We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases’ substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information  using publically available MS data, including breast, colon, and ovarian cancer models. The benchmarking reveals that co-phosphorylation analysis can significantly improve prediction performance when static information is available (about 35% of sites) while providing reliable predictions for the remainder, thus tripling the KSAs available from the experimental MS data providing to a comprehensive and reliable characterization of the landscape of kinase-substrate interactions well beyond current limitations. Kinases play an important role in cellular regulation and have emerged as an important class of drug targets for many diseases, particularly cancers. Comprehensive identification of the links between kinases and their substrates enhances our ability to understand the underlying mechanism of diseases and signalling networks to drive drug discovery. Most of the current computational methods for prediction of kinase-substrate associations use static information such as sequence motifs and physical interactions to generate predictions. However, phosphorylation is a dynamic process and these static predictions may overlook unique features of cellular context, where kinases may be rewired. In this manuscript, we propose a computational method, CoPhosK, which uses the mass spectrometry based phosphoproteomics data to predict the kinase for all identified phosphosites in the experiment. We show that our approach complements and extends existing approaches. PLOS Computational Biology Methods paper.This is a Protein phosphorylation (PP) is a post-translational modification that is central to cellular signalling where networks composed of kinases, phosphatases, and their substrates regulate the sites and levels of phosphorylation at the molecular level. MS-based approaches based on phosphopeptide enrichment can report the identity and intensity of thousands of protein phosphorylation sites in the context of their phosphopeptides –4 and thPrevious KSA prediction methods have focused mainly on the consensus sequence motifs recognized by the active sites of kinases –11. The co-phosphorylation patterns from MS data to provide global predictions of KSAs  Patient-Derived Xenograft (PDX) tissues: Huang et al. used the proteomic method called isobaric tags for relative and absolute quantification (iTRAQ) to identify 56874 phosphosites in 24 breast cancer PDX models [et al, the reference sample used to compute fold change is comprised of a standard sample from 16 out of 24 tumours with equal amounts from each tumour.X models . In thisOvarian Cancer (OC) tumors: The Clinical Proteomic Tumor Analysis Consortium conducted an extensive MS based phosphoproteomic of ovarian HGSC tumors characterized by The Cancer Genome Atlas [me Atlas . They haWe applied the proposed method to predict KSAs for the above data and assessed the performance of the methods in four additional independent datasets:BC PDX: The NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) conducted an extensive MS based phosphoproteomics analysis of TCGA breast cancer samples [ samples . After sBreast Cancer (BC) PDX and Ovarian Cancer (OC) PDX: This dataset was generated to analyse the effect of delayed cold Ischemia on the stability of phosphoproteins in tumor samples using quantitative LC-MS/MS. The phosphorylation level of the tumor samples was measured across 3 time points [e points . After pColorectal cancer (CC): Abe et al. performed immobilized metal-ion affinity chromatography-based phosphoproteomics and highly sensitive pY proteomic analyses to obtain data from 4 different colorectal cancer cell lines [ll lines . The datphosphorylation profile of a phosphosite for which co-phosphorylation will be evaluated using fold-change values (vs. the reference sample) as the entries in this vector. For assessing the correlations of these vectors across the data matrix, we compared the suitability of various mathematical methods from the literature. For example, for gene co-expression, Pearson’s correlation is the most popular correlation measure used to capture linear relations between the expression profiles of genes [et al. proposed biweight-midcorrelation as an alternative, and showed that it outperforms mutual information in terms elucidating pairwise relationships between genes; it is also more robust than Pearson correlation with respect to outliers [We used the data above to address two initial questions related to the appropriate development of our phosphorylation analysis method. First, we considered the type of correlation analysis approach appropriate for these data and second; we considered the effect of experimental design (e.g. number of independent samples). For the latter, we intuited that a larger number of samples should provide greater opportunity to sample a wider range of phosphorylation states based on sampling a wider range of biological variations. For the analysis, we define the vector containing the phosphorylation levels of a phosphosite across a number of biological states as the of genes , while mof genes . Song etoutliers . TherefoAs a second consideration, we reasoned that the number of biological states  may have a significant effect on the interpretation and reliability of our co-phosphorylation analysis, thus we investigated the effect of number of states on the distribution of co-phosphorylation across all pairs of phosphosites. Using the 24 breast cancer PDX samples for testing, we randomly selected 3, 6 and 12 samples and analysed the co-phosphorylation distribution for phosphorylation profiles composed of these randomly selected samples. Then, we randomized the phosphoproteomics data (see below) and compared the distribution of co-phosphorylation among phosphosites in the original data against the randomized data . We obseWe compared the distribution of co-phosphorylation for both ovarian (12-dimensions) and breast cancer (24-dimensions) datasets against three different null models to assess the statistical significance of co-phosphorylation for pairs of phosphosites that were detected in these experiments. These null models are constructed via (i) random permutation of the phosphorylation intensities of phosphosites within each sample , (ii) random permutation of phosphorylation intensities representing the phosphorylation profiles across biological states for each phosphosite, and (iii) random permutation of all intensity values in the phosphosite-biological state matrix. The results of this analysis for the breast cancer cell line dataset are presented in The distribution of co-phosphorylation for ovarian cancer samples is shown in Supplementary et al showed that phosphorylated sites that are modified together tend to participate in similar biological process [shared-kinase pairs). We thus compared the co-phosphorylation distributions  for substrates from the same kinase, as annotated by gold standards, to the original distribution. Using KSAs from PhosphoSitePlus (PSP) for each of the 347 reported kinases, we quantified the co-phosphorylation of all pairs of phosphosites that are listed as that kinase’s substrates . The distribution of co-phosphorylation of shared-kinase pairs as compared to all other phosphosite pairs is shown in As Li  process , we hypoAlthough this positive correlation of substrate pairs can be potentially explained by shared kinase annotation, the data also contains negative correlations of phosphosite pairs  that poi+. This software is designed to take three main elements into consideration to predict KSAs: Sequence motifs associated with targets of kinases, the network proximity of kinases and substrates in the protein association network, and co- phosphorylation of substrates of kinases  and we use other reported KSAs to rank the likely kinases for that phosphosite. To enhance the reliability of the predictions, we only consider kinases that have at least two reported substrates in the database. For each phosphosite in the dataset, we rank all kinases based on the scores computed by CophosK, CoPhosK+ and KinomeXplorer and determine the rank of the target kinase.To test the effectiveness of CoPhosK and CophosK+ can exploit any potential synergy between dynamic co-phosphorylation and static predictions, we investigated the performance of CophosK+ using the leave-one-out cross validation method described above. +, KinomeXplorer, and PUEL, an alternative analysis approach [+, our algorithm that comprehensively integrates co-phosphorylation, sequence motifs, and protein interactions, improves the accuracy of KSA predictions over all approaches.To investigate