Wee1-like protein kinase (WEE1) restrains activities of cyclin-dependent kinases (CDKs) in S and G2 phase. Inhibition of WEE1 evokes drastic increase in CDK activity, which perturbs replication dynamics and compromises cell cycle checkpoints. Notably, WEE1 inhibitors such as adavosertib are tested in cancer treatment trials; however, WEE1-regulated phosphoproteomes and their dynamics have not been systematically investigated. In this study, we identified acute time-resolved alterations in the cellular phosphoproteome following WEE1 inhibition with adavosertib. These treatments acutely elevated CDK activities with distinct phosphorylation dynamics revealing more than 600 potential uncharacterized CDK sites. Moreover, we identified a major role for WEE1 in controlling CDK-dependent phosphorylation of multiple clustered sites in the key DNA repair factors MDC1, 53BP1, and RIF1. Functional analysis revealed that WEE1 fine-tunes CDK activities to permit recruitment of 53BP1 to chromatin. Thus, our findings uncover WEE1-controlled targets and pathways with translational potential for the clinical application of WEE1 inhibitors.  •We deciphered acute phosphoproteome alterations upon WEE1 kinase inhibition•The changes mainly reflected activation of CDK1 and CDK2, but not other CDKs•WEE1 indirectly suppresses phosphorylation of DNA repair and DNA replication factors•WEE1 fine-tunes CDK activities to guide recruitment of 53BP1 to chromatin Molecular biology; Cancer; Omics As the process is unidirectional, cells employ cell cycle checkpoints to ensure the cellular demands are met before proceeding forward. Excessive genotoxic stress, as exemplified by the presence of double-strand breaks (DSBs), elicits cell cycle DNA damage checkpoints that delay progression via CDK inhibition. Accordingly, checkpoint activation provides time for repair, and in the case of DSBs, it is predominantly carried out by two repair pathways: classical non-homologs end joining (NHEJ) and homologous recombination (HR).,,,,,The Wee1-like protein kinase (WEE1) governs a pathway that keeps CDK activity in check by inhibitory phosphorylation of Y15 on CDK1 and CDK2.clinicaltrials.gov, 2021-11). Although, most studies aim to specifically exploit the role of WEE1 in the G2/M checkpoint, evidence has emerged that the S phase impacts of WEE1 inhibition contribute to cancer-specific lethality.,,,Cancer cells frequently display elevated expression of WEE1, likely to counterbalance the effects of oncogene activation or loss of p53 that otherwise trigger high levels of replication stress.,,WEE1 inhibition has acute effect leading to CDK hyperactivation and phenotype impacts within the first hour of treatment.Z score combination method was used to combine standardized phosphorylation site abundances into a single value for each time point  showed a stable change in phosphorylation levels, with a trend that was either linear or the relative phosphorylation levels quickly shifted at 20 min and remained elevated afterward A. Sites We set out to determine if phosphosites in clusters 7 and 8 were previously identified and assigned to CDKs. However, only a small fraction of the detected phosphorylation sites could be linked with CDK1 and CDK2 kinases based on PhosphositePlus and SIGNOR databases A. Lack oTo substantiate the validity of our identification, we compared the predicted sites with an independent study that used chemical inhibition of CDK1 with 5 μM RO-3306 to identify potential CDK1 sites.We were surprised that phosphopeptides in both clusters 7 and 8 had similar motifs and CDK probability scores, even though they appeared to exhibit distinct phosphorylation dynamic patterns A and 3B.,,To characterize this “slow” and “fast” dichotomy, we extracted the predicted and known CDK sites found in these clusters and analyzed different features selected based on known CDK specificity determinants that might be affecting the phosphorylation rate.,CDKs are recruited to their substrates with the help of cyclins, which recognize cyclin-binding (Cy) motifs on the target protein. In yeast, it has been shown that functional Cy motifs need to be within 20–80 amino acids from the phosphorylation site to function and can play an important role in regulating substrate phosphorylation timing.Finally, we performed gene set overrepresentation to investigate if there were significant differences in cellular compartmentalization between the clusters. The quickly phosphorylated sites were enriched for chromosomal association, albeit the slow sites did not exhibit the same enrichment, suggesting that sites found on proteins with specific cellular localization are more likely to exhibit fast phosphorylation dynamics F. As exa,,,,,,,,The phosphoproteomic datasets are natural starting points for hypothesis-based exploration. Interestingly, after 20 min of adavosertib treatment, phosphorylation sites on RIF1 (S2260:S2263) and MCM4 (S120) proteins have the highest response. The two proteins play vital roles in DNA replication and DNA repair.,Based on the observed phosphorylation clusters, we hypothesized that adavosertib could trigger untimely hyperphosphorylation of RIF1 and 53BP1 suppressing their function. To determine if this was indeed the case, we treated cells with genotoxic agents in combination with adavosertib to allow easy detection of 53BP1 localization. Furthermore, we utilized quantitative image-based cytometry to quantify the levels of replication stress (chromatin-bound RPA), DNA damage (γH2AX), and 53BP1 foci formation. Since RIF1 association with DNA damage sites is dependent on 53BP1,,,Our data indicate that the genotoxic stress-induced association of 53BP1 with chromatin is promoted by WEE1. To determine if this is due to CDK activity control, we performed rescue experiments with inhibitors designed to target CDK1 or CDK2.Our study utilized deep phosphoproteome profiling in combination with bioinformatic analysis to reveal downstream kinase-mediated signaling pathways upon the inactivation of WEE1 with adavosertib. Inhibition of WEE1 rapidly triggers high CDK1 and CDK2 activity, which in turn could affect other kinase signaling pathways. We identified the specific temporal WEE1-regulated phosphorylation patterns of CDK substrates and features that are governing them. Furthermore, we show that parallel to its mitotic role, WEE1 can indirectly control factors involved in DNA replication and repair. Specifically, we identified that dysregulation of the WEE1-dependent CDK activity impacted 53BP1 by blocking its association with chromatin.,in vitro kinome profiling for adavosertib and assessed both binding and effects on kinase activity.,WEE1 serves a vital function in controlling CDKs, which are pivotal kinases that are intertwined with numerous cellular functions. Loss of WEE1 activity rapidly induces high levels of CDK-driven DNA damage; for this reason, fast inactivation of WEE1 is required to confidently elucidate the role of WEE1 without confounding effects.,,,An important aspect of our study is that the controlled dysregulation of CDK activity facilitates detection of putative new phosphosites. We constructed PSSM based on previously identified CDK1 and CDK2 phosphorylation sites and combined it with our experimental data to predict tentative CDK phosphorylation sites A. We ide,The timing of CDK-driven substrate phosphorylation plays a paramount role in the ordering of the cell cycle. Ectopic target phosphorylation has been shown to perturb the order of cell cycle stages,,,,,,,,,,,,,Mechanisms that maintain genome integrity not only play a role in preventing malignant transformation but they are also targeted in cancer therapy.,,,,,,Although WEE1 has a major role in regulating the G2/M checkpoint, its role in S phase is also important.,,,,,,,,,,,,In this study, we utilize computational analysis of large-scale phosphoproteomic datasets. By utilizing different algorithms, we establish links between multiple kinases and the WEE1-CDK axis and predict putative CDK sites. Certain limitations of our chosen data acquisition and analysis approach have to be highlighted. In accordance with previous studies, we use a relatively high concentration of adavosertib, reminiscent of the blood serum concentration in clinical studies.claus.storgaard@bric.ku.dk).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Claus Storgaard Sørensen  supplemented with 10% foetal bovine serum  and 1% Penicillin-Streptomycin . Human mammary gland cell line MCF10A was cultured in Dulbecco’s Modified Eagle Medium/Nutrient Mixture F-12  supplemented with 5% horse serum (Gibco), 1% Penicillin-Streptomycin, 20 ng/mL EGF (PeproTech), 10 mg/mL insulin (Sigma-Aldrich), 0.5 mg/mL hydrocortisone (Sigma-Aldrich), and 100 ng/mL cholera toxin (Sigma-Aldrich). High grade serous ovarian cancer cell lines OVACAR3 and OVCAR8 were cultured in Roswell Park Memorial Institute 1640 medium  supplemented with 10 % feotal bovine serum and 1 % Penicillin-Streptomycin. All cells were cultured in 37°C with 5% CO2 and checked for mycoplasma infection regularly.oC. After determining protein concentration with Pierce™ Rapid Gold BCA Protein Assay Kit (Thermo Fisher), 200ug were taken forward for digestion. Samples were diluted 1:3 with 10% Acetonitrile, 50 mM HEPES pH 8.5, LysC  was added in a 1:50 (enzyme to protein) ratio, and samples were incubated at 37oC for 4hrs. Samples were further diluted to 1:10 with 10% Acetonitrile, 50 mM HEPES pH 8.5, trypsin  was added in a 1:100 (enzyme to protein) ratio and samples were incubated overnight at 37oC. Enzyme activity was quenched by adding 2% trifluoroacetic acid (TFA) to a final concentration of 1%. Prior to TMT labeling, the peptides were desalted with the use of SOLAμ™ SPE Plate (Thermo Fisher). The sorbent was activated by 40μl of 100% Methanol , then 40μ of 80% Acetonitrile, 0.1% formic acid. The plate was subsequently equilibrated 2x with 40 μL of 1%TFA, 3% Acetonitrile, after which