal epidemic, which allows teasing this factor apart from other drivers of virus occurrence. Applying JSDMs to interpret the effects of host genotype and local population context, we find that while the population context has a strong influence on virus communities within individual hosts, not accounting for the host genotype might underestimate the role host genotypes have in generating variation in pathogen communities. Such variation in within-host pathogen diversity may have far reaching implications for all key aspects of disease: transmission, virulence suffered by the host, and pathogen evolution. With these results, we are one step closer to binding together the different spatial scales and processes that underpin pathogen metacommunities.Plantago lanceolata is a globally occurring perennial herbaceous plant97. It is an obligate outcrosser with wind-dispersed pollen, also capable of vegetative reproduction97. In the Åland Islands, SW of Finland, it typically grows on dry meadows, forming a network of approximately 4000 small connected populations81. The size and location of the populations have been monitored since the early 1990s as a part of the metapopulation studies of the Glanville fritillary butterfly and powdery mildew Podosphaera plantaginis98. In the Åland Islands, P. lanceolata also hosts a diverse community of viruses that vary in their occurrence among P. lanceolata populations and among the individuals within populations69. We used five recently characterized viruses from the Åland Islands, to study within-host viral communities69: Plantago lanceolata latent virus in genus Capulavirus and Plantago lanceolata caulimovirus in genus Caulimovirus with DNA-genomes, and Plantago betapartitivirus in genus Betapartitivirus, Plantago enamovirus in genus Enamovirus, and Plantago closterovirus in genus Closterovirus with RNA-genomes. The viruses are hereafter referred to by their genus for understandability. These viruses were initially identified from P. lanceolata in the Åland Islands by sequencing plant small RNAs69. Plants use RNA-silencing mechanism and produce short interfering RNA (SiRNA) molecules in a defense response against viral infection99. Hence, these viruses trigger an active defense response in P. lanceolata. Also, although not directly demonstrating their pathogenic nature, Susi et al.69 found that plants with virotic symptoms (necrotic spots/yellow colour) are more likely to carry a virus infection. Currently, the detailed transmission dynamics and vector species, as well as the viruses’ distribution outside the Åland Islands remain unknown. More detailed information of the virus families is compiled in Supplementary Table P. lanceolata in the Åland Islands. To obtain genetically uniform plant material, we cloned four greenhouse-grown maternal P. lanceolata plants into 80 replicates each. The maternal plants originate from natural P. lanceolata populations in the Åland Islands, and were grown from seeds in an insect free greenhouse at the University of Helsinki. The plants are expected to represent four different genotypes , as their maternal plants originated from distant populations 7–40 kilometres apart. Their resistance against viruses is currently unknown, but they represent different mildew resistance phenotypes as has been confirmed during laboratory maintenance of P. plantaginis. The maternal plant individuals used in the experiment were confirmed to be free of target viruses, that would have been the result of seed-borne infection, by PCR-testing using specific primers. Each maternal plant was cloned into 80 replicates by placing maternal plants on pots containing vermiculate and kept on a tray containing fertilized water.To study the effect of plant host genotype on the variation of within-host virus communities, we set up an experiment using sentinel trap plants in natural populations of P. lanceolata populations  were recorded after two weeks of exposure, and again after seven weeks of exposure. Plant size was measured during the first week of exposure by counting the number of leaves, and by measuring the length and width of the longest leaf. Based on these measurements we calculated plant size by using the equation n × A, where n is the number of leaves, and leaf area A is calculated using the equation of ellipse area: A = πab, where a is a half axis of the width of the longest leaf, and b is the half axis of the length of the longest leaf. For those 13 plants missing measurement data, an average over all recorded values for all plants was used, in order to not to lose any virus occurrence data from the analysis.Twenty replicates of each sentinel plant genotype were placed into each of the four 100. Two additional phenol cleaning steps prior chloroform cleaning of the RNA were performed. In the additional cleaning steps, we used 800 ml of equal volumes of phenol solution (pH 4.5) and chloroform-isoamylalcohol, mixed with isolation buffer containing the sample, vortexed, and centrifuged for phase-separation in 14 800 rpm for 15 min. For the PCR detection of the RNA viruses, RNA was translated into cDNA. For reverse transcription, we used 2 ng of total RNA, mixed with 2 µl random hexamer primers (Promega) and sterile nuclease free water in 17,125 µL volume incubated for 5 min in 70 °C. Subsequently, 1 µL Moloney Murine Leukemia Virus Reverse Transcriptase , 5 µL M-MLV RT buffer, 1.25 µL of dNTP (10 mM) mix, and 0.625 µL of RiboLock RNaseinhibitor were added and the 37.41 µL reaction mix was incubated in 37 °C for 60 min. For virus detection PCR, we used specific primers101 as well as two additional primer pairs for capulavirus (PiLVi2_forward_1 5′-GTGTTTAACAATGAAGTGAGCC-3′ and PiLVi2_reverse_4 5′-AATCCATCCACACATCCAATC-3′) and caulimovirus (forward primer 5′-AGGAGATGCCCATACTTTACC-3′ and reverse primer 5′-GACTTGCCAGAACCTGATTTAC-3′). PCR reactions to detect viruses were performed in final volume of 10 µL containing of 1–3 µL of DNA or cDNA, and GoTaq Green® polymerase 5x Mastermix  according to manufacturer’s instructions. Samples were subjected to initial denaturation in 95 °C for 2 min, following 35 cycles of denaturation in 95 °C for 40 s, annealing 53–60 °C for 40 s, and extension 72 °C for 1 min with a final extension step of 72 °C for 5 min. The full protocol with virus specific PCR conditions is described in the To detect the viruses infecting plants during the growing season, leaf samples were collected for nuclear acid extractions after two weeks and again after 7 weeks of exposure to the natural virus and vector communities. Samples were collected from a single leaf of similar age (young but large enough for sampling) from each plant. For DNA extraction, we collected a 1 cm² piece of leaf from each plant. Samples were stored in −20 °C until DNA extraction with E.Z.N.A. Plant Kit  at the Institute of Biotechnology at University of Helsinki. For RNA extractions, 3 cm² leaf samples were collected, immediately deep-frozen in liquid nitrogen, and stored in −80 °C before RNA-extraction. Total RNA was extracted using phenol-chloroform extraction with a modified method from Chang et al.102 and its identically named function, and applied a probabilistic model103 which calculates expected frequencies of species co-occurrences based on a distribution of random, independent species. By comparing the expected and observed co-occurrences the applied algorithm gives the probabilities of co-occurrence greater than or less than what is observed in the data analytically, without relying on randomisations or test statistics, under the condition that the probability of occurrence for a species at each sentinel plant is equal to its observed frequency among all the sentinel plants, i.e. in this case the prevalence of the virus103.For all the statistical analysis, we pooled the detected occurrences of the five focal viruses over the two timepoints of sampling by collapsing the occurrence data so that each sentinel plant had one observed virus community. Only when a sentinel plant had not been infected by a certain virus in either of the timepoints accounted as an absence of the virus while infection in one or both timepoints was accounted for as virus presence. To understand whether the co-occurrence of viruses differs from expected co-occurrences calculated solely from the prevalences of these viruses, we first analysed the co-occurrence patterns both in the full data set as well as separately for each sentinel host genotype and plant population Fig. . We used104), which is a multivariate Bayesian hierarchical generalised linear latent variable model. Essentially, HMSC is a multivariate generalised linear model, enabling the modelling of the whole community of viruses as opposed to fitting individual single-species distribution models105. In addition, HMSC is a latent variable model70. Latent variable models include unobserved, i.e. latent predictors, which are typically included to model correlation, or to account for missing predictors70. Hence, in this context, the latent variables are random effects that model the co-occurrences between species due to either biotic interactions or some other effects not included in the fixed part of the model, such as unmeasured effects of the environment. For a more detailed description of JSDMs and latent variable models, please see the comprehensive review by Warton et al.70.For addressing our study questions about the effects of host genotype and characteristics as well as local population context on the (co-)occurrence patterns of the viruses, as well as the possible signals of biotic interactions between the viruses on virus community assembly, we applied a joint species distribution modelling (JSDM) framework ‘Hierarchical Modelling of Species Communities’ , Lij is the linear predictor for the occurrence of virus j on sentinel plant i, which is further divided to fixed  we used the local plant population context , which is a proxy for the plant population-level effects, such as variation in abiotic conditions, vector communities, and disease pool ; and at the level of the sentinel host plants, we include the plant size (a continuous variable), signs of herbivory , as well the genotype of the sentinel host plant . To examine the residual co-occurrence patterns among hosts, we also included the sentinel plant individual as a latent variable random effect.We fitted three JSDM variants to the data by varying the way the sentinelt plant genotype was included in the model Table . As explX. Then, we fitted a model including also the sentinel plant genotype, i.e. the full set of fixed explanatory variables (variant 2). With both of these model variants (1 and 2), we included random effects at the level of sentinel plants individuals. Finally, we fitted a model with the same full set of fixed explanatory variables X as with model variant 2, but we modified the random effects by allowing these residual patterns to covary with the sentinel plant genotype (variant 3), details of which are explained by Tikhonov et al.107. In this case, the latent factor loadings Λ are furthermore modelled as a linear regression of the selected fixed explanatory variables, which in this case was the sentinel plant genotype. Hence, as a summary, our model variants vary in terms of what is included in the matrix X of explanatory variables, namely if sentinel plant genotype is included (variant 2) or not (variant 1), and do we allow the residual associations between viruses to covary among