Ciona robusta, Ciona savignyi, and Styela clava from the northeast region of China using morphological characteristics and mitochondrial cytochrome c oxidase subunit I (cox1) as genetic marker. We additionally used phylogenetics to aid in the identification of these three species. The results of a population genetic analysis showed that among the three species, the level of haplotype diversity was particularly high within C. savignyi, and nucleotide diversity varied moderately. We divided the three species separately into native and invasive populations using 170 cox1 sequences from global resources to explore population genetic structure and invasive potential. Although in the network analysis Ciona spp. formed haplogroups of native and invasive populations, some haplotypes were still shared. We found that the haplotypes did not cluster within the network of S. clava. Our AMOVA results also showed that Ciona spp. had a weak genetic structure, and less genetic differentiation was present in S. clava. These data suggest that there are extensive incursions of these three ascidians into different geographical regions. Global comparisons of ascidian populations will help in the understanding of their population genetic structure and invasive potential, hence providing important insights regarding conservation as well as management.Ascidians are sessile marine chordate invertebrates found along seashores worldwide and are typically regarded as invasive organisms. Knowledge concerning their global genetic structure and subsequent invasive potential is limited. Here, we identified three ascidians— Ciona robusta, Ciona savignyi, and Styela clava were identified from the northeast region of China using morphological characteristics and mitochondrial cytochrome c oxidase subunit I (cox1) as genetic marker. C. savignyi has particularly high level of haplotype diversity and nucleotide diversity varied moderately. Ciona spp. had a weak genetic structure, and less genetic differentiation was present in S. clava. These data suggest that there are extensive incursions of these three ascidians into different geographical regions.Three ascidians— A comparative analysis of these species isolated from China with the samples elsewhere in the world using cox1 as a genetic marker was also performed to distinguish native from invasive ascidian populations.Here, we identified three ascidian species 22.1C. robusta, C. savignyi, and S. clava adults were dissected, and internal tissues were collected for DNA extraction and sequencing.Adult animals were collected from the Rongcheng Bay area  and were maintained in the laboratory in seawater tanks with aeration and constant illumination. Ascidian species were identified morphologically. 2.2cox1 from C. robusta, C. savignyi, and S. clava were designed according to published mitogenomes  of Halocynthia roretzi was used as an out‐group.The ld Table  were retld Table  hosted bld Table . Only thld Table . Neighbold Table .The barc2.4cox1 from three ascidians were performed separately in ClustalW , number of segregating sites (S), and nucleotide diversity (Pi), were estimated using DnaSP software  were identified in C. savignyi. All these sequences were deposited in the NCBI database, and the accession numbers are listed in Table We cloned the full length of the cox1 barcode sequences from the NCBI database  from China did not form a single clade in either NJ  was comparatively higher in C. savignyi (0.993 + 0.038) than that in C. robusta (0.912 + 0.059), and S. clava (0.947 + 0.038). Similarly, the detected average number of nucleotide difference (K) in C. savignyi (20.618) was higher than that in C. robusta (8.143) and S. clava (11.550). Nucleotide diversity (Pi) and average number of mutations (θ) were also relatively higher in C. savignyi  compared with C. robusta  and S. clava , respectively , indicating that there was a excess of low‐frequency polymorphisms, and the C. savignyi population was expanding. However, in the C. robusta/S. clava populations, the values were not statistically significant, indicating that these two species populations did not deviate from the neutral expectations. Similarly, for Fu and Li's D* statistic, negative values were observed in all three species . The results from these two analytical approaches indicate that the population of C. savignyi is undergoing positive selection and expansion.Next, we carried out the neutrality test. The Tajima's es Table , but thees Table . The val3.4C. robusta  and C. savignyi , but these values were not statistically significant , indicating that there was no population differentiation , C. savignyi , and S. clava , there were significant variations, with the highest level of variation appearing in C. savignyi , followed by C. savignyi  and C. robusta ; data curation ; formal analysis ; visualization ; writing‐original draft ; writing‐review and editing . Runyu Qiao: Data curation ; formal analysis ; validation . Bo Dong: Conceptualization ; formal analysis ; funding acquisition (lead); project administration (lead); resources (lead); supervision (lead); writing‐review and editing (lead).Table S1Click here for additional data file.


PCa) has a broad spectrum of clinical behavior; hence, biomarkers are urgently needed for risk stratification. Here, we aim to find potential biomarkers for risk stratification, by utilizing a gene co‐expression network of transcriptomics data in addition to laser‐microdissected proteomics from human and murine prostate FFPE samples. We show up‐regulation of oxidative phosphorylation (OXPHOS) in PCa on the transcriptomic level and up‐regulation of the TCA cycle/OXPHOS on the proteomic level, which is inversely correlated to STAT3 expression. We hereby identify gene expression of pyruvate dehydrogenase kinase 4 (PDK4), a key regulator of the TCA cycle, as a promising independent prognostic marker in PCa. PDK4 predicts disease recurrence independent of diagnostic risk factors such as grading, staging, and PSA level. Therefore, low PDK4 is a promising marker for PCa with dismal prognosis.Prostate cancer ( PCa) reveal high TCA/OXPHOS activity and low PDK4 expression in low STAT3 tumors. PDK4 is a promising independent predictor of biochemical recurrence in PCa.Transcriptomic and proteomic analyses in prostate cancer ( These results were corroborated in additional data sets and by findings in shotgun proteomics of laser‐microdissected formalin‐fixed and paraffin‐embedded (FFPE) PCa material from human and murine samples. Furthermore, we found that gene expression of PDK4, which inhibits pyruvate oxidation through the TCA cycle and thereby negatively impacts OXPHOS  , “low STAT3” of the 0.2nd quantile (n = 100), and “medium STAT3” of all samples in between (n = 298).To compare low P‐value ≤ 0.05, We compared low STAT3 to high STAT3 samples and found 1,194 genes to be significantly differentially expressed  method  form. Therefore, we correlated STAT3 log counts per million (cpm) with TCGA PRAD Reverse Phase Protein Array (RPPA) data for pY‐STAT3  showed a positive correlation with STAT3 expression, “OXPHOS” cluster 2  and “Ribosomal” cluster 3  were negatively correlated , STAT3, Suppressor Of Cytokine Signaling 3 (SOCS3), Janus Kinase 1 (JAK1), Janus Kinase 2 (JAK2), Tyrosine Kinase 2 (TYK2), and “STAT3 TARGETS UP” genes derived from Carpenter and Lo  and pT risk , and pathological lymph node staging , GSC 7 , and GSC 9 .To verify that y ANOVAs . Again, STAT3‐correlated clusters 2 and 3. We wanted to know which genes from clusters 2 and 3 overlapped with the differentially expressed genes resulting from the low STAT3 to high STAT3 comparison. From 1,194 differentially expressed genes, 316 overlapped with “OXPHOS” cluster 2 and 103 with “Ribosome” cluster 3 , while at the same time being highly associated with the respective gene cluster (ρ ≥ 0.8). We analyzed those genes for enriched GO biological process terms. The top 50 cluster 2 genes were associated with GO biological process “OXPHOS” (45.71%), “mitochondrial ATP synthesis coupled protein transport” (42.86%), and “mitochondrial translational elongation” (11.43%)    data sets. All three data sets showed a significant negative Pearson correlation of STAT3 log cpm with KEGG “OXPHOS”  and KEGG “Ribosome” . RAS was negatively correlated, but not significant . Correlations for “STAT3 TARGETS UP” were significantly negatively correlated in NCI  and negatively correlated in VPC , whereas RAS was again not significant.We also correlated the STAT3 target signatures “AZARE STAT3 TARGETS” and “STAT3 TARGETS UP” to KEGG “OXPHOS” and KEGG “Ribosome” in these data sets . In VPC n = 6 in each group, GSC 7–8) and a healthy prostate control group (n = 7). Control samples consisted of healthy prostates, derived after prostatectomy of bladder cancer patients. Low and high STAT3 groups were chosen from a patient cohort tissue microarray (TMA) after immunohistochemistry (IHC) staining of STAT3. We confirmed STAT3 levels by additional IHC STAT3 staining of the selected samples  or concomitant loss of Pten and Stat3 (PtenStat3pc−/−) in the prostate epithelium. Whereas Ptenpc−/− mice show slow, localized tumor progression, the additional deletion of Stat3 leads to rapid tumor growth, dissemination, and early death  and performed LMD and LFQ shotgun proteomics on FFPE tumors and controls. We were able to detect 2,994 proteins on average , with 1,510 being differentially expressed between all three groups . PCA showed a clear separation between groups , Idh2 , and Idh1 , were differentially expressed in PtenStat3pc−/− compared to WT mice , but the TCA cycle was not. In the direct comparison of PtenStat3pc−/− with Ptenpc−/−, OXPHOS and the TCA cycle were not significantly differentially expressed. We hypothesize that the metabolic differences between the two mouse models are too subtle to show significant differences upon direct comparison. However, the additional effect of Stat3‐deficiency becomes visible if we compare PtenStat3pc−/− versus WT with Ptenpc−/− versus WT enriched gene sets.As we observed metabolic changes in human samples, we further investigated these in our PCa mouse model. Therefore, we did additional gene set testing with metabolic KEGG pathways . We obseways Fig D. Accord WT mice . In contStat3‐dependent changes of metabolites involved in the TCA cycle. We performed a targeted metabolomics experiment on WT, Ptenpc−/−, and PtenStat3pc−/− mice . We measured absolute amounts (nmol/μg) of pyruvate, citrate, α‐ketoglutarate, succinate, fumarate, and malate in mouse prostate tumors and WT prostates. PtenStat3pc−/− prostate tumors showed significantly higher amounts of pyruvate , fumarate , and malate  compared to WT tumors . Generally, measured metabolite concentrations showed a trend to be higher in PtenStat3pc−/− compared to Ptenpc−/− mice, but due to the high variability