whether CophosKapproach , when anNext, we investigate the coverage of the phosphoproteome provided by the proposed KSA prediction methods. The number of phosphosites annotated by each method  is shown in + and CoPhosK together capture the trade-off between adding new annotations and improving existing annotations. Using CophosK+ the annotations of phosphosites with existing “static” annotations can be enhanced. Using CoPhosK, on the other hand, new annotations can be also developed for previously uncharacterized phosphoproteins. Thus, the methods create a set of medium confidence predictions for all the phosphosites and high confidence predictions (65%) for the better annotated subset. The kinases predicted for all phosphosites by CophosK and CophosK+ are available at compbio.case.edu/cophosk. Any phosphoproteomics data and pre-defined KSAs data can be used for predictions. For example, the results obtained by the application of the proposed methods using phospho.ELM  is reported in Since we can compute the co-phosphorylation among all the identified phosphosites in these studies, CoPhosK can predict the associated kinase for all of the phosphosites identified in these studies, providing annotations for 12,000 phosphosites that had no kinase annotation previously available. A downside of these predictions is that their current estimated accuracy is just over 50%, if we consider ranking in the top five a true positive . To thisThe runtime of CophosK depends on the number of kinases that should be scored for each phosphosite. We assess the runtime of CophosK using a workstation with an AMD Opteron CPU with a 1.9 GHz processor with 64G RAM. For ovarian cancer data, it takes approximately 1 hour and 20 minutes to score 75 kinases for 5017 phosphosites. For breast cancer data, CophosK scores 101 kinases for 15780 phosphosites in approximately 18 hours. Please note that the reporting time is for a single thread process. Since the scoring procedure of each kinase and substrate is independent of each other, this process is highly parallelizable and can be optimized.+ are reproducible, we compared the prediction results from datasets I and II with the other two independent public MS-based phosphoproteomics datasets from human ovarian tumors and breast cancer xenograft tissue (IV and V). 543 phosphosites appear in both ovarian cancer datasets. 373 out of 543 phosphosites have a predicted kinase in KinomeXplorer and consequently in CophosK+. We have run CophosK+ on these new datasets and crosschecked the KSA predictions. Our results showed that the top predicted kinase of 155 phosphosites in this new ovarian cancer data is identical to the predicted kinase in the previous ovarian cancer data (i.e. 155/373 = 41% reproducibility rate for the top-ranked prediction). Moreover, the top-ranked kinase for 349 of the 373 phosphosites in the previous ovarian cancer dataset are ranked in the top 5 predicted kinases in this new dataset (i.e. 349/373 = 93% reproducibility rate for top-1 vs. top-5). There were 1899 common phosphosites between the two breast cancer datasets. CophosK+ has kinase predictions for 1079 out of 1899 phosphosites. Our result showed that, for these datasets, there are 22% and 40% reproducibility in top 1 and top 5 predicted KSAs, respectively.To investigate whether the KSA predictions provided by CoPhosK+ based KSA predictions in the two biological contexts we consider (breast cancer and ovarian cancer). For this analysis, considering the top-ranked kinase as the prediction of CoPhosK+, we cluster the predictions by CophosK+ into three categories: 1. Predictions of CophosK+ that are consistent with those reported in PhosphoSitePlus. These include 402 predictions with 80 in common between breast and ovarian cancer datasets. 2. Prediction of CophosK+ is different from the kinase reported in PhosphoSitePlus. These include 393 predictions, with 29 in common. 3. No kinase annotation is available for that phosphosite in PhosphoSitePlus. These include 6425 phosphosites that are newly annotated with respect to a predicted KSA in this CophosK+ analysis, with 678 in common. Note that the predictions in the second category do not necessarily represent false positives, since a phosphosite can be targeted by multiple kinases and the annotations provided by PhosphoSitePlus are limited. The top-ranked kinase according to CophosK+ is identical for 109 phosphosites in categories 1 and 2. Among these, the kinase that is reported in PhosphoSitePlus is identical to that reported by CophosK+ for 80 phosphosites. If we define precision as the number of target kinases that are ranked first (category 1) divided by the total number phosphosites with a known target kinases in PhosphoSitePlus (union of category 1 and category 2), we can expect that (at least) 73% of top predictions which are identical between two different datasets will be correct. This improved accuracy also may be applicable in the context of expanded coverage for the 678 phosphosites that do not have any annotation in PhosphoSitePlus (category 3).In Motivated by the observation that predictions that are supported by two datasets have improved accuracy as compared to predictions that are supported by a single dataset, we also investigate the effect of the number of datasets supporting a prediction on the accuracy of that prediction. To characterize the trade-off between accuracy and coverage in integrating multiple datasets, we also assessed the number of predictions that are supported by multiple datasets as a function of the number of datasets. The results of this analysis are tabulated in + captures context-specificity of KSAs, we identify common phosphosites between two datasets such that the kinase ranked as the top kinase by CophosK+ is different for different datasets, but all the kinase annotations are reported in PhosphoSitePlus , an integrated correlation analysis framework for multiple PTMs must ultimately be developed to explain phenotype and may be effective in defining relationships between types of PTMs. Nevertheless, CophosK provides a strong data-driven approach for prediction of KSAs and drastically increases the coverage of the phosphosites for which kinase associations can be predicted. Thus, generation of more high-throughput MS-based phosphoproteomics data representing a variety of biological contexts can be used in conjunction with CophosK to better enable drug discovery and provide a deeper understanding of biological signalling.x ∈ Rm1× and y ∈ Rm1× is computed asCo-phosphorylation between phosphorylation sites is computed using Biweight midcorrelation. In contrast to other measures of correlation that use the mean to standardize observations, biweight midcorrelation uses the median. Biweight midcorrelation of two vectors In order to prioritize the kinases for phosphorylation sites, we apply Bayes’ rule to derive the score for every pair of kinase-substrate.T denote a set of all phosphorylation sites in PhosphoSitePlus and P denote the set of all phosphorylation sites that are present in the experimental data. We create a complete graph G in which the nodes represent the phosphosites in P. The weights of each edge are computed as the co-phosphorylation between the two corresponding phosphosites. Namely, for phosphosites p,q ∈ P, we denote the co-phosphorylation of p and q as cpq.Let P). Let A denote the distribution of co-phosphorylation among all pairs of phosphosites in P  ∈ P × P). On the other hand, kinase information is available for the phosphosites that are in T. We call two phosphosites a shared-kinase pair if the two phosphosites are annotated as being regulated by the same kinase in PhosphoSitePlus. We denote the distribution of co-phosphorylation among shared-kinase pairs as Ѕ  ∈ (T ∩ P) × (T ∩ P)).Note that it is possible to compute the co-phosphorylation of two phosphosites only if both phosphosites are present in the MS data  ∈ P, Pr(Cpq > cpq|S) is the probability that the co-phosphorylation of p and q would be higher than cpq given that p and q share a kinase. On the other hand, Pr(Cpq > cpq|A) represents the probability that the co-phosphorylation of p and q would be higher than cpq for any pair of phosphosites. Using the Bayesian rule, we compute the log-likelihood of the association of phosphosite p with kinase k using the weights of edges between p and its neighbours that are in Tk:For a kinase p ∈ P, CoPhosK computes all h values (i.e. the log likelihood of association of kinase k and phosphosite p) for all kinases k and ranks the kinases in decreasing order of h, where a larger value of h indicates that k is more likely to be a kinase that phosphorylates p.For a given phosphosite x. We compute CophosK+ score ) for phosphosite p and kinase k by combining CophosK score and KinomeXplorer score as follows:To integrate the co-phosphorylation-based scores with static information, we have downloaded the pre-computed data for all available predictors on known phosphorylation sites from KinomeXporer-DB.version59. The scores reported by KinomeXplorer-DB represent the likelihood of association of kinases and substrates based on the integration of protein interaction based scoring and sequence-based scoring. Assume that the KinomeXplorer score for the interaction between kinase k to phosphosite p is M for all phosphosite-kinase pairs, and ranks kinases for each phosphosites such that a larger value of M indicates that k is more likely to be a kinase that phosphorylates p.As in CoPhosK, CoPhosK+ also computes We downloaded the jar file provided byYang et al. and used the default parameters as initial parameters . For each kinase, we run PUEL on our data using the known kinase-substrate interactions downloaded from PhosphoSitePlus as the training set. Then, for each phosphosite, we rank the kinases using the computed scores.S1 FigUsing the 24 breast cancer PDX samples, (a) we randomly selected subsets of samples and plot the co-phosphorylation distribution among phosphosites (solid line). We also randomize that data to compare against the co-phosphorylation distribution in original data (dashed line). (b) The standard deviation of the co-phosphorylation distribution is shown in the blue line and red line shows changes of Kolmogorov-Smirnov test statistic  in different number of dimensions.(TIF)Click here for additional data file.S2 FigWe generated 1000 random vectors from a normal distribution with zero mean and a standard deviation of one and plotted the distribution of correlation among all pairs of vectors. Each panel shows the histogram of correlations among pairs of vectors for a specific number of dimensions (denoted d).(TIF)Click here for additional data file.S3 FigUsing the 24 breast cancer PDX samples, we randomly select 6, 12 and 18 subsets of samples 100 times and plot the co-phosphorylation distribution among phosphosites.(TIF)Click here for additional data file.S4 FigThe blue histogram shows the distribution of co-phosphorylation (the correlation between the phosphorylation levels) of all pairs of phosphosites in ovarian cancer . The pink histogram in each panel shows the distribution of co-phosphorylation of all pairs of phosphosites in 100 permutation tests representing (a) randomization of all entries in the phosphorylation matrix , (b) permutation of all entries across phosphosites for each state , and (c) permutation of all entries across states within each phosphosite . The distribution of co-phosphorylation in the original dataset is significantly different as compared to the distribution of co-phosphorylation in all permutations (Kolmogorov-Smirnov (KS) test p-value << 1E-9).(TIF)Click here for additional data file.S5 FigThe blue histogram shows the distribution of co-phosphorylation (the correlation between the phosphorylation levels) of all pairs of phosphosites in breast cancer . The pink histogram in each panel shows the distribution of co-phosphorylation of all pairs of phosphosites in 100 permutation tests representing (a) randomization of all entries in the phosphorylation matrix , (b) permutation of all entries across phosphosites for each state , and (c) permutation of all entries across states within each phosphosite . The distribution of co-phosphorylation in the original dataset is significantly different as compared to the distribution of co-phosphorylation in all permutations (Kolmogorov-Smirnov (KS) test p-value << 1E-9).(TIF)Click here for additional data file.S6 FigThe blue histogram shows the distribution of co-phosphorylation (the correlation between the phosphorylation levels) of all pairs of phosphosites in breast cancer . The pink histogram in each panel shows the distribution of co-phosphorylation of all pairs of phosphosites in 100 permutation tests representing (a) randomization of all entries in the phosphorylation matrix , (b) permutation of all entries across phosphosites for each state , and (c) permutation of all entries across states within each phosphosite . The distribution of co-phosphorylation in the original dataset is significantly different as compared to the distribution of co-phosphorylation in all permutations (Kolmogorov-Smirnov (KS) test p-value << 1E-9).(TIF)Click here for additional data file.S7 FigComparison of co-phosphorylation distribution among phosphosite pairs in (a) breast cancer PDX and (b) ovarian cancer tumors using biweight-midcorrelation (blue curve) and Pearson correlation (red curve).(TIF)Click here for additional data file.S8 FigNumber of annotated phosphosites and the methods’ performance separated on specific residue is reported in (a) and (b), respectively.(TIF)Click here for additional data file.S9 FigNumber of phosphosites in each dataset that are annotated by each method is shown.(TIF)Click here for additional data file.S10 FigThere is 2427 KSAs reported in the Phospho.ELM dataset and 1350 KSAs are common between PhosphoSitePlus and Phospho.ELM(TIF)Click here for additional data file.S1 Table+ identifies previously reported, but different kinases as the top-ranked candidate based on each dataset.The phosphosites listed in this table are reported to have more than one kinase in PhosphoSitePlus. CophosK(TIF)Click here for additional data file.


Understanding how the Antarctic ice sheet will respond to global warming relies on knowledge of how it has behaved in the past. The use of numerical models, the only means to quantitatively predict the future, is hindered by limitations to topographic data both now and in the past, and in knowledge of how subsurface oceanic, glaciological and hydrological processes interact. Incorporating the variety and interplay of such processes, operating at multiple spatio-temporal scales, is critical to modeling the Antarctic’s system evolution and requires direct observations in challenging locations. As these processes do not observe disciplinary boundaries neither should our future research. Understudied in the Antarctic system are the subsurface interfaces between ice-sheet, ocean and geological substrate. Here, the authors review our understanding of these components and propose new avenues of holistic dynamic modeling to achieve a unified understanding of past, present and future polar climate. Once this threshold is reached, global sea-level rise of several, possibly tens, of meters on timescales of centuries to millenia becomes inevitable4. Currently, ice-sheet models disagree on the origin and magnitude of the main processes driving AIS retreat and on the proportion of its contribution to projected sea-level rise by 2100 and beyond6, as well as in the past8.The Antarctic Ice Sheet (AIS) is at risk of reaching a tipping point  under the COP21 + 2 °C atmospheric warming limit9, with atmospheric temperature rise playing a relatively minor role. It has been observed and modeled that in areas where the bed slopes toward the continental interior, ice shelf thinning can lead to a Marine Ice-Sheet Instability (MISI)11. Both numerical simulations and observations show that if the buttressing support of the floating ice shelves is removed from the grounded ice, ice-sheet flow to the ocean may be enhanced12, leading to accelerated and substantial mass loss in a few years to a few decades13. Direct measurements of physical processes and their feedbacks leading to MISI are difficult to aquire over the spatio-temporal scales of glaciological changes, however. Alternatively, past evidence, testifying to repeated rapid retreats of both the East and West components of the AIS (EAIS and WAIS) over the past 5 million years14, provides a valuable basis for validation of the physics of numerical climate and ice-sheet models, allowing calibration between the past and future in our assessments of ice sheet evolution. As a consequence, the Intergovernmental Panel on Climate Change commissioned a special “Oceans and Cryosphere” report to urgently identify the knowledge gaps concerning the ice–ocean interactions.Over the last decade, ocean heat supply to the continental shelves bordering the Southern Ocean has been shown to be the main cause of thinning and retreat observed on several floating Antarctic ice shelves15, and to calculate the intermittent and highly-localized incursions of oceanic warm waters across the continental shelf break, partly caused by short-term mesoscale eddy formation . The resulting glacio-isostatic adjustment induces feedbacks that enhance or dampen ice retreat on various spatial