the sentinel genotype (variant 3) or not (variant 2).First, we fitted a model with only the local population context, plant size and signs of herbivory (variant 1) as fixed explanatory variables 108, except that for the parameter Λ of species loadings, of the random part of the model. While the HMSC framework is usually not very sensitive to the choices of priors, when data is sufficient, they can be sensitive to the prior chosen for Λ. The multiplicative gamma process shrinking prior109 for the species loadings Λ has several prior parameters, but out of those, the user is advised to pay attention to the choice of α, a vector of two values, which can be used to adjust the level of shrinkage that the prior implies for the matrix Ω of species associations106. Hence, we used two alternative priors. First, we used the default of α = , which imposes a lot of shrinkage. We refer to this group of model variants as our main model variants. Second, we used α = , which imposes much less shrinkage, but as a trade-off, also increases the risk of overfitting.We used the default priors of the package ‘Hmsc’R2 coefficient of determination, a statistic that has been recommended to be used as a standard measure of explanatory power for binary outcomes110. The coefficient is obtained by calculating the mean of the predicted probabilities of presences and absences, and then taking the difference between those two means. Hence, a high coefficient value implies high predicted probabilities for presences and low probabilities for absences. When interpreting it, it is good to note that with sparse data, the probabilities of presence tend to be low in the first place, and thus the Tjur R2 coefficient can remain rather low as well. Nevertheless, if the model is completely uninformative and predicts a 50% probability for both presence and absence, the coefficient value will be zero, thus revealing a poor model fit. For examining explanatory power, we fit the model to the full data set and base our comparison on predictions made for the same data. To examine the predictive power of the model, we conducted a 10-fold cross-validation and compared the model variants based on the same Tjur R2 coefficient as with explanatory power, but calculated from the predictions made to new, unknown host plants. To complement our comparison based on model accuracy, we calculated the widely applicable information criterion (WAIC)111 for all the variants.The model variant comparison approach allows us to examine the relevance of sentinel plant genotype as a predictor of virus community composition (comparison of model variants 1 and 2), as well as to see whether the residual co-occurrences between the viruses differ between the sentinel plant genotypes (variant 3). The comparison of different priors enables us to examine how sensitive our models were for these choices. We compared the model variants in terms of their explanatory and predictive performance, where the first tells us how well the model predicts the data used to fit it, whereas the latter illustrates how well the model predicts independent data which has not been used for model fitting. We calculated the Tjur We also conducted a partitioning of the variance explained by the best-performing model variant, to assess how different (groups of) variables are contributing to the overall variance explained by the model at the level of the linear predictor. Finally, we used the best-performing model variant to simulate predicted coinfections profiles.108). The performance comparison, variance partitioning and predictions were conducted with the tools provided in the package. For a full formal description of the structure of the modelling framework, please see Ovaskainen et al.106, and for the covariate-dependent latent variables used in model variant 3, please see Tikhonov et al.107. The analytical pipeline and an R package along with the data used is available in Zenodo (10.5281/zenodo.4117739). For all the statistical analysis, we used R version 4.0.0112. For more details on the statistical analysis, please see We implemented our analyses with the R package ‘Hmsc’ (version 3.0-7Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Hepeviridae and is responsible for acute hepatitis in humans worldwide. These viruses show a relatively strict host specificity, e.g., rodent-related, avian-related, or even bat-related virus groups. However, similar (HEV-like) viruses have been identified in carnivores; some of them form a new genetically separated group, while others show a close evolutionary relationship with the rodent-related group, thus makes the strict host-specificity questionable and the classification of these new strains uncertain. Herein, we investigated feces of red foxes, the most widespread carnivore species worldwide, to identify the Hepatitis E virus and to ascertain their evolutionary origin via sequencing. The non-invasively collected fecal samples can provide information about the presence of viruses specific to the host and viruses derived from their prey as well. The virus we detected from our samples showed a very close relationship (91% identity) with rodent-related HEV described before from common voles, whilst a more distant relationship (85%) with fox-specific HEV strains was observed. Our results strongly support “the dietary-origin” of unclassified HEV-like strains described from various predator species.Orthohepeviruses, commonly known as Hepatitis E virus (HEV), is a diverse virus group belonging to the family of Microtus arvalis. In contrast, it shared 85% nucleotide similarity with HEV strains described previously in red foxes. Our results strongly support “the dietary-origin” of unclassified HEV-like strains described from predators that usually prey on rodents.Orthohepeviruses (HEV) can infect a wide range of animals, showing a relatively strict host specificity; however, its zoonotic potential, natural transmission in the wildlife are less known. Several new HEV-like viruses have been identified in various animal species, including carnivores; however, the phylogenetic relationship among these viruses is poorly resolved, since some of them were known as rodent-related so far. The red fox, the most widespread carnivore worldwide, is a known reservoir of several viruses that transmit from wildlife to humans or domestic animals; they might have a defined role in the circulation of rodent-borne HEV. In this study, we performed a HEV survey by heminested RT-PCR (Reverse Transcription PCR) on red fox fecal samples to investigate the presence of HEV in red foxes living in natural conditions, and to explore the origin of the virus via phylogenetic analysis. Out of the 26 investigated samples, HEV RNA was identified in one sample. Following Sanger sequencing, the novel sequence displayed 91% identity on the nucleotide level with recently published European common vole-HEV derived from  Hepeviridae) show a relatively strict host specificity, as supported by the latest classification (ICTV), which was based on phylogeny and host range of these viruses .The phylogenetic analysis supports the aforementioned results. Based on the phylogeny , our samArvicola amphibius). Remains of other foods did not present in the sample.Based on the diet composition analysis, two rodent species (belong to Cricetidae: Arvicolinae) were determined from the feces of the HEV-positive red fox. These species were the common vole and European water vole  [Orthohepevirus C, their origin becomes questionable, as all of these novel variants were found in fecal samples, it might be more likely that the virus identified from predator animals derived originally from prey rodent species. Multiple detections of HEV strains in ferrets from different geographical locations  supported them as the most likely reservoir of this virus [Orthohepevirus C, classified as Carnivore-HEV (or genotype HEV-C2) and then referred all strains came from carnivores  to this group [Within the wide range of Hepatitis E viruses, vegicus) , severalvegicus) ,4. Howevis virus . Since tis virus . Furtheris group ,10. MeanOrthohepevirus C species, since their common vole-derived sequences separated clearly from other Muridae-associated strains (rats and other Apodemus species) [Orthohepevirus C are associated with rodents only and refuted an evolutionary origin of these viruses in avian hosts [The potential “dietary-origin hypothesis” has been discussed before. For instance, HEV RNA detected in common voles from Hungary showed a close genetic relationship to the previously described kestrel-derived HEV-strain from the same country, and to fox-derived strain from the Netherlands, supported the dietary-origin of strains detected from predators, while ferret-HEV strains segregated from them, suggesting that ferrets may carry a different virus strain than foxes or kestrels . Based ospecies) . This asspecies) , who invan hosts . HoweverUrsus arctos syriacus) in a German zoo was PCR positive for Rat-HEV without any disease symptoms, and serological evidence of Hepatitis E virus infection was confirmed in this animal, assumed a spillover infection of Rat-HEV from free-living Norway rats [Orthohepevirus C group. Since it is unknown if HEV associated clinical signs in foxes, it is assumed that they can be reservoir hosts for the virus [Our results are consistent and complementary to the abovementioned conclusions, as we found a strain very similar to cvHEV, in addition to the presence of common vole in the fox’s feces, which is native and common species of the investigated wetland area, and belong to the natural diet of red foxes . Consideway rats . Recentlway rats  performehe virus .Othohepevirus A species were associated with zoonotic potential and human disease, represent the most common cause of acute hepatitis in humans worldwide [The knowledge about the pathomechanisms in wildlife and the zoonotic potential of these viruses are still poorly understood. Whereas Cricetidae-specific HEV genotype was only recently discovered, further investigations of rodent species belong to this taxa are needed to get more insights about this new genotype. The investigation of prevalence, genomic diversity and most importantly the infectivity of fox-derived HEV samples may reveal additional ecological and transmission aspects of Ortohepevirus genus. For a better exploration, more complex investigations would be necessary for the future, including serological studies, concurrent PCR tests for feces and serum, as well as traditional diet composition analysis  of infecorldwide . Besidesorldwide , and in orldwide . Furtherorldwide  and liveorldwide , highligAs the red fox is the most common wild-living carnivore, it is optimal for targeted assessments of pathogens. Although in our study, red fox feces were investigated, these non-invasively collected samples can provide information about the presence of known and unknown viruses specific for the host and viruses derived from their prey as well. That methodology could be applied more widely to survey the geographic distribution of viruses, without the necessity of invasive survey techniques, for example, live trapping or shooting animals. Although HEV was detected in one sample only in the examined natural habitat, it indicates the feasibility of fox samples as practical indicators for HEV presence in a particular area, and applicability of the fox as a sentinel for the detection and eventually monitoring of hepevirus in the environment.