the samples were loaded. The plates were then washed 2 times with 200 μL of 0.1% formic acid and the bound peptides were eluted into clean 500 μL Eppendorf tubes using 40% Acetonitrile, 0.1% formic acid. The eluted peptides were concentrated in an Eppendorf Speedvac and re-constituted in 50 mM HEPES (pH8.5) for TMT labeling. TMTPro 16plex (Thermo Fisher) was used according to manufacturer’s instructions. After labeling the peptides pooled with the same ratio and 200 μL of Ti-IMAC HP (ReSyn Bioscience) was used for enrichment of phosphopeptides. The Ti-IMAC particles were equilibrated with 200 L of 70% ethanol for 5 min and subsequently with 200 μL 1% NaNH3 solution for 10 min. Particles were then washed three times with 200 μL of 80% Acetonitrile, 1M glycolic acid, 5% TFA . Pooled peptides that have been diluted 1:1 with 80% Acetonitrile, 1M glycolic acid, 5% TFA were loaded onto the particles and incubated for 30 min with gentle agitation. Supernatant was then removed and kept as the proteome sample. The particles were washed twice with 400μL of 80% Acetonitrile, 1% TFA for 2 min and twice 400 μL of 10% Acetonitrile 0.1% TFA. The phosphopeptides were then eluted in three rounds by adding 100 μL of 1 % NaNH3 solution for 20 min. The eluted phosphopeptide solution was then acidified with 40 μL of TFA and the supernantant proteome samples were diluted to reduce the Acetonitrile concentration to 5%. Peptide solutions were then desalted with the use of SOLAμ™ SPE Plate and subsequently peptides were concentrated in an Eppendorf Speedvac. Concentrated peptides were stored in -80oC.U2OS cells were treated for 0, 20, 40, 60 and 90 min (n=3) for the time-course experiment and for 0 or 90 min (n = 4) for the single time point dataset with 1 μM of adavosertib. After treatment, the cells were subsequently harvested by scrapping and processed according to . Briefly, harvested cells were lysed using 100 μL of lysis buffer . Samples were boiled at 95°C for 5 min, after which they were sonicated on high for 5x 30 seconds in a Bioruptor sonication water bath (Diagenode) at 4Prior to mass spectrometry analysis, the peptides were fractionated using an offline ThermoFisher Ultimate3000 liquid chromatography system using high pH fractionation  at 5μl/min flowrate. 30μg of peptides were separated over a 120 min gradient (5% to 35% Acetonitrile), while collecting fractions every 120 sec. The resulting 60 fractions were pooled into 30 final fractions, acidified to pH < 2 with 1% TFA and loaded onto EvoSep stagetips according to manufacturer’s protocol. For the enriched phospho-peptides, the number of fractions was adjusted to the total amount of phospho-peptide available, to ensure the collection of ∼500ng per fraction.For each fraction, peptides were analysed using the pre-set ’30 samples per day’ or ’15 samples per day’ method on the EvoSep One instrument. Global proteome peptides were eluted over a 44-min gradient, and analysed on a Q-Exactive Exploris 480 instrument (Thermo Fisher Scientific) running in a DD-MS2 method. Full MS spectra were collected at a resolution of 120,000, with an AGC target of 3×106 or maximum injection time of 50 ms and a scan range of 350–1500 m/z. The MS2 spectra were obtained at a resolution of 45,000, with an AGC target value of 1×105 or maximum injection time of 86 ms, a normalized collision energy of 32 and an intensity threshold of 1e5. First mass was set to 110 m/z to ensure capture of the TMT reporter ions. Dynamic exclusion was set to 60 s, and ions with a charge state <2, >6 and unknown were excluded. For phosphorylated peptides, the gradient length was doubled to 88mins. MS performance was verified for consistency by running complex cell lysate quality control standards, and chromatography was monitored to check for reproducibility.The raw files were analyzed using Proteome Discoverer 2.4. TMT reporter ion quantitation was enabled in the processing and consensus steps, and spectra were matched against the 9606 Human database obtained from UniProt. Dynamic modifications were set as Oxidation (M), Deamidation  and Acetyl on protein N-termini. Cysteine carbamidomethyl (on C residues) and TMTPro (on peptide N-termini and K residues) were set as static modifications. All results were filtered to a 1% FDR, and protein quantitation done using the built-in Reporter Ions Quantifier with statistical significance testing was done with the built-in t-test.Samples for label-free quantification were process as described above. Both phosphoproteome and proteome peptides were analyzed with a “20 samples per day” method on the EvoSep One instrument and analysed on Q-Exactive Exploris 480 instrument (Thermo Fisher Scientific) running a high-resolution MS1 data-independent acquisition method. Full MS spectra were collected at 120 000 with AGC target of 3x10ˆ6 or maximum injection time of 50 ms. Scan range from 400-1000 m/z was used. The MS2 spectra were obtained with 60,000 resolution, with a AGC target of 10e5. 75 windows windows of 8 m/z were used with a MS1 scan every 200 m/z. The raw files were the analysis with Spectronaut 16 software.I,i. Subsequently, artificial internal reference standards were generated protein/peptide specific scaling factor for each replicate and used for normalization to reduce the variation due to biological replicate processing:j in biological replicate i and n is the number of unique conditions (time points) in the biological replicate. Biological replicate scaling factors are then calculated similar as for sample loading with m is the number of biological replicates and j in biological replicate replicate i. Finally, protein/peptide abundances are corrected Protein group abundances were used for the proteome and phosphopeptide abundances for the phosphoproteome. Data normalization was carried out in two steps. First, sample loading was adjusted by multiplying the abundances by a sample specific scaling factor:Equation ARSSFi,j=Sj¯ARSi,jAi,jARS=AZ score method for each sample:RKA is the relative kinase k activity, S is the scaled abundance of a phosphopeptide for a specific kinase k and n is the number of phosphosites that correspond to kinase k in the dataset. A mean RKA score was calculated for each sample with a linear model of the form Relative kinase activity was calculated by first annotating the detected phosphorylation with kinases based on the PhosphoSitePlus and SIGNOR databases downloaded via the Omnipath.Vm and K.To identify patterns of phosphorylation dynamic fuzzy c-means clustering was used.Binomial probability modela residue score a specific position in the motif, N – is the total number of all residues at that position , K – is the observed count of residue a at a specific position, p – is the background amino acid frequency of residue a. The respective probabilities are calculated as follows:Phosphorylation sites in disordered protein regions, to avoid enriching our motifs with residues whose frequencies are highly dependent on protein disorder, we calculated the disordered scores for all protein amino acid sequences in the human proteome obtained from UniProt,Ai is the count of an amino acid i, T is the count of total amino acids. Both values are after disorder propensity filtering.S is the score of a phosphorylation site for a given kinase PSSM and RS is binomial probability model score for amino acid a at position b. To ensure comparability between kinases logistic regression is used to calibrate the kinase scores. The known phosphorylation sites (without curation effort cut-off) were then separated into 80% training and 20 % testing dataset. Since, some of the testing dataset examples were used to generate the PSSM, the accuracy characteristics will be favorably skewed, however the test set serves a good sanity check for the logistic regression step. Since logistic regression is sensitive to imbalanced datasets, we use down sampling, to match the number of positive and negative instances that are used for logistic regression. Generalized linear modeling was used to fit the logistic regression function with a logit link function:We predicted kinases for the unknown phosphorylation sites by constructing position specific scoring matrixes (PSSMs) for the amino acid sequences flanking the known phosphorylation sites. We extracted the phosphorylation sites from the PhosphositePlust and SIGNOR databases via the Omnipath. Sites with only a curation effort of above 2 were considered and PSSM were generated only for kinases that had at least 20 phosphorylation sites after the filtration. Amino acid sequences surrounding the phosphorylation site (+/- 10) were then extracted from the full-length protein amino acid sequences that have been downloaded from the UniProt database. The binomial probability model was then used to calculate scores for every amino acid for all the possible positions generating a 21x20 matrix. The phosphorylation sites were then scored for each kinase with the following equation:CDK1, CDK2 and CDK5 prediction accuracy was the analysis by receiver operating characteristic analysis with the used of the “pROC” package.To identify preferences in the amino acid residues flanking the phosphorylation site amino acid frequencies between the “slow” and “fast” CDK substrates directly compared. To obtain an uncertainty measure in our comparisons we utilized bootstrapping. Phosphorylation sites with their flanking amino acid sequences were samples from the clusters 100 times, to generate resampled datasets with different compositions. Then amino acid frequency was calculated for each dataset and the “slow” cluster frequencies were subtracted from the “fast”. The mean and standard deviation of the frequency differences was then calculated for each amino acid in all possible positions. Under the null hypothesis the frequency difference should be 0, to test this we calculated the probability of observing the calculated frequency difference from a normal distribution parametrized