STAT3 gene signature that stratifies GBM patients into STAT3-high and -low cohorts. STAT3 inhibitor treatment selectively mitigates STAT3-high cell viability and tumorigenicity in orthotopic mouse xenograft models. We show the mechanism underlying resistance in STAT3-low cells by combining STAT3 signature analysis with kinome screen data on STAT3 inhibitor-treated cells. This allows us to draw connections between kinases affected by STAT3 inhibitors, their associated transcription factors and target genes. We demonstrate that dual inhibition of IGF-1R and STAT3 sensitizes STAT3-low cells and improves survival in mice. Our study underscores the importance of serially profiling tumors so as to accurately target individuals who may demonstrate molecular subtype switching.Intratumoral heterogeneity is a hallmark of glioblastoma (GBM) tumors, thought to negatively influence therapeutic outcome. Previous studies showed that mesenchymal tumors have a worse outcome than the proneural subtype. Here we focus on STAT3 as its activation precedes the proneural-mesenchymal transition. We first establish a  STAT3 activates distinct transcriptional programmes within cancer cells. In this study, the authors find that, in glioma, a STAT3-mediated expression signature can stratify patients for targeted precision therapy. Tumor recurrence and the development of resistance toward standard-of-care treatment regimens are key reasons for the poor outcome and have been attributed to the cellular and molecular heterogeneity of tumor tissue5. Over the past decade, several large, publicly funded efforts demonstrated that gene expression drives brain tumor disease progression and clinical outcome3. Primary adult GBM tumors that are predominantly isocitrate dehydrogenase 1—wild type (IDH-WT) are subdivided into three glioma-intrinsic (GI) transcriptomic subtypes  after separating out microglial- and stromal cell-type contribution4. The revised World Health Organization (WHO) classification scheme for brain tumors incorporates these molecular markers to influence treatment decision1. These efforts highlight the necessity to prescribe treatment regimens based on a stratified population.Patients with glioblastoma (GBM) frequently survive no >15 months despite surgical intervention with chemotherapy and radiation6. The proneural–mesenchymal transition (PMT) process has been associated with recurrent tumors and, more recently, chemotherapeutic and radiation resistance, thought to arise from the selection of transitional glioma-initiating cell clones harboring gain in PMT transcriptomic patterns6. Thus targeting the STAT3 signaling axis is pivotal in disease management.We focused on signal transducers and activators of transcription (STAT3) where its activation has been demonstrated to effect a transition in molecular subtype to the aggressive mesenchymal profile7. In cancer, increased IL-6 levels result in hyperactivation of JAK/STAT3 signaling, typically associated with poorer prognosis11. In most myeloproliferative cancers, the genes encoding JAK enzymes, particularly JAK2, frequently contain gain-of-function mutations12. However, no such mutations can be detected in GBM tumors, thus implicating other mechanisms of STAT3 activation. STAT3 has also been shown to regulate the self-renewal potential of glioma cells, suggesting that its inhibition would lead to a more curative and sustained outcome14. Over the past decade, much effort has been spent at evaluating JAK inhibitors predominantly in chronic inflammation and hematological disorders, with its application in solid tumors largely unexplored. Tofacitinib, Ruxolitinib, and pacritinib are the most advanced drugs in preclinical development. Furthermore, oncology-based clinical trials of STAT3 inhibitors have yet to take into consideration the implications arising from The Cancer Genome Atlas (TCGA) findings and whether the development of subsequent patient stratification methods would lead to significant improvement in prognostic outcomes. We are therefore interested in assessing JAK/STAT3-inhibitory agents in GBM tumors, with an ultimate goal in identification of potential responders and non-responders.The interleukin 6/Janus kinase/STAT3 (IL-6/JAK/STAT3) pathway is involved in the pathogenesis of many human malignancies. Increased IL-6 levels are also found in conditions associated with inflammation, such as rheumatoid arthritis and inflammatory bowel disease and increasingly in hematological disorders and solid tumors, such as GBM3. In the evaluation of upstream kinases that lead to active STAT3 signaling, the human epidermal growth factor receptor (EGFR), a member of the ErbB/HER family of receptor tyrosine kinases (RTKs); the family of IL-6–type (IL-6) cytokine receptors that form complexes with gp130 and JAKs; and several G-protein-coupled receptors have been described in previous literature15. Multiple growth factors , platelet-derived growth factor, and colony-stimulating factor 1) and cytokines , IL-6, leukemia inhibitory factor, cardiotrophin-1, ciliary neurotrophic factor, IL-10, IL-11, and Oncostatin-M) have been shown to activate STAT3. Elevated levels of STAT3-activating ligands, such as IGF-1, TGFα, or IL-6, have also been detected in the serum and/or the tumor microenvironment of patients with various malignancies. In the case of IGF-1 receptor (IGF-1R) where several small-molecule candidates are under evaluation in pharmaceutical pipelines, the downstream cell-intrinsic activation of STAT3 remains unclear in GBM tumors.TCGA efforts identified molecular subtypes driven by key signaling pathwaysSTAT3 knockdown (KD), with candidates regulated in similar direction across the STAT3 axis in large, public clinical databases. This strategy allows us to prioritize clinically relevant gene candidates in an otherwise statistically underpowered cell line collection, as with all such studies. We then systematically rank the STAT3 signaling axis, as defined by a gene signature, with key patient characteristics and clinical indicators17. This allows us to predict patient cohorts most likely to benefit from a STAT3 inhibition therapeutic approach. Furthermore, by analyzing the upregulated genes in the other non-responder cohort, we select key kinases for which inhibitory small molecules are currently evaluated in clinical trials. To substantiate our bioinformatical analyses, we prioritize clinically relevant  kinases using a novel computational pipeline to set the threshold for a kinome screen conducted on STAT3-perturbed GBM cells. We successfully identify IGF-1R in an as yet undescribed STAT3-IGF-1 forward feedback loop. Our study provides preclinical evidence for the implementation of anti-STAT3 therapy in selected patient cohorts, while defining a method to sensitize non-responders.We first combine gene candidates inversely implicated in the STAT3 response pathway in patient-derived GBM cells in the presence of 4, suggesting that druggable pathways may be revealed through genomic and transcriptomic profiles. STAT3 represents the final molecular switch that is activated prior to the PMT process that typifies highly aggressive and recurrent GBMs6. We hypothesize that the STAT3 pathway stratifies patients for their likely response to STAT3 inhibition therapy. As any signaling pathway is better represented by a set of genes than a single candidate, we established a transcriptomic signature reflecting the STAT3 pathway activation status , and the proneural molecular subtype with enrichment of 1p/19q co-deletion and IDH-Mut (mutant) status, representing tumors of better prognosis and responsiveness to current chemotherapy   presented the best statistical model accounting for patient survival  are 2.42 times more likely to be IDH-WT than those with a positive signature score   Fig. .Fig. 1NNSTAT3-high and -low subtypes , we extended our analyses to exclusively GBM tumors in both Gravendeel and TCGA databases Fig. . SimilarSTAT3 gene signature and observed a consistent expression of increased phospho-STAT3 (pSTAT3) in the STAT3-high group , in contrast to the STAT3-low cells   upon treatment with STAT3 inhibitors, compared to STAT3-low cells  mouse model, mice implanted with AZD1480-treated NNI-24 (STAT3-high) cells demonstrated better survival in a dose-dependent manner  mice, NNI-24 mice demonstrated an approximate 2.5-fold increased median survival difference between matched dimethyl sulfoxide (DMSO) solvent control and AZD1480-treated groups , neural precursor cell expressed, developmentally downregulated 9 (NEDD9), synaptosomal-associated protein 23 (SNAP23), guanosine diphosphate (GDP)-mannose pyrophosphorylase A (GMPPA), E26 transformation-specific containing gene , and kelch domain containing 8A (KLHDC8A). By surveying literature, we prioritized IGFBP2 as it is one of the six similar genes that sequester intracellular IGF-1 and for which clinical trials are currently in progress to evaluate anti-IGF-1R inhibitors in a variety of solid tumors. Since kinases represent dominant therapeutic targets in major pharmaceutical pipelines, we established the approach of using biological evidence to substantiate our computational predictions, by measuring phosphorylation levels of 144 kinases in STAT3-signature-stratified GPCs using the PamChip kinome screen. We developed a novel computational pipeline on kinome assay data by integrating phospho-chemical interactions with functional genomics data through kinase-substrate databases   upon AZD1480 treatment, compared to STAT3-high cells   Fig. . Further24) Fig. . Our datIGFBP2, using lentiviral-mediated short hairpin RNA (shRNA) KD  with AZD1480 and 20–200 μM TMZ. We evaluated the combination index (CI) plot where increased synergism with TMZ correlated with CI values of <1  in oth Fig. . Similaroth Fig. . We alsoion Fig. . The ratSTAT3-high and -low GBM cells, respectively, and synergize with TMZ, thus suggesting the advancement of both therapeutic approaches in a clinical setting.Collectively, our in vitro data provide strong evidence for both STAT3 inhibition and dual STAT3/IGF-1R inhibition in STAT3-high GBM tumors. The premise of our approach lies in TCGA studies showing that transcriptomic expression drives GBM disease progression and prognostic outcome4. We tapped into a recent article where drug and disease signature integration identifies synergistic combinations in GBM33. This study utilized the Library of Integrated Network-Based Cellular Signatures (LINCS) database where several commercial cancer cell lines were treated with Food and Drug Administration-approved and experimental small molecule drugs, and the transcriptomic profile of each treated cell line was acquired. Primary GBM cells were similarly treated with these drugs including TMZ with radiation for the purpose that synergistic interactions could be assessed with the standard-of-care treatment regimen for GBM patients. The authors further mapped the association of transcriptomic patterns to prognostic information in TCGA, thus identifying clinically relevant drug combinations capable of reversing the disease transcriptomic profile. In our specific scenario, the disease pattern is defined by our STAT3 functionally tuned gene signature for which we previously demonstrated phenotypic effects and prognostic association  score), we showed that there was no significant correlation of H-score to either the NTP score or IC50 values , whereas the STAT3-low group underwent subtype switching to STAT3-high (100%) . Specifically, classical STAT3-high patients had an equal chance to undergo subtype switching to STAT3-low. Although these results strongly support that mesenchymal patient groups can potentially benefit from STAT3 inhibition therapy, they also indicate that the dual inhibition strategy would most likely benefit patients who had undergone STAT3 inhibition therapy at first diagnosis but gained the resistant STAT3-low profile upon recurrence.To assess the utility of STAT3 inhibitors in recurrent patient tumors, a likely clinical scenario where individuals may be prescribed novel agents, and when PMT characterizes aggressive recurrence, we evaluated TCGA glioma-intrinsic subtypes (GISs) with STAT3 status as defined by the 34. The tumor purity score of our resource is 62%, comparable with 59% of the TCGA collection  penetration. In polycythemia vera, the STAT3 pathway correlates with poorer prognosis and is constitutively active due to the presence of the 36. Such a strategy facilitates the quantitative analysis of multi-dimensional data represented as molecular information, magnetic resonance imaging scans, and