Enteric viral co-infections, infections involving more than one virus, have been reported for a diverse group of etiological agents, including rotavirus, norovirus, astrovirus, adenovirus, and enteroviruses. These pathogens are causative agents for acute gastroenteritis and diarrheal disease in immunocompetent and immunocompromised individuals of all ages globally. Despite virus–virus co-infection events in the intestine being increasingly detected, little is known about their impact on disease outcomes or human health. Here, we review what is currently known about the clinical prevalence of virus–virus co-infections and how co-infections may influence vaccine responses. While experimental investigations into enteric virus co-infections have been limited, we highlight in vivo and in vitro models with exciting potential to investigate viral co-infections. Many features of virus–virus co-infection mechanisms in the intestine remain unclear, and further research will be critical. Every year, millions of children are infected with viruses that target the gastrointestinal tract to cause acute gastroenteritis (AGE), which is the inflammation of the stomach or small or large intestine ,2. IndeeIn the past, AGE was generally attributed to a single causative agent known to be associated with clinical symptoms. However, due to their high prevalence, exposure to multiple viruses at similar times can potentially occur in the same host, and these viruses may either infect simultaneously or within a short window of time. The outcomes of either infection may thus be influenced by the combined contribution of multiple agents. With increasing capacity to efficiently analyze samples for the presence of multiple putative pathogens using quantitative real-time PCR approaches, our appreciation that more than one virus may be present in an infected individual has grown. Enteric virus co-infections are commonly identified for all viruses linked to AGE  Table 1Table 1. Here, we will review what is known about the clinical aspects of enteric virus co-infection and discuss potential models to investigate viral co-infection in vitro and in vivo. We will first summarize what is known about enteric viral co-infection prevalence and its impact on health and disease, as well as how vaccine responses may be influenced by co-infection events. Second, we will present several of the possible cellular and molecular consequences of enteric viral co-infections. Finally, we will conclude by exploring past and current model systems used to study viral infections in the gut that could be applied to co-infections.By quantity and diversity, prokaryotic and eukaryotic viruses in the human intestinal microbiota likely outnumber their bacterial counterparts, with the size of the virome still remaining largely unknown ,16,17. IRecently, the metagenomic sequencing analysis of stool samples of several cohorts of healthy neonates revealed a stepwise viral colonization model wherein prophages may be the viral pioneers in the gut at one month after birth while, by four months, eukaryotic viruses became prominent ,23,24. TRisk factors for acquiring enteric co-infections include young age, daycare attendance, and households with more than three children , as wellReoviridae: Despite the successful implementation of vaccine programs against rotavirus (RV) starting in 2006, it continues to be the leading cause of viral diarrheal mortality in children under the age of 5 globally, causing approximately 200,000 deaths per year [per year . To dateper year ,34: the per year . RV grouper year .Caliciviridae: Norovirus (NoV) has emerged as a leading cause of pediatric gastroenteritis [Caliciviridae family that causes diarrheal illness similar to NoV, especially in young children and the elderly, and frequently closely follows NoV in prevalence [nteritis , in addinteritis . NoV GIInteritis . Indeed,nteritis . NoV hasnteritis ,41. Thouevalence ,43. Althevalence ,45, therAstroviridae: Astroviruses (AstV) are causative agents of diarrheal disease particularly in children and immunocompromised patients. Few studies report AstV-mediated AGE in normal healthy adults, but most children are infected with AstV and develop antibodies to the virus early in life [ in life ,47. Clas in life . Some no in life ,50,51,52 in life .Picornaviridae: Enterovirus species A and B (enteroviruses and coxsackieviruses), and C (polioviruses (PV)) are prevalent enteric pathogens causing a range of diseases, including AGE. PV is an enteric virus transmitted through the fecal–oral route, which during the pre-vaccine era could spread from the gut to the central nervous system to cause poliomyelitis, a disease leading to temporary or permanent paralysis [aralysis ,55. Follaralysis . PV vaccaralysis  and a liaralysis . Both vaaralysis . Howeveraralysis , and recaralysis . Other haralysis . Enterovaralysis . In genearalysis ,64,65. Waralysis ,67,68.Adenoviridae: Adenoviruses (AdV) are non-enveloped DNA viruses known to cause conjunctivitis, upper and lower respiratory disease, and AGE. AdV can be transmitted via respiratory droplets, fomites, and fecal–oral transmission, and predominantly infects the respiratory and gastrointestinal epithelia [pithelia . Epidemipithelia ,72,73,74pithelia . In patipithelia ,77,78, wpithelia . Althougpithelia . AdV is pithelia .Coronaviridae: Coronaviruses have been previously considered as possible rare causes of AGE in infants [ infants . However infants ,13. Infe infants ,82,83. I infants ,85. Alth infants . It has  infants . While i infants ,89, the Though co-infections are more commonly thought of as viruses from different viral families mediating simultaneous infection, they can, of course, also include concurrent infections by multiple viral strains or species of the same viral genus. An investigation by the Rota-net Italy Study Group demonstrated that mixed infections with two or more RV strains were present in 7.6% of their samples, with G1 + G9P(8) strains representing the most common mixed infections in children with AGE . In a stDuring epidemic AGE outbreaks, such as those that occur from contaminated water, co-infection with multiple unrelated viruses can be common. For example, in a pediatric AGE outbreak in Finland, in which drinking water was contaminated by sewage, RV was detected in 66% of cases, calicivirus including either NoV or SaV in 62%, and both in 40% . SimilarIn analyses pre-dating the RV vaccine, one study conducted in Spanish children found that ~5% of the cohort showed mixed infections associated with acute diarrhea. Virus–virus co-infections were more frequent, with RV–AstV and RV–AdV being most common, than virus–bacteria co-infections . SimilarThe introduction of the RV vaccine altered the prevalence of RV infection, but it continues to be frequently detected during AGE. A study of enteric RNA virus co-infections in diarrheic children and adults in Southwestern Canada found that 2.8% of total RNA virus-positive samples showed combinations of NoV GII-SaV, NoV GII-RV, NoV GII-AstV, SaV-AstV, and SaV-RV at varying percentages . A similEven less clear, however, are the impacts of co-infection in immunocompromised patients. Immunocompromised patients have been reported to have only slightly higher prevalence of individual enteric viral pathogens as the general population ,104, butBoth human gut virome studies and investigations specifically testing for viral pathogens have shown that individuals with detectable enteric virus loads in stool may be free of clinical symptoms or disease pathology ,26,113. While there are fewer studies that have examined viral co-infection in asymptomatic individuals, those that have support frequent enteric viral co-infection in the absence of AGE. Up to 17 different enteric viruses were detected in fecal samples from children without diarrhea in Bangladesh, and among the positive samples, 53% demonstrated co-infection with multiple viruses . SimilarWhile co-infections are thus prevalent even in the absence of symptoms, the literature does suggest that, in matched cohorts, co-infection rates tend to be higher in cases of AGE than in asymptomatic controls. The analysis of samples from a Japanese daycare revealed that 1.6% of stools from asymptomatic children exhibited viral co-infections, whereas viral co-infections accounted for 5.7% of AGE cases . SimilarAs discussed above, vaccines are currently available for PV and RV, but not for other enteric viruses associated with AGE. These vaccines are administered in early childhood, which is also the most common period for viral co-infections to occur. The inhibitory effect of human enteroviruses on OPV efficacy has been recognized for many decades, and indeed has contributed to attempts to vaccinate children in months when these viruses were circulating at lower levels ,119. A sWhile some recent studies have suggested that co-infections with bacterial or viral enteropathogens do not alter RV vaccine efficacy in a statistically significant manner, across these studies there is a trend for an 8–11% increase in vaccine efficacy against severe RV diarrhea in children who were not co-infected ,125,126.While most enteric viruses causing AGE do not have specific treatments available, an important note is that frequently diarrheal episodes are treated with antibiotics which have no efficacy against viral pathogens. In the MAL-ED multi-site study, almost half of all diarrheal episodes were treated with antibiotics. Analysis later showed that 91.7% of these antibiotic courses were inappropriate . Thus, iDespite frequent reports of viral co-infections, their