by a mean of 0 and an amino acid position specific standard deviation.The regular expression – [R/K]-x-[L/V/I] was used to identify all the potential Cy motifs in the full-length protein amino acid sequence. The closes Cy motif to the detected phosphorylation site was extracted on both left and right sides of the modification. The distances were visualized by binning them into a histogram or generating a cumulative mass function. Statistical significance was tested with the Kolmogorov-Smirnov test.Gene-set overrepresentation analysis was carried out with the use of the ClusterProfiler R package.Cells growing on 96-well microplates (Greiner-BIO) were treated with different combinations of drugs and genotoxic agents for variable time intervals. After the appropriate treatment, the media was quickly removed and the cells were incubated in pre-extraction buffer  on ice for 2 min and immediately fixed in formaldehyde 4% (VWR) for 10 min at the room temperature. For the analysis of the mitotic cell number the pre-extraction step was omitted. Primary antibodies  were diluted in filtered DMEM containing 10% FBS and 5% Bovine Serum Albumin . Incubations with the primary antibodies were performed at RT for 1 h. Microplates were washed three times with 0.05% PBS-Tween20 and incubated in DMEM/FBS/BSA containing secondary fluorescently labeled antibodies  and DAPI  for 1 h at RT. Images were obtained automatically with the ScanR acquisition software controlling a motorized Olympus IX-83 wide-field microscope. Olympus Universal Plan Super Apo 10x Objective was used for all QIBC data. However, 53BP1 foci images were obtained with a 40x objective. Images were processed and quantified using the ScanR image analysis software for total nuclear pixel intensities for DAPI (Arbitrary units: A.U.) and mean  nuclear intensities (A.U.) for γH2AX, chromatin-bound RPA and 53BP1 foci. Further analysis was then carried out with R statistical software. Kolmogorov-Smirnov test was used to establish statistical significance by comparing the signal intensity distributions.3 and incubated overnight with primary antibodies at 4oC. The membrane was then washed 3 x 6 min in PBS + 0.1% Tween and incubated with secondary HRP conjugated antibodies for 1h at room temperature. Membranes were again washed 3x6 min with PBS + 0.1% Tween and incubated with Classico/Crescendo Western HRP substrate for 3 min. Protein bands were visualized on Amersham Hyperfilm/AGFA Curix Ortho films.Cells were lysed in RIPA (Sigma) buffer containing EDTA free protease inhibitor cocktail (Roche) and 2mM DTT. Lysates were sonicated with Bioruptor sonication device for 10 cycles 30 s on and 30 s off intervals. Lysates were centrifuged for 15 min at 20 000 RFC at 4C. Protein concentration was then measured with Bradford assay and adjusted accordingly to ensure equal loading. Lysates were mixed with 4x LSB and boiled for 10 min at 95°C. Samples were run on NuPAGE Bis-Tris 4-12 % gels according to manufacturer instructions. Proteins were then transferred to a nitrocellulose membrane and blocked with PBS + 0.1% Tween + 5 % Milk powder + 0.01 % NaNAnalysis of data was carried out in the statistical R software with use of tidyverse environmentAll statistical analysis was carried out in R (version 4.2.2) in the Visual Studio Code editor environment (version 1.73), all packaged used can be found in the


Therefore, it is important to identify the molecular entity that exhibits this activity. Here, for this purpose, we established the Proteomics-based Identification of transporter by Crosslinking substrate in Keyhole (PICK) method, which combines photo-affinity labeling with comprehensive proteomics analysis using SWATH-MS. Using preselected criteria, the PICK method generated sixteen candidate proteins. From these, knockdown screening in hCMEC/D3 cells, an in vitro BBB model, identified two proteins, TM7SF3 and LHFPL6, as candidates for the H+/OC antiporter. We synthesized a novel H+/OC antiporter substrate for functional analysis of TM7SF3 and LHFPL6 in hCMEC/D3 cells and HEK293 cells. The results suggested that both TM7SF3 and LHFPL6 are components of the H+/OC antiporter.A proton-coupled organic cation (H In. In+/OC  the BBB . In huma the BBB . Thus, Tk effect , strongl+/OC antiporter substrates. Both transient and stable expression of TM7SF3 tended to increase the substrate uptake, whereas the expression of LHFPL6 had little impact (In the gain-of-function analysis, the HEK293 expression system was used to investigate the contributions of TM7SF3 and LHFPL6 to the uptake of He impact . In parte impact  was obse+/OC antiporter is present, it is important to use a compound for which plasma membrane permeability is rate-limiting. Here, we synthesized a new compound, pyrilamine analogue (+/OC antiporter (+/OC antiporter.Drug permeation through biological membranes is affected by the unstirred water layer surrounding the membrane, the plasma membrane, and intracellular binding. Therefore, to detect phenomena on the plasma membrane where the Hanalogue . The inianalogue , implyintiporter . This sutiporter A. On thetiporter . Furthertiporter C. All of+/OC antiporter, which is responsible for the transport of various CNS drugs at the BBB. Sixteen candidate proteins were picked up based on predefined criteria. The results of knockdown and inhibitor studies in hCMEC/D3 cells, as well as uptake studies with overexpressing cells, indicated that TM7SF3 and LHFPL6 are H+/OC antiporter components. This information is expected to promote the development of effective CNS drugs and novel drug delivery systems. We anticipate that the PICK method will be useful for the identification of various transporters. When screening to identify the responsible transporters for compounds of interest, researchers frequently focus on known ABC, SLC and MFS transporters, and consequently may miss transporters that do not belong to these families. Furthermore, transporter complexes consisting of multiple proteins are difficult to identify. The PICK method can overcome these limitations and is expected to accelerate transporter discovery.We developed the PICK method, which combines photo-affinity labeling and comprehensive proteomic analysis using SWATH-MS, to identify the molecular components of the H


During mechanical recycling, polypropylene typically is reprocessed using a single- or twin-screw extruder. The degradation of polypropylene during this reprocessing reduces the polymer’s molecular weight and, consequently, limits the performance of the recycled resin. This work investigated the impact of a quad screw extruder (QSE), which has greater free volume, on the reprocessing of an impact copolymer polypropylene. To mimic the recycling process, the polypropylene was subjected to three processing cycles using a QSE and a comparable twin-screw extruder (TSE) operated at three screw speeds. The reprocessed materials were characterized for their rheological, morphological, and mechanical properties. For both extruders, increasing the number of reprocessing cycles and the screw speed resulted in higher melt flow indices, decreases in zero-shear viscosity, and shifting of the crossover points for the storage and loss moduli, which indicate reductions in the molecular weight and narrowing of the molecular weight distribution of the polypropylene. The QSE exhibited greater reductions in molecular weight compared to the TSE, probably due to the higher stresses associated with the three intermeshing points along its screws. Reprocessing caused a significant reductions in the Izod impact strength of the reprocessed polypropylene, which correlated with reductions in the particle size and particle size distribution of the dispersed rubbery phase in the polypropylene during reprocessing. Polypropylene (PP) is the second most commonly used polymer after polyethylene. It is available in three families: homopolymer polypropylene (HPP), random polypropylene (RCP), and impact copolymer polypropylene (ICP). In view of its ease of processing, low density, good chemical resistance, and low cost, polypropylene is suitable for use in a wide range of applications, including packaging, consumer goods, building and construction, and automotive ,2. The uMechanical recycling is the most common technique for reducing waste polymer, and its use has increased due to economic and environmental benefits. This mechanical recycling consists of collecting and sorting the waste, grinding the waste, and then reprocessing the waste . DependiStudying multiple-extrusion history has been found to be a very useful method to simulate mechanical recycling by melt processing ,18 and tThe general consensus was that degradation of polypropylene during reprocessing can be reduced by lowering the barrel temperature profile and reducing the screw speeds. In addition, more effective stabilization packages and greater concentration of stabilizers can help to prevent polypropylene from degrading and improve recyclability ,5,18,26.In contrast to co-rotating twin-screw extruders, quad-screw extruders (QSEs) have not been investigated for to reprocessing of polymers. A quad-screw extruder consists of four co-rotating parallel fully-intermeshing screws. Compared to twin-screw extruders, QSEs have greater free volume, which has produced greater throughputs and lower shear rates . The thrTherefore, the objective of this work was to evaluate the impact of a QSE on the recyclability of polypropylene. The virgin impact copolymer polypropylene was subjected to multiple reprocessing cycles at several screw speeds in a QSE and compared with the performance for a similar TSE. The effect of reprocessing was assessed by extruder parameters (melt temperature and head pressure) as well as the melt flow index, rheological properties, impact strength, and morphology of the reprocessed polypropylene.A commercial grade of a heterophasic polypropylene copolymer supplied by Borealis  was used