and temporal scales  Fig. . In this18). Basal water, where present, may lubricate the base of an ice sheet or a glacier, causing ice flow acceleration19 and enhancing erosion of the bed18. Depending on bed morphology and conditions, subglacial water is now understood to exist in three ways  sub21  and the availability of water at their beds29. On longer timescales, groundwater could accumulate in subglacial aquifers during glaciations30 and be released during interglacials, when the ice sheet thins and retreats, inducing further ice flow changes.Thanks to radio-echo sounding (RES) campaigns, more than 400 subglacial lakes, scattered across the continent, have been identified to date21 Fig. . Their b21 Fig.  or ice-sors Fig. . How wat18  of the bed18 Fig. . Some ic 31 Fig. . This fo34 precludes detailed knowledge of basal water pathways over most of Antarctica and impedes precise simulations of basal hydrology within ice-sheet models. As micro-scale roughness is largely unknown, ice-sheet models use satellite observations of surface velocities in fast-flowing areas to “invert” for total basal friction, enabling ice stream dynamics to be simulated35. Continent-wide models have achieved success with simulations using this and related techniques36. Inverting ice velocities does not provide explicit information on the basal hydrology nor on past or future changes in basal conditions. Consequently, when climate conditions depart too much from present-day state and lead to substantial expansion or retreat of the AIS away from its present-day margins, this technique fails. Hence, there is an urgent need to refine the parameters for basal hydrologic processes in ice-sheet models. A hydrological scheme bridging the gap between relatively small-scale geophysically observed phenomena and their representation in continental-scale ice flow models has been implemented and tested within the Parallel Ice Sheet Model37. This mass-conservative scheme includes a subglacial deformable sediment layer combined with a distributed system of linked, water-filled cavities that open as a consequence of sliding and close due to the creep of ice over a range of spatial and timescales. To validate the physics of this scheme, present-day simulations should predict locations of at least the larger (> 10 km long) subglacial lakes.Despite localized high-resolution bed observations for some areas, the current low spatial resolution of subglacial topography from BEDMAP238 or for a limited area domain39. At a continent scale, the best results will be achieved once high-resolution bed topography, geothermal heat flux distribution42, and the location and thickness of subglacial sediments are known well. More direct observations of these are needed to account for hydrological processes and to improve ice-sheet models. A great utility of RES observations is in the identification and location of basal water, and mechanisms by which water may flow. RES methods are incapable of sounding through water, however, and there is a lack of information beneath suglacial water surfaces. This shortfall can be addressed by other methods such as sonar, gravity inversion, and seismic surveying that may reveal the thickness and extent of soft water-saturated basal sediments44  are needed to simulate realistic past basal hydrology and thus ice flow, in order to constrain past AIS dynamics.The need for high-resolution subglacial bed topography and seabed bathymetry is demonstrated by recent work that reveals a relict hydrological network dating back to the last glaciation and evidence for grounding line retreat across the Ross Sea shallow continental shelf50 and seismic stratigraphy54 reveal evidence for widespread past erosional and depositional modifications to the seabed over the last 34 Ma. These data show that sedimentary units deposited in the Pliocene (5–3 Ma) were eroded during late-Pleistocene glaciations in many places. Despite this evidence, paleo ice-sheet simulations of key climate intervals commonly use modern subglacial bed topography and seabed bathymetry from BEDMAP55, BEDMAP234, and IBCSO56 as boundary conditions if no other paleo-reconstructions are available57. However, inherent inaccuracies arising from heterogeneous spatial data coverage , global climate cooling led to the expansion of grounded ice and the cold-based area underneath the AIS4 Ma, glo66. The time of the reconstruction is for the presumed onset of continental glaciation at the Eocene-Oligocene Transition (EOT). Other climate transitions of specific interest for paleogeographic reconstructions include the mid-Miocene (≈ 14 Ma), mid-Pliocene (≈ 3.3 Ma), and some of the late Pleistocene super interglacials  or glacials . These times, marked in some cases by a substantial retreat of the AIS, may yield insights that allow the cryosphere community to identify upper and lower bounds for past WAIS and EAIS tipping points that may be surpassed in the future. This objective creates an impetus to develop Antarctic bathymetry/bed topography reconstructions for these selected intervals.The dynamic geological history of the AIS margins clearly demonstrates how past continental shelf morphologies differed from the modern one. However, a continent-wide paleotopographic reconstruction, partly achieved using backstripping, exists for just one important climate interval, so far, at ≈ 33.8–33.5 Ma72, however, the paucity of stratigraphic constraints hinders the comprehensive pan-Antarctic correlation of known horizons and pan-Antarctic bathymetric reconstructions. The drilling expeditions by the International Ocean Discovery Program scheduled in 2018–2020 will greatly expand knowledge of the WAIS margin , paleo-landscape, and identification of pan-Antarctic seismic horizons of regional extent, will be required. Some regional maps of key Cenozoic horizons and sequences have recently been publishedgin Fig. .73 highlight the large spread in simulated ice volumes and extents produced by the uncertainties, or the lack of definition, of past bed morphologies. For example, simulations of AIS dynamics across the EOT using a maximum, mostly emergent, topography lead to larger ice volume and extent than when using a minimum, more subdued topography. Finally, the variations in bed over time likely had consequences for the AIS response to atmospheric and ocean forcing. When tested in ice-sheet simulations, AIS sensitivity to changes in ocean temperature increases along with a gradual deepening of the continental shelves. This is because the area of ice shelves that is exposed to ocean heat increases with a deepening of the bathymetry74.Additional geophysical and drilling campaigns will be necessary to fill gaps in circum-Antarctic coverage and provide pan-Antarctic past boundary conditions to ice-sheet models. Recent numerical studies75 that restore tectonic subsidence, decompact sediment, and remove water load for a given time. The backstripping procedure consists of stripping off stratigraphic units in sequence from the youngest to the oldest, calculating the decompaction of remaining underlying sedimentary units, and isostatic rebound after removal of upper sedimentary units, at each step. The depth at the time of sediment burial  is restored for each horizon (red and yellow), deposited during the post-rift history (t0). The paleo-depth calculation takes into account the sedimentary units age  and thickness (S), water (Wd), and eustatic sea-level changes relative to present day (ΔSL). Isostatic rebound, caused by removal of sediment units and of water load, is usually calculated with either local (no rigidity and effective elastic thickness Te = 0) or flexural (Te > 0) isostasy.Paleobathymetry of a continental margin can be calculated using backstripping techniquesϕ) with depth, for each sediment type. Glacial environments undergo an additional local sediment overcompaction due to the advance of grounding ice over the sediments, accounted for using the physical properties of sediments measured at drill sites . In the case of rifted provinces, such as West Antarctica, an additional correction accounts for the subsidence caused by mantle cooling (Tm) or convection78. Using the age of rifting, the time of post-rift sedimentation, and a crustal stretching factor, thermal subsidence (TS) is determined. Tectonic structures active during the burial history can be considered when restoring the paleo-depths. In glacial environments, a final correction for glacio-isostasy is applied to the backstripping maps to account for both the eustatic and regional sea-level changes that result from the growth or decay of an ice sheet. Finally, cross comparisons between restored paleo-depths, seismic facies  and