consequences and underlying mechanisms are poorly understood. Here, we discuss potential consequences of co-infections, suggestions which will need to be bolstered by careful future studies .Co-infections with related strains facilitate viral recombination, which can occur via homologous or non-homologous recombination, or reassortment. Improvements in viral genomic sequencing methods have facilitated the detection of recombinants, leading to accelerated detection of these events over the past decade. Enteroviruses have been frequently observed to recombine to permit the emergence of new viral strains ,128, andRecent studies have revealed that multiple enteric eukaryotic viruses, including RV, NoV, and PV may be transmitted in aggregates or viral clusters ,135. GroViral interference is a phenomenon first recognized in the 1950’s, wherein a cell infected by a virus is resistant to a second super-infecting virus, mediated by interferons (IFNs) . This prWhile mechanistic explorations of viral co-infections in the intestine have been limited, there are several useful model systems available to explore the pathogenesis of individual viruses which could be applied to co-infection studies . Here, wChlorocebus pygerythrus), Rhesus macaques (Macaca mulatta), and pigtailed macaques (Macaca nemestrina) have been used extensively to study enteric viral infections [Non-human primates (NHPs), including Vervet monkeys , which can be maintained free of some of these enteric pathogens, have been highly instrumental in studying enteric viral infections including pathogenesis and immune responses. Mice are easy to manipulate and can recapitulate many of the human enteric viral–host relationships . Human NOverall, there is a clear need, and opportunity for, the exploration of enteric viral co-infections in mouse models, in order to interrogate infection outcomes for individual viruses as well as innate and adaptive immune responses and how these are governed by the presence of multiple viruses during infection.D. melanogaster, can be used to study oral infection by bacterial and viral pathogens in well-characterized, fast-growing, and readily genetically-manipulatable hosts [D. melanogaster. Microbiota regulation of antiviral immunity can restrict enteric viral infections in D. melanogaster [Drosophila can be infected orally by human arboviruses, permitting the exploration of both fly and human virus co-infections [Drosophila has important promise as a model for enteric viral co-infections.Insect models, including the fruit fly le hosts ,180,181.nogaster , supportfections . Taken tC. elegans is the model organism with the most biologically simple intestine, comprised of just 20 clonal enterocytes [C. elegans has a number of advantages, as it has a rapid generation time and can be efficiently screened using forward- and reverse-genetic, as well as drug or small molecule screening approaches. It has been instrumental in the discovery of multiple molecular pathways, such as RNA interference [C. elegans are potentially amenable to infection with non-natural nematode viruses via microinjection [C. elegans is thus a potential model to identify host-pathogen interactions during enteric virus co-infections.The nematode erocytes . C. elegrference . There arference . Furthernjection . C. elegCaco-2 and HT-29 lines, derived from human colorectal adenocarcinomas, are the most widely used in vitro immortalized cell systems to study mechanisms underlying host–pathogen interactions in the intestine ,188. TheIn the last decade, pioneering work from several groups has demonstrated the capacity of pluripotent intestinal stem cells to develop into complex intestinal structures in cell culture, closely mimicking their in vivo counterparts. Two types of intestinal organoids are used for studying enteric viruses—organoids derived from pluripotent stem cells, known as human intestinal organoids (HIOs), or organoids derived from intestinal crypts grown in three dimensional cultures, known as human intestinal enteroids (HIEs) ,204,205.Both HIOs and HIEs are widely used to study enteric viral pathogenesis and innate immune responses in the intestinal epithelium. HIOs support the replication of clinical isolates of RV, and HIEs have been used to study host restriction and innate antiviral responses to RV ,207. MulSingle pathogens have been classically understood to be the etiologies of AGE. As the evidence for virus–virus co-infections grows, however, it is evident that the causative agents for this disease are often co-infecting viruses, and indeed that these co-infections may be occurring even in the absence of symptoms. However, our understanding of the consequences of enteric virus co-infection is still in its infancy. There is a great remaining need for careful exploration of the effects of co-infection on symptom severity and duration, as well as development of protective immunity, in clinical cohorts.Notably, the frequency of co-infection, and the identity of implicated viruses, can be drastically altered with the introduction of immunization programs. For example, PV and RV vaccination programs dramatically reduced infection rates in communities worldwide. Thus, as vaccines are developed for other enteric viral pathogens, there may continue to be robust shifts in viral co-infection prevalence. We are also now developing an increasing appreciation that the commensal microbiota, diet, and host genetics may have complex regulatory effects on enteric viruses, all of which may variably contribute to disease outcomes from infection as well. Thus, there remains much to be discovered in terms of physiological effects of co-infections and other host factors on disease severity, immune defense mechanisms and vaccine responses. Although the different models discussed in this review have been used to study the pathogenesis of individual enteric viruses, our knowledge regarding enteric viral co-infections lags far behind. We suggest that these models could be used as a toolkit for targeted studies exploring clinically relevant enteric virus co-infections, thereby allowing us to carefully dissect the individual contributions of the many complex factors that control these pathogens.


Results show that the raw materials used to manufacture the majority of the glass most probably originated in Egypt, the Levantine coast and the Middle East. One bead is of uncertain provenance and shows similarities with glass found in the Iberian Peninsula and in South Africa. One bead fragment found inside a tomb is a modern production, probably linked to recent plundering. All of these ancient beads were exchanged along the trans-Saharan trade routes active during the rise of the first Sahelian states, such as the Ghana and the Gao kingdoms, and show strong similarities with the other West African bead assemblages that have been analysed. Despite the remoteness of their location in the Dogon Country and in the Falémé River valley, the beads studied were therefore included in the long-distance trade network, via contacts with the urban commercial centers located at the edge of the Sahara along the Niger River and in current southern Mauretania. These results bring a new light on the relationships between international and regional trade in Africa and highlight the complementarity between centres of political and economic power and their peripheries, important because of resources like gold for eastern Senegal.The presence of glass beads in West African archaeological sites provides important evidence of long-distance trade between this part of the continent and the rest of the world. Until recently, most of these items came from historical Sub-Saharan urban centers, well known for their role in the medieval trans-Saharan trade. We present here the chemical analysis by Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) of 16 glass beads found in three rural sites excavated during the past decade: the funerary site of Dourou-Boro and settlement sites of Sadia, in central Mali, as well as the settlement site of Djoutoubaya, in eastern Senegal, in contexts dated between the 7 Almost every society is or has been involved in the use of beads made from a great variety of materials. These objects are interesting as they can provide insightful information about these societies, from their cultural contexts to the political and social customs, and from their economic system to the technological questions linked to bead production .Glass was invented almost four millennia ago and the production technologies and raw materials used for glassmaking differ throughout history. Beads are among the first objects produced with this synthetic material and the chemical analysis of archaeological glass beads is useful for identifying provenance and ancient trade routes , 3.th-9th and the 11th-13th centuries CE. The sites of Dourou-Boro and Sadia, in Central Mali, and Djoutoubaya, in Eastern Senegal, were excavated between 2007 and 2018 by the research team of the University of Geneva’s laboratory Archéologie et Peuplement de l’Afrique (APA), within the research project Human Population and Palaeoenvironment in Africa (HPPA) led by professor Éric Huysecom. The aim of this work is to identify the origin of the glass beads analysed and to evaluate the implications in relation to the trade of these items in West Africa at the time of the first Sahelian states. A particularly interesting research question concerns the integration of the hinterland with the trans-Saharan trade networks and the evaluation of the connection between the urban centres directly involved in the long-distance