for this work. The reported melt-flow index was 1.3 dg/min (230 °C/2.16 kg). The heterophasic copolymer consists of a homopolymer polypropylene matrix with a dispersed rubbery copolymer phase (ethylene propylene rubber).The reprocessing of the polypropylene resin was carried out using a co-rotating twin-screw extruder (TSE) from Technovel Corporation  and a quad-screw extruder (QSE) from Technovel Corporation . Both extruders had 15 mm diameter screws with length-to-diameter ratios of 45:1. Moreover, both extruders had the same screw configuration. The screw configuration contained only one set of 90° kneading blocks and two sets of 45° forward-kneading blocks immediately after the first feed port to help to melt polymer; the rest of the screw consisted of conveying elements. This screw design was used to reduce the shear on the polymer. The barrel temperature profile was set to 200–230 °C from the first feed section to the strand die. The extrudate was cooled using a water bath, dried by pressurized air, and then strand-pelletized by a rotary cutter. The feed rate was held constant at 2.3 kg/h because it was the maximum feed rate tolerated by the TSE. The reprocessing was performed at screw speeds of 500, 1000, and 1500 rpm. For each screw speed, all samples were recycled three times at the same processing conditions. The melt temperature and head pressure were collected from the machines’ readouts to evaluate machine response. The melt temperature was measured using a type K thermocouple  protruding 6 mm into the melt stream, whereas the head pressure was measured using a pressure transducer  located before the die.o), i.e., ratios of the reprocessed samples’ average MFIs to virgin sample’s average MFI, were reported.The melt flow index (MFI) measurements were performed using an extrusion plastometer . ASTM D1238-20 (Procedure A) was followed with the temperature set at 230 °C and weight load at 2.16 kg. For each reprocessing condition, five measurements were carried out, and the average values were calculated. The relative melt flow indices . The specimen disks were prepared using micro-injection molding  at a temperature of 230 °C and soak time of 3 min. The parallel plates had a diameter of 25 mm and gap of 1.5 mm; the set temperature was 230 °C. For these measurements, a strain sweep was first performed over strain range of 0.1 to 100% and at fixed frequency of 10 Hz to determine the range of linear viscoelastic behavior of the polypropylene. Then, frequency sweep measurements were performed for a frequency range of 0.02 to 15.92 Hz and at a constant strain of 2%, which was within the region of linear viscoelastic region for the polymer.o), the non-linear curves of complex viscosity as function of frequency were fitted to the Cross model [w) using [1/MW2) was calculated as:W1 is the molecular weight of the unprocessed material, MW2 is the molecular weight of the reprocessed material, ηo1 is zero-shear viscosity of the unprocessed material, and ηo2 is zero-shear viscosity of the reprocessed material.To determine the zero-shear viscosity (ss model  using Trw) using :(1)ηo =kStorage modulus (G’) and loss modulus (G”) curves also were obtained from the parallel plate rheology. These curves were analyzed by the shift in crossover points (G*) of the storage modulus (G’) and loss modulus (G”) curves. As shown in The molded specimens of each sample were examined by field-emission scanning electron microscopy (FESEM) using a JEOL, model: JSM 7401F . The accelerating voltage was maintained constant at 3 kV. Prior to imaging, the injection-molded samples were fractured under liquid nitrogen to avoid any disturbance to the molecular structure and then etched with xylene at room temperature for 24 h to extract the elastomeric (ethylene propylene rubber) phase. Then, the etched samples were coated with platinum for 120 s using a vacuum sputter coater .Notched Izod impact testing was performed using an Izod Impact Tester  and a 1.36 J. pendulum. In accordance with ASTM D256-10, the impact properties were tested at room temperature and 40% relative humidity. Prior impact testing, the specimens were molded using Arburg, model: Allrounder 320, injection molding machine  operated at 230 °C. More than five measurements were performed for each reprocessed sample, and the average values were reported.In both extruders, the head pressure decreased as the screw speed and number of reprocessing cycles increased b. The deo). As expected, there was a substantial increase in MFI with the number of cycles and the screw speed due to the thermomechanical degradation of the polypropylene. Over the three processing cycles in the TSE, the MFI/MFIo increased from 1.3 to 2.0, 1.7 to 3.3, and 2.5 to 5.1 for screw speeds of 500, 1000, and 1500 rpm, respectively. These results were in agreement with other authors’ work on polypropylene homopolymers and copolymers reprocessed using single- and twin-screw extruders [o that were similar to those of the TSE. Slightly larger increases in MFI/MFIo (2.0 to 3.8) occurred for a screw speed to 1000 rpm in the QSE. With the highest screw speed, however, the QSE exhibited a significant increase in MFI/MFIo (2.7 to 9.0) with a greater number of reprocessing cycles. These results suggest that QSE produced more degradation at higher screw speeds (>1000 rpm); this finding was consistent with prior work on mixing, which indicated that high screw speeds in the QSE produced greater material degradation [The melt flow index (MFI) provides essential information regarding the material’s flowability because it indicates the polymer’s molecular weight and viscosity. xtruders ,13,17,18radation .The storage modulus (G’) represents the elastic behavior and the amount of stored energy, whereas the loss modulus (G”) characterizes the amount of dissipate energy. The changes in G’ and G” with frequency reflect the mobility of the polymer chains in bulk . The croAs shown in 2) for these fits were 0.9999. Since these values were very close to 1.0, the data were a good fit for the Cross model. Therefore, the calculated zero-shear viscosities could be used to analyze the impact of reprocessing on the polypropylene’s molecular weight.The complex viscosity–frequency curves did not exhibit a clear lower Newtonian plateau . To predW1/MW2) of the reprocessed polypropylene samples as a function of the reprocessing cycle in the TSE and QSEs and at different screw speeds; MW1/MW2 was calculated using Equation (2). The molecular weight ratio was used to quantify the reduction of the molecular weight due to the chain session reactions. In general, the molecular weight ratio for the reprocessed samples decreased with increasing screw speed and the number of reprocessing cycles. For the TSE, these molecular weight reductions were consistent with prior work that attributed reductions in molecular weight to chain scission during reprocessing processes [rocesses ,12,27. MAs shown in The SEM micrographs of the cross-section morphology of the unprocessed polypropylene and the polypropylene reprocessed in the TSE and QSE at screw speeds of 500 rpm and 1500 rpm (cycles 1 and 3) are presented in To quantify the drop in particle size and the changes in particle size distribution, the micrographs for the unprocessed PP and the first cycle of reprocessed samples by TSE and QSE at 500 rpm were analyzed particle size and particle size distribution using an image processing and analysis software (ImageJ). The particle size distribution was determined by using OriginPro 8.1 software. As shown in Similar findings have previously been reported for impact copolymer polypropylene and related copolymers. For a twin-screw extruder operated at 300 rpm, Bouaziz et al.  reportedInterestingly, with this work, the changes particle size and particle distribution seem to correlate with the Izod impact strength results. The highest impact strength was associated with the unprocessed polypropylene, which had the larger EPR particles and a Gaussian particle size distribution. The somewhat lower impact strength correlated with the smaller EPR particles with flat particle size distribution (produced with the TSE at 500 rpm). Finally, the impact strengths of 100–200 J/m were typical for the samples with the smaller particles having a narrow particle size distribution (such as was produced by processing the polypropylene with a QSE at 500 rpm).In this work, a heterophasic copolymer polypropylene was reprocessed three times at different screw speeds  in a quad-screw extruder (QSE) and a comparable co-rotating twin-screw extruder (TSE). Both extruders showed higher shear with increased screw speeds. For both the TSE and QSE, increasing the number of reprocessing cycles and the screw speed resulted in higher melt flow indices, decreases in zero-shear viscosity, and shifting of the crossover points for the storage and loss moduli, which indicate a reduction in the molecular weight and a narrowing of the molecular weight distribution. From these measurements, the QSE exhibited greater reductions in molecular weight compared to the TSE due to the higher shear stresses associated the three intermeshing points along the screws. Reprocessing using the TSE and QSE caused a significant reductions in the Izod impact strength of the reprocessed polypropylene. SEM imaging and subsequent analyses showed reductions in the particle size and particle size distribution of the dispersed rubbery phase size in the polypropylene during reprocessing. These changes in the morphology of the dispersed rubbery phase correlated with reductions in the Izod impact strength of the polypropylene samples. Compared to the TSE, the QSE exhibited a higher melt temperatures due to the higher shear stresses and a lower head pressure because of the higher free volume. As a result of changes in polypropylene viscosity, the melt temperature and the head pressure decreased as the number of reprocessing cycles was increased.


