Accumulation of epicardial adipose tissue (EAT) is considered to be a cardiovascular risk factor independent from visceral adiposity, obesity, hypertension and diabetes. We explored the parameters related to EAT accumulation, aiming to clarify the novel pathophysiological roles of EAT in subjects with type 2 diabetes (T2DM).We examined the laboratory values, including cystatinC, and surrogate markers used for evaluating atherosclerosis. EAT was measured as the sum of the adipose tissue area, obtained by plain computed tomography scans in 208 subjects with T2DM but no history of coronary artery disease.EAT correlated positively with age, body mass index (BMI), visceral fat area, leptin, cystatin C and C-peptide, while correlating negatively with adiponectin, estimated glomerular filteration rate (eGFR) and the liver-to-spleen ratio. Multiple linear regression analysis revealed serum cystatin C (β = 0.175), leptin (β = 0.536), BMI (β = 0.393) and age (β = 0.269) to be the only parameters showing independent statistically significant associations with EAT. When cystatin C was replaced with eGFR, eGFR showed no significant correlation with EAT. In reverse analysis, serum cystatin C was significantly associated with EAT after adjustment in multivariate analysis.EAT accumulation and elevated cystatin C have been independently regarded as risk factors influencing atherosclerosis. The strong association between EAT and cystatin C demonstrated herein indicates that EAT accumulation may play an important role in Cystatin C secretion, possibly contributing to cardiometabolic risk in T2DM patients. Epicardial adipose tissue (EAT) has recently been recognized not only as fat deposited around the pericardium, but also as a metabolically active tissue, secreting various humoral factors . The volEAT is a source of bioactive molecules, including adipocyokines and growth factors, directly impacting inflammation of the myocardium and coronary arteries. Similar to visceral fat, increasing obesity accompanied by epicardial adipocyte enlargement, leads to deterioration of adipocytokine signaling, including enhancement of plasminogen activator inhibitor (PAI)-1, tumor necrosis factor (TNF)-α and leptin expressions and a decrease in adiponectin expression . This poThe recent advances in imaging technology enable us to quantify EAT using modalities such as echocardiography , magnetiWhile several studies have shown the importance of EAT accumulation in the development of atherosclerosis, the EAT associated factors influencing atherosclerosis as not as yet fully understand. Thus, we designed this cross-sectional study to examine associations among EAT, humoral factors and atherosclerotic surrogate markers in T2DM patients, who are known to be at risk for CAD. In this study, we explored the parameters related to EAT accumulation, aiming to clarify the novel pathophysiological roles of EAT in subjects with T2DM.-1 1.73 m-2), any malignancy, an infectious disorder, collagen disease or a past history of CAD. This study was approved by the Institutional Review Board of Iwate Medical University . The concent of the study was informed and obtained by written form.The study subjects were T2DM patients admitted to Iwate Medical University Hospital during the period from January 2014 to July 2016. Two hundred and eight subjects, all of whom underwent cardiac MDCT, were enrolled in this study. Patients were excluded if they had renal dysfunction  were quantified on ECG-gated diagnostic cardiac CT scans with some modification , 14. A V2) was performed using software programs . The EAT area was calculated by manually tracing a region of interest (ROI), which was placed outside the line of the visceral pericardium to exclude pericardial fluid. A density range between -200 and -30 Hounsfield Units was used to isolate adipose tissue [3). A representative image is shown in Measurements of EAT were performed by CT scanning with cross-sectional axial views employing imageswith 3mm gaps. The range for measurement of EAT was set as the origin of the left main coronary trunk for the superior border and 6cm below the superior border for the basal border . Quantife tissue , 16. TheThe total CACS were analyzed according to the Agatston method  and wereThe ABI  and brachial ankle pulse wave velocity (baPWV) were measured using an automatic waveform analyzer . The intima-media thickness (IMT) of the carotid arteries was measured using ultrasound diagnostic equipment  and the max IMT, i.e. the thickest portion detected in the scanned regions, was determined as described previously . These mLaboratory values were measured employing routine techniques on blood and urine samples obtained after a 12-h overnight fast in T2DM patients. The value of low dencity lipoprotein cholesterol (LDL-C) was measured using a direct assay method . The serum levels of adipocytekines, including leptin and adiponectin, as well as those of oxidative stress markers, such as urinary 8-isoprostane and 8-hydroxydeoxyguanosine, and serum malondialdehyde-LDL cholesterol, and various unsaturated fatty acid were measured by SRL, Inc. .2) = 194 x cre-1.094 x age-0.287 , 194 x cre-1.094 x age-0.287 x 0.739 . Serum cystatin C (cys) based eGFR was defined as eGFRcys (mL/min/1.73m2) = (104 x cys-1.019 x 0.996 age) - 8 , (104 x cys-1.019 x 0.996 age x 0.929) - 8 .Estimated glomerular filtration rates (eGFR) were calculated as shown below . Serum ct test and the chi-square test or, when the data showed a non-normal distribution, the Mann–Whitney U-test. The level of significance was set at P < 0.05. Multiple linear regression analyses were performed to evaluate parameters independently showing significant correlations with EAT and cystatin C. Clinical parameters, showing significant simple correlations with EAT or cystatin C, were assigned as independent variables in multivariate linear regression analysis, unless there was extreme collinearity. All statistical analyses were carried out using SPSS version 21 .Quantitative data are presented as means ± standard deviation (SD) or as medians with inter quartile range when the data showed a non-normal distribution. Comparisons between the subjects were performed employing the student 2 and 3.1, respectively, indicating moderately obesity and the presence of insulin resistance as compared to Japanese subjects with T2DM in general. The mean EAT values was 52.4 cm3.The clinical characteristics of the 208 enrolled subjects are shown in The volume of EAT correlated positively with age , BMI , visceral fat area , levels of serum dihomo-gamma-linolenic acid levels (DGLA) , leptin , cystatin C  and C-peptide , as well as with HOMA-R (r = 0.262 p < 0.01) . The EATNext, we performed multiple linear regression analyses to identify variables independently related to EAT values . MultivaTo investigate the effects of cystatin C on clinical parameters in our study subjects, we performed simple and multiple linear regression analyses for cystatin C. The serum cystatin C level showed significant correlation with age, diabetes duration, visceral fat area, EAT, IMT, PWV and CACS . FurtherIn addition, there were no significant differences either EAT volume or cystatin C values between users and non-users of various medications, including drugs for diabetes, statins and renin-angiotensin system inhibitors.This study is the first, to our knowledge, to demonstrate a close relationship between EAT accumulation and the serum level of cystatin C, independent from glomerular filtration rate in Japanese T2DM. Because the incidence of CAD in the Japanese population has been rising in recent decades, identification of factors contributing to the development of atherosclerosis resulting from adiposity is important. Our present observations shed light on the mechanism of atherosclerosis development in T2DM patients with fat accumulation.EAT arises from brown adipose tissue as well as visceral adipose tissue  and possThe volume of EAT reportedly showed association with surrogate markers of atherosclerosis, including CACS , carotidCystatin C, a 13-kD endogenous cysteine proteinase inhibitor, is ubiquitously expressed, mainly in the brain, testis, lung, spleen and adipose tissue . CystatiEpidemiological studies have shown serum cystatin C to be increased in humans with obesity . Naour eOn the other hand, elevated cystatin C is reportedly associated with the presence or likely development of cardiovascular disease in subjects without chronic kidney disease , 40. MorThe strengths of this study include the employment of numerous surrogate markers for atherosclerosis and other obesity-related disease as independent variables in conducting correlation analyses focusing on EAT. Among the obesity-related markers, such as adipocytokines, inflammatory cytokines and polyunsaturated fatty acids, studied herein, a particularly strong association between cystatin C and EAT was revealed. Serum cystatin C showed a significant association with EAT even after adjustment for several confounding factors. This study supports the hypothesis, and its clinical implications, that cystatin C elevation, related to EAT accumulation, exerts an additional impact on atherosclerosis development. Surrogate markers for evaluating atherosclerosis, including EAT volume, are informative but somewhat inconvenient and expensive to obtain, due to the equipment necessary for performing the measurement. In contrast, measuring serum cystatin C is non-invasive and low-cost. Therefore, assessment of serum cystatin C may allow early detection of atherosclerosis.The major limitation of this study is its cross-sectional design, raising the possibility that our results show only associations. Therefore, the possible casual relationships between EAT and cystatin C cannot be confirmed, and further prospective study is required. Second, the extent of cardiac CT scanning for EAT evaluation is restricted, to within a range of 6cm from the origin of the left main coronary trunk. Third, the sample size was rather small for performing multivariate analysis incorporating large number of variables. In addition, despite the various clinical characteristics of the enrolled subjects varying rather markedly, the subjects were analyzed as a single group.EAT accumulation and a high cystatin C concentration have been independently regarded as risk factors influencing atherosclerosis. This study showed a strong association between EAT and cystatin C independent of several confounders, including renal function parameters and several humoral factors. EAT accumulation may play an important role in Cystatin C secretion, thereby possibly contributing to cardiometabolic risk in T2DM.


