Mesoamerica is a historically and culturally defined geographic area comprising current central and south Mexico, Belize, Guatemala, El Salvador, and border regions of Honduras, western Nicaragua, and northwestern Costa Rica. The permanent settling of Mesoamerica was accompanied by the development of agriculture and pottery manufacturing (2500 BCE–150 CE), which led to the rise of several cultures connected by commerce and farming. Hence, Mesoamericans probably carried an invaluable genetic diversity partly lost during the Spanish conquest and the subsequent colonial period. Mesoamerican ancient DNA (aDNA) research has mainly focused on the study of mitochondrial DNA in the Basin of Mexico and the Yucatán Peninsula and its nearby territories, particularly during the Postclassic period (900–1519 CE). Despite limitations associated with the poor preservation of samples in tropical areas, recent methodological improvements pave the way for a deeper analysis of Mesoamerica. Here, we review how aDNA research has helped discern population dynamics patterns in the pre-Columbian Mesoamerican context, how it supports archaeological, linguistic, and anthropological conclusions, and finally, how it offers new working hypotheses. Genomics, namely the study of the complete set of genetic information carried by an individual, has become a powerful tool to map the peopling of the world in detail. Ancient DNA (aDNA) adds a crucial layer of information by providing a snapshot of genetic diversity in past populations, most of which is often hardly accessible in modern-day populations due to recent demographic events. Therefore, aDNA provides evidence in addition to archaeology, linguistics, and anthropology to better comprehend human expansions and demographic events. Within the Americas, the genetic history of the Arctic, northern North America, and South America have been recently studied using aDNA ,2,3,4. HMesoamerica is a historically and culturally defined geographic area comprising current central and south Mexico, Belize, Guatemala, El Salvador, and border regions of Honduras, western Nicaragua, and northwestern Costa Rica  Figure . MesoameArchaeological evidence indicates a continuous occupation of Mesoamerica since the end of the Late Pleistocene, specifically the Lithic period (~13000–5000 BCE)  11]. Ho. Ho11]. The permanent settling of Mesoamerica took place in the Preclassic (or Formative) period 2500 BCE–150 CE), with the development of agriculture and pottery manufacturing. Agricultural practices intensified and improved during the Classic and Epiclassic periods (150–900 CE). The number of large population centres rapidly increased and cultural differences incremented between regions. During the Postclassic period (900–1519 CE), Mesoamerica was inhabited by large intermingled cultural groups connected by trade and agriculture, which fostered migrations that intensified admixture and built up the diversity patterns found across Mesoamerica. The Postclassic period ended with the start of the Spanish conquest (1519–1521 CE) and the subsequent colonial period (1521–1821 CE) ,15. The 0 CE, witMesoamerica was home to different human cultures connected by commerce and farming, and whose varied genetic backgrounds created an invaluable source of genetic diversity in the continent. Previous studies have attempted to uncover the peopling process of Mesoamerica using genetic approaches on modern-day Indigenous and cosmopolitan populations, mostly from Mexico and Guatemala. These analyses mainly focused on studying the so-called uniparental markers: the mitochondrial DNA (mtDNA) ,25,26,27Most of these genetic studies proposed a single origin for Mesoamerican populations ,28,29,33Inferring the population history of Mesoamerica through genetic studies that solely incorporate modern-day populations can be challenging due to recent historical events, such as population admixture with European, African, or Asian populations, that can distort pre-colonisation genetic signals. For instance, present-day Mexicans have, in general, three different ancestries: Native American, European (closely related to Iberians), and African. While the African component is generally minimal, the European component has a much lower proportion in Native Mexicans than what is found in cosmopolitan Mexicans. Notably, the Native American ancestry component can be divided into six separate ancestries . Three oIn spite of the extensive archaeological, linguistic, and anthropological research on pre-Columbian Mesoamerica, there have been surprisingly few aDNA studies. Therefore, there is a current lack of genetic knowledge about the genetic history of the region, probably because of the highly degraded nature of genetic material from tropical environments. Thus, currently available ancient genetic data rely mostly on mtDNA studies. The human mtDNA is a circular double-stranded DNA molecule that is maternally inherited, does not recombine ,38, and Interestingly, existing aDNA data come from the Central and Southeast areas of Mesoamerica, corresponding to the Basin of Mexico and the Yucatán Peninsula and its nearby territories, respectively. Therefore, this review will focus on the research that has been performed in these two geographical areas. Nonetheless, it should be noted that Mesoamerica contains other regions with extensive archaeological and anthropological records that have not been investigated in aDNA studies yet. These regions include the Gulf Coast and Oaxaca. The Gulf Coast was initially peopled by the Olmecs (the earliest known major Mesoamerican civilisation) and was later home to the city of El Tajín. Oaxaca comprises the current Mexican state of Oaxaca as well as the border regions of Guerrero, Puebla, and Veracruz. It was home to the Mixtec and Zapotec civilisations, the Preclassic–Classic city of Monte Albán being the most important archaeological site. In contrast, the northern and western regions of Mesoamerica are largely understudied from an archaeological and anthropological perspective and have been severely looted. The North was the transition area between Mesoamerica and Aridoamerica inhabited by both hunter-gatherers and farmers and it was nearly abandoned during the Postclassic period. The West was a geographically diverse area that led to the rise of highly diverse societies characterised by the shaft tomb tradition in the Preclassic and Classic periods and the Tarascan state during the Postclassic .The Basin of Mexico is a highland plateau that presently includes Mexico City and parts of the states of Mexico, Hidalgo, Tlaxcala, Morelos, and Puebla. The Basin of Mexico previously contained five lakes drained by Spanish colonists in order to expand Mexico City and prevent flooding . This arMesoamerica’s settling took place during the Early Preclassic period with the emergence of agrarian villages with egalitarian societies. The Middle Preclassic was defined by the development of agriculture, the increase in population sizes in settlements with hierarchical societies, and a strong influence from the Gulf Coast. This influence later disappeared during the Late Preclassic, when some settlements  became capital towns with strong economic and political power. At the end of this period, Teotihuacan rose as the undisputed power in Mesoamerica . Teotihuacan was a powerful political and military centre that controlled trade routes and attracted large masses of people from distant regions, coordinating them into an effective multi-ethnic system. The metropolis grew into the largest city of Mesoamerica and one of the world’s largest and most urbanised population centres of the first millennium, reaching 80,000–100,000 inhabitants by approximately 150 CE . TeotihuTeotihuacan was first occupied ~500 BCE and consisted of villages of local people as the valley offered several advantages: an abundance of freshwater springs, volcanic and raw materials for construction and technology, and a crucial location along the route from the Gulf Coast to the Basin of Mexico, skirting the Trans-Mexican Volcanic Belt also known as Sierra Nevada .During the first century CE, Teotihuacan was the destination of numerous migrations from Puebla and Tlaxcala, probably due to the Popocatépetl volcano eruption . It has Around 350 CE, a new episode of population displacement from Cuicuilco and surrounding regions likely took place when the Xitle volcano erupted . This miFollowing the political crisis, Teotihuacan became a multi-ethnic city inhabited by locals, returnee migrants, people from the Tlaxcala–Hidalgo–Puebla corridor, and individuals from distant regions, such as the Gulf Coast, Oaxaca, and Chiapas. This pattern has been sustained by archaeological studies , trace eAround 550 CE, Teotihuacan’s population size declined by half, and the city collapsed without any clear explanation. Although some of the earliest theories postulated that Teotihuacan was sacked by outside invaders , the mosThe Epiclassic, also known as Late Classic in other parts of Mesoamerica, was marked by the decadence of Teotihuacan and extensive population movements. Various ephemeral centres of power emerged throughout the Basin of Mexico, such as Xochicalco, Teotenango, Cacaxtla, and Cholula . The latThe study of the Postclassic includes not only archaeology and anthropology, but also linguistic information from accounts written in Spanish and different Indigenous languages. While the Classic period was a peaceful epoch of cultural climax, the Postclassic period was marked by political instability and war. This period emerges with the apogee of Tula , the capital of the Toltec Empire. Even though the political system and area of influence of the Toltec culture remain a point of discussion among scholars, especially in the city of Chichen Itza, inhabited by the Mayas in the Yucatán Peninsula, it has been proposed that its influence was widespread and persisted through time in central Mesoamerica . After the collapse of the Toltec Empire, numerous city-states emerged and fought one another for the control of political power. Even though Azcapotzalco  initially ruled most of the Basin of Mexico, the Triple Alliance of the city-states of Tenochtitlan, Texcoco, and Tlacopan reduced Tepanec influence and allowed the rise of the Aztec Empire. The Aztecs conquered a large number of cities across Mesoamerica over the following years and established a large Empire that lasted until the Spanish conquest . For exaIn contrast, various studies have focused on the archaeological site of Tlatelolco, which is buried beneath modern-day Mexico City and was founded around 1337 CE. Even though Tlatelolco was a sister city to Tenochtitlan , religious and cultural practices were distinct between the two cities. Tlatelolco was the biggest and most diversified marketplace in Mesoamerica during the Late Postclassic . The mosThe Yucatán Peninsula is located in southeastern Mexico and separates the Caribbean Sea from the Gulf of Mexico. Currently, it comprises the Mexican states of Yucatán, Campeche, and Quintana Roo, as well as part of Guatemala and almost the whole of Belize. The first humans inhabiting this area were hunter-gatherers. In fact, the