Hyperexcitability in fear circuits is suggested to be important for development of pathological anxiety and trauma from adaptive mechanisms of fear. Hyperexcitability is proposed to be due to acquired sensitization in fear circuits that progressively becomes more severe over time causing changing symptoms in early and late pathology. We use the metaphor and mechanisms of kindling to examine gains and losses in function of one excitatory and one inhibitory neuropeptide, corticotrophin releasing factor and somatostatin, respectively, to explore this sensitization hypothesis. We suggest amygdala kindling induced hyperexcitability, hyper-inhibition and loss of inhibition provide clues to mechanisms for hyperexcitability and progressive changes in function initiated by stress and trauma. In this paper we explore and update the idea that pathological anxiety develops from hyperexcitability of neural circuits for adaptive fear. More than 20 years ago we presented a view that pathological anxiety developed through a process of neural sensitization or kindling-like processes that initiate changes in the brain’s adaptive fear circuits leading to enhanced perception and response to subsequent threat and danger . This noWe use the metaphor and mechanisms of kindling induced-seizure development as a basis for a progression of hyperexcitability in excitatory fear circuits to move from adaptive fear to pathological anxiety and trauma disorders. We extend this idea further to include kindling and sensitization induced hyper-inhibitory and disinhibitory processes interacting with excitatory neurons which produce their own effects in fear circuits and symptoms associated with anxiety and trauma, such as numbness, aloofness, and lack of detail and loss of context in the memories of traumatic and anxious incidents.Hyperexcitability and hyper-inhibition in neural circuits responsible for normal adaptive fear states and behaviors may lead to exaggeration or persistence of these states and behaviors that behaviorally is seen as maladaptive fear. At the level of neural circuits, the amygdala and its connections are central for both normal fear and pathological anxiety. We suggest that while hyperexcitability in the amygdala is responsible for hypervigilance and exaggerated responses to threat in pathological anxiety, hyper-inhibition, and progressive loss of inhibitory processes in the hippocampus and prefrontal cortex also contribute to the spectrum of symptoms. Kindling-like processes might produce both hyperexcitable and hyper-inhibitory states as adaptation to excessive activation of neural circuits.Humans inherently use metaphors as a reasoning tool to make sense of our ourselves and the physical world . The conElectrical kindling can be induced in many areas of the brain, but in most research the amygdala or hippocampus of rodents is stimulated because these regions are typically the loci for temporal lobe epilepsy in humans . KindlinAlthough kindling is a model of seizure development, it has fallen out of favor as an epilepsy model in recent years because it does not readily lead to spontaneous seizures that are part of the clinical syndrome of the epilepsies . NeverthThe kindling metaphor is a useful way of thinking about how anxiety, depression and bipolar disorders may develop with repeated exposure to psychosocial stressors . Robert Kindling as metaphor acts as a rubric for many types of experimental paradigms modeling post traumatic stress disorder (PTSD), anxiety disorders and pathological fear. Acute or chronic stress through restraint/immobilization, chronic unpredictable stress, repeated shocks, exposure to predators and predator odors, isolation, single prolonged stress, social defeat, and early maternal care stress are all paradigms that “kindle” fear circuits to become hyperexcitable and more sensitive to the perception of threatening events leading to exaggerated behavioral and psychological responses [for review see ]. For exIn the rest of the paper, we will highlight how information gleaned from kindling might lead to clues about neural mechanisms of hyperexcitability and compensatory changes that may regulate hyperexcitability and hyper-inhibition altering adaptive fear leading toward pathological anxiety. This will be highlighted in the amygdala, hippocampus, and medial prefrontal cortex .per se that is of interest here, but the increased responsiveness to threat and augmented neuronal excitability in the amygdala that the kindling produces  prefrontal cortex , howevervia increasing somatostatinergic activity to inhibit parvalbumin interneurons, disinhibiting excitatory principal neurons , which normally inhibits Gas mediated signaling through CRF1 receptors. Reducing RGS2 would increase CRF1 receptor linked signal transduction of cAMP, enhancing neuronal hyperexcitability of neurons with CRF1 receptors.Kindling induces changes in signal transduction of CRF1 receptors and excitability. Kindling generates a switch from CRF being inhibitory to excitatory in the pyriform cortex . This chAn important CRF pathway for fear is from the lateral division of the central nucleus of the amygdala (CeAL) to the bed nucleus of the stria terminalis BNST, . While tThe BNST plays a central role in fear. Early ideas suggested that the central amygdala mediated phasic, short lasting fear responses, while the BNST mediated sustained fear behavior, similar to some views on the distinction between fear and anxiety . More reCRF containing neurons in the lateral division of the central nucleus of the amygdala which project to the BNST appear to be important for sensitizing responses to threatening situations and stimuli . SimilarCRF in the amygdala-BNST pathway also facilitates fear learning to weak aversive stimulation . ChemogePharmacological studies antagonizing CRF receptors in the bed nucleus of the stria terminalis and inhibition of CRF production in the lateral division of the central nucleus of the amygdala block the retention of contextual fear memory . OptogenNot everyone who experiences severe fear and trauma develops anxiety or trauma disorders. Only about 19% of Vietnam veterans developed PTSD and 11% still have PTSD symptoms 40 years later . There iFKBP5)]  are showing up in these analyses. Genetic polymorphisms associated with anxiety and trauma disorders are found in the glucocorticoid receptor , genes tFKBP5)]  and genereceptor  and pituGABAergic and somatostatinergic genes also have a genetic association with anxiety and affective disorders. There is an association between glutamic acid decarboxylase (the glutamate to GABA converting enzyme and anxiety and depression  and an aGenetic strains of rats that kindle fast or slow have been developed by selective breeding to assess genetic predispositional contributions to acquiring epilepsy and reactivity to uncertainty and threat . InitialOne study examined the release of CRF to stressors in fast and slow kindling rats . PredatoWe have explored kindling-induced hyperexcitability and hyper-inhibition as a metaphor for development of pathological anxiety and trauma disorders from adaptive neurocircuits of fear. Kindling induces hyperexcitability in the amygdala which affects many cortical and subcortical regions of the temporal lobe . This isper se, maladaptive anxiety becomes triggered by lowering the threshold for threatening stimuli to activate amygdala fear circuitry. Loss of inhibitory somatostatin interneurons in basolateral amygdala may play a large role in this. Increases in CRF excitation, both in the basolateral and central amygdala and BNST further facilitates sensitization. Other inhibitory and excitatory neuropeptide systems also likely contribute.One is that through kindling-like or fear induced non-associative sensitization processes, and not fear conditioning These kindling-induced changes over time may be similar to what trauma and stress produce. This is reminiscent of the effects of few versus many traumatic episodes in PTSD patients where hypervigilance may change to aloofness and detachment with more trauma episodes . While kArun Asok edited the article in collaboration with Eric R. Kandel, Columbia University, United States.JR and JS contributed equally to the conception