The retreating Pine Island Glacier (PIG), West Antarctica, presently contributes ~5–10% of global sea-level rise. PIG’s retreat rate has increased in recent decades with associated thinning migrating upstream into tributaries feeding the main glacier trunk. To project future change requires modelling that includes robust parameterisation of basal traction, the resistance to ice flow at the bed. However, most ice-sheet models estimate basal traction from satellite-derived surface velocity, without a priori knowledge of the key processes from which it is derived, namely friction at the ice-bed interface and form drag, and the resistance to ice flow that arises as ice deforms to negotiate bed topography. Here, we present high-resolution maps, acquired using ice-penetrating radar, of the bed topography across parts of PIG. Contrary to lower-resolution data currently used for ice-sheet models, these data show a contrasting topography across the ice-bed interface. We show that these diverse subglacial landscapes have an impact on ice flow, and present a challenge for modelling ice-sheet evolution and projecting global sea-level rise from ice-sheet loss. Projecting the future retreat and thus global sea level contributions of Antarctica’s Pine Island Glacier is hampered by a poor grasp of what controls flow at the ice base. Here, via high-resolution ice-radar imaging, the authors show diverse landscapes beneath the glacier fundamentally influence ice flow. The potential for this contribution to accelerate further as a consequence of the ice sheet’s dynamical instability8 poses a threat to the long-term security and prosperity of the planet’s coastal populations and infrastructure1. However, despite advances in Earth-system modelling to constrain future change, recent projections of the Antarctic Ice Sheet’s future global sea-level contribution still have a significant uncertainty10. One of the greatest sources of this uncertainty is the parameterisation of basal boundary conditions in the ice-sheet models used for projection12. This is most acute for the fast-flowing ice streams that discharge >90% of ice from Antarctica. A particular challenge for these models is the simulation of the basal traction generated by ice flow over its bed, which is the primary restraint on ice flow15. Few observational data exist beneath Antarctic ice streams at the sub-km scale required to parameterise this complex, but crucial, interaction.Over 40 years of satellite observations, the West Antarctic Ice Sheet (WAIS) has consistently lost ice and contributed up to a 10th of the observed rise in global sea levels16, with thinning having propagated progressively upstream from the floating ice shelf, along the main trunk and into its tributaries over the interior basin6. Several modelling studies have projected that the retreat and progressive upstream thinning of PIG will continue in the coming decades, potentially increasing its contributions to global sea-level rise20. However, the magnitude and rate of the projected contribution critically depend on the pace at which the retreat may propagate into the interior basin, which is largely controlled by the poorly constrained basal boundary conditions21.Here, we present high-resolution images of the topography underlying West Antarctica’s Pine Island Glacier . The data, obtained by 1–3 MHz over-snow radar during three austral field seasons , were typically acquired in 10 × 15-km patches. The data comprise a total of nine patches imaged at 40 m (cross-flow) × 100 m  grid resolution  that allows the model to match the observed ice-surface velocity and/or elevation changes37. It is clear that the short-wavelength bed roughness shown in our data can explain much of the variability in the traction coefficient  on length scales of ~5–50% of the ice thickness.Many state-of-the-art Antarctic ice-sheet models use the ‘Bedmap2’ basal topography12. However, while basal friction is likely to be a highly dynamic field, evolving as water melts, flows and refreezes, and subglacial till is mobilised and refrozen41, form drag is likely to be considerably more static, defined primarily by the size and orientation of bedrock undulations and protuberances. Recent changes in PIG already indicate the potential importance of this factor, in as much as satellite data have shown that the rate of upstream propagation of ice thinning on PIG varies considerably between the tributaries, the southerly tributaries with rough beds showing 2–3 times slower propagation than the smoother tributaries6  monopulse radars towed by snowmobiles operated at a constant speed of 12 ± 2 km hData from 2007/08 were acquired with 40-m half-dipole antennae, giving a centre frequency of ~1 MHz, and the transmitter was fired at a pulse-repetition rate of 3 kHz. Data from 2010/11 and 2013/14 were acquired with 20-m half-dipole antennae, giving a centre frequency of ~3 MHz, with the transmitter fired at 1-kHz repetition rate. In all years, however, multiple radar returns were first stacked in the oscilloscope buffer to reduce the signal-to-noise ratio, so that each trace of the final record was built up of the data acquired over an along-track distance of ~4–6 m.The digital oscilloscope used in 2007/8 and 2010/11 had a sampling rate of 100 MHz, giving a time resolution of 10 ns, equivalent to 1.68-m vertical resolution on ice. The sample rate in 2013/14 was 250 MHz, giving a 4-ns time resolution equivalent to 0.67-m vertical resolution. The system was therefore capable of detecting a change in ice thickness from trace to trace of <2 m when a high-amplitude bed reflection was present.45 and Carlson Inlet46. In each of the survey ‘patches’, extra radar profiles acquired at different orientations  were used as an additional check for consistency in data acquisition across the patch . This is an online service that uses the precise GNSS satellite orbit ephemerides to produce corrected x, y, z coordinates of a constant ‘absolute’ accuracy. We submitted all data in RINEX format, and processed them in kinematic mode in the International Terrestrial Reference Frame. Each output was provided in WGS84 coordinates, with z values given with respect to the WGS84 ellipsoid.Before radar processing, the dual-frequency GPS data collected during each survey were processed using the Canadian Spatial Reference System (CSRS) Precise Point Positioning (PPP) service  but that the single-frequency GPS z coordinates had a vertical accuracy of ±10 m (confirming their non-usability). For the affected radar profiles, we therefore recovered z from surface elevation data generated from DigitalGlobe WorldView-1 and WorldView-2 along-track stereo image data47. For each of iSTARt1, iSTARt5 and iSTARt6, we first generated two DEMs: one a weighted average of WorldView-generated DEMs from 2010 to 2015 (hence spanning our 2013/14 survey), and the other a 50-m-resolution gridded output of the CSRS-PPP-processed surface elevation data generated from the radar profiles where the dual-frequency GPS had worked (which was the majority of radar profiles for each survey patch). From these two products for each survey patch, which showed a maximum elevation difference of 3 m but usually ~1 m, we generated difference maps from which we recovered z coordinates to fit to the single-frequency x, y coordinates for the affected radar profiles.For a few radar profiles acquired in 2013/14, comprising small proportions of the data collected across three of the survey patches , dual-frequency GPS files were not collected or were overwritten due to a GPS software malfunction. For the affected radar profiles . We first assigned positioning information to every recorded radar trace from the CSRS-PPP output (or its substitute data from WorldView DEMs) as described above. We suppressed noise induced by the arrival of the direct wave using an average filter, and then used a band-pass filter to increase the signal-to-noise ratio. The data were then given an amplitude-scaling proportional to the two-way travel time to compensate for spherical spreading of the radar wavefront with depth, and were migrated using the Kirchoff function to reassign energy back to source points. Finally, a further band-pass filter removed processing-induced noise. The data were archived in SEG-Y format.x, y, t coordinates, where x and y were the coordinates in South Pole stereographic projection, and t was the two-way travel time. We converted t into depth h using a single value of radiowave speed through ice of 0.168 m ns−1, and adding 10 m to correct for propagation of the radiowaves through a layer of firn at the surface, based on prior experience45. The elevation values for the bed at each radar trace were then calculated by subtracting h from the surface elevation, giving bed elevations with an estimated vertical precision of ±3 m. We acknowledge that using the single value for radiowave propagation is a standard simplification that neglects possible, but unquantifiable, variability in attenuation at each survey patch, which may arise due to variations in ice temperature or chemistry; however, we argue that making such an assumption is reasonable over the relatively local areas covered by each survey patch. Varying the radiowave speed across a range of 0.167–0.170 m ns−1, a plausible range based on reported values across Antarctica49, has the effect of varying ice thickness (hence bed elevation) across a range of ~35 m for ice between 1.5 and 2-km thick. This does not have an impact on the essential shapes of the bed topography maps per survey patch, and affects the trough-crest heights of MSGL  × 100 m  grid oriented orthogonal to the profile lines using a natural neighbour algorithm with a 5:1 anisotropy ratio aligned along the ice flow direction. This interpolation scheme preserves the continuity of the features that are elongate in the ice-flow direction while preserving some of the high spatial sampling along the cross-flow radar tracks.To produce the images of the bed for Figs. 50. The code enables the derivation of roughness along 2D tracks using moving windows of predefined length. We applied the code along 2-km moving windows, which are sufficiently long to capture multiple streamlined landforms when applied across flow, yet small enough to capture roughness variability within each of the survey patches.Bed roughness for Table http://gis.istar.ac.uk/.All the data in this paper are available from the lead author by request and will be made available on the NERC/iSTAR GIS site, Peer Review File


In addition, all compounds reacted in a non-competitive manner with the substrate. Docking analysis results for complexes with BACE1 indicated that SER10 and THR232 residues of BACE1 hydrogen bonded with two oxygen atoms of tangeretin, while three additional BACE1 residues  interacted with three oxygen atoms of nobiletin. Furthermore, sinensetin formed four hydrogen bonds through nitrogen atoms of TYR71, LYS75, and TRP76, and an oxygen atom of TYR198. Furthermore, the lowest-energy conformations of the most proposed complexes of sinensetin, nobiletin, and tangeretin with BACE1 were −7.2, −7.0, and −6.8 kcal/mol, respectively. Taken together, our results suggest that these polymethoxyflavones (PMFs) might be considered as promising BACE1 inhibitory agents that could lower Aβ production in AD.Beta-site amyloid precursor protein (APP) cleaving enzyme1 (BACE1) catalyzes the rate-limiting step of amyloid-β protein (Aβ) generation, and is considered as a prime target for Alzheimer’s disease (AD). In search of a candidate for AD prevention, our efforts exploring the natural BACE1 inhibitor have led to the finding of nobiletin, tangeretin, and sinensetin—representative compounds of polymethoxyflavones (PMFs). Tangeretin exhibited the strongest BACE1 inhibition (IC Alzheimer’s disease (AD) is a devastating neurodegenerative disorder that alters the mental capacity of patients suffering from the disease . Patholoγ-Secretase is a membrane protein complex composed of presenilin 1 or 2 (PS1 or PS2) as the catalytic subunit, Aph-1a or -b, nicastrin (NCT), and presenilin enhancer-2 (PEN-2). PS1 and PS2 are two integral membrane proteins found in the endoplasmic reticulum and Golgi apparatus, and are the key targets for γ-secretase inhibition in the treatment of AD . HoweverBACE1 knockout mice were initially reported to be free of negative phenotypes; subsequent investigations identified BACE1 null abnormalities such as axon guidance defects, hypomyelination, memory deficits, spin density reduction, impairment of synaptic plasticity, neurogenesis and astrogenesis abnormalities, etc., suggesting that BACE1 inhibitors might produce mechanism-based side-effects . HoweverCitrus, and are particularly more abundant in the peel than in other edible parts of the fruit [2O2-stimulated HT-22 neurons [Recently, much attention has been paid to the screening of products from natural sources, because they are usually considered to be less toxic and have fewer side effects than products from synthetic sources. In particular, the wide biochemical functions of polymethoxyflavones (PMFs) have been studied extensively. They are of particular interest because of their broad range of biological activities, including antioxidant, anticarcinogenic, and anti-inflammatory properties ,14,15. Phe fruit . The neu neurons . In addi neurons . NobiletNobiletin (>98% purity) was purchased from Santa Cruz Biotechnology . Tangeretin, sinensetin, (>95% purity), and resveratrol (>99% purity) were obtained from Sigma-Aldrich . BACE1 inhibition was determined by enzymatic assay using the fluorescent resonance energy transfer (FRET)-based BACE1 kit from Invitrogen . α-Secretase  and substrate were obtained from R&D Systems . Trypsin, chymotrypsin, elastase, and their substrates were obtained from Sigma-Aldrich . The measurement of fluorescence and optical density was performed with a BioTEK ELISA microplate fluorescence reader FLx 800 and BioTEK ELx 808 , respectively.2 as substrates. Trypsin, chymotrypsin, and elastase were assayed according to the manual described in the reference using N-benzoyl-l-Arg-pNA, N-benzoyl-l-Tyr-pNA, and N-succinyl-Ala-Ala-Ala-pNA as substrates, respectively.BACE1, TACE, chymotrypsin, trypsin and elastase assays were performed according to previous methods . FluoromTM module of SigmaPlotTM version 12.3 .The kinetic mechanisms of the different samples towards BACE1 were determined by the graphical views of Dixon and Lineweaver–Burk plots. The inhibitory constants (Ki) value was defined by interpretation of the Dixon plot, where the value of the x-axis implies -Ki. Maximum velocity (Vmax) and Michaelis constant (Km) were obtained by Lineweaver–Burk plots, using initial velocities obtained over a substrate concentration ranging from 250 to 750 nM. The kinetic parameters were then calculated using Enzyme KineticA computational ligand–target docking method was used to investigate structural complexes of the BACE1 (target) with PMFs (ligand) in order to understand the structural basis of this protein target specificity . SpecifiAll experiments were presented as the mean ± standard deviation (SD) of three independent experiments. Significant differences were conducted by Duncan’s multiple range tests using Statistical Analysis System (SAS) version 9.3 .p < 0.001). Tangeretin had the highest BACE1 inhibitory property , followed by nobiletin  and sinensetin . The common structures of nobiletin, tangeretin, and sinensetin include three methoxy groups at C5, C6, and C7 in the A ring and one methoxy group at C4′ in the B ring, which provide a partial BACE1-suppressive potency. Interestingly, the presence of C3′-OCH3 in the B ring in nobiletin and sinensetin reduced their inhibitory potency. However, an additional C8-OCH3 in the A ring of tangeretin noticeably enhanced its anti-BACE1 activity. Therefore, the C8-OCH3 in the A ring was considered an enhancer of the anti-BACE1 activity, whereas the anti-BACE1 activity decreased in the presence of C3′-OCH3 in the B ring.The chemical structures of nobiletin, tangeretin, and sinensetin are shown in To prove the enzyme specificity of PMFs against BACE1, their inhibitory activities against BACE1 were compared with their inhibitory activities against TACE and other serine proteases  . None of−5 M for nobiletin, 3.7 × 10−5 M for tangeretin, and 3.8 × 10−5 M for sinensetin. A lower Ki value represents a stronger bond between an inhibitor and an enzyme, thus implying greater effectiveness of the inhibitor. Thus, our present results suggest that PMFs could be good BACE1 inhibitors.In the present study, different graphical analyses were carried out to distinguish the type of inhibition by PMFs. Analysis of kinetic parameters obtained from the Dixon and Lineweaver–Burk plots showed that nobiletin, tangeretin, and sinensetin are noncompetitive inhibitors. These compounds decreased the Vmax of FRET substrate decomposition reaction by BACE1, whereas the Km value remained unchanged in the Lineweaver–Burk plot . As showThe AutoDock program uses a semi-empirical energy force field to guess the binding of protein–ligand complexes of known structure and binding energies. The docking results of the BACE1–PMFs complexes revealed that nobiletin, tangeretin, and sinensetin were stably positioned in allosteric sites of the BACE1 residues, all of which were 5 Å from the inhibitor in the original complex . The oxy1-42 and Aβ1-40 in the brain [1-40 and reactive oxygen species (ROS) in the brain and in the hippocampus of 3XTg-AD mice [Recently, PMFs were shown to possess neuroprotective effects in both cell and animal models. Nakajima et al. and Nagase et al. reported that the administration of nobiletin for 11 days significantly recovered olfactory bulbectomy-induced memory impairment ,23. In ahe brain . In addi-AD mice . Tangere-AD mice . Subchro-AD mice . Sinense-AD mice . Although not fully understood, the metabolites of PMFs have been studied, and it was found that they undergo in vivo biotransformation, producing metabolites with different bioactivities and pharmacological properties. The bioinformation study of nobiletin has shown that the compound undergoes a demethylation pathway with the formation of mono-demethyl nobiletin (DMN) as major metabolites such as 3′-, 4′-, 6-, or 7-DMN. Through di-demethylation, nobiletin transforms to 3′, 4′-di-DMN or 6, 7-di-DMN ,29. LikeDespite the evidence for PMF-mediated neuroprotection, bioavailability in the brain is important for active compounds to be effective in therapeutic for AD, because an inability to cross the blood–brain barrier (BBB) can limit the prevention and/or treatment applications of compounds . When noCitrus reticulata peel extract treatment for 1 year could prevent the progression of the cognitive impairment in donepezil-preadministered AD patients with no adverse side effects [Oral acute and chronic toxicity studies are important to determine the safety of drugs and plant products for human use. However, to date, information on the toxicology profile for PMFs is still limited. Ting et al. (2015)  reported effects . It is important to reiterate that the risk of mechanism-based toxic effects might depend on the level of BACE1 inhibition. Partial inhibition of BACE1 activity could represent a feasible approach. For example, the currently tested BACE1 inhibitor MK-8931 has been safe and tolerated after multiple-dose administration for at least 18 months in human subjects . Since nOur findings suggest that PMFs have a significant inhibitory activity against BACE1, whereas they lack any inhibitory property against TACE and other serine proteases. Enzyme kinetics was evaluated using the Dixon and Lineweaver–Burk plots to identify compound inhibition modes. In addition, molecular docking studies indicated strong hydrogen bonding with several important amino acid residues, as evidenced by negative binding energies at the allosteric site in BACE1; this can explain the potency of these compounds. Although further BACE1 selectivity over cathepsins D and BACE2 and in vivo studies are required to confirm our findings, these PMFs showed significant and selective inhibitory activities against BACE1, and can be used as potential agents for preventing and/or treating AD.


Dimensionality reduction has been applied in various brain areas to study the activity of populations of neurons. To interpret the outputs of dimensionality reduction, it is important to first understand its outputs for brain areas for which the relationship between the stimulus and neural response is well characterized. Here, we applied principal component analysis (PCA) to trial-averaged neural responses in macaque primary visual cortex (V1) to study two fundamental, population-level questions. First, we characterized how neural complexity relates to stimulus complexity, where complexity is measured using relative comparisons of dimensionality. Second, we assessed the extent to which responses to different stimuli occupy similar dimensions of the population activity space using a novel statistical method. For comparison, we performed the same dimensionality reduction analyses on the activity of a recently-proposed V1 receptive field model and a deep convolutional neural network. Our results show that the dimensionality of the population response changes systematically with alterations in the properties and complexity of the visual stimulus. A central goal in systems neuroscience is to understand how large populations of neurons work together to enable us to sense, to reason, and to act. To go beyond single-neuron and pairwise analyses, recent studies have applied dimensionality reduction methods to neural population activity to reveal tantalizing evidence of neural mechanisms underlying a wide range of brain functions. To aid in interpreting the outputs of dimensionality reduction, it is important to vary the inputs to a brain area and ask whether the outputs of dimensionality reduction change in a sensible manner, which has not yet been shown. In this study, we recorded the activity of tens of neurons in the primary visual cortex (V1) of macaque monkeys while presenting different visual stimuli. We found that the dimensionality of the population activity grows with stimulus complexity, and that the population responses to different stimuli occupy similar dimensions of the population firing rate space, in accordance with the visual stimuli themselves. For comparison, we applied the same analysis methods to the activity of a recently-proposed V1 receptive field model and a deep convolutional neural network. Overall, we found dimensionality reduction to yield interpretable results, providing encouragement for the use of dimensionality reduction in other brain areas. Dimensionality reduction has been applied to neural population activity to study decision making , 2, motoTo aid in interpreting the outputs of dimensionality reduction in such settings, it is important to vary the inputs to a brain area and ask whether the outputs of dimensionality reduction change in a sensible way. This is most readily done for a brain area close to the sensory periphery, such as the primary visual cortex V1). Here, we apply dimensionality reduction to V1 and ask two fundamental, population-level questions. First, how is neural complexity related to stimulus or task complexity? Previous studies have used dimensionality reduction to analyze population activity in a reduced space . Implicpopulation firing rate space) in which each axis represents the firing rate of a recorded neuron  how these dimensions are oriented within the population firing rate space. In this three-neuron example, the population activity is two-dimensional, where the dimensions are defined by the orthogonal basis patterns  to the trial-averaged neural responses to different classes of visual stimuli, including sinusoidal gratings, a natural movie, and white noise. In addition, we develop a new method (termed the pattern aggregation method) to measure how the basis patterns extracted from the population responses to each stimulus relate to each other. This method allows one to characterize how the similarity of the dimensions being occupied by the population activity changes with the similarity of the stimuli. A key advantage of studying these questions in V1 is that there are well-established receptive field (RF) models. By applying the same dimensionality reduction methods to the outputs of an RF model, we can more deeply understand the relationship between the outputs of dimensionality reduction and known properties of V1 neurons. The results described in this work show that the outputs of dimensionality reduction, when applied to V1 population activity, are sensible, and thus may be fruitfully applied to other brain areas.We first investigated how changing the complexity of the visual stimulus changes the dimensionality of trial-averaged population responses using drifting sinusoidal gratings. To change the stimulus complexity, we included different numbers of consecutive grating orientations in the analysis (ranging from 1 to 12 orientations). For example, the least complex stimulus included a single orientation, and more complex stimuli included two or five consecutive orientations A.We asked how quickly the dimensionality of the population activity grows as the number of orientations increases. At one extreme, it may be that the population response to each orientation uses an entirely different set of basis patterns . In this case, the dimensionality for two orientations would be two times the dimensionality for one orientation. At the other extreme, it may be that the population response to each orientation resides in the same set of dimensions. In other words, the population response is formed using the same basis patterns, but linearly combined using different weights for different orientations. In this case, the dimensionality for two orientations would be the same as the dimensionality for one orientation.p < 10−5). In other words, for consecutive orientations separated by 30°, the population responses share about half of their basis patterns. As more orientations were included, the dimensionality of population responses remained lower than expected by chance , and identifying the patterns explaining the greatest variance in the population response . To assess the dimensionality of the population response to multiple orientations, we developed the pattern aggregation method see , which fy chance B, indicat to determine how different basis patterns need to be before they define separate dimensions. We