in metabolite levels of Ptenpc−/− mice, significance was not reached in these samples. Higher levels of TCA metabolites suggest higher activity of the TCA cycle. Of interest were also the high pyruvate levels in PtenStat3pc−/− tissue. Since we measured metabolites in the whole tissue and not specifically in mitochondria, measured pyruvate could be both mitochondrial and cytosolic. Mitochondrial pyruvate is either converted to acetyl‐CoA, which enters the TCA cycle, or converted to oxaloacetate , which is located in the inner mitochondrial membrane. SDH/CII participates in both the TCA cycle by oxidizing succinate to fumarate and OXPHOS by shuttling electrons . Moreover, Gleason grade (GL) 5 areas showed a stronger expression of both SDHB  and IDH2 , when compared to GL3 or GL4 areas , but were higher in GL5 than in GL3 and GL4 , they were not correlated in the respective Gleason grades. Neither SDHB nor IDH2 was correlated with nuclear or cytoplasmic STAT3  , but SDHB  and IDH2  did not  was significantly down‐regulated in low STAT3 samples  1–1.98], P‐value = 0.05, PDK4 groups was significant for Gleason 6 . Nevertheless, eight out of 10 patients who died were in the low PDK4/high‐risk group after a median split of sample groups . In those, PDK4 did not reach significance, presumably because of the smaller sample sizes. However, there is a trend of low PDK4 showing risk of earlier BCR , MYC Proto‐Oncogene (c‐MYC), and CCR4‐NOT transcription complex subunit 1 (CNOT1), were not or only weakly predictive of increased risk to earlier disease recurrence  by a median split of both STAT3 and PDK4  and in a Cox PH model with high STAT3/high PDK4 as reference . Mixed groups did not show a significant difference to the reference group  compared to a scrambled control  knockdowns of PDK4 could be a direct transcriptional target of STAT3, we used chromatin immunoprecipitation DNA‐sequencing (ChIP‐Seq) data created by the ENCODE Consortium  in the Genome Browser ( http://genome.ucsc.edu)  using PDK4 promoter‐specific primer pairs  and in the VPC data set . The NCI  and RAS  data sets showed no correlation between STAT3 and PDK4. However, PDK4 was positively correlated to a STAT3 target gene signature (“AZARE STAT3 TARGETS”)  as well as in the VPC data set   and “STAT3 TARGETS UP” signatures  in TCGA PRAD  Fig B. PDK4 wPRAD Fig C.et al, et al, et al, et al, PDK4 expression , whereas c‐MYC was not correlated to STAT3 .Besides STAT3, also HIF‐1α and c‐MYC are possible regulators of PDK4 as well as TCA/OXPHOS. HIF‐1α induction suppresses TCA/OXPHOS, is linked to STAT3 signaling  and 7 TCA/OXPHOS genes   and again not correlated to c‐MYC . After hierarchical clustering of correlated genes, STAT3 and PDK4 clustered together, whereas HIF‐1α clustered with PDK3 and PDK1. The TCA/OXPHOS genes formed a large cluster of positively correlated genes, but c‐MYC did not cluster with any of these genes. c‐MYC was weakly negatively correlated to PDK4 .Using the MSKCC data set, we correlated PDK4 in this setting, but other factors, such as HIF‐1α, should also be considered. This needs to be further evaluated in future studies.Our data suggest that STAT3 and HIF‐1α influence TCA/OXPHOS gene expression in this setting, but not c‐MYC. As a conclusion, STAT3 does play a role in the transcription of PDK4 as a highly relevant independent candidate prognostic marker in PCa. We here demonstrate for the first time that PCa patients with low PDK4 expression have a higher risk of earlier disease recurrence, independent of ISUP grading and tumor staging. Moreover, in low‐/intermediate‐risk T1c–T2c tumors, PDK4 proves to be a significant predictor of earlier BCR, independent of ISUP grading. Therefore, PDK4 is a strong candidate marker for risk stratification of the large group of T1c–T2c tumors, which are prone to over‐ or under‐treatment. By comparing low STAT3 to high STAT3 tumors, we show an association of low STAT3 with high TCA/OXPHOS both on transcriptomic and proteomic levels. Also, STAT3 expression is correlated with PDK4 and knockdown of STAT3 in 22Rv1 cells results in reduced PDK4 levels. ChIP‐Seq data and ChIP assays show binding of STAT3 at the promoter region of PDK4.In this study, we used gene co‐expression network analysis in addition to proteomics from LMD human and murine FFPE samples to identify et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, et al, These data are of interest considering the peculiar energy metabolism of the prostate cell: The generation of mitochondrial adenosine triphosphate (ATP) through aerobic respiration via TCA/OXPHOS is the primary source of energy in most healthy cells  and Ras homolog enriched in brain  cell lines , Vienna, Austria. The FFPE material originated from 84 primary PCa patients and seven bladder cancer patients who underwent radical prostatectomy at the General Hospital of Vienna from 1993 to 2015. Use of patient FFPE material in this study was approved by the Research Ethics Committee of the Medical University Vienna, Austria (1877/2016).Pten (Ptenpc−/−) were received from Prof. Johannes Schmidt  mice   in the prostate epithelium   method  were used in this study, to focus on the comparison between low STAT3 and high STAT3 tumors. Samples were ranked according to STAT3 expression and assigned to groups: “high STAT3” consisted of the 1–0.8th quantile (n = 100), “low STAT3” of the 0.2nd quantile (n = 100), and “medium STAT3” of all samples in between (n = 298). RNA‐Seq data from The NCI  . In this setting, it is not possible to include normal samples, as they would require the generation of a separate network. Gene expression data were voom‐transformed with limma v3.40.6 R package  of each cluster was calculated and clusters with similar eigengenes (ρ > 0.75) were merged. Genes in each gene cluster were tested for over‐representation of GO biological process terms with clusterProfiler v3.10.1  was calculated from the adjacency matrix  and STAT3 expression  was calculated by Pearson correlation. Student's asymptotic P‐values were calculated and adjusted with Benjamini–Hochberg method. Significance was defined by an adj. P‐value ≤ 0.05.Gene clusters were associated with external traits by correlating MEs with trait data (= cluster‐ trait correlation) by Pearson correlation. Student's asymptotic P‐value ≤ 0.01). For both clusters, genes were sorted for their MM and GS. The top 50 genes with a MM ≥ 0.8 and a GS ≤ −0.6  were used for overexpression analysis with clusterProfiler v3.10.1. GO biological process enrichment was additionally performed using Cytoscape v.3.6.1. . Whole‐mount prostate FFPE blocks were sliced into 3‐μm‐thick sections, mounted on slides, and stained with hematoxylin and eosin. Subsequently, a pathologist marked the respective areas on the slides. To generate the TMA, cores of 2 mm diameter were cut out of the donor block and placed into the recipient TMA block using a manual tissue arrayer (Beecher Instruments). Tissue sections were placed onto superfrost slides.et al, Immunohistochemistry was performed on FFPE TMAs using consecutive sections. After deparaffinization, heat‐induced antigen retrieval was performed with Tris‐EDTA buffer, and primary antibodies were incubated as listed in the table below, followed by DAB and hematoxylin staining, respectively. The following antibodies were used: anti‐IDH2 , anti‐SDHB , and anti‐STAT3 . Antibodies were validated for FFPE IHC by using human colon cancer for IDH2, human muscle tissue for SDHB, and human pancreas for STAT3 as positive controls. STAT3 antibody was validated as described previously  at 37°C under 5% CO2. For IL‐6 stimulation, cells were treated with 100 ng/ml recombinant human IL‐6 (PeproTech) for 30 min and immediately harvested.Human PCa 22Rv1 cells  were grown in RPMI‐1640 medium (Sigma) supplemented with 10% FBS, 1% penicillin and streptomycin, 25 mM HEPES, 0.4 mM et al  constructs from the Mission TRC shRNA library (Sigma) were used: scrambled control shRNA (SHC002), shSTAT3#456 (TRCN0000071456), and shSTAT3#843 (TRCN0000020843). Transduced cells were selected for puromycin resistance, and the knockdown was verified via WB.Short hairpin‐mediated knockdown was performed as previously described in Eberl et al . For theFor WB analysis, cells were lysed in RIPA buffer  containing 1 mM sodium fluoride, 1 mM sodium orthovanadate, 1 mM PMSF, 1 μg/ml leupeptin, 10 μg/ml aprotinin, and cOmplete Mini protease inhibitors  or Hunt buffer  in the presence of cOmplete protease inhibitors and PhosSTOP™ (Roche). 20 μg of each sample was loaded onto a 10% SDS–PAGE minigel (Invitrogen) for electrophoresis. For IL‐6 stimulation of 22Rv1 cells, 40 μg protein extract was separated on a 4–20% precast gradient TGX™ SDS–PAGE (Bio‐Rad) and transferred onto nitrocellulose membranes with the Trans‐Blot Turbo Transfer system (Bio‐Rad). Membranes were blocked with 5% BSA in 1× TBS/0.1% Tween‐20 for 1 h and incubated with the primary antibody overnight at 4°C. Primary antibodies were reactive to STAT3 , PDK4 , β‐Tubulin , Phospho‐STAT3 Tyr705 , total STAT3 , and GAPDH . For protein quantification, Image Lab software v5.2.1 (Bio‐Rad) was used. Phospho‐STAT3 signal was normalized to TGX total protein lanes and total STAT3 expression.et al, et al (http://genome.ucsc.edu). For the generation of PDK4 primer pairs, a STAT3 binding site in the promoter region of PDK4 detected by ENCODE STAT3 ChIP‐Seq HeLa‐S3 was extracted  as low STAT3 group and seven patients with GSC 7–8 from groups 2 and 3 as high STAT3 group. Additionally, seven healthy prostate FFPE samples were included as control group, stemming from bladder cancer patients. To facilitate LMD, we created a TMA for each patient. Whole‐mount prostate FFPE blocks were sliced into 3‐μm‐thick sections, mounted on slides, and stained with hematoxylin and eosin. A pathologist marked tumor areas with GL4 or GL5 on the slides. For each patient, a TMA block was created with 2‐mm‐diameter spots using a manual tissue arrayer (Beecher Instruments).Since PCa tumors are heterogeneous and respective punches in TMAs cover only a small part of the tumor, we reconfirmed STAT3 levels in our samples by IHC. The low STAT3 group had ≤ 20% positive nuclei in the tumor and intensities of the staining ranged between 0 and 2 on a scale of 0–3. The high STAT3 group had ≥ 80% positive nuclei in the tumors, with intensities of the staining ranging between 2 and 3. One sample in the high STAT3 group was STAT3 negative and therefore excluded from data analysis following LC/MS‐MS. One low STAT3 sample did not include any tumor and was excluded as well.Ptenpc−/−, and PtenStat3pc−/− mice (n = 3 for each genotype). Blocks were sliced into 3‐μm‐thick sections, mounted on slides, and stained with hematoxylin and eosin. Tumor areas were marked by a pathologist. Since mouse tumors are much smaller and contain only few stroma compared to