Acanthodactylus, contains two sub-Saharan species with unresolved phylogenetic relationship and unknown climatic preferences. We here aim to understand how and when adaptation to arid conditions occurred in Acanthodactylus and when tropical habitats where entered. Using whole mitogenomes from fresh and archival DNA and published sequences we recovered a well-supported Acanthodactylus phylogeny and underpinned the timing of diversification with environmental niche analyses of the sub-Saharan species A. guineensis and A. boueti in comparison to all arid Acanthodactylus. We found that A. guineensis represents an old lineage that splits from a basal node in the Western clade, and A. boueti is a derived lineage and probably not its sister. Their long branches characterize them—and especially A. guineensis—as lineages that may have persisted for a long time without further diversification or have undergone multiple extinctions. Environmental niche models verified the occurrence of A. guineensis and A. boueti in hot humid environments different from the other 42 arid Acanthodactylus species. While A. guineensis probably remained in tropical habitat from periods prior to the Eocene–Oligocene boundary, A. boueti entered tropical environments independently at a later period. Our results provide an important baseline for studying adaptation and the transition from humid to arid environments in Lacertidae.Climate has a large impact on diversity and evolution of the world’s biota. The Eocene–Oligocene transition from tropical climate to cooler, drier environments was accompanied by global species turnover. A large number of Old World lacertid lizard lineages have diversified after the Eocene–Oligocene boundary. One of the most speciose reptile genera in the arid Palearctic,  Across the tree of life, diversity patterns of organisms are linked to climate, although the details of these patterns differ among major groups5. Present and past climatic conditions are particularly influential in shaping diversity, distribution, physiology and molecular evolution of ectothermic animals, as recently exemplified in lizards of the family Lacertidae6.The world’s biota is largely influenced by the environment, including climate and its change over time and space , A. savignyi , A. boueti Chabanaud, 1917 and A. guineensis  (fide18).Among these lineages, the genus 9Acanthodactylus belongs to a subclade containing three other mainly arid Palearctic genera , with Ophisops also reaching the Oriental zoogeographic region. Herein, we propose calling this subclade the ‘Northern arid clade’. The Northern arid clade is sister to what has been named the ‘Equatorial African clade’ comprising the genera Adolfus, Congolacerta, Gastropholis, Holaspis7, herein renamed to ‘Equatorial African-Arabian clade’ as it also includes the two relic species of Omanosaura from the Arabian Peninsula6. From the Northern arid clade, only two Acanthodactylus taxa—A. boueti and A. guineensis—occur exclusively south of the Sahara. These represent a biogeographic enigma, especially because their phylogenetic relationships remain largely unresolved19 despite enormous progress in Acanthodactylus systematics largely triggered by recent molecular analyses26. While the affiliation of A. boueti to the Western clade of Acanthodactylus is relatively well established despite an unstable phylogenetic position14, the relationship of A. guineensis remains uncertain. Different trees placed the species either at unsupported nodes basal to the entire Northern arid clade containing Acanthodactylus, Eremias, Mesalina and Ophisops6, basal to the Acanthodactylus genus (in a BI dated tree14) or basal to the Acanthodactylus Western clade (in a BEAST consensus tree14). This uncertainty is mainly the result of very limited sampling with only 791 base pairs from two mitochondrial gene fragments  and cytochrome b (COB)) from a single individual . Due to the high genetic distance of A. boueti and A. guineensis, however, it was suggested that the two species are not closely related14. This in turn would indicate that either (i) these two species independently entered sub-Saharan and potentially wetter environments after the ancestor of the genus already adapted to desert conditions, or (ii) A. guineensis, and maybe also A. boueti, are the sister to all other Acanthodactylus and the genus thus may have originated in tropical Africa before it adapted to drier conditions and diversified, or (iii) these species, especially the enigmatic A. guineensis, do not belong to the genus Acanthodactylus at all.Within the Saharo-Eurasian clade of EremiadiniAcanthodactylus. In order to do so we first required an extended genetic sampling of one of the hitherto missing key species, A. guineensis. Since no fresh material has been collected recently  we used tissue samples collected from museum specimens that often yield low quality and highly fragmented DNA. Then we applied a shotgun next generation sequencing strategy, a method that is commonly used to obtain sufficient genetic material from low-quality samples for subsequent assembly of whole mitogenomes28. We aimed to achieve a reliable understanding of the evolutionary relationships of Acanthodactylus spp. in general and resolve the phylogenetic position of A. guineensis specifically using whole mitogenomes of A. guineensis, A. schmidti (for which we had genomic data available from an ongoing study), twelve lacertid taxa assembled over the course of a previous study6 and published data on mitogenomes and common mitochondrial marker genes of crucial additional taxa. We updated the distribution range of A. guineensis and assembled data on habitat and environmental conditions prevailing within its distribution range. With the improved phylogeny as a backbone, we aimed to compare the environmental niche of both sub-Saharan species A. guineensis and A. boueti with all arid-adapted Acanthodactylus spp. in order to evaluate the climatic history and adaptation of this diverse and widespread genus. We focused especially on A. guineensis and hypothesized that it represents an early-derived members of Acanthodactylus or even its own genus and has persisted for a long time in tropical environmental conditions.In this study we aimed to understand how and when adaptation to arid conditions occurred in the genus A. guineensis and A. schmidti as well as the twelve additional taxa of Gallotiinae and Eremiadini each consist of 13 protein-coding genes, 22 transfer RNA (tRNA) genes, 2 ribosomal RNA (rRNA) genes typical for vertebrates. Gene order was the same as in other known lacertid mitogenomes31. We also recovered partial sequences of the control region for all mitogenomes. The assembly lengths varied between 15,552 and 17,143 bp (Supplementary Table S1 online). The final mitogenome assemblies of three species presented low coverage regions within genes that were coded as consecutive unidentified bases (N) in the final MITObim output: these included 48 and 29 bp in the COB gene for A. guineensis and A. schmidti, respectively, and 149 bp comprising the 3′ end of cytochrome c oxidase subunit I gene and the tRNA Serine 2 gene for A. erythrurus.The nearly complete newly assembled mitogenomes of Mesalina, Acanthodactylus and Eremias) and the Ethiopian clade  with high support33; Fig. Podarcis and Algyroides which switched places (with moderate node support). Within the Eremiadini the majority of nodes received 100% ultrafast bootstrap node support values (posterior probabilities = 1) and no node was supported with less than 96%. The tree further confirms specimen ZFMK 59511 from Burkina Faso to be a member of the genus Acanthodactylus  with 100% node support . However, the tree is informative at more shallow nodes, and it reconstructed with high support the previously established relationships of most Acanthodactylus species included. Specifically, the three major clades Western, Eastern and scutellatus26 were recovered and confirmed with high node support , the Middle Eastern tristrami group , and the African erythrurus group  and pardalis group . The newly sequenced specimen of A. guineensis  but instead placed with high support  as sister to the pardalis group within the Western clade.The second sub-Saharan A. schmidti (SB 642) from Abu Dhabi (UAE) clustered with A. schmidti (KJ652757.1) from Iran24 and was sister to A. blanfordii in the so-called blanfordii group14.The individual of Acanthodactylus guineensis in the collection of ZMB . One of them (ZMB 25479) is the rediscovered holotype of Eremias mandjarum Sternfeld, 1916, which based on morphological characters we consider to be a synonym of A. guineensis35 and not of Heliobolus nitidus36. Morphological data on the new specimens is provided in the Supplementary Material online.Over the course of this study, we discovered undetected specimens of Acanthodactylus guineensis comprises the following West and Central African countries: Mali, Burkina Faso, Ghana, Benin , Niger, Nigeria, Cameroon, and Central African Republic .After including the new records, the updated distribution range of Acanthodactylus, A. guineensis and A. boueti inhabit wet and warm habitats and there is no climatic niche overlap between the two sub-Saharan species and all xerophile species with regards to annual temperatures and precipitation  are above 1000 m , but others are in the ‘Montane Grasslands & Shrublands’ and ‘Tropical & Subtropical Moist Broadleaf Forests’ biomes . Since there is a chance for misidentification when seen from a distance, we decided to exclude this specimen from our habitat analyses. According to the literature, the majority of habitats of A. guineensis are sand dominated and located near water  but there are occasional records also from harder soil and rocky habitat structures45.According to our analysis, mes Fig. c. The onA. boueti is known from eleven localities in Ghana, Togo, Benin and Nigeria. Here, it inhabits mainly the ‘Tropical & Subtropical Grasslands, Savannas and Shrublands’ biome  while one record is probably from ‘Tropical & Subtropical Moist Broadleaf Forests’ (‘Eastern Guinean forests’). However, the coordinates of the latter locality have low accuracy  where it experiences a slightly warmer median average annual temperature (27.3 °C) and slightly higher median average annual precipitation (1085 mm)  species are not desert/arid-adapted