There are thousands of known cellular phosphorylation sites, but the paucity of ways to identify kinases for particular phosphorylation events remains a major roadblock for understanding kinase signaling. To address this, we here develop a generally applicable method that exploits the large number of kinase inhibitors that have been profiled on near-kinome-wide panels of protein kinases. The inhibition profile for each kinase provides a fingerprint that allows identification of unknown kinases acting on target phosphosites in cell extracts. We validate the method on diverse known kinase-phosphosite pairs, including histone kinases, EGFR autophosphorylation, and Integrin β1 phosphorylation by Src-family kinases. We also use our approach to identify the previously unknown kinases responsible for phosphorylation of INCENP at a site within a commonly phosphorylated motif in mitosis , and of BCL9L at S915 (PKA). We show that the method has clear advantages over in silico and genetic screening. Identifying kinases responsible for specific phosphorylation events remains challenging. Here, the authors leverage kinase inhibitor profiles for the identification of kinase-substrate site pairs in cell extracts, developing a method that can identify the enzymes responsible for unassigned phosphorylation events. Advances in phosphoproteomics mean that tens of thousands of phosphorylation sites are now known3. Large catalogues of specific phosphorylation events that occur within particular cell types or organelles, or during specific treatments or phases of the cell cycle, have been generated8. However, understanding the biology controlled by a particular phosphorylation event depends on being able to identify the kinase(s) responsible for catalyzing it. Unfortunately, the methods available for assigning kinase-phosphosite dependencies are limited. Consequently, our ability to benefit from the expanding knowledge of cellular phosphorylation is hampered.Protein phosphorylation is a vital regulatory system in cells, with 90% of all proteins undergoing phosphorylation9. Indeed, we are not aware of an experimental method that has found widespread use that can selectively identify direct upstream kinases for any substrate. Broadly, existing approaches can be divided into three categories: (i) in silico predictions; (ii) screens in intact cells, and (iii) biochemical methods, often using cell extracts or recombinant kinases.Of the techniques that have been developed to interrogate kinase-phosphosite dependencies, most focus on identifying the targets of a kinase of interest; far fewer perform the similarly valuable task of identifying the kinase(s) that phosphorylate a particular residue within a substrate12. A known phosphosite can be compared to these motifs to identify candidate kinases in silico13. However, such motifs have been produced for only about half of all human kinases and validated for far fewer. It is clear that false positives and negatives are frequent using prediction methods, even when combined with contextual information such as protein-protein interaction data, co-expression, or co-occurrence in literature abstracts16.For a subset of kinases, consensus or optimal phosphorylation motifs have been determined20. Indeed, these indirect effects can make it difficult to identify the direct kinase for a particular phosphorylation site. For example, if RNAi for a kinase impedes cell cycle progression, it is difficult to determine whether its effect on a substrate is direct21.The second category includes techniques such as kinome-wide RNAi, CRISPR/Cas9 or overexpression screens. Such screens are tremendously useful to biologists, but they often identify pathways or networks of kinases that are indirectly required for phosphorylation of a particular substrate rather than (or in addition to) the direct kinase22, but such libraries are incomplete, expensive, not widely available, and may not reflect the activity state of kinases in cells. Co-immunoprecipitation or other methods can be used to identify proteins that bind to substrates, but these techniques are not specific for kinases, nor for particular phosphosites, and kinase-substrate interactions may not be robust enough to be detected. Methods that make use of modified forms of substrates and/or ATP in an effort to crosslink substrates to their cognate kinases in cell extracts are promising. However, where tested, the specificity of kinase crosslinking has been low, these approaches require additional steps such as mass spectrometry to identify the crosslinked kinase25, and they have not yet been used to identify unknown kinases. Tracking a specific kinase activity through biochemical enrichment steps can also be carried out, but this often requires large amounts of cell extract, multiple steps, and protein sequencing methods to identify candidate kinases28.Finally, in the third category, a handful of approaches have been outlined. Arrays of recombinant kinases can be tested for their ability to phosphorylate substrates in vitro34. Conventional wisdom suggests small molecule inhibitors are not appropriate tools for finding direct kinases because all such inhibitors inhibit more than one target, and signaling cascades in cells mean that indirect effects are common29. Our method, however, which we term kinase inhibitor profiling to identify kinases (KiPIK), circumvents these problems by treating the inhibition information for each recombinant kinase as a fingerprint for the identification of kinases acting on target phosphorylation sites in cell extracts. In this way, rather than ignoring or trying to minimize the influence of the off-target activity of inhibitors, the approach exploits this information. Here we describe the technique, validate it on diverse known kinase-phosphosite pairs, and use it to identify cellular kinases for as yet unassigned phosphosites on the Chromosomal Passenger Complex protein INCENP and the transcriptional activator BCL9L.Here, we develop a broadly applicable method to identify kinases for specific phosphorylation sites that overcomes many of these limitations. Our approach exploits the fact that, in recent years, large numbers of kinase inhibitors have been profiled for inhibitory activity on large panels of recombinant kinases35. Together these provide unique inhibition patterns, or fingerprints, for ~80% of human protein kinases. Second, it utilizes the rapid action of kinase inhibitors in cell extracts to focus on defined biological states and to minimize indirect effects. The method relies on the finding that inhibition profiles of kinase inhibitors in cell extracts are generally similar to those established in vitro38. Like other approaches discussed earlier, it also rests on the assumption that kinase reactions in cell extracts preserve physiological kinase-phosphosite dependencies, a notion with strong anecdotal support, including the observation that extracts can carry out complex processes such as steps of the cell cycle41.KiPIK uses two key principles to allow identification of direct kinases for particular phosphorylation sites. First, it makes use of detailed selectivity profiles of small molecule kinase inhibitors obtained using large panels of recombinant kinases in vitroIn brief, KiPIK screening proceeds as follows Fig. . Initial35 and a library of inhibitors assembled by a team at EMD Millipore32 and expanded at the Fox Chase Cancer Center31. The inhibitory properties of compounds in these libraries have been determined on large kinase panels in vitro using radiolabeled ATP incorporation assays32, substrate electrophoretic mobility shift assays34, or inhibitor binding assays35. Together, these profiles provide inhibition fingerprints for kinases encoded by 416 of the ~535 human protein kinase genes (Supplementary Data 42.An informative KIPIK screen requires a library of kinase inhibitors for which in vitro inhibitory information data is available across a range of recombinant kinases. Suitable libraries include the protein kinase inhibitor sets (PKIS1 and PKIS2) which consist of published GlaxoSmithKline compounds selected by the Structural Genomics ConsortiumA cell extract containing the phosphorylation activity being screened must also be prepared. In principle, any cell or tissue could be used as a source, but here we have focused on cultured cell lines. Prior to lysis, the cells can be treated to enhance the phosphorylation signal, or to probe phosphorylation dependencies in a particular context. For example, when investigating a mitotic phosphorylation, enriching the mitotic cell population by nocodazole treatment may be beneficial, while EGF stimulation can be used to explore phosphorylation downstream of EGFR. After lysis in the presence of phosphatase inhibitors, extracts are immediately flash frozen. A number of methods are available to assay substrate phosphorylation in cell extracts and a number of these are likely to be compatible with the KiPIK procedure. Here, we have used short biotinylated peptides encompassing phosphosites of interest as substrates, coupled with the use of phospho-specific antibodies to detect peptide phosphorylation in enzyme-linked immunosorbent assays (ELISA).Using microplate robotics, many extract kinase reactions can be performed in parallel, each in the presence of a single kinase inhibitor from the characterized inhibitor library. Quantification of substrate phosphorylation in the presence of each inhibitor yields the required inhibition fingerprint that is characteristic of the predominant kinase acting on the substrate in the extract. This fingerprint is then compared with the known inhibition patterns previously determined for recombinant kinases challenged with the same set of kinase inhibitors. The kinase(s) with inhibition patterns showing the highest similarity to that of the unknown kinase activity  are the top hits in the screen.43. We made use of 312 kinase inhibitors from the PKIS1 panel . Extracts of HeLa cells that had been arrested in mitosis for 12 h in nocodazole were used as the source of relevant kinases. To create an inhibition fingerprint for the H3S28 kinase, the phosphorylation of an H3(21–44)-GK-biotin peptide was assayed using anti-H3S28ph antibodies in the presence of each inhibitor individually at 10 µM. For the H3S28 kinase fingerprint, the top three correlation scores were all from the Nanosyn 1 µM profiling dataset, and all were with Aurora family kinases  was with Aurora B. Notably, the top three hits within the Nanosyn 0.1 µM dataset were also all Aurora kinases. The DSF dataset did not contain the Aurora kinases, and no kinase in this dataset had a correlation score above 0.52. Therefore, the KiPIK method unambiguously identified Aurora family kinases as those most likely responsible for H3S28 phosphorylation in mitosis. Aurora B, the established kinase, was the top hit.To validate this methodology, we sought to identify the direct kinase responsible for histone 3 serine 28 (H3S28) phosphorylation in mitosis, previously reported to be Aurora B44, and the second was the Y1016 autophosphorylation site in the carboxy-terminal tail of EGFR (Y992 in mature EGFR)46. In both cases, we used generic phospho-tyrosine antibodies to detect phosphorylation of biotinylated substrate peptides in extracts of A431 cells treated for 5 min with 50 ng/ml EGF, and the PKIS1 library of kinase inhibitors.We wished to validate KiPIK screening for diverse kinases. We, therefore, conducted further screens to identify kinases for two tyrosine residues. The first was Y795 in the cytoplasmic tail of integrin β1A, thought to be phosphorylated by Src family kinasesIn the screen for kinases that phosphorylate integrin β1A Y795, the top 6 hits, and 10 of the top 11 overall, were members of the Src family kinase family Fig. . TherefoThese results demonstrate that, for peptides containing a single tyrosine, generic phospho-specific antibodies can be used effectively for KiPIK screening, avoiding the need for phosphosite-specific antibodies. They also lend further support to the idea that the method can be used successfully to identity kinases for a variety of specific phosphorylation sites, including on receptor and non-receptor