human PCa, there was no need to create sample TMAs for LMD.For LMD of murine samples, FFPE tumor material was used from WT, For LMD of human samples, a PALM Zeiss Microbeam 4 was used. Sample TMA blocks were cut into 10‐μm‐thick sections and mounted on superfrost slides. For LMD of mouse samples, a Leica LMD6000 was used. Tissue blocks were cut into 10‐μm‐thick sections and mounted on membrane slides . LMD was conducted similarly for mouse and human samples: For each sample, a slide was stained with hematoxylin and eosin for inspection before LMD. To obtain the minimum amount of tissue (100 nl = 0.1 mm³) necessary for consecutive LC‐MS/MS analysis, at least 10 mm² of target area were laser‐microdissected. To obtain proteomic profiles solely from the tumor, stroma and immune cells were excluded from dissection. In tumor samples, only cancerous prostate glands were dissected. Microdissected FFPE samples were stored at −20°C before LC‐MS/MS analysis.2 of 10 μm slides) of FFPE material per sample was used for analysis. Lysis of microdissected tissue was carried out in 50% trifluoroethanol (TFE), 5 mM dithiothreitol (DTT), 25 mM ammonium bicarbonate (ABC) at 99°C for 45 min. followed by 5‐min. sonication . After centrifugation at 16,000 g for 10 min., the cleared protein lysate was alkylated with 20 mM iodoacetamide for 30 min. at room temperature. Upon vacuum centrifugation, digestion was carried out in 5% TFE, 50 mM ABC to which 0.15 μg of LysC and 0.15 μg of trypsin were added for digestion overnight at 37°C. The following day, digestion was arrested by adding trifluoroacetic acid (TFA) to 1% and the digestion buffer removed by vacuum centrifugation. Peptides were suspended in 2% acetonitrile and 0.1% TFA and purified on C18 StageTips. Finally, purified peptides were resolved in 2% acetonitrile and 0.1% TFA, and the entire sample was injected for MS analysis in a single‐shot measurement. Protocols were adapted from Roulhac et al  followed by high‐energy collisional dissociation (HCD) fragmentation of the five most abundant ions with charge ≥ 2 . Dynamic exclusion was set to 20 s to avoid repeated sequencing. Data were acquired with the Xcalibur software (Thermo Scientific).LC‐MS/MS analysis was performed on an EASY‐nLC 1000 system (Thermo Fisher Scientific) coupled online to a Q Exactive HF mass spectrometer (Thermo Fisher Scientific) with a nanoelectrospray ion source (Thermo Fisher Scientific). Peptides were loaded in buffer A (0.1% formic acid) into a 50‐cm‐long, 75‐μm inner diameter column in house packed with ReproSil‐Pur C18‐AQ 1.9 μm resin (Dr. Maisch HPLC GmbH) and separated over a 270‐min gradient of 2–60% buffer B  at a 250 nl/min flow rate. The Q Exactive HF operated in a data‐dependent mode with full MS scans , methanol (MeOH), and water were of LC‐MS grade and ordered at Fisher Scientific  or Sigma‐Aldrich . ABC, ammonium formate, and ammonium hydroxide were purchased as the eluent additive for LC‐MS at Sigma‐Aldrich. Formic acid was also of LC‐MS grade and ordered at VWR International . Sodium hydroxide (NaOH) was obtained from Sigma‐Aldrich . Metabolite standards were purchased from Sigma‐Aldrich  or Carbosynth .n = 5 biological replicates of wild‐type and Ptenpc−/− mice and n = 3 biological replicates of PtenStat3pc−/− mice. For WT and Ptenpc−/−, technical replicates (n = 3) were made from two biological samples in each group. For PtenStat3pc−/− mice, technical replicates (n = 3) were made for all biological samples. The prostates of sacrificed 19‐week‐old mice were immediately collected, quickly washed in fresh PBS, snap‐frozen in liquid N2, and stored on dry ice in Petri dishes until extraction. Tissue pieces were transferred into glass vials, and 50 μl of fully 13C labeled internal standard from ISOtopic solutions e.U.  and 950 μl extraction solvent were added . Subsequently, the tissue was homogenized with a probe sonicator head  in the extraction solvent. After homogenization, the contents of the glass tubes were transferred to a 2‐ml Eppendorf tube and the glass tubes were washed two more times with 500 μl extraction solvent to transfer all tissue content. The Eppendorf tubes were thoroughly vortexed and kept on dry ice during the processing of other samples.Analysis was conducted with g, 4°C, 20 min), four 400 μl aliquots were extracted into LC vials, and 3 × 100 μl was used for pooled quality controls (QC) for each sample, respectively. Remaining extraction solvent on the pellets was discarded. Aliquots were evaporated until dryness in a vacuum centrifuge. The dried samples and the high molecular pellets were stored at −80°C until measurement.The samples were centrifuged  was utilized with a 15‐min long gradient and 10 mM ABC pH 9.2/10% ACN and 100% ACN as eluents. Sample measurements were randomized, and within every 10 injections, a pooled QC sample, a QC with standards and a blank, was injected. HRMS was conducted on a high‐field Thermo Scientific™ Q Exactive HF™ quadrupole‐Orbitrap mass spectrometer equipped with an electrospray source. Full mass scan data with resolution of 12,000, maximum injection time (IT) of 200 ms, automatic gain control (AGC) target of 1e6 in the mass range of 65–900 m/z were acquired with positive–negative polarity switching.The LC‐HRMS measurement was adopted from Schwaiger et al . Shortly− ion was extracted with 5 ppm mass tolerance.Targeted analysis of the metabolomics data was carried out with Thermo Trace Finder 4.1 software. In all cases, the [M–H]The pellets resulting from extraction with 80% MeOH were dissolved in 0.2M NaOH solution, diluted 1:10, and quantified for total protein content with the Micro BCA Protein Assay kit from Thermo , according to the manufacturer's instructions.Absolute metabolite amounts were normalized to the protein content. If multiple technical replicates were available from the same organ, the sum of metabolite concentrations was calculated for each metabolite and it was normalized with the sum of total protein content for that organ. This way, absolute metabolite amounts (nmol) were normalized to the total protein content (μg) from the tissue of origin.https://cran.r-project.org/) and are described in detail in the following sections.Statistical analyses were performed using the R software environment v3.5.1 and v3.6.2. ”. Generally, correlations were assessed by Pearson correlation and Student's asymptotic Larsson, . OverexpFor statistical evaluation of a human TMAs, IHC stainings on tumor and normal tissue were evaluated by a pathologist. Stainings were quantified by evaluating staining intensities and percentage of positive cells, described by the IHC expression level:P‐value ≤ 0.05, and adjustment method was Benjamini–Hochberg. Statistical tests were performed using the R software environment with packages DescTools v.0.99.28  ranges from 0 to 3 and percentage of positive cells (Perc) from 0 to 100. Therefore, expression levels can take values from 0 to 3. To compare GLs, expression levels were evaluated separately for each present GL in a spot. To test for significant differences between groups, Kruskal–Wallis test was applied after rejection of null hypothesis for normality testing with Pearson chi‐square normality test. Visual inspection of the distribution of data was conducted using Q–Q (quantile–quantile) plots and density plots. Pairwise comparisons were done using Dunn's all‐pairs test. Significance was defined by adj. P‐values were adjusted by Benjamini–Hochberg method for each family of tests. Significance was defined by an adj. P‐value ≤ 0.05. For detailed description of correlations in the WGCNA, please refer to respective section.Correlation analyses were performed with either Pearson correlation or Spearman correlation after testing for normality as described in the section on TMA quantification above. R packages stats, Hmisc v4.3‐0  method. Significance was defined by an adj. P‐value ≤ 0.05. Multivariate Cox PH models were fitted for PDK4. As a rule of thumb, one predictor per 10 events was included in the model. In addition, Kaplan–Meier curves and log‐rank tests were performed after a median split of samples by gene expression. All statistical tests were considered significant with an adj. P‐value ≤ 0.05 after adjustment with BH method. BCR is defined by an increase of > 0.2 ng/ml PSA in serum on two occasions.Survival analyses were performed using R packages survival v3.1‐8 . Median split was used for PDK4 in the TCGA PRAD and Lapointe data set, maximizing split for the remainder. Risk groups were analyzed by a concurrent Cox model and used for Kaplan–Meier plots and log‐rank tests. All statistical tests were considered significant with a P‐value ≤ 0.05.Additional data sets were analyzed using SurvExpress online tool   , implemented in R package limma v3.40.6 ”. Quantification of gene set activities (“signatures”) was assessed using single‐sample Gene Set Enrichment analysis (ssGSEA) . Since this was an exploratory experiment used for hypothesis generation, no P‐value adjustment was performed between the six individual ANOVAs. Results of all ANOVAs can be found in Table To evaluate differences between three groups for six metabolites, ANOVA and Tukey honest significant differences (HSD) test were performed for each metabolite after normality testing. Normality was tested using Pearson chi‐square normality test and Levene's test for homogeneity of variance (center = median). Visual inspection of the distribution of data was conducted using Q–Q plots and density plots. Significance was defined as Conceptualization: MO, BH, LK; Data curation: MO, MP, MR, MBo; Formal analysis: MO, MP, MR, MBo, GO; Funding acquisition: LK, WW, BH, MBr; Investigation: MO, MP, MR, GO, MW, PH, EG, MS, TL, SL, JPe, PK, GS, SG‐G, MBo, RW, TW, AHo; Project administration: MO, BH, LK; Resources: GO, SH, FA, J‐PT, AHa, MBr, WW, TM, JPo, GK, MM, LK; Supervision: GO, JG, TM, GE, BH, LK; Validation: MO, MP, MR, TL; Visualization: MO, AJ; Writing—original draft: MO; Writing—review and editing: MO, GO, SL, SG‐G, FA, MBr, JG, GE, BH, LK.LK is a member of the scientific advisory board of CBmed‐Center for Biomarker Research in Medicine GmbH. MBr was and WW is a member of the Scientific Board of CBmed.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Table EV1Click here for additional data file.Table EV2Click here for additional data file.Table EV3Click here for additional data file.Table EV4Click here for additional data file.Table EV5Click here for additional data file.Table EV6Click here for additional data file.Source Data for Expanded View/Source Data for Expanded View and AppendixClick here for additional data file.Review Process FileClick here for additional data file.Source Data for Figure 4Click here for additional data file.Source Data for Figure 5Click here for additional data file.Source Data for Figure 7Click here for additional data file.