lizards. We visualized this simply by plotting annual mean temperature and precipitation . The populations in southern Burkina Faso, northern Ghana, Nigeria, northern Cameroon and western Central African Republic were predicted with high occurrence probability  with decreasing occurrence probability linked to increasing forest cover. Another 4.9% to the model are contributed by ‘Precipitation of the wettest week’ (bio13) with increasing occurrence probability in areas with higher rainfall . The model thus confirms that the species has a certain dependence on humidity and rainfall.The environmental variable with the highest contribution (68.2%) to the final model was ‘Potential Evapotranspiration of the Wettest Quarter’ (PETwet)—A. boueti achieved even higher accuracy than the A. guineensis model (mean AUC = 0.977 ± 0.006). The best predictors are predominantly linked to rainfall and comprised ‘Precipitation of coldest quarter’ , ‘Precipitation of driest week’  and ‘Mean temperature of coldest quarter’ . The inferred potential distribution comprised Côte d’Ivoire in the West, and central Nigeria, central Cameroon and parts of Central African Republic  that A. guineensis indeed belongs to the genus Acanthodactylus, a finding that is essential for reconstructing the phylogenetic and paleoclimatic history and deepening our understanding of the evolution of that genus. Previous molecular phylogenies that contained the only other A. guineensis specimen (A27514) have found the species at very disparate places on the lacertid phylogeny, sometimes not within Acanthodactylus at all6. The reliable placement of this taxon within the family of Lacertidae was therefore a priority in lacertid systematics and constitutes one of the key outcomes of our study.The approach using a shotgun sequencing strategy on preserved museum specimens to assemble whole mitogenomes proved successful and our phylogenetic reconstruction placed Acanthodactylus is still the most likely biogeographical hypothesis14, with independent expansions of two (Western and Eastern) of the three main clades into the Middle East (and east further into Asia in the case of the Eastern clade).Considering the new results, an African origin of the genus A. guineensis match the detailed first description40 and those of the two known synonyms Eremias (Taenieremias) benuensis47 and Eremias mandjarum48 with their rather unique combination of morphological features such as the nasal scale arrangement, the toe fringes, the almost coadunate collar and the absence of an occipital scale . Minor morphological variation  was detected .Morphologically, all examined individuals of Acanthodactylus guineensis currently occupies a relatively large distribution range for a lacertid lizard, spanning almost 3000 km  from Burkina Faso in the West to the eastern Central African Republic . Our newly detected specimens in the MfN collection, including the new synonym Eremias mandjarum Sternfeld, 1916, and the record from the Pendjari National Park in Benin38, close previously apparent range gaps between the easternmost record and the Cameroonian records and between Ghana and Nigeria . The elevational range the species occupies from sea level to 1300 m is quite large, and there is a distinct gap between 900 and 1000 m where no individual was recorded , but the current notion is that A. guineensis predominantly inhabits sandy habitats, often along rivers45. This is in agreement with the presence of toe fringes in A. guineensis, similar to all other Acanthodactylus spp. Nevertheless, A. guineensis has also occasionally been recorded from harder soil and rocky habitat structures43; JFT pers. obs.). Although Meinig and Böhme44 did not find morphological variation among A. guineensis along an East–West transect that would allow recognition of subspecies, their dataset did not include the specimens from Central African Republic. Our extended dataset including the latter, underpinned with climate, altitude and additional (macro-) habitat data provide at least some indication that there is still hidden diversity among these lizards. Additional in-depth assessment of A. guineensis (and A. boueti) taxonomy is consequently still required.lic Fig. 5. Our neA. guineensis, A. boueti) are not sister taxa despite occupying roughly similar niches in precipitation-rich and hot tropical environments, opposed to the mainly xeric adapted remaining 42 species of Acanthodactylus. It is worth noting that the phylogenetic position of A. boueti is not fully concordant among the phylogeny presented here and another published tree6 calculated with a very similar data set for these taxa, as no phylogenomic backbone data for relationships within Acanthodactylus was available. In fact the previous studies were based on the same individual of A. boueti, and largely on the same sequences 14 as herein. The added c-mos sequence may be responsible for a slightly different position of A. boueti in previous studies, where it was placed sister to the Western clade, while our mitochondrial gene tree places it in a nested position within this clade. In any case, a possible sister-group relationship A. boueti and A. guineensis has not been recovered in any species or gene tree reconstruction to date14. Although for a full clarification of its relationships more extensive, preferably phylogenomic data sets are necessary, we consider it as very unlikely that A. guineensis and A. boueti are closely related to each other given their high genetic distance in the genes analysed14.Our new phylogeny combined with environmental niche models suggests that the only two sub-Saharan species of the genus (A. guineensis (and A. boueti) rather unique among the entire Northern arid clade . In the otherwise robust molecular timetree generated by Garcia-Porta et al.6A. guineensis had unresolved relationships and was sometimes even recovered as basal to the entire Northern arid clade6, but only two mitochondrial genes of one individual of A. guineensis were available for that study.Looking at a larger scale, the tropical habitat makes 49. We therefore did not reconstruct a new time-calibrated phylogeny, but for discussion rather rely on diversification dates from the phylogenomic analysis of Garcia-Porta et al.6. Because our analysis places A. guineensis within the Acanthodactylus crown group, it is unlikely that the timing of the crown group diversification of the genus during the tropical Eocene epoch (36.7 mya) recovered by Garcia-Porta et al.6 will be strongly affected by the improved phylogenetic position of this species.Inferring a timetree from a purely mitochondrial phylogeny as provided herein, without inclusion of nuclear DNA sequences, would likely result in overestimated divergence times especially for old nodesAcanthodactylus and Mesalina (the Northern arid clade) and a second subclade with Adolfus, Holaspis, and Omanosaura 6. However, the relationships of several other genera in the Saharo-Eurasian clade remains largely unresolved. Congolacerta and Gastropholis appear to belong to the Equatorial African-Arabian clade, and Eremias and Ophisops to the Northern arid clade6 but the exact phylogenetic placement of these genera are poorly supported50. Our mitogenomic tree, in agreement with another study50 places the purely arid-adapted Mesalina sister to Acanthodactylus (including A. guineensis), and the equally arid-adapted Eremias sister to this clade. This topology, with Acanthodactylus nested within the arid-adapted genera and A. guineensis furthermore not being sister to all other species of its genus, suggests adaptation to arid conditions was ancestral for Acanthodactylus, and the ancestor of A. guineensis colonized very early on, maybe before the Eocene–Oligocene extinction event (~ 33.7 MYA), the woodland and forest/open habitat ecotones it inhabits today. The very long branch connecting A. guineensis to its sister group (only comparable in length to that of the other sub-Saharan species A. boueti) suggests that this species represents a relic lineage that may have persisted for a long time without diversification, or, alternatively, has experienced severe extinctions along the branch.Within the Saharo-Eurasian clade of Eremiadini, the available phylogenomic evidence supports a subclade with Acanthodactylus. The second tropical Acanthodactylus with sub-Saharan distribution—A. boueti—albeit occupying similar environmental conditions as A. guineensis, likely entered humid tropical environments independently at a later time, as indicated by its rather distant relationship to A. guineensis  in combination with its estimated divergence time within the Western clade of Acanthodactylus after the Eocene–Oligocene extinction event at between 29.7 MYA6 and 17.8 MYA14.In this scenario, the drastic change in climatic conditions from Eocene to Oligocene then triggered the extensive diversification of arid-adapted A. guineensis to humid biomes may represent an ancestral condition of the genus or the Northern arid clade, and several colonization events and radiations into more arid biomes may have taken place. Given the current phylogenetic knowledge this scenario is clearly not parsimonious, but it should be reconsidered if future phylogenomic studies would support a split of Acanthodactylus from a more basal node in the Northern arid clade, and/or an (unlikely) phylogenetic position of A. guineensis sister to all other Acanthodactylus.However, considering the poorly resolved relationships among several genera in the Saharo-Eurasian clade, an alternative hypothesis should not be completely discarded: the adaptation of A. guineensis and A. boueti constitute tropical species. For both species, occurrence probability rises with increasing values for environmental parameters linked with humidity or direct rainfall . Nevertheless, while being tropical species both appear to be open habitat and not forest lizards45 and only briefly enter more densely vegetated areas as indicated by our habitat analyses and the decreasing occurrence probability with an increase in forest cover in the case of A. guineensis .The Maxent niche models and the temperature-precipitation regression Fig. a support51. Major species turnovers have been documented as a result of the Eocene–Oligocene extinction event, both on land  and in marine environments54. The continuous climatic cooling and drying since the