of the manuscript. JR wrote the first draft. Both authors contributed to the manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Aedes aegypti trypsin inhibitor (AaTI), which does not inhibit FXIIa, to better understand Kazal-type protease specificity and determine the structural components responsible for inhibition. There are only three residue differences in the reactive-site loop between AaTI and Inf4. Through site-directed mutagenesis, we show that the reactive-site loop is only partially responsible for the inhibitory specificity of these proteases. The protein scaffold of AaTI is unstable due to an elongated C5C6 region. Through chimeric study, we show that swapping the protease-binding loop and the C5C6 region from Inf4 with that of AaTI can partially enhance the inhibitory activity of the AaTI_Inf4 chimera. Furthermore, the additional substitution of Asn at the P14′ position of AaTI with Gly (Gly27 in Inf4) absolves the steric clashing between AaTI and the surface 140-loop of FXIIa, and increases the inhibition of the chimeric AaTI to match that of wild-type Inf4. Our findings suggest that ancillary regions in addition to the reactive-site loop sequence are important factors driving Kazal-type inhibitor specificity.Kazal-type protease inhibitors strictly regulate Factor XIIa (FXIIa), a blood-clotting serine protease. However, when negatively charged surface of prosthetic device come into contact with FXII, it undergoes conformational change and auto-activation, leading to thrombus formation. Some research suggests that Kazal-type protease inhibitor specificity against FXIIa is governed solely by the reactive-site loop sequence, as this sequence makes most-if not all-of the direct contacts with FXIIa. Here, we sought to compare the inhibitory properties of two Kazal-type inhibitors, Infestin-4 (Inf4), a potent inhibitor of FXIIa, and  Factor XIIa (FXIIa), also known as Hageman factor, is a serine protease that plays a pivotal role in the intrinsic pathway of the blood coagulation cascade. Studies have reported that zymogen FXII circulates in the blood and becomes auto-activated upon exposure to negatively charged surfaces , throughm), and Cys-IV from Cys-V (n). Classical Kazal-type inhibitors are found with spacer lengths in the range m = 8–16 and n = 2, whereas non-classical Kazal-type inhibitors have m = 1–5 and n = 3–8  vs. percentage normalized inhibition was plotted using a non-linear regression curve function, created with Kaleidagraph 4.5.2 . All data represent the mean ± SD for triplicate values. 50 values of all Inf4 and AaTI mutants and chimeras used in the present study toward FXIIa.For IC50 values, instead of Ki values, are chosen for quantitative comparison of changes in inhibitory activity instead of binding affinity between wild-type and mutant proteins. Inf4 displayed potent inhibitory activity against FXIIa with an IC50 value of 3.1 ± 0.5 nM. In contrast, AaTI did not show any significant inhibition of FXIIa up to 10 μM  or C6 (IC50 = 6.2 ± 0.8 nM) regions with those regions of AaTI caused only a slight reduction in the inhibitory activity of Inf4 on FXIIa (50 = 63.6 ± 13.8 nM) and C5C6 (IC50 = 28.2 ± 7.3 nM) regions of AaTI caused profound drops in inhibitory activity; this was particularly evident for Inf4_AaTI C3C4, which showed an activity similar to that of the reactive-site loop triple mutant (IC50 = 70.5 ± 9.7 nM) (50 = 4680 ± 3834.6 nM) than wild-type AaTI; albeit the activity does not compare with that of wild-type Inf4 (IC50 = 3.1 ± 0.5 nM)  C and 3B. 0.5 nM) C. Togeth50 values of 63.6 ± 13.8 nM for AaTI_Inf4 C1C3+C3C4 and 28.2 ± 7.3 nM for AaTI_Inf4 C1C3+C5C6 (50 = 610.3 ± 205.9 nM) (50 = 28.2 ± 7.3 nM), suggesting that the steric clash may come from residues in regions other than the reactive-site loop.Finally, we tested a combined replacement, whereby we replaced the C1C3 region of Inf4 along with either the C3C4 or C5C6 regions of Inf4 in AaTI. Both of these chimeras showed significant recovery of AaTI inhibition on FXIIa, with IC1C3+C5C6 C. The lo05.9 nM) C. Of notin silico molecular model of the complex between Inf4 and FXIIa. The model suggested that most of the direct interactions between the two proteins occurs via the reactive-site loop of Inf4. However, residue Gly25 at P14′ of Inf4 lies close to residue Tyr151 on the surface 140-loop of FXIIa , suggesting that most of the steric clashing from the C3C4 region is contributed by Asn27. Finally, the inclusion of a stable scaffold (C5C6 region) from Inf4 rendered the AaTI chimera (AaTI_Inf4 C1C3+C5C6+N27G) a potent inhibitor of FXIIa, with an IC50 value (11.4 ± 3.1 nM) very close to that of wild-type Inf4 (IC50 = 3.1 ± 0.5 nM)  B.8ACFRNYV14’) inserts into the catalytic groove of FXIIa and establishes direct close contacts with various subsites of the enzyme. The well-conserved P1 residue, Arg11, in Inf4 makes extensive interactions with FXIIa by extending deep inside the S1 subsite pocket of the protease and establishing salt-bridge interactions with Asp189 within the extremity of the pocket. In contrast, the P2 residue, Phe10, extends outwards from the S2 pocket; this observation is attributed to the fact that a Tyr residue (Tyr99) from the ‘99-loop’ occupies the S2 pocket in FXIIa, making the S2 subsite narrow and shielded from the P2 residue. Previously, we showed that the P2 Phe residue of Inf4 is essential for the inhibition of plasmin due to a ‘94-shunt’ and thus exposure of the S2 subsite, which is filled by the P2 Phe10 residue of Inf4 during binding [All available crystal structures of Kazal-type inhibitors in complex with a protease show that the reactive-site loop makes most-if not all-of the direct contacts with the protease . Indeed, binding . The absIf we were to assume that the reactive site loop was the only direct point of contact between the inhibitor and the protease, additivity-based principles could be used to design a novel inhibitor based on sequence. As per the Laskowski mechanism of canonical inhibition , the inh. that the specificity of serpin is not dictated only by the reactive centre loop sequence, but also by conformational dynamics and protein stability [We previously compared the inhibition of plasmin by AaTI, Inf4 and Inf1, and showed that, in addition to the reactive-site loop sequence, the stability of the scaffold plays an important role in Kazal-type inhibitor specificity . We showtability .We show that the P14′ residue is an important determinant in FXIIa inhibition by Kazal-type inhibitors. Whereas this P14′ position is usually occupied by small amino acid residues such as Gly, Ala and Ser in most Kazal-type inhibitors, it is replaced by an Asn residue in AaTI . Mutation of the P14′ Gly residue in Inf4 to Asn caused a significant 15-fold reduction in its inhibitory activity towards FXIIa. Despite similar structural topology of the chymo(trypsin)-like β-barrel architecture, serine proteases display differential enzyme specificity and selectivity, mainly attributed to variability in length and sequence of eight surface loops in proximity of the active site cleft . The ‘14The Tyr residues in the 140-loop of trypsin do not always cause steric clashing but may instead enhance and stabilize binding of the inhibitor to the enzyme. For example, in the Inf1:trypsin complex, the active-site cleft of trypsin is narrowed due to the presence of a bulky Tyr residue in the 140-loop. The Ser24 residue at the P14′ position of Inf1 fits well into this active-site groove and establishes hydrogen-bonding interactions with the Tyr residue . AaTI isOverall, our results highlight two critical aspects of inhibition by Kazal-type inhibitors in addition to the reactive-site loop: (1) scaffold-dependent stabilization of the reactive-site loop; and (2) length and sequence of the protease surface loops to avoid steric clashes with the inhibitor. Only through three substitutions could we convert AaTI into a reasonable FXIIa inhibitor: we required substitution of the protease-binding loop and region C5C6 of Inf4 as well as an Asn to Gly mutation at position 27. These ancillary regions play essential scaffolding roles in dictating inhibitor specificity, in addition to the reactive-site loop.Click here for additional data file.