P) is correspondingly assumed representative for population fitness and eco-evolutionary dynamics. For 8356 populations across the globe, we report that plants systematically display much lower GDP than animals, and that life history traits shape GDP patterns both directly , and indirectly by mediating core-periphery patterns . Particularly in some plant groups, peripheral populations can sustain similar GDP as core populations, emphasizing their potential conservation value. We further find surprisingly weak support for general latitudinal GDP trends. Finally, contemporary rather than past climate contributes to the spatial distribution of GDP, suggesting that contemporary environmental changes affect global patterns of GDP. Our findings generate new perspectives for the conservation of genetic resources at worldwide and taxonomic-wide scales.Understanding how biological and environmental factors interactively shape the global distribution of plant and animal genetic diversity is fundamental to biodiversity conservation. Genetic diversity measured in local populations (GD A global analysis of population-level variation in genetic diversity for 727 plant and animal species finds that biogeography, life history traits and climate are important for predicting the distribution of local genetic diversity, and should be considered together when assessing the local conservation status of species. Through its relation with inbreeding and demographic processes7, population genetic diversity (GDP) is often used to support and complement the identification of “evolutionary significant units”  or with biogeography (e.g. core vs. periphery effects10). However, these studies typically focus on one particular driver of GDP, or consider them independently, whereas variation in GDP more likely results from interactions between life-history-related, climatic, historical and biogeographic factors. For instance, wind-pollinated plant populations with high outcrossing rates and/or wind-dispersed seeds generally sustain high genetic diversity due to positive effects of these traits on effective population size (NE) and rates of molecular evolution20, yet other studies have questioned the generality of this pattern21. Similarly, populations of small animals with high fecundity and short longevity, large geographic ranges, long-distance dispersal and/or with generalist lifestyles have often been found to harbour relatively high levels of genetic diversity27, whereas other studies could not validate these life-history-related GDP patterns30.Several studies have evaluated how GD33. Other studies, however, failed to find evidence for this core-periphery hypothesis35. Furthermore, while insular conditions have long been acknowledged to limit GDP due to founder effects and reduced gene flow39, recent work has questioned the generality of this theory41.From a biogeographic point of view, populations at species’ distribution edges are often characterized by relatively low genetic diversity due to founder effects and low connectivity, whereas core populations generally harbour higher levels of genetic diversity due to increased admixture of lineages with distinct evolutionary and/or demographic trajectoriesP. Like species diversity, GDP is expected to be higher around the Equator because (i) climatic conditions have been much more stable in the last 10–20,000 years with no major glaciations that could otherwise have generated severe demographic bottlenecks, and (ii) the higher temperatures around the tropics may boost mutation rates and hence GDp43 (but see ref. 44). Latitudinal clines in GDP have been informed for some species46, but the exact mechanisms sustaining this pattern are still poorly understood and it remains unknown to what extent temperature and precipitation contribute to spatial patterns of GDP. Together, the marked heterogeneity in GDP patterns across taxa and space urges for a better global understanding of how species traits, biogeography and climate interactively mediate variation in GDP at a large spatial scale.Finally, environmental—and in particular climatic—gradients are also thought to affect GDP are lacking, various quantitative reviews considered both life-history traits and spatial factors as drivers of genetic diversity across populations, i.e., at the species level  than species occurring near the poles, and that GDS is lower for long-lived or low-fecundity species than for short-lived or high-fecundity species. However, contrary to GDP  patterns across large spatial (worldwide) and taxonomic  scales, and to identify the main life-history-related, biogeographic and climatic factors sustaining these patterns of GDP. Our study involves 8356 populations distributed across the globe and from 242 eudicot, 10 magnolid, 82 monocot, 50 pinopsida (hereafter pines), 51 amphibian, 36 reptile, 44 mollusc, 139 mammal, and 73 bird species  identify spatial patterns of GDP across phyla and across the globe, and (ii) determine how life-history traits, past and current climatic conditions and biogeography interactively affect GDP. Our study provides unique insights into the determinants of GDP and its underlying processes, and improves our general understanding of the drivers of molecular diversity in nature. We discuss the conservation implications of our results and we propose additional endeavours that aim to unravel how various biological and environmental factors impact natural genetic variation.Here, we synthetize published datasets to inform GDies Fig. –S3. We qP across the plant and animal kingdoms , GDP slightly (but significantly) increased with increasing distance from the equator only in eudicots , life-history traits , contemporary climate  and long-term temperature stability during the Last Glacial Maximum (LGM), and during the mid Holocene (MH) on GDP. Together, these fixed effects explained 32.1%  and 10.2% (plants) of global GDP patterns. Models’ residuals did not display signs of spatial autocorrelation, whereas spatial autocorrelation was detected at a very fine spatial scale for the raw GDP data especially for animals    had a systematic and significant impact on GDge) Fig. . In partion Fig. . In addigy) Fig. . The rel. P Fig. . Consistnge Fig. . Endemicies Fig. . As oppoyla Fig. . Specifiene Fig. , and in ene Fig. . In addiive Fig. .Fig. 3ReP were consistent across phyla (no interactions of variables with phylum), including precipitation and lifeform that were among the most important variables   Fig. .Fig. 5EfP, plant GDP was lowest for endemic species and highest in core populations, with a subtle decrease in GDP towards the edges of species’ ranges  was mainly driven by plant species with a seed dispersal sustained by animal movements  calls for a new paradigm regarding the worldwide distribution of population genetic diversity.Our results provide surprisingly weak support for the frequently hypothesized relationship between the distance to equator (expressed as absolute latitude) and genetic diversityP and GDS, with GDP being sensitive to the biogeographic context of the population. Conservation-wise, edge populations should be considered in light of the species’ life-history traits, or genetically compared to core populations to avoid potential loss of unique genetic variation by prioritizing core populations in conservation planning.Our results support to some extent the core-periphery hypothesis, a major biogeographic theory predicting higher genetic diversity in the core than in the periphery of species’ ranges. Importantly, the decline in population genetic diversity from core to edge habitats seems moderated by dispersal (in plants) and fecundity . In addition, insular populations did not systematically harbour low population genetic diversity compared to mainland populations, except in animal species Fig. . Thus, a62. Here, accounting for molecular marker types and species as weighing and random factors in the models allowed revealing patterns of GDP variation independent of this methodological noise. Nonetheless, it is also possible that the “species” random term aggregates unmeasured characteristics of species that might be important for explaining population genetic diversity (and species genetic diversity), and we call for future studies investigating further these potential unmeasured variables. In parallel, the high species specificity of GDP emphasizes the importance of accumulating more data on individual species for further understanding which taxonomic GDP patterns are key to conservation planning.Noteworthy, in both plant and animals, a non-negligible amount of variance in population genetic diversity was explained by the random “species” effect . As the only vertebrate group with a positive relationship between LGM temperature stability and population genetic diversity, mammals seem to manifest the longest-lasting imprint of temperature stability on population genetic diversity. This result suggests that mammals exerted relatively rapid postglacial recolonization, explaining why mammal population genetic diversity coincides with LGM rather than with MH climate stability.Animal population genetic diversity varied substantially among phyla, with reptiles, mammals and birds sustaining particularly high levels of genetic diversity. While we demonstrated that population genetic diversity decreases with latitude for amphibians and molluscs, no trends were observed for other animal phyla Fig. . The neg 68 Fig. . Limited71. Independent from these climatic effects, we found a weak, but expected, negative relation between population genetic diversity and elevation, suggesting that populations are more isolated at higher elevation. This finding is in line with the core-periphery effect that was most pronounced in the animal kingdom  ecosystems can carry larger animal populations and are less prone to genetic driftdom Fig. .73. In compliance with these studies, we argue that species that are naturally rare with respect to their geographic range can still reach high-effective population sizes, possibly because their populations have been historically more stable and less affected by postglacial colonization dynamics. This seems to be especially valid for highly fecund, endemic animal species, which exhibited particularly high population genetic diversity  stressful environments away from the equator trigger a shift in energy allocation from individual growth to reproductive output, thereby increasing population genetic diversity. While stressful environments may favour reproductive output (e.g. fruit production), a meta-analysis based on 164 published studies rejected the hypothesis that drought stress triggers a shift in energy allocation from biomass to reproductive tissues78. Interspecific competition thus represents a more likely driver of global patterns of population genetic diversity. We also identified a consistent and significant—but weak—negative relationship between precipitation and plant population genetic diversity, reinforcing the notion that stressful (dry) environments might favour high levels of genetic diversity in plant populations  correlation between population genetic diversity and distance to the equator , seemingly driven by species’ life-history traits, by the location of a population relative to the species’ distribution range, and by contemporary climate. A given level of population genetic diversity will thus have different conservation implications for different populations even if they belong to the same species, and low population genetic diversity may not necessarily translate into low fitness or low evolutionary potential51; an effect attributed to temperature-dependent mutation and diversification rates. Our results illustrate that population genetic diversity (GDP) and genetic diversity at the species level (GDS) can have strongly different spatial patterns, likely as a result of the interplay between biogeography, climate and species traits together shaping local effective population size. This result has strong implications for the management of local populations with distinct evolutionary histories 90 is related to the adaptive potential of natural populations. Thus, while neutral genetic diversity is informative with respect to inbreeding and demographic processes, adaptive genetic diversity is indicative of long-term adaptive potential and persistence in light of environmental changes. Third, epigenetic variation has been increasingly shown to be tightly linked to environmental variation93. How epigenetic variation is driven by biogeography, life-history, climate and anthropogenic stressors, however, remains unexplored and its assessment could reveal novel insights into the spatial distribution, determinants and conservation implications of molecular diversity.We used expected heterozygosity of neutral nuclear markers, which is the most widely used index of genetic diversity, to explain population genetic diversity patterns across space and taxa. A major improvement would be a similar exercise that uses (i) allelic richness, (ii) adaptive genetic variation, or (iii) epigenetic variation as response variables. First, allelic richness has been suggested to respond more readily to anthropogenic stressors, and may therefore be particularly useful when addressing questions related to human-mediated changes in genetic diversityP explained by molecular marker and study effects (captured by our “species” variable) demonstrate that a more standardized methodology is crucial for allowing comparison between species and studies. The enormous discrepancy in population genetic diversity estimates among studies questions their use in biodiversity conservation, and compromises the development and operationalization of a unified population genetic diversity framework for monitoring global biodiversity95. The ongoing transformation from population genetics into population genomics is nevertheless promising, since individual outlier SNPs typically have a much more reduced impact on population genetic diversity estimates than microsatellite loci with suspicious allele distributions. To further ensure the comparability and usefulness of published population genetic diversity metrics, a detailed description of the study species and of the geographical position