Eocene climatic optimum further resulted in changes in vegetation, for example, in Europe, Asia and North America, dense Eocene forests were replaced by more open country52. In marine environments, especially the colder winter temperatures—a climate parameter which can impact terrestrial ectothermic lizards greatly as species might be required to adopt a hibernation period to survive unsuitable cold periods—have been determined to being largely influential in facilitating faunal turnover during the Eocene–Oligocene transitional period53. We here present another example where the major climatic changes from the tropical Eocene to the cooler and drier Oligocene had an impact in shaping diversity, distribution, and molecular evolution of reptiles, in this case of the predominantly Palearctic lizard genus Acanthodactylus.The transitional period from the greenhouse climate of the early Eocene to the cooler and drier Oligocene is considered the most significant interval in Earth history since the extinction of the dinosaursAcanthodactylus, A. guineensis is certainly a member of this genus. We wish to stress, however, that the genetic material analyzed was not from a name-giving type specimen. Acanthodactylus guineensis appears to have remained in tropical habitat from periods prior to the Eocene–Oligocene transitional period ~ 33.7 MYA, while the major Acanthodactylus radiation into arid environments happened after the mass extinction event accompanied by climatic changes from tropical to cooler and drier environments. Acanthodactylus boueti likely re-entered tropical environments independently at a later period of time. While A. guineensis is clearly not a desert taxon like the majority of Acanthodactylus species, it nevertheless seems to inhabit mainly sand habitats even within its precipitation-rich environment. The preference for sandy substrate explains the toe-fringes typical for the genus45, even though in A. guineensis they are poorly developed. The rather unexpected report of A. guineensis also inhabiting harder soil and rocky habitat structure45 in combination with some morphological variation we detected and a possible gap in elevational distribution corroborate the necessity of increased sampling effort and in-depth molecular analyses of the species using whole genomes or at least nuclear markers.To conclude, our molecular results demonstrate that despite some divergent morphological features compared to other 55):The following museum acronyms are used ;CAS: California Academy of Sciences, San Francisco (USA);MHNC: Musée d’histoire naturelle, La Chaux-de-Fonds (CH);MHNG: Muséum d’Histoire Naturelle, Genève (CH);MNHN: Muséum national d’Histoire naturelle, Paris (F);SMNS: Staatliches Museum für Naturkunde, Stuttgart (D);UWBM The Washington State Museum of Natural History and Culture/Burke Museum, University of Washington (USA);ZMB: Museum für Naturkunde Berlin, formerly Zoologisches Museum Berlin (D).Acanthodactylus was first described as a subgenus of Lacerta Cuvier (sic)56, the type species is A. boskianus . The few meristic characters described to be diagnostic for Acanthodactylus56 include: collar connected in the center but free on the sides, tempora squamosal, i.e., temporal region covered by small scales rather than larger shields, ventral scales rectangular and arranged in longitudinal rows, digits acutely fimbriate-denticulate forming “toe fringes”. Fringed toes have evolved in various shapes multiple times in lizards and in Lacertidae, and when triangular, projectional or conical as in Acanthodactylus they are commonly seen as adaptation to windblown sand substrate57.Acanthodactylus guineensis is among the lesser-known species of the Lacertidae. Only limited information is available regarding the species’ morphology, habitat and distribution . Based on one young specimen, A. guineensis was described as member of the genus Eremias Fitzinger, 1834 by Boulenger40. Boulenger does not mention the slightly projecting third row of scales around the toes and fingers (“fringed toes”) of the type specimen in his quite comprehensive description40, probably because he did not notice it in such a small (SVL 24 mm) specimen and because the projection is indeed rather minor compared to other members of the genus. This is probably also the reason why he did not place guineensis in the genus Acanthodactylus Wiegmann, 1834 but in Eremias Fitzinger, 1834 (in56).The enigmatic Eremias and divided it into five sections he assumed to be natural associations59: (1) Taenieremias Boulenger, 1918—monotypic, type species Eremias guineensis Boulenger, 1887 (currently Acanthodactylus guineensis); (2) Lampreremias Boulenger, 1918—type species Eremias nitida Günther, 1872 (currently Heliobolus nitidus); (3) Pseuderemias Boettger, 1883—type species Eremias mucronata  (currently Pseuderemias mucronata); (4) Mesalina Gray, 1838—type species Eremias rubropunctata  ; (5) Eremias s. str. Fitzinger, 1834—type Eremias velox  (currently Eremias velox).About 30 years later, Boulenger revised the genus 47 described Eremias (Taenieremias) benuensis from Cameroon based on a few minor morphological differences compared to E. guineensis, but in 1969 E. benuensis was synonymized with E. guineensis60.Monard17 and one year later also Arnold18 in their respective major revisions of the genus Acanthodactylus both found that Eremias (Taenieremias) guineensis agrees with all the characteristic morphological features of Acanthodactylus with the exception of the arrangement of scales around the nostril. However, it was suggested that the E. guineensis condition with an extra suture across the area occupied by the first upper labial scale to produce a smaller first upper labial is easily derived from that found in Acanthodactylus, evidenced by BMNH 1966.430, a juvenile A. erythrurus lineomaculatus (sic) with a similar scale arrangement18. Within Acanthodactylus, both authors placed guineensis in the Western clade and in the Acanthodactylus erythrurus group which was assumed to consist of A. erythrurus, A. savignyi, A. boueti, A. guineensis, and A. blanci which was considered a subspecies of A. savignyi at the time20.SalvadorAcanthodactylus using whole mitochondrial DNA sequences, assembled by means of a shotgun next generation sequencing strategy. Analyses of whole mitogenomes have been shown to resolve many nodes of the lacertid tree with high statistical support (e.g.31). We retrieved muscle tissue from a museum voucher of A. guineensis  that likely was never in contact with formalin for preservation and therefore offered good chances to obtain sufficient amounts of DNA of decent quality for sequencing. We extracted genomic DNA using the Qiagen DNeasy Blood and Tissue Kit  following the protocol provided by the manufacturer. We additionally sequenced a tissue sample of a freshly caught individual of Acanthodactylus schmidti (SB 642) from Abu Dhabi (UAE) to increase sampling of Acanthodactylus spp. in the mitogenomic tree. The lizard was euthanized by injection of an aqueous solution of benzocaine (20%) into the body cavity. Subsequently, a sample of muscle tissue was taken from the thigh, preserved in 98% Ethanol and stored in − 80 °C. Handling, euthanizing and collection of tissue samples of A. schmidti individuals was approved by the NYUAD Institutional Animal Care and Use Committee (IACUC 19-0002) and UAE No Objection Certificate (NOC 8416), and all applied methods were performed in accordance with the relevant guidelines and regulations.We aimed at reconstructing a well-supported phylogeny of the genus A. guineensis and A. schmidti, respectively, were used for library preparation.Genomic DNA of SB 642 was extracted from ethanol-preserved muscle tissue using the Qiagen MagAttract HMW DNA Kit  for high molecular weight DNA. We determined DNA yields on a Qubit fluorometer  with a dsDNA high sensitivity kit. Totals of 52 ng and 80 ng of DNA ) from . All mitogenome assemblies and original alignments were deposited in Figshare under  https://doi.org/10.6084/m9.figshare.13754083.v1 .ZFMK 59511 libraries were prepared with NEB Ultra II FS DNA  kit as per protocol instructions with input below 100 ng. For sample SB642, linked reads were generated on a 10X Genomics Chromium following Genome reagent kits v2 instructions. Resulting libraries’ concentration, size distribution and quality were assessed on a Qubit fluorometer  with a dsDNA high sensitivity kit and on an Agilent 2100 Bioanalyzer  using a High Sensitivity DNA kit. Subsequently, libraries were normalized and pooled, and pools quantified with a KAPA Library quantification kit for Illumina platforms  on a ABI StepOnePlus qPCR machine , then loaded on a SP flowcell and paired-end sequenced (2 × 150 bp) on an Illumina NovaSeq 6000 next generation sequencer , and a S2 flowcell for linked read library. Raw reads were deposited in the Sequence Read Archive (SRA) under BioProject ID PRJNA700414 61. The reads where then passed through Trimmomatic v0.3662 (parameters ILLUMINACLIP: trimmomatic_adapter.fa:2:30:10 TRAILING:3 LEADING:3 SLIDINGWINDOW:4:15 MINLEN:36) for quality trimming and adapter sequence removal. Following the quality trimming, the reads were assessed again using FastQC. The executed workflows were performed using BioSAILs63.Raw FASTQ sequenced reads were first assessed for quality using FastQC v0.11.5A. schmidti), we received 436,370,000 single-end reads (average read length 139.5 bases (b)) with a raw coverage of 29.29X and a scaffold N50 of 21.12 kilobases (kb). For the MITObim analysis, read number for SB 642 was randomly reduced to 1,000,000 reads  using the awk command. For ZFMK 59511 (A. guineensis), we received 57,936,345 paired-end reads . Subsequently, the two sets of reads were converted to interleaved format. We used the quality filtered reads to assemble the mitogenomes of A. guineensis and A. schmidti using an iterative mapping strategy in MITObim v. 1.9.164. We used the Acanthodactylus aureus mitogenome  as seed for both samples; this rendered an initial mapping of a conserved region from a more distantly related individual unnecessary64. We therefore applied the –quick option in MITObim and iterations were run until no additional reads could be incorporated into the assembly .For SB 642 (6 (see Supplementary methods in6 for extraction protocol and sequencing methods and Supplementary Table S1 online in6) with the Podarcis muralis complete mitogenome (GB accession number NC_011607) as reference sequence. The raw data of each individual was quality filtered using Trimmomatic v0.3662 (parameter MINLEN:45) and assembled using MITObim v. 1.9.1. All multiple alignment files generated in the final MITObim iteration were imported to Geneious R11 (https://www.geneious.com) to check for assembly quality and coverage.We also assembled complete or nearly complete mitogenomes for additional twelve species of Gallotiinae and Eremiadini using anchored hybrid enrichment sequence data from Garcia-Porta et al.65 using defaults settings with the vertebrata database as a reference. The annotated assemblies were imported into PhyloSuite66 together with existing mitogenomic sequences of Lacertidae, and Blanus cinereus  available in GenBank (as of July 2020). Details of all mitogenomic sequences included in this study and the corresponding GenBank accession numbers are provided in Supplementary Table S1 online. Using PhyloSuite, we exported all protein coding sequences plus the two rRNAs. The resulting files were aligned with MAFFT67 using “auto” settings. Alignments of coding sequences were refined with MACSE68 to account for open reading frame structure during the alignments. The protein coding and rRNAs alignments were finally concatenated into a single alignment delimiting partitions by marker and, for the protein-coding genes, by codons within markers. Mitogenome phylogenies were inferred using a maximum likelihood approach with IQ-TREE v. 1.5.4 software69 and Bayesian inference (BI) analysis with MrBayes 3.270.The resulting assemblies were annotated with MITOS70. Results of two independent runs of 10 million generations, each comprising four Markov Chains (three heated and one cold), were sampled every 1000 generation. Chain mixing and stationery was assessed by examining the standard deviation of split frequencies and by plotting the –lnL per generation using Tracer 1.5 software71. Samples corresponding to the initial phase of the Markov chain (25%) were discarded as burn-in and the remaining results were combined to obtain a majority rule consensus tree and the respective posterior probabilities of nodes.For the maximum likelihood approach, best-fitting partitioning schemes and substitution models were selected based on the Akaike Information Criterion (AIC) using the heuristic algorithms implemented in the MFP + MERGE option (Supplementary Table S3 online). After ML inference, branch support was assessed with 1000 ultrafast bootstrap replicates. As the alignments used for phylogenetic inference contained more than one sequence for some species, these terminals in the resulting tree were collapsed a-posteriori for aesthetic reasons using FigTree v1.4 , COB, and NADH-dehydrogenase subunit (ND4) were later added to the alignment of6 using –add and –keeplength options in MAFFT (Supplementary Table S5 online).An additional, more comprehensive, alignment was produced containing all currently known A. guineensis and A. boueti including new discoveries in museum collections from the current study , all known records from the literature77 and from the Global Biodiversity Information Facility (GBIF78). We provide all coordinates as latitude , longitude . Due to the general scarcity of records for A. guineensis and A. boueti, we added two localities for A. guineensis  and one locality for A. boueti  published in Trape et al.45 that were not found on GBIF or in other publications, by extracting the center coordinates of the respective 1 × 1 degree grid. These additional localities have coordinates with very low accuracy and the corresponding environmental variables extracted from a 1 × 1 km resolution grid  therefore have high degrees of uncertainty. When examined, the majority of values fell within the total range for the respective environmental variable (the two exceptions are mentioned in the Results section), we consequently decided to keep them for our analysis. We reduced the final locality dataset to one record per km2  to avoid pseudoreplication using the R package spThin79. We then tested whether the remaining presence points are randomly dispersed or clustered using the Nearest Neighbor Index NNI in the R packages sp80 and spatialEco81. NNI was 1.4 for A. guineensis and 1.2 for A. boueti, indicating that the filtered point dataset consists of randomly distributed points which are not spatially autocorrelated.As basis for characterizing the species’ bioclimatic envelopes, we compiled an updated distribution map for 82, global bioclimate and elevation layers39, data on potential evapotranspiration83 and global aridity index84 with a spatial resolution of 30 arc-seconds (~ 1 km2 near the equator). Although the climate datasets are interpolated from data from weather stations much farther apart from one another and are consequently not measurements of actual environmental conditions, they were rigorously cross-validated with observed data (including satellite data) during the development of the dataset and generally showed high correspondence with observations 39. Since for many applications such as our species distribution models data at high spatial resolution  are preferable over lower resolution to capture variation for example across steep climate gradients in mountains39 we chose the 30 arc-seconds dataset over the 5 arc-minutes dataset (~ 9 × 9 km near the equator) for the ecoregions and climate datasets. In addition, we downloaded spatial layers with forest and grassland/scrub/woodland cover from the harmonized soil database . We downscaled the forest and grassland/scrub/woodland dataset to 30 arc-seconds resolution despite the resulting slight inaccuracy, which we kept in mind during interpretation but did not consider relevant for the vegetation datasets.As environmental parameters we downloaded spatial layers of the Terrestrial Ecoregions of the World (TEOW)A. guineensis and A. boueti to all other species of the genus Acanthodactylus we downloaded all available records of the other currently known Acanthodactylus species from GBIF. We cleaned the downloaded datasheet by deleting all entries with missing data for either latitude, longitude, species epithet, or all of those, and removed duplicates or spelling mistakes. We further removed all GBIF entries with imprecise coordinates, i.e. records with coordinates that when plotted landed just outside of coastal areas in the ocean instead of on land . In addition, we added several curated locality data from Garcia-Porta et al.6 as available from Figshare (https://doi.org/10.6084/m9.figshare.8866271.v1/). Following Tamar et al.14 we treated A. lineomaculatus as a junior synonym of A. erythrurus and changed the respective records in our database accordingly. For species that did not have records published on GBIF we added further locality data from the literature (Supplementary Table S6 online). The final database contained 4286 records from all 44 currently recognized species of Acanthodactylus. We acknowledge that our database is by no means complete, however, we wanted to follow the most conservative approach and use only records that are supported by a voucher specimen and can be traced back using published databases. We trust that our record list covers a nearly complete representation of the ecological conditions inhabited by all currently accepted Acanthodactylus species.In order to compare the prevailing climate within the distribution range of 39 we plotted annual mean temperature (bio1) and annual precipitation (bio12) for all locality records in our Acanthodactylus species database and compared the respective data for A. guineensis and A. boueti with its congeners. We developed species distribution models using Maxent 3.4.146 for A. guineensis and A. boueti. Maxent applies the maximum entropy principle86 for model fitting under the basic premise that the estimated species distribution deviates from a uniform distribution as minimally as required to explain the observations46. We used the cleaned datasets of A. guineensis and A. boueti with only one record per km2 as input presence locality data. We extracted environmental data for all presence sites from the 19 bioclim variables (bio1-19), elevation , aridity index (AI), percentage cover of forest (forest) and grassland, scrub and woodland (grass) per grid cell, as well as four parameters comprising potential evapotranspiration (PET): PET of the wettest, driest, warmest and coldest quarter of the year . To avoid issues resulting from correlated parameters we reduced the number of environmental predictors by performing multicollinearity analyses using the Variance Inflation Factor (VIF) with a threshold < 10 for non-collinearity87 in the R package usdm88. The VIF, one of the most commonly used factors in collinearity analyses, is the result from regressing the predictor variable against all other predictor variables. The VIF measures how strongly each predictor can be explained by the rest of predictors89. After removing all variables with collinearity problems our final environmental input variable dataset comprised bio3, bio11, bio13, bio14, bio18, bio19, PETcold, PETdry, PETwet, forest, grass. We ran the Maxent algorithm with the following settings: we chose 10,000 random background points from a rectangular area extending an additional 500 km in every compass direction from the outermost presence sites of A. guineensis for both species90, allowed only linear and quadratic features and chose the output format cloglog. For A. guineensis we had 38 presence sites, of which we used 70% for model training and 30% for testing with 25 model replicates of type "subsampling" and a regularization multiplier of 2.591. For A. boueti, due to low number of presence sites (N = 11), we did not use a training dataset but ran the model with eleven replicates and bootstrapping  and a regularization multiplier of 1. The predictive model performance was evaluated using the area under the receiver operating curve (AUC) which measures the ability of predictions to discriminate between presences and absences, irrespective of the absolute value of the predictions92. Finally, we plotted the median of all models to predict the potential range of each species and determined the most important environmental predictors.Using the bioclim datasetSupplementary Information.