repeated the above analysis for different choices of t and a fixed variance threshold of 90% , following the same trend shown in To test this possibility, we performed two analyses. First, we assessed the direction selectivity of each neuron (direction index = 1 − null response / preferred response), and found that 16 of 183 neurons had a direction index greater than 0.5, consistent with . If nonep < 10−5), indicating that the population activity does not use identical basis patterns for opposite drift directions. Because these dimensionalities were computed differently (the pattern aggregation method for 180° offset and a variance threshold for 0° offset), we aggregated an equal number of patterns (identified over many subsampled runs) for 0° offset as that for 180° offset, and still found a higher dimensionality for 180° offset than for 0° offset (p < 10−5).Second, we assessed how the dimensionality of the population activity for two orientations varies with the angular offset between the orientations D. This iThis result, combined with the change in slope of the dimensionality curve B, indicaWe also visualized the basis patterns describing the largest percentage of variance for three different orientations E. These We next sought to determine how the dimensionality of the trial-averaged population activity varies with the complexity of the visual stimulus for a wider range of stimuli. We presented three movie stimuli A: a sequHaving measured the relative complexity of the movie stimuli, we then asked how the dimensionality of the population responses to the movie stimuli varies with stimulus complexity. We found that for a 90% variance threshold, the dimensionality of the trial-averaged population responses (20 ms bins) was ordered in the same way as the stimulus complexity C; namelyHaving compared the dimensionality of the population activity across stimuli, we next asked how the basis patterns of the population activity (corresponding to the dimensions being occupied by the population activity) compare across stimuli. Previous studies have found that the ability of a RF model to predict a neuron’s response can depend on the stimulus class on which the model was trained –22, suggWe first asked whether there are qualitative differences in the coefficients of the basis patterns for population responses to the stimulus movies A. As in s, for which s > 0 indicates that the patterns are more similar  than expected by chance and s < 0 indicates that the patterns are less similar  than expected by chance . Second, the dimensions occupied by the noise movie include many of the dimensions for the other two stimuli. This is indicated by the fact that the dimensionality corresponding to any combination of stimuli that included the noise movie  was less than the dimensionality that would be expected by chance . Note that, in all cases, the chance dimensionality was near the maximum dimensionality  because the dimensionality of the pixel space  was much larger than the dimensionalities of the individual movies.As a baseline, we assessed the extent to which the visual stimuli themselves reside in the same dimensions in pixel space. We compared the dimensionalities of the individual movies B to thosance see . There as > 0.39, p < 10−5). Second, the dimensions occupied by the population response to the noise movie include most of the dimensions for the other two stimuli. This is indicated by the fact that the dimensionality corresponding to any combination of stimuli that included the noise movie was less than the dimensionality expected by chance , and close to entirely overlapping.We used the same approach to analyze the population responses as we did the visual stimuli. We compared the dimensionality of the population responses to individual movies C to thatN-dimensional population firing rate space can be achieved (where N = 61 for monkey 1 and N = 81 for monkey 2). Previous studies indicate that the population activity may only be able to occupy a subset of dimensions due to underlying network constraints , 3817, 3Our work lays a solid foundation to assess the dimensionality and similarity of basis patterns of neural population activity. Because V1 is a well-studied brain area and is close to the sensory input, our results can be compared with expectations based on our intuition and well-established RF models. Moving forward, these methods can be applied broadly to other brain areas and behavioral tasks to examine how the complexity of the population response changes due to conditions such as attentional state, learning, and contextual modulation.μg/kg/hr). Eye movements were minimized with a continuous intravenous infusion of vecuronium bromide (100–150 μg/kg/hr). Experiments typically lasted 5–7 days. All experimental procedures followed guidelines approved by the Institutional Animal Care and Use Committee of the Albert Einstein College of Medicine at Yeshiva University, and were in full compliance with the guidelines set forth in the US Public Health Service Guide for the Care and Use of Laboratory Animals.Details of the neural recordings have been described previously , 55. Bri2 and had an electrode length of 1 mm. The electrodes were inserted to a nominal depth of 0.6 mm to confine recordings mostly to layers 2–3. Recordings were performed in parafoveal V1, with RFs within 5 degrees of the fovea. Voltage waveform segments that passed a separately-chosen threshold for each channel were later spike-sorted offline. For comparisons of population responses to different orientation gratings, we spike sorted responses together across all orientations, thereby obtaining a common set of units. Similarly, we spike sorted responses together across all three movies. We included sorted units for which the voltage waveform had a signal-to-noise ratio (SNR) greater than 1.5, where SNR is defined as the ratio of the average waveform amplitude to the standard deviation of the waveform noise  (where V: N × (kA + kB)) and designate kAB to be the effective column rank (defined below) of V. In the case where kA > kB and the space spanned by the condition A patterns includes the space spanned by the condition B patterns, then kAB will be max = kA. On the other hand, if the space spanned by the condition A patterns is orthogonal to the space spanned by the condition B patterns, then kAB will be kA + kB. For more than two conditions, this method is easily extended by aggregating the patterns for all conditions into V = [UAUBUC…].To overcome the issues described above, we developed an alternative approach which guaranteed that V is the number of “large” singular values of V. The definition of “large” requires setting a threshold  for the singular values. The rank threshold determines how different two basis patterns (cf. vB) from a position of overlap with another unit vector  to a position of orthogonality (90°)  A. We vark of two B. In othk of two B. In oth degrees B. We alskAB to a chance level, rather than simply asking whether kAB is closer to the minimum dimensionality bound max or maximum dimensionality bound kA + kB. The reason is that the chance level depends on the relative values of kA, kB, and N, and does not simply lie halfway in between max and kA + kB. For example, if N is large relative to kA and kB, the chance level would lie near kA + kB because randomly drawn patterns in a high-dimensional space tend to be orthogonal. We compute the chance level by drawing patterns randomly from the N-dimensional space, and compute a distribution of aggregated dimensionalities. Specifically, we repeatedly draw kA orthonormal patterns and kB orthonormal patterns in an N-dimensional space, and measure their aggregated dimensionality using the same method as applied to the data.To assess whether two sets of patterns are similar or dissimilar, it is necessary to compare N-dimensional space can be shown in the data. In some cases, there is reason to believe that not all patterns in the N-dimensional space can be achieved. For example, the underlying neural circuitry may constrain the space of activity patterns that a population of neurons is capable of producing  are defined below. Thus, the Gabor filter is the product of a Gaussian envelope H and a sinusoid S. To orient the Gabor, the pixel locations  are first rotated by orientation angle θ. To incorporate direction selectivity,  are further rotated by angle β, which is either 0° or 180°. These rotations are achieved with a rotation matrix, R:x′, y′, t′]T = R × T and the scaled and rotated x, y, t) are scaled by  before rotation to align the Gaussian envelope with the orientation angle. The Gabor filter G is normalized by its Frobenius norm.The first component for each model neuron is an oriented Gabor filter applied to the input image. Because  incorporxloc, yloc) were drawn from a Gaussian with mean 160 and variance 20 to be partially overlapping at the center of the 320 × 320 pixel image. The spatial phase ϕ was drawn from a uniform distribution between 0 and 2π. The temporal frequency γt = 6.25 Hz was fixed at the same temporal frequency as that for the drifting gratings. Orientation angle θ was drawn from a uniform distribution between 0 and π. The direction of drift β was randomly chosen from the set {0, π}. The sizes of the Gaussian envelope were σx = 20 + |ϵ| (where ϵ was drawn from a standard Gaussian) and σy = cσx  with the sequence of images  was computed as: R1 = max.To compute the output of this model component at timestep R1 the response of an untuned suppressive filter to the current image. The untuned suppressive filter Guntuned was computed as a difference of 2-d Gaussians:xloc, yloc) were kept the same as those for the first model component, and the spreads of the filters were fixed at σ1 = 20, σ2 = 30. The dot product of the untuned Gabor filter Guntuned and the current image I, computed as S2 = max{0, ωL2}, where ω was drawn from the distribution reported in . We considered 5 values for α: 0° , 45°, 90°, 135°, 180° . Third, we transformed the pink noise images  to white noise by raising the power spectrum of the pink noise to different fractional exponents. The intuition behind this is that the power spectrum of natural images falls off as 1/f2, where f is frequency [f2 fall-off to a 1/f fall-off. To ensure the average magnitude of the power spectrum was similar for each transformed image, we normalized the power spectrum of each image by dividing by its sum of magnitudes λ. We then raised the normalized power spectrum to 5 different exponents: 1 , 3/4, 1/2, 1/4, 0 . Finally, we scaled the resulting power spectrum by λ.To study how dimensionality of the model outputs changes as we parametrically alter the visual stimuli, we performed three analyses. First, we varied the contrast of the images of the natural movie in 5 different increments: 100%, 75%, 50%, 25%, 5%. For each image, we subtracted the mean luminance of that image, scaled the result by one of the percentages above, and added back the mean luminance. Second, we transformed the images of the natural movie to pink noise by randomizing the phase of the 2-d Fourier transform for each image. To avoid removing local contrast of the natural images , we adderequency . Raising3 to 106) that performed the same operation  but on different spatial regions of the input. Each layer comprised a group of units . We used an instantiation of a CNN called GoogLeNet , trainedshown in A. We assS1 FigWe randomly subsampled a smaller number of neurons from the 61 neurons of monkey 1 (left panel) and from the 81 neurons of monkey 2 (right panel). We then computed the dimensionality for the subsampled population responses to each movie separately. We assessed dimensionality as the number of dimensions needed to explain 90% of the variance. Error bars represent the standard deviation for 50 different subsamples.(EPS)Click here for additional data file.S2 FigA: Projection for the pixel intensities for the visual stimuli, capturing 18% of the total variance. Each dot is an image, and v1 and v2 are orthonormal projection vectors of the high-dimensional pixel space. B: Projections for the population responses for monkey 1  and monkey 2 . Each trajectory traces out the population activity timecourse for one movie . The orthonormal projection vectors v1 and v2 of the high-dimensional firing rate spaces are computed separately for each monkey.Projections were found using the DataHigh software , to show(EPS)Click here for additional data file.S3 Figr and ask how the number of points N contained within that sphere grows as r increases. The faster N grows with r, the higher the dimensionality. We plot log(N) versus log(r), where the slope is defined to be the fractal dimensionality. A: Fractal dimensionality of the pixel intensities of the gratings (blue), natural (green), and noise (red) movies. Slopes (Δ) were computed by linear regression between the bounds of 6 and 12 of the log number of neighbors. B: Fractal dimensionality of the population responses to the gratings, natural, and noise movies for monkey 1 (left panel) and monkey 2 (right panel). Slopes were computed by linear regression between the bounds of 4 and 10 of the log number of neighbors.Fractal dimensionality was computed by the same method as in . The int(EPS)Click here for additional data file.