clinical indicators used to assess the patient’s disease and functional status. We previously demonstrated the utility of such a strategy in determining tumor cell resistance and invasiveness41. In this study, we identified STAT3-high to describe a cohort of both glioma and GBM patients who had poorer prognosis. This subgroup comprised of genes previously implicated in ATP-binding cassette (ABC) drug transporters, RTK signaling, and tumor cell invasiveness. Our method to winnow down the gene list associated with STAT3 combined functional validation with co-expressed genes in clinical databases. This reduced our scope to only clinically relevant genes with phenotypic changes in STAT3-perturbed primary GBM cells and PDX mouse models. Our STAT3 signature is not confounded by current clinical and molecular classification, thereby emphasizing the molecular heterogeneity contributed by this mechanistic pathway. While we showed significant extended survival after implanting AZD1480-pretreated STAT3-high GBM cells in immunocompromised mice, we also identified the top ranking causative mechanism responsible for conferring increased resistance after STAT3 inhibition therapy in STAT3-low patients and validated its biochemical activity using a kinome screen. GBM tumor cell resistance to targeted therapy is often attributed to the compensatory activation of RTKs44. Studies have described the frequent activation of insulin receptor (InsR) and IGF-1R in GBM specimens and PDX cells at conferring resistance to EGFR inhibitors45, both frequently activated but rarely amplified or mutated in GBM according to TCGA (<2%)46. IGFBP2 is the second most abundant IGF-binding protein (after IGFBP3), functions as a carrier for IGF-1 and likely promotes tumor progression through IGF-1R pathway47. In gliomas, IGFBP2 is also often overexpressed48; moreover, increased expression of IGFBP2 has been implicated in reduced survival and resistance to chemotherapy49. Therefore, our data support that the InsR/IGF-1R pathway may possibly be activated through an autocrine mechanism in a subgroup of GBM tumors. A novel pipeline for analysis of the kinome screen data was implemented in this study, which involved assigning a “biological threshold” to the otherwise voluminous data typical of such screens. The successful application of this method was subsequently confirmed in vitro and in mouse models implanted with Linsitinib-pretreated cells, a drug targeting IGF-1R. We thus propose a model where STAT3 activation results in binding to nuclear IGFBP2, with resultant secretion of IGF-1 cytokine that contributes in a novel feed-forward loop leading to IGF-1R activation. We envisage that this autocrine mechanism can contribute in part to STAT3 activation, since both AZD1480 and Linsitinib dual targeting conferred a significant mitigation of tumor cell growth and proliferation. As proof-of-concept, we treated STAT3-low  and STAT3-high  GBM cells with NT157, a selective inhibitor of insulin receptor substrate (IRS-1/2) that has the potential to inhibit both IGF-1R and STAT3 signaling pathways in cancer and stromal cells of the tumor microenvironment50. We observed significant reduction of viability and self-renewal of STAT3-low cells, at levels comparable to dual inhibition using AZD1480 and Linsitinib  and AZD1480/Linsitinib (in STAT3-low GBM cells) synergized with TMZ to mitigate in vitro tumor cell viability. Using transcriptomic information gleaned from clinical and small molecule-treated cell databases, we further identified Ruxolitinib and AZD1480 among the top ranked small molecules capable of reversing the STAT3-high disease transcriptomic profile. While IGF-1R inhibition induces responses as monotherapy in sarcomas and with chemotherapy or targeted agents in common cancers, negative Phase 2/3 trials in unselected patients prompted the cessation of several pharma-led programs51. We believe that, with TCGA studies in various cancers, intertumor and intratumor molecular heterogeneity could conceivably play an essential role in patient stratification. Our study suggests the application of IGF-1R and STAT3 inhibition, in combination with TMZ, in STAT3-low GBM tumors.We hypothesize that most signaling pathways, such as the IL-6/STAT3 axis, could be represented by a set of genes defining key regulatory modules. The premise of our hypothesis rests in being able to map these modules in clinical databases comprising molecular information and indicators used by the physicianSTAT3 functionally tuned gene signature stratified survival and significantly enriched for the IDH-WT (wild-type) status, suggesting that the latter could act as a clinical molecular indicator for administering STAT3 inhibition therapy  tumors underwent molecular switching upon recurrence. In particular, classical tumors at first diagnosis (64%) switched subtypes at recurrence . This finding has three implications. First, it is imperative that serial molecular profiling be carried out on tumors at all stages to provide a clear decision to the use of STAT3 inhibitory molecules. The failure to stratify patients can potentially result in an unfavorable outcome caused by increased resistance in the STAT3-low cohort. Second, as STAT3 is the key switch effecting PMT, its early implementation when the tumor is STAT3-high and non-mesenchymal could possibly mitigate its subtype switching. Lastly, we suggest that other mechanisms beside EGFR activation can contribute to STAT3 signaling. Recent work by Bonni and colleagues suggested that EGFRvIII-GBM tumors are constitutively active for STAT3, through co-receptor binding of EGFR and OSM52. They further postulated that EGFR-wild-type GBM tumors require EGF and OSM cytokines, beside co-receptor binding, to maintain active STAT3 signaling. The classical subtype of GBM tumors is represented by EGFR gain-of-function mutations, such as EGFRvIII3. Our earlier observation of subtype switching in classical tumors that were originally STAT3-high thus suggests that additional mechanisms can contribute to STAT3 activation and that combinational therapies may be prescribed. Collectively, our effort identifies potential drug agents applicable to both STAT3-high and -low patient cohorts.We considered the likely scenario of tumor recurrence, typical of the disease’s highly infiltrative nature. Our analysis included profiling GBM tumors at first diagnosis and at recurrence, with the finding that mesenchymal 53. All experiments were conducted with low-passage GPCs for which we previously demonstrated maintenance of phenotypic, transcriptomic, and karyotypic features similar to the primary tumor53. Briefly, tumors were processed according to Gritti et al. with slight modifications54. Cells were seeded at a density of 2500 cells/cm2 in chemically defined serum-free selection growth medium consisting of basic fibroblast growth factor , EGF , heparin , and serum-free supplement  in a 3:1 mix of Dulbecco’s modified Eagle’s medium (Sigma-Aldrich) and Ham’s F-12 Nutrient Mixture . The cultures were incubated at 37 °C in a water-saturated atmosphere containing 5% CO2 and 95% air. To maintain the undifferentiated state of neurosphere cultures, growth factors were replenished every 2 days. Successful neurosphere cultures (1–4 weeks) were expanded by mechanical trituration using a flame-drawn glass Pasteur pipette, and cells were reseeded at 100,000 cells/ml in fresh medium.GBM tumor specimens from the National Neuroscience Institute (NNI) were obtained with informed consent and de-identified in accordance with the SingHealth Centralised Institutional Review Board A, and GPC culture methods are described below50 concentration. TMZ was obtained from Sigma-Aldrich and used at concentrations of 20, 50, 100, and 200 μM. Human lentiviral shRNA clones targeting STAT3 and IGFBP2 in pLKO.1 backbone were from GE Life Science . Lentiviral shRNA vectors were co-transfected using the Lenti-X HTX Packaging System  into HEK293T cells according to the manufacturer’s instruction. Viral titer of supernatant collected was determined using Lenti-X™ p24 Rapid Titer Kit (Clontech) according to the manufacturer’s instructions. IGF-1R C-terminal-deleted overexpression vectors were constructed using pCDH-CMV-MCS-EF1- GFP+ Puro vector (System Biosciences). The amplified product was digested with XbaI and NotI and ligated into pCDH vector. Lentiviral particles were generated as described above.Small molecule inhibitor AZD1480 was obtained from SelleckChem and used at concentrations of 0.1, 0.5, 1, or 2 μM. Other STAT3 and IGF-1R small molecule inhibitors were obtained from SelleckChem and used at their respective IC53. Dose–response curves for each cell line were generated from a mean of triplicate experiments using GraphPad Prism  and IC50 values were computed from 10-point titration curves ranging from 10−4 to 102 μM. For invasion assay, 50,000 cells were added to the upper compartment of the Corning® BioCoat™ Matrigel® invasion chamber  and 2% fetal calf serum was supplemented into the lower compartment. Cells were incubated for 24 h and the lower surface was subsequently stained with 0.005% crystal violet (Sigma-Aldrich). The number of cells from five random fields having migrated to the bottom chamber was counted.Dose–response curves and cell viability were assessed using alamarBlue® cell viability assay  5 and 10 days post-treatment, respectively32.CI values based on Loewe’s additivity model were determined to assess the nature of drug–drug interactions that can be additive (CI = 1), antagonistic (CI > 1), or synergistic (CI < 1) and effect levels (fraction affected (Fa)). CI and Fa values were calculated using the CompuSyn software , following the method by Chou et al.Cells were lysed in buffer containing 0.5% sodium deoxycholate, 1% NP-40 detergent, 0.1% sodium dodecyl sulfate (SDS), 0.15 M NaCl, 10 mM Tris-HCl pH7.4, and protease and phosphatase inhibitor cocktail tablets . Approximately 25 µg of heat-denatured protein lysate were resolved on 8% SDS polyacrylamide gel and electrotransferred onto polyvinylidene difluoride membranes (Millipore). The following antibodies were used for protein analysis: anti-pSTAT3 , anti-STAT3 , anti-IGFBP2 , anti-pIGF-1R , anti-IGF-1R , and anti-β-actin . Anti-mouse or -rabbit  IgG horseradish peroxidase -linked secondary antibody was used. All antibodies were diluted in 5% bovine serum albumin in 10 mM Tris-HCl pH 7.4, 100 mM NaCl, and 0.1% Tween® 20 (Merck). Membranes were processed per standard procedures and detected using the chemiluminescence detection kit SuperSignal West Pico or Femto (Thermo Scientific) according to the manufacturer’s instructions. Protein bands were visualized using SYNGENE G:Box, iChemiXT. Protein expression was quantitated with the Quantity One® software (Bio-Rad Laboratories), normalized against β-actin levels.Cells were lysed in buffer containing 0.5% sodium deoxycholate, 1% NP-40 detergent, 0.1% SDS, 0.15 M NaCl, 10 mM Tris-HCl pH7.4, and protease and phosphatase inhibitor cocktail tablets . One μg of protein lysates was analyzed on enzyme-linked immunosorbent assay kits as per the manufacturer’s protocol in triplicates.Pfu DNA Polymerase (Promega) according to the manufacturer’s protocol. Cycle parameters were: initial denaturation at 95 °C for 5 min, followed by 40 cycles of 95 °C for 30 s, 50 °C for 30 s, 72 °C for 90 s, and a final extension at 72 °C for 7 min. PCR products were purified using Wizard® SV Gel and PCR Clean-up System  and sequenced using the BigDye® Terminator v3.1 Cycle Sequencing Kit . Primers used are as follows:Genomic DNA was extracted from cell lines and PDXs using the DNeasy Blood & Tissue Kit  in accordance with the manufacturer’s protocol. PCR amplifications was performed in a total volume of 25 μl with 50 ng of sample (including no template control), using IDH1 Forward 5′-AATGAGCTCTATATGCCATCACTG-3′;IDH1 Reverse 5′-TTCATACCTTGCTTAATGGGTGT-3′;IDH1 sequencing 5′-AATGAGCTCTATATGCCATCACTG-3′;IDH2 Forward 5′-ATTCTGGTTGAAAGATGGCG-3′;IDH2 Reverse 5′-CAGAAGAAAGGAAAGCCACG-3′;IDH2 sequencing 5′-ATTCTGGTTGAAAGATGGCG-3′.H-scores were then derived from both the staining intensity  and the percentage of positive cells (0–100%), generated a score ranging from 0 to 3. Briefly, the percentage of weakly stained cells was multiplied by one plus moderately stained cells multiplied by two plus strongly stained cells multiplied by three. At least five random fields were counted and scoring was performed blinded to clinical data.Tissue sections were stained with the following antibodies: anti-pSTAT3 antibody  and anti-IGF-1R antibody . For quantitative