Higher precipitation and lower temperatures were associated with higher levels of SOC stocks in majority of ecoregions. Changes in land cover types (vegetation properties) was important in drier ecosystem as North American deserts, whereas soil types and topography were more important in American prairies. Wetlands of the Everglades was highly sensitive to projected temperature changes. The SOC stocks did not change under SSP126 until 2100, however SOC stocks decreased up to 21% under SSP585. Our results, based on environmental controllers of SOC stocks, help to predict impacts of changing environmental conditions on SOC stocks more reliably and may reduce uncertainties found in both, geospatial and Earth System Models. In addition, the description of different environmental controllers for US ecoregions can help to describe the scope and importance of global and local models.Understanding the influence of environmental factors on soil organic carbon (SOC) is critical for quantifying and reducing the uncertainty in carbon climate feedback projections under changing environmental conditions. We explored the effect of climatic variables, land cover types, topographic attributes, soil types and bedrock geology on SOC stocks of top 1 m depth across conterminous United States (US) ecoregions. Using 4559 soil profile observations and high-resolution data of environmental factors, we identified dominant environmental controllers of SOC stocks in 21 US ecoregions using geographically weighted regression. We used projected climatic data of SSP126 and SSP585 scenarios from GFDL-ESM 4 Earth System Model of Coupled Model Intercomparison Project phase 6 to predict SOC stock changes across continental US between 2030 and 2100. Both baseline and predicted changes in SOC stocks were compared with SOC stocks represented in GFDL-ESM4 projections. Among 56 environmental predictors, we found 12 as dominant controllers across all ecoregions. The adjusted geospatial model with the 12 environmental controllers showed an R Understanding the relationship between SOC and its environmental controllers is key for accurately predicting climate and land use change impacts on SOC and reducing uncertainties in large scale carbon climate feedback projections.Soils store the largest amount of carbon in terrestrial ecosystems containing around 1500 Pg C 8. O'Rourke, et al.9 stated that linking specific environmental controllers to SOC functions can be a way to toward more realistic projections in ESMs. Examples of this approach were applied for Australian10, Brazilian11 and other soils using geospatial modelling. The same way Weintraub, et al.12 highlighted the potential of soil observation networks to accelerate process representation in SOC models and improve our current capacity to make predictions.Statistical geospatial modeling can be used to quantify the heterogeneity of environmental controllers on SOC stocks from regional to national and global scales. Also, environmental controllers’ spatial gradient can be used in a space-for-time substitution approach to predict the spatiotemporal variation in SOC stocksIn this study we used a database composed of 4559 soil profile observations in conterminous US and 56 environmental predictors representing climate, topography, soil type, geology, land use and water dynamics to fit a geographically weighted regression model. We used the model to predict and generate maps of current and future SOC in 21 US ecoregions. The maps were generated considering current and two shared socioeconomic pathways SSP126 and SSP585 from GFDL-EMS4 of Coupled Model Intercomparison Project phase 6 (CMIP6), corresponding of IPCC RCP 2.6 and 8.5 scenarios. To produce the SSP126 and SSP585 pathways maps, we used future temperature and precipitation regimes from GFDL-EMS4 predictions in conterminous US. Finally, the results were compared with total SOC stocks from GFDL-ESM4 model predictions.−1 C.The chosen model included 12 individual variables from the original 56 (Table 2) of 0.48 . The GWR approach consistently underestimated the SOC stocks, bias is higher in the ecoregion with higher observed SOC stocks  showed coefficient of variation R of 0.48 SOC stocks were controlled by different environmental factors across US ecoregions Fig. . The eco12 g) respectively  (Table −1) was almost double the SSP126 (2506 Mg ha−1), total SOC stocks  was lower  , sea level rise, frequent wildfires, increase methane emissions and temperature, the effect of climate change on SOC stocks is still not well known22. The complexity of the water cycle makes it difficult to capture all the feedbacks and peatlands/wetlands may emit more carbon than sequester17. It’s not clear whether climate change will increase or decrease wetland area over continental US, but the net primary production should increase23. Primary vegetation is expected to move South to North in continental US due to increase in temperature in higher latitude24. Isolated events like wildfires has increase in the last years in west conterminous US, resulting in tree mortality25. Although carbon emission from wildfires is certain, its effect is still not well understood because of vegetation regeneration26. On the other hand, US deserts may increase biomass production18.Some other factors used to predict SOC stocks are also expected to change in the future . According to the fifth IPCC report8. Although, when comparing our results with GFDL-ESM4 model simulations that accounted for carbon climate feedbacks, the same pattern was observed, considering total SOC stocks reduction for SSP126 and SSP585.The extreme SOC values showed no difference for record low between SSP126 and 585, but the record high was 41 to 500% higher in SSP585 compared to 126. Soil organic carbon stocks reduction accounting for conterminous US for SSP585 was highly pronounced compared to 126, highlighting SOC sensitivity to climatic factors. The main exception, Florida, showed an increase in SOC stocks Table . Tempera12. This shows the importance of local models as an alternative to produce more accurate predictions on its represented region. The development of appropriate observation networks may provide data for the development, benchmarking, and validation of statistical and process-based models, increasing our understanding of SOC cycle at different scales and improving the predictive accuracy27. In addition, the description of different environmental controllers for US ecoregions can help to describe the scope and importance of global and local models.Although SOC dynamics may be similarly affected by the explored predictors in all the studied region, our results indicated relative importance, since SOC stocks were limited for different predictors across different ecoregions. This way, changes in SOC stocks inside the same ecoregion was related to variation on the specific limiting factors. This is especially important for improve soil process representations in Earth System Models, where treat specific important factors more accurately in different ecoregions can be a strategy for improve models’ predictions. Higher precipitation and lower temperatures were associated with higher levels of SOC stocks in majority of ecoregions. Changes in land cover types (vegetation properties) was important in drier ecosystem as North American deserts, whereas soil types and topography were more important in American prairies. Wetlands of the Everglades was highly sensitive to projected temperature changes. Although represent these behaviors in Earth System Models can be a way to produce more accurate global predictions, interaction among factors can result in emergent complex interactions, difficult to derive especially when data is a limiting factor28 project 29, land cover and soil types. A total of 31,472 samples describe the soil profiles, 77%  were distributed between 0 and 100 cm, which was used for this study. Thus, approximately five samples per soil profile were used for calculating soil carbon stocks at 0–100 cm depth. SOC was measured by dry combustion and bulk density was modelled30. For calculating SOC stocks for each pedon/profile, a fixed depth approach was used. A more complete description of the methods used to analyze the samples can be obtained in the Rapid Carbon Assessment: Methodology, Sampling and Summary15. The measured SOC ranged from 1.37 to 11,981.0 Mg C ha−1 and with a mean of 209.6 Mg C ha−1. The lowest stocks were measured in desert biomes and highest in wetlands. The SOC stocks showed a unimodal positively skewed distribution to normalize the data for the modelling activities we applied a box-cox transformation.We performed this study in the continental US using 4559 georeferenced soil profile observations obtained from the Rapid Carbon Assessmentect Fig. . The soihttps://data.nal.usda.gov/dataset/gridded-soil-survey-geographic-database-gssurgo) and aggregated into 10 soil orders: Alfisols, Andisols, Aridisols, Entisols, Histosols, Inceptisols, Mollisols, Spodosols, Ultisols, Vertisols. We obtained the bedrock geology types information31 from the United States Geological Survey (https://www.usgs.gov/products/maps/geologic-maps) map with 1 km resolution and aggregated the main geological features into 23 groups.Soil orders were obtained from the conterminous United States digital soil map derived from the gridded Soil Survey Geographic Database (gSSURGO) in 30 m resolution . Monthly mean net radiation was derived from NASA (https://neo.sci.gsfc.nasa.gov/) with 0.25 degrees resolution and compiled into average annual net radiation datasets. The potential evapotranspiration maps were acquired from Consultative Group on International Agricultural Research—Consortium for Spatial Information (https://cgiarcsi.community/) with spatial resolution of 30 arc-seconds (≈ 1 km at equator). The mean annual NDVI and net primary productivity datasets were obtained from NASA Earth observations at 10 km resolution (https://neo.sci.gsfc.nasa.gov/). We obtained US land cover data of 30 m resolution from multi resolution land characteristics (https://www.mrlc.gov/nlcd2011.php) and aggregated land cover types into 6 major categories, briefly: Open Water and Perennial Ice/Snow were grouped as Water, Developed Open Space, Low, Medium and High Intensity were grouped as Urban, Deciduous, Evergreen and Mixed Forest were grouped as Forest, Woody and Emergent Herbaceous Wetlands were grouped as Wetlands and the other classes were Rock, Cropland, Grassland, Pasture and Scrub. The