Staphylococcus aureus enterotoxins (SAEs). Aspirin-exacerbated respiratory disease is a severe form of chronic rhinosinusitis with nasal polyps in which nearly all patients express anti-SAEs.Chronic rhinosinusitis with nasal polyps is associated with local immunoglobulin hyperproduction and the presence of IgE antibodies against We aimed to understand antibodies reactive to SAEs and determine whether they recognize SAEs through their complementarity-determining regions (CDRs) or framework regions.1, and those of high affinity for specific SAEs, assayed by means of ELISA and surface plasmon resonance, were recloned as IgE and antigen-binding fragments. IgE activities were tested in basophil degranulation assays.Labeled staphylococcal enterotoxin (SE) A, SED, and SEE were used to isolate single SAE-specific B cells from the nasal polyps of 3 patients with aspirin-exacerbated respiratory disease by using fluorescence-activated cell sorting. Recombinant antibodies with “matched” heavy and light chains were cloned as IgG1 or antigen-binding fragments of each anti-SEE enhanced degranulation by the other anti-SEE.Thirty-seven SAE-specific, IgG- or IgA-expressing B cells were isolated and yielded 6 anti-SAE clones, 2 each for SEA, SED, and SEE. Competition binding assays revealed that the anti-SEE antibodies recognize nonoverlapping epitopes in SEE. Unexpectedly, each anti-SEE mediated SEE-induced basophil degranulation, and IgG1s can enhance the activity of anti-SEE IgEs as conventional antibodies through CDRs or simultaneously as conventional antibodies and as “superantibodies” through CDRs and framework regions to SEEs in SEE–anti-SEE IgE-FcεRI complexes.SEEs can activate basophils by simultaneously binding as antigens in the conventional manner to CDRs and as superantigens to framework regions of anti-SEE IgE in anti-SEE IgE-FcεRI complexes. Anti-SEE IgG Staphylococcus aureus and its superantigens are implicated in the intense inflammatory processes of the upper and lower airways in patients with allergic diseases.Staphylococcus aureus enterotoxins (SAEs), are strongly associated with chronic rhinosinusitis with nasal polyps (CRSwNP), particularly in the subpopulation of patients with aspirin-exacerbated respiratory disease (AERD), as well as those with allergic rhinitis, asthma, and atopic dermatitis.in vitro suggests that they can also act as B-cell superantigens by binding to the common structural framework regions in the immunoglobulin heavy-chain variable region (VH) domains shared by immunoglobulins with different CDRs,S aureus to escape immune surveillance.16H2 inflammation, local immunoglobulin production, and eosinophil infiltration driven by IL-5 and eotaxin.in vitro.in vivo to cause the symptoms of atopic dermatitis.Nasal polyps in patients with CRSwNP are inflammatory outgrowths of the paranasal sinus mucosa, which are generally characterized by TAntibody production in nasal polyps of patients with CRSwNP is driven by local activation and differentiation into plasma cells of B cells on exposure to various aeroallergens and microbial antigens.Nasal polyps and sera were collected from 3 patients with AERD  at the Royal National Throat, Nose and Ear Hospital, London, United Kingdom. The patients were male, with a mean age of 56 years. Only patient HPK-014 had positive skin prick test responses to aeroallergens. The ethical committee representing the Royal National Throat, Nose and Ear Hospital approved the study, and all patients provided written informed consent before the study commenced.www.jacionline.org. All sera from blood samples and homogenates from nasal polyps were assayed for total IgE and specific IgE to SEA, SEB, SEC1, SED, SEE, and TSST-1 by using the UniCAP system .Detailed methods for processing sera and nasal polyp homogenates are available in the 1  and 1 U/mL Liberase TL  at 37°C. Samples were stained for CD19, CD138, SEA, SED, and SEE. SAE-specific single B cells were sorted by means of fluorescence-activated cell sorting (FACS) into 96-well PCR plates for cDNA synthesis and immunoglobulin expression cloning as IgG1 . Full de1. Large-scale production of IgE, IgG1, and antigen-binding fragments (Fabs) was carried out with Expi293 cells . The IgG1 antibodies were purified by means of protein A–Sepharose , the IgE antibodies by omalizumab-coupled NHS-activated Sepharose , and the Fabs by LambdaFabSelect agarose . Anti-SAE IgG1 antibodies were identified in culture supernatants by means of routine ELISA (www.jacionline.org). Further details are available in the SEA-, SED-, and SEE-specific antibodies were expressed in human embryonic kidney 293T cells  as IgGne ELISA , and thehttp://www.ncbi.nlm.nih.gov/genbank), and their accession numbers are listed in www.jacionline.org.Immunoglobulin gene sequences were analyzed by using the IMGT/V-Quest tool,Surface plasmon resonance (SPR) with a Biacore T200 instrument  was performed to determine binding specificity, kinetics, and affinity of the antigen-antibody interactions. Further details are available in the Degranulation of cells of the rat basophilic cell line RBL SX-38, which stably expresses the α, β, and γ chains of human FcεRI,Three patients with AERD, HPK-014, HPK-016, and HPK-018, with titers of specific anti-SAEs in their serum or nasal polyp homogenate were studied see . Specifi+CD19+CD138− 7-aminoactinomycin D [7-AAD]−) identified by means of FACS (www.jacionline.org). Matched immunoglobulin VH and light-chain variable region  DNA pairs were amplified from the cDNAs of 37 SAE+CD19+CD138− single B cells . We expressed all 40 of the putative SAE-specific matched variable regions as human IgG1 antibodies.Viable SAE-positive B cells  and HPK-016_1F3 ; 2 anti-SED antibodies, HPK-014_1A4  and HPK-018_2D6 ; and 2 anti-SEE antibodies, HPK-014_1G2  and HPK-016_203 , were identified .The V(D)J rearrangement and CDR signatures of the 6 anti-SAE antibodies are shown in 1 antibodies with the SAEs . The 1G2 and 203 antibodies were specific for SEE, although both displayed cross-reactivity with SEC1 (27% amino acid sequence homology with SEE) to a different extent rather than the more homologous SEA and SED .D-F).ELISA was used to determine the specificity of the purified recombinant IgG1 antibodies was substituted with the human ε constant region and the resulting IgEs were expressed and purified to examine the activities of the corresponding anti-SAE IgEs (see B). Basophil degranulation activities of the 6 anti-SAE IgEs in the presence of the cognate SAE are shown in The γ1 chain constant region of the IgGIgEs see , B. BasoA) and the anti-SEDs (B) behaved as expected, whereas both anti-SEE IgEs, 1G2 and 203, were active in this assay (C). Basophil activation would require either 2 identical epitopes in the SEE because it formed a dimer like SEDD).Conventional antigen binding through CDRs to 2 identical receptor-bound monoclonal IgE molecules, each of which recognizes a single epitope in a monomeric antigen, should not induce cross-linking of FcεRI on the surfaces of basophils to cause basophil degranulation. The anti-SEAs , A and tnti-SEDs , B behavis assay , C. Baso1 (see www.jacionline.org). Also, SPR revealed that binding of saturating concentrations of 1G2 IgG1 to immobilized SEE did not prevent the simultaneous binding of soluble 203 IgG1 (A). The reverse was also observed: after saturating all of the 203 binding sites on immobilized SEE, 1G2 IgG1 could still bind with high affinity (B). Both ELISA and SPR assays confirmed that the 2 anti-SEE antibodies bind to nonoverlapping epitopes on SEE.To map the epitopes of anti-SEE 1G2 and 203 antibodies on SEE, we carried out sandwich binding assays using ELISA and SPR. ELISA revealed that saturation of the 1G2 binding site, which was achieved through addition of SEE to immobilized 1G2 Fab and IgE, did not prevent binding of 203 IgG203 IgG1 , A. The affinity , B. Both1, were tested in pairwise combinations in basophil degranulation assays to characterize their interplay. As expected, because of competition for epitope binding to SEE, the 1G2 IgG1 inhibited SEE-induced degranulation of basophils sensitized with the 1G2 IgE, and the 203 IgG1 inhibited degranulation of basophils sensitized with the 203 IgE (A).1 that recognized the nonoverlapping epitope on IgE enhanced degranulation (A) by cross-linking 2 SEE molecules bound to adjacent receptor-bound IgE molecules; this is illustrated below in B. The 1F3 antibody, which is specific for SEA, had no such effects, demonstrating that antigen specificity is crucial for the modulation of IgE activity by IgG1.The different anti-SEE isotypes, IgE and IgG 203 IgE , A.34, 3nulation , A by cr1 Fabs (B). As expected, the Fabs exhibited the same inhibitory activity as the whole IgG1 antibodies (because of competitive inhibition) when Fab and IgE were of the same specificity. Cross-linking of adjacent IgE-SEE complexes on the basophil surface cannot occur with a monomeric Fab, even one that binds to the nonoverlapping epitope. Paradoxically, the Fab recognizing the nonoverlapping epitope retains the enhancing activity of the whole IgG1 (B).We repeated the pairwise assays by using the 2 anti-SEE IgG1 Fabs , B. As eole IgG1 , B.C, illustrates the mechanism by which receptor cross-linking can occur. We suggest that high-affinity binding of receptor-bound anti-SEE IgE to SEE through its CDRs to the epitope represented in green (C) facilitates subsequent low-affinity binding through a site on SEE, which is represented in red, that binds to a framework region of an adjacent IgG (or Fab) that recognizes a nonoverlapping SEE epitope, which is represented in blue. We call such an antibody, in principle of any isotype that acts synergistically to enhance IgE effector functions, a “superantibody.”in green , C facil1 and then converted to IgE and Fab.Several human mAbs against specific allergens have been obtained from allergic patients by means of phage display,A).We began with the hypothesis that SAEs can act as B-cell superantigens, as well as T-cell superantigens. Indeed, this suggestion has often been made before. It is consistent with the evidence that SEA and SED bind weakly to immunoglobulins bearing VH3 and VH4, respectively.S aureus commensal infections.ELISA assays used to select the 6 antibodies described in the present study  would no1 antibodies , although these 2 IgG1 clones were not among the 6 clones that exhibited high affinity for the SAEs  and a candidate for binding in a superantigen-like manner.1 and expresses a member of the VH3 family .In previous studies it has been demonstrated that certain antibodies against a specific allergen are more frequently observed than others in atopic patients; such allergens, that is, Der p 1 and Der p 2 in patients with house dust mite allergy, are said to be dominant allergens.SEE was first documented in a case of food poisoning.in vitro,4 or IgA1 precursors, could have activated a variety of effector cells and contribute to the proinflammatory environment in the patients' nasal polyps. This is compatible with evidence from the work of Gevaert et alBasophils were activated to release granular mediators by allergens and SEB when sensitized by IgE in homogenates from the nasal polyps of patients with CRSwNP H2 disease. However, similar antibodies can occur in patients with allergic diseases and asthma associated with commensal S aureus infections and dominant allergens.1 can contribute to the pathogenesis of CRSwNP and allergic disease.Anti-SAE IgE antibodies alone or together with IgGIn summary, our results provide proof of concept that SAES can act as B-cell superantigens and that anti-SAE antibodies of any isotype can behave as superantibodies. We have isolated antibodies with potential superantibody activity from nasal polyps of patients with AERD, a highly inflammatory T