analysis, the percentage of stained tumor cells and intensity of staining were evaluated under high-power fields (×400) on tissue sections using optical microscopy. RNA was isolated using TRI Reagent® (Sigma-Aldrich) and reverse transcribed into cDNA using the Superscript® III First-Strand Synthesis System Kit (Life Technologies). Cycle parameters were: 40 cycles of 95 °C for 10 s, 55 °C for 10 s, and 72 °C for 5 s. Real-time PCR was performed on Roche LightCycler® 96 Instrument using FastStart Essential DNA Green Master (Roche Life Science). Each real-time PCR was done in triplicate, and the level of each gene’s expression was determined relative to hypoxanthine phosphoribosyltransferase.Gene-specific primers used are as follows:STAT3 F-GGGAGAGATTGACCAGCAGT, R-CTGCACTCTCTTCCGGACAT;ELK3 F-TCAAGACGGAGAAGCTGGAG, R-CCGAGATGAGAAGGGTGAGG;BIRC2 F-CTCCAGCCTTTCTCCAAACC, R-AGTTACTGAGCTTCCCACCA;FZD1 F- GCCCTCCTACCTCAACTACC, R-CAGCCGGACAAGAAGATGA;SLF35F5 F-CTGTGGGGAAACTTACTGCA, R-CCAGTACAACGCCTCCAATG;KLHDC8A F-CGGGTCTACTGCTCCCTG, R-TGTTGTACATCTCCACGACCT;GMPPA F-TCACCCAGTTCCTAGAAGCC, R-CTGTTAGCCGTAGTGCCAAG;SNAP23 F-AGGATGCAGGAATCAAGACCA, R-CTCCACCATCTCCCCATGTT;NEDD9 F-AGCTCAGGACAAAAGGCTCT, R-GCAACAGCTCCCTTGACAAA;DTX3L F- TCACAAGCAGAAACACCGTC; R-GTCACCACACACCTTCTCA;CTNNA1 F-GCAGCCAAAAGACAACAGGA, R-TGTGAGGCATCGTCTGAGG;NAA38 F-GTCAAGCAGCAAGATGGAGG, R-GCGCATAGTCTTGTTGAGCA;ITFG3 F-ACACCAACAGCAGCAACAATT, R-AATGAAAGAACTGGGTCTGCC;IGFBP2 F-GGCTTGGTTGGAAGACTGAT, R-CATTTTCAAAGGCCTCACGCg-Prkdcscid II2rgtm1Wjl/SzJ, Jackson Laboratories) as described below40. Five hundred thousand pretreated cells were injected into the following coordinates: antero-posterior = +1 mm; medio-lateral = +2 mm; dorso-ventral = −2.5 mm. Mice were euthanized by transcardiac perfusion with 4% paraformaldehyde upon presentation of neurological deficits. Kaplan–Meier survival curves were plotted to show survival differences. A logrank test was adapted to estimate the survival difference between the STAT3-high and STAT3-low patient group using Prism 5 . Multivariate Cox Regression model was fitted to identify the significant clinical covariates associated with survival. A p value of <0.05 was defined as significant association of covariates for survival. The statistical significance of correlation was evaluated using Spearman’s rank correlation test.Mice were handled according to approved guidelines of the Institutional Animal Care and Use Committee of the National Neuroscience Institute, Singapore. Briefly, orthotopic intracranial implantations were carried out using 6–8-week-old NOD-SCID gamma mice  of at least three independent experiments. Student’s STAT3 knockdown GPCs were profiled on Affymetrix GeneChip® Human Genome U133 Plus 2.0 Array using the 3’ IVT Express Kit. The Gene Expression Omnibus (GEO) accession number for the microarray data is GSE117905. Raw cel files were summarized with mas5 algorithm and log2-scaled and gene expression dataset was created. All data pre-processing analysis was carried out by R/Bioconductor packages. A linear model was regressed to assess the differentially expressed genes between STAT3 KD and non-targeting control profiles  in NNI GPCs (N = 3) as described in R/limma packages27. False discovery rate (FDR)-adjusted p value of <0.05 was considered as statistically significantly perturbed genes upon STAT3 KD. A subset of differential genes was extracted as STAT3 KD gene signature by applying a stringent criterion of 2-log2 fold change between KD clones and the control profiles.STAT3 functionally tuned gene signature, we utilized the gene expression data from the Rembrandt glioma patient database (N = 390). First, we built a correlation matrix for 44,950 probesets by estimating the pair-wise rank correlation coefficient for each probeset. The STAT3 co-expressed module was defined by the probesets that had a coefficient value >0.3. Both positively and negatively correlated probesets were combined as the STAT3 co-expressed genes in primary tumor samples. The gene list was subsequently narrowed down by selecting only those candidates that showed inverse expression upon STAT3 KD. We considered the intersection of genes upon genetic KD of STAT3 and STAT3 co-regulated transcript modules as the STAT3 functionally tuned gene signature (N = 207). The list of genes comprising the STAT3 functionally tuned gene signature is available as Supplementary Data To identify the STAT3 functionally tuned gene signature was performed on three glioma patient database resources: Gravendeel (N = 276), TCGA all glioma patients (N = 672), and TCGA GBM microarray cohort (N = 558)55. All Affymetrix microarray profiles were processed using standard MAS5 scaling algorithm available in R/affy packages56. For TCGA database, gene expression profiles and clinical data were downloaded using R/TCGAbiolinks package57, and raw cel files for Gravendeel database were downloaded from GEO database (GSE16011). To evaluate the predictive ability of our gene signature, all patient database was treated as an independent validation cohort and was interrogated using the NTP method available in R/CMScaller package59. The predicted classes for patient tumors with statistical significance  were further evaluated for the prognostic association for overall survival in both glioma or GBM patient cohorts.The evaluation of our 60. To understand the clinical association of 2016 WHO classification marker IDH1 status with STAT3 signature-stratified classes, we employed the relative odds estimation61. The relative odds score was estimated from the proportion of highest fifth distribution and lowest fifth distribution of STAT3 signature score values for IDH1 phenotypes. BIC score was calculated for each regression model for survival variability.GIS signatures defining the three molecular subgroups were interrogated using single-sample Gene Set Enrichment Analysis (GSEA) with resampling classification strategySTAT3 KD transcriptome data against established molecular signatures available in public signature databases62. We interrogated 186 KEGG pathway gene sets from the Molecular Signatures Database with the complete transcriptome as our background in the enrichment analysis. As our STAT3 KD transcriptome was generated on Affymetrix GeneChip® Human Genome U133 Plus 2.0 Array using 3’ IVT Express Kit, we used max_probe option to collapse the gene expression values of genes with multiple probesets. The overrepresentation of ranked genes score was calculated using weighted running sum statistic option (p = 2). The signal-to-noise ratio was applied to rank the genes enriched with the background pathway gene sets using 1000 permutations. The statistical significance of gene ranking between non-target and STAT3 KD clones was defined by the nominal p value <0.05. The GSEA ranked list is available as Supplementary Data GSEA was performed using the desktop GSEA software v.2.2.2) to identify the enrichment of .2.2 to iSTAT3 signature-stratified GBM cells using the PamChip technology. The computational pipeline is represented in the flowchart . First, we interrogated TCGA GBM patients (N = 558) in microarray database with our STAT3 functionally tuned gene signature using the NTP method available in R/CMScaller package59. The predicted classes for patient tumors with statistical significance  were further evaluated to identify the differential disease gene signature. A disease signature of 6359 genes was identified to be differential between STAT3-high versus STAT3-low GBM patients . This STAT3-high GBM signature was interrogated as a disease signature, with TMZ as the reference compound in the SynergySeq pipeline. The LINCS compounds that displayed high disease discordance and low concordance with the reference compound were determined as synergistic small molecule candidates capable of reversing the disease signature based on the Loewe additive model65. The current evaluation included compounds from both the LINCS database and GBM-JQ1 study from the SynergySeq project (N = 1679).We utilized the SynergySeq platform to identify compounds synergistic with TMZ to reverse Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Source Data


Fatal human cases of avian-origin H10N8 influenza virus infections have raised concern about their potential for human-to-human transmission. H10 subtype avian influenza viruses (AIVs) have been isolated from wild and domestic aquatic birds across Eurasia and North America. We isolated eight H10 AIVs  from live poultry markets in Bangladesh. Genetic analyses demonstrated that all eight isolates belong to the Eurasian lineage. HA phylogenetic and antigenic analyses indicated that two antigenically distinct groups of H10 AIVs are circulating in Bangladeshi live poultry markets. We evaluated the virulence of four representative H10 AIV strains in DBA/2J mice and found that they replicated efficiently in mice without prior adaptation. Moreover, H10N6 and H10N1 AIVs caused high mortality with systemic dissemination. These results indicate that H10 AIVs pose a potential threat to human health and the mechanisms of their transmissibility should be elucidated. Orthomyxoviridae and are classified on the basis of their surface glycoproteins hemagglutinin (HA) and neuraminidase (NA). Currently, 18 HA subtypes (H1–H18) and 11 NA subtypes (N1–N11) have been identified and, with exception of the H17N10 and H18N11 subtypes found in bats, all AIV subtypes are found in aquatic birds4. Only subtypes H1, H2, and H3 are known to be transmissible between humans or associated with human pandemics. However, subtypes H5, H6, H7, H9, and H10 can cross the species barrier from birds to mammals, including humans, and cause sporadic infections but have not yet acquired the ability to transmit between humans8. The reasons for this remain unclear. In 2013, three subtypes of AIVs were able to cross the species barrier and infect humans. In February 2013, an H7N9 AIV emerged in China and 1,566 laboratory-confirmed cases of human infection resulted in 613 deaths9. In May 2013, H6N1 AIV infections in humans occurred in Taiwan10. In December 2013, three human cases of H10N8 AIV infection were reported in China5.Avian influenza viruses (AIVs) pose continual challenges to human and animal health worldwide. AIVs belong to the family 11. Since then, H10 AIVs have been isolated with increasing frequency from wild and domestic aquatic and terrestrial avian species. H10 AIVs are now divided into two lineages: Eurasian and North American12. The first human infections with H10N7 AIV were reported in 2004 in Egypt13. An additional two cases were then confirmed in Australia in 201014. Each of these human cases was associated with either interactions with, or proximity to, live poultry. H10N7 AIVs were isolated from hunted wild ducks in a live bird market in Egypt and from chickens processed on a farm in Australia that was associated with the 2010 H10N7 poultry outbreak. Those who acquired H10N8 infections were known to have a history of visiting live poultry markets (LPMs) or exposure to live poultry5. Currently, sustained human-to-human transmission has not been reported for H10 AIVs.A subtype H10 influenza virus was first isolated from a chicken in Germany in 194916. H10N8 AIV was infectious among feral dogs in LPMs in Guangdong Province, China17. Several Eurasian and North American–origin H10 AIVs replicate and cause weight loss in ferrets18. Therefore, H10 AIVs can cause disease in mammals and have the potential to pose a potential public health threat.H10 AIVs have been isolated from various mammalian hosts, including harbor seals in northwestern Europe19. The circulation of H5N1 with other AIVs subtypes may increase the likelihood for emerging pandemic strains through reassortment. Reassortment between the polymerase basic 1 (PB1) gene from the H5N1 and H9N2 AIVs has been observed21. Recently, we described the emergence of a novel genotype H5N1 AIV containing the HA, M, and NA genes of circulating Bangladeshi H5N1 AIVs and five genes from low pathogenic Eurasian-lineage AIVs22.LPMs play a critical role in maintaining, amplifying, and disseminating AIVs among poultry species and from poultry to humans. After the first outbreak of highly pathogenic H5N1 AIV in 2007, ongoing active surveillance of poultry in Bangladesh has been conducted. A variety of AIV subtypes, including H5 and H9, have been isolated in LPMs in BangladeshDuring our active surveillance in Bangladesh, we isolated H10 AIVs from LPMs from 2008 to 2017. To better understand the evolution of these H10 AIVs, we sequenced their full genomes and analyzed their genetic and