Water, Urban and Bare Rock Land cover types were excluded from the analysis. All the chosen categorical variables were treated as individual predictors .We obtained 30 years (1981–2010) mean annual average data of precipitation, maximum, minimum and mean temperature, mean dew point temperature and minimum and maximum vapor pressure deficit at 800 m resolution from PRISM website  and derived the following terrain attributes: Elevation, aspect, slope, plan curvature, profile curvature and total curvature in GIS environment using ArcGIS v 1033. The environmental predictors used are described in Table We obtained a digital elevation model (DEM) with 30 m resolutionr > 0.70) one of the predictors was removed, we choose keeping the one which its effect over SOC dynamics is better known according to current theory. To measure the model fit, we divided the database into training  and testing  datasets. The training dataset was used to fit the model and testing set to measure the model prediction capacity. We treated the nominal/categorical variables as dummy variables .The SOC data in the same ecoregion that were outside of 1.5*IQR where removed as outliers. First, we used plots to identify non-linear relationships that could not be captured by linear modelling. Further, to avoid including unnecessary environmental predictors in the model we generated Pearson correlation coefficients for all numerical predictors paired in a correlation matrix. When the pair showed a high correlation coefficient  for each local and ecoregion. The spatial variation of the coefficients can be used to explore local ecoregion specific controllers for SOC stocks. A more complete description of GWR approach can be found in Fotheringham book37. The model was then applied to the test dataset which had an R2 of 0.48. We then used the GWR model to generate a SOC stocks map in in Mg ha−133. Using the coefficients adjusted for the predictor variables in the GWR model, we calculated a correlation matrix and performed a principal component analysis to visualize the relationship between environmental predictors and ecoregions.After pre-processing, we fitted multiple regression models and used three selection criteria to choose the optimum set of linear predictors, variable significance according to F test at 38. The increase in atmospheric CO2 is expected to increase temperature and change precipitation averages. We obtained maps of mean air temperature and precipitation from National Climate Assessment  and GFDL-ESM4 models  for every 10 years from 2030 to 2100 and used the GWR model to explore the effect of these changes on SOC stocks. Briefly, all the model predictors were kept constant with exception of temperature and precipitation (obtained from GFDL-ESM4 predictions), then the model was run to produce SOC maps. The generated SOC stock maps were also compared with GFDL-ESM4 model SOC stock maps for current and future climate change scenarios . This model was chosen because produced better predictions of SOC stocks in American biomes compared to other Earth System Models4. The maps were subtracted  for generate SOC stocks difference maps and those were used for comparing the differences between scenarios. All modelling processes were performed using R v. 3.6.139.We investigated the changes in SOC stocks until 2100 under SSP126 and SSP585 climate change scenarios of CMIP6Supplementary Information


The aim of this study was to determine whether the association of calcium voltage-gated channel subunit alpha1 C (CACNA1C) rs1006737 with essential hypertension (EH) exists in both Chinese Han and ethnic Russian populations of Northeast Asia. We used a case-control study of 2 ethnic groups in the same latitude geographical area to investigate the association between the susceptibility of EH and rs1006737 polymorphism. A total of 1512 EH patients and 1690 controls in Chinese Han people , 250 EH patients, and 250 controls in ethnic Russian people , participated in this study. All participants were genotyped using the TaqMan SNP genotyping assay (Agena Company). Baseline characteristics and the minor allele frequencies of rs1006737 vary substantially among common Chinese Han and ethnic Russian people. Allele A was found to be a risk factor for EH in Chinese Han  and ethnic Russian . The GA genotype was significantly associated with an increased risk of hypertension  for Chinese Han people, and the AA genotype  for ethnic Russian people. The results of this study indicate that the A allele of the variant rs1006737 in the CACNA1C gene may be a useful genetic marker for EH risk prediction in Chinese Han and ethnic Russian populations.Voltage-gated Ca Hypertension is a major contributor to health burden in both China and Russia due to its large impact on a number of cardiovascular diseases.–5 A genetic association study is difficult to repeat in 2 races due to enrollment and environmental factors; however, statements about geographical factors such as latitude, solar radiation, and ambient temperature are often not referenced.,7 It is plausible that geographic factors impact the results of a research study. Therefore, to determine shared genetic susceptibility of genes, a case-control study of Chinese and Russian people in the same geographical area may be a better design to minimize the impact of geographical factors.Essential hypertension (EH) is the consequence of an interaction between genetic, environmental, and lifestyle factors and does not follow Mendelian genetic law.2+ channels serve as key transducer coupling changes in cell surface membrane potential with local intracellular calcium (Ca2+) pathways. The L-type α1c subunit plays a central role in the regulation of cardiac function and blood pressure. The human alpha 1C subunit of the L-type voltage-gated calcium channel (CACNA1C) gene is a potential candidate gene for EH development, which is located at chromosomal position 12p13.33 . EH has been shown to be heritable and shared genetic risk components with other diseases. Previous studies have shown that patients with psychiatric disorders, such as bipolar disorder, schizophrenia, or major depression, have a greater risk for hypertension than those without such pathologies.CACNA1C gene rs1006737 polymorphism was reported to be associated with psychiatric disorders.–13 It is not yet known whether EH and psychiatric disorders more broadly share biological underpinnings. Therefore, we hypothesized that the allele of rs1006737 might contribute to elevated blood pressure (BP) over time in humans.Calcium channel is an important target of anti-hypertensive drugs, and voltage-gated CaCACNA1C rs1006737 polymorphism with EH in ethnic Russians. The aim of this study was to assess the association between human CACNA1C gene rs1006737 and susceptibility of EH in different races, including Chinese Han and ethnic Russian people in the same geographical area, further supporting the hypothesis that EH and psychiatric disorders share the same SNP locus in genetic susceptibility.From 2016, we carried out Sino-Russian scientific research on cardiovascular and cerebrovascular diseases in the Northeast Asia region. The results of our previous study showed an association of 22.1In this study, all Chinese Han patients with EH were recruited from the No.2 affiliated hospital of Qiqihar Medical University and Mohe People's Hospital , and healthy volunteers were enrolled by health examination in the Heilongjiang region from December 2016 to July 2019. Russian participants were enrolled from City Clinical Hospital No. 1 and Railway Clinical Hospital at Chita-2 Station JSC RZhD  from September 2010 to June 2017. All Russian participants were of Caucasian origin, from the Chita population. Informed consent was obtained from each participant, and all protocols were approved by the Medical Ethics Committee of Qiqihar University and Chita State Medical Academy. All Chinese participants were of northern Han Chinese within 3 generations, and are currently residing in Qiqihar  and Mohe  region of Heilongjiang province. All Russian participants were from Chita within 3 generations, and are currently residing in Chita  of Zabaykalsky Krai, in the southeast of Russia  was calculated by height and weight, which were obtained without shoes and subjects wearing light clothes.The measuring data of blood pressure came from Riva-Rocci sphygmomanometer. After resting for ten minutes, the blood pressure of the subjects was measured 5 minutes apart there times on the right arm by experienced doctors according to the standard protocol recommended by the American Heart Association.In detail, a total of 1512 unrelated Chinese Han patients  with EH and 1690 Chinese Han normotensive subjects (790 men and 900 women) were included in this investigation after completion of matching inclusion criteria. Russian participants comprised 250 patients  and 250 healthy individuals .Subjects aged >18 years, who consistently had systolic blood pressure (SBP) >140 mm Hg and/or diastolic blood pressure (DBP) >90 mm Hg, or with a history of hypertension were diagnosed as hypertensive. The control group was composed of subjects aged >40 years old with blood pressure SBP <120 mm Hg and DBP <80 mm Hg, without personal or family history of hypertension and other cardiovascular diseases, and patients with minor illness without hypertension, hyperlipidaemia, diabetes mellitus, tumor in their previous records. Secondary hypertension and pregnancy were excluded from this study.2.2 and it is not yet known whether there is biological overlap in etiology.EH and psychiatric disorders such as schizophrenia and bipolar disorder may share the same susceptibility locus, The calcium channel is an important target in the treatment of hypertension and calcium channel blockers are widely used as antihypertensive agents. The CACNA1C gene, located on chromosome 12p13.33, encodes an alpha-1 subunit of a voltage-dependent calcium channel that mediates the influx of calcium ions into the cell upon membrane polarization. GWAS have identified rs1006737 in the α1c-subunit of the L-type calcium channel (CACNA1C) gene as a susceptibility locus for psychiatric disorders.,18Candidate gene studies have implicated aberrations in ion channels and ion channel regulation in EH. Genome-wide association studies (GWAS) have detected more than 50 BP loci, most previously unsuspected in EH.Many gene polymorphism loci have been extensively studied in various populations, but the results of these studies are often inconsistent