oldest individual (~10500 BCE) with published genetic data in Mesoamerica comes from this region. This individual was found in Hoyo Negro , a submeHaplogroup D1 was actually found in two genetically related individuals ~5300 BCE) from the archaeological site of Saki Tzul in the Maya Mountains (Belize) 00 BCE fr that expThe entire Yucatán Peninsula together with the Mexican states of Tabasco and Chiapas, the rest of Guatemala and Belize, as well as the border regions of Honduras and El Salvador, were the home to the Maya civilisation , one of The geographical diversity of the Yucatán Peninsula was one of the most important traits for the Mayan settling in the Preclassic period, allowing the emergence of different cultural focuses that led to the rise of various linguistic families. Despite the existence of a cultural influence from the Gulf Coast during the Middle Preclassic, it disappeared in the Late Preclassic when cities started to urbanise with architectural gigantism . The ClaRegarding genetic studies of the region, Ochoa-Lugo and colleagues  performeAdditionally, other aDNA studies just focused on the analysis of mtDNA haplogroups in one archaeological site during a specific period of time. For example, the largest mtDNA collection from Mesoamerica to date comes from the Classic–Epiclassic site of Midnight Terror Cave (Belize), which is believed to have been a site of human sacrifice . A totalFinally, a further study focused on the Epiclassic–Postclassic site of Xcaret (Mexico), one of the most active ports in the eastern Mayan coast . This reThe Greater Nicoya was an ancient Central American cultural area currently located in the Pacific Nicaragua and northwestern Costa Rica. It was situated between southern Mesoamerica and northern Isthmo-Colombia . This reRecent improvements in the field of aDNA have revolutionised the study of the history of past populations, providing additional information highly valuable for archaeologists, linguists, and anthropologists. Given that Mesoamerica is the geographical link between the North and South American continents, aDNA studies from this region may be essential to understand the migration patterns of populations across the region, as well as their territorial expansions and interactions. For example, the aDNA study performed by Moreno-Mayar and colleagues  describeArchaeological evidence strongly suggests that after the settling of Mesoamerica, most cultures remained connected by trade, agriculture, culture, politics, empires, and conquests. Therefore, it is essential to include genetic analysis to examine the patterns of population structure, biological adaptation, cultural impacts, and natural phenomena  on the evolutionary and demographic history of Mesoamerican populations. Crucially, aDNA can help to discern these patterns in pre-Columbian Mesoamerica and provide further support to archaeological studies as well as offer new insights and working hypotheses. This is particularly important for the highly understudied Preclassic period, which could benefit from interdisciplinary approaches that help to unravel the demographic and genetic architecture of the settling process of Mesoamerica. Regarding the Basin of Mexico, a detailed study focused on samples from the Classic city of Teotihuacan, concretely in multi-ethnic neighbourhoods such as Teopancazco, could not only help to determine aspects of population demography of Teotihuacan, but also be useful to infer and contextualize the population history of Mesoamerica during the Classic period. This should be aided by similar studies focusing on the understudied Epiclassic and the Postclassic periods, which could help to explain how people were connected after the fall of Teotihuacan and during the empires that emerged before the Spanish conquest, respectively. Although Mayan culture remained in the Yucatán peninsula throughout all these cultural changes, it was never isolated and was also affected by the Classic collapse. A genetic study focusing on the Mayas could further expand our knowledge of this important civilisation and reveal connections with other regions of Mesoamerica. In fact, a knowledge gap currently exists in several Mesoamerican areas that could help to define population interactions in the region. These include the Gulf Coast, the North, the West, and Oaxaca regions, as well as the cultural and historical areas of Isthmo-Colombia, Aridoamerica, and Oasisamerica.Mesoamerica represents a critical tropical transition in the southward journey of the First Peoples of the Americas. A thorough description of genetic variation in ancient Mesoamerican populations through space and time may enable us to identify mechanisms of adaptation to changing environments, climates, lifestyles, and social structures, with potential consequence for present-day people’s health. For instance, this kind of research could target the Mesoamerican Classic collapse and investigate a possible genetic link to severe droughts or epidemic diseases, and potential consequences to the genetic make-up of contemporary Mesoamerican populations. Thus, it is highly important to reconstruct the genetic past of Mesoamerica before and after European arrival and estimate the genetic impact of European colonisation in the demographic and adaptive history of these populations, which currently remains unknown.


Caretta caretta) faces multiple conservation challenges due to its migratory nature and philopatric behaviour. Here, using 4207 mtDNA sequences, we analysed the colonisation patterns and distribution of genetic diversity within a major ocean basin (the Atlantic), a regional rookery (Cabo Verde Archipelago) and a local island . Data analysis using hypothesis-driven population genetic models suggests the colonization of the Atlantic has occurred in two distinct waves, each corresponding to a major mtDNA lineage. We propose the oldest lineage entered the basin via the isthmus of Panama and sequentially established aggregations in Brazil, Cabo Verde and in the area of USA and Mexico. The second lineage entered the Atlantic via the Cape of Good Hope, establishing colonies in the Mediterranean Sea, and from then on, re-colonized the already existing rookeries of the Atlantic. At the Cabo Verde level, we reveal an asymmetric gene flow maintaining links across island-specific nesting groups, despite significant genetic structure. This structure stems from female philopatric behaviours, which could further be detected by weak but significant differentiation amongst beaches separated by only a few kilometres on the island of Boa Vista. Exploring biogeographic processes at diverse geographic scales improves our understanding of the complex evolutionary history of highly migratory philopatric species. Unveiling the past facilitates the design of conservation programmes targeting the right management scale to maintain a species’ evolutionary potential.Understanding the processes that underlie the current distribution of genetic diversity in endangered species is a goal of modern conservation biology. Specifically, the role of colonization and dispersal events throughout a species’ evolutionary history often remains elusive. The loggerhead sea turtle ( Currently, the distribution of biodiversity reflects species' evolutionary history as well as eco-evolutionary dynamics within and across systems2. Because environmental factors (e.g.3) and past events of colonization (e.g.4) leave genetic signatures, characterizing the distribution of genetic diversity sheds light on the mechanisms underlying the species and populations’ distributions6. Understanding those mechanisms is crucial for the implementation of management and conservation measures to maintain species’ evolutionary potentials8. The recent years have seen an increasing number of tools available to perform model-based inferences in evolutionary genetics9. Inferences are made over likelihood estimates of a certain set of pre-defined parameters constituting different evolutionary scenarios10. Likelihood estimates can be obtained, for example, with exact calculations and usually use coalescent theory and Bayesian statistics to simulate phylogenies with Markov Chain Monte Carlo (MCMC) samplers11. Alternatively, likelihood estimates can be obtained with Approximate Bayesian Computation (ABC) methods which emerged as an alternative that derives likelihood estimates through comparisons of summary statistics of simulated datasets with those obtained from observed data9. Being less demanding computationally, ABC facilitates hypothesis-testing to explain phylogeographic patterns that would be otherwise challenging to explore through computing exact likelihood estimates. For marine species, model-based inferences have contributed, for example, to understand oceanic divergence of humpback whales or the post glacial distribution of blacknose sharks13. ABC methods hence are promising to investigate the complex demographic patterns of philopatric, yet highly migratory species.The distribution of species is shaped by environmental variation acting both at macro and micro evolutionary scales15, impacts the genetic structure of species, forming groups of individuals of similar matrilineage17. This evolutionary strategy is common in the aquatic realm . Particularly, sea turtles are capable of homing on a scale of a few kilometres24. Upon hatching, neonates enter the ocean, find the major currents, which tend to guide them in actively escaping predator-rich coastal waters27, and disappear for a period known as the “lost years”28. At sexual maturity, turtles return to their natal rookery likely using a combination of geomagnetic and olfactory cues30. Given this strong site fidelity, it is not surprizing that some genomic regions demonstrate patterns of local adaptation16.Philopatry, the tendency of an organism to return to its home area or natal site to reproduce33. The loggerhead turtle (Caretta caretta) in particularly is widely distributed in tropical and temperate regions, with nesting aggregations ranging from South Africa to Virginia (USA), including the world’s largest rookeries located in Florida  and Masirah Island  haplotypes in a global haplotype network36. Both hypotheses stem from the existence of two divergent mtDNA haplogroups: Haplogroup I—CCA1 and Haplogroup II—CCA238. Approximately 40 distinct haplotypes identified over 3000 turtles revealed a surprising divergence of up to 34-point mutations separating the two major haplogroups41. Despite the suggestion of at least two colonization waves36, such scenarios have not been formally tested, separating the history of those haplogroups and their origins within the Atlantic Ocean.There are currently two main hypotheses that explain the colonization history of loggerhead rookeries in the Atlantic. On the one hand, it has been proposed that the American rookery, ranging from southern Florida to Northern Carolina, is the oldest rookery in the Atlantic and among the first colonized—a conclusion drawn from the high haplotype and nucleotide diversity detected in this aggregation36. The Eastern Atlantic supports the third largest nesting aggregation of loggerhead turtles in the Archipelago of Cabo