These findings point to differences in Cdk1-mediated mechanisms of regulation between human and yeast Cdc14 orthologues.Cdc14 enzymes compose a family of highly conserved phosphatases that are present in a wide range of organisms, including yeast and humans, and that preferentially reverse the phosphorylation of Cyclin-Dependent Kinase (Cdk) substrates. The budding yeast Cdc14 orthologue has essential functions in the control of late mitosis and cytokinesis. In mammals, however, the two Cdc14 homologues, Cdc14A and Cdc14B, do not play a prominent role in controlling late mitotic events, suggesting that some Cdc14 functions are not conserved across species. Moreover, in yeast, Cdc14 is regulated by changes in its subcellular location and by phosphorylation events. In contrast, little is known about the regulation of human Cdc14 phosphatases. Here, we have studied how the human Cdc14A orthologue is regulated during the cell cycle. We found that Cdc14A is phosphorylated on Ser411, Ser453 and Ser549 by Cdk1 early in mitosis and becomes dephosphorylated during late mitotic stages. Interestingly,  They are highly conserved and are present in eukaryotes ranging from yeast to mammals. Their functions are quite well established in yeast. In Saccharomyces cerevisiae, Cdc14 exerts an essential role in regulating late mitotic events, mitotic exit and cytokinesis by reversing phosphorylation of different CDK substrates5. In the fission yeast Schizosaccharomyces pombe, the Cdc14 orthologue, Flp1/Clp1 (referred to as Flp1 hereafter), although a non-essential gene, also targets mitotic cyclin-dependent Cdk1 substrates to regulate late mitotic events and cytokinesis10. Moreover, both budding and fission yeast Cdc14 orthologues have been involved in DNA damage response mechanisms13. The ability of human Cdc14 phosphatases, Cdc14A and Cdc14B, to rescue Cdc14-deficient yeast cells suggests conserved functions15, although they do not play a prominent role in controlling late mitotic events16. Mammalian Cdc14s have been involved in several cellular processes such as the centrosome duplication cycle, control of Cdk1 activity, cytokinesis, cell adhesion and migration, transcription regulation and also DNA damage response29.Cdc14 family members are dual-specificity phosphatases that preferentially reverse Cdk-dependent phosphorylations30. Regulation of Cdc14 is also fairly well characterized in yeast. In S. cerevisiae the activity of Cdc14 is largely controlled at the level of subcellular localization. Thus, Cdc14 is maintained in a nucleolar-bound inactive form during interphase and in a nucleolar-released active state during late mitosis. Cdc14 nucleolar release and activation starts at the onset of anaphase, the time at which Cdc14 initiates essential roles for nuclear and cytoplasmic divisions, and are promoted by the coordinated and consecutive action of the mitotic networks FEAR (fourteen early anaphase release) and MEN (mitotic exit network)32. Proteomics studies have identified CDK-dependent phosphorylation sites in Cdc14 in the budding yeast; some of these sites seem to be specific for one or several cell cycle phases36. In particular, the CDK complex formed by Cyclin-Cdc28 phosphorylates Cdc14 to decrease its activity specifically during S-phase36. In the fission yeast S. pombe, Flp1 regulation also involves a change in its subcellular localization. Thus, the interphase nucleolar Flp1 is released from the nucleolus early in mitosis to concentrate on the kinetochores and contractile ring and to disperse throughout the nucleus and cytoplasm37. In this case, there is also an additional mechanism to control its catalytic activity, although it differs from the S. cerevisiae one. Flp1 is phosphorylated by Cdk1 during early mitosis to stay inactive until mitotic exit, the time at which the protein is activated by autodephosphorylation to participate in the orderly dephosphorylation of Cdk1 substrates30.All Cdc14 phosphatases share a highly conserved N-terminal domain, which comprises the PTP (protein-tyrosine phosphatase) catalytic motif, and have a variable C-terminal domain, whose length and sequence conservation differs among the different species. The carboxyl terminus contains a nuclear export sequence and has been shown to be subjected to post-translational modification29. We have previously shown that Cdc14A modulates the timing of mitotic entry through the regulation of both positive and negative Cdk1 regulators, Cdc25B phosphatase and Wee1 kinase, respectively28. Cdc14A has also been involved in late mitotic processes, such as chromosome segregation, and later on, cytokinesis39. These observations suggest that Cdc14A phosphatase participates in the dynamic control of protein phosphorylation during mitosis, and that it should therefore be subjected to strict spatiotemporal regulation.As in yeast, human Cdc14 phosphatases have different localizations throughout the cell cycle. Thus, Cdc14A and Cdc14B, concentrated in the centrosomes and nucleolus, respectively, during interphase, become dispersed throughout the cell upon entry into mitosisHere, we describe mitotic-specific phosphorylation of human Cdc14A by Cdk1-Cyclin B1 complexes. Cdc14A gets hyperphosphorylated during early mitosis and then, at the same time as Cdk1 inactivation during late mitosis, Cdc14A becomes dephosphorylated. In addition, we discovered that although Cdc14A has autodephosphorylation capacity, its dephosphorylation during mitotic exit is regulated by other phosphatases. Moreover, we found that Cdk1-mediated Cdc14A phosphorylation does not regulate either its catalytic activity (in contrast to what has been observed in yeast) or its subcellular localization or stability. However, Cdk1-mediated Cdc14A phosphorylation in early mitosis may modulate its protein interaction pattern. These results suggest a clear divergence between yeast and human Cdc14 phosphatases, regarding to the mechanisms of their regulation through the cell cycle.29. When ectopically expressed, we routinely noticed that electrophoretic mobility of the inactive form of Cdc14A, phosphatase dead or Cdc14A(PD), appeared slightly decreased when compared with the wild-type protein  stable cell lines expressing low levels of the proteins. Ectopic expression of these proteins did not have any effect on cell cycle progression, or on its specific subcellular localization  cell line. As expected, Cdc14A(PD) showed higher phosphorylation levels than the active form at all time points analyzed , showing its ability to autodephosphorylate in trans  at low but detectable levels. Nocodazole treatment was used to synchronize cells in mitosis and these cells were collected by shake off and released into fresh medium to allow progression through mitosis. As shown in the Fig. 42. We found that Cdc14A interacts with PP2A α-catalytic subunit , and MALDI-TOF. Three serine phosphorylation residues corresponding to minimum Cdk phosphorylation consensus sites and located at the carboxyl terminus were identified  for Cdk1 phosphorylation Fig. ; two of in vitro. We found that while Cdk1 efficiently phosphorylated Cdc14A(PD), the mutation of these three serines  drastically reduced the level of Cdc14A(PD) phosphorylation  mutants ion Fig. . Mutatioion Fig. , indicat28, coinciding with the Cdc14A hyperphosphorylation peak, we considered the possibility that mitotic phosphorylation might negatively regulate its catalytic activity, halting its inhibition of Cdk1 during early mitotic stages. In fact, it has been demonstrated that in the fission yeast S. pombe phosphorylation of Flp1 by Cdk1 during early mitosis reduces its catalytic activity to ensure proper progression through mitosis30. Based on this finding, we evaluated the contribution of Cdk1 phosphorylation on the catalytic activity of human Cdc14A. Immunopurified Cdc14A from asynchronous or mitotic U-2-OS-Flag-Cdc14A cells was analyzed for its ability to hydrolyze in vitro the universal phosphatase substrate pNPP (4-Nitrophenyl phosphate disodium). We found that the rate at which Cdc14A samples hydrolyzed pNPP was similar in both asynchronous and mitotic samples  and the effect on Wee1 was analyzed by changes in its electrophoretic mobility. As previously reported, Cdc14A, but not its catalytically inactive form, was able to dephosphorylate Wee1, as observed by its faster migration26  and the cytoplasm with the same pattern as wild type and the nonphosphorylatable mutant EGFP-Cdc14A-3A  were quite similar  and the non-phosphorylatable Cdc14A(PD)−3A mutant bind equally to Cdh1  laying on its carboxyl domain. The identification of these three major phosphoresidues allowed us to generate specific phosphorylation mutants for studying the biological meaning of this mitotic modification.28, we considered the possibility that Cdk1 phosphorylation at the early mitosis negatively regulates Cdc14A catalytic activity. Based on in vitro phosphatase assays, it has been reported that the enzymatic activity of Cdc14A does not significantly change during the cell cycle29. Since Cdc14A has autodephosphorylation capacity, it could be possible that it reversed the potential phosphorylation-mediated inhibition of its activity during in vitro reactions. To discard this possibility, we performed in vitro phosphatase assays with the different Cdc14A phosphorylation mutants, and found that both the nonphosphorylatable and the phosphomimetic mutants had a catalytic activity similar to the wild-type protein. Similar results were obtained when the activity of Cdc14A mutants was analyzed in vivo using Wee1 kinase and KIBRA as endogenous substrates45. Although we cannot exclude that phosphomimetic mutations could fail to reproduce original phosphorylations, these results indicate that phosphorylation by Cdk1-Cyclin B1 complexes at early mitosis does not regulate human Cdc14A phosphatase activity.Since Cdc14A negatively regulates Cdk1 activity at the G2/M transition36. It is therefore possible that Cdc14 phosphorylation could be involved in the interaction with its substrates along the cell cycle; indeed, previous reports suggest that in budding yeast the Cdc14 to Cdk1 ratio determines the order of CDK substrates dephosphorylation at the exit from mitosis49 and that this mechanism seems to depend on Cdc14 different affinity for CDK substrates50. All these data support the idea that Cdk1 phosphorylation regulates Cdc14 activity and/or substrate specificity, suggesting that, at least in part, this mechanism might be evolutionary conserved.Furthermore, this evidence shows that although Cdc14A shares the same Cdk1-mediated phosphorylation dynamics as its orthologue Flp1, the biological consequences of this modification are not the same. It has been reported that phosphorylation by Cdk1 also has a negative effect on the catalytic activity of Cdc14 in budding yeast. In this case, the S phase specific CDK complexes, Clb5-Cdc28, phosphorylate Cdc14 in its disordered tail to decrease its activity during S phase, which most likely modulates the balance of CDK and phosphatase activity. This phosphorylation persists through mitosis and diminishes after it30. The PPP family members PP2A and PP1 have prominent roles in dephosphorylating Cdk1 substrates at this cell cycle stage in mammals51. We have shown that Cdc14A interacts in vivo with PP2A-Cα, suggesting that PP2A may be the phosphatase that dephosphorylates it. Further work will be required to explore this possibility.Another