A hierarchy of environmental controls ostensibly underpins this complex signature: lithology, topography and climate, though it is basal thermodynamics that ultimately regulates erosion, which can be variously protective, pervasive, or, highly selective. Our analysis highlights the remarkable yet fickle nature of glacial erosion — critically modulated by transient ice-sheet dynamics — with its capacity to impart a profound but piecemeal geological legacy across mid- and high latitudes.Ice can sculpt extraordinary landscapes, yet the efficacy of, and controls governing, glacial erosion on geological timescales remain poorly understood and contended, particularly across Polar continental shields. Here, we assimilate geophysical data with modelling of the Eurasian Ice Sheet — the third largest Quaternary ice mass that spanned 49°N to 82°N — to decipher its erosional footprint during the entire last ~100 ka glacial cycle. Our results demonstrate extreme spatial and temporal heterogeneity in subglacial erosion, with rates ranging from 0 to 5 mm a Glacial erosion processes over geological timescales are poorly understood. Here the authors apply an observationally constrained model to reconstruct the evolving thermodynamics and transient erosional signature of the Eurasian Ice Sheet over the last 100,000-year ice age to reveal how it profoundly sculpted but also protected the landscape visible today Moreover, despite half a century of thorough investigation, the drivers of, and controls on, glacial erosion are still unclear4, with — to differing degrees — assertion of the primary dominance of tectonics, lithology, climate and other proxies over ice-sheet dynamics. For example, it has been argued that rates of glacial erosion that exceed 1 mm a−1 are driven by rapid tectonic uplift, focused on active margins in maritime/temperate climatic settings where fast flow is promoted by high mass-balance turnover and temperate basal regimes that are independent of subglacial processes per se9. Likewise, a glacial-buzzsaw mechanism has also gained traction, where it is argued that climate is the critical control restricting the maximum elevations that mountain regions can attain. Here, mountain glaciers and ice caps — self-limited by their equilibrium line altitude — implicitly designate a climatic/altitudinal threshold at and above which they are highly effective erosive agents but below which their erosive capacity rapidly declines10.As pervasive geological agents of erosion and deposition, ice sheets can drive the evolution of continental shields, shelves, mountain ranges and passive margins. However, the long-term erosive efficacy of ice sheets has been questioned, particularly across low-relief continental shields and high latitudes with cold subglacial regimes11, was in excess of 2500 m thick in places, and had a considerable latitudinal range spanning 49°N to 82°N12. The impact of the EIS on long-term landscape development was profound, moulding the North Atlantic passive margin and north-western European continental shelf through kilometre-scale denudation, sediment evacuation, and related uplift during repeated Quaternary glacial cycles13. Despite this, broad swathes of cold-based inter-fjord uplands and the terrestrial hinterland, including the Baltic Shield, survived multiple glaciations with virtually no modification16. Recently, these views of a cold-based “relict” landscape have been somewhat challenged by cosmogenic exposure measurements that demonstrate significant glacial modification of high plateaus between the fjords of western Fennoscandia18. Erosion of these high plateau surfaces has been linked to long-term equilibrium line altitudes that yield a distinctive bimodal erosion/elevation distribution due to consequent zonation of cold and warm-based glacier action. This observed bimodal erosive signature is claimed to provide further, nuanced, evidence for a glacial buzzsaw mechanism19.In this study, we aim to address and reconcile some of these issues by reference to the time-transgressive erosional footprint of the Eurasian ice sheet (EIS) over the last ~100 ka glacial cycle. At its Last Glacial Maximum (LGM) extent, the EIS was the third largest Quaternary ice complex after Antarctica and the Laurentide20. The model is coupled to a modern-day reference climate via a distributed temperature index scheme based on mean (1950–2000) seasonal temperature and precipitation patterns and is forced by perturbations in these fields scaled to the NGRIP δ18O record and eustatic sea-level change  sediments and 10Be cosmogenic nuclide measurements (see “Methods”) across three primary geological provinces that characterize the Eurasian domain: Shield, Younger Basement, and Platform, delimited by rock type and age . By applying previously defined velocity constraints28 we estimate a total GIII sediment volume of 62,105 km3 31.The Bjørnøyrenna catchment dominated ice drainage from the western Barents Sea shelf, extending inland towards ice domes over Fennoscandia, the central Barents Sea and Svalbard. Outcropping strata within this catchment is largely characterized by the thick sedimentary succession  that infilled the Barents Sea basin during the Mesozoic and generally have similar mechanical properties to the Mesozoic sedimentary rocks found farther south in the Eurasian domain. Towards the southern and western Barents Sea shelf, the platform is divided into minor highs and sub-basins mainly influenced by salt tectonics km3 Fig. . Assumin3 of sediment has been deposited, equivalent to 13,000 km3 of bedrock given a compaction ratio of 20%32 due to the varying lithologies of the source areas . In lieu of more precise chronologies for the glacigenic deposits found offshore, we conservatively estimate 50% of this bedrock volume — 6500 km3 — was eroded since the Eemian, taking into account a reduced ice presence on the mid-Norwegian margin during the early stages of the last glacial cycle , 16,300 km10Be measurements from bedrock samples  and patterns of modelled ice discharge  when compared to the softer Mesozoic sedimentary rocks found elsewhere across other platform areas in the domain39, highlighting the local complexities associated with geological controls when modelling potential erosion at the continental/ice-sheet scale.When aggregated over the glacial cycle, long-term patterns of erosion correspond well with independent (bulk) empirical estimates, particularly within marine-terminating source areas  — in a series of experiments conducted during peak glaciation at the LGM . A comprehensive suite of sensitivity experiments has previously been conducted to investigate the impact of incremental changes in internal and external parameters on our model resultsLGM Fig. . During 40. Analysis of our modelled subglacial erosion patterns across the Northwest Eurasian domain fails to yield statistically significant support for a synoptic-scale link between glacial erosion and latitude  versus plucking 42, generally we find no significant difference in erosional efficacy between these two provinces once our model results are normalized for the duration of ice-sheet occupancy  timescales, climate a priori controls glacial erosion since ice-sheet mass balance, volume and dynamic activity ultimately scale to snow accumulation. We find that peak erosion of 5–7 km3 a−1 occurs during three phases: 88 ka, 61 ka and 23 ka BP, coeval with maximum EIS volume and positive mass balance  within the Younger Basement province account for ~19% of the total sediment flux to offshore depocenters  fluxes and trends throughout the last glacial cycle.Glacial erosion and transport of sediments, including those suspended in proglacial runoff, loaded numerous TMFs distributed along the marine shelf edge offshore Scotland, Norway, and the Barents Sea Fig. . The qua−1  to meltwater runoff discharge calculated from the mass balance algorithm of the ice-flow model. Assuming a continual source of subglacial sediments, fluxes from catchments along the North Atlantic and Arctic Ocean margins peaked during phases of rapid climate warming and deglaciation, attaining a maximum of 12.78 ± 5.1 Gt a56, subglacial mobilization of those sediments as suspended loads is phase-lagged by millennia and driven by episodes of abrupt warming and the associated rapid increases in meltwater and runoff delivered to the bed  timespans to inform long-term landscape evolution models7.While subglacial erosive efficacy broadly scales to EIS flow dynamics, linked to the progressive influence of ice-stream activity through ice-sheet evolutionbed Fig. . This prIn summary, our analyses demonstrate that the EIS, which at its maximum spanned over 5500 km of temperate through extreme polar conditions from 49 to 82°N, was an extremely effective agent of erosion. Despite this, its spatiotemporal signature was also highly heterogenous and transient, with its erosion potential determined by an interacting hierarchy of environmental and internal controls: climate, lithology, topographic relief, and the dynamically evolving, thermomechanical ice-sheet configuration. The time-transgressive perspective presented here reveals the complex and highly nuanced impact of glacial erosion on long-term landscape evolution, which, at a continental ice-sheet scale, rudimentary environmental proxies such as latitude or climatic regime fail to capture.62. Moreover, the highly selective subglacial erosion across shield margins, along with episodes of extreme rates driven by abrupt disequilibrium within the climate-cryosphere-landscape system, provide essential context to contemporary process studies that imply exceptionally high rates of erosion — or rather the mobilization of reworked subglacial sediments — as ice sheets respond to increased meltwater and runoff fluxes to the basal environment and deglaciate under abrupt climate warming.Though our analysis discounts the case for any deep, uniform glacial erosion of the Fennoscandian shield, the internal dynamic modulation demonstrated by our transient analysis provides support for the view that ice sheets can be able or feckless erosive agents in the same place at different times20 to model the Late Weichselian Eurasian ice sheet (EIS) (Supplementary Table 18O) and eustatic sea level63  coefficient, used to encompass the effects of crystal anisotropy and impurities on bulk ice deformation65. Modifying the strain rate towards softer ice tends to increase ice-flow speeds. Basal sliding is related to the basal shear stress through a Weertman-style sliding law, that is introduced at sub pressure-melting temperatures using an exponential decay function67: at 0.75 K below the pressure-melting point, sliding is 0.47 of its value at the pressure-melting point. While this classical sliding law is based on the assumptions of a glacier flowing over hard bedrock, its applicability can break down in subglacial environments characterised by widespread cavitation or ‘soft’/till-laden beds that induce a Coulomb-type regime where basal shear stress is proportional to the effective pressure. Coulomb friction is thus more representative of sliding near the grounding line. Real-world sensitivity analyses regarding the choice of friction law on grounding line dynamics reveals a varied transient response, with Weertman laws systematically predicting the smallest changes in ice volume above flotation68, though the exact nature of the effect on ice dynamics due to parameter choices in sliding laws can be highly region-dependent69.Ice velocity, composed of internal deformation and basal motion, is determined through flow and sliding relations. Internal deformation uses an adaption of Glen’s flow law70, and derives total melt from integrated monthly