tyrosine kinases as well as on a mitotic serine/threonine kinase.47. However, in cells, Aurora B inhibition also reduces H3T3ph because Aurora B activates Haspin and inhibits the RepoMan-PP1 phosphatase that dephosphorylates H3T3ph48. In contrast, in cell extracts containing phosphatase inhibitors, Aurora B inhibitors do not compromise generation of H3T3ph41, presumably because Haspin is already phosphorylated and activated, and phosphatases are inactive. Consistent with this, when we carried out a kinome-wide RNAi screen to identify kinases required for H3T3ph in mitotic HeLa cells, Haspin was the top hit, but Aurora B kinase was also identified in the top 3 , while Haspin was the top hit (ρ = 0.66). In addition, the kinase Plk1, which also has a role in activating Haspin in cells50, was in the top 5 protein kinase hits in the RNAi screen but was in the bottom 20% of the KiPIK screen. Therefore, when compared to an RNAi screen, a KiPIK screen for H3T3ph was relatively insensitive to the kinases upstream of the direct kinase.To substantiate the idea that KiPIK screens are less susceptible to indirect upstream kinase activity than genetic screens, we turned to a kinase that we know is under the control of an upstream kinase (Aurora B) that is active in KiPIK conditions. Haspin is the direct kinase that phosphorylates Histone H3T3 during mitosisp 3 Fig. . Howeverp 3 Fig. , none of53. In addition, active Src family kinases can feed back to activate EGFR56. When we conducted KiPIK screens in EGF-stimulated A431 extracts for the Src-family substrate integrin β1A Y795, EGFR was present among the top ten kinases  were not identified. For one of these motifs, P-X-pS-X-X-[K/R], we identified a site in the Inner Centromere Protein (INCENP) for which a phosphospecific antibody had been raised: INCENP S446ph57. Phosphorylation of this site was previously shown to be sensitive to the Aurora B inhibitor Hesperadin57, but this inhibitor is relatively non-selective58 and the site does not match the classical Aurora kinase consensus. To identify the kinase responsible for phosphorylation of this site, we conducted a KiPIK screen using a biotin-INCENP(439–453) peptide (GPREPPQSARRKRSY), and extracts of mitotic HeLa cells as a source of kinases. This screen unambiguously identified Cyclin-dependent kinases as the best hits in all three profiling datasets. The master regulator of mitosis, Cyclin B1-Cdk1, was the top hit  in recombinant INCENP residues 825–918. In contrast, Aurora B was able to phosphorylate its known target, the TSS motif of INCENP, but not S446 Fig. . Full leted Fig. . In contted Fig. . Also coted Fig. . This pated Fig.  which is61, and the extent to which different compound sets give similar results provides an indication of the robustness of the method.We have shown that a single inhibitor panel, PKIS1, is suitable for use in KiPIK screening. We wished to determine if additional inhibitor panels and profiling datasets could be utilized. Where alternative inhibitor panels have been profiled on additional kinases, this allows the number of kinases covered by KiPIK screening to be increased. Furthermore, not all kinase inhibition profiles are necessarily concordant between different profiling efforts33, 76 of the 178 inhibitors profiled at 0.5 µM on 300 kinases by Anastassiadis et al. (“Anastassiadis”) using conventional in vitro kinase assays at a fixed concentration of 10 µM ATP31, and 55 of the 158 inhibitors profiled at 1 µM or 10 µM on 234 kinases by Gao et al. (“Gao 1 µM” and “Gao 10 µM”), also using in vitro kinase assays, but at near-Km concentrations of ATP32. Together, this encompasses 431 kinases  was with Haspin in the Gao 1 µM dataset, and Haspin was also the top scoring kinase (ρ = 0.66) in the Anastassiadis dataset. Haspin was not present in the Davis dataset, and no kinase in this panel had a correlation coefficient exceeding 0.52  was with Aurora B in the Davis dataset, and Aurora B was also the top scoring kinase (ρ = 0.66) in the Anastassiadis dataset , a phosphosite identified by mass spectrometry among proteins enriched using a phospho-AMPK substrate motif (L-X-R-X-X-[pS/pT]) antibody62. BCL9L S915ph is upregulated by phorbol ester treatment , suggesting that it lies within a PKC-triggered signaling pathway. To identify the relevant direct kinase, we conducted a KiPIK screen using 399 PKIS2 inhibitors, a biotin-BCL9L(908–922) peptide, extracts of TPA-treated HeLa cells as a source of kinases, and an anti-BCL9L S915ph antibody. This screen clearly identified the AGC kinase family as the source of the best hits . The top hit was the cAMP-dependent kinase PKA-β (ρ = 0.87) and PKA-α was also within the top ten, as were PKC-δ, -η and -ε. To test the robustness of the screen, we repeated it using all 485 available PKIS2 inhibitors and PKA-β was again the top hit 32 also decreased BCL9L S915ph, while inhibitors with activity against AKT and LATS kinases 64 did not  RNAi of PKA-α/β diminished BCL9L S915 phosphorylation in TPA-stimulated HeLa cells  anot Fig. ; (iv) BCnot Fig. ; and (v)not Fig. . TogetheIn cases where the kinase of interest is present in the profiling dataset, we have shown that KiPIK is able to identify the correct kinase or kinase sub-family in the six cases we have tested. However, because only 80% of known protein kinases are covered by the profiling data it is possible, for some screens, that the unknown kinase is not present in the profiling data. Nevertheless, the top hits are still likely to share properties with the unknown kinase, and therefore can help prioritize candidate kinases for further study. As we have shown above, a simple ranking of correlation coefficients readily identifies relevant candidates, but we explored two additional approaches to enhance this analysis.42 can be annotated with correlation coefficients derived from KiPIK screening . This generates kinome trees in which kinases are clustered more closely if they are inhibited by similar sets of inhibitors  that has recently been shown to be heavily phosphorylated by Cyclin B1-Cdk1 on other residues, and therefore may be an additional site that modulates the association of INCENP with microtubules70. We also found that, after phorbol ester treatment of cells, PKA can phosphorylate S915 of BCL9L. This is consistent with findings that phorbol esters can trigger PKA activity, perhaps via the phosphorylation of adenylate cyclase by PKC63. BCL9L functions in the Wnt signaling pathway by interacting with β-catenin to promote TCF-mediated transcription72. Our findings suggest that it may be fruitful to investigate if phosphorylation of BCL9L by PKA, as well as the reported phosphorylation of β-catenin by PKA74, underlies crosstalk between PKA and Wnt signaling75.Here we describe a method for identifying the kinases for specific protein phosphorylation sites that outperforms in silico and genetic screening approaches in key respects. KiPIK identified the previously determined kinase for all examples we tested, including serine/threonine kinases that act on histones in mitosis, and both receptor and non-receptor tyrosine kinases. We also tested the method on two orphan phosphorylation sites. For a residue in the Chromosomal Passenger Complex protein INCENP (S446) that sits within a consensus motif commonly phosphorylated in mitosis by unknown kinase(s)76. Our method differs substantially from this because it utilizes extracts of cells harvested in a state where signaling pathways are already active and the presence of phosphatase inhibitors minimizes the requirement for the ongoing activity of upstream kinases, biasing KiPIK towards identification of the direct kinases for particular substrate residues. This allows, for example, the successful identification of Cyclin B1-Cdk1 as a direct kinase for INCENP S446 kinase, even though loss of Cdk1 prevents cells from entering mitosis21, and allows the direct and indirect contributions of Haspin and Aurora B to H3T3ph to be uncoupled. Inhibitors also allow analysis of a broad range of cells and tissues which may be difficult to transfect. In addition, numerous kinases have kinase-activity-independent functions, and inhibitors provide a powerful way to focus on the function of kinase activity. In practice, however, many kinase inhibitors are poorly selective, and this significantly hampers their use in conventional screening approaches. In KiPIK screening, however, we harness knowledge of this incomplete selectivity to enhance the information content of inhibitor screens to identify candidate kinases for particular phosphorylation sites.KiPIK has a number of advantages over current methods for identifying kinases for phosphorylation sites. For example, biochemical approaches that exploit substrate-kinase interactions or attempt kinase purification by tracing enzyme activity require large samples and customized methods that are not easily transferable to additional substrates. In contrast, KiPIK is a relatively standardized approach that can be applied to a variety of substrates. Methods such as RNAi and CRISPR/Cas9 (or overexpression) can be used, but they require long timeframes, causing indirect effects and limiting analysis at different stages of a process such as the cell cycle. These methods also require efficient transfection, and genetic manipulation prohibits simple analysis of the latter stages of a cellular or developmental process if there is a defect in an earlier step. Small molecule inhibitors, in contrast, act in minutes and can be used to determine the effect of kinase inhibition on a particular biological state without affecting a preceding step. A recently published approach using inhibitor panels in intact cells appears useful, but it identifies networks of kinases upstream of substrate phosphorylation events14. Indeed, compared with a spectrum of in silico prediction methods, including those that attempt to integrate information on biological context, KiPIK was the most reliable approach for identifying the kinases for the six substrates examined here  are present and active, and over 400 human kinases are currently covered by the method. Kinases that are not expressed in the relevant cell are excluded, and only kinases that efficiently phosphorylate the substrate in competition with other kinases and substrates in the extract will be identified. In contrast, the majority of computational methods are limited in the number of kinases for which they can make predictions, and they do not account for factors such as the activation state or expression level of the kinase34, we cannot be sure that all kinases are equally discoverable. For an individual screen, KiPIK provides an unambiguous ranking of candidate kinases that can be followed up with additional experiments. In addition, z-scores provide an indication of the robustness of the results. Screens in which all z-scores are below 6 may be questionable, but future utilization of the method should allow this boundary to be refined. The coverage of the human kinome is approximately 80%, but there remain over 100 kinases for which in vitro inhibition profiles are unknown. The combination of KiPIK screening data with sequence or inhibition-based dendrograms  and A431 (ATCC CCR-1555) cells were grown in DMEM with 5% (v/v) FBS and 100 U/ml penicillin–streptomycin, at 37 °C and 5% CO47, INCENP , INCENP-S446ph 57, INCENP-TSSph 82, BCL9L S915ph , Neurogranin S36ph  and γ-tubulin ; rabbit monoclonal antibodies to the S-pT-P motif , Cyclin B1  and Vinculin ; mouse monoclonal antibodies to H3S28ph (CMA315)83 and phospho-tyrosine ; and sheep polyclonal antibodies to Aurora B 84 and BCL9L  were used. Antibodies used for siRNA screening were mouse monoclonal anti-H3T3ph (16B2)85 and rabbit polyclonal anti-H2BS6ph86. Secondary antibodies were: donkey anti-sheep IgG-HRP , goat anti-rabbit IgG-HRP , horse anti-mouse IgG-HRP , donkey anti-mouse Alexa Fluor 488, anti-rabbit Alexa Fluor 594 and anti-sheep Alexa 488 Fluor .For KiPIK screening, immunoblotting and immunofluorescence microscopy, rabbit polyclonal antibodies to H3T3ph (B8634)KiPIK inhibitor details are provided in Supplementary Data BCL9L(908–922) peptide: biotin-RGLGRRPSDLTISIN .EGFR peptide: biotin-ADEYLIPQQ .H3(1–21) peptide: ARTKQTARKSTGGKAPRKQLA-GGK-biotin .H3(21–44) peptide: ATKAARKSAPATGGVKKPHRYRPG-GK-biotin .INCENP peptide: biotin-GPREPPQSARRKRSY .Integrin β1A tail peptide: biotin-GG-KSAVTTVVNPKYEGK .Neurogranin(28–43) peptide: biotin-LC-AAKIQASFRGHMARKK .2, 2 mM EDTA, 1 mM DTT, pH 7.5 with protease inhibitor cocktail (Sigma P8340), PhosSTOP (Merck), 1 mM PMSF, 0.1 µM okadaic acid, 10 mM NaF, 20 mM β-glycerophosphate, at approximately 30 × 106 cells/ml. Extracts were immediately flash frozen in liquid nitrogen.To prepare mitotic cell extract, HeLa cells were treated with 300 nM nocodazole for 9 h and then collected by shake off. To prepare TPA-stimulated cell extracts, HeLa cells were treated with 200 nM TPA for 30 min. To prepare EGF-stimulated cell extracts, A431 cells were trypsinised, collected in prewarmed DMEM supplemented with 50 ng/ml human EGF  and incubated at 37 °C for 5 min. After these treatments, cells were washed once with PBS and lysed at 4 °C in 50 mM Tris, 0.25 M NaCl, 0.1% Triton X100, 10 mM MgCl2, 1 mM EGTA, 10 mM NaF, 20 mM β-glycerophosphate, 1 mM PMSF, pH 7.5 with PhosSTOP (Merck). Reactions were carried out in duplicate in 384-well microplates (ThermoFisher), at 30 °C for 30 min, in a total volume of 35 µl/well.Kinase reactions contained 10 µM kinase inhibitor or DMSO (vehicle control), 0.1–0.35 µM peptide, 0.2–0.7 mM ATP, 0.5–5% cell extract, in KiPIK buffer  were washed thrice with TBS, 0.05% Tween20. In some cases, 10 µl/well of 500 mM EDTA was added. Then, 35 µl/well of completed KiPIK extract kinase reaction was added and incubated at room temperature for 2 h. After washing, primary phospho-specific antibodies were added at 40 µl/well in TBS, 0.05% Tween20, 0.1% BSA, for 2 h at room temperature . After washing, 40 µl/well of HRP-conjugated secondary antibodies in TBS, 0.05% Tween20, 0.1% BSA were added for 1 h at room temperature. After washing, HRP-conjugated antibody binding (1:3000) was detected using TMB substrate (New England Biolabs) according to the manufacturer’s instructions. All pipetting was performed with a Biomek FX liquid handling robot (Beckman Coulter). Absorbance readings were made using a Polarstar Omega microplate reader (BMG Labtech).ρ) was then calculated for this fingerprint against the inhibition profiles of each of the kinases profiled in vitro against that inhibitor library, excluding mutant kinases. Notably, because Davis et al. determined Kd values for each kinase-inhibitor interaction in their panels, rather than single point percent inhibition values33, we made estimates of the %inhibition expected at 0.5 µM, using the Hill equation: %inhibition = 100/(1 + (Kd/500)). To calculate z-scores, we used a VBA macro in Excel (Microsoft) to permutate inhibitor name labels and generate 100 randomised sets of %inhibition results. From these, we calculated 100 sets of Pearson correlation coefficients to estimate the null distribution of correlation coefficients for that screen /standard deviation of null distribution.Using the mean of duplicate determinations, a standard score was calculated for each inhibitor treatment where standard score = (mean absorbance in presence of inhibitor - mean absorbance of multiple DMSO controls)/standard deviation of DMSO controls. The inhibitory effect of each compound was defined as follows: %inhibition = 100 × (standard score for inhibitor/lowest standard score on plate). The lowest standard score on the plate was either from the EDTA-treated control wells or from those containing the most inhibitory compound. The %inhibition scores for all inhibitors were then compiled as the inhibition fingerprint of the phosphorylation event probed. Using Prism (GraphPad), Pearson’s correlation  n inhibitors and checked how many times the true hit was contained in the top k kinases ranked by correlation with the unknown kinase . By setting k = 1, 5 or 10  and setting n = 3,…,N and sampling 10,000 times, we generated curves representing how the uncertainty in hit identification varies with library size.To investigate how robust KiPIK results were to the size of the inhibitor library used, we downsampled inhibitor libraries and tested how easy it was to identify true hits using the downsampled versions. From existing libraries of size hclust and heatmap in the ComplexHeatmap v1.10.2 package87 implemented in R version 3.4.0 (2017–04–21)88, calculating distances using (1 − Pearson correlation coefficient) and clustering using the method Ward.D289. For bootstrapping, we used hclust within pvclust package 4 with 10,000 iterations, producing two types of p-value: Approximately Unbiased (AU) p-value and Bootstrap Probability (BP) value. The AU value is considered the best indication of robustness90.For hierarchical clustering of inhibition fingerprints, we used t-test for each siRNA treatment. Approximately 300 cells were measured for each siRNA treatment.Hela cells were grown in clear-bottomed microplates . Transfection was carried out using Interferin-HTS (Polyplus), according to the manufacturer’s instructions. The siRNA library targeted 840 genes from the Dharmacon Human Druggable siGENOME library, of which 712 were kinase-related, 110 were G-protein coupled receptor-related and 18 were phosphatase-related. After 24 h, cells were accumulated in mitosis for 6 h by addition of 200 nM nocodazole, immunofluorescently stained with mouse anti-H3T3ph (0.1 µg/ml) and rabbit anti-H2BS6ph antibodies (1:1000), followed by fluorophore-conjugated secondary antibodies, and the intensity of staining quantified by High Content Imaging. Experiments were carried out in quadruplicate. The standard score for condition was then calculated, where standard score = (mean staining intensity in presence of siRNA − mean staining intensity for entire plate)/standard deviation of staining intensity for the entire plate; p-values were calculated using one-sample 2O, samples were mounted using Prolong Gold (Invitrogen) for cover slips, or Fluoromount-G with DAPI (Invitrogen) for microplates.Cells were fixed for 10 min with 2% paraformaldehyde in PBS, washed twice in PBS, then permeabilized for 2 min with 0.5% Triton X100, PBS. After washing twice in PBS, cells were incubated for 1 h in 5% milk in PBS, 0.05% Tween20 at room temperature, and then with primary antibodies at 37 °C in 5% milk in PBS, 0.05% Tween20 . After washing twice with PBS, 0.05% Tween20, and twice with PBS, cells were incubated for 45 min with 2 µg/ml fluorophore-conjugated secondary antibodies at 37 °C in 5% milk in PBS, 0.05% Tween20. After washing twice with PBS, 0.05% Tween20, twice with PBS, and once with milliQ HFor High Content imaging of cells in microplates, the average fluorescence intensity (integrated) of mitotic cells (defined as cells positive for either stained mitotic histone mark) was measured using a widefield Nikon Eclipse Ti-E microscope equipped with an automated stage, DS-Qi1MC camera (Nikon) and High Content Analysis software (Nikon Elements with JOBS 4.12) using a Plan Apo Lambda 20x/0.8 objective and 1.5x internal magnification. Cells on coverslips were imaged with a Zeiss AxioImager Z2 microscope using a Plan-Apochromat 100x/1.40 oil objective and ZEN 2.3 software (Zeiss). Optical sections were acquired every 0.1 µm using an AxioCam MR R3 camera. Image stacks are displayed as maximum intensity projections.91 with a 6xHis coding sequence added to the 3′-end. Expression in E. coli strain BL21-CodonPlus DE3 (Agilent Technologies) was induced with 1 mM IPTG for 20 h at 18 °C. Bacterial pellets were resuspended in lysis buffer  with cOmplete™ Mini EDTA-free Protease Inhibitors (Merck). Cells were lysed by sonication, polyethylenimine was added to precipitate cellular debris and, after centrifugation twice at 3600 g for 5 min, the supernatant was incubated with glutathione Sepharose 4B resin (GE Life Sciences) for 15 min. After washing with 5 volumes of lysis buffer, the resin was incubated for 10 min in lysis buffer containing 5 mM ATP, 5 mM MgCl2 to eliminate bacterial chaperones. Recombinant proteins were then eluted with 150 mM Tris-HCl, 0.5 M NaCl, 1 mM DTT, 50 mM reduced glutathione, pH 9.2 and dialysed into 50 mM Tris-HCl, 0.3 M NaCl, 0.5 mM EDTA, 1 mM DTT, pH 8 overnight at 4 °C, in the presence of PreScission protease (Merck) to cleave GST. Following a second dialysis into 20 mM NaPO4, 75 mM NaCl, pH 7 at 4 °C for 4 h, and removal of precipitates by centrifugation at 3600 × g for 5 min, free GST was removed by adding glutathione Sepharose 4B. Recombinant proteins were further purified by ion exchange chromatography , elution with increasing concentration of NaCl, and then dialysis into 20 mM NaPO4, 150 mM NaCl, 1 mM DTT, pH 7. Finally, purified INCENP-Aurora B was treated with λ phosphatase (New England Biolabs) to remove existing phosphorylation.GST-CDK1/GST-CycB kinase was from Cell Signaling Technology (#7518), PKA-α from Millipore (14-440), PKC-δ from Promega (V3401), and Aurora B/INbox(825–918) from Millipore (14-835). Purified recombinant fragments of INCENP were from Novus Biologicals (pPEPTIDE-INCENP(369–583); NBP2-37471CUSTOM) and Upstate Biotechnology (GST-INCENP(825–918); 12-534). Purified full length INCENP-Aurora B complex was produced using the plasmid pGEX-hAurora B-rbs-hINCENP2, 0.14 M NaCl, 3 mM KCl, pH 7.4 with ~100 nM of each substrate and 10 nM of recombinant kinase for 30 min at 30 °C. When using full length INCENP-Aurora B substrate, reactions were carried out in 50 mM Tris, 1 mM ATP, 10 mM MgCl2, 1 mM EGTA, 10 mM NaF, 20 mM β-glycerophosphate, pH 7.5 with PhosSTOP phosphatase inhibitors (Merck) and approximately 70 nM of INCENP substrate and 7 nM of recombinant Cyclin B1-Cdk1 for 30 min at 37 °C. Aurora B inhibitor ZM447439 was included at 0.5 µM. To assay full-length BCL9L phosphorylation, HeLa cells lysed in 50 mM Tris, 0.15 M NaCl, 0.1% SDS, 1% Triton X-100, 0.5% sodium deoxycholate, pH 7.5, with 2.5 U/µl Benzonase (Merck) and cOmplete™ Protease Inhibitor Cocktail (Roche) were subjected to immunoprecipitation with 5 µg/ml anti-BCL9L antibodies and Dynabeads Protein G (ThermoFisher). After 1 h, beads were washed three times in PBS, split into 3 equal fractions and incubated with 1 nM PKA-α or PKC-δ, or without kinase, at 1 mM ATP in the conditions described above for peptides. After washing, bound proteins were eluted by boiling in SDS-PAGE sample buffer and analyzed by immunoblotting. When using peptide substrates, reactions were carried out at 37 °C for 30 min with 0.1 µM peptide and 0.1 mM ATP and either PKC-α in 50 mM Tris, 10 mM MgCl2, 1 mM EGTA, 10 mM NaF and 20 mM β-glycerolphosphate, pH 7.5, or with PKC-δ in 40 mM Tris, 20 mM MgCl2, 0.1 mg/ml BSA, 50 μM DTT, 50 µg/ml phosphatidylserine, 5 µg/ml 1-stearoyl-2-linoleoyl-sn-glycerol, 5 µg/ml 1-oleoyl-2-acetyl-sn-glycerol, pH 7.5, and phosphorylation was detected using antibodies as described for KiPIK screens .When using INCENP fragments as substrates, reactions were carried out in 20 mM HEPES, 1 mM ATP, 5 mM MgCl47 were treated with 0.5 µg/ml nocodazole for 8 h. After collecting mitotic cells by shake off and replating, either 100 µM Roscovitine or DMSO (vehicle control) was added together with 20 µM MG132 in the continued presence of nocodazole. After 2 h, cell lysates were prepared in 50 mM Tris, 0.3 M NaCl, 0.5% Triton X-100, 10 mM MgCl2, 5 mM EDTA, 1 mM dithiothreitol, 1 mM PMSF, 0.1 μM okadaic acid, 10 mM NaF, 20 mM β-glycerophosphate, 0.25 U/µl Benzonase, cOmplete™ Mini EDTA-free Protease Inhibitors (Merck), pH7.4 and subjected to immunoprecipitation with rabbit anti-INCENP (diluted 1:30) and Dynabeads Protein G (ThermoFisher). After washing, bound proteins were eluted by boiling in SDS-PAGE sample buffer.HeLa 1C8 cellsHeLa cells were transfected with 50 nM Mission siRNAs using Lipofectamine RNAiMAX according to the manufacturer’s instructions (ThermoFisher). After 48 h, cells were treated with 200 nM TPA for 30 min before lysis in NuPAGE loading buffer (ThermoFisher) containing 100 mM DTT, 0.25 U/µl Benzonase, and PhosSTOP (Merck). The siRNAs were: BCL9L, EH054121; AKT1, EHU083501; AKT3, EHU067201; PKA-α, EHU132541; PKA-β, EHU075621; PKC-δ, EHU067081; PKC-ε, SIHK1828; PKC-η, SIHK1833; PKG1 #1, SIHK1862; PKG1 #5, SIHK1866; PKG2 #1, SIHK1867; PKG2 #2, SIHK1868; LATS2, SIHK1044 (Sigma-Aldrich). Alternatively, cells were treated with 200 nM TPA or 10 µM Forskolin for various time periods, or were pretreated with 0.5 µM SU-14813, 0.2 µM Gö 6983, 30 µM H-89 or 10 µM MK-2206 for 30 min and then stimulated with 200 nM TPA for 30 min before lysis in 50 mM Tris, 0.15 M NaCl, 0.1% SDS, 1% Triton X-100, 0.5% sodium deoxycholate, pH 7.5, with 2.5 U/µl Benzonase (Merck) and cOmplete™ Protease Inhibitor Cocktail (Roche).SDS-PAGE was carried out using the NuPAGE Bis-Tris (ThermoFisher) or BioRad systems. Proteins were transferred onto PVDF or Nitrocellulose membranes using standard techniques. Membranes were blocked with 3–5% milk in TBS, 0.1% Tween20 for 1 h, then with primary antibodies in 3–5% milk, TBS, 0.1% Tween20 overnight at 4 °C . After washing 3 times with TBS, 0.1% Tween20, they were incubated for 1 h with HRP-conjugated secondary antibodies  in 3–5% milk, TBS, 0.1% Tween20, washed three times with TBS, 0.1% Tween20 and once with TBS, before development using ECL substrate  and exposure to X-ray film. Stripping of immunoblots was carried out with Restore Western Blot Stripping Buffer (ThermoFisher).Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