point of divergence between Flp1 and Cdc14A orthologues is that although both have autophosphatase activity, Cdc14A does not fully autoremove Cdk1 phosphorylation at the end of mitosis like Flp1 does16. Different regions of Cdc14A, including the C-terminal domain, are involved in its centrosome localization18. Our data suggest that Cdc14A centrosome release at the G2/M transition is independent of phosphorylation by Cdk1 at its carboxyl terminus. Whether Cdk1 phosphorylation could act in concert with other phosphorylations or additional modifications to regulate Cdc14A localization and/or catalytic activity, is not yet known.As in yeast, human Cdc14 localization changes during the cell cycle. Thus, Cdc14A, concentrated in centrosome during interphase, is distributed all over the cell in mitosis21, cell division28, and transcription25. Future work is needed to confirm these interactions, which will contribute to further understand both the regulatory mechanisms of the human Cdc14A isoform and its still poorly characterized functions, especially those related with mitosis. Different research studies have pointed out that yeast Cdc14 functions are not fully conserved in other organisms (reviewed by16). Our work provides new data about the regulation of human Cdc14A by Cdk1 phosphorylation, which also differs from yeast.Finally, we considered the possibility that Cdk1-mediated Cdc14A phosphorylation might play such a regulatory role, contributing to determine the specific Cdc14A protein interaction landscape during this cell cycle window. Consistent with this idea, immunoprecipitation analysis showed that some proteins seem to associate with Cdc14A specifically during mitosis, and that these interactions might be Cdk1-phosphorylation dependent. The interactions studied, focused on some of its already characterized substrates or regulators  proved, however, to be independent of the Cdc14A phosphorylation state, suggesting that other proteins, likely not known yet as Cdc14A-related, are involved in these interactions. We have identified a pool of potential Cdc14A-associated proteins that may bind Cdc14A in a Cdk1-dependent phosphorylation manner. Although none of them corresponds to known Cdc14A interactors, some are linked to biological process in which Cdc14A has been already involved, such as cell polarity−/− cells were a kind gift from the laboratory of Dr. P. Jallepalli16. U-2-OS, HeLa and HEK293T cells were cultured in Dulbecco Modified Eagle’s Medium (DMEM) supplemented with 2 mM glutamine, 100U/ml penicillin G, 0.1 mg/ml streptomycin and 10% fetal bovine serum at 37 °C/5% CO2. HEK293T and U-2-OS cells were transfected with the calcium phosphate/HEBS  method or Lipofectamine 2000 (GIBCO Invitrogen), respectively. Double thymidine block was used to synchronize U-2-OS cells in G1/S phase. First block was performed adding 2.5 mM thymidine for 24 hours; then, thymidine was washed out, allowing cell cycle to progress for 10 hours. A second thymidine block was performed in identical conditions than the first one. Finally, cells were extensively washed with PBS and released into fresh medium. To avoid cell cycle progression further than mitosis, nocodazole (50 ng/ml) was added where indicated. Mitotic cells were selected by shake off after 12 hours of treatment with nocodazole (50 ng/ml). HEK293T cells were enriched in mitosis by a 12 hr treatment with nocodazole. When indicated, cells were treated with 10 μM purvalanol A and 10 μM RO-3306 , 0.5 μM Okadaic acid (Sigma). To analyse protein stability, cultures expressing different Cdc14A constructions were treated with 25 μg/ml Cycloheximide (Sigma) for the indicated times.U-2-OS and HeLa cell lines stably expressing HA-Cdc14A or Flag-Cdc14A alleles, respectively, were generated by infection with specific retroviruses produced in HEK293T. RPE-Cdc14AE. coli. GST- recombinant proteins were purified on glutathione agarose beads using as lysis buffer PBS containing 1% Triton X-100 and supplemented with complete protease inhibitor mixture (Roche). Where indicated, treatment with PreScission protease  was used following the manufacturer’s instructions to remove GST- tag. All the material generated during the current study is available from the corresponding author upon request.Human Cdc14A cDNA was cloned into pCEFL-HA, pCEFL-Flag, pCDNA3-Myc or pEGFP-N1 mammalian expression vectors using suitable restriction enzymes. Two truncated Cdc14A constructions corresponding to its N- amino (aas 1-332) and C- carboxyl domains (aas 333–623) were generated by polymerase chain reaction (PCR) using the corresponding primers. Cdc14A phosphorylation mutants were generated by site-directed mutagenesis. For retroviral infection, wild-type and mutant Cdc14A cDNAs were cloned into pBabe-puro expression vector. These constructions were transfected in HEK293T cells to produce the corresponding retroviruses that were subsequently used to infect U-2-OS or HeLa cells. Puromycin (0.5 μg/ml) was used to select the infected cells and create stable cell lines. Infection efficiency was monitored by pBabe-puro-eGFP expression. Wild-type and mutant Cdc14A/B constructs were cloned into a pGEX-4T-GST or pGEX-6T-GST vectors and expressed in Cells were fixed in ice-cold PBS-70% ethanol for at least 1 hour and subsequently stained with PBS containing 20 μg/ml-RNaseA and 8 μg/ml propidium iodide for 1 hour at 37 °C. To determine the percentage of phospho-histone H3 positive cells, cells were harvested and fixed in ice-cold PBS-70% ethanol, permeabilized with PBS-0.15% Triton X-100 and stained with rabbit anti-phospho-histone-H3(S10)  and Fluorescein isothiocyanate-conjugated goat anti rabbit IgG  antibodies. Subsequently, they were incubated with 20 μg/ml-RNase A and 8 μg/ml-propidium iodide solution for 1 hour at 37 °C. Stained cells were acquired using a FACSCalibur device (Becton Dickinson) and cell cycle distribution was analysed using Cell Quest Pro.3VO4 and supplemented with complete protease inhibitor mixture (Roche). Protein samples were run on 8, 10 or 12% SDS-acrylamide gels. For phosphorylation analyses, Phos-tag (Wako Lab. Chem.) and MnCl2 (100 mM) were added to the SDS-acrylamide mix following the manufacturer’s instructions. The primary antibodies used were mouse HA-tag , mouse Flag-tag  or Rat anti-Flag, , mouse Myc-tag , mouse Cdk1 , rabbit phospho-Tyr15-Cdk1 , rabbit phospho-(Ser) CDKs , goat Cdc14A , rabbit Cyclin B1 , mouse PP2A , rabbit Wee1 , mouse Plk1 , rabbit phospho-histone-H3(S10)  and mouse β-Actin . Specificity of anti-HA and anti-Flag antibodies were checked using control wild type U-2-OS or HEK293T cells that do not express the corresponding tagged-Cdc14A forms. For the immunoprecipitation studies, cell lysates  were incubated with the corresponding antibodies (2 μg/mg protein extracts) and protein G Dynabeads  (10 μl/mg protein) for 3–4 hours at 4 °C. The beads were collected by slow centrifugation, washed five times with 1 ml of lysis buffer, eluted by boiling with Laemli buffer at 95 °C for 5 min and resolved by SDS-PAGE electrophoresis and immunoblot analysis.For western blotting, cells were collected and lysis was performed with buffer containing 0.5% NP40, 20 mM Tris-Cl pH8.0, 150 mM NaCL, 1 mM DTT, 10 mM βM DTT, 10 pH8.0, 150 mM NaF, 1 mM Na32P)ATP with or without sodium vanadate (1 mM) for 30 minutes.All recombinant GST- proteins produced in bacteria were purified on glutathione beads (GST). Approximately 0.5 mg of recombinant GST-Cdc14A, GST-Cdc14B, GST-Cdc14A(PD) or GST-Cdc14B(PD) were phosphorylated by Cdk1-Cyclin B1  at 30 °C in the presence of 0.10 μCi of γ(32P)ATP for 30 minutes at 30 °C. Beads were washed extensively in phosphatase buffer  to remove Cdk1 and non-incorporated γ(32P)ATP, and then incubated in the mentioned phosphatase buffer in the presence of recombinant GST-Cdc14A, GST-Cdc14B, GST-Cdc14A(PD) or GST-Cdc14B(PD) at 30 °C for 30–45 min. Laemmli buffer was added to stop the reactions followed by 5 minutes incubation at 95 °C. Samples were then resolved on SDS-PAGE gels and proteins were visualized by Coomassie staining and autoradiography. Where indicated, recombinant GST-Cdc14A forms were purified from E. coli and processed with the PreScission protease  following the manufacturer’s instructions to remove the tag. Samples were then incubated in the presence of Cdk1-Cyclin B1 complexes an cold-nonhydrolyzable ATP/γ(32P)ATP (100:1) and assayed for activity on pNPP.For phosphatase assays, recombinant GST-Cdc14A(PD) was phosphorylated by Cdk1-Cyclin B1 in the presence of 0.10 μCi of γ, Flag-Cdc14A immunoprecipitates were resuspended in phosphatase buffer to 100 mg/200 ml in 96-well plates. pNPP was added to a final concentration of 5 mM and absorbance at 405 nm was monitored at 30 °C for 30 minutes every 5 minutes. Reactions were made in duplicate or triplicate as stated. Absorbance readings were plotted and the rates of the reactions were determined using linear regression analysis, where the slope was used as indicative of the reaction speed and therefore the activity of the protein. The 25% of the immunoprecipitates was detected by Western blot and quantified for normalization.TM3-conjugated goat anti-mouse IgG , respectively, for 30 minutes to visualize the primary antibodies. Staining specificity of anti-HA antibodies was checked using control wild type U-2-OS cells. No staining signal was detected in these cells. To detect EGFP-Cdc14A, U-2-OS cells were transfected using lipofectamine 2000 reagents (Invitrogen) with the different pEGFP-N1-Cdc14A DNA plasmids. After 36 hr of transfection, cells were fixed and permeabilized as above indicated, and incubated with mouse anti-γ-tubulin antibody . Cells were washed three times with PBS and DNA was stained with DAPI. Images were acquired using a Leica DM6000B or a Zeiss Axioimager Apotome microscopes (objective 40X) and processed using OpenLab 4.0.3 or Zeiss Zen lite softwares.U-2-OS-HA-Cdc14A cell lines were seeded onto sterile, Poly-L-Lysine (Sigma) -treated coverslips in 6-well plates, fixed with paraformaldehyde (4%) and permeabilized with methanol. Then, cells were blocked in PBS with 1% bovine serum albumin (BSA) (Sigma) and incubated in the same buffer with rabbit anti-HA  and mouse anti-γ-tubulin  antibodies for 1 hour. Cells were washed three times with PBS/1% BSA and incubated with Fluorescein isothiocyanate-conjugated goat anti rabbit IgG  and Cy52 using a 1:50 trypsin:protein ratio; the peptide mixture was analyzed by LC-MS/MS in an nanoAcquity UPLC  coupled with an LTQ - Orbitrap Velos . Peptides were trapped and separated by a 30 min gradient to 35% B in a Symmetry and BEH C18 columns . A TOP 20 followed by CID and TOP 10 followed by CID MSA data-dependent methods were used for peptide and CDC14 phosphopeptide analysis respectively. Protein identification was done using Sequest algorithm and Homo sapiens RefSeq database release January 2014 from NCBI including common contaminant sequences. Percolator was used for peptide validation. In addition, phosphopeptides were confirmed by manual interpretation of MSA ion spectra. The mass spectometry proteomics data are available via ProteomeXchange with identifier PXD009481.Immunoprecipitates of Myc-Cdc14A and Myc-Cdc14A(PD) from untreated or Okadaic acid-treated HEK293T cells (0.5 μM OA during 2 hours) were resolved on SDS-PAGE gels and silver stained. Gel bands were processed as reportedSupplementary information