Imperatorin, a dietary furocoumarin, is found not only in medicinal plants, but also in popular culinary herbs, such as parsley and fennel. Recently, imperatorin has been shown to activate GPR119 in cells. Another GPR, GPR131, also called TGR5 or G-protein-coupled bile acid receptor 1 (GPBAR1), is known to regulate glucose metabolism. Additionally, TGR5 activation increases glucagon-like peptide (GLP-1) secretion to lower blood sugar levels in animals. Therefore, the present study aims to determine whether the effects of imperatorin on GLP-1 secretion are mediated by TGR5. First, we transfected cultured Chinese hamster ovary cells (CHO-K1 cells) with the TGR5 gene. Glucose uptake was confirmed in the transfected cells using a fluorescent indicator. Moreover, NCI-H716 cells, which secrete GLP-1, were used to investigate the changes in calcium concentrations and GLP-1 levels. In addition, streptozotocin (STZ)-induced type 1-like diabetic rats were used to identify the effects of imperatorin in vivo. Imperatorin dose-dependently increased glucose uptake in CHO-K1 cells expressing TGR5. In STZ diabetic rats, similar to the results in NCI-H716 cells, imperatorin induced a marked increase of GLP-1 secretion that was reduced, but not totally abolished, by a dose of triamterene that inhibited TGR5. Moreover, increases in GLP-1 secretion induced by imperatorin and GPR119 activation were shown in NCI-H716 cells. We demonstrated that imperatorin induced GLP-1 secretion via activating TGR5 and GPR119. Therefore, imperatorin shall be considered as a TGR5 and GPR119 agonist. Diabetes mellitus (DM) is a metabolic disorder that is characterized by pancreatic islet dysfunction . The preAfter food ingestion, enteroendocrine cells in the intestinal mucosa release hormones that stimulate insulin secretion from the endocrine pancreas, thus reducing blood glucose levels; this process is known as the incretin effect ,6. Two tAngelica dahurica, or Baizhi in China. The beneficial effects of Baizhi on obesity and fatty liver have been demonstrated in mice [Herbal extracts have been widely applied as complementary and alternative medicines for the treatment of T2DM . Antioxi in mice . Additio in mice . HoweverGPR131, another GPR, is also known as Takeda G-protein-coupled receptor 5 (TGR5), G-protein-coupled bile acid receptor 1 (GPBAR1) and M-BAR; this GPR has been identified as a bile acid binding receptor ,16,17. AThus, we are interested in understanding the effects of imperatorin on plasma GLP-1 levels. In the present study, the direct effects of imperatorin on TGR5 are identified by using cultured cells. Additionally, imperatorin-induced increases in plasma GLP-1 levels associated with TGR5 activation are characterized in diabetic rats. Therefore, for the first time, we demonstrate that imperatorin can activate TGR5 and GPR119 sites to promote GLP-1 secretion.Imperatorin (purity > 98%) and triamterene  were diluted in dimethyl sulfoxide (DMSO) as the stock solution. Additionally, sitagliptin phosphate , an inhibitor of dipeptidyl peptidase-4 (DPP-4), was diluted in normal saline.Male Sprague-Dawley (SD) rats weighing 250–280 g were obtained from the National Laboratory Animal Center . Animals used in all experiments were anesthetized with sodium pentobarbital  i.p.) to minimize suffering. The experimental protocols were approved by the Institutional Animal Ethics Committee (103120201) of Chi-Mei Medical Center. All experiments conformed to the Guide for the Care and Use of Laboratory Animals, as well as the guidelines of the Animal Welfare Act. To induce type 1-like diabetes, streptozotocin (STZ)-diabetic rats were intravenously injected (i.v.) with STZ (65 mg/kg) according to our previous report . Rats weDiabetic rats were treated orally with 2 mg/kg/day sitagliptin (a DPP-4 inhibitor) or a vehicle for one week before the administration of the testing substance. Then, following the methods of our previous report , we collv/v) fetal bovine serum (FBS) and 2 mM l-glutamine at 5% CO2. Additionally, CHO-K1 cells (ATCC No. CCL-61) were cultured in F-12K growth medium containing 10% (v/v) FBS. In general, we subcultured the cells once every 3 days using trypsin  and changed the medium every 2–3 days.We purchased cell lines from the Culture Collection and Research Center of the Food Industry Institute . Human NCI-H716 cells (ATCC No. CCL-251) were cultured in RPMI 1640 supplemented with 10% amino)-2-deoxyglucose (2-NBDG) was used as an indicator of glucose uptake [2-(e uptake . The expe uptake  with min2+)i) concentrations were detected using the fluorescent probe fura-2 [2+)i were then determined as described in a previous report [2+)i were calculated from the fluorescence values measured at 340 nm and 380 nm. Additionally, the effectiveness of the inhibitors, such as triamterene and others, was compared by using a 30-min pretreatment.Changes in intracellular calcium  (ON-TARGETplus Human GPR119 (139760) siRNA-SMARTpool) from a commercial source . ONT-ARGETplus SMARTpool non-targeting siRNA was used as a negative control. Lipofectamine 2000  was used to transfer siRNA, according to our previous method . The suc5 cells per well) were treated with imperatorin at the indicated concentration under 37 °C or 1 h. In some experiments, a 30-min incubation with triamterene was conducted prior to imperatorin treatment. The supernatants were then collected and assayed by using a GLP-1 active ELISA kit . Each assay was performed in duplicate for the indicated samples.NCI-H716 cells (5 × 105 cells per well) were treated with imperatorin at the indicated concentrations for 72 h. In some experiments, a 30-min incubation with triamterene was conducted prior to imperatorin treatment. Intracellular cAMP levels were determined using a commercial ELISA kit  according to the manufacturer's instructions. Each assay was performed in duplicate for the indicated samples.The cells (5 × 10n) of each group. One-way analysis of variance (ANOVA) was performed and followed by Tukey’s post hoc test. The datasets of the two sample groups were analyzed by independent t-tests. A p-value of 0.05 or less was considered significant.Results are indicated as the mean ± SEM for the sample number (The exogenous TGR5 gene was successfully transfected into CHO-K1 cells and confirmed by Western blots a. The exAdditionally, the increased 2-NBDG concentrations induced by imperatorin were markedly inhibited in a dose-dependent manner by pretreatment with triamterene . The effThe cultured intestinal NCI-H716 cell line that expresses TGR5 receptors is widely used to study GLP-1 secretion . TherefoIn STZ-induced diabetic rats, an established type 1-like diabetic model, imperatorin dose-dependently attenuated hyperglycemia a. AdditiIn STZ-induced diabetic rats, imperatorin dose-dependently increased plasma GLP-1 levels a. The inIn STZ-induced diabetic rats, the residual insulin levels in plasma were not affected by imperatorin a. IntereAs stated above, triamterene did not completely abolish the effects of imperatorin in NCI-H716 cells and STZ-diabetic rats. It has been demonstrated that imperatorin can activate GPR119 . TherefoIn the present study, we found that imperatorin can activate TGR5 in both cells and animals. We demonstrated the direct effect of imperatorin on TGR5 using CHO-K1-cells transiently transfected with the TGR5 gene. Additionally, the effects of imperatorin on TGR5 were observed in cultured cells that secrete GLP-1. Then, we confirmed the increase in plasma GLP-1 levels induced by imperatorin via TGR5 activation by showing that GLP-1 levels were markedly decreased by the TGR5 inhibitor triamterene in diabetic rats. Therefore, imperatorin should be considered as a TGR5 agonist both in vitro and in vivo.Until now, in addition to its role in metabolic regulation, TGR5 has been shown to be involved in many functions of biological systems . TherefoThe direct effects of imperatorin on TGR5 were first identified in CHO-K1-cells transiently transfected with the TGR5 gene. Imperatorin increased glucose uptake in cells expressing TGR5. Additionally, imperatorin increased the levels of cAMP, which serves as a TGR5 signal in these cells. Neither of these effects of imperatorin can be observed in CHO-K1 cells not expressing TGR5. Thus, imperatorin has been shown to mediate TGR5. These results are consistent with those of our previous report  that exa+-sparing [The effects of imperatorin were further investigated in NCI-H716 cells, which are widely used for the study of GLP-1 secretion . TGR5 ac-sparing . The fun-sparing .GLP-1 regulates plasma glucose levels through several mechanisms, including insulin secretion stimulation and proinsulin gene expression, β-cell proliferation and anti-apoptosis increases . TherefoWe found that imperatorin can activate TGR5 in animals and demonstrated for the first time that imperatorin increases GLP-1 secretion through TGR5 and GPR119 activation in cells. Therefore, imperatorin should be considered as an agonist of both TGR5 and GPR119 for progression from bench to bedside in the future.