exchanges, on which the research has been mainly focusing on, and the peripheral rural areas.Presented in this article is the techno-stylistic analysis of 21 glass beads and the chemical analysis by LA-ICP-MS of a subgroup of 16 glass beads found in three West African archaeological sites in contexts dating between the 7st millennium BCE and the 1st millennium CE have steadily increased in the recent decades, but the research has been mainly focused on the Eastern Mediterranean basin and in Western Asia. Current models of glass production and distribution might therefore be implicitly affected by this preferential focal point and more systematic studies need to be directed towards other geographical areas in order to avoid a biased picture.In order to understand the provenance and trade of glass beads, it is essential to differentiate between primary production centres, where raw glass was produced from raw materials, and secondary working sites, where glass objects were manufactured. Especially in ancient times, the glassworking centres were numerous and disseminated, whereas the glassmaking sites were limited in number and generally located in proximity of the raw material sources –4. Throuth and the 15th centuries CE the active glassmaking centres were mainly located in the Mediterranean basin and the Middle East (mostly producing plant ash glass), in the south and south-east of Asia and in the Far East , and in continental Europe and Italy  Institut supra). The reduced composition has been calculated following the method of Brill [of Brill , excludi2O3: 3.4%). The samples of the main group show a content of SiO2* ranging from 61.1 to 76.3 wt%, Na2O* from 12.8 to 19.4 wt%, CaO* from 3.5 to 7.9 wt%, and Al2O3* from 0.6 to 6.7 wt%. These values indicate that the beads were made with a soda-lime-silica glass produced from more or less pure sands. Moreover, except for sample DB-12t, the strontium content of the glasses (251–1062 ppm) suggests a coastal origin for the sand, which is characterised by Sr > 300 ppm due to the presence of beach shells [infra). Concerning the flux, MgO* and K2O* concentrations range from 0.9 to 5.9 wt% and from 1.2 and 3.5 wt% respectively, their total being always higher than 3.6 wt%, indicating therefore that soda plant ash was probably used to lower the melting temperature of the silica. This is confirmed by the high P2O5* level (0.15–1.0 wt%), typical of this kind of flux [Two principal glass compositions were identified, namely soda-lime-silica glass (17 samples) and soda-alumina-silica glass with a high boron content , soda-lime-silica glass with high magnesia (4 samples), and soda-lime-silica glass with high alumina (1 sample). Trace element analysis confirms this subdivision and additionally reveals various subgroups within the two main clusters .The full chemical composition of the samples, grouped into the chemical glass types identified, is presented in 2O and lower concentrations of K2O and MgO compared to the other soda-lime-silica glasses from Dourou-Boro and Djoutoubaya, suggesting the use of different plant ashes as a flux (cf. infra). Moreover, glass fragment DB-12t shows particularly low concentrations of elements linked to the vitrifier  suggesting that it was made with either pure silica sand or quartz pebbles. On the contrary, sample DJ16-5t from Djoutoubaya shows a higher concentration of these elements, probably linked to the use of less pure sand. Three subgroups become evident when considering the trace elements content, the principal group composed of beads DB-6t, DB-7t, DB-8t, DB-10t, DB-12t, DB-SM1t, DB-SM2t, DJ16-12t, and DJ17-5t, the second group composed of beads DB-2pu and DB-3be, and the third group composed by bead DJ16-5t , DB-SM2t, DJ16-5t, DJ16-12t, and DJ17-5t. Of these beads, ten are turquoise, coloured with copper oxide, whereas two are respectively purple and beige, both probably coloured by manganese and iron. It is important to note that the elemental composition of the latter two glasses is extremely similar despite the colour difference; an analysis using UV-Vis-NIR and Raman spectroscopy would provide more information about the nature of the colouring agents. The glasses in this group show on an average higher levels of Na DJ16-5t . It must2O5, as well as a higher concentration of CaO compared to the other soda-lime-silica samples analysed. The concentrations of the elements linked to the flux, such as MgO, K2O, P2O5, Li, and B [Three glass beads from Dourou-Boro and one glass bead from Djoutoubaya show this composition, namely DB-4y, DB-9t, DB-11t, and DJ16-3b. All samples contain low levels of Cl and among the lowest levels of Pi, and B  suggest supra) but they were produced using different raw materials, the raw glass probably having a different origin. In addition to the different plant ash used, in fact, these two samples show a particularly high level of Sr  and a very low concentration of Zr , as well as the lowest concentrations of alumina, titanium oxide, and REE, which could be explained by the use of a fairly pure source of silica as vitrifier.It is interesting to notice that samples DB-9t and DB-11t are typologically consistent with the other turquoise beads from Dourou-Boro , TiO2 (0.22 wt%), Ba (368 ppm), and Rb (32 ppm), suggesting the use of an alkali feldspar rich sand as silica source [The only sample belonging to this glass type is the green glass bead SA11-2g from Sadia, in Mali. The glass shows high concentrations of Ala source . On the 2O3 (14.1 wt%) and B2O3 (3.5 wt%), as well as low concentrations of MgO, CaO, Cl, and P2O5. The red colour of the glass is produced by the complex formed by Cd (4522 ppm) and Se (1814 ppm).The red glass fragment DB-5r found in Dourou-Boro, Mali is the only sample presenting this unusual composition. This glass contains very high concentrations of Alst and the first half of the 2nd millennium CE must be taken into account. In view of the composition of the above discussed samples, it is possible to confirm that the red glass fragment from Dourou-Boro falls outside this chronology, since it is a modern production: soda-alumina-silica glass with high boron was in fact developed after the 19th century CE [th century or after the turn of the 20th century but was commonly used in decorative pressed glass by the 1920s or 1930s [In order to determine the possible origin of the samples, the glassmaking centres active during the second half of the 1ntury CE  and the or 1930s . This glAs for the other samples from Dourou-Boro and Djoutoubaya, various collections of archaeological plant-ash glasses found in several sites located in the Mediterranean basin and the Middle East were used as reference material .th century CE [st millennium CE [As mentioned above, plant ash flux was reintroduced instead of natron in the near-eastern productions west of the Euphrates River in the 8ntury CE , 44, 45.nnium CE , 46–48. nnium CE , 50. Thennium CE , as wellnnium CE  have beennium CE , 52; typnnium CE , 47, as nnium CE  show a Minfra).If the sub-Saharan African samples are compared with the reference materials , we can These attributions are confirmed by the trace elements composition of the samples, as shown by the Cr to La and Zr to Ti ratios . The chret al. was taken into account in order to better understand the origin of the Levantine glass [2 to Al2O3 ratio. The composition of Islamic glass weights and stamps from Egypt dated between 700 and 1020 CE analysed by Schibille ne glass . In thisFinally, it needs to be mentioned that the possibility of some beads being made by mixing glass from different sources cannot be ruled out, especially considering their lead content, as well as the variability of the concentration of the trace elements linked to the colorants.2O3 content. High alumina glass is actually more typical of south and southeast Asia, where it was produced principally with a mineral flux [vNCA glass in [st century BCE and the 1st century CE; these were not taken into account as reference material in view of the chronological discrepancy. On the other hand, high alumina glass, glass waste, and beads were found in contexts within the chronological range of the Sadia bead in the Iberian Peninsula [th century CE) and Ciudad de Vascos  in Spain (th-14th century CE) and Zimbabwe bead series (14th -15th century CE) from southern Africa, as well as to the glasses from Mtwapa (10th -18th century CE) in Kenya and from Quseir el-Qadim (13th -14th century CE) in Egypt . Nevertheless, considering the unknown origin of the glasses taken into account for comparison, it is impossible to make assumptions on the actual provenance of the Sadia bead, although the exchange with northern Africa through the Sahara seems more plausible on account of geographical considerations.th and the 15th centuries CE. v-Na-Ca), plant-ash soda-lime glass with low magnesia content (v-Na-Ca low Mg), and plant-ash soda-lime glass with high alumina content (v-Na-Ca high Al). The bibliographic references for the chemical compositions are also indicated.As mentioned in the introduction, many glass beads were found in several West African archaeological sites dated between the 8By plotting the alumina content versus the magnesia to lime ratio and considering the Eastern Mediterranean and Mesopotamian grouping by Phelps , 50, it th-13th and the 14th century CE. However, the low alumina beads DB-9t and DB-11t seem to have a composition less common in West Africa, which is otherwise found in al-Basra, Morocco. On the other hand, the low lime and magnesia glasses from Dourou-Boro and Djoutoubaya, previously identified as probable