of the sampled populations helps contextualizing and comparing estimates of genetic diversity.The amount of variance in GDP, additional traits may deserve more attention in future projects, including animal gamete dispersal , and strategies of space use . For example, particularly low dispersal abilities and high habitat specificity within the amphibian clade may partly explain the generally low genetic diversity in amphibian species  may be strong moderators of population genetic diversity. Indeed, the important role of fecundity in the animal kingdom suggests that the low amount of explained variance of plant GDP may be due to the lack of fecundity metrics in our plant models. Seed set is correspondingly considered an important fitness trait104, with inbreeding frequently resulting in reduced seed set105. The exploration of anthropogenic impacts on population genetic diversity may finally uncover additional global patterns, and allows accounting for anthropogenic effects while establishing natural baseline population genetic diversity values.To further elucidate the global drivers of GDies Fig. 66. TheseComprehensively considering the wide array of factors that can simultaneously and interactively affect genetic diversity at the local (population) scale is crucial for understanding the processes affecting evolutionary dynamics and long-term sustainability of populations. Here, we provide a global map of within-population genetic diversity, and we demonstrate surprisingly distinct patterns and drivers of population genetic diversity between plants and animals. We show that patterns of genetic diversity at the population scale (linked to effective population size) do not align with those typically identified for genetic diversity measured at the species level (linked to diversification rates), and that genetic diversity at the population scale is shaped by a complex interplay between historical climate, current climate, biogeography and life history. This interplay questions several conventional assumptions, such as the expectation that island, edge and endemic populations systematically harbour low levels of genetic diversity. Our findings have major conservation implications, and raise questions regarding the efficacy of formulating management solely based on genetic diversity measured at the species level or on range-wide measures of population size. Instead, our results encourage the development of a conservation framework based on genetic diversity measured at the population scale and accounting for biogeographic context and life-history traits.P)106 was chosen as the response variable as it is the most studied index of within-population genetic diversity, fundamentally descriptive and consequently predicted to be relatively insensitive to publication biases. Moreover, it is directly related to effective population size108. Although expected heterozygosity may be slower at responding to recent demographic setbacks than allelic richness, it is less sensitive to difference in sample size. Moreover, recent longitudinal studies have shown that HE is sensitive to anthropogenic disturbance, and that significant reductions in expected heterozygosity can be observed several generations after a population bottleneck110. For example, a South-African lion population suffered a 10–13% reduction in expected heterozygosity as compared to museum samples collected 100 years earlier (ca. 14 generations), even though the census population remained locally abundant110. This demonstrates the importance of considering expected heterozygosity as a proxy of GDP in addition to census population counts for a more accurate assessment of a population’s conservation status.Expected heterozygosity (hereafter GDP is very sensitive to marker type (e.g. GDP is restricted between 0 and 0.5 in AFLP markers and between 0 and 1 in microsatellite markers), GDP values from each marker type were standardized (mean = 0 and variance = 1) to make them comparable across studies. Standardized GDP values were then normalized as /(max-min) to range from 0 to 1.Because GDUsing the terms “expected heterozygosity” AND “genetic marker” AND “populations” AND “plant” OR “amphibian” OR “reptile” OR “bird” OR “mammal” OR “mollusc”, from 2000 up to 2015, we searched for articles that estimated population genetic diversity using Google Scholar. Taxonomic variants  are specified in the PRISMA diagram provided in Supplementary Fig. P estimate were extracted using the general WGS84 coordinate system to obtain the spatial variables “Longitude”, “Latitude” and “|Latitude|”, the latter reflecting the distance from the equator. We downloaded georeferenced raster files to characterize local and current climatic data (worldclim.org) in the form of the three synthetic predictors “Temperature”, “Precipitation” and “Humidity” using a principal component analysis (see Supplementary Methods). In addition, because historical climate variability may have imprinted GDP (populations may have persisted much longer in regions featured by stable climates as opposed to more variable climates where population turnover and bottlenecks are more frequent), we used averaged temperatures for the Mid-Holocene climate  and for the Last Glacial Maximum climate , and calculated “MH stability” and “LGM stability” as the standardized differences between the current temperature and the past temperature calculated either from LGM or MH, respectively, (see Supplementary Methods). We classified the biogeographic position of each population (“Position”) on the mainland as edge (the outer 25% of the species’ distribution area), subedge (25–50%), core (within 50% of the species’ distribution area) or endemic (species restricted to an area <10.000 km²). “Position” also included the classes island (>10.000 km²) and isle (<10.000 km²) to account for effects of isolation and area restrictions on GDP. “Elevation” was also retrieved for each population. Although elevation encompasses climatic variation among populations, climatic variation is accounted for by the three climatic variables described above. Remaining elevation effects are therefore considered as a proxy for the isolation of populations. We expected GDP to be lower at higher elevation due to an increase in spatial isolation (i.e. decrease in gene flow).For each population, we collected information on the geography, climatic conditions (past and contemporary), biogeography and species life history. The geographical coordinates of each population with a GDE (Supplementary Data vegan), the first one (hereafter “PC_SizeLongevity”) being positively associated with Size and Longevity and the second one (hereafter “PC_Fecundity”) being positively associated with Fecundity (Supplementary Methods). To characterize plant life history, we included gamete dispersal (“Pollen”: biotic [n = 2341 populations] vs. abiotic [1913]), zygote dispersal , “Mating” , and “Lifeform”  (Supplementary Methods). The number of classes in categorical variables was limited to avoid model overfitting. As a proxy for a species’ habitat specificity and/or dispersal capacity, we used “Range”, representing the size of a species’ distribution. A detailed account of the composition of all environmental, life history and geographical variables, as well as the Pearson correlation coefficients between all covariates , are provided in Supplementary Methods and Supplementary Tables For each species, we obtained information on life-history traits and distribution ranges from iucn.org, utheria.org, animaldiversity.org, try-db.org, eol.org and the source papers used for collecting H111 and allowing to take into account the precision of the estimate E estimation methods and sampling protocols.In all models, we (i) weighted model residuals by the number of markers and sample sizes through a frequently used weighing factor in meta-regressionsP across the globe. Because distance to equator is thought to be an important moderator of genetic diversity through its association with temperature, productivity, long-term stability and historical range expansions, we implemented a linear mixed modelling (LMM) approach testing the impacts of |Latitude|*Kingdom on GDP while controlling for non-independence within marker types and species (see above).In a first, descriptive step, we aimed to test whether plant and animal species have distinct levels of GDP varied across broad taxonomic groups within kingdoms, i.e. for plants (eudicots vs. monocots vs. magnolids vs. pines) and animals  separately. To this end, we replaced the “Kingdom” predictor by a “Phylum” predictor in the LMM described above, and added the “Kingdom” effect as a random term to the model to account for non-independence within the plant and animal kingdoms.We then assessed whether GDP was modelled, first for animals  and then for plants (plant kingdom model). Because the impact of life history on GDP may depend on the biogeographic position of a population, we included the pairwise interactions between position and each life-history variable. In addition, we assessed whether the effects of life-history traits, current climate, climate stability, elevation and biogeography on GDP were consistent across phyla, and thus included all possible pairwise interactions with “Phylum”. Species with unknown life-history traits were excluded from the models, leaving 2544 data points (229 species) for the animal kingdom model and 4254 (308 species) for the plant kingdom model. Because life-history trait information is extremely scarce for molluscs, this phylum was poorly represented in the animal kingdom model .In a second, more comprehensive step, the mediating role of life-history traits, current climate, past climate stability and biogeography on GDMuMln, and the relative importance (RI) of all simple and interaction terms was quantified as the sum of the AIC weights across all the models with a ΔAICc < 4  when RI > 50%, and these terms were included in the final models used to infer estimates associated to each term. The proportion of variance explained by the final model’s fixed effects  and the proportion of variance explained by both the fixed and random factors  were obtained using the R package MuMln.All possible variants of the full animal and plant kingdom models were tested using the ‘dredge’ function in the R package 109. We did not find evidence for patterns of spatial autocorrelation neither for the animal kingdom model nor for the plant kingdom model  and random (species) structure of our model may affect model parameter robustness due to potential overfitting, we consecutively left out all data points from (i) each random species  or (ii) each occupied squared geographical area  using a jacknife procedure, to obtain 95% confidence intervals about each parameter. Our models appear highly robust to both geographic and taxonomic variations (Supplementary Fig. Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1–9


Paintings on the cave walls of Chauvet, Lascaux, and other sites bear witness to the presence of modern humans within France’s borders over the past 35,000 years, rendering this country an exciting place to do human population genetic studies. However, France’s finer genetic structure has so far been described only with respect to selected geographic regions. Saint Pierre et al.  fill thiAfter a first wave of papers describing the broad genetic structure of European populations with respect to common variants in 2008, namely the papers by Lao and Lu et al. , and HeaThe subtle (and not so subtle) differences demonstrated by this study in the genetic makeup of France’s population are of importance for future studies. While population stratification may be well controlled in common-variant population-based association studies, this is not necessarily the case when studying rare variants. In fact, the broad genetic divide between Northeastern and Southwestern France, coupled with different environmental exposures and perhaps different lifestyle habits, may turn out to be a prime example for this concern in future rare-variant studies. As theoretically investigated by Mathieson and McVean  nearly aThis study also exposes the often-strong dependency of the analysis results from genetic studies on the particular sample set at hand, in particular for those that focus on rare variation. While both study cohorts, comprising close to 800 and about 1400 samples, respectively, yielded broadly concordant results, there were numerous differences in the details. The application of identical statistical approaches to two different cohorts covering the same geographic area is one of the particular strengths of this study in that, it well illustrates the impact of sampling variance, while also providing an assessment of the robustness of the obtained results. This may serve as a warning against overinterpretation of results when only one data set is available, which is naturally the case for most population genetic studies. Notably, this also applies to studies based on ancient DNA which are very often characterized by an extremely sparse sampling in both time and space and by limited genetic data.Estimating proportions of genetic heritage from primordial European hunter-gatherers, distant Steppe people and migrating early farmers and assessing population size fluctuations are fascinating endeavors and offer glimpses into our past. However, such events may also resemble a distant mirror of our future. Given the comparatively large genetic homogeneity of humankind, today’s interconnected world is more at risk than ever to see Black Death-style pandemics occur, this time on a global rather than a local scale and at higher frequency. Furthermore, this study reminds us that events dating back to medieval times, the Bronze and even the Stone Age significantly contributed to shaping the genetic structure of the French and other extant populations. Such events are therefore still relevant to human genetic research. Finally, the study results yield further evidence that all extant human populations are the product of multiple admixture events during their formation. A population that would have developed independently from all others from the start of humankind would be a very rare exception, if such a population exists at all.