Verde42. This archipelago is located approximately 600 km off the Western coast of Africa. It consists of 10 volcanic islands with the oldest ~ 20My in the East (Maio) and the youngest aged of ~ 8My in the West43. There, turtles lay well over 15.000 nests per year42 and this number continues to grow. The majority of nesting events occurs on the island of Boa Vista, Maio, Sal and tends to reduce westwards. The existence of two very divergent lineages suggests that at least two independent colonization events occurred44. Similarly, the asymmetric distribution of turtle density in the archipelago calls for the investigation of the directionality of gene flow to better understand the pattern of distribution of genetic diversity. Such knowledge will determine the source and sink island-specific nesting groups, facilitating management of this rookery. Noteworthy, here we refer to island-specific nesting groups, the turtles that nest on a given island since they form the local management unit16. Nesting density on a smaller geographic scale is also heterogeneous: a beach of 15 km length along the south eastern coast of Boa Vista island supports around > 50% of all nesting activity in Cabo Verde42.Interestingly, the role of the Eastern Atlantic rookery in a colonization scenario remains to be completely understoodIn this study, we conducted population genetic analyses on loggerhead turtle rookeries ranging from the large geographic scale of the Atlantic Ocean and Mediterranean Sea, to the regional scale of the Cabo Verde archipelago, and to the local scale of the Island of Boa Vista. We aimed to (1) revisit hypotheses of the Atlantic Ocean colonization to clarify the role of the whole Cabo Verde nesting aggregation in this process, using hypothesis-driven population genetics modelling; (2) determine the impact of philopatry on the distribution of genetic diversity and demographic parameters at various geographical scales from the Cabo Verde archipelago to the island level.47, 2107 from the USA, which included the whole South Eastern Coast from South Florida to North Carolina44, 131 from Brazil36, 175 from Mexico36 and 392 from Cabo Verde44. In total, 3326 sequences were obtained from the published literature , while the USA rookery had the highest value of nucleotide diversity (πUSA = 0.023). The Mediterranean rookery showed the lowest values of haplotype (HdMediterranean = 0.348) and nucleotide diversity (πMediterranean = 0.001), suggesting this rookery has been the most recently colonized or is the result of fewer events of colonization. The Cabo Verdean rookery showed one of the highest indices of haplotype diversity (HdCabo Verde = 0.572) but one of the lowest of nucleotide diversity  between the rookeries of Brazil and the Mediterranean Sea to 0.303 (p < 0.001) between the rookeries of Cabo Verde and that of the USA  while those from São Vicente showed the highest nucleotide diversity (πSaoVicente = 0.020). Both islands belong to the northern area of the archipelago. The lowest values of haplotype diversity were observed in turtles from Santo Antão (HdSantoAntao = 0.438) and the lowest nucleotide diversities were detected in turtles from four islands: Santo Antão, Santiago, Fogo and Maio  and CCA17.1 (n = 294), both belonging to the oldest Haplogroup I—CCA1 lineage. As expected, haplotype and nucleotide diversities showed less variation at the regional level than at the global level, however they remained high and diverse  correction for multiple comparisons  to 0.294 with Maio (p < 0.001) , suggesting an absence of isolation by distance.Pairwise Fons Fig. b. The ge1) Table . Exact tST significantly varied across island-specific nesting groups , increasing in a linear manner from East to West  and increased westwards . This result demonstrates that the islands with higher turtle abundance in the east, such as Boa Vista, act as sources of migrants towards the other islands westwards , while Boa Esperança exhibited the lowest values in both indices . Interestingly, those beaches are adjacent to each other. Furthermore, haplotype diversity observed at the local scale was comparable to that of the regional level but reduced of about half for the nucleotide diversity  Fig.  to invesST = 0.220, p = 0.000), Boa Esperança and Curral Velho , Boa Esperança and Lacacão  and Boa Esperança and Norte ’s hypothesis, as the Brazilian rookery is the oldest in the Atlantic and is composed only by the turtles carrying haplotypes of the haplogroup I. The colonization from Cabo Verde to north America can be explained by the warm saline waters that were gradually introduced to northern latitudes as the Central American Seaway shallowed until its total closure50. As loggerhead turtles do not tolerate cold water and require sand temperatures of at least 25° for successful nest incubation31, North American habitats might not have been suitable for nesting until after the closure of the isthmus of Panama.The existence of two highly divergent mitochondrial haplogroups that shape the phylogeographic patterns of the ocean basins underlies the loggerhead’s global biogeography36. The major haplogroup II pathway favoured by the scenario modelling supports the nearly consensual hypothesis that the Mediterranean Sea supports the youngest rookeries with connection to the Atlantic Ocean45.On a more recent timescale, individuals carrying haplotypes of the haplogroup II colonized the Mediterranean Sea. Likely after the last glaciations, migration occurred to Cabo Verde, where haplogroup II individuals established on already formed colonies, further proceeding to populate USA/Mexico. The pathway here presented thus supports Shamblin’s et al. hypothesis, specifically, the retention in the Indian ocean and the subsequent entry into the Atlantic Ocean via South AfricaST, combined with testing colonization hypotheses suggests USA and/or Mexico as the oldest rookeries. The apparent paradox can be explained by the physical convergence of the two divergent lineages in two waves of colonization in this region of the Atlantic Ocean. The admixture of lineages from multiple genetic backgrounds is well known to increase diversity at sink populations47. Thus, by being the last rookery to receive individuals carrying the highly diverse haplogroup I, USA/Mexico hold a signature that could be interpreted as that of an ancestral rookery, as observed for loggerhead turtles by Reis et al. (2010). Overall, the Atlantic can be understood as a secondary contact zone of major haplogroups that have accumulated divergence in allopatry. While we cannot exclude demographic effects that could have erased the presence of genetic variants at a local scale, one can parsimoniously assume that once admixed rookeries became established, those demographic effects would affect haplogroups differentiation equally.Our biogeographic reconstruction of colonization pathways that assumes two distinct haplogroups as distinct ancestral populations clarifies key elements of the genetic distribution in loggerhead turtles in the Atlantic Ocean. On the one hand, our colonization pathway rejects Central and North American rookeries as the oldest in the Atlantic. However, our summary statistics of diversity and F16. This hypothesis is further reinforced by our scenario simulations of the Atlantic Ocean colonization.Regionally, the Cabo Verdean rookery shows both high haplotype and nucleotide diversities with the presence of both mtDNA haplogroups. Particularly, turtles nesting on the island of São Vicente exhibit a nucleotide diversity at least 3 times higher than that observed in other island-specific nesting groups of the archipelago. This is mainly linked to the presence of the most divergent haplotype CC.2 being frequent on this island. The presence of this haplotype can be traced mostly to one specific beach called Lazareto on the North-West of the island representing ~ 70% of the individuals. The presence of this Indo-Pacific haplotype also present in the Mediterranean Sea is interpreted as evidence for a second wave of colonization44 and management unit36, while early signs of differentiations have been detected16. The analyses of the more extensive dataset in this study confirms the highly significant genetic differentiation congruent with both FST and exact tests. This structure arises clearly from the island of São Vicente and the frequent divergent haplotype but not only. Indeed, a clear geographic pattern exists where the island-specific nesting groups from the western range of the archipelago show increased average differentiation compared to those of the eastern range. Noteworthy, signatures on mtDNA stem from female-mediated gene flow. Because bi-parentally inherited markers show an increase gene flow eastwards linked to opportunistic mating from Western males encountering more females on the East16, our results suggest that the densely-populated easternmost islands of Sal, Boa Vista and Maio are extremely important in maintaining the functioning of the rookery, acting as evolutionary source populations, and therefore requiring specific management. Here, we also reveal another geographic pattern, where gene flow of nesting females has propagated from the centre of the distribution to the edge. This pattern might be the ancient signature of colonization within the Archipelago: after the evolution of site fidelity to the firstly colonized islands, populations were established in all other islands through sporadic nesting events from the long-distance migrants, as it has recently been described to occur in the Mediterranean Sea51. Altogether, the observed genetic structure is likely the result of a strong female philopatric behaviour as often observed in loggerhead turtles53.From a population structure point of view, Cabo Verde was considered a single nesting population54. This signature does not necessarily represent the recent poaching peaks of the 1990s and 2000s. Instead, it may be the footprint of poaching activity, known since the human occupation of the archipelago in the fifteenth century55.From a demographic perspective, estimates of effective population sizes suggest that after a small decline, the most abundant island-specific nesting groups show expansion. This is particularly the case for an East–West corridor entailing Boa Vista to São Vicente islands. While dating the decline is complex, the Cabo Verde rookery may hold the signature of intense poaching which has significantly impacted the census population size42. Reduced genetic diversity is likely the result of genetic isolation as demonstrated by the consistent significant differentiation of turtles nesting on Boa Esperança with turtles nesting on the surrounded beaches. Given that the sample size from this beach is ~ 70, this significant differentiation is unlikely to arise from random fluctuation linked to a small sample size. The parsimonious explanation is a high accuracy of philopatric behaviour on Boa Vista which can be detected away from the centre of the nesting activity, on the other side of the island, as the result of leakage of nesting behaviours.At the local scale, our results show that turtles nesting on one beach of the island of Boa Vista, Boa