Helicobacter pylori-mediated gastric carcinogenesis is initiated by a plethora of signaling events in the infected gastric epithelial cells (GECs). The E3 ubiquitin ligase seven in absentia homolog 2 (Siah2) is induced in GECs in response to H. pylori infection. Posttranslational modifications of Siah2 orchestrate its function as well as stability. The aim of this study was to evaluate Siah2 phosphorylation status under the influence of H. pylori infection and its impact in gastric cancer progression.H. pylori-infected various GECs, gastric tissues from H. pylori-infected GC patients and H. felis-infected C57BL/6 mice were evaluated for Siah2 phosphorylation by western blotting or immunofluorescence microscopy. Coimmunoprecipitation assay followed by mass spectrometry were performed to identify the kinases interacting with Siah2. Phosphorylation sites of Siah2 were identified by using various plasmid constructs generated by site-directed mutagenesis. Proteasome inhibitor MG132 was used to investigate proteasome degradation events. The importance of Siah2 phosphorylation on tumorigenicity of infected cells were detected by using phosphorylation-null mutant and wild type Siah2 stably-transfected cells followed by clonogenicity assay, cell proliferation assay, anchorage-independent growth and transwell invasion assay.H. pylori-infected GECs as well as in metastatic GC tissues at residues serine6 (Ser6) and threonine279 (Thr279). Phosphorylation of Siah2 was mediated by MRCKβ, a Ser/Thr protein kinase. MRCKβ was consistently expressed in uninfected GECs and noncancer gastric tissues but its level decreased in infected GECs as well as in metastatic tissues which had enhanced Siah2 expression. Infected murine gastric tissues showed similar results. MRCKβ could phosphorylate Siah2 but itself got ubiquitinated from this interaction leading to the proteasomal degradation of MRCKβ and use of proteasomal inhibitor MG132 could rescue MRCKβ from Siah2-mediated degradation. Ser6 and Thr279 phosphorylated-Siah2 was more stable and tumorigenic than its non-phosphorylated counterpart as revealed by the proliferation, invasion, migration abilities and anchorage-independent growth of stable-transfected cells.Siah2 was phosphorylated in 6 and Thr279-phosphorylated-Siah2 and downregulated MRCKβ were prominent histological characteristics of Helicobacter-infected gastric epithelium and metastatic human GC. MRCKβ-dependent Siah2 phosphorylation stabilized Siah2 which promoted anchorage-independent survival and proliferative potential of GECs. Phospho-null mutants of Siah2 (S6A and T279A) showed abated tumorigenicity.Increased level of Ser H. pylori (a class I carcinogen) is the prime factor for increased risk of GC. This pathogen infects human stomach and acquires a number of adaptations to survive in its harsh ecological niche, the pylorus part of the human stomach. Infection of H. pylori initiates various pathophysiological events in the gastric epithelium which promote GC [GC progresses through multiple stages and involves various factors. omote GC –4.H. pylori infection upregulates Twist-related protein 1 and E26 transformation-specific sequence 2 which transcriptionally increase Siah2 level. Increase in Siah2 aids in H. pylori-mediated GC progression [H. pylori and induces Siah2-mediated prolyl hydroxylase 3 degradation, thus contributing to infection-induced hypoxia [H. pylori-mediated GC remain elusive.Siah proteins belonging to the E3 ubiquitin ligase family are associated with several cancers including GC –7. H. pygression . Siah2 egression . Siah2 f hypoxia , 11. Pho hypoxia . p53 kin hypoxia . Serine- hypoxia . HoweverH. pylori-infected gastric epithelium and investigated the effect of phospho-Siah2 in GC progression. Here we report that H. pylori infection enhances Siah2 phosphorylation through myotonic dystrophy kinase-related Cdc42-binding kinase β (MRCKβ), a Ser/Thr kinase. MRCKβ remains anchored by a tight junction protein zonula occludens-1 at the leading-edge of the invasive cell and controls migration [6 and Thr279 increases Siah2 stability and positively contributes in the GC progression. The presence of phosphorylated Siah2 in the human GC biopsy tissues also reveal its potential diagnostic and therapeutic importance.Keeping in purview the crucial role of Siah2 proteins in GC progression, we studied Siah2 phosphorylation status in the igration . Squamouigration  and ErbBigration  are MRCKigration  and HeLaigration . This stcag PAI positive H. pylori strain 26695, cag PAI negative H. pylori strain 8–1 and H. felis strain 49179 were purchased from the American Type Culture Collection . Immortalized non-neoplastic HFE145 GECs, other mentioned cell lines and H. pylori were maintained as per established protocols [H. pylori using various multiplicity of infection (MOI) for the indicated durations. Proteasomal inhibitor MG132 or Z-Leu-Leu-Leu-al  at 50 µM concentration was used in assays aiming to identify involvement of proteasomal degradation. Gentamicin  was used in clonogenicity assay to kill extracellular H. pylori at 150 μg/ml concentration for 3 h.MKN45 and AGS cells , rotocols . Cells wsiah2  and eukaryotic expression vector pcDNA3.1+  were purchased. Various siah2 mutants were generated from the siah2 WT constructs by site-directed mutagenesis  using suggested procedure. Primers used were as follows:Full-length human siah2 S6A: 5′ CATGAGCCGCCCGTCCGCCACCGGCCCCAGCGC 3’.For siah2 T275A: 5′ GGGAACCGGCGGAGATTGGCCTGGGAGGCCACGCCCC 3’.For siah2 T279A: 5′ AGATTGACCTGGGAGGCCGCGCCCCGTTCGATTCATG 3’.For siah2 S282A: 5′ TGGGAGGCCACGCCCCGTGCGATTCATGACGGTGTGG 3’.For mrckβ) was gifted by Prof. Naoki Mochizuki, National Cerebral and Cardiovascular Center, Osaka, Japan.pEGFP-N1-Cdc42BPB (mrckβ siRNA (Santa Cruz Biotechnology) using Lipofectamine 3000. Similar protocol was used for transfection of control siRNA (Origene) and siRNA of siah2 (Origene). G418 solution (Millipore-Sigma) was used to select stably-transfected clones following standard protocol.24 h post-plating and 1 h prior to transfection, spent media was discarded and replenished by 10% FBS (HiMedia) containing RPMI1640 media (HiMedia). Cells were transfected with DNA, P3000 reagent and Lipofectamine 3000  as per the company’s instructions. When required, cells were transfected with control siRNA  and H. pylori infection), run on SDS-PAGE and electro-transferred onto PVDF membrane. MRCKβ and Siah2 (Santa Cruz Biotechnology) Phospho-Serine (Millipore-Sigma), Phospho-Threonine , Ubiquitin  and GAPDH  primary antibodies were used to probe electro-blotted membranes. Customized human P-Ser6-Siah2 and P-Thr279-Siah2 antibodies were generated . Immunoblots were detected by Thermo Scientific SuperSignal West Femto Chemiluminescent Substrate kit  and images were captured using BioRad Chemidoc XRS+. Densitometric analysis was done using ImageLab software . Whole-cell lysates were immunoprecipitated using Siah2 antibody and immunoprecipitates were detected by western blotting.Western blotting was performed following standard protocols , 9, 20. H. pylori  and processed for immunofluorescence microscopy. Cells were incubated with desired specific antibodies at 4 °C for overnight using manufacturer’s recommended dilution. Fluorophore Alexa Fluor-488 and 594-tagged secondary antibodies  were used. 4′, 6-Diamidino-2-phenylindoledihydrochloride  was used for 20 min to stain nuclei. Digital images were taken by Nikon microscope  fitted with the digital monochrome camera DS Qi2 (Nikon).GECs were cultured on coverslips, infected with Confocal images were captured on Leica DMi8 confocal microscope  using Leica Application Suite X software. Captured images were processed and analysed using NIS Advanced Research software (Nikon).H. pylori eradication were not included in the study. The protocol used for biopsy collection was approved by the Institutional Ethics Committee for Human Research, National Institute of Science Education and Research and was in compliance with the Helsinki Declaration (2013), World Medical Association.Metastatic stage IV antral gastric adenocarcinoma biopsy samples were obtained from the gastric antrum of urease-test positive GC patients (n = 9). A noncancerous biopsy sample from the adjacent area was also obtained from each patient which was used as a paired-control. A prior-informed signed consent was obtained for all patients and patient privacy was preserved. Patients who were treated for H. felis or 1X PBS following a standard protocol [7–8 weeks old C57BL/6 mice  were blindly divided into uninfected (n = 16) and infected (n = 16) irrespective of their sex and were infected with protocol . After 1siah2 WT or siah2 phospho-null mutant (siah2 S6A and siah2 T279A) stable-transfected MKN45 cells infected with 200 MOI H. pylori for 12 h was assessed using a MTT assay kit  following the company’s recommended protocol.Proliferation of pcDNA3.1+, Stably-transfected MKN45 cells were used to assess anchorage-independent growth following our previously-established protocols , 9.siah2-expressing MKN45 stable cells were seeded in 24-well cell culture dishes with a density of 16.6 × 104 followed by infection for 12 h at 200 MOI. Next, extracellular pathogens were killed by using 150 μg/ml gentamicin for 3 h. Cells were trypsinized followed by seeding in 60 mm2 dishes with 1000 cells/dish. After 3 weeks of incubation, cells were fixed with 4% paraformaldehyde. After fixing and staining with crystal violet (0.5%), the number of colonies formed were counted.pcDNA3.1+, WT, S6A and T279A siah2-expressing AGS stable cells were seeded on cell culture inserts with 0.3 cm2 polyethylene terephthalate membrane  coated with growth factor–reduced standard Matrigel matrix  and further processed according to our previously reported protocol [pcDNA3.1+, WT, S6A and T279A protocol . Images siah2-expressing MKN45 stable cells were seeded in 24 well plate at a density of 16.6 × 104 cells/well. After 24 h of incubation, cells were infected with 200 MOI of H. pylori for 12 h. 150 μg/ml gentamicin was used for 3 h to kill extracellular bacteria. Next, cells were trypsinized and 10,000 cells were seeded in 24 well plate. After seeding, the number of cells were counted on day 2, 3 and 4. The population doubling (PD) was calculated using the formula below [pcDNA3.1+, WT, S6A and T279A la below , 24 wherThe cumulative population doubling (cPD) level was calculated by summation of the PD values obtained for each day and represented as graphs with cPD at y-axis .P < 0.05 determined statistical significance using either one-way or two-way ANOVA followed by Tukey’s post hoc test. All results represent at least three independent experimental repeats.GraphPad Prism 7.00 software  was used for statistical analyses. Values represent mean ± sem. H. pylori 12 h after infection and at 200 MOI [siah2 overexpression plasmid or the vector control followed by H. pylori 26695 (cag PAI + strain) infection for 12 h at 200 MOI. Whole cell lysates