Levantine productions, are similar to the v-Na-Ca beads from Kissi and Essouk (10th-11th to 12th century CE), having however a lower alumina content. Finally, beads with high alumina content similar to the Sadia bead have been found in Essouk, Igbo-Ukwu, and al-Barsa.High magnesia glasses of probable Middle Eastern production from Dourou-Boro and Djoutoubaya find thus correspondences in the Jenne-Jeno, Kissi, Gao, Igbo-Ukwu, and al-Barsa assemblages, as well as in the Essouk assemblage that was found in contexts dated between the 12supra) are present in this collection, in spite of the fact that beads with this specific composition were found in many contemporary West African archaeological sites. This could be explained by the structure of the sub-Saharan trade routes at the time, which were mainly developed on a north-south axis, linking the cities located along the Niger River and in southern Mauretania to the Mediterranean basin and the Levant, as opposed to an east-west axis through the savannah regions.It is noteworthy that no high lime high alumina glass bead of Nigerian primary production  and of the Falémé river valley (Djoutoubaya). While the first two are situated in a region rich in archaeological sites that have imported glass beads assemblages, the last one is far from the urban centres involved in the long-distance trade. Djoutoubaya is actually located in one of the areas of gold production that was active between late 1ennia CE , 66, a rbi Saleh . Interesth and the 13th centuries CE in Dourou-Boro and Sadia, Mali, and in Djoutoubaya, Senegal, were analysed by LA-ICP-MS. Two beads from Dourou-Boro previously analysed in another laboratory were included in the study as well. Four chemical groups were identified based on the main composition of the glasses, namely soda-lime silica glass with low lime and magnesia (12 samples), soda-lime silica glass with high magnesia (4 samples), soda-lime silica glass with high alumina (1 sample), and soda-alumina-silica glass with high boron (1 sample). The glass of the first three groups were produced using sands with differing levels of purity as vitrifier and various plant ash types as flux, whereas the glass fragment from the last group is a modern production, introduced on site probably by grave looters. Each main group was further divided into subgroups based on the trace element composition, indicating that different raw materials were probably used for production. This is also true for the typologically identical turquoise beads from Dourou-Boro, for which at least two sources of raw materials have been identified. The comparison of the compositions with the chemical data of contemporary glasses suggests an Eastern Mediterranean production for the low lime and magnesia glasses, pointing specifically towards Egypt and the Levantine coast, with similarities as well with the type 1 glasses from the primary production site of al-Raqqa, in Syria; on the other hand, the origin of the high magnesia glasses is probably the Mesopotamian region, east of the Euphrates River, with at least two different sources of raw materials; finally, the provenance of the high alumina glass bead is unclear, but its composition can be related to some Iberian glasses from Ciudad the Vascos, and beads of the Mapungubwe Oblate series from South Africa, both of unknown origin.A total of 16 glass beads found in West African archaeological contexts dated between the 7The analysed glass beads were therefore imported in West Africa through the trans-Saharan trade routes during the early phase of the Islamic expansion in North Africa, and in the context of the development of the first states in Sub-Saharan Africa, such as the Ghana and the Gao kingdoms. This is confirmed by the fact that the composition of the beads is in line with the compositions of glass beads found in several other West African sites, and in particular in the major urban centres involved in the trans-Saharan trade. Two different parts of the commercial network linking both edges of the Sahara were probably used to reach Djoutoubaya on one side, and Dourou-Boro and Sadia on the other, according to their geographical proximity to different urban trade centres, respectively Tegdaoust and Koumbi Saleh on one side, and Essouk, Gao, and Timbuktu on the other. The three sites analysed here were linked to these trade routes in spite of the remoteness of their location, attesting the distribution of this kind of small imported luxury goods into the rural areas of West Africa through regional and local exchange networks reaching villages. Lastly, the Djoutoubaya discoveries bring new insights on the integration of the gold production areas into this multi-scalar trade network. Finally, it questions the role of these peripheral sites producing important resources, like gold or ivory, within the political and economic organization of the first Sub-Saharan states.


The overexpressed S100P is located mainly in nuclei and microtubule organising centres (MTOC) and it significantly reduces their number, slows down tubulin polymerisation and enhances cell migration in S100P-induced COS-7 or HeLa cells. It fails, however, to significantly reduce cell adhesion, in contrast with NMIIA-containing S100P-inducible HeLa cells. When taxol is used to stabilise MTs or colchicine to dissociate MTs, S100P's stimulation of migration is abolished. Affinity-chromatography of tryptic digests of α and β-tubulin on S100P-bound beads identifies multiple S100P-binding sites consistent with S100P binding to all four half molecules in gel-overlay assays. When screened by NMR and ITC for interacting with S100P, four chemically synthesised peptides show interactions with low micromolar dissociation constants. The two highest affinity peptides significantly inhibit binding of S100P to α,β-tubulin and, when tagged for cellular entry, also inhibit S100P-induced reduction in tubulin polymerisation and S100P-enhancement of COS-7 or HeLa cell migration. A third peptide incapable of interacting with S100P also fails in this respect. Thus S100P can interact directly with two different cytoskeletal filaments to independently enhance cell migration, the most important step in the metastatic cascade.Overexpression of S100P promotes breast cancer metastasis in animals and elevated levels in primary breast cancers are associated with poor patient outcomes. S100P can differentially interact with nonmuscle myosin (NM) isoforms (IIA > IIC > IIB) leading to the redistribution of actomyosin filaments to enhance cell migration. Using COS-7 cells which do not naturally express NMIIA, S100P is now shown to interact directly with α,β-tubulin  This differential interaction causes the redistribution of the microfilaments and focal adhesion sites and thereby facilitates, in part, increases in migration of the S100P-overexpressing cells [in vitro and on metastasis in vivo. This interaction between the cytoskeleton and cell motility can also occur in noncancerous cells such as trophoblasts [The major cause of death from the commonly occurring cancers is due to metastasis, the process of tumour dissemination from its primary site to distant organs . This ding cells ,12 demonhoblasts .The cytoskeleton, however, is composed of three types of filaments, microfilaments containing nonmuscle actin and myosin, microtubules (MTs) containing α, β-tubulins, and intermediate filaments containing cytokeratins/vimentin. The first two types of filaments are both important in cell migration . Nonmuscinter alia with either NM or with tubulin, although with different binding affinities [in vivo and is an essential component in the MAP-kinase-p38-induced migration of macrophages [The individual S100 proteins have been reported to interact finities . For exafinities  and disrfinities . Decreasfinities . In controphages . Howeverrophages  and studRama 37 and S100P-inducible Rama 37, HeLa and S100P-inducible HeLa cells were produced and cultured as described previously . The COSCell migration assays were performed using Boyden chamber transwells separated by a membrane with 8µm diameter pores, as described previously . Usually5 uninduced or induced cells in 1 ml were added to 24 well tissue culture dishes and allowed to adhere at 37°C. At the given time (30 min to 2 h) cells were washed 2× with PBS and detached with 250 µl 0.05% (w/v) trypsin in versene for 5 min at 37°C, neutralised in serum-containing media (SCM) [For kinetics of attachment either HeLa-A3 or COS-7 S10 cells were induced with 1 µg/ml doxycycline 48 h prior to experiment and 2 × 10E. coli, as described previously [The coding sequences for full length (human) α-tubulin and for N-terminal and C-terminal halves of α or β-tubulin were generated using PCR with specific primers shown in eviously . β-tubulEqual molar proteins (1 μM) were subjected to SDS PAGE on 10% (w/v) polyacrylamide gels. One gel was stained with Coomassie Blue to check equal loading and the other electroblotted onto PVDF membrane. Three µg/ml S100P in Gel Overlay Buffer were added to the membrane and the contents incubated at 4°C overnight. After extensive washing, the presence of bound S100P was probed with mouse anti-S100P as described for Western blotting.20 µg cell lysates were subjected to SDS–PAGE and electrically blotted on to PVDF membrane. After blocking with 2% (w/v) BSA, the blot was probed with rabbit anti-β-tubulin (Abcam), rabbit anti-actin (Abcam), rabbit anti-NMIIA (Abcam) or rabbit anti-S100P (R&D Systems). After extensive washing, anti-rabbit-HRP (Santa Cruz) 1/10,000 was used and the bands were visualised using ECL and their densities