Esperança, harbour the lowest nucleotide diversity. This beach is situated about 12kms away from Ponta Ervatao & Ponta Cosme, the beaches with the highest nesting density in Cabo Verde and in the eastern Atlantic29. In addition, and due to the highly philopatric behaviour that has been consistently reported in this species, and largely reinforced with this work, conservation-driven actions should be enhanced to preserve the genetic biodiversity at the island-level.Our results offer a fresh perspective on the biogeography of the loggerhead sea turtle. Understanding the Atlantic Ocean as a secondary contact zone for two highly divergent lineages showed that current hypotheses can be merged into a single colonization and dispersal scenario. The expansion pattern exposed here allows to investigate major climatic and geological changes that shaped the pathways of colonization—such as the impact of the gradual closure of the isthmus of Panama in opening suitable nesting habitats in Northern latitudes. Our work also suggests that interpreting ancestry from genetic diversity estimates should be done cautiously. As we have shown here, the presence of divergent lineages does not necessarily imply ancestry. Hypothesis testing with population genetic modelling may help to clarify conflicting views on global biogeography questions. Despite extensive migration, significant degrees of population differentiation spanned all the analysed geographic levels. Therefore, we have demonstrated that turtles are capable of extreme site fidelity and of forming independent nesting groups at highly localized geographic scales. Lastly, the role of Cabo Verde as the stepping stone connecting major rookeries on each side of the Atlantic supports the critical need to maintain protecting and monitoring loggerhead census sizes across this archipelago47, 2107 from the USA, which included the whole South Eastern Coast from South Florida to North Carolina44, 131 from Brazil36, 175 from Mexico36 and 392 others from published studies in Cabo Verde44. In total, 3326 sequences were obtained from published literature, . Elution was conducted in twice 75 μl of AE Buffer. All other steps followed the manufacturer’s protocol.To complement the already existing dataset and improve the resolution at the regional level, field surveys took place in the Cabo Verde archipelago in 2011, 2012 and 2013 during the nesting seasons from June to October. Turtles nesting on nine different islands were sampled: Boa Vista, Fogo, Maio, Sal, Santa Luzia, Santiago, Santo Antão, São Nicolau and São Vicente. On the island of Boa Vista, where the turtle density is the highest44. A 10 μl PCR reaction consisted of 1 μl 10 × Buffer, 1 μl dNTP’s (10 mM), 0.3 μl MgCl2 (5 nM), 3.6 μl HPLC water, 0.1 μl Taq Polymerase (Invitek), 1 μl of each primer (5 pmol/μl) and 1 μl template DNA (~ 20 μg). The reactions were carried out under the following thermo-cycling conditions: An initial denaturation step of 95 °C for 2 min, followed by a second cycle that was repeated 40 times with denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s and elongation at 72 °C for 1 min. A final elongation step of 7 min at 72 °C was carried out.The long fragment (~ 780 bp) of control region of the mitochondrial DNA was amplified using the Primers LCM15382 (5′-GCTTAACCCTAAAGCATTGG-3′) and H950 (5′-GTCTCGGATTTAGGGGTTTG-3′)https://accstr.ufl.edu). The entire data set was aligned in Muscle v8.3.157. All unique sequences can be found in supplementary file PCR products were cleaned with ExoSAP-IT following the manufacturer’s protocol. Cycle sequencing reactions were performed with Big Dye Terminator v3.1 Cycle Sequencing Kit . Sequences were obtained from the forward direction (primer LCM15382). Where insufficient fragment lengths were retrieved, sequences from the reverse direction were also obtained and sequences were concatenated into contigs. Sequencing was performed with an ABI 3730 Genetic Analyzer . Sequences were assembled in Codon Code Aligner v5.0  and ambiguities were corrected by hand investigating carefully the electropherograms for heteroplasmy for instance. All the amplified mitochondrial sequences were classified accordingly to the standardized nomenclature of the Archie Carr Centre for Sea Turtle Research (58 for each of the major rookeries in the Atlantic and Mediterranean Sea. Relationships among haplotypes were investigated in a neighbour-joining network using Network v4.6.1.3 (https://www.fluxus-engineering.com), and visualized with PopArt (https://popart.otago.ac.nz). Population structure amongst those major rookeries was investigated using FST estimates in Arlequin v3.5.1.3 (10.000 permutations). Because the role of Cabo Verde rookery is still yet to be clarified in the broader context of rookeries’ structure, we performed a hierarchic Analysis of Molecular Variance (AMOVA) considering different grouping scenarios, i.e. grouping Cabo Verde with all rookeries separately. The considered rookeries were Brazil, Mediterranean Sea, Mexico and USA. Most likely grouping was identified based on the FCT statistics.Haplotype (Hap) and nucleotide diversity (π) indices were computed in Arlequin v3.5.1.341. The initial tree root height was set to initial split between the genus Caretta and the genus Lepidochelys, 4.09 million years before present37. Monophyly was enforced in all rookeries in order to constrain tree topology during the course of MCMC sampling. These analyses were performed in BEAST v.1.848 and based on haplotype sequences without considering their frequencies. The substitution model was set to HKY as it was found to be the best-fit model of nucleotide substitution chosen through Akaike Information Criteria (AICc) in Mega v6.0659 and the mutation rate was set to 3.24*10–3 substitutions/site/million year, as estimated for marine turtles37. The tree prior was set to coalescent and constant size. The MCMC chain length was set to 108 steps. Convergence was inspected in Tracer v1.660, and models were compared by applying the AICM criteria (1000 bootstraps)61.Rookeries’ ancestry and colonization routes within the Atlantic basin and the Mediterranean Sea were explored by comparing the likelihood ratios of model-based inferences. Phylogenetic models were alternatively built with either the Mediterranean, Mexican, Cabo Verdean, USA and the Brazilian rookery as fixed at the root of the tree49. Models consisted of different scenarios of rookeries serving as source of migrants, from which only emigration was allowed to occur. In total, we explored 12 possible colonization hypotheses  and effective population size (θ) were set as uniform with upper and lower boundaries explored by preliminary tests . Thermodynamic integration with 4 chains with different temperatures  was performed in order to improve the search for parameter space and allow comparisons of models with Bayes factor. A total of 5 × 105 steps were recorded in each chain after a burn-in of 104 states. Three independent replicates were performed for each scenario within each run, in order to ensure convergence. A total of 1.5 × 108 parameters values were visited. Marginal likelihoods were used for model comparisons with Bayes Factor62.To complement the Bayesian phylogenies intended to infer the most likely ancestral rookery, we built possible colonization models in order to (1) strengthen phylogenetic conclusions and (2) infer colonization routes within the Atlantic basin. Colonization hypotheses were tested with models that weight the roles of migration, i.e. gene flow, and mutations as sources of genetic novelty within a population. Colonization models were built and compared in the software Migrate-n v.3.6 through Bayesian inferenceses Fig. . Due to 63. DIYABC allows the generation of simulated datasets and selection of those closest to the true dataset, and the estimate posterior distribution of specific statistics. The objective was to test the possibility that the two major mtDNA Haplogroups (Haplogroup I—CCA1 and Haplogroup II—CCA2) have distinct evolutionary and colonization histories after the split from a common ancestor.We further explored colonization hypothesis with approximate Bayesian computation implemented in the software DIYABC v2.1.0The genetic composition of contemporary Atlantic rookeries would therefore reflect several instances of secondary contact between proto-populations composed by individuals carrying haplotypes belonging to Haplogroup I—CCA1 and Haplogroup II—CCA2. DIYABC scenarios were built to test both migrate-n the 3 highest rank models and two others that could not be tested with migrate-n due to increased structure complexity.ST pairwise comparisons among rookeries from the observed dataset to situate our data with the simulated parameter spaces of the 5 scenarios. Priors were defined as following: uniform population sizes min = 100, max = 500; uniform branching times (in generations) calibrated for the Last Glacial Maximum (LGM) at t1, considering turtle generation time of 50 years—estimated for Chelonia midas to be of 46 years64. Thus, the t1 priors, in turtle generations, of min = 80, max = 100 place the foundation of the Mediterranean rookery approximately 4000 to 5000 years ago, safely outside the estimates of LGM for the Northern Hemisphere, which is estimated to have taken place approximately 20.000ya66. The priors for the remaining t were as following: t2 min = 2000, max = 3999; t3 min = 4000, max = 5999; t4 min = 6000, max = 8999 and t5 min = 9000, max = 10,000. Mutation rate was also allowed to vary uniformly between 10–3 and 10–7, with substitution model Kimura-2.In total, we constructed and compared 5 scenarios Fig. . ReferenST) and perform exact tests for estimation of population differentiation (10.000 permutations). With the exception of calculating genetic indices, and to not influence direct comparisons due to exceptionally high sample size of Boa Vista, we randomly picked 100 sequences from Boa Vista and kept them for all downstream analyses. Results were corrected for multiple comparisons using the modified Benjamini-Yekutieli false discovery rate (FDR) method67, as suggested by Narum68. Furthermore, we calculated the average FST for each island-specific nesting group in order to investigate whether a geographic pattern of population structure exists across the archipelago.Arlequin v3.5.1.3 was used to calculate the nucleotide and haplotype diversity for each island-specific nesting group, to compute Wright’s fixation index , sum of squares deviation (SSD), a measurement of goodness-of-fit, the raggedness index gene flow (M) and effective population size (θ) were set as uniform with upper and lower boundaries of θ = 0–100 and M = 0–1000. A total of 106 steps were recorded in each chain after a burn-in of 104 states. Two independent replicates were performed for each scenario within each run. A total of 2 × 108 parameters values were visited. We ran it three times, averaged θ and M, and calculated effective migration rates (ENI) with the equation (θaverage*Maverage)/2. Geographic distances were calculated from the GPS coordinates of each island. Direction was inferred in relation to the longitudinal position of each island pair. ENIs and geographic distances were log transformed and incorporated in a linear model as response and explanatory variables, respectively, while direction (eastwards or westwards) of gene flow was incorporated as a factor. Statistical analyses were conducted in R v3.2.371.In order to further investigate the migration along the archipelago’s West–East axis, we calculated the effective number of migrants (ENI) per generation across islands and related it to geographic distance and direction with a linear model. Migration estimates were obtained with migrate-n. For this model, prior distribution for 2. It is the Cabo Verdean Island where the majority of the nesting events takes place42. Diversity indices (haplotype and nucleotide diversities), pairwise FST comparisons and exact tests of population differentiation amongst beaches were computed in Arlequin v3.5.1.3. Bayesian skylines were performed as mentioned above. Test were corrected for multiple testing using FDR.Fine-scale variation in the distribution of genetic diversity was investigated for turtles nesting on the island of Boa Vista across 7 different beaches. Boa Vista is the eastern most island of the Archipelago and has an area of 631.1 kmSupplementary file1Supplementary file2Supplementary file3Supplementary file4Supplementary file5Supplementary file6Supplementary file7Supplementary file8Supplementary file9Supplementary file10Supplementary file11Supplementary file12