in different races, so we chose rs1006737 as a biomarker to conduct a case-control study between Chinese Han and ethnic Russian people in the same geographical region.Blood samples were collected from all participants and anticoagulated with 2% ethylenediaminetetraacetic acid. Genomic DNA was extracted from peripheral blood leukocytes using a DNA extraction kit  and stored at –80°C. Genotyping was performed using the TaqMan SNP genotyping assay (Agena Company). Primers and probes were chosen from the information available on the ABI website.2.3U test. The Chi-Squared test was used to calculate categorical variables. After single-factor analysis, multiple logistic regression models were used to analyze the independent effect of each genetic variant on the risk of EH. The allele frequency, genotype, and factor between the groups were examined by unconditional logistic regression analysis. A P value of less than .05 was considered statistically significant.Data management and statistical analysis were performed with Statistical Product and Service Solutions (SPSS version 21.0). The quantitative variables are presented as the mean (SD), which are normally distributed. Hardy-Weinberg equilibrium (HWE) was tested in the healthy group using a Chi-Squared test. The difference in continuous data was compared using the Mann–Whitney 33.1P < .05). In the Russian part of Table P < .01). However, no statistical significance was found in sex or weight (P > .05) in the ethnic Russian population. There were significant differences in Cholesterol (CHOL), Triglyceride (TG), and Glucose (GLU) between the 2 groups in Chinese Han people (P < .05). However, no statistical significance was found in CHOL and GLU (P > .05) in the ethnic Russian population. Baseline characteristics of healthy Chinese Han and ethnic Russian individuals aged 40–50 years are shown in Table A total of 3202 Chinese Han (1512 patients with EH and 1690 controls) and 500 ethnic Russian (250 patients with EH and 250 controls) individuals participated in this study. Table A multiple logistic regression model showed that CHOL , BMI , rs1006737 , TG , and GLU  to be significantly correlated with EH in the Chinese Han population. TG  and rs1006737  were independent risk factors in the ethnic Russian population.Unconditional logistic regression model showed correlative risk factors for EH Table . Compare3.2CACNA1C genotype conformed to Hardy-Weinberg equilibrium (HWE) with P = .299 (Chinese Han control people) and P = .304 (Russian control people), respectively.The distribution of the The MAF of rs1006737 increased with advancing age in Chinese Han hypertension and control groups, from 0.0294 (age < 40 years) to 0.0795 (age >60 years) in the hypertension group, from 0.0242 (age <50 years old) to 0.0571 (age >60 years old) in the control group, the results were the same in the male and female groups  (Chinese Han) and  (ethnic Russian).Single-locus analyses of genotype and allele distributions in normotensive and EH patients for these SNPs are summarized in Table P = .001) for Chinese Han people. The AA genotype frequency of rs1006737 was significantly associated with an increased risk of hypertension  for ethnic Russian people. In the dominant model, GA+AA genotype carriers had a 61.8% increased risk of EH relative to those with the GG genotype  for Chinese Han people and 49.6% increased risk  for ethnic Russian people. In the recessive model, AA genotype carriers had a 108.4% increased risk of EH relative to those with GG+GA genotype  for ethnic Russian people. Following advanced age, the GA genotype had higher OR values in the Chinese Han hypertension group, OR = 1.105 (age <50 years), OR = 1.597 (aged 50–59 years), OR = 2.295 (age >60 years old). Similar trends were observed for AA genotype in ethnic Russian people: OR = 1.932 (age <40 years old), OR = 3.060 (aged 40–49 years old), OR = 1.072 (age >50 years old) , which has the highest latitude in China.Asian and white adults were reported to have higher rates of diagnosed hypertension from an analysis of the 2010 to 2016 National Health Interview Survey.From baseline characteristics of healthy populations aged 40 to 50 years, we have proven that there are large differences in demographic data between the 2 races, especially in male populations. From the results of this study, ethnic Russians suffer from EH earlier than Chinese Han in the Northeast Asia region, which involves a mix of genetic and environmental factors.Although the MAFs of Chinese Han and ethnic Russian populations are different, the trend of the A allele of rs1006737 risk is similar in this study. These results come from the A allele of rs1006737, which is a risk factor for EH, even if MAF rs1006737 is lower in Chinese Han people. Although genome-wide studies have provided valuable insights into the genetic basis of EH, candidate genes for EH are more powerful for detecting risk variants than to detect protective variants, especially when MAF <0.05. Calcium-channel blockers are widely used to treat hypertension because they are more effective than other types of antihypertensive agents.CACNA1C gene encodes protein is expressed in neurons and other electrically excitable tissues , sensory, and endocrine cells, which play an important role in regulating BP.EH and psychiatric disorders shared the rs1006737 locus in genetic susceptibility from the results of our study. Some patients with psychiatric disorders also take antihypertensive drugs to treat their mental illness.the human CACNA1C gene rs1006737 is associated with patients with psychiatric disorders. While researchers have achieved satisfactory experimental results, people may have overlooked biological hints that complex diseases might share the same susceptibility locus and pathological process. There is little evidence that hypertension and psychiatric disorders share the same genetic basis for common diseases. For most common diseases, polygenic inheritance plays a greater role than rare monogenic mutations.Shared genetic susceptibility suggests that some complex diseases might share the same pathological process in the early stages of the disease. In a genome-wide association study, the present study demonstrated that the human CACNA1C gene, although the MAFs of the 2 races are different. An allele of rs1006737 in Chinese Han populations shows more power to detect the risk of EH than ethnic Russian people. The OR values of the A allele become higher following the increase in age. There is a similar trend in ethnic Russian people. Because of the limited sample size in Russian people, the OR value of the allele shows a different trend in the age >50 group.There is the same susceptibility locus of hypertension rs1006737 in This study had several strengths: to minimize the impact of geographical factors, the use of 2 cohorts from China and Russia in similar latitudes in the Northeast Asia region, and the population-based design of data collection for 2 populations. However, our study also had some limitations. Our study sample was relatively small, which are hardly representative of the whole Chinese Han and Russian population, but effects of biogeographic and climatic factors were considered. We selected only 1 SNP locus as the research object, which is insufficient to explain the pathogenesis of a polygenic trait such as hypertension. However, other purpose of designing experiments like this is to remind other researchers that people often overlook the influence of geographic factors on disease. This, on the other hand, may explain why the results of genetic association studies are difficult to repeat. Environmental factors such as geography and lifestyle may influence complex diseases more than genetic factors. Russian cohorts had a small sample size and relatively concentrated age, and the control group was younger. We consider that this might be due to a small population in Russia. Participants using antihypertensive drugs were classified as hypertensive. Ambulatory BP was not performed, which might have caused errors in the estimation of white coat and masked hypertensive subjects. These factors might affect the average blood pressure levels.4.1This kind of design provides a new view of genetic association studies; rs1006737 was also shown to contribute to EH and psychiatric disorders, further supporting the hypothesis that EH and psychiatric disorders show biological overlap in genetic susceptibility. EH and psychiatric disorders are now recognized as leading causes of morbidity and affect the human population around the world. Increasing evidence suggests an overlap in genetic susceptibility across the traditional classification systems of disease. Future identification of EH and psychosis susceptibility genes will have a major impact on our understanding of disease pathophysiology and will lead to changes in classification and the clinical practice of disease.Achieving precision therapy requires molecular level detection, and new molecular level experimental results may promote disease reclassification. We will treat seemingly irrelevant diseases together. Although a key public health need is to identify individuals at high risk for common disease to enable enhanced screening or preventive therapies, it has not yet been possible to use polygenic predictors to identify individuals at high risk compared to monogentic disease. Multiple SNP tests may be used to improve risk prediction models in conjunction with clinical assessments. In the future, humans need to create a type of polygenic score to identify individuals at clinically significantly increased risk or multivariable risk prediction model to predict the probability of developing EH at the gene level. Shared susceptibility may be a key hint to find a valued locus of SNP, and previous studies have shown that common susceptibility variants are shared in common complex disorders.We thank the subjects for participating in our study. We thank Emelyanov Arthur, Romanyuk Svetlana. We gratefully acknowledge the assistance of the clinical, field, and laboratory staff that made this work possible.Conceptualization: Yuri Vitkovsky, Changchun Qiu.Data curation: Mingyu Cong.Formal analysis: Ningning Wang.Funding acquisition: Hao Zhang, Changchun Qiu.Investigation: Jiexin Zhou, Yuyuan Mu, Olga Bolshakova.Methodology: Baiyu Jian.Resources: Keyong Zhang, Jicheng Liu.Software: Ming Jin.Writing – original draft: Hao Zhang, Boris Pushkarev.Writing – review & editing: Hao Zhang, Sandeep Shrestha, Changchun Qiu.