The late Pleistocene Ice Complex  encompasses ice-rich permafrost formed when alluvial and/or aeolian sediments accumulated under cold climatic conditions. Three metagenomes obtained from Yedoma deposits continually frozen for periods up to 60,000 years are reported here. The late Pleistocene Ice Complex  encompasses ice-rich permafrost formed when alluvial and/or aeolian sediments accumulated under cold climatic conditions. Three metagenomes obtained from Yedoma deposits continually frozen for periods up to 60,000 years are reported here. Permafr−1 , and the−1  before r−1 , with op−1 , to find−1 . Trimmed−1 . The sub−1 . Prodiga−1  was usedatabases . After tActinobacteria was the most abundant phylum in both the BP (22.4% of reads) and the OR (36.3%) samples, whereas Proteobacteria dominated in the GP (17.7%) samples. Archaea were more abundant in the GP samples (3.4% of reads) than in the BP (0.5%) and the OR (1.6%) samples. Of all reads assigned to Archaea in the GP samples, 61% belonged to Euryarchaeota, with which many methanogens are taxonomically affiliated. Genes encoding methyl-coenzyme M reductase (based upon the presence of mcrABG genes) that can catalyze the final step of methanogenesis were identified only within the GP samples, which was consistent with the presence of biogenic methane (pmoABC-amoABC genes), suggesting that microorganisms in these two locations have the potential to oxidize methane to methanol and/or ammonia to hydroxylamine. In addition, enzymes such as endoglucanase (based upon the presence of Enzyme Commission number [EC] 3.2.1.4) and xylan 1,4-beta-xylosidase (based upon the presence of the xynB gene) involved in the degradation of complex polysaccharides were identified in all three metagenomes.Based upon the taxonomic analysis on trimmed reads, n the GP .7% samplOrganic-bearing ice-rich Yedoma sediments are vulnerable to environmental changes, including thawing, and these sediments might be considered a potential GHG source in permafrost. A comprehensive investigation of Yedoma metagenomes could provide a basis for more reliable predictions of future biochemical reactions within these sediments in response to climate change.SRX2163490, SRX2163491, and SRX2163492.The raw sequences for these metagenomes were deposited to the NCBI Sequence Read Archive (SRA) under the accession numbers


Escherichia coli 20 years ago but remains relatively understudied. The present work shows that this phosphatase modulates the nitrogen-related phosphotransferase system, a pathway that is regulated by nitrogen and carbon metabolism and affects diverse aspects of bacterial physiology. Until now, there was no known mechanism for removing phosphoryl groups from this pathway.One common means to regulate protein activity is through phosphorylation. Protein phosphatases exist to reverse this process, returning the protein to the unphosphorylated form. The vast majority of protein phosphatases that have been identified target phosphoserine, phosphotheronine, and phosphotyrosine. A widely conserved phosphohistidine phosphatase was identified in  Escherichia coli histidine kinase ArcB. The present work analyzes an ArcB-independent growth defect of a sixA deletion in E. coli. A screen for suppressors, analysis of various mutants, and phosphorylation assays indicate that SixA modulates phosphorylation of the nitrogen-related phosphotransferase system (PTSNtr). The PTSNtr is a widely conserved bacterial pathway that regulates diverse metabolic processes through the phosphorylation states of its protein components, EINtr, NPr, and EIIANtr, which receive phosphoryl groups on histidine residues. However, a mechanism for dephosphorylating this system has not been reported. The results presented here suggest a model in which SixA removes phosphoryl groups from the PTSNtr by acting on NPr. This work uncovers a new role for the phosphohistidine phosphatase SixA and, through factors that affect SixA expression or activity, may point to additional inputs that regulate the PTSNtr.SixA, a well-conserved protein found in proteobacteria, actinobacteria, and cyanobacteria, is the only reported example of a bacterial phosphohistidine phosphatase. A single protein target of SixA has been reported to date: the  Phosphorylation is a common mechanism for modulating protein function. Much of its utility depends on the reversibility of the process, which is often mediated by protein phosphatases. Phosphorylation of histidine and aspartate residues is frequently encountered in bacterial regulatory systems, as well as in plants and some unicellular eukaryotes, and there is a growing appreciation for the importance of histidine phosphorylation in animal cells. However, the vast majority of phosphatases reported to date dephosphorylate serine, threonine, or tyrosine.Escherichia coli SixA  was one of the first phosphohistidine phosphatases to be discovered and, together with the eukaryotic PGAM5 and PHPT1  domain of ArcB . We further show that the phosphorylation state of EIIANtr, one of the protein components of the PTSNtr, is affected by SixA, and we propose a model in which SixA dephosphorylates the PTSNtr protein NPr.Here, we reexamine the role of SixA in sixA deletion in Escherichia coli MG1655 grows poorly in glycerol minimal medium. In aerobic cultures at 37°C, the wild-type and ΔsixA strains reached similar optical densities (OD) at stationary phase, but the ΔsixA strain grew more slowly, with an exponential-phase-doubling time twice that of the wild-type strain .We also found that the Δnditions . Data frsixA strain depends on the ArcB/ArcA two-component system. To our surprise, deletion of sixA produced a growth defect in strains lacking either arcA or arcB , we confirmed that ptsN overexpression suppresses the ΔsixA growth defect  . Co. CosixA  defects . The douNtr inhibits YcgO, and slow growth results from loss of this inhibition. We reasoned that the absence of EINtr or NPr, the first two components of the PTSNtr   . The PTSmponents , 18–21. orylated . We therl medium . These oNtr phosphorylation depends on NPr, we overexpressed SixA in the presence or absence of NPr and monitored levels of EIIANtr and EIIANtr-P. We observed significant EIIANtr phosphorylation in the absence of NPr for cells growing on LB  domain  domain . Furtherpiration , 7. Howepiration , and anopiration . Thus, wNtr phosphorylation. As with other phosphotransferase systems, the activity of the PTSNtr depends on the phosphorylation states of its protein components. For example, many pathways are reportedly mediated by the unphosphorylated form of EIIANtr 2SO4, and 0.5 g Na citrate dihydrate) with 0.2% of the indicated carbon source and 1 mM MgSO4 added after autoclaving. Minimal medium plates contained 1.5% agar. Bacteria were grown at 37°C, except during some steps of strain construction, when cultures were grown at 30°C to maintain temperature-sensitive plasmids. The final concentrations of the antibiotics ampicillin, kanamycin, and chloramphenicol were 50 to 100, 25 to 50, and 12 to 25 µg/ml, respectively. Plasmids containing sixA or ptsN were used without IPTG  induction and thus expressed the corresponding proteins from the basal activity of the trc promoter.Rich medium consisted of LB Miller medium  contain  and were grown for 7 to 8 doublings anaerobically.For anaerobic growth, minimal medium containing 0.2% glucose and 0.1% Casamino Acids was inoculated with a single colony and transferred to an anaerobic chamber, and the culture was grown overnight to stationary phase. The medium for the following day's cultures, which was the same as that used for the overnight cultures but also contained 40 mM KNOvir as described in reference Phage transduction was performed with P1ptsO strain, a PCR fragment containing FRT-cat-FRT was amplified from pKD3 with primers psyn-u1 and oriR6kseqprim1. The resulting DNA segment was then electroporated into strain JES185/pEL8, and cells were selected on 12 μg/ml chloramphenicol to exchange FRT-kan-FRT with FRT-cat-FRT. The resulting strain was confirmed to be kanamycin and ampicillin sensitive, indicating loss of both the kanamycin resistance gene and plasmid pEL8.To construct JES270, a chloramphenicol-resistant and kanamycin-sensitive ΔThe insertions in all engineered plasmids were verified to be correct by DNA sequencing.sixA expression plasmid pSixA, primers NcoI-sixA-F and HindIII-sixA-R were used to amplify the sixA gene from MG1655 genomic DNA. The resulting DNA segment was digested with NcoI and HindIII and cloned into pTrc99a, which was digested with the same enzymes. A plasmid expressing the H8A sixA mutant was constructed by site-directed mutagenesis as follows: plasmid pSixA was amplified with primers sixA-dead-F and sixA-deadCRIM-R2, the ends were phosphorylated with T4 polynucleotide kinase, and the resulting DNA was circularized by blunt-end ligation with T4 DNA ligase, generating pSixA(H8A).To construct ycgO expression plasmid pYcgO, the ycgO gene and roughly 500 bp of upstream DNA were amplified from MG1655 genomic DNA with primers EcoRI-PcvrA-F and HindIII-cvrA-R. The resulting PCR product was digested with EcoRI and HindIII and ligated into EcoRI- and HindIII-digested pMG91, a derivative of the single-copy plasmid pSMART.To construct the ptsN expression was constructed as follows. The ptsN-3×FLAG sequence from Z501 genomic DNA was amplified with primers EcoRI-ptsN-F and HindIII-ptsN-3×FLAG-R2. The resulting PCR product was cloned into EcoRI- and HindIII-digested pEB52, a derivative of pTrc99a, generating pEIIANtr.The plasmid for FLAG-tagged 600) of each was measured. For determination of the wild-type strain’s growth curve in glycerol minimal medium , which were grown with aeration in a roller drum. Incubation times were chosen to catch wild-type cultures in exponential phase.β-Galactosidase assays were performed essentially as described previously . To ensusixA JES13 was transformed with pRL27, which contains a hyperactive Tn5 transposase (4 and 0.4% glucose added after autoclaving) for over 1 h, the cells were incubated in SOC medium with kanamycin for 6 h to select for mutants. Aliquots were then plated on maltose minimal medium agar, and >16,000 colonies were screened to identify suppressor mutants. Genomic DNA sequences flanking the sites of transposon insertions were identified as described previously . The limit of quantification was determined to be 0.1 mg/liter. Potassium content was determined as nanomoles of potassium per OD600 of the culture prior to harvesting.LB Miller overnight cultures inoculated from single colonies were used to inoculate 2-ml cultures of glycerol minimal medium, which were grown overnight in a roller drum to optical densities of approximately 0.2. Cultures were harvested by centrifugation through a layer of a 2:1 (vol/vol) mixture of dibutyl phthalate and dioctyl phthalate following the procedure described in reference Ntr phosphorylation assays were performed similarly to those described previously  membrane (Millipore) using a semidry transfer apparatus and transfer buffer consisting of 20% methanol, 25 mM Tris, and 192 mM glycine (pH 8.3). The membrane was blocked with 5% milk–Tris-buffered saline with Tween 20 (TBST) . Anti-FLAG mouse antibody , digital anti-mouse horseradish peroxidase (HRP) antibody (Kindle Biosciences), and a KwikQuant Western blot detection kit and imager (Kindle Biosciences) were used to probe and image membranes.