Our current understanding of the natural evolution of RNA viruses comes largely from consensus level genetic analyses which ignore the diverse mutant swarms that comprise within-host viral populations. The breadth and composition of viral mutant swarms impact viral fitness and adaptation, and the capacity for swarm plasticity is likely to be particularly important for arthropod-borne viruses (arboviruses) that cycle between taxonomically divergent hosts. Despite this, characterization of the relationship between the selective pressures and genetic signatures of the mutant swarm and consensus sequences is lacking. To clarify this, we analyzed previously generated whole genome, deep-sequencing data from 548 West Nile virus samples isolated from avian tissues or mosquitoes in New York State from 1999–2018. Both consensus level (interhost) and minority level (intrahost) nucleotide and amino acid sequences were analyzed, and diversity at each position was calculated across the genome in order to assess the relationship between minority and consensus sequences for individual genes and hosts. Our results indicate that consensus sequences are an inept representation of the overall genetic diversity. Unique host and gene-specific signatures and selective pressures were identified. These data demonstrate that an accurate and comprehensive understanding of arbovirus evolution and adaptation within and between hosts requires consideration of minority genotypes. Flavivirus consists of arthropod-borne viruses (arboviruses) of significant public health importance, collectively causing over fifty million cases annually worldwide 36] but iAcross the entire genome and regardless of host origin, interhost diversity was poorly predictive of intrahost diversity. In fact, some of the most diverse intrahost regions were perfectly conserved in the interhost data. These areas of high intrahost diversity are critical in understanding viral evolution, as minority variants have been shown to interact cooperatively, altering viral fitness and pathogenicity ,53. Furtin vitro [Perhaps most significantly, inter and intrahost diversity differed in individual genes and subunits. While previous studies have made broad conclusions regarding host-specific selective pressures and diversity, genetic correlates of viral fitness, and adaptive or evolutionary trade-offs, the extent to which these characteristics are region-specific has rarely been considered ,54. Regiin vitro . NS1, whin vitro ,59,60, cin vitro . The E, in vitro ,61,62,63Both NS4A and NS2A have high intrahost AA diversity and are known to inhibit IFN signaling acting to suppress the host immune system ,65. NS2ANS3 and NS5 are the primary drivers of flaviviral replication and mutations in NS5 can alter fidelity and thus, mutant swarm diversity ,23,35,71Comparison of mutational frequencies of mosquito and avian isolates revealed additional differences in substitution bias between consensus and minority genomes. Specifically, intrahost analysis revealed a higher ratio of transversions and nonsynonymous mutations relative to consensus genotypes. Our results thus support the selective hypothesis for explaining the transition-transversion substitutional bias. Simply, the selective hypothesis states that transitions are more likely to result in conservative amino acid changes, which are more likely to maintain viral fitness ,75. FixeOur results demonstrate the importance of analysis of both intra and interhost level sequencing data for arboviruses. Consensus sequences are clearly poor representations of mutational landscapes, and therefore do not accurately reflect diversity hotspots, substitution bias, or host-specific selective pressures. A large-scale study by Parameswaran et al. analyzed whole-genome intrahost diversity for DENV highlighted differences between inter and intrahost genotypes, although it focused exclusively on isolates from human sera . In agre


Arabitragus jayakari) occurs only in the mountains of northern Oman and the United Arab Emirates. The species is classified as Endangered due to its small declining population. In this study, we combined genetic and landscape ecology techniques in order to inform landscape scale conservation and genetic management of Arabian tahr. Using 540 base pairs of mitochondrial control region in a dataset of 53 samples, we found eight haplotypes, which fell into two haplogroups. Population genetic analysis using a panel of 14 microsatellite loci also showed a weak, but significant division. Analyses of landscape connectivity supported the genetic results showing poor connectivity between populations in the far south of the study area and those in the north. The most likely location of corridors connecting Arabian tahr populations were identified. Many corridors between tahr populations are impeded by multi-lane highways and restoration of these connections is required to maintain population viability of Arabian tahr. Owing to limited genetic samples outside of Wadi Sareen, further sampling is needed to elucidate both mtDNA and the nuclear structure of Arabian tahr more fully. Our study provides a toolkit that may be used for future genetic and connectivity monitoring of the Arabian tahr population.The Arabian tahr (The online version of this article (10.1007/s42991-020-00072-4) contains supplementary material, which is available to authorized users. Anthropogenic habitat loss and fragmentation is known to negatively affect biodiversity by reducing suitable habitat area, habitat quality and connectivity between populations, and is recognised as the greatest threat to global biodiversity , genetic management should be considered an important part of conservation management.Over the long-term, reduced connectivity results in increased extinction risk, particularly for Endangered species with small populations Frankham . As popuThe Arabian tahr is an Endangered caprid endemic to the Hajar Mountains  provide a conservation genetic toolkit for Arabian tahr to facilitate future assessment and monitoring of the species; (2) evaluate genetic diversity of remaining Arabian tahr populations; (3) identify population genetic structure; (4) assess landscape connectivity and how it may impact Arabian tahr gene flow and population persistence; and (5) provide management recommendations to maintain population connectivity.We searched for genetic samples of the Arabian tahr while deploying camera traps at 336 locations across the Hajar Mountains of Oman and while trapping Arabian tahr at three locations in Wadi Sareen Natural Park, between September 2011 and June 2015. Sixty-seven genetic samples were collected over seven areas of the south-eastern Hajar Mountains (Table DNA extraction from blood and tissue samples was conducted using the following protocols FujiFilm Tissue/Blood Kit (Raytek Scientific Ltd.). For faecal samples, a combination of Isohelix DNA Isolation Kit (Cell Projects Ltd.) and QIAamp DNA Stool Mini Kit (Qiagen Ltd.) were used for DNA extraction following the protocol described in Werhahn et al. . In ordeFor each sample, 540 base pairs of mitochondrial control region (d-loop) was amplified with the following primers TAH D-loop1 forward (5′-GAAGTTCTACTTAAACTATTCCCTG-3′) and primers TAH D-loop1 reverse (5′-AGAAAGAACCAGATGTCTGATA-3′) according to the following conditions, 95 °C for 5 min; 40 cycles of 95 °C for 30 s, 58 °C for 30 s and 72 °C for 1 min; 72 °C for 10 min.For samples where d-loop sequences were not obtainable, universal mammalian cytochrome B primers  and 1 μl of 10 nM of each primer. PCR product was then purified using 1 μl mix of Exonuclease I (ThermoFisher Scientific Ltd.) and FastAP Thermosensitive Alkaline Phosphatase (ThermoFisher Scientific Ltd.) with 1:1 ratio. All products were sequenced in the forward direction  using a BigDye Terminator v3.1 Cycle Sequencing Kit (ThermoFisher Scientific Ltd.) with PCR conditions: 96 °C for 1 min; 25 cycles of 96 °C for 10 s, 50 °C for 5 s and 60 °C for 4 min; 4 °C for 10 min. For every reaction a negative control was included and analysed in parallel with the samples. If any positive signal was found in the negative control the results were rejected and the entire run was repeated.https://www.geneious.com).The mitochondrial DNA sequences were edited with the software Geneious 8.1.6.  (for details and references see Online Appendix 1). After markers elimination for monomorphism, poor scoring or non-amplification was applied, 15 markers were then selected for the final three multiplex panels (see Online Appendix 2).2O and 0.5 μl of each forward and reverse primer. As for the mtDNA sequencing, for every PCR reaction a negative control was included and if a positive signal was observed the entire run was repeated. To confirm PCR amplification, the products were then visualised on a 1.5% agarose gel. For non-invasive samples  each sample was repeated three times. Consensus calls were generated from three identical homozygous profiles or two identical heterozygous protocols and ambiguous profiles were scored another two times and the consensus taken. We used Geneious version 8.1.6 to score the 15 microsatellites loci.The microsatellite panels were run under the following PCR conditions: 95 °C for 5 min; 40 cycles of 95 °C for 30 s, 50 °C for 30 s and 72 °C for 1 min; 72 °C for 10 min. Total 10 μl reaction was performed for PCR amplification: 1 μl template DNA, 5 μl Type-it and 1 μl Q-solution , 2 μl of ddHMicrosatellite loci were tested for the presence of null alleles with the software micro-checker  with suitable non-degraded DNA selected for the library preparation stage. DNA was quantified using a Qubit Assay (Thermofisher Scientific) and normalised to 7 ng–1 µl.A double digest RAD (ddRAD) library was produced according to a modified protocol of Peterson et al.  see als for the https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and the reads were then demultiplexed by barcode using the process_radtags module of the Stacks 1.44 bioinformatics pipeline  to assemble and create a catalogue of genetic loci contained in the data. The Stacks scripts export_sql.pl  and Stacks scripts populations (-m 10) and Plink v1.07 toolset , observed heterozygosity (Ho) and inbreeding coefficient (Fis) were calculated for the genetic cluster defined by the STRUCTURE results with the software Genetix v4.05.2   of the microsatellite and SNP data were conducted in the adegenet package of R (1.3-1) (Jombart K) from one to six. The CLUMPAK web interface was used to summarise the results and graphs  was determined using the calculation of delta K (ΔK) described in Evanno et al. . In order to support the results from STRUCTURE we used the software GENELAND to create a spatially explicit model for the inference of the number of genetic cluster (K) and the spatial location of genetic discontinuities between those clusters. We ran the software for 1–6 clusters (K) using 1,000,000 iterations and a thinning of 100. The allele frequency model was set to correlated and ten independent runs were realised. The rest of the parameters were left to the default.Principle component analysis (PCA) and estimation of the number of genetic cluster . Although tahr are known to occupy areas of lower occupancy probability, our criteria ensured that enough tahr inhabited the core areas to act as breeding and dispersing source populations from where current could be inputted into the connectivity circuit  of a receiver operating characteristic curve  to the path length (PL). The ratio indicated the average resistance an individual experiences when using the optimal path to move between populations . Three failed samples were found not to be Arabian tahr following sequencing at cytochrome B Table . These wThe mitochondrial control region sequence data consisted of 8 haplotypes which were named A–H and deposited on GenBank under the accession numbers MN563184—MN563191. One further haplotype from an animal from the United Arab Emirates was included from the NCBI GenBank accession FJ207523  and 2  divided by 10 stepwise mutations. Within each haplogroup, haplotypes were divided by maximally 3 step-wise mutations from their nearest neighbour . However, a significant proportion of the variance was also attributable to the difference among samples within populations . The difference between populations within the southern haplogroup was not significant .Geographical segregation of the haplotypes appeared to be fairly strong with recovered haplotypes being restricted to a single sample area, apart from haplotype E found in Jabal Qahwan and As Saleel and Haplotype G found in Wadi Sareen and Jabal Qahwan Fig. . The mosHe) ranged from 0.18 (BM4008) to 0.0.77 (MCM38) and observed heterozygosity (Ho) ranged from 0.19 (BM4008) to 0.78 (MCM38)  which could have potentially been accounted for by allelic dropout; however, these animals were blood-sampled individuals that were given individual identification marks following blood sampling, so accidental resampling of these animals can be ruled out and it can be taken that the genotypes are from two different animals.–8 and 3.8 × 10–4 for Wadi Sareen and respectively 5.0 × 10–8 and 5.4 × 10–4 for the populations south of Wadi Sareen.The probability of identity (PID) and probability of identity for siblings (PIDsibs) were respectively 3.8 × 10K was 2 (ΔK2 = 14.75) while ΔK was ΔK3 = 0.83 . On the other hand the STRUCTURE bar plot graphs , hinting for further substructure in one of the clusters. The map of probability distribution created with GENELAND shows a similar clustering of the different sampling sites as the software STRUCTURE  which did not allow us to estimate a precise number of genetic clusters using this method. Principle component analysis (PCA) of the data showed some degree of spatial separation .The analysis of molecular variance (AMOVA) is summarised in Table Fst between the two populations was 0.0651 . Regarding the genetic diversity indices He was 0.509 ± 0.136 (s.d)—0.472 ± 0.255 (s.d) and Ho was 0.512 ± 0.161 (s.d)—0.364 ± 0.206 (s.d) for Wadi Sareen and south of Wadi Sareen respectively. The inbreeding coefficient (Fis) was 0.012 : − 0.088 to 0.077) for Wadi Sareen and 0.299 (CI: − 0.004 to 0.408) south of Wadi Sareen.Pairwise He) was 0.288 ± 0.157 (s.d). Mean observed heterozygosity (Ho) was 0.312 ± 0.199 (s.d). Of these variable SNPs 342 had flanking region > 50 bp either side of the SNP, making them suitable for future assay design.Following filtering of 12.3 million reads, 1314 variable SNPs loci were identified across the dataset. Out of the 21 samples analysed, 19 could be used for further analysis. Mean expected heterozygosity (K = 1–3) and the find.cluster function from the adegenet package revealed no clear substructure within Wadi Sareen. However, the presence of admixed individuals could be observed (Online Appendix 5).Analysis of the dataset with structure , which indicated a low level of genetic exchange between the north and south. However, while genetic data suggested the split between the populations was located immediately south of Wadi Sareen, the landscape connectivity data indicated the split was south of Ras Ash Shajar and Tiwi. As a very small number of genetic samples were tested from this area, one individual from Tiwi and three from Ras Ash Shajar, it may be that the Tiwi and Ras Ash Shajar populations are genetically closer to Wadi Sareen but the few individuals we tested happened to be genetically closer to Jabal Qahwan. For this reason, we believe the split between populations is most likely south of Tiwi, isolating only the As Saleel and Jabal Qahwan populations.The best connectivity model indicated a total of 11 corridors impeded by highways, these impediments affected five separate tahr populations Fig. . These iThis study provides useful molecular and landscape recommendations and tools that improve our understanding of population structure, genetic diversity and landscape management of the Arabian tahr. These management tools and recommendations facilitate the preservation of population connectivity and the genetic integrity of tahr within the species range.The use of multiple genetic methods in this study allowed comparison of the reliability of results from a number of techniques. The mitochondrial control regions (d-loop) provided important information about the genetic structure thanks to the presence of nine different haplotypes. These haplotypes presented a strong geographical segregation permitting us to define different genetic haplogroups in our dataset. Other studies on related species also successfully used mitochondrial DNA in order to identify monophyletic haplogroups    and probability of identity for siblings (PIDsibs) results showed that the microsatellite panel was informative enough to carry out individual and sibling identification. This suggests that this panel could be used to conduct mark-recapture population surveys of Arabian tahr using non-invasive genetic samples. Mark-recapture methods have been used on many other similarly elusive mammals such as the Eurasian Otter  suggested two main genetic clusters in the study area, the Wadi Sareen cluster in the north and a cluster south of Wadi Sareen. Given that we would expect a time lag on the effects of barriers to gene flow, it is likely that the genetic differentiation that we observed was the result of local environmental conditions occurring 30 or more years ago  and a lower inbreeding coefficient (Fis) than the southern cluster. The Wadi Sareen population is the largest contiguous Arabian tahr population in existence and is the least threatened population in our sampling area  populations in Oman culverts are known to be used by gazelle , although we currently have no evidence that Arabian tahr use culverts, in the right habitats it is likely that they do. Culverts may therefore help mitigate the issue of fragmentation caused by highways, however, some steps are required to facilitate the use of culverts as corridors. These include: (1) initiating research to understand the use of culverts by wildlife in Oman and identify habitat characteristics of preferred crossing areas; (2) protection of habitat surrounding important culvert crossings for connectivity; (3) remediation and provision of corridor habitats to connect occupied tahr habitats and provide essential cover habitat, through planting trees, providing large rocks and other cover to encourage wildlife utilisation.Our study highlighted highways as a threat to wildlife connectivity in the study area. A total of 11 important tahr corridors were negatively impacted by highways Fig. . ConnectThe preservation and restoration of Arabian tahr movement and connectivity between Oman’s protected area network and important populations is vital for maintaining genetic diversity. Human activities have restricted movement of Arabian tahr in the Hajar Mountains and are most likely contributing towards reduced gene flow. In light of our results, research and actions are required to understand and restore functional connectivity to assure the long-term persistence of Arabian tahr and other wildlife species in Oman.Supplementary file1 (XLSX 10 kb)Supplementary file2 (XLSX 11 kb)Supplementary file3 (XLSX 9 kb)Supplementary file4 (PNG 210 kb)Supplementary file5 (PNG 641 kb)Supplementary file6 (PDF 1319 kb)Supplementary file7 (PDF 111 kb)Below is the link to the electronic supplementary material.