antigenic characteristics and receptor-binding properties. We determined the growth kinetics and antiviral susceptibility for these viruses and evaluated their pathogenic potential in mice.19. We noted that eight H10 subtype AIVs were isolated from samples collected from three different LPMs in Bangladesh. All of the AIVs were isolated from ducks, with the exception of one H10N7 AIV that was isolated from a chicken . We then subtyped all samples that tested negative for the H5 subtype by HA and NA sequencing. We detected eight different HA subtypes of AIVs, including H1 (5 isolates), H3 (12 isolates), H4 (4 isolates), H5 (182 isolates), H6 (3 isolates), H7 (1 isolate), H9 (1165 isolates), and H10 (8 isolates)To better understand the genetic relationship of the H10 viruses, we sequenced the complete genomes of eight H10 AIVs isolated from LPMs in Bangladesh. Phylogenetic analysis of the HA sequences revealed that they belonged to the Eurasian lineage. We classified the HA genes into two groups based on branch clustering of the phylogenetic tree , indicating that they were low pathogenic strains. We examined the HA proteins in these AIVs for the presence of N-linked glycosylation motifs by interrogating the proteins for N-(P-[S/T]-P) motifs, which indicate potential N-linked glycosylation sites. We identified four potential glycosylation sites in HA1 at positions 22, 38, 242, and 296, which are highly conserved in all H10 AIVs.Phylogenetic analysis of the NA genes revealed that all NA subtypes  belonged to the Eurasian lineage  genes revealed that they formed two distinct groups within the Eurasian lineage, all belonging to the Eurasian lineage  and nucleoprotein (NP) genes of the H10 AIVs clustered into two groups, all of which were of the Eurasian lineage  genes of eight H10 AIVs clustered into two groups and belonged to the Eurasian lineage  of each AIV. Three mice in each group were euthanized on 2, 4, 6, and 8 days post inoculation (dpi) to determine viral titers in the nasal turbinate, lung, spleen, heart, intestine, and brain. We observed the remaining five mice in each group for 14 days to determine their susceptibility to these AIVs. Mice infected with DK/BD24035/14 (H10N1), Dk/BD24268/15 (H10N6), and DK/BD821/09 (H10N7) experienced significant weight loss (>25%)  Fig. . Four vi5%) Fig. .Figure 210 EID50  and Dk/BD24268/15 (H10N6) AIVs that caused high mortality in mice and were detected in the heart, liver, and brain tissues. In addition, we detected the DK/BD24035/14 (H10N1) AIV in the spleen at 2 and 4 dpi  AIV revealed minimal viral antigens in bronchiolar epithelial cells and none in alveolar cells. Infection with the DK/BD821/09 (H10N7) AIV caused intermittent infection of bronchiolar epithelial cells and minimal spread to alveoli. Both the DK/BD24035/14 (H10N1) and Dk/BD24268/15 (H10N6) AIVs caused diffuse infection of bronchiolar epithelial cells and spread to surrounding alveolar epithelial cells  values ranging from 0.12 to 3.06 nM  AIV and the other H10 AIVs that correlated with substitutions in the HA at residues 137 and 83 at antigenic sites A and E, respectively. Overall, we observed limited antigenic drift among the North American and Eurasian lineages.Our phylogenetic analysis of the H10 HA gene revealed that two different groups of Eurasian H10 AIVs were introduced into Bangladesh. The group 2 AIVs H10N1 and H10N6 were similar to H10N8 AIVs that formed a stable lineage in Jiangxi Province, China29. We previously showed that the viruses isolated from Tanguar haor area played a role in the development of a new genotype of H5N1 clade 2.3.2.1a through reassortment22. Therefore, further study is needed to address the link between AIVs isolated from free-range ducks in the Tanguar haor area and the diversity and evolution of AIVs circulating in LPMs in Bangladesh.Migratory birds are important in the evolution, maintenance, and spread of AIVs. Bangladesh is located in the Central Asian flyway and is near the Eastern Asian-Australian and Black Sea-Mediterranean flyways, and is a stopover site for migratory birds. The interaction between wild birds and domestic and/or free-range ducks, which introduces AIVs into poultry systems, was responsible for emerging new strains that infected humans. This was observed in the emergence of novel H7N9 and H10N8 strains in China in 201332. We found that all H10 AIVs infected and replicated in mice without prior adaptation, some of the AIVs caused some degree of weight loss, and some were lethal. Furthermore, these AIVs replicated to higher titers in the lungs than in the nasal turbinates, which is consistent with previous findings showing that H10N8 AIVs isolated from LPMs in China replicated well in the respiratory tracts of mice, with higher viral loads in the lungs than in nasal turbinates33.The mouse model has been widely used to evaluate the virulence of AIVs in mammals. We used DBA/2J mice to evaluate the disease potential of H10 AIVs because DBA/2J mice are more sensitive to influenza A infections than are other strains34. Recent studies demonstrated that many low pathogenic H1 and H7 AIVs isolated from wild birds in North America caused mortality in mice prior to adaptation35. Recently, we described the replication and pathogenic potential of H3, H7, and H15 viruses from free-range ducks in Bangladesh using mice. We observed that H7N1 and H7N9 viruses caused 100% and 60% mortality respectively, and expanded their tissue tropism beyond the pulmonary tissues to heart, liver, and brain36. Our current study showed this for both H10N1 and H10N6 AIVs as well as high mortality in mice.Mouse studies can be useful to investigate the potential of low pathogenic AIVs to replicate and cause disease and also can be used to discover the molecular markers which are important for adaptation to a mammalian host38. All of the H10 AIVs we isolated did not contain the PB2 E627K substitution. The E627K substitution is present in viruses obtained from human samples infected with H10N8 AIVs but not in poultry isolates27, suggesting that this substitution is required to improve replication in mammalian hosts. The NS gene of H10N4 AIV has also been shown to contribute to virulence39. In 2014, an H10N7 AIV was isolated from seals in northeastern Europe that caused severe mortality41. An H10N4 AIV was also detected in mink farms in Sweden42. We observed that all H10 AIVs had mutations in their NS1 genes that resulted in a P42S substitution, which is associated with virulence and pathogenicity in mammals25.The PB2 E627K substitution has been shown to confer increased virulence of AIVs in mammals43. All of the H10 AIVs we tested in this study were susceptible to NA inhibitors. However, monitoring influenza viruses for the presence of drug-resistance markers is important to predict the emergence of strains with such markers.AIV mutations conferring resistance to oseltamivir emerged after the antiviral was used to treat patients with H7N9 and H10N8 AIV infections46 and has acquired mammalian host–specific mutations in its internal genes, which have been shown to facilitate transmission from avian species to humans44. H9N2 AIVs are significant donors of genetic material to emerging zoonotic viruses such as H5Nx, H7N9, and H10N8 AIVs posing an enormous threat to both human health and poultry industry. The wide circulation of H9N2 AIVs in Bangladeshi LPMs affords H9N2 with more opportunities for reassortment with other AIV subtypes, such as H10 AIVs. Transmission of H10 AIVs to humans has resulted in some fatal cases, and serologic evidence indicates that H10 AIVs were previously transmitted among turkey farmers in the United States47. This should raise concern about the potential for human-to-human transmission of H10 AIVs. Evaluation of the biologic properties of H10 and other subtypes of AIVs circulating in LPMs is essential for understanding the emergence and evolution of these viruses and to reduce their potential pandemic threat to public health.The H9N2 AIV is endemic in BangladeshAll mouse experiments were approved and performed according to the guidelines set by the Animal Care and Use Committee of St. Jude Children’s Research Hospital in an enhanced Animal Biosafety Level 3 containment facility.50 in the allantoic cavities of 10-day-old embryonated chicken eggs at 35 °C for 48 h. Virus titers were determined by injecting 100 μL of serial 10-fold dilutions of virus into the allantoic cavities of 10-day-old embryonated chicken eggs and then calculating the EID50 according to the method of Reed and Muench48.The H10 AIVs used in this study were isolated from LPMs between 2008 and 2017 in Bangladesh during routine surveillance. The HA and NA subtypes of the AIVs were identified via qRT-PCR. All viruses were propagated and titrated for EID50. For phylogenetic analysis, multiple-sequence alignments for each data set were conducted using the MAFFT program51. MEGA6 was used to construct phylogenetic trees by applying the neighbor-joining method with the Kimura two-parameter distance model and 1,000 bootstrap replicates52. Several virus sequences of the North American lineage were also used as an out group in phylogenetic trees.Viral RNA was extracted by using an RNeasy kit (QIAGEN); conventional RT-PCR was then performed by using a SuperScript III first-strand synthesis kit (Invitrogen) with the Uni12 influenza primer. Multiplex PCR of all eight gene segments was conducted by using PCR Supermix HiFi (Invitrogen) with the Uni12/13 primers. PCR products were purified on a spin column (QIAGEN). DNA libraries were prepared by using NEXTera XT DNA-Seq library prep kits (Illumina) with 96 dual-index barcodes, according to manufacturer instructions. Pooled libraries were sequenced with an Illumina MiSeq Personal Genome Sequencer and 150 bp paired-end reads. The closest relatives of the viral genes sequenced of the eight isolated viruses were identified in the Global Initiative on Sharing Avian Influenza Data (GISAID) database and the Influenza Sequence Database (ISD)6 EID50/mL viruses and then boosted after 3 weeks by intramuscular injection of virus with adjuvant. Blood was collected 1 week later for serum isolation. A panel of three mouse monoclonal antibodies  prepared against the H10 HA of A/chicken/Jiangxi/34609/2013 (H10N8) virus. Monoclonal antibodies were prepared at St. Jude Children’s Research Hospital by ClonaCell-HY Hybridoma Kit (StemCell Technologies) according to manufacturer instructions. Anti-H10 monoclonal antibodies were submitted to BEI Research Resources https://www.beiresources.org/ under catalog no. NR-50408, NR-50409, and NR-50410. The HI test was performed according to WHO protocols53.The HI assay was used to antigenically characterize the viruses. The four H10 AIVs were tested by using reference antisera against H10 AIVs selected from the North American and Eurasian lineages. The antisera against DK/BD24035/14 (H10N1) was generated for this study. Briefly, ferrets were intranasally infected with 1 mL of 106 EID50/mL virus, and an uninfected group was anesthetized and intranasally inoculated with 30 µL of phosphate-buffered saline, pH 7.2 (PBS) as controls. Upon virus challenge, five mice per group were monitored for 14 dpi for disease signs, weight loss, and mortality. Mortality was recorded as actual death or loss of ≥25% body weight, which is the threshold in which animals are required to be euthanized according to our animal protocol. Three mice per group were euthanized via CO2 asphyxiation at 2, 4, 6, and 8 dpi. Lung, intestine, liver, brain, spleen, and heart were collected to determine viral titers. Whole organs of lung, liver, brain, spleen, heart, and parts from intestine were homogenized in 1 ml PBS containing antibiotics with a Qiagen Tissue Lyser II . Organ homogenates were centrifuged at 2000g for 10 min, and the supernatants were transferred to clean tubes from which 10-fold dilutions (10−1 to 10−10) were made in a solution of sterile PBS and antibiotics. For each dilution, three 10- to 11-day-old chicken embryos were inoculated via the allantoic cavity to determine the viral titers for each organ in terms of EID50.Four H10 isolates were selected to investigate H10 AIVs pathogenicity in DBA/2J mice. Groups of seventeen mice were anesthetized with isoflurane and intranasally inoculated with 30 µL of 10The lungs of mice (n = 3) were collected at 3 dpi and fixed via intratracheal infusion and immersion in a 10% neutral buffered formalin solution. Tissues were embedded in paraffin, sectioned, and stained with hematoxylin and eosin. Immunohistochemical staining was performed with