were subjected to immunoprecipitation with Siah2 antibody and immunocomplexes were separated by SDS-PAGE followed by staining of the gel with Coomassie brilliant blue (R-250). Bands were cut and mass spectrometry was done. Among the identified kinases from the Siah2-immunocomplex was MRCKβ, a Ser/Thr kinase and two isoforms of 6-phosphofructokinase (6-PFK). Our analysis further revealed that MRCKβ has four partial and one full Siah-degron motifs but 6-PFK isoforms have only one partial degron motif  for various time periods. Western blot results revealed that MRCKβ was significantly low in the infected cells at 12 h post infection as compared to the uninfected lanes  or WT  or WT mrproteome  but its siah2 WT, Siah2 phospho-null mutant-expressing stable cells were transfected with mrckβ and were infected with H. pylori or were left uninfected. Siah2 phosphorylation was increased in all H. pylori-infected cells except for S6A as detected by western blotting. In the case of phospho-null mutant T279A-expressing cells, the increase in phosphorylation post-infection was lesser than the WT and T275A-infected cells indicating that Thr279 was the prime site for Siah2 phosphorylation in infected GECs  showed abrogation of Siah2 phosphorylation as well as Siah2 downregulation by mrckβ suppression  by site-directed mutagenesis. To consistently study phosphorylation events, we generated pcDNA3.1+, WT Siah2 and Siah2 S6A, T275A, T279A and S282A phospho-null mutant stably-expressing MKN45 cells. In order to identify the phosphorylated residues of Siah2, MKN45 pcDNA3.1+, 6 and Thr279-phosphorylated Siah2 in GECs, AGS and MKN45 cells were infected with H. pylori for 12 h and western blotting was performed. Results showed enhanced level of Ser6 and Thr279 phosphorylated Siah2 in infected MKN45 as well as AGS cells  showed enhanced levels of P-Ser6-Siah2 and P-Thr279-Siah2 in H. pylori-infected GEC  and P-Thr279-Siah2 (below) as compared to uninfected controls  and the rest do not stain for the protein (v19.proteinatlas.org/ENSG00000198752-CDC42BPB/pathology) while 80% of GC tissues show expression of Siah2 protein (v19.proteinatlas.org/ENSG00000181788-SIAH2/pathology) [MRCKβ belongs to a subfamily of Rho GTPases within the AGC family of kinases . MRCKβ interacts with Cdc42 and regulates myosin II-regulatory light chain phosphorylation important for the regulation of actin-myosin cytoskeleton dynamics , 36–38. g cancer . This stg cancer . Most ofg cancer , 42 and thology) . Cdc42 ithology) . Cdc42 rthology) , 41. Sinthology) , 44, it H. pylori ensures GC progression, targeting Siah2 stability looks very promising for therapeutic intervention of GC.In conclusion, our study shows that MRCKβ-mediated phosphorylation of Siah2 increases proliferation, survival, anchorage-independent nature and invasiveness of GECs implicating a crucial link of the mechanism with GC progression. In addition, this study establishes that phosphorylation-mediated increased stability of Siah2 and concomitant decrease in MRCKβ are important immunohistochemical characteristics of metastatic GC. As Siah2 stabilization by Additional file 1: Fig. S1. Result of LR_PPI analysis showing the probability of Siah2-MRCKβ interaction. Fig. S2. Identification of novel phosphorylatable residues. (A) Novel Siah2 phosphorylatable residues along with their predicted scores as identified using NetPhos 3.0. (B) Sequence and multi-domain schematic depiction of human Siah2 protein. Identified highly probable phosphorylatable amino acids are underlined in the sequence and represented by vertical lines in the domain structure. Fig. S3i. Human gastric biopsy tissues exhibit enhanced Siah2 phosphorylation and MRCKβ downregulation. Immunofluorescence microscopy images showing the status of Siah2, P-Ser6-Siah2, P-Thr279-Siah2 and MRCKβ in Patient #1–3. Objective: 20X and scale bar represents 50 μm. Fig. S3ii. Human gastric biopsy tissues exhibit enhanced Siah2 phosphorylation and MRCKβ downregulation. Immunofluorescence microscopy images showing the status of Siah2, P-Ser6-Siah2, P-Thr279-Siah2 and MRCKβ in Patient #4–6. Objective: 20X and scale bar represents 50 μm. Fig. S3iii. Human gastric biopsy tissues exhibit enhanced Siah2 phosphorylation and MRCKβ downregulation. Immunofluorescence microscopy images showing the status of Siah2, P-Ser6-Siah2, P-Thr279-Siah2 and MRCKβ in Patient #7–9. Objective: 20X and scale bar represents 50 μm. Fig. S4. Phosphorylated Siah2 is enhanced upon mrckβ overexpression. Immunofluorescence microscopy images of AGS cells overexpressing the empty vector or mrckβ and infected with 200 MOI H. pylori for 12 h showing enhanced P-Ser6-Siah2 and P-Thr279-Siah2. Objective: 60X and scale bar represents 20 μm.


For decades, disintegrin and metalloproteinase 17 (ADAM17) has been the object of deep investigation. Since its discovery as the tumor necrosis factor convertase, it has been considered a major drug target, especially in the context of inflammatory diseases and cancer. Nevertheless, the development of drugs targeting ADAM17 has been harder than expected. This has generally been due to its multifunctionality, with over 80 different transmembrane proteins other than tumor necrosis factor α (TNF) being released by ADAM17, and its structural similarity to other metalloproteinases. This review provides an overview of the different roles of ADAM17 in disease and the effects of its ablation in a number of in vivo models of pathological conditions. Furthermore, here, we comprehensively encompass the approaches that have been developed to accomplish ADAM17 selective inhibition, from the newest non-zinc-binding ADAM17 synthetic inhibitors to the exploitation of iRhom2 to specifically target ADAM17 in immune cells. The proteolytic release of transmembrane proteins, the so-called ectodomain shedding, is a key mechanism in several biological processes, including cell-to-cell communication and immunity . A numbe2+ ion for the catalysis that is coordinated to three histidines of the conserved binding motif HEXXHXXGXXH . Th. Th11]. ADAM17 is ubiquitously expressed in mammals . Its conThe role of ADAM17 in inflammation became evident as soon as it was identified as the enzyme responsible for the release of soluble TNF. Since then, ADAM17 has been discovered to control the release of other proteins involved in this process, including IL-6R and L-selectin. This highlights the complexity of ADAM17 function in inflammation, which orchestrates not only one, but multiple key molecular pathways of the inflammatory process.Escherichia coli-mediated peritoneal sepsis, which was associated with a reduction in systemic proinflammatory cytokine levels, including TNF, and bacterial burden . H. H176]. The X-ray structure of the ADAM10 ectodomain has been recently solved and revealed a high similarity with the ADAM17 catalytic domain . They slThe majority of the small molecule ADAM17 inhibitors reported so far contain a peptido-like backbone allocated in the “primed” side, a lipophilic substituent fitting the S1′ specificity pocket and a zinc-binding group (ZBG). The most common structure comprises a sulfonamide or amide group as hydrogen bond acceptor, an aromatic or polyaromatic group interacting with the S1′ pocket and a hydroxamic acid as ZBG. For an ADAM17 inhibitor to be selective over the other ADAMs and MMPs, thus avoiding the off-target side effects, it must have a functional interaction with the “L-shaped” S1′ pocket. The majority of small molecule inhibitors of ADAM17 reported in the last 10 years have been extensively reviewed in several articles ,181,182.1 (Inspired by the Apratastat (TMI-005) and DPC-333 structure, Ouvry et al. carried out a survey of the literature to understand which are the fundamental groups of these two potent inhibitors to be exploited for the synthesis of new ADAM17 inhibitors for a topical application . Apratas1 , featuri1  preclude1, the same group adopted an assay based on TNF inhibition in human peripheral blood mononuclear cells (PBMC) to directly evaluate activity of a new series of sulfonamide-based hydroxamate derivatives . T. T8   .10, 10 , it was able to prevent the shedding of different substrates such as EGFR ligands (heregulin in A549 cells), receptors (discoidin receptor 1 (DDR1) in HCC1806 breast cancer cells and protein kinase 7 (PTK7) in human fibrosarcoma cell lines HT1080 and L622D) and cytokines  in different cell models. This substrate-selective inhibition, never reported before, was hypothesized to be due to the interaction of compound 10 with just one exosite of ADAM17 responsible for the binding of specific substrates.Even though compound 11, In 2018, a promising non-zinc-binding inhibitor, the thioxodihydro pyrimidindione ZLDI-8 11, , was disThe same group reported the activity of ZLDI-8 as an ADAM17-specific inhibitor able to disrupt the Notch pathway in hepatocellular carcinoma (HCC) cells, avoiding the NICD (Intracellular domain of Notch) accumulation in the nucleus, and inhibiting the EMT process of HCC cells . The inhIn a further study, ZLDI-8 was shown to inhibit migration and invasion in a highly aggressive type of HCC cells (MHCC97-H and LM3) in vitro and to block lung metastasis in vivo . In 2019i for ADAM17 when it is used in trans, majorly due to the cysteine rich and disintegrin domain that structurally hinder the access of TDP to the active site when ADAM17 is already folded. Secondly, TDP does not fold correctly when expressed in E. coli. Aided refolding of TPD inclusion bodies results in altered binding properties of the inhibitor against ADAM17. Thus, production of functional TDP has been complicated and only recently, due to improved biotechnology techniques, use of TPD in ADAM17-associated diseases has received growing interest [Specific ADAM17 inhibition through synthetic compounds has been proven to be challenging due to the highly conserved catalytic domain of different metalloproteases. Thus, there has been growing interest in developing ADAM17 inhibitory biomolecules based on the biology of the enzyme and its physiological mechanisms of inhibition. One of the most investigated mechanisms for its therapeutical potential is the auto-inhibition of ADAM17 by its prodomain (TACE pro-domain or TPD). This strategy is based on the evolutionary function of TDP, which is essential for a correct folding of ADAM17 and ensures its inactivation throughout the secretory pathway, thus preventing unspecific cleavage of ER-associated proteins. In contrast to the high similarity of the catalytic domain among different metalloproteases, TDP has only 23% similarity with the prodomain of ADAM10, ADAM17 closest relative within the ADAM family. Despite its potential, developing an efficient ADAM17 TDP to be used in therapy has been challenging for a number of reasons. First, TDP functions as a chaperone for the catalytic domain of ADAM17, and it has limited access when the catalytic domain is already folded. Thus, TDP has a relatively low Kinterest . ADAM17 interest . Local iinterest . In addiinterest . In addiinterest .Analysis of ADAM substrates revealed that glycosylation is often present in the vicinity of the scissile bond, and this post-translational modification enhances their susceptibility to ADAM17 by forming kinetic interactions with exosites of the enzyme. A large compound library was screened in order to identify molecules that differentially inhibit the cleavage of glycosylated or non-glycosylated substrates of ADAM17 . InhibitSolomon and colleagues have developed a strategy for ADAM17 inhibition based on a deletion mutant of the enzyme, lacking the catalytic domain . This pr2+ in the active site, and a threonine to glycine mutation in position 2 (T2G), which disrupts the interaction of TIMP-3 with the S1′ specificity pocket of MMPs. Interestingly, both -1Ala-TIMP-3 and T2G-TIMP-3 lost their inhibitory capability against MMPs, but retained it against ADAM17. These two mutants efficiently blocked TNF release in PMA stimulated macrophage-like cells. In addition, -1Ala-TIMP-3 and T2G-TIMP-3 were able to inhibit the aggrecanases (ADAMTS-4 and -5) and block aggrecan degradation, which is, together with TNF release, a key feature of arthritis [Among the four