FST = 0.03), where two distinct subpopulations were identified.Unexpectedly, elephants in the Lake Manyara region appear to be more closely related to those across the East African Rift wall in the Ngorongoro Conservation Area than they are to the neighboring Tarangire subpopulations. We concluded that the Rift wall has had a negligible influence on genetic differentiation up to this point, but differentiation may accelerate in the future because of ongoing loss of corridors in the area. Interestingly, relatively high genetic similarity was found between elephants in Tarangire and Ruaha although they are separated by >400 km. In southern Tanzania, there was little evidence of female‐mediated gene flow between Ruaha and Selous, probably due to the presence of the Udzungwa Mountains between them. Despite observing evidence of significant isolation, the populations of elephants we examined generally exhibited robust levels of allelic richness (mean AR = 9.96), heterozygosity (mean µHE = 0.73), and effective population sizes (mean Ne = 148). Our results may inform efforts to restore wildlife corridors between protected areas in Tanzania in order to facilitate gene flow for long‐term survival of elephants and other species.Increasing human population growth, exurban development, and associated habitat fragmentation is accelerating the isolation of many natural areas and wildlife populations across the planet. In Tanzania, rapid and ongoing habitat conversion to agriculture has severed many of the country's former wildlife corridors between protected areas. To identify historically linked protected areas, we investigated the genetic structure and gene flow of African savanna elephants in Tanzania using microsatellite and mitochondrial DNA markers in 688 individuals sampled in 2015 and 2017. Our results indicate distinct population genetic structure within and between ecosystems across Tanzania, and reveal important priority areas for connectivity conservation. In northern Tanzania, elephants sampled from the Tarangire‐Manyara ecosystem appear marginally, yet significantly isolated from elephants sampled from the greater Serengeti ecosystem (mean  We used microsatellite loci and mtDNA to identify population structure of African elephants in Northern Tanzania. We identified two subpopulations: Serengeti elephants were genetically distinct from Tarangire Manyara. We also found that the Tarangire elephants showed higher genetic similarity with the Ruaha elephants than the Serengeti. Tanzania alone has lost over 60%  of its elephant population between 2009 and 2014 , range and habitat fragmentation remain a significant long‐term threat to the species’ survival  to what degree does the rift valley wall influence gene flow between the Serengeti and other metapopulations in northern Tanzania? (b) what is the genetic history of elephants that recolonized the Serengeti following poaching‐induced extirpation? (c) has genetic differentiation occurred within regions such as the Serengeti in the absence of barriers to migration and gene flow? (d) how has the recent blockage of the wildlife corridor between Tarangire and Ruaha impacted gene flow and genetic differentiation? (e) what is the extent of gene flow mediated by males versus females? (f) are Ruaha elephants genetically isolated from those of Selous and is the isolation recent and due to habitat loss or were the populations distinct even before the intensification of human activities?22.1To conduct a regional assessment of the genetic structure, we selected four major ecosystems in Tanzania that have the largest elephant populations. These are Serengeti and Tarangire‐Manyara in northern Tanzania and Ruaha and Selous ecosystems in central and southern Tanzania Figure . We then2.1.1The Serengeti ecosystem (SE) is in the north‐east of Tanzania between 34°450–35°500E and 2°–3°200S and includes multiple levels of protected area  comprises three protected areas: Tarangire National Park (TNP), Lake Manyara National Park (LMNP), and Manyara Ranch (MANR) which acts a migratory corridor between the two parks Figure . TNP and2.1.32 . The number of elephants in the Ruaha ecosystem in 2015 was 8,272  which is an important bird area and important farming areas especially for rice and sugar cane , Rungwa, Kisigo and Muhesi Game Reserves and Lunda‐Mkwambi Game Controlled Area. Ruaha National Park is the largest national park in Tanzania Figure , and it  TANAPA, . The Gre TAWIRI, . Between2.2We used DNA extracted from elephant fecal samples to genotype 11 microsatellite loci and mitochondrial DNA sequences of a hypervariable region in cytochrome b in the control region of D‐loop. To ensure a higher concentration of DNA, we only collected samples from fresh dung. Scrapings of 5g were taken from the outer layer of the dung bolus, where most of the epithelial cells are found, and stored in Queen's College Buffer in 50 ml conical tubes, following methods established by Ahlering et al. . Where p2.3DNA was extracted and purified using company protocols for commercially available DNA extraction kits (QIAamp DNA Stool Mini Kit) with minor modifications . Eleven loci that had previously been found to be polymorphic in elephants were used: LAT06, LAT08, LAT13, LAT24 , and individual alleles in each amplified loci were manually verified and compiled into working multi‐locus genotypes for each fecal sample collected. In order to ensure quality control standards, individual samples underwent two separate amplification, fragment analysis, and genotyping rounds, to avoid inconsistent allele patterns or weak peak fragment identification  in GenAIEx version 6.502. We tested for deviations from expectations under Hardy–Weinberg equilibrium (HWE) and for linkage disequilibrium (LD) within protected areas in GENEPOP  was then plotted against geographic distance (km) to determine if the two variables were significantly correlated. FSTAT  and allelic richness (AR) which represents the number of alleles standardized to the smallest sample size in the study area. GenIEx  and observed (HO) heterozygosities. FST values were normalized by dividing pairwise FST to the geographic distance between subpopulations and denoted as FST/km.The IBD program Bohonak,  was used Goudet,  was usedK within the population, where K in most cases is unknown  repetition value of 1 × 106 was set. STRUCTURE HARVESTER was used to identify all primary  ΔK estimates  implemented in GenAIEX  by comparing to a reference sequence for the hypervariable control region of elephants  and nucleotide diversity (π) were calculated using Arlequin version 3.5  and were used in the analyses of nuclear DNA. One locus, FH60, consistently showed evidence of a null allele for all sampling locations and deviated from HWE after applying a Bonferroni correction for multiple comparisons and was thusly removed from subsequent analyses, which therefore relied on 10 loci.We successfully genotyped 714 samples of the 800 fecal samples collected between 2015 and 2017 Appendix . We idenFST = 0.03) (Tables AR = 9.96), heterozygosity (mean µHE = 0.73) (Table Ne = 148). Both NSE and SSE had the highest effective population size (Ne) of 309 and 310, respectively, but the NCA and SGR had the lowest Ne of 95 elephants  Tables  and 3. D3) Table , and effts Table . For RNPK = 2; Figure K = 7; Figure Hierarchical STRUCTURE analysis indicated that the uppermost level of population structure was represented by two clusters  was performed to determine the number of clusters using r) implemented in GenAIEX . Elephants that are closer to each other show higher genetic similarity than those far apart. Results from a Mantel test showed a positive correlation between genetic distance and geographic distance , as expected. However, one notable exception was the finding of a relatively high FST value between TNP and MAR subpopulations despite being only 60 km apart  and nuclear loci  between Ruaha and Selous.Spatial autocorrelation  the mtDNA FST/km between TNP‐MAR was ~25× larger than the FST/km between TNP‐RNP and the microsatellites FST/km was ~9× larger. (2) Although the geographic distance between NCA‐MAR (76 km) is larger than the distance between MAR‐TNP (60 km) and the NCA and MAR are separated by the Rift wall, the microsatellites and mtDNA FST/km values are approximately three times smaller. This strongly suggests that elephants have been traversing the Rift wall between the NCA and Manyara resulting in gene flow whereas gene flow between Manyara and Tarangire has been ablated.All on Table . The FS3.2h) ranged from 0.29 in RNP to 0.91 in SGR , and Savanna (S), which is further subdivided into three subclades: Savanna‐wide (SW), East‐central (EC), and Southeast‐savanna (SS) .Tanzania is bisected by the East African Rift, with major elephant populations west of the Rift in the greater Serengeti ecosystem and populations east of the Rift including Manyara, Tarangire, and Ruaha National Parks and the Selous Game Reserve and adjoining protected areas. A hierarchical STRUCTURE analysis of our genetic data indeed identified two metapopulations divided by the Rift. When the analysis is restricted to northern Tanzania, excluding Ruaha and Selous, the result is the same: The first level of STRUCTURE analysis identifies two metapopulations. Both nuclear and mtDNA results revealed that the Tarangire‐Manyara Ecosystem (TME) and the Serengeti Ecosystem (SE) are genetically distinct with limited gene flow among them. All pairwise FST/km) was twenty‐five times lower between Tarangire and Ruaha elephants than for Tarangire and Manyara. This suggests extensive gene flow over the long distance (>400km) between Tarangire and Ruaha prior to the eventual closure of corridors between them that occurred over the past 30 years  size for nuclear loci will frequently be four times that of mtDNA loci , our microsatellite analysis provides insights into the presence of genetic differentiation within the Serengeti. Elephants from northern Serengeti (NSE) formed one cluster whereas elephants from southern Serengeti (SSE) formed another cluster. Pairwise Analysis of the hypervariable control region in D‐loop of the cytochrome b gene of mtDNA has frequently been used to assess nucleotide and haplotype diversity among mammals. The mtDNA is inherited in a haploid fashion because it is passed through the maternal lineage. Majority of Serengeti elephants carry haplotypes in the F‐clade which is also found among forest elephants. The presence of F‐clade in Serengeti indicates many generations of hybridization between forest and savanna elephants during the geological history of Africa  in Tanzania uncovered significant genetic differentiation between lions of Selous and Ruaha  identified unexpected clear, distinct lineages between Ruaha and Selous which was attributed to the presence of the Eastern Arc Mountains . Indeed, the future of wildlife conservation in Tanzania, particularly for far‐ranging species, and in the face of increasing isolation of protected areas, is reliant upon participation of communities whose livelihoods depend on these same areas. Every effort must be made to ensure it is a mutually beneficial arrangement as recommended in Table There is a need to recognize the importance of conserving wildlife in human‐dominated areas ; Data curation (lead); Formal analysis (lead); Funding acquisition ; Investigation ; Methodology (lead); Project administration ; Resources ; Supervision (supporting); Validation (lead); Visualization ; Writing‐original draft (lead); Writing‐review & editing (lead). Thomas Casey Weathers: Conceptualization (supporting); Data curation (supporting); Formal analysis ; Investigation ; Methodology ; Validation (supporting); Writing‐review & editing (supporting). Anna B. Estes: Conceptualization (supporting); Formal analysis (supporting); Funding acquisition (supporting); Investigation (supporting); Methodology (supporting); Resources (supporting); Supervision (supporting); Validation ; Writing‐original draft ; Writing‐review & editing . Barbara C. McGrath: Conceptualization (supporting); Data curation ; Formal analysis ; Funding acquisition (supporting); Investigation (supporting); Methodology ; Project administration (supporting); Resources (lead); Supervision ; Validation ; Writing‐original draft ; Writing‐review & editing . Douglas R. Cavener: Conceptualization (lead); Data curation ; Formal analysis (lead); Funding acquisition (lead); Investigation (supporting); Methodology (supporting); Project administration (lead); Resources (lead); Software ; Supervision (lead); Validation (lead); Visualization ; Writing‐original draft (supporting); Writing‐review & editing .Appendix S1‐S4Click here for additional data file.