The individual observed heterozygosity levels of the tested horses varied within 23.1–100%, and the population’s parameters of observed heterozygosity tended to decrease. The relationship between the level of inbreeding and the rate of homozygosity was not significant .The Thoroughbred horse breed provides a unique model for studying the theoretical bases and practical methods of selection in horse breeding. To assess the diversity in the modern population of Russian Thoroughbred horses, we used classic parameters, such as Wright’s inbreeding coefficient, the heterozygosity level, and population inbreeding level, over the period 1990–2018. The average inbreedinn = 9680) for the period from 1990 to 2018 using polymorphisms of 17 microsatellite loci. We found that the genetic structure of the TB breed in Russia is represented by 100 alleles of panel STR (short tandem repeat) loci and has been stable for the past three decades. The conducted monitoring revealed a slight increase in the Wright’s inbreeding coefficient in all age and sex groups of TB horses  from 0.68% to 0.90%, which was followed by a decrease in the degree of heterozygosity, Ho, from 68.5% to 67.6%. The Spearman’s rank correlation coefficient between the level of inbreeding and the degree of homozygosity was estimated . The obtained data on the DNA genotypes of horses of different breeds provide a unique base for the evaluation of genetic variability and the control of genetic variability of horses in selection programs.The Thoroughbred (TB) horse has hugely impacted the development of horse breeding around the world. This breed has unique genetic qualities due to having had a closed studbook for approximately 300 years. In Russia, TBs have been bred since the second half of the 18th century. Here, we analyzed the genetic diversity and the inbreeding level in TB horses ( The Thoroughbred (TB) breed of horses, created in England in the 17–18th centuries, was perfected using purebred breeding for many generations. The breed was formed as a result of crossing local mares with stallions of Eastern origin. The breed has three foundation stallions: the Byerley Turk, the Darley Arabian, and the Godolphin Arabian . TB horsThe long-term intensive selection of horses for racing performance and the wide use of the best stallions in breeding led to the accumulation of well-known names in their pedigrees. According to Cunningham et al. , TB horsMating among relatives is one of the most important breeding methods used to consolidate the hereditary qualities of animals. However, the negative effects of related mating are well known, and are expressed in an increase in the homozygosity rate of animals and inbred depression ,8,9. TheThe practice of related breeding shows that animal breeds differ in the rates at which homozygosity increases . A particular inbreeding level exists for each breed, and exceeding this level leads to inbred depression. According to Pern , inbred The measurement of the actual homozygosity level at the individual and population levels only became possible at the end of the 20th century due to the development of methods for studying genes and DNA polymorphisms. As a result of the widespread use of microsatellite loci in the control of animal origins, this type of highly polymorphic genetic marker has been successfully used to study the genetic features of breeds and their phylogenetic relationships ,13,14,15Assessment of the inbreeding and homozygosity levels in TB horses is particularly relevant, as the breed has been improved only through purebred breeding for more than three centuries. Monitoring the state of genetic diversity in the Russian population of TB horses in the period from 1980 to 2005 revealed the tendency towards a certain decrease in the level of polymorphisms and the rate of heterozygosity, especially in the group of stallions .Due to the import of a relatively large number of TB horses from different countries around the world to Russia in recent decades, changes have occurred in the genealogical structure of the Russian horse population, which has led to the descendants of Northern Dancer (30.9%) and Nasrullah (16.7%) dominating in the linear structure of the breed. These significant changes could have a certain impact on the level of the genetic diversity of the population .The purpose of our research was to assess the genetic diversity and the level of inbreeding in the TB breed in Russia over the period 1990–2018.All data for this study were obtained in the Laboratory of Genetics of the All-Russian Research Institute for Horse Breeding, certified by ISAG  based on the Horse Comparison Test (HCT) results.TM DNA Prep 200” and “DiatomTM DNA Prep 200” kits  in the Laboratory of Genetics of the All-Russian Research Institute for Horse Breeding. The individual inbreeding coefficient of horses was determined using Wright’s formula [The materials for the research included the database of the DNA typing of 9680 TB horses registered in the Russian Thoroughbred Stud Book for the period of 1996–2018, which includes approximately 98% of the horses bred in Russia. The studied population included 957 stallions, 3194 broodmares, and their offspring. Genomic DNA samples were prepared from hair follicles or blood samples of TB horses by standard procedure using the “Extra GeneThe dynamics of the changes in the level of inbreeding in groups of stallion, mares, and foals was analyzed considering three periods , corresponding to the generation change interval in horse breeding.®, Applied Biosystems, Foster City, CA, USA). The kit includes 17 loci  recommended for Thoroughbred parentage testing by the Standardization Committee of the ISAG [Isolated DNA probes were amplified on a Thermocycler 2730  using 17-plex kit primers for horses (StockMarksaN), the effective number of alleles (eA), the number of alleles per loci (MNA), the observed (oH) and expected (eH) heterozygosity, and the Hardy–Weinberg equilibrium (HWE). Intra-breed inbreeding was estimated using F-statistics [The genetic diversity in groups of TB horses was evaluated using basic parameters, including the total number of allele variants , which was associated with the intensive import of TB horses from Europe and the USA. During this period, the allele pool of the domestic population was supplemented with three new rare alleles, ASB17H, HMS1L, and LEX3I, due to the genotypes of the imported mares. However, in the next generation, in horses born in 2010–2018, the HMS1L allele was lost. The total number of alleles on 17 STR loci was only 98 in the modern Russian population of TB horses (third period). The breed stably maintained its genetic structure despite continued imports of horses from other countries. The intensive system of breeding TB horses for racing performance was not found to contradict the fairly stable state of the current genetic structure of the breed, which changed insignificantly over the studied 30 years. We found slight fluctuations in the frequency of occurrence of both typical and rare alleles, which invariably retained their status. During the analyzed period, we noted a trend of an increase in the concentration of the major allele HMS2L (0.648–0.749) (data not shown), found in the genotypes of the best stallions. Earlier analysis showed that this locus may be associated with genes that influence the distance abilities of Thoroughbred horses .Na varied between 5.71 and 5.77 from 1990 to 2018, with a maximum of 5.82 reached in 2000–2009; Ae varied between 3.508 and 3.411; and Ho varied between 0.682 and 0.674. The highest level of genetic diversity of the Russian population of TB horses was observed in the late 1990s (first period), when the largest number of breeding broodmares in the country was registered with studs and on breeding farms. Increased imports of TB horses in the second period slightly increased the variability of rare alleles but did not significantly change the genetic structure of the breed  was registered only in the second period, and, as the analysis of genotypes showed, this occurred due to the gene drift that accompanied the increased import of TB horses from other countries. p < 0.001) (data not shown).Due to the imported mares, the allele pool of the Russian population of the TB breed was supplemented with the new rare alleles ASB17H, HMS1L, and LEX3I. Mares born in other countries and imported in 1995–2009 were characterized by a significantly higher frequency of the alleles ANT5K, ASB2Q, HMS7J, and HTG4 K , which averaged 0.68–0.81–0.90% over the periods.In the 1990s, the share of outbred animals in the analyzed groups of horses ranged from 43.37% to 47.76%, while the highest rate was observed among the stallions. Inbreeding coefficients of 3.1% and higher were only found in 4% of stallions, 7.4% of mares, and 7.1% of foals.In the first decade of the 21st century, with the background of an increase in the inbreeding coefficient and an increase in the degree of homozygosity of STR loci, the percentage of outbred horses in groups decreased to 37.7–41.4%. However, the share of horses with inbreeding above 3.1% increased slightly to 6.1–7.2% (data not shown).In the third period (2009–2018), the tendency of a reduction in the number of horses with outbred pedigrees in the livestock structure remained. As a result, the share of outbred young animals decreased to 29.7%, and there was a slight increase in the number of horses (36.5–42.7%) with a low inbreeding rate, at the up to 1% level. Inbreeding at the level of 3.1% and higher occurred in 5.6–8.1% of horses, while the maximum indicator was identified in the group of broodmares .The data presented in The lowest level of homozygosity was found in the group of stallions, but, in general, the differences between the compared groups of horses were slight . StallioChanges in the level of homozygosity in different groups of TB horses were insignificant, whereas a slight increase in this indicator was observed in stallions and broodmares with an inbreeding coefficient of 3.1–4.0%.r = 0.022; p > 0.05). A statistically significant value of this indicator was found only in the group of broodmares  (data not shown).The Spearman rank correlation coefficient between the inbreeding level and degree of homozygosity of the Thoroughbred horses was close to zero  [The history of TB horse breeding in a closed Stud Book for three centuries could result in a reduction of the genetic diversity of the TB horses. The relatively high degree of homozygosity in the horses of this breed was confirmed using microsatellite loci by other researchers ,14,15,16The studied population steadily maintained a certain level of heterozygosity (Ho = 0.685–0.676), both during the period of partial isolation (1990–1999) and during the intensive import of horses (2000–2009). This provides proof of the hypothesis of the existence of optimal heterozygosity of populations . This caThe inbreeding coefficient at the pedigree level does not adequately reflect the degree of homozygosity of an individual ; a more The Wright’s inbreeding coefficient approximately reflected the degree of homozygosity of the animals; therefore, genome monitoring is important for further improvement of the Thoroughbred breed. We have planned further research using SNP chips on Russian TB horses to potentially provide interesting new findings, e.g., the possibility to detect subpopulation structures or selection signatures.Na (5.71–5.77), Ae (3.508–3.411), and Ho (0.685–0676). The highest level of genetic diversity of the Russian population of TB horses was observed in the late 1990s (first period). The monitoring revealed a trend of a steady increase in the inbreeding coefficient in all age and sex groups of Thoroughbred horses , which averaged, over the periods, 0.68–0.81–0.90%. The increase in the degree of inbred horses was accompanied by a decrease in the degree of heterozygosity of STR loci from 68.5% to 67.6%, which generally reflected the reduced genetic diversity in this breed.The study of the genetic structure of TB horses in Russia by 17 microsatellite loci demonstrated 100 alleles that were typical of the global allelofond of the breed and that these were stable for the past three decades. A comparative analysis of the main genetic and population indicators by period showed that over three generations, from 1990 to 2018, the studied characteristics changed slightly: The trend of an increase in the level of inbreeding and the degree of homozygosity of TB horses in Russia generally reflects the global trend of reduced genetic diversity in this breed due to changes in the modern genealogical structure and the strict selection for racing performance, associated with a wider use of the descendants of outstanding stallions and broodmares in breeding.