Mammalian female fertility is limited by the number and quality of oocytes in the ovarian reserve. The number of oocytes is finite since all germ cells cease proliferation to become oocytes in fetal life. Moreover, 70–80% of the initial oocyte population is eliminated during fetal and neonatal development, restricting the ovarian reserve. Why so many oocytes are lost during normal development remains an enigma. In Meiotic Prophase I (MPI), oocytes go through homologous chromosome synapsis and recombination, dependent on formation and subsequent repair of DNA double strand breaks (DSBs). The oocytes that have failed in DSB repair or synapsis get eliminated mainly in neonatal ovaries. However, a large oocyte population is eliminated before birth, and the cause or mechanism of this early oocyte loss is not well understood. In the current paper, we show that the oocyte loss in fetal ovaries was prevented by a deficiency of Caspase 9 (CASP9), which is the hub of the mitochondrial apoptotic pathway. Furthermore, CASP9 and its downstream effector Caspase 3 were counteracted by endogenous X-linked Inhibitor of Apoptosis (XIAP) to regulate the oocyte population; while XIAP overexpression mimicked CASP9 deficiency, XIAP deficiency accelerated oocyte loss. In the CASP9 deficiency, more oocytes were accumulated at the pachytene stage with multiple γH2AFX foci and high LINE1 expression levels, but with normal levels of synapsis and overall DSB repair. We conclude that the oocytes with LINE1 overexpression were preferentially eliminated by CASP9-dependent apoptosis in balance with XIAP during fetal ovarian development. When such oocytes were retained, however, they get eliminated by a CASP9-independent mechanism during neonatal development. Thus, the oocyte is equipped with multiple surveillance mechanisms during MPI progression to safe-guard the quality of oocytes in the ovarian reserve. In addition, 70–80% of the initial oocyte population gets eliminated during fetal and neonatal development, restricting the ovarian reserve. The cause or mechanism of this major oocyte loss remains an enigma.The number of oocytes in the ovarian reserve is finite since all germ cells cease proliferation to become oocytes in fetal life. Although the controversy over oocyte stem cells in the adult ovary remains5. Three major causes have been identified using mutant mice. The first cause is a failure in the repair of DNA double strand breaks (DSBs). In the oocyte, the last round of DNA replication is followed by formation of numerous DSBs, which are essential for homologous synapsis and recombination8. However, DSBs are toxic DNA lesions and must be promptly repaired using the homologous chromosome as a template. The interaction between homologous chromosomes is secured by synaptonemal complex (SC), composed of lateral and transversal elements, SCP2/3 and SCP1, respectively10. The second cause is a failure in homologous synapsis15. Transcriptional repression of unsynapsed chromatin regions, named Meiotic Silencing of Unsynapsed Chromatin (MSUC), has been proposed to determine the life or death of oocytes dependent on the repertoire of silenced genes17. These two causes may contribute to the elimination of oocytes at or beyond the pachytene stage but not at earlier stages when both DSBs and unsynapsed chromosomes are normally present in all oocytes.The oocyte goes through Meiotic Prophase I (MPI) in fetal life, when homologous chromosomes synapse and recombine. Proper homologous recombination is essential for correct chromosome segregation at meiotic divisions, which determines the success in embryonic development. Therefore, it has been postulated that a surveillance mechanism operates to eliminate the oocytes with meiotic defects to promote the quality of oocytes in the ovarian reserve. We have previously shown that the oocyte population continuously declines throughout MPI progression, suggesting the presence of multiple causes for oocyte elimination18. LINE1 is usually silenced by DNA methylation, but it gets derepressed while germ cells undergo global DNA demethylation prior to entering meiosis. Overexpression of L1NE1 ORF1 protein (L1ORF1p) is associated with meiotic defects such as DNA damage, asynapsis, and unrepaired DSBs, and subsequent greater oocyte loss compared to the wild-type (WT) female. Furthermore, treatment of pregnant WT mice with a pan-inhibitor of reverse transcriptases prevents prenatal oocyte loss. This cause may explain the oocyte elimination at the pachytene stage or earlier in the WT ovary. However, it is still unclear whether physiological levels of L1ORF1p are sufficient for oocyte demise.The third cause for oocyte elimination is the overexpression of the Long Interspersed Nuclear Element (LINE1) gene19. However, CASP9 appears to be constitutively activated in most oocytes and hence not directly lead to oocyte demise. In this paper, we examined the role of endogenous cellular Inhibitor of Apoptosis Protein (IAP), which may protect oocytes from apoptotic demise in the presence of activated CASP9. Four members of the IAP family, XIAP, cIAP1, cIAP2, and NIAP, are known in mammals20. Of these, only XIAP inhibits cleaved CASP9, as well as effector Caspase 3 (CASP3) and Caspase 7, with a physiologically relevant potency. In the current paper, we first extended our study on the Casp9−/− ovary. Since Casp9−/− mice die prenatally, we could not previously follow the fate of preserved oocytes after birth. Hence, we used ovarian culture to examine Casp9−/− ovaries at later developmental stages. Since counting oocytes in neonatal ovaries using histological sections or dissociate ovarian cells is inaccurate, we established confocal microscopic analyses of oocytes in wholemount ovaries. We then examined the role of XIAP in the oocyte population dynamics using both its null and transgenic mice in vivo.We anticipated that by identifying the molecular mechanism of oocyte elimination, we can block oocyte loss and then assess the oocytes which must have been eliminated in the WT ovary. We have previously reported that oocyte loss in fetal ovaries is prevented by a deficiency of Caspase 9 (CASP9), which is the hub of the mitochondrial apoptotic pathwayMus musculus, on the C57BL/6 (B6) genetic background  including Casp-9 null (Casp9−/−) , Xiap null (Xiap-/-), RING-free Xiap mutant (ΔR-Xiap) , and transgenic mouse in which human XIAP is expressed under a ubiquitin promoter (Xiap-tg) , as well as their corresponding controls. Heterozygous null males and females were crossed to generate −/−, + /−, and + / + progeny while B6 females and Xiap-tg males were crossed to generate heterozygous Xiap-tg and WT progeny. The day when a plug was observed was defined as 0.5 day postcoitum (dpc). Delivery usually occurred at 19.5 dpc, but we used dpc to define postnatal ages for consistency. Ovaries were isolated from fetal and neonatal female mice at 15.5–23.5 dpc and processed for various experiments. A piece of liver was taken from each mouse for identifying its genotype by PCR amplification, using primers as listed in Supplementary Table All animal procedures were performed in accordance with the Canadian Council on Animal Care and approved by the McGill University Animal Care Committee. All experiments were carried out using mice, 2 and humidity as previously described21. Ovarian explants were collected on the third day of culture or further incubated in fresh culture medium supplemented with 10 µM fulvestrant (Sigma I4409), an estrogen receptor antagonist, to simulate folliculogenesis. Two days later, half of the media was changed with fresh culture medium supplemented with fulvestrant. After two more days in culture, ovaries were collected for further analyses.Fetal ovaries isolated at 16.5 dpc were transferred individually onto Nucleopore membranes (1.0 µm pore size) floating on pre-equilibrated MEM-α medium (GIBCO 12571063) containing 10% heat-inactivated horse serum (GIBCO 26050088) and 100 U/ml penicillin/streptomycin (GIBCO 15140122) in 24-well culture dishes (Corning 353847), and incubated at 37 °C with 5% CO22. In brief, ovaries were rehydrated in 1:1 methanol:phosphate buffered saline (PBS) for 30 min and washed thrice in holding buffer  with 1% TritonX-100 (HBT) for 1 h at room temperature. Ovaries were further incubated with the primary antibody overnight at room temperature with gentle shaking, washed thrice in HBT for 1 h, and incubated with the secondary antibody and DAPI overnight in dark at 4 °C. Ovaries were then washed thrice in PBS with 1% TritonX-100 in dark at room temperature, serially dehydrated in 25%, 50%, 75%, and 100% methanol, and finally cleared in benzyl alcohol:benzoyl benzoate (1:2) overnight at room temperature. Details of the primary and secondary antibodies are given in Supplementary Tables Ovaries isolated at 19.5–23.5 dpc were fixed in a mixture of cold methanol:DMSO (4:1) and stored at −20 °C at least overnight before immunofluorescence (IF)-staining as previously described24. The cell suspension was incubated in a hypotonic solution  within a QuadriPERM 4-well chamber on histology slide (Lab-Tek II 154917) for 10 min and spun down, followed by fixation and washing. The slides were vacuum dried and stored in sealed boxes with silica gel at −20 °C. The microspread ovarian cells on histology slides were incubated with the primary antibodies overnight, the secondary antibodies for 1 h, and avidin-FITC for 1 h all at room temperature. , SuperSignal™ West Femto Maximum Sensitivity Substrate (Thermo Fisher Scientific 34096) or SuperSignal West Pico PLUS Luminol/Enhancer (Thermo Fisher Scientific 34579), and visualized by autoradiography or Bio-Rad ChemiDoc XRS + system. After detection, the membranes were stripped with a ReBlot Plus Mild Antibody stripping solution (Millipore 2502) and reblotted with other antibodies. For comparing protein cleavage levels, the bands corresponding to cleaved and uncleaved forms within the same blot were quantified using the Image Lab software, and the ratio was used to indicate the relative cleavage level. To combine the results from different WB, the cleavage level in the mutant ovary was normalized against that in the WT ovary in each blot.Ovaries at 16.5, 18.5, and 23.5 dpc were individually placed in 1.5 ml microfuge tubes, snap frozen in liquid nitrogen and kept at −80 °C until use. Two ovaries were pooled, unless specified, lysed and fractionated in SDS–PAGE according to the protocols, as previously describedt-test or one-way ANOVA followed by Tukey’s multiple comparison test.All experiments were performed at least three times, and the values were presented as the mean ± SEM. In each experiment, at least three females from two litters were used, and one ovary of each pair was subjected to data analyses. Significant differences between experimental and control groups were analyzed by either Student’s Casp9−/− ovaries compared to Casp9+/+ ovaries at 19.5 dpc by counting GCNA1-positive cells in microspread ovarian cells19. However, this method has a disadvantage of losing many oocytes during dissociation procedure. Furthermore, GCNA1 is downregulated in oocytes by the mid diplotene stage and does not represent all oocytes at or after birth. Instead, we established a method to count the oocytes in wholemount ovaries, which were IF-stained with TRA98 and anti-TAp63α antibodies. TAp63α is expressed in the oocyte nucleus at the late pachytene stage and onwards26 whereas TRA98 recognizes the same epitope as GCNA127. In Casp9+/+ ovaries at 19.5 dpc   Fig. . This reCasp9−/− ovaries at 16.5 and 18.5 dpc by WB  of CASP3 and PARP1 in  WB Fig. . We chosXiap−/− ovaries were significantly higher than those in Xiap+/−or Xiap+/+ ovaries at 18.5 dpc, but not at 16.5 or 23.5 dpc  while somatic cells do neither actively proliferate nor die in fetal ovaries, unlikely impacting the overall results. Importantly, the results using various mutants were consistent to support the role of CASP9-XIAP interplay in oocyte elimination. Therefore, we believe that a change in the oocyte population was largely reflected into WB results. Nonetheless, further studies are needed