positive temperatures. Both temperature and precipitation adjust to the evolving ice-sheet surface through applied lapse rates derived from multiple-regression analyses of meteorological observations at a resolution of 1 km from the WorldClim database (Version 1.4)71. To account for the large variations in climate regime across the Eurasian domain, regional reference climates and associated forcing are tuned independently for each of the three major ice-sheet accumulation centres . An additional mass-balance term incorporated is the net water–vapour flux to and from the ice-sheet surface — a predominant component of ablation in cold continental settings where humidity can be very low72.Surface mass balance is determined by a positive degree-day (PDD) scheme73. The sensitivity of calving to, for example, variations in ocean temperature and sea-ice buttressing has been controlled spatially and temporally through a depth-scaled calving parameterization74. In the absence of explicit calculations of such external feedbacks, this depth-related calving coefficient provides a pragmatic and computationally efficient parameterization for determining mass loss at marine terminating margins of the EIS. The model is applied to a 10 km finite-difference mesh with the inclusion of grounding-line dynamics based on the analytical boundary treatment of Schoof75 and adapted in 2D by Pollard and DeConto76, which defines the ice flux at the grounding line as a function of ice thickness linearly interpolated between the adjacent node that brackets floating and grounded ice.Calving losses at marine-terminating margins are coupled to relative sea level using a standard empirical function relating the calving flux to ice thickness and water depth77. We apply a simple and tested correction factor to the PDD melt scheme (Mcorr), making it possible to tune the insolation-independent parameters from present-day data, while relying on the insolation term to handle changes in palaeo conditions:Minsol is the insolation-related melt, S is the incoming solar radiation at the top of the atmosphere, and S0 is the spatially and seasonally explicit insolation fixed to present-day levels. The spatially and seasonally varying coefficient, a, can be considered to be representative of average local conditions of surface albedo and atmospheric transmissivity for the ice sheet, in effect acting as a substitute for a full surface-albedo model. The dependence on temperature means that a cannot be taken as a constant, but must contain some seasonal variation. To better capture this local variability, a  is parameterized as a piecewise linear function of the local mean daily temperature (T) and the day of the year (d):amax and Tmax were chosen to be constant77. Tmin – the minimum temperature threshold for melt corrections — was parameterized as a nonlinear sinusoidal function of the time of year between Tmax in winter and Tmin,sum in summer:amax, Tmax, Tmin,sum, and the exponent p used are based on tuning to best match the diagnosed values of a across the Greenland ice sheet77.Several modifications were made to the ice-flow model to make it suitable for running over full glacial timescales. The first accounts for insolation variations and their impact on the surface mass balance. This parameter can account for 20–50% of the surface melt anomaly, although its effects are not homogenous over the ice sheet since the contribution of insolation to melt is modulated by the local surface albedoIce-modelling experiments were initiated for the Eemian climatic optimum at 123 ka BP to allow small ice caps to equilibrate with Eemian climate. Two important assumptions for these experiments are that the Eemian topography was similar to that of the present-day in terms of isostatic relaxation and relief, and that Eemian interglacial glaciers had reduced to a negligible size.12, though the magnitude of these climate gradients were tuned separately for the previous two ice-sheet advances during the last glacial cycle. One major divergence from the original setup was to add a dynamic adjustment to mimic a Kara Sea precipitation shadow by scaling the precipitation gradient with ice-surface elevation over central Novaya Zemlya. The resulting effect freely encourages ice growth over the Kara Sea during phases of ice build-up but prevents runaway ice expansion once the central ice dome thickens.Manually applied perturbations of climatic patterns, such as major rain shadow effects, were kept consistent with those applied during the optimum Late Weichselian experiments of Patton et al.The aim of these modelling experiments is to produce a glaciologically feasible EIS simulation that fits within the broad geological framework related to ice extension during the last glacial cycle Fig. . The unc12, highlighting the variation of the maximum ice-sheet configuration to a range of key climate and internal parameter choices. Furthermore, the pattern and rate of retreat across the Eurasian Arctic after the LGM was independently validated and compared with other model reconstructions using a suite of relative-sea level curves from around the Barents Sea78.Sensitivity experiments of the LGM reconstruction with an essentially identical setup were carried out by Patton et al.B) with mean ice discharge (ti):The long-term mean erosion rate, q¯) Fig.  and the −1. The resulting linear fit between the rate of erosion (mm a−1) and local ice discharge within both erosion catchments is thus described . Cosmogenic 10Be production rates are calculated based on the nuclide-specific LSD method92 with production from muons calibrated against the Beacon Heights depth-profile data93 and using a global average reference spallation production rate of 3.98 ± 0.22 atoms g−1 a−1. The time-dependent 10Be production rate is calculated based on sample location , topographic shielding, sample depth, bedrock density, and history of ice cover. We assume full shielding from cosmic rays and a zero cosmogenic nuclide production rate through all periods of ice cover. The local ice discharge and ice cover periods from the ice-sheet model are used for the last glacial cycle. Similar to the ice-sheet model, glacial erosion occurs only when depth-averaged velocities are at least 10 m a−1. For the time 2.6 Ma to 123 ka, we use the LR04 stack33 as a proxy for ice cover, with an ice cover cut-off value determined by the modelled total duration of ice cover in the last glacial cycle with linear interpolation of the LR04 cut-off value from 3.2‰ (123 ka ice cover) to 5.0‰ (0 ka ice cover). For a particular cosmogenic nuclide location, we use the average ice discharge from the last glacial cycle for all ice cover periods prior to 123 ka. Because the last duration of exposure to cosmic rays is commonly critical for the 10Be concentration, we adjust the timing of the last deglaciation to match the reconstructed deglaciation from DATED-194. The deglaciation adjustment is only applied to the ice cover history (shielding from cosmic rays), and it does not affect the total glacial erosion derived from the ice-sheet model. For the ice-free periods we assume no interglacial erosion. For twelve samples collected from sites below the highest shoreline in Sweden, the production rate is adjusted for the water depth calculated from the uplift model by Påsse and Daniels95, with uplift following pre-LGM ice cover periods based on the duration of ice cover and the uplift history following the last deglaciation. We then calculate the central value of F and use a Monte Carlo approach with 10,000 iterations to estimate positive and negative uncertainties, applying the reported 10Be concentration uncertainty, the reference production-rate uncertainty, 10Be decay constant uncertainty97, a 5% uncertainty for bedrock density, a 1 ka uncertainty for the DATED-194 deglaciation age, a 10% uncertainty for the vertical uplift for samples located under the highest shoreline, a 0.1‰ uncertainty for the LR04 ice cover break value for the glaciations prior to the last glacial cycle, and a one-sided positive uncertainty of 0.005 mm a−1 for the interglacial erosion rate. For full details of the calibration, we refer to the provided supplementary code , which could bias estimates of erosion. For example, the sampling of plucked bedrock surfaces to yield reliable deglaciation ages by avoiding cosmogenic-nuclide inheritance99, leads to unconstrained erosion estimates in a landscape where glacial erosion otherwise was minimal, creating a complex and inconsistent patchwork of constraining point data. Similarly, samples can also be taken to establish non-glacial erosion rates, such as on blockfields or tors where geomorphic evidence indicates negligible glacial erosion.The use of cosmogenic nuclide data as a proxy for constraining erosion rates, while arguably more precise, is limited by the shallow depth of cosmogenic-nuclide production in rock and, consequently, a high sensitivity to glacial erosion depths during the last ice cover period (erosion deeper than the depth of production cannot be constrained) and durations of exposure/shielding during the post-glacial period. Modest, local variation in glacial erosion depths can also yield nearby surfaces with highly varying cosmogenic-nuclide concentrations52. Volume-based sedimentation rate estimates derived from shield sources, which fed sink areas south and east of the EIS, are, however, poorly constrained, and generally limited by uncertainty in the initial terrestrial sediment volumes. One estimate for Quaternary sediments deposited by the EIS yields a mean thickness of ~55 m across the Eastern European Plain, increasing to 84 m within the LGM domain100. With a source area representing erosion of the Shield in Finland and the Platform in the eastern Baltic, Gulf of Finland and East European Plain, these estimated sediment volumes provide an order-of-magnitude indication of glacial erosion on the scale of up to tens of metres over the last 2.7 Ma, aligning with mean modelled estimates of glacial denudation of 6.95 m (0.06 mm a−1) across Shield areas for the last glacial cycle  using Landsat7 Enhanced Thematic Mapper and Earth Observation-1 Advanced Land Imager (ALI) imagery and in-situ sampling. Sediment concentrations were analyzed over the active river-discharge summer season for 14 years, yielding a dataset honouring the large variation in discharge events and highly nonlinear nature of sediment transport. The median suspended-sediment concentration examined was found to be 992 mg l−1. Direct upscaling of these observations yielded a total ice-sheet contribution of 1.28 ± 0.51 Gt a−1, associated with a mean yearly runoff of 418 km3 (RACMO2.3 1999–2013)54. An uncertainty of 40% in the total estimate stems from the errors in the reconstructed suspended-sediment concentration from all 160 river outlets, and the estimated error in the modelled runoff (20%). By determining riverine suspended-sediment concentrations through a first-order erosion model linked to glacial dynamics instead, an alternative estimate for the total sediment load from Greenland amounts to 0.892 ± 0.374 Gt a−1 (with a relative error of 42%)54.Suspended sediment fluxes were scaled across the Eurasian domain using observations from the modern-day Greenland ice sheet. We incorporate the analyses of Overeem et al.20.We directly scale the former estimated suspended-sediment flux to meltwater fluxes calculated within the mass balance budget of the ice-sheet model through the last glacial cycle, assuming a consistent sediment concentration through time. Sediment load fluxes were further partitioned according to broad ice-sheet scale catchments defined in Patton et al.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2