serial histologic sections to determine the distribution of AIV antigens. A goat primary polyclonal antibody  against anti-influenza A, USSR (H1N1) was used  on tissue sections that were previously subjected to antigen retrieval for 30 min at 98 °C . To quantify the extent of viral infection in the lungs, digital images of whole lung sections stained for viral antigens were acquired with an Aperio ScanScope XT Slide Scanner . Both uninfected and virus-positive regions were then manually outlined, and the areas of the outlined regions were determined with ImageScope software (Leica Biosystems).50) with DK/BD821/09 (H10N7), DK/BD8988/10 (H10N9), DK/BD24035/14 (H10N1), and Dk/BD24268/15 (H10N6). Cells were incubated at 37 °C in RPMI containing 0.2 µg/mL of L-1-tosylamido-2-phenylethyl chloromethyl ketone–treated trypsin . Supernatants were collected at different time points and titrated in MDCK cells by TCID50.Monolayers of A549 cells were inoculated at a multiplicity of infection of 0.01 50% tissue culture infectious dose  and Neu5Acα2-3Galβ 1,4Glc β-PAA-biotin (3′SL)] and α2,6 . A/Aichi/2/1968 (H3N2) was used as a specificity control for α2,6 binding and A/laughing gull/Delaware Bay/42/2006 (H7N3) was used as a specificity control for α2,3 binding. Binding was detected by measuring absorbance at 450 nm.Receptor specificity of H10 AIVs was analyzed by using a solid-phase direct-binding assay, as described previously50 values56.The NA inhibitors oseltamivir carboxylate , zanamivir , and peramivir  were used to test the sensitivity of the H10 AIVs to NA inhibitors. A fluorescence-based NA inhibition assay with the fluorogenic substrate 2′-(4-methylumbelliferyl)-α-D-N-acetylneuraminic acid  was used to determine ICAll statistical analyses were performed by two-way ANOVA in combination with Bonferroni multiple comparison tests by using GraphPad Prism 5.0 . P values < 0.05 were considered statistically significant.The nucleotide sequences of the eight H10 AIVs described in this study were deposited in the GenBank database with the accession numbers shown in Table Supplementary Information


Nyssorhynchus (Anopheles) darlingi is the most aggressive and effective malaria vector. In endemic areas, behavioral aspects of anopheline vectors such as host preference, biting time and resting location post blood meal have a key impact on malaria transmission dynamics and vector control interventions. Nyssorhynchus darlingi presents a range of feeding and resting behaviors throughout its broad distribution.In the Amazon Basin, Ny. darlingi in two settlement types in Acre, Brazil: Granada  and Remansinho . Mosquitoes were classified by the location of collection (indoors or outdoors) and time (dusk or dawn).To investigate the genetic diversity related to biting behavior, we collected host-seeking FST values, model-based STRUCTURE detected considerable admixture of these populations.Genome-wide SNPs, used to assess the degree of genetic divergence and population structure, identified non-random distributions of individuals in the PCA for both location and time analyses. Although genetic diversity related to behavior was confirmed by non-model-based analyses and Ny. darlingi. Additional ecological and genomic studies may help to understand the genetic basis of mosquito behavior and address appropriate surveillance and vector control.To our knowledge, this is the first study to detect genetic markers associated with biting behavior in The online version of this article (10.1186/s13071-019-3498-4) contains supplementary material, which is available to authorized users. Nyssorhynchus (Anopheles) darlingi [Plasmodium transmission [Ny. darlingi presence and abundance and thus malaria risk [darlingi  is the msmission –4. Follosmission , 6. In Bsmission . Human msmission –11. Deforia risk –14.Ny. darlingi based on morphological characters of the egg, fourth-instar larva, pupa, male and female adults. Thenceforth, successive studies reported morphological, behavioral, ecological and genetic heterogeneity throughout its broad distribution from Central to South America [Ny. darlingi had been considered to be a monophyletic species until Emerson et al. [Ny. darlingi populations, at a continental scale, leading to diversification within this species. However, additional genetic diversity at a local scale has been demonstrated for this vector based on ecological differences [Root  first de America –19. Neven et al.  proposedferences , 22 and ferences .Nyssorhynchus darlingi demonstrates high anthropophilic behavior in many areas, sometimes combined with opportunistic zoophilic (non-human) feeding [At least three aspects of mosquito behavior are especially important in determining pathogen transmission rates to humans: anthropophily, endophagy and endophily. The first is the predilection of a vector to blood-feed on humans instead of other animals, the second is a preference for biting inside houses, and the last is indoor resting behavior after a blood meal. These traits are known to vary within and between Anophelinae mosquito species that transmit malaria . Nyssorh feeding , 26. Dep feeding , 28 or e feeding  behavior feeding , 30, 31. feeding , 33.Anopheles farauti in the Solomon Islands identified a single population with individuals which fed indoors and outdoors, and early in the evening as well as late at night [An. arabiensis analyzed 34 coding SNPs in 8 clock genes, but found no linkage between these phenotypes and the candidate clock genes known to influence behavior in other Diptera [Heterogeneity of vector behavior could be the result of genetically differentiated subpopulations or behavioral plasticity, i.e. individuals with the same genotype adopt different behaviors. Behavioral plasticity seems to be the more likely explanation in malaria vectors. For example, studies on at night . Further Diptera .Ny. darlingi on a microgeographical scale and its association with biting behavior. We tested the hypothesis that there was population genetic structure in this species associated with blood-feeding location (indoors or outdoors) and time (dusk or dawn) using genome-wide SNPs. Understanding the genetic contribution to mosquito biting behavior could lead to the development of molecular tools for more precise vector surveillance and malaria control.With these findings in mind, and a general dearth of such studies in Neotropical malaria vectors, the present article investigated the extent of population genetic diversity in Nyssorhynchus darlingi adults were performed outdoors  and indoors in two rural settlements Acre State in April 2011, the older more settled Granada  and the newer, more recently deforested Remansinho  . Hybridized customized adapters P1 (0.3 µM) and P2 (4.8 µM) were ligated to the digested DNA  as described in Campos et al. [DNA was individually extracted using ReliaPrep™ Blood gDNA kit  and DNA concentration was estimated using a Qubit fluorometer . Double restriction digestion of 200 ng of high-quality genomic DNA with s et al. . After aprocess_radtags script. Then, each individual’s sequence reads were aligned to the Ny. darlingi reference genome [ref_map.pl Perl script in Stacks, using a minimum of 5 reads (−m) to report a stack and join the catalog. The dataset was corrected using another Stacks script called rxstacks with the following parameters: prune out non-biological haplotypes unlikely to occur in the population (−prune_haplo), minimum log-likelihood required was −10.0 (−lnl_lim). A new catalog was built by cstacks Stacks’ script and each individual was matched against the catalog with sstacks script. We then used the populations script in Stacks to filter loci that were called in at least 50% (−r) of all samples, i.e. the first step was performed without population information to avoid population bias in the SNP selection. This latter step was run with minimum coverage of 5 (−m) and a random single SNP for each RAD locus was selected (write_random_snp). The populations script produced genotype output in several formats  and summary statistics such as nucleotide diversity, pairwise FST and private alleles.Raw ddRAD reads were processed to identify SNP loci within and between individuals using scripts implemented in Stacks v1.31 . First, e genome  using Boe genome . AlignedAdegenet [K-means calculated. Number of retained PCs for DAPC analysis was calculated as described [K value that ranged from 1 to 6. STRUCTURE was run with admixture model and correlated allele frequencies, ‛burn-inʼ of 100,000 generations and MCMC chain of 1,000,000 generations. The Evanno method [K for runs implemented in structureHarvester [Principal components analysis (PCA) and discriminant analysis of principal components (DAPC) were performed in the R package Adegenet . The forescribed . For modo method  was usedarvester .n = 96) and Remansinho (n = 32). We considered four collection categories: indoor collection at dusk, i.e. 18:00–21:00 h; indoors at dawn, i.e. 3:00–6:00 h; outdoors at dusk and outdoors at dawn  , and the lowest was between indoors samples  , and the lowest was between outdoors samples   in Ny. darlingi females. Previous studies with other species such as An. gambiae (s.l.) and An. funestus showed genetic differentiation regarding divergent behavior using chromosomal inversion karyotypes frequencies [An. arabiensis linked to two paracentromeric chromosome inversions [Ny. darlingi genome assembly does not provide. Thus, we are aware of the limitations of the present study in identifying the precise location of the causal genetic variants.Despite numerous entomological studies investigating the different aspects of quencies –51. Moreversions . HoweverReduced representation genome-sequencing methods, such as ddRAD, have proven to be powerful tools for the assessment of a large number of SNPs on a genome-wide scale, with considerably lower library construction and sequencing effort , 54. HowFST values are significant between these groups, and a consistently lower value was detected for endophagic populations, no different clustering assignment were found using STRUCTURE analysis. The single endophagic group could be explained by similar selection of Ny. darlingi females related to environmental conditions within houses such as temperature, shelter and blood meal availability. In fact, Paaijmans & Thomas [Ny. darlingi from two localities in Loreto, Peru, Cahuide and Lupuna, detected no genetic differentiation between endophagic and exophagic specimens, nor between specimens collected at dusk vs dawn [Our results, using 994 genome-wide SNPs, showed a lack of population structuring related to indoor and outdoor biting behavior. On the other hand, PCA and DAPC analysis showed that the same markers are present in indoor mosquitoes collected at both Granada and Remansinho Fig. . Althoug& Thomas  showed t vs dawn . However vs dawn . We hypoNy. darlingi, the present study showed a non-random distribution of individuals in the PCA and clustering in DAPC, besides lack of population structuring. To avoid confounding effects of microgeographical population structure, only one collection point was used in this analysis. As shown in the first set of analysis, PC1 segregated endophagic and exophagic Ny. darlingi females (Fig. Ny. darlingi populations present a range of peak biting times and patterns, i.e. unimodal, bimodal, trimodal [Ny. darlingi has been associated with seasonality [Ny. darlingi showing similar behavior patterns.In terms of the time of biting behavior in les Fig. . In additrimodal , 33. Bitsonality , 57 and sonality . AdditioNy. darlingi contributed to its dispersion in different niches within the Amazon region. Together with recent events directly attributable to human presence, such as deforestation and land use changes on a massive scale [Ny. darlingi and may explain the behavior heterogeneity observed in these different studies. A better understanding of the genetic diversity in Ny. darlingi regarding its behavior may help to predict and improve vector control methods and effectiveness of malaria frontline interventions.It is widely accepted that behavioral aspects of anopheline species such as host preference, local and time of biting and resting location after a blood meal have a major impact on malaria transmission dynamics in endemic areas , 60. Theve scale , 11, 12 Ny. darlingi and showed that this association is not due to population structuring. Mosquito behavior is one of the multiple factors directly influencing in the impact of measures of vector control. Molecular tools, such as presented in this study, could help to address appropriate vector surveillance and control on a local-scale perspective. It is emphasized that additional ecological and genomic studies may help to understand the genetic basis of Ny. darlingi behavior by the identification of relevant genes and/or genomic regions.In this study, we provided evidence of genetic diversity associated with biting behavior in the major Neotropical malaria vector Additional file 1. Per-individual An. darlingi detail of the number of sequence reads and unique stacks genotyped.