to clarify the molecular interaction between XIAP, CASP9, and CASP3 in oocytes.In Casp9−/− ovaries at birth were swiftly eliminated in postnatal development in culture. This finding is consistent with the report that the oocytes accumulated by inhibition of LINE1 expression were lost after birth18. The oocytes spared in Xiap-tg ovaries were also diminished by 23.5 dpc, but to a lesser extent than in Casp9−/− ovaries. This difference can be attributed to the oocyte population dynamics in vivo vs. in vitro. It is also possible that XIAP overexpression suppressed CASP3 that contributed to oocyte loss beside the CASP9-dependent pathway. Regardless of the mechanism, the oocytes that had circumvented the CASP9-dependent apoptotic demise were eliminated by a CASP9-indepdendent mechanism that became available in postnatal ovaries. It is well established that the oocytes with unrepaired DSBs or unsynapsed chromosomes are eliminated by the CHK2-TAp63 apoptotic pathway, which becomes active only after birth45. It remains to be determined if the CHK2 pathway can operate in the CASP9 deficiency. Autophagy is an alternative mechanism for oocyte demise47. In fact, apoptosis and autophagy share many molecules such as XIAP and CASP348. However, we found no difference in the LC3I/LC3II ratio, a hallmark of autophagy49, between Casp9−/− and Casp9+/+ ovaries after culture (data not shown). Necroptosis is known to operate under the apoptosis deficient conditions51. Examples are so far limited to the apoptosis initiated by extrinsic causes. Further studies are needed to identify the mechanism of CASP9-indepdendent oocyte elimination in postnatal ovaries.The oocytes spared in Our current results revealed two phases of oocyte surveillance during MPI progression. The first phase takes place during fetal development, during which oocytes undergo DSB formation and homologous synapsis, dependent on the CASP9-mediated apoptotic pathway. The oocytes that have survived in the CASP9 deficiency appear to carry high L1ORF1p expression levels and locally unrepaired DSBs along SC axes, but no defect in overall DSB repair or synapsis. XIAP appears to counterbalance the CASP9 activity to regulate the oocyte population. The second phase of oocyte surveillance takes place during neonatal development, during which oocytes pass the pachytene stage and reach the diplotene resting stage, independent of CASP9. These results suggest that the oocyte is equipped with multiple surveillance mechanisms during MPI progression to safe-guard the quality of oocytes in the ovarian reserve.Supplemental Material


Accelerated shrinkage of the Arctic sea ice cover is the main reason for the recent Arctic amplification of global warming. There is growing evidence that the ocean is involved in this phenomenon, but to what extent remains unknown. Here, a unique dataset of hydrographic profiles is used to infer the regional pattern of recent subsurface ocean warming and construct a skillful predictor for surface climate variability in the Barents Sea region - a hotspot of the recent climate change. It is shown that, in the era of satellite observations (1981–2018), summertime temperature anomalies of Atlantic water heading for the Arctic Ocean explain more than 80% of the variance of the leading mode of variability in the following winter sea ice concentration over the entire Northern Hemisphere, with main centers of action just in the Barents Sea region. Results from empirical forecast experiments demonstrate that predictability of the wintertime sea ice cover in the Barents Sea from subsurface ocean heat anomalies might have increased since the Arctic climate shift of the mid-2000s. In contrast, the corresponding predictability of the sea ice cover in the nearby Greenland Sea has been lost. Dramatic changes have been recently observed in all components of the Arctic climate system, including the polar atmosphere6, cryosphere9 and the Arctic Ocean interior13. These changes may have potentially far-reaching consequences not only for Arctic ecology and human activities in the high North14 but also for extreme weather events and climate anomalies at lower latitudes22. Over recent decades, surface atmospheric warming in the Arctic has occurred at a rate that is at least two times higher than globally23. This phenomenon, known as Arctic amplification, is most pronounced during the ice-growth season (autumn and winter). Arctic amplification has risen above the level of climate noise in the late 1990s16. Its apparent emergence at that particular time might have been related to a temporary slowdown in the surface temperature warming trend over the rest of the globe24. This slowdown, if at all significant25, did not represent a reduced pace in warming of the climate system. It rather reflected a modulation of global surface warming by external drivers other than anthropogenic forcing, warming of increasingly deeper layers of the ocean, and redistribution of energy within the oceans by natural modes of climate variability that changed their phase in the 1990s29. In fact, all oceans have experienced significant basin-averaged warming since 199828. In any case, surface global warming has accelerated in recent years31 due to a further enhanced air temperature rise in the Arctic32.The Arctic climate system is particularly vulnerable to global warming instigated by anthropogenic increases of carbon dioxide and other greenhouse gases37, this phenomenon is profoundly related to the diminishing Arctic sea ice cover via the ice-albedo and other effects38. The sea ice loss is most spectacular in summer, at the end of the melt season, but is also significant in other seasons39. In the Barents Sea  may control Arctic warming via their influence on atmospheric circulation41. There is also growing evidence that changing inflows of warm and salty Atlantic water (AW) to the Barents Sea and the Arctic Ocean contribute to the shrinkage of the Arctic sea ice cover43. The wintertime sea ice decline is consistent with recent warming signals detected in the Barents Sea45 as well as in the pathway of AW to the Arctic Ocean through Fram Strait47, and then eastward along the Arctic Ocean margin48.While several atmospheric processes may contribute to Arctic amplification51. Remarkably high predictability of the total wintertime sea ice area (SIA) in the Barents and Nordic (Greenland-Iceland-Norwegian) Seas region from summertime anomalies of Atlantic water temperature (AWT) was reported from an analysis of observations in the period 1982–200649. In that period, the ocean acted as a coordinator of the regional SIC variability. Indeed, the oceanic impact on the sea ice cover in the Barents Sea and the Greenland Sea considered separately was not as strong as its impact on the coherent SIC variability in these seas49. The main objective of the present study is to verify the hypothesis that the high predictability of the sea ice cover in the Barents/Nordic Seas region from AWT anomalies survived through the most recent changes in the Arctic climate system. This survival is not a priori granted since a sharp sea ice decline observed in the northern Barents Sea in the mid-2000s53 could be linked to reduced wintertime sea ice import from the nearby Kara Sea54, which could limit the ability of AWT anomalies to control the sea ice extent in the Barents Sea.The ocean “memory” due to the large heat capacity of seawater combined with long ocean advective timescales make the Arctic sea ice changes, especially those in the Barents Sea, to some extent predictable form upstream oceanic conditions55 supplemented with in situ temperature observations from other sources (see Methods).Here, the variability of the Arctic climate system is investigated using statistical analyses applied to SIC, SST and atmospheric fields, as well as subsurface ocean data covering the era of satellite observations (ESO period) from 1981 to 2018. In addition to indices based on area-averaged quantities, indices of climate variability are derived using the empirical orthogonal function (EOF) technique (see Methods). A unique subsurface ocean dataset is used to analyse the recent ocean warming in the Barents/Nordic Seas region and construct a reliable AWT time series employed in empirical forecast experiments (see Methods). This dataset consists of ocean temperature profiles from the newly compiled Unified Database for Arctic and Subarctic Hydrography (UDASH)BS index defined as the ocean area covered by sea ice within the BS box in Fig. BS exhibits considerable interannual variability but practically no trend  appear as departures from a marked long-term decline (the linear trend significant at p = 0.02). Through the LATE period, SIABS declined by as much as 58% (from 9.8 × 1011 to 4.1 × 1011 m2). The decline occurred in three winter-to-winter pulses, first from 2003/04 to 2004/05, then from 2010/11 to 2011/12 and finally from 2014/15 to 2015/16, separated by events of recoveries to relatively heavy ice conditions. The decline culminated with unprecedentedly low values of SIABS in the last three (LAST3) winters (2015/16–2017/18), with a record of −2.5 standard deviations in 2016/17.Variability of the wintertime sea ice cover in the Barents Sea region is characterised by the December-February (DJF) mean SIA54 (see Methods). In SIABS, the OT point of the sea ice decline  is qualified as a regime shift using a regime change detection method. The method is based on a comparison of means in sliding segments and subsequent data in the given time series56 (see Methods). According to this method, also the abrupt sea ice decline from 2014/15 to 2015/16 can be qualified as a regime shift. In Fig. BS are marked by a square and triangle, respectively. Two regime shifts during the ESO period, one at the transition between the EARLY and LATE periods and one leading to abnormal conditions in the LAST3 winters are also identified in other indices of Arctic climate variability. Results of the search for the CP and OT points in selected indices are included in Table pLATE). Whether the transition near the end of the LATE period is a genuine regime shift or an interannual bump in the time series cannot be determined at present.In order to objectively identify the onset time (OT) of recent changes in the Barents Sea ice cover and other Arctic climate variables, an algorithm for detection of nonlinear transitions is employed. The algorithm is based on data departures from a simplified form of the given time series48. A remarkable sea ice retreat was also observed in the northern Barents Sea52. The recent sea ice decline in these areas is illustrated by thick black and red lines in Fig. SIC−BS) of the leading EOF mode of the winter mean SIC variability in the Barents Sea region during the ESO period . This mode explains 62% of the regional SIC variance. Its temporal variability is practically indistinguishable from the variability of the area-averaged sea ice cover in the Barents Sea . The correlation between PC1SIC−BS and SIABS is negative because the upward trend in PC1SIC−BS corresponds to the sea ice decline , known as the Whalers Bay, was observedsBS index) is defined as SSTs averaged over the southern Barents Sea  during the EARLY period followed by a significant trend (p = 0.01) during the LATE period . In the LATE period, this index is characterised by winter-to-winter warming pulses that coincide with the pulses of the Barents Sea ice decline. Over the entire ESO period, SSTsBS correlates with SIABS at a high level (r = −0.94), suggesting that the wintertime sea ice extent in the Barents Sea region is controlled by SST anomalies.The recent sea ice retreat in the Barents Sea region was accompanied by ocean warming, as shown by the pattern of the wintertime SST difference between the means in the LAST3 years and the EARLY period  of the leading EOF mode of the winter mean SST variability in the Barents Sea region  and PC1SIC−BS  after removal of either their respective linear trends over the ESO period or their continuous piecewise linear trends with the breakpoint in winter 2003/04  correlates highly with PC1SIC−BS  with PC1SST−BS , when SIC anomalies are small in the Greenland Sea but large in the Barents Sea, than in the EARLY period (r = 0.93). It is also consistent with a slightly stronger sea ice decline in the EARLY period associated with PC1SIC−A40 than PC1SIC−BS  of the leading EOF mode of the winter mean SAT variability in the high Arctic north of 70°N , covers the entire Arctic Ocean, but the largest anomalies appear just over the northern Barents Sea. PC1SAT−A70 correlates highly with PC1SIC−A40  and indices of the sea ice cover and ocean temperature variability in the Barents Sea region , the onset time of the recent change obtained from the objective OT detection method is the same (winter 2003/04) as in all indices of the sea ice cover and SST variability in the Barents Sea region. When the method is applied to smoothed time series (11-yr running mean data), the onset time appears simultaneously (in winter 2000/01) in all SST and sea ice cover indices, including PC1SIC−A40  mean gridded fields of the vertically-averaged temperature in the 150–250 m depth layer (T150−250) are constructed from scattered observations (see Methods). The 150–250 m depth range corresponds to the warmest, uppermost part of the AW layer below the Arctic Ocean halocline near the northern Barents Sea boundary48 and encompasses the AW salinity maximum in the open water at the entrance to the Barents Sea61.In the Atlantic domain of the shallow Barents Sea, the warm and salty AW extends nearly throughout the entire water column and is ventilated via air-sea interactions during the cooling season when most of the heat advected with AW to the Barents Sea is lost to the atmosphereT150−250 also appear in the corresponding LATE period climatology, although the subsurface waters are generally warmer in the LATE period  conditions and the summers preceding the winters with prevailing “icy” (ICE+) conditions during the EARLY and LATE periods, respectively. The selection of the ICE− and ICE+ winters (see Methods for details) is based on the SIABNS index defined as the sea ice area over the Barents and Nordic Seas  is constructed (see Methods) using temperature profiles in the southern Svalbard slope area  and significant warming through the LATE period (p = 0.03). It correlates highly with the following winter SIABNS index . These high correlations and the correspondence between the onset time of the recent changes, identified by the objective OT detection method in winter 1997/98 for SIABNS and the preceding summer for AWTSSS  differs considerably between the EARLY and LATE periods  appear only in the northeastern Barents Sea while in the LATE period such anomalies extend across the entire northern Barents Sea. In addition, there are differences in the seasonal timing of the strongest sea ice response to oceanic forcing, as indicated by the time-lagged correlations of the summer AWTSSS anomalies with the seasonal (3-month running) mean anomalies of the sea ice cover over the Barents/Nordic Seas region (SIABNS index) and the Barents Sea alone (SIABS index) in the EARLY and LATE periods  in winter at lag +6 months  and occurs slightly later, in late winter  at lag +7 months  and occurs already in early winter  at lag +5 months . The increase of the correlation in the LATE period is evident for all indices of the variability in the Barents Sea region. It is significant (prΔ between 10−3 and 10−2) for the area-averaged SICs in the Barents Sea, the PC1s of the SIC and SST variability in that region, and area-averaged SATs in the northern Barents Sea area. The increase is most significant (prΔ ≈ 10−4) for the area-averaged SSTs in the southern Barents Sea. This increase suggests that the enhanced impact of summertime anomalies of the subsurface ocean temperature on the wintertime climate variability in the Barents Sea region reflects their stronger control of postsummer SSTs on the open water side of the sea ice edge (see the next section but one for possible mechanisms).To test the statistical significance of the recent changes in the relationship between the wintertime surface climate indices and the previous summer subsurface ocean temperature in the Barents Sea region, a Monte Carlo method is employed (see Methods). Empirical BNS in the EARLY and LATE periods, respectively. The forecast models are based on the linear regression with the leave-1-yr-out cross-validation (see Methods) and use the summertime AWTSSS index as the predictor. The standardised time series of the predicted and observed interannual (detrended) SIABNS anomalies are compared in Fig. BNS anomalies are slightly higher in the LATE period (CSS = 0.90 and PEV = 0.80) than the EARLY period (CSS = 0.84 and PEV = 0.71). Similarly, the skill scores of the forecast of the raw SIABNS index from the undetrended AWTSSS anomalies are higher in the LATE period (CSS = 0.94 and PEV = 0.88) than the EARLY period (CSS = 0.82 and PEV = 0.67).In order to demonstrate the predictive value of the lagged relations identified above, empirical forecast models are constructed for the winter and early winter SIABS in the LATE period (0.87) is higher by about 0.35 than the PEV score for the late winter SIABS in the EARLY period (see Table sBS index) than the EARLY period (PEV = 0.40 for the late winter SSTsBS index). The most remarkable increase in predictability is observed for the atmospheric hotspot over the northern Barents Sea where the SATs were not predictable in the EARLY period but to a large extent predictable in the LATE period (PEV = 0.70 for the early winter SATnBS index).The increase of predictability is more pronounced for the sea ice cover in the Barents Sea alone. For instance, the PEV score for the early winter SIASSS index  and the summer AWTSSS index, shown in Fig. r = 0.77 for the interannual anomalies and r = 0.86 for the raw data) occur in spring (lag −3 months), and maximum postsummer correlations  are found in early winter (lag +5 months).We have demonstrated that variations of the wintertime sea ice cover in the Barents Sea are strongly coupled to the concurrent SST anomalies on the open water side of the ice edge Fig.  as well ex Table  are cons62. In the LATE period, this response is suggested by the pattern of the difference in the surface wind vector (us) between early winters 2004/05 and 2003/04  averaged over the eastern Barents Sea  in late autumn (lag +4 months) and early winter. For the raw data  as for the interannual anomalies. The postsummer AWTSSS-covariant anomalies of veBS become nonsignificant already in winter (lag +6 months), while the corresponding SST anomalies remain significant up to the following spring . Seasonal mean fields of SAT and surface wind vector (us) are computed from the monthly mean fields of air temperature at the 2-m height and wind at the 10-m height, respectively, derived from the NCEP/NCAR reanalysis75 and provided on a gaussian grid of approximately 1.8-degree resolution (https://www.esrl.noaa.gov/psd/).Seasonal mean fields of SST and SIC are constructed from the NOAA Optimum Interpolation Sea Surface Temperature Version 2 monthly mean fields derived from remote and Basic indices of regional surface climate variability are obtained by averaging of the seasonal mean SIC, SST and SAT fields over the Barents/Nordic Seas , Barents Sea , southern Barents Sea  and northern Barents Sea  domains. The averaged SIC (fraction of the ocean surface covered by sea ice) multiplied by the total area of the domain of interest is referred to as the sea ice area (SIA). While the focus is on the winter  season, some indices are also computed for other seasons in order to explore time-lagged relationships.76 serve as supplementary indices of variability. The field anomalies are obtained by subtracting local mean values from the raw data. In order to account for different areas represented at each grid point, the anomalies are weighted by the square root of the cosine of latitude prior to the EOF decomposition. Only the first leading modes that account for the largest fraction of the total variance in the data are analysed. The modes are computed for the SIC and SST anomalies over the Barents Sea region  time series of the EOF decomposition of wintertime field anomalies55. It consists of thoroughly quality-checked profiles of temperature and salinity measured mainly with conductivity/temperature/depth probes, bottles, mechanical thermographs and expandable thermographs from all publicly available data sources, including the Oceanographic Database of the International Council for the Exploration of the Sea (ICES)78. Additional hydrographic data from the ICES from 2016 and 2017 are used to extend the UDASH archive by two years. The UDASH/ICES archive is filtered to retain only summertime  temperature profiles and augmented with temperature profiles from the Arctic Experiment (AREX) database of the Institute of Oceanology, Sopot, Poland. The latter compiles measurements from summertime cruises of R/V Oceania in the West Spitsbergen Current area since the late 1980s80. The final dataset spans all summers from 1981 to 2017.In order to construct fields of subsurface ocean temperature anomalies, data from three sources are used. The main database (UDASH) is a unified collection of hydrographic observations for the Arctic Ocean and subarctic seas north of 65°N for the period 1980–2015 compiled by the Alfred Wegener Institute, Bremerhaven, GermanyTi at each hydrographic station si is then corrected for the seasonal cycle using the monthly mean temperature data from the University of Washington Polar Science Center Hydrographic Climatology 1.0 (PHC)81, which are vertically-averaged and interpolated onto the location of si. Each Ti is referenced to the middle of the season of interest. Climatological averages of the summer means of the vertically-averaged temperature are then constructed for the EARLY (summers 1981–2003) and LATE (summers 2004–2017) subperiods. Anomalies are computed for all summers, and their composite means are constructed for selected sets of years. To this end, first, the local average Tl is computed at the location of each station. The average at the given station sl is calculated using data from all stations inside a circular domain with the radius R0 around sl except for the stations, if present, classified as outliers (those with the temperature differing from Tl by at least five standard deviations). If no station is found inside the search area, station sl is eliminated. The distance of half a degree of latitude (about 56 km) is selected as R0.All summertime temperature profiles are vertically averaged over the layer between selected depth levels and then gridded. Stations not spanning the entire layer are disregarded, as are repeated stations. The vertically-averaged temperature Tl at stations sl found inside a circular domain of influence with the radius R0 around the centre of the given grid cell. Local inverse distance weighting is employed with weights wl = (R02−dl2)/(R02 + dl2), where dl is the distance of station sl from the cell centre. The weights are normalised so that they sum to one. The Tl outliers (classified as such based on the 5-standard-deviation criterion applied to the Tl data within the search area) are excluded from the computation of the climatological means. The means are calculated only for the grid cells with at least two stations in the search area. The gridded temperature anomalies are constructed using the same spatial averaging procedure but applied, for each summer, to the departures Ta of the vertically-averaged temperatures Ti from the corresponding local climatological means Tl in the EARLY period. This procedure is a modified version of the method applied earlier to construct temperature anomalies from scattered hydrographic observations in the North Atlantic82.The gridded fields of the vertically-averaged temperature are computed with a resolution of 0.5° in latitude and 1.5° in longitude. The climatological mean values are calculated by weighted averaging of the local averages SSS index) is constructed using temperature data averaged over the 100–300 m depth layer in the southern Svalbard