lithofacies from drill sites are crucial to validate the backstripping applied to the sedimentary units. In fact, the interpretation relies on a number of parameters and physical simplifications that approximate water, sediments, lithosphere, and mantle rheological properties and behavior52.Sediment decompaction is calculated considering the exponential decay of porosity  the geometry and draft of the ice-shelf base; (2) water properties; (3) circulation in the sub-shelf cavity ; and (4) the connection to water masses outside of the cavity ; and  the conn82. The long-term bathymetric evolution of the basins that accommodate circumpolar circulation is adequately known and reveals that the gross circumpolar circulation systems within the Southern Ocean basins were established in the Eocene/Oligocene Epoch (≈ 40–25 Ma) with the opening of the Tasmanian Gateway and the deepening of Drake Passage83. Climate simulations of the EOT have shown that the opening of the gateways did not substantially change the moisture supply to the AIS enough to explain its complete glaciation84, but the gateway opening did contribute to a large-scale cooling, leading to the gradual expansion of ice sheets85.The Antarctic Circumpolar Current (ACC) position is, to a great extent, constrained by the bathymetry of the ocean basins and gateways, and by the topography of the continental landmasses that affects the position of the local maximum of the Westerly winds86, leading to a more vigorous ACC, enhanced advection and volume of Circumpolar Deep Water (CDW), and strong-bottom Ekman transport and vice versa under cold climate conditions82. An intensification and poleward shift of near-surface ocean winds, attributed to positive Southern Annular Mode-like trends (atmospheric teleconnection) is projected for warmer climates by most climate models by 2100 86. However, Coupled Model Intercomparison Project Phase 587 (CMIP) and Paleo-modeling Intercomparison Project phase 388 (PMIP) reveal a large spread in simulated strength of the ACC among models, suggesting that the position of the Westerly winds in climate simulations is model-dependent. This is of consequence, because heat transport to southern latitudes is in part regulated by the strength of the ACC.Although the mean position of the ACC is controlled by long-term changes in ocean gateways, its strength and vigor depend upon the strength and relative position of the Westerly winds, in turn determined by the mean climate state. In general, a warmer climate is associated with a southward shift and strengthening of the Westerly winds89. Coupled atmosphere–ocean model simulations produce a more vigorous Weddell Sea gyre, with a southward shift from its current location, for times in the mid-Miocene (≈ 14–17 Ma) when the continental shelf break was located more southerly .In models, the location of the continental shelf break determines the position of regional oceanic circulation systems such as the Weddell Sea and Ross Sea gyres Fig. . The modrly Fig.  than todrly Fig. . When th2 forcing and sea-ice cover change. The degree of local bathymetric control upon ocean circulation is strongly dependent on the mean climate state. Furthermore, during the last deglaciation and the Pliocene warm periods, paleoceanographic data point to increased upwelling of CDW and incursion onto the continental shelf on suborbital timescales93 , or from physically based sub-shelf melting parameterizations in stand-alone ice-sheet models102. Those implementations are nevertheless useful to investigate how, under warmer than present mean climate states, ice sheets display threshold behavior in landward-deepening subglacial basins in response to relatively short-lived high-intensity ocean heat supply104.High-resolution bathymetry and accurate paleobathymetric reconstructions are clearly needed to properly capture kilometer-scale oceanic processes leading to heat and salt exchange at the shelf break105. It highlights the processes least understood, poorly investigated or not implemented in models, that are understood to operate in a connected manner  regulates the long-term and short-term heat/salt (AABW outflow and CDW inflow) and carbon/nutrients transfer across the shelf break and the ice-shelf cavities. The review also draws attention to the short-term interactions between ocean circulation in the ice-shelf cavities and ice-sheet dynamics across the grounding zone, which are seldom observed and largely unknown in Antarctica. These interactions potentially induce MISI. Although those processes may not operate on policy-relevant timescales, the ability to predict the AIS response to ocean warming and freshening, including the surpassing of tipping point(s) and contribution to global mean sea level, relies on advances in our understanding of interrelated processes and the feedbacks spanning the three realms in the past, present and future.This review provides insights into processes operating at the interfaces between the ice sheet, its bed, the ocean and the continental margins around Antarctica Fig. , all of ner Fig. . Atmosph107. High spatial and temporal resolution bed data are needed for models that simulate ocean circulation, including mesoscale eddy transport, basal hydrology, and marine-based ice-sheet dynamics, at the present-day margins of the ice sheet. Such data are also required to identify regions of Antarctica where the grounding line may migrate under atmospheric and ocean warming.Due to the lack of integrated observations of the AIS basal processes and ocean circulation, there is little direct knowledge of the precise range of temporal and spatial scales of interactions between these realms. The knowledge gap has consequences for climate and ice-sheet model development, and experimental design strategies, because observations are needed to validate those recently developed or to develop new parameterizations of ocean–ice-sheet interactionsTo quantify AIS contribution to past, present and future global mean sea-level variations, paleo-polar-change and contemporary polar-change communities must converge toward the same objective, which is to identify precursor signals to MISI or AIS tipping points, with substantiated timescales. Investigating the AIS sensitivity to climate changes, whether it is in the past, ongoing, or projected, requires the study of the grounding line response to a variety of external perturbations, such as sea-level rise, atmospheric and oceanic warming, local to regional wind effects (katabatic winds and polynyas), sub-shelf circulation changes and subglacial hydrology influences Fig. . Timesca108.One of the major challenges for the polar community to surmount is the representation of the interplay of long-term, large-scale processes, and small-scale, short-term processes from both observational and modeling points of view Fig. . Process110, which will allow coupled regional or global models to optimize computational resources, while simulating processes and feedbacks that occur at a variety of spatial and temporal scales.High-resolution regional simulations, and long-term simulations of the past, entail significant computational cost, so the capability of supercomputing centers must increase and/or model codes must be optimized for computation of essential feedbacks. The use of unstructured grids in climate and ice-sheet models is a good example of optimization and is under development. It allows for nested areas of horizontal mesh refinement to or higher than 1 km 111, the ongoing ROSETTA-Ice Project ; http://www.ldeo.columbia.edu/res/pi/rosetta/), whose objective is to retrieve the bathymetry beneath the Ross Ice Shelf, and forthcoming BEDMAP3, reflect continued progress in gradually filling this gap. High-resolution modeling that investigates past AIS-ocean interactions during key intervals, such as the mid-Miocene (≈ 14 Ma), mid-Pliocene (≈ 3 Ma), and some of the late Pleistocene interglacials, is also needed. The reconstruction of realistic past subglacial bed and seabed morphologies, which persists as a major challenge, is one of the major objectives for the SCAR scientific research program Past Antarctic Ice Sheet dynamics (PAIS) (https://www.scar.org/science/pais/).To support high-resolution modeling, improved continental shelf and sub-shelf bathymetry (among other quantities) is a priority. Updated and new near-shore R-TOPO2 bathymetry for the circum Antarctica112), the PMIP now in phase 4113, the PLIOcene Model Intercomparison Project114 , and the Pliocene Ice Sheet Modeling Intercomparison Project17, have highlighted the large range of climate and ice-sheet projections that result from gaps in the model physics and observations. In developing a regional polar system model for the past, present, and future, improvements in global climate models are required. This can only be achieved if present-day and paleo-climate data coverage improves, both in the polar areas and in the representation of the essential teleconnections between the equatorial region and Antarctica. Our understanding of the Polar system requires strong synergies among the polar and global climate communities to investigate interrelated processes and feedbacks in a common framework aimed at assessing the onset and surpassing of tipping point(s), and global mean sea-level changes in the past, present and future.Strong research synergies have developed between the paleo and present-day observational and modeling communities over the last two decades. Major community initiatives such as CMIP, now in phase 6 (present day and future), which includes for the first time the Ice Sheet Model Intercomparison Project 6 (sea-level contribution from the Greenland and Antarctic ice sheets