Fusarium head blight (FHB), a devastating wheat disease, results in loss of yield and production of mycotoxins including deoxynivalenol (DON) in infected grains. DON is harmful to human and animal health and facilitates the spread of FHB symptoms. Its conversion into DON-3-glucoside (D3G) by UDP-glycosyltransferases (UGTs) is correlated with FHB resistance, and only few gene members in wheat have been investigated. Here, Fusarium graminearum and DON-induced TaUGT6 expression in the resistant cultivar Sumai 3 was cloned and characterized. TaUGT6::GFP was subcellularly located throughout cells. Purified TaUGT6 protein could convert DON into D3G to some extent in vitro. Transformation of TaUGT6 into Arabidopsis increased root tolerance when grown on agar plates containing DON. Furthermore, TaUGT6 overexpression in wheat showed improved resistance to Fusarium spread after F. graminearum inoculation. Overall, this study provides useful insight into a novel UGT gene for FHB resistance in wheat. Triticum aestivum L.) is the third largest grain crop after corn and rice in terms of production, with > 700 million tons consumed by 2.5 billion people worldwide (Fusarium head blight (FHB), primarily caused by Fusarium graminearum, is one of the most devastating wheat diseases in China and other parts of the world, where frequent rainfall occurs from the flowering to early grain filling stages , and approximately 500 QTLs have been identified since 1999 when the first FHB resistance QTL was published for wheat  were injected into one floret at intermediate positions of spikelets and covered with plastic bags for 3 days to retain moisture. The infected spikelets were harvested at 12, 24, 48, and 72 h after inoculation. During DON inoculation, 5 μL DON (10 mg/L)  was injected into florets and collected after 2, 4, 8, 12, and 24 h of inoculation. All samples were frozen immediately in liquid nitrogen and stored at -80°C. A. thaliana were grown in a chamber under a 16/8-h light/dark condition at 21°C.Sumai 3, Annong 8455, and Fielder were grown in plastic pots or in single-row-plots in greenhouses at campus of Jiangsu Academy of Agricultural Sciences, Nanjing, China. Spikelets at the early anthesis stage were chosen for eviously . Ten micTubulin in wheat and actin in A. thaliana were used as internal reference genes. RT-qPCR was performed as described previously , and first-strand cDNAs were synthesized using the PrimeScript 1st strand cDNA Synthesis Kit . RT-qPCR was performed using a Roche thermal cycler 96 using the SYBR Green reagent . eviously . Triplict method . The priTaUGT6 was cloned at the Xho I and Spe I sites of the pA7-eGFP plasmid with the primers listed in Full-length cDNA of 1. Multiple sequence alignment of UGT protein sequences was performed by MUSCLE2 and adjusted manually using GeneDoc3. Neighbor-joining trees based on the full-length UGT protein sequences were constructed using MEGA 7.04 with the following parameters: Poisson correction, pairwise deletion, and bootstrap .TaUGT6 and several other UGT proteins  that had been previously reported to contribute to FHB resistance were selected for alignment by DNAMAN using the pairwise method . BLAST aTaUGT6 was cloned into a prokaryote expression plasmid pGEX-4T-1, and the GST-tagged TaUGT6 recombinant protein was expressed in E. coli and purified using affinity chromatography , which had been treated with 0.1% methanoic acid . Then, it was centrifuged for 5 min at 3,500 g, and 2 ml of the supernatant was purified with an extraction salt pack . After centrifugation, the supernatant was transferred to a new tube and concentrated under nitrogen. Before analysis, the extracts were diluted with 0.6 mL extract solution, filtered using 0.22 μm filters, and then were analyzed by HPLC-MS/MS.The procedure of the HPLC-MS/MS was performed as described by The infected spikelets at 3 days after inoculation (dai) were fixed in 3 % glutardialdehyde for 2 h, dehydrated in a graded alcohol series, coated with gold and examined using Zeiss EVO LS10 scanning electron microscope .UGT genes that were upregulated after F. graminearum inoculation (TRIAE_CS42_5BL_TGACv1_404184_AA1288920 (TraesCS5B02G436300), termed TaUGT6, was chosen for further characterization in the present study. We firstly performed quantitative reverse transcription polymerase chain reaction (RT-qPCR) to determine the expression level of TaUGT6 following F. graminearum inoculation of spikes at different time points in the FHB-resistant cultivar Sumai 3 and susceptible cultivar Annong 8455. TaUGT6 transcript level was induced at 48 h after F. graminearum inoculation (hai) and rapidly increased at 72 hai in Sumai 3. This increase was much lower in Annong 8455  after reaching a peak at 8 hai in Sumai 3, whereas was hardly induced in Annong 8455 treated with DON , TaUGT2 (35.08%), TaUGT4 (22.36%), and TaUGT5 (25.54%) at the amino acid level, respectively. However, all comprised a 44-amino acid UGT consensus sequence, also referred to as PSPG signature box at the C-terminus  searches were conducted using the full-length amino acid sequence of TaUGT6 in the Phytozome database, and an unrooted tree was constructed using the neighbor-joining method . Close hTaUGT6 was distributed throughout cells including in cell membrane and nuclei, similar to that in pA7-eGFP  than the WT line were chosen for further analysis  (To test whether the overexpression of  < 0.01) . The qua < 0.01) . Further < 0.01) .F. graminearum interaction was incorporated in this study. In the past few years, massive efforts have been made to fight against FHB such as successful cloning of Fhb1 genes and application of this QTL into practical breeding as well as production of improved cultivars  in wheat have been transformed into plants to prove their role in FHB resistance and DON tolerance  of TaUGT6 was carried out 3 hours after reaction, and D3G was detected. Determining the optimum reaction conditions may get a better catalytic efficiency. TaUGT6 was able to transform DON into the less toxic form D3G to some extent in vitro, which suggested that it might play a potential role in FHB resistance. DON reportedly inhibited root growth when grown on agar medium, and functional DON detoxification of UGTs could reduce this inhibition  in a susceptible background in order to achieve a better resistance level in the future breeding.Mycotoxins degradation is a beneficial method to increase FHB resistance, and enzymes that can degrade mycotoxins are urgently required . TaUGT6 The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.YH and HM conceived the experiments. YH, LW, XL, and LY performed most of the experiments and analyzed the data. PJ and XZ provided analytical tools and assisted in analyzing the data. JQ and GW performed toxin and HPLC-MS/MS analysis. XZ provided project administration. YH, HM, and XZ wrote the article. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Ciona intestinalis. A previous study using microsatellites analysed samples collected along the Swedish west coast and showed the presence of genetically distinct lineages in deep and shallow waters. Using 1,653 single nucleotide polymorphisms (SNPs) from newly collected samples , we first confirmed the presence of this depth‐defined genomic divergence along the Swedish coast. We then used approximate Bayesian computation to infer the historical relationship among sites from the North Sea, the English Channel and the northwest Atlantic and found evidence of ancestral divergence between individuals from deep waters off Sweden and individuals from the English Channel. This divergence was followed by a secondary contact that led to a genetic admixture between the ancestral populations , which originated the genotypes found in shallow Sweden. We then revealed that the colonization of C. intestinalis in the northwest Atlantic was as a result of an admixture between shallow Sweden and the English Channel genotypes across the introduced range. Our results showed the presence of both past and recent genetic admixture events that together may have promoted the successful colonizations of C. intestinalis. Our study suggests that secondary contacts potentially reshape the evolutionary trajectories of invasive species through the promotion of intraspecific hybridization and by altering both colonization patterns and their ecological effects in the introduced range.Research on the genetics of invasive species often focuses on patterns of genetic diversity and population structure within the introduced range. However, a growing body of literature is demonstrating the need to study how native genotypes affect both ecological and evolutionary mechanisms within the introduced range. Here, we used genotyping‐by‐sequencing to study both native and introduced ranges of the amphiatlantic marine invertebrate  We hypothesized that the colonization success of C. intestinalis across its introduced range has been affected by the historic divergence of ancestral genotypes, the levels of genetic admixture between divergent lineages, and the intensity of gene flow between native and introduced ranges.Here, we used 22.1C. intestinalis were collected from 20 sites within the putative native and introduced range of the species  according to the manufacturer's protocol. Gel electrophoresis and the QuantiFluor® dsDNA System (Promega) were used to assess quality and quantity of extracted DNA, respectively. DNA was shipped to the University of Wisconsin Biotechnology Center where it was genotyped using the genotyping‐by‐sequencing methodology . As the C. intestinalis genome is not yet available, we used the de novo assembly method, which requires no prior genomic resources and used ipyrad to trim Illumina adapter reads. As we were working with only one species, we set the level of sequence similarity for clustering to be 90% . Following the iterative filtering framework outlined by O'Leary, Puritz, Willis, Hollenbeck, and Portnoy  to find candidate loci excessively related to population structure. We classified loci that were recovered by both BayeScan and pcadapt as putatively under natural selection and removed them for the following analyses. Finally, we created a more conservative dataset that excluded all loci recovered by BayeScan and pcadapt and ran the whole set of analyses again  per population. Finally, we ran an analysis of molecular variance (AMOVA) test using site clusters as inferred by ADMIXTURE and DAPC plots and also using only shallow Sweden sites to test whether there was an effect of substratum . AMOVAs were performed in Arlequin v.3.5.  method using DIYABC v.2.0.1 , which was likely caused by poor DNA quality or secondary contaminants within the samples  and identified the presence of historical genetic admixture among individuals from these regions. This seems to have resulted in genotypically and phenotypically distinctive individuals that are currently found in shallow sites in Sweden. In addition, we revealed genomic patterns suggesting secondary contacts and postulate that this may have promoted intraspecific hybridization. Our result supported the presence of genetic admixture during the spread to and colonization of the northwest Atlantic. More specifically, we found evidence of genetic admixture between genotypes from the English Chanel and genotypes from the shallow North Sea. While we found here no direct evidence that intraspecific hybridization influences colonization, our results indicate that this may be a possible mechanism promoting successful colonization of sites with new environmental conditions, such as trans‐oceanic introductions. This builds on a growing number of studies showing that the mixing of divergent genotypes as a result of human mediated transport of species has the potential to fundamentally alter colonization patterns and to unprecedentedly alter ecological and evolutionary patterns  and the south‐western North Sea (Rees, Waldock, Matthiessen, & Pendle, It is well established that the recent spread of C. intestinalis along the west coast of Sweden. While this genomic differentiation among populations appears surprising as some deep and shallow sites are geographically close to one another, this can be explained due to the effect of the aforementioned pycnocline promoting depth‐defined divergence that has also been observed in corals (Prada & Hellberg, Our study corroborated the findings of Johannesson et al.  by identC. intestinalis is restricted to the east coast of North America. It has been documented in eastern Canada since at least the mid‐1800s (Carver, Mallet, & Vercaemer, The northwest Atlantic range of FIS values significantly greater than zero; Table C. intestinalis (Bouchemousse, Lévêque, Dubois, & Viard, Ciona spp. (Hudson et al., Heterozygote deficiency at nine of our sites (C. intestinalis to the western Atlantic coast is complex, starting with historical divergence in the native range involving two groups (England, Jersey, France and deep Sweden lineages, Figure The history of the introduction of None declared, Click here for additional data file.