The survival of INA-6 human multiple myeloma cells is strictly dependent upon the Interleukin-6-activated transcription factor STAT3. Although transcriptional analyses have revealed many genes regulated by STAT3, to date no protein-coding STAT3 target gene is known to mediate survival in INA-6 cells. Therefore, the aim here was to identify and analyze non-protein-coding STAT3 target genes. In addition to the oncogenic microRNA-21, we previously described five long noncoding RNAs (lncRNAs) induced by STAT3, named STAiRs. Here, we focus on STAT3-induced RNA 18 (STAiR18), an mRNA-like, long ncRNA that is duplicated in the human lineage. One STAiR18 locus is annotated as the already well described LINC00152/CYTOR, however, the other harbors the MIR4435-2HG gene and is, up to now, barely described.CAPTURE-RNA-sequencing was used to analyze STAiR18 transcript architecture. To identify the STAiR18 and STAT3 phenotype, siRNA-based knockdowns were performed and microarrays were applied to identify their target genes. RNA-binding partners of STAiR18 were determined by Chromatin-Isolation-by-RNA-Purification (ChIRP) and subsequent sequencing. STAT3 expression in dependence of STAiR18 was investigated by immunoblots, chromatin- and RNA-immunoprecipitations.As identified by CAPTURE-RNA sequencing, a complex splice pattern originates from both STAiR18 loci, generating different transcripts. Knockdown of the most abundant STAiR18 isoforms dramatically decreased INA-6 cell vitality, suggesting a functional role in myeloma cells. Additionally, STAiR18 and STAT3 knockdowns yielded overlapping changes of transcription patterns in INA-6 cells, suggesting a close functional interplay between the two factors. Moreover, Chromatin isolation by RNA purification (ChIRP), followed by genome-wide RNA sequencing showed that STAiR18 associates specifically with the STAT3 primary transcript. Furthermore, the knockdown of STAiR18 reduced STAT3 levels on both the RNA and protein levels, suggesting a positive feedback between both molecules. Furthermore, STAiR18 knockdown changes the histone methylation status of the STAT3 locus, which explains the positive feedback and indicates that STAiR18 is an epigenetic modulator.Hence, STAiR18 is an important regulator of myeloma cell survival and is strongly associated with the oncogenic function of STAT3. The close functional interplay between STAT3 and STAiR18 suggests a novel principle of regulatory interactions between long ncRNAs and signaling pathways. Interleukin-6 (IL-6) is a hallmark of a wide range of biological functions, including immune regulation, hematopoiesis, inflammation and tumor development . IL-6 opINA-6 cells were kindly provided by the Gramatzki group , who removed these cells originally from an 80 year old multiple myeloma patient and brought them into cell culture. Cells were maintained in RPMI1640 + GlutaMAX™ , supplemented with 10% fetal calf serum  and 1% penicillin/streptomycin (LIFE Technologies), and 1 ng IL-6 per ml medium. Where indicated, INA-6 cells were withdrawn from IL-6 for at least 12 h with an optional IL-6 restimulation.n ≥ 3). Data are shown as means, and error bars represent standard deviation (SD). A two-sided and unpaired t-test was used to assess statistical significance.Each experiment was performed in a minimum of three independent biological replicates (Apoptosis rates were determined by either the Dead Cell Apoptosis Kit (LIFE Technologies) or the Caspase-Glo® 3/7 Assay  according to the manufacturerˈs protocols. Cell vitality was examined by CellTiter-Glo® Luminescent Cell Viability Assay (Promega). Analysis was carried out by either FACSCalibur™  together with the corresponding software CellQuest™ or LUMIstar Optima  together with the corresponding software Optima, respectively.RNA and DNA were isolated with TRIzol (LIFE Technologies) and Phenol-Chloroform-Isoamylalcohol , respectively, following the manufacturerˈs protocols. RNA was DNase-digested using TURBO-DNA-free Kit (LIFE Technologies). Reverse transcription of RNA was conducted using the RevertAid First Strand cDNA synthesis kit . Analyses of DNA and cDNA were performed using Light Cycler® Fast Start DNA Master Plus SYBR Green kit (Roche) as described by the manufacturer using Light Cycler®. Primers are listed in the Additional file INA-6 cells were treated with 5 μg/ml Actinomycin D (in DMSO) for up to 6 h. Cells were harvested after indicated time points and transcript half-life was assessed by qPCR in comparison to DMSO control.23 mol− 1), the molar mass of a single base pair (650 g/mol), the plasmid size (5872 bp for pcDNA-STAiR18 and 6079 for pcDNA-cypB), its concentration (both 1 μg/μl) and volume (5 μl) used for qPCR by the following equation:To determine the copy number of STAiR18 and cyclophillin B a sequential dilution of pcDNA 3.1 (+) harboring either STAiR18 or cypB inserts  according to the manufacturer’s instructions. Three pulses of 1600 V and 10 ms were applied.Cells were transfected with 200 pmol stealth-siRNAs . After a Bradford analysis, 30 μg of lysates were supplemented with Laemmli buffer, boiled, separated in a 10–15% SDS gel and blotted to a polyvinylidene fluoride membrane. Protein bands were visualized using primary and secondary antibodies . Where indicated, a quantitative analysis of band signals was performed using the software XStella.Proteins were isolated using cell lysis buffer . Data are shown as means and error bars are given as standard deviations (SD).Chromatin-Immunoprecipitation (ChIP) was performed using the EZ ChIP kit  according to the manufacturerˈs instructions together with antibodies targeting H3K4me3, H3K27me3 , H3K36me3 , and an IgG negative control . For each IP approach, 5 μg antibody was used for 5 × 10n = 4) was analyzed using SurePrint G3 Human Gene Expression v2 8x60K Microarrays  and the belonging OneColor Quick Amp Labeling kit according to the manufacturer’s instructions. Quality controlled libraries were hybridized to the array and signal detection occurred by microarray scanner (Agilent Technologies). Raw data files were processed by the GeneSpring 13.0 software (Agilent Technologies). A paired t-test without a multiple testing correction was executed for statistical analysis. Data were deposited in the GEO database [GEO:GSE71092].Gene expression of an experiment performed in four independent biological replicates . Of note, the probes captured the short, spliced RNAs with higher efficiency than the long macroRNA transcripts (data not shown). Hence, CAPTURE-RNA-sequencing data under-represented the levels of unspliced variants. However, when expression levels were compared by qPCR, a well-measurable amount of unspliced transcripts could be detected (data not shown). As shown in Additional file The STAiR18 phenotype in INA-6 cells was studied by knockdown experiments in combination with different vitality assays see Fig. . We usedThe knockdown of STAT3 was carried out as a control as the cells are expected to undergo apoptosis. Targeting STAiR18 with different siRNAs comparably reduced cell vitality and induced apoptosis in INA-6 cells. In this respect, STAiR18 knockdown was as efficient as knockdown of STAT3. To exclude off-target effects, we used additional, distinct siRNA oligonucleotides targeting exons 1 and 2 in multiple positions. Both induced apoptosis to a comparable extent  over lacZ negative control oligos is shown in Fig. As shown in Fig. As the regulation of STAT3 by STAiR18 seems to occur at the level of transcription 6A) and an association of STAiR18 with nascent STAT3 mRNA within the first intron could be observed, we next asked whether STAiR18 might influence the epigenetic status of the STAT3 locus. Thus, ChIP experiments in INA-6 cells revealed that H3K4 trimethylation of the STAT3 promoter and H3K36 trimethylation in the transcribed region were significantly reduced after STAiR18 knockdown  within the first intron of STAT3 pre-mRNA. Since STAiR18 is essential to induce or maintain H3K36 trimethylations as activating histone marks in the STAT3 locus, we suggest an interaction with the histone-modifying complex SETD2, which is indeed regulated by STAiR18 within the STAT3 locus. Furthermore, we demonstrated that STAT3 expression on RNA and protein levels is increased by STAiR18, constituting a positive feedback loop between both molecules. Therefore, the tight interplay of STAT3 and STAiR18 as well as the regulation of their downstream targets turns out to be essential for multiple myeloma cell survival. As a mediator of survival, STAT3 conveys this effect largely by the miR21 . HoweverLINC00152, which we later termed STAiR18 due to its STAT3-dependent expression [Homo sapiens from its primate ancestors. Using locus-specific PCR primers, we demonstrated that both loci are active and seem to produce similar yet not identical splice patterns. It is intriguing to speculate that the highly conserved STAiR18 provides a mechanistic principle that can be dynamically modulated by more rapidly evolving additional exons during phylogeny, and that the duplication further extends this functional plasticity in the human species. Many splice variants are derived from the STAiR18 loci, as demonstrated by CAPTURE-RNA-sequencing. Exons 1 and 2 show phylogenetic conservation in all mammals, while conservation of other exons seems restricted to primates. This suggests an evolutionarily preserved functional role of STAiR18.Here, we focused on STAiR18 in multiple myeloma and initially found the LINC00152 locus to be duplicated on chromosome 2 within the human genome (first published by Diederichs in HeLa 2017). Furthermore, the duplication of the STAiR18 locus appears particularly intriguing as it occurred very recently during the evolutionary development of As mentioned above, investigations of other groups revealed that LINC00152 (STAiR18-A transcript variant) shows elevated expression or hypomethylation . KnockdoHere, we present a completely undescribed mechanism of LINC00152/STAiR18 in myeloma cells. Microarray studies upon STAT3 and STAiR18 knockdown revealed a highly overlapping set of target genes. This observation suggests that STAiR18 represents a downstream regulator mediating part of the transcriptional responses of STAT3. In fact, closer inspection of our microarray data revealed that those STAT3 targets, which are known to be induced by IL-6-triggered activation of the transcription factor, including SOCS3, JunB, GADD45β, Pim-1, and others, do not appear to be amongst the STAiR18 targets. In contrast, STAT3 and STAiR18 seem to primarily share target genes such as those that are regulated upon STAT3 knockdown but not via STAT3 tyrosine phosphorylation. In fact, STAT3 has been shown to regulate a number of target genes and functions without being tyrosine phosphorylated . Thus, SAmongst the genes directly regulated by STAiR18 and STAT3 we identified several targets involved in cell cycle progression and cancer cell survival, like the protein tyrosine phosphatase type 4 member 1 (PTP4A1) –22 and tSurvival of INA-6 multiple myeloma cells depends on IL-6-mediated STAT3 activation. STAT3 as a transcription factor induces the expression of the ncRNA STAiR18 , which in turn facilitates a positive feedback to STAT3. Moreover, both factors are strong survival key players in myeloma cells. In conclusion, the interplay of both molecules unravels an enthralling potential mechanism.Additional file 1: Figure S1. Vector map pcDNA 3.1 (+)-STAiR18. Figure S2. Vector map pcDNA 3.1 (+)-cypB. Figure S3. Survival of INA-6 cells depends on IL-6-induced STAT3 activation. Figure S4. Determination of CyclophillinB copy number. Figure S5. INA-6 cell vitality after ActinomycinD treatment. Figure S6. Identification of STAiR18 isoforms by capture RNA-sequencing. Figure S7. STAiR18 polyadenylation. Figure S8. Survival of INA-6 myeloma cells depends on STAiR18. Figure S9. Survival of myeloma cells depends on STAiR18. Figure S10. Comparison of genes regulated by STAT3 and STAiR18 knockdown. Figure S11. STAT3 protein levels are STAiR18 regulated. Figure S12. STAiR18 associates with specific RNA and DNA targets. Table S1. (q) PCR primers. Table S2. Stealth siRNAs. Table S3. Antibodies. Table S4. ChIRP oligonucleotides. Table S5. Duplication of STAiR18 in the Neandertal and Denisova genomes. Table S6. Absence of STAiR18 duplication in other primate genomes. Table S7. Genes regulated by STAT3 knockdown. Table S8. Genes regulated by STAiR18 knockdown. Table S9. Genes regulated by STAT3 and STAiR18 knockdown. Table S10. RNA interaction partners of STAiR18.