Immunoglobulin E (IgE) is the key immunoglobulin in the pathogenesis of IgE associated allergic diseases affecting 30% of the world population. Recent data suggest that allergen-specific IgE levels in serum of allergic patients are sustained by two different mechanisms: inducible IgE production through allergen exposure, and continuous IgE production occurring even in the absence of allergen stimulus that maintains IgE levels. This assumption is supported by two observations. First, allergen exposure induces transient increases of systemic IgE production. Second, reduction in IgE levels upon depletion of IgE from the blood of allergic patients using immunoapheresis is only temporary and IgE levels quickly return to pre-treatment levels even in the absence of allergen exposure. Though IgE production has been observed in the peripheral blood and locally in various human tissues , the origin and main sites of IgE production in humans remain unknown. Furthermore, IgE-producing cells in humans have yet to be fully characterized. Capturing IgE-producing cells is challenging not only because current staining technologies are inadequate, but also because the cells are rare, they are difficult to discriminate from cells bearing IgE bound to IgE-receptors, and plasma cells express little IgE on their surface. However, due to the central role in mediating both the early and late phases of allergy, free IgE, IgE-bearing effector cells and IgE-producing cells are important therapeutic targets. Here, we discuss current knowledge and unanswered questions regarding IgE production in allergic patients as well as possible therapeutic approaches targeting IgE. Immunoglobulin E (IgE) associated allergic diseases in their various forms affect approximately 30% of the world population. Symptoms may range from relatively mild, such as rhinoconjunctivitis, to potentially life-threatening, such as asthma or anaphylaxis. Development of allergic disease is associated both with environmental and individual genetic factors . The lat−5 mg/mL [1, lasting 21 days [Among all immunoglobulin subclasses, IgE stands out with respect to function, half-life, and low serum concentration. With a serum concentration of 5 × 10−5 mg/mL  it repre−5 mg/mL . Its hal 21 days . In cont 21 days . It has  21 days . This shThe central role of IgE in mediating allergic diseases makes it an important and attractive target for development of novel therapeutic approaches ,15,16. SThe pathway of B cell differentiation with respect to the nature and location of potential IgE+ memory cells as well as long-lived IgE producing plasma cells in allergy is still not completely understood. Investigation of human IgE responses are impeded by the fact that IgE-producing cells and B cells are rare in human tissues that are easy to access, such as blood, nasal mucosa, or tonsils ,25,26,271 in mice [4 subclass responses like allergic patients. With regard to allergen epitope specific Ig responses, the two species differ. Recent studies have indicated that the allergen-specific IgE responses in mice develop by switching from IgG1 to IgE [Murine models are very valuable for our understanding of general mechanisms of allergy in humans as well as for testing novel therapeutic approaches, but it is necessary to bear several differences in mind . The cyt in mice , both IL in mice . In addi1 to IgE . In this1 to IgE . However1 to IgE . In cont1 to IgE ,36. This1 to IgE ,38.With respect to the composition of the immune system, there are further differences to consider. While lymphocytes are the predominant mononuclear cells present in the peripheral blood of mice , neutroAnother caveat is that mouse models mimicking allergic rhinitis, a key feature of human disease, are rare and are hampered by technical difficulties such as measuring isolated nasal obstruction. Additionally, the nasal anatomy of mice differs from human anatomy ,55. In aIn humans, allergen-specific IgE memory persists over years or even decades, even in the absence of antigenic stimulation . At the Aside from continuous IgE production there appears to be a second mechanism where IgE production is boosted upon allergen contact as seasonal allergen exposure via the respiratory mucosa has been shown to induce a strong increase of allergen-specific IgE levels ,61. A ri1 expressing cells [However, there is also evidence indicating that sequential class switching occurs in humans ,50,51,52ng cells ,52. In tng cells ,72,73,74ng cells , their sIn summary, continuous IgE production, most likely by long-lived plasma cells as well as by memory cells rapidly responding to allergen by IgE production, seem to contribute to the maintenance of allergen-specific IgE levels in allergic patients despite the short half-life of IgE.Another open question is where the major sites of IgE production are in humans. The majority of studies investigating mechanisms of human IgE production have used peripheral blood mononuclear cells (PBMCs) ,75,76,77The assumption that IgE-producing cells are mainly localized in the bone marrow and other human lymphoid compartments is difficult to prove because these tissues are quite difficult to access in humans. It seems to be more convenient to work with human peripheral blood; however, the amount of IgE-producing cells in human blood is minimal and there are numerous cell types that carry IgE on their surface but do not produce it. In this section, we summarize current approaches for the identification of IgE-producing cells.One possible method for detecting IgE-producing cells in the blood is the ELISpot assay. This method is more effective for plasmablasts than for the determination of memory B cells, as the latter do not secrete antibodies. In order to obtain antibody-secreting cells from B cells, stimulation with CD40L and IL-4 or allergen is usually performed ,87. HoweThe most commonly used method for detecting and isolating putative IgE-producing B-lymphocytes is flow cytometry, which is based on the determination of surface markers of target cells. There are several approaches for IgE+ B lymphocyte detection. For the step-by-step determination of the total subpopulation of IgE+ B lymphocytes from the PBMC pool, first, non-relevant cells  are excluded by cells expressing the following markers: CD3, CD14, CD16, CD235a, and CD123. Consequently, IgE+ B lymphocytes are identified by positive staining for CD19 as well IgE. However, this approach does not exclusively identify IgE+ memory cells as IgE can be present on the B cell surface not only in the form of the IgE B cell receptor (BCR) but also bound to its low affinity receptor CD23 . This faIn addition to the above-stated panel, antibodies to CD23 can be added, which should help to exclude false-positive events from the gate of IgE+ cells. However, by excluding all CD23+ lymphocytes, one may potentially remove the subset of IgE-producing cells that express CD23 at the same time.Compared to the previous approach a more successful strategy may be enrichment of B lymphocytes by using magnetic separation or RosetteSep prior to antibody staining ,90. ThisAntigen-specific labeling of B lymphocytes is in general a successful method for isolating B cells responding to a particular antigen . A cruciTaking into consideration the low percentage of target cells compared to background levels, it may be preferable to use several antigen conjugates labeled with different fluorescent labels in order to show reproducible target cell isolation. For a more reliable isolation of IgE+ B lymphocytes, this strategy can be combined with additional staining for other surface Igs  ,94. JiméFurthermore, circulating IgE+ B lymphocytes can be divided into plasmablasts and