This article describes how CGIAR centers and partners are using genomic sequence information to promote the conservation and sustainable use of crop genetic diversity, and to generate and share benefits derived from those uses. The article highlights combined institutional, and benefit-sharing-related challenges that need to be addressed to support expanded use of digital sequence information in agricultural research and development. DNA sequences are unique from one organism to the next; they can aid in taxonomic classification, identification of unique genes, and gene combinations that encode valuable traits for sustainable agriculture under changing climates. While whole-genome sequences are increasingly available as a result of new-generation technologies,2 our collective capacity to actually analyze and benefit from that data is lagging behind.3 Currently, genetic markers—minuscule segments of DNA scattered throughout the whole genome—are successfully deployed to genotype individuals and, with some success, to identify individuals in breeding programs that contain traits of agronomic importance. Genotyping is a powerful tool to help identify gaps in collections of plant genetic resources for food and agriculture (PGRFA).5 Unique genetic fingerprints of existing crop varieties can provide a baseline to assess losses of farmers' varieties, concomitant priorities for in situ conservation, and reintroduction into local production systems of “lost” farmers' varieties that have been conserved ex situ.TPlant varieties  represent combinations of genetic sequences that underpin the traits of each particular variety in interaction with the environment where the variety grows. While a variety may be unique as a whole, individual genetic sequences  may be expressed in the same manner in many different varieties. The reverse can also occur: the same trait, for example, early-maturing, can also be under the control of different sequences in different varieties. The expression of any given gene can be widely influenced by environment. The uniqueness of a variety comes from the combination of those genetic sequences and the environment's influence. The combination in any particular variety is the result of hundreds and thousands of years of random, environmental, farmer, and breeder selection.6–8 A crop's ability to tolerate drought, for example, depends on the anatomy and architecture of roots, leaves and stems; the rate of progress through the life cycle in relation to the development of drought; and difficult-to-measure attributes of photosynthetic, respiratory, and other biochemical and physiological capacities of the plant.While a powerful tool, genetic sequence data cannot be used in isolation from other technologies, including phenotyping . While genome sequencing and genetic fingerprinting may help to distinguish “what is the same” and “what is different” genetically, morphological (phenotypic) data are needed to fully interpret the digital sequence. Most traits, particularly those related to abiotic stresses, are under complex genetic control involving multiple forms of multiple genes interacting in networks.10This article describes how CGIAR centers and partners are using genomic sequence information to conserve and sustainably use crop genetic diversity, and to generate and share benefits associated with those uses. The article also includes reflections from CGIAR centers concerning technical and policy challenges that need to be addressed for the enhanced use of genomic sequence information in the future.11 cassava,12 chickpea,13 cowpea, groundnut,14 millets,15 maize, pigeon-pea,16 potato,17 rice,18 sorghum, sweetpotato, wheat, and yam.19CGIAR's mission is to advance agricultural science and innovation to enable poor people to better nourish their families, and improve productivity and resilience so they can share economic growth and manage natural resources in the face of climatic changes and other challenges. Fifteen CGIAR centers carry out research for development under this mission in close collaboration with hundreds of partners, including national and regional research institutes, civil society organizations, academia, development organizations, and the private sector. Most of CGIAR's  work is focused on developing countries and regions. Eleven CGIAR centers host international PGRFA collections under the framework of the International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA). These centers use genomic sequence information to improve their conservation and sustainable use of crop genetic diversity. Some CGIAR centers also generate and use fish, livestock, and microbial genomic data. CGIAR centers are involved, either as leaders or members of research consortia, in whole- or high-density genome sequencing and genotyping of a number of crop plants, including banana,ex situ collections have long sought to define the diversity of crops they hold, to know better what they are conserving and to identify gaps in their collections. Morphological descriptors have been developed and used to characterize ex situ collections, but these descriptors have limited value in assessing closely related individuals and in defining the range of genetic diversity that exists within and between landraces. Characterization of diversity found in situ remains an elusive goal. However, it will be achievable when whole ex situ collections and representative samples of in situ diversity can be sequenced.20 DNA sequence data, once obtained on a sufficiently large scale, will be the best tool for describing and analyzing the extent of diversity in situ and to aid long-term conservation of maximum diversity so that all elements of the diversity present in any one crop are conserved long-term for humankind.Managers of in situ conservation and use programs.22 For example, the International Potato Center (CIP) recently genotyped their entire ex situ collection of cultivated landrace potato and sweetpotato, laying the foundation for assessing the diversity present in such collection  markers and associated techniques have been developed. Some of them, suitable for research on population diversity, evolution, gene flow, and inheritance of traits, are increasingly used to better understand the diversity conserved in international collections and facilitate researchers and breeders' work by identifying potential sources of useful traits . CGIAR hllection .23 Among24 For example, CIMMYT  recently developed a core set representing the diversity of Mexican wheat landraces. Introduced into the Americas between the 16th and 18th centuries, wheat became gradually adapted to local environments, giving rise to Mexican wheat landraces known as “creole wheats.” As such, they should have useful genetic variation for heat and drought stress tolerance. Scientists from CIMMYT and Mexico's Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP) and the Punjab Biodiversity Board (India) carried out a study to: (1) characterize the collection of Mexican wheat landraces conserved at the CIMMYT gene bank; and (2) develop a core set using multiple variables. Eight thousand four hundred sixteen wheat landraces representing a range of Mexican agro-ecologies were characterized using genetic markers (DArTseq) and phenotypically for yield potential, drought and heat tolerance, and yellow rust resistance to identify a core set capturing 89% of the rare alleles present in the complete set , has genotyped 124 traditional varieties of rice to show how related each variety is to others from the same location or from other provinces. This work will increase farmers' knowledge of their heirloom rice diversity and their market opportunities.26Genomic sequence information is very useful in creating these core sets, ensuring representation of collections' genetic diversity in the core set. Genomic sequence information also provides guidance for breeders to make use of these core sets in effective, efficient ways. Some CGIAR centers, in collaboration with local and national organizations, have applied genomic tools to genetically characterize landrace diversity found 27 Researchers from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) have identified the molecular markers for quantitative trait loci (QTL) influencing grain iron and zinc content in sorghum, with the long-range potential impact of reducing malnutrition in sorghum-producing and -consuming countries.28 Researchers from the Centro Internacional de Agricultura Tropical (CIAT) and the International Institute of Tropical Agriculture (IITA) have identified QTL associated with resistance to cassava green mite, cassava mosaic disease, cassava brown streak disease, high provitamin A, and high dry matter content in storage roots.30 CIMMYT researchers have developed and deployed breeder-ready production markers for provitamin A content, maize streak virus resistance, maize lethal necrosis (MLN) resistance, and high haploid induction rate.Conventional plant breeding relies on natural or induced genetic variation combined with efficient selection of favorable genetic combinations and evaluation of phenotypes to identify variants of interest for desirable traits. The use of genomic information can shorten these processes and make them more precise. The use of molecular markers  has allo32 maize,34 peanut,35 sorghum,36 banana,37 and cassava39 have led to the identification of useful diversity and molecular markers for a wide range of traits. For example, ICRISAT identified genetic markers associated with 36 agronomically important traits in peanut gene bank accessions, opening new prospects for future breeding programs.35Digital genetic sequence data obtained from high-throughput genotyping techniques have dramatically enhanced CGIAR centers' ability to screen highly diverse germplasm from international collections at a large scale to identify allelic variation linked to agronomic traits. Genome-wide association studies conducted by CGIAR researchers for rice,CGIAR centers are also involved in using genetic information to conserve and enhance underutilized plant species. The World Agroforestry Centre (ICRAF) is a member of the African Orphan Crops Consortium (AOCC), which seeks to improve the livelihoods of smallholder farmers by improving 101 orphan crops, including trees, and strengthening associated value chains. As part of this work, AOCC is sequencing whole genomes and developing single nucleotide polymorphism (SNP) panels. Through the African Plant Breeding Academy, AOCC is training ∼120 African plant breeders in national agricultural research programs to use genomics tools to take advantage of the genomic sequence data that is being generated.40CGIAR centers and partners are also using genetic sequence information to make useful contributions to farmer-led improvement programs, as a means of completing farmers' knowledge and adding to the knowledge base for local-level management and future enhancement efforts. For example, Bioversity International's Seeds for Needs programme uses a combination of participatory approaches, genomics, and quantitative genetics to trace the genetic basis of smallholder farmer preferences of durum wheat traits in Ethiopia. Communities evaluated 400 Ethiopian wheat varieties, mostly landraces, for traits of local interest in two locations in the Ethiopian highlands. For each wheat variety, farmers provided quantitative evaluations of their preference for flowering time, spike morphology, tillering capacity, and overall quality. Ten agronomic and phenology traits were simultaneously measured on the same varieties, providing the means to compare them with farmer traits. The durum wheat varieties were genotyped for more than 80,000 SNP markers, and the resulting data were used in a genome-wide association study that resulted in a molecular dissection of smallholder farmers' choice criteria. One hundred twenty-four putative QTL affecting farmer-preferred traits and 30 putative QTL affecting metric traits were found. The study showed that smallholder farmers' traditional knowledge can be associated with