slope (SSS) area of the western Barents Sea opening . First, the local anomalies of the vertically-averaged temperature from its EARLY period climatology are computed for all stations inside the SSS area. These anomalies are supplemented with the local anomalies at two additional stations per each summer, by one at (or closest to) the southern and northern limits of the SSS area. Overall, a set of 1601 local anomalies is used. The set has a very irregular time distribution but includes at least ten data in each summer. Then, for each summer, the local anomalies are interpolated and averaged following the procedure outlined in an earlier study63. The resulting time series of the AWTSSS anomalies are standardised by subtracting their mean value (0.25 °C) and dividing by their standard deviation (0.6 °C). Its correlations with all analysed indices of climate variability in the following winter are included in Table rAWT).A reliable time series of the summer mean AWT anomalies  is found, the whole procedure is repeated to find the start point of the second (CP2) and next new regimes, if present. The results of the search for the CP points using 11 years as the cut-off length l are reported in Table l.An automatic calculation of regime shifts in the analysed indices of climate variability is carried out using the method devised in an earlier study54 is also used here. First, the beginning and end points of a given time series are set as base points of the analysis. Next, the orthogonal distance between each datum and the straight line joining the base points is calculated, and the year j of the maximum distance is found. If the maximum distance is greater than a threshold ε, year j is marked as a potential onset time of a new regime (referred to as OT in the main text). The time series is then split at year j into two segments, and other potential OTs are sought, by one in each segment. The procedure is applied recursively to all segments resulting from splitting each segment from the previous iteration until the orthogonal distance to the line joining the base points of the current segment is less than or equal to ε. The value of ε is set to 1.5 of the standard deviation of the original time series. From all potential OTs, the one which yields a minimum root mean square error (RMSE) of fitting the initial time series to the piecewise linear trend with the breakpoint at the given potential OT is selected as the final, single OT. If two or more potential OTs yield an equal (to within 0.5%) RMSE, the one is retained for which the onward linear trend is the largest. The same procedure is applied to obtain estimates of the onset time, denoted as OT11, based on the 11-yr running mean of the time series. The OT and OT11 points for all analysed indices of climate variability are reported in Table As the above method of regime change detection does not detect early onset times of nonlinear but initially gradual transitions to new regimes, a version of the method for objective detection of such transitions employed in an earlier studyr is estimated using a two-sided Student’s t-test. The serial correlation in the time series is taken into account by employing an effective sample size defined as Neff = N0(1 − rarb)/(1 + rarb), where N0 is the length of the series while ra and rb are the lag-one autocorrelations of the correlated series a and b83. The statistical significance of linear trends is estimated with a one-tailed Students’s t-test. The effective sample size for this test is obtained using the lag-one autocorrelation of the regression residuals84. The 95% confidence level is used as the threshold for marking significant anomalies in all regression maps and maps of differences between composite means. The statistical significance of the composite mean differences is tested using the standard two-sample t-test76.Correlation analysis is carried out to quantify relationships between different indices of climate variability and between these indices and the regressed fields. The statistical significance of the correlation coefficient −) and heavy ice (ICE+) winters. The ICE− and ICE+ winters are selected using the SIABNS index  are retained. In the EARLY period, this criterion yields six ICE− winters  and six ICE+ winters . In the LATE period, the LAST3 winters (2015/16–2017/18) of the extremely low ice cover are selected as the ICE− winters and four winters (2008/09–2010/11 and 2014/15) with the highest values of SIABNS after 2003/04 are selected as the ICE+ winters.Composite mean differences of the subsurface ocean temperature are calculated only for the grid cells for which data from at least two summers contribute to the mean in each of the epochs being compared. These differences are computed between the LATE and EARLY subperiods and, in these subperiods, between summers preceding light ice  synthetic realizations of 23 pairs of data of the AWTSSS index and a given wintertime climate index I are generated by randomly subsampling the data from the EARLY period. Then, the number n of the realizations for which the correlation between the synthetic AWTSSS and I indices exceeds or is equal to the correlation between the observed 15-year-long time series of these indices in the LATE period is found. The ratio n/N yields an estimate of the p-value from the Monte Carlo test for the difference between correlations in the EARLY and LATE periods. This estimate for all analysed wintertime variables is included in Table prΔ).Monte Carlo simulations are carried out to test whether the recent increase of correlations between indices of wintertime climate variability and the preceding summer AWTN set to 04 syntheBNS index) in the EARLY and LATE subperiods of the ESO period. The summertime Atlantic water temperature in the southern Svalbard slope area (AWTSSS index) is used as the predictor. The predictor and predictand time series are either undetrended or linearly detrended over the forecast period. Additional forecast experiments are carried out for the undetrended sea ice cover in the Barents Sea alone (SIABS index), area-averaged SSTs in its southern part (SSTsBS index), and area-averaged SATs in its northern part (SATnBS index). The forecasts are based on the standard linear regression method with the leave-1-yr-out cross-validation scheme85. In the leave-1-yr-out experiments, the training data are strictly separated from the testing data. One year is first excluded from K years of data. The forecast model is then trained on the data from the remaining years and tested on the excluded data. The procedure is repeated for each year, providing K test results from which two forecast skill scores are estimated. The first one is the correlation skill score (CSS) defined as the correlation coefficient between the forecasts F  and their targets T . The second one is the proportion of explained variance (PEV) defined as PEV = 1 − σ2(F − T)/σ2(T), where σ2 stands for the variance76. PEV is positive if the regression model is better than the climate reference model (F = 0 in our case). It becomes unity for a perfect model, that is, when σ2(F − T) = 0, and minus unity for a random forecast.Forecast models are constructed for the wintertime sea ice area over the Barents/Nordic Seas region (SIA


N2,N4-diamino quinazoline analogues with differing PDE-5 inhibitory activities and varying physicochemical properties were assessed in both endothelium-intact and denuded rat pulmonary arteries to gain greater insight into their mode of action. All analogues produced vasorelaxant effects with EC50s ranging from 0.58 ± 0.22 µM to ˃30 µM. It was observed that vasodilation response in intact vessels was highly correlated with that of denuded vessels. The ~10% drop in activity is consistent with a loss of the nitric oxide mediated cyclic guanosine monophosphate (NO/cGMP) pathway in the latter case. A moderate correlation between the vasodilation response and PDE-5 inhibitory activity in the intact vessels was observed. Experimental protocol using the alpha-adrenergic (α1) receptor agonist, phenylephrine (PE), was undertaken to assess whether quinazoline derivatives showed competitive behavior similar to the α1 receptor blocker, prazosin, itself a quinazoline derivative, or to the PDE-5 inhibitor, sildenafil. Competitive experiments with the α1-adrenergic receptor agonist point to quinazoline derivatives under investigation here act via PDE-5 inhibition and not the former. The pre-incubation of pulmonary arterial rings with quinazoline test compounds (10 μM) reduced the contractile response to PE around 40–60%. The most promising compound (9) possessed ~32 folds higher selectivity in terms of vasodilation to its mammalian A549 cell cytotoxicity. This study provides experi0 0mental basis for PDE-5 inhibition as the mode of action for vasodilation by N2,N4-diamino quinazoline analogues along with their safety studies that may be beneficial in the treatment of various cardiovascular pathologies.Pulmonary arterial hypertension (PAH) is a rare and progressive disease arising from various etiologies and pathogenesis. PAH decreases life expectancy due to pulmonary vascular remodeling, elevation of mean pulmonary arterial pressure, and ultimately progresses to heart failure. While clinical treatments are available to reduce the associated symptoms, a complete cure has yet to be found. Phosphodiesterase-5 (PDE-5) inhibition has been identified as a possible intervention point in PAH treatment. The functional vasodilation response to  Pulmonary arterial hypertension (PAH) is a rare (~15 cases/million) . This diPhosphodiesterase-5 (PDE-5) inhibitors are considered to improve clinical hemodynamics in combination with other treatment regimens based on patient response and tolerability studies ,12. This1) receptor  piperazine-N′-[2-ethane-sulfonic acid] (HEPES) 10; KH2PO4 0.5; NaH2PO4 0.5; MgCl2 1; CaCl2 1.8; and glucose 11 adjusted to pH 7.3 using 1 M NaOH. The pulmonary artery was isolated from the lungs and cut into rings approximately 2 mm in length before mounting in the organ bath chambers. Endothelial cells were mechanically removed in some experiments by gentle rubbing of the inner surface with forceps. Pulmonary arterial rings were mounted on intraluminal wires via stainless steel hooks maintaining the resting tension at 1 g and rings were equilibrated for 30 min before obtaining a stable contraction with 10−5 M phenylephrine (PE) -2,5-diphenyl-2reported . A549 ce50). Prism 6.0 GraphPad Software Inc.,  was utilized for data fitting. 0.5% DMSO was used as the negative control.The activity was presented as the inhibitory concentration required to produce a 50% response . The saturated solution was then filtered using a nylon syringe filter. A standard solution of each sample was prepared by dissolving a weighed amount of the sample in 1 mL of DMSO. The concentration of sample dissolved in the PBS solution was determined by comparing the peak area of the PBS and DMSO standard using an Agilent 1100 series HPLC with a Sunshell C8 column . A 100 µL injection of the aqueous sample and 5 µL of DMSO solution (pre-diluted 100 fold using DMSO) were used .Substructure searches of the ChEMBL database  were undThe relationship between PDE-5 inhibitory activity, vasodilation, cytotoxicity, solubility, and computed parameters was assessed using principle components analysis (PCA). All computed parameters were obtained from Chemaxon JCHEM  . PCA modN2,N4-disubstituted quinazoline 2,4-diamines produced vasorelaxation, acting through both endothelium and smooth muscle cells via inhibition of PDE-5. Despite structural similarity to the α1-adrenergic receptor blocker, prazosin, competitive experiments against PE in the rat pulmonary artery model appear to suggest they may function via a different mode of action. The quinazoline compounds displayed a phenotypic response in-line with the PDE-5 inhibitor, sildenafil, which is consistent with their known biochemical activities [tivities . This wa8 and 9 were identified as the most potent vasodilators, with EC50s of 0.58 and 1.03 μM, respectively. This corresponds to a 19.6 and 31.7 folds selectivity over their mammalian cytotoxicity. In comparison, sildenafil showed a vasodilation EC50 of 0.14 μM while prazosin, being a direct competitive blocker of the alpha-adrenergic (α1) receptor, showed an EC50 of 0.03 μM.Compounds N2,N4-disubstituted quinazoline 2,4-diamines, as exemplified by compounds 8 and 9, have clear potential as lead compounds for the treatment of vascular pathologies, including PAH. Additional work is needed to establish and improve the PDE-5 inhibitory and vasodilation activity, as well as increased compound solubility along with exploration of the pharmacokinetic profile of the series.These results indicate that