mammalian TIMPs, TIMP-3 is unique in its ability to inhibit ADAM17. Before that the crystal structure of ADAM17/TIMP-3 complex was resolved, Gillian Murphy’s group had already identified determinants of the ADAM17 inhibition by TIMP-3 and developed engineered TIMPs with high specificity for the proteinase . Based orthritis . Ultimatrthritis .Phage display technology has been extensively used to produce therapeutical molecules, including inhibitory antibodies that are able to block the activity of specific proteases . Given tADAM17 has been a major drug target since its discovery, as has been extensively discussed within this review. Nevertheless, development of such therapeutic molecules has been much harder than expected, majorly due to difficulties in accomplishing 1) an ADAM17 selective inhibition over other metalloproteases and 2) its specific inhibition in immune cells, but not other tissues, which would impede TNF release without affecting all other physiological functions of the protease. iRhoms are able to discriminate between ADAM17 and its closest relative ADAM10 ,64. ThusSince 1992, when it was identified as the enzyme responsible for the release of soluble TNF, ADAM17 has been considered as a major research focus and a promising drug target for the therapy of chronic inflammatory diseases. However, therapeutic inhibition of ADAM17 has been historically complicated due to two major reasons: its multifunctionality and its high similarity with other metalloproteases. ADAM17 is ubiquitously expressed in human, and it has been identified as the sheddase responsible for cleavage of over 80 substrates other than TNF. Thus, its systemic inhibition, other than blocking TNF release, ends up in mechanism-side effects for deregulation of physiological processes in diverse tissues from immune cells. In addition, its catalytic domain is highly conserved not only among ADAMs, but also among other metalloproteases of the related families of MMPs and ADAMTs. Inhibitors developed to block ADAM17 activity resulted in cross-reacting with other metalloproteases, thus deregulating physiological processes in which these enzymes play a role and leading to off-target side-effects. To overcome these limitations, several approaches have been utilized to develop molecules able to discriminate between ADAM17 and its relatives, and to inhibit ADAM17 in a specific tissue or cell-type. This review encompassed all these methods, with a major focus on those recently developed, which span from the chemical synthesis of highly specific molecules to engineering of endogenous inhibitors of ADAM17. Intriguingly, what researchers have been struggling to accomplish already exists in nature. In this regard, the discovery of iRhoms showed a revolutionary, yet physiological, way to selectively inhibit ADAM17 in immune cells and over other proteases of the same family. This regulatory mechanism offers an unprecedented opportunity to develop ADAM17-targeting therapeutics, especially in the context of chronic inflammatory diseases that are characterized by a deregulated release of TNF.


Response inhibition describes the cognitive processes mediating the suppression of unwanted actions. A network involving the basal ganglia mediates two forms of response inhibition: reactive and proactive inhibition. Reactive inhibition serves to abruptly stop motor activity, whereas proactive inhibition is goal-orientated and results in slowing of motor activity in anticipation of stopping. Due to its impairment in several psychiatric disorders, the neurochemistry of response inhibition has become of recent interest. Dopamine has been posed as a candidate mediator of response inhibition due to its role in functioning of the basal ganglia and the observation that patients with Parkinson's disease on dopamine agonists develop impulse control disorders. Although the effects of dopamine on reactive inhibition have been studied, substantial literature on the role of dopamine on proactive inhibition is lacking. To fill this gap, we devised a double-blind, placebo-controlled study of 1 mg ropinirole (a dopamine agonist) on response inhibition in healthy volunteers. We found that whilst reactive inhibition was unchanged, proactive inhibition was impaired when participants were on ropinirole relative to when on placebo. To investigate how ropinirole mediated this effect on proactive inhibition, we used hierarchical drift-diffusion modelling. We found that ropinirole impaired the ability to raise the decision threshold when proactive inhibition was called upon. Our results provide novel evidence that an acute dose of ropinirole selectively reduces proactive inhibition in healthy participants. These results may help explain how ropinirole induces impulse control disorders in susceptible patients with Parkinson's disease. •Proactive but not reactive inhibition is impaired under the influence of ropinirole vs placebo.•Ropinirole impairs the ability to raise the decision threshold when proactive inhibition is called upon.•We provide novel evidence that an acute dose of ropinirole selectively reduces proactive inhibition in healthy participants. It can 1 receptor antagonist into the rat striatum improves reactive motor inhibition, whereas infusions of a D2 receptor antagonist impairs it , the measure of proactive inhibition, under ropinirole, assuming an intraclass correlation coefficient of 0.655 with 80% power at an alpha level of 0.05. Therefore, 30 healthy volunteers  aged 19–30  participated in this within-subject study.2.2The conditional stop-signal task (CSST), shown in Reaction times were measured as the time interval between the imperative stimulus (right arrow) onset and the button press. Trials were first organised into whether they were to the critical or non-critical direction. For ease, the remainder of this section will refer to ‘right’ as the critical direction and left as the non-critical direction. The critical go reaction time was the reaction time measured on critical (right arrow) go trials and the non-critical go reaction time was the reaction time measured during non-critical (left arrow) go trials. p(inhibit) was calculated as the proportion of successful stop trials (where the participant correctly aborted their response) to the critical (right) direction. The reaction time on failed stop trials  was also calculated (Stop respond reaction time). The stop-signal reaction time (SSRT) was calculated using the integration method , which i2.3The study was approved by the Local Ethics Committee (UCL Ethics ID: 9669/002). Written, informed consent was obtained from each participant. After performing one block of the CSST, as a practice block, participants were then given one of two pills (ropinirole 1 mg or placebo). Both the participants and the experimenter were blinded and only a third-party investigator, not part of the study, knew the identity of the pills. For each participant, the pill given was selected using a random number generator. The participant stayed in the room for 1 h, a time period consistent for ropinirole to reach an appropriate blood level to have CNS effects . Followi2.41), activity increases towards one of two decision boundaries  at a rate (v). When activity reaches one of the thresholds, the choice is selected; after another delay for motor execution (n2), the choice is made. Usually, the delays for sensory processing and motor execution are combined into one non-decision time (n).A Bayesian hierarchical drift-diffusion model (HDDM)  was usedWe used the HDDM to investigate how ropinirole modulated decision-making parameters during the CSST. Crucially, the HDDM results in better model fits by taking advantage of both the similarity and differences between the participants’ performance; individual participant parameters are drawn from a group posterior such that if participants are similar, the variance in the group distribution is small. We used the HDDM toolbox  to estim2.5We were particularly interested in how behavioural inhibition was altered under the influence of the dopamine agonist, ropinirole. To this end, we performed paired t-tests between conditions (ropinirole and placebo) for the following parameters: RDE (proactive inhibition), SSRT (reactive inhibition), CIS, and go discrimination errors. We considered p values of <0.05 as statistically significant.Using the HDDM allowed for Bayesian analysis of posterior probability distributions for each parameter, between drug (ropinirole vs placebo) and trial type  conditions. Posterior probabilities of greater than 95% were considered significant.33.1Behavioural measurements are shown in 3.2t-test between the RDE for placebo and ropinirole and found significantly reduced proactive inhibition on ropinirole . The SSRT is a measure of reactive inhibition; it is the estimated time taken for stopping on presentation of the stop signal. A paired t-test comparing the SSRTs between drug conditions showed that reactive inhibition was unchanged on ropinirole relative to placebo . We also found that under the influence of ropinirole, participants made more go discrimination errors than when on placebo . CIS was unchanged on ropinirole compared to placebo .We performed a paired 3.3Of the seven models we tested, the one with the lowest DIC was a model allowing boundary separation, drift rate and non-decision time to vary ; parametFrom the behavioural analysis, we saw that the RDE was smaller on ropinirole than on placebo; indicating that participants engaged in less proactive inhibition on ropinirole. To investigate how this could arise, we looked for interaction effects between drug and trial type for each DDM parameter. We found that ropinirole specifically impaired the ability to raise the decision threshold on trials when stopping might be required (posterior probability 98.60%). A significant interaction between drug and trial type was not present for drift rate (posterior probability 85.43%) or non-decision time (posterior probability 48.99%).44.13 affinity. The data show that proactive inhibition is reduced with administration of ropinirole, indexed by a significant decrease in the RDE. This was accompanied by an increase in the number of go discrimination errors on ropinirole relative to placebo. This is a rather surprising finding since the imperative stimuli are unambiguous, but consistent with a previous report that dopamine replacement in PD resulted in more perceptual decision-making errors on a random motion dots task  availability and response inhibition  show impairments in a variety of tasks probing response inhibition , althoughibition . Intervehibition  and dopahibition , 2011.A role of ropinirole in response inhibition has been previously explored in healthy adults. In that study, participants were randomised to receive a placebo, 0.5 mg or 1 mg of ropinirole and asked to perform two tasks of response inhibition: a the stop signal task to measure reactive inhibition and a balloon analogue risk task, measuring decisional inhibition, which could be regarded as a form of proactive inhibition. Interestingly, this group also genotyped their participants to derive a dopamine genetic risk score , which indexed basal dopamine neurotransmission. They found that ropinirole modulated both forms of inhibition depending on individuals' genetic risk score and concluded, as have others , that do1 and D2 gene polymorphisms have been shown to predict engagement of proactive inhibition (measured as post-error slowing in a go/no-go task), such that increased D1 and decreased D2 receptor expression are associated with increased proactive inhibition (3 receptors, our work extends the mediators of proactive inhibition to include D3 receptors, as well.Although the aforementioned studies have informed the role of dopamine in reactive inhibition, the literature concerning dopamine and proactive inhibition remains scarce. Dhibition . Given t4.2We investigated the strategy that participants employed when stopping might be required. Under both ropinirole and placebo, our participants decreased their non-decision time, reduced their drift rate and increased their boundary separation on trials when stopping might be required, the latter confirming