QTL for desired phenotypes. The combination of participatory variety selection and modern plant breeding can speed up the genetic gain in breeding that targets smallholder farming systems and also lead to improved varieties more closely addressing smallholder farmers' needs.Since the 1970s, the focus of so-called “genetic modification” has been to target the insertion of new genetic material into the genomes of organisms and to control their expression. A distinct new generation of technologies, commonly known as genomic editing, is now available to induce mutations at preselected loci to disrupt the function of one or more specific genes; to alter existing sequences to reproduce ancient alleles or to introduce novel alleles; and to introduce new genetic material into specific loci or regions of the genome. Genome editing could vastly reduce the time to create new varieties by facilitating the direct modification of (un)favorable alleles in germplasm, thus reducing the number of breeding cycles required. Equally importantly, genome editing now offers a fast and economical method to validate the function of native genes, thus speeding up conventional marker-assisted breeding.CIMMYT is using the CRISPR/Cas9 to edit genes for stress tolerance and quality traits in maize and MLN, a disease prevalent in East Africa that poses a significant threat to food security in that region. CIMMYT will gene-edit wheat for durable rust resistance and improved metal ions (zinc and iron). CIAT started genome-editing research in 2014 with rice, and has since expanded its work to cassava and beans. CIAT's work on rice focuses on developing resistance to viruses and bacteria, improving nutritional quality and attaining hybrid seeds; its work on cassava seeks to improve starch quality and generate resistance to herbicides. The objective of CIAT's work on beans is to increase nutritional quality and early detection of pathogens for diagnostic purposes. Other CGIAR centers are also include gene-editing techniques as part of their crop improvement programs.41 and the Musa Germplasm Information System42  under the framework of the ITPGRFA. CGIAR gene banks have started to issue digital object identifiers (DOI) for all genetic resources they maintain in their international PGRFA collections. These DOIs will provide a means of ensuring that research results, including genomic research, are associated with and traceable to the germplasm held in trust.CGIAR centers share information on genetic resources held in trust through their own databases and through the global online gateway Genesys.c A growing number of developing countries are urging these bodies to require benefit-sharing from commercial users of genomic sequence data. CGIAR has made submissions to these bodies concerning CGIAR centers' uses of genomic sequence information.43In recent years, there has been a surge in interest, on the part of intergovernmental bodies dealing with the conservation and use of biological diversity, concerning the governance of genomic sequence information. The conference of the parties of the Convention on Biological Diversity (CBD) and the meeting of the parties of its Nagoya Protocol, the governing body of the ITPGRFA, and the FAO Commission on Genetic Resources for Food and Agriculture have all launched processes to gather information about how genomic sequence data are used, to set the stage for future discussions of governance options.Improved plant varieties—including the increasing proportion of those whose development depends in some way on the use of genomic sequence data—are used by farmers the world over, generating household income and food security; at a larger scale, they contribute to national and regional economic development. Under the CBD, Nagoya Protocol, and ITPGRFA, income generated by farmers from the use of improved resources and local, national, and regional economic development are considered nonmonetary benefits. Monetary benefit-sharing is more narrowly construed to refer to payments from organizations who commercialize new genetic resource products to the providers of the genetic resources from which the products are derived.CGIAR pursues its mission by working through partnerships to create nonmonetary benefits for farmers and national programs in developing countries. Perhaps the most important overall benefit from CGIAR's use of genomic sequence information is “food and livelihood security benefits” (one of the nonmonetary benefits listed in the Annex to the Nagoya Protocol). In the previous sections, we have provided examples where genomic information played a critically important role in the discovery of genes that contributes to the development of crop varieties that are more productive, nutritious, disease-resistant, less reliant on chemical inputs, and adapted to changing climatic conditions. Given the relatively recent emergence of next-generation sequencing technologies, there are still not many examples of fully developed research and development chains that start with the generation of raw sequence data and end with released cultivars and breeds adopted by farmers. However, it is clear that use and reliance of genomics data in crop improvement programs will increase over time.44 They must also be partners in the actual conservation, research, and development work.10 In the following paragraphs we provide examples of how CGIAR centers partner with, train, and transfer technology to agricultural research organizations in developing countries.One challenge related to the evolution of new genomic-sequencing technologies will be to ensure they are used for sustainable development, generating benefits for resource-poor farmers in developing countries. There is a risk that new technologies/methods may disproportionately benefit developed countries, and perpetuate rather than diminish the existing North/South technology divide. To ensure next-generation sequencing technologies benefit the Global South, national agricultural research organizations of developing countries must be engaged as equal partners in identifying challenges to be addressed through genomics-based research.45CIMMYT, Bioversity, International Centre for Agricultural Research in the Dry Areas (ICARDA), and IRRI have partnered with national research organizations from 13 countries in Africa and South Asia to co-generate and share technologies for genetic characterization and marker-assisted improvement of wheat, barley, and rice, focusing on traits and alleles that are important for the crops' adaptation to climatic changes. These efforts are taking place under four projects funded by the ITPGRFA Benefit Sharing Fund (BSF). Training of plant researchers, breeders, and informaticians from the countries involved and from other target countries on the use of genomic tools is a central part of the four projects.https://sweetpotatogenomics.cals.ncsu.edu).fCIP is a partner in the Genomic Tools for Sweetpotato Improvement Project that aims to develop modern genomic, genetic, and bioinformatics tools to facilitate crop improvement and improve genetic gains in sweetpotato. Sweetpotato is an important food security and cash crop with potential to alleviate hunger, vitamin A deficiency, and poverty in Sub-Saharan Africa. It is mainly grown by poor women farmers in small plots. The project supports capacity development. Traditional and web-based training workshops and seminars are organized for partner organizations in Africa to facilitate access to and use of molecular markers in breeding  in popcorn maize, and searching for sources of resistance to Karnal bunt disease in wheat. Forty-two students have pursued PhD, MSc, or BSc thesis projects within MasAgro Biodiversidad .The CIMMYT-led “MasAgro Biodiversidad” promotes use of wheat genetic resources through the creation of a platform linking germplasm, data, software tools and services, and capacity-building. It has developed free distance-learning modules focusing on (1) the theory of genotypic data, and technologies to generate and analyze it, and (2) practical use of an Android application to record phenotypic data of maize and wheat varieties in the field47Shared research results based on genetic resources are another critically important benefit. CGIAR's default policy is to make all research results, including data, freely available, as set out in the CGIAR Principles for Management of Intellectual Assets in 2012 and open access policy in 2013. The Intellectual Assets Principles allow CGIAR centers to restrict access to an intellectual asset if necessary for the further development or dissemination of that asset in furtherance of the CGIAR mission. However, even where such restrictions are justified, the policy specifies that the asset must be freely available to public organizations in developing countries for research and breeding.The ITPGRFA establishes the monetary benefit-sharing rules that apply to most of the CGIAR centers' research and development work that involved accessing, conserving, improving, and distributing PGRFA. By extension, recipients of PGRFA from CGIAR centers are also bound by the terms of the ITPGRFA's multilateral system of access and benefit-sharing. Accordingly, commercializers of new PGRFA products derived from germplasm they receive from CGIAR centers are obliged to pay 0.77% of sales to the ITPGRFA's BSF if those products are not made available for use by others for further research or breeding.The ITPGRFA (like the CBD and the Nagoya Protocol) applies in scope to material genetic resources and not to genomic sequence information per se, so it does not require benefit-sharing from users of genomic sequence data alone.The terms of access and benefit-sharing under the Treaty are currently under review with the objective of increasing the flow of payments from users to the BSF, and possibly increase the scope of materials in the multilateral system. The review process was launched because, at the time, no mandatory payments have been made by commercial users. Very recently, the BSF received its first payment pursuant to the mandatory benefit-sharing conditions. Generally speaking, it appears that those users who might be obliged to make payments—for example, those whose product development and delivery strategy includes patenting new PGRFA products—have chosen not to obtain materials from the multilateral system. Some delegations have stated they will only endorse a revised multilateral system if it includes benefit-sharing from commercial users of genomic sequence information.One option that is being considered is to create a subscription system, whereby PGRFA users could choose to subscribe to the multilateral system for 10 years, during which they would make annual payments to the BSF based on all their seed sales, regardless of whether or not all of their commercialized PGRFA products actually incorporate materials from the multilateral system of access and benefit-sharing. The subscription system could be the key to resolving tensions concerning genomic data. Payments from subscribers could be construed to reflect the commercial value of their own use of both genetic material and sequence data. In this way, the governance of both, both genetic materials and genomic data could be addressed under the same policy framework. Of course, all regions will need to agree on the rate of payments.Another option that seems rational and straightforward from the point of view of research and development organizations conserving, accessing, distributing, and using genetic resources—but which is not currently getting much support from a number of contracting parties—would be for contracting parties to agree to make payments themselves to the ITPGRA benefit-sharing fund, based on total seed sales within their jurisdictions. In return, all users in those countries would enjoy facilitated access to genetic resources in the multilateral system and related genomic information. In this scenario, similar to the subscription option described earlier, the distinction between access and use of material genetic resources and genetic sequence data disappears for practical purposes, as do challenges linked to attribution, because payments to the benefit-sharing fund would presumably reflect the value of access to the genetic resource or gene sequence data, or both. It is hoped that these kinds of simplified approaches can help avoid disincentives rooted in unresolved controversies over benefit-sharing, and instead encourage proactive use of new-generation technologies in sustainable, equitable agricultural research and development.