Antarctic coastal polynyas are regions of persistent open water and are thought to be key bio-physical features within the sea-ice zone. However, their use by the upper trophic levels of ecosystems remains unclear. A unique bio-physical dataset recorded by southern elephant seals reveals that East Antarctic polynyas are a key winter foraging habitat for male seals. During their post-moult trips from Isles Kerguelen to the Antarctic continental shelf, a total of 18 out of 23 seals visited 9 different polynyas, spending on average 25 ± 20% (up to 75%) of their total trip time inside polynyas. Changes in seal foraging and diving behaviours are observed inside polynyas as compared to outside polynyas. Two polynya usages by seals are observed for the inactive and active polynya phases, pointing to different seasonal peaks in prey abundance. During the active polynya phase, we link seal foraging behaviour to changes in the physical stability of the water-column, which likely impact the seasonal biological dynamics within polynyas. Antarctic coastal polynyas are largely latent heat polynyas because they are mechanically driven and form in regions of divergent ice motion due to prevailing winds, oceanic currents, and/or dynamical barriers  blocking the passage of pack ice. Such conditions promote the loss of heat from the ocean to the atmosphere, and the formation of new sea-ice3. Surface waters associated with polynyas often have relatively high biological production as they are the first polar marine systems in spring to be exposed to solar radiation; either because they are not covered by sea-ice or their thin ice is more susceptible to early breakout4. The associated phytoplankton blooms, which persist in summer after sea-ice has melted away, maintain the highest phytoplankton biomass on the continental shelf4. The edges of polynyas, characterized by complex and deformed sea-ice, provide favourable habitat for sea-ice algae grazers . While enhanced primary production occurs primarily between early spring and summer, in polynyas, zooplankton growth and reproduction is extended into late summer and early autumn9. Enhanced vertical carbon flux in polynyas can also support rich benthic communities11. Consequently, Antarctic coastal polynyas provide favourable physical and biological conditions for productive marine ecosystems accessible to air-breathing populations of seabirds and marine mammals to feed in throughout the ice season14.Coastal polynyas are persistent and recurrent regions of open water  that occur within the polar sea-ice zone, ranging in size from tens to tens of thousands of square kilometerse.g.13, and14). However, the seasonal usage of polynyas by mid- to upper trophic levels is still poorly known , as are the underlying biophysical mechanisms that make these regions profitable for foraging. It is unclear if and how marine mammals and seabirds use polynyas either as an air-breathing refuge or as foraging habitat during winter. The role of polynyas as a predictable open water access to food has been suggested ; acting to reduce commute time and energy expenditure between colonies and food supply. The spring/summer use of polynyas by upper trophic levels19 can be associated with the seasonal primary production bloom, for example, the magnitude of primary production in polynyas was positively linked with Adélie penguin colony size4 and the probability of Weddell seals producing pups20. However, the ecological role of each polynya differs between regions (e.g.21), and might be associated with local environmental conditions, such as bathymetry, ‘icescape’ , hydrological properties, polynya size and sea-ice production, some of which vary seasonally. Understanding the role of polynyas in the ecosystem requires a thorough bio-physical assessment of the polynya environment throughout the year, which is logistically challenging during the active sea-ice growth period (March-October). Although a polynya effectively disappears during spring-summer when the sea ice melts around it, its impact on the water column remains and we therefore also pay attention to this period, referred to hereinafter as the “pre/post-polynya” phase.Previous studies have investigated the timing of primary production within Antarctic polynyas and the controlling environmental factors . Circumpolar deep-diving animals, SES come ashore twice-yearly to breed and moult22. Otherwise, SES spend 80% of their time at-sea in transit between their colonies on sub-Antarctic Islands and their foraging grounds24. Key foraging habitat for SES have been found to be sex-dependent26, as well as variable amongst breeding subpopulations28. Among SES from the Kerguelen Islands, there are two main post-moult (i.e. winter) foraging strategies: some individuals use the Kerguelen shelf and/or frontal systems of the Antarctic Circumpolar Current (ACC), while others travel south to sea-ice covered areas and the Antarctic shelf26.We investigated the use of coastal polynyas in East Antarctica by a common mesopelagic predator of the Southern Ocean, the southern elephant seal  to assess (i) whether polynyas were used by SES and if so, the specific timing, (ii) diving behaviour inside polynyas to examine whether they are more favourable foraging grounds than the surrounding shelf/slope areas; and, (iii) the seasonal oceanographic conditions that make these habitats more suitable than adjacent waters.Despite not being considered an “ice obligate” species, some SES spend their entire post-moult foraging trip (January-October) within the Antarctic sea-ice region4 and17: 1. Lützoh-Holm Bay, 2. Cape Borle, 3. Cape Darnley, 4. Mackenzie, 5. Barrier, 6. West Ice Shelf, 7. Shackleton, 8. Bowman Island, 9. Vincennes Bay, 10. Cape Poinsett, 11. Dalton, 12. Paulding Bay, 13. Dibble, 14. Mertz. Eighteen of the 23 tracked seals visited 9 of the 14 identified polynya areas from January to November , 32 ± 25% in core 2 (5 m.y−1 ≤ SIPyear < 10 m.y−1) and 8 ± 14% in core 3 (10 m.y−1 ≤ SIPyear). Seals both dived benthically and pelagically (definition of benthic and pelagic dives followed the method developed in ref.26) whilst in polynyas (54 ± 34% and 46 ± 34% (SEM) of their time, respectively, Table i.e. 100% pelagic (n = 3) or benthic (n = 1) dives. The different metrics describing the use of polynyas by each seal are given in Table A total of 14 polynyas were identified and named following refsber Fig. : seals sber Fig. . When seber Fig. . When inTwo strategies were observed in how seals used polynyas seasonally Fig. : half 9; (ii) thOver the seals’ entire trips, hunting time was significantly (i.e. linear mixed effect models) greater inside  than outside polynyas ) compared to outside  and outside polynyas. However, diving in core areas 3 occurred less frequently, only 6.7% within polynyas. Moreover, only 5 out of the 9 polynyas visited had core 3 areas for at least one year . Finally, no significant differences were found in dive duration inside and outside polynyas and between the different core areas within the Antarctic shelf region  where mCDW can exist below the AASW if there is a mechanism to bring it onto the shelf.Three key water masses layers of the Southern Ocean south of the Polar Front define the principal water masses of the continental slope  with a seasonal mixed layer at the surface in summer. In coastal polynya regions with sufficient sea ice formation, the WML can reach the ocean bed and so transforms the AASW (and any mCDW) into dense Shelf Water (DSW). DSW is the precursor of the offshore AABW layer. Cold, saline shelf water with insufficient density to form AABW is termed LSSW. LSSW can be simply early season DSW formation that has not had sufficient build-up of salinity from brine-rejection or a mixing between DSW and new intrusions of mCDW in spring/summer after the active convection from sea ice growth has ended. As DSW is exported from the continental shelf and mixes down the slope to form AABW, it is modified, hence the transitional water mass is termed mSW and links DSW on the shelf and AABW below the continental rise. In regions where there is ice shelf interaction, the majority of ISW is produced when the deepest water mass on the shelf (DSW or mCDW) accesses the grounding line and produces glacial meltwater. ISW is the product of positively buoyant glacial meltwater ascending/mixing until it reaches a level of neutral buoyancy, above or within the layer of its source water mass.When on approach to/departure from the shelf region, over the Antarctic slope, the seals encountered AASW, mCDW, and mSW , the autumn period is one of transition, with the remnant summer stratification (summer mixed layer in the AASW layer and mCDW below) being ‘conditioned’ by atmospheric cooling and early sea ice growth. By winter, the entire water column is primed for top-to-bottom convection and increasing salinification of the shelf waters. In contrast, latent heat polynyas near the slope (e.g. Cape Poinsett polynya) have weaker sea ice formation relative to true coastal polynyas and in the absence of true DSW formation, the convection simply deepens the WML within the AASW layer. This deepening AASW layer can reach the bottom in the vicinity of the shelf break.Seals entered polynyas for the first time between March and May and made successive exits and re-entries  and the daily average of hunting time per dive , 56% of the SES foraged in frontal areas of the Antarctic Circumpolar Current (ACC), 44% forage south of 55°S within the seasonal sea ice zone (SSIZ) and 34% inside polynyas (78% of those visiting the SSIZ). This reinforces the importance of both SSIZ and polynyas as SES foraging habitats. Significant changes in the foraging and diving behaviour were evident inside polynyas. Enhanced primary production in coastal polynyas14, especially diatom blooms, appears to extend feeding and reproduction of secondary producers into late summer and early autumn (reviewed by ref.9), resulting in higher resources available inside polynyas during the post-moult period of SES (January – October). This increase in primary and secondary resources may explain the marked increase in seals’ foraging activity in polynyas and the large proportion of time these subadult male seals spent in polynyas during their post-moult foraging trip.Our novel observations of seal behaviour coupled with 4, but then left for different foraging areas. Seals from this group may avoid the risk of being trapped in sea ice over winter. Seals from the second group appear to have exploited polynyas throughout the winter period, potentially taking advantage of both the open water access polynyas provide within dense pack/fast ice, and also the prolonged secondary production available here in contrast to surrounding ice covered waters.The use of polynyas fell into two strategies over the post-moult period: one group of 9 seals spent relatively short times early in the season (January–April) in pre-polynyas; while the second group of 9 seals remained in polynyas during the pre-polynya phase and the entire winter (February – October). Early in the season, seals of the first group probably took advantage of the enhanced biological activity from spring blooms that took place in post-polynyas38 on high resolution dive data of SES investigating the link between bottom duration and Prey Encounter Events (PEE) derived from accelerometers were interesting for our study: (i) they showed that for 90% of dives, the bottom time is a good indicator of foraging success (i.e. bottom time increased with the number of PEE at depth greater than 250 m); (ii) they also found that beyond 550 m dive depth, bottom time starts decreasing with increasing dive depth regardless whether or not the dive was successful and unsuccessful (presence or absence of PEE). Hunting time encompasses foraging effort both at the bottom and transit phase and is well correlated with bottom time. The validity of hunting time is thus dependent on diving depth, and below 550 m it may be biased as shorter bottom times  may be associated with good foraging success. However we found that the average dive depth of male seals foraging within the sea ice region was around 330 m, so this bias may only concern deep dives within canyon along the Antarctic shelf or along the shelf break. We are thus confident that this index is reliable for evaluating foraging activity of SES within the sea ice region.The hunting time per dive was used as a proxy for foraging activity. Regarding the validity of this index, findings from the recent work of ref.SES foraging activity was higher and diving depths shallower inside polynyas (core areas 1 & 2) compared with dives performed over the shelf outside polynyas. This pattern presumably reflects the presence of aggregated/abundant prey resources in the upper surface layer in polynyas following extensive primary production in spring. Only a small fraction of dives inside polynyas (6.7%) took place in core 3, and hunting times and diving depths there did not differ markedly from those outside of the polynyas. We suggest that these central zones within the polynyas are likely less available overall .39 than the polynyas’ central waters as it often harbours more diatoms compared to central