previous literature . We thenModulation of the dopaminergic system has been shown to alter strategy used during decision-making. Beste and co-workers used methylphenidate, a dopamine/noradrenaline reuptake inhibitor, to study perceptual decision making in healthy individuals using the random motion dots task. They found that administration of methylphenidate significantly increased drift rates compared to placebo and showed that modulation of the dopaminergic system can selectively modulate evidence accumulation . PolymorThe speed-accuracy tradeoff is a feature of decision-making, whereby decisions that are made faster suffer from decreased accuracy, whereas those that are more accurate are generally slower. The mechanisms by which this tradeoff occurs have been explained using DDMs, with increases in boundary separation resulting in greater accuracy and slower reaction times . Given t2 receptors, ropinirole has a relatively higher affinity for D3 receptors than bromocriptine, and ropinirole has a higher affinity for D3 receptors than D2 receptors (3 receptors. These studies and our own highlight that the role of dopamine in perceptual decision-making is complex, with interactions between neurotransmitter/receptor specificity and design of behavioural tasks.Winkel and colleagues have investigated the role of a dopamine agonist, bromocriptine, on the speed-accuracy tradeoff using the random motion dots task. They failed to find an effect of bromocriptine on boundary separation in mediating the tradeoff . Crucialeceptors . Furthereceptors . The ran4.33 receptors, we are aware that it acts on the family of D2-like receptors, which include D2, D3 and D4 receptors (3 receptor activity. Whether our findings can be generalized to all dopamine agonists remains to be established; the effect observed in this study may be specific to ropinirole. Future studies could therefore aim to explore a number of dopamine agonists with varying D3 receptor activity. If causative, then deficits in proactive inhibition should scale with dopamine agonist D3 receptor affinity.We are aware that a multitude of factors, such as BMI or smoking status, affect drug pharmacokinetics and pharmacodynamics. Despite going unmeasured, these confounds are limited by a within-subject design. Although ropinirole has a relatively high affinity for Deceptors . TherefoWhilst we have drawn parallels between our findings and impulsivity in PD, we note two important caveats; our participants have a preserved dopaminergic state, whereas patients with PD have variable dopaminergic denervation in motor and limbic regions. Furthermore, the doses received by patients with PD typically exceed those used in this study. In view of these differences, conclusions of acute dopaminergic administration in healthy populations cannot be extrapolated to patients with PD. Although response inhibition is impaired in PD patients irrespective of dopaminergic medication load , the spe53 affinity to induce impulse control disorders in susceptible patients with Parkinson's disease.Our results from a placebo controlled, double-blind study provide novel evidence that an acute dose of ropinirole selectively reduces proactive inhibition in healthy participants. The results of the HDDM further indicate that ropinirole results in less context-specific adjustment of the boundary separation when stopping is potentially required. These results may have implications for the potential of this dopamine agonist with high DMedical Research Council UK. The authors have nothing to disclose.This study was funded by doctoral training grant MR/K501268/1 from the Vishal Rawji: collected and analysed data, contributed in the design of the study, involved in the drafting and revisions of the manuscript, approved this manuscript. Lorenzo Rocchi: involved in the drafting and revisions of the manuscript, contributed in the design of the study, approved this manuscript. Tom Foltynie: involved in the drafting and revisions of the manuscript, contributed in the design of the study, provided the ropinirole and placebo used in the study, approved this manuscript. John C. Rothwell: involved in the drafting and revisions of the manuscript, contributed in the design of the study, approved this manuscript. Marjan Jahanshahi: involved in the drafting and revisions of the manuscript, contributed in the design of the study, approved this manuscript.


The strength of the alongshore wind controls the thickness of the inflowing warm water layer and the rate of basal melting.Mass loss from the Antarctic ice sheet, Earth’s largest freshwater reservoir, results directly in global sea-level rise and Southern Ocean freshening. Observational and modeling studies have demonstrated that ice shelf basal melting, resulting from the inflow of warm water onto the Antarctic continental shelf, plays a key role in the ice sheet’s mass balance. In recent decades, warm ocean-cryosphere interaction in the Amundsen and Bellingshausen seas has received a great deal of attention. However, except for Totten Ice Shelf, East Antarctic ice shelves typically have cold ice cavities with low basal melt rates. Here we present direct observational evidence of high basal melt rates (7–16 m yr East Antarctic ice shelves typically have cold ice cavities with low basal melt rates. Here the authors direct observational evidence of high basal melt rates beneath Shirase Glacier Tongue in East Antarctica, driven by inflowing warm water guided by a deep continuous trough extending to the continental slope. Most mass loss from the Antarctic ice sheet results from iceberg calving and basal melting of its marginal ice shelves, with basal melting contributing more than half of the total4. As ice shelves have a buttressing effect on seaward glacial flow5, their thinning or retreat due to increased basal melt will cause an acceleration in ice discharge into the ocean, and subsequent sea-level rise.Recent satellite observations have demonstrated accelerated ice flow from the Antarctic ice sheet4 seem to be commonly found in regions where the Antarctic Circumpolar Current (ACC), transporting warm Circumpolar Deep Water (CDW), approaches the continental slope, or CDW-origin water is transported poleward along the eastern limb of cyclonic gyres. In this context, the Amundsen and Bellingshausen Sea (ABS) ice shelves in West Antarctica are those that are most susceptible to ocean heat flux that results from CDW inflow8. An increase in basal melting and consequent thinning of ABS ice shelves coupled with increased ice sheet mass loss is principally driven by strengthening of CDW inflows9.Ice shelves with high basal melt rates10, with East Antarctic ice shelves typically having cold ice cavities. An exception is Totten Ice Shelf (TIS) whose continental slope is located near the eastern limb of a cyclonic gyre in the Australian-Antarctic Basin12. Recent hydrographic observations have revealed relatively warm, modified CDW inflows into TIS cavity14, which could explain basal melt rates (>10 m yr−1) comparable with those for the ABS ice shelves4.In contrast, East Antarctic continental shelves are primarily occupied by “cold waters”15  with relatively high satellite-derived basal melt rates (7.0 ± 2.0 m yr−1)4. Until recently, heavy land-fast sea ice has prevented shipboard observations even in summer, and so we have no direct hydrographic evidence to determine the effect of the ice tongue on the ocean, or the processes by which the ocean heat gains access to the ice base. In austral autumn 2016, however, large areas of fast ice broke up18, allowing us to access the bay in January and February 2017, to make the first comprehensive shipboard observations in front of SGT  in Lützow-Holm Bay (LHB), where the continental slope is located near the eastern limb of the Weddell Gyre in the Weddell-Enderby Basin15 Fig. . ShiraseSGT Fig. ; MethodsoC), fresh (~34.23), and oxygen-rich (~6.8 ml L−1) Winter Water (WW: defined as T < −1.5 oC)19 is found from the sea surface to 350–400 dbar. Warm (~0.69 oC), saline (~34.66), and oxygen-poor (~4.6 ml L−1) modified Circumpolar Deep Water (mCDW) lies beneath the WW, filling the deep trough to the ice front , indicating modification of mCDW during its journey from shelf break to ice front. However, the mCDW’s temperature at the ice front still exceeds the in-situ freezing point by more than 2.7 °C, with the potential to cause strong melting at the base of the SGT. The mCDW therefore represents a large source of ocean heat that would cause strong melting were it to come into contact with SGT’s base. Although our CTD profiles did not reach the sea floor (Methods), the absence of dense shelf waters in LHB suggests that near-sea floor mCDW temperatures are comparable to or warmer than the deepest observation at each station.An important topographic feature in LHB is a deep glacial trough in the center of bay, providing a connection from the shelf break to the SGT ocean cavity Fig. . It is ~ont Fig. . The oce, fresh ~4.23, and20  were observed near the surface  and salinity  are along a line connecting endmembers of mCDW and glacial meltwater. The lowest δ18O value ~−0.8‰, which is comparable to the surface water in front of Dotson Ice Shelf21, is found near surface . Ice front waters with properties lying along the mCDW-glacial melt line are a result of ocean-SGT interaction; that is, the lower δ18O waters contain larger glacial meltwater fractions. In contrast, the δ18O-salinity relationships deviate from the mCDW-glacial melt line with distance from the ice front, and δ18O values near the surface layer are much higher at the bay mouth (~−0.5~−0.4‰).Here, we identify the presence of glacial meltwater from relationships between ity Fig. . Overallδ18O can be used as conservative parameters to quantify the respective freshwater contribution from glacial and sea-ice meltwaters22 (see Methods). The largest glacial meltwater fraction ~2%, comparable with those observed near Pine Island Glacier Ice Shelf24, is found near the sea surface at the ice front stations  and spring (October) in 1990 also show anomalous warm signals within the subsurface layer 200–400 dbar), with the subsurface warm signals gradually diminishing with distance from the ice front  as a result of enhanced Ekman convergence forced by intensified easterly winds, while the WW layer thickness is decreased  when the easterly wind relaxes in summer. The seasonal variation in prevailing wind is associated with equatorward (in summer) and poleward  shifts of the atmospheric convergence line around the Antarctic continent25. Alongshore wind speed from the ERA-Interim reanalysis on the northeast continental slope  × v, using subsurface mooring data at 300 dbar near the ice front  to examine the seasonal variability in SGT basal melting and attempt to provide supporting evidence for its link to variability in alongshore wind stress over the continental slope. The coupled model reproduced the major observed features: a circulation pattern comprising mCDW inflow into the SGT cavity, and northward outflow from beneath it, and the water mass structure and properties along and across the deep trough  is maximum in summer ~14.5 m yr−1 and minimum in fall ~6.0 m yr−1, corresponding to the seasonal variability of ocean heat flux from mCDW flowing into the SGT cavity  from a site on the SGT strongly supports the seasonality in the SGT basal melting deduced from the results of the hydrographic observation and model simulation, although the ice radar measurement was conducted at a single point located ~45 km south of the SGT ice front and ~16 km north of the southernmost SGT grounding line Fig. . The tim−1 in January, which is broadly consistent with simulated (14.5 m yr−1) and ApRES-derived (15–18 m yr−1) estimates , which is somewhat larger than but the same order as the value from the simulation 4 estimates.Here, we estimate SGT’s basal melt rate, using across-trough distributions of observed glacial meltwater Fig.  and the tes Fig. . By assutes Fig. , the annThe first comprehensive ship-based observations, available past data, wind reanalyses, coupled ocean–sea ice–ice shelf model, and ice radar measurement on the SGT reveal (1) strong basal melting beneath the SGT by the year-round warm water inflows and (2) that the seasonal variation in the prevailing alongshore wind on the continental slope is a key factor controlling the ocean heat flux carried by mCDW into the ice cavity, and, therefore, the magnitude of SGT basal melting.