were measured using Image Lab software.For Western blotting, S100P was detected using mouse anti-S100P monoclonal antibody (mAb) (1 : 50 dilution) (BD Biosciences) which showed no cross-reaction with S100A1, A2 or A4 proteins . Anti-tu2 or 1.0 mM EGTANa2. After brief sonication, the lysates were centrifuged at 15,000g for 30 min to remove insoluble components. The supernatants were then applied to His-S100P columns or control column with His-binding beads only. After extensive washings with 10 mM Tris–HCl, pH 7.4, 150 mM NaCl, 0.5% (v/v) Tween 20, the bound proteins were eluted with 300 mM imidazole and then subjected to Western blotting using mouse anti-S100P mAb and anti-tubulin antibodies .For pulldown assays, S100P-inducible COS-7 cells were incubated with doxycycline for 16 h and lysed in 20 mM Tris–HCl pH 8.0, 150 mM NaCl and 1% (v/v) Nonidet P-40 (NP-40) with 0.5 mM CaCl2. After sonication and centrifugation as described above, control antibody (rabbit IgG from Sigma), anti-tubulin or anti-S100P were added to three separate tubes containing 0.7 ml each of cell lysate and incubated overnight at 4°C. Then 100 µl Dynabeads® ProteinG (Thermo Fisher Scientific) were added to each tube and the contents incubated for 4 h at 4°C. After extensive washings with washing buffer  Triton X-100, 0.2 mM sodium orthovanadate), the bound proteins were eluted with 0.1 M glycine pH 2.5 and then subjected to Western blotting using the paired antibodies together with a sample of the input and the last wash.For co-immunoprecipitations, S100P-inducible COS-7 cells were incubated with 1 µg/ml doxycycline for 16 h and lysed in RIPA buffer  with 0.5 mM CaCl3 for use in Biocore X100. Recombinant S100P produced as described previously [Kd) from the association/dissociation rate constants and from the extent of binding near equilibrium  glass coverslips in a 24-well plate. After a further 48 h, cells were washed once in Cytoskeleton Buffer  ethanesulfonic acid, pH 6.1) prior to fixing with 3.7% (w/v) paraformaldehyde in CB at 37°C for 20 min. Cells were permeabilised with 1% (v/v) Triton X-100 in CB for 2 min and blocked with 5% (v/v) goat serum in CB for 60 min before incubation with rabbit anti-EB3 (Abcam ab157217) and mouse anti-α-tubulin (Sigma T5168) (dilution between 1/400 and 1/500) in 1% (v/v) goat serum in CB for 60 min at room temperature. After extensive washing, cells were incubated with the appropriate secondary antibodies: anti-rabbit FITC (Dako F0205) and anti-mouse Alexa Fluor (Molecular Probes A11004), respectively, for 60 min at room temperature. After washing, coverslips were mounted on slides in Vectashield , before being viewed in an Epifluorescence Leica DMI400B microscope equipped with objective lens 63× Leica PH3 FLUOTAR 25 Oil and excitation at 488 nm, emission at 520 nm for FITC and excitation at 540 nm and emission at 570 nm for TRITC. Images were obtained using a Leica DFC360FX digital camera and the Leica LAS AF 6000 software. FITC-labelled antibodies to EB3 were used for EB3 staining. Quantification of the presence of EB3 clusters at the cell periphery or well-defined MTOC was performed by counting the number per cell in about 100 cells selected at random. Results are presented as percentages of the cell population containing either an EB3 network at the cell periphery or well-defined MTOCs. For MTOC quantification, cells and MTOC were graded as shown in Immunofluorescent staining was carried out as previously described . COS-7 SS100P fused to yellow fluorescent protein (YFP), and EB3 fused to cyan fluorescent protein (CFP) (EB3-CFP) expression plasmids, pYFP-S100P and pCFP-EB3 were constructed, respectively. COS-7 cells were transiently transfected with the two plasmids and images were recorded using a LSM 5 confocal microscope 24 h after transfection. CFP: excitation = 436 nm, emission = 480 nm; YFP: excitation = 500 nm, emission = 520 nm.HeLa and S100P-inducible HeLa, COS-7 and S100P-inducible COS-7 cells were routinely cultured with or without doxycycline (1 µg/ml) and treated with 33 µM nocodazole for 24 h to synchronise the cells. Then the nocodazole was washed away (First release) and the synchronised cells were cultured in normal medium for 4 h to allow them to complete cell division. The nocodazole was then added again for 4 h to dissociate all the MTs. After this time, nocodazole was again removed by extensive washings (Second release) and tubulin was allowed to polymerise in the interphase of the cell cycle for different times ,33. The 2 and 0.5% (v/v) Tween 20) and applied to a S100P-conjugated CNBr-Sepharose 4B column in the presence of 0.5 mM CaCl2. After extensive washings, the bound peptides were eluted with 0.1 M glycine pH 2.5 and neutralised immediately. These eluates were analysed using the mass spectrometry service provided by the University of Dundee using nanoLC–MS–MS. The raw data was analysed further using Peak software 7.0 and all identified peptides were superimposed on the amino acid sequence of α or β-tubulin.Fifty micrograms of α and separately β-tubulins were incubated with 5 µg trypsin in 200 μl total volume at 37°C overnight in 25 mM ammonium bicarbonate to ensure complete digestion. Resultant peptides were diluted in 10 ml Binding Buffer , 50 mM NaCl, 0.5 mM CaCl2, 2 mM Tris-carboxyethyl-phosphine (TCEP), pH 6.1 buffer as described previously [15N-labelled S100P and 2–5 mM peptide solutions to obtain a 1 : 1 mixture of 0.1 mM of S100P and 0.1 mM of each peptide. Initially, peptide solutions were prepared by weight to produce 5 mM peptide concentrations. Some peptides had low solubility and did not fully dissolve, leading to visibly cloudy solutions. In these cases, buffer was added to reduce the peptide concentration to 2 mM. In the case of two peptides, DLV and VNA, solutions remained cloudy. These peptides were added to the protein as cloudy suspensions to produce 0.1 mM solutions; some precipitations were still detected. To estimate the peptide concentrations, equivalent peptide solutions without the protein were prepared. All solutions were centrifuged before the NMR measurements. NMR spectra were collected on Bruker Avance III 800 MHz spectrometer equipped with CryoProbe. Data were processed and analysed in TopSpin 3.5 .eviously . Samples2 (for Ca2+-form of S100P) at 25°C. Due to low solubility, peptide solutions at concentrations of 10–400 µM were placed in the cell and titrated with S100P at concentrations of 0.5–3 mM, depending on the peptide concentration. Data were integrated and fitted to a single site binding equation using Origin 7 software which was included with the ITC module .Measurements were conducted using an ITC-200 instrument  as previously described . ITC titt-test P ≤ 0.022), there was no significant decrease in the rate of adhesion (P ≥ 0.18) upon addition of 1 µg/ml of doxycyclin. These results indicate that S100P can enhance cell migration through mechanisms other than via NMIIA and cell adhesion in the COS-7 cells.In our previous study, we reported that S100P enhances cell migration by binding to and affecting distribution of NMIIA filaments in HeLa and Rama 37 cells  and thisadhesion  or signiadhesion  of S100P2 but not EGTA was originally present. Neither S100P nor tubulins were pulled down by blank beads control  fused to End Binding Protein (EB3) (CFP-EB3) to highlight MT termini  and simuTo determine whether altering S100P levels would lead to changes in MT-dependent organisation, S100P-inducible S10 cells were treated with 1 µg/ml doxycycline and seeded on coverslips with or without fibronectin, fixed and immunofluorescently stained red for tubulin and green for EB3 using TRITC and FITC labels, respectively. Some clear disruption could be seen in MTOC and in EB3 localisation at the cell periphery when S100P was expressed  as well t-test P < 0.05) by ∼30% after 4 h than that without S100P induction, in both S100P-inducible HeLa-A19 (31% reduced) and COS-7 S10 cells (32% reduced) . The Triton-X100-insoluble tubulins (representing the newly formed MTs) were then detected at successive time points by Western blotting  and theireduced) . In contreduced) . These dIt has been reported that stabilising MTs by taxol significantly reduced cell migration in almost all cell types tested , whilst To confirm that S100P-tubulin interaction is a major cause of S100P-enhanced cell migration, a specific disruptor of this interaction is required. To achieve this goal, we first mapped the binding sites of S100P on α, β-tubulin using a gel overlay assay with intact and half molecule tubulin fragments. However, S100P interacted with both α and β-tubulin subunits and with all four half molecules, suggesting the involvement of multiple binding sites . Therefo15N-labelled S100P, with similar signals affected . The unexpectedly high stoichiometry of ∼2 (peptide:protein) for VNA may be due to some peptide aggregation, which is also suggested by the reduction in the intensity of its signals in the HSQC spectrum. HSQC signals were also weak for the DLV peptide, solutions of which were visibly cloudy even at low concentrations of 0.1 mM, clearly demonstrating peptide aggregation , and 1 µM S100P were applied to the chip. We found that DIC and DLV peptides significantly inhibited the extent of binding of S100P by over 50% . In contAlthough doxycycline-induction of S100P significantly increased cell migration with all of the tagged peptides at 20 µM, the extents were very different. The S100P-induced 3.4 or 3.5 fold increase in the presence of DMSO or Tag was significantly reduced to 1.4 or 1.5 fold with T-DIC or T-DLV, respectively, but not