Background: Opioid use has emerged as a leading cause of death in the US. Given that 1 in 300 opioid-naive patients exposed to opioids after cesarean birth will become persistent users, hospitals should strive to limit exposure to these medications. We set out to evaluate whether transitioning to a standardized order set based on multimodal combination analgesic therapy decreases the exposure to opioids after cesarean delivery. Methods: Our health system’s post-cesarean pain management electronic medical record (EMR) order set was changed from standing NSAIDs (Ibuprofen 600 mg every 6 h) and additional acetaminophen and opioid medications  as needed (PRN) to a multimodal combination therapy with acetaminophen (975 mg every 6 h) and NSAIDs (Ibuprofen 600 mg every 6 h) as primary analgesics and opioids PRN (Oxycodone immediate release (IR) 5 mg every 3 h for moderate to severe pain). We performed a retrospective analysis across seven hospitals comparing inpatient opioid use, administration of other analgesics, and severe pain episodes (pain score ≥ 7) between the patients who were treated before and after implementation of the multimodal order set. Chi square and Student t-test were used for statistical analysis with significance determined as p < 0.05. Results: A total of 12,898 cesarean births were included (8696 prior and 4202 after implementation). The multimodal order set was associated with marked decrease in the incidence of post cesarean opioid use , lower average opioid dose , and increased dose of acetaminophen , while severe pain scores were less frequent . Conclusions: Multimodal analgesic therapy for post-cesarean pain management reduces inpatient opioid use while improving pain control. Incorporation of a multimodal order set as a default in the EMR facilitates effective and widespread implementation on a large scale. Obstetric units should consider standardizing post-cesarean pain management orders to include routine (not PRN) multimodal combination therapy with acetaminophen and NSAIDs as primary analgesics. Opioid use has emerged as a leading cause of death in the United States, reaching epidemic proportions in recent years . In factPost-surgical opioid prescriptions are widespread and are frequently the first time a patient will be exposed to opioids. Cesarean delivery is the most common inpatient surgical procedure in the United States, affecting approximately 32% of all births . Most woMultimodal pain therapy utilizes scheduled dosing of several different medications with varying mechanisms of action to control pain while limiting exposure to opioids and thus their potential negative consequences. The American College of Obstetricians and Gynecologists (ACOG) and other societies including the American Society of Anesthesiologists Task Force on Acute Pain Management recommend the use of multimodal analgesic therapy whenever possible for patients with acute postoperative pain ,8,9. NevThe purpose of our study was to evaluate the potential impact of adopting a standardized order set based on multimodal combination analgesic therapy on both opioid usage as well as quality of pain control in the immediate post cesarean delivery period in a large healthcare system with multiple obstetrical units.This is a retrospective analysis of data on all patients who had cesarean delivery between January 2018 and December 2019 in seven hospitals in our health system that has over 30,000 combined annual births and share an enterprise electronic medical record (EMR). Data were extracted directly from the EMR, which contains all post-operative orders, medications administration data , and pain scale assessments. Information regarding medication administration and dosages as well as pain scale assessments are available on patients from admission to discharge.In May 2019, our institution’s post-cesarean pain management order set was changed in our EMR to include a default treatment with combination of agents  based on the recommendations from the ACOG . The anaWe divided our cohort into two groups based on the date of cesarean delivery reflecting the type of post cesarean pain management order set used . The first group consisted of all cesarean deliveries in the 16 months prior to implementing the multimodal analgesic order set (between January 2018 and April 2019), i.e., ‘old’ order set. This group was compared to the group of patients who had cesarean delivery in the first eight months following the implementation of the multimodal analgesic order set (May 2019 through December 2019) i.e., the ‘Multimodal’ order set.The primary outcome was the overall incidence of inpatient opioid use, defined as opioid intake by the patient. Secondary outcomes included stratification of the incidence of opioid use for the first and second 24-h post-operative periods, as well as the last 24 h of hospitalization; the dose of opioid used, expressed in milligrams of oxycodone ; the incidence of administration and dose of other analgesic medications (acetaminophen and ibuprofen); and incidence of severe pain episodes, defined as pain score ≥7 on a 10 point scale.We included in our analyses all cesarean deliveries that occurred during the study period. We excluded cases with incomplete records regarding the cesarean delivery, the administered analgesic medications or dosage, or the post-operative pain score assessment, as well as women with a known opioid addiction. In addition, charts with incomplete data or extreme values of pre-pregnancy body mass index (BMI) (≥60 or ≤18.5), charts with missing maternal age, or charts with maternal age ≥50 were excluded as chart reviews indicate they were likely to represent data entry errors or a unique patient population.Baseline demographic and clinical characteristics such as maternal age, race, ethnicity, pre-pregnancy BMI and weight category, abortion history, parity, and type of cesarean delivery (primary versus repeat) were compared between the two groups as well.t-test was used for continuous variables. Differences in the proportions of inpatient opioid administration over time were evaluated with an interrupted time-series analysis using ordinary least-squares (OLS) regression [p < 0.05, and the proper assumptions were tested and met. All statistical analyses were conducted using Stata 14.2.Pearson’s Chi square was used for categorical variables and presented as odds ratios with 95% confidence intervals, while Student gression . The intThe study received an IRB exemption given the retrospective nature of the analyses and evaluation of de-identified data.‘Old’ vs. Multimodal post cesarean pain management order sets:In the United States, it has become a common practice, as it has been at our institution, to have a pre-selected order set for post-cesarean pain management. The ‘old’ post cesarean pain management order set reviewed in this study included a pre-selected standing treatment with ibuprofen 600 mg every 6 h. Oxycodone 5 mg/acetaminophen 325 mg every 3 h for moderate pain (defined as pain score of 4–6), and oxycodone 10 mg/acetaminophen 650 mg every 6 h for severe pain (defined as pain score 7–10) were pre-selected as PRN order (as needed). It did not include a standing order for acetaminophen . The newIn addition, physicians continued to be able to prescribe “a la carte”, individualized, analgesics (narcotic and non-narcotic) based on clinical judgement and patient specific needs. Of note, oxycodone was the only opioid medication used in both the old and multimodal order sets .There were 13,233 cesarean deliveries during the study period (January 2018 to December 2019). After excluding 335 women with incomplete data, we assessed a total of 12,898 cesarean births, 8696 of which delivered before the implementation of the multimodal order set . There were 4202 cesarean deliveries in the post implementation period between 1 May 2019, and 31 December 2019 . p < 0.0001). The mean total dose of oxycodone for patients who received opioids decreased 27%, from 36.6 mg to 26.7 mg (p < 0.0001).There was a statistically significant difference in the distribution of the specific weight categories between the two groups, although the mean BMI was similar. There were no significant differences in the other demographic and baseline characteristics between the two groups . The comp < 0.001). Furthermore, following this abrupt decrease, a continuous mild reduction in opioid exposure is seen throughout the following eight months since implementation, at a range of 0.25% reduction per week (An interrupted time series demonstrating the average percentage of patients that received opioids in the immediate post cesarean recovery throughout the study period is presented in per week .p < 0.0001). In the same three time periods, significant opioid dose reduction was seen, with a decrease of 17.2% (from 6.5 mg to 5.3 mg), 23.2% (from 17.6 mg to 13.5 mg), and 20.2% (from 18.8 mg to 15.0 mg), in the first, second, and last 24 h periods, respectively (p < 0.0001).Clinically and statistically significant differences were also seen in the incidence of opioid use for each of the 24 h time periods in our stratification: the first 24 h post-operatively ; the second 24 h post-operatively ; and the final 24 h of hospitalization  (p < 0.0001), the actual dose of acetaminophen increased substantially (from 4563 mg to 8422 mg) in the old and multimodal groups, respectively (p < 0.0001). Substantial increases in acetaminophen doses were seen in both patients that were exposed to opioids , as well as in patients that did not take opioids . Significant increase in the dose of acetaminophen were noted for each of the 24 h time periods with dose increase from 802 mg to 1897 mg, from 1355 mg to 2656 mg, and from 1636 mg to 2796 mg, in the first, second, and last 24 h periods, respectively. The difference in ibuprofen dose was statistically but not clinically significant .Although the incidence of administration of acetaminophen was high in both groups, and increased only mildly . Similarly, statistically significant improvements in pain control based on pain scores were further demonstrated during the first and second 24 h post-operative periods, as well as the last 24 h of hospitalization.Severe pain scores decreased from 56.6% with the old order set to 46.3% with the new, multimodal, order set , while the average opioid dose decreased by 27% . Of interest, the new, multimodal order set led to improved pain control, with severe pain episodes (pain score ≥ 7) decreasing by 18.2%, from 56.6% with the old order set to 46.3% with the multimodal regimen .After implementing a standardized post-cesarean order set for pain management employing multimodal combination therapy, we found a substantial reduction in both the rate and dose of opioid administration. Post-cesarean opioid use decreased by 32.7%, from 67.5% with the old order set to 45.4% with the multimodal order set  at a dose of 0.15–0.3 mg. In cases where epidural anesthesia was already administered during labor, the Duramorph dose used was 3–4 mgIn conclusion, we demonstrated that a standardized post-cesarean order set based on multimodal therapy resulted in substantially decreased exposure to opioids while simultaneously improving pain scores. Embedding such order sets as a default for post-cesarean pain management in the EMR facilitates effective and widespread implementation on a large scale. Hospitals should consider standardizing post-cesarean pain management orders in their EMR to include multimodal combination therapy with acetaminophen and NSAIDS as primary analgesics.