−1)33, except for Totten Ice Shelf4. The observed mCDW temperature at the SGT ice front 4 in East Antarctica.A striking outcome of this study is the identification of a new hot spot of strong glacial ice-ocean interaction in East Antarctica dominantly driven by warm mCDW inflows Fig. , which io–40oE) is located at the eastern limb of the Weddell Gyre where the southern boundary of the ACC begins to deflect southward 36 than for the SGT cavity  is associated with a strengthening and poleward shift of the Southern Ocean westerly windsThe bathymetry data from ship-based multibeam echo sounders, point echo sounding via sea-ice drill holes, hydrographic survey-based nautical charts made by the Japan Coast Guard (JCG), and the global relief model ETOPO1 were used when making the bathymetric grid in this study.Shirase. The sounder has 205 beams with a transducer of 2 (transmission) by 2 (receiving) degrees. The data were acquired during following five expeditions: the Japanese Antarctic Research Expeditions (JARE) 51st during December 2009 to February 2010, JARE 52nd during December 2010 to February 2011, JARE 53rd during December 2011 to March 2012, JARE 54th during December 2012 to February 2013, and JARE 55th during December 2013 to February 2014  installed on the Japanese icebreaker 44). The measurements were carried out using echo sounders of the GS-3 (28.5 kHz)45 and NSL-1300 (20 kHz)46. Based on results of repeated measurements at the same position, difference of measured depth values from each instrument was less than less 15 meters for ~600-meters-deep seafloor44. The survey lines were basically set from east toward west with 1-km intervals. This operation was conducted by using two or more flags and interval distance was measured by the distancemeter of the snow vehicle. Each position was checked up by surveying technique such as triangulation, and resulted error in location of sounding stations was less than 1 km in the greater part of the survey area44. These data were compiled into the submarine topographic map of Lützow-Holm Bay and published47.The point echo sounding data via sea-ice drill holes were acquired in winter seasons during the JARE 9th in 1968 to JARE 22nd in 1981 . This observational campaign was conducted during the 58th Japanese Antarctic Research Expedition (JARE 58th), under the ROBOTICA (Research of Ocean-ice BOundary InTeraction and Change around Antarctica) project of National Institute of Polar Research. The distribution of observation stations within LHB  analysis, which were processed at the laboratory of Institute of Low Temperature Science, Hokkaido University in Japan. Water samples were nominally taken at 20, 50, 100, 200, 300, and 500 dbar. In addition, an expendable CTD probe  was deployed on the northeast slope region of LHB  in 2017, we carried out comprehensive ship-based hydrographic observations at 31 stations in Lützow-Holm Bay , meteoric water , and mCDW  observed at the mouth of LHB (Sta.G3).Salinity and oxygen isotope ratio T and S, DO and T, and DO and S) using CTD-measured temperature, salinity, and DO (dissolved oxygen) as conservative parameters , WW , and glacial meltwater . Representative values observed at the regions of northeast continental slope and bay mouth are adopted as values for endmembers of mCDW and WW. Temperature of pure glacial meltwater is the intercept of the extrapolated Gade Line with S = 0 54.Assuming that the observed water mass is explained as a mixture of three endmembers of modified Circumpolar Deep Water (mCDW), Winter Water (WW), and glacial meltwater, the three-component mass balance is described as follows:With the results from in-situ hydrographic observations and a coupled ocean–sea ice–ice shelf model, we estimate a basal melt rate of SGT. This estimate needs to posit the premise that all the observed glacial meltwaters at the ice front Fig.  are (1) 2), we obtain a glacial meltwater transport of ~20.9 Gt yr−1 when we adopt mean January values of the observed glacial meltwater fraction of 1.06% 4 yields a basal melt rate of ~25.4 m yr−1 for January. By assuming the simulated seasonal variation in basal melt rate  to regionally enhance the horizontal resolution around the Lützow-Holm Bay (LHB) region, while keeping the model domain circumpolar Southern Ocean with the artificial northern boundary at around 30°S.This study used a coupled ocean–sea ice–ice shelf model55. The sea ice component used one-layer thermodynamics56 and a two-category ice thickness representation57. Prognostic equations for momentum, mass, and concentration were taken from Mellor and Kantha58. Internal ice stress was formulated by the elastic-viscous-plastic rheology59 and sea ice salinity was fixed at 5 psu.The vertical coordinate system of the ocean model was z coordinate. The vertical grid spacing was 5 m (4 grid levels) and 20 m (49 grid levels) for 0–20 m and 20–1000 m depth range, respectively. In 1000–2000 m/2000–3000 m/3000–5000 m range, we used 20/10/10 grid levels with a spacing of 50/100/200 m. The maximum ocean depth in the model was set to 5000 m to save on computational resources. A partial step representation was adopted for both the bottom topography and ice shelf draft to represent them optimally in the z-coordinate ocean model61. This model did not include tidal forcing, and thus we used the velocity-independent coefficients for the thermal and salinity exchange velocities 60. The modeled meltwater flux and the associated heat flux were imposed on the ice shelf–ocean interface. Note that the modeled basal melt rate and amount depend on the selected values of the coefficients61. Estimates of basal melt rate from the oceanographic and ice radar measurements support that our choice of the parameters is reasonable in this modeling framework.In the ice shelf component, we assumed a steady shape in the horizontal and vertical directions. The freshwater flux at the base of ice shelves was calculated with a three equation scheme, based on a pressure-dependent freezing point equation and conservation equations for heat and salinity62, while ice shelf draft and bathymetry under the ice shelf were obtained from the 1-min refined topography (RTopo-2) dataset63. The bottom topography in the LHB region (35–40°E and 68–70°S) was replaced with a detailed topography that blended multi-beam survey in JARE 51st–55th, depth information at control points from the Japan Coast Guard, and ETOPO1 (see Methods for details of the acquisition and processing of bathymetric data used in this model). Fast ice is sea ice that is fastened to the Antarctic coastline and the edges of ice shelves. Extensive fast ice has been identified along the East Antarctic coast64. We introduced areas of multiyear fast ice into the model as constant-thickness (5 m) ice shelf grid cells. Although in reality the horizontal distribution and thickness of fast ice vary seasonally and interannually65, as a first approximation the spatial distribution of multiyear fast ice in the model was assumed to be constant.The horizontal grid spacing over the LHB region was less than 2.5 km. This relatively high horizontal resolution enabled us to produce a realistic coastline and bottom topography. The bathymetry for the Southern Ocean in this model was derived from the ETOPO166 throughout the water column with a damping time scale of 10 days. Outside of the focal region the horizontal resolution becomes coarser than 10 km, and sea surface salinity was restored to the monthly mean climatology to suppress unrealistic deep convection in some regions . Daily surface boundary conditions for the model were surface winds, air temperature, specific humidity, downward shortwave, downward longwave, and freshwater flux. To calculate the wind stress and sensible and latent heat fluxes, we used the bulk formula67. When the surface air temperature was below 0 °C, precipitation was treated as snow. Daily reanalysis atmospheric conditions were calculated from the ERA-Interim dataset68. The ice-ocean model was first integrated with fast ice for 20 years using 2005 forcing, and then hindcast simulations with and without fast ice (FI and NOFI cases) were carried out for the period of 2006–2017 with interannually varying forcing. In this study we utilized the model results from the NOFI case, as the observations were carried out when the fast ice was absent.North of 40°S, temperature and salinity were restored to the monthly mean climatology of the World Ocean Database69.The autonomous phase-sensitive radio echo sounder (ApRES) is an active radar that was deployed on Shirase Glacier Tongue. It uses the frequency modulated continuous wave approach, transmitting a tone that scans from 200 to 400 MHz over a period of 1 s, at an output power of 100 mW. The instrument measures the change in distance between the radar antennas and the ice base, assuming an appropriate speed of radio waves in ice, and, after appropriate processing, the data yield a time series of basal melt rates70. The result is a sequence of radar returns, which retain both the phase and amplitude of the signal.The ApRES dataset covered a period of 354 days from February 2, 2018. Once every hour the instrument collected a burst of 20 measurements over a period of about 21 s. To process the data, each burst was averaged, and then its Fourier transform calculated using the methodology described by Brennan et al.−7 m. However, the thickness variation is the result of a combination of several effects: basal melting, strain thinning in the ice column, vertical compaction in the firn, sinking of the antennas in the snow, and the temperature sensitivity of instrument itself. The accuracy of the melt rate estimates is therefore set by the ability to account for these other effects.Each return shows a strong reflecting horizon at a depth of about 500 m, indicating the depth of the ice base below the surface, assuming a dielectric permittivity of 3.18 throughout the ice column. The accurate depth of the ice base is not required here, but the phase sensitivity of the measurement means that the vertical motion of the base with respect to the radar antennas can be monitored with a precision that formally depends on the signal to noise ratio of the signal: a high signal to noise ratio of 60 dB, as in the case of the bed reflection in the present study, yields a range precision of less than 10A key assumption is that the shape of the ice base does not change with time. This is not the case at the Shirase site, and so some care was needed to reduce the effect of the time-variation of the shape of the basal echo on the extracted ice thickness time series. This was achieved by calculating the phase of the cross correlation between the first ten meters of each basal return and the subsequent one. Although the details of the resulting time series is sensitive to which ten-meter portion of basal echo is used, the broad pattern was robust.−1.By assuming that internal reflecting horizons from within the ice column are fixed in the ice, we use their changes in range to determine the non-melt induced contributions to the thickness change, including the apparent contribution from the temperature-induced variations in the instrument. In principle, this allows us to find the thickness change contribution due to basal melting. As the strength of internal reflections is relatively low, their phase is known with less precision, and short-term , non-melt-induced variations in thickness are difficult to extract. This is important for tidally-induced vertical strain, which is expected to make a strong contribution. For that reason, we filter the signal to remove variability at timescales of 36 h and shorter. The final result is a basal melt rate time series uncontaminated by other factors affecting the ice shelf thickness. As a result of the effects discussed above, the estimated error of the derived melt rate is 0.5 m yrSupplementary InformationPeer Review File