memory cells based on antibodies against CD27, CD38, and CD138 . Using tTo summarize, the major obstacles in any attempt to characterize IgE-producing plasma cells and potentially IgE memory B cells in the blood of allergic patients are the small numbers of allergen-specific lymphocytes ,86 and tIn an attempt to overcome this obstacle, we have recently developed an approach where cells were stained for CD19 in combination with a fluorescently labelled monoclonal anti-IgE antibody, which discriminates membrane-bound IgE from receptor-bound IgE in the blood of birch pollen allergic patients . To detect IgE-producing B lymphocytes of patients with seasonal birch pollen allergy, we isolated PBMCs and stained them with a cocktail of antibodies CD19-PE, CD3-FITC, CD14-FITC, CD16-FITC, and anti-IgE-APC. Though both IgE-producing plasma cells and IgE+ memory cells have been observed in the blood of allergic patients ,46,86,89As sites known to house large number of memory cells, both the spleen and bone marrow are potential sites of interest ,110,111.The presence of IgE production has so far been shown at various lymphatic sites; however, the relative contribution of each of these areas to the continuous and de novo production in allergic responses remains unanswered. One may speculate that lymphatic tissues at the sites of allergen exposure  contain IgE+ memory cells, which can be activated, whereas plasma cells in bone marrow may be responsible for the continuous long-lived IgE production. Nevertheless, further investigations are needed to identify the sites of IgE production in allergic patients.IgE plays an important role in propagating allergic responses both in the early and late phases of allergic immune responses. In the early phase, IgE is central for mast and basophil degranulation upon allergen contact; however, the timely interplay between increases in IgE levels in the blood upon allergen challenge and potential rises in effector cell sensitivity are not yet fully understood. In the late phase, IgE-bearing B cells contribute to increased allergen-specific T cell response via internalization and presentation of IgE-allergen complexes via CD23 in a process called IgE-facilitated allergen presentation. The major players propagating the early symptoms experienced by allergic individuals in immediate hypersensitivity reactions are basophils and mast cells. Both of these cells utilize IgE to mediate their allergic responses, and thus understanding the kinetics of how circulating IgE influences the reactivity profile of these cells is crucial to treating the disease. Basophils and mast cells are similar in that they are both granulocytes, both are derived from hematopoietic stem cells, and both contain pre-formed intracellular granules that are rapidly exocytosed from the cell membrane upon FcεRI cross linking in response to IgE allergen recognition . DespiteDue to the high affinity of FcεRI for IgE, receptor bound IgE—in contrast to free IgE—has a relatively long half-life of 2–3 weeks . AdditioNasal allergen exposure in pre-sensitized individuals leads to a rise in allergen specific IgE with peak serum levels occurring 4–6 weeks after exposure ,127,128.The activation of allergen-specific T cells by APCs plays an important role in the development of an allergic reaction, especially in mediating late phase reactions . In factIt is therefore important to understand the modes of how IgE-allergen complexes bind to CD23  and the As IgE is central to mediating symptoms of allergic diseases, it represents an important and attractive target for developing novel therapeutics Table A11 antibody directed towards IgE, has been shown to reduce free IgE levels by 99% within 2 h of administration, and reduces human basophil responsiveness within 3 months [1 antibody for neutralization of free IgE domain and binds selectively the Cε3 and Cε4 region of IgE [The heavy chain of IgE is composed of four constant Cε domains. The binding site for IgE to its high and low affinity receptors has been mapped to the Cε3 domain ,156. Thu3 months ,160. It 3 months . It is s3 months ,162. Rec3 months  and is c3 months . Phase In of IgE . It bearn of IgE . Howevern of IgE . DARPinsn of IgE . Recentln of IgE . The appn of IgE ,171 and n of IgE ,20. ImmuTripterygium wilfordii and capable of potentiating TNF-induced apoptosis [Another strategy aims at reducing the number of effector cells. In mouse models it has been shown that amelioration of allergic disease can be achieved by targeting mast cells and basophils with anti-FcεRIa Fab coated micelles loaded with celastrol . The latpoptosis . CTLA4Fcpoptosis . CTLA4FcSeveral approaches have been made to eliminate IgE-producing cells. For example, quilizumab is directed towards the M1 domain of the membrane-bound IgE B cell receptor and was shown to reduce IgE levels as well as numbers of IgE-producing plasma cells in a murine model . DespiteThough IgE is the least abundant class of immunoglobulins with an extremely short half-life it plays a central role in allergic disease. Allergen-specific IgE production in allergic patients seems to consist of two modes: a continuous mode, which maintains IgE levels even in the absence of an allergen stimulus, and a reactionary mode, where increases of IgE production occur after exogenous allergen stimulus. Neither the precise sites nor the nature of the IgE-producing cells involved in these two modes of IgE production in allergic patients are known. Another as yet unanswered matter is the timely interplay between rises in IgE production occurring in response to allergen exposure and the loading of effector cells capable of binding IgE via their high and low affinity receptors. Due to fundamental differences between allergic patients and experimental animal models for allergy, research in patients will be required to address the open research questions regarding IgE-producing cells, mechanisms, and sites of IgE production, as well as the loading of IgE to effectors cells, which is responsible for allergic inflammation. Clinical experience with therapeutic strategies depleting IgE and preventing IgE binding to its receptors indicates that the inactivation IgE-mediated effector cell activation is not harmful. The elimination of IgE-producing cells may therefore represent a safe therapeutic strategy, which may lead to a cure of allergy but will require the identification and characterization of IgE-producing cells in allergic patients.


Of the five Kampo medicines, YKS showed the strongest inhibition (IC50 = 1.94 mg/ml), which was attributed to Uncariae Uncis Cum Ramulus. Unfortunately, we could not find the active ingredients responsible for its action. Finally, the Igut/IC50 values for the five Kampo medicines were calculated, and the DDI risk was objectively evaluated according to the criteria in the DDI guidance issued by the Japanese Ministry of Health, Labor, and Welfare and the US Food and Drug Administration. The Igut/IC50 values for the five Kampo medicines were ≤3.4. As these values were <10, they were evaluated as having a weak P-gp inhibitory effect that does not require further verification in humans, suggesting that the DDI risk due to P-gp inhibition for these Kampo medicines is low. The results should provide useful clinical information on the safety and efficacy of the combined use of Kampo and Western medicines.In modern medical care in which Kampo and Western drugs are often combined, it is extremely important to clarify drug–drug interaction (DDI) to ensure safety and efficacy. However, there is little evidence of DDI in Kampo medicines. Therefore, as part of our studies to clarify the DDI risk for Kampo medicines, we evaluated the effects of five Kampo medicines  that are widely used in Japan, on drug transporter P-glycoprotein (P-gp) using a Caco-2 permeability assay. These Kampo medicines inhibited the P-gp transport of digoxin through a Caco-2 cell monolayer. The ICThe online version of this article (10.1007/s11418-018-1222-x) contains supplementary material, which is available to authorized users. Many reports have demonstrated that various metabolizing enzymes and transporters have important roles in the pharmacokinetics of xenobiotics –4. P-glyCaco-2 cells derived from human colon carcinoma cells, which express P-gp and metabolizing enzymes such as peptidase, are widely used as an in vitro model of human small intestinal mucosa to predict the absorption of orally administered drugs  because Many traditional Japanese Kampo medicines approved by the Japanese Ministry of Health, Labor, and Welfare (MHLW) have been used for the treatment of various diseases in Japan for many years. In modern medical care in which Kampo and Western drugs are often combined, it is important to clarify DDI to ensure the safety and efficacy of their combined use. However, there is little evidence of DDI in Kampo medicines. Yokukansan (YKS), rikkunshito (RKT), hangeshashinto (HST), goshajinkigan (GJG), and shakuyakukanzoto (SKT) are Kampo medicines which have been used for treating the behavioral and psychological symptoms of dementia, including aggression and insomnia , functioIgut/IC50 values for the five Kampo medicines were calculated, and the DDI risk was objectively evaluated according to the criteria in the DDI guidance of the MHLW -Digoxin (26.3–39.8 Ci/mmol), [3H]-mannitol (17.2 Ci/mmol), and ULTIMA Gold scintillation fluid were obtained from PerkinElmer Life and Analytical Sciences . Digoxin was obtained from Alfa Aesar . Mannitol was obtained from Wako Pure Chemical Industries . Verapamil hydrochloride was obtained from Enzo Life Sciences .