mixed waters42. In contrast, well mixed shelf waters may harbour rich benthic communities benefiting from enhanced vertical carbon flux10. Other parameters varying from one polynya to another such as intrusion of nutrient rich mCDW and bottom depth may also influence the switch in seal diving behaviour. Below we develop concepts on the underlying oceanographic mechanisms coupling primary production, secondary producers and predators.A bimodal distribution in the diving depths was observed inside polynyas, reflecting the presence in similar proportion of both benthic and pelagic dives Fig. . The pro43 reported similar behaviour in post-moulting female SES from Macquarie Island and foraging in the Commonwealth Bay polynya  during the summer-autumn transition (i.e. Feb–April). The authors hypothesized that the continuous foraging activity they observed was likely due to favourable feeding conditions resulting from ongoing secondary production interpreting their observations as follows. In summer, the shallower seasonal mixed layer forms above the winter mixed layer, in some cases in conjunction with a deep chlorophyll maximum. After summer, atmospheric cooling initiates re-freezing, sea ice growth and the development of the new winter mixed layer , the Mackenzie polynya is isolated from the rest of Prydz Bay below depths of about 700 m and may harbor deep prey for SES such as cephalopods. Indeed, the abundant Antarctic squids Galiteuthis glacialis and Psychroteuthis glacialis are restricted to cold Antarctic waters48. We hypothesized that these species may inhabit the Mackenzie depression as adults of G. glacialis were found to be concentrated in the 800–1000 m layer (mainly beyond the shelf break) and adults of P. glacialis were found near the bottom close to the shelf break within the Prydz Bay region49. The West Ice Shelf polynya has only minor sea ice production and it appears that the seals dived both pelagically and benthically in AASW, presumably chasing the prey aggregated at the boundary of the Winter Mixed layer that can sometimes reach the bottom. Finally, Cape Poinsett is mostly formed due to divergence in the westward flow of ice with the coastal current. There is an important presence of mCDW at depth in that region; any deep convection may cause heat to come to the surface, stopping sea ice production, but possibly assisting in keeping the polynya open. Our results show that in autumn and winter, the seals mainly hunted pelagically in the AASW, most likely chasing the prey aggregating near the boundary of the WML as seen in the West Ice Shelf polynya.The three polynyas studied have different oceanographic structure and properties and in accordance seal foraging behaviour varied among the three polynyas. Mackenzie polynya is the polynya among the three, where active sea ice formation allows the formation of DSW. Here, the WML convects all the way to the sea-floor. Within this polynya, seals mostly visited shelf waters in autumn and then dived only benthically within dense shelf waters in winter. Mooring data in that location suggests convection to 1000 m in winter10 and specific bathymetric features which then lead to rich benthic communities and prey.To conclude, we suggest that during the autumn-winter (April to October), in low sea ice production polynyas, seals dived pelagically to reach the bottom of the WML, remaining in the AASW, where they may benefit from increased resources from convection Fig. . In cont20, the mechanism by which polynya size enhances productivity remains unclear. As detailed by ref.14, polynya size does not directly influence primary production via (i) mixed layer depth (as mixed layer depths in polynyas of different sizes are similar), (ii) nutrient availability, (iii) sea ice melt releasing iron , or (iv) light availability . Further studies are needed to establish linkages between polynya size, productivity and implications across trophic levels within polynya ecosystems. Moreover, all polynyas are different in terms of their physical and biological capabilities.Polynya size, nor its variability in terms of thin ice area influenced seals’ usage of polynyas or their foraging activity. While marine mammals and birds are reportedly more abundant in large and productive polynyasUsing seven years of tracking, we provide clear evidence that subadult male SES from Iles Kerguelen that forage in East Antarctica during their post-moult trip use coastal polynyas to feed. Both pre-polynyas during late summer and active polynyas from autumn through the end of the winter season were used by seals, and foraging activity increased significantly inside polynyas. Unique biological and physical features characterize polynyas as winter oases for these marine predators: (i) open water access to breathe at the surface throughout the winter; (ii) secondary production in autumn from mixing associated with the formation of the WML and water column de-stratification; and (iii) rich benthic communities from likely enhanced vertical water column mixing and carbon flux in strong sea ice production polynyas.e.g. opportunistic feeding when high prey patches are present, or predator avoidance and thus may remain in the polynyas as sea ice extends for the whole winter. The role of innate behaviours such as opportunism, predator avoidance or the role of memory is difficult to unravel when analyzing the foraging ecology of predators. Although this study was based on a unique bio-physical dataset recorded by southern elephant seals across 11 years representing an important research effort, our conclusions are still limited by a relative small sample size (n = 23) leading to potential high degree of individual variability in foraging behavior.It remains unclear why some polynyas were used more than others. Inter-polynya differences in oceanographic conditions, topography, proximity to the Antarctic Slope Front, and sea ice conditions may account for some of the differences in seal behaviour we observed. However, seals may also choose polynyas through innate behaviours, 50. Conversely, physical drivers of vertical mixing important for ecological processes may be slowed or shut down in weaker polynyas. Thus, the ecological importance of key coastal polynyas may change for meso-predators that rely on them as a foraging ground. Understanding how meso-predators rely on particular sea-ice features deepens our much-needed knowledge on under-ice biological habitats. Indeed, knowing how environmental structure and dynamics affect productivity is central to understand the ecology of the Southern Ocean and provides the basis for predicting consequences of future changes on this unique ecosystem.Finally, given the spatio-temporal complexity of polynya processes, it is worth considering how these key habitats might respond to climatic changes. Phytoplankton biomass and primary production in coastal polynyas may increase in the future as stirring of sediments, sea ice and Antarctic ice shelf melt release iron, which may explain some of the variance in primary production in polynyas26. All animals in this study were handled in accordance with the French Polar Institute  ethical and Polar Environment Committees guidelines. The experimental protocols were approved by The Ethics Committee of IPEV and Polar Environment Committees.We used satellite location and dive depth data from 23 post-moulting Kerguelen subadult male SES (weight: 559 ± 244 kg (SD); Supplementary, Table 51, which estimates the time spent hunting during a dive. For each dive, the time spent in segments with a vertical velocity lower or equal to 0.4 m.s−1 was calculated. This time was the estimated hunting time per dive and was used as a proxy for foraging activity.The proxy for foraging activity for each seal was developed at the dive scale using the methodology developed by ref.For each individual, the time spent within or outside polynyas, in the different polynya core areas (defined below), spatial zones  and using benthic or pelagic diving strategies were calculated from the dive duration of the dive data  following refs54 using updated data from ref.3. First, the thin ice thickness algorithm was applied using 85 and 37 GHz brightness temperature retrieved from SSM/I. Next, sea ice production was estimated by heat flux calculation during the freezing period (from March to October) using thin ice thickness and surface atmospheric data. The air-sea-ice surface heat flux is obtained by assuming that the sum of radiative and turbulent fluxes at the ice surface is balanced by the conductive heat flux in the ice. The European Centre for Medium-Range Weather Forecasts Re-Analysis data  and the National Centres for Environmental Prediction/Department of Energy Re-Analysis data (NCEP2: 1992–2014) were used for this calculation. The calculation was performed twice a day over the entire Southern Ocean on the SSM/I Equal Area Scalable Earth-Grid (12.5 km X 12.5 km) from 1992 to 2014.Polynya location and extent was based on estimated sea ice production , 5 m.y−1 ≤ SIPyear < 10 m.y−1  and SIPyear ≥ 10 m.y−1   we contoured a polygon of thin ice  was computed  Fig. . For eac004 Fig. . A total57. The minimum accuracies of post processed data were estimated to be ±0.03 °C in temperature and ±0.05 psu, increasing to ±0.01 °C and ±0.02 psu in the best cases57. Data are made freely available by the International MEOP consortium  and the national programs that contribute to it.Among the 23 male SES, 21 had usable CTD (Conductivity–Temperature–Depth) profiles for a total of 8568 profiles  profiles per day, n = 21) with foraging activity and dive parameters (e.g. dive duration) each CTD profile was compared with the closest dive in time and depth collected by the same individual as follows: for each CTD profile only (i) dives with a maximum depth shallower or equal to the CTD depth, and (ii) within ±6 hours of the CTD timestamp were retained; (iii) from this selection, the dive with the closest depth was retained; (iv) if more than one dive was retained from the previous selection, the closest in time was selected. The stratification within the upper 100 meters of the water column has been computed as the frequency of Brunt-Väisälä, −3, g is the gravitational acceleration of the Earth (−2), and To obtain temperature and salinity vertical profiles at regularised depths, a linear interpolation every 5 m was applied while the average depth difference between points of each profile among all individuals were 35 ± 45 m (mean ± SD). CTD positions were corrected by interpolating between filtered locations at the CTD timestamp. For each CTD profile, we identified the water masses used when the seals were at the dive bottom, as this is where most of the foraging activity is expected to occuryn characteristics60, in conjunction with some specific geospatial conditions. Criteria to define these water masses  Antarctic Surface Water (AASW); (2 & 3) Circumpolar Deep Water (CDW) and modified CDW (mCDW); (4) modified Shelf Water (mSW), north and south of the shelf break; (5) Antarctic Bottom Water (AABW); (6) Ice Shelf Water (ISW); (7) Dense shelf water (DSW); (8) Low Salinity Shelf Water (LSSW).Water masses sampled during the transit of seals from 55°S to the Antarctic continent were determined from their temperature, salinity and neutral density nlme66 using restricted maximum likelihood (REML). The 6 different models tested the difference in hunting times, dive duration and maximum depths inside and outside polynyas ; in the 3 different yearly cores and outside polynyas . Models were based on dive data on a total of 23 males .To quantify the influence of (i) polynyas on seal foraging activity, a total of 6 linear mixed effects models (LMMs) were fitted with the R package 67. The significance of each factor relative to the first level of factor was assessed using p-values with a threshold of 0.01. Summary of the regression coefficients for the 6 LMMS are presented in Supplementary, Table For each model, the response variable was centred and scaled for each seal prior to analysis to correct for non-Gaussian distribution. We first determined the optimal model structure by assessing if individual seals as a random intercept term contributed to the model fit. The final model was then fitted using restricted maximum likelihood (REML). Model validation was checked by plotting Pearson residuals against fitted values, and against the explanatory variables, to verify homogeneity and normality of residualsitsadug . Outliers in the explanatory variables were checked. Individual seals were included as a random intercept and slope terms. Model validation and term significance were evaluated as described above. Summary of the regression coefficients for the GAMM are presented in Supplementary, Table Finally, a Gaussian additive mixed effects model (GAMM) was fitted to examine the statistical relationships between seal hunting time per dive and the ocean stratification inside polynyas. These focussed upon the three longest time-series available inside polynyas . The model was computed with the R packages The datasets analysed and generated during the current study are available from the corresponding author on reasonable request.Supplementary Materials