The study of European rabbit immunoglobulin genes has contributed decisively to the current knowledge on antibody structure and diversification. The European rabbit has also been increasingly used as an animal model for the study of many human diseases, such as syphilis, tuberculosis, and AIDS. As such, the study of its immune system genes is of crucial relevance, but the study of rabbit immunoglobulins has focused only on the IgG and IgA antibodies. In this study, we added to the knowledge of the rabbit immune system by investigating the genetic diversity of two antibodies, IgM and IgE, in wild and domestic rabbits as well as other rabbit close species. With the data obtained in this study, we showed a high similarity between the different rabbit close species studied and we pointed out important genetic differences in the wild and domestic rabbits. Our findings are a valuable tool for the management of rabbit wild populations and domestic breeds and may contribute to the identification of immunoglobulins genetic variants with greater efficiency against pathogens.Oryctolagus cuniculus) has been an important model for immunological studies but the study of its immunoglobulins (Ig) has been restricted to its unique IgA and IgG. Here, we studied the genetic diversity of IgM and IgE in several species of leporids and performed population genetics studies on European rabbit wild populations and domestic breeds. The leporids sequencing showed that these Ig are well conserved (98% sequence similarity among leporids), For IgM the Cµ1 and Cµ4 were the most diverse and most conserved domains, respectively, while for IgE the Cε1 was the most diverse domain and Cε2 and Cε3 the most conserved domains. The differences in the pattern of most conserved and most diverse domain between the Ig isotypes are most likely related to each isotype function. The genetic population data showed contrasting results for IgM and IgE. For both Ig, as expected, a greater diversity was observed in the original species range, the Iberian Peninsula. However, unexpectedly the genetic diversity found for IgE in the domestic animals is higher than that for the French wild populations. These results will increase knowledge of the genetic diversity of leporids and wild and domestic rabbit populations and are important tools for the management of wild populations and rabbitries.The European rabbit ( Immunoglobulins (Igs) are one of the most emblematic components of the jawed vertebrate immune system. These heterodimeric glycoproteins act as a critical part of the immune system by recognizing and binding to antigens and subsequently triggering mechanisms that lead to its destruction . In euthOryctolagus cuniculus) has been used as an animal model for diseases and immunological studies . One ofgermline ,4. IgM abclasses . For IgAbclasses ,7 being bclasses ,9,10,11)IgM, in its transmembrane form, is the first isotype to be expressed during B-cell development. As such the VH and VL regions associated with this Ig have not undergone much antigenic driven diversification being able to recognize a wider range of antigens than other isotypes, although showing low affinity . In mammO. cuniculus algirus in the south-west area of the IP and O. cuniculus cuniculus in the north east area. After the last glacial peak, O. c. cuniculus expanded its range north, across the Pyrennes, towards France [Brachylagus, Bunolagus, Caprolagus, Lepus, Nesolagus, Oryctolagus, Pentalagus, Poelagus, Pronolagus, Romerolagus and Sylvilagus) [Lepus, a polytypic and cosmopolitan genus, and Nesolagus, Pronolagus and Sylvilagus, which comprise two, four and more than 16 species, respectively [Pronolagus, and that Romerolagus is at the second basal position [The European rabbit originated on the Iberian Peninsula (IP) , where ts France , from whs France ,16. It bvilagus) ,18. Mostectively ,23,24,25position ,27,28. IGHM and IGHE genes for six additional extant leporid genera: Bunolagus, Brachylagus, Lepus, Pentalagus, Romerolagus and Sylvilagus. Additionally, we compared the genetic diversity of these Igs between wild rabbit populations of O. c. algirus and wild and domestic populations of O. c. cuniculus.In this study, we extend the knowledge on IgM and IgE isotypes in leporids by sequencing the complete IGHE and IGHM evolution in leporids, we analysed specimens of Bunolagus, Brachylagus, Lepus, Pentalagus, Romerolagus and Sylvilagus genera. To assess the genetic diversity of European rabbit we analysed 10 individuals for each of three wild populations: O. c. algirus collected in Portugal and O. c. cuniculus collected in Spain and in France  [PCR amplifications of the four Y386696) . A fragmSequences obtained in this study were edited and aligned using CLUSTAL W  as impleSequence nucleotide diversity was estimated using DnaSP version 5.10 . HaplotySequences obtained in this study were deposited in GenBank (Accession Numbers: MN650919-MN651079).IGHM and IGHE genes for six leporid genera: Bunolagus, Brachylagus, Lepus, Pentalagus, Romerolagus and Sylvilagus. The gene structure is similar to the European rabbit IGHM and IGHE and we thus inferred the intron-exon organization from published rabbit IGHM and IGHE sequences. Splicing signals are present at the intron boundaries and therefore we assumed that all studied leporids share the same IGHM and IGHE exon organization as the European rabbit.We newly sequenced the IGHM domains with 24 amino acid variable positions, the majority of which involve one substitution and 11 are conservative regarding the amino acid properties. For the Cµ1 domain some genera specific residues are observed: Arg 117 for the European rabbit, Asn 3 and Ala 122 for Pentalagus and Asn 115, Gln 117 and Asn 118 for Romerolagus ; the remaining haplotypes are rare or uncommon (0.01–0.10 frequencies). Our results show a greater diversity of IGHE in the IP populations: of the 26 SNPs observed, 16 were found for the uniculus . Surprisuniculus . The nucuniculus . We recoO. c. algirus: M and F at CH2 residues 81 and 116, respectively; Spanish wild O. c. cuniculus: I and M at CH1 residues 4 and 84, respectively, and K at CH4 residue 82; and for the domestic breeds: K, A and T at CH1 residues 12, 44 and 45, respectively, G at CH2 residue 1.7 and V at CH3 residue 85 . The raIGHM and IGHE have been fairly conserved (each Ig has 98% sequence similarity among leporids), similarly to what has been found for IGHG [IGHM constant domains diversity was observed, with the Cµ1 domain being the most diverse and the Cµ4 the most conserved. This gradient in domain diversity was not observed for IGHE, the Cε1 is the most diverse domain but Cε4 shows greater diversity than Cε2 or Cε3 domains, the most conserved domains. For leporid IGHG a gradient in the constant domains diversity was described from the most conserved Cγ1 domain to the most diverse Cγ3 domain [The results obtained in this study show that throughout Leporid evolution for IGHG . A gradi3 domain  and for 3 domain ,7. The d3 domain ,38,39 an3 domain ,41,42, t3 domain ,44, agai3 domain , the mos3 domain ,7 and du3 domain . As for 3 domain ,47,48 a 3 domain . Thus, Ith century the rabbit was domesticated from French wild European rabbit populations [O. c. cuniculus [O.c. algirus populations, in agreement with the previously described pattern of highest genetic diversity in IP populations. Interestingly, the genetic diversity we found for the domestic animals IgE is higher than that for the French wild O. c. cuniculus  involve non-synonymous substitutions, in spite of the observed 31 SNPs only seven polymorphic amino acid positions were detected. IgM is the most ancient Ig isotype being present with the same function in all gnathostomes [The results we obtained for IgM, on the other hand, revealed a higher nucleotide and haplotype diversity for the iversity . Considehostomes . As suchIGHM and IGHE have been fairly conserved. Despite this, variable amino acid residues were identified for IgE which may improve the binding of IgE to its Fc receptors. The observed pattern of genetic diversity of IGHM and IGHE along the wild rabbit populations from Iberia and France is contradictory, being, as expected, higher for the Iberian populations for IGHE but surprisingly higher for French populations for IGHM. This is the first time that these two immunoglobulin isotypes were fully characterized for Leporids and for European rabbit wild populations and domestic breeds. These findings will be useful for the management of rabbit populations and for the understanding of the rabbit adaptive immunity against pathogens.Our findings show that throughout Leporid evolution