Cassia auriculata, Passiflora edulis, Sesbania grandiflora, Olax zeylanica, Gymnema lactiferum, and Centella asiatica. The anti-inflammatory activity of methanolic extracts of leafy vegetables was evaluated using four in vitro-based assays: hemolysis inhibition, proteinase inhibition, protein denaturation inhibition, and lipoxygenase inhibition. Results showed that the percent inhibition of hemolysis from these leaf extracts (25–100 µg/mL dry weight basis (DW)) was within the range from 5.4% to 14.9%, and the leaves of P. edulis and O. zeylanica showed a significantly higher (p < 0.05) inhibition levels. Percent inhibition of protein denaturation of these leafy types was within the range of 36.0–61.0%, and the leaf extract of C. auriculata has exhibited a significantly higher (p < 0.05) inhibition level. Proteinase inhibitory activity of these leaf extracts was within the range of 20.2–25.9%. The lipoxygenase inhibition was within the range of 3.7–36.0%, and the leaf extract of G. lactiferum showed an improved ability to inhibit lipoxygenase activity. In conclusion, results revealed that all the studied leaves possess anti-inflammatory properties at different levels, and this could be due to the differences in the composition and concentration of bioactive compounds.The study investigated the anti-inflammatory activity of the hydro methanolic extract of six leafy vegetables, namely  Inflammation is generally referred to as a complex biological response of vascular tissues to harmful stimuli. As well, inflammation is associated with pain, and it involves in an increase of protein denaturation, an increase of vascular permeability, and membrane alteration, among others . InflammCassia auriculata, Passiflora edulis, Gymnema lactiferum, Sesbania grandiflora, and Olax zeylanica, are some of the leafy vegetables rich in polyphenols, carotenoids, and antioxidant activities [Centella asiatica is one of the leafy vegetables that possess anti-inflammatory properties [Various epidemiological studies provide convincing evidence that natural dietary constituents, such as polyphenols and flavonoids, that humans consume as food, possess many biological activities ,6. Furthtivities ,9. Furthoperties . Though Passiflora edulis, Olax zeylanica, Gymnema lactiferum, Sesbania grandiflora, Centella asiatica, and Cassia auriculata L. were gathered from the Negombo and Makandura areas in Sri Lanka, and they were cleaned and freeze-dried. A sample from each leafy type were deposited in a herbarium (Herbarium 2014-1) at the Department of Food Science and Technology, Faculty of Livestock, Fisheries & Nutrition, Wayamba University of Sri Lanka.Fresh green leafy vegetable samples, Bovine serum albumin, trypsin, Tris-HCl, perchloric acid, casein, lipoxygenase, linoleic acid, lutein, rutin, β-carotene, and methanol, were obtained from Sigma Aldrich, St. Louis, MO, USA, through Analytical Instrument Pvt Ltd., Colombo, Sri Lanka. All other chemicals used were of analytical grade.v/v) and vortexed at high speed for about five minutes, and then centrifuged  for 10 min at 4500 rpm, and the supernatants were collected. Then, the extracts were filtered through a filter paper (WhatmanNo.42), and then the residue that remained was re-extracted with 80% methanol with the same procedure, and the supernatants obtained were combined with those from the first extraction. The solvent in the combined mixture was evaporated in a rotary evaporator  at 40 °C. The prepared concentrated extracts of leafy vegetables were dried at 40 °C for 12 h in an oven, and then dried extracts were stored at −18 °C in air-tight screw-capped glass vials, until used for the anti-inflammatory bioassays, within one week. The extracts collected were dissolved in methanol to obtain a concentration of 3 mg/mL for each assay.One gram of freeze-dried sample was mixed with 20 mL of methanol (80% v/v) suspension with isotonic buffer solution (10 mM sodium phosphate buffer pH 7.4). Composition of the buffer solution (g/L) used was NaH2PO4 (0.2), Na2HPO4 (1.15), and NaCl (9.0).Erythrocyte suspension was prepared according to the method described in Shin de et al. , with soThis test was carried out as described by Okoli et al. , with soThe level of hemolysis was calculated using the following equation based on the Okoli et al. :% inhibiProtein denaturation assay was done according to the method described by Gambhire et al. , with sow/v) casein was added, and the mixture was further incubated for an additional 20 min. At the end of incubation, 2 mL of 70% perchloric acid was added to terminate the reaction. The mixture was centrifuged, and the absorbance of the supernatant was measured at 210 nm against buffer as the blank. Phosphate buffer solution was used as the control. The percentage inhibition of protein denaturation was calculated by using the following formula:Proteinase inhibitory activity of the leaf extracts was performed according to the method of Sakat et al. , which iLipoxygenase inhibition activity of the extracts of leafy vegetables was assayed according to the method of Wu , with soPolyphenol contents were measured as described by Gunathilake et al. , carotenr) with the level of significance (p ≤ 0.05) (2-tailed) for flavonoids, carotenoids, and polyphenols versus anti-inflammatory assays.All data are presented as the mean ± standard deviation for the all in vitro assays tested, and each analysis was done in triplicate. One-way analysis of variance (ANOVA) was performed using MINITAB 15 software and Pearson’s correlation coefficient (The percent inhibition of heat-induced hemolysis of red blood cells at different concentrations of each leafy vegetable (DW), in the range of 25–100 µg/mL, is shown in Passiflora edulis and O. zeylanica showed significantly higher (p < 0.05) levels of hemolysis inhibition compared to other leafy types studied, and C. asiatica showed the least inhibition levels among the six leaves. The order of the inhibition % of extracts of leaves varieties was P. edulis > O. zeylanica > C. auriculata > G. lactiferum > S. grandiflora > C. asiatica. Methanolic extracts of leafy vegetables were able to inhibit hemolysis in a concentration-dependent manner. Inhibition % of hemolysis from these leaf extracts were within the range from 3.8% to 23.1%, at the concentrations of 25–100 µg/mL. Leaves of Methanolic extracts of leafy vegetables were able to inhibit protein denaturation in a concentration-dependent manner, and the inhibitory effect of different leafy vegetables at different concentrations (25–100 µg/mL) on protein denaturation is shown in C. auriculata exhibited a significantly higher (p < 0.05) level of inhibition compared to other leafy types studied, whereas leaves of C. asiatica showed the lowest inhibition levels. The order of the inhibition of the extracts of leaves varieties was C. auriculata > P. edulis > O. zeylanica > G. lactiferum > S. grandiflora > C. asiatica.Inhibition % of protein denaturation of these leafy vegetables was within the range from 36.0% to 75.0% at the concentration range of 25–100 µg/mL. Leaves of O. zeylanica and S. grandiflora have shown significantly higher (p < 0.05) proteinase inhibition level compared with other leafy types.Proteinase inhibitory activity of different leafy vegetables is shown in Results for lipoxygenase inhibitory activity of different leafy vegetables are shown graphically in G. lactiferum showed an improved ability to inhibit lipoxygenase activity (about 50.0%) at 100 µg/mL concentration, whereas C. asiatica has shown the least inhibitory activity (9.0%) at the same concentration, among the leafy vegetables studied. The order of the lipoxygenase inhibitory activity of the leaf varieties was G. lactiferum > C. auriculata > S. grandiflora > P.edulis > O. zeylanica > C. asiatica.Inhibition levels were within the range of 3.7–50.0% within the concentrations of 25–100 µg/mL. The leaves of The results of this study also showed that there was a significant correlation between the studied anti-inflammatory properties  and their estimated total polyphenol, flavonoid, and carotenoid contents . These cCellular infiltration, due to the pivotal role played by leukocytes, is an important aspect of an inflammatory response . During P. edulis leaves, and its derived fractions, slow down the inflammation-related responses induced by carrageenan and histamine in the mouse air-pouch model. Furthermore, these extracts inhibit the production of a variety of pro-inflammatory cytokines, mediators, and enzymes at inflammatory sites [O. zeylanica leaves. In an in vitro study on the effect of Fagra zanthoxiloides, Olax subscorpioidea, and Tetrapleura tetraptera, on membrane stabilization, showed that they inhibited the heat- and hypotonicity-induced lysis of red blood cells [Albuca setosa aqueous extract, at the concentration 125–500 μg/mL, may protect the lysis of erythrocyte membrane induced by heat solution [In a previous study, it was reported that the aqueous extract of ry sites . Howeverod cells . In anotsolution . Howeversolution  have indsolution .Semecarpus anacardium bark on bovine albumin [Wedelia trilobataon bovine albumin [Albucas etosaon egg albumen [Denaturation of protein molecules is well documented in the literature, and it is due to an inflammation process in conditions like arthritis . One of  albumin , an etha albumin , Albucas albumen , etc. Th albumen . Lysosom albumen .Proteinases have been associated with arthritic reactions. Neutrophils, in their lysosomal granules, carry many serine proteinases . ProteinSemecarpus anacardium bark [Wedelia trilobata [In many previous studies, methanolic extracts of ium bark , and an rilobata  have exhcis, trans-conjugated diene hydroperoxides, such as leukotrienes, which are essential mediators in a variety of inflammatory events [Leptadenia pyrotechnica [Mahonia aquifolium [G. lactiferum has a potentially high anti-inflammatory effect compared with other leafy vegetables studied, which might be related to the polyphenol content and antioxidant property of the extract. Previous studies have shown that polyphenols may block or interfere with the cascade process of arachidonic acid metabolism by inhibiting lipoxygenase activity and, also, they may serve as scavengers of various reactive free radicals which are produced during arachidonic acid metabolism [Lipoxygenases are the key enzymes in the biosynthesis of leukotrienes. Leukotrienes play an important role in several inflammatory diseases, such as arthritis, asthma, cancer, and allergic diseases . The mecy events . Previoutechnica  and Mahouifolium . These rtabolism . Howevertabolism , most ofC. auriculata, P. edulis, G. lactiferum, S. grandiflora, O. zeylanica, and C. asiatica possess anti-inflammatory properties at varying levels. Leaves of O. zeylanica and P. edulis showed higher hemolysis inhibition activity. Leaves of C. auriculata possess higher protein denaturation inhibition properties whereas G. lactiferum showed higher lipoxygenase inhibition ability. Leaves of O. zeylanica, S. grandiflora, and C. asiatica exhibited good proteinase inhibition properties. Pearson’s correlation studies showed that there were significant correlations between estimated bioactives and anti-inflammatory properties. Results indicate that these anti-inflammatory activities may be due to the occurrence of bioactive compounds, such as polyphenols, flavonoids, and carotenoids in these leafy types.In conclusion, results indicate that the hydromethanol extracts of leaves of