significantly with T-IMN peptide . Thus inin vivo has been demonstrated by pulldown experiments from lysed cell extracts using antibodies to either component separately . In contrast, when they bind to their target proteins, Ca2+ binding can increase by as much as 200 to 400 fold [2+ concentrations of 1 to 4 mM were employed. Thus it may be that the local intracellular concentrations of Ca2+ peak at higher values than those observed on average in the cytoplasm of activated cells and/or the requirement for Ca2+ ions is greatly reduced by the configuration of S100P-tubulin perhaps complexed with additional proteins/ions in vivo compared to that in vitro.Cell migration is a complicated biological process involving many disparate cellular machines and regulatory systems . The cytt 200 µM . These ctivation . These ceticulum . These c in vivo . Howeveragnitude . In addi400 fold . Moreove400 fold  or V dom400 fold  using eiKd of the interaction between S100P and tubulin from SPR analysis is 2–3 × 10−7 M, as calculated from estimates at or near equilibrium or 1–2 × 10−7 from a ratio of forward and backward rate constants [In our hands, the onstants . Moreoveonstants ,25,62,63The chemicals taxol and colchicine have much stronger effects on tubulin polymerisation than those of S100P . Taxol, Despite the many reports which have shown that disruption of MTs can impair cell migration, particularly directional migration, cells can still retain the ability of persistent motility without MTs , presumaTo prove that the effect of S100P on tubulin is the major mechanism of S100P-enhanced cell migration in NMIIA-depleted COS-7 cells, we have used the two strongest S100P-binding peptides tested in ITC, the 30mer starting with DLV from α-tubulin and the 26mer starting with DIC from β-tubulin, as well as a very weak or nonbinder as a control, the 30mer starting with IMN from β-tubulin . The locIn the case of NMIIA-containing HeLa cells, the two inhibitory peptides DIC and DLV still significantly blocked the S100P-induced increase in cell migration, but only by 49% and 54%, respectively. This result suggests that, although part of the S100P-induced stimulation of cell migration in HeLa cells is caused by S100P binding to tubulin, the remainder is caused by another mechanism, in this case probably S100P binding to NMIIA  and α, β


In this configuration, the combination of both functional perovskites constitutes an artificial multiferroic system with potential applications in spintronic devices based on the magnetoelectric effect. La2/3Sr1/3MnO3 single layers and BaTiO3/La2/3Sr1/3MnO3 bilayers using the pulsed-laser deposition technique. We analyzed the films structurally through X-ray reciprocal space maps and high-angle annular dark field microscopy, and magnetically via thermal demagnetization curves and in-plane magnetization versus applied magnetic field loops at room temperature. Our results indicate that the BaTiO3 layer induces an additional strain in the La2/3Sr1/3MnO3 layers close to their common interface. The presence of BaTiO3 on the surface of tensile-strained La2/3Sr1/3MnO3 films transforms the in-plane biaxial magnetic anisotropy present in the single layer into an in-plane uniaxial magnetic anisotropy. Our experimental evidence suggests that this change in the magnetic anisotropy only occurs in tensile-strained La2/3Sr1/3MnO3 film and is favored by an additional strain on the La2/3Sr1/3MnO3 layer promoted by the BaTiO3 film. These findings reveal an additional mechanism that alters the magnetic behavior of the ferromagnetic layer, and consequently, deserves further in-depth research to determine how it can modify the magnetoelectric coupling of this hybrid multiferroic system.We studied in detail the in-plane magnetic properties of heterostructures based on a ferroelectric BaTiO In all cases, we noted that for the LSMO reflections, the position of Qx coincides (within the measurement margin of error) with those of the substrates, while the position of Qz is quite different from that of each substrate, being almost superimposed for LSMO grown on LSAT  and out-of-plane (fc-system) lattice parameter of each film, defining fa-system and fc-system as:From a and c correspond to the in-plane and out-of-plane lattice parameter of the system and the subscript film and bulk are associated to the lattice parameters measured in the film and that reported for the bulk of each material, respectively. According to fa-system correspond to tensile (compressive) strain. In fa-LSMO = −2.3%) and moderate tensile (fa-LSMO = +0.7%) strain, respectively. A very weak compressive strain (fa-LSMO = −0.2%) is observed in the LSMO film grown on LSAT. According to Poisson’s effect, each tensile (compressive) in-plane strain led to shrinkage (elongation) of cLSMO, where the higher the fa-LSMO magnitude the higher the fc-LSMO magnitude. For the BTO layers, the in-plane lattice parameter is close to that of the bulk value, whereas the cBTO reveals two values corresponding to the two spots observed in the RSMs  a bilayer with a compressive-strained LSMO film on LAO substrate; 2) a bilayer with a weakly compressive-strained LSMO film on LSAT substrate and 3) a bilayer with a tensile-strained LSMO film on STO substrate. M(H)/Ms) as a function of the applied magnetic field for 27 nm thick LSMO films (plots to the left), and for BTO (140 nm)/LSMO (27 nm) bilayers (plots to the right) grown on STO (plots on the top), LSAT , and LAO (plots at the bottom) substrates. Hysteresis loops were measured by applying the magnetic field in the plane of the film along the three high-symmetry axes: [100] (black squares), [110] (blue circle), and [010] (red triangles) directions. We observe that the magnetization loop shape depends on both substrate and the applied field direction. For the LSMO film grown on STO, there is almost no difference among the three narrow hysteresis loops , revealing that such direction constitutes either a magnetization easy axis, for a given in-plane anisotropy, or a magnetization intermediate axis for an out-of-plane anisotropy. For BTO/LSMO samples grown on LSAT  is reduced when compared with the loop measured along the [010] direction (red triangles).is loops . For theis loops . For theis loops . For all on LSAT  and LAO  on LSAT  we do no on LSAT , the Mr Mr/Ms as a function of the in-plane applied magnetic field at 300 K for a LSMO layer and for BTO/LSMO bilayers grown on different substrates and LSMO thicknesses. tLSMO) bilayer grown on STO with tLSMO = 20 nm (blue circles), 27 nm (green triangles) and 40 nm (black squares). For the LSMO/STO system . This type of biaxial anisotropy has been observed in virtually unstrained, and tensile-strained LSMO films grown on cubic-crystal substrate  directions. Such unexpected change could reflect that a 140 nm-thick BTO layer grown onto a 27 nm-thick tensile-strained LSMO film distorts its biaxial magnetic anisotropy. Moreover, a similar measurement in bilayers  and biaxial (k1) anisotropy constants for the LSMO layer and BTO/LSMO bilayer grown on STO substrate. First, we determine the anisotropy energy ratios (ku/k1) by fitting the polar plots and determining the angles that minimize the general anisotropy energy equation. To separate ku/k1, we obtain ku by calculating the anisotropy field through the slope of the derivative of the magnetization with respect to the field (H) at H = 0 in the in-plane hard axis magnetization loop  to 0.55 kJ/m3 (for tLSMO = 40 nm). Thus, our experimental results allow us to conclude that the deposition of BTO in the top layer of the tensile-strained LSMO drastically changes the magnetic anisotropy of the ferromagnetic layer.Following a similar approach presented in , where tented in ). For th6 octahedra to favor epitaxial growth  and εzz = [100 × (cfilm-system − cSTO)/cSTO], where aSTO and cSTO correspond to the in-plane and out-of-plane lattice parameters of the STO substrate. We can then relate the in-plane lattice deformation fa-system and in-plane deformation maps εxx, as well as the out-of-plane strain fc-system and out-of-plane deformation maps εzz, by means of the following equations:The local color variations observed in the strain maps reflect homogeneous and dislocation-free STO and LSMO layers, and a BTO layer with several linear defects, most of which are concentrated close to the BTO/LSMO interfaces. Misfit dislocation-free epitaxial growth of LSMO films under (001)-oriented STO substrates is expected and widely reported ,47–50 duxx and εzz extracted from the GPA maps are plotted in fa-system and fc-system.For a quantitative strain analysis, profiles of εxx is equal to zero, on average, inside the STO and LSMO systems which presents a discontinuity around the BTO dislocation and finally follows an increasing tendency, reaching a value of 1.54% at 37 nm thickness of BTO  to then progressively increase until 0.57%. This fact evidences that the BTO layer locally induces an additional tensile deformation to afilm-LSMO, up to twice that already induced by the substrate. This extra elongation of afilm-LSMO seems to help the relaxation of the BTO layer, hence horizontal misfit dislocations are not formed. Similar profiles were extracted from the εzz strain maps. Compared to the εxx, the εzz profiles present noisier behavior inherent to the GPA method  vertical strain profile that covers the entire thickness of the BTO layer.This file contains two figures showing magnetization hysteresis loops performed in the BTO/LSMO/STO bilayer to prove that the magnetization easy axis has an in-plane orientation and the in-plane [εFile 1Additional figures.