Apis mellifera) in identification of evolutionary lineages or subspecies and, to a lesser extent, in assessing genetic structure within subspecies. Due to bias in the production of sterile females (workers) in a colony, most studies have used workers leaving the males (drones) as a neglected group. However, considering their importance as reproductive individuals, the use of drones should be incorporated in these analyses in order to better understand diversity patterns and underlying evolutionary processes. Here, we assessed the usefulness of drone wings, as well as the power of wing geometric morphometrics, in capturing the signature of complex evolutionary processes by examining wing shape data, integrated with geographical information, from 711 colonies sampled across the entire distributional range of Apis mellifera iberiensis in Iberia. We compared the genetic patterns reconstructed from spatially-explicit shape variation extracted from wings of both sexes with that previously reported using 383 genome-wide SNPs (single nucleotide polymorphisms). Our results indicate that the spatial structure retrieved from wings of drones and workers was similar (r = 0.93) and congruent with that inferred from SNPs , corroborating the clinal pattern that has been described for A. m. iberiensis using other genetic markers. In addition to showing that drone wings carry valuable genetic information, this study highlights the capability of wing geometric morphometrics in capturing complex genetic patterns, offering a reliable and low-cost alternative for preliminary estimation of population structure.Wing geometric morphometrics has been applied to honey bees ( Morphological markers have long been used to understand the diversity of life and the relationships among different taxa . HoweverApis mellifera L.) is no exception [Traditional morphological methods typically employ multivariate statistics to quantitative variables such as length, height and width of structures, distances between certain landmarks and sometimes angles and ratios ,9,10. Hoxception ,20,21,22A. mellifera taxonomy and biogeography, Friedrich Ruttner [Apis mellifera intermissa and Apis mellifera sahariensis together with the European subspecies Apis mellifera mellifera and Apis mellifera iberiensis in the western European (M) lineage, later phylogeographic studies using nuclear DNA markers [The father of  Ruttner , applied Ruttner , a wide  Ruttner ,29,30,31 Ruttner ,22. Howe Ruttner  placed t markers ,33,34 anA. m. iberiensis, offers a powerful model system to address this issue for two main reasons. First, the Iberian honey bee exhibits a complex phylogeographic pattern which, contrarily to other subspecies, has been largely resilient to confounding beekeeper-mediated processes [Wing geometric morphometrics has proved capable of identifying lineages, subspecies, and even hybrids ,20,21,22rocesses . Second,rocesses ,36, allorocesses ,37, mitorocesses ,46,47,48rocesses ,49,50,51rocesses ,52, and rocesses . Early srocesses ,37. Howerocesses ,39,40,41rocesses ,49,51 anrocesses ,49,51 darocesses  and evenrocesses , which rThe vast amount of knowledge generated by all those works enables unprecedented comparisons between markers as to their power in retrieving complex diversity patterns. Using the Iberian honey bee as a model system, the first goal of this study was to assess the efficiency of geometric morphometrics in capturing genetic structure within a subspecies range. To that end, a comprehensive geometric morphometrics dataset was integrated with geographical information to disentangle global structure from local structure and random noise. If geometric morphometrics would prove efficient in describing structure in a subspecies with a complex phylogeographic pattern, it could be used as a low-cost alternative for preliminary genetic surveys.Geometric morphometrics can be applied to different parts of an organism, but wings, mainly from females, have been the major target in Diptera and Hymenoptera . Insect A. m. iberiensis sampled across three north-south Iberian transects. The 711 colonies were previously analysed with 383 genome-wide SNPs genotyped in drones, which showed the existence of a strong global structure formed by two genetic clusters [To achieve these goals, we used a geometrics morphometrics approach to examine the forewing shapes of multiple drones and workers belonging to 711 georeferenced colonies of clusters . Using tclusters , we addrSamples of drones and workers were collected in 2010 from 711 colonies distributed in the Iberian Peninsula along three transects : the AtlST values  the number of detached forewings was lower than five see . The lanT values  obtainedSpatial structure was inferred from drone and worker forewing shapes using the model-free multivariate spatial principal component analysis (sPCA). The sPCA of the drone SNPs was performed in the software package adegenet , using t−4. This test validates whether genetic variance is distributed at random on the connection network (null hypothesis), or display global or local spatial structure . In each permutation, the maximum of the mean of determination coefficient (max(t)) is computed. The coefficient of determination is estimated by linear regression between the matrix of allelic frequencies or morphometric measures and the eigen analysis of spatial connectivity among individuals. If the observed max(t) associated with the local or global structure is different from most simulated values and the P-value ≤ 1 × 10−4, the spatial distribution of the genetic variance is not random and the null hypothesis can be rejected. Spatial structure inferred from drone SNPs and wing geometric morphometrics data of both drones and workers were compared through the Pearson correlation coefficient (r) calculated in R 3.6.0 [The sPCA is a modification of PCA and considers the genetic variance of individuals or populations together with their spatial autocorrelation measured by Moran’s I, which ranges from −1 to 1 . To calc R 3.6.0  using thP-value < 2.2 × 10−16). Similar correlations were obtained between FST values inferred from SNPs  and workers .The canonical variate analysis (CVA) of drone and worker forewing shapes showed no clear structure in the Iberian honey bee . Pairwisrom SNPs  and Procrom SNPs  inferredThe patterns of forewing shape variation of both sexes were further examined using a spatially explicit approach. The first global score  for drones and workers was associated with moderate autocorrelations . On the other hand, the first local score  was low either for drones (Moran’s I = −0.0178) or workers (Moran’s I = −0.0208). The interpolation of the first global score for both sexes detected a well-defined cline dividing Iberia along a northeast–southwest axis a,b, beinP-value < 1 × 10−4; P-value < 1 × 10−4; P-value = 0.8670; P-value = 0.2772; As reported in Chávez-Galarza, et al.  for SNPsP-value < 2.2 × 10−16) but also between SNPs and wing shapes of drones  and workers , suggesting that the spatial analysis is more powerful in detecting population structure as compared to the traditional CVA and Procrustes distances.Spatial patterns inferred from wing shape data of drones and workers and from SNPs  were coA. mellifera, geometric morphometrics of wing vein junctions has been used to discriminate amongst evolutionary lineages and subspecies [A. m. iberiensis as the model system to unequivocally show, for the first time in A. mellifera, the power of wing geometric morphometrics in retrieving complex phylogeographic patterns, particularly when wing shape data is analysed using a spatially explicit framework. The landmark-based geometric morphometrics is acknowledge as the most rigorous and powerful morphometric technique currently available. It has been applied to a variety of structures  and organisms  to describe inter- and intra-specific variation and underlying evolutionary processes ,76,77,78bspecies ,18,19,21P-value < 1 × 10−4). Interpolation of the first global scores, computed from variations carried by the wing landmarks of both sexes, was able to unambiguously retrieve the clinal shape of variation  and 0.90 (drones’ wing shape versus SNPs) for the spatial genetic patterns inferred from both markers. The ability of wing traits to capture a genetic pattern so strikingly similar to that inferred from hundreds of molecular loci spread across the 16 honey bee chromosomes ,52 couldA. mellifera, that the genetic information carried by drone wings for identifying genetic structure is similar to that of workers, the commonly used form in diversity studies [In Hymenoptera, wing geometric morphometrics has been used to examine sexual dimorphism ,57,58. W studies ,20,21,22 studies . DespiteIn this study, we examined concordance between genetic patterns captured by different genetic markers and sexes in a honey bees subspecies that is free of introgression from commercial strains ,49,52,53This study highlights the power of wing geometric morphometrics in capturing genetic structure within honey bee subspecies, especially when shape data is integrated with geographical information and analysed using spatial statistics. Spatial analysis of wing shape variation was able to detect a well-defined cline that bisects Iberia along a northeast–southwest axis, a pattern that remarkably parallels that of genome-wide SNPs. Moreover, in spite of the haploid nature of drones, allowing more frequent phenotypic expression of wing abnormalities, the genetic structure retrieved from drones was similar to that of workers, suggesting that drone forewings carry usable genetic information for diversity studies. Geometric morphometrics has the benefit over molecular methods of being cheaper and easier to use ,18, offe