-mannitol (0.058 µmol/l) was loaded into apical chambers, and the monolayer permeability of mannitol was measured in the same procedure as that of the following test substances. The integrity of a cell monolayer was confirmed by measuring the TEER, and mannitol permeability was used for evaluation of the test substance.The transepithelial electrical resistance (TEER) to evaluate the integrity of the cell monolayer was measured using the Millicell ER-2 . For further verification of the tight junctions in the monolayer, warm HBSS-HEPES buffer containing mannitol (10 µmol/l) and [3H]-digoxin (0.025–0.038 µmol/l) was added into either the apical or basolateral chamber in the absence or presence of various concentrations of test substances or verapamil (50 µmol/l). Aliquot solution in the apical or basolateral chamber was collected after incubation at 37 °C for 120 min with slow rotation in the horizontal direction and addition of an ULTIMA GOLD scintillation cocktail. The mixture was immediately injected into a liquid scintillation counter  to measure the radioactivity.To evaluate the test substance effect on the permeability of digoxin via the cell monolayer, warm HBSS-HEPES buffer containing digoxin (10 µmol/l) and [Papp) of digoxin was calculated as follows:Q/dt is the slope of the linear portion of the permeated amount versus time curve (µmol/l/s), A is the effective surface area of the Transwell insert (0.3 cm2), C0 is the initial concentration of the digoxin applied at t = 0 (µmol/l), and V is the volume of the receiver chamber (ml).The apparent permeability  or from the least square regression line. The concentration–response correlative formula and correlation coefficient (R2) for each crude drug constituting YKS were determined by regression analysis using Excel .The efflux ratios in each experiment were expressed as the mean ± standard deviation (SD). The statistical significance was evaluated by one-way analysis of variance (ANOVA) and Dunnett’s multiple comparisons test. A The viabilities of Caco-2 cells treated with various concentrations of test substances are summarized in Supplementary Table S1. From the results, the concentrations of test substances that showed little cell toxicity  were used in the digoxin transport assay using a Caco-2 cell monolayer.2. Therefore, Caco-2 cell monolayers showing a TEER value of ≥250 Ω cm2 were first selected for this experiment. Next, the flux of mannitol across the cell membrane was examined to verify the integrity of the monolayer. The permeability of mannitol was < 1.5 × 10−6 cm/s, which indicated that the tight junctions of the cell monolayer were completed.The TEER value of the Caco-2 cell monolayer guarantees tight junction formation, which depends on the cell source, passage number, number of seeded cells, and culture conditions . Based oFurthermore, to confirm the expression and function of P-gp in the completed cell monolayer, transport of digoxin via the cell monolayer was investigated. The efflux ratio, an index for permeability of digoxin (6.98), was inhibited (0.832) by co-treating a P-gp inhibitor, verapamil (50 µmol/l). The inhibition rate was 88.1%, which shows that P-gp was expressed in the cell monolayer and functioned.F5,18 = 69.3, P < 0.001; RKT, F5,12 = 129.0, P < 0.001; SKT, F5,12 = 12.0, P < 0.001; HST, F6,14 = 35.2, P < 0.001; GJG, F5,12 = 32.0, P < 0.001). The IC50 values of YKS, RKT, SKT, HST, and GJG calculated by non-linear regression analysis were 1.94, 3.36, 10.80, 6.37, and 6.34 mg/ml, and the Igut/IC50 values as P-gp inhibition index were 3.4, 2.4, 0.5, 1.4, and 1.4, respectively. However, the IC50 values of SKT and HST were calculated as the estimated values, because both medicines did not show 50% inhibition in the examined concentration range.Figure F3,12 = 229.0, P < 0.001; Poria, F3,12 = 58.4, P < 0.001; GR, F3,12 = 471.0, P < 0.001; UUCR, F2,9 = 577.0, P < 0.001). The correlativity was observed as y = − 0.031x + 95.264 and the correlation coefficient as (R2) = 0.9537 for ALR, y = − 0.033x + 96.634 and R2 = 0.9379 for Poria, y = − 0.046x + 104.790 and R2 = 0.9757 for GR, and y = − 0.191x + 97.706 and R2 = 0.9926 for UUCR, where y is the efflux ratio and x is the drug concentration. From these formulas, the IC50 values were calculated to be 1,470 µg/ml for ALR, 1,430 µg/ml for Poria, 1,190 µg/ml for GR, and 249 µg/ml for UUCR. Other crude drugs  had low correlation, and the IC50 values could not be determined in the concentration range examined.Next, the P-gp inhibition rates of seven crude drugs constituting YKS showing the strongest suppression of P-gp were examined. As shown in Fig. 50 concentration  compared to those in the control, and the IC50 values were 16.5 and 56.0 µmol/l, respectively.Next, the effects of GM and RP, which are UUCR alkaloids in YKS, were evaluated using a Caco-2 permeability assay Fig. . GM (>10The concentrations of GM and RP were 0.238 nmol/ml (0.238 µmol/l) and 0.179 nmol/ml (0.179 µmol/l) in the 1.94 mg/ml of YKS, respectively. To clarify the contributions of both ingredients to the inhibitory effect of YKS, although the inhibition rate at the concentration of each ingredient contained in the YKS extract were examined in Fig. We examined the inhibitory effect of the five Kampo medicines  widely used in clinical treatments for various diseases in Japan on P-gp transport of digoxin across a Caco-2 cell monolayer. Djuv and Nilsen  suggeste50: 10.80 mg/ml). This result suggests that the GR level contained in each Kampo medicine may not contribute much to the P-gp inhibitory action of the five Kampo medicines, including YKS. On the other hand, UUCR is a crude drug that was contained only in YKS among the Kampo medicines examined in the present study, among which the inhibitory action of UUCR was stronger than the others  that were available for this study in order to to clarify the components contributing to the inhibitory activity of UCCR, and found that both ingredients had P-gp inhibitory activity. However, the P-gp inhibition rate at the GM and RP concentrations in the YKS extract powder was slight (≤1.5%), suggesting that other ingredients are involved in the inhibitory activity of UCCR. Because many other active alkaloids have been identified in YKS in addition to GM and RP , it is nIgut/IC50 ≥10 are likely to be P-gp inhibitors. For test substances evaluated as P-gp inhibitors it is stated that more detailed DDI tests should be conducted in humans. Therefore, the Igut/IC50 values for the five Kampo medicines were calculated, and the DDI risk was objectively evaluated according to the criteria in the DDI guidance. The rates of YKS, RKT, SKT, HST, and GJG examined in the present study were 3.4, 2.4, 0.5, 1.4, and 1.4, respectively. These values were <10, which was evaluated as a weak P-gp inhibitory effect that does not require further verification in humans, suggesting that the DDI risk due to P-gp inhibition for these Kampo medicines may be low. Furthermore, it has been reported that the inhibitory potential of YKS and RKT against P-gp was low in an ATPase assay [Both the Japanese MHLW  and US Fse assay  and did se assay . Ito et se assay  and Sorase assay  also evase assay , and it All Kampo medicines examined  inhibited the P-gp transport of digoxin in a Caco-2 permeability assay but to a low degree according to the criteria in the DDI draft guidance of the MHLW and FDA. These results suggest that the risk of drug interaction due to inhibition of P-gp in combination therapy with Western medicines is extremely low for these Kampo medicines. This finding should provide useful clinical information on the safety and efficacy of combinations of Kampo medicines